@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:a,extractMainBlockParams:s}=Zn(o,e),i=r("entry_flow/conv_in"),c=a("entry_flow/reduction_block_0"),m=a("entry_flow/reduction_block_1"),p={conv_in:i,reduction_block_0:c,reduction_block_1:m},u={};it(t,0,1).forEach(y=>{u[`main_block_${y}`]=s(`middle_flow/main_block_${y}`)});let f=a("exit_flow/reduction_block"),l=n("exit_flow/separable_conv"),b={reduction_block:f,separable_conv:l};return B(o,e),{params:{entry_flow:p,middle_flow:u,exit_flow:b},paramMappings:e}}function Wo(o,t,e){return S.add(S.conv2d(o,t.filters,e,"same"),t.bias)}function mo(o,t,e=!0){let r=e?S.relu(o):o;return r=H(r,t.separable_conv0,[1,1]),r=H(S.relu(r),t.separable_conv1,[1,1]),r=S.maxPool(r,[3,3],[2,2],"same"),r=S.add(r,Wo(o,t.expansion_conv,[2,2])),r}function Kn(o,t){let e=H(S.relu(o),t.separable_conv0,[1,1]);return e=H(S.relu(e),t.separable_conv1,[1,1]),e=H(S.relu(e),t.separable_conv2,[1,1]),e=S.add(e,o),e}var _r=class extends A{constructor(e){super("TinyXception");this._numMainBlocks=e}forwardInput(e){let{params:r}=this;if(!r)throw new Error("TinyXception - load model before inference");return S.tidy(()=>{let n=S.cast(e.toBatchTensor(112,!0),"float32"),s=rt(n,[122.782,117.001,104.298]).div(255),i=S.relu(Wo(s,r.entry_flow.conv_in,[2,2]));return i=mo(i,r.entry_flow.reduction_block_0,!1),i=mo(i,r.entry_flow.reduction_block_1),it(this._numMainBlocks,0,1).forEach(c=>{i=Kn(i,r.middle_flow[`main_block_${c}`])}),i=mo(i,r.exit_flow.reduction_block),i=S.relu(H(i,r.exit_flow.separable_conv,[1,1])),i})}async forward(e){return this.forwardInput(await C(e))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(e){return Ao(e,this._numMainBlocks)}extractParams(e){return Lo(e,this._numMainBlocks)}};function ko(o){let t=[],{extractWeights:e,getRemainingWeights:r}=R(o),n=lr(e,t),a=n(512,1,"fc/age"),s=n(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:s}}}}function Bo(o){let t=[],e=Y(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:{age:r("fc/age"),gender:r("fc/gender")}};return B(o,t),{params:n,paramMappings:t}}var Tr=(e=>(e.FEMALE="female",e.MALE="male",e))(Tr||{});var He=class extends A{constructor(e=new _r(2)){super("AgeGenderNet");this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){let{params:r}=this;if(!r)throw new Error(`${this._name} - load model before inference`);return ft.tidy(()=>{let n=e instanceof ut?this.faceFeatureExtractor.forwardInput(e):e,a=ft.avgPool(n,[7,7],[2,2],"valid").as2D(n.shape[0],-1),s=$e(a,r.fc.age).as1D(),i=$e(a,r.fc.gender);return{age:s,gender:i}})}forwardInput(e){return ft.tidy(()=>{let{age:r,gender:n}=this.runNet(e);return{age:r,gender:ft.softmax(n)}})}async forward(e){return this.forwardInput(await C(e))}async predictAgeAndGender(e){let r=await C(e),n=await this.forwardInput(r),a=ft.unstack(n.age),s=ft.unstack(n.gender),i=a.map((m,p)=>({ageTensor:m,genderTensor:s[p]})),c=await Promise.all(i.map(async({ageTensor:m,genderTensor:p})=>{let u=m.dataSync()[0],f=p.dataSync()[0],l=f>.5,b=l?"male":"female",y=l?f:1-f;return m.dispose(),p.dispose(),{age:u,gender:b,genderProbability:y}}));return n.age.dispose(),n.gender.dispose(),r.isBatchInput?c:c[0]}getDefaultModelName(){return"age_gender_model"}dispose(e=!0){this.faceFeatureExtractor.dispose(e),super.dispose(e)}loadClassifierParams(e){let{params:r,paramMappings:n}=this.extractClassifierParams(e);this._params=r,this._paramMappings=n}extractClassifierParams(e){return ko(e)}extractParamsFromWeightMap(e){let{featureExtractorMap:r,classifierMap:n}=gr(e);return this.faceFeatureExtractor.loadFromWeightMap(r),Bo(n)}extractParams(e){let n=e.slice(0,e.length-1539),a=e.slice(e.length-1539);return this.faceFeatureExtractor.extractWeights(n),this.extractClassifierParams(a)}};var G=v(x());var Fe=class extends Te{postProcess(t,e,r){let n=r.map(({width:s,height:i})=>{let c=e/Math.max(i,s);return{width:s*c,height:i*c}}),a=n.length;return G.tidy(()=>{let s=(u,f)=>G.stack([G.fill([68],u,"float32"),G.fill([68],f,"float32")],1).as2D(1,136).as1D(),i=(u,f)=>{let{width:l,height: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(G.fill([a,136],e,"float32")).sub(G.stack(Array.from(Array(a),(u,f)=>s(c(f),m(f))))).div(G.stack(Array.from(Array(a),(u,f)=>s(n[f].width,n[f].height))))})}forwardInput(t){return G.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 C(t))}async detectLandmarks(t){let e=await C(t),r=G.tidy(()=>G.unstack(this.forwardInput(e))),n=await Promise.all(r.map(async(a,s)=>{let i=Array.from(a.dataSync()),c=i.filter((p,u)=>rr(u)),m=i.filter((p,u)=>!rr(u));return new Gt(Array(68).fill(0).map((p,u)=>new g(c[u],m[u])),{height:e.getInputHeight(s),width:e.getInputWidth(s)})}));return r.forEach(a=>a.dispose()),e.isBatchInput?n:n[0]}getClassifierChannelsOut(){return 136}};var Kt=class extends Fe{constructor(t=new ve){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var De=v(x());function Ro(o){let t=[],{extractDenseBlock3Params:e}=br(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2")};return B(o,t),{params:r,paramMappings:t}}function $o(o){let t=[],{extractWeights:e,getRemainingWeights:r}=R(o),{extractDenseBlock3Params:n}=dr(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i}}}var wr=class extends A{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyFaceFeatureExtractor - load model before inference");return De.tidy(()=>{let r=De.cast(t.toBatchTensor(112,!0),"float32"),a=rt(r,[122.782,117.001,104.298]).div(255),s=pr(a,e.dense0,!0);return s=pr(s,e.dense1),s=pr(s,e.dense2),s=De.avgPool(s,[14,14],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await C(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return Ro(t)}extractParams(t){return $o(t)}};var ze=class extends Fe{constructor(t=new wr){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var po=class extends Kt{};var nt=v(x());var Ee=v(x());var Pr=v(x());function Oo(o,t){return Pr.add(Pr.mul(o,t.weights),t.biases)}function uo(o,t,e,r,n="same"){let{filters:a,bias:s}=t.conv,i=Ee.conv2d(o,a,e,n);return i=Ee.add(i,s),i=Oo(i,t.scale),r?Ee.relu(i):i}function Ho(o,t){return uo(o,t,[1,1],!0)}function fo(o,t){return uo(o,t,[1,1],!1)}function Fr(o,t){return uo(o,t,[2,2],!0,"valid")}var j=v(x());function Qn(o,t){function e(i,c,m){let p=o(i),u=p.length/(c*m*m);if($r(u))throw new Error(`depth has to be an integer: ${u}, weights.length: ${p.length}, numFilters: ${c}, filterSize: ${m}`);return j.tidy(()=>j.transpose(j.tensor4d(p,[c,u,m,m]),[2,3,1,0]))}function r(i,c,m,p){let u=e(i,c,m),f=j.tensor1d(o(c));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/bias`}),{filters:u,bias:f}}function n(i,c){let m=j.tensor1d(o(i)),p=j.tensor1d(o(i));return t.push({paramPath