diff --git a/dist/face-api.esm-nobundle.js b/dist/face-api.esm-nobundle.js index cca7aa3..2bfe350 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 Er=o=>Ge(o,"__esModule",{value:!0});var ho=(o,t)=>()=>(t||(t={exports:{}},o(t.exports,t)),t.exports),Mr=(o,t)=>{for(var e in t)Ge(o,e,{get:t[e],enumerable:!0})},lt=(o,t,e)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of ln(t))!fn.call(o,r)&&r!=="default"&&Ge(o,r,{get:()=>t[r],enumerable:!(e=hn(t,r))||e.enumerable});return o},b=o=>o&&o.__esModule?o:lt(Er(Ge(o!=null?dn(un(o)):{},"default",{value:o,enumerable:!0})),o);import*as Ma from"@tensorflow/tfjs/dist/index.js";import*as Ca from"@tensorflow/tfjs-backend-wasm";var g=ho(xn=>{Er(xn);lt(xn,Ma);lt(xn,Ca)});var 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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 so(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: ${JSON.stringify(o.meanRgb)}`)}var Q=b(g());var K=b(g());function ge(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Tt(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),ge(e)})}var At=b(g());function Pt(o,t){return At.tidy(()=>{let e=At.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=At.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=At.add(e,t.bias),ge(e)})}var io=b(g());function qn(o,t){let e=me(o,t);function r(s,i){let c=io.tensor1d(o(s)),m=io.tensor1d(o(s));return t.push({paramPath:`${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"),_t=m(h,y,"conv5"),wt=T?m(y,T,"conv6"):void 0,Dt=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:_t,conv6:wt,conv7:Dt,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"),_t=c(h,y,"conv5"),wt=c(y,T,"conv6"),Dt=c(T,F,"conv7"),te=i(F,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:_t,conv6:wt,conv7:Dt,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 co=class extends S{constructor(t){super("TinyYolov2");so(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=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=Tt(r,e.conv6),r=Tt(r,e.conv7),zt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?ge(zt(t,e.conv0,"valid",!1)):Pt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Pt(r,e.conv6):r,r=e.conv7?Pt(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 Ar(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Et(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||co.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,_t=G-it/2,wt=et-X/2,Dt={row:y,col:T,anchor:F},{classScore:te,label:lo}=this.withClassScores?await this.extractPredictedClass(f,Dt):{classScore:1,label:0};v.push({box:new re(_t,wt,_t+it,wt+X),score:L,classScore:L*te,label:lo,...Dt})}}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=co;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 mo=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 mo.Tensor?await ie(t,a):await se(t,a)),i=await e(s);return s.forEach(c=>c instanceof mo.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 po=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},we=class extends po{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 po{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 Wt(this,this.input)}},Qt=class extends De{withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Bt(this,this.input)}};var uo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Pe=class extends uo{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 uo{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 Wt(this,this.input)}},Zt=class extends _e{withFaceExpressions(){return new Qt(this,this.input)}withFaceDescriptor(){return new Bt(this,this.input)}};var vr=class extends tt{constructor(t,e){super();this.parentTask=t;this.input=e}},Wt=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)}},Bt=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 Wt(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 Bt(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 fo(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 bt)return s;if(s instanceof Float32Array)return new bt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new bt(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=>fo(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=>bt.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,Wt as ComputeAllFaceDescriptorsTask,vr as ComputeFaceDescriptorsTaskBase,Bt as ComputeSingleFaceDescriptorTask,Fr as DetectAllFaceLandmarksTask,Ye as DetectAllFacesTask,yr as DetectFaceLandmarksTaskBase,Pr as DetectFacesTaskBase,Tr as DetectSingleFaceLandmarksTask,_r as DetectSingleFaceTask,A as Dimensions,qr as FACE_EXPRESSION_LABELS,M as FaceDetection,Uo as FaceDetectionNet,cr as FaceExpressionNet,Lt 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,yt as Gender,Ce as LabeledBox,bt as LabeledFaceDescriptors,gt as NetInput,S as NeuralNetwork,Et 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,Gr as awaitMediaLoaded,zr 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,to as draw,w as env,fo 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,Xr as fetchJson,Cn as fetchNetWeights,Gt as fetchOrThrow,$ as getContext2dOrThrow,Yt as getMediaDimensions,Vr as imageTensorToCanvas,Ur as imageToSquare,vn as inverseSigmoid,kr as iou,qe as isMediaElement,Ie as isMediaLoaded,$n as isWithAge,pt as isWithFaceDetection,Zr as isWithFaceExpressions,Vt as isWithFaceLandmarks,On as isWithGender,da as loadAgeGenderModel,ua as loadFaceDetectionModel,pa as loadFaceExpressionModel,ia as loadFaceLandmarkModel,ca as loadFaceLandmarkTinyModel,ma as loadFaceRecognitionModel,cn as loadSsdMobilenetv1Model,aa as loadTinyFaceDetectorModel,sa as loadTinyYolov2Model,Jr as loadWeightMap,fa as locateFaces,Nn as matchDimensions,Sr as minBbox,P as nets,Ar as nonMaxSuppression,ot as normalize,Wr as padToSquare,na as predictAgeAndGender,oa as recognizeFaceExpressions,pn as resizeResults,Ht as resolveInput,gn as shuffleArray,Ee as sigmoid,an as ssdMobilenetv1,va as tf,Qn as tinyFaceDetector,ta as tinyYolov2,E as toNetInput,Cr as utils,so as validateConfig,Ta as version}; +var dn=Object.create,Ge=Object.defineProperty,un=Object.getPrototypeOf,fn=Object.prototype.hasOwnProperty,ln=Object.getOwnPropertyNames,hn=Object.getOwnPropertyDescriptor;var 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yt;(function(o){o.FEMALE="female",o.MALE="male"})(yt||(yt={}));var pr=class extends S{constructor(t=new ro(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return ut.tidy(()=>{let r=t instanceof gt?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 so(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: ${JSON.stringify(o.meanRgb)}`)}var Q=b(g());var K=b(g());function ge(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Tt(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),ge(e)})}var At=b(g());function Pt(o,t){return At.tidy(()=>{let e=At.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=At.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=At.add(e,t.bias),ge(e)})}var io=b(g());function qn(o,t){let e=me(o,t);function r(s,i){let c=io.tensor1d(o(s)),m=io.tensor1d(o(s));return t.push({paramPath:`${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"),_t=m(h,y,"conv5"),wt=T?m(y,T,"conv6"):void 0,Dt=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:_t,conv6:wt,conv7:Dt,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"),_t=c(h,y,"conv5"),wt=c(y,T,"conv6"),Dt=c(T,F,"conv7"),te=i(F,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:_t,conv6:wt,conv7:Dt,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 co=class extends S{constructor(t){super("TinyYolov2");so(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=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=Tt(r,e.conv6),r=Tt(r,e.conv7),zt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?ge(zt(t,e.conv0,"valid",!1)):Pt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Pt(r,e.conv6):r,r=e.conv7?Pt(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 Ar(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Et(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||co.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,_t=G-it/2,wt=et-X/2,Dt={row:y,col:T,anchor:F},{classScore:te,label:lo}=this.withClassScores?await this.extractPredictedClass(f,Dt):{classScore:1,label:0};v.push({box:new re(_t,wt,_t+it,wt+X),score:L,classScore:L*te,label:lo,...Dt})}}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=co;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 mo=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 mo.Tensor?await ie(t,a):await se(t,a)),i=await e(s);return s.forEach(c=>c instanceof mo.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 po=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},we=class extends po{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 po{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 Wt(this,this.input)}},Qt=class extends De{withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Bt(this,this.input)}};var uo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Pe=class extends uo{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 uo{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 Wt(this,this.input)}},Zt=class extends _e{withFaceExpressions(){return new Qt(this,this.input)}withFaceDescriptor(){return new Bt(this,this.input)}};var vr=class extends tt{constructor(t,e){super();this.parentTask=t;this.input=e}},Wt=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)}},Bt=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 Wt(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 Bt(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 fo(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 bt)return s;if(s instanceof Float32Array)return new bt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new bt(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=>fo(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=>bt.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,Wt as ComputeAllFaceDescriptorsTask,vr as ComputeFaceDescriptorsTaskBase,Bt as ComputeSingleFaceDescriptorTask,Fr as DetectAllFaceLandmarksTask,Ye as DetectAllFacesTask,yr as DetectFaceLandmarksTaskBase,Pr as DetectFacesTaskBase,Tr as DetectSingleFaceLandmarksTask,_r as DetectSingleFaceTask,A as Dimensions,qr as FACE_EXPRESSION_LABELS,M as FaceDetection,Uo as FaceDetectionNet,cr as FaceExpressionNet,Lt 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,yt as Gender,Ce as LabeledBox,bt as LabeledFaceDescriptors,gt as NetInput,S as NeuralNetwork,Et 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,Gr as awaitMediaLoaded,zr 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,to as draw,w as env,fo 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,Xr as fetchJson,Cn as fetchNetWeights,Gt as fetchOrThrow,$ as getContext2dOrThrow,Yt as getMediaDimensions,Vr as imageTensorToCanvas,Ur as imageToSquare,vn as inverseSigmoid,kr as iou,qe as isMediaElement,Ie as isMediaLoaded,$n as isWithAge,pt as isWithFaceDetection,Zr as isWithFaceExpressions,Vt as isWithFaceLandmarks,On as isWithGender,da as loadAgeGenderModel,ua as loadFaceDetectionModel,pa as loadFaceExpressionModel,ia as loadFaceLandmarkModel,ca as loadFaceLandmarkTinyModel,ma as loadFaceRecognitionModel,cn as loadSsdMobilenetv1Model,aa as loadTinyFaceDetectorModel,sa as loadTinyYolov2Model,Jr as loadWeightMap,fa as locateFaces,Nn as matchDimensions,Sr as minBbox,P as nets,Ar as nonMaxSuppression,ot as normalize,Wr as padToSquare,na as predictAgeAndGender,oa as recognizeFaceExpressions,pn as resizeResults,Ht as resolveInput,gn as shuffleArray,Ee as sigmoid,an as ssdMobilenetv1,va as tf,Qn as tinyFaceDetector,ta as tinyYolov2,E as toNetInput,Cr as utils,so as validateConfig,Ta as version}; //# sourceMappingURL=face-api.esm-nobundle.js.map diff --git a/dist/face-api.esm.js b/dist/face-api.esm.js index 5f0d770..47466dd 100644 --- a/dist/face-api.esm.js +++ b/dist/face-api.esm.js @@ -4045,7 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Please use 'channelsLast'.`);let B=a.makeOutput(h.outShape,"float32"),W=a.dataIdMap.get(B.dataId).id;return p2(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,v,P,x,N,T,C,F,$,W),B}var ite={kernelName:Ps,backendName:"wasm",setupFunc:rte,kernelFunc:ste},ote=!1,lte=fn(Vo,ote,"bool"),ute=Sn(Ls);function Jv(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)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Pa({inputs:{x:r},backend:a,attrs:{shape:o}})}var cte={kernelName:Uo,backendName:"wasm",kernelFunc:Jv};function pte(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var dte={kernelName:hc,backendName:"wasm",kernelFunc:pte},d2;function hte(e){d2=e.wasm.cwrap(Ho,null,["number","number","number","number","number","number"])}function mte(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,c,u]=a.shape;return d2(s,o,l,c,u,i),r}var fte={kernelName:Ho,backendName:"wasm",kernelFunc:mte,setupFunc:hte},gte=Sn(zs),yte=!1,bte=fn(Bs,yte),h2;function xte(e){h2=e.wasm.cwrap(Ws,null,["number","number","number","number","number","number","number"])}function vte(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=c!=null?t.dataIdMap.get(c.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return h2(u,p,d,h,m,r,g),f}var wte={kernelName:Ws,backendName:"wasm",setupFunc:xte,kernelFunc:vte},m2;function kte(e){m2=e.wasm.cwrap(xi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ite(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d),g=Sp[h];if(g==null)throw new Error(`${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 ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,F=f.padInfo.right,$=f.padInfo.bottom,P=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,K=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return m2(y,K,ne,J,b,N,T,x,C,F,$,P,X,B,W,G,q,j,v,g,oe,m||0,Q),se}var Nte={kernelName:xi,backendName:"wasm",setupFunc:kte,kernelFunc:Ite},f2;function Tte(e){f2=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 Ste(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=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d,!0),g=Sp[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 ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,F=f.padInfo.right,$=f.padInfo.bottom,P=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,K=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return f2(y,K,ne,J,b,N,T,x,C,F,$,P,X,B,W,G,q,j,v,g,oe,m||0,Q),se}var Cte={kernelName:vi,backendName:"wasm",setupFunc:Tte,kernelFunc:Ste},g2;function _te(e){g2=e.wasm.cwrap(qo,null,["number","number","number","number","number","number","array","number"])}function Ete(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=by.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 g2(d,Vn[a.dtype],h,i,p,o,m,f),c}var Ate={kernelName:qo,backendName:"wasm",setupFunc:_te,kernelFunc:Ete},y2;function Fte(e){y2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function $te(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=Pa({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),d=Pa({inputs:{x:s},attrs:{shape:[c.batchSize,p/c.batchSize]},backend:t}),h=[c.batchSize,c.outerSize,p/c.batchSize,c.sliceSize],m=t.makeOutput(h,r.dtype);if(k.sizeFromShape(r.shape)===0)return m;let f=u.shape.length-1,g=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(d.dataId).id,b=t.dataIdMap.get(m.dataId).id,v=new Uint8Array(new Int32Array(k.computeStrides(u.shape)).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer);return y2(g,Vn[r.dtype],v,f,y,c.batchSize,x,b),t.disposeData(u.dataId),t.disposeData(d.dataId),m.shape=c.outputShape,m}var Dte={kernelName:jo,backendName:"wasm",setupFunc:Fte,kernelFunc:$te},Rte=!1,Mte=fn(Ko,Rte,"bool"),Pte=!1,Ote=fn(Vs,Pte,"bool"),b2;function Lte(e){b2=e.wasm.cwrap(Gs,null,["number","number","number"])}function zte(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;b2(r,n,i)}return s}var Bte={kernelName:Gs,backendName:"wasm",setupFunc:Lte,kernelFunc:zte},Wte=!1,Vte=fn(Jo,Wte,"bool"),Ute=!1,Gte=fn(Qo,Ute,"bool"),Hte=Sn(Hs),jte=!1,qte=fn(tl,jte,"bool"),x2;function Kte(e){x2=e.wasm.cwrap(js,null,["number, number, number"])}function Xte(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;_.assertAxesAreInnerMostDims("max",u,h);let[m,f]=_.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;x2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=_.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var Yte={kernelName:js,backendName:"wasm",setupFunc:Kte,kernelFunc:Xte},Zte=!1,Jte=fn(qs,Zte),v2;function Qte(e){v2=e.wasm.cwrap(Ks,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ene(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=_.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"),F=a.dataIdMap.get(C.dataId).id;return v2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,v,x,N,T,F),C}var tne={kernelName:Ks,backendName:"wasm",setupFunc:Qte,kernelFunc:ene},w2;function nne(e){w2=e.wasm.cwrap(Xs,null,["number, number, number"])}function ane(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=_.getInnerMostAxes(m.length,c.shape.length))}_.assertAxesAreInnerMostDims("mean",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=c;c.dtype!=="float32"&&(b=Qm({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;w2(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(v.shape,d);v.shape=x}return c.dtype!=="float32"&&t.disposeData(b.dataId),v}var rne={kernelName:Xs,backendName:"wasm",setupFunc:nne,kernelFunc:ane},k2;function sne(e){k2=e.wasm.cwrap(Ys,null,["number, number, number"])}function ine(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;_.assertAxesAreInnerMostDims("min",p,m);let[f,g]=_.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;k2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var one={kernelName:Ys,backendName:"wasm",setupFunc:sne,kernelFunc:ine},lne=!1,une=fn(Zs,lne),cne=!0,pne=fn(Js,cne),dne=Sn(al);function Qv(e,t){let n=new 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yne(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=N2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=Qv(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var bne={kernelName:il,backendName:"wasm",setupFunc:gne,kernelFunc:yne},T2;function xne(e){T2=e.wasm.cwrap(ol,"number",["number","number","number","number","number","number"])}function vne(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=T2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=Qv(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var 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Iw=["neutral","happy","sad","angry","fearful","disgusted","surprised"],ms=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Iw.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return Iw.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var gf=class extends zp{constructor(t=new Op){super("FaceExpressionNet",t)}forwardInput(t){return D(()=>Ta(this.runNet(t)))}async forward(t){return this.forwardInput(await gt(t))}async predictExpressions(t){let n=await gt(t),a=await this.forwardInput(n),r=await Promise.all(dt(a).map(async i=>{let o=await i.data();return i.dispose(),o}));a.dispose();let s=r.map(i=>new ms(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Nw(e){return e.expressions instanceof ms}function 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$r(e,t){return D(()=>{let n=Zn(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Fi(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=Z(n,t.bias),Au(n)})}function Bre(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 SC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=bn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=Bre(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"),F=c(f,g,"conv4"),$=c(g,y,"conv5"),P=b?c(y,b,"conv6"):void 0,B=v?c(b,v,"conv7"):void 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n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Wre(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return yn(e,n),{params:i,paramMappings:n}}var ur=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Rw=class extends an{constructor(t){super("TinyYolov2");Dw(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Fr(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=Fr(a,n.conv6),a=Fr(a,n.conv7),no(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Au(no(t,n.conv0,"valid",!1)):$r(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=n.conv6?$r(a,n.conv6):a,a=n.conv7?$r(a,n.conv7):a,no(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=ce(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?La(r,this.config.meanRgb):r,r=r.div(de(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await gt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new ur(n),s=await gt(t),i=await this.forwardInput(s,a),o=D(()=>dt(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 lw(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new ds(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return CC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Rw.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 ${a} filterSizes in config`);return SC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),v=y.slice([0,0,0,4],[c,c,u,1]),x=this.withClassScores?Ta(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):de(0);return[b,v,x]}),m=[],f=await d.array(),g=await p.array();for(let y=0;ya){let N=(b+Ap(g[y][b][v][0]))/c*o,T=(y+Ap(g[y][b][v][1]))/c*l,C=Math.exp(g[y][b][v][2])*this.config.anchors[v].x/c*o,F=Math.exp(g[y][b][v][3])*this.config.anchors[v].y/c*l,$=N-C/2,P=T-F/2,B={row:y,col:b,anchor:v},{classScore:W,label:G}=this.withClassScores?await this.extractPredictedClass(h,B):{classScore:1,label:0};m.push({box:new bu($,P,$+C,P+F),score:x,classScore:x*W,label:G,...B})}}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=Rw;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:vC,classes:["face"],...t?{anchors:kC,meanRgb:IC}:{anchors:wC,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?TC:NC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Vre(e,t=!0){let n=new $u(t);return n.extractWeights(e),n}var Tf=class extends ur{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var wa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function io(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>ao(l)?r(l):l.detection),i=a||(t instanceof Ae?await Iu(t,s):await ku(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ae&&l.dispose()),o}async function Du(e,t,n,a,r){return io([e],t,async s=>n(s[0]),a,r)}var _C=.4,EC=[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)],AC=[117.001,114.697,97.404];var Ru=class extends Fu{constructor(){let t={withSeparableConvs:!0,iouThreshold:_C,classes:["face"],anchors:EC,meanRgb:AC,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 nt={ssdMobilenetv1:new so,tinyFaceDetector:new Ru,tinyYolov2:new $u,faceLandmark68Net:new _u,faceLandmark68TinyNet:new xf,faceRecognitionNet:new Eu,faceExpressionNet:new gf,ageGenderNet:new bf},FC=(e,t)=>nt.ssdMobilenetv1.locateFaces(e,t),Ure=(e,t)=>nt.tinyFaceDetector.locateFaces(e,t),Gre=(e,t)=>nt.tinyYolov2.locateFaces(e,t),$C=e=>nt.faceLandmark68Net.detectLandmarks(e),Hre=e=>nt.faceLandmark68TinyNet.detectLandmarks(e),jre=e=>nt.faceRecognitionNet.computeFaceDescriptor(e),qre=e=>nt.faceExpressionNet.predictExpressions(e),Kre=e=>nt.ageGenderNet.predictAgeAndGender(e),DC=e=>nt.ssdMobilenetv1.load(e),Xre=e=>nt.tinyFaceDetector.load(e),Yre=e=>nt.tinyYolov2.load(e),Zre=e=>nt.faceLandmark68Net.load(e),Jre=e=>nt.faceLandmark68TinyNet.load(e),Qre=e=>nt.faceRecognitionNet.load(e),ese=e=>nt.faceExpressionNet.load(e),tse=e=>nt.ageGenderNet.load(e),nse=DC,ase=FC,rse=$C;var Mw=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Ou=class extends Mw{async run(){let t=await this.parentTask,n=await io(t,this.input,async a=>Promise.all(a.map(r=>nt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>yf(a,n[r]))}withAgeAndGender(){return new Mu(this,this.input)}},Lu=class extends Mw{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 yf(t,n)}withAgeAndGender(){return new Pu(this,this.input)}},uo=class extends Ou{withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptors(){return new fs(this,this.input)}},co=class extends Lu{withAgeAndGender(){return new lo(this,this.input)}withFaceDescriptor(){return new gs(this,this.input)}};var Pw=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Mu=class extends Pw{async run(){let t=await this.parentTask,n=await io(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 kf(If(a,i,o),s)})}withFaceExpressions(){return new Ou(this,this.input)}},Pu=class extends Pw{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 kf(If(t,a,r),n)}withFaceExpressions(){return new Lu(this,this.input)}},oo=class extends Mu{withFaceExpressions(){return new uo(this,this.input)}withFaceDescriptors(){return new fs(this,this.input)}},lo=class extends Pu{withFaceExpressions(){return new co(this,this.input)}withFaceDescriptor(){return new gs(this,this.input)}};var Sf=class extends wa{constructor(t,n){super();this.parentTask=t;this.input=n}},fs=class extends Sf{async run(){let t=await this.parentTask;return(await io(t,this.input,a=>Promise.all(a.map(r=>nt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>wf(t[r],a))}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}},gs=class extends Sf{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 wf(t,n)}withFaceExpressions(){return new co(this,this.input)}withAgeAndGender(){return new lo(this,this.input)}};var Cf=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?nt.faceLandmark68TinyNet:nt.faceLandmark68Net}},_f=class extends Cf{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Ae?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 Ae&&s.dispose()),t.map((s,i)=>Cu(s,r[i]))}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptors(){return new fs(this,this.input)}},Ef=class extends Cf{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ae?await Iu(this.input,[n]):await ku(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ae&&s.dispose()),Cu(t,r)}withFaceExpressions(){return new co(this,this.input)}withAgeAndGender(){return new lo(this,this.input)}withFaceDescriptor(){return new gs(this,this.input)}};var Af=class extends wa{constructor(t,n=new va){super();this.input=t;this.options=n}},Vp=class extends Af{async run(){let{input:t,options:n}=this,a=n instanceof Tf?r=>nt.tinyFaceDetector.locateFaces(r,n):n instanceof va?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 _f(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Ou(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Mu(this.runAndExtendWithFaceDetections(),this.input)}},Ff=class extends Af{async run(){let t=await new Vp(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 Ef(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Lu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Pu(this.runAndExtendWithFaceDetection(),this.input)}};function sse(e,t=new va){return new Ff(e,t)}function $f(e,t=new va){return new Vp(e,t)}async function RC(e,t){return $f(e,new va(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function ise(e,t={}){return $f(e,new ur(t)).withFaceLandmarks().withFaceDescriptors()}var ose=RC;function Ow(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 Df=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 _r)return i;if(i instanceof Float32Array)return new _r(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new _r(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=>Ow(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Fp(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>_r.fromJSON(a));return new Df(n,t.distanceThreshold)}};function lse(e){let t=new Ru;return t.extractWeights(e),t}function MC(e,t){let{width:n,height:a}=new gn(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>MC(r,{width:n,height:a}));if(ao(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Cu(Ji(e,r),s)}return or(e)?Ji(e,e.detection.forSize(n,a)):e instanceof ta||e instanceof bt?e.forSize(n,a):e}var use=typeof process!="undefined",cse=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",pse={faceapi:Q2,node:use,browser:cse};export{bf as AgeGenderNet,bu as BoundingBox,ut as Box,wa as ComposableTask,fs as ComputeAllFaceDescriptorsTask,Sf as ComputeFaceDescriptorsTaskBase,gs as ComputeSingleFaceDescriptorTask,_f as DetectAllFaceLandmarksTask,Vp as DetectAllFacesTask,Cf as DetectFaceLandmarksTaskBase,Af as DetectFacesTaskBase,Ef as DetectSingleFaceLandmarksTask,Ff as DetectSingleFaceTask,gn as Dimensions,Iw as FACE_EXPRESSION_LABELS,bt as FaceDetection,xC as FaceDetectionNet,gf as FaceExpressionNet,ms as FaceExpressions,_u as FaceLandmark68Net,xf as FaceLandmark68TinyNet,oC as FaceLandmarkNet,ta as FaceLandmarks,H2 as FaceLandmarks5,vu as FaceLandmarks68,Fp as FaceMatch,Df as FaceMatcher,Eu as FaceRecognitionNet,Ar as Gender,$p as LabeledBox,_r as LabeledFaceDescriptors,Er as NetInput,an as NeuralNetwork,ds as ObjectDetection,De as Point,j2 as PredictedBox,xu as Rect,so as SsdMobilenetv1,va as SsdMobilenetv1Options,Ru as TinyFaceDetector,Tf as TinyFaceDetectorOptions,$u as TinyYolov2,ur as TinyYolov2Options,ose as allFaces,RC as allFacesSsdMobilenetv1,ise as allFacesTinyYolov2,yw as awaitMediaLoaded,bw as bufferToImage,jre as computeFaceDescriptor,wu as createCanvas,Mp as createCanvasFromMedia,zre as createFaceDetectionNet,_re as createFaceRecognitionNet,bC as createSsdMobilenetv1,lse as createTinyFaceDetector,Vre as createTinyYolov2,$f as detectAllFaces,$C as detectFaceLandmarks,Hre as detectFaceLandmarksTiny,rse as detectLandmarks,sse as detectSingleFace,Cw as draw,st as env,Ow as euclideanDistance,kf as extendWithAge,wf as extendWithFaceDescriptor,Ji as extendWithFaceDetection,yf as extendWithFaceExpressions,Cu as extendWithFaceLandmarks,If as extendWithGender,Iu as extractFaceTensors,ku as extractFaces,bre as fetchImage,ww as fetchJson,xre as fetchNetWeights,to as fetchOrThrow,Cn as getContext2dOrThrow,eo as getMediaDimensions,xw as imageTensorToCanvas,vw as imageToSquare,ure as inverseSigmoid,iw as iou,of as isMediaElement,Rp as isMediaLoaded,Ere as isWithAge,or as isWithFaceDetection,Nw as isWithFaceExpressions,ao as isWithFaceLandmarks,Are as isWithGender,tse as loadAgeGenderModel,nse as loadFaceDetectionModel,ese as loadFaceExpressionModel,Zre as loadFaceLandmarkModel,Jre as loadFaceLandmarkTinyModel,Qre as loadFaceRecognitionModel,DC as loadSsdMobilenetv1Model,Xre as loadTinyFaceDetectorModel,Yre as loadTinyYolov2Model,kw as loadWeightMap,ase as locateFaces,vre as matchDimensions,ow as minBbox,nt as nets,lw as nonMaxSuppression,La as normalize,uw as padToSquare,Kre as predictAgeAndGender,qre as recognizeFaceExpressions,MC as resizeResults,Qi as resolveInput,lre as shuffleArray,Ap as sigmoid,FC as ssdMobilenetv1,Ug as tf,Ure as tinyFaceDetector,Gre as tinyYolov2,gt as toNetInput,nw as utils,Dw as validateConfig,pse as version}; + `}};function oee(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=_.getAxesPermutation([c],o),p=r;u!=null&&(p=Tn({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(p),c=_.getInnerMostAxes(1,o)[0]);let d=_.segment_util.computeOutShape(p.shape,c,i),h=k.sizeFromShape([p.shape[c]]),m=be({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=nh(r.dtype),g=(x,N,T,C,F)=>{let $=x.shape[0],P=x.shape[1],B=_.segment_util.segOpComputeOptimalWindowSize(P,F),W={windowSize:B,inSize:P,batchSize:$,numSegments:F},G=new iee(W,N),q=n.compileAndRun(G,[x,T],C);if(l.push(q),q.shape[1]===F)return q;let j=KS({backend:n,attrs:{start:0,stop:F,step:1,dtype:"float32"}}),X=ZS({inputs:{x:j},backend:n,attrs:{reps:[P/B]}});return l.push(j),l.push(X),g(q,N,X,C,F)},y=g(m,"unsortedSegmentSum",s,f,i),b=be({inputs:{x:y},backend:n,attrs:{shape:d}}),v=b;if(u!=null){l.push(b);let x=_.getUndoAxesPermutation(u);v=Tn({inputs:{x:v},backend:n,attrs:{perm:x}})}return 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Sp;(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"})(Sp||(Sp={}));var JS;function dee(e){JS=e.wasm.cwrap(bi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function hee(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 F=n.dataIdMap.get(i.dataId);if(F.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${F.shape.length}.`);m=F.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=Sp[u];if(g==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let 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s(i){let{backend:o,inputs:l}=i,{a:c,b:u}=l,p=o.dataIdMap.get(c.dataId).id,d=o.dataIdMap.get(u.dataId).id,h=n!=null?n:c.dtype,m=_.assertAndGetBroadcastShape(c.shape,u.shape),f=o.makeOutput(m,h);if(k.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(c.shape).buffer),y=new Uint8Array(new Int32Array(u.shape).buffer),b=o.dataIdMap.get(f.dataId).id,v=()=>a(p,g,c.shape.length,d,y,u.shape.length,Vn[c.dtype],b);if(t&&c.dtype==="float32")return v(),f;let x=_.getBroadcastDims(c.shape,m),N=_.getBroadcastDims(u.shape,m),T=x.every((F,$)=>F===$),C=N.every((F,$)=>F===$);if(T&&C)return v(),f;throw new Error(`Broadcasting along outer dims is not yet supported for ${c.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var gee=!0,yee=fn(zr,gee),QS;function bee(e){QS=e.wasm.cwrap(Ss,null,["array","number","number","number"])}function xee(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(k.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return QS(s,r.length,Vn[a.dtype],i),a}var vee={kernelName:Ss,backendName:"wasm",setupFunc:bee,kernelFunc:xee};function Zm(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var wee={kernelName:Us,backendName:"wasm",kernelFunc:Zm},e2;function kee(e){e2=e.wasm.cwrap(yi,null,["number","array","number","number","number","array","number"])}function Jm(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=Nee(t.x.shape,a.perm),i=!0;for(let m=0;m=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var Tee={kernelName:yi,backendName:"wasm",kernelFunc:Jm,setupFunc:kee};function gu(e,t,n){let a=e.shape,r=e.shape.length,s=k.parseAxisParam(t,a),i=s,o=_.getAxesPermutation(i,r),l=null,c=!1;if(o!=null){let u=new Array(r);for(let d=0;d`new shape: ${i}, old shape: ${a.shape}. 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Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return m2(y,K,ne,J,b,N,T,x,C,F,$,P,X,B,W,G,q,j,v,g,oe,m||0,Q),se}var Nte={kernelName:xi,backendName:"wasm",setupFunc:kte,kernelFunc:Ite},f2;function Tte(e){f2=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 Ste(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=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d,!0),g=Sp[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 ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,F=f.padInfo.right,$=f.padInfo.bottom,P=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,K=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return f2(y,K,ne,J,b,N,T,x,C,F,$,P,X,B,W,G,q,j,v,g,oe,m||0,Q),se}var Cte={kernelName:vi,backendName:"wasm",setupFunc:Tte,kernelFunc:Ste},g2;function _te(e){g2=e.wasm.cwrap(qo,null,["number","number","number","number","number","number","array","number"])}function Ete(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=by.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 g2(d,Vn[a.dtype],h,i,p,o,m,f),c}var Ate={kernelName:qo,backendName:"wasm",setupFunc:_te,kernelFunc:Ete},y2;function Fte(e){y2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function $te(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=Pa({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),d=Pa({inputs:{x:s},attrs:{shape:[c.batchSize,p/c.batchSize]},backend:t}),h=[c.batchSize,c.outerSize,p/c.batchSize,c.sliceSize],m=t.makeOutput(h,r.dtype);if(k.sizeFromShape(r.shape)===0)return m;let f=u.shape.length-1,g=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(d.dataId).id,b=t.dataIdMap.get(m.dataId).id,v=new Uint8Array(new Int32Array(k.computeStrides(u.shape)).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer);return y2(g,Vn[r.dtype],v,f,y,c.batchSize,x,b),t.disposeData(u.dataId),t.disposeData(d.dataId),m.shape=c.outputShape,m}var Dte={kernelName:jo,backendName:"wasm",setupFunc:Fte,kernelFunc:$te},Rte=!1,Mte=fn(Ko,Rte,"bool"),Pte=!1,Ote=fn(Vs,Pte,"bool"),b2;function Lte(e){b2=e.wasm.cwrap(Gs,null,["number","number","number"])}function zte(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;b2(r,n,i)}return s}var Bte={kernelName:Gs,backendName:"wasm",setupFunc:Lte,kernelFunc:zte},Wte=!1,Vte=fn(Jo,Wte,"bool"),Ute=!1,Gte=fn(Qo,Ute,"bool"),Hte=Sn(Hs),jte=!1,qte=fn(tl,jte,"bool"),x2;function Kte(e){x2=e.wasm.cwrap(js,null,["number, number, number"])}function Xte(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;_.assertAxesAreInnerMostDims("max",u,h);let[m,f]=_.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;x2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=_.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var Yte={kernelName:js,backendName:"wasm",setupFunc:Kte,kernelFunc:Xte},Zte=!1,Jte=fn(qs,Zte),v2;function Qte(e){v2=e.wasm.cwrap(Ks,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ene(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=_.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"),F=a.dataIdMap.get(C.dataId).id;return v2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,v,x,N,T,F),C}var tne={kernelName:Ks,backendName:"wasm",setupFunc:Qte,kernelFunc:ene},w2;function nne(e){w2=e.wasm.cwrap(Xs,null,["number, number, number"])}function ane(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=_.getInnerMostAxes(m.length,c.shape.length))}_.assertAxesAreInnerMostDims("mean",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=c;c.dtype!=="float32"&&(b=Qm({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;w2(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(v.shape,d);v.shape=x}return c.dtype!=="float32"&&t.disposeData(b.dataId),v}var rne={kernelName:Xs,backendName:"wasm",setupFunc:nne,kernelFunc:ane},k2;function sne(e){k2=e.wasm.cwrap(Ys,null,["number, number, number"])}function ine(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;_.assertAxesAreInnerMostDims("min",p,m);let[f,g]=_.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;k2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var one={kernelName:Ys,backendName:"wasm",setupFunc:sne,kernelFunc:ine},lne=!1,une=fn(Zs,lne),cne=!0,pne=fn(Js,cne),dne=Sn(al);function Qv(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 I2;function hne(e){I2=e.wasm.cwrap(sl,"number",["number","number","number","number","number"])}function mne(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=I2(c,u,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=Qv(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var fne={kernelName:sl,backendName:"wasm",setupFunc:hne,kernelFunc:mne},N2;function gne(e){N2=e.wasm.cwrap(il,"number",["number","number","number","number","number","bool"])}function yne(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=N2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=Qv(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var bne={kernelName:il,backendName:"wasm",setupFunc:gne,kernelFunc:yne},T2;function xne(e){T2=e.wasm.cwrap(ol,"number",["number","number","number","number","number","number"])}function vne(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=T2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=Qv(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var wne={kernelName:ol,backendName:"wasm",setupFunc:xne,kernelFunc:vne},kne=!1,Ine=fn(rl,kne,"bool"),S2;function Nne(e){S2=e.wasm.cwrap(Qs,null,["number","number","number","number","number"])}function Tne(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 S2(u,s,i,o,c),l}var Sne={kernelName:Qs,backendName:"wasm",setupFunc:Nne,kernelFunc:Tne};function Cne(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var _ne={kernelName:ll,backendName:"wasm",kernelFunc:Cne};function Ene(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Jv({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 dtypes")});let o=t.map(l=>Jv({inputs:{input:l},backend:n,attrs:{dim:r}}));return s2({inputs:o,backend:n,attrs:{axis:r}})}var Ane={kernelName:ul,backendName:"wasm",kernelFunc:Ene},C2;function Fne(e){C2=e.wasm.cwrap(ei,null,["number","array","number","number","array","array","number","number"])}function $ne(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,constantValue:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),u=a.map(m=>m[0]),p=a.map(m=>m[1]),d=new Uint8Array(new Int32Array(u).buffer),h=new Uint8Array(new Int32Array(p).buffer);return C2(i,c,t.shape.length,Vn[t.dtype],d,h,r,l),o}var Dne={kernelName:ei,backendName:"wasm",kernelFunc:$ne,setupFunc:Fne},Rne=!1,Mne=fn(ti,Rne),_2;function Pne(e){_2=e.wasm.cwrap(ni,null,["number","number","number"])}function One(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 _2(s,i,l),o}var Lne={kernelName:ni,backendName:"wasm",setupFunc:Pne,kernelFunc:One},E2;function zne(e){E2=e.wasm.cwrap(cl,null,["number","number","number","number"])}function Bne(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=_.getInnerMostAxes(m.length,c.shape.length))}_.assertAxesAreInnerMostDims("prod",m,c.shape.length);let[f,g]=_.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;E2(l,y,Vn[b.dtype],v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var Wne={kernelName:cl,backendName:"wasm",setupFunc:zne,kernelFunc:Bne},Vne=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=Iv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},Une={kernelName:xc,backendName:"wasm",kernelFunc:Vne},Gne=!0,Hne=fn(Os,Gne),jne=Sn(ai),qne=Sn(si),A2;function Kne(e){A2=e.wasm.cwrap(ri,null,["number","number","number","number","number","number","number","number","number","number"])}function Xne(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=Qm({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 A2(y,u,p,d,h,l,c,s?1:0,i?1:0,v),g!=null&&t.disposeData(g.dataId),b}var Yne={kernelName:ri,backendName:"wasm",setupFunc:Kne,kernelFunc:Xne},F2;function Zne(e){F2=e.wasm.cwrap(ii,null,["number","array","number","array","number","number"])}function Jne(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 Zm({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);F2(l,u,i.length,p,r.shape.length,c);let d=Pa({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),d}var Qne={kernelName:ii,backendName:"wasm",kernelFunc:Jne,setupFunc:Zne},$2;function eae(e){$2=e.wasm.cwrap(Sl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function tae(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]=_.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 $2(c,p,d,h,m,s,f,g,x,v.length,u),l}var nae={kernelName:Sl,backendName:"wasm",kernelFunc:tae,setupFunc:eae},aae=Sn(oi),rae=Sn(li),D2;function sae(e){D2=e.wasm.cwrap(hl,null,["number","number","number","number","number","number","array","number","number"])}function iae(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}=xy.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 D2(h,m,Vn[s.dtype],l,c,u,f,d,g),o}var oae={kernelName:hl,backendName:"wasm",setupFunc:sae,kernelFunc:iae},R2;function lae(e){R2=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function uae(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 R2(i,o,l,h,u),c}var cae={kernelName:ml,backendName:"wasm",kernelFunc:uae,setupFunc:lae},M2;function pae(e){M2=e.wasm.cwrap(ci,null,["number","number"])}function dae(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||M2(a,s),r}var hae={kernelName:"Sigmoid",backendName:"wasm",setupFunc:pae,kernelFunc:dae},mae=Sn(ui);function ef(e){let{inputs:{x:t},attrs:{begin:n,size:a},backend:r}=e,[s,i]=cn.parseSliceParams(t,n,a),o=cn.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=cn.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=Dm(l,s,i,t.shape,t.dtype);return p.stringBytes=m,c}let d=r.typedArrayFromHeap(c),h=t.shape.length;if(h===2)fae(l,u[0],d,s,i);else if(h===3)gae(l,u[0],u[1],d,s,i);else if(h===4)yae(l,u[0],u[1],u[2],d,s,i);else{let m=Dm(l,s,i,t.shape,t.dtype);d.set(m)}return c}function fae(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=ef({inputs:{x:r},attrs:{begin:c,size:d},backend:a});return c[o]+=p,h})}var Iae={kernelName:vl,backendName:"wasm",kernelFunc:kae},Nae=Sn(pi),Tae=Sn(kc),Sae=!0,Cae=fn(mi,Sae),O2;function 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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 ut(this._box).rescale(this.imageDims.reverse())}forSize(t,n){return new ds(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var bt=class extends ds{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 bt(a,r,s)}};function iw(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 ow(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 o=r,l=[];for(let c=0;cl[u]<=n)}return s}function La(e,t){return D(()=>{let[n,a,r]=t,s=wn([...e.shape.slice(0,3),1],n,"float32"),i=wn([...e.shape.slice(0,3),1],a,"float32"),o=wn([...e.shape.slice(0,3),1],r,"float32"),l=Qe([s,i,o],3);return fe(e,l)})}function uw(e,t=!1){return D(()=>{let[n,a]=e.shape.slice(1);if(n===a)return e;let r=Math.abs(n-a),s=Math.round(r*(t?.5:1)),i=n>a?2:1,o=d=>{let h=e.shape.slice();return h[i]=d,wn(h,0,"float32")},l=o(s),c=r-l.shape[i],p=[t&&c?o(c):null,e,l].filter(d=>!!d).map(d=>ce(d,"float32"));return Qe(p,i)})}function lre(e){let t=e.slice();for(let n=t.length-1;n>0;n--){let a=Math.floor(Math.random()*(n+1)),r=t[n];t[n]=t[a],t[a]=r}return t}function Ap(e){return 1/(1+Math.exp(-e))}function ure(e){return Math.log(e/(1-e))}var xu=class extends ut{constructor(t,n,a,r,s=!1){super({x:t,y:n,width:a,height:r},s)}};var cre=.5,pre=.43,dre=.45,ta=class{constructor(t,n,a=new De(0,0)){let{width:r,height:s}=n;this._imgDims=new gn(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 ut(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/dre),l=Zi(t),c=Math.floor(Math.max(0,l.x-cre*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=ow(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var H2=class extends ta{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],Zi([t[3],t[4]])]}};var vu=class extends ta{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(Zi)}};var Fp=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}${t?` (${Yi(this.distance)})`:""}`}};var $p=class extends ut{static assertIsValidLabeledBox(t,n){if(ut.assertIsValidBox(t,n),!Oa(t.label))throw new Error(`${n} - expected property label (${t.label}) to be a number`)}constructor(t,n){super(t);this._label=n}get label(){return this._label}};var _r=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 _r(t.label,n)}};var j2=class extends $p{static assertIsValidPredictedBox(t,n){if($p.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 bt}function Ji(e,t){return{...e,...{detection:t}}}function cw(){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 environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:e,readFile:t}}function rf(e){let t="";if(!e)try{e=require("fs")}catch(a){t=a.toString()}return{readFile:e?a=>new Promise((r,s)=>{e.readFile(a,(i,o)=>i?s(i):r(o))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)}}}function pw(){let e=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,n=()=>{if(e)return new e;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},a=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},r=global.fetch,s=rf();return{Canvas:e||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:n,createImageElement:a,fetch:r,...s}}function dw(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var hw=aE(K2()),Jt;function fre(){if(!Jt)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return Jt}function mw(e){Jt=e}function fw(){return dw()?mw(cw()):hw.isNodejs()?mw(pw()):null}function gre(e){if(Jt||fw(),!Jt)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=Jt.Canvas,Image:n=Jt.Image}=e;Jt.Canvas=t,Jt.Image=n,Jt.createCanvasElement=e.createCanvasElement||(()=>new t),Jt.createImageElement=e.createImageElement||(()=>new n),Jt.ImageData=e.ImageData||Jt.ImageData,Jt.Video=e.Video||Jt.Video,Jt.fetch=e.fetch||Jt.fetch,Jt.readFile=e.readFile||Jt.readFile}var st={getEnv:fre,setEnv:mw,initialize:fw,createBrowserEnv:cw,createFileSystem:rf,createNodejsEnv:pw,monkeyPatch:gre,isBrowser:dw,isNodejs:hw.isNodejs};fw();function Qi(e){return!st.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function Cn(e){let{Canvas:t,CanvasRenderingContext2D:n}=st.getEnv();if(e instanceof n)return e;let a=Qi(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 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 Dp=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}},hs=class{constructor(t,n,a={}){this.text=typeof t=="string"?[t]:t instanceof hs?t.text:t,this.anchor=n,this.options=new Dp(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 gw=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 Dp({...i,...s})}},sf=class{constructor(t,n={}){this.box=new ut(t),this.options=new gw(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 hs([c],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function yre(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof bt?a.score:or(a)?a.detection.score:void 0,s=a instanceof bt?a.box:or(a)?a.detection.box:new ut(a),i=r?`${Yi(r)}`:void 0;new sf(s,{label:i}).draw(e)})}function Rp(e){let{Image:t,Video:n}=st.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function yw(e){return new Promise((t,n)=>{if(e instanceof st.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 bw(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 eo(e){let{Image:t,Video:n}=st.getEnv();return e instanceof t?new gn(e.naturalWidth,e.naturalHeight):e instanceof n?new gn(e.videoWidth,e.videoHeight):new gn(e.width,e.height)}function wu({width:e,height:t}){let{createCanvasElement:n}=st.getEnv(),a=n();return a.width=e,a.height=t,a}function Mp(e,t){let{ImageData:n}=st.getEnv();if(!(e instanceof n)&&!Rp(e))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:a,height:r}=t||eo(e),s=wu({width:a,height:r});return e instanceof n?Cn(s).putImageData(e,0,0):Cn(s).drawImage(e,0,0,a,r),s}async function xw(e,t){let n=t||st.getEnv().createCanvasElement(),[a,r,s]=e.shape.slice(ea(e)?1:0),i=D(()=>e.as3D(a,r,s).toInt());return await Ni.toPixels(i,n),i.dispose(),n}function of(e){let{Image:t,Canvas:n,Video:a}=st.getEnv();return e instanceof t||e instanceof n||e instanceof a}function vw(e,t,n=!1){let{Image:a,Canvas:r}=st.getEnv();if(!(e instanceof a||e instanceof r))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");let s=eo(e),i=t/Math.max(s.height,s.width),o=i*s.width,l=i*s.height,c=wu({width:t,height:t}),u=e instanceof r?e:Mp(e),p=Math.abs(o-l)/2,d=n&&o{if(Cr(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(ea(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input 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n=this.getInputWidth(t),a=this.getInputHeight(t);return sw({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 Ae){let o=ea(i)?i:i.expandDims();return o=uw(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=Za.resizeBilinear(o,[t,t])),o.as3D(t,t,3)}if(i instanceof st.getEnv().Canvas)return Ni.fromPixels(vw(i,t,n));throw new Error(`toBatchTensor - at batchIdx ${s}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${i}`)});return $t(a.map(s=>ce(s,"float32"))).as4D(this.batchSize,t,t,3)})}};async function gt(e){if(e instanceof Er)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 ${r}:`:"",a=t.map(Qi);return a.forEach((r,s)=>{if(!of(r)&&!Cr(r)&&!ea(r))throw typeof t[s]=="string"?new Error(`toNetInput -${n(s)} string passed, but could not resolve HTMLElement for element id ${t[s]}`):new Error(`toNetInput -${n(s)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(ea(r)){let i=r.shape[0];if(i!==1)throw new Error(`toNetInput -${n(s)} tf.Tensor4D with batchSize ${i} passed, but not supported in input array`)}}),await Promise.all(a.map(r=>of(r)&&yw(r))),new Er(a,Array.isArray(e))}async function ku(e,t){let{Canvas:n}=st.getEnv(),a=e;if(!(e instanceof n)){let i=await gt(e);if(i.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let o=i.getInput(0);a=o instanceof n?o:await xw(o)}let r=Cn(a);return t.map(i=>i instanceof bt?i.forSize(a.width,a.height).box.floor():i).map(i=>i.clipAtImageBorders(a.width,a.height)).map(({x:i,y:o,width:l,height:c})=>{let u=wu({width:l,height:c});return Cn(u).putImageData(r.getImageData(i,o,l,c),0,0),u})}async function Iu(e,t){if(!Cr(e)&&!ea(e))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(ea(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return D(()=>{let[n,a,r]=e.shape.slice(ea(e)?1:0);return t.map(o=>o instanceof bt?o.forSize(a,n).box:o).map(o=>o.clipAtImageBorders(a,n)).map(({x:o,y:l,width:c,height:u})=>Vl(e.as3D(n,a,r),[l,o,0],[u,c,r]))})}async function to(e,t){let{fetch:n}=st.getEnv(),a=await n(e,t);if(!(a.status<400))throw new Error(`failed to fetch: (${a.status}) ${a.statusText}, from url: ${a.url}`);return a}async function bre(e){let t=await to(e),n=await t.blob();if(!n.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${n.type}, for url: ${t.url}`);return bw(n)}async function ww(e){return(await to(e)).json()}async function xre(e){return new Float32Array(await(await to(e)).arrayBuffer())}function lf(e,t){let n=`${t}-weights_manifest.json`;if(!e)return{modelBaseUri:"",manifestUri:n};if(e==="/")return{modelBaseUri:"/",manifestUri:`/${n}`};let a=e.startsWith("http://")?"http://":e.startsWith("https://")?"https://":"";e=e.replace(a,"");let r=e.split("/").filter(o=>o),s=e.endsWith(".json")?r[r.length-1]:n,i=a+(e.endsWith(".json")?r.slice(0,r.length-1):r).join("/");return i=e.startsWith("/")?`/${i}`:i,{modelBaseUri:i,manifestUri:i==="/"?`/${s}`:`${i}/${s}`}}async function kw(e,t){let{manifestUri:n,modelBaseUri:a}=lf(e,t),r=await ww(n);return Ht.loadWeights(r,a)}function vre(e,t,n=!1){let{width:a,height:r}=n?eo(t):t;return e.width=a,e.height=r,{width:a,height:r}}var an=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:n,objProp:a}=this.traversePropertyPath(t);return n[a]}reassignParamFromPath(t,n){let{obj:a,objProp:r}=this.traversePropertyPath(t);a[r].dispose(),a[r]=n}getParamList(){return 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a=_n(a,t.separable_conv0,[1,1]),a=_n(Ye(a),t.separable_conv1,[1,1]),a=Ft(a,[3,3],[2,2],"same"),a=Z(a,nC(e,t.expansion_conv,[2,2])),a}function Tre(e,t){let n=_n(Ye(e),t.separable_conv0,[1,1]);return n=_n(Ye(n),t.separable_conv1,[1,1]),n=_n(Ye(n),t.separable_conv2,[1,1]),n=Z(n,e),n}var Ew=class extends an{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return D(()=>{let a=ce(t.toBatchTensor(112,!0),"float32"),s=La(a,[122.782,117.001,104.298]).div(de(256)),i=Ye(nC(s,n.entry_flow.conv_in,[2,2]));return i=_w(i,n.entry_flow.reduction_block_0,!1),i=_w(i,n.entry_flow.reduction_block_1),ir(this._numMainBlocks,0,1).forEach(o=>{i=Tre(i,n.middle_flow[`main_block_${o}`])}),i=_w(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 gt(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return tC(t,this._numMainBlocks)}extractParams(t){return eC(t,this._numMainBlocks)}};function aC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=bn(e),r=cf(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: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function rC(e){let t=[],n=Un(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return yn(e,t),{params:r,paramMappings:t}}var Ar;(function(e){e.FEMALE="female",e.MALE="male"})(Ar||(Ar={}));var bf=class extends an{constructor(t=new Ew(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof Er?this.faceFeatureExtractor.forwardInput(t):t,r=Xn(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Lp(r,n.fc.age).as1D(),i=Lp(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:Ta(a)}})}async forward(t){return this.forwardInput(await gt(t))}async predictAgeAndGender(t){let n=await gt(t),a=await this.forwardInput(n),r=dt(a.age),s=dt(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?Ar.MALE:Ar.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 aC(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=ff(t);return this.faceFeatureExtractor.loadFromWeightMap(n),rC(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 Bp=class extends zp{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)=>$t([wn([68],p,"float32"),wn([68],d,"float32")],1).as2D(1,136).as1D(),o=(p,d)=>{let{width:h,height:m}=r[p];return 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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 hC(t)}extractParams(t){return dC(t)}};function bC(e){let t=new so;return t.extractWeights(e),t}function zre(e){return bC(e)}var xC=class extends so{};var vC=.4,wC=[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)],kC=[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)],IC=[117.001,114.697,97.404],NC="tiny_yolov2_model",TC="tiny_yolov2_separable_conv_model";var Nf=e=>typeof e=="number";function Dw(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(!Nf(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=>Nf(t.x)&&Nf(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(Nf)))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,de(.10000000149011612));return Z(Ye(fe(e,t)),t)})}function Fr(e,t){return D(()=>{let n=Zn(e,[[0,0],[1,1],[1,1],[0,0]]);return n=At(n,t.conv.filters,[1,1],"valid"),n=fe(n,t.bn.sub),n=L(n,t.bn.truediv),n=Z(n,t.conv.bias),Au(n)})}function $r(e,t){return D(()=>{let n=Zn(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Fi(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=Z(n,t.bias),Au(n)})}function Bre(e,t){let n=Nu(e,t);function a(i,o){let l=tt(e(i)),c=tt(e(i));return t.push({paramPath:`${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,`${l}/bn`);return{conv:c,bn:u}}let s=Tu(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function SC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=bn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=Bre(r,i),u;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,v]=a,x=t.isFirstLayerConv2d?o(p,d,3,"conv0"):c(p,d,"conv0"),N=c(d,h,"conv1"),T=c(h,m,"conv2"),C=c(m,f,"conv3"),F=c(f,g,"conv4"),$=c(g,y,"conv5"),P=b?c(y,b,"conv6"):void 0,B=v?c(b,v,"conv7"):void 0,W=o(v||b||y,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:F,conv5:$,conv6:P,conv7:B,conv8:W}}else{let[p,d,h,m,f,g,y,b,v]=a,x=l(p,d,"conv0"),N=l(d,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),F=l(f,g,"conv4"),$=l(g,y,"conv5"),P=l(y,b,"conv6"),B=l(b,v,"conv7"),W=o(v,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:F,conv5:$,conv6:P,conv7:B,conv8:W}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function Wre(e,t){let n=Un(e,t);function a(o){let l=n(`${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(`${o}/bn`);return{conv:l,bn:c}}let i=Su(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function CC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Wre(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return yn(e,n),{params:i,paramMappings:n}}var ur=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Rw=class extends an{constructor(t){super("TinyYolov2");Dw(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Fr(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=Fr(a,n.conv6),a=Fr(a,n.conv7),no(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Au(no(t,n.conv0,"valid",!1)):$r(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=n.conv6?$r(a,n.conv6):a,a=n.conv7?$r(a,n.conv7):a,no(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=ce(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?La(r,this.config.meanRgb):r,r=r.div(de(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await gt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new ur(n),s=await gt(t),i=await this.forwardInput(s,a),o=D(()=>dt(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 lw(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new ds(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return CC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Rw.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 ${a} filterSizes in config`);return SC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),v=y.slice([0,0,0,4],[c,c,u,1]),x=this.withClassScores?Ta(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):de(0);return[b,v,x]}),m=[],f=await d.array(),g=await p.array();for(let y=0;ya){let N=(b+Ap(g[y][b][v][0]))/c*o,T=(y+Ap(g[y][b][v][1]))/c*l,C=Math.exp(g[y][b][v][2])*this.config.anchors[v].x/c*o,F=Math.exp(g[y][b][v][3])*this.config.anchors[v].y/c*l,$=N-C/2,P=T-F/2,B={row:y,col:b,anchor:v},{classScore:W,label:G}=this.withClassScores?await this.extractPredictedClass(h,B):{classScore:1,label:0};m.push({box:new bu($,P,$+C,P+F),score:x,classScore:x*W,label:G,...B})}}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=Rw;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:vC,classes:["face"],...t?{anchors:kC,meanRgb:IC}:{anchors:wC,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?TC:NC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Vre(e,t=!0){let n=new $u(t);return n.extractWeights(e),n}var Tf=class extends ur{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var wa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function io(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>ao(l)?r(l):l.detection),i=a||(t instanceof Ae?await Iu(t,s):await ku(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ae&&l.dispose()),o}async function Du(e,t,n,a,r){return io([e],t,async s=>n(s[0]),a,r)}var _C=.4,EC=[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)],AC=[117.001,114.697,97.404];var Ru=class extends Fu{constructor(){let t={withSeparableConvs:!0,iouThreshold:_C,classes:["face"],anchors:EC,meanRgb:AC,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 nt={ssdMobilenetv1:new so,tinyFaceDetector:new Ru,tinyYolov2:new $u,faceLandmark68Net:new _u,faceLandmark68TinyNet:new xf,faceRecognitionNet:new Eu,faceExpressionNet:new gf,ageGenderNet:new bf},FC=(e,t)=>nt.ssdMobilenetv1.locateFaces(e,t),Ure=(e,t)=>nt.tinyFaceDetector.locateFaces(e,t),Gre=(e,t)=>nt.tinyYolov2.locateFaces(e,t),$C=e=>nt.faceLandmark68Net.detectLandmarks(e),Hre=e=>nt.faceLandmark68TinyNet.detectLandmarks(e),jre=e=>nt.faceRecognitionNet.computeFaceDescriptor(e),qre=e=>nt.faceExpressionNet.predictExpressions(e),Kre=e=>nt.ageGenderNet.predictAgeAndGender(e),DC=e=>nt.ssdMobilenetv1.load(e),Xre=e=>nt.tinyFaceDetector.load(e),Yre=e=>nt.tinyYolov2.load(e),Zre=e=>nt.faceLandmark68Net.load(e),Jre=e=>nt.faceLandmark68TinyNet.load(e),Qre=e=>nt.faceRecognitionNet.load(e),ese=e=>nt.faceExpressionNet.load(e),tse=e=>nt.ageGenderNet.load(e),nse=DC,ase=FC,rse=$C;var Mw=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Ou=class extends Mw{async run(){let t=await this.parentTask,n=await io(t,this.input,async a=>Promise.all(a.map(r=>nt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>yf(a,n[r]))}withAgeAndGender(){return new Mu(this,this.input)}},Lu=class extends Mw{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 yf(t,n)}withAgeAndGender(){return new Pu(this,this.input)}},uo=class extends Ou{withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptors(){return new fs(this,this.input)}},co=class extends Lu{withAgeAndGender(){return new lo(this,this.input)}withFaceDescriptor(){return new gs(this,this.input)}};var Pw=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Mu=class extends Pw{async run(){let t=await this.parentTask,n=await io(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 kf(If(a,i,o),s)})}withFaceExpressions(){return new Ou(this,this.input)}},Pu=class extends Pw{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 kf(If(t,a,r),n)}withFaceExpressions(){return new Lu(this,this.input)}},oo=class extends Mu{withFaceExpressions(){return new uo(this,this.input)}withFaceDescriptors(){return new fs(this,this.input)}},lo=class extends Pu{withFaceExpressions(){return new co(this,this.input)}withFaceDescriptor(){return new gs(this,this.input)}};var Sf=class extends wa{constructor(t,n){super();this.parentTask=t;this.input=n}},fs=class extends Sf{async run(){let t=await this.parentTask;return(await io(t,this.input,a=>Promise.all(a.map(r=>nt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>wf(t[r],a))}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}},gs=class extends Sf{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 wf(t,n)}withFaceExpressions(){return new co(this,this.input)}withAgeAndGender(){return new lo(this,this.input)}};var Cf=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?nt.faceLandmark68TinyNet:nt.faceLandmark68Net}},_f=class extends Cf{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Ae?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 Ae&&s.dispose()),t.map((s,i)=>Cu(s,r[i]))}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptors(){return new fs(this,this.input)}},Ef=class extends Cf{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ae?await Iu(this.input,[n]):await ku(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ae&&s.dispose()),Cu(t,r)}withFaceExpressions(){return new co(this,this.input)}withAgeAndGender(){return new lo(this,this.input)}withFaceDescriptor(){return new gs(this,this.input)}};var Af=class extends wa{constructor(t,n=new va){super();this.input=t;this.options=n}},Vp=class extends Af{async run(){let{input:t,options:n}=this,a=n instanceof Tf?r=>nt.tinyFaceDetector.locateFaces(r,n):n instanceof va?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 _f(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Ou(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Mu(this.runAndExtendWithFaceDetections(),this.input)}},Ff=class extends Af{async run(){let t=await new Vp(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 Ef(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Lu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Pu(this.runAndExtendWithFaceDetection(),this.input)}};function sse(e,t=new va){return new Ff(e,t)}function $f(e,t=new va){return new Vp(e,t)}async function RC(e,t){return $f(e,new va(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function ise(e,t={}){return $f(e,new ur(t)).withFaceLandmarks().withFaceDescriptors()}var ose=RC;function Ow(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 Df=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 _r)return i;if(i instanceof Float32Array)return new _r(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new _r(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=>Ow(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Fp(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>_r.fromJSON(a));return new Df(n,t.distanceThreshold)}};function lse(e){let t=new Ru;return t.extractWeights(e),t}function MC(e,t){let{width:n,height:a}=new gn(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>MC(r,{width:n,height:a}));if(ao(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Cu(Ji(e,r),s)}return or(e)?Ji(e,e.detection.forSize(n,a)):e instanceof ta||e instanceof bt?e.forSize(n,a):e}var use=typeof process!="undefined",cse=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",pse={faceapi:Q2,node:use,browser:cse};export{bf as AgeGenderNet,bu as BoundingBox,ut as Box,wa as ComposableTask,fs as ComputeAllFaceDescriptorsTask,Sf as ComputeFaceDescriptorsTaskBase,gs as ComputeSingleFaceDescriptorTask,_f as DetectAllFaceLandmarksTask,Vp as DetectAllFacesTask,Cf as DetectFaceLandmarksTaskBase,Af as DetectFacesTaskBase,Ef as DetectSingleFaceLandmarksTask,Ff as DetectSingleFaceTask,gn as Dimensions,Iw as FACE_EXPRESSION_LABELS,bt as FaceDetection,xC as FaceDetectionNet,gf as FaceExpressionNet,ms as FaceExpressions,_u as FaceLandmark68Net,xf as FaceLandmark68TinyNet,oC as FaceLandmarkNet,ta as FaceLandmarks,H2 as FaceLandmarks5,vu as FaceLandmarks68,Fp as FaceMatch,Df as FaceMatcher,Eu as FaceRecognitionNet,Ar as Gender,$p as LabeledBox,_r as LabeledFaceDescriptors,Er as NetInput,an as NeuralNetwork,ds as ObjectDetection,De as Point,j2 as PredictedBox,xu as Rect,so as SsdMobilenetv1,va as SsdMobilenetv1Options,Ru as TinyFaceDetector,Tf as TinyFaceDetectorOptions,$u as TinyYolov2,ur as TinyYolov2Options,ose as allFaces,RC as allFacesSsdMobilenetv1,ise as allFacesTinyYolov2,yw as awaitMediaLoaded,bw as bufferToImage,jre as computeFaceDescriptor,wu as createCanvas,Mp as createCanvasFromMedia,zre as createFaceDetectionNet,_re as createFaceRecognitionNet,bC as createSsdMobilenetv1,lse as createTinyFaceDetector,Vre as createTinyYolov2,$f as detectAllFaces,$C as detectFaceLandmarks,Hre as detectFaceLandmarksTiny,rse as detectLandmarks,sse as detectSingleFace,Cw as draw,st as env,Ow as euclideanDistance,kf as extendWithAge,wf as extendWithFaceDescriptor,Ji as extendWithFaceDetection,yf as extendWithFaceExpressions,Cu as extendWithFaceLandmarks,If as extendWithGender,Iu as extractFaceTensors,ku as extractFaces,bre as fetchImage,ww as fetchJson,xre as fetchNetWeights,to as fetchOrThrow,Cn as getContext2dOrThrow,eo as getMediaDimensions,xw as imageTensorToCanvas,vw as imageToSquare,ure as inverseSigmoid,iw as iou,of as isMediaElement,Rp as isMediaLoaded,Ere as isWithAge,or as isWithFaceDetection,Nw as isWithFaceExpressions,ao as isWithFaceLandmarks,Are as isWithGender,tse as loadAgeGenderModel,nse as loadFaceDetectionModel,ese as loadFaceExpressionModel,Zre as loadFaceLandmarkModel,Jre as loadFaceLandmarkTinyModel,Qre as loadFaceRecognitionModel,DC as loadSsdMobilenetv1Model,Xre as loadTinyFaceDetectorModel,Yre as loadTinyYolov2Model,kw as loadWeightMap,ase as locateFaces,vre as matchDimensions,ow as minBbox,nt as nets,lw as nonMaxSuppression,La as normalize,uw as padToSquare,Kre as predictAgeAndGender,qre as recognizeFaceExpressions,MC as resizeResults,Qi as resolveInput,lre as shuffleArray,Ap as sigmoid,FC as ssdMobilenetv1,Ug as tf,Ure as tinyFaceDetector,Gre as tinyYolov2,gt as toNetInput,nw as utils,Dw as validateConfig,pse as version}; /** * @license * Copyright 2017 Google LLC. All Rights Reserved. diff --git a/dist/face-api.js b/dist/face-api.js index 7f9f148..6a0533e 100644 --- a/dist/face-api.js +++ b/dist/face-api.js @@ -4045,7 +4045,7 @@ return a / b;`,bJ=` } setOutput(${l}); } - `}};function Lee(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=_.getAxesPermutation([c],o),p=r;u!=null&&(p=Sn({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(p),c=_.getInnerMostAxes(1,o)[0]);let d=_.segment_util.computeOutShape(p.shape,c,i),h=k.sizeFromShape([p.shape[c]]),m=be({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=vh(r.dtype),g=(x,N,T,C,F)=>{let $=x.shape[0],P=x.shape[1],B=_.segment_util.segOpComputeOptimalWindowSize(P,F),W={windowSize:B,inSize:P,batchSize:$,numSegments:F},G=new Oee(W,N),q=n.compileAndRun(G,[x,T],C);if(l.push(q),q.shape[1]===F)return q;let j=r2({backend:n,attrs:{start:0,stop:F,step:1,dtype:"float32"}}),X=o2({inputs:{x:j},backend:n,attrs:{reps:[P/B]}});return 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nte={kernelName:Ii,backendName:"wasm",kernelFunc:yf,setupFunc:Qee};function Cu(e,t,n){let a=e.shape,r=e.shape.length,s=k.parseAxisParam(t,a),i=s,o=_.getAxesPermutation(i,r),l=null,c=!1;if(o!=null){let u=new Array(r);for(let d=0;d`new shape: ${i}, old shape: ${a.shape}. 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Got input batch dimensions of (${m}) and (${f}).`);let v=(g>y?r.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,h]);k.assert(u===p,()=>`Error in matMul: inner shapes (${u}) and (${p}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,u,d]:[g,d,u],N=o?[y,h,p]:[y,p,h],T=Oa({inputs:{x:r},backend:n,attrs:{shape:x}}),C=Oa({inputs:{x:s},backend:n,attrs:{shape:N}}),F=n.dataIdMap.get(T.dataId).id,$=n.dataIdMap.get(C.dataId).id,P=i?T.shape[2]:T.shape[1],B=o?C.shape[1]:C.shape[2],W=Math.max(g,y),G=n.makeOutput([W,P,B],T.dtype),q=n.dataIdMap.get(G.dataId).id,j=new Uint8Array(new Int32Array(T.shape).buffer),X=new Uint8Array(new Int32Array(C.shape).buffer);return h2(F,j,T.shape.length,$,X,C.shape.length,i,o,q),n.disposeData(T.dataId),n.disposeData(C.dataId),G.shape=v,G}var dte={kernelName:Ms,backendName:"wasm",setupFunc:cte,kernelFunc:pte};function bf(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var hte={kernelName:Ps,backendName:"wasm",kernelFunc:bf},mte=Cn(Os),m2;function fte(e){m2=e.wasm.cwrap(Gr,null,["number","number","number","number"])}function gte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(l.dataId).id;return m2(o,s,i,c),l}var yte={kernelName:Gr,backendName:"wasm",setupFunc:fte,kernelFunc:gte};function f2(e){let{inputs:t,backend:n}=e,a=k.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=_.computeOutShape(t.map(h=>h.shape),a),s=t.filter(h=>k.sizeFromShape(h.shape)>0);if(s.length===1)return gf({inputs:{x:s[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(k.sizeFromShape(r)===0)return i;let o=s.map(h=>h.shape);if(_.assertParamsConsistent(o,a),s[0].dtype==="string"){let h=s.map(v=>{let x=k.sizeFromShape(v.shape.slice(a));return Oa({inputs:{x:v},backend:n,attrs:{shape:[-1,x]}})}),m=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape}));r=_.computeOutShape(h.map(v=>v.shape),1);let f=h[0].shape[0]===1,g=Pv(m,r,t[0].dtype,f),y=_.computeOutShape(s.map(v=>v.shape),a);i.shape=y;let b=n.dataIdMap.get(i.dataId);return b.stringBytes=_.fromStringArrayToUint8(g),i}let l=k.sizeFromShape(s[0].shape.slice(0,a)),c=0,u=s.map(h=>{let m=k.sizeFromShape(h.shape.slice(a));return c+=m,m}),p=s.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h`cumsum does not support ${r.dtype} tensors in the WASM backend`);let c=_.getAxesPermutation([s],l),u=r;c!==null&&(u=yf({inputs:{x:r},attrs:{perm:c},backend:n}));let p=_.getInnerMostAxes(1,l)[0];_.assertAxesAreInnerMostDims("cumsum",[p],l);let d=n.makeOutput(u.shape,u.dtype),h=u.shape[p],m=n.dataIdMap.get(u.dataId).id,f=n.dataIdMap.get(d.dataId).id;x2(m,i?1:0,o?1:0,h,f,Vn[r.dtype]);let g=d;if(c!==null){let y=_.getUndoAxesPermutation(c);g=yf({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(u.dataId),n.disposeData(d.dataId)}return g}var Fte={kernelName:Ws,backendName:"wasm",setupFunc:Ete,kernelFunc:Ate},v2;function $te(e){v2=e.wasm.cwrap(Yo,null,["number","number","number","array","number","array","array","number","number"])}function Dte(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],c=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=c*s,h=u/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),v=new Uint8Array(new Int32Array(k.computeStrides(m)).buffer),x=t.dataIdMap.get(f.dataId).id;return v2(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,v,m.length,x),f}var Rte={kernelName:Yo,backendName:"wasm",setupFunc:$te,kernelFunc:Dte},w2;function Mte(e){w2=e.wasm.cwrap(Vs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Pte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:p}=n,d=c==null?[1,1]:c,h=_.computeConv2DInfo(r.shape,s.shape,l,d,u,p,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,N=h.dilationWidth,T=h.strideHeight,C=h.strideWidth,F=h.inChannels,$=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let B=a.makeOutput(h.outShape,"float32"),W=a.dataIdMap.get(B.dataId).id;return w2(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,v,P,x,N,T,C,F,$,W),B}var Ote={kernelName:Vs,backendName:"wasm",setupFunc:Mte,kernelFunc:Pte},Lte=!1,zte=gn(Qo,Lte,"bool"),Bte=Cn(Gs);function mw(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Oa({inputs:{x:r},backend:a,attrs:{shape:o}})}var Wte={kernelName:el,backendName:"wasm",kernelFunc:mw};function Vte(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var Ute={kernelName:vc,backendName:"wasm",kernelFunc:Vte},k2;function Gte(e){k2=e.wasm.cwrap(nl,null,["number","number","number","number","number","number"])}function Hte(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,c,u]=a.shape;return k2(s,o,l,c,u,i),r}var jte={kernelName:nl,backendName:"wasm",kernelFunc:Hte,setupFunc:Gte},qte=Cn(Hs),Kte=!1,Xte=gn(js,Kte),I2;function Yte(e){I2=e.wasm.cwrap(qs,null,["number","number","number","number","number","number","number"])}function Zte(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=c!=null?t.dataIdMap.get(c.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return I2(u,p,d,h,m,r,g),f}var Jte={kernelName:qs,backendName:"wasm",setupFunc:Yte,kernelFunc:Zte},N2;function Qte(e){N2=e.wasm.cwrap(Ti,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ene(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d),g=Dp[h];if(g==null)throw new Error(`${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 ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,F=f.padInfo.right,$=f.padInfo.bottom,P=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,K=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return N2(y,K,ne,J,b,N,T,x,C,F,$,P,X,B,W,G,q,j,v,g,oe,m||0,Q),se}var tne={kernelName:Ti,backendName:"wasm",setupFunc:Qte,kernelFunc:ene},T2;function nne(e){T2=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 ane(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=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d,!0),g=Dp[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 ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,F=f.padInfo.right,$=f.padInfo.bottom,P=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,K=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return T2(y,K,ne,J,b,N,T,x,C,F,$,P,X,B,W,G,q,j,v,g,oe,m||0,Q),se}var rne={kernelName:Si,backendName:"wasm",setupFunc:nne,kernelFunc:ane},S2;function sne(e){S2=e.wasm.cwrap(rl,null,["number","number","number","number","number","number","array","number"])}function ine(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Ry.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 S2(d,Vn[a.dtype],h,i,p,o,m,f),c}var one={kernelName:rl,backendName:"wasm",setupFunc:sne,kernelFunc:ine},C2;function lne(e){C2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function une(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=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=Oa({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),d=Oa({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 C2(g,Vn[r.dtype],v,f,y,c.batchSize,x,b),t.disposeData(u.dataId),t.disposeData(d.dataId),m.shape=c.outputShape,m}var cne={kernelName:al,backendName:"wasm",setupFunc:lne,kernelFunc:une},pne=!1,dne=gn(sl,pne,"bool"),hne=!1,mne=gn(Ks,hne,"bool"),_2;function fne(e){_2=e.wasm.cwrap(Ys,null,["number","number","number"])}function gne(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;_2(r,n,i)}return s}var yne={kernelName:Ys,backendName:"wasm",setupFunc:fne,kernelFunc:gne},bne=!1,xne=gn(ul,bne,"bool"),vne=!1,wne=gn(cl,vne,"bool"),kne=Cn(Zs),Ine=!1,Nne=gn(dl,Ine,"bool"),E2;function Tne(e){E2=e.wasm.cwrap(Js,null,["number, number, number"])}function Sne(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;_.assertAxesAreInnerMostDims("max",u,h);let[m,f]=_.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;E2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=_.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var Cne={kernelName:Js,backendName:"wasm",setupFunc:Tne,kernelFunc:Sne},_ne=!1,Ene=gn(Qs,_ne),A2;function Ane(e){A2=e.wasm.cwrap(ei,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Fne(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=_.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"),F=a.dataIdMap.get(C.dataId).id;return A2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,v,x,N,T,F),C}var $ne={kernelName:ei,backendName:"wasm",setupFunc:Ane,kernelFunc:Fne},F2;function Dne(e){F2=e.wasm.cwrap(ti,null,["number, number, number"])}function Rne(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 x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x,m=_.getInnerMostAxes(m.length,c.shape.length))}_.assertAxesAreInnerMostDims("mean",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=c;c.dtype!=="float32"&&(b=bf({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;F2(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(v.shape,d);v.shape=x}return c.dtype!=="float32"&&t.disposeData(b.dataId),v}var Mne={kernelName:ti,backendName:"wasm",setupFunc:Dne,kernelFunc:Rne},$2;function Pne(e){$2=e.wasm.cwrap(ni,null,["number, number, number"])}function One(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;_.assertAxesAreInnerMostDims("min",p,m);let[f,g]=_.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;$2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var Lne={kernelName:ni,backendName:"wasm",setupFunc:Pne,kernelFunc:One},zne=!1,Bne=gn(ai,zne),Wne=!0,Vne=gn(ri,Wne),Une=Cn(ml);function fw(e,t){let n=new 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n=e(`${t}/filters`,4),a=e(`${t}/bias`,1);return{filters:n,bias:a}}}function Vf(e,t){let n=Gn(e,t),a=Wf(n),r=Pu(n);function s(o,l=!1){let c=l?a(`${o}/conv0`):r(`${o}/conv0`),u=r(`${o}/conv1`),p=r(`${o}/conv2`);return{conv0:c,conv1:u,conv2:p}}function i(o,l=!1){let c=l?a(`${o}/conv0`):r(`${o}/conv0`),u=r(`${o}/conv1`),p=r(`${o}/conv2`),d=r(`${o}/conv3`);return{conv0:c,conv1:u,conv2:p,conv3:d}}return{extractDenseBlock3Params:s,extractDenseBlock4Params:i}}function uC(e){let t=[],{extractDenseBlock4Params:n}=Vf(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2"),dense3:n("dense3")};return bn(e,t),{params:a,paramMappings:t}}var Bp=class extends Qt{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceFeatureExtractor - load model before inference");return D(()=>{let a=ce(t.toBatchTensor(112,!0),"float32"),s=wa(a,[122.782,117.001,104.298]).div(de(255)),i=zp(s,n.dense0,!0);return 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this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof pr?this.faceFeatureExtractor.forwardInput(t):t;return Wp(a.as2D(a.shape[0],-1),n.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return cC(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=Uf(t);return this.faceFeatureExtractor.loadFromWeightMap(n),pC(a)}extractParams(t){let n=this.getClassifierChannelsIn(),a=this.getClassifierChannelsOut(),r=a*n+a,s=t.slice(0,t.length-r),i=t.slice(t.length-r);return this.faceFeatureExtractor.extractWeights(s),this.extractClassifierParams(i)}};var 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this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var ks=class extends Qt{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return D(()=>{let a=ce(t.toBatchTensor(512,!1),"float32"),r=fe(L(a,de(.007843137718737125)),de(1)),s=EC(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=$C(s.out,s.conv11,n.prediction_layer);return FC(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await gt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new na(n),s=await gt(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(F=>F*g),[T,C]=[Math.max(0,y[v][1]),Math.min(1,y[v][3])].map(F=>F*f);return new yt(u[v],new so(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 _C(t)}extractParams(t){return CC(t)}};function Lw(e){let t=new ks;return t.extractWeights(e),t}function DC(e){return Lw(e)}var zw=class extends ks{};var RC=.4,MC=[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)],PC=[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)],OC=[117.001,114.697,97.404],LC="tiny_yolov2_model",zC="tiny_yolov2_separable_conv_model";var Kf=e=>typeof e=="number";function Xf(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(!Kf(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=>Kf(t.x)&&Kf(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(Kf)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Ou(e){return D(()=>{let t=L(e,de(.10000000149011612));return Z(Ye(fe(e,t)),t)})}function Dr(e,t){return D(()=>{let n=Jn(e,[[0,0],[1,1],[1,1],[0,0]]);return n=At(n,t.conv.filters,[1,1],"valid"),n=fe(n,t.bn.sub),n=L(n,t.bn.truediv),n=Z(n,t.conv.bias),Ou(n)})}function Rr(e,t){return D(()=>{let n=Jn(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Oi(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=Z(n,t.bias),Ou(n)})}function lse(e,t){let n=Ru(e,t);function a(i,o){let l=nt(e(i)),c=nt(e(i));return t.push({paramPath:`${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,`${l}/bn`);return{conv:c,bn:u}}let s=Mu(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function BC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=xn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=lse(r,i),u;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,v]=a,x=t.isFirstLayerConv2d?o(p,d,3,"conv0"):c(p,d,"conv0"),N=c(d,h,"conv1"),T=c(h,m,"conv2"),C=c(m,f,"conv3"),F=c(f,g,"conv4"),$=c(g,y,"conv5"),P=b?c(y,b,"conv6"):void 0,B=v?c(b,v,"conv7"):void 0,W=o(v||b||y,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:F,conv5:$,conv6:P,conv7:B,conv8:W}}else{let[p,d,h,m,f,g,y,b,v]=a,x=l(p,d,"conv0"),N=l(d,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),F=l(f,g,"conv4"),$=l(g,y,"conv5"),P=l(y,b,"conv6"),B=l(b,v,"conv7"),W=o(v,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:F,conv5:$,conv6:P,conv7:B,conv8:W}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function use(e,t){let n=Gn(e,t);function a(o){let l=n(`${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(`${o}/bn`);return{conv:l,bn:c}}let i=Pu(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function WC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=use(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return bn(e,n),{params:i,paramMappings:n}}var Wa=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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 Bw=class extends Qt{constructor(t){super("TinyYolov2");Xf(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=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=Dr(a,n.conv6),a=Dr(a,n.conv7),co(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Ou(co(t,n.conv0,"valid",!1)):Rr(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=n.conv6?Rr(a,n.conv6):a,a=n.conv7?Rr(a,n.conv7):a,co(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=ce(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?wa(r,this.config.meanRgb):r,r=r.div(de(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await gt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Wa(n),s=await gt(t),i=await this.forwardInput(s,a),o=D(()=>dt(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 Sf(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new Fr(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return WC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Bw.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 ${a} filterSizes in config`);return BC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),v=y.slice([0,0,0,4],[c,c,u,1]),x=this.withClassScores?Sa(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):de(0);return[b,v,x]}),m=[],f=await d.array(),g=await p.array();for(let y=0;ya){let N=(b+Eu(g[y][b][v][0]))/c*o,T=(y+Eu(g[y][b][v][1]))/c*l,C=Math.exp(g[y][b][v][2])*this.config.anchors[v].x/c*o,F=Math.exp(g[y][b][v][3])*this.config.anchors[v].y/c*l,$=N-C/2,P=T-F/2,B={row:y,col:b,anchor:v},{classScore:W,label:G}=this.withClassScores?await this.extractPredictedClass(h,B):{classScore:1,label:0};m.push({box:new ro($,P,$+C,P+F),score:x,classScore:x*W,label:G,...B})}}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=Bw;Lu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var go=class extends Lu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:RC,classes:["face"],...t?{anchors:PC,meanRgb:OC}:{anchors:MC,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 yt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?zC:LC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function VC(e,t=!0){let n=new go(t);return n.extractWeights(e),n}var Jp=class extends Wa{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var aa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function yo(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>ws(l)?r(l):l.detection),i=a||(t instanceof Ae?await uo(t,s):await lo(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ae&&l.dispose()),o}async function zu(e,t,n,a,r){return yo([e],t,async s=>n(s[0]),a,r)}var UC=.4,GC=[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)],HC=[117.001,114.697,97.404];var bo=class extends Lu{constructor(){let t={withSeparableConvs:!0,iouThreshold:UC,classes:["face"],anchors:GC,meanRgb:HC,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 yt(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 ks,tinyFaceDetector:new bo,tinyYolov2:new go,faceLandmark68Net:new ho,faceLandmark68TinyNet:new qp,faceRecognitionNet:new mo,faceExpressionNet:new Up,ageGenderNet:new Hp},Ww=(e,t)=>et.ssdMobilenetv1.locateFaces(e,t),jC=(e,t)=>et.tinyFaceDetector.locateFaces(e,t),qC=(e,t)=>et.tinyYolov2.locateFaces(e,t),Vw=e=>et.faceLandmark68Net.detectLandmarks(e),KC=e=>et.faceLandmark68TinyNet.detectLandmarks(e),XC=e=>et.faceRecognitionNet.computeFaceDescriptor(e),YC=e=>et.faceExpressionNet.predictExpressions(e),ZC=e=>et.ageGenderNet.predictAgeAndGender(e),Uw=e=>et.ssdMobilenetv1.load(e),JC=e=>et.tinyFaceDetector.load(e),QC=e=>et.tinyYolov2.load(e),e_=e=>et.faceLandmark68Net.load(e),t_=e=>et.faceLandmark68TinyNet.load(e),n_=e=>et.faceRecognitionNet.load(e),a_=e=>et.faceExpressionNet.load(e),r_=e=>et.ageGenderNet.load(e),s_=Uw,i_=Ww,o_=Vw;var Gw=class extends aa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Vu=class extends Gw{async run(){let t=await this.parentTask,n=await yo(t,this.input,async a=>Promise.all(a.map(r=>et.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>Gp(a,n[r]))}withAgeAndGender(){return new Bu(this,this.input)}},Uu=class extends Gw{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 Gp(t,n)}withAgeAndGender(){return new Wu(this,this.input)}},wo=class extends Vu{withAgeAndGender(){return new xo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},ko=class extends Uu{withAgeAndGender(){return new vo(this,this.input)}withFaceDescriptor(){return new Pr(this,this.input)}};var Hw=class extends aa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Bu=class extends Hw{async run(){let t=await this.parentTask,n=await yo(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 Yp(Zp(a,i,o),s)})}withFaceExpressions(){return new Vu(this,this.input)}},Wu=class extends Hw{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 Yp(Zp(t,a,r),n)}withFaceExpressions(){return new Uu(this,this.input)}},xo=class extends Bu{withFaceExpressions(){return new wo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},vo=class extends Wu{withFaceExpressions(){return new ko(this,this.input)}withFaceDescriptor(){return new Pr(this,this.input)}};var Qp=class extends aa{constructor(t,n){super();this.parentTask=t;this.input=n}},Mr=class extends Qp{async run(){let t=await this.parentTask;return(await yo(t,this.input,a=>Promise.all(a.map(r=>et.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>Xp(t[r],a))}withFaceExpressions(){return new wo(this,this.input)}withAgeAndGender(){return new xo(this,this.input)}},Pr=class extends Qp{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 Xp(t,n)}withFaceExpressions(){return new ko(this,this.input)}withAgeAndGender(){return new vo(this,this.input)}};var ed=class extends aa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?et.faceLandmark68TinyNet:et.faceLandmark68Net}},td=class extends ed{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Ae?await uo(this.input,n):await lo(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Ae&&s.dispose()),t.map((s,i)=>po(s,r[i]))}withFaceExpressions(){return new wo(this,this.input)}withAgeAndGender(){return new xo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},nd=class extends ed{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ae?await uo(this.input,[n]):await lo(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ae&&s.dispose()),po(t,r)}withFaceExpressions(){return new ko(this,this.input)}withAgeAndGender(){return new vo(this,this.input)}withFaceDescriptor(){return new Pr(this,this.input)}};var ad=class extends aa{constructor(t,n=new na){super();this.input=t;this.options=n}},Gu=class extends ad{async run(){let{input:t,options:n}=this,a=n instanceof Jp?r=>et.tinyFaceDetector.locateFaces(r,n):n instanceof na?r=>et.ssdMobilenetv1.locateFaces(r,n):n instanceof Wa?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 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this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>Yf(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 id(n,t.distanceThreshold)}};function p_(e){let t=new bo;return t.extractWeights(e),t}function qw(e,t){let{width:n,height:a}=new ln(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>qw(r,{width:n,height:a}));if(ws(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return po(gs(e,r),s)}return za(e)?gs(e,e.detection.forSize(n,a)):e instanceof Un||e instanceof yt?e.forSize(n,a):e}var pse=typeof process!="undefined",dse=typeof navigator!="undefined"&&typeof 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Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return N2(y,K,ne,J,b,N,T,x,C,F,$,P,X,B,W,G,q,j,v,g,oe,m||0,Q),se}var tne={kernelName:Ti,backendName:"wasm",setupFunc:Qte,kernelFunc:ene},T2;function nne(e){T2=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 ane(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=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d,!0),g=Dp[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 ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,F=f.padInfo.right,$=f.padInfo.bottom,P=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,K=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return T2(y,K,ne,J,b,N,T,x,C,F,$,P,X,B,W,G,q,j,v,g,oe,m||0,Q),se}var rne={kernelName:Si,backendName:"wasm",setupFunc:nne,kernelFunc:ane},S2;function sne(e){S2=e.wasm.cwrap(rl,null,["number","number","number","number","number","number","array","number"])}function ine(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Ry.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 S2(d,Vn[a.dtype],h,i,p,o,m,f),c}var one={kernelName:rl,backendName:"wasm",setupFunc:sne,kernelFunc:ine},C2;function lne(e){C2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function une(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=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=Oa({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),d=Oa({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 C2(g,Vn[r.dtype],v,f,y,c.batchSize,x,b),t.disposeData(u.dataId),t.disposeData(d.dataId),m.shape=c.outputShape,m}var cne={kernelName:al,backendName:"wasm",setupFunc:lne,kernelFunc:une},pne=!1,dne=gn(sl,pne,"bool"),hne=!1,mne=gn(Ks,hne,"bool"),_2;function fne(e){_2=e.wasm.cwrap(Ys,null,["number","number","number"])}function gne(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;_2(r,n,i)}return s}var yne={kernelName:Ys,backendName:"wasm",setupFunc:fne,kernelFunc:gne},bne=!1,xne=gn(ul,bne,"bool"),vne=!1,wne=gn(cl,vne,"bool"),kne=Cn(Zs),Ine=!1,Nne=gn(dl,Ine,"bool"),E2;function Tne(e){E2=e.wasm.cwrap(Js,null,["number, number, number"])}function Sne(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;_.assertAxesAreInnerMostDims("max",u,h);let[m,f]=_.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;E2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=_.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var Cne={kernelName:Js,backendName:"wasm",setupFunc:Tne,kernelFunc:Sne},_ne=!1,Ene=gn(Qs,_ne),A2;function Ane(e){A2=e.wasm.cwrap(ei,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Fne(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=_.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"),F=a.dataIdMap.get(C.dataId).id;return A2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,v,x,N,T,F),C}var $ne={kernelName:ei,backendName:"wasm",setupFunc:Ane,kernelFunc:Fne},F2;function Dne(e){F2=e.wasm.cwrap(ti,null,["number, number, number"])}function Rne(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 x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x,m=_.getInnerMostAxes(m.length,c.shape.length))}_.assertAxesAreInnerMostDims("mean",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=c;c.dtype!=="float32"&&(b=bf({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;F2(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(v.shape,d);v.shape=x}return c.dtype!=="float32"&&t.disposeData(b.dataId),v}var Mne={kernelName:ti,backendName:"wasm",setupFunc:Dne,kernelFunc:Rne},$2;function Pne(e){$2=e.wasm.cwrap(ni,null,["number, number, number"])}function One(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;_.assertAxesAreInnerMostDims("min",p,m);let[f,g]=_.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;$2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var Lne={kernelName:ni,backendName:"wasm",setupFunc:Pne,kernelFunc:One},zne=!1,Bne=gn(ai,zne),Wne=!0,Vne=gn(ri,Wne),Une=Cn(ml);function fw(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 D2;function Gne(e){D2=e.wasm.cwrap(gl,"number",["number","number","number","number","number"])}function Hne(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=D2(c,u,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=fw(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var jne={kernelName:gl,backendName:"wasm",setupFunc:Gne,kernelFunc:Hne},R2;function qne(e){R2=e.wasm.cwrap(yl,"number",["number","number","number","number","number","bool"])}function Kne(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=R2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=fw(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var Xne={kernelName:yl,backendName:"wasm",setupFunc:qne,kernelFunc:Kne},M2;function Yne(e){M2=e.wasm.cwrap(bl,"number",["number","number","number","number","number","number"])}function Zne(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}=fw(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:bl,backendName:"wasm",setupFunc:Yne,kernelFunc:Zne},Qne=!1,eae=gn(fl,Qne,"bool"),P2;function tae(e){P2=e.wasm.cwrap(si,null,["number","number","number","number","number"])}function nae(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 P2(u,s,i,o,c),l}var aae={kernelName:si,backendName:"wasm",setupFunc:tae,kernelFunc:nae};function rae(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var sae={kernelName:xl,backendName:"wasm",kernelFunc:rae};function iae(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return mw({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 dtypes")});let o=t.map(l=>mw({inputs:{input:l},backend:n,attrs:{dim:r}}));return f2({inputs:o,backend:n,attrs:{axis:r}})}var oae={kernelName:vl,backendName:"wasm",kernelFunc:iae},O2;function lae(e){O2=e.wasm.cwrap(ii,null,["number","array","number","number","array","array","number","number"])}function uae(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,constantValue:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),u=a.map(m=>m[0]),p=a.map(m=>m[1]),d=new Uint8Array(new Int32Array(u).buffer),h=new Uint8Array(new Int32Array(p).buffer);return O2(i,c,t.shape.length,Vn[t.dtype],d,h,r,l),o}var cae={kernelName:ii,backendName:"wasm",kernelFunc:uae,setupFunc:lae},pae=!1,dae=gn(oi,pae),L2;function hae(e){L2=e.wasm.cwrap(li,null,["number","number","number"])}function mae(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 L2(s,i,l),o}var fae={kernelName:li,backendName:"wasm",setupFunc:hae,kernelFunc:mae},z2;function gae(e){z2=e.wasm.cwrap(wl,null,["number","number","number","number"])}function yae(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=_.getInnerMostAxes(m.length,c.shape.length))}_.assertAxesAreInnerMostDims("prod",m,c.shape.length);let[f,g]=_.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;z2(l,y,Vn[b.dtype],v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var bae={kernelName:wl,backendName:"wasm",setupFunc:gae,kernelFunc:yae},xae=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=zv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},vae={kernelName:Sc,backendName:"wasm",kernelFunc:xae},wae=!0,kae=gn(Us,wae),Iae=Cn(ui),Nae=Cn(pi),B2;function Tae(e){B2=e.wasm.cwrap(ci,null,["number","number","number","number","number","number","number","number","number","number"])}function Sae(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=bf({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 B2(y,u,p,d,h,l,c,s?1:0,i?1:0,v),g!=null&&t.disposeData(g.dataId),b}var Cae={kernelName:ci,backendName:"wasm",setupFunc:Tae,kernelFunc:Sae},W2;function _ae(e){W2=e.wasm.cwrap(di,null,["number","array","number","array","number","number"])}function Eae(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 gf({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);W2(l,u,i.length,p,r.shape.length,c);let d=Oa({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),d}var Aae={kernelName:di,backendName:"wasm",kernelFunc:Eae,setupFunc:_ae},V2;function Fae(e){V2=e.wasm.cwrap(Ol,null,["number","number","number","number","number","number","number","number","array","number","number"])}function $ae(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=a,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(l.dataId).id,[p,d,h,m]=r.shape,[f,g]=_.getImageCenter(o,d,h),y=i===0,b=255,v=typeof i=="number"?[i,i,i,y?0:b]:[...i,b],x=new Uint8Array(new Int32Array(v).buffer);return V2(c,p,d,h,m,s,f,g,x,v.length,u),l}var Dae={kernelName:Ol,backendName:"wasm",kernelFunc:$ae,setupFunc:Fae},Rae=Cn(hi),Mae=Cn(mi),U2;function Pae(e){U2=e.wasm.cwrap(Nl,null,["number","number","number","number","number","number","array","number","number"])}function Oae(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}=My.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 U2(h,m,Vn[s.dtype],l,c,u,f,d,g),o}var Lae={kernelName:Nl,backendName:"wasm",setupFunc:Pae,kernelFunc:Oae},G2;function zae(e){G2=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function Bae(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 G2(i,o,l,h,u),c}var Wae={kernelName:Tl,backendName:"wasm",kernelFunc:Bae,setupFunc:zae},H2;function Vae(e){H2=e.wasm.cwrap(gi,null,["number","number"])}function Uae(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||H2(a,s),r}var Gae={kernelName:"Sigmoid",backendName:"wasm",setupFunc:Vae,kernelFunc:Uae},Hae=Cn(fi);function 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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 ot(this._box).rescale(this.imageDims.reverse())}forSize(t,n){return new Fr(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var yt=class extends Fr{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 yt(a,r,s)}};function Nf(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 Tf(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 o=r,l=[];for(let c=0;cl[u]<=n)}return s}function wa(e,t){return D(()=>{let[n,a,r]=t,s=kn([...e.shape.slice(0,3),1],n,"float32"),i=kn([...e.shape.slice(0,3),1],a,"float32"),o=kn([...e.shape.slice(0,3),1],r,"float32"),l=Qe([s,i,o],3);return fe(e,l)})}function Cf(e,t=!1){return D(()=>{let[n,a]=e.shape.slice(1);if(n===a)return e;let r=Math.abs(n-a),s=Math.round(r*(t?.5:1)),i=n>a?2:1,o=d=>{let h=e.shape.slice();return h[i]=d,kn(h,0,"float32")},l=o(s),c=r-l.shape[i],p=[t&&c?o(c):null,e,l].filter(d=>!!d).map(d=>ce(d,"float32"));return Qe(p,i)})}function tC(e){let t=e.slice();for(let n=t.length-1;n>0;n--){let a=Math.floor(Math.random()*(n+1)),r=t[n];t[n]=t[a],t[a]=r}return t}function Eu(e){return 1/(1+Math.exp(-e))}function nC(e){return Math.log(e/(1-e))}var so=class extends ot{constructor(t,n,a,r,s=!1){super({x:t,y:n,width:a,height:r},s)}};var zre=.5,Bre=.43,Wre=.45,Un=class{constructor(t,n,a=new De(0,0)){let{width:r,height:s}=n;this._imgDims=new ln(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 yt?t.box.floor():new ot(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/Wre),l=ao(t),c=Math.floor(Math.max(0,l.x-zre*o)),u=Math.floor(Math.max(0,l.y-Bre*o));return new so(c,u,Math.min(o,this.imageWidth+c),Math.min(o,this.imageHeight+u))}alignMinBbox(t){let n=Tf(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var ww=class extends Un{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],ao([t[3],t[4]])]}};var io=class extends Un{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(ao)}};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?` (${no(this.distance)})`:""}`}};var Fu=class extends ot{static assertIsValidLabeledBox(t,n){if(ot.assertIsValidBox(t,n),!La(t.label))throw new Error(`${n} - expected property label (${t.label}) to be a number`)}constructor(t,n){super(t);this._label=n}get label(){return this._label}};var ur=class{constructor(t,n){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(n)||n.some(a=>!(a instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=n}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let n=t.descriptors.map(a=>new Float32Array(a));return new ur(t.label,n)}};var kw=class extends Fu{static assertIsValidPredictedBox(t,n){if(Fu.assertIsValidLabeledBox(t,n),!_u(t.score)||!_u(t.classScore))throw new Error(`${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 za(e){return e.detection instanceof yt}function gs(e,t){return{...e,...{detection:t}}}function Iw(){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 environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:e,readFile:t}}function _f(e){let t="";if(!e)try{e=require("fs")}catch(a){t=a.toString()}return{readFile:e?a=>new Promise((r,s)=>{e.readFile(a,(i,o)=>i?s(i):r(o))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)}}}function Nw(){let e=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,n=()=>{if(e)return new e;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},a=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},r=global.fetch,s=_f();return{Canvas:e||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:n,createImageElement:a,fetch:r,...s}}function Tw(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var Sw=RE(rC()),Jt;function Gre(){if(!Jt)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return Jt}function Cw(e){Jt=e}function _w(){return Tw()?Cw(Iw()):Sw.isNodejs()?Cw(Nw()):null}function Hre(e){if(Jt||_w(),!Jt)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=Jt.Canvas,Image:n=Jt.Image}=e;Jt.Canvas=t,Jt.Image=n,Jt.createCanvasElement=e.createCanvasElement||(()=>new t),Jt.createImageElement=e.createImageElement||(()=>new n),Jt.ImageData=e.ImageData||Jt.ImageData,Jt.Video=e.Video||Jt.Video,Jt.fetch=e.fetch||Jt.fetch,Jt.readFile=e.readFile||Jt.readFile}var rt={getEnv:Gre,setEnv:Cw,initialize:_w,createBrowserEnv:Iw,createFileSystem:_f,createNodejsEnv:Nw,monkeyPatch:Hre,isBrowser:Tw,isNodejs:Sw.isNodejs};_w();function ys(e){return!rt.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function yn(e){let{Canvas:t,CanvasRenderingContext2D:n}=rt.getEnv();if(e instanceof n)return e;let a=ys(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 Op=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}},bs=class{constructor(t,n,a={}){this.text=typeof t=="string"?[t]:t instanceof bs?t.text:t,this.anchor=n,this.options=new Op(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 Ew=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 Op({...i,...s})}},Ef=class{constructor(t,n={}){this.box=new ot(t),this.options=new Ew(n)}draw(t){let n=yn(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 bs([c],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function jre(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof yt?a.score:za(a)?a.detection.score:void 0,s=a instanceof yt?a.box:za(a)?a.detection.box:new ot(a),i=r?`${no(r)}`:void 0;new Ef(s,{label:i}).draw(e)})}function $u(e){let{Image:t,Video:n}=rt.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function Af(e){return new Promise((t,n)=>{if(e instanceof rt.getEnv().Canvas||$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 Ff(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=rt.getEnv().createImageElement();r.onload=()=>t(r),r.onerror=n,r.src=a.result},a.onerror=n,a.readAsDataURL(e)})}function xs(e){let{Image:t,Video:n}=rt.getEnv();return e instanceof t?new ln(e.naturalWidth,e.naturalHeight):e instanceof n?new ln(e.videoWidth,e.videoHeight):new ln(e.width,e.height)}function oo({width:e,height:t}){let{createCanvasElement:n}=rt.getEnv(),a=n();return a.width=e,a.height=t,a}function Du(e,t){let{ImageData:n}=rt.getEnv();if(!(e instanceof n)&&!$u(e))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:a,height:r}=t||xs(e),s=oo({width:a,height:r});return e instanceof n?yn(s).putImageData(e,0,0):yn(s).drawImage(e,0,0,a,r),s}async function $f(e,t){let n=t||rt.getEnv().createCanvasElement(),[a,r,s]=e.shape.slice(ta(e)?1:0),i=D(()=>e.as3D(a,r,s).toInt());return await Ai.toPixels(i,n),i.dispose(),n}function Lp(e){let{Image:t,Canvas:n,Video:a}=rt.getEnv();return e instanceof t||e instanceof n||e instanceof a}function Df(e,t,n=!1){let{Image:a,Canvas:r}=rt.getEnv();if(!(e instanceof a||e instanceof r))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");let s=xs(e),i=t/Math.max(s.height,s.width),o=i*s.width,l=i*s.height,c=oo({width:t,height:t}),u=e instanceof r?e:Du(e),p=Math.abs(o-l)/2,d=n&&o{if(Ar(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(ta(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=a,this._inputDimensions[r]=a.shape.slice(1);return}let s=a instanceof rt.getEnv().Canvas?a:Du(a);this._canvases[r]=s,this._inputDimensions[r]=[s.height,s.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return lr(this.batchSize,0,1).map((t,n)=>this.getReshapedInputDimensions(n))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let n=this.getInputWidth(t),a=this.getInputHeight(t);return vw({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 Ae){let o=ta(i)?i:i.expandDims();return o=Cf(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=Qa.resizeBilinear(o,[t,t])),o.as3D(t,t,3)}if(i instanceof rt.getEnv().Canvas)return Ai.fromPixels(Df(i,t,n));throw new Error(`toBatchTensor - at batchIdx ${s}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${i}`)});return $t(a.map(s=>ce(s,"float32"))).as4D(this.batchSize,t,t,3)})}};async function gt(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 ${r}:`:"",a=t.map(ys);return a.forEach((r,s)=>{if(!Lp(r)&&!Ar(r)&&!ta(r))throw typeof t[s]=="string"?new Error(`toNetInput -${n(s)} string passed, but could not resolve HTMLElement for element id ${t[s]}`):new Error(`toNetInput -${n(s)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(ta(r)){let i=r.shape[0];if(i!==1)throw new Error(`toNetInput -${n(s)} tf.Tensor4D with batchSize ${i} passed, but not supported in input array`)}}),await Promise.all(a.map(r=>Lp(r)&&Af(r))),new pr(a,Array.isArray(e))}async function lo(e,t){let{Canvas:n}=rt.getEnv(),a=e;if(!(e instanceof n)){let i=await gt(e);if(i.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let o=i.getInput(0);a=o instanceof n?o:await $f(o)}let r=yn(a);return t.map(i=>i instanceof yt?i.forSize(a.width,a.height).box.floor():i).map(i=>i.clipAtImageBorders(a.width,a.height)).map(({x:i,y:o,width:l,height:c})=>{let u=oo({width:l,height:c});return yn(u).putImageData(r.getImageData(i,o,l,c),0,0),u})}async function uo(e,t){if(!Ar(e)&&!ta(e))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(ta(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return D(()=>{let[n,a,r]=e.shape.slice(ta(e)?1:0);return t.map(o=>o instanceof yt?o.forSize(a,n).box:o).map(o=>o.clipAtImageBorders(a,n)).map(({x:o,y:l,width:c,height:u})=>Ql(e.as3D(n,a,r),[l,o,0],[u,c,r]))})}async function vs(e,t){let{fetch:n}=rt.getEnv(),a=await n(e,t);if(!(a.status<400))throw new Error(`failed to fetch: (${a.status}) ${a.statusText}, from url: ${a.url}`);return a}async function sC(e){let t=await vs(e),n=await t.blob();if(!n.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${n.type}, for url: ${t.url}`);return Ff(n)}async function Rf(e){return(await vs(e)).json()}async function iC(e){return new Float32Array(await(await vs(e)).arrayBuffer())}function Mf(e,t){let n=`${t}-weights_manifest.json`;if(!e)return{modelBaseUri:"",manifestUri:n};if(e==="/")return{modelBaseUri:"/",manifestUri:`/${n}`};let a=e.startsWith("http://")?"http://":e.startsWith("https://")?"https://":"";e=e.replace(a,"");let r=e.split("/").filter(o=>o),s=e.endsWith(".json")?r[r.length-1]:n,i=a+(e.endsWith(".json")?r.slice(0,r.length-1):r).join("/");return i=e.startsWith("/")?`/${i}`:i,{modelBaseUri:i,manifestUri:i==="/"?`/${s}`:`${i}/${s}`}}async function Pf(e,t){let{manifestUri:n,modelBaseUri:a}=Mf(e,t),r=await Rf(n);return Ht.loadWeights(r,a)}function oC(e,t,n=!1){let{width:a,height:r}=n?xs(t):t;return e.width=a,e.height=r,{width:a,height:r}}var Qt=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:n,objProp:a}=this.traversePropertyPath(t);return n[a]}reassignParamFromPath(t,n){let{obj:a,objProp:r}=this.traversePropertyPath(t);a[r].dispose(),a[r]=n}getParamList(){return 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a=_n(a,t.separable_conv0,[1,1]),a=_n(Ye(a),t.separable_conv1,[1,1]),a=Ft(a,[3,3],[2,2],"same"),a=Z(a,fC(e,t.expansion_conv,[2,2])),a}function Zre(e,t){let n=_n(Ye(e),t.separable_conv0,[1,1]);return n=_n(Ye(n),t.separable_conv1,[1,1]),n=_n(Ye(n),t.separable_conv2,[1,1]),n=Z(n,e),n}var Dw=class extends Qt{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return D(()=>{let a=ce(t.toBatchTensor(112,!0),"float32"),s=wa(a,[122.782,117.001,104.298]).div(de(256)),i=Ye(fC(s,n.entry_flow.conv_in,[2,2]));return i=$w(i,n.entry_flow.reduction_block_0,!1),i=$w(i,n.entry_flow.reduction_block_1),lr(this._numMainBlocks,0,1).forEach(o=>{i=Zre(i,n.middle_flow[`main_block_${o}`])}),i=$w(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 gt(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return mC(t,this._numMainBlocks)}extractParams(t){return hC(t,this._numMainBlocks)}};function gC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),r=Lf(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: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function yC(e){let t=[],n=Gn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return bn(e,t),{params:r,paramMappings:t}}var dr;(function(e){e.FEMALE="female",e.MALE="male"})(dr||(dr={}));var Hp=class extends Qt{constructor(t=new Dw(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof pr?this.faceFeatureExtractor.forwardInput(t):t,r=Yn(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Wp(r,n.fc.age).as1D(),i=Wp(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:Sa(a)}})}async forward(t){return this.forwardInput(await gt(t))}async predictAgeAndGender(t){let n=await gt(t),a=await this.forwardInput(n),r=dt(a.age),s=dt(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 gC(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=Uf(t);return this.faceFeatureExtractor.loadFromWeightMap(n),yC(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 jp=class extends Vp{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)=>$t([kn([68],p,"float32"),kn([68],d,"float32")],1).as2D(1,136).as1D(),o=(p,d)=>{let{width:h,height:m}=r[p];return 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ise(e){let t=dt(Ue(e,[1,0])),n=[fe(t[2],t[0]),fe(t[3],t[1])],a=[Z(t[0],ve(n[0],de(2))),Z(t[1],ve(n[1],de(2)))];return{sizes:n,centers:a}}function ose(e,t){let{sizes:n,centers:a}=ise(e),r=dt(Ue(t,[1,0])),s=ve(L(dn(ve(r[2],de(5))),n[0]),de(2)),i=Z(L(ve(r[0],de(10)),n[0]),a[0]),o=ve(L(dn(ve(r[3],de(5))),n[1]),de(2)),l=Z(L(ve(r[1],de(10)),n[1]),a[1]);return Ue($t([fe(i,s),fe(l,o),Z(i,s),Z(l,o)]),[1,0])}function FC(e,t,n){return D(()=>{let a=e.shape[0],r=ose(H(Xa(n.extra_dim,[a,1,1]),[-1,4]),H(e,[-1,4]));r=H(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=H(i,[a,i.shape[1]]);let o=dt(r),l=dt(i);return{boxes:o,scores:l}})}function fo(e,t){return D(()=>{let n=e.shape[0],a=H(co(e,t.box_encoding_predictor),[n,-1,1,4]),r=H(co(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function $C(e,t,n){return D(()=>{let 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this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var ks=class extends Qt{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return D(()=>{let a=ce(t.toBatchTensor(512,!1),"float32"),r=fe(L(a,de(.007843137718737125)),de(1)),s=EC(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=$C(s.out,s.conv11,n.prediction_layer);return FC(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await gt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new na(n),s=await gt(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(F=>F*g),[T,C]=[Math.max(0,y[v][1]),Math.min(1,y[v][3])].map(F=>F*f);return new yt(u[v],new so(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 _C(t)}extractParams(t){return CC(t)}};function Lw(e){let t=new ks;return t.extractWeights(e),t}function DC(e){return Lw(e)}var zw=class extends ks{};var RC=.4,MC=[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)],PC=[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)],OC=[117.001,114.697,97.404],LC="tiny_yolov2_model",zC="tiny_yolov2_separable_conv_model";var Kf=e=>typeof e=="number";function Xf(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(!Kf(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=>Kf(t.x)&&Kf(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(Kf)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Ou(e){return D(()=>{let t=L(e,de(.10000000149011612));return Z(Ye(fe(e,t)),t)})}function Dr(e,t){return D(()=>{let n=Jn(e,[[0,0],[1,1],[1,1],[0,0]]);return n=At(n,t.conv.filters,[1,1],"valid"),n=fe(n,t.bn.sub),n=L(n,t.bn.truediv),n=Z(n,t.conv.bias),Ou(n)})}function Rr(e,t){return D(()=>{let n=Jn(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Oi(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=Z(n,t.bias),Ou(n)})}function lse(e,t){let n=Ru(e,t);function a(i,o){let l=nt(e(i)),c=nt(e(i));return t.push({paramPath:`${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,`${l}/bn`);return{conv:c,bn:u}}let s=Mu(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function BC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=xn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=lse(r,i),u;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,v]=a,x=t.isFirstLayerConv2d?o(p,d,3,"conv0"):c(p,d,"conv0"),N=c(d,h,"conv1"),T=c(h,m,"conv2"),C=c(m,f,"conv3"),F=c(f,g,"conv4"),$=c(g,y,"conv5"),P=b?c(y,b,"conv6"):void 0,B=v?c(b,v,"conv7"):void 0,W=o(v||b||y,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:F,conv5:$,conv6:P,conv7:B,conv8:W}}else{let[p,d,h,m,f,g,y,b,v]=a,x=l(p,d,"conv0"),N=l(d,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),F=l(f,g,"conv4"),$=l(g,y,"conv5"),P=l(y,b,"conv6"),B=l(b,v,"conv7"),W=o(v,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:F,conv5:$,conv6:P,conv7:B,conv8:W}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function use(e,t){let n=Gn(e,t);function a(o){let l=n(`${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(`${o}/bn`);return{conv:l,bn:c}}let i=Pu(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function WC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=use(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return bn(e,n),{params:i,paramMappings:n}}var Wa=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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 Bw=class extends Qt{constructor(t){super("TinyYolov2");Xf(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=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=Dr(a,n.conv6),a=Dr(a,n.conv7),co(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Ou(co(t,n.conv0,"valid",!1)):Rr(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=n.conv6?Rr(a,n.conv6):a,a=n.conv7?Rr(a,n.conv7):a,co(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=ce(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?wa(r,this.config.meanRgb):r,r=r.div(de(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await gt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Wa(n),s=await gt(t),i=await this.forwardInput(s,a),o=D(()=>dt(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 Sf(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new Fr(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return WC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Bw.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 ${a} filterSizes in config`);return BC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),v=y.slice([0,0,0,4],[c,c,u,1]),x=this.withClassScores?Sa(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):de(0);return[b,v,x]}),m=[],f=await d.array(),g=await p.array();for(let y=0;ya){let N=(b+Eu(g[y][b][v][0]))/c*o,T=(y+Eu(g[y][b][v][1]))/c*l,C=Math.exp(g[y][b][v][2])*this.config.anchors[v].x/c*o,F=Math.exp(g[y][b][v][3])*this.config.anchors[v].y/c*l,$=N-C/2,P=T-F/2,B={row:y,col:b,anchor:v},{classScore:W,label:G}=this.withClassScores?await this.extractPredictedClass(h,B):{classScore:1,label:0};m.push({box:new ro($,P,$+C,P+F),score:x,classScore:x*W,label:G,...B})}}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=Bw;Lu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var go=class extends Lu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:RC,classes:["face"],...t?{anchors:PC,meanRgb:OC}:{anchors:MC,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 yt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?zC:LC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function VC(e,t=!0){let n=new go(t);return n.extractWeights(e),n}var Jp=class extends Wa{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var aa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function yo(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>ws(l)?r(l):l.detection),i=a||(t instanceof Ae?await uo(t,s):await lo(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ae&&l.dispose()),o}async function zu(e,t,n,a,r){return yo([e],t,async s=>n(s[0]),a,r)}var UC=.4,GC=[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)],HC=[117.001,114.697,97.404];var bo=class extends Lu{constructor(){let t={withSeparableConvs:!0,iouThreshold:UC,classes:["face"],anchors:GC,meanRgb:HC,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 yt(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 ks,tinyFaceDetector:new bo,tinyYolov2:new go,faceLandmark68Net:new ho,faceLandmark68TinyNet:new qp,faceRecognitionNet:new mo,faceExpressionNet:new Up,ageGenderNet:new Hp},Ww=(e,t)=>et.ssdMobilenetv1.locateFaces(e,t),jC=(e,t)=>et.tinyFaceDetector.locateFaces(e,t),qC=(e,t)=>et.tinyYolov2.locateFaces(e,t),Vw=e=>et.faceLandmark68Net.detectLandmarks(e),KC=e=>et.faceLandmark68TinyNet.detectLandmarks(e),XC=e=>et.faceRecognitionNet.computeFaceDescriptor(e),YC=e=>et.faceExpressionNet.predictExpressions(e),ZC=e=>et.ageGenderNet.predictAgeAndGender(e),Uw=e=>et.ssdMobilenetv1.load(e),JC=e=>et.tinyFaceDetector.load(e),QC=e=>et.tinyYolov2.load(e),e_=e=>et.faceLandmark68Net.load(e),t_=e=>et.faceLandmark68TinyNet.load(e),n_=e=>et.faceRecognitionNet.load(e),a_=e=>et.faceExpressionNet.load(e),r_=e=>et.ageGenderNet.load(e),s_=Uw,i_=Ww,o_=Vw;var Gw=class extends aa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Vu=class extends Gw{async run(){let t=await this.parentTask,n=await yo(t,this.input,async a=>Promise.all(a.map(r=>et.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>Gp(a,n[r]))}withAgeAndGender(){return new Bu(this,this.input)}},Uu=class extends Gw{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 Gp(t,n)}withAgeAndGender(){return new Wu(this,this.input)}},wo=class extends Vu{withAgeAndGender(){return new xo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},ko=class extends Uu{withAgeAndGender(){return new vo(this,this.input)}withFaceDescriptor(){return new Pr(this,this.input)}};var Hw=class extends aa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Bu=class extends Hw{async run(){let t=await this.parentTask,n=await yo(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 Yp(Zp(a,i,o),s)})}withFaceExpressions(){return new Vu(this,this.input)}},Wu=class extends Hw{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 Yp(Zp(t,a,r),n)}withFaceExpressions(){return new Uu(this,this.input)}},xo=class extends Bu{withFaceExpressions(){return new wo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},vo=class extends Wu{withFaceExpressions(){return new ko(this,this.input)}withFaceDescriptor(){return new Pr(this,this.input)}};var Qp=class extends aa{constructor(t,n){super();this.parentTask=t;this.input=n}},Mr=class extends Qp{async run(){let t=await this.parentTask;return(await yo(t,this.input,a=>Promise.all(a.map(r=>et.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>Xp(t[r],a))}withFaceExpressions(){return new wo(this,this.input)}withAgeAndGender(){return new xo(this,this.input)}},Pr=class extends Qp{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 Xp(t,n)}withFaceExpressions(){return new ko(this,this.input)}withAgeAndGender(){return new vo(this,this.input)}};var ed=class extends aa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?et.faceLandmark68TinyNet:et.faceLandmark68Net}},td=class extends ed{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Ae?await uo(this.input,n):await lo(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Ae&&s.dispose()),t.map((s,i)=>po(s,r[i]))}withFaceExpressions(){return new wo(this,this.input)}withAgeAndGender(){return new xo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},nd=class extends ed{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ae?await uo(this.input,[n]):await lo(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ae&&s.dispose()),po(t,r)}withFaceExpressions(){return new ko(this,this.input)}withAgeAndGender(){return new vo(this,this.input)}withFaceDescriptor(){return new Pr(this,this.input)}};var ad=class extends aa{constructor(t,n=new na){super();this.input=t;this.options=n}},Gu=class extends ad{async run(){let{input:t,options:n}=this,a=n instanceof Jp?r=>et.tinyFaceDetector.locateFaces(r,n):n instanceof na?r=>et.ssdMobilenetv1.locateFaces(r,n):n instanceof Wa?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 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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 nn(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 mo=class extends S{constructor(t){super("TinyYolov2");io(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 Wr(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 nn(t,this.config)}extractParams(t){let e=this.config.filterSizes||mo.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 on(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:ho}=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:ho,...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=mo;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:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function 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 po=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 po.Tensor?await se(t,a):await ae(t,a)),i=await e(s);return s.forEach(c=>c instanceof po.Tensor&&c.dispose()),i}async function ye(o,t,e,r,n){return Xt([o],t,async a=>e(a[0]),r,n)}var an=.4,sn=[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)],cn=[117.001,114.697,97.404];var Fe=class extends ge{constructor(){let t={withSeparableConvs:!0,iouThreshold:an,classes:["face"],anchors:sn,meanRgb:cn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var P={ssdMobilenetv1:new Ut,tinyFaceDetector:new Fe,tinyYolov2:new ve,faceLandmark68Net:new fe,faceLandmark68TinyNet:new dr,faceRecognitionNet:new xe,faceExpressionNet:new cr,ageGenderNet:new pr},mn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ca=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ma=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=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),dn=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=dn,Ta=mn,Pa=pn;var uo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},_e=class extends uo{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 uo{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 lo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Te=class extends lo{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 lo{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 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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 mo=class extends S{constructor(t){super("TinyYolov2");io(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 Wr(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 nn(t,this.config)}extractParams(t){let e=this.config.filterSizes||mo.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 on(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:ho}=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:ho,...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=mo;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:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function 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 po=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 po.Tensor?await se(t,a):await ae(t,a)),i=await e(s);return s.forEach(c=>c instanceof po.Tensor&&c.dispose()),i}async function ye(o,t,e,r,n){return Xt([o],t,async a=>e(a[0]),r,n)}var an=.4,sn=[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)],cn=[117.001,114.697,97.404];var Fe=class extends ge{constructor(){let t={withSeparableConvs:!0,iouThreshold:an,classes:["face"],anchors:sn,meanRgb:cn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var P={ssdMobilenetv1:new Ut,tinyFaceDetector:new Fe,tinyYolov2:new ve,faceLandmark68Net:new fe,faceLandmark68TinyNet:new dr,faceRecognitionNet:new xe,faceExpressionNet:new cr,ageGenderNet:new pr},mn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ca=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ma=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=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),dn=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=dn,Ta=mn,Pa=pn;var uo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},_e=class extends uo{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 uo{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 lo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Te=class extends lo{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 lo{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 un(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=un;function fo(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=>fo(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 ln(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>ln(n,{width:e,height:r}));if(zt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return le($t(o,n),a)}return pt(o)?$t(o,o.detection.forSize(e,r)):o instanceof V||o instanceof M?o.forSize(e,r):o}var Ca=typeof process!="undefined",Na=typeof navigator!="undefined"&&typeof 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p=s(`Prediction/BoxPredictor_${m}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${m}/box_encoding_predictor`),d=s(`Prediction/BoxPredictor_${m}/ClassPredictor`,`prediction_layer/box_predictor_${m}/class_predictor`);return{box_encoding_predictor:p,class_predictor:d}}function c(){return{conv_0:r("Prediction",0,"prediction_layer/conv_0"),conv_1:r("Prediction",1,"prediction_layer/conv_1"),conv_2:r("Prediction",2,"prediction_layer/conv_2"),conv_3:r("Prediction",3,"prediction_layer/conv_3"),conv_4:r("Prediction",4,"prediction_layer/conv_4"),conv_5:r("Prediction",5,"prediction_layer/conv_5"),conv_6:r("Prediction",6,"prediction_layer/conv_6"),conv_7:r("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:i(0),box_predictor_1:i(1),box_predictor_2:i(2),box_predictor_3:i(3),box_predictor_4:i(4),box_predictor_5:i(5)}}return{extractMobilenetV1Params:a,extractPredictionLayerParams:c}}function Go(o){let t=[],{extractMobilenetV1Params:e,extractPredictionLayerParams:r}=Zn(o,t),n=o["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!ht(n))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${n}`);let a={mobilenetv1:e(),prediction_layer:r(),output_layer:{extra_dim:n}};return W(o,t),{params:a,paramMappings:t}}var yt=b(g());var kt=b(g());function q(o,t,e){return kt.tidy(()=>{let r=kt.conv2d(o,t.filters,e,"same");return r=kt.add(r,t.batch_norm_offset),kt.clipByValue(r,0,6)})}var Kn=.0010000000474974513;function Qn(o,t,e){return yt.tidy(()=>{let r=yt.depthwiseConv2d(o,t.filters,e,"same");return r=yt.batchNorm(r,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,Kn),yt.clipByValue(r,0,6)})}function ta(o){return[2,4,6,12].some(t=>t===o)?[2,2]:[1,1]}function zo(o,t){return yt.tidy(()=>{let e,r=q(o,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((a,s)=>{let i=s+1,c=ta(i);r=Qn(r,a.depthwise_conv,c),r=q(r,a.pointwise_conv,[1,1]),i===11&&(e=r)}),e===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:r,conv11:e}})}function ea(o,t,e){let r=o.arraySync(),n=Math.min(r[t][0],r[t][2]),a=Math.min(r[t][1],r[t][3]),s=Math.max(r[t][0],r[t][2]),i=Math.max(r[t][1],r[t][3]),c=Math.min(r[e][0],r[e][2]),m=Math.min(r[e][1],r[e][3]),p=Math.max(r[e][0],r[e][2]),d=Math.max(r[e][1],r[e][3]),u=(s-n)*(i-a),l=(p-c)*(d-m);if(u<=0||l<=0)return 0;let v=Math.max(n,c),_=Math.max(a,m),h=Math.min(s,p),y=Math.min(i,d),T=Math.max(h-v,0)*Math.max(y-_,0);return T/(u+l-T)}function Vo(o,t,e,r,n){let a=o.shape[0],s=Math.min(e,a),i=t.map((p,d)=>({score:p,boxIndex:d})).filter(p=>p.score>n).sort((p,d)=>d.score-p.score),c=p=>p<=r?1:0,m=[];return i.forEach(p=>{if(m.length>=s)return;let 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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 Xo(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=zo(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=Xo(a.out,a.conv11,e.prediction_layer);return Uo(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 Go(t)}extractParams(t){return Yo(t)}};function Jo(o){let t=new Ut;return t.extractWeights(o),t}function na(o){return Jo(o)}var qo=class extends Ut{};var Zo=.4,Ko=[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)],tn=[117.001,114.697,97.404],en="tiny_yolov2_model",rn="tiny_yolov2_separable_conv_model";var N=b(g());var br=o=>typeof o=="number";function io(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: ${JSON.stringify(o.meanRgb)}`)}var Q=b(g());var K=b(g());function be(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Ft(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),be(e)})}var St=b(g());function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),be(e)})}var co=b(g());function aa(o,t){let e=ce(o,t);function r(s,i){let c=co.tensor1d(o(s)),m=co.tensor1d(o(s));return t.push({paramPath:`${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 on(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(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=sa(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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 mo=class extends S{constructor(t){super("TinyYolov2");io(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 Wr(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 nn(t,this.config)}extractParams(t){let e=this.config.filterSizes||mo.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 on(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:ho}=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:ho,...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=mo;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:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ia(o,t=!0){let e=new ve(t);return e.extractWeights(o),e}var gr=class extends 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D(this._box).rescale(this.imageDims.reverse())}forSize(t,e){return new Dt(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:e})}};var M=class extends Dt{constructor(t,e,r){super(t,t,"",e,r)}forSize(t,e){let{score:r,relativeBox:n,imageDims:a}=super.forSize(t,e);return new M(r,n,a)}};function Sr(o,t,e=!0){let r=Math.max(0,Math.min(o.right,t.right)-Math.max(o.left,t.left)),n=Math.max(0,Math.min(o.bottom,t.bottom)-Math.max(o.top,t.top)),a=r*n;return e?a/(o.area+t.area-a):a/Math.min(o.area,t.area)}function Ar(o){let t=o.map(i=>i.x),e=o.map(i=>i.y),r=t.reduce((i,c)=>ccii({score:s,boxIndex:i})).sort((s,i)=>s.score-i.score).map(s=>s.boxIndex),a=[];for(;n.length>0;){let s=n.pop();a.push(s);let i=n,c=[];for(let m=0;mc[p]<=e)}return a}var mt=b(g());function ot(o,t){return mt.tidy(()=>{let[e,r,n]=t,a=mt.fill([...o.shape.slice(0,3),1],e,"float32"),s=mt.fill([...o.shape.slice(0,3),1],r,"float32"),i=mt.fill([...o.shape.slice(0,3),1],n,"float32"),c=mt.concat([a,s,i],3);return 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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 ko(o,t,e){return I.add(I.conv2d(o,t.filters,e,"same"),t.bias)}function ro(o,t,e=!0){let r=e?I.relu(o):o;return r=$(r,t.separable_conv0,[1,1]),r=$(I.relu(r),t.separable_conv1,[1,1]),r=I.maxPool(r,[3,3],[2,2],"same"),r=I.add(r,ko(o,t.expansion_conv,[2,2])),r}function Gn(o,t){let e=$(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 oo=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(ko(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 oo(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return ut.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t,n=ut.avgPool(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),a=Ae(n,e.fc.age).as1D(),s=Ae(n,e.fc.gender);return{age:a,gender:s}})}forwardInput(t){return ut.tidy(()=>{let{age:e,gender:r}=this.runNet(t);return{age:e,gender:ut.softmax(r)}})}async forward(t){return this.forwardInput(await E(t))}async predictAgeAndGender(t){let e=await E(t),r=await this.forwardInput(e),n=ut.unstack(r.age),a=ut.unstack(r.gender),s=n.map((c,m)=>({ageTensor:c,genderTensor:a[m]})),i=await Promise.all(s.map(async({ageTensor:c,genderTensor:m})=>{let p=(await c.data())[0],d=(await 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r=q(o,e.conv_0,[1,1]),n=q(r,e.conv_1,[2,2]),a=q(n,e.conv_2,[1,1]),s=q(a,e.conv_3,[2,2]),i=q(s,e.conv_4,[1,1]),c=q(i,e.conv_5,[2,2]),m=q(c,e.conv_6,[1,1]),p=q(m,e.conv_7,[2,2]),d=Vt(t,e.box_predictor_0),u=Vt(o,e.box_predictor_1),l=Vt(n,e.box_predictor_2),v=Vt(s,e.box_predictor_3),_=Vt(c,e.box_predictor_4),h=Vt(p,e.box_predictor_5),y=$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=zo(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=Xo(a.out,a.conv11,e.prediction_layer);return Uo(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 Go(t)}extractParams(t){return Yo(t)}};function Jo(o){let t=new Ut;return t.extractWeights(o),t}function na(o){return Jo(o)}var qo=class extends Ut{};var Zo=.4,Ko=[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)],tn=[117.001,114.697,97.404],en="tiny_yolov2_model",rn="tiny_yolov2_separable_conv_model";var N=b(g());var br=o=>typeof o=="number";function io(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: ${JSON.stringify(o.meanRgb)}`)}var Q=b(g());var K=b(g());function be(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Ft(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),be(e)})}var St=b(g());function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),be(e)})}var co=b(g());function aa(o,t){let e=ce(o,t);function r(s,i){let c=co.tensor1d(o(s)),m=co.tensor1d(o(s));return t.push({paramPath:`${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 on(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(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=sa(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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 mo=class extends S{constructor(t){super("TinyYolov2");io(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 Wr(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 nn(t,this.config)}extractParams(t){let e=this.config.filterSizes||mo.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 on(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:ho}=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:ho,...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=mo;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:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function 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 po=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 po.Tensor?await se(t,a):await ae(t,a)),i=await e(s);return s.forEach(c=>c instanceof po.Tensor&&c.dispose()),i}async function ye(o,t,e,r,n){return Xt([o],t,async a=>e(a[0]),r,n)}var an=.4,sn=[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)],cn=[117.001,114.697,97.404];var Fe=class extends ge{constructor(){let t={withSeparableConvs:!0,iouThreshold:an,classes:["face"],anchors:sn,meanRgb:cn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var P={ssdMobilenetv1:new Ut,tinyFaceDetector:new Fe,tinyYolov2:new ve,faceLandmark68Net:new fe,faceLandmark68TinyNet:new dr,faceRecognitionNet:new xe,faceExpressionNet:new cr,ageGenderNet:new pr},mn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ca=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ma=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=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),dn=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=dn,Ta=mn,Pa=pn;var uo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},_e=class extends uo{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 uo{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 lo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Te=class extends lo{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 lo{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 un(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=un;function fo(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=>fo(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 ln(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>ln(n,{width:e,height:r}));if(zt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return le($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:No,node:Ca,browser:Na}; //# sourceMappingURL=face-api.node.js.map diff --git a/example/index.js b/example/index.js index 34aa115..5f63dee 100644 --- a/example/index.js +++ b/example/index.js @@ -5,7 +5,7 @@ const modelPath = 'https://vladmandic.github.io/face-api/model/'; // path to mod // const modelPath = '../model/'; // path to model folder that will be loaded using http const imgSize = 512; // maximum image size in pixels const minScore = 0.1; // minimum score -const maxResults = 5; // maximum number of results to return +const maxResults = 10; // maximum number of results to return const samples = ['sample (1).jpg', 'sample (2).jpg', 'sample (3).jpg', 'sample (4).jpg', 'sample (5).jpg', 'sample (6).jpg']; // sample images to be loaded using http // helper function to pretty-print json object to string @@ -122,8 +122,8 @@ async function main() { await faceapi.tf.ready(); // check version - log(`Version: TensorFlow/JS ${str(faceapi.tf?.version_core || '(not loaded)')} FaceAPI ${str(faceapi?.version || '(not loaded)')} Backend: ${str(faceapi.tf?.getBackend() || '(not loaded)')}`); - log(`Flags: ${JSON.stringify(faceapi.tf.ENV.flags)}`); + log(`Version: FaceAPI ${str(faceapi?.version.faceapi || '(not loaded)')} TensorFlow/JS ${str(faceapi?.tf?.version_core || '(not loaded)')} Backend: ${str(faceapi?.tf?.getBackend() || '(not loaded)')}`); + log(`Flags: ${JSON.stringify(faceapi?.tf?.ENV.flags || { tf: 'not loaded' })}`); // load face-api models log('Loading FaceAPI models'); diff --git a/example/webcam.html b/example/webcam.html index d58ddcb..ca7b161 100644 --- a/example/webcam.html +++ b/example/webcam.html @@ -7,9 +7,9 @@ - + -
+
diff --git a/example/webcam.js b/example/webcam.js index 26f7358..7e2c53d 100644 --- a/example/webcam.js +++ b/example/webcam.js @@ -116,6 +116,17 @@ async function setupCamera() { const settings = track.getSettings(); log(`Camera active: ${track.label} ${str(constraints)}`); log(`Camera settings: ${str(settings)}`); + canvas.addEventListener('click', () => { + // @ts-ignore + if (video && video.readyState >= 2) { + // @ts-ignore + if (video.paused) video.play(); + // @ts-ignore + else video.pause(); + } + // @ts-ignore + log(`Camera state: ${video.paused ? 'paused' : 'playing'}`); + }); return new Promise((resolve) => { video.onloadeddata = async () => { // @ts-ignore @@ -162,8 +173,8 @@ async function main() { await faceapi.tf.ready(); // check version - log(`Version: TensorFlow/JS ${str(faceapi.tf?.version_core || '(not loaded)')} FaceAPI ${str(faceapi?.version || '(not loaded)')} Backend: ${str(faceapi.tf?.getBackend() || '(not loaded)')}`); - log(`Flags: ${str(faceapi.tf.ENV.flags)}`); + log(`Version: FaceAPI ${str(faceapi?.version.faceapi || '(not loaded)')} TensorFlow/JS ${str(faceapi?.tf?.version_core || '(not loaded)')} Backend: ${str(faceapi?.tf?.getBackend() || '(not loaded)')}`); + log(`Flags: ${JSON.stringify(faceapi?.tf?.ENV.flags || { tf: 'not loaded' })}`); setupFaceAPI(); setupCamera();