@vladmandic/face-api/dist/face-api.node-gpu.js

FaceAPI: AI-powered Face Detection & Rotation Tracking, Face Description & Recognition, Age & Gender & Emotion Prediction for Browser and NodeJS using TensorFlow/JS

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

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e=[],{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:s,extractMainBlockParams:a}=qn(o,e),i=r("entry_flow/conv_in"),c=s("entry_flow/reduction_block_0"),m=s("entry_flow/reduction_block_1"),p={conv_in:i,reduction_block_0:c,reduction_block_1:m},u={};ct(t,0,1).forEach(T=>{u[`main_block_${T}`]=a(`middle_flow/main_block_${T}`)});let f=s("exit_flow/reduction_block"),l=n("exit_flow/separable_conv"),b={reduction_block:f,separable_conv:l};return W(o,e),{params:{entry_flow:p,middle_flow:u,exit_flow:b},paramMappings:e}}function Ao(o,t,e){return N.add(N.conv2d(o,t.filters,e,"same"),t.bias)}function mo(o,t,e=!0){let r=e?N.relu(o):o;return r=O(r,t.separable_conv0,[1,1]),r=O(N.relu(r),t.separable_conv1,[1,1]),r=N.maxPool(r,[3,3],[2,2],"same"),r=N.add(r,Ao(o,t.expansion_conv,[2,2])),r}function Zn(o,t){let e=O(N.relu(o),t.separable_conv0,[1,1]);return e=O(N.relu(e),t.separable_conv1,[1,1]),e=O(N.relu(e),t.separable_conv2,[1,1]),e=N.add(e,o),e}var yr=class extends 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e=await D(t),r=await this.forwardInput(e),n=ft.unstack(r.age),s=ft.unstack(r.gender),a=n.map((c,m)=>({ageTensor:c,genderTensor:s[m]})),i=await Promise.all(a.map(async({ageTensor:c,genderTensor:m})=>{let p=c.dataSync()[0],u=m.dataSync()[0],f=u>.5,l=f?"male":"female",b=f?u:1-u;return c.dispose(),m.dispose(),{age:p,gender:l,genderProbability:b}}));return r.age.dispose(),r.gender.dispose(),e.isBatchInput?i:i[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return Wo(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=br(t);return this.faceFeatureExtractor.loadFromWeightMap(e),ko(r)}extractParams(t){let r=t.slice(0,t.length-1539),n=t.slice(t.length-1539);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(n)}};var V=v(x());var we=class extends _e{postProcess(t,e,r){let n=r.map(({width:a,height:i})=>{let c=e/Math.max(i,a);return{width:a*c,height:i*c}}),s=n.length;return V.tidy(()=>{let a=(u,f)=>V.stack([V.fill([68],u,"float32"),V.fill([68],f,"float32")],1).as2D(1,136).as1D(),i=(u,f)=>{let{width:l,height:b}=n[u];return f(l,b)?Math.abs(l-b)/2:0},c=u=>i(u,(f,l)=>f<l),m=u=>i(u,(f,l)=>l<f);return t.mul(V.fill([s,136],e,"float32")).sub(V.stack(Array.from(Array(s),(u,f)=>a(c(f),m(f))))).div(V.stack(Array.from(Array(s),(u,f)=>a(n[f].width,n[f].height))))})}forwardInput(t){return V.tidy(()=>{let e=this.runNet(t);return this.postProcess(e,t.inputSize,t.inputDimensions.map(([r,n])=>({height:r,width:n})))})}async forward(t){return this.forwardInput(await D(t))}async detectLandmarks(t){let e=await D(t),r=V.tidy(()=>V.unstack(this.forwardInput(e))),n=await Promise.all(r.map(async(s,a)=>{let i=Array.from(s.dataSync()),c=i.filter((p,u)=>er(u)),m=i.filter((p,u)=>!er(u));return new 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Error("TinyFaceFeatureExtractor - load model before inference");return Fe.tidy(()=>{let r=Fe.cast(t.toBatchTensor(112,!0),"float32"),s=rt(r,[122.782,117.001,104.298]).div(255),a=mr(s,e.dense0,!0);return a=mr(a,e.dense1),a=mr(a,e.dense2),a=Fe.avgPool(a,[14,14],[2,2],"valid"),a})}async forward(t){return this.forwardInput(await D(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return Bo(t)}extractParams(t){return Ro(t)}};var ze=class extends we{constructor(t=new Tr){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var po=class extends Zt{};var nt=v(x());var De=v(x());var Pr=v(x());function $o(o,t){return Pr.add(Pr.mul(o,t.weights),t.biases)}function uo(o,t,e,r,n="same"){let{filters:s,bias:a}=t.conv,i=De.conv2d(o,s,e,n);return i=De.add(i,a),i=$o(i,t.scale),r?De.relu(i):i}function Oo(o,t){return uo(o,t,[1,1],!0)}function fo(o,t){return uo(o,t,[1,1],!1)}function 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