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

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/*
Face-API
homepage: <https://github.com/vladmandic/face-api>
author: <https://github.com/vladmandic>'
*/
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e=o(`${t}/filters`,4),r=o(`${t}/bias`,1);return{filters:e,bias:r}}}function Lr(o,t){let e=H(o,t),r=Ir(e),n=ye(e);function a(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`);return{conv0:m,conv1:p,conv2:d}}function s(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`),u=n(`${i}/conv3`);return{conv0:m,conv1:p,conv2:d,conv3:u}}return{extractDenseBlock3Params:a,extractDenseBlock4Params:s}}function $o(o){let t=[],{extractDenseBlock4Params:e}=Lr(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2"),dense3:e("dense3")};return B(o,t),{params:r,paramMappings:t}}var We=class extends S{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceFeatureExtractor - load model before inference");return Rt.tidy(()=>{let r=Rt.cast(t.toBatchTensor(112,!0),"float32"),a=Z(r,[122.782,117.001,104.298]).div(Rt.scalar(255)),s=Se(a,e.dense0,!0);return s=Se(s,e.dense1),s=Se(s,e.dense2),s=Se(s,e.dense3),s=Rt.avgPool(s,[7,7],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return $o(t)}extractParams(t){return Ro(t)}};var Ho=b(g());var Fe=b(g());function Be(o,t){return Fe.tidy(()=>Fe.add(Fe.matMul(o,t.weights),t.bias))}function Oo(o,t,e){let r=[],{extractWeights:n,getRemainingWeights:a}=R(o),i=Mr(n,r)(t,e,"fc");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:r,params:{fc:i}}}function jo(o){let t=[],e=H(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:r("fc")};return B(o,t),{params:n,paramMappings:t}}function kr(o){let t={},e={};return Object.keys(o).forEach(r=>{let n=r.startsWith("fc")?e:t;n[r]=o[r]}),{featureExtractorMap:t,classifierMap:e}}var Re=class extends S{constructor(t,e){super(t);this._faceFeatureExtractor=e}get 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forward(t){return this.forwardInput(await E(t))}async computeFaceDescriptor(t){var a;if((a=t==null?void 0:t.shape)==null?void 0:a.some(s=>s<=0))return new Float32Array(128);let e=await E(t),r=U.tidy(()=>U.unstack(this.forwardInput(e))),n=await Promise.all(r.map(s=>s.data()));return r.forEach(s=>s.dispose()),e.isBatchInput?n:n[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return tn(t)}extractParams(t){return Qo(t)}};function en(o){let t=new ee;return t.extractWeights(o),t}function ze(o,t){return{...o,...{descriptor:t}}}function rn(o){return typeof o.age=="number"}function Ve(o,t){return{...o,...{age:t}}}function on(o){return(o.gender===ht.MALE||o.gender===ht.FEMALE)&&de(o.genderProbability)}function Ue(o,t,e){return{...o,...{gender:t,genderProbability:e}}}var it=b(g());var st=b(g());function Fa(o,t){function e(c,m){let p=st.tensor4d(o(3*3*c),[3,3,c,1]),d=st.tensor1d(o(c)),u=st.tensor1d(o(c)),l=st.tensor1d(o(c)),v=st.tensor1d(o(c));return t.push({paramPath:`${m}/filters`},{paramPath:`${m}/batch_norm_scale`},{paramPath:`${m}/batch_norm_offset`},{paramPath:`${m}/batch_norm_mean`},{paramPath:`${m}/batch_norm_variance`}),{filters:p,batch_norm_scale:d,batch_norm_offset:u,batch_norm_mean:l,batch_norm_variance:v}}function r(c,m,p,d,u){let l=st.tensor4d(o(c*m*p*p),[p,p,c,m]),v=st.tensor1d(o(m));return t.push({paramPath:`${d}/filters`},{paramPath:`${d}/${u?"batch_norm_offset":"bias"}`}),{filters:l,bias:v}}function n(c,m,p,d){let{filters:u,bias:l}=r(c,m,p,d,!0);return{filters:u,batch_norm_offset:l}}function a(c,m,p){let d=e(c,`${p}/depthwise_conv`),u=n(c,m,1,`${p}/pointwise_conv`);return{depthwise_conv:d,pointwise_conv:u}}function s(){let 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et=b(g());var tt=b(g());function we(o){return tt.tidy(()=>{let t=tt.mul(o,tt.scalar(.10000000149011612));return tt.add(tt.relu(tt.sub(o,t)),t)})}function _t(o,t){return et.tidy(()=>{let e=et.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=et.conv2d(e,t.conv.filters,[1,1],"valid"),e=et.sub(e,t.bn.sub),e=et.mul(e,t.bn.truediv),e=et.add(e,t.conv.bias),we(e)})}var Yt=b(g());function wt(o,t){return Yt.tidy(()=>{let e=Yt.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Yt.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=Yt.add(e,t.bias),we(e)})}var ho=b(g());function Ca(o,t){let e=ge(o,t);function r(s,i){let c=ho.tensor1d(o(s)),m=ho.tensor1d(o(s));return t.push({paramPath:`${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=ve(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}}function gn(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=R(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=Ca(n,s),p;if(t.withSeparableConvs){let[d,u,l,v,w,x,y,T,F]=r,L=t.isFirstLayerConv2d?i(d,u,3,"conv0"):m(d,u,"conv0"),z=m(u,l,"conv1"),rt=m(l,v,"conv2"),mt=m(v,w,"conv3"),q=m(w,x,"conv4"),Et=m(x,y,"conv5"),Mt=T?m(y,T,"conv6"):void 0,Ct=F?m(T,F,"conv7"):void 0,pe=i(F||T||y,5*e,1,"conv8");p={conv0:L,conv1:z,conv2:rt,conv3:mt,conv4:q,conv5:Et,conv6:Mt,conv7:Ct,conv8:pe}}else{let[d,u,l,v,w,x,y,T,F]=r,L=c(d,u,"conv0"),z=c(u,l,"conv1"),rt=c(l,v,"conv2"),mt=c(v,w,"conv3"),q=c(w,x,"conv4"),Et=c(x,y,"conv5"),Mt=c(y,T,"conv6"),Ct=c(T,F,"conv7"),pe=i(F,5*e,1,"conv8");p={conv0:L,conv1:z,conv2:rt,conv3:mt,conv4:q,conv5:Et,conv6:Mt,conv7:Ct,conv8:pe}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function Na(o,t){let e=H(o,t);function r(i){let c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=ye(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function vn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=Na(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return B(o,e),{params:s,paramMappings:e}}var ct=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var xo=class extends S{constructor(t){super("TinyYolov2");$r(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=_t(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=_t(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=_t(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=_t(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=_t(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=_t(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=_t(r,e.conv6),r=_t(r,e.conv7),Kt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?we(Kt(t,e.conv0,"valid",!1)):wt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=wt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=wt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=wt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=wt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=wt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?wt(r,e.conv6):r,r=e.conv7?wt(r,e.conv7):r,Kt(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?Z(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 ct(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(x=>x.box),d=m.map(x=>x.score),u=m.map(x=>x.classScore),l=m.map(x=>this.config.classes[x.label]);return ur(p.map(x=>x.rescale(r)),d,this.config.iouThreshold,!0).map(x=>new vt(d[x],u[x],l[x],p[x],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return vn(t,this.config)}extractParams(t){let e=this.config.filterSizes||xo.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 gn(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=[],w=await u.array(),x=await d.array();for(let y=0;y<m;y++)for(let T=0;T<m;T++)for(let F=0;F<p;F++){let L=ue(w[y][T][F][0]);if(!r||L>r){let z=(T+ue(x[y][T][F][0]))/m*i,rt=(y+ue(x[y][T][F][1]))/m*c,mt=Math.exp(x[y][T][F][2])*this.config.anchors[F].x/m*i,q=Math.exp(x[y][T][F][3])*this.config.anchors[F].y/m*c,Et=z-mt/2,Mt=rt-q/2,Ct={row:y,col:T,anchor:F},{classScore:pe,label:Po}=this.withClassScores?await this.extractPredictedClass(l,Ct):{classScore:1,label:0};v.push({box:new Ut(Et,Mt,Et+mt,Mt+q),score:L,classScore:L*pe,label:Po,...Ct})}}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)}},Pe=xo;Pe.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var oe=class extends Pe{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:un,classes:["face"],...t?{anchors:fn,meanRgb:hn}:{anchors:ln,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?bn:xn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function yn(o,t=!0){let e=new oe(t);return e.extractWeights(o),e}var qe=class extends ct{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Ke=b(g());var bo=b(g());async function ne(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>$t(c)?n(c):c.detection),s=r||(t instanceof bo.Tensor?await Zt(t,a):await qt(t,a)),i=await e(s);return s.forEach(c=>c instanceof bo.Tensor&&c.dispose()),i}async function De(o,t,e,r,n){return ne([o],t,async a=>e(a[0]),r,n)}var Fn=.4,Tn=[new h(1.603231,2.094468),new h(6.041143,7.080126),new h(2.882459,3.518061),new h(4.266906,5.178857),new h(9.041765,10.66308)],_n=[117.001,114.697,97.404];var ae=class extends Pe{constructor(){let t={withSeparableConvs:!0,iouThreshold:Fn,classes:["face"],anchors:Tn,meanRgb:_n,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 _={ssdMobilenetv1:new Ht,tinyFaceDetector:new ae,tinyYolov2:new oe,faceLandmark68Net:new te,faceLandmark68TinyNet:new Ye,faceRecognitionNet:new ee,faceExpressionNet:new $e,ageGenderNet:new je},go=(o,t)=>_.ssdMobilenetv1.locateFaces(o,t),wn=(o,t)=>_.tinyFaceDetector.locateFaces(o,t),Pn=(o,t)=>_.tinyYolov2.locateFaces(o,t),vo=o=>_.faceLandmark68Net.detectLandmarks(o),Dn=o=>_.faceLandmark68TinyNet.detectLandmarks(o),En=o=>_.faceRecognitionNet.computeFaceDescriptor(o),Mn=o=>_.faceExpressionNet.predictExpressions(o),Cn=o=>_.ageGenderNet.predictAgeAndGender(o),yo=o=>_.ssdMobilenetv1.load(o),Nn=o=>_.tinyFaceDetector.load(o),In=o=>_.tinyYolov2.load(o),Ln=o=>_.faceLandmark68Net.load(o),kn=o=>_.faceLandmark68TinyNet.load(o),Sn=o=>_.faceRecognitionNet.load(o),An=o=>_.faceExpressionNet.load(o),Wn=o=>_.ageGenderNet.load(o),Bn=yo,Rn=go,$n=vo;var Fo=class extends J{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Ce=class extends Fo{async run(){let t=await this.parentTask,e=await ne(t,this.input,async r=>Promise.all(r.map(n=>_.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>Oe(r,e[n]))}withAgeAndGender(){return new Ee(this,this.input)}},Ne=class extends Fo{async run(){let t=await this.parentTask;if(!t)return;let e=await De(t,this.input,r=>_.faceExpressionNet.predictExpressions(r),this.extractedFaces);return Oe(t,e)}withAgeAndGender(){return new Me(this,this.input)}},ce=class extends Ce{withAgeAndGender(){return new se(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},me=class extends Ne{withAgeAndGender(){return new ie(this,this.input)}withFaceDescriptor(){return new Dt(this,this.input)}};var To=class extends J{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Ee=class extends To{async run(){let t=await this.parentTask,e=await ne(t,this.input,async r=>Promise.all(r.map(n=>_.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return Ve(Ue(r,s,i),a)})}withFaceExpressions(){return new Ce(this,this.input)}},Me=class extends To{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await De(t,this.input,a=>_.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Ve(Ue(t,r,n),e)}withFaceExpressions(){return new Ne(this,this.input)}},se=class extends Ee{withFaceExpressions(){return new ce(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},ie=class extends Me{withFaceExpressions(){return new me(this,this.input)}withFaceDescriptor(){return new Dt(this,this.input)}};var Ze=class extends J{constructor(t,e){super();this.parentTask=t;this.input=e}},Pt=class extends Ze{async run(){let t=await this.parentTask;return(await ne(t,this.input,r=>Promise.all(r.map(n=>_.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>ze(t[n],r))}withFaceExpressions(){return new ce(this,this.input)}withAgeAndGender(){return new se(this,this.input)}},Dt=class extends Ze{async run(){let t=await this.parentTask;if(!t)return;let e=await De(t,this.input,r=>_.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return ze(t,e)}withFaceExpressions(){return new me(this,this.input)}withAgeAndGender(){return new ie(this,this.input)}};var Qe=class extends J{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?_.faceLandmark68TinyNet:_.faceLandmark68Net}},tr=class extends Qe{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof Ke.Tensor?await Zt(this.input,e):await qt(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Ke.Tensor&&a.dispose()),t.map((a,s)=>Qt(a,n[s]))}withFaceExpressions(){return new ce(this,this.input)}withAgeAndGender(){return new se(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},er=class extends Qe{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Ke.Tensor?await Zt(this.input,[e]):await qt(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Ke.Tensor&&a.dispose()),Qt(t,n)}withFaceExpressions(){return new me(this,this.input)}withAgeAndGender(){return new ie(this,this.input)}withFaceDescriptor(){return new Dt(this,this.input)}};var rr=class extends J{constructor(t,e=new X){super();this.input=t;this.options=e}},Ie=class extends rr{async run(){let{input:t,options:e}=this,r;if(e instanceof qe)r=_.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=_.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof ct)r=_.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>It({},r)))})}withFaceLandmarks(t=!1){return new tr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Ce(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Ee(this.runAndExtendWithFaceDetections(),this.input)}},or=class extends rr{async run(){let t=await new Ie(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?It({},e):void 0)})}withFaceLandmarks(t=!1){return new er(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Ne(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Me(this.runAndExtendWithFaceDetection(),this.input)}};function On(o,t=new X){return new or(o,t)}function nr(o,t=new X){return new Ie(o,t)}async function _o(o,t){return nr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function jn(o,t={}){return nr(o,new ct(t)).withFaceLandmarks().withFaceDescriptors()}var Hn=_o;function Or(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 ar=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 ut)return s;if(s instanceof Float32Array)return new ut(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new ut(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>Or(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new le(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distance<r.distance?e:r)}findBestMatch(t){let e=this.matchDescriptor(t);return e.distance<this.distanceThreshold?e:new le("unknown",e.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>ut.fromJSON(r));return new ar(e,t.distanceThreshold)}};function Yn(o){let t=new ae;return t.extractWeights(o),t}function wo(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>wo(n,{width:e,height:r}));if($t(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Qt(It(o,n),a)}return nt(o)?It(o,o.detection.forSize(e,r)):o instanceof j||o instanceof M?o.forSize(e,r):o}var Ia=typeof process!="undefined",La=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",zn={faceapi:Yo,node:Ia,browser:La};0&&(module.exports={AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf,tinyFaceDetector,tinyYolov2,toNetInput,utils,validateConfig,version});
//# sourceMappingURL=face-api.node-gpu.js.map
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