face-api/dist/face-api.esm-nobundle.js

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

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s(m,p,d){let u=e(m,`${d}/depthwise_conv`),l=a(m,p,1,`${d}/pointwise_conv`);return{depthwise_conv:u,pointwise_conv:l}}function i(){let m=a(3,32,3,"mobilenetv1/conv_0"),p=s(32,64,"mobilenetv1/conv_1"),d=s(64,128,"mobilenetv1/conv_2"),u=s(128,128,"mobilenetv1/conv_3"),l=s(128,256,"mobilenetv1/conv_4"),f=s(256,256,"mobilenetv1/conv_5"),b=s(256,512,"mobilenetv1/conv_6"),F=s(512,512,"mobilenetv1/conv_7"),h=s(512,512,"mobilenetv1/conv_8"),g=s(512,512,"mobilenetv1/conv_9"),y=s(512,512,"mobilenetv1/conv_10"),v=s(512,512,"mobilenetv1/conv_11"),E=s(512,1024,"mobilenetv1/conv_12"),W=s(1024,1024,"mobilenetv1/conv_13");return{conv_0:m,conv_1:p,conv_2:d,conv_3:u,conv_4:l,conv_5:f,conv_6:b,conv_7:F,conv_8:h,conv_9:g,conv_10:y,conv_11:v,conv_12:E,conv_13:W}}function c(){let m=a(1024,256,1,"prediction_layer/conv_0"),p=a(256,512,3,"prediction_layer/conv_1"),d=a(512,128,1,"prediction_layer/conv_2"),u=a(128,256,3,"prediction_layer/conv_3"),l=a(256,128,1,"prediction_layer/conv_4"),f=a(128,256,3,"prediction_layer/conv_5"),b=a(256,64,1,"prediction_layer/conv_6"),F=a(64,128,3,"prediction_layer/conv_7"),h=r(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),g=r(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),y=r(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),v=r(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),E=r(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),W=r(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),j=r(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),V=r(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),R=r(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),rt=r(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),ot=r(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),nt=r(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:m,conv_1:p,conv_2:d,conv_3:u,conv_4:l,conv_5:f,conv_6:b,conv_7:F,box_predictor_0:{box_encoding_predictor:h,class_predictor:g},box_predictor_1:{box_encoding_predictor:y,class_predictor:v},box_predictor_2:{box_encoding_predictor:E,class_predictor:W},box_predictor_3:{box_encoding_predictor:j,class_predictor:V},box_predictor_4:{box_encoding_predictor:R,class_predictor:rt},box_predictor_5:{box_encoding_predictor:ot,class_predictor:nt}}}return{extractMobilenetV1Params:i,extractPredictionLayerParams:c}}function Or(o){let t=[],{extractWeights:e,getRemainingWeights:r}=I(o),{extractMobilenetV1Params:a,extractPredictionLayerParams:s}=Co(e,t),i=a(),c=s(),p={extra_dim:n.tensor3d(e(5118*4),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{params:{mobilenetv1:i,prediction_layer:c,output_layer:p},paramMappings:t}}function No(o,t){let e=A(o,t);function r(p,d,u){let l=e(`${p}/Conv2d_${d}_pointwise/weights`,4,`${u}/filters`),f=e(`${p}/Conv2d_${d}_pointwise/convolution_bn_offset`,1,`${u}/batch_norm_offset`);return{filters:l,batch_norm_offset:f}}function a(p){let d=`mobilenetv1/conv_${p}`,u=`MobilenetV1/Conv2d_${p}_depthwise`,l=`${d}/depthwise_conv`,f=`${d}/pointwise_conv`,b=e(`${u}/depthwise_weights`,4,`${l}/filters`),F=e(`${u}/BatchNorm/gamma`,1,`${l}/batch_norm_scale`),h=e(`${u}/BatchNorm/beta`,1,`${l}/batch_norm_offset`),g=e(`${u}/BatchNorm/moving_mean`,1,`${l}/batch_norm_mean`),y=e(`${u}/BatchNorm/moving_variance`,1,`${l}/batch_norm_variance`);return{depthwise_conv:{filters:b,batch_norm_scale:F,batch_norm_offset:h,batch_norm_mean:g,batch_norm_variance:y},pointwise_conv:r("MobilenetV1",p,f)}}function s(){return{conv_0:r("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:a(1),conv_2:a(2),conv_3:a(3),conv_4:a(4),conv_5:a(5),conv_6:a(6),conv_7:a(7),conv_8:a(8),conv_9:a(9),conv_10:a(10),conv_11:a(11),conv_12:a(12),conv_13:a(13)}}function i(p,d){let u=e(`${p}/weights`,4,`${d}/filters`),l=e(`${p}/biases`,1,`${d}/bias`);return{filters:u,bias:l}}function c(p){let d=i(`Prediction/BoxPredictor_${p}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${p}/box_encoding_predictor`),u=i(`Prediction/BoxPredictor_${p}/ClassPredictor`,`prediction_layer/box_predictor_${p}/class_predictor`);return{box_encoding_predictor:d,class_predictor:u}}function m(){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:c(0),box_predictor_1:c(1),box_predictor_2:c(2),box_predictor_3:c(3),box_predictor_4:c(4),box_predictor_5:c(5)}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:m}}function jr(o){let t=[],{extractMobilenetV1Params:e,extractPredictionLayerParams:r}=No(o,t),a=o["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Z(a))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${a}`);let s={mobilenetv1:e(),prediction_layer:r(),output_layer:{extra_dim:a}};return N(o,t),{params:s,paramMappings:t}}function O(o,t,e){return n.tidy(()=>{let r=n.conv2d(o,t.filters,e,"same");return r=n.add(r,t.batch_norm_offset),n.clipByValue(r,0,6)})}var Io=.0010000000474974513;function Lo(o,t,e){return n.tidy(()=>{let r=n.depthwiseConv2d(o,t.filters,e,"same");return r=n.batchNorm(r,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,Io),n.clipByValue(r,0,6)})}function ko(o){return[2,4,6,12].some(t=>t===o)?[2,2]:[1,1]}function Hr(o,t){return n.tidy(()=>{let e,r=O(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((s,i)=>{let c=i+1,m=ko(c);r=Lo(r,s.depthwise_conv,m),r=O(r,s.pointwise_conv,[1,1]),c===11&&(e=r)}),e===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:r,conv11:e}})}function So(o,t,e){let r=o.arraySync(),a=Math.min(r[t][0],r[t][2]),s=Math.min(r[t][1],r[t][3]),i=Math.max(r[t][0],r[t][2]),c=Math.max(r[t][1],r[t][3]),m=Math.min(r[e][0],r[e][2]),p=Math.min(r[e][1],r[e][3]),d=Math.max(r[e][0],r[e][2]),u=Math.max(r[e][1],r[e][3]),l=(i-a)*(c-s),f=(d-m)*(u-p);if(l<=0||f<=0)return 0;let b=Math.max(a,m),F=Math.max(s,p),h=Math.min(i,d),g=Math.min(c,u),y=Math.max(h-b,0)*Math.max(g-F,0);return y/(l+f-y)}function Yr(o,t,e,r,a){let s=o.shape[0],i=Math.min(e,s),c=t.map((d,u)=>({score:d,boxIndex:u})).filter(d=>d.score>a).sort((d,u)=>u.score-d.score),m=d=>d<=r?1:0,p=[];return c.forEach(d=>{if(p.length>=i)return;let u=d.score;for(let l=p.length-1;l>=0;--l){let f=So(o,d.boxIndex,p[l]);if(f!==0&&(d.score*=m(f),d.score<=a))break}u===d.score&&p.push(d.boxIndex)}),p}function Ao(o){let t=n.unstack(n.transpose(o,[1,0])),e=[n.sub(t[2],t[0]),n.sub(t[3],t[1])],r=[n.add(t[0],n.div(e[0],2)),n.add(t[1],n.div(e[1],2))];return{sizes:e,centers:r}}function Wo(o,t){let{sizes:e,centers:r}=Ao(o),a=n.unstack(n.transpose(t,[1,0])),s=n.div(n.mul(n.exp(n.div(a[2],5)),e[0]),2),i=n.add(n.mul(n.div(a[0],10),e[0]),r[0]),c=n.div(n.mul(n.exp(n.div(a[3],5)),e[1]),2),m=n.add(n.mul(n.div(a[1],10),e[1]),r[1]);return n.transpose(n.stack([n.sub(i,s),n.sub(m,c),n.add(i,s),n.add(m,c)]),[1,0])}function Gr(o,t,e){return n.tidy(()=>{let r=o.shape[0],a=Wo(n.reshape(n.tile(e.extra_dim,[r,1,1]),[-1,4]),n.reshape(o,[-1,4]));a=n.reshape(a,[r,a.shape[0]/r,4]);let s=n.sigmoid(n.slice(t,[0,0,1],[-1,-1,-1])),i=n.slice(s,[0,0,0],[-1,-1,1]);i=n.reshape(i,[r,i.shape[1]]);let c=n.unstack(a),m=n.unstack(i);return{boxes:c,scores:m}})}function ht(o,t){return n.tidy(()=>{let e=o.shape[0],r=n.reshape(lt(o,t.box_encoding_predictor),[e,-1,1,4]),a=n.reshape(lt(o,t.class_predictor),[e,-1,3]);return{boxPredictionEncoding:r,classPrediction:a}})}function zr(o,t,e){return n.tidy(()=>{let r=O(o,e.conv_0,[1,1]),a=O(r,e.conv_1,[2,2]),s=O(a,e.conv_2,[1,1]),i=O(s,e.conv_3,[2,2]),c=O(i,e.conv_4,[1,1]),m=O(c,e.conv_5,[2,2]),p=O(m,e.conv_6,[1,1]),d=O(p,e.conv_7,[2,2]),u=ht(t,e.box_predictor_0),l=ht(o,e.box_predictor_1),f=ht(a,e.box_predictor_2),b=ht(i,e.box_predictor_3),F=ht(m,e.box_predictor_4),h=ht(d,e.box_predictor_5),g=n.concat([u.boxPredictionEncoding,l.boxPredictionEncoding,f.boxPredictionEncoding,b.boxPredictionEncoding,F.boxPredictionEncoding,h.boxPredictionEncoding],1),y=n.concat([u.classPrediction,l.classPrediction,f.classPrediction,b.classPrediction,F.classPrediction,h.classPrediction],1);return{boxPredictions:g,classPredictions:y}})}var G=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 At=class extends C{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return n.tidy(()=>{let r=n.cast(t.toBatchTensor(512,!1),"float32"),a=n.sub(n.div(r,127.5),1),s=Hr(a,e.mobilenetv1),{boxPredictions:i,classPredictions:c}=zr(s.out,s.conv11,e.prediction_layer);return Gr(i,c,e.output_layer)})}async forward(t){return this.forwardInput(await P(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:a}=new G(e),s=await P(t),{boxes:i,scores:c}=this.forwardInput(s),m=i[0],p=c[0];for(let v=1;v<i.length;v++)i[v].dispose(),c[v].dispose();let d=Array.from(p.dataSync()),l=Yr(m,d,r,.5,a),f=s.getReshapedInputDimensions(0),b=s.inputSize,F=b/f.width,h=b/f.height,g=m.arraySync(),y=l.map(v=>{let[E,W]=[Math.max(0,g[v][0]),Math.min(1,g[v][2])].map(R=>R*h),[j,V]=[Math.max(0,g[v][1]),Math.min(1,g[v][3])].map(R=>R*F);return new D(d[v],new Gt(j,E,V-j,W-E),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return m.dispose(),p.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return jr(t)}extractParams(t){return Or(t)}};function Bo(o){let t=new At;return t.extractWeights(o),t}function Cu(o){return Bo(o)}var Ro=class extends At{};var Vr=.4,Ur=[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)],Xr=[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)],Jr=[117.001,114.697,97.404],qr="tiny_yolov2_model",Zr="tiny_yolov2_separable_conv_model";var we=o=>typeof o=="number";function Kr(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(!we(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: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>we(t.x)&&we(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: 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s=Lt(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function Qr(o,t,e,r){let{extractWeights:a,getRemainingWeights:s}=I(o),i=[],{extractConvParams:c,extractConvWithBatchNormParams:m,extractSeparableConvParams:p}=$o(a,i),d;if(t.withSeparableConvs){let[u,l,f,b,F,h,g,y,v]=r,E=t.isFirstLayerConv2d?c(u,l,3,"conv0"):p(u,l,"conv0"),W=p(l,f,"conv1"),j=p(f,b,"conv2"),V=p(b,F,"conv3"),R=p(F,h,"conv4"),rt=p(h,g,"conv5"),ot=y?p(g,y,"conv6"):void 0,nt=v?p(y,v,"conv7"):void 0,_t=c(v||y||g,5*e,1,"conv8");d={conv0:E,conv1:W,conv2:j,conv3:V,conv4:R,conv5:rt,conv6:ot,conv7:nt,conv8:_t}}else{let[u,l,f,b,F,h,g,y,v]=r,E=m(u,l,"conv0"),W=m(l,f,"conv1"),j=m(f,b,"conv2"),V=m(b,F,"conv3"),R=m(F,h,"conv4"),rt=m(h,g,"conv5"),ot=m(g,y,"conv6"),nt=m(y,v,"conv7"),_t=c(v,5*e,1,"conv8");d={conv0:E,conv1:W,conv2:j,conv3:V,conv4:R,conv5:rt,conv6:ot,conv7:nt,conv8:_t}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:d,paramMappings:i}}function Oo(o,t){let e=A(o,t);function r(c){let m=e(`${c}/sub`,1),p=e(`${c}/truediv`,1);return{sub:m,truediv:p}}function a(c){let m=e(`${c}/filters`,4),p=e(`${c}/bias`,1);return{filters:m,bias:p}}function s(c){let m=a(`${c}/conv`),p=r(`${c}/bn`);return{conv:m,bn:p}}let i=kt(e);return{extractConvParams:a,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function to(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}=Oo(o,e),i;if(t.withSeparableConvs){let c=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?r("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:c>7?s("conv6"):void 0,conv7:c>8?s("conv7"):void 0,conv8:r("conv8")}}else i={conv0:a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:a("conv6"),conv7:a("conv7"),conv8:r("conv8")};return N(o,e),{params:i,paramMappings:e}}var et=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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 Ze=class extends C{constructor(t){super("TinyYolov2");Kr(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=Q(t,e.conv0);return r=n.maxPool(r,[2,2],[2,2],"same"),r=Q(r,e.conv1),r=n.maxPool(r,[2,2],[2,2],"same"),r=Q(r,e.conv2),r=n.maxPool(r,[2,2],[2,2],"same"),r=Q(r,e.conv3),r=n.maxPool(r,[2,2],[2,2],"same"),r=Q(r,e.conv4),r=n.maxPool(r,[2,2],[2,2],"same"),r=Q(r,e.conv5),r=n.maxPool(r,[2,2],[1,1],"same"),r=Q(r,e.conv6),r=Q(r,e.conv7),lt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?Wt(lt(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,lt(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 a=n.cast(t.toBatchTensor(e,!1),"float32");return a=this.config.meanRgb?X(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,r):this.runTinyYolov2(a,r)})}async forward(t,e){return this.forwardInput(await P(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:a}=new et(e),s=await P(t),i=await this.forwardInput(s,r),c=n.tidy(()=>n.unstack(i)[0].expandDims()),m={width:s.getInputWidth(0),height:s.getInputHeight(0)},p=await this.extractBoxes(c,s.getReshapedInputDimensions(0),a);i.dispose(),c.dispose();let d=p.map(h=>h.box),u=p.map(h=>h.score),l=p.map(h=>h.classScore),f=p.map(h=>this.config.classes[h.label]);return fr(d.map(h=>h.rescale(r)),u,this.config.iouThreshold,!0).map(h=>new ut(u[h],l[h],f[h],d[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return to(t,this.config)}extractParams(t){let e=this.config.filterSizes||Ze.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 Qr(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:a,height:s}=e,i=Math.max(a,s),c=i/a,m=i/s,p=t.shape[1],d=this.config.anchors.length,[u,l,f]=n.tidy(()=>{let g=t.reshape([p,p,d,this.boxEncodingSize]),y=g.slice([0,0,0,0],[p,p,d,4]),v=g.slice([0,0,0,4],[p,p,d,1]),E=this.withClassScores?n.softmax(g.slice([0,0,0,5],[p,p,d,this.config.classes.length]),3):n.scalar(0);return[y,v,E]}),b=[],F=await l.array(),h=await u.array();for(let g=0;g<p;g++)for(let y=0;y<p;y++)for(let v=0;v<d;v++){let E=ce(F[g][y][v][0]);if(!r||E>r){let W=(y+ce(h[g][y][v][0]))/p*c,j=(g+ce(h[g][y][v][1]))/p*m,V=Math.exp(h[g][y][v][2])*this.config.anchors[v].x/p*c,R=Math.exp(h[g][y][v][3])*this.config.anchors[v].y/p*m,rt=W-V/2,ot=j-R/2,nt={row:g,col:y,anchor:v},{classScore:_t,label:mr}=this.withClassScores?await this.extractPredictedClass(f,nt):{classScore:1,label:0};b.push({box:new Yt(rt,ot,rt+V,ot+R),score:E,classScore:E*_t,label:mr,...nt})}}return u.dispose(),l.dispose(),f.dispose(),b}async extractPredictedClass(t,e){let{row:r,col:a,anchor:s}=e,i=await t.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[r][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},Bt=Ze;Bt.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ne=class extends Bt{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Vr,classes:["face"],...t?{anchors:Xr,meanRgb:Jr}:{anchors:Ur,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(a=>new D(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?Zr:qr}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Dl(o,t=!0){let e=new ne(t);return e.extractWeights(o),e}var Ke=class extends et{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var z=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function xt(o,t,e,r,a=({alignedRect:s})=>s){let s=o.map(m=>St(m)?a(m):m.detection),i=r||(t instanceof n.Tensor?await Xt(t,s):await Ut(t,s)),c=await e(i);return i.forEach(m=>m instanceof n.Tensor&&m.dispose()),c}async function Rt(o,t,e,r,a){return xt([o],t,async s=>e(s[0]),r,a)}var eo=.4,ro=[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)],oo=[117.001,114.697,97.404];var ae=class extends Bt{constructor(){let t={withSeparableConvs:!0,iouThreshold:eo,classes:["face"],anchors:ro,meanRgb:oo,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(a=>new D(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var T={ssdMobilenetv1:new At,tinyFaceDetector:new ae,tinyYolov2:new ne,faceLandmark68Net:new ee,faceLandmark68TinyNet:new ze,faceRecognitionNet:new oe,faceExpressionNet:new Be,ageGenderNet:new Ye},jo=(o,t)=>T.ssdMobilenetv1.locateFaces(o,t),tf=(o,t)=>T.tinyFaceDetector.locateFaces(o,t),ef=(o,t)=>T.tinyYolov2.locateFaces(o,t),Ho=o=>T.faceLandmark68Net.detectLandmarks(o),rf=o=>T.faceLandmark68TinyNet.detectLandmarks(o),of=o=>T.faceRecognitionNet.computeFaceDescriptor(o),nf=o=>T.faceExpressionNet.predictExpressions(o),af=o=>T.ageGenderNet.predictAgeAndGender(o),Yo=o=>T.ssdMobilenetv1.load(o),sf=o=>T.tinyFaceDetector.load(o),cf=o=>T.tinyYolov2.load(o),mf=o=>T.faceLandmark68Net.load(o),pf=o=>T.faceLandmark68TinyNet.load(o),df=o=>T.faceRecognitionNet.load(o),uf=o=>T.faceExpressionNet.load(o),lf=o=>T.ageGenderNet.load(o),ff=Yo,hf=jo,xf=Ho;var Qe=class extends z{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},jt=class extends Qe{async run(){let t=await this.parentTask,e=await xt(t,this.input,async r=>Promise.all(r.map(a=>T.faceExpressionNet.predictExpressions(a))),this.extractedFaces);return t.map((r,a)=>Re(r,e[a]))}withAgeAndGender(){return new $t(this,this.input)}},Ht=class extends Qe{async run(){let t=await this.parentTask;if(!t)return;let e=await Rt(t,this.input,r=>T.faceExpressionNet.predictExpressions(r),this.extractedFaces);return Re(t,e)}withAgeAndGender(){return new Ot(this,this.input)}},Ft=class extends jt{withAgeAndGender(){return new vt(this,this.input)}withFaceDescriptors(){return new bt(this,this.input)}},Tt=class extends Ht{withAgeAndGender(){return new yt(this,this.input)}withFaceDescriptor(){return new gt(this,this.input)}};var tr=class extends z{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},$t=class extends tr{async run(){let t=await this.parentTask,e=await xt(t,this.input,async r=>Promise.all(r.map(a=>T.ageGenderNet.predictAgeAndGender(a))),this.extractedFaces);return t.map((r,a)=>{let{age:s,gender:i,genderProbability:c}=e[a];return Je(qe(r,i,c),s)})}withFaceExpressions(){return new jt(this,this.input)}},Ot=class extends tr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:a}=await Rt(t,this.input,s=>T.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Je(qe(t,r,a),e)}withFaceExpressions(){return new Ht(this,this.input)}},vt=class extends $t{withFaceExpressions(){return new Ft(this,this.input)}withFaceDescriptors(){return new bt(this,this.input)}},yt=class extends Ot{withFaceExpressions(){return new Tt(this,this.input)}withFaceDescriptor(){return new gt(this,this.input)}};var er=class extends z{constructor(t,e){super();this.parentTask=t;this.input=e}},bt=class extends er{async run(){let t=await this.parentTask;return(await xt(t,this.input,r=>Promise.all(r.map(a=>T.faceRecognitionNet.computeFaceDescriptor(a))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,a)=>Xe(t[a],r))}withFaceExpressions(){return new Ft(this,this.input)}withAgeAndGender(){return new vt(this,this.input)}},gt=class extends er{async run(){let t=await this.parentTask;if(!t)return;let e=await Rt(t,this.input,r=>T.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Xe(t,e)}withFaceExpressions(){return new Tt(this,this.input)}withAgeAndGender(){return new yt(this,this.input)}};var rr=class extends z{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?T.faceLandmark68TinyNet:T.faceLandmark68Net}},or=class extends rr{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof n.Tensor?await Xt(this.input,e):await Ut(this.input,e),a=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof n.Tensor&&s.dispose()),t.map((s,i)=>Qt(s,a[i]))}withFaceExpressions(){return new Ft(this,this.input)}withAgeAndGender(){return new vt(this,this.input)}withFaceDescriptors(){return new bt(this,this.input)}},nr=class extends rr{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof n.Tensor?await Xt(this.input,[e]):await Ut(this.input,[e]),a=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof n.Tensor&&s.dispose()),Qt(t,a)}withFaceExpressions(){return new Tt(this,this.input)}withAgeAndGender(){return new yt(this,this.input)}withFaceDescriptor(){return new gt(this,this.input)}};var ar=class extends z{constructor(t,e=new G){super();this.input=t;this.options=e}},Pe=class extends ar{async run(){let{input:t,options:e}=this,r;if(e instanceof Ke)r=T.tinyFaceDetector.locateFaces(t,e);else if(e instanceof G)r=T.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof et)r=T.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=>Dt({},r)))})}withFaceLandmarks(t=!1){return new or(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new jt(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new $t(this.runAndExtendWithFaceDetections(),this.input)}},sr=class extends ar{async run(){let t=await new Pe(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?Dt({},e):void 0)})}withFaceLandmarks(t=!1){return new nr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Ht(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Ot(this.runAndExtendWithFaceDetection(),this.input)}};function fh(o,t=new G){return new sr(o,t)}function ir(o,t=new G){return new Pe(o,t)}async function Go(o,t){return ir(o,new G(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function yh(o,t={}){return ir(o,new et(t)).withFaceLandmarks().withFaceDescriptors()}var Fh=Go;function no(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((a,s)=>a-r[s]).reduce((a,s)=>a+s**2,0))}var cr=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 a=1,s=()=>`person ${a++}`;this._labeledDescriptors=r.map(i=>{if(i instanceof at)return i;if(i instanceof Float32Array)return new at(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new at(s(),[i.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=>no(r,t)).reduce((r,a)=>r+a,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.distance<r.distance?e:r)}findBestMatch(t){let e=this.matchDescriptor(t);return e.distance<this.distanceThreshold?e:new me("unknown",e.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>at.fromJSON(r));return new cr(e,t.distanceThreshold)}};function Ih(o){let t=new ae;return t.extractWeights(o),t}function zo(o,t){let{width:e,height:r}=new L(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(a=>zo(a,{width:e,height:r}));if(St(o)){let a=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(a.box.width,a.box.height);return Qt(Dt(o,a),s)}return K(o)?Dt(o,o.detection.forSize(e,r)):o instanceof $||o instanceof D?o.forSize(e,r):o}var Vo=typeof process!="undefined",Uo=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",ox={faceapi:Mr,node:Vo,browser:Uo};export{Ye as AgeGenderNet,Yt as BoundingBox,w as Box,z as ComposableTask,bt as ComputeAllFaceDescriptorsTask,er as ComputeFaceDescriptorsTaskBase,gt as ComputeSingleFaceDescriptorTask,or as DetectAllFaceLandmarksTask,Pe as DetectAllFacesTask,rr as DetectFaceLandmarksTaskBase,ar as DetectFacesTaskBase,nr as DetectSingleFaceLandmarksTask,sr as DetectSingleFaceTask,L as Dimensions,Dr as FACE_EXPRESSION_LABELS,D as FaceDetection,Ro as FaceDetectionNet,Be as FaceExpressionNet,ft as FaceExpressions,ee as FaceLandmark68Net,ze as FaceLandmark68TinyNet,Do as FaceLandmarkNet,$ as FaceLandmarks,fo as FaceLandmarks5,zt as FaceLandmarks68,me as FaceMatch,cr as FaceMatcher,oe as FaceRecognitionNet,ct as Gender,pe as LabeledBox,at as LabeledFaceDescriptors,it as NetInput,C as NeuralNetwork,ut as ObjectDetection,x as Point,ho as PredictedBox,Gt as Rect,At as SsdMobilenetv1,G as SsdMobilenetv1Options,ae as TinyFaceDetector,Ke as TinyFaceDetectorOptions,ne as TinyYolov2,et as TinyYolov2Options,Fh as allFaces,Go as allFacesSsdMobilenetv1,yh as allFacesTinyYolov2,xr as awaitMediaLoaded,br as bufferToImage,of as computeFaceDescriptor,Ct as createCanvas,fe as createCanvasFromMedia,Cu as createFaceDetectionNet,Md as createFaceRecognitionNet,Bo as createSsdMobilenetv1,Ih as createTinyFaceDetector,Dl as createTinyYolov2,ir as detectAllFaces,Ho as detectFaceLandmarks,rf as detectFaceLandmarksTiny,xf as detectLandmarks,fh as detectSingleFace,To as draw,_ as env,no as euclideanDistance,Je as extendWithAge,Xe as extendWithFaceDescriptor,Dt as extendWithFaceDetection,Re as extendWithFaceExpressions,Qt as extendWithFaceLandmarks,qe as extendWithGender,Xt as extractFaceTensors,Ut as extractFaces,si as fetchImage,yr as fetchJson,di as fetchNetWeights,Nt as fetchOrThrow,S as getContext2dOrThrow,Mt as getMediaDimensions,gr as imageTensorToCanvas,vr as imageToSquare,On as inverseSigmoid,ur as iou,We as isMediaElement,le as isMediaLoaded,Id as isWithAge,K as isWithFaceDetection,Er as isWithFaceExpressions,St as isWithFaceLandmarks,Ad as isWithGender,lf as loadAgeGenderModel,ff as loadFaceDetectionModel,uf as loadFaceExpressionModel,mf as loadFaceLandmarkModel,pf as loadFaceLandmarkTinyModel,df as loadFaceRecognitionModel,Yo as loadSsdMobilenetv1Model,sf as loadTinyFaceDetectorModel,cf as loadTinyYolov2Model,Fr as loadWeightMap,hf as locateFaces,vi as matchDimensions,lr as minBbox,T as nets,fr as nonMaxSuppression,X as normalize,hr as padToSquare,af as predictAgeAndGender,nf as recognizeFaceExpressions,zo as resizeResults,Et as resolveInput,Ln as shuffleArray,ce as sigmoid,jo as ssdMobilenetv1,n as tf,tf as tinyFaceDetector,ef as tinyYolov2,P as toNetInput,co as utils,Kr as validateConfig,ox as version};
//# sourceMappingURL=face-api.esm-nobundle.js.map