From 8bbd6b875d1c9ed5d265583880e43e214e56ed52 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 9 Mar 2021 13:28:55 -0500 Subject: [PATCH] update --- CHANGELOG.md | 4 ++-- README.md | 1 - dist/face-api.esm-nobundle.js | 2 +- dist/face-api.esm.js | 2 +- dist/face-api.js | 2 +- dist/face-api.node-cpu.js | 2 +- dist/face-api.node-gpu.js | 2 +- dist/face-api.node.js | 2 +- 8 files changed, 8 insertions(+), 9 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 04a16b8..2d0a619 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,6 @@ # @vladmandic/face-api -Version: **1.0.0** +Version: **1.0.1** Description: **FaceAPI: AI-powered Face Detection, Face Embedding & Recognition Using Tensorflow/JS** Author: **Vladimir Mandic ** @@ -9,7 +9,7 @@ Repository: **** ## Changelog -### **HEAD -> master** 2021/03/08 mandic00@live.com +### **1.0.1** 2021/03/09 mandic00@live.com - add badges - optimize for npm diff --git a/README.md b/README.md index 0fffa5c..ac57afb 100644 --- a/README.md +++ b/README.md @@ -421,4 +421,3 @@ npm run build ![Downloads](https://img.shields.io/npm/dm/@vladmandic/face-api?style=flat-square) ![Stars](https://img.shields.io/github/stars/vladmandic/face-api?style=flat-square) ![Code Size](https://img.shields.io/github/languages/code-size/vladmandic/face-api?style=flat-square) -![Commit Activity](https://img.shields.io/github/commit-activity/m/vladmandic/face-api?style=flat-square) diff --git a/dist/face-api.esm-nobundle.js b/dist/face-api.esm-nobundle.js index 159f5cc..9fa6079 100644 --- a/dist/face-api.esm-nobundle.js +++ b/dist/face-api.esm-nobundle.js @@ -5,5 +5,5 @@ author: ' */ -var pn=Object.create,Ye=Object.defineProperty,dn=Object.getPrototypeOf,un=Object.prototype.hasOwnProperty,fn=Object.getOwnPropertyNames,ln=Object.getOwnPropertyDescriptor;var Dr=o=>Ye(o,"__esModule",{value:!0});var lo=(o,t)=>()=>(t||(t={exports:{}},o(t.exports,t)),t.exports),Er=(o,t)=>{for(var e in 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vt;(function(o){o.FEMALE="female",o.MALE="male"})(vt||(vt={}));var mr=class extends k{constructor(t=new eo(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 dt.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t,n=dt.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 dt.tidy(()=>{let{age:e,gender:r}=this.runNet(t);return{age:e,gender:dt.softmax(r)}})}async forward(t){return this.forwardInput(await D(t))}async predictAgeAndGender(t){let e=await D(t),r=await this.forwardInput(e),n=dt.unstack(r.age),a=dt.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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c=n(1024,256,1,"prediction_layer/conv_0"),m=n(256,512,3,"prediction_layer/conv_1"),p=n(512,128,1,"prediction_layer/conv_2"),d=n(128,256,3,"prediction_layer/conv_3"),u=n(256,128,1,"prediction_layer/conv_4"),f=n(128,256,3,"prediction_layer/conv_5"),g=n(256,64,1,"prediction_layer/conv_6"),P=n(64,128,3,"prediction_layer/conv_7"),h=r(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),v=r(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),F=r(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),y=r(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),I=r(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),Y=r(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),tt=r(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),st=r(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),U=r(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),Pt=r(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),_t=r(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),wt=r(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:c,conv_1:m,conv_2:p,conv_3:d,conv_4:u,conv_5:f,conv_6:g,conv_7:P,box_predictor_0:{box_encoding_predictor:h,class_predictor:v},box_predictor_1:{box_encoding_predictor:F,class_predictor:y},box_predictor_2:{box_encoding_predictor:I,class_predictor:Y},box_predictor_3:{box_encoding_predictor:tt,class_predictor:st},box_predictor_4:{box_encoding_predictor:U,class_predictor:Pt},box_predictor_5:{box_encoding_predictor:_t,class_predictor:wt}}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:i}}function 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p=`mobilenetv1/conv_${m}`,d=`MobilenetV1/Conv2d_${m}_depthwise`,u=`${p}/depthwise_conv`,f=`${p}/pointwise_conv`,g=e(`${d}/depthwise_weights`,4,`${u}/filters`),P=e(`${d}/BatchNorm/gamma`,1,`${u}/batch_norm_scale`),h=e(`${d}/BatchNorm/beta`,1,`${u}/batch_norm_offset`),v=e(`${d}/BatchNorm/moving_mean`,1,`${u}/batch_norm_mean`),F=e(`${d}/BatchNorm/moving_variance`,1,`${u}/batch_norm_variance`);return{depthwise_conv:{filters:g,batch_norm_scale:P,batch_norm_offset:h,batch_norm_mean:v,batch_norm_variance:F},pointwise_conv:r("MobilenetV1",m,f)}}function a(){return{conv_0:r("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:n(1),conv_2:n(2),conv_3:n(3),conv_4:n(4),conv_5:n(5),conv_6:n(6),conv_7:n(7),conv_8:n(8),conv_9:n(9),conv_10:n(10),conv_11:n(11),conv_12:n(12),conv_13:n(13)}}function s(m,p){let d=e(`${m}/weights`,4,`${p}/filters`),u=e(`${m}/biases`,1,`${p}/bias`);return{filters:d,bias:u}}function i(m){let p=s(`Prediction/BoxPredictor_${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 Oo(o){let t=[],{extractMobilenetV1Params:e,extractPredictionLayerParams:r}=Yn(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 A(o,t),{params:a,paramMappings:t}}var yt=b();var kt=b();function J(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 Gn=.0010000000474974513;function zn(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,Gn),yt.clipByValue(r,0,6)})}function Vn(o){return[2,4,6,12].some(t=>t===o)?[2,2]:[1,1]}function jo(o,t){return yt.tidy(()=>{let e,r=J(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=Vn(i);r=zn(r,a.depthwise_conv,c),r=J(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 Un(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),f=(p-c)*(d-m);if(u<=0||f<=0)return 0;let g=Math.max(n,c),P=Math.max(a,m),h=Math.min(s,p),v=Math.min(i,d),F=Math.max(h-g,0)*Math.max(v-P,0);return F/(u+f-F)}function Ho(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 d=p.score;for(let u=m.length-1;u>=0;--u){let f=Un(o,p.boxIndex,m[u]);if(f!==0&&(p.score*=c(f),p.score<=n))break}d===p.score&&m.push(p.boxIndex)}),m}var l=b();function Xn(o){let t=l.unstack(l.transpose(o,[1,0])),e=[l.sub(t[2],t[0]),l.sub(t[3],t[1])],r=[l.add(t[0],l.div(e[0],l.scalar(2))),l.add(t[1],l.div(e[1],l.scalar(2)))];return{sizes:e,centers:r}}function Jn(o,t){let{sizes:e,centers:r}=Xn(o),n=l.unstack(l.transpose(t,[1,0])),a=l.div(l.mul(l.exp(l.div(n[2],l.scalar(5))),e[0]),l.scalar(2)),s=l.add(l.mul(l.div(n[0],l.scalar(10)),e[0]),r[0]),i=l.div(l.mul(l.exp(l.div(n[3],l.scalar(5))),e[1]),l.scalar(2)),c=l.add(l.mul(l.div(n[1],l.scalar(10)),e[1]),r[1]);return l.transpose(l.stack([l.sub(s,a),l.sub(c,i),l.add(s,a),l.add(c,i)]),[1,0])}function Yo(o,t,e){return l.tidy(()=>{let r=o.shape[0],n=Jn(l.reshape(l.tile(e.extra_dim,[r,1,1]),[-1,4]),l.reshape(o,[-1,4]));n=l.reshape(n,[r,n.shape[0]/r,4]);let 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 Oe=b();var $e=b();function Ut(o,t){return $e.tidy(()=>{let e=o.shape[0],r=$e.reshape(zt(o,t.box_encoding_predictor),[e,-1,1,4]),n=$e.reshape(zt(o,t.class_predictor),[e,-1,3]);return{boxPredictionEncoding:r,classPrediction:n}})}function Go(o,t,e){return Oe.tidy(()=>{let r=J(o,e.conv_0,[1,1]),n=J(r,e.conv_1,[2,2]),a=J(n,e.conv_2,[1,1]),s=J(a,e.conv_3,[2,2]),i=J(s,e.conv_4,[1,1]),c=J(i,e.conv_5,[2,2]),m=J(c,e.conv_6,[1,1]),p=J(m,e.conv_7,[2,2]),d=Ut(t,e.box_predictor_0),u=Ut(o,e.box_predictor_1),f=Ut(n,e.box_predictor_2),g=Ut(s,e.box_predictor_3),P=Ut(c,e.box_predictor_4),h=Ut(p,e.box_predictor_5),v=Oe.concat([d.boxPredictionEncoding,u.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding,P.boxPredictionEncoding,h.boxPredictionEncoding],1),F=Oe.concat([d.classPrediction,u.classPrediction,f.classPrediction,g.classPrediction,P.classPrediction,h.classPrediction],1);return{boxPredictions:v,classPredictions:F}})}var q=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${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 Xt=class extends k{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return at.tidy(()=>{let r=at.cast(t.toBatchTensor(512,!1),"float32"),n=at.sub(at.mul(r,at.scalar(.007843137718737125)),at.scalar(1)),a=jo(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=Go(a.out,a.conv11,e.prediction_layer);return Yo(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await D(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:n}=new q(e),a=await D(t),{boxes:s,scores:i}=this.forwardInput(a),c=s[0],m=i[0];for(let y=1;y{let[I,Y]=[Math.max(0,v[y][0]),Math.min(1,v[y][2])].map(U=>U*h),[tt,st]=[Math.max(0,v[y][1]),Math.min(1,v[y][3])].map(U=>U*P);return new E(p[y],new oe(tt,I,st-tt,Y-I),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return c.dispose(),m.dispose(),F}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return Oo(t)}extractParams(t){return $o(t)}};function zo(o){let t=new Xt;return t.extractWeights(o),t}function qn(o){return zo(o)}var Vo=class extends Xt{};var Uo=.4,Xo=[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)],Jo=[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)],qo=[117.001,114.697,97.404],Zo="tiny_yolov2_model",Ko="tiny_yolov2_separable_conv_model";var C=b();var xr=o=>typeof o=="number";function ao(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(!xr(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=>xr(t.x)&&xr(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(xr)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(o.meanRgb)}`)}var K=b();var Z=b();function be(o){return Z.tidy(()=>{let t=Z.mul(o,Z.scalar(.10000000149011612));return Z.add(Z.relu(Z.sub(o,t)),t)})}function Ft(o,t){return K.tidy(()=>{let e=K.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=K.conv2d(e,t.conv.filters,[1,1],"valid"),e=K.sub(e,t.bn.sub),e=K.mul(e,t.bn.truediv),e=K.add(e,t.conv.bias),be(e)})}var St=b();function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),be(e)})}var so=b();function Zn(o,t){let e=ce(o,t);function r(s,i){let c=so.tensor1d(o(s)),m=so.tensor1d(o(s));return t.push({paramPath:`${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 Qo(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=W(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=Zn(n,s),p;if(t.withSeparableConvs){let[d,u,f,g,P,h,v,F,y]=r,I=t.isFirstLayerConv2d?i(d,u,3,"conv0"):m(d,u,"conv0"),Y=m(u,f,"conv1"),tt=m(f,g,"conv2"),st=m(g,P,"conv3"),U=m(P,h,"conv4"),Pt=m(h,v,"conv5"),_t=F?m(v,F,"conv6"):void 0,wt=y?m(F,y,"conv7"):void 0,te=i(y||F||v,5*e,1,"conv8");p={conv0:I,conv1:Y,conv2:tt,conv3:st,conv4:U,conv5:Pt,conv6:_t,conv7:wt,conv8:te}}else{let[d,u,f,g,P,h,v,F,y]=r,I=c(d,u,"conv0"),Y=c(u,f,"conv1"),tt=c(f,g,"conv2"),st=c(g,P,"conv3"),U=c(P,h,"conv4"),Pt=c(h,v,"conv5"),_t=c(v,F,"conv6"),wt=c(F,y,"conv7"),te=i(y,5*e,1,"conv8");p={conv0:I,conv1:Y,conv2:tt,conv3:st,conv4:U,conv5:Pt,conv6:_t,conv7:wt,conv8:te}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function Kn(o,t){let e=O(o,t);function r(i){let c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function tn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=Kn(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return A(o,e),{params:s,paramMappings:e}}var ut=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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 io=class extends k{constructor(t){super("TinyYolov2");ao(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=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=Ft(r,e.conv6),r=Ft(r,e.conv7),zt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(zt(t,e.conv0,"valid",!1)):Tt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Tt(r,e.conv6):r,r=e.conv7?Tt(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 C.tidy(()=>{let n=C.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?rt(n,this.config.meanRgb):n,n=n.div(C.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await D(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new ut(e),a=await D(t),s=await this.forwardInput(a,r),i=C.tidy(()=>C.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 Sr(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Dt(d[h],u[h],f[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return tn(t,this.config)}extractParams(t){let e=this.config.filterSizes||io.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 Qo(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]=C.tidy(()=>{let v=t.reshape([m,m,p,this.boxEncodingSize]),F=v.slice([0,0,0,0],[m,m,p,4]),y=v.slice([0,0,0,4],[m,m,p,1]),I=this.withClassScores?C.softmax(v.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):C.scalar(0);return[F,y,I]}),g=[],P=await u.array(),h=await d.array();for(let v=0;vr){let Y=(F+De(h[v][F][y][0]))/m*i,tt=(v+De(h[v][F][y][1]))/m*c,st=Math.exp(h[v][F][y][2])*this.config.anchors[y].x/m*i,U=Math.exp(h[v][F][y][3])*this.config.anchors[y].y/m*c,Pt=Y-st/2,_t=tt-U/2,wt={row:v,col:F,anchor:y},{classScore:te,label:fo}=this.withClassScores?await this.extractPredictedClass(f,wt):{classScore:1,label:0};g.push({box:new re(Pt,_t,Pt+st,_t+U),score:I,classScore:I*te,label:fo,...wt})}}return d.dispose(),u.dispose(),f.dispose(),g}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=io;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:Uo,classes:["face"],...t?{anchors:Jo,meanRgb:qo}:{anchors:Xo,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 E(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?Ko:Zo}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Qn(o,t=!0){let e=new ve(t);return e.extractWeights(o),e}var br=class extends ut{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Q=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var je=b();var co=b();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 co.Tensor?await se(t,a):await ae(t,a)),i=await e(s);return s.forEach(c=>c instanceof co.Tensor&&c.dispose()),i}async function ye(o,t,e,r,n){return Jt([o],t,async a=>e(a[0]),r,n)}var en=.4,rn=[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)],on=[117.001,114.697,97.404];var Fe=class extends ge{constructor(){let t={withSeparableConvs:!0,iouThreshold:en,classes:["face"],anchors:rn,meanRgb:on,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 E(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var T={ssdMobilenetv1:new Xt,tinyFaceDetector:new Fe,tinyYolov2:new ve,faceLandmark68Net:new le,faceLandmark68TinyNet:new pr,faceRecognitionNet:new xe,faceExpressionNet:new ir,ageGenderNet:new mr},nn=(o,t)=>T.ssdMobilenetv1.locateFaces(o,t),ta=(o,t)=>T.tinyFaceDetector.locateFaces(o,t),ea=(o,t)=>T.tinyYolov2.locateFaces(o,t),an=o=>T.faceLandmark68Net.detectLandmarks(o),ra=o=>T.faceLandmark68TinyNet.detectLandmarks(o),oa=o=>T.faceRecognitionNet.computeFaceDescriptor(o),na=o=>T.faceExpressionNet.predictExpressions(o),aa=o=>T.ageGenderNet.predictAgeAndGender(o),sn=o=>T.ssdMobilenetv1.load(o),sa=o=>T.tinyFaceDetector.load(o),ia=o=>T.tinyYolov2.load(o),ca=o=>T.faceLandmark68Net.load(o),ma=o=>T.faceLandmark68TinyNet.load(o),pa=o=>T.faceRecognitionNet.load(o),da=o=>T.faceExpressionNet.load(o),ua=o=>T.ageGenderNet.load(o),fa=sn,la=nn,ha=an;var mo=class extends Q{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},_e=class extends mo{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>T.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>cr(r,e[n]))}withAgeAndGender(){return new Te(this,this.input)}},we=class extends mo{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>T.faceExpressionNet.predictExpressions(r),this.extractedFaces);return cr(t,e)}withAgeAndGender(){return new Pe(this,this.input)}},Kt=class extends _e{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Qt=class extends we{withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var po=class extends Q{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Te=class extends po{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>T.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return lr(hr(r,s,i),a)})}withFaceExpressions(){return new _e(this,this.input)}},Pe=class extends po{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await ye(t,this.input,a=>T.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return lr(hr(t,r,n),e)}withFaceExpressions(){return new we(this,this.input)}},qt=class extends Te{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Zt=class extends Pe{withFaceExpressions(){return new Qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var gr=class extends Q{constructor(t,e){super();this.parentTask=t;this.input=e}},At=class extends gr{async run(){let t=await this.parentTask;return(await Jt(t,this.input,r=>Promise.all(r.map(n=>T.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>fr(t[n],r))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}},Wt=class extends gr{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>T.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return fr(t,e)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}};var vr=class extends Q{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?T.faceLandmark68TinyNet:T.faceLandmark68Net}},yr=class extends vr{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)=>fe(a,n[s]))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Fr=class extends vr{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()),fe(t,n)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var Tr=class extends Q{constructor(t,e=new q){super();this.input=t;this.options=e}},He=class extends Tr{async run(){let{input:t,options:e}=this,r;if(e instanceof br)r=T.tinyFaceDetector.locateFaces(t,e);else if(e instanceof q)r=T.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof ut)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=>Ot({},r)))})}withFaceLandmarks(t=!1){return new yr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new _e(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Te(this.runAndExtendWithFaceDetections(),this.input)}},Pr=class extends Tr{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?Ot({},e):void 0)})}withFaceLandmarks(t=!1){return new Fr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new we(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Pe(this.runAndExtendWithFaceDetection(),this.input)}};function xa(o,t=new q){return new Pr(o,t)}function _r(o,t=new q){return new He(o,t)}async function cn(o,t){return _r(o,new q(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function ba(o,t={}){return _r(o,new ut(t)).withFaceLandmarks().withFaceDescriptors()}var ga=cn;function uo(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 wr=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=>uo(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 wr(e,t.distanceThreshold)}};function va(o){let t=new Fe;return t.extractWeights(o),t}function mn(o,t){let{width:e,height:r}=new S(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=>mn(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 fe(Ot(o,n),a)}return mt(o)?Ot(o,o.detection.forSize(e,r)):o instanceof z||o instanceof E?o.forSize(e,r):o}var Fa=typeof process!="undefined",Ta=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",Pa={faceapi:Do,node:Fa,browser:Ta};export{mr as AgeGenderNet,re as BoundingBox,w as Box,Q as ComposableTask,At as ComputeAllFaceDescriptorsTask,gr as ComputeFaceDescriptorsTaskBase,Wt as ComputeSingleFaceDescriptorTask,yr as DetectAllFaceLandmarksTask,He as DetectAllFacesTask,vr as DetectFaceLandmarksTaskBase,Tr as DetectFacesTaskBase,Fr as DetectSingleFaceLandmarksTask,Pr as DetectSingleFaceTask,S as Dimensions,Jr as FACE_EXPRESSION_LABELS,E as FaceDetection,Vo as FaceDetectionNet,ir as FaceExpressionNet,It as FaceExpressions,le as FaceLandmark68Net,pr as FaceLandmark68TinyNet,So as FaceLandmarkNet,z as FaceLandmarks,xo as FaceLandmarks5,ne as FaceLandmarks68,Ee as FaceMatch,wr as FaceMatcher,xe as FaceRecognitionNet,vt as Gender,Me as LabeledBox,xt as LabeledFaceDescriptors,bt as NetInput,k as NeuralNetwork,Dt as ObjectDetection,x as Point,bo as PredictedBox,oe as Rect,Xt as SsdMobilenetv1,q as SsdMobilenetv1Options,Fe as TinyFaceDetector,br as 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Jr=["neutral","happy","sad","angry","fearful","disgusted","surprised"],It=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Jr.forEach((e,r)=>{this[e]=t[r]})}asSortedArray(){return Jr.map(t=>({expression:t,probability:this[t]})).sort((t,e)=>e.probability-t.probability)}};var ir=class extends We{constructor(t=new Se){super("FaceExpressionNet",t)}forwardInput(t){return ue.tidy(()=>ue.softmax(this.runNet(t)))}async forward(t){return this.forwardInput(await D(t))}async predictExpressions(t){let e=await D(t),r=await this.forwardInput(e),n=await Promise.all(ue.unstack(r).map(async s=>{let i=await s.data();return s.dispose(),i}));r.dispose();let a=n.map(s=>new It(s));return e.isBatchInput?a:a[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function qr(o){return o.expressions instanceof It}function 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vt;(function(o){o.FEMALE="female",o.MALE="male"})(vt||(vt={}));var mr=class extends k{constructor(t=new eo(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return dt.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t,n=dt.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 dt.tidy(()=>{let{age:e,gender:r}=this.runNet(t);return{age:e,gender:dt.softmax(r)}})}async forward(t){return this.forwardInput(await D(t))}async predictAgeAndGender(t){let e=await D(t),r=await this.forwardInput(e),n=dt.unstack(r.age),a=dt.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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s=(d,u)=>j.stack([j.fill([68],d,"float32"),j.fill([68],u,"float32")],1).as2D(1,136).as1D(),i=(d,u)=>{let{width:f,height:g}=n[d];return u(f,g)?Math.abs(f-g)/2:0},c=d=>i(d,(u,f)=>ui(d,(u,f)=>fs(c(u),m(u))))).div(j.stack(Array.from(Array(a),(d,u)=>s(n[u].width,n[u].height))))})}forwardInput(t){return j.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=j.tidy(()=>j.unstack(this.forwardInput(e))),n=await Promise.all(r.map(async(a,s)=>{let i=Array.from(await a.data()),c=i.filter((p,d)=>Ge(d)),m=i.filter((p,d)=>!Ge(d));return new ne(Array(68).fill(0).map((p,d)=>new x(c[d],m[d])),{height:e.getInputHeight(s),width:e.getInputWidth(s)})}));return r.forEach(a=>a.dispose()),e.isBatchInput?n:n[0]}getClassifierChannelsOut(){return 136}};var le=class extends Be{constructor(t=new 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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 Oo(o){let 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e,r=J(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=Vn(i);r=zn(r,a.depthwise_conv,c),r=J(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 Un(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),f=(p-c)*(d-m);if(u<=0||f<=0)return 0;let g=Math.max(n,c),P=Math.max(a,m),h=Math.min(s,p),v=Math.min(i,d),F=Math.max(h-g,0)*Math.max(v-P,0);return F/(u+f-F)}function Ho(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 d=p.score;for(let u=m.length-1;u>=0;--u){let f=Un(o,p.boxIndex,m[u]);if(f!==0&&(p.score*=c(f),p.score<=n))break}d===p.score&&m.push(p.boxIndex)}),m}var l=b();function Xn(o){let t=l.unstack(l.transpose(o,[1,0])),e=[l.sub(t[2],t[0]),l.sub(t[3],t[1])],r=[l.add(t[0],l.div(e[0],l.scalar(2))),l.add(t[1],l.div(e[1],l.scalar(2)))];return{sizes:e,centers:r}}function Jn(o,t){let{sizes:e,centers:r}=Xn(o),n=l.unstack(l.transpose(t,[1,0])),a=l.div(l.mul(l.exp(l.div(n[2],l.scalar(5))),e[0]),l.scalar(2)),s=l.add(l.mul(l.div(n[0],l.scalar(10)),e[0]),r[0]),i=l.div(l.mul(l.exp(l.div(n[3],l.scalar(5))),e[1]),l.scalar(2)),c=l.add(l.mul(l.div(n[1],l.scalar(10)),e[1]),r[1]);return l.transpose(l.stack([l.sub(s,a),l.sub(c,i),l.add(s,a),l.add(c,i)]),[1,0])}function Yo(o,t,e){return l.tidy(()=>{let r=o.shape[0],n=Jn(l.reshape(l.tile(e.extra_dim,[r,1,1]),[-1,4]),l.reshape(o,[-1,4]));n=l.reshape(n,[r,n.shape[0]/r,4]);let 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 Oe=b();var $e=b();function Ut(o,t){return $e.tidy(()=>{let e=o.shape[0],r=$e.reshape(zt(o,t.box_encoding_predictor),[e,-1,1,4]),n=$e.reshape(zt(o,t.class_predictor),[e,-1,3]);return{boxPredictionEncoding:r,classPrediction:n}})}function Go(o,t,e){return Oe.tidy(()=>{let 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oe(tt,I,st-tt,Y-I),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return c.dispose(),m.dispose(),F}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return Oo(t)}extractParams(t){return $o(t)}};function zo(o){let t=new Xt;return t.extractWeights(o),t}function qn(o){return zo(o)}var Vo=class extends Xt{};var Uo=.4,Xo=[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)],Jo=[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)],qo=[117.001,114.697,97.404],Zo="tiny_yolov2_model",Ko="tiny_yolov2_separable_conv_model";var C=b();var xr=o=>typeof o=="number";function ao(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(!xr(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=>xr(t.x)&&xr(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(xr)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(o.meanRgb)}`)}var K=b();var Z=b();function be(o){return Z.tidy(()=>{let t=Z.mul(o,Z.scalar(.10000000149011612));return Z.add(Z.relu(Z.sub(o,t)),t)})}function Ft(o,t){return K.tidy(()=>{let e=K.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=K.conv2d(e,t.conv.filters,[1,1],"valid"),e=K.sub(e,t.bn.sub),e=K.mul(e,t.bn.truediv),e=K.add(e,t.conv.bias),be(e)})}var St=b();function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),be(e)})}var so=b();function Zn(o,t){let e=ce(o,t);function r(s,i){let c=so.tensor1d(o(s)),m=so.tensor1d(o(s));return t.push({paramPath:`${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 Qo(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=W(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=Zn(n,s),p;if(t.withSeparableConvs){let[d,u,f,g,P,h,v,F,y]=r,I=t.isFirstLayerConv2d?i(d,u,3,"conv0"):m(d,u,"conv0"),Y=m(u,f,"conv1"),tt=m(f,g,"conv2"),st=m(g,P,"conv3"),U=m(P,h,"conv4"),Pt=m(h,v,"conv5"),_t=F?m(v,F,"conv6"):void 0,wt=y?m(F,y,"conv7"):void 0,te=i(y||F||v,5*e,1,"conv8");p={conv0:I,conv1:Y,conv2:tt,conv3:st,conv4:U,conv5:Pt,conv6:_t,conv7:wt,conv8:te}}else{let[d,u,f,g,P,h,v,F,y]=r,I=c(d,u,"conv0"),Y=c(u,f,"conv1"),tt=c(f,g,"conv2"),st=c(g,P,"conv3"),U=c(P,h,"conv4"),Pt=c(h,v,"conv5"),_t=c(v,F,"conv6"),wt=c(F,y,"conv7"),te=i(y,5*e,1,"conv8");p={conv0:I,conv1:Y,conv2:tt,conv3:st,conv4:U,conv5:Pt,conv6:_t,conv7:wt,conv8:te}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function Kn(o,t){let e=O(o,t);function r(i){let c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function tn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=Kn(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return A(o,e),{params:s,paramMappings:e}}var ut=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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 io=class extends k{constructor(t){super("TinyYolov2");ao(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=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=Ft(r,e.conv6),r=Ft(r,e.conv7),zt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(zt(t,e.conv0,"valid",!1)):Tt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Tt(r,e.conv6):r,r=e.conv7?Tt(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 C.tidy(()=>{let n=C.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?rt(n,this.config.meanRgb):n,n=n.div(C.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await D(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new ut(e),a=await D(t),s=await this.forwardInput(a,r),i=C.tidy(()=>C.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 Sr(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Dt(d[h],u[h],f[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return tn(t,this.config)}extractParams(t){let e=this.config.filterSizes||io.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 Qo(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]=C.tidy(()=>{let v=t.reshape([m,m,p,this.boxEncodingSize]),F=v.slice([0,0,0,0],[m,m,p,4]),y=v.slice([0,0,0,4],[m,m,p,1]),I=this.withClassScores?C.softmax(v.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):C.scalar(0);return[F,y,I]}),g=[],P=await u.array(),h=await d.array();for(let v=0;vr){let Y=(F+De(h[v][F][y][0]))/m*i,tt=(v+De(h[v][F][y][1]))/m*c,st=Math.exp(h[v][F][y][2])*this.config.anchors[y].x/m*i,U=Math.exp(h[v][F][y][3])*this.config.anchors[y].y/m*c,Pt=Y-st/2,_t=tt-U/2,wt={row:v,col:F,anchor:y},{classScore:te,label:fo}=this.withClassScores?await this.extractPredictedClass(f,wt):{classScore:1,label:0};g.push({box:new re(Pt,_t,Pt+st,_t+U),score:I,classScore:I*te,label:fo,...wt})}}return d.dispose(),u.dispose(),f.dispose(),g}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=io;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:Uo,classes:["face"],...t?{anchors:Jo,meanRgb:qo}:{anchors:Xo,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 E(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?Ko:Zo}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Qn(o,t=!0){let e=new ve(t);return e.extractWeights(o),e}var br=class extends ut{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Q=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var je=b();var co=b();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 co.Tensor?await se(t,a):await ae(t,a)),i=await e(s);return s.forEach(c=>c instanceof co.Tensor&&c.dispose()),i}async function ye(o,t,e,r,n){return Jt([o],t,async a=>e(a[0]),r,n)}var en=.4,rn=[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)],on=[117.001,114.697,97.404];var Fe=class extends ge{constructor(){let t={withSeparableConvs:!0,iouThreshold:en,classes:["face"],anchors:rn,meanRgb:on,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 E(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var T={ssdMobilenetv1:new Xt,tinyFaceDetector:new Fe,tinyYolov2:new ve,faceLandmark68Net:new le,faceLandmark68TinyNet:new pr,faceRecognitionNet:new xe,faceExpressionNet:new ir,ageGenderNet:new mr},nn=(o,t)=>T.ssdMobilenetv1.locateFaces(o,t),ta=(o,t)=>T.tinyFaceDetector.locateFaces(o,t),ea=(o,t)=>T.tinyYolov2.locateFaces(o,t),an=o=>T.faceLandmark68Net.detectLandmarks(o),ra=o=>T.faceLandmark68TinyNet.detectLandmarks(o),oa=o=>T.faceRecognitionNet.computeFaceDescriptor(o),na=o=>T.faceExpressionNet.predictExpressions(o),aa=o=>T.ageGenderNet.predictAgeAndGender(o),sn=o=>T.ssdMobilenetv1.load(o),sa=o=>T.tinyFaceDetector.load(o),ia=o=>T.tinyYolov2.load(o),ca=o=>T.faceLandmark68Net.load(o),ma=o=>T.faceLandmark68TinyNet.load(o),pa=o=>T.faceRecognitionNet.load(o),da=o=>T.faceExpressionNet.load(o),ua=o=>T.ageGenderNet.load(o),fa=sn,la=nn,ha=an;var mo=class extends Q{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},_e=class extends mo{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>T.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>cr(r,e[n]))}withAgeAndGender(){return new Te(this,this.input)}},we=class extends mo{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>T.faceExpressionNet.predictExpressions(r),this.extractedFaces);return cr(t,e)}withAgeAndGender(){return new Pe(this,this.input)}},Kt=class extends _e{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Qt=class extends we{withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var po=class extends Q{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Te=class extends po{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>T.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return lr(hr(r,s,i),a)})}withFaceExpressions(){return new _e(this,this.input)}},Pe=class extends po{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await ye(t,this.input,a=>T.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return lr(hr(t,r,n),e)}withFaceExpressions(){return new we(this,this.input)}},qt=class extends Te{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Zt=class extends Pe{withFaceExpressions(){return new Qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var gr=class extends Q{constructor(t,e){super();this.parentTask=t;this.input=e}},At=class extends gr{async run(){let t=await this.parentTask;return(await Jt(t,this.input,r=>Promise.all(r.map(n=>T.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>fr(t[n],r))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}},Wt=class extends gr{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>T.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return fr(t,e)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}};var vr=class extends Q{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?T.faceLandmark68TinyNet:T.faceLandmark68Net}},yr=class extends vr{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)=>fe(a,n[s]))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Fr=class extends vr{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()),fe(t,n)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var Tr=class extends Q{constructor(t,e=new q){super();this.input=t;this.options=e}},He=class extends Tr{async run(){let{input:t,options:e}=this,r;if(e instanceof br)r=T.tinyFaceDetector.locateFaces(t,e);else if(e instanceof q)r=T.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof ut)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=>Ot({},r)))})}withFaceLandmarks(t=!1){return new yr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new _e(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Te(this.runAndExtendWithFaceDetections(),this.input)}},Pr=class extends Tr{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?Ot({},e):void 0)})}withFaceLandmarks(t=!1){return new Fr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new we(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Pe(this.runAndExtendWithFaceDetection(),this.input)}};function xa(o,t=new q){return new Pr(o,t)}function _r(o,t=new q){return new He(o,t)}async function cn(o,t){return _r(o,new q(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function ba(o,t={}){return _r(o,new ut(t)).withFaceLandmarks().withFaceDescriptors()}var ga=cn;function uo(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 wr=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=>uo(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 wr(e,t.distanceThreshold)}};function va(o){let t=new Fe;return t.extractWeights(o),t}function mn(o,t){let{width:e,height:r}=new S(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>mn(n,{width:e,height:r}));if(Vt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return fe(Ot(o,n),a)}return mt(o)?Ot(o,o.detection.forSize(e,r)):o instanceof z||o instanceof E?o.forSize(e,r):o}var Fa=typeof process!="undefined",Ta=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",Pa={faceapi:Do,node:Fa,browser:Ta};export{mr as AgeGenderNet,re as BoundingBox,w as Box,Q as ComposableTask,At as ComputeAllFaceDescriptorsTask,gr as ComputeFaceDescriptorsTaskBase,Wt as ComputeSingleFaceDescriptorTask,yr as DetectAllFaceLandmarksTask,He as DetectAllFacesTask,vr as DetectFaceLandmarksTaskBase,Tr as DetectFacesTaskBase,Fr as DetectSingleFaceLandmarksTask,Pr as DetectSingleFaceTask,S as Dimensions,Jr as FACE_EXPRESSION_LABELS,E as FaceDetection,Vo as FaceDetectionNet,ir as FaceExpressionNet,It as FaceExpressions,le as FaceLandmark68Net,pr as FaceLandmark68TinyNet,So as FaceLandmarkNet,z as FaceLandmarks,xo as FaceLandmarks5,ne as FaceLandmarks68,Ee as FaceMatch,wr as FaceMatcher,xe as FaceRecognitionNet,vt as Gender,Me as LabeledBox,xt as LabeledFaceDescriptors,bt as NetInput,k as NeuralNetwork,Dt as ObjectDetection,x as Point,bo as PredictedBox,oe as Rect,Xt as SsdMobilenetv1,q as SsdMobilenetv1Options,Fe as TinyFaceDetector,br as TinyFaceDetectorOptions,ve as TinyYolov2,ut as TinyYolov2Options,ga as allFaces,cn as allFacesSsdMobilenetv1,ba as allFacesTinyYolov2,Yr as awaitMediaLoaded,Gr as bufferToImage,oa as computeFaceDescriptor,Yt as createCanvas,Ie as createCanvasFromMedia,qn as createFaceDetectionNet,$n as createFaceRecognitionNet,zo as createSsdMobilenetv1,va as createTinyFaceDetector,Qn as createTinyYolov2,_r as detectAllFaces,an as detectFaceLandmarks,ra as detectFaceLandmarksTiny,ha as detectLandmarks,xa as detectSingleFace,Qr as draw,_ as env,uo as euclideanDistance,lr as extendWithAge,fr as extendWithFaceDescriptor,Ot as extendWithFaceDetection,cr as extendWithFaceExpressions,fe as extendWithFaceLandmarks,hr as extendWithGender,se as extractFaceTensors,ae as extractFaces,Mn as fetchImage,Ur as fetchJson,Cn as fetchNetWeights,Gt as fetchOrThrow,R as getContext2dOrThrow,Ht as getMediaDimensions,zr as imageTensorToCanvas,Vr as imageToSquare,vn as inverseSigmoid,Lr as iou,Je as isMediaElement,Ne as isMediaLoaded,On as isWithAge,mt as isWithFaceDetection,qr as isWithFaceExpressions,Vt as isWithFaceLandmarks,jn as isWithGender,ua as loadAgeGenderModel,fa as loadFaceDetectionModel,da as loadFaceExpressionModel,ca as loadFaceLandmarkModel,ma as loadFaceLandmarkTinyModel,pa as loadFaceRecognitionModel,sn as loadSsdMobilenetv1Model,sa as loadTinyFaceDetectorModel,ia as loadTinyYolov2Model,Xr as loadWeightMap,la as locateFaces,Nn as matchDimensions,kr as minBbox,T as nets,Sr as nonMaxSuppression,rt as normalize,Ar as padToSquare,aa as predictAgeAndGender,na as recognizeFaceExpressions,mn as resizeResults,jt as resolveInput,gn as shuffleArray,De as sigmoid,nn as ssdMobilenetv1,ya as tf,ta as tinyFaceDetector,ea as tinyYolov2,D as toNetInput,Mr as utils,ao as validateConfig,Pa as version}; //# sourceMappingURL=face-api.esm-nobundle.js.map diff --git a/dist/face-api.esm.js b/dist/face-api.esm.js index d864cee..72f02fa 100644 --- a/dist/face-api.esm.js +++ b/dist/face-api.esm.js @@ -4045,7 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Kte={kernelName:sl,backendName:"wasm",setupFunc:jte,kernelFunc:qte},Xte=!1,Yte=gn(nl,Xte,"bool"),k2;function Jte(e){k2=e.wasm.cwrap(Js,null,["number","number","number","number","number"])}function Qte(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 k2(u,s,i,o,c),l}var Zte={kernelName:Js,backendName:"wasm",setupFunc:Jte,kernelFunc:Qte};function ene(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var tne={kernelName:il,backendName:"wasm",kernelFunc:ene};function nne(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(u=>{w.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===u.dtype,()=>"All tensors passed to stack must have matching 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mne={kernelName:ll,backendName:"wasm",setupFunc:dne,kernelFunc:hne},fne=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=yv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},gne={kernelName:oc,backendName:"wasm",kernelFunc:fne},yne=!0,bne=gn(Ms,yne),xne=An(ti),vne=An(ai),S2;function wne(e){S2=e.wasm.cwrap(ni,null,["number","number","number","number","number","number","number","number","number","number"])}function kne(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=Wm({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(w.sizeFromShape(r.shape)===0)return b;let x=t.dataIdMap.get(b.dataId).id;return S2(y,u,p,d,h,l,c,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),b}var Ine={kernelName:ni,backendName:"wasm",setupFunc:wne,kernelFunc:kne},C2;function Tne(e){C2=e.wasm.cwrap(ri,null,["number","array","number","array","number","number"])}function Nne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=w.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);C2(l,u,i.length,p,r.shape.length,c);let d=Ma({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),d}var Sne={kernelName:ri,backendName:"wasm",kernelFunc:Nne,setupFunc:Tne},_2;function Cne(e){_2=e.wasm.cwrap(Tl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function 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new Error("extractFaces - batchSize > 1 not supported");let o=i.getInput(0);a=o instanceof n?o:await hw(o)}let r=$n(a);return t.map(i=>i instanceof gt?i.forSize(a.width,a.height).box.floor():i).map(i=>i.clipAtImageBorders(a.width,a.height)).map(({x:i,y:o,width:l,height:c})=>{let u=Zi({width:l,height:c});return l>0&&c>0&&$n(u).putImageData(r.getImageData(i,o,l,c),0,0),u})}async function vu(e,t){if(!Sr(e)&&!aa(e))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(aa(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return D(()=>{let[n,a,r]=e.shape.slice(aa(e)?1:0);return t.map(o=>o instanceof gt?o.forSize(a,n).box:o).map(o=>o.clipAtImageBorders(a,n)).map(({x:o,y:l,width:c,height:u})=>Bl(e.as3D(n,a,r),[l,o,0],[u,c,r]))})}async function eo(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 Hae(e){let t=await eo(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: ${t.url}`);return dw(n)}async function fw(e){return(await eo(e)).json()}async function jae(e){return new Float32Array(await(await eo(e)).arrayBuffer())}function Xm(e,t){let n=`${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 gw(e,t){let{manifestUri:n,modelBaseUri:a}=Xm(e,t),r=await fw(n);return Ht.loadWeights(r,a)}function qae(e,t,n=!1){let{width:a,height:r}=n?Qi(t):t;return e.width=a,e.height=r,{width:a,height:r}}var sn=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 this._paramMappings.map(({paramPath:t})=>({path:t,tensor:this.getParamFromPath(t)}))}getTrainableParams(){return this.getParamList().filter(t=>t.tensor instanceof Hr)}getFrozenParams(){return this.getParamList().filter(t=>!(t.tensor instanceof Hr))}variable(){this.getFrozenParams().forEach(({path:t,tensor:n})=>{this.reassignParamFromPath(t,n.variable())})}freeze(){this.getTrainableParams().forEach(({path:t,tensor:n})=>{let a=Yn(n.dataSync());n.dispose(),this.reassignParamFromPath(t,a)})}dispose(t=!0){this.getParamList().forEach(n=>{if(t&&n.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${n.path}`);n.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:t})=>Array.from(t.dataSync())).reduce((t,n)=>t.concat(n)))}async load(t){if(t instanceof Float32Array){this.extractWeights(t);return}await this.loadFromUri(t)}async loadFromUri(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let n=await gw(t,this.getDefaultModelName());this.loadFromWeightMap(n)}async loadFromDisk(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile:n}=rt.getEnv(),{manifestUri:a,modelBaseUri:r}=Xm(t,this.getDefaultModelName()),s=c=>Promise.all(c.map(u=>n(u).then(p=>p.buffer))),i=Ht.weightsLoaderFactory(s),o=JSON.parse((await n(a)).toString()),l=await i(o,r);this.loadFromWeightMap(l)}loadFromWeightMap(t){let{paramMappings:n,params:a}=this.extractParamsFromWeightMap(t);this._paramMappings=n,this._params=a}extractWeights(t){let{paramMappings:n,params:a}=this.extractParams(t);this._paramMappings=n,this._params=a}traversePropertyPath(t){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let n=t.split("/").reduce((s,i)=>{if(!s.nextObj.hasOwnProperty(i))throw new Error(`traversePropertyPath - object does not have property ${i}, for path ${t}`);return{obj:s.nextObj,objProp:i,nextObj:s.nextObj[i]}},{nextObj:this.params}),{obj:a,objProp:r}=n;if(!a||!r||!(a[r]instanceof Ee))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${t}`);return{obj:a,objProp:r}}};function Dn(e,t,n){return D(()=>{let a=Ei(e,t.depthwise_filter,t.pointwise_filter,n,"same");return a=J(a,t.bias),a})}function Ym(e,t,n=!1){return D(()=>{let a=qe(n?J(Ft(e,t.conv0.filters,[2,2],"same"),t.conv0.bias):Dn(e,t.conv0,[2,2])),r=Dn(a,t.conv1,[1,1]),s=qe(J(a,r)),i=Dn(s,t.conv2,[1,1]);return qe(J(a,J(r,i)))})}function Tp(e,t,n=!1,a=!0){return D(()=>{let r=qe(n?J(Ft(e,t.conv0.filters,a?[2,2]:[1,1],"same"),t.conv0.bias):Dn(e,t.conv0,a?[2,2]:[1,1])),s=Dn(r,t.conv1,[1,1]),i=qe(J(r,s)),o=Dn(i,t.conv2,[1,1]),l=qe(J(r,J(s,o))),c=Dn(l,t.conv3,[1,1]);return qe(J(r,J(s,J(o,c))))})}function to(e,t,n="same",a=!1){return D(()=>{let r=J(Ft(e,t.filters,[1,1],n),t.bias);return a?qe(r):r})}function bn(e,t){Object.keys(e).forEach(n=>{t.some(a=>a.originalPath===n)||e[n].dispose()})}function wu(e,t){return(n,a,r,s)=>{let i=Sa(e(n*a*r*r),[r,r,n,a]),o=Qe(e(a));return t.push({paramPath:`${s}/filters`},{paramPath:`${s}/bias`}),{filters:i,bias:o}}}function Jm(e,t){return(n,a,r)=>{let s=Na(e(n*a),[n,a]),i=Qe(e(a));return t.push({paramPath:`${r}/weights`},{paramPath:`${r}/bias`}),{weights:s,bias:i}}}var Qm=class{constructor(t,n,a){this.depthwise_filter=t;this.pointwise_filter=n;this.bias=a}};function ku(e,t){return(n,a,r)=>{let s=Sa(e(3*3*n),[3,3,n,1]),i=Sa(e(n*a),[1,1,n,a]),o=Qe(e(a));return t.push({paramPath:`${r}/depthwise_filter`},{paramPath:`${r}/pointwise_filter`},{paramPath:`${r}/bias`}),new Qm(s,i,o)}}function Iu(e){return t=>{let n=e(`${t}/depthwise_filter`,4),a=e(`${t}/pointwise_filter`,4),r=e(`${t}/bias`,1);return new Qm(n,a,r)}}function jn(e,t){return(n,a,r)=>{let s=e[n];if(!qi(s,a))throw new Error(`expected weightMap[${n}] to be a Tensor${a}D, instead have ${s}`);return t.push({originalPath:n,paramPath:r||n}),s}}function xn(e){let t=e;function n(r){let s=t.slice(0,r);return t=t.slice(r),s}function a(){return t}return{extractWeights:n,getRemainingWeights:a}}function Zm(e,t){let n=wu(e,t),a=ku(e,t);function r(i,o,l,c=!1){let u=c?n(i,o,3,`${l}/conv0`):a(i,o,`${l}/conv0`),p=a(o,o,`${l}/conv1`),d=a(o,o,`${l}/conv2`);return{conv0:u,conv1:p,conv2:d}}function s(i,o,l,c=!1){let{conv0:u,conv1:p,conv2:d}=r(i,o,l,c),h=a(o,o,`${l}/conv3`);return{conv0:u,conv1:p,conv2:d,conv3:h}}return{extractDenseBlock3Params:r,extractDenseBlock4Params:s}}function H2(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),{extractDenseBlock4Params:r}=Zm(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2"),l=r(128,256,"dense3");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o,dense3:l}}}function ef(e){return t=>{let n=e(`${t}/filters`,4),a=e(`${t}/bias`,1);return{filters:n,bias:a}}}function tf(e,t){let n=jn(e,t),a=ef(n),r=Iu(n);function s(o,l=!1){let c=l?a(`${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 j2(e){let t=[],{extractDenseBlock4Params:n}=tf(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 Np=class extends sn{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceFeatureExtractor - load model before inference");return D(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=Oa(a,[122.782,117.001,104.298]).div(pe(255)),i=Tp(s,n.dense0,!0);return i=Tp(i,n.dense1),i=Tp(i,n.dense2),i=Tp(i,n.dense3),i=Qn(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await ht(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return j2(t)}extractParams(t){return H2(t)}};function Sp(e,t){return D(()=>J(ze(e,t.weights),t.bias))}function q2(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=xn(e),o=Jm(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function K2(e){let t=[],n=jn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return bn(e,t),{params:r,paramMappings:t}}function nf(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var Cp=class extends sn{constructor(t,n){super(t);this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof _r?this.faceFeatureExtractor.forwardInput(t):t;return Sp(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 q2(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=nf(t);return this.faceFeatureExtractor.loadFromWeightMap(n),K2(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 yw=["neutral","happy","sad","angry","fearful","disgusted","surprised"],gs=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);yw.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return yw.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var af=class extends Cp{constructor(t=new Np){super("FaceExpressionNet",t)}forwardInput(t){return D(()=>Ta(this.runNet(t)))}async forward(t){return this.forwardInput(await ht(t))}async predictExpressions(t){let n=await ht(t),a=await this.forwardInput(n),r=await Promise.all(ut(a).map(async i=>{let o=await i.data();return i.dispose(),o}));a.dispose();let s=r.map(i=>new gs(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function bw(e){return e.expressions instanceof gs}function rf(e,t){return{...e,...{expressions:t}}}function Kae(e,t,n=.1,a){(Array.isArray(t)?t:[t]).forEach(s=>{let i=s instanceof gs?s:bw(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let l=i.asSortedArray().filter(p=>p.probability>n),c=sr(s)?s.detection.box.bottomLeft:a||new De(0,0);new fs(l.map(p=>`${p.expression} (${Ki(p.probability)})`),c).draw(e)})}function no(e){return sr(e)&&e.landmarks instanceof ra&&e.unshiftedLandmarks instanceof ra&&e.alignedRect instanceof gt}function Xae(e){let t=(o,l,c,u)=>Math.atan2(u-l,c-o)%Math.PI,n=o=>o*180/Math.PI,a={roll:void 0,pitch:void 0,yaw:void 0};if(!e||!e._positions||e._positions.length!==68)return a;let r=e._positions;a.roll=-t(r[36]._x,r[36]._y,r[45]._x,r[45]._y),a.pitch=t(0,Math.abs(r[0]._x-r[30]._x)/r[30]._x,Math.PI,Math.abs(r[16]._x-r[30]._x)/r[30]._x);let s=r.reduce((o,l)=>oo>l._y?o:l._y,-Infinity);return a.yaw=Math.PI*(e._imgDims._height/(i-s)/1.4-1),a}function Tu(e,t){let{box:n}=e.detection,a=t.shiftBy(n.x,n.y),r=a.align(),{imageDims:s}=e.detection,i=new gt(e.detection.score,r.rescale(s.reverse()),s),o=Xae(t);return{...e,...{landmarks:a,unshiftedLandmarks:t,alignedRect:i,angle:o}}}var xw=class{constructor(t={}){let{drawLines:n=!0,drawPoints:a=!0,lineWidth:r,lineColor:s,pointSize:i,pointColor:o}=t;this.drawLines=n,this.drawPoints=a,this.lineWidth=r||1,this.pointSize=i||2,this.lineColor=s||"rgba(0, 255, 255, 1)",this.pointColor=o||"rgba(255, 0, 255, 1)"}},vw=class{constructor(t,n={}){this.faceLandmarks=t,this.options=new xw(n)}draw(t){let n=$n(t),{drawLines:a,drawPoints:r,lineWidth:s,lineColor:i,pointSize:o,pointColor:l}=this.options;if(a&&this.faceLandmarks instanceof bu&&(n.strokeStyle=i,n.lineWidth=s,Nr(n,this.faceLandmarks.getJawOutline()),Nr(n,this.faceLandmarks.getLeftEyeBrow()),Nr(n,this.faceLandmarks.getRightEyeBrow()),Nr(n,this.faceLandmarks.getNose()),Nr(n,this.faceLandmarks.getLeftEye(),!0),Nr(n,this.faceLandmarks.getRightEye(),!0),Nr(n,this.faceLandmarks.getMouth(),!0)),r){n.strokeStyle=l,n.fillStyle=l;let c=u=>{n.beginPath(),n.arc(u.x,u.y,o,0,2*Math.PI),n.fill()};this.faceLandmarks.positions.forEach(c)}}};function Yae(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof ra?a:no(a)?a.landmarks:void 0;if(!r)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new vw(r).draw(e)})}var X2="1.0.0";function Jae(e,t){let n=wu(e,t),a=ku(e,t);function r(i,o,l){let c=a(i,o,`${l}/separable_conv0`),u=a(o,o,`${l}/separable_conv1`),p=n(i,o,1,`${l}/expansion_conv`);return{separable_conv0:c,separable_conv1:u,expansion_conv:p}}function s(i,o){let l=a(i,i,`${o}/separable_conv0`),c=a(i,i,`${o}/separable_conv1`),u=a(i,i,`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:c,separable_conv2:u}}return{extractConvParams:n,extractSeparableConvParams:a,extractReductionBlockParams:r,extractMainBlockParams:s}}function Y2(e,t){let n=[],{extractWeights:a,getRemainingWeights:r}=xn(e),{extractConvParams:s,extractSeparableConvParams:i,extractReductionBlockParams:o,extractMainBlockParams:l}=Jae(a,n),c=s(3,32,3,"entry_flow/conv_in"),u=o(32,64,"entry_flow/reduction_block_0"),p=o(64,128,"entry_flow/reduction_block_1"),d={conv_in:c,reduction_block_0:u,reduction_block_1:p},h={};rr(t,0,1).forEach(y=>{h[`main_block_${y}`]=l(128,`middle_flow/main_block_${y}`)});let m=o(128,256,"exit_flow/reduction_block"),f=i(256,512,"exit_flow/separable_conv"),g={reduction_block:m,separable_conv:f};if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:n,params:{entry_flow:d,middle_flow:h,exit_flow:g}}}function Qae(e,t){let n=jn(e,t),a=ef(n),r=Iu(n);function s(o){let l=r(`${o}/separable_conv0`),c=r(`${o}/separable_conv1`),u=a(`${o}/expansion_conv`);return{separable_conv0:l,separable_conv1:c,expansion_conv:u}}function i(o){let l=r(`${o}/separable_conv0`),c=r(`${o}/separable_conv1`),u=r(`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:c,separable_conv2:u}}return{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}}function J2(e,t){let n=[],{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}=Qae(e,n),o=a("entry_flow/conv_in"),l=s("entry_flow/reduction_block_0"),c=s("entry_flow/reduction_block_1"),u={conv_in:o,reduction_block_0:l,reduction_block_1:c},p={};rr(t,0,1).forEach(f=>{p[`main_block_${f}`]=i(`middle_flow/main_block_${f}`)});let d=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:d,separable_conv:h};return bn(e,n),{params:{entry_flow:u,middle_flow:p,exit_flow:m},paramMappings:n}}function Q2(e,t,n){return J(Ft(e,t.filters,n,"same"),t.bias)}function kw(e,t,n=!0){let a=n?qe(e):e;return a=Dn(a,t.separable_conv0,[1,1]),a=Dn(qe(a),t.separable_conv1,[1,1]),a=At(a,[3,3],[2,2],"same"),a=J(a,Q2(e,t.expansion_conv,[2,2])),a}function Zae(e,t){let n=Dn(qe(e),t.separable_conv0,[1,1]);return n=Dn(qe(n),t.separable_conv1,[1,1]),n=Dn(qe(n),t.separable_conv2,[1,1]),n=J(n,e),n}var Iw=class extends sn{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return D(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=Oa(a,[122.782,117.001,104.298]).div(pe(256)),i=qe(Q2(s,n.entry_flow.conv_in,[2,2]));return i=kw(i,n.entry_flow.reduction_block_0,!1),i=kw(i,n.entry_flow.reduction_block_1),rr(this._numMainBlocks,0,1).forEach(o=>{i=Zae(i,n.middle_flow[`main_block_${o}`])}),i=kw(i,n.exit_flow.reduction_block),i=qe(Dn(i,n.exit_flow.separable_conv,[1,1])),i})}async forward(t){return this.forwardInput(await ht(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return J2(t,this._numMainBlocks)}extractParams(t){return Y2(t,this._numMainBlocks)}};function Z2(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),r=Jm(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: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function eC(e){let t=[],n=jn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return bn(e,t),{params:r,paramMappings:t}}var Er;(function(e){e.FEMALE="female",e.MALE="male"})(Er||(Er={}));var sf=class extends sn{constructor(t=new Iw(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof _r?this.faceFeatureExtractor.forwardInput(t):t,r=Qn(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Sp(r,n.fc.age).as1D(),i=Sp(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 ht(t))}async predictAgeAndGender(t){let n=await ht(t),a=await this.forwardInput(n),r=ut(a.age),s=ut(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?Er.MALE:Er.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 Z2(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=nf(t);return this.faceFeatureExtractor.loadFromWeightMap(n),eC(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 _p=class extends Cp{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)=>$t([Sn([68],p,"float32"),Sn([68],d,"float32")],1).as2D(1,136).as1D(),o=(p,d)=>{let{width:h,height:m}=r[p];return d(h,m)?Math.abs(h-m)/2:0},l=p=>o(p,(d,h)=>do(p,(d,h)=>hi(l(d),c(d))))).div($t(Array.from(Array(s),(p,d)=>i(r[d].width,r[d].height))))})}forwardInput(t){return D(()=>{let n=this.runNet(t);return this.postProcess(n,t.inputSize,t.inputDimensions.map(([a,r])=>({height:a,width:r})))})}async forward(t){return this.forwardInput(await ht(t))}async detectLandmarks(t){let n=await ht(t),a=D(()=>ut(this.forwardInput(n))),r=await Promise.all(a.map(async(s,i)=>{let o=Array.from(await s.data()),l=o.filter((u,p)=>Gm(p)),c=o.filter((u,p)=>!Gm(p));return new bu(Array(68).fill(0).map((u,p)=>new De(l[p],c[p])),{height:n.getInputHeight(i),width:n.getInputWidth(i)})}));return a.forEach(s=>s.dispose()),n.isBatchInput?r:r[0]}getClassifierChannelsOut(){return 136}};var Nu=class extends _p{constructor(t=new Np){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function tC(e){let t=[],{extractDenseBlock3Params:n}=tf(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return bn(e,t),{params:a,paramMappings:t}}function nC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),{extractDenseBlock3Params:r}=Zm(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o}}}var Tw=class extends sn{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return D(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=Oa(a,[122.782,117.001,104.298]).div(pe(255)),i=Ym(s,n.dense0,!0);return i=Ym(i,n.dense1),i=Ym(i,n.dense2),i=Qn(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await ht(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return tC(t)}extractParams(t){return nC(t)}};var of=class extends _p{constructor(t=new Tw){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var aC=class extends Nu{};function rC(e,t){return J(L(e,t.weights),t.biases)}function Nw(e,t,n,a,r="same"){let{filters:s,bias:i}=t.conv,o=Ft(e,s,n,r);return o=J(o,i),o=rC(o,t.scale),a?qe(o):o}function sC(e,t){return Nw(e,t,[1,1],!0)}function Sw(e,t){return Nw(e,t,[1,1],!1)}function lf(e,t){return Nw(e,t,[2,2],!0,"valid")}function ere(e,t){function n(o,l,c){let u=e(o),p=u.length/(l*c*c);if(Qv(p))throw new Error(`depth has to be an integer: ${p}, weights.length: ${u.length}, numFilters: ${l}, filterSize: ${c}`);return D(()=>Ve(Sa(u,[l,p,c,c]),[2,3,1,0]))}function a(o,l,c,u){let p=n(o,l,c),d=Qe(e(l));return t.push({paramPath:`${u}/filters`},{paramPath:`${u}/bias`}),{filters:p,bias:d}}function r(o,l){let c=Qe(e(o)),u=Qe(e(o));return t.push({paramPath:`${l}/weights`},{paramPath:`${l}/biases`}),{weights:c,biases:u}}function s(o,l,c,u){let p=a(o,l,c,`${u}/conv`),d=r(l,`${u}/scale`);return{conv:p,scale:d}}function i(o,l,c,u,p=!1){let d=s((p?.5:1)*o,l,c,`${u}/conv1`),h=s(o,l,c,`${u}/conv2`);return{conv1:d,conv2:h}}return{extractConvLayerParams:s,extractResidualLayerParams:i}}function iC(e){let{extractWeights:t,getRemainingWeights:n}=xn(e),a=[],{extractConvLayerParams:r,extractResidualLayerParams:s}=ere(t,a),i=r(4704,32,7,"conv32_down"),o=s(9216,32,3,"conv32_1"),l=s(9216,32,3,"conv32_2"),c=s(9216,32,3,"conv32_3"),u=s(36864,64,3,"conv64_down",!0),p=s(36864,64,3,"conv64_1"),d=s(36864,64,3,"conv64_2"),h=s(36864,64,3,"conv64_3"),m=s(147456,128,3,"conv128_down",!0),f=s(147456,128,3,"conv128_1"),g=s(147456,128,3,"conv128_2"),y=s(589824,256,3,"conv256_down",!0),b=s(589824,256,3,"conv256_1"),x=s(589824,256,3,"conv256_2"),v=s(589824,256,3,"conv256_down_out"),N=D(()=>Ve(Na(t(256*128),[128,256]),[1,0]));if(a.push({paramPath:"fc"}),n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{params:{conv32_down:i,conv32_1:o,conv32_2:l,conv32_3:c,conv64_down:u,conv64_1:p,conv64_2:d,conv64_3:h,conv128_down:m,conv128_1:f,conv128_2:g,conv256_down:y,conv256_1:b,conv256_2:x,conv256_down_out:v,fc:N},paramMappings:a}}function tre(e,t){let n=jn(e,t);function a(i){let o=n(`${i}/scale/weights`,1),l=n(`${i}/scale/biases`,1);return{weights:o,biases:l}}function r(i){let o=n(`${i}/conv/filters`,4),l=n(`${i}/conv/bias`,1),c=a(i);return{conv:{filters:o,bias:l},scale:c}}function s(i){return{conv1:r(`${i}/conv1`),conv2:r(`${i}/conv2`)}}return{extractConvLayerParams:r,extractResidualLayerParams:s}}function oC(e){let t=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=tre(e,t),r=n("conv32_down"),s=a("conv32_1"),i=a("conv32_2"),o=a("conv32_3"),l=a("conv64_down"),c=a("conv64_1"),u=a("conv64_2"),p=a("conv64_3"),d=a("conv128_down"),h=a("conv128_1"),m=a("conv128_2"),f=a("conv256_down"),g=a("conv256_1"),y=a("conv256_2"),b=a("conv256_down_out"),{fc:x}=e;if(t.push({originalPath:"fc",paramPath:"fc"}),!Jv(x))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${x}`);let v={conv32_down:r,conv32_1:s,conv32_2:i,conv32_3:o,conv64_down:l,conv64_1:c,conv64_2:u,conv64_3:p,conv128_down:d,conv128_1:h,conv128_2:m,conv256_down:f,conv256_1:g,conv256_2:y,conv256_down_out:b,fc:x};return bn(e,t),{params:v,paramMappings:t}}function La(e,t){let n=sC(e,t.conv1);return n=Sw(n,t.conv2),n=J(n,e),n=qe(n),n}function Ep(e,t){let n=lf(e,t.conv1);n=Sw(n,t.conv2);let a=Qn(e,2,2,"valid"),r=xt(a.shape),s=a.shape[3]!==n.shape[3];if(a.shape[1]!==n.shape[1]||a.shape[2]!==n.shape[2]){let o=[...n.shape];o[1]=1;let l=xt(o);n=Je([n,l],1);let c=[...n.shape];c[2]=1;let u=xt(c);n=Je([n,u],2)}return a=s?Je([a,r],3):a,n=J(a,n),n=qe(n),n}var Su=class extends sn{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceRecognitionNet - load model before inference");return D(()=>{let 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Ar(e,t){return D(()=>{let n=ea(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ei(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=J(n,t.bias),Cu(n)})}function mre(e,t){let n=wu(e,t);function a(i,o){let l=Qe(e(i)),c=Qe(e(i));return t.push({paramPath:`${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,`${l}/bn`);return{conv:c,bn:u}}let s=ku(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function kC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=xn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=mre(r,i),u;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,x]=a,v=t.isFirstLayerConv2d?o(p,d,3,"conv0"):c(p,d,"conv0"),N=c(d,h,"conv1"),T=c(h,m,"conv2"),S=c(m,f,"conv3"),A=c(f,g,"conv4"),$=c(g,y,"conv5"),R=b?c(y,b,"conv6"):void 0,B=x?c(b,x,"conv7"):void 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n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=fre(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 or=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 _w=class extends sn{constructor(t){super("TinyYolov2");Cw(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=At(a,[2,2],[2,2],"same"),a=Fr(a,n.conv1),a=At(a,[2,2],[2,2],"same"),a=Fr(a,n.conv2),a=At(a,[2,2],[2,2],"same"),a=Fr(a,n.conv3),a=At(a,[2,2],[2,2],"same"),a=Fr(a,n.conv4),a=At(a,[2,2],[2,2],"same"),a=Fr(a,n.conv5),a=At(a,[2,2],[1,1],"same"),a=Fr(a,n.conv6),a=Fr(a,n.conv7),to(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Cu(to(t,n.conv0,"valid",!1)):Ar(t,n.conv0);return a=At(a,[2,2],[2,2],"same"),a=Ar(a,n.conv1),a=At(a,[2,2],[2,2],"same"),a=Ar(a,n.conv2),a=At(a,[2,2],[2,2],"same"),a=Ar(a,n.conv3),a=At(a,[2,2],[2,2],"same"),a=Ar(a,n.conv4),a=At(a,[2,2],[2,2],"same"),a=Ar(a,n.conv5),a=At(a,[2,2],[1,1],"same"),a=n.conv6?Ar(a,n.conv6):a,a=n.conv7?Ar(a,n.conv7):a,to(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=ue(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?Oa(r,this.config.meanRgb):r,r=r.div(pe(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await ht(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new or(n),s=await ht(t),i=await this.forwardInput(s,a),o=D(()=>ut(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 nw(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new ms(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return IC(t,this.config)}extractParams(t){let n=this.config.filterSizes||_w.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 ${a} filterSizes in config`);return kC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),x=y.slice([0,0,0,4],[c,c,u,1]),v=this.withClassScores?Ta(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):pe(0);return[b,x,v]}),m=[],f=await d.array(),g=await p.array();for(let y=0;ya){let N=(b+bp(g[y][b][x][0]))/c*o,T=(y+bp(g[y][b][x][1]))/c*l,S=Math.exp(g[y][b][x][2])*this.config.anchors[x].x/c*o,A=Math.exp(g[y][b][x][3])*this.config.anchors[x].y/c*l,$=N-S/2,R=T-A/2,B={row:y,col:b,anchor:x},{classScore:V,label:W}=this.withClassScores?await this.extractPredictedClass(h,B):{classScore:1,label:0};m.push({box:new gu($,R,$+S,R+A),score:v,classScore:v*V,label:W,...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)}},_u=_w;_u.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Eu=class extends _u{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:gC,classes:["face"],...t?{anchors:bC,meanRgb:xC}:{anchors:yC,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 gt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?wC:vC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function gre(e,t=!0){let n=new Eu(t);return n.extractWeights(e),n}var hf=class extends or{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 so(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>no(l)?r(l):l.detection),i=a||(t instanceof Ee?await vu(t,s):await xu(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ee&&l.dispose()),o}async function Fu(e,t,n,a,r){return so([e],t,async s=>n(s[0]),a,r)}var TC=.4,NC=[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)],SC=[117.001,114.697,97.404];var Au=class extends _u{constructor(){let t={withSeparableConvs:!0,iouThreshold:TC,classes:["face"],anchors:NC,meanRgb:SC,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 gt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var Ze={ssdMobilenetv1:new ro,tinyFaceDetector:new Au,tinyYolov2:new Eu,faceLandmark68Net:new Nu,faceLandmark68TinyNet:new of,faceRecognitionNet:new Su,faceExpressionNet:new af,ageGenderNet:new sf},CC=(e,t)=>Ze.ssdMobilenetv1.locateFaces(e,t),yre=(e,t)=>Ze.tinyFaceDetector.locateFaces(e,t),bre=(e,t)=>Ze.tinyYolov2.locateFaces(e,t),_C=e=>Ze.faceLandmark68Net.detectLandmarks(e),xre=e=>Ze.faceLandmark68TinyNet.detectLandmarks(e),vre=e=>Ze.faceRecognitionNet.computeFaceDescriptor(e),wre=e=>Ze.faceExpressionNet.predictExpressions(e),kre=e=>Ze.ageGenderNet.predictAgeAndGender(e),EC=e=>Ze.ssdMobilenetv1.load(e),Ire=e=>Ze.tinyFaceDetector.load(e),Tre=e=>Ze.tinyYolov2.load(e),Nre=e=>Ze.faceLandmark68Net.load(e),Sre=e=>Ze.faceLandmark68TinyNet.load(e),Cre=e=>Ze.faceRecognitionNet.load(e),_re=e=>Ze.faceExpressionNet.load(e),Ere=e=>Ze.ageGenderNet.load(e),Fre=EC,Are=CC,$re=_C;var Ew=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Ru=class extends Ew{async run(){let t=await this.parentTask,n=await so(t,this.input,async a=>Promise.all(a.map(r=>Ze.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>rf(a,n[r]))}withAgeAndGender(){return new $u(this,this.input)}},Mu=class extends Ew{async run(){let t=await this.parentTask;if(!t)return;let n=await Fu(t,this.input,a=>Ze.faceExpressionNet.predictExpressions(a),this.extractedFaces);return rf(t,n)}withAgeAndGender(){return new Du(this,this.input)}},lo=class extends Ru{withAgeAndGender(){return new io(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},uo=class extends Mu{withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var Fw=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},$u=class extends Fw{async run(){let t=await this.parentTask,n=await so(t,this.input,async a=>Promise.all(a.map(r=>Ze.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return cf(pf(a,i,o),s)})}withFaceExpressions(){return new Ru(this,this.input)}},Du=class extends Fw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Fu(t,this.input,s=>Ze.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return cf(pf(t,a,r),n)}withFaceExpressions(){return new Mu(this,this.input)}},io=class extends $u{withFaceExpressions(){return new lo(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},oo=class extends Du{withFaceExpressions(){return new uo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var mf=class extends wa{constructor(t,n){super();this.parentTask=t;this.input=n}},ys=class extends mf{async run(){let t=await this.parentTask;return(await so(t,this.input,a=>Promise.all(a.map(r=>Ze.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>uf(t[r],a))}withFaceExpressions(){return new lo(this,this.input)}withAgeAndGender(){return new io(this,this.input)}},bs=class extends mf{async run(){let t=await this.parentTask;if(!t)return;let n=await Fu(t,this.input,a=>Ze.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return uf(t,n)}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}};var ff=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?Ze.faceLandmark68TinyNet:Ze.faceLandmark68Net}},gf=class extends ff{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Ee?await vu(this.input,n):await xu(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Ee&&s.dispose()),t.map((s,i)=>Tu(s,r[i]))}withFaceExpressions(){return new lo(this,this.input)}withAgeAndGender(){return new io(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},yf=class extends ff{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ee?await vu(this.input,[n]):await xu(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ee&&s.dispose()),Tu(t,r)}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var bf=class extends wa{constructor(t,n=new va){super();this.input=t;this.options=n}},Fp=class extends bf{async run(){let{input:t,options:n}=this,a;if(n instanceof hf)a=Ze.tinyFaceDetector.locateFaces(t,n);else if(n instanceof va)a=Ze.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof or)a=Ze.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>Yi({},a)))})}withFaceLandmarks(t=!1){return new gf(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Ru(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new $u(this.runAndExtendWithFaceDetections(),this.input)}},xf=class extends bf{async run(){let t=await new Fp(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?Yi({},n):void 0)})}withFaceLandmarks(t=!1){return new yf(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Mu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Du(this.runAndExtendWithFaceDetection(),this.input)}};function Dre(e,t=new va){return new xf(e,t)}function vf(e,t=new va){return new Fp(e,t)}async function FC(e,t){return vf(e,new va(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Rre(e,t={}){return vf(e,new or(t)).withFaceLandmarks().withFaceDescriptors()}var Mre=FC;function Aw(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 wf=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 Cr)return i;if(i instanceof Float32Array)return new Cr(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Cr(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=>Aw(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new xp(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>Cr.fromJSON(a));return new wf(n,t.distanceThreshold)}};function Pre(e){let t=new Au;return t.extractWeights(e),t}function AC(e,t){let{width:n,height:a}=new yn(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>AC(r,{width:n,height:a}));if(no(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Tu(Yi(e,r),s)}return sr(e)?Yi(e,e.detection.forSize(n,a)):e instanceof ra||e instanceof gt?e.forSize(n,a):e}var Ore=typeof process!="undefined",Lre=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",zre={faceapi:X2,node:Ore,browser:Lre};export{sf as AgeGenderNet,gu as BoundingBox,lt as Box,wa as ComposableTask,ys as ComputeAllFaceDescriptorsTask,mf as ComputeFaceDescriptorsTaskBase,bs as ComputeSingleFaceDescriptorTask,gf as DetectAllFaceLandmarksTask,Fp as DetectAllFacesTask,ff as DetectFaceLandmarksTaskBase,bf as DetectFacesTaskBase,yf as DetectSingleFaceLandmarksTask,xf as DetectSingleFaceTask,yn as Dimensions,yw as FACE_EXPRESSION_LABELS,gt as FaceDetection,fC as FaceDetectionNet,af as FaceExpressionNet,gs as FaceExpressions,Nu as FaceLandmark68Net,of as FaceLandmark68TinyNet,aC as FaceLandmarkNet,ra as FaceLandmarks,W2 as FaceLandmarks5,bu as FaceLandmarks68,xp as FaceMatch,wf as FaceMatcher,Su as FaceRecognitionNet,Er as Gender,vp as LabeledBox,Cr as LabeledFaceDescriptors,_r as NetInput,sn as NeuralNetwork,ms as ObjectDetection,De as Point,V2 as PredictedBox,yu as Rect,ro as SsdMobilenetv1,va as SsdMobilenetv1Options,Au as TinyFaceDetector,hf as TinyFaceDetectorOptions,Eu as TinyYolov2,or as TinyYolov2Options,Mre as allFaces,FC as allFacesSsdMobilenetv1,Rre as allFacesTinyYolov2,pw as awaitMediaLoaded,dw as bufferToImage,vre as computeFaceDescriptor,Zi as createCanvas,Ip as createCanvasFromMedia,hre as createFaceDetectionNet,nre as createFaceRecognitionNet,mC as createSsdMobilenetv1,Pre as createTinyFaceDetector,gre as createTinyYolov2,vf as detectAllFaces,_C as detectFaceLandmarks,xre as detectFaceLandmarksTiny,$re as detectLandmarks,Dre as detectSingleFace,ww as draw,rt as env,Aw as euclideanDistance,cf as extendWithAge,uf as extendWithFaceDescriptor,Yi as extendWithFaceDetection,rf as extendWithFaceExpressions,Tu as extendWithFaceLandmarks,pf as extendWithGender,vu as extractFaceTensors,xu as extractFaces,Hae as fetchImage,fw as fetchJson,jae as fetchNetWeights,eo as fetchOrThrow,$n as getContext2dOrThrow,Qi as getMediaDimensions,hw as imageTensorToCanvas,mw as imageToSquare,Pae as inverseSigmoid,ew as iou,Km as isMediaElement,kp as isMediaLoaded,are as isWithAge,sr as isWithFaceDetection,bw as isWithFaceExpressions,no as isWithFaceLandmarks,rre as isWithGender,Ere as loadAgeGenderModel,Fre as loadFaceDetectionModel,_re as loadFaceExpressionModel,Nre as loadFaceLandmarkModel,Sre as loadFaceLandmarkTinyModel,Cre as loadFaceRecognitionModel,EC as loadSsdMobilenetv1Model,Ire as loadTinyFaceDetectorModel,Tre as loadTinyYolov2Model,gw as loadWeightMap,Are as locateFaces,qae as matchDimensions,tw as minBbox,Ze as nets,nw as nonMaxSuppression,Oa as normalize,aw as padToSquare,kre as predictAgeAndGender,wre as recognizeFaceExpressions,AC as resizeResults,Ji as resolveInput,Mae as shuffleArray,bp as sigmoid,CC as ssdMobilenetv1,Og as tf,yre as tinyFaceDetector,bre as tinyYolov2,ht as toNetInput,Yv as utils,Cw as validateConfig,zre as version}; + `}};function RZ(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=Fn({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=w.sizeFromShape([p.shape[c]]),m=ye({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=Gd(r.dtype),g=(v,N,T,S,A)=>{let $=v.shape[0],R=v.shape[1],B=_.segment_util.segOpComputeOptimalWindowSize(R,A),V={windowSize:B,inSize:R,batchSize:$,numSegments:A},W=new DZ(V,N),G=n.compileAndRun(W,[v,T],S);if(l.push(G),G.shape[1]===A)return G;let H=GS({backend:n,attrs:{start:0,stop:A,step:1,dtype:"float32"}}),X=qS({inputs:{x:H},backend:n,attrs:{reps:[R/B]}});return l.push(H),l.push(X),g(G,N,X,S,A)},y=g(m,"unsortedSegmentSum",s,f,i),b=ye({inputs:{x:y},backend:n,attrs:{shape:d}}),x=b;if(u!=null){l.push(b);let v=_.getUndoAxesPermutation(u);x=Fn({inputs:{x},backend:n,attrs:{perm:v}})}return 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Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),ne=a.dataIdMap.get(se.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return p2(y,q,te,Q,b,N,T,v,S,A,$,R,X,B,V,W,G,H,x,g,ie,m||0,ne),se}var ete={kernelName:bi,backendName:"wasm",setupFunc:Qee,kernelFunc:Zee},d2;function tte(e){d2=e.wasm.cwrap(Ho,null,["number","number","number","number","number","number","array","number"])}function nte(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=dy.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 d2(d,Hn[a.dtype],h,i,p,o,m,f),c}var ate={kernelName:Ho,backendName:"wasm",setupFunc:tte,kernelFunc:nte},h2;function rte(e){h2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function ste(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=w.parseAxisParam(i,r.shape)[0],c=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=Ma({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),p=w.sizeFromShape(s.shape),d=Ma({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(w.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,x=new Uint8Array(new Int32Array(w.computeStrides(u.shape)).buffer),v=new Uint8Array(new Int32Array(w.computeStrides(h)).buffer);return h2(g,Hn[r.dtype],x,f,y,c.batchSize,v,b),t.disposeData(u.dataId),t.disposeData(d.dataId),m.shape=c.outputShape,m}var ite={kernelName:Go,backendName:"wasm",setupFunc:rte,kernelFunc:ste},ote=!1,lte=gn(jo,ote,"bool"),ute=!1,cte=gn(Bs,ute,"bool"),m2;function pte(e){m2=e.wasm.cwrap(Vs,null,["number","number","number"])}function dte(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(w.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;m2(r,n,i)}return s}var hte={kernelName:Vs,backendName:"wasm",setupFunc:pte,kernelFunc:dte},mte=!1,fte=gn(Yo,mte,"bool"),gte=!1,yte=gn(Jo,gte,"bool"),bte=An(Us),xte=!1,vte=gn(Zo,xte,"bool"),f2;function wte(e){f2=e.wasm.cwrap(Gs,null,["number, number, number"])}function kte(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}=mu(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=w.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;f2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=_.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var Ite={kernelName:Gs,backendName:"wasm",setupFunc:wte,kernelFunc:kte},Tte=!1,Nte=gn(Hs,Tte),g2;function Ste(e){g2=e.wasm.cwrap(js,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Cte(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,x=u.strideHeight,v=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 S=a.makeOutput(u.outShape,"float32"),A=a.dataIdMap.get(S.dataId).id;return g2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,x,v,N,T,A),S}var _te={kernelName:js,backendName:"wasm",setupFunc:Ste,kernelFunc:Cte},y2;function Ete(e){y2=e.wasm.cwrap(qs,null,["number, number, number"])}function Fte(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}=mu(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("mean",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=w.sizeFromShape(g),b=c;c.dtype!=="float32"&&(b=Wm({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let x=t.makeOutput(f,"float32");if(w.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;y2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(x.shape,d);x.shape=v}return c.dtype!=="float32"&&t.disposeData(b.dataId),x}var Ate={kernelName:qs,backendName:"wasm",setupFunc:Ete,kernelFunc:Fte},b2;function $te(e){b2=e.wasm.cwrap(Ks,null,["number, number, number"])}function Dte(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=mu(i,r,t);if(h){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x)}let m=c.shape.length;_.assertAxesAreInnerMostDims("min",p,m);let[f,g]=_.computeOutAndReduceShapes(c.shape,p),y=w.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(w.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;b2(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var Rte={kernelName:Ks,backendName:"wasm",setupFunc:$te,kernelFunc:Dte},Mte=!1,Pte=gn(Xs,Mte),Ote=!0,Lte=gn(Ys,Ote),zte=An(tl);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 x2;function Bte(e){x2=e.wasm.cwrap(al,"number",["number","number","number","number","number"])}function Wte(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=x2(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 Vte={kernelName:al,backendName:"wasm",setupFunc:Bte,kernelFunc:Wte},v2;function Ute(e){v2=e.wasm.cwrap(rl,"number",["number","number","number","number","number","bool"])}function Gte(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=v2(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 Hte={kernelName:rl,backendName:"wasm",setupFunc:Ute,kernelFunc:Gte},w2;function jte(e){w2=e.wasm.cwrap(sl,"number",["number","number","number","number","number","number"])}function qte(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=w2(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 Kte={kernelName:sl,backendName:"wasm",setupFunc:jte,kernelFunc:qte},Xte=!1,Yte=gn(nl,Xte,"bool"),k2;function Jte(e){k2=e.wasm.cwrap(Js,null,["number","number","number","number","number"])}function Qte(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 k2(u,s,i,o,c),l}var Zte={kernelName:Js,backendName:"wasm",setupFunc:Jte,kernelFunc:Qte};function ene(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var tne={kernelName:il,backendName:"wasm",kernelFunc:ene};function nne(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(u=>{w.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let p=jv({inputs:{input:u},backend:n,attrs:{dim:r}});return o.push(p),p}),c=t2({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(u=>n.disposeData(u.dataId)),c}var ane={kernelName:ol,backendName:"wasm",kernelFunc:nne},I2;function rne(e){I2=e.wasm.cwrap(Qs,null,["number","array","number","number","array","array","number","number"])}function sne(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 I2(i,c,t.shape.length,Hn[t.dtype],d,h,r,l),o}var ine={kernelName:Qs,backendName:"wasm",kernelFunc:sne,setupFunc:rne},one=!1,lne=gn(Zs,one),T2;function une(e){T2=e.wasm.cwrap(ei,null,["number","number","number"])}function cne(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 T2(s,i,l),o}var pne={kernelName:ei,backendName:"wasm",setupFunc:une,kernelFunc:cne},N2;function dne(e){N2=e.wasm.cwrap(ll,null,["number","number","number","number"])}function hne(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}=mu(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("prod",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=w.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(w.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;N2(l,y,Hn[b.dtype],x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var mne={kernelName:ll,backendName:"wasm",setupFunc:dne,kernelFunc:hne},fne=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=yv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},gne={kernelName:oc,backendName:"wasm",kernelFunc:fne},yne=!0,bne=gn(Ms,yne),xne=An(ti),vne=An(ai),S2;function wne(e){S2=e.wasm.cwrap(ni,null,["number","number","number","number","number","number","number","number","number","number"])}function kne(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=Wm({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(w.sizeFromShape(r.shape)===0)return b;let x=t.dataIdMap.get(b.dataId).id;return S2(y,u,p,d,h,l,c,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),b}var Ine={kernelName:ni,backendName:"wasm",setupFunc:wne,kernelFunc:kne},C2;function Tne(e){C2=e.wasm.cwrap(ri,null,["number","array","number","array","number","number"])}function Nne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=w.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);C2(l,u,i.length,p,r.shape.length,c);let d=Ma({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),d}var Sne={kernelName:ri,backendName:"wasm",kernelFunc:Nne,setupFunc:Tne},_2;function Cne(e){_2=e.wasm.cwrap(Tl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function 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a=qe(n?J(Ft(e,t.conv0.filters,[2,2],"same"),t.conv0.bias):Dn(e,t.conv0,[2,2])),r=Dn(a,t.conv1,[1,1]),s=qe(J(a,r)),i=Dn(s,t.conv2,[1,1]);return qe(J(a,J(r,i)))})}function Tp(e,t,n=!1,a=!0){return D(()=>{let r=qe(n?J(Ft(e,t.conv0.filters,a?[2,2]:[1,1],"same"),t.conv0.bias):Dn(e,t.conv0,a?[2,2]:[1,1])),s=Dn(r,t.conv1,[1,1]),i=qe(J(r,s)),o=Dn(i,t.conv2,[1,1]),l=qe(J(r,J(s,o))),c=Dn(l,t.conv3,[1,1]);return qe(J(r,J(s,J(o,c))))})}function to(e,t,n="same",a=!1){return D(()=>{let r=J(Ft(e,t.filters,[1,1],n),t.bias);return a?qe(r):r})}function bn(e,t){Object.keys(e).forEach(n=>{t.some(a=>a.originalPath===n)||e[n].dispose()})}function wu(e,t){return(n,a,r,s)=>{let i=Sa(e(n*a*r*r),[r,r,n,a]),o=Qe(e(a));return t.push({paramPath:`${s}/filters`},{paramPath:`${s}/bias`}),{filters:i,bias:o}}}function Jm(e,t){return(n,a,r)=>{let s=Na(e(n*a),[n,a]),i=Qe(e(a));return t.push({paramPath:`${r}/weights`},{paramPath:`${r}/bias`}),{weights:s,bias:i}}}var 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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 j2(e){let t=[],{extractDenseBlock4Params:n}=tf(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 Np=class extends sn{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceFeatureExtractor - load model before inference");return D(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=Oa(a,[122.782,117.001,104.298]).div(pe(255)),i=Tp(s,n.dense0,!0);return i=Tp(i,n.dense1),i=Tp(i,n.dense2),i=Tp(i,n.dense3),i=Qn(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await ht(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return j2(t)}extractParams(t){return H2(t)}};function Sp(e,t){return D(()=>J(ze(e,t.weights),t.bias))}function q2(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=xn(e),o=Jm(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function K2(e){let t=[],n=jn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return bn(e,t),{params:r,paramMappings:t}}function nf(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var Cp=class extends sn{constructor(t,n){super(t);this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof _r?this.faceFeatureExtractor.forwardInput(t):t;return Sp(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 q2(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=nf(t);return this.faceFeatureExtractor.loadFromWeightMap(n),K2(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 yw=["neutral","happy","sad","angry","fearful","disgusted","surprised"],gs=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);yw.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return yw.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var af=class extends Cp{constructor(t=new Np){super("FaceExpressionNet",t)}forwardInput(t){return D(()=>Ta(this.runNet(t)))}async forward(t){return this.forwardInput(await ht(t))}async predictExpressions(t){let n=await ht(t),a=await this.forwardInput(n),r=await Promise.all(ut(a).map(async i=>{let o=await i.data();return i.dispose(),o}));a.dispose();let s=r.map(i=>new gs(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function bw(e){return e.expressions instanceof gs}function rf(e,t){return{...e,...{expressions:t}}}function Kae(e,t,n=.1,a){(Array.isArray(t)?t:[t]).forEach(s=>{let i=s instanceof gs?s:bw(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let l=i.asSortedArray().filter(p=>p.probability>n),c=sr(s)?s.detection.box.bottomLeft:a||new De(0,0);new fs(l.map(p=>`${p.expression} (${Ki(p.probability)})`),c).draw(e)})}function no(e){return sr(e)&&e.landmarks instanceof ra&&e.unshiftedLandmarks instanceof ra&&e.alignedRect instanceof gt}function Xae(e){let t=(o,l,c,u)=>Math.atan2(u-l,c-o)%Math.PI,n=o=>o*180/Math.PI,a={roll:void 0,pitch:void 0,yaw:void 0};if(!e||!e._positions||e._positions.length!==68)return a;let r=e._positions;a.roll=-t(r[36]._x,r[36]._y,r[45]._x,r[45]._y),a.pitch=t(0,Math.abs(r[0]._x-r[30]._x)/r[30]._x,Math.PI,Math.abs(r[16]._x-r[30]._x)/r[30]._x);let s=r.reduce((o,l)=>oo>l._y?o:l._y,-Infinity);return a.yaw=Math.PI*(e._imgDims._height/(i-s)/1.4-1),a}function Tu(e,t){let{box:n}=e.detection,a=t.shiftBy(n.x,n.y),r=a.align(),{imageDims:s}=e.detection,i=new gt(e.detection.score,r.rescale(s.reverse()),s),o=Xae(t);return{...e,...{landmarks:a,unshiftedLandmarks:t,alignedRect:i,angle:o}}}var xw=class{constructor(t={}){let{drawLines:n=!0,drawPoints:a=!0,lineWidth:r,lineColor:s,pointSize:i,pointColor:o}=t;this.drawLines=n,this.drawPoints=a,this.lineWidth=r||1,this.pointSize=i||2,this.lineColor=s||"rgba(0, 255, 255, 1)",this.pointColor=o||"rgba(255, 0, 255, 1)"}},vw=class{constructor(t,n={}){this.faceLandmarks=t,this.options=new xw(n)}draw(t){let n=$n(t),{drawLines:a,drawPoints:r,lineWidth:s,lineColor:i,pointSize:o,pointColor:l}=this.options;if(a&&this.faceLandmarks instanceof bu&&(n.strokeStyle=i,n.lineWidth=s,Nr(n,this.faceLandmarks.getJawOutline()),Nr(n,this.faceLandmarks.getLeftEyeBrow()),Nr(n,this.faceLandmarks.getRightEyeBrow()),Nr(n,this.faceLandmarks.getNose()),Nr(n,this.faceLandmarks.getLeftEye(),!0),Nr(n,this.faceLandmarks.getRightEye(),!0),Nr(n,this.faceLandmarks.getMouth(),!0)),r){n.strokeStyle=l,n.fillStyle=l;let c=u=>{n.beginPath(),n.arc(u.x,u.y,o,0,2*Math.PI),n.fill()};this.faceLandmarks.positions.forEach(c)}}};function Yae(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof ra?a:no(a)?a.landmarks:void 0;if(!r)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new vw(r).draw(e)})}var X2="1.0.1";function Jae(e,t){let n=wu(e,t),a=ku(e,t);function r(i,o,l){let c=a(i,o,`${l}/separable_conv0`),u=a(o,o,`${l}/separable_conv1`),p=n(i,o,1,`${l}/expansion_conv`);return{separable_conv0:c,separable_conv1:u,expansion_conv:p}}function s(i,o){let l=a(i,i,`${o}/separable_conv0`),c=a(i,i,`${o}/separable_conv1`),u=a(i,i,`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:c,separable_conv2:u}}return{extractConvParams:n,extractSeparableConvParams:a,extractReductionBlockParams:r,extractMainBlockParams:s}}function Y2(e,t){let n=[],{extractWeights:a,getRemainingWeights:r}=xn(e),{extractConvParams:s,extractSeparableConvParams:i,extractReductionBlockParams:o,extractMainBlockParams:l}=Jae(a,n),c=s(3,32,3,"entry_flow/conv_in"),u=o(32,64,"entry_flow/reduction_block_0"),p=o(64,128,"entry_flow/reduction_block_1"),d={conv_in:c,reduction_block_0:u,reduction_block_1:p},h={};rr(t,0,1).forEach(y=>{h[`main_block_${y}`]=l(128,`middle_flow/main_block_${y}`)});let m=o(128,256,"exit_flow/reduction_block"),f=i(256,512,"exit_flow/separable_conv"),g={reduction_block:m,separable_conv:f};if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:n,params:{entry_flow:d,middle_flow:h,exit_flow:g}}}function Qae(e,t){let n=jn(e,t),a=ef(n),r=Iu(n);function s(o){let l=r(`${o}/separable_conv0`),c=r(`${o}/separable_conv1`),u=a(`${o}/expansion_conv`);return{separable_conv0:l,separable_conv1:c,expansion_conv:u}}function i(o){let l=r(`${o}/separable_conv0`),c=r(`${o}/separable_conv1`),u=r(`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:c,separable_conv2:u}}return{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}}function J2(e,t){let n=[],{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}=Qae(e,n),o=a("entry_flow/conv_in"),l=s("entry_flow/reduction_block_0"),c=s("entry_flow/reduction_block_1"),u={conv_in:o,reduction_block_0:l,reduction_block_1:c},p={};rr(t,0,1).forEach(f=>{p[`main_block_${f}`]=i(`middle_flow/main_block_${f}`)});let d=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:d,separable_conv:h};return bn(e,n),{params:{entry_flow:u,middle_flow:p,exit_flow:m},paramMappings:n}}function Q2(e,t,n){return J(Ft(e,t.filters,n,"same"),t.bias)}function kw(e,t,n=!0){let a=n?qe(e):e;return a=Dn(a,t.separable_conv0,[1,1]),a=Dn(qe(a),t.separable_conv1,[1,1]),a=At(a,[3,3],[2,2],"same"),a=J(a,Q2(e,t.expansion_conv,[2,2])),a}function Zae(e,t){let n=Dn(qe(e),t.separable_conv0,[1,1]);return n=Dn(qe(n),t.separable_conv1,[1,1]),n=Dn(qe(n),t.separable_conv2,[1,1]),n=J(n,e),n}var Iw=class extends sn{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return D(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=Oa(a,[122.782,117.001,104.298]).div(pe(256)),i=qe(Q2(s,n.entry_flow.conv_in,[2,2]));return i=kw(i,n.entry_flow.reduction_block_0,!1),i=kw(i,n.entry_flow.reduction_block_1),rr(this._numMainBlocks,0,1).forEach(o=>{i=Zae(i,n.middle_flow[`main_block_${o}`])}),i=kw(i,n.exit_flow.reduction_block),i=qe(Dn(i,n.exit_flow.separable_conv,[1,1])),i})}async forward(t){return this.forwardInput(await ht(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return J2(t,this._numMainBlocks)}extractParams(t){return Y2(t,this._numMainBlocks)}};function Z2(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),r=Jm(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: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function eC(e){let t=[],n=jn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return bn(e,t),{params:r,paramMappings:t}}var Er;(function(e){e.FEMALE="female",e.MALE="male"})(Er||(Er={}));var sf=class extends sn{constructor(t=new Iw(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof _r?this.faceFeatureExtractor.forwardInput(t):t,r=Qn(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Sp(r,n.fc.age).as1D(),i=Sp(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 ht(t))}async predictAgeAndGender(t){let n=await ht(t),a=await this.forwardInput(n),r=ut(a.age),s=ut(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?Er.MALE:Er.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 Z2(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=nf(t);return this.faceFeatureExtractor.loadFromWeightMap(n),eC(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 _p=class extends Cp{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([Sn([68],p,"float32"),Sn([68],d,"float32")],1).as2D(1,136).as1D(),o=(p,d)=>{let{width:h,height:m}=r[p];return 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t=[],{extractDenseBlock3Params:n}=tf(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return bn(e,t),{params:a,paramMappings:t}}function nC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),{extractDenseBlock3Params:r}=Zm(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o}}}var Tw=class extends sn{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return D(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=Oa(a,[122.782,117.001,104.298]).div(pe(255)),i=Ym(s,n.dense0,!0);return i=Ym(i,n.dense1),i=Ym(i,n.dense2),i=Qn(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await ht(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return tC(t)}extractParams(t){return nC(t)}};var of=class extends _p{constructor(t=new Tw){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var aC=class extends Nu{};function rC(e,t){return J(L(e,t.weights),t.biases)}function Nw(e,t,n,a,r="same"){let{filters:s,bias:i}=t.conv,o=Ft(e,s,n,r);return o=J(o,i),o=rC(o,t.scale),a?qe(o):o}function sC(e,t){return Nw(e,t,[1,1],!0)}function Sw(e,t){return Nw(e,t,[1,1],!1)}function lf(e,t){return Nw(e,t,[2,2],!0,"valid")}function ere(e,t){function n(o,l,c){let u=e(o),p=u.length/(l*c*c);if(Qv(p))throw new Error(`depth has to be an integer: ${p}, weights.length: ${u.length}, numFilters: ${l}, filterSize: ${c}`);return D(()=>Ve(Sa(u,[l,p,c,c]),[2,3,1,0]))}function a(o,l,c,u){let p=n(o,l,c),d=Qe(e(l));return t.push({paramPath:`${u}/filters`},{paramPath:`${u}/bias`}),{filters:p,bias:d}}function r(o,l){let c=Qe(e(o)),u=Qe(e(o));return t.push({paramPath:`${l}/weights`},{paramPath:`${l}/biases`}),{weights:c,biases:u}}function s(o,l,c,u){let p=a(o,l,c,`${u}/conv`),d=r(l,`${u}/scale`);return{conv:p,scale:d}}function i(o,l,c,u,p=!1){let d=s((p?.5:1)*o,l,c,`${u}/conv1`),h=s(o,l,c,`${u}/conv2`);return{conv1:d,conv2:h}}return{extractConvLayerParams:s,extractResidualLayerParams:i}}function iC(e){let{extractWeights:t,getRemainingWeights:n}=xn(e),a=[],{extractConvLayerParams:r,extractResidualLayerParams:s}=ere(t,a),i=r(4704,32,7,"conv32_down"),o=s(9216,32,3,"conv32_1"),l=s(9216,32,3,"conv32_2"),c=s(9216,32,3,"conv32_3"),u=s(36864,64,3,"conv64_down",!0),p=s(36864,64,3,"conv64_1"),d=s(36864,64,3,"conv64_2"),h=s(36864,64,3,"conv64_3"),m=s(147456,128,3,"conv128_down",!0),f=s(147456,128,3,"conv128_1"),g=s(147456,128,3,"conv128_2"),y=s(589824,256,3,"conv256_down",!0),b=s(589824,256,3,"conv256_1"),x=s(589824,256,3,"conv256_2"),v=s(589824,256,3,"conv256_down_out"),N=D(()=>Ve(Na(t(256*128),[128,256]),[1,0]));if(a.push({paramPath:"fc"}),n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{params:{conv32_down:i,conv32_1:o,conv32_2:l,conv32_3:c,conv64_down:u,conv64_1:p,conv64_2:d,conv64_3:h,conv128_down:m,conv128_1:f,conv128_2:g,conv256_down:y,conv256_1:b,conv256_2:x,conv256_down_out:v,fc:N},paramMappings:a}}function tre(e,t){let n=jn(e,t);function a(i){let o=n(`${i}/scale/weights`,1),l=n(`${i}/scale/biases`,1);return{weights:o,biases:l}}function r(i){let o=n(`${i}/conv/filters`,4),l=n(`${i}/conv/bias`,1),c=a(i);return{conv:{filters:o,bias:l},scale:c}}function s(i){return{conv1:r(`${i}/conv1`),conv2:r(`${i}/conv2`)}}return{extractConvLayerParams:r,extractResidualLayerParams:s}}function oC(e){let t=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=tre(e,t),r=n("conv32_down"),s=a("conv32_1"),i=a("conv32_2"),o=a("conv32_3"),l=a("conv64_down"),c=a("conv64_1"),u=a("conv64_2"),p=a("conv64_3"),d=a("conv128_down"),h=a("conv128_1"),m=a("conv128_2"),f=a("conv256_down"),g=a("conv256_1"),y=a("conv256_2"),b=a("conv256_down_out"),{fc:x}=e;if(t.push({originalPath:"fc",paramPath:"fc"}),!Jv(x))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${x}`);let v={conv32_down:r,conv32_1:s,conv32_2:i,conv32_3:o,conv64_down:l,conv64_1:c,conv64_2:u,conv64_3:p,conv128_down:d,conv128_1:h,conv128_2:m,conv256_down:f,conv256_1:g,conv256_2:y,conv256_down_out:b,fc:x};return bn(e,t),{params:v,paramMappings:t}}function La(e,t){let n=sC(e,t.conv1);return n=Sw(n,t.conv2),n=J(n,e),n=qe(n),n}function Ep(e,t){let n=lf(e,t.conv1);n=Sw(n,t.conv2);let a=Qn(e,2,2,"valid"),r=xt(a.shape),s=a.shape[3]!==n.shape[3];if(a.shape[1]!==n.shape[1]||a.shape[2]!==n.shape[2]){let o=[...n.shape];o[1]=1;let l=xt(o);n=Je([n,l],1);let c=[...n.shape];c[2]=1;let u=xt(c);n=Je([n,u],2)}return a=s?Je([a,r],3):a,n=J(a,n),n=qe(n),n}var Su=class extends sn{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceRecognitionNet - load model before inference");return D(()=>{let a=ue(t.toBatchTensor(150,!0),"float32"),s=Oa(a,[122.782,117.001,104.298]).div(pe(256)),i=lf(s,n.conv32_down);i=At(i,3,2,"valid"),i=La(i,n.conv32_1),i=La(i,n.conv32_2),i=La(i,n.conv32_3),i=Ep(i,n.conv64_down),i=La(i,n.conv64_1),i=La(i,n.conv64_2),i=La(i,n.conv64_3),i=Ep(i,n.conv128_down),i=La(i,n.conv128_1),i=La(i,n.conv128_2),i=Ep(i,n.conv256_down),i=La(i,n.conv256_1),i=La(i,n.conv256_2),i=Ep(i,n.conv256_down_out);let o=i.mean([1,2]);return ze(o,n.fc)})}async forward(t){return this.forwardInput(await ht(t))}async computeFaceDescriptor(t){var s;if((s=t==null?void 0:t.shape)==null?void 0:s.some(i=>i<=0))return new Float32Array(128);let n=await ht(t),a=D(()=>ut(this.forwardInput(n))),r=await Promise.all(a.map(i=>i.data()));return a.forEach(i=>i.dispose()),n.isBatchInput?r:r[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return oC(t)}extractParams(t){return iC(t)}};function nre(e){let t=new Su;return t.extractWeights(e),t}function uf(e,t){return{...e,...{descriptor:t}}}function are(e){return typeof e.age=="number"}function cf(e,t){return{...e,...{age:t}}}function rre(e){return(e.gender===Er.MALE||e.gender===Er.FEMALE)&&fu(e.genderProbability)}function pf(e,t,n){return{...e,...{gender:t,genderProbability:n}}}function sre(e,t){function n(l,c){let u=Sa(e(3*3*l),[3,3,l,1]),p=Qe(e(l)),d=Qe(e(l)),h=Qe(e(l)),m=Qe(e(l));return t.push({paramPath:`${c}/filters`},{paramPath:`${c}/batch_norm_scale`},{paramPath:`${c}/batch_norm_offset`},{paramPath:`${c}/batch_norm_mean`},{paramPath:`${c}/batch_norm_variance`}),{filters:u,batch_norm_scale:p,batch_norm_offset:d,batch_norm_mean:h,batch_norm_variance:m}}function a(l,c,u,p,d){let h=Sa(e(l*c*u*u),[u,u,l,c]),m=Qe(e(c));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/${d?"batch_norm_offset":"bias"}`}),{filters:h,bias:m}}function r(l,c,u,p){let{filters:d,bias:h}=a(l,c,u,p,!0);return{filters:d,batch_norm_offset:h}}function s(l,c,u){let p=n(l,`${u}/depthwise_conv`),d=r(l,c,1,`${u}/pointwise_conv`);return{depthwise_conv:p,pointwise_conv:d}}function i(){let l=r(3,32,3,"mobilenetv1/conv_0"),c=s(32,64,"mobilenetv1/conv_1"),u=s(64,128,"mobilenetv1/conv_2"),p=s(128,128,"mobilenetv1/conv_3"),d=s(128,256,"mobilenetv1/conv_4"),h=s(256,256,"mobilenetv1/conv_5"),m=s(256,512,"mobilenetv1/conv_6"),f=s(512,512,"mobilenetv1/conv_7"),g=s(512,512,"mobilenetv1/conv_8"),y=s(512,512,"mobilenetv1/conv_9"),b=s(512,512,"mobilenetv1/conv_10"),x=s(512,512,"mobilenetv1/conv_11"),v=s(512,1024,"mobilenetv1/conv_12"),N=s(1024,1024,"mobilenetv1/conv_13");return{conv_0:l,conv_1:c,conv_2:u,conv_3:p,conv_4:d,conv_5:h,conv_6:m,conv_7:f,conv_8:g,conv_9:y,conv_10:b,conv_11:x,conv_12:v,conv_13:N}}function o(){let l=r(1024,256,1,"prediction_layer/conv_0"),c=r(256,512,3,"prediction_layer/conv_1"),u=r(512,128,1,"prediction_layer/conv_2"),p=r(128,256,3,"prediction_layer/conv_3"),d=r(256,128,1,"prediction_layer/conv_4"),h=r(128,256,3,"prediction_layer/conv_5"),m=r(256,64,1,"prediction_layer/conv_6"),f=r(64,128,3,"prediction_layer/conv_7"),g=a(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),y=a(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),b=a(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),x=a(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),v=a(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),N=a(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),T=a(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),S=a(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),A=a(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),$=a(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),R=a(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),B=a(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:l,conv_1:c,conv_2:u,conv_3:p,conv_4:d,conv_5:h,conv_6:m,conv_7:f,box_predictor_0:{box_encoding_predictor:g,class_predictor:y},box_predictor_1:{box_encoding_predictor:b,class_predictor:x},box_predictor_2:{box_encoding_predictor:v,class_predictor:N},box_predictor_3:{box_encoding_predictor:T,class_predictor:S},box_predictor_4:{box_encoding_predictor:A,class_predictor:$},box_predictor_5:{box_encoding_predictor:R,class_predictor:B}}}return{extractMobilenetV1Params:i,extractPredictionLayerParams:o}}function lC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),{extractMobilenetV1Params:r,extractPredictionLayerParams:s}=sre(n,t),i=r(),o=s(),c={extra_dim:Kd(n(5118*4),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:{mobilenetv1:i,prediction_layer:o,output_layer:c},paramMappings:t}}function ire(e,t){let n=jn(e,t);function a(c,u,p){let d=n(`${c}/Conv2d_${u}_pointwise/weights`,4,`${p}/filters`),h=n(`${c}/Conv2d_${u}_pointwise/convolution_bn_offset`,1,`${p}/batch_norm_offset`);return{filters:d,batch_norm_offset:h}}function r(c){let u=`mobilenetv1/conv_${c}`,p=`MobilenetV1/Conv2d_${c}_depthwise`,d=`${u}/depthwise_conv`,h=`${u}/pointwise_conv`,m=n(`${p}/depthwise_weights`,4,`${d}/filters`),f=n(`${p}/BatchNorm/gamma`,1,`${d}/batch_norm_scale`),g=n(`${p}/BatchNorm/beta`,1,`${d}/batch_norm_offset`),y=n(`${p}/BatchNorm/moving_mean`,1,`${d}/batch_norm_mean`),b=n(`${p}/BatchNorm/moving_variance`,1,`${d}/batch_norm_variance`);return{depthwise_conv:{filters:m,batch_norm_scale:f,batch_norm_offset:g,batch_norm_mean:y,batch_norm_variance:b},pointwise_conv:a("MobilenetV1",c,h)}}function s(){return{conv_0:a("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:r(1),conv_2:r(2),conv_3:r(3),conv_4:r(4),conv_5:r(5),conv_6:r(6),conv_7:r(7),conv_8:r(8),conv_9:r(9),conv_10:r(10),conv_11:r(11),conv_12:r(12),conv_13:r(13)}}function i(c,u){let p=n(`${c}/weights`,4,`${u}/filters`),d=n(`${c}/biases`,1,`${u}/bias`);return{filters:p,bias:d}}function o(c){let u=i(`Prediction/BoxPredictor_${c}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${c}/box_encoding_predictor`),p=i(`Prediction/BoxPredictor_${c}/ClassPredictor`,`prediction_layer/box_predictor_${c}/class_predictor`);return{box_encoding_predictor:u,class_predictor:p}}function l(){return{conv_0:a("Prediction",0,"prediction_layer/conv_0"),conv_1:a("Prediction",1,"prediction_layer/conv_1"),conv_2:a("Prediction",2,"prediction_layer/conv_2"),conv_3:a("Prediction",3,"prediction_layer/conv_3"),conv_4:a("Prediction",4,"prediction_layer/conv_4"),conv_5:a("Prediction",5,"prediction_layer/conv_5"),conv_6:a("Prediction",6,"prediction_layer/conv_6"),conv_7:a("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:o(0),box_predictor_1:o(1),box_predictor_2:o(2),box_predictor_3:o(3),box_predictor_4:o(4),box_predictor_5:o(5)}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:l}}function uC(e){let t=[],{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=ire(e,t),r=e["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Sr(r))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${r}`);let s={mobilenetv1:n(),prediction_layer:a(),output_layer:{extra_dim:r}};return bn(e,t),{params:s,paramMappings:t}}function xa(e,t,n){return D(()=>{let a=Ft(e,t.filters,n,"same");return a=J(a,t.batch_norm_offset),qt(a,0,6)})}var ore=.0010000000474974513;function lre(e,t,n){return D(()=>{let a=Qr(e,t.filters,n,"same");return a=fr(a,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,ore),qt(a,0,6)})}function ure(e){return[2,4,6,12].some(t=>t===e)?[2,2]:[1,1]}function cC(e,t){return D(()=>{let n,a=xa(e,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 o=i+1,l=ure(o);a=lre(a,s.depthwise_conv,l),a=xa(a,s.pointwise_conv,[1,1]),o===11&&(n=a)}),n===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:a,conv11:n}})}function cre(e,t,n){let a=e.arraySync(),r=Math.min(a[t][0],a[t][2]),s=Math.min(a[t][1],a[t][3]),i=Math.max(a[t][0],a[t][2]),o=Math.max(a[t][1],a[t][3]),l=Math.min(a[n][0],a[n][2]),c=Math.min(a[n][1],a[n][3]),u=Math.max(a[n][0],a[n][2]),p=Math.max(a[n][1],a[n][3]),d=(i-r)*(o-s),h=(u-l)*(p-c);if(d<=0||h<=0)return 0;let m=Math.max(r,l),f=Math.max(s,c),g=Math.min(i,u),y=Math.min(o,p),b=Math.max(g-m,0)*Math.max(y-f,0);return b/(d+h-b)}function pC(e,t,n,a,r){let s=e.shape[0],i=Math.min(n,s),o=t.map((u,p)=>({score:u,boxIndex:p})).filter(u=>u.score>r).sort((u,p)=>p.score-u.score),l=u=>u<=a?1:0,c=[];return o.forEach(u=>{if(c.length>=i)return;let p=u.score;for(let d=c.length-1;d>=0;--d){let h=cre(e,u.boxIndex,c[d]);if(h!==0&&(u.score*=l(h),u.score<=r))break}p===u.score&&c.push(u.boxIndex)}),c}function pre(e){let t=ut(Ve(e,[1,0])),n=[me(t[2],t[0]),me(t[3],t[1])],a=[J(t[0],xe(n[0],pe(2))),J(t[1],xe(n[1],pe(2)))];return{sizes:n,centers:a}}function dre(e,t){let{sizes:n,centers:a}=pre(e),r=ut(Ve(t,[1,0])),s=xe(L(dn(xe(r[2],pe(5))),n[0]),pe(2)),i=J(L(xe(r[0],pe(10)),n[0]),a[0]),o=xe(L(dn(xe(r[3],pe(5))),n[1]),pe(2)),l=J(L(xe(r[1],pe(10)),n[1]),a[1]);return Ve($t([me(i,s),me(l,o),J(i,s),J(l,o)]),[1,0])}function dC(e,t,n){return D(()=>{let a=e.shape[0],r=dre(U(Ha(n.extra_dim,[a,1,1]),[-1,4]),U(e,[-1,4]));r=U(r,[a,r.shape[0]/a,4]);let s=ca(We(t,[0,0,1],[-1,-1,-1])),i=We(s,[0,0,0],[-1,-1,1]);i=U(i,[a,i.shape[1]]);let o=ut(r),l=ut(i);return{boxes:o,scores:l}})}function ao(e,t){return D(()=>{let n=e.shape[0],a=U(to(e,t.box_encoding_predictor),[n,-1,1,4]),r=U(to(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function hC(e,t,n){return D(()=>{let a=xa(e,n.conv_0,[1,1]),r=xa(a,n.conv_1,[2,2]),s=xa(r,n.conv_2,[1,1]),i=xa(s,n.conv_3,[2,2]),o=xa(i,n.conv_4,[1,1]),l=xa(o,n.conv_5,[2,2]),c=xa(l,n.conv_6,[1,1]),u=xa(c,n.conv_7,[2,2]),p=ao(t,n.box_predictor_0),d=ao(e,n.box_predictor_1),h=ao(r,n.box_predictor_2),m=ao(i,n.box_predictor_3),f=ao(l,n.box_predictor_4),g=ao(u,n.box_predictor_5),y=Je([p.boxPredictionEncoding,d.boxPredictionEncoding,h.boxPredictionEncoding,m.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding],1),b=Je([p.classPrediction,d.classPrediction,h.classPrediction,m.classPrediction,f.classPrediction,g.classPrediction],1);return{boxPredictions:y,classPredictions:b}})}var va=class{constructor({minConfidence:t,maxResults:n}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=n||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 ro=class extends sn{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return D(()=>{let a=ue(t.toBatchTensor(512,!1),"float32"),r=me(L(a,pe(.007843137718737125)),pe(1)),s=cC(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=hC(s.out,s.conv11,n.prediction_layer);return dC(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await ht(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new va(n),s=await ht(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],c=o[0];for(let x=1;x{let[v,N]=[Math.max(0,y[x][0]),Math.min(1,y[x][2])].map(A=>A*g),[T,S]=[Math.max(0,y[x][1]),Math.min(1,y[x][3])].map(A=>A*f);return new gt(u[x],new yu(T,v,S-T,N-v),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),c.dispose(),b}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return uC(t)}extractParams(t){return lC(t)}};function mC(e){let t=new ro;return t.extractWeights(e),t}function hre(e){return mC(e)}var fC=class extends ro{};var gC=.4,yC=[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)],bC=[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)],xC=[117.001,114.697,97.404],vC="tiny_yolov2_model",wC="tiny_yolov2_separable_conv_model";var df=e=>typeof e=="number";function Cw(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(!df(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=>df(t.x)&&df(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(df)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Cu(e){return D(()=>{let t=L(e,pe(.10000000149011612));return J(qe(me(e,t)),t)})}function Fr(e,t){return D(()=>{let n=ea(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ft(n,t.conv.filters,[1,1],"valid"),n=me(n,t.bn.sub),n=L(n,t.bn.truediv),n=J(n,t.conv.bias),Cu(n)})}function Ar(e,t){return D(()=>{let n=ea(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ei(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=J(n,t.bias),Cu(n)})}function mre(e,t){let n=wu(e,t);function a(i,o){let l=Qe(e(i)),c=Qe(e(i));return t.push({paramPath:`${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,`${l}/bn`);return{conv:c,bn:u}}let s=ku(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function kC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=xn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=mre(r,i),u;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,x]=a,v=t.isFirstLayerConv2d?o(p,d,3,"conv0"):c(p,d,"conv0"),N=c(d,h,"conv1"),T=c(h,m,"conv2"),S=c(m,f,"conv3"),A=c(f,g,"conv4"),$=c(g,y,"conv5"),R=b?c(y,b,"conv6"):void 0,B=x?c(b,x,"conv7"):void 0,V=o(x||b||y,5*n,1,"conv8");u={conv0:v,conv1:N,conv2:T,conv3:S,conv4:A,conv5:$,conv6:R,conv7:B,conv8:V}}else{let[p,d,h,m,f,g,y,b,x]=a,v=l(p,d,"conv0"),N=l(d,h,"conv1"),T=l(h,m,"conv2"),S=l(m,f,"conv3"),A=l(f,g,"conv4"),$=l(g,y,"conv5"),R=l(y,b,"conv6"),B=l(b,x,"conv7"),V=o(x,5*n,1,"conv8");u={conv0:v,conv1:N,conv2:T,conv3:S,conv4:A,conv5:$,conv6:R,conv7:B,conv8:V}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function fre(e,t){let n=jn(e,t);function a(o){let l=n(`${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(`${o}/bn`);return{conv:l,bn:c}}let i=Iu(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function IC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=fre(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return bn(e,n),{params:i,paramMappings:n}}var or=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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 _w=class extends sn{constructor(t){super("TinyYolov2");Cw(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=At(a,[2,2],[2,2],"same"),a=Fr(a,n.conv1),a=At(a,[2,2],[2,2],"same"),a=Fr(a,n.conv2),a=At(a,[2,2],[2,2],"same"),a=Fr(a,n.conv3),a=At(a,[2,2],[2,2],"same"),a=Fr(a,n.conv4),a=At(a,[2,2],[2,2],"same"),a=Fr(a,n.conv5),a=At(a,[2,2],[1,1],"same"),a=Fr(a,n.conv6),a=Fr(a,n.conv7),to(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Cu(to(t,n.conv0,"valid",!1)):Ar(t,n.conv0);return a=At(a,[2,2],[2,2],"same"),a=Ar(a,n.conv1),a=At(a,[2,2],[2,2],"same"),a=Ar(a,n.conv2),a=At(a,[2,2],[2,2],"same"),a=Ar(a,n.conv3),a=At(a,[2,2],[2,2],"same"),a=Ar(a,n.conv4),a=At(a,[2,2],[2,2],"same"),a=Ar(a,n.conv5),a=At(a,[2,2],[1,1],"same"),a=n.conv6?Ar(a,n.conv6):a,a=n.conv7?Ar(a,n.conv7):a,to(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=ue(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?Oa(r,this.config.meanRgb):r,r=r.div(pe(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await ht(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new or(n),s=await ht(t),i=await this.forwardInput(s,a),o=D(()=>ut(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 nw(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new ms(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return IC(t,this.config)}extractParams(t){let n=this.config.filterSizes||_w.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 ${a} filterSizes in config`);return kC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),x=y.slice([0,0,0,4],[c,c,u,1]),v=this.withClassScores?Ta(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):pe(0);return[b,x,v]}),m=[],f=await d.array(),g=await p.array();for(let y=0;ya){let N=(b+bp(g[y][b][x][0]))/c*o,T=(y+bp(g[y][b][x][1]))/c*l,S=Math.exp(g[y][b][x][2])*this.config.anchors[x].x/c*o,A=Math.exp(g[y][b][x][3])*this.config.anchors[x].y/c*l,$=N-S/2,R=T-A/2,B={row:y,col:b,anchor:x},{classScore:V,label:W}=this.withClassScores?await this.extractPredictedClass(h,B):{classScore:1,label:0};m.push({box:new gu($,R,$+S,R+A),score:v,classScore:v*V,label:W,...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)}},_u=_w;_u.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Eu=class extends _u{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:gC,classes:["face"],...t?{anchors:bC,meanRgb:xC}:{anchors:yC,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 gt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?wC:vC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function gre(e,t=!0){let n=new Eu(t);return n.extractWeights(e),n}var hf=class extends or{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 so(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>no(l)?r(l):l.detection),i=a||(t instanceof Ee?await vu(t,s):await xu(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ee&&l.dispose()),o}async function Fu(e,t,n,a,r){return so([e],t,async s=>n(s[0]),a,r)}var TC=.4,NC=[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)],SC=[117.001,114.697,97.404];var Au=class extends _u{constructor(){let t={withSeparableConvs:!0,iouThreshold:TC,classes:["face"],anchors:NC,meanRgb:SC,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 gt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var Ze={ssdMobilenetv1:new ro,tinyFaceDetector:new Au,tinyYolov2:new Eu,faceLandmark68Net:new Nu,faceLandmark68TinyNet:new of,faceRecognitionNet:new Su,faceExpressionNet:new af,ageGenderNet:new sf},CC=(e,t)=>Ze.ssdMobilenetv1.locateFaces(e,t),yre=(e,t)=>Ze.tinyFaceDetector.locateFaces(e,t),bre=(e,t)=>Ze.tinyYolov2.locateFaces(e,t),_C=e=>Ze.faceLandmark68Net.detectLandmarks(e),xre=e=>Ze.faceLandmark68TinyNet.detectLandmarks(e),vre=e=>Ze.faceRecognitionNet.computeFaceDescriptor(e),wre=e=>Ze.faceExpressionNet.predictExpressions(e),kre=e=>Ze.ageGenderNet.predictAgeAndGender(e),EC=e=>Ze.ssdMobilenetv1.load(e),Ire=e=>Ze.tinyFaceDetector.load(e),Tre=e=>Ze.tinyYolov2.load(e),Nre=e=>Ze.faceLandmark68Net.load(e),Sre=e=>Ze.faceLandmark68TinyNet.load(e),Cre=e=>Ze.faceRecognitionNet.load(e),_re=e=>Ze.faceExpressionNet.load(e),Ere=e=>Ze.ageGenderNet.load(e),Fre=EC,Are=CC,$re=_C;var Ew=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Ru=class extends Ew{async run(){let t=await this.parentTask,n=await so(t,this.input,async a=>Promise.all(a.map(r=>Ze.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>rf(a,n[r]))}withAgeAndGender(){return new $u(this,this.input)}},Mu=class extends Ew{async run(){let t=await this.parentTask;if(!t)return;let n=await Fu(t,this.input,a=>Ze.faceExpressionNet.predictExpressions(a),this.extractedFaces);return rf(t,n)}withAgeAndGender(){return new Du(this,this.input)}},lo=class extends Ru{withAgeAndGender(){return new io(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},uo=class extends Mu{withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var Fw=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},$u=class extends Fw{async run(){let t=await this.parentTask,n=await so(t,this.input,async a=>Promise.all(a.map(r=>Ze.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return cf(pf(a,i,o),s)})}withFaceExpressions(){return new Ru(this,this.input)}},Du=class extends Fw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Fu(t,this.input,s=>Ze.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return cf(pf(t,a,r),n)}withFaceExpressions(){return new Mu(this,this.input)}},io=class extends $u{withFaceExpressions(){return new lo(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},oo=class extends Du{withFaceExpressions(){return new uo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var mf=class extends wa{constructor(t,n){super();this.parentTask=t;this.input=n}},ys=class extends mf{async run(){let t=await this.parentTask;return(await so(t,this.input,a=>Promise.all(a.map(r=>Ze.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>uf(t[r],a))}withFaceExpressions(){return new lo(this,this.input)}withAgeAndGender(){return new io(this,this.input)}},bs=class extends mf{async run(){let t=await this.parentTask;if(!t)return;let n=await Fu(t,this.input,a=>Ze.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return uf(t,n)}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}};var ff=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?Ze.faceLandmark68TinyNet:Ze.faceLandmark68Net}},gf=class extends ff{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Ee?await vu(this.input,n):await xu(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Ee&&s.dispose()),t.map((s,i)=>Tu(s,r[i]))}withFaceExpressions(){return new lo(this,this.input)}withAgeAndGender(){return new io(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},yf=class extends ff{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ee?await vu(this.input,[n]):await xu(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ee&&s.dispose()),Tu(t,r)}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var bf=class extends wa{constructor(t,n=new va){super();this.input=t;this.options=n}},Fp=class extends bf{async run(){let{input:t,options:n}=this,a;if(n instanceof hf)a=Ze.tinyFaceDetector.locateFaces(t,n);else if(n instanceof va)a=Ze.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof or)a=Ze.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>Yi({},a)))})}withFaceLandmarks(t=!1){return new gf(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Ru(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new $u(this.runAndExtendWithFaceDetections(),this.input)}},xf=class extends bf{async run(){let t=await new Fp(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?Yi({},n):void 0)})}withFaceLandmarks(t=!1){return new yf(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Mu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Du(this.runAndExtendWithFaceDetection(),this.input)}};function Dre(e,t=new va){return new xf(e,t)}function vf(e,t=new va){return new Fp(e,t)}async function FC(e,t){return vf(e,new va(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Rre(e,t={}){return vf(e,new or(t)).withFaceLandmarks().withFaceDescriptors()}var Mre=FC;function Aw(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 wf=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 Cr)return i;if(i instanceof Float32Array)return new Cr(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Cr(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=>Aw(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new xp(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>Cr.fromJSON(a));return new wf(n,t.distanceThreshold)}};function Pre(e){let t=new Au;return t.extractWeights(e),t}function AC(e,t){let{width:n,height:a}=new yn(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>AC(r,{width:n,height:a}));if(no(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Tu(Yi(e,r),s)}return sr(e)?Yi(e,e.detection.forSize(n,a)):e instanceof ra||e instanceof gt?e.forSize(n,a):e}var Ore=typeof process!="undefined",Lre=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",zre={faceapi:X2,node:Ore,browser:Lre};export{sf as AgeGenderNet,gu as BoundingBox,lt as Box,wa as ComposableTask,ys as ComputeAllFaceDescriptorsTask,mf as ComputeFaceDescriptorsTaskBase,bs as ComputeSingleFaceDescriptorTask,gf as DetectAllFaceLandmarksTask,Fp as DetectAllFacesTask,ff as DetectFaceLandmarksTaskBase,bf as DetectFacesTaskBase,yf as DetectSingleFaceLandmarksTask,xf as DetectSingleFaceTask,yn as Dimensions,yw as FACE_EXPRESSION_LABELS,gt as FaceDetection,fC as FaceDetectionNet,af as FaceExpressionNet,gs as FaceExpressions,Nu as FaceLandmark68Net,of as FaceLandmark68TinyNet,aC as FaceLandmarkNet,ra as FaceLandmarks,W2 as FaceLandmarks5,bu as FaceLandmarks68,xp as FaceMatch,wf as FaceMatcher,Su as FaceRecognitionNet,Er as Gender,vp as LabeledBox,Cr as LabeledFaceDescriptors,_r as NetInput,sn as NeuralNetwork,ms as ObjectDetection,De as Point,V2 as PredictedBox,yu as Rect,ro as SsdMobilenetv1,va as SsdMobilenetv1Options,Au as TinyFaceDetector,hf as TinyFaceDetectorOptions,Eu as TinyYolov2,or as TinyYolov2Options,Mre as allFaces,FC as allFacesSsdMobilenetv1,Rre as allFacesTinyYolov2,pw as awaitMediaLoaded,dw as bufferToImage,vre as computeFaceDescriptor,Zi as createCanvas,Ip as createCanvasFromMedia,hre as createFaceDetectionNet,nre as createFaceRecognitionNet,mC as createSsdMobilenetv1,Pre as createTinyFaceDetector,gre as createTinyYolov2,vf as detectAllFaces,_C as detectFaceLandmarks,xre as detectFaceLandmarksTiny,$re as detectLandmarks,Dre as detectSingleFace,ww as draw,rt as env,Aw as euclideanDistance,cf as extendWithAge,uf as extendWithFaceDescriptor,Yi as extendWithFaceDetection,rf as extendWithFaceExpressions,Tu as extendWithFaceLandmarks,pf as extendWithGender,vu as extractFaceTensors,xu as extractFaces,Hae as fetchImage,fw as fetchJson,jae as fetchNetWeights,eo as fetchOrThrow,$n as getContext2dOrThrow,Qi as getMediaDimensions,hw as imageTensorToCanvas,mw as imageToSquare,Pae as inverseSigmoid,ew as iou,Km as isMediaElement,kp as isMediaLoaded,are as isWithAge,sr as isWithFaceDetection,bw as isWithFaceExpressions,no as isWithFaceLandmarks,rre as isWithGender,Ere as loadAgeGenderModel,Fre as loadFaceDetectionModel,_re as loadFaceExpressionModel,Nre as loadFaceLandmarkModel,Sre as loadFaceLandmarkTinyModel,Cre as loadFaceRecognitionModel,EC as loadSsdMobilenetv1Model,Ire as loadTinyFaceDetectorModel,Tre as loadTinyYolov2Model,gw as loadWeightMap,Are as locateFaces,qae as matchDimensions,tw as minBbox,Ze as nets,nw as nonMaxSuppression,Oa as normalize,aw as padToSquare,kre as predictAgeAndGender,wre as recognizeFaceExpressions,AC as resizeResults,Ji as resolveInput,Mae as shuffleArray,bp as sigmoid,CC as ssdMobilenetv1,Og as tf,yre as tinyFaceDetector,bre as tinyYolov2,ht as toNetInput,Yv as utils,Cw as validateConfig,zre as version}; /** * @license * Copyright 2017 Google LLC. 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Please use 'channelsLast'.`);let B=a.makeOutput(h.outShape,"float32"),V=a.dataIdMap.get(B.dataId).id;return y2(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,x,R,v,N,T,S,A,$,V),B}var cte={kernelName:Ws,backendName:"wasm",setupFunc:lte,kernelFunc:ute},pte=!1,dte=yn(Yo,pte,"bool"),hte=$n(Us);function lw(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(w.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Pa({inputs:{x:r},backend:a,attrs:{shape:o}})}var mte={kernelName:Jo,backendName:"wasm",kernelFunc:lw};function fte(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var gte={kernelName:lc,backendName:"wasm",kernelFunc:fte},b2;function yte(e){b2=e.wasm.cwrap(Zo,null,["number","number","number","number","number","number"])}function bte(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,c,u]=a.shape;return b2(s,o,l,c,u,i),r}var xte={kernelName:Zo,backendName:"wasm",kernelFunc:bte,setupFunc:yte},vte=$n(Gs),wte=!1,kte=yn(Hs,wte),x2;function Ite(e){x2=e.wasm.cwrap(js,null,["number","number","number","number","number","number","number"])}function Tte(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(w.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return x2(u,p,d,h,m,r,g),f}var Nte={kernelName:js,backendName:"wasm",setupFunc:Ite,kernelFunc:Tte},v2;function Ste(e){v2=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 Cte(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=wp[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,x=f.outChannels,v=0;if(i!=null){let Z=a.dataIdMap.get(i.dataId);if(Z.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${Z.shape}) does not match the number of output channels (${x})`);v=Z.id}let N=f.filterHeight,T=f.filterWidth,S=f.padInfo.top,A=f.padInfo.right,$=f.padInfo.bottom,R=f.padInfo.left,B=f.dilationHeight,V=f.dilationWidth,W=f.strideHeight,G=f.strideWidth,H=f.inChannels,X=f.padInfo.type==="SAME"?1:0,q=f.batchSize,te=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),ne=a.dataIdMap.get(se.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return v2(y,q,te,Q,b,N,T,v,S,A,$,R,X,B,V,W,G,H,x,g,ie,m||0,ne),se}var _te={kernelName:Ti,backendName:"wasm",setupFunc:Ste,kernelFunc:Cte},w2;function Ete(e){w2=e.wasm.cwrap(Ni,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 Fte(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=wp[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,x=f.outChannels,v=0;if(i!=null){let Z=a.dataIdMap.get(i.dataId);if(Z.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${Z.shape}) does not match the number of output channels (${x})`);v=Z.id}let N=f.filterHeight,T=f.filterWidth,S=f.padInfo.top,A=f.padInfo.right,$=f.padInfo.bottom,R=f.padInfo.left,B=f.dilationHeight,V=f.dilationWidth,W=f.strideHeight,G=f.strideWidth,H=f.inChannels,X=f.padInfo.type==="SAME"?1:0,q=f.batchSize,te=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),ne=a.dataIdMap.get(se.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return w2(y,q,te,Q,b,N,T,v,S,A,$,R,X,B,V,W,G,H,x,g,ie,m||0,ne),se}var Ate={kernelName:Ni,backendName:"wasm",setupFunc:Ete,kernelFunc:Fte},k2;function $te(e){k2=e.wasm.cwrap(tl,null,["number","number","number","number","number","number","array","number"])}function Dte(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=_y.prepareAndValidate(a,r),c=t.makeOutput(s,a.dtype);if(i===0)return c;let u=r.shape,p=u[u.length-1],d=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(c.dataId).id;return k2(d,Hn[a.dtype],h,i,p,o,m,f),c}var Rte={kernelName:tl,backendName:"wasm",setupFunc:$te,kernelFunc:Dte},I2;function Mte(e){I2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Pte(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=w.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=w.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(w.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,x=new Uint8Array(new Int32Array(w.computeStrides(u.shape)).buffer),v=new Uint8Array(new Int32Array(w.computeStrides(h)).buffer);return I2(g,Hn[r.dtype],x,f,y,c.batchSize,v,b),t.disposeData(u.dataId),t.disposeData(d.dataId),m.shape=c.outputShape,m}var Ote={kernelName:el,backendName:"wasm",setupFunc:Mte,kernelFunc:Pte},Lte=!1,zte=yn(nl,Lte,"bool"),Bte=!1,Wte=yn(qs,Bte,"bool"),T2;function Vte(e){T2=e.wasm.cwrap(Xs,null,["number","number","number"])}function Ute(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(w.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;T2(r,n,i)}return s}var Gte={kernelName:Xs,backendName:"wasm",setupFunc:Vte,kernelFunc:Ute},Hte=!1,jte=yn(il,Hte,"bool"),qte=!1,Kte=yn(ol,qte,"bool"),Xte=$n(Ys),Yte=!1,Jte=yn(ul,Yte,"bool"),N2;function Qte(e){N2=e.wasm.cwrap(Js,null,["number, number, number"])}function Zte(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:p,inputWasTransposed:d}=Tu(i,r,t);if(d){let b=t.dataIdMap.get(c.dataId).id;l=c,o=b}let h=l.shape.length;_.assertAxesAreInnerMostDims("max",u,h);let[m,f]=_.computeOutAndReduceShapes(l.shape,u),g=w.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;N2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=_.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var ene={kernelName:Js,backendName:"wasm",setupFunc:Qte,kernelFunc:Zte},tne=!1,nne=yn(Qs,tne),S2;function ane(e){S2=e.wasm.cwrap(Zs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function rne(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=_.computePool2DInfo(r.shape,i,o,1,l,c),p=u.filterHeight,d=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,g=u.padInfo.left,y=u.dilationHeight,b=u.dilationWidth,x=u.strideHeight,v=u.strideWidth,N=u.inChannels,T=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let S=a.makeOutput(u.outShape,"float32"),A=a.dataIdMap.get(S.dataId).id;return S2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,x,v,N,T,A),S}var sne={kernelName:Zs,backendName:"wasm",setupFunc:ane,kernelFunc:rne},C2;function ine(e){C2=e.wasm.cwrap(ei,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}=Tu(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("mean",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=w.sizeFromShape(g),b=c;c.dtype!=="float32"&&(b=sf({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let x=t.makeOutput(f,"float32");if(w.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;C2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(x.shape,d);x.shape=v}return c.dtype!=="float32"&&t.disposeData(b.dataId),x}var lne={kernelName:ei,backendName:"wasm",setupFunc:ine,kernelFunc:one},_2;function une(e){_2=e.wasm.cwrap(ti,null,["number, number, number"])}function cne(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}=Tu(i,r,t);if(h){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x)}let m=c.shape.length;_.assertAxesAreInnerMostDims("min",p,m);let[f,g]=_.computeOutAndReduceShapes(c.shape,p),y=w.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(w.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;_2(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var pne={kernelName:ti,backendName:"wasm",setupFunc:une,kernelFunc:cne},dne=!1,hne=yn(ni,dne),mne=!0,fne=yn(ai,mne),gne=$n(pl);function uw(e,t){let n=new 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n(a)).toString()),l=await i(o,r);this.loadFromWeightMap(l)}loadFromWeightMap(t){let{paramMappings:n,params:a}=this.extractParamsFromWeightMap(t);this._paramMappings=n,this._params=a}extractWeights(t){let{paramMappings:n,params:a}=this.extractParams(t);this._paramMappings=n,this._params=a}traversePropertyPath(t){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let n=t.split("/").reduce((s,i)=>{if(!s.nextObj.hasOwnProperty(i))throw new Error(`traversePropertyPath - object does not have property ${i}, for path ${t}`);return{obj:s.nextObj,objProp:i,nextObj:s.nextObj[i]}},{nextObj:this.params}),{obj:a,objProp:r}=n;if(!a||!r||!(a[r]instanceof Ee))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${t}`);return{obj:a,objProp:r}}};function Dn(e,t,n){return D(()=>{let a=Pi(e,t.depthwise_filter,t.pointwise_filter,n,"same");return a=J(a,t.bias),a})}function Nf(e,t,n=!1){return D(()=>{let a=qe(n?J(Ft(e,t.conv0.filters,[2,2],"same"),t.conv0.bias):Dn(e,t.conv0,[2,2])),r=Dn(a,t.conv1,[1,1]),s=qe(J(a,r)),i=Dn(s,t.conv2,[1,1]);return qe(J(a,J(r,i)))})}function Cp(e,t,n=!1,a=!0){return D(()=>{let r=qe(n?J(Ft(e,t.conv0.filters,a?[2,2]:[1,1],"same"),t.conv0.bias):Dn(e,t.conv0,a?[2,2]:[1,1])),s=Dn(r,t.conv1,[1,1]),i=qe(J(r,s)),o=Dn(i,t.conv2,[1,1]),l=qe(J(r,J(s,o))),c=Dn(l,t.conv3,[1,1]);return qe(J(r,J(s,J(o,c))))})}function lo(e,t,n="same",a=!1){return D(()=>{let r=J(Ft(e,t.filters,[1,1],n),t.bias);return a?qe(r):r})}function xn(e,t){Object.keys(e).forEach(n=>{t.some(a=>a.originalPath===n)||e[n].dispose()})}function Au(e,t){return(n,a,r,s)=>{let i=Ca(e(n*a*r*r),[r,r,n,a]),o=Ze(e(a));return t.push({paramPath:`${s}/filters`},{paramPath:`${s}/bias`}),{filters:i,bias:o}}}function Sf(e,t){return(n,a,r)=>{let s=Sa(e(n*a),[n,a]),i=Ze(e(a));return t.push({paramPath:`${r}/weights`},{paramPath:`${r}/bias`}),{weights:s,bias:i}}}var Cf=class{constructor(t,n,a){this.depthwise_filter=t;this.pointwise_filter=n;this.bias=a}};function $u(e,t){return(n,a,r)=>{let s=Ca(e(3*3*n),[3,3,n,1]),i=Ca(e(n*a),[1,1,n,a]),o=Ze(e(a));return t.push({paramPath:`${r}/depthwise_filter`},{paramPath:`${r}/pointwise_filter`},{paramPath:`${r}/bias`}),new Cf(s,i,o)}}function Du(e){return t=>{let n=e(`${t}/depthwise_filter`,4),a=e(`${t}/pointwise_filter`,4),r=e(`${t}/bias`,1);return new Cf(n,a,r)}}function qn(e,t){return(n,a,r)=>{let s=e[n];if(!eo(s,a))throw new Error(`expected weightMap[${n}] to be a Tensor${a}D, instead have ${s}`);return t.push({originalPath:n,paramPath:r||n}),s}}function vn(e){let t=e;function n(r){let s=t.slice(0,r);return t=t.slice(r),s}function a(){return t}return{extractWeights:n,getRemainingWeights:a}}function _f(e,t){let n=Au(e,t),a=$u(e,t);function r(i,o,l,c=!1){let u=c?n(i,o,3,`${l}/conv0`):a(i,o,`${l}/conv0`),p=a(o,o,`${l}/conv1`),d=a(o,o,`${l}/conv2`);return{conv0:u,conv1:p,conv2:d}}function 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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 sC(e){let t=[],{extractDenseBlock4Params:n}=Ff(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2"),dense3:n("dense3")};return xn(e,t),{params:a,paramMappings:t}}var _p=class extends Zt{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceFeatureExtractor - load model before inference");return D(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=wa(a,[122.782,117.001,104.298]).div(pe(255)),i=Cp(s,n.dense0,!0);return i=Cp(i,n.dense1),i=Cp(i,n.dense2),i=Cp(i,n.dense3),i=Zn(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await ht(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return sC(t)}extractParams(t){return rC(t)}};function Ep(e,t){return D(()=>J(ze(e,t.weights),t.bias))}function iC(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=vn(e),o=Sf(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function oC(e){let t=[],n=qn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return xn(e,t),{params:r,paramMappings:t}}function Af(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var Fp=class extends Zt{constructor(t,n){super(t);this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof ur?this.faceFeatureExtractor.forwardInput(t):t;return Ep(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 iC(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=Af(t);return this.faceFeatureExtractor.loadFromWeightMap(n),oC(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 $f=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Ar=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);$f.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return $f.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var Ap=class extends Fp{constructor(t=new _p){super("FaceExpressionNet",t)}forwardInput(t){return D(()=>Na(this.runNet(t)))}async forward(t){return this.forwardInput(await ht(t))}async predictExpressions(t){let n=await ht(t),a=await this.forwardInput(n),r=await Promise.all(ut(a).map(async i=>{let o=await i.data();return i.dispose(),o}));a.dispose();let s=r.map(i=>new Ar(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Df(e){return e.expressions instanceof Ar}function $p(e,t){return{...e,...{expressions:t}}}function vre(e,t,n=.1,a){(Array.isArray(t)?t:[t]).forEach(s=>{let i=s instanceof Ar?s:Df(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let l=i.asSortedArray().filter(p=>p.probability>n),c=La(s)?s.detection.box.bottomLeft:a||new De(0,0);new vs(l.map(p=>`${p.expression} (${to(p.probability)})`),c).draw(e)})}function Ts(e){return La(e)&&e.landmarks instanceof jn&&e.unshiftedLandmarks instanceof jn&&e.alignedRect instanceof mt}function wre(e){let t=(o,l,c,u)=>Math.atan2(u-l,c-o)%Math.PI,n=o=>o*180/Math.PI,a={roll:void 0,pitch:void 0,yaw:void 0};if(!e||!e._positions||e._positions.length!==68)return a;let r=e._positions;a.roll=-t(r[36]._x,r[36]._y,r[45]._x,r[45]._y),a.pitch=t(0,Math.abs(r[0]._x-r[30]._x)/r[30]._x,Math.PI,Math.abs(r[16]._x-r[30]._x)/r[30]._x);let s=r.reduce((o,l)=>oo>l._y?o:l._y,-Infinity);return a.yaw=Math.PI*(e._imgDims._height/(i-s)/1.4-1),a}function uo(e,t){let{box:n}=e.detection,a=t.shiftBy(n.x,n.y),r=a.align(),{imageDims:s}=e.detection,i=new mt(e.detection.score,r.rescale(s.reverse()),s),o=wre(t);return{...e,...{landmarks:a,unshiftedLandmarks:t,alignedRect:i,angle:o}}}var Tw=class{constructor(t={}){let{drawLines:n=!0,drawPoints:a=!0,lineWidth:r,lineColor:s,pointSize:i,pointColor:o}=t;this.drawLines=n,this.drawPoints=a,this.lineWidth=r||1,this.pointSize=i||2,this.lineColor=s||"rgba(0, 255, 255, 1)",this.pointColor=o||"rgba(255, 0, 255, 1)"}},Nw=class{constructor(t,n={}){this.faceLandmarks=t,this.options=new Tw(n)}draw(t){let n=bn(t),{drawLines:a,drawPoints:r,lineWidth:s,lineColor:i,pointSize:o,pointColor:l}=this.options;if(a&&this.faceLandmarks instanceof so&&(n.strokeStyle=i,n.lineWidth=s,_r(n,this.faceLandmarks.getJawOutline()),_r(n,this.faceLandmarks.getLeftEyeBrow()),_r(n,this.faceLandmarks.getRightEyeBrow()),_r(n,this.faceLandmarks.getNose()),_r(n,this.faceLandmarks.getLeftEye(),!0),_r(n,this.faceLandmarks.getRightEye(),!0),_r(n,this.faceLandmarks.getMouth(),!0)),r){n.strokeStyle=l,n.fillStyle=l;let c=u=>{n.beginPath(),n.arc(u.x,u.y,o,0,2*Math.PI),n.fill()};this.faceLandmarks.positions.forEach(c)}}};function kre(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof jn?a:Ts(a)?a.landmarks:void 0;if(!r)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new Nw(r).draw(e)})}var lC="1.0.0";function Ire(e,t){let n=Au(e,t),a=$u(e,t);function r(i,o,l){let c=a(i,o,`${l}/separable_conv0`),u=a(o,o,`${l}/separable_conv1`),p=n(i,o,1,`${l}/expansion_conv`);return{separable_conv0:c,separable_conv1:u,expansion_conv:p}}function s(i,o){let l=a(i,i,`${o}/separable_conv0`),c=a(i,i,`${o}/separable_conv1`),u=a(i,i,`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:c,separable_conv2:u}}return{extractConvParams:n,extractSeparableConvParams:a,extractReductionBlockParams:r,extractMainBlockParams:s}}function uC(e,t){let n=[],{extractWeights:a,getRemainingWeights:r}=vn(e),{extractConvParams:s,extractSeparableConvParams:i,extractReductionBlockParams:o,extractMainBlockParams:l}=Ire(a,n),c=s(3,32,3,"entry_flow/conv_in"),u=o(32,64,"entry_flow/reduction_block_0"),p=o(64,128,"entry_flow/reduction_block_1"),d={conv_in:c,reduction_block_0:u,reduction_block_1:p},h={};ir(t,0,1).forEach(y=>{h[`main_block_${y}`]=l(128,`middle_flow/main_block_${y}`)});let m=o(128,256,"exit_flow/reduction_block"),f=i(256,512,"exit_flow/separable_conv"),g={reduction_block:m,separable_conv:f};if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:n,params:{entry_flow:d,middle_flow:h,exit_flow:g}}}function Tre(e,t){let n=qn(e,t),a=Ef(n),r=Du(n);function s(o){let l=r(`${o}/separable_conv0`),c=r(`${o}/separable_conv1`),u=a(`${o}/expansion_conv`);return{separable_conv0:l,separable_conv1:c,expansion_conv:u}}function i(o){let l=r(`${o}/separable_conv0`),c=r(`${o}/separable_conv1`),u=r(`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:c,separable_conv2:u}}return{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}}function cC(e,t){let n=[],{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}=Tre(e,n),o=a("entry_flow/conv_in"),l=s("entry_flow/reduction_block_0"),c=s("entry_flow/reduction_block_1"),u={conv_in:o,reduction_block_0:l,reduction_block_1:c},p={};ir(t,0,1).forEach(f=>{p[`main_block_${f}`]=i(`middle_flow/main_block_${f}`)});let d=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:d,separable_conv:h};return xn(e,n),{params:{entry_flow:u,middle_flow:p,exit_flow:m},paramMappings:n}}function pC(e,t,n){return J(Ft(e,t.filters,n,"same"),t.bias)}function Sw(e,t,n=!0){let a=n?qe(e):e;return a=Dn(a,t.separable_conv0,[1,1]),a=Dn(qe(a),t.separable_conv1,[1,1]),a=At(a,[3,3],[2,2],"same"),a=J(a,pC(e,t.expansion_conv,[2,2])),a}function Nre(e,t){let n=Dn(qe(e),t.separable_conv0,[1,1]);return n=Dn(qe(n),t.separable_conv1,[1,1]),n=Dn(qe(n),t.separable_conv2,[1,1]),n=J(n,e),n}var Cw=class extends Zt{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return D(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=wa(a,[122.782,117.001,104.298]).div(pe(256)),i=qe(pC(s,n.entry_flow.conv_in,[2,2]));return i=Sw(i,n.entry_flow.reduction_block_0,!1),i=Sw(i,n.entry_flow.reduction_block_1),ir(this._numMainBlocks,0,1).forEach(o=>{i=Nre(i,n.middle_flow[`main_block_${o}`])}),i=Sw(i,n.exit_flow.reduction_block),i=qe(Dn(i,n.exit_flow.separable_conv,[1,1])),i})}async forward(t){return this.forwardInput(await ht(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return cC(t,this._numMainBlocks)}extractParams(t){return uC(t,this._numMainBlocks)}};function dC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=vn(e),r=Sf(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: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function hC(e){let t=[],n=qn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return xn(e,t),{params:r,paramMappings:t}}var cr;(function(e){e.FEMALE="female",e.MALE="male"})(cr||(cr={}));var Dp=class extends Zt{constructor(t=new Cw(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof ur?this.faceFeatureExtractor.forwardInput(t):t,r=Zn(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Ep(r,n.fc.age).as1D(),i=Ep(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:Na(a)}})}async forward(t){return this.forwardInput(await ht(t))}async predictAgeAndGender(t){let n=await ht(t),a=await this.forwardInput(n),r=ut(a.age),s=ut(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?cr.MALE:cr.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 dC(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=Af(t);return this.faceFeatureExtractor.loadFromWeightMap(n),hC(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 Rp=class extends Fp{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([Cn([68],p,"float32"),Cn([68],d,"float32")],1).as2D(1,136).as1D(),o=(p,d)=>{let{width:h,height:m}=r[p];return 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t=[],{extractDenseBlock3Params:n}=Ff(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return xn(e,t),{params:a,paramMappings:t}}function fC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=vn(e),{extractDenseBlock3Params:r}=_f(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o}}}var _w=class extends Zt{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return D(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=wa(a,[122.782,117.001,104.298]).div(pe(255)),i=Nf(s,n.dense0,!0);return i=Nf(i,n.dense1),i=Nf(i,n.dense2),i=Zn(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await 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n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Ore(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return xn(e,n),{params:i,paramMappings:n}}var Ba=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Rw=class extends Zt{constructor(t){super("TinyYolov2");Of(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=$r(t,n.conv0);return a=At(a,[2,2],[2,2],"same"),a=$r(a,n.conv1),a=At(a,[2,2],[2,2],"same"),a=$r(a,n.conv2),a=At(a,[2,2],[2,2],"same"),a=$r(a,n.conv3),a=At(a,[2,2],[2,2],"same"),a=$r(a,n.conv4),a=At(a,[2,2],[2,2],"same"),a=$r(a,n.conv5),a=At(a,[2,2],[1,1],"same"),a=$r(a,n.conv6),a=$r(a,n.conv7),lo(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Ru(lo(t,n.conv0,"valid",!1)):Dr(t,n.conv0);return a=At(a,[2,2],[2,2],"same"),a=Dr(a,n.conv1),a=At(a,[2,2],[2,2],"same"),a=Dr(a,n.conv2),a=At(a,[2,2],[2,2],"same"),a=Dr(a,n.conv3),a=At(a,[2,2],[2,2],"same"),a=Dr(a,n.conv4),a=At(a,[2,2],[2,2],"same"),a=Dr(a,n.conv5),a=At(a,[2,2],[1,1],"same"),a=n.conv6?Dr(a,n.conv6):a,a=n.conv7?Dr(a,n.conv7):a,lo(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=ue(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?wa(r,this.config.meanRgb):r,r=r.div(pe(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await ht(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Ba(n),s=await ht(t),i=await this.forwardInput(s,a),o=D(()=>ut(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 mf(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 OC(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 ${a} filterSizes in config`);return PC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),x=y.slice([0,0,0,4],[c,c,u,1]),v=this.withClassScores?Na(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):pe(0);return[b,x,v]}),m=[],f=await d.array(),g=await p.array();for(let y=0;ya){let N=(b+Su(g[y][b][x][0]))/c*o,T=(y+Su(g[y][b][x][1]))/c*l,S=Math.exp(g[y][b][x][2])*this.config.anchors[x].x/c*o,A=Math.exp(g[y][b][x][3])*this.config.anchors[x].y/c*l,$=N-S/2,R=T-A/2,B={row:y,col:b,anchor:x},{classScore:V,label:W}=this.withClassScores?await this.extractPredictedClass(h,B):{classScore:1,label:0};m.push({box:new ao($,R,$+S,R+A),score:v,classScore:v*V,label:W,...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)}},Mu=Rw;Mu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var mo=class extends Mu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:FC,classes:["face"],...t?{anchors:$C,meanRgb:DC}:{anchors:AC,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 mt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?MC:RC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function LC(e,t=!0){let n=new mo(t);return n.extractWeights(e),n}var Bp=class extends Ba{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var ia=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function fo(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>Ts(l)?r(l):l.detection),i=a||(t instanceof Ee?await oo(t,s):await io(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ee&&l.dispose()),o}async function Pu(e,t,n,a,r){return fo([e],t,async s=>n(s[0]),a,r)}var zC=.4,BC=[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)],WC=[117.001,114.697,97.404];var go=class extends Mu{constructor(){let t={withSeparableConvs:!0,iouThreshold:zC,classes:["face"],anchors:BC,meanRgb:WC,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 mt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var Qe={ssdMobilenetv1:new Ns,tinyFaceDetector:new go,tinyYolov2:new mo,faceLandmark68Net:new co,faceLandmark68TinyNet:new Mp,faceRecognitionNet:new po,faceExpressionNet:new Ap,ageGenderNet:new Dp},Mw=(e,t)=>Qe.ssdMobilenetv1.locateFaces(e,t),VC=(e,t)=>Qe.tinyFaceDetector.locateFaces(e,t),UC=(e,t)=>Qe.tinyYolov2.locateFaces(e,t),Pw=e=>Qe.faceLandmark68Net.detectLandmarks(e),GC=e=>Qe.faceLandmark68TinyNet.detectLandmarks(e),HC=e=>Qe.faceRecognitionNet.computeFaceDescriptor(e),jC=e=>Qe.faceExpressionNet.predictExpressions(e),qC=e=>Qe.ageGenderNet.predictAgeAndGender(e),Ow=e=>Qe.ssdMobilenetv1.load(e),KC=e=>Qe.tinyFaceDetector.load(e),XC=e=>Qe.tinyYolov2.load(e),YC=e=>Qe.faceLandmark68Net.load(e),JC=e=>Qe.faceLandmark68TinyNet.load(e),QC=e=>Qe.faceRecognitionNet.load(e),ZC=e=>Qe.faceExpressionNet.load(e),e_=e=>Qe.ageGenderNet.load(e),t_=Ow,n_=Mw,a_=Pw;var Lw=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},zu=class extends Lw{async run(){let t=await this.parentTask,n=await fo(t,this.input,async a=>Promise.all(a.map(r=>Qe.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>$p(a,n[r]))}withAgeAndGender(){return new Ou(this,this.input)}},Bu=class extends Lw{async run(){let t=await this.parentTask;if(!t)return;let n=await Pu(t,this.input,a=>Qe.faceExpressionNet.predictExpressions(a),this.extractedFaces);return $p(t,n)}withAgeAndGender(){return new Lu(this,this.input)}},xo=class extends zu{withAgeAndGender(){return new yo(this,this.input)}withFaceDescriptors(){return new Rr(this,this.input)}},vo=class extends Bu{withAgeAndGender(){return new bo(this,this.input)}withFaceDescriptor(){return new Mr(this,this.input)}};var zw=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Ou=class extends zw{async run(){let t=await this.parentTask,n=await fo(t,this.input,async a=>Promise.all(a.map(r=>Qe.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Lp(zp(a,i,o),s)})}withFaceExpressions(){return new zu(this,this.input)}},Lu=class extends zw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Pu(t,this.input,s=>Qe.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Lp(zp(t,a,r),n)}withFaceExpressions(){return new Bu(this,this.input)}},yo=class extends Ou{withFaceExpressions(){return new xo(this,this.input)}withFaceDescriptors(){return new Rr(this,this.input)}},bo=class extends Lu{withFaceExpressions(){return new vo(this,this.input)}withFaceDescriptor(){return new Mr(this,this.input)}};var Wp=class extends ia{constructor(t,n){super();this.parentTask=t;this.input=n}},Rr=class extends Wp{async run(){let t=await this.parentTask;return(await fo(t,this.input,a=>Promise.all(a.map(r=>Qe.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>Op(t[r],a))}withFaceExpressions(){return new xo(this,this.input)}withAgeAndGender(){return new yo(this,this.input)}},Mr=class extends Wp{async run(){let t=await this.parentTask;if(!t)return;let n=await Pu(t,this.input,a=>Qe.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return Op(t,n)}withFaceExpressions(){return new vo(this,this.input)}withAgeAndGender(){return new bo(this,this.input)}};var Vp=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?Qe.faceLandmark68TinyNet:Qe.faceLandmark68Net}},Up=class extends Vp{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Ee?await oo(this.input,n):await io(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Ee&&s.dispose()),t.map((s,i)=>uo(s,r[i]))}withFaceExpressions(){return new xo(this,this.input)}withAgeAndGender(){return new yo(this,this.input)}withFaceDescriptors(){return new Rr(this,this.input)}},Gp=class extends Vp{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ee?await oo(this.input,[n]):await io(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ee&&s.dispose()),uo(t,r)}withFaceExpressions(){return new vo(this,this.input)}withAgeAndGender(){return new bo(this,this.input)}withFaceDescriptor(){return new Mr(this,this.input)}};var Hp=class extends ia{constructor(t,n=new sa){super();this.input=t;this.options=n}},Wu=class extends Hp{async run(){let{input:t,options:n}=this,a;if(n instanceof Bp)a=Qe.tinyFaceDetector.locateFaces(t,n);else if(n instanceof sa)a=Qe.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof Ba)a=Qe.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>bs({},a)))})}withFaceLandmarks(t=!1){return new Up(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new zu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Ou(this.runAndExtendWithFaceDetections(),this.input)}},jp=class extends Hp{async run(){let t=await new Wu(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?bs({},n):void 0)})}withFaceLandmarks(t=!1){return new Gp(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Bu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Lu(this.runAndExtendWithFaceDetection(),this.input)}};function r_(e,t=new sa){return new jp(e,t)}function qp(e,t=new sa){return new Wu(e,t)}async function Bw(e,t){return qp(e,new sa(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function s_(e,t={}){return qp(e,new Ba(t)).withFaceLandmarks().withFaceDescriptors()}var i_=Bw;function Lf(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 Kp=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 or)return i;if(i instanceof Float32Array)return new or(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new or(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=>Lf(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Cu(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>or.fromJSON(a));return new Kp(n,t.distanceThreshold)}};function o_(e){let t=new go;return t.extractWeights(e),t}function Ww(e,t){let{width:n,height:a}=new un(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>Ww(r,{width:n,height:a}));if(Ts(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return uo(bs(e,r),s)}return La(e)?bs(e,e.detection.forSize(n,a)):e instanceof jn||e instanceof mt?e.forSize(n,a):e}var zre=typeof process!="undefined",Bre=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",l_={faceapi:lC,node:zre,browser:Bre};return Lre;})(); + 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Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),ne=a.dataIdMap.get(se.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return v2(y,q,te,Q,b,N,T,v,S,A,$,R,X,B,V,W,G,H,x,g,ie,m||0,ne),se}var _te={kernelName:Ti,backendName:"wasm",setupFunc:Ste,kernelFunc:Cte},w2;function Ete(e){w2=e.wasm.cwrap(Ni,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 Fte(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=wp[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,x=f.outChannels,v=0;if(i!=null){let Z=a.dataIdMap.get(i.dataId);if(Z.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${Z.shape}) does not match the number of output channels (${x})`);v=Z.id}let N=f.filterHeight,T=f.filterWidth,S=f.padInfo.top,A=f.padInfo.right,$=f.padInfo.bottom,R=f.padInfo.left,B=f.dilationHeight,V=f.dilationWidth,W=f.strideHeight,G=f.strideWidth,H=f.inChannels,X=f.padInfo.type==="SAME"?1:0,q=f.batchSize,te=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),ne=a.dataIdMap.get(se.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return w2(y,q,te,Q,b,N,T,v,S,A,$,R,X,B,V,W,G,H,x,g,ie,m||0,ne),se}var Ate={kernelName:Ni,backendName:"wasm",setupFunc:Ete,kernelFunc:Fte},k2;function $te(e){k2=e.wasm.cwrap(tl,null,["number","number","number","number","number","number","array","number"])}function Dte(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=_y.prepareAndValidate(a,r),c=t.makeOutput(s,a.dtype);if(i===0)return c;let u=r.shape,p=u[u.length-1],d=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(c.dataId).id;return k2(d,Hn[a.dtype],h,i,p,o,m,f),c}var Rte={kernelName:tl,backendName:"wasm",setupFunc:$te,kernelFunc:Dte},I2;function Mte(e){I2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Pte(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=w.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=w.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(w.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,x=new Uint8Array(new Int32Array(w.computeStrides(u.shape)).buffer),v=new Uint8Array(new Int32Array(w.computeStrides(h)).buffer);return I2(g,Hn[r.dtype],x,f,y,c.batchSize,v,b),t.disposeData(u.dataId),t.disposeData(d.dataId),m.shape=c.outputShape,m}var Ote={kernelName:el,backendName:"wasm",setupFunc:Mte,kernelFunc:Pte},Lte=!1,zte=yn(nl,Lte,"bool"),Bte=!1,Wte=yn(qs,Bte,"bool"),T2;function Vte(e){T2=e.wasm.cwrap(Xs,null,["number","number","number"])}function Ute(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(w.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;T2(r,n,i)}return s}var Gte={kernelName:Xs,backendName:"wasm",setupFunc:Vte,kernelFunc:Ute},Hte=!1,jte=yn(il,Hte,"bool"),qte=!1,Kte=yn(ol,qte,"bool"),Xte=$n(Ys),Yte=!1,Jte=yn(ul,Yte,"bool"),N2;function Qte(e){N2=e.wasm.cwrap(Js,null,["number, number, number"])}function Zte(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:p,inputWasTransposed:d}=Tu(i,r,t);if(d){let b=t.dataIdMap.get(c.dataId).id;l=c,o=b}let h=l.shape.length;_.assertAxesAreInnerMostDims("max",u,h);let[m,f]=_.computeOutAndReduceShapes(l.shape,u),g=w.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;N2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=_.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var ene={kernelName:Js,backendName:"wasm",setupFunc:Qte,kernelFunc:Zte},tne=!1,nne=yn(Qs,tne),S2;function ane(e){S2=e.wasm.cwrap(Zs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function rne(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=_.computePool2DInfo(r.shape,i,o,1,l,c),p=u.filterHeight,d=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,g=u.padInfo.left,y=u.dilationHeight,b=u.dilationWidth,x=u.strideHeight,v=u.strideWidth,N=u.inChannels,T=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let S=a.makeOutput(u.outShape,"float32"),A=a.dataIdMap.get(S.dataId).id;return S2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,x,v,N,T,A),S}var sne={kernelName:Zs,backendName:"wasm",setupFunc:ane,kernelFunc:rne},C2;function ine(e){C2=e.wasm.cwrap(ei,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}=Tu(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("mean",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=w.sizeFromShape(g),b=c;c.dtype!=="float32"&&(b=sf({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let x=t.makeOutput(f,"float32");if(w.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;C2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(x.shape,d);x.shape=v}return c.dtype!=="float32"&&t.disposeData(b.dataId),x}var lne={kernelName:ei,backendName:"wasm",setupFunc:ine,kernelFunc:one},_2;function une(e){_2=e.wasm.cwrap(ti,null,["number, number, number"])}function cne(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}=Tu(i,r,t);if(h){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x)}let m=c.shape.length;_.assertAxesAreInnerMostDims("min",p,m);let[f,g]=_.computeOutAndReduceShapes(c.shape,p),y=w.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(w.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;_2(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var pne={kernelName:ti,backendName:"wasm",setupFunc:une,kernelFunc:cne},dne=!1,hne=yn(ni,dne),mne=!0,fne=yn(ai,mne),gne=$n(pl);function uw(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 E2;function yne(e){E2=e.wasm.cwrap(hl,"number",["number","number","number","number","number"])}function bne(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=E2(c,u,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=uw(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var xne={kernelName:hl,backendName:"wasm",setupFunc:yne,kernelFunc:bne},F2;function vne(e){F2=e.wasm.cwrap(ml,"number",["number","number","number","number","number","bool"])}function wne(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=F2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=uw(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var kne={kernelName:ml,backendName:"wasm",setupFunc:vne,kernelFunc:wne},A2;function Ine(e){A2=e.wasm.cwrap(fl,"number",["number","number","number","number","number","number"])}function Tne(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=A2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=uw(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var Nne={kernelName:fl,backendName:"wasm",setupFunc:Ine,kernelFunc:Tne},Sne=!1,Cne=yn(dl,Sne,"bool"),$2;function _ne(e){$2=e.wasm.cwrap(ri,null,["number","number","number","number","number"])}function Ene(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 $2(u,s,i,o,c),l}var Fne={kernelName:ri,backendName:"wasm",setupFunc:_ne,kernelFunc:Ene};function Ane(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:gl,backendName:"wasm",kernelFunc:Ane};function Dne(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return lw({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{w.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let p=lw({inputs:{input:u},backend:n,attrs:{dim:r}});return o.push(p),p}),c=p2({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(u=>n.disposeData(u.dataId)),c}var Rne={kernelName:yl,backendName:"wasm",kernelFunc:Dne},D2;function Mne(e){D2=e.wasm.cwrap(si,null,["number","array","number","number","array","array","number","number"])}function Pne(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 D2(i,c,t.shape.length,Hn[t.dtype],d,h,r,l),o}var One={kernelName:si,backendName:"wasm",kernelFunc:Pne,setupFunc:Mne},Lne=!1,zne=yn(ii,Lne),R2;function Bne(e){R2=e.wasm.cwrap(oi,null,["number","number","number"])}function Wne(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 R2(s,i,l),o}var Vne={kernelName:oi,backendName:"wasm",setupFunc:Bne,kernelFunc:Wne},M2;function Une(e){M2=e.wasm.cwrap(bl,null,["number","number","number","number"])}function Gne(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}=Tu(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("prod",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=w.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(w.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;M2(l,y,Hn[b.dtype],x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var Hne={kernelName:bl,backendName:"wasm",setupFunc:Une,kernelFunc:Gne},jne=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=Dv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},qne={kernelName:mc,backendName:"wasm",kernelFunc:jne},Kne=!0,Xne=yn(Vs,Kne),Yne=$n(li),Jne=$n(ci),P2;function Qne(e){P2=e.wasm.cwrap(ui,null,["number","number","number","number","number","number","number","number","number","number"])}function Zne(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,[u,p,d,h]=r.shape,m=[u,l,c,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=sf({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(w.sizeFromShape(r.shape)===0)return b;let x=t.dataIdMap.get(b.dataId).id;return P2(y,u,p,d,h,l,c,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),b}var eae={kernelName:ui,backendName:"wasm",setupFunc:Qne,kernelFunc:Zne},O2;function tae(e){O2=e.wasm.cwrap(pi,null,["number","array","number","array","number","number"])}function nae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=w.parseAxisParam(s,r.shape);if(r.shape.length===0)return af({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);O2(l,u,i.length,p,r.shape.length,c);let d=Pa({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),d}var aae={kernelName:pi,backendName:"wasm",kernelFunc:nae,setupFunc:tae},L2;function rae(e){L2=e.wasm.cwrap(Rl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function sae(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=a,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(l.dataId).id,[p,d,h,m]=r.shape,[f,g]=_.getImageCenter(o,d,h),y=i===0,b=255,x=typeof i=="number"?[i,i,i,y?0:b]:[...i,b],v=new Uint8Array(new Int32Array(x).buffer);return L2(c,p,d,h,m,s,f,g,v,x.length,u),l}var iae={kernelName:Rl,backendName:"wasm",kernelFunc:sae,setupFunc:rae},oae=$n(di),lae=$n(hi),z2;function uae(e){z2=e.wasm.cwrap(wl,null,["number","number","number","number","number","number","array","number","number"])}function cae(e){let{backend:t,inputs:n,attrs:a}=e,{indices:r,updates:s}=n,{shape:i}=a,o=t.makeOutput(i,s.dtype);if(w.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:c,sliceSize:u,strides:p,outputSize:d}=Ey.calculateShapes(s,r,i),h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(p).buffer),g=t.dataIdMap.get(o.dataId).id;return z2(h,m,Hn[s.dtype],l,c,u,f,d,g),o}var pae={kernelName:wl,backendName:"wasm",setupFunc:uae,kernelFunc:cae},B2;function dae(e){B2=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function hae(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(s.dataId).id,c=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(c.dataId).id,p=a.shape.length,d=r.shape.length,h=p===0||p>1||d===1?1:w.sizeFromShape(r.shape.slice(1));return B2(i,o,l,h,u),c}var mae={kernelName:kl,backendName:"wasm",kernelFunc:hae,setupFunc:dae},W2;function fae(e){W2=e.wasm.cwrap(fi,null,["number","number"])}function gae(e){let{backend:t,inputs:{x:n}}=e,a=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(r.dataId).id;return w.sizeFromShape(r.shape)===0||W2(a,s),r}var yae={kernelName:"Sigmoid",backendName:"wasm",setupFunc:fae,kernelFunc:gae},bae=$n(mi);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 it(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 mt=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 mt(a,r,s)}};function df(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 hf(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=Cn([...e.shape.slice(0,3),1],n,"float32"),i=Cn([...e.shape.slice(0,3),1],a,"float32"),o=Cn([...e.shape.slice(0,3),1],r,"float32"),l=Je([s,i,o],3);return me(e,l)})}function ff(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,Cn(h,0,"float32")},l=o(s),c=r-l.shape[i],p=[t&&c?o(c):null,e,l].filter(d=>!!d).map(d=>ue(d,"float32"));return Je(p,i)})}function J2(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 Su(e){return 1/(1+Math.exp(-e))}function Q2(e){return Math.log(e/(1-e))}var ro=class extends it{constructor(t,n,a,r,s=!1){super({x:t,y:n,width:a,height:r},s)}};var dre=.5,hre=.43,mre=.45,jn=class{constructor(t,n,a=new De(0,0)){let{width:r,height:s}=n;this._imgDims=new un(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 mt?t.box.floor():new it(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/mre),l=no(t),c=Math.floor(Math.max(0,l.x-dre*o)),u=Math.floor(Math.max(0,l.y-hre*o));return new ro(c,u,Math.min(o,this.imageWidth+c),Math.min(o,this.imageHeight+u))}alignMinBbox(t){let n=hf(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var fw=class extends jn{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],no([t[3],t[4]])]}};var so=class extends jn{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(no)}};var Cu=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?` (${to(this.distance)})`:""}`}};var _u=class extends it{static assertIsValidLabeledBox(t,n){if(it.assertIsValidBox(t,n),!Oa(t.label))throw new Error(`${n} - expected property label (${t.label}) to be a number`)}constructor(t,n){super(t);this._label=n}get label(){return this._label}};var or=class{constructor(t,n){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(n)||n.some(a=>!(a instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=n}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let n=t.descriptors.map(a=>new Float32Array(a));return new or(t.label,n)}};var gw=class extends _u{static assertIsValidPredictedBox(t,n){if(_u.assertIsValidLabeledBox(t,n),!Nu(t.score)||!Nu(t.classScore))throw new Error(`${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 La(e){return e.detection instanceof mt}function bs(e,t){return{...e,...{detection:t}}}function yw(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser environment");return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:e,readFile:()=>{throw new Error("readFile - filesystem not available for browser environment")}}}function gf(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 bw(){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=gf();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 xw(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var vw=lE(eC()),Qt;function yre(){if(!Qt)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return Qt}function ww(e){Qt=e}function kw(){return xw()?ww(yw()):(0,vw.isNodejs)()?ww(bw()):null}function bre(e){if(Qt||kw(),!Qt)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=Qt.Canvas,Image:n=Qt.Image}=e;Qt.Canvas=t,Qt.Image=n,Qt.createCanvasElement=e.createCanvasElement||(()=>new t),Qt.createImageElement=e.createImageElement||(()=>new n),Qt.ImageData=e.ImageData||Qt.ImageData,Qt.Video=e.Video||Qt.Video,Qt.fetch=e.fetch||Qt.fetch,Qt.readFile=e.readFile||Qt.readFile}var tt={getEnv:yre,setEnv:ww,initialize:kw,createBrowserEnv:yw,createFileSystem:gf,createNodejsEnv:bw,monkeyPatch:bre,isBrowser:xw,isNodejs:vw.isNodejs};kw();function xs(e){return!tt.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function bn(e){let{Canvas:t,CanvasRenderingContext2D:n}=tt.getEnv();if(e instanceof n)return e;let a=xs(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 Np=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}},vs=class{constructor(t,n,a={}){this.text=typeof t=="string"?[t]:t instanceof vs?t.text:t,this.anchor=n,this.options=new Np(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 Iw=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 Np({...i,...s})}},yf=class{constructor(t,n={}){this.box=new it(t),this.options=new Iw(n)}draw(t){let n=bn(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 vs([c],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function xre(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof mt?a.score:La(a)?a.detection.score:void 0,s=a instanceof mt?a.box:La(a)?a.detection.box:new it(a),i=r?`${to(r)}`:void 0;new yf(s,{label:i}).draw(e)})}function Eu(e){let{Image:t,Video:n}=tt.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function bf(e){return new Promise((t,n)=>{if(e instanceof tt.getEnv().Canvas||Eu(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 xf(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=tt.getEnv().createImageElement();r.onload=()=>t(r),r.onerror=n,r.src=a.result},a.onerror=n,a.readAsDataURL(e)})}function ws(e){let{Image:t,Video:n}=tt.getEnv();return e instanceof t?new un(e.naturalWidth,e.naturalHeight):e instanceof n?new un(e.videoWidth,e.videoHeight):new un(e.width,e.height)}function ks({width:e,height:t}){let{createCanvasElement:n}=tt.getEnv(),a=n();return a.width=e,a.height=t,a}function Fu(e,t){let{ImageData:n}=tt.getEnv();if(!(e instanceof n)&&!Eu(e))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:a,height:r}=t||ws(e),s=ks({width:a,height:r});return e instanceof n?bn(s).putImageData(e,0,0):bn(s).drawImage(e,0,0,a,r),s}async function vf(e,t){let n=t||tt.getEnv().createCanvasElement(),[a,r,s]=e.shape.slice(ra(e)?1:0),i=D(()=>e.as3D(a,r,s).toInt());return await Ei.toPixels(i,n),i.dispose(),n}function Sp(e){let{Image:t,Canvas:n,Video:a}=tt.getEnv();return e instanceof t||e instanceof n||e instanceof a}function wf(e,t,n=!1){let{Image:a,Canvas:r}=tt.getEnv();if(!(e instanceof a||e instanceof r))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return ks({width:1,height:1});let s=ws(e),i=t/Math.max(s.height,s.width),o=i*s.width,l=i*s.height,c=ks({width:t,height:t}),u=e instanceof r?e:Fu(e),p=Math.abs(o-l)/2,d=n&&o0&&u.height>0&&bn(c).drawImage(u,d,h,o,l),c}var ur=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((a,r)=>{if(Er(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(ra(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 tt.getEnv().Canvas?a:Fu(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 ir(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 mw({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 Ee){let o=ra(i)?i:i.expandDims();return o=ff(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=Ja.resizeBilinear(o,[t,t])),o.as3D(t,t,3)}if(i instanceof tt.getEnv().Canvas)return Ei.fromPixels(wf(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=>ue(s,"float32"))).as4D(this.batchSize,t,t,3)})}};async function ht(e){if(e instanceof ur)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 ${r}:`:"",a=t.map(xs);return a.forEach((r,s)=>{if(!Sp(r)&&!Er(r)&&!ra(r))throw typeof t[s]=="string"?new Error(`toNetInput -${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(ra(r)){let i=r.shape[0];if(i!==1)throw new Error(`toNetInput -${n(s)} tf.Tensor4D with batchSize ${i} passed, but not supported in input array`)}}),await Promise.all(a.map(r=>Sp(r)&&bf(r))),new ur(a,Array.isArray(e))}async function io(e,t){let{Canvas:n}=tt.getEnv(),a=e;if(!(e instanceof n)){let i=await ht(e);if(i.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let o=i.getInput(0);a=o instanceof n?o:await vf(o)}let r=bn(a);return t.map(i=>i instanceof mt?i.forSize(a.width,a.height).box.floor():i).map(i=>i.clipAtImageBorders(a.width,a.height)).map(({x:i,y:o,width:l,height:c})=>{let u=ks({width:l,height:c});return l>0&&c>0&&bn(u).putImageData(r.getImageData(i,o,l,c),0,0),u})}async function oo(e,t){if(!Er(e)&&!ra(e))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(ra(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return D(()=>{let[n,a,r]=e.shape.slice(ra(e)?1:0);return t.map(o=>o instanceof mt?o.forSize(a,n).box:o).map(o=>o.clipAtImageBorders(a,n)).map(({x:o,y:l,width:c,height:u})=>Yl(e.as3D(n,a,r),[l,o,0],[u,c,r]))})}async function Is(e,t){let{fetch:n}=tt.getEnv(),a=await n(e,t);if(!(a.status<400))throw new Error(`failed to fetch: (${a.status}) ${a.statusText}, from url: ${a.url}`);return a}async function tC(e){let t=await Is(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: ${t.url}`);return xf(n)}async function kf(e){return(await Is(e)).json()}async function nC(e){return new Float32Array(await(await Is(e)).arrayBuffer())}function If(e,t){let n=`${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 Tf(e,t){let{manifestUri:n,modelBaseUri:a}=If(e,t),r=await kf(n);return Ht.loadWeights(r,a)}function aC(e,t,n=!1){let{width:a,height:r}=n?ws(t):t;return e.width=a,e.height=r,{width:a,height:r}}var Zt=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 this._paramMappings.map(({paramPath:t})=>({path:t,tensor:this.getParamFromPath(t)}))}getTrainableParams(){return this.getParamList().filter(t=>t.tensor instanceof Xr)}getFrozenParams(){return this.getParamList().filter(t=>!(t.tensor instanceof Xr))}variable(){this.getFrozenParams().forEach(({path:t,tensor:n})=>{this.reassignParamFromPath(t,n.variable())})}freeze(){this.getTrainableParams().forEach(({path:t,tensor:n})=>{let a=Jn(n.dataSync());n.dispose(),this.reassignParamFromPath(t,a)})}dispose(t=!0){this.getParamList().forEach(n=>{if(t&&n.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${n.path}`);n.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:t})=>Array.from(t.dataSync())).reduce((t,n)=>t.concat(n)))}async load(t){if(t instanceof Float32Array){this.extractWeights(t);return}await this.loadFromUri(t)}async loadFromUri(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let n=await Tf(t,this.getDefaultModelName());this.loadFromWeightMap(n)}async loadFromDisk(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile:n}=tt.getEnv(),{manifestUri:a,modelBaseUri:r}=If(t,this.getDefaultModelName()),s=c=>Promise.all(c.map(u=>n(u).then(p=>p.buffer))),i=Ht.weightsLoaderFactory(s),o=JSON.parse((await 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a=qe(n?J(Ft(e,t.conv0.filters,[2,2],"same"),t.conv0.bias):Dn(e,t.conv0,[2,2])),r=Dn(a,t.conv1,[1,1]),s=qe(J(a,r)),i=Dn(s,t.conv2,[1,1]);return qe(J(a,J(r,i)))})}function Cp(e,t,n=!1,a=!0){return D(()=>{let r=qe(n?J(Ft(e,t.conv0.filters,a?[2,2]:[1,1],"same"),t.conv0.bias):Dn(e,t.conv0,a?[2,2]:[1,1])),s=Dn(r,t.conv1,[1,1]),i=qe(J(r,s)),o=Dn(i,t.conv2,[1,1]),l=qe(J(r,J(s,o))),c=Dn(l,t.conv3,[1,1]);return qe(J(r,J(s,J(o,c))))})}function lo(e,t,n="same",a=!1){return D(()=>{let r=J(Ft(e,t.filters,[1,1],n),t.bias);return a?qe(r):r})}function xn(e,t){Object.keys(e).forEach(n=>{t.some(a=>a.originalPath===n)||e[n].dispose()})}function Au(e,t){return(n,a,r,s)=>{let i=Ca(e(n*a*r*r),[r,r,n,a]),o=Ze(e(a));return t.push({paramPath:`${s}/filters`},{paramPath:`${s}/bias`}),{filters:i,bias:o}}}function Sf(e,t){return(n,a,r)=>{let s=Sa(e(n*a),[n,a]),i=Ze(e(a));return t.push({paramPath:`${r}/weights`},{paramPath:`${r}/bias`}),{weights:s,bias:i}}}var Cf=class{constructor(t,n,a){this.depthwise_filter=t;this.pointwise_filter=n;this.bias=a}};function $u(e,t){return(n,a,r)=>{let s=Ca(e(3*3*n),[3,3,n,1]),i=Ca(e(n*a),[1,1,n,a]),o=Ze(e(a));return t.push({paramPath:`${r}/depthwise_filter`},{paramPath:`${r}/pointwise_filter`},{paramPath:`${r}/bias`}),new Cf(s,i,o)}}function Du(e){return t=>{let n=e(`${t}/depthwise_filter`,4),a=e(`${t}/pointwise_filter`,4),r=e(`${t}/bias`,1);return new Cf(n,a,r)}}function qn(e,t){return(n,a,r)=>{let s=e[n];if(!eo(s,a))throw new Error(`expected weightMap[${n}] to be a Tensor${a}D, instead have ${s}`);return t.push({originalPath:n,paramPath:r||n}),s}}function vn(e){let t=e;function n(r){let s=t.slice(0,r);return t=t.slice(r),s}function a(){return t}return{extractWeights:n,getRemainingWeights:a}}function _f(e,t){let n=Au(e,t),a=$u(e,t);function r(i,o,l,c=!1){let u=c?n(i,o,3,`${l}/conv0`):a(i,o,`${l}/conv0`),p=a(o,o,`${l}/conv1`),d=a(o,o,`${l}/conv2`);return{conv0:u,conv1:p,conv2:d}}function 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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 sC(e){let t=[],{extractDenseBlock4Params:n}=Ff(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2"),dense3:n("dense3")};return xn(e,t),{params:a,paramMappings:t}}var _p=class extends Zt{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceFeatureExtractor - load model before inference");return D(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=wa(a,[122.782,117.001,104.298]).div(pe(255)),i=Cp(s,n.dense0,!0);return i=Cp(i,n.dense1),i=Cp(i,n.dense2),i=Cp(i,n.dense3),i=Zn(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await ht(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return sC(t)}extractParams(t){return rC(t)}};function Ep(e,t){return D(()=>J(ze(e,t.weights),t.bias))}function iC(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=vn(e),o=Sf(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function oC(e){let t=[],n=qn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return xn(e,t),{params:r,paramMappings:t}}function Af(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var Fp=class extends Zt{constructor(t,n){super(t);this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof ur?this.faceFeatureExtractor.forwardInput(t):t;return Ep(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 iC(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=Af(t);return this.faceFeatureExtractor.loadFromWeightMap(n),oC(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 $f=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Ar=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);$f.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return $f.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var Ap=class extends Fp{constructor(t=new _p){super("FaceExpressionNet",t)}forwardInput(t){return D(()=>Na(this.runNet(t)))}async forward(t){return this.forwardInput(await ht(t))}async predictExpressions(t){let n=await ht(t),a=await this.forwardInput(n),r=await Promise.all(ut(a).map(async i=>{let o=await i.data();return i.dispose(),o}));a.dispose();let s=r.map(i=>new Ar(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Df(e){return e.expressions instanceof Ar}function $p(e,t){return{...e,...{expressions:t}}}function vre(e,t,n=.1,a){(Array.isArray(t)?t:[t]).forEach(s=>{let i=s instanceof Ar?s:Df(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let l=i.asSortedArray().filter(p=>p.probability>n),c=La(s)?s.detection.box.bottomLeft:a||new De(0,0);new vs(l.map(p=>`${p.expression} (${to(p.probability)})`),c).draw(e)})}function Ts(e){return La(e)&&e.landmarks instanceof jn&&e.unshiftedLandmarks instanceof jn&&e.alignedRect instanceof mt}function wre(e){let t=(o,l,c,u)=>Math.atan2(u-l,c-o)%Math.PI,n=o=>o*180/Math.PI,a={roll:void 0,pitch:void 0,yaw:void 0};if(!e||!e._positions||e._positions.length!==68)return a;let r=e._positions;a.roll=-t(r[36]._x,r[36]._y,r[45]._x,r[45]._y),a.pitch=t(0,Math.abs(r[0]._x-r[30]._x)/r[30]._x,Math.PI,Math.abs(r[16]._x-r[30]._x)/r[30]._x);let s=r.reduce((o,l)=>oo>l._y?o:l._y,-Infinity);return a.yaw=Math.PI*(e._imgDims._height/(i-s)/1.4-1),a}function uo(e,t){let{box:n}=e.detection,a=t.shiftBy(n.x,n.y),r=a.align(),{imageDims:s}=e.detection,i=new mt(e.detection.score,r.rescale(s.reverse()),s),o=wre(t);return{...e,...{landmarks:a,unshiftedLandmarks:t,alignedRect:i,angle:o}}}var Tw=class{constructor(t={}){let{drawLines:n=!0,drawPoints:a=!0,lineWidth:r,lineColor:s,pointSize:i,pointColor:o}=t;this.drawLines=n,this.drawPoints=a,this.lineWidth=r||1,this.pointSize=i||2,this.lineColor=s||"rgba(0, 255, 255, 1)",this.pointColor=o||"rgba(255, 0, 255, 1)"}},Nw=class{constructor(t,n={}){this.faceLandmarks=t,this.options=new Tw(n)}draw(t){let n=bn(t),{drawLines:a,drawPoints:r,lineWidth:s,lineColor:i,pointSize:o,pointColor:l}=this.options;if(a&&this.faceLandmarks instanceof so&&(n.strokeStyle=i,n.lineWidth=s,_r(n,this.faceLandmarks.getJawOutline()),_r(n,this.faceLandmarks.getLeftEyeBrow()),_r(n,this.faceLandmarks.getRightEyeBrow()),_r(n,this.faceLandmarks.getNose()),_r(n,this.faceLandmarks.getLeftEye(),!0),_r(n,this.faceLandmarks.getRightEye(),!0),_r(n,this.faceLandmarks.getMouth(),!0)),r){n.strokeStyle=l,n.fillStyle=l;let c=u=>{n.beginPath(),n.arc(u.x,u.y,o,0,2*Math.PI),n.fill()};this.faceLandmarks.positions.forEach(c)}}};function kre(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof jn?a:Ts(a)?a.landmarks:void 0;if(!r)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new Nw(r).draw(e)})}var lC="1.0.1";function Ire(e,t){let n=Au(e,t),a=$u(e,t);function r(i,o,l){let c=a(i,o,`${l}/separable_conv0`),u=a(o,o,`${l}/separable_conv1`),p=n(i,o,1,`${l}/expansion_conv`);return{separable_conv0:c,separable_conv1:u,expansion_conv:p}}function s(i,o){let l=a(i,i,`${o}/separable_conv0`),c=a(i,i,`${o}/separable_conv1`),u=a(i,i,`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:c,separable_conv2:u}}return{extractConvParams:n,extractSeparableConvParams:a,extractReductionBlockParams:r,extractMainBlockParams:s}}function uC(e,t){let n=[],{extractWeights:a,getRemainingWeights:r}=vn(e),{extractConvParams:s,extractSeparableConvParams:i,extractReductionBlockParams:o,extractMainBlockParams:l}=Ire(a,n),c=s(3,32,3,"entry_flow/conv_in"),u=o(32,64,"entry_flow/reduction_block_0"),p=o(64,128,"entry_flow/reduction_block_1"),d={conv_in:c,reduction_block_0:u,reduction_block_1:p},h={};ir(t,0,1).forEach(y=>{h[`main_block_${y}`]=l(128,`middle_flow/main_block_${y}`)});let m=o(128,256,"exit_flow/reduction_block"),f=i(256,512,"exit_flow/separable_conv"),g={reduction_block:m,separable_conv:f};if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:n,params:{entry_flow:d,middle_flow:h,exit_flow:g}}}function Tre(e,t){let n=qn(e,t),a=Ef(n),r=Du(n);function s(o){let l=r(`${o}/separable_conv0`),c=r(`${o}/separable_conv1`),u=a(`${o}/expansion_conv`);return{separable_conv0:l,separable_conv1:c,expansion_conv:u}}function i(o){let l=r(`${o}/separable_conv0`),c=r(`${o}/separable_conv1`),u=r(`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:c,separable_conv2:u}}return{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}}function cC(e,t){let n=[],{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}=Tre(e,n),o=a("entry_flow/conv_in"),l=s("entry_flow/reduction_block_0"),c=s("entry_flow/reduction_block_1"),u={conv_in:o,reduction_block_0:l,reduction_block_1:c},p={};ir(t,0,1).forEach(f=>{p[`main_block_${f}`]=i(`middle_flow/main_block_${f}`)});let d=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:d,separable_conv:h};return xn(e,n),{params:{entry_flow:u,middle_flow:p,exit_flow:m},paramMappings:n}}function pC(e,t,n){return J(Ft(e,t.filters,n,"same"),t.bias)}function Sw(e,t,n=!0){let a=n?qe(e):e;return a=Dn(a,t.separable_conv0,[1,1]),a=Dn(qe(a),t.separable_conv1,[1,1]),a=At(a,[3,3],[2,2],"same"),a=J(a,pC(e,t.expansion_conv,[2,2])),a}function Nre(e,t){let n=Dn(qe(e),t.separable_conv0,[1,1]);return n=Dn(qe(n),t.separable_conv1,[1,1]),n=Dn(qe(n),t.separable_conv2,[1,1]),n=J(n,e),n}var Cw=class extends Zt{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return D(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=wa(a,[122.782,117.001,104.298]).div(pe(256)),i=qe(pC(s,n.entry_flow.conv_in,[2,2]));return i=Sw(i,n.entry_flow.reduction_block_0,!1),i=Sw(i,n.entry_flow.reduction_block_1),ir(this._numMainBlocks,0,1).forEach(o=>{i=Nre(i,n.middle_flow[`main_block_${o}`])}),i=Sw(i,n.exit_flow.reduction_block),i=qe(Dn(i,n.exit_flow.separable_conv,[1,1])),i})}async forward(t){return this.forwardInput(await ht(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return cC(t,this._numMainBlocks)}extractParams(t){return uC(t,this._numMainBlocks)}};function dC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=vn(e),r=Sf(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: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function hC(e){let t=[],n=qn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return xn(e,t),{params:r,paramMappings:t}}var cr;(function(e){e.FEMALE="female",e.MALE="male"})(cr||(cr={}));var Dp=class extends Zt{constructor(t=new Cw(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof ur?this.faceFeatureExtractor.forwardInput(t):t,r=Zn(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Ep(r,n.fc.age).as1D(),i=Ep(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:Na(a)}})}async forward(t){return this.forwardInput(await ht(t))}async predictAgeAndGender(t){let n=await ht(t),a=await this.forwardInput(n),r=ut(a.age),s=ut(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?cr.MALE:cr.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 dC(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=Af(t);return this.faceFeatureExtractor.loadFromWeightMap(n),hC(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 Rp=class extends Fp{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)=>$t([Cn([68],p,"float32"),Cn([68],d,"float32")],1).as2D(1,136).as1D(),o=(p,d)=>{let{width:h,height:m}=r[p];return 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this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Ns=class extends Zt{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return D(()=>{let a=ue(t.toBatchTensor(512,!1),"float32"),r=me(L(a,pe(.007843137718737125)),pe(1)),s=NC(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=_C(s.out,s.conv11,n.prediction_layer);return CC(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await ht(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new sa(n),s=await ht(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],c=o[0];for(let x=1;x{let[v,N]=[Math.max(0,y[x][0]),Math.min(1,y[x][2])].map(A=>A*g),[T,S]=[Math.max(0,y[x][1]),Math.min(1,y[x][3])].map(A=>A*f);return new mt(u[x],new ro(T,v,S-T,N-v),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),c.dispose(),b}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return TC(t)}extractParams(t){return IC(t)}};function $w(e){let t=new Ns;return t.extractWeights(e),t}function EC(e){return $w(e)}var Dw=class extends Ns{};var FC=.4,AC=[new De(.738768,.874946),new De(2.42204,2.65704),new De(4.30971,7.04493),new De(10.246,4.59428),new De(12.6868,11.8741)],$C=[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)],DC=[117.001,114.697,97.404],RC="tiny_yolov2_model",MC="tiny_yolov2_separable_conv_model";var Pf=e=>typeof e=="number";function Of(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(!Pf(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=>Pf(t.x)&&Pf(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(Pf)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Ru(e){return D(()=>{let t=L(e,pe(.10000000149011612));return J(qe(me(e,t)),t)})}function $r(e,t){return D(()=>{let n=ta(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ft(n,t.conv.filters,[1,1],"valid"),n=me(n,t.bn.sub),n=L(n,t.bn.truediv),n=J(n,t.conv.bias),Ru(n)})}function Dr(e,t){return D(()=>{let n=ta(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Pi(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=J(n,t.bias),Ru(n)})}function Pre(e,t){let n=Au(e,t);function a(i,o){let l=Ze(e(i)),c=Ze(e(i));return t.push({paramPath:`${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,`${l}/bn`);return{conv:c,bn:u}}let s=$u(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function PC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=vn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=Pre(r,i),u;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,x]=a,v=t.isFirstLayerConv2d?o(p,d,3,"conv0"):c(p,d,"conv0"),N=c(d,h,"conv1"),T=c(h,m,"conv2"),S=c(m,f,"conv3"),A=c(f,g,"conv4"),$=c(g,y,"conv5"),R=b?c(y,b,"conv6"):void 0,B=x?c(b,x,"conv7"):void 0,V=o(x||b||y,5*n,1,"conv8");u={conv0:v,conv1:N,conv2:T,conv3:S,conv4:A,conv5:$,conv6:R,conv7:B,conv8:V}}else{let[p,d,h,m,f,g,y,b,x]=a,v=l(p,d,"conv0"),N=l(d,h,"conv1"),T=l(h,m,"conv2"),S=l(m,f,"conv3"),A=l(f,g,"conv4"),$=l(g,y,"conv5"),R=l(y,b,"conv6"),B=l(b,x,"conv7"),V=o(x,5*n,1,"conv8");u={conv0:v,conv1:N,conv2:T,conv3:S,conv4:A,conv5:$,conv6:R,conv7:B,conv8:V}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function Ore(e,t){let n=qn(e,t);function a(o){let l=n(`${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(`${o}/bn`);return{conv:l,bn:c}}let i=Du(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function OC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Ore(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return xn(e,n),{params:i,paramMappings:n}}var Ba=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Rw=class extends Zt{constructor(t){super("TinyYolov2");Of(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=$r(t,n.conv0);return a=At(a,[2,2],[2,2],"same"),a=$r(a,n.conv1),a=At(a,[2,2],[2,2],"same"),a=$r(a,n.conv2),a=At(a,[2,2],[2,2],"same"),a=$r(a,n.conv3),a=At(a,[2,2],[2,2],"same"),a=$r(a,n.conv4),a=At(a,[2,2],[2,2],"same"),a=$r(a,n.conv5),a=At(a,[2,2],[1,1],"same"),a=$r(a,n.conv6),a=$r(a,n.conv7),lo(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Ru(lo(t,n.conv0,"valid",!1)):Dr(t,n.conv0);return a=At(a,[2,2],[2,2],"same"),a=Dr(a,n.conv1),a=At(a,[2,2],[2,2],"same"),a=Dr(a,n.conv2),a=At(a,[2,2],[2,2],"same"),a=Dr(a,n.conv3),a=At(a,[2,2],[2,2],"same"),a=Dr(a,n.conv4),a=At(a,[2,2],[2,2],"same"),a=Dr(a,n.conv5),a=At(a,[2,2],[1,1],"same"),a=n.conv6?Dr(a,n.conv6):a,a=n.conv7?Dr(a,n.conv7):a,lo(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=ue(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?wa(r,this.config.meanRgb):r,r=r.div(pe(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await ht(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Ba(n),s=await ht(t),i=await this.forwardInput(s,a),o=D(()=>ut(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 mf(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 OC(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 ${a} filterSizes in config`);return PC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),x=y.slice([0,0,0,4],[c,c,u,1]),v=this.withClassScores?Na(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):pe(0);return[b,x,v]}),m=[],f=await d.array(),g=await p.array();for(let y=0;ya){let N=(b+Su(g[y][b][x][0]))/c*o,T=(y+Su(g[y][b][x][1]))/c*l,S=Math.exp(g[y][b][x][2])*this.config.anchors[x].x/c*o,A=Math.exp(g[y][b][x][3])*this.config.anchors[x].y/c*l,$=N-S/2,R=T-A/2,B={row:y,col:b,anchor:x},{classScore:V,label:W}=this.withClassScores?await this.extractPredictedClass(h,B):{classScore:1,label:0};m.push({box:new ao($,R,$+S,R+A),score:v,classScore:v*V,label:W,...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)}},Mu=Rw;Mu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var mo=class extends Mu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:FC,classes:["face"],...t?{anchors:$C,meanRgb:DC}:{anchors:AC,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 mt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?MC:RC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function LC(e,t=!0){let n=new mo(t);return n.extractWeights(e),n}var Bp=class extends Ba{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var ia=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function fo(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>Ts(l)?r(l):l.detection),i=a||(t instanceof Ee?await oo(t,s):await io(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ee&&l.dispose()),o}async function Pu(e,t,n,a,r){return fo([e],t,async s=>n(s[0]),a,r)}var zC=.4,BC=[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)],WC=[117.001,114.697,97.404];var go=class extends Mu{constructor(){let t={withSeparableConvs:!0,iouThreshold:zC,classes:["face"],anchors:BC,meanRgb:WC,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 mt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var Qe={ssdMobilenetv1:new Ns,tinyFaceDetector:new go,tinyYolov2:new mo,faceLandmark68Net:new co,faceLandmark68TinyNet:new Mp,faceRecognitionNet:new po,faceExpressionNet:new Ap,ageGenderNet:new Dp},Mw=(e,t)=>Qe.ssdMobilenetv1.locateFaces(e,t),VC=(e,t)=>Qe.tinyFaceDetector.locateFaces(e,t),UC=(e,t)=>Qe.tinyYolov2.locateFaces(e,t),Pw=e=>Qe.faceLandmark68Net.detectLandmarks(e),GC=e=>Qe.faceLandmark68TinyNet.detectLandmarks(e),HC=e=>Qe.faceRecognitionNet.computeFaceDescriptor(e),jC=e=>Qe.faceExpressionNet.predictExpressions(e),qC=e=>Qe.ageGenderNet.predictAgeAndGender(e),Ow=e=>Qe.ssdMobilenetv1.load(e),KC=e=>Qe.tinyFaceDetector.load(e),XC=e=>Qe.tinyYolov2.load(e),YC=e=>Qe.faceLandmark68Net.load(e),JC=e=>Qe.faceLandmark68TinyNet.load(e),QC=e=>Qe.faceRecognitionNet.load(e),ZC=e=>Qe.faceExpressionNet.load(e),e_=e=>Qe.ageGenderNet.load(e),t_=Ow,n_=Mw,a_=Pw;var Lw=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},zu=class extends Lw{async run(){let t=await this.parentTask,n=await fo(t,this.input,async a=>Promise.all(a.map(r=>Qe.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>$p(a,n[r]))}withAgeAndGender(){return new Ou(this,this.input)}},Bu=class extends Lw{async run(){let t=await this.parentTask;if(!t)return;let n=await Pu(t,this.input,a=>Qe.faceExpressionNet.predictExpressions(a),this.extractedFaces);return $p(t,n)}withAgeAndGender(){return new Lu(this,this.input)}},xo=class extends zu{withAgeAndGender(){return new yo(this,this.input)}withFaceDescriptors(){return new Rr(this,this.input)}},vo=class extends Bu{withAgeAndGender(){return new bo(this,this.input)}withFaceDescriptor(){return new Mr(this,this.input)}};var zw=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Ou=class extends zw{async run(){let t=await this.parentTask,n=await fo(t,this.input,async a=>Promise.all(a.map(r=>Qe.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Lp(zp(a,i,o),s)})}withFaceExpressions(){return new zu(this,this.input)}},Lu=class extends zw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Pu(t,this.input,s=>Qe.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Lp(zp(t,a,r),n)}withFaceExpressions(){return new Bu(this,this.input)}},yo=class extends Ou{withFaceExpressions(){return new xo(this,this.input)}withFaceDescriptors(){return new Rr(this,this.input)}},bo=class extends Lu{withFaceExpressions(){return new vo(this,this.input)}withFaceDescriptor(){return new Mr(this,this.input)}};var Wp=class extends ia{constructor(t,n){super();this.parentTask=t;this.input=n}},Rr=class extends Wp{async run(){let t=await this.parentTask;return(await fo(t,this.input,a=>Promise.all(a.map(r=>Qe.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>Op(t[r],a))}withFaceExpressions(){return new xo(this,this.input)}withAgeAndGender(){return new yo(this,this.input)}},Mr=class extends Wp{async run(){let t=await this.parentTask;if(!t)return;let n=await Pu(t,this.input,a=>Qe.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return Op(t,n)}withFaceExpressions(){return new vo(this,this.input)}withAgeAndGender(){return new bo(this,this.input)}};var Vp=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?Qe.faceLandmark68TinyNet:Qe.faceLandmark68Net}},Up=class extends Vp{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Ee?await oo(this.input,n):await io(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Ee&&s.dispose()),t.map((s,i)=>uo(s,r[i]))}withFaceExpressions(){return new xo(this,this.input)}withAgeAndGender(){return new yo(this,this.input)}withFaceDescriptors(){return new Rr(this,this.input)}},Gp=class extends Vp{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ee?await oo(this.input,[n]):await io(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ee&&s.dispose()),uo(t,r)}withFaceExpressions(){return new vo(this,this.input)}withAgeAndGender(){return new bo(this,this.input)}withFaceDescriptor(){return new Mr(this,this.input)}};var Hp=class extends ia{constructor(t,n=new sa){super();this.input=t;this.options=n}},Wu=class extends Hp{async run(){let{input:t,options:n}=this,a;if(n instanceof Bp)a=Qe.tinyFaceDetector.locateFaces(t,n);else if(n instanceof sa)a=Qe.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof Ba)a=Qe.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>bs({},a)))})}withFaceLandmarks(t=!1){return 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on(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=B(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=sa(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,te=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:te}}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"),te=i(F,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:Pt,conv6:_t,conv7:wt,conv8:te}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function ia(o,t){let e=j(o,t);function r(i){let c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=ia(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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),zt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(zt(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,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 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:te,label:ho}=this.withClassScores?await this.extractPredictedClass(l,wt):{classScore:1,label:0};v.push({box:new re(Pt,_t,Pt+it,_t+X),score:L,classScore:L*te,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 ca(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 Jt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Vt(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 Jt([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 Xt,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),ma=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),pa=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),da=o=>P.faceLandmark68TinyNet.detectLandmarks(o),ua=o=>P.faceRecognitionNet.computeFaceDescriptor(o),la=o=>P.faceExpressionNet.predictExpressions(o),fa=o=>P.ageGenderNet.predictAgeAndGender(o),dn=o=>P.ssdMobilenetv1.load(o),ha=o=>P.tinyFaceDetector.load(o),xa=o=>P.tinyYolov2.load(o),ba=o=>P.faceLandmark68Net.load(o),ga=o=>P.faceLandmark68TinyNet.load(o),va=o=>P.faceRecognitionNet.load(o),ya=o=>P.faceExpressionNet.load(o),Fa=o=>P.ageGenderNet.load(o),Ta=dn,Pa=mn,_a=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 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 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)}},Kt=class extends _e{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Qt=class extends we{withAgeAndGender(){return new Zt(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 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 _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)}},qt=class extends Te{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Zt=class extends Pe{withFaceExpressions(){return new Qt(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 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)=>fr(t[n],r))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(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 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 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 Kt(this,this.input)}withAgeAndGender(){return new qt(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 Qt(this,this.input)}withAgeAndGender(){return new Zt(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;if(e instanceof gr)r=P.tinyFaceDetector.locateFaces(t,e);else if(e instanceof Z)r=P.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof lt)r=P.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 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faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return Co.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t;return Ae(r.as2D(r.shape[0],-1),e.fc)})}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 Eo(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=ir(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Mo(r)}extractParams(t){let e=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),n=r*e+r,a=t.slice(0,t.length-n),s=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(s)}};var Zr=["neutral","happy","sad","angry","fearful","disgusted","surprised"],It=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Zr.forEach((e,r)=>{this[e]=t[r]})}asSortedArray(){return Zr.map(t=>({expression:t,probability:this[t]})).sort((t,e)=>e.probability-t.probability)}};var cr=class extends We{constructor(t=new Se){super("FaceExpressionNet",t)}forwardInput(t){return ue.tidy(()=>ue.softmax(this.runNet(t)))}async forward(t){return this.forwardInput(await E(t))}async predictExpressions(t){let e=await E(t),r=await this.forwardInput(e),n=await Promise.all(ue.unstack(r).map(async s=>{let i=await s.data();return s.dispose(),i}));r.dispose();let a=n.map(s=>new It(s));return e.isBatchInput?a:a[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Kr(o){return o.expressions instanceof It}function 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lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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 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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:te,label:ho}=this.withClassScores?await this.extractPredictedClass(l,wt):{classScore:1,label:0};v.push({box:new re(Pt,_t,Pt+it,_t+X),score:L,classScore:L*te,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 ca(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 Jt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Vt(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 Jt([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 Xt,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),ma=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),pa=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),da=o=>P.faceLandmark68TinyNet.detectLandmarks(o),ua=o=>P.faceRecognitionNet.computeFaceDescriptor(o),la=o=>P.faceExpressionNet.predictExpressions(o),fa=o=>P.ageGenderNet.predictAgeAndGender(o),dn=o=>P.ssdMobilenetv1.load(o),ha=o=>P.tinyFaceDetector.load(o),xa=o=>P.tinyYolov2.load(o),ba=o=>P.faceLandmark68Net.load(o),ga=o=>P.faceLandmark68TinyNet.load(o),va=o=>P.faceRecognitionNet.load(o),ya=o=>P.faceExpressionNet.load(o),Fa=o=>P.ageGenderNet.load(o),Ta=dn,Pa=mn,_a=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 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 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)}},Kt=class extends _e{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Qt=class extends we{withAgeAndGender(){return new Zt(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 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 _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)}},qt=class extends Te{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Zt=class extends Pe{withFaceExpressions(){return new Qt(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 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)=>fr(t[n],r))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(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 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 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 Kt(this,this.input)}withAgeAndGender(){return new qt(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 Qt(this,this.input)}withAgeAndGender(){return new Zt(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;if(e instanceof gr)r=P.tinyFaceDetector.locateFaces(t,e);else if(e instanceof Z)r=P.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof lt)r=P.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=>$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 wa(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 Da(o,t={}){return wr(o,new lt(t)).withFaceLandmarks().withFaceDescriptors()}var Ea=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 Ma(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: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>ln(n,{width:e,height:r}));if(Vt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return le($t(o,n),a)}return pt(o)?$t(o,o.detection.forSize(e,r)):o instanceof V||o instanceof M?o.forSize(e,r):o}var Na=typeof process!="undefined",Ia=typeof navigator!="undefined"&&typeof 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p=`mobilenetv1/conv_${m}`,d=`MobilenetV1/Conv2d_${m}_depthwise`,u=`${p}/depthwise_conv`,l=`${p}/pointwise_conv`,v=e(`${d}/depthwise_weights`,4,`${u}/filters`),_=e(`${d}/BatchNorm/gamma`,1,`${u}/batch_norm_scale`),h=e(`${d}/BatchNorm/beta`,1,`${u}/batch_norm_offset`),y=e(`${d}/BatchNorm/moving_mean`,1,`${u}/batch_norm_mean`),T=e(`${d}/BatchNorm/moving_variance`,1,`${u}/batch_norm_variance`);return{depthwise_conv:{filters:v,batch_norm_scale:_,batch_norm_offset:h,batch_norm_mean:y,batch_norm_variance:T},pointwise_conv:r("MobilenetV1",m,l)}}function a(){return{conv_0:r("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:n(1),conv_2:n(2),conv_3:n(3),conv_4:n(4),conv_5:n(5),conv_6:n(6),conv_7:n(7),conv_8:n(8),conv_9:n(9),conv_10:n(10),conv_11:n(11),conv_12:n(12),conv_13:n(13)}}function s(m,p){let d=e(`${m}/weights`,4,`${p}/filters`),u=e(`${m}/biases`,1,`${p}/bias`);return{filters:d,bias:u}}function i(m){let p=s(`Prediction/BoxPredictor_${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}=Kn(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 Qn=.0010000000474974513;function ta(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,Qn),yt.clipByValue(r,0,6)})}function ea(o){return[2,4,6,12].some(t=>t===o)?[2,2]:[1,1]}function zo(o,t){return yt.tidy(()=>{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 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 Xo(o,t,e){return $e.tidy(()=>{let r=q(o,e.conv_0,[1,1]),n=q(r,e.conv_1,[2,2]),a=q(n,e.conv_2,[1,1]),s=q(a,e.conv_3,[2,2]),i=q(s,e.conv_4,[1,1]),c=q(i,e.conv_5,[2,2]),m=q(c,e.conv_6,[1,1]),p=q(m,e.conv_7,[2,2]),d=Ut(t,e.box_predictor_0),u=Ut(o,e.box_predictor_1),l=Ut(n,e.box_predictor_2),v=Ut(s,e.box_predictor_3),_=Ut(c,e.box_predictor_4),h=Ut(p,e.box_predictor_5),y=$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 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=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 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 Go(t)}extractParams(t){return Yo(t)}};function Jo(o){let t=new Xt;return t.extractWeights(o),t}function aa(o){return Jo(o)}var qo=class extends Xt{};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: ${JSON.stringify(o.meanRgb)}`)}var Q=b(g());var K=b(g());function be(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Ft(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),be(e)})}var St=b(g());function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),be(e)})}var co=b(g());function sa(o,t){let e=ce(o,t);function r(s,i){let c=co.tensor1d(o(s)),m=co.tensor1d(o(s));return t.push({paramPath:`${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}=sa(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,te=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:te}}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"),te=i(F,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:Pt,conv6:_t,conv7:wt,conv8:te}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function ia(o,t){let e=j(o,t);function r(i){let c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=ia(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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),zt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(zt(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,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 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 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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=ea(i);r=ta(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 ra(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 d=p.score;for(let u=m.length-1;u>=0;--u){let l=ra(o,p.boxIndex,m[u]);if(l!==0&&(p.score*=c(l),p.score<=n))break}d===p.score&&m.push(p.boxIndex)}),m}var f=b(g());function oa(o){let t=f.unstack(f.transpose(o,[1,0])),e=[f.sub(t[2],t[0]),f.sub(t[3],t[1])],r=[f.add(t[0],f.div(e[0],f.scalar(2))),f.add(t[1],f.div(e[1],f.scalar(2)))];return{sizes:e,centers:r}}function na(o,t){let{sizes:e,centers:r}=oa(o),n=f.unstack(f.transpose(t,[1,0])),a=f.div(f.mul(f.exp(f.div(n[2],f.scalar(5))),e[0]),f.scalar(2)),s=f.add(f.mul(f.div(n[0],f.scalar(10)),e[0]),r[0]),i=f.div(f.mul(f.exp(f.div(n[3],f.scalar(5))),e[1]),f.scalar(2)),c=f.add(f.mul(f.div(n[1],f.scalar(10)),e[1]),r[1]);return f.transpose(f.stack([f.sub(s,a),f.sub(c,i),f.add(s,a),f.add(c,i)]),[1,0])}function Uo(o,t,e){return f.tidy(()=>{let r=o.shape[0],n=na(f.reshape(f.tile(e.extra_dim,[r,1,1]),[-1,4]),f.reshape(o,[-1,4]));n=f.reshape(n,[r,n.shape[0]/r,4]);let 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 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 Xo(o,t,e){return $e.tidy(()=>{let r=q(o,e.conv_0,[1,1]),n=q(r,e.conv_1,[2,2]),a=q(n,e.conv_2,[1,1]),s=q(a,e.conv_3,[2,2]),i=q(s,e.conv_4,[1,1]),c=q(i,e.conv_5,[2,2]),m=q(c,e.conv_6,[1,1]),p=q(m,e.conv_7,[2,2]),d=Ut(t,e.box_predictor_0),u=Ut(o,e.box_predictor_1),l=Ut(n,e.box_predictor_2),v=Ut(s,e.box_predictor_3),_=Ut(c,e.box_predictor_4),h=Ut(p,e.box_predictor_5),y=$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 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=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 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 Go(t)}extractParams(t){return Yo(t)}};function Jo(o){let t=new Xt;return t.extractWeights(o),t}function aa(o){return Jo(o)}var qo=class extends Xt{};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: ${JSON.stringify(o.meanRgb)}`)}var Q=b(g());var K=b(g());function be(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Ft(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),be(e)})}var St=b(g());function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),be(e)})}var co=b(g());function sa(o,t){let e=ce(o,t);function r(s,i){let c=co.tensor1d(o(s)),m=co.tensor1d(o(s));return t.push({paramPath:`${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}=sa(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,te=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:te}}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"),te=i(F,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:Pt,conv6:_t,conv7:wt,conv8:te}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function ia(o,t){let e=j(o,t);function r(i){let c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=ia(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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),zt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(zt(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,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 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:te,label:ho}=this.withClassScores?await this.extractPredictedClass(l,wt):{classScore:1,label:0};v.push({box:new re(Pt,_t,Pt+it,_t+X),score:L,classScore:L*te,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 ca(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 Jt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Vt(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 Jt([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 Xt,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),ma=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),pa=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),da=o=>P.faceLandmark68TinyNet.detectLandmarks(o),ua=o=>P.faceRecognitionNet.computeFaceDescriptor(o),la=o=>P.faceExpressionNet.predictExpressions(o),fa=o=>P.ageGenderNet.predictAgeAndGender(o),dn=o=>P.ssdMobilenetv1.load(o),ha=o=>P.tinyFaceDetector.load(o),xa=o=>P.tinyYolov2.load(o),ba=o=>P.faceLandmark68Net.load(o),ga=o=>P.faceLandmark68TinyNet.load(o),va=o=>P.faceRecognitionNet.load(o),ya=o=>P.faceExpressionNet.load(o),Fa=o=>P.ageGenderNet.load(o),Ta=dn,Pa=mn,_a=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 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 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bt=class{constructor(t,e=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=e,this._batchSize=t.length,t.forEach((r,n)=>{if(ht(r)){this._imageTensors[n]=r,this._inputDimensions[n]=r.shape;return}if(z(r)){let s=r.shape[0];if(s!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${s} passed, but not supported in input array`);this._imageTensors[n]=r,this._inputDimensions[n]=r.shape.slice(1);return}let a=r instanceof w.getEnv().Canvas?r:Ie(r);this._canvases[n]=a,this._inputDimensions[n]=[a.height,a.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 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Zr=["neutral","happy","sad","angry","fearful","disgusted","surprised"],It=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Zr.forEach((e,r)=>{this[e]=t[r]})}asSortedArray(){return Zr.map(t=>({expression:t,probability:this[t]})).sort((t,e)=>e.probability-t.probability)}};var cr=class extends We{constructor(t=new Se){super("FaceExpressionNet",t)}forwardInput(t){return ue.tidy(()=>ue.softmax(this.runNet(t)))}async forward(t){return this.forwardInput(await E(t))}async predictExpressions(t){let e=await E(t),r=await this.forwardInput(e),n=await Promise.all(ue.unstack(r).map(async s=>{let i=await s.data();return s.dispose(),i}));r.dispose();let a=n.map(s=>new It(s));return e.isBatchInput?a:a[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Kr(o){return o.expressions instanceof It}function 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u=a("exit_flow/reduction_block"),l=n("exit_flow/separable_conv"),v={reduction_block:u,separable_conv:l};return W(o,e),{params:{entry_flow:p,middle_flow:d,exit_flow:v},paramMappings:e}}function ko(o,t,e){return I.add(I.conv2d(o,t.filters,e,"same"),t.bias)}function ro(o,t,e=!0){let r=e?I.relu(o):o;return r=$(r,t.separable_conv0,[1,1]),r=$(I.relu(r),t.separable_conv1,[1,1]),r=I.maxPool(r,[3,3],[2,2],"same"),r=I.add(r,ko(o,t.expansion_conv,[2,2])),r}function zn(o,t){let e=$(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(`${this._name} - load model before inference`);return ut.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t,n=ut.avgPool(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),a=Ae(n,e.fc.age).as1D(),s=Ae(n,e.fc.gender);return{age:a,gender:s}})}forwardInput(t){return ut.tidy(()=>{let{age:e,gender:r}=this.runNet(t);return{age:e,gender:ut.softmax(r)}})}async forward(t){return this.forwardInput(await E(t))}async predictAgeAndGender(t){let e=await E(t),r=await this.forwardInput(e),n=ut.unstack(r.age),a=ut.unstack(r.gender),s=n.map((c,m)=>({ageTensor:c,genderTensor:a[m]})),i=await Promise.all(s.map(async({ageTensor:c,genderTensor:m})=>{let p=(await c.data())[0],d=(await 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r=q(o,e.conv_0,[1,1]),n=q(r,e.conv_1,[2,2]),a=q(n,e.conv_2,[1,1]),s=q(a,e.conv_3,[2,2]),i=q(s,e.conv_4,[1,1]),c=q(i,e.conv_5,[2,2]),m=q(c,e.conv_6,[1,1]),p=q(m,e.conv_7,[2,2]),d=Ut(t,e.box_predictor_0),u=Ut(o,e.box_predictor_1),l=Ut(n,e.box_predictor_2),v=Ut(s,e.box_predictor_3),_=Ut(c,e.box_predictor_4),h=Ut(p,e.box_predictor_5),y=$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 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=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 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 Go(t)}extractParams(t){return Yo(t)}};function Jo(o){let t=new Xt;return t.extractWeights(o),t}function aa(o){return Jo(o)}var qo=class extends Xt{};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: ${JSON.stringify(o.meanRgb)}`)}var Q=b(g());var K=b(g());function be(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Ft(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),be(e)})}var St=b(g());function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),be(e)})}var co=b(g());function sa(o,t){let e=ce(o,t);function r(s,i){let c=co.tensor1d(o(s)),m=co.tensor1d(o(s));return t.push({paramPath:`${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}=sa(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,te=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:te}}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"),te=i(F,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:Pt,conv6:_t,conv7:wt,conv8:te}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function ia(o,t){let e=j(o,t);function r(i){let c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=ia(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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),zt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(zt(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,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 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:te,label:ho}=this.withClassScores?await this.extractPredictedClass(l,wt):{classScore:1,label:0};v.push({box:new re(Pt,_t,Pt+it,_t+X),score:L,classScore:L*te,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 ca(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 Jt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Vt(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 Jt([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 Xt,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),ma=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),pa=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),da=o=>P.faceLandmark68TinyNet.detectLandmarks(o),ua=o=>P.faceRecognitionNet.computeFaceDescriptor(o),la=o=>P.faceExpressionNet.predictExpressions(o),fa=o=>P.ageGenderNet.predictAgeAndGender(o),dn=o=>P.ssdMobilenetv1.load(o),ha=o=>P.tinyFaceDetector.load(o),xa=o=>P.tinyYolov2.load(o),ba=o=>P.faceLandmark68Net.load(o),ga=o=>P.faceLandmark68TinyNet.load(o),va=o=>P.faceRecognitionNet.load(o),ya=o=>P.faceExpressionNet.load(o),Fa=o=>P.ageGenderNet.load(o),Ta=dn,Pa=mn,_a=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 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 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)}},Kt=class extends _e{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Qt=class extends we{withAgeAndGender(){return new Zt(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 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 _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)}},qt=class extends Te{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Zt=class extends Pe{withFaceExpressions(){return new Qt(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 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)=>fr(t[n],r))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}},Wt=class extends vr{async run(){let t=await this.parentTask;if(!t)return;let e=await 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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 sa(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}=sa(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,te=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:te}}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"),te=i(F,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:Pt,conv6:_t,conv7:wt,conv8:te}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function ia(o,t){let e=j(o,t);function r(i){let c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=ia(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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),zt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(zt(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,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 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:te,label:ho}=this.withClassScores?await this.extractPredictedClass(l,wt):{classScore:1,label:0};v.push({box:new re(Pt,_t,Pt+it,_t+X),score:L,classScore:L*te,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 ca(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 Jt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Vt(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 Jt([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 Xt,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),ma=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),pa=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),da=o=>P.faceLandmark68TinyNet.detectLandmarks(o),ua=o=>P.faceRecognitionNet.computeFaceDescriptor(o),la=o=>P.faceExpressionNet.predictExpressions(o),fa=o=>P.ageGenderNet.predictAgeAndGender(o),dn=o=>P.ssdMobilenetv1.load(o),ha=o=>P.tinyFaceDetector.load(o),xa=o=>P.tinyYolov2.load(o),ba=o=>P.faceLandmark68Net.load(o),ga=o=>P.faceLandmark68TinyNet.load(o),va=o=>P.faceRecognitionNet.load(o),ya=o=>P.faceExpressionNet.load(o),Fa=o=>P.ageGenderNet.load(o),Ta=dn,Pa=mn,_a=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 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 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)}},Kt=class extends _e{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Qt=class extends we{withAgeAndGender(){return new Zt(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 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 _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)}},qt=class extends Te{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Zt=class extends Pe{withFaceExpressions(){return new Qt(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 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)=>fr(t[n],r))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(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 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 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 Kt(this,this.input)}withAgeAndGender(){return new qt(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 Qt(this,this.input)}withAgeAndGender(){return new Zt(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;if(e instanceof gr)r=P.tinyFaceDetector.locateFaces(t,e);else if(e instanceof Z)r=P.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof lt)r=P.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=>$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 wa(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 Da(o,t={}){return wr(o,new lt(t)).withFaceLandmarks().withFaceDescriptors()}var Ea=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 Ma(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: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>ln(n,{width:e,height:r}));if(Vt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return le($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 Na=typeof process!="undefined",Ia=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",La={faceapi:No,node:Na,browser:Ia}; //# sourceMappingURL=face-api.node.js.map