diff --git a/README.md b/README.md
index ce43b59..94aa91b 100644
--- a/README.md
+++ b/README.md
@@ -116,11 +116,18 @@ Note that `@tensorflow/tfjs-node` or `@tensorflow/tfjs-node-gpu` must be install
 
 Face-API ships with several pre-build versions of the library:
 
-- `dist/face-api.js`: IIFE format for client-side Browser execution *with* TFJS pre-bundled
-- `dist/face-api.esm.js`: ESM format for client-side Browser execution *with* TFJS pre-bundled
-- `dist/face-api.esm-nobundle.js`: ESM format for client-side Browser execution *without* TFJS pre-bundled
-- `dist/face-api.node.js`: CommonJS format for server-side NodeJS execution *without* TFJS pre-bundled
-- `dist/face-api.node-gpu.js`: CommonJS format for server-side NodeJS execution *without* TFJS pre-bundled and optimized for CUDA GPU acceleration
+- `dist/face-api.js`: IIFE format for client-side Browser execution  
+   *with* TFJS pre-bundled
+- `dist/face-api.esm.js`: ESM format for client-side Browser execution  
+   *with* TFJS pre-bundled
+- `dist/face-api.esm-nobundle.js`: ESM format for client-side Browser execution  
+   *without* TFJS pre-bundled
+- `dist/face-api.node.js`: CommonJS format for server-side NodeJS execution  
+   *without* TFJS pre-bundled
+- `dist/face-api.node-gpu.js`: CommonJS format for server-side NodeJS execution  
+   *without* TFJS pre-bundled and optimized for CUDA GPU acceleration
+- `dist/face-api.node-cpu.js`: CommonJS format for server-side NodeJS execution  
+   *without* TFJS pre-bundled and using JS engine for platforms where tensorflow binary library version is not available
 
 Defaults are:
 ```json
@@ -239,6 +246,18 @@ And then use with:
   const faceapi = require('@vladmandic/face-api/dist/face-api.node-gpu.js'); // this loads face-api version with correct bindings for tfjs-node-gpu
 ```
 
+If you want to use `Face-API` in a NodeJS on platforms where NodeJS binary libraries are not supported, you can use JavaScript CPU backend.  
+
+```shell
+  npm install @tensorflow/tfjs
+  npm install @vladmandic/face-api 
+```
+And then use with:
+```js
+  const tf = require('@tensorflow/tfjs')
+  const faceapi = require('@vladmandic/face-api/dist/face-api.node-pcu.js');
+```
+
 <br>
 <hr>
 <br>
diff --git a/dist/face-api.esm-nobundle.js b/dist/face-api.esm-nobundle.js
index e0be1cc..ddf0960 100644
--- a/dist/face-api.esm-nobundle.js
+++ b/dist/face-api.esm-nobundle.js
@@ -5,5 +5,5 @@
   author: <https://github.com/vladmandic>'
   */
 
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tr=b(g());function me(o,t){return(e,r,n,a)=>{let s=tr.tensor4d(o(e*r*n*n),[n,n,e,r]),i=tr.tensor1d(o(r));return t.push({paramPath:`${a}/filters`},{paramPath:`${a}/bias`}),{filters:s,bias:i}}}var er=b(g());function rr(o,t){return(e,r,n)=>{let a=er.tensor2d(o(e*r),[e,r]),s=er.tensor1d(o(r));return t.push({paramPath:`${n}/weights`},{paramPath:`${n}/bias`}),{weights:a,bias:s}}}var Se=b(g());var or=class{constructor(t,e,r){this.depthwise_filter=t;this.pointwise_filter=e;this.bias=r}};function pe(o,t){return(e,r,n)=>{let a=Se.tensor4d(o(3*3*e),[3,3,e,1]),s=Se.tensor4d(o(e*r),[1,1,e,r]),i=Se.tensor1d(o(r));return t.push({paramPath:`${n}/depthwise_filter`},{paramPath:`${n}/pointwise_filter`},{paramPath:`${n}/bias`}),new or(a,s,i)}}function de(o){return t=>{let e=o(`${t}/depthwise_filter`,4),r=o(`${t}/pointwise_filter`,4),n=o(`${t}/bias`,1);return new or(e,r,n)}}function j(o,t){return(e,r,n)=>{let a=o[e];if(!Rt(a,r))throw new Error(`expected weightMap[${e}] to be a Tensor${r}D, instead have 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s=ke(s,e.dense1),s=ke(s,e.dense2),s=ke(s,e.dense3),s=It.avgPool(s,[7,7],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return Po(t)}extractParams(t){return To(t)}};var Do=b(g());var ue=b(g());function We(o,t){return ue.tidy(()=>ue.add(ue.matMul(o,t.weights),t.bias))}function _o(o,t,e){let r=[],{extractWeights:n,getRemainingWeights:a}=B(o),i=rr(n,r)(t,e,"fc");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:r,params:{fc:i}}}function wo(o){let t=[],e=j(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:r("fc")};return W(o,t),{params:n,paramMappings:t}}function ir(o){let t={},e={};return Object.keys(o).forEach(r=>{let n=r.startsWith("fc")?e:t;n[r]=o[r]}),{featureExtractorMap:t,classifierMap:e}}var Be=class extends S{constructor(t,e){super(t);this._faceFeatureExtractor=e}get 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c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=de(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function en(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=Zn(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var ft=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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 co=class extends S{constructor(t){super("TinyYolov2");so(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=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=Tt(r,e.conv6),r=Tt(r,e.conv7),Vt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?ge(Vt(t,e.conv0,"valid",!1)):Pt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Pt(r,e.conv6):r,r=e.conv7?Pt(r,e.conv7):r,Vt(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 ft(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),f=m.map(h=>this.config.classes[h.label]);return Ar(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Et(d[h],u[h],f[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return en(t,this.config)}extractParams(t){let e=this.config.filterSizes||co.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 tn(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]=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;y<m;y++)for(let T=0;T<m;T++)for(let F=0;F<p;F++){let L=Ee(_[y][T][F][0]);if(!r||L>r){let G=(T+Ee(h[y][T][F][0]))/m*i,et=(y+Ee(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,_t=G-it/2,wt=et-X/2,Dt={row:y,col:T,anchor:F},{classScore:ee,label:lo}=this.withClassScores?await this.extractPredictedClass(f,Dt):{classScore:1,label:0};v.push({box:new oe(_t,wt,_t+it,wt+X),score:L,classScore:L*ee,label:lo,...Dt})}}return d.dispose(),u.dispose(),f.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)}},ve=co;ve.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ye=class extends ve{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Xo,classes:["face"],...t?{anchors:qo,meanRgb:Zo}:{anchors:Jo,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?Qo:Ko}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Kn(o,t=!0){let e=new ye(t);return e.extractWeights(o),e}var gr=class extends ft{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 He=b(g());var mo=b(g());async function qt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Ut(c)?n(c):c.detection),s=r||(t instanceof mo.Tensor?await ie(t,a):await se(t,a)),i=await e(s);return s.forEach(c=>c instanceof mo.Tensor&&c.dispose()),i}async function Fe(o,t,e,r,n){return qt([o],t,async a=>e(a[0]),r,n)}var rn=.4,on=[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)],nn=[117.001,114.697,97.404];var Te=class extends ve{constructor(){let t={withSeparableConvs:!0,iouThreshold:rn,classes:["face"],anchors:on,meanRgb:nn,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 Jt,tinyFaceDetector:new Te,tinyYolov2:new ye,faceLandmark68Net:new he,faceLandmark68TinyNet:new dr,faceRecognitionNet:new be,faceExpressionNet:new cr,ageGenderNet:new pr},an=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),Qn=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ta=(o,t)=>P.tinyYolov2.locateFaces(o,t),sn=o=>P.faceLandmark68Net.detectLandmarks(o),ea=o=>P.faceLandmark68TinyNet.detectLandmarks(o),ra=o=>P.faceRecognitionNet.computeFaceDescriptor(o),oa=o=>P.faceExpressionNet.predictExpressions(o),na=o=>P.ageGenderNet.predictAgeAndGender(o),cn=o=>P.ssdMobilenetv1.load(o),aa=o=>P.tinyFaceDetector.load(o),sa=o=>P.tinyYolov2.load(o),ia=o=>P.faceLandmark68Net.load(o),ca=o=>P.faceLandmark68TinyNet.load(o),ma=o=>P.faceRecognitionNet.load(o),pa=o=>P.faceExpressionNet.load(o),da=o=>P.ageGenderNet.load(o),ua=cn,fa=an,la=sn;var po=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},we=class extends po{async run(){let t=await this.parentTask,e=await qt(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 Pe(this,this.input)}},De=class extends po{async run(){let t=await this.parentTask;if(!t)return;let e=await Fe(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return mr(t,e)}withAgeAndGender(){return new _e(this,this.input)}},Qt=class extends we{withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptors(){return new Wt(this,this.input)}},te=class extends De{withAgeAndGender(){return new Kt(this,this.input)}withFaceDescriptor(){return new Bt(this,this.input)}};var uo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Pe=class extends uo{async run(){let t=await this.parentTask,e=await qt(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 we(this,this.input)}},_e=class extends uo{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Fe(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return hr(xr(t,r,n),e)}withFaceExpressions(){return new De(this,this.input)}},Zt=class extends Pe{withFaceExpressions(){return new Qt(this,this.input)}withFaceDescriptors(){return new Wt(this,this.input)}},Kt=class extends _e{withFaceExpressions(){return new te(this,this.input)}withFaceDescriptor(){return new Bt(this,this.input)}};var vr=class extends tt{constructor(t,e){super();this.parentTask=t;this.input=e}},Wt=class extends vr{async run(){let t=await this.parentTask;return(await qt(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>lr(t[n],r))}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}},Bt=class extends vr{async run(){let t=await this.parentTask;if(!t)return;let e=await Fe(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return lr(t,e)}withFaceExpressions(){return new te(this,this.input)}withAgeAndGender(){return new Kt(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 He.Tensor?await ie(this.input,e):await se(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof He.Tensor&&a.dispose()),t.map((a,s)=>le(a,n[s]))}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptors(){return new Wt(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 He.Tensor?await ie(this.input,[e]):await se(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof He.Tensor&&a.dispose()),le(t,n)}withFaceExpressions(){return new te(this,this.input)}withAgeAndGender(){return new Kt(this,this.input)}withFaceDescriptor(){return new Bt(this,this.input)}};var Pr=class extends tt{constructor(t,e=new Z){super();this.input=t;this.options=e}},Ye=class extends Pr{async run(){let{input:t,options:e}=this,r=e instanceof gr?n=>P.tinyFaceDetector.locateFaces(n,e):e instanceof Z?n=>P.ssdMobilenetv1.locateFaces(n,e):e instanceof ft?n=>P.tinyYolov2.locateFaces(n,e):null;if(!r)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return r(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>jt({},r)))})}withFaceLandmarks(t=!1){return new Fr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new we(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Pe(this.runAndExtendWithFaceDetections(),this.input)}},_r=class extends Pr{async run(){let t=await new Ye(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?jt({},e):void 0)})}withFaceLandmarks(t=!1){return new Tr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new De(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new _e(this.runAndExtendWithFaceDetection(),this.input)}};function ha(o,t=new Z){return new _r(o,t)}function wr(o,t=new Z){return new Ye(o,t)}async function mn(o,t){return wr(o,new Z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function xa(o,t={}){return wr(o,new ft(t)).withFaceLandmarks().withFaceDescriptors()}var ba=mn;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 bt)return s;if(s instanceof Float32Array)return new bt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new bt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>fo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Me(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distance<r.distance?e:r)}findBestMatch(t){let e=this.matchDescriptor(t);return e.distance<this.distanceThreshold?e:new Me("unknown",e.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>bt.fromJSON(r));return new Dr(e,t.distanceThreshold)}};function ga(o){let t=new Te;return t.extractWeights(o),t}function pn(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=>pn(n,{width:e,height:r}));if(Ut(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return le(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof V||o instanceof M?o.forSize(e,r):o}var ya=typeof process!="undefined",Fa=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",Ta={faceapi:Eo,node:ya,browser:Fa};export{pr as AgeGenderNet,oe as BoundingBox,D as Box,tt as ComposableTask,Wt as ComputeAllFaceDescriptorsTask,vr as ComputeFaceDescriptorsTaskBase,Bt as ComputeSingleFaceDescriptorTask,Fr as DetectAllFaceLandmarksTask,Ye as DetectAllFacesTask,yr as DetectFaceLandmarksTaskBase,Pr as DetectFacesTaskBase,Tr as DetectSingleFaceLandmarksTask,_r as DetectSingleFaceTask,A as Dimensions,qr as FACE_EXPRESSION_LABELS,M as FaceDetection,Uo as FaceDetectionNet,cr as FaceExpressionNet,Lt as FaceExpressions,he as FaceLandmark68Net,dr as FaceLandmark68TinyNet,Ao as FaceLandmarkNet,V as FaceLandmarks,bo as FaceLandmarks5,ae as FaceLandmarks68,Me as FaceMatch,Dr as FaceMatcher,be as FaceRecognitionNet,yt as Gender,Ce as LabeledBox,bt as LabeledFaceDescriptors,gt as NetInput,S as NeuralNetwork,Et as ObjectDetection,x as Point,go as PredictedBox,ne as Rect,Jt as SsdMobilenetv1,Z as SsdMobilenetv1Options,Te as TinyFaceDetector,gr as TinyFaceDetectorOptions,ye as TinyYolov2,ft as TinyYolov2Options,ba as allFaces,mn as allFacesSsdMobilenetv1,xa as allFacesTinyYolov2,Gr as awaitMediaLoaded,zr as bufferToImage,ra as computeFaceDescriptor,Gt as createCanvas,Le as createCanvasFromMedia,Jn as createFaceDetectionNet,Rn as createFaceRecognitionNet,Vo as createSsdMobilenetv1,ga as createTinyFaceDetector,Kn as createTinyYolov2,wr as detectAllFaces,sn as detectFaceLandmarks,ea as detectFaceLandmarksTiny,la as detectLandmarks,ha as detectSingleFace,to as draw,w as env,fo as euclideanDistance,hr as extendWithAge,lr as extendWithFaceDescriptor,jt as extendWithFaceDetection,mr as extendWithFaceExpressions,le as extendWithFaceLandmarks,xr as extendWithGender,ie as extractFaceTensors,se as extractFaces,Mn as fetchImage,Xr as fetchJson,Cn as fetchNetWeights,zt as fetchOrThrow,$ as getContext2dOrThrow,Yt as getMediaDimensions,Vr as imageTensorToCanvas,Ur as imageToSquare,vn as inverseSigmoid,kr as iou,qe as isMediaElement,Ie as isMediaLoaded,$n as isWithAge,pt as isWithFaceDetection,Zr as isWithFaceExpressions,Ut as isWithFaceLandmarks,On as isWithGender,da as loadAgeGenderModel,ua as loadFaceDetectionModel,pa as loadFaceExpressionModel,ia as loadFaceLandmarkModel,ca as loadFaceLandmarkTinyModel,ma as loadFaceRecognitionModel,cn as loadSsdMobilenetv1Model,aa as loadTinyFaceDetectorModel,sa as loadTinyYolov2Model,Jr as loadWeightMap,fa as locateFaces,Nn as matchDimensions,Sr as minBbox,P as nets,Ar as nonMaxSuppression,ot as normalize,Wr as padToSquare,na as predictAgeAndGender,oa as recognizeFaceExpressions,pn as resizeResults,Ht as resolveInput,gn as shuffleArray,Ee as sigmoid,an as ssdMobilenetv1,va as tf,Qn as tinyFaceDetector,ta as tinyYolov2,E as toNetInput,Cr as utils,so as validateConfig,Ta as version};
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c=n(`${i}/separable_conv0`),m=n(`${i}/separable_conv1`),p=n(`${i}/separable_conv2`);return{separable_conv0:c,separable_conv1:m,separable_conv2:p}}return{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:a,extractMainBlockParams:s}}function Co(o,t){let e=[],{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:a,extractMainBlockParams:s}=Sn(o,e),i=r("entry_flow/conv_in"),c=a("entry_flow/reduction_block_0"),m=a("entry_flow/reduction_block_1"),p={conv_in:i,reduction_block_0:c,reduction_block_1:m},d={};ct(t,0,1).forEach(_=>{d[`main_block_${_}`]=s(`middle_flow/main_block_${_}`)});let u=a("exit_flow/reduction_block"),f=n("exit_flow/separable_conv"),v={reduction_block:u,separable_conv:f};return W(o,e),{params:{entry_flow:p,middle_flow:d,exit_flow:v},paramMappings:e}}function No(o,t,e){return I.add(I.conv2d(o,t.filters,e,"same"),t.bias)}function eo(o,t,e=!0){let r=e?I.relu(o):o;return 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instanceof gt?this.faceFeatureExtractor.forwardInput(t):t,n=ut.avgPool(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),a=We(n,e.fc.age).as1D(),s=We(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 m.data())[0],u=d>.5,f=u?yt.MALE:yt.FEMALE,v=u?d:1-d;return c.dispose(),m.dispose(),{age:p,gender:f,genderProbability:v}}));return 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ft=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 co=class extends S{constructor(t){super("TinyYolov2");so(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=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=Tt(r,e.conv6),r=Tt(r,e.conv7),Vt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?ge(Vt(t,e.conv0,"valid",!1)):Pt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Pt(r,e.conv6):r,r=e.conv7?Pt(r,e.conv7):r,Vt(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 ft(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),f=m.map(h=>this.config.classes[h.label]);return Ar(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Et(d[h],u[h],f[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return en(t,this.config)}extractParams(t){let e=this.config.filterSizes||co.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 tn(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]=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;y<m;y++)for(let T=0;T<m;T++)for(let F=0;F<p;F++){let L=Ee(_[y][T][F][0]);if(!r||L>r){let G=(T+Ee(h[y][T][F][0]))/m*i,et=(y+Ee(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,_t=G-it/2,wt=et-X/2,Dt={row:y,col:T,anchor:F},{classScore:ee,label:lo}=this.withClassScores?await this.extractPredictedClass(f,Dt):{classScore:1,label:0};v.push({box:new oe(_t,wt,_t+it,wt+X),score:L,classScore:L*ee,label:lo,...Dt})}}return d.dispose(),u.dispose(),f.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)}},ve=co;ve.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ye=class extends ve{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Xo,classes:["face"],...t?{anchors:qo,meanRgb:Zo}:{anchors:Jo,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?Qo:Ko}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Kn(o,t=!0){let e=new ye(t);return e.extractWeights(o),e}var gr=class extends ft{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 He=b(g());var mo=b(g());async function qt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Ut(c)?n(c):c.detection),s=r||(t instanceof mo.Tensor?await ie(t,a):await se(t,a)),i=await e(s);return s.forEach(c=>c instanceof mo.Tensor&&c.dispose()),i}async function Fe(o,t,e,r,n){return qt([o],t,async a=>e(a[0]),r,n)}var rn=.4,on=[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)],nn=[117.001,114.697,97.404];var Te=class extends ve{constructor(){let t={withSeparableConvs:!0,iouThreshold:rn,classes:["face"],anchors:on,meanRgb:nn,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 Jt,tinyFaceDetector:new Te,tinyYolov2:new ye,faceLandmark68Net:new he,faceLandmark68TinyNet:new dr,faceRecognitionNet:new be,faceExpressionNet:new cr,ageGenderNet:new pr},an=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),Qn=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ta=(o,t)=>P.tinyYolov2.locateFaces(o,t),sn=o=>P.faceLandmark68Net.detectLandmarks(o),ea=o=>P.faceLandmark68TinyNet.detectLandmarks(o),ra=o=>P.faceRecognitionNet.computeFaceDescriptor(o),oa=o=>P.faceExpressionNet.predictExpressions(o),na=o=>P.ageGenderNet.predictAgeAndGender(o),cn=o=>P.ssdMobilenetv1.load(o),aa=o=>P.tinyFaceDetector.load(o),sa=o=>P.tinyYolov2.load(o),ia=o=>P.faceLandmark68Net.load(o),ca=o=>P.faceLandmark68TinyNet.load(o),ma=o=>P.faceRecognitionNet.load(o),pa=o=>P.faceExpressionNet.load(o),da=o=>P.ageGenderNet.load(o),ua=cn,fa=an,la=sn;var po=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},we=class extends po{async run(){let t=await this.parentTask,e=await qt(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 Pe(this,this.input)}},De=class extends po{async run(){let t=await this.parentTask;if(!t)return;let e=await Fe(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return mr(t,e)}withAgeAndGender(){return new _e(this,this.input)}},Qt=class extends we{withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptors(){return new Wt(this,this.input)}},te=class extends De{withAgeAndGender(){return new Kt(this,this.input)}withFaceDescriptor(){return new Bt(this,this.input)}};var uo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Pe=class extends uo{async run(){let t=await this.parentTask,e=await qt(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 we(this,this.input)}},_e=class extends uo{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Fe(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return hr(xr(t,r,n),e)}withFaceExpressions(){return new De(this,this.input)}},Zt=class extends Pe{withFaceExpressions(){return new Qt(this,this.input)}withFaceDescriptors(){return new Wt(this,this.input)}},Kt=class extends _e{withFaceExpressions(){return new te(this,this.input)}withFaceDescriptor(){return new Bt(this,this.input)}};var vr=class extends tt{constructor(t,e){super();this.parentTask=t;this.input=e}},Wt=class extends vr{async run(){let t=await this.parentTask;return(await qt(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>lr(t[n],r))}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}},Bt=class extends vr{async run(){let t=await this.parentTask;if(!t)return;let e=await Fe(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return lr(t,e)}withFaceExpressions(){return new te(this,this.input)}withAgeAndGender(){return new Kt(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 He.Tensor?await ie(this.input,e):await se(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof He.Tensor&&a.dispose()),t.map((a,s)=>le(a,n[s]))}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptors(){return new Wt(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 He.Tensor?await ie(this.input,[e]):await se(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof He.Tensor&&a.dispose()),le(t,n)}withFaceExpressions(){return new te(this,this.input)}withAgeAndGender(){return new Kt(this,this.input)}withFaceDescriptor(){return new Bt(this,this.input)}};var Pr=class extends tt{constructor(t,e=new Z){super();this.input=t;this.options=e}},Ye=class extends Pr{async run(){let{input:t,options:e}=this,r=e instanceof gr?n=>P.tinyFaceDetector.locateFaces(n,e):e instanceof Z?n=>P.ssdMobilenetv1.locateFaces(n,e):e instanceof ft?n=>P.tinyYolov2.locateFaces(n,e):null;if(!r)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return r(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>jt({},r)))})}withFaceLandmarks(t=!1){return new Fr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new we(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Pe(this.runAndExtendWithFaceDetections(),this.input)}},_r=class extends Pr{async run(){let t=await new Ye(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?jt({},e):void 0)})}withFaceLandmarks(t=!1){return new Tr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new De(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new _e(this.runAndExtendWithFaceDetection(),this.input)}};function ha(o,t=new Z){return new _r(o,t)}function wr(o,t=new Z){return new Ye(o,t)}async function mn(o,t){return wr(o,new Z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function xa(o,t={}){return wr(o,new ft(t)).withFaceLandmarks().withFaceDescriptors()}var ba=mn;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 bt)return s;if(s instanceof Float32Array)return new bt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new bt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>fo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Me(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distance<r.distance?e:r)}findBestMatch(t){let e=this.matchDescriptor(t);return e.distance<this.distanceThreshold?e:new Me("unknown",e.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>bt.fromJSON(r));return new Dr(e,t.distanceThreshold)}};function ga(o){let t=new Te;return t.extractWeights(o),t}function pn(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=>pn(n,{width:e,height:r}));if(Ut(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return le(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof V||o instanceof M?o.forSize(e,r):o}var ya=typeof process!="undefined",Fa=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",Ta={faceapi:Eo,node:ya,browser:Fa};export{pr as AgeGenderNet,oe as BoundingBox,D as Box,tt as ComposableTask,Wt as ComputeAllFaceDescriptorsTask,vr as ComputeFaceDescriptorsTaskBase,Bt as ComputeSingleFaceDescriptorTask,Fr as DetectAllFaceLandmarksTask,Ye as DetectAllFacesTask,yr as DetectFaceLandmarksTaskBase,Pr as DetectFacesTaskBase,Tr as DetectSingleFaceLandmarksTask,_r as DetectSingleFaceTask,A as Dimensions,qr as FACE_EXPRESSION_LABELS,M as FaceDetection,Uo as FaceDetectionNet,cr as FaceExpressionNet,Lt as FaceExpressions,he as FaceLandmark68Net,dr as FaceLandmark68TinyNet,Ao as FaceLandmarkNet,V as FaceLandmarks,bo as FaceLandmarks5,ae as FaceLandmarks68,Me as FaceMatch,Dr as FaceMatcher,be as FaceRecognitionNet,yt as Gender,Ce as LabeledBox,bt as LabeledFaceDescriptors,gt as NetInput,S as NeuralNetwork,Et as ObjectDetection,x as Point,go as PredictedBox,ne as Rect,Jt as SsdMobilenetv1,Z as SsdMobilenetv1Options,Te as TinyFaceDetector,gr as TinyFaceDetectorOptions,ye as TinyYolov2,ft as TinyYolov2Options,ba as allFaces,mn as allFacesSsdMobilenetv1,xa as allFacesTinyYolov2,Gr as awaitMediaLoaded,zr as bufferToImage,ra as computeFaceDescriptor,Gt as createCanvas,Le as createCanvasFromMedia,Jn as createFaceDetectionNet,Rn as createFaceRecognitionNet,Vo as createSsdMobilenetv1,ga as createTinyFaceDetector,Kn as createTinyYolov2,wr as detectAllFaces,sn as detectFaceLandmarks,ea as detectFaceLandmarksTiny,la as detectLandmarks,ha as detectSingleFace,to as draw,w as env,fo as euclideanDistance,hr as extendWithAge,lr as extendWithFaceDescriptor,jt as extendWithFaceDetection,mr as extendWithFaceExpressions,le as extendWithFaceLandmarks,xr as extendWithGender,ie as extractFaceTensors,se as extractFaces,Mn as fetchImage,Xr as fetchJson,Cn as fetchNetWeights,zt as fetchOrThrow,$ as getContext2dOrThrow,Yt as getMediaDimensions,Vr as imageTensorToCanvas,Ur as imageToSquare,vn as inverseSigmoid,kr as iou,qe as isMediaElement,Ie as isMediaLoaded,$n as isWithAge,pt as isWithFaceDetection,Zr as isWithFaceExpressions,Ut as isWithFaceLandmarks,On as isWithGender,da as loadAgeGenderModel,ua as loadFaceDetectionModel,pa as loadFaceExpressionModel,ia as loadFaceLandmarkModel,ca as loadFaceLandmarkTinyModel,ma as loadFaceRecognitionModel,cn as loadSsdMobilenetv1Model,aa as loadTinyFaceDetectorModel,sa as loadTinyYolov2Model,Jr as loadWeightMap,fa as locateFaces,Nn as matchDimensions,Sr as minBbox,P as nets,Ar as nonMaxSuppression,ot as normalize,Wr as padToSquare,na as predictAgeAndGender,oa as recognizeFaceExpressions,pn as resizeResults,Ht as resolveInput,gn as shuffleArray,Ee as sigmoid,an as ssdMobilenetv1,va as tf,Qn as tinyFaceDetector,ta as tinyYolov2,E as toNetInput,Cr as utils,so as validateConfig,Ta as version};
 //# sourceMappingURL=face-api.esm-nobundle.js.map
diff --git a/dist/face-api.esm.js b/dist/face-api.esm.js
index c072cef..3548de9 100644
--- a/dist/face-api.esm.js
+++ b/dist/face-api.esm.js
@@ -4045,7 +4045,7 @@ return a / b;`,B9=`
         }
         setOutput(${l});
       }
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New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var Fee={kernelName:hl,backendName:"wasm",kernelFunc:Pa},a2;function $ee(e){a2=e.wasm.cwrap(Es,null,["number","array","number","number","array","number","number","number","number"])}function Dee(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,c=s.shape.length,u=i?r.shape[l-2]:r.shape[l-1],p=o?s.shape[c-1]:s.shape[c-2],d=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[c-2]:s.shape[c-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=k.sizeFromShape(m),y=k.sizeFromShape(f),b=g===y||g===1||y===1;k.assert(l>=2&&c>=2&&b,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. 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Please use 'channelsLast'.`);let B=a.makeOutput(h.outShape,"float32"),W=a.dataIdMap.get(B.dataId).id;return p2(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,v,P,x,N,T,C,F,$,W),B}var ste={kernelName:Ps,backendName:"wasm",setupFunc:ate,kernelFunc:rte},ite=!1,ote=fn(Uo,ite,"bool"),lte=Sn(Ls);function Jv(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&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Pa({inputs:{x:r},backend:a,attrs:{shape:o}})}var ute={kernelName:Go,backendName:"wasm",kernelFunc:Jv};function cte(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var pte={kernelName:hc,backendName:"wasm",kernelFunc:cte},d2;function dte(e){d2=e.wasm.cwrap(jo,null,["number","number","number","number","number","number"])}function hte(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,c,u]=a.shape;return d2(s,o,l,c,u,i),r}var mte={kernelName:jo,backendName:"wasm",kernelFunc:hte,setupFunc:dte},fte=Sn(zs),gte=!1,yte=fn(Bs,gte),h2;function bte(e){h2=e.wasm.cwrap(Ws,null,["number","number","number","number","number","number","number"])}function xte(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=c!=null?t.dataIdMap.get(c.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return h2(u,p,d,h,m,r,g),f}var vte={kernelName:Ws,backendName:"wasm",setupFunc:bte,kernelFunc:xte},m2;function wte(e){m2=e.wasm.cwrap(xi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function kte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d),g=Sp[h];if(g==null)throw new Error(`${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,v=f.outChannels,x=0;if(i!=null){let ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,F=f.padInfo.right,$=f.padInfo.bottom,P=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,K=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return m2(y,K,ne,J,b,N,T,x,C,F,$,P,X,B,W,G,q,j,v,g,oe,m||0,Q),se}var Ite={kernelName:xi,backendName:"wasm",setupFunc:wte,kernelFunc:kte},f2;function Nte(e){f2=e.wasm.cwrap(vi,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 Tte(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=Sp[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,v=f.outChannels,x=0;if(i!=null){let ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,F=f.padInfo.right,$=f.padInfo.bottom,P=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,K=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return f2(y,K,ne,J,b,N,T,x,C,F,$,P,X,B,W,G,q,j,v,g,oe,m||0,Q),se}var Ste={kernelName:vi,backendName:"wasm",setupFunc:Nte,kernelFunc:Tte},g2;function Cte(e){g2=e.wasm.cwrap(Ko,null,["number","number","number","number","number","number","array","number"])}function _te(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=by.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 g2(d,Vn[a.dtype],h,i,p,o,m,f),c}var Ete={kernelName:Ko,backendName:"wasm",setupFunc:Cte,kernelFunc:_te},y2;function Ate(e){y2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Fte(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=k.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=k.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(k.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,v=new Uint8Array(new Int32Array(k.computeStrides(u.shape)).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer);return y2(g,Vn[r.dtype],v,f,y,c.batchSize,x,b),t.disposeData(u.dataId),t.disposeData(d.dataId),m.shape=c.outputShape,m}var $te={kernelName:qo,backendName:"wasm",setupFunc:Ate,kernelFunc:Fte},Dte=!1,Rte=fn(Xo,Dte,"bool"),Mte=!1,Pte=fn(Vs,Mte,"bool"),b2;function Ote(e){b2=e.wasm.cwrap(Gs,null,["number","number","number"])}function Lte(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,t.dtype);if(k.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;b2(r,n,i)}return s}var zte={kernelName:Gs,backendName:"wasm",setupFunc:Ote,kernelFunc:Lte},Bte=!1,Wte=fn(Qo,Bte,"bool"),Vte=!1,Ute=fn(el,Vte,"bool"),Gte=Sn(Hs),Hte=!1,jte=fn(nl,Hte,"bool"),x2;function qte(e){x2=e.wasm.cwrap(js,null,["number, number, number"])}function Kte(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:p,inputWasTransposed:d}=yu(i,r,t);if(d){let b=t.dataIdMap.get(c.dataId).id;l=c,o=b}let h=l.shape.length;_.assertAxesAreInnerMostDims("max",u,h);let[m,f]=_.computeOutAndReduceShapes(l.shape,u),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;x2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=_.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var Xte={kernelName:js,backendName:"wasm",setupFunc:qte,kernelFunc:Kte},Yte=!1,Zte=fn(qs,Yte),v2;function Jte(e){v2=e.wasm.cwrap(Ks,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Qte(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=_.computePool2DInfo(r.shape,i,o,1,l,c),p=u.filterHeight,d=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,g=u.padInfo.left,y=u.dilationHeight,b=u.dilationWidth,v=u.strideHeight,x=u.strideWidth,N=u.inChannels,T=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let C=a.makeOutput(u.outShape,"float32"),F=a.dataIdMap.get(C.dataId).id;return v2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,v,x,N,T,F),C}var ene={kernelName:Ks,backendName:"wasm",setupFunc:Jte,kernelFunc:Qte},w2;function tne(e){w2=e.wasm.cwrap(Xs,null,["number, number, number"])}function nne(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}=yu(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("mean",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=c;c.dtype!=="float32"&&(b=Qm({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let v=t.makeOutput(f,"float32");if(k.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(v.dataId).id;w2(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(v.shape,d);v.shape=x}return c.dtype!=="float32"&&t.disposeData(b.dataId),v}var ane={kernelName:Xs,backendName:"wasm",setupFunc:tne,kernelFunc:nne},k2;function rne(e){k2=e.wasm.cwrap(Ys,null,["number, number, number"])}function sne(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}=yu(i,r,t);if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v)}let m=c.shape.length;_.assertAxesAreInnerMostDims("min",p,m);let[f,g]=_.computeOutAndReduceShapes(c.shape,p),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;k2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var ine={kernelName:Ys,backendName:"wasm",setupFunc:rne,kernelFunc:sne},one=!1,lne=fn(Zs,one),une=!0,cne=fn(Js,une),pne=Sn(rl);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 I2;function dne(e){I2=e.wasm.cwrap(il,"number",["number","number","number","number","number"])}function hne(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=I2(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 mne={kernelName:il,backendName:"wasm",setupFunc:dne,kernelFunc:hne},N2;function fne(e){N2=e.wasm.cwrap(ol,"number",["number","number","number","number","number","bool"])}function gne(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=N2(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 yne={kernelName:ol,backendName:"wasm",setupFunc:fne,kernelFunc:gne},T2;function bne(e){T2=e.wasm.cwrap(ll,"number",["number","number","number","number","number","number"])}function xne(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=T2(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 vne={kernelName:ll,backendName:"wasm",setupFunc:bne,kernelFunc:xne},wne=!1,kne=fn(sl,wne,"bool"),S2;function Ine(e){S2=e.wasm.cwrap(Qs,null,["number","number","number","number","number"])}function Nne(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 S2(u,s,i,o,c),l}var Tne={kernelName:Qs,backendName:"wasm",setupFunc:Ine,kernelFunc:Nne};function Sne(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var Cne={kernelName:ul,backendName:"wasm",kernelFunc:Sne};function _ne(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(l=>{k.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===l.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=t.map(l=>Jv({inputs:{input:l},backend:n,attrs:{dim:r}}));return s2({inputs:o,backend:n,attrs:{axis:r}})}var Ene={kernelName:cl,backendName:"wasm",kernelFunc:_ne},C2;function Ane(e){C2=e.wasm.cwrap(ei,null,["number","array","number","number","array","array","number","number"])}function Fne(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 C2(i,c,t.shape.length,Vn[t.dtype],d,h,r,l),o}var $ne={kernelName:ei,backendName:"wasm",kernelFunc:Fne,setupFunc:Ane},Dne=!1,Rne=fn(ti,Dne),_2;function Mne(e){_2=e.wasm.cwrap(ni,null,["number","number","number"])}function Pne(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 _2(s,i,l),o}var One={kernelName:ni,backendName:"wasm",setupFunc:Mne,kernelFunc:Pne},E2;function Lne(e){E2=e.wasm.cwrap(pl,null,["number","number","number","number"])}function zne(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}=yu(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("prod",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;E2(l,y,Vn[b.dtype],v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var Bne={kernelName:pl,backendName:"wasm",setupFunc:Lne,kernelFunc:zne},Wne=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=Iv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},Vne={kernelName:xc,backendName:"wasm",kernelFunc:Wne},Une=!0,Gne=fn(Os,Une),Hne=Sn(ai),jne=Sn(si),A2;function qne(e){A2=e.wasm.cwrap(ri,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=Qm({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(k.sizeFromShape(r.shape)===0)return b;let v=t.dataIdMap.get(b.dataId).id;return A2(y,u,p,d,h,l,c,s?1:0,i?1:0,v),g!=null&&t.disposeData(g.dataId),b}var Xne={kernelName:ri,backendName:"wasm",setupFunc:qne,kernelFunc:Kne},F2;function Yne(e){F2=e.wasm.cwrap(ii,null,["number","array","number","array","number","number"])}function Zne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=k.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);F2(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 Jne={kernelName:ii,backendName:"wasm",kernelFunc:Zne,setupFunc:Yne},$2;function Qne(e){$2=e.wasm.cwrap(Cl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function eae(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,v=typeof i=="number"?[i,i,i,y?0:b]:[...i,b],x=new Uint8Array(new Int32Array(v).buffer);return $2(c,p,d,h,m,s,f,g,x,v.length,u),l}var tae={kernelName:Cl,backendName:"wasm",kernelFunc:eae,setupFunc:Qne},nae=Sn(oi),aae=Sn(li),D2;function rae(e){D2=e.wasm.cwrap(ml,null,["number","number","number","number","number","number","array","number","number"])}function sae(e){let{backend:t,inputs:n,attrs:a}=e,{indices:r,updates:s}=n,{shape:i}=a,o=t.makeOutput(i,s.dtype);if(k.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:c,sliceSize:u,strides:p,outputSize:d}=xy.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 D2(h,m,Vn[s.dtype],l,c,u,f,d,g),o}var iae={kernelName:ml,backendName:"wasm",setupFunc:rae,kernelFunc:sae},R2;function oae(e){R2=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function lae(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:k.sizeFromShape(r.shape.slice(1));return R2(i,o,l,h,u),c}var uae={kernelName:fl,backendName:"wasm",kernelFunc:lae,setupFunc:oae},M2;function cae(e){M2=e.wasm.cwrap(ci,null,["number","number"])}function pae(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 k.sizeFromShape(r.shape)===0||M2(a,s),r}var dae={kernelName:"Sigmoid",backendName:"wasm",setupFunc:cae,kernelFunc:pae},hae=Sn(ui);function ef(e){let{inputs:{x:t},attrs:{begin:n,size:a},backend:r}=e,[s,i]=cn.parseSliceParams(t,n,a),o=cn.isSliceContinous(t.shape,s,i),l=r.readSync(t.dataId),c=r.makeOutput(i,t.dtype),u=k.computeStrides(t.shape),p=r.dataIdMap.get(c.dataId);if(o){let m=cn.computeFlatOffset(s,u);return t.dtype==="string"?p.stringBytes=l.slice(m,m+k.sizeFromShape(i)):r.typedArrayFromHeap(c).set(l.subarray(m,m+k.sizeFromShape(i))),c}if(t.dtype==="string"){let m=Dm(l,s,i,t.shape,t.dtype);return p.stringBytes=m,c}let d=r.typedArrayFromHeap(c),h=t.shape.length;if(h===2)mae(l,u[0],d,s,i);else if(h===3)fae(l,u[0],u[1],d,s,i);else if(h===4)gae(l,u[0],u[1],u[2],d,s,i);else{let m=Dm(l,s,i,t.shape,t.dtype);d.set(m)}return c}function mae(e,t,n,a,r){let s=0,i=a[0],o=a[1],l=i+r[0];for(let c=i;c<l;c++){let u=c*t+o;n.set(e.subarray(u,u+r[1]),s),s+=r[1]}}function fae(e,t,n,a,r,s){let i=0,o=r[0],l=r[1],c=r[2],u=o+s[0],p=l+s[1];for(let d=o;d<u;d++)for(let h=l;h<p;h++){let m=d*t+h*n+c;a.set(e.subarray(m,m+s[2]),i),i+=s[2]}}function 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a=n/Math.max(t,e);return new gn(Math.round(e*a),Math.round(t*a))}function Zi(e){return e.reduce((t,n)=>t.add(n),new De(0,0)).div(new De(e.length,e.length))}function ir(e,t,n){return Array(e).fill(0).map((a,r)=>t+r*n)}function Oa(e){return!!e&&e!==Infinity&&e!==-Infinity&&!Number.isNaN(e)||e===0}function bu(e){return Oa(e)&&e>=0&&e<=1}var De=class{constructor(t,n){this._x=t,this._y=n}get x(){return this._x}get y(){return this._y}add(t){return new De(this.x+t.x,this.y+t.y)}sub(t){return new De(this.x-t.x,this.y-t.y)}mul(t){return new De(this.x*t.x,this.y*t.y)}div(t){return new De(this.x/t.x,this.y/t.y)}abs(){return new De(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new De(Math.floor(this.x),Math.floor(this.y))}};var ut=class{static isRect(t){return!!t&&[t.x,t.y,t.width,t.height].every(Oa)}static assertIsValidBox(t,n,a=!1){if(!ut.isRect(t))throw new Error(`${n} - invalid box: ${JSON.stringify(t)}, expected object with properties x, 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De(this.left,this.bottom)}get bottomRight(){return new De(this.right,this.bottom)}round(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.round(s));return new ut({x:t,y:n,width:a,height:r})}floor(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.floor(s));return new ut({x:t,y:n,width:a,height:r})}toSquare(){let{x:t,y:n,width:a,height:r}=this,s=Math.abs(a-r);return a<r&&(t-=s/2,a+=s),r<a&&(n-=s/2,r+=s),new ut({x:t,y:n,width:a,height:r})}rescale(t){let n=af(t)?t.width:t,a=af(t)?t.height:t;return new ut({x:this.x*n,y:this.y*a,width:this.width*n,height:this.height*a})}pad(t,n){let[a,r,s,i]=[this.x-t/2,this.y-n/2,this.width+t,this.height+n];return new ut({x:a,y:r,width:s,height:i})}clipAtImageBorders(t,n){let{x:a,y:r,right:s,bottom:i}=this,o=Math.max(a,0),l=Math.max(r,0),c=s-o,u=i-l,p=Math.min(c,t-o),d=Math.min(u,n-l);return new ut({x:o,y:l,width:p,height:d}).floor()}shift(t,n){let{width:a,height:r}=this,s=this.x+t,i=this.y+n;return new 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this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(Zi)}};var Fp=class{constructor(t,n){this._label=t,this._distance=n}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${Yi(this.distance)})`:""}`}};var $p=class extends ut{static assertIsValidLabeledBox(t,n){if(ut.assertIsValidBox(t,n),!Oa(t.label))throw new Error(`${n} - expected property label (${t.label}) to be a number`)}constructor(t,n){super(t);this._label=n}get label(){return this._label}};var _r=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 _r(t.label,n)}};var j2=class extends $p{static assertIsValidPredictedBox(t,n){if($p.assertIsValidLabeledBox(t,n),!bu(t.score)||!bu(t.classScore))throw new Error(`${n} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,n,a,r){super(t,n);this._score=a,this._classScore=r}get score(){return this._score}get classScore(){return this._classScore}};function or(e){return e.detection instanceof bt}function Ji(e,t){return{...e,...{detection:t}}}function cw(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser 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lr;(function(e){e.TOP_LEFT="TOP_LEFT",e.TOP_RIGHT="TOP_RIGHT",e.BOTTOM_LEFT="BOTTOM_LEFT",e.BOTTOM_RIGHT="BOTTOM_RIGHT"})(lr||(lr={}));var Dp=class{constructor(t={}){let{anchorPosition:n,backgroundColor:a,fontColor:r,fontSize:s,fontStyle:i,padding:o}=t;this.anchorPosition=n||lr.TOP_LEFT,this.backgroundColor=a||"rgba(0, 0, 0, 0.5)",this.fontColor=r||"rgba(255, 255, 255, 1)",this.fontSize=s||14,this.fontStyle=i||"Georgia",this.padding=o||4}},hs=class{constructor(t,n,a={}){this.text=typeof t=="string"?[t]:t instanceof hs?t.text:t,this.anchor=n,this.options=new Dp(a)}measureWidth(t){let{padding:n}=this.options;return this.text.map(a=>t.measureText(a).width).reduce((a,r)=>a<r?r:a,0)+2*n}measureHeight(){let{fontSize:t,padding:n}=this.options;return this.text.length*t+2*n}getUpperLeft(t,n){let{anchorPosition:a}=this.options,r=a===lr.BOTTOM_RIGHT||a===lr.TOP_RIGHT,s=a===lr.BOTTOM_LEFT||a===lr.BOTTOM_RIGHT,i=this.measureWidth(t),o=this.measureHeight(),l=r?this.anchor.x-i:this.anchor.x,c=s?this.anchor.y-o:this.anchor.y;if(n){let{width:u,height:p}=n,d=Math.max(Math.min(l,u-i),0),h=Math.max(Math.min(c,p-o),0);return{x:d,y:h}}return{x:l,y:c}}draw(t){let n=Qi(t),a=Cn(n),{backgroundColor:r,fontColor:s,fontSize:i,fontStyle:o,padding:l}=this.options;a.font=`${i}px ${o}`;let c=this.measureWidth(a),u=this.measureHeight();a.fillStyle=r;let p=this.getUpperLeft(a,n);a.fillRect(p.x,p.y,c,u),a.fillStyle=s,this.text.forEach((d,h)=>{let m=l+p.x,f=l+p.y+(h+1)*i;a.fillText(d,m,f)})}};var gw=class{constructor(t={}){let{boxColor:n,lineWidth:a,label:r,drawLabelOptions:s}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=a||2,this.label=r;let i={anchorPosition:lr.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Dp({...i,...s})}},sf=class{constructor(t,n={}){this.box=new ut(t),this.options=new gw(n)}draw(t){let n=Cn(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:c}=this.options;c&&new hs([c],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function gre(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof bt?a.score:or(a)?a.detection.score:void 0,s=a instanceof bt?a.box:or(a)?a.detection.box:new ut(a),i=r?`${Yi(r)}`:void 0;new sf(s,{label:i}).draw(e)})}function Rp(e){let{Image:t,Video:n}=st.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function yw(e){return new Promise((t,n)=>{if(e instanceof st.getEnv().Canvas||Rp(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 bw(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=st.getEnv().createImageElement();r.onload=()=>t(r),r.onerror=n,r.src=a.result},a.onerror=n,a.readAsDataURL(e)})}function eo(e){let{Image:t,Video:n}=st.getEnv();return e instanceof t?new gn(e.naturalWidth,e.naturalHeight):e instanceof n?new gn(e.videoWidth,e.videoHeight):new gn(e.width,e.height)}function to({width:e,height:t}){let{createCanvasElement:n}=st.getEnv(),a=n();return a.width=e,a.height=t,a}function Mp(e,t){let{ImageData:n}=st.getEnv();if(!(e instanceof n)&&!Rp(e))throw new Error("createCanvasFromMedia - media has not finished 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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=Su(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function CC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Bre(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 yn(e,n),{params:i,paramMappings:n}}var ur=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 an{constructor(t){super("TinyYolov2");Dw(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=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=Fr(a,n.conv6),a=Fr(a,n.conv7),ao(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Au(ao(t,n.conv0,"valid",!1)):$r(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=n.conv6?$r(a,n.conv6):a,a=n.conv7?$r(a,n.conv7):a,ao(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=ce(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?La(r,this.config.meanRgb):r,r=r.div(de(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await gt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new ur(n),s=await gt(t),i=await this.forwardInput(s,a),o=D(()=>dt(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 lw(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new ds(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return CC(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 ${a} filterSizes in config`);return SC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),v=y.slice([0,0,0,4],[c,c,u,1]),x=this.withClassScores?Ta(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):de(0);return[b,v,x]}),m=[],f=await d.array(),g=await p.array();for(let y=0;y<c;y++)for(let b=0;b<c;b++)for(let v=0;v<u;v++){let x=Ap(f[y][b][v][0]);if(!a||x>a){let N=(b+Ap(g[y][b][v][0]))/c*o,T=(y+Ap(g[y][b][v][1]))/c*l,C=Math.exp(g[y][b][v][2])*this.config.anchors[v].x/c*o,F=Math.exp(g[y][b][v][3])*this.config.anchors[v].y/c*l,$=N-C/2,P=T-F/2,B={row:y,col:b,anchor:v},{classScore:W,label:G}=this.withClassScores?await this.extractPredictedClass(h,B):{classScore:1,label:0};m.push({box:new xu($,P,$+C,P+F),score:x,classScore:x*W,label:G,...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)}},Fu=Rw;Fu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var $u=class extends Fu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:vC,classes:["face"],...t?{anchors:kC,meanRgb:IC}:{anchors:wC,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 bt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?TC:NC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Wre(e,t=!0){let n=new $u(t);return n.extractWeights(e),n}var Tf=class extends ur{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 oo(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>ro(l)?r(l):l.detection),i=a||(t instanceof Ae?await Iu(t,s):await ku(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ae&&l.dispose()),o}async function Du(e,t,n,a,r){return oo([e],t,async s=>n(s[0]),a,r)}var _C=.4,EC=[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)],AC=[117.001,114.697,97.404];var Ru=class extends Fu{constructor(){let t={withSeparableConvs:!0,iouThreshold:_C,classes:["face"],anchors:EC,meanRgb:AC,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 bt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var nt={ssdMobilenetv1:new io,tinyFaceDetector:new Ru,tinyYolov2:new $u,faceLandmark68Net:new _u,faceLandmark68TinyNet:new xf,faceRecognitionNet:new Eu,faceExpressionNet:new gf,ageGenderNet:new bf},FC=(e,t)=>nt.ssdMobilenetv1.locateFaces(e,t),Vre=(e,t)=>nt.tinyFaceDetector.locateFaces(e,t),Ure=(e,t)=>nt.tinyYolov2.locateFaces(e,t),$C=e=>nt.faceLandmark68Net.detectLandmarks(e),Gre=e=>nt.faceLandmark68TinyNet.detectLandmarks(e),Hre=e=>nt.faceRecognitionNet.computeFaceDescriptor(e),jre=e=>nt.faceExpressionNet.predictExpressions(e),qre=e=>nt.ageGenderNet.predictAgeAndGender(e),DC=e=>nt.ssdMobilenetv1.load(e),Kre=e=>nt.tinyFaceDetector.load(e),Xre=e=>nt.tinyYolov2.load(e),Yre=e=>nt.faceLandmark68Net.load(e),Zre=e=>nt.faceLandmark68TinyNet.load(e),Jre=e=>nt.faceRecognitionNet.load(e),Qre=e=>nt.faceExpressionNet.load(e),ese=e=>nt.ageGenderNet.load(e),tse=DC,nse=FC,ase=$C;var Mw=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Ou=class extends Mw{async run(){let t=await this.parentTask,n=await oo(t,this.input,async a=>Promise.all(a.map(r=>nt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>yf(a,n[r]))}withAgeAndGender(){return new Mu(this,this.input)}},Lu=class extends Mw{async run(){let t=await this.parentTask;if(!t)return;let n=await Du(t,this.input,a=>nt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return yf(t,n)}withAgeAndGender(){return new Pu(this,this.input)}},co=class extends Ou{withAgeAndGender(){return new lo(this,this.input)}withFaceDescriptors(){return new fs(this,this.input)}},po=class extends Lu{withAgeAndGender(){return new uo(this,this.input)}withFaceDescriptor(){return new gs(this,this.input)}};var Pw=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Mu=class extends Pw{async run(){let t=await this.parentTask,n=await oo(t,this.input,async a=>Promise.all(a.map(r=>nt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return kf(If(a,i,o),s)})}withFaceExpressions(){return new Ou(this,this.input)}},Pu=class extends Pw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Du(t,this.input,s=>nt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return kf(If(t,a,r),n)}withFaceExpressions(){return new Lu(this,this.input)}},lo=class extends Mu{withFaceExpressions(){return new co(this,this.input)}withFaceDescriptors(){return new fs(this,this.input)}},uo=class extends Pu{withFaceExpressions(){return new po(this,this.input)}withFaceDescriptor(){return new gs(this,this.input)}};var Sf=class extends wa{constructor(t,n){super();this.parentTask=t;this.input=n}},fs=class extends Sf{async run(){let t=await this.parentTask;return(await oo(t,this.input,a=>Promise.all(a.map(r=>nt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>wf(t[r],a))}withFaceExpressions(){return new co(this,this.input)}withAgeAndGender(){return new lo(this,this.input)}},gs=class extends Sf{async run(){let t=await this.parentTask;if(!t)return;let n=await Du(t,this.input,a=>nt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return wf(t,n)}withFaceExpressions(){return new po(this,this.input)}withAgeAndGender(){return new uo(this,this.input)}};var Cf=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?nt.faceLandmark68TinyNet:nt.faceLandmark68Net}},_f=class extends Cf{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Ae?await Iu(this.input,n):await ku(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Ae&&s.dispose()),t.map((s,i)=>Cu(s,r[i]))}withFaceExpressions(){return new co(this,this.input)}withAgeAndGender(){return new lo(this,this.input)}withFaceDescriptors(){return new fs(this,this.input)}},Ef=class extends Cf{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ae?await Iu(this.input,[n]):await ku(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ae&&s.dispose()),Cu(t,r)}withFaceExpressions(){return new po(this,this.input)}withAgeAndGender(){return new uo(this,this.input)}withFaceDescriptor(){return new gs(this,this.input)}};var Af=class extends wa{constructor(t,n=new va){super();this.input=t;this.options=n}},Vp=class extends Af{async run(){let{input:t,options:n}=this,a=n instanceof Tf?r=>nt.tinyFaceDetector.locateFaces(r,n):n instanceof va?r=>nt.ssdMobilenetv1.locateFaces(r,n):n instanceof ur?r=>nt.tinyYolov2.locateFaces(r,n):null;if(!a)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return a(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>Ji({},a)))})}withFaceLandmarks(t=!1){return new _f(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Ou(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Mu(this.runAndExtendWithFaceDetections(),this.input)}},Ff=class extends Af{async run(){let t=await new Vp(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?Ji({},n):void 0)})}withFaceLandmarks(t=!1){return new Ef(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Lu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Pu(this.runAndExtendWithFaceDetection(),this.input)}};function rse(e,t=new va){return new Ff(e,t)}function $f(e,t=new va){return new Vp(e,t)}async function RC(e,t){return $f(e,new va(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function sse(e,t={}){return $f(e,new ur(t)).withFaceLandmarks().withFaceDescriptors()}var ise=RC;function Ow(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 Df=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 _r)return i;if(i instanceof Float32Array)return new _r(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new _r(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>Ow(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Fp(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distance<a.distance?n:a)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this.distanceThreshold?n:new Fp("unknown",n.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>_r.fromJSON(a));return new Df(n,t.distanceThreshold)}};function ose(e){let t=new Ru;return t.extractWeights(e),t}function MC(e,t){let{width:n,height:a}=new gn(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>MC(r,{width:n,height:a}));if(ro(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Cu(Ji(e,r),s)}return or(e)?Ji(e,e.detection.forSize(n,a)):e instanceof ta||e instanceof bt?e.forSize(n,a):e}var lse=typeof process!="undefined",use=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",cse={faceapi:Q2,node:lse,browser:use};export{bf as AgeGenderNet,xu as BoundingBox,ut as Box,wa as ComposableTask,fs as ComputeAllFaceDescriptorsTask,Sf as ComputeFaceDescriptorsTaskBase,gs as ComputeSingleFaceDescriptorTask,_f as DetectAllFaceLandmarksTask,Vp as DetectAllFacesTask,Cf as DetectFaceLandmarksTaskBase,Af as DetectFacesTaskBase,Ef as DetectSingleFaceLandmarksTask,Ff as DetectSingleFaceTask,gn as Dimensions,Iw as FACE_EXPRESSION_LABELS,bt as FaceDetection,xC as FaceDetectionNet,gf as FaceExpressionNet,ms as FaceExpressions,_u as FaceLandmark68Net,xf as FaceLandmark68TinyNet,oC as FaceLandmarkNet,ta as FaceLandmarks,H2 as FaceLandmarks5,wu as FaceLandmarks68,Fp as FaceMatch,Df as FaceMatcher,Eu as FaceRecognitionNet,Ar as Gender,$p as LabeledBox,_r as LabeledFaceDescriptors,Er as NetInput,an as NeuralNetwork,ds as ObjectDetection,De as Point,j2 as PredictedBox,vu as Rect,io as SsdMobilenetv1,va as SsdMobilenetv1Options,Ru as TinyFaceDetector,Tf as TinyFaceDetectorOptions,$u as TinyYolov2,ur as TinyYolov2Options,ise as allFaces,RC as allFacesSsdMobilenetv1,sse as allFacesTinyYolov2,yw as awaitMediaLoaded,bw as bufferToImage,Hre as computeFaceDescriptor,to as createCanvas,Mp as createCanvasFromMedia,Lre as createFaceDetectionNet,Cre as createFaceRecognitionNet,bC as createSsdMobilenetv1,ose as createTinyFaceDetector,Wre as createTinyYolov2,$f as detectAllFaces,$C as detectFaceLandmarks,Gre as detectFaceLandmarksTiny,ase as detectLandmarks,rse as detectSingleFace,Cw as draw,st as env,Ow as euclideanDistance,kf as extendWithAge,wf as extendWithFaceDescriptor,Ji as extendWithFaceDetection,yf as extendWithFaceExpressions,Cu as extendWithFaceLandmarks,If as extendWithGender,Iu as extractFaceTensors,ku as extractFaces,yre as fetchImage,ww as fetchJson,bre as fetchNetWeights,no as fetchOrThrow,Cn as getContext2dOrThrow,eo as getMediaDimensions,xw as imageTensorToCanvas,vw as imageToSquare,lre as inverseSigmoid,iw as iou,of as isMediaElement,Rp as isMediaLoaded,_re as isWithAge,or as isWithFaceDetection,Nw as isWithFaceExpressions,ro as isWithFaceLandmarks,Ere as isWithGender,ese as loadAgeGenderModel,tse as loadFaceDetectionModel,Qre as loadFaceExpressionModel,Yre as loadFaceLandmarkModel,Zre as loadFaceLandmarkTinyModel,Jre as loadFaceRecognitionModel,DC as loadSsdMobilenetv1Model,Kre as loadTinyFaceDetectorModel,Xre as loadTinyYolov2Model,kw as loadWeightMap,nse as locateFaces,xre as matchDimensions,ow as minBbox,nt as nets,lw as nonMaxSuppression,La as normalize,uw as padToSquare,qre as predictAgeAndGender,jre as recognizeFaceExpressions,MC as resizeResults,Qi as resolveInput,ore as shuffleArray,Ap as sigmoid,FC as ssdMobilenetv1,Ug as tf,Vre as tinyFaceDetector,Ure as tinyYolov2,gt as toNetInput,nw as utils,Dw as validateConfig,cse as version};
+    `}};function iee(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=Tn({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=k.sizeFromShape([p.shape[c]]),m=be({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=nh(r.dtype),g=(x,N,T,C,F)=>{let $=x.shape[0],P=x.shape[1],B=_.segment_util.segOpComputeOptimalWindowSize(P,F),W={windowSize:B,inSize:P,batchSize:$,numSegments:F},G=new see(W,N),q=n.compileAndRun(G,[x,T],C);if(l.push(q),q.shape[1]===F)return q;let j=KS({backend:n,attrs:{start:0,stop:F,step:1,dtype:"float32"}}),X=ZS({inputs:{x:j},backend:n,attrs:{reps:[P/B]}});return l.push(j),l.push(X),g(q,N,X,C,F)},y=g(m,"unsortedSegmentSum",s,f,i),b=be({inputs:{x:y},backend:n,attrs:{shape:d}}),v=b;if(u!=null){l.push(b);let x=_.getUndoAxesPermutation(u);v=Tn({inputs:{x:v},backend:n,attrs:{perm:x}})}return 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e2(u,h,l.shape.length,Vn[l.dtype],p,d,s.length),c}function kee(e,t){let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];return n}function Iee(e,t){let n=[],a=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&a.push(t[r]);for(let r=0;r<a.length;++r){let s=-1;for(let i=0;i<a.length;++i)a[i]>=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var Nee={kernelName:yi,backendName:"wasm",kernelFunc:Jm,setupFunc:wee};function yu(e,t,n){let a=e.shape,r=e.shape.length,s=k.parseAxisParam(t,a),i=s,o=_.getAxesPermutation(i,r),l=null,c=!1;if(o!=null){let u=new Array(r);for(let d=0;d<u.length;d++)u[d]=a[o[d]];i=_.getInnerMostAxes(i.length,r),l=Jm({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==p&&(c=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:c}}var t2;function Tee(e){t2=e.wasm.cwrap(Cs,null,["number","number","number","number","number"])}function See(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:c,axes:u,inputWasTransposed:p}=yu(s,r,t);if(p){let y=t.dataIdMap.get(c.dataId).id;y!==i&&(l=c,o=y)}let d=l.shape.slice(0,-1),h=t.makeOutput(d,"int32"),m=t.dataIdMap.get(h.dataId).id,f=k.sizeFromShape(h.shape),g=l.shape[u[0]];return t2(o,Vn[l.dtype],f,g,m),p&&t.disposeData(c.dataId),h}var Cee={kernelName:Cs,backendName:"wasm",kernelFunc:See,setupFunc:Tee},n2;function _ee(e){n2=e.wasm.cwrap(_s,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Eee(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.strideHeight,b=u.strideWidth,v=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. 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Please use 'channelsLast'.`);let W=a.makeOutput(m.outShape,"float32"),G=a.dataIdMap.get(W.dataId).id;return i2(i,r.shape[0],r.shape[1],r.shape[2],o,f,g,y,b,v,x,B,N,T,C,F,$,P,G),W}var Uee={kernelName:$s,backendName:"wasm",setupFunc:Wee,kernelFunc:Vee},o2;function Gee(e){o2=e.wasm.cwrap(Ds,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Hee(e){let{backend:t,inputs:n,attrs:a}=e,{dy:r,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,inputShape:u}=a,p=1,d=_.convertConv2DDataFormat(l),h=_.computeConv2DInfo(u,s.shape,i,p,o,c,!1,d),{batchSize:m,filterHeight:f,filterWidth:g,inChannels:y,inHeight:b,inWidth:v,outChannels:x,outHeight:N,outWidth:T,strideHeight:C,strideWidth:F}=h,$=f-1-h.padInfo.top,P=g-1-h.padInfo.left,B=h.dataFormat==="channelsLast",W=k.computeStrides(h.inShape),G=k.computeStrides(r.shape),[q,j,X]=k.computeStrides(s.shape),K=W[0],ne=B?W[1]:W[2],J=B?W[2]:1,se=B?1:W[1],Q=G[0],oe=B?G[1]:G[2],ae=B?G[2]:1,pe=B?1:G[1],ue=t.makeOutput(h.inShape,"float32"),ge=t.dataIdMap.get(ue.dataId).id,ye=t.dataIdMap.get(r.dataId).id,ke=t.dataIdMap.get(s.dataId).id;return o2(ye,ke,m,f,g,b,v,y,N,T,x,C,F,$,P,q,j,X,K,ne,J,se,Q,oe,ae,pe,ge),ue}var jee={kernelName:Ds,backendName:"wasm",setupFunc:Gee,kernelFunc:Hee},qee=Sn(Rs),Zv;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(Zv||(Zv={}));var l2;function Kee(e){l2=e.wasm.cwrap(zo,null,["number","number","number","number","array","number","number","number","number","number"])}function Xee(e){let{backend:t,inputs:n,attrs:a}=e,{method:r,extrapolationValue:s,cropSize:i}=a,{image:o,boxes:l,boxInd:c}=n,u=l.shape[0],[p,d]=i,h=[u,p,d,o.shape[3]],m=t.dataIdMap.get(o.dataId),f;o.dtype!=="float32"&&(f=Qm({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(f.dataId));let g=m.id,y=t.dataIdMap.get(l.dataId).id,b=t.dataIdMap.get(c.dataId).id,v=t.makeOutput(h,"float32"),x=t.dataIdMap.get(v.dataId).id,N=new Uint8Array(new Int32Array(o.shape).buffer);return l2(g,y,b,u,N,p,d,Zv[r],s,x),f!=null&&t.disposeData(f.dataId),v}var Yee={kernelName:zo,backendName:"wasm",setupFunc:Kee,kernelFunc:Xee},u2;function Zee(e){u2=e.wasm.cwrap(Ms,null,["number","number","number","number","number","number"])}function Jee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;k.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let c=_.getAxesPermutation([s],l),u=r;c!==null&&(u=Jm({inputs:{x:r},attrs:{perm:c},backend:n}));let p=_.getInnerMostAxes(1,l)[0];_.assertAxesAreInnerMostDims("cumsum",[p],l);let d=n.makeOutput(u.shape,u.dtype),h=u.shape[p],m=n.dataIdMap.get(u.dataId).id,f=n.dataIdMap.get(d.dataId).id;u2(m,i?1:0,o?1:0,h,f,Vn[r.dtype]);let g=d;if(c!==null){let y=_.getUndoAxesPermutation(c);g=Jm({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(u.dataId),n.disposeData(d.dataId)}return g}var Qee={kernelName:Ms,backendName:"wasm",setupFunc:Zee,kernelFunc:Jee},c2;function ete(e){c2=e.wasm.cwrap(Bo,null,["number","number","number","array","number","array","array","number","number"])}function tte(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],c=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=c*s,h=u/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),v=new Uint8Array(new Int32Array(k.computeStrides(m)).buffer),x=t.dataIdMap.get(f.dataId).id;return c2(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,v,m.length,x),f}var nte={kernelName:Bo,backendName:"wasm",setupFunc:ete,kernelFunc:tte},p2;function ate(e){p2=e.wasm.cwrap(Ps,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function rte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:p}=n,d=c==null?[1,1]:c,h=_.computeConv2DInfo(r.shape,s.shape,l,d,u,p,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,N=h.dilationWidth,T=h.strideHeight,C=h.strideWidth,F=h.inChannels,$=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let B=a.makeOutput(h.outShape,"float32"),W=a.dataIdMap.get(B.dataId).id;return p2(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,v,P,x,N,T,C,F,$,W),B}var ste={kernelName:Ps,backendName:"wasm",setupFunc:ate,kernelFunc:rte},ite=!1,ote=fn(Uo,ite,"bool"),lte=Sn(Ls);function Jv(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&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Pa({inputs:{x:r},backend:a,attrs:{shape:o}})}var ute={kernelName:Go,backendName:"wasm",kernelFunc:Jv};function cte(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var pte={kernelName:hc,backendName:"wasm",kernelFunc:cte},d2;function dte(e){d2=e.wasm.cwrap(jo,null,["number","number","number","number","number","number"])}function hte(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,c,u]=a.shape;return d2(s,o,l,c,u,i),r}var mte={kernelName:jo,backendName:"wasm",kernelFunc:hte,setupFunc:dte},fte=Sn(zs),gte=!1,yte=fn(Bs,gte),h2;function bte(e){h2=e.wasm.cwrap(Ws,null,["number","number","number","number","number","number","number"])}function xte(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=c!=null?t.dataIdMap.get(c.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return h2(u,p,d,h,m,r,g),f}var vte={kernelName:Ws,backendName:"wasm",setupFunc:bte,kernelFunc:xte},m2;function wte(e){m2=e.wasm.cwrap(xi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function kte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d),g=Sp[h];if(g==null)throw new Error(`${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,v=f.outChannels,x=0;if(i!=null){let ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,F=f.padInfo.right,$=f.padInfo.bottom,P=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,K=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return m2(y,K,ne,J,b,N,T,x,C,F,$,P,X,B,W,G,q,j,v,g,oe,m||0,Q),se}var Ite={kernelName:xi,backendName:"wasm",setupFunc:wte,kernelFunc:kte},f2;function Nte(e){f2=e.wasm.cwrap(vi,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 Tte(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=Sp[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,v=f.outChannels,x=0;if(i!=null){let ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,F=f.padInfo.right,$=f.padInfo.bottom,P=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,K=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return f2(y,K,ne,J,b,N,T,x,C,F,$,P,X,B,W,G,q,j,v,g,oe,m||0,Q),se}var Ste={kernelName:vi,backendName:"wasm",setupFunc:Nte,kernelFunc:Tte},g2;function Cte(e){g2=e.wasm.cwrap(Ko,null,["number","number","number","number","number","number","array","number"])}function _te(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=by.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 g2(d,Vn[a.dtype],h,i,p,o,m,f),c}var Ete={kernelName:Ko,backendName:"wasm",setupFunc:Cte,kernelFunc:_te},y2;function Ate(e){y2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Fte(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=k.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=k.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(k.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,v=new Uint8Array(new Int32Array(k.computeStrides(u.shape)).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer);return y2(g,Vn[r.dtype],v,f,y,c.batchSize,x,b),t.disposeData(u.dataId),t.disposeData(d.dataId),m.shape=c.outputShape,m}var $te={kernelName:qo,backendName:"wasm",setupFunc:Ate,kernelFunc:Fte},Dte=!1,Rte=fn(Xo,Dte,"bool"),Mte=!1,Pte=fn(Vs,Mte,"bool"),b2;function Ote(e){b2=e.wasm.cwrap(Gs,null,["number","number","number"])}function Lte(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,t.dtype);if(k.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;b2(r,n,i)}return s}var zte={kernelName:Gs,backendName:"wasm",setupFunc:Ote,kernelFunc:Lte},Bte=!1,Wte=fn(Qo,Bte,"bool"),Vte=!1,Ute=fn(el,Vte,"bool"),Gte=Sn(Hs),Hte=!1,jte=fn(nl,Hte,"bool"),x2;function qte(e){x2=e.wasm.cwrap(js,null,["number, number, number"])}function Kte(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:p,inputWasTransposed:d}=yu(i,r,t);if(d){let b=t.dataIdMap.get(c.dataId).id;l=c,o=b}let h=l.shape.length;_.assertAxesAreInnerMostDims("max",u,h);let[m,f]=_.computeOutAndReduceShapes(l.shape,u),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;x2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=_.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var Xte={kernelName:js,backendName:"wasm",setupFunc:qte,kernelFunc:Kte},Yte=!1,Zte=fn(qs,Yte),v2;function Jte(e){v2=e.wasm.cwrap(Ks,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Qte(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=_.computePool2DInfo(r.shape,i,o,1,l,c),p=u.filterHeight,d=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,g=u.padInfo.left,y=u.dilationHeight,b=u.dilationWidth,v=u.strideHeight,x=u.strideWidth,N=u.inChannels,T=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let C=a.makeOutput(u.outShape,"float32"),F=a.dataIdMap.get(C.dataId).id;return v2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,v,x,N,T,F),C}var ene={kernelName:Ks,backendName:"wasm",setupFunc:Jte,kernelFunc:Qte},w2;function tne(e){w2=e.wasm.cwrap(Xs,null,["number, number, number"])}function nne(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}=yu(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("mean",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=c;c.dtype!=="float32"&&(b=Qm({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let v=t.makeOutput(f,"float32");if(k.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(v.dataId).id;w2(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(v.shape,d);v.shape=x}return c.dtype!=="float32"&&t.disposeData(b.dataId),v}var ane={kernelName:Xs,backendName:"wasm",setupFunc:tne,kernelFunc:nne},k2;function rne(e){k2=e.wasm.cwrap(Ys,null,["number, number, number"])}function sne(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}=yu(i,r,t);if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v)}let m=c.shape.length;_.assertAxesAreInnerMostDims("min",p,m);let[f,g]=_.computeOutAndReduceShapes(c.shape,p),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;k2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var ine={kernelName:Ys,backendName:"wasm",setupFunc:rne,kernelFunc:sne},one=!1,lne=fn(Zs,one),une=!0,cne=fn(Js,une),pne=Sn(rl);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 I2;function dne(e){I2=e.wasm.cwrap(il,"number",["number","number","number","number","number"])}function hne(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=I2(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 mne={kernelName:il,backendName:"wasm",setupFunc:dne,kernelFunc:hne},N2;function fne(e){N2=e.wasm.cwrap(ol,"number",["number","number","number","number","number","bool"])}function gne(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=N2(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 yne={kernelName:ol,backendName:"wasm",setupFunc:fne,kernelFunc:gne},T2;function bne(e){T2=e.wasm.cwrap(ll,"number",["number","number","number","number","number","number"])}function xne(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=T2(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 vne={kernelName:ll,backendName:"wasm",setupFunc:bne,kernelFunc:xne},wne=!1,kne=fn(sl,wne,"bool"),S2;function Ine(e){S2=e.wasm.cwrap(Qs,null,["number","number","number","number","number"])}function Nne(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 S2(u,s,i,o,c),l}var Tne={kernelName:Qs,backendName:"wasm",setupFunc:Ine,kernelFunc:Nne};function Sne(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var Cne={kernelName:ul,backendName:"wasm",kernelFunc:Sne};function _ne(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(l=>{k.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===l.dtype,()=>"All tensors passed to stack must have matching 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Xne={kernelName:ri,backendName:"wasm",setupFunc:qne,kernelFunc:Kne},F2;function Yne(e){F2=e.wasm.cwrap(ii,null,["number","array","number","array","number","number"])}function Zne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=k.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);F2(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 Jne={kernelName:ii,backendName:"wasm",kernelFunc:Zne,setupFunc:Yne},$2;function Qne(e){$2=e.wasm.cwrap(Cl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function 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gae(e,t,n,a,r,s,i){let o=0,l=s[0],c=s[1],u=s[2],p=l+i[0],d=c+i[1],h=u+i[2],m=s[3];for(let f=l;f<p;f++)for(let g=c;g<d;g++)for(let y=u;y<h;y++){let b=f*t+g*n+y*a+m;r.set(e.subarray(b,b+i[3]),o),o+=i[3]}}var yae={kernelName:yl,backendName:"wasm",kernelFunc:ef},P2;function bae(e){P2=e.wasm.cwrap(hi,null,["number","number","number","number"])}function xae(e){let{backend:t,inputs:{logits:n},attrs:{dim:a}}=e,r=t.dataIdMap.get(n.dataId).id,s=t.makeOutput(n.shape,n.dtype),i=t.dataIdMap.get(s.dataId).id,o=n.shape[a],l=k.sizeFromShape(n.shape)/o;return k.sizeFromShape(s.shape)===0||P2(r,i,o,l),s}var vae={kernelName:hi,backendName:"wasm",setupFunc:bae,kernelFunc:xae};function wae(e){let{inputs:t,attrs:n,backend:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=k.parseAxisParam(i,r.shape)[0],l=_.prepareSplitSize(r,s,o),c=new Array(r.shape.length).fill(0),u=r.shape.slice();return l.map(p=>{let d=[...u];d[o]=p;let h=ef({inputs:{x:r},attrs:{begin:c,size:d},backend:a});return c[o]+=p,h})}var kae={kernelName:wl,backendName:"wasm",kernelFunc:wae},Iae=Sn(pi),Nae=Sn(kc),Tae=!0,Sae=fn(mi,Tae),O2;function Cae(e){O2=e.wasm.cwrap(Vr,null,["number","number","number"])}function _ae(e){let{backend:t,inputs:n,attrs:a}=e,{alpha:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=t.makeOutput(s.shape,s.dtype),l=t.dataIdMap.get(o.dataId).id;return O2(i,r,l),o}var Eae={kernelName:Vr,backendName:"wasm",setupFunc:Cae,kernelFunc:_ae},L2;function Aae(e){L2=e.wasm.cwrap(kl,null,["number","array","number","array","array","array","array","array","number","number"])}function Fae(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{begin:s,end:i,strides:o}=a;o==null&&(o=new Array(s.length));let{beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:p,shrinkAxisMask:d}=a,h=_.slice_util.maskToAxes(u);if(h.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(u!==0&&p!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(u!==0&&d!==0)throw new Error("Using both 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this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(Zi)}};var Fp=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}${t?` (${Yi(this.distance)})`:""}`}};var $p=class extends ut{static assertIsValidLabeledBox(t,n){if(ut.assertIsValidBox(t,n),!Oa(t.label))throw new Error(`${n} - expected property label (${t.label}) to be a number`)}constructor(t,n){super(t);this._label=n}get label(){return this._label}};var _r=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 _r(t.label,n)}};var j2=class extends $p{static assertIsValidPredictedBox(t,n){if($p.assertIsValidLabeledBox(t,n),!bu(t.score)||!bu(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 or(e){return e.detection instanceof bt}function Ji(e,t){return{...e,...{detection:t}}}function cw(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser 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isBrowser()");let{Canvas:t=Jt.Canvas,Image:n=Jt.Image}=e;Jt.Canvas=t,Jt.Image=n,Jt.createCanvasElement=e.createCanvasElement||(()=>new t),Jt.createImageElement=e.createImageElement||(()=>new n),Jt.ImageData=e.ImageData||Jt.ImageData,Jt.Video=e.Video||Jt.Video,Jt.fetch=e.fetch||Jt.fetch,Jt.readFile=e.readFile||Jt.readFile}var st={getEnv:mre,setEnv:mw,initialize:fw,createBrowserEnv:cw,createFileSystem:rf,createNodejsEnv:pw,monkeyPatch:fre,isBrowser:dw,isNodejs:hw.isNodejs};fw();function Qi(e){return!st.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function Cn(e){let{Canvas:t,CanvasRenderingContext2D:n}=st.getEnv();if(e instanceof n)return e;let a=Qi(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 Dp=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}},hs=class{constructor(t,n,a={}){this.text=typeof t=="string"?[t]:t instanceof hs?t.text:t,this.anchor=n,this.options=new Dp(a)}measureWidth(t){let{padding:n}=this.options;return this.text.map(a=>t.measureText(a).width).reduce((a,r)=>a<r?r:a,0)+2*n}measureHeight(){let{fontSize:t,padding:n}=this.options;return this.text.length*t+2*n}getUpperLeft(t,n){let{anchorPosition:a}=this.options,r=a===lr.BOTTOM_RIGHT||a===lr.TOP_RIGHT,s=a===lr.BOTTOM_LEFT||a===lr.BOTTOM_RIGHT,i=this.measureWidth(t),o=this.measureHeight(),l=r?this.anchor.x-i:this.anchor.x,c=s?this.anchor.y-o:this.anchor.y;if(n){let{width:u,height:p}=n,d=Math.max(Math.min(l,u-i),0),h=Math.max(Math.min(c,p-o),0);return{x:d,y:h}}return{x:l,y:c}}draw(t){let n=Qi(t),a=Cn(n),{backgroundColor:r,fontColor:s,fontSize:i,fontStyle:o,padding:l}=this.options;a.font=`${i}px ${o}`;let c=this.measureWidth(a),u=this.measureHeight();a.fillStyle=r;let p=this.getUpperLeft(a,n);a.fillRect(p.x,p.y,c,u),a.fillStyle=s,this.text.forEach((d,h)=>{let m=l+p.x,f=l+p.y+(h+1)*i;a.fillText(d,m,f)})}};var gw=class{constructor(t={}){let{boxColor:n,lineWidth:a,label:r,drawLabelOptions:s}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=a||2,this.label=r;let i={anchorPosition:lr.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Dp({...i,...s})}},sf=class{constructor(t,n={}){this.box=new ut(t),this.options=new gw(n)}draw(t){let n=Cn(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:c}=this.options;c&&new hs([c],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function gre(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof bt?a.score:or(a)?a.detection.score:void 0,s=a instanceof bt?a.box:or(a)?a.detection.box:new ut(a),i=r?`${Yi(r)}`:void 0;new sf(s,{label:i}).draw(e)})}function Rp(e){let{Image:t,Video:n}=st.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function yw(e){return new Promise((t,n)=>{if(e instanceof st.getEnv().Canvas||Rp(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 bw(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=st.getEnv().createImageElement();r.onload=()=>t(r),r.onerror=n,r.src=a.result},a.onerror=n,a.readAsDataURL(e)})}function eo(e){let{Image:t,Video:n}=st.getEnv();return e instanceof t?new gn(e.naturalWidth,e.naturalHeight):e instanceof n?new gn(e.videoWidth,e.videoHeight):new gn(e.width,e.height)}function to({width:e,height:t}){let{createCanvasElement:n}=st.getEnv(),a=n();return a.width=e,a.height=t,a}function Mp(e,t){let{ImageData:n}=st.getEnv();if(!(e instanceof n)&&!Rp(e))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:a,height:r}=t||eo(e),s=to({width:a,height:r});return e instanceof n?Cn(s).putImageData(e,0,0):Cn(s).drawImage(e,0,0,a,r),s}async function xw(e,t){let n=t||st.getEnv().createCanvasElement(),[a,r,s]=e.shape.slice(ea(e)?1:0),i=D(()=>e.as3D(a,r,s).toInt());return await Ni.toPixels(i,n),i.dispose(),n}function of(e){let{Image:t,Canvas:n,Video:a}=st.getEnv();return e instanceof t||e instanceof n||e instanceof a}function vw(e,t,n=!1){let{Image:a,Canvas:r}=st.getEnv();if(!(e instanceof a||e instanceof r))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return to({width:1,height:1});let s=eo(e),i=t/Math.max(s.height,s.width),o=i*s.width,l=i*s.height,c=to({width:t,height:t}),u=e instanceof r?e:Mp(e),p=Math.abs(o-l)/2,d=n&&o<l?p:0,h=n&&l<o?p:0;return u.width>0&&u.height>0&&Cn(c).drawImage(u,d,h,o,l),c}var Er=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(Cr(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(ea(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=a,this._inputDimensions[r]=a.shape.slice(1);return}let s=a instanceof st.getEnv().Canvas?a:Mp(a);this._canvases[r]=s,this._inputDimensions[r]=[s.height,s.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get 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d=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:d,separable_conv:h};return yn(e,n),{params:{entry_flow:u,middle_flow:p,exit_flow:m},paramMappings:n}}function nC(e,t,n){return Z(At(e,t.filters,n,"same"),t.bias)}function _w(e,t,n=!0){let a=n?Ye(e):e;return a=_n(a,t.separable_conv0,[1,1]),a=_n(Ye(a),t.separable_conv1,[1,1]),a=Ft(a,[3,3],[2,2],"same"),a=Z(a,nC(e,t.expansion_conv,[2,2])),a}function Nre(e,t){let n=_n(Ye(e),t.separable_conv0,[1,1]);return n=_n(Ye(n),t.separable_conv1,[1,1]),n=_n(Ye(n),t.separable_conv2,[1,1]),n=Z(n,e),n}var Ew=class extends an{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return D(()=>{let a=ce(t.toBatchTensor(112,!0),"float32"),s=La(a,[122.782,117.001,104.298]).div(de(256)),i=Ye(nC(s,n.entry_flow.conv_in,[2,2]));return i=_w(i,n.entry_flow.reduction_block_0,!1),i=_w(i,n.entry_flow.reduction_block_1),ir(this._numMainBlocks,0,1).forEach(o=>{i=Nre(i,n.middle_flow[`main_block_${o}`])}),i=_w(i,n.exit_flow.reduction_block),i=Ye(_n(i,n.exit_flow.separable_conv,[1,1])),i})}async forward(t){return this.forwardInput(await gt(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return tC(t,this._numMainBlocks)}extractParams(t){return eC(t,this._numMainBlocks)}};function aC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=bn(e),r=cf(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: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function rC(e){let t=[],n=Un(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return yn(e,t),{params:r,paramMappings:t}}var Ar;(function(e){e.FEMALE="female",e.MALE="male"})(Ar||(Ar={}));var bf=class extends an{constructor(t=new Ew(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof Er?this.faceFeatureExtractor.forwardInput(t):t,r=Xn(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Lp(r,n.fc.age).as1D(),i=Lp(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 gt(t))}async predictAgeAndGender(t){let n=await gt(t),a=await this.forwardInput(n),r=dt(a.age),s=dt(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?Ar.MALE:Ar.FEMALE,m=d?p:1-p;return 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256}};function sC(e){let t=[],{extractDenseBlock3Params:n}=mf(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return yn(e,t),{params:a,paramMappings:t}}function iC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=bn(e),{extractDenseBlock3Params:r}=df(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: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o}}}var Aw=class extends an{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return D(()=>{let a=ce(t.toBatchTensor(112,!0),"float32"),s=La(a,[122.782,117.001,104.298]).div(de(255)),i=uf(s,n.dense0,!0);return i=uf(i,n.dense1),i=uf(i,n.dense2),i=Xn(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await 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SC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=bn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=zre(r,i),u;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,v]=a,x=t.isFirstLayerConv2d?o(p,d,3,"conv0"):c(p,d,"conv0"),N=c(d,h,"conv1"),T=c(h,m,"conv2"),C=c(m,f,"conv3"),F=c(f,g,"conv4"),$=c(g,y,"conv5"),P=b?c(y,b,"conv6"):void 0,B=v?c(b,v,"conv7"):void 0,W=o(v||b||y,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:F,conv5:$,conv6:P,conv7:B,conv8:W}}else{let[p,d,h,m,f,g,y,b,v]=a,x=l(p,d,"conv0"),N=l(d,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),F=l(f,g,"conv4"),$=l(g,y,"conv5"),P=l(y,b,"conv6"),B=l(b,v,"conv7"),W=o(v,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:F,conv5:$,conv6:P,conv7:B,conv8:W}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function Bre(e,t){let n=Un(e,t);function a(o){let l=n(`${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(`${o}/bn`);return{conv:l,bn:c}}let i=Su(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function CC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Bre(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return yn(e,n),{params:i,paramMappings:n}}var ur=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Rw=class extends an{constructor(t){super("TinyYolov2");Dw(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Fr(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=Fr(a,n.conv6),a=Fr(a,n.conv7),ao(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Au(ao(t,n.conv0,"valid",!1)):$r(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=n.conv6?$r(a,n.conv6):a,a=n.conv7?$r(a,n.conv7):a,ao(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=ce(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?La(r,this.config.meanRgb):r,r=r.div(de(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await gt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new ur(n),s=await gt(t),i=await this.forwardInput(s,a),o=D(()=>dt(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 lw(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new ds(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return CC(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 ${a} filterSizes in config`);return SC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),v=y.slice([0,0,0,4],[c,c,u,1]),x=this.withClassScores?Ta(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):de(0);return[b,v,x]}),m=[],f=await d.array(),g=await p.array();for(let y=0;y<c;y++)for(let b=0;b<c;b++)for(let v=0;v<u;v++){let x=Ap(f[y][b][v][0]);if(!a||x>a){let N=(b+Ap(g[y][b][v][0]))/c*o,T=(y+Ap(g[y][b][v][1]))/c*l,C=Math.exp(g[y][b][v][2])*this.config.anchors[v].x/c*o,F=Math.exp(g[y][b][v][3])*this.config.anchors[v].y/c*l,$=N-C/2,P=T-F/2,B={row:y,col:b,anchor:v},{classScore:W,label:G}=this.withClassScores?await this.extractPredictedClass(h,B):{classScore:1,label:0};m.push({box:new xu($,P,$+C,P+F),score:x,classScore:x*W,label:G,...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)}},Fu=Rw;Fu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var $u=class extends Fu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:vC,classes:["face"],...t?{anchors:kC,meanRgb:IC}:{anchors:wC,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 bt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?TC:NC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Wre(e,t=!0){let n=new $u(t);return n.extractWeights(e),n}var Tf=class extends ur{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 oo(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>ro(l)?r(l):l.detection),i=a||(t instanceof Ae?await Iu(t,s):await ku(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ae&&l.dispose()),o}async function Du(e,t,n,a,r){return oo([e],t,async s=>n(s[0]),a,r)}var _C=.4,EC=[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)],AC=[117.001,114.697,97.404];var Ru=class extends Fu{constructor(){let t={withSeparableConvs:!0,iouThreshold:_C,classes:["face"],anchors:EC,meanRgb:AC,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 bt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var nt={ssdMobilenetv1:new io,tinyFaceDetector:new Ru,tinyYolov2:new $u,faceLandmark68Net:new _u,faceLandmark68TinyNet:new xf,faceRecognitionNet:new Eu,faceExpressionNet:new gf,ageGenderNet:new bf},FC=(e,t)=>nt.ssdMobilenetv1.locateFaces(e,t),Vre=(e,t)=>nt.tinyFaceDetector.locateFaces(e,t),Ure=(e,t)=>nt.tinyYolov2.locateFaces(e,t),$C=e=>nt.faceLandmark68Net.detectLandmarks(e),Gre=e=>nt.faceLandmark68TinyNet.detectLandmarks(e),Hre=e=>nt.faceRecognitionNet.computeFaceDescriptor(e),jre=e=>nt.faceExpressionNet.predictExpressions(e),qre=e=>nt.ageGenderNet.predictAgeAndGender(e),DC=e=>nt.ssdMobilenetv1.load(e),Kre=e=>nt.tinyFaceDetector.load(e),Xre=e=>nt.tinyYolov2.load(e),Yre=e=>nt.faceLandmark68Net.load(e),Zre=e=>nt.faceLandmark68TinyNet.load(e),Jre=e=>nt.faceRecognitionNet.load(e),Qre=e=>nt.faceExpressionNet.load(e),ese=e=>nt.ageGenderNet.load(e),tse=DC,nse=FC,ase=$C;var Mw=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Ou=class extends Mw{async run(){let t=await this.parentTask,n=await oo(t,this.input,async a=>Promise.all(a.map(r=>nt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>yf(a,n[r]))}withAgeAndGender(){return new Mu(this,this.input)}},Lu=class extends Mw{async run(){let t=await this.parentTask;if(!t)return;let n=await Du(t,this.input,a=>nt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return yf(t,n)}withAgeAndGender(){return new Pu(this,this.input)}},co=class extends Ou{withAgeAndGender(){return new lo(this,this.input)}withFaceDescriptors(){return new fs(this,this.input)}},po=class extends Lu{withAgeAndGender(){return new uo(this,this.input)}withFaceDescriptor(){return new gs(this,this.input)}};var Pw=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Mu=class extends Pw{async run(){let t=await this.parentTask,n=await oo(t,this.input,async a=>Promise.all(a.map(r=>nt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return kf(If(a,i,o),s)})}withFaceExpressions(){return new Ou(this,this.input)}},Pu=class extends Pw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Du(t,this.input,s=>nt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return kf(If(t,a,r),n)}withFaceExpressions(){return new Lu(this,this.input)}},lo=class extends Mu{withFaceExpressions(){return new co(this,this.input)}withFaceDescriptors(){return new fs(this,this.input)}},uo=class extends Pu{withFaceExpressions(){return new po(this,this.input)}withFaceDescriptor(){return new gs(this,this.input)}};var Sf=class extends wa{constructor(t,n){super();this.parentTask=t;this.input=n}},fs=class extends Sf{async run(){let t=await this.parentTask;return(await oo(t,this.input,a=>Promise.all(a.map(r=>nt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>wf(t[r],a))}withFaceExpressions(){return new co(this,this.input)}withAgeAndGender(){return new lo(this,this.input)}},gs=class extends Sf{async run(){let t=await this.parentTask;if(!t)return;let n=await Du(t,this.input,a=>nt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return wf(t,n)}withFaceExpressions(){return new po(this,this.input)}withAgeAndGender(){return new uo(this,this.input)}};var Cf=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?nt.faceLandmark68TinyNet:nt.faceLandmark68Net}},_f=class extends Cf{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Ae?await Iu(this.input,n):await ku(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Ae&&s.dispose()),t.map((s,i)=>Cu(s,r[i]))}withFaceExpressions(){return new co(this,this.input)}withAgeAndGender(){return new lo(this,this.input)}withFaceDescriptors(){return new fs(this,this.input)}},Ef=class extends Cf{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ae?await Iu(this.input,[n]):await ku(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ae&&s.dispose()),Cu(t,r)}withFaceExpressions(){return new po(this,this.input)}withAgeAndGender(){return new uo(this,this.input)}withFaceDescriptor(){return new gs(this,this.input)}};var Af=class extends wa{constructor(t,n=new va){super();this.input=t;this.options=n}},Vp=class extends Af{async run(){let{input:t,options:n}=this,a=n instanceof Tf?r=>nt.tinyFaceDetector.locateFaces(r,n):n instanceof va?r=>nt.ssdMobilenetv1.locateFaces(r,n):n instanceof ur?r=>nt.tinyYolov2.locateFaces(r,n):null;if(!a)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return a(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>Ji({},a)))})}withFaceLandmarks(t=!1){return new _f(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Ou(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Mu(this.runAndExtendWithFaceDetections(),this.input)}},Ff=class extends Af{async run(){let t=await new Vp(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?Ji({},n):void 0)})}withFaceLandmarks(t=!1){return new Ef(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Lu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Pu(this.runAndExtendWithFaceDetection(),this.input)}};function rse(e,t=new va){return new Ff(e,t)}function $f(e,t=new va){return new Vp(e,t)}async function RC(e,t){return $f(e,new va(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function sse(e,t={}){return $f(e,new ur(t)).withFaceLandmarks().withFaceDescriptors()}var ise=RC;function Ow(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 Df=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 _r)return i;if(i instanceof Float32Array)return new _r(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new _r(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>Ow(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Fp(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distance<a.distance?n:a)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this.distanceThreshold?n:new Fp("unknown",n.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>_r.fromJSON(a));return new Df(n,t.distanceThreshold)}};function ose(e){let t=new Ru;return t.extractWeights(e),t}function MC(e,t){let{width:n,height:a}=new gn(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>MC(r,{width:n,height:a}));if(ro(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Cu(Ji(e,r),s)}return or(e)?Ji(e,e.detection.forSize(n,a)):e instanceof ta||e instanceof bt?e.forSize(n,a):e}var lse=typeof process!="undefined",use=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",cse={faceapi:Q2,node:lse,browser:use};export{bf as AgeGenderNet,xu as BoundingBox,ut as Box,wa as ComposableTask,fs as ComputeAllFaceDescriptorsTask,Sf as ComputeFaceDescriptorsTaskBase,gs as ComputeSingleFaceDescriptorTask,_f as DetectAllFaceLandmarksTask,Vp as DetectAllFacesTask,Cf as DetectFaceLandmarksTaskBase,Af as DetectFacesTaskBase,Ef as DetectSingleFaceLandmarksTask,Ff as DetectSingleFaceTask,gn as Dimensions,Iw as FACE_EXPRESSION_LABELS,bt as FaceDetection,xC as FaceDetectionNet,gf as FaceExpressionNet,ms as FaceExpressions,_u as FaceLandmark68Net,xf as FaceLandmark68TinyNet,oC as FaceLandmarkNet,ta as FaceLandmarks,H2 as FaceLandmarks5,wu as FaceLandmarks68,Fp as FaceMatch,Df as FaceMatcher,Eu as FaceRecognitionNet,Ar as Gender,$p as LabeledBox,_r as LabeledFaceDescriptors,Er as NetInput,an as NeuralNetwork,ds as ObjectDetection,De as Point,j2 as PredictedBox,vu as Rect,io as SsdMobilenetv1,va as SsdMobilenetv1Options,Ru as TinyFaceDetector,Tf as TinyFaceDetectorOptions,$u as TinyYolov2,ur as TinyYolov2Options,ise as allFaces,RC as allFacesSsdMobilenetv1,sse as allFacesTinyYolov2,yw as awaitMediaLoaded,bw as bufferToImage,Hre as computeFaceDescriptor,to as createCanvas,Mp as createCanvasFromMedia,Lre as createFaceDetectionNet,Cre as createFaceRecognitionNet,bC as createSsdMobilenetv1,ose as createTinyFaceDetector,Wre as createTinyYolov2,$f as detectAllFaces,$C as detectFaceLandmarks,Gre as detectFaceLandmarksTiny,ase as detectLandmarks,rse as detectSingleFace,Cw as draw,st as env,Ow as euclideanDistance,kf as extendWithAge,wf as extendWithFaceDescriptor,Ji as extendWithFaceDetection,yf as extendWithFaceExpressions,Cu as extendWithFaceLandmarks,If as extendWithGender,Iu as extractFaceTensors,ku as extractFaces,yre as fetchImage,ww as fetchJson,bre as fetchNetWeights,no as fetchOrThrow,Cn as getContext2dOrThrow,eo as getMediaDimensions,xw as imageTensorToCanvas,vw as imageToSquare,lre as inverseSigmoid,iw as iou,of as isMediaElement,Rp as isMediaLoaded,_re as isWithAge,or as isWithFaceDetection,Nw as isWithFaceExpressions,ro as isWithFaceLandmarks,Ere as isWithGender,ese as loadAgeGenderModel,tse as loadFaceDetectionModel,Qre as loadFaceExpressionModel,Yre as loadFaceLandmarkModel,Zre as loadFaceLandmarkTinyModel,Jre as loadFaceRecognitionModel,DC as loadSsdMobilenetv1Model,Kre as loadTinyFaceDetectorModel,Xre as loadTinyYolov2Model,kw as loadWeightMap,nse as locateFaces,xre as matchDimensions,ow as minBbox,nt as nets,lw as nonMaxSuppression,La as normalize,uw as padToSquare,qre as predictAgeAndGender,jre as recognizeFaceExpressions,MC as resizeResults,Qi as resolveInput,ore as shuffleArray,Ap as sigmoid,FC as ssdMobilenetv1,Ug as tf,Vre as tinyFaceDetector,Ure as tinyYolov2,gt as toNetInput,nw as utils,Dw as validateConfig,cse as version};
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/dist/face-api.js b/dist/face-api.js
index 7958474..95106fd 100644
--- a/dist/face-api.js
+++ b/dist/face-api.js
@@ -4045,7 +4045,7 @@ return a / b;`,yJ=`
         }
         setOutput(${l});
       }
-    `}};function Oee(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=Sn({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=k.sizeFromShape([p.shape[c]]),m=be({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=vh(r.dtype),g=(x,N,T,C,F)=>{let $=x.shape[0],P=x.shape[1],B=_.segment_util.segOpComputeOptimalWindowSize(P,F),W={windowSize:B,inSize:P,batchSize:$,numSegments:F},G=new Pee(W,N),q=n.compileAndRun(G,[x,T],C);if(l.push(q),q.shape[1]===F)return q;let j=r2({backend:n,attrs:{start:0,stop:F,step:1,dtype:"float32"}}),X=o2({inputs:{x:j},backend:n,attrs:{reps:[P/B]}});return l.push(j),l.push(X),g(q,N,X,C,F)},y=g(m,"unsortedSegmentSum",s,f,i),b=be({inputs:{x:y},backend:n,attrs:{shape:d}}),v=b;if(u!=null){l.push(b);let x=_.getUndoAxesPermutation(u);v=Sn({inputs:{x:v},backend:n,attrs:{perm:x}})}return 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New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var lte={kernelName:Il,backendName:"wasm",kernelFunc:Oa},h2;function ute(e){h2=e.wasm.cwrap(Ps,null,["number","array","number","number","array","number","number","number","number"])}function cte(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,c=s.shape.length,u=i?r.shape[l-2]:r.shape[l-1],p=o?s.shape[c-1]:s.shape[c-2],d=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[c-2]:s.shape[c-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=k.sizeFromShape(m),y=k.sizeFromShape(f),b=g===y||g===1||y===1;k.assert(l>=2&&c>=2&&b,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. 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Please use 'channelsLast'.`);let W=a.makeOutput(m.outShape,"float32"),G=a.dataIdMap.get(W.dataId).id;return g2(i,r.shape[0],r.shape[1],r.shape[2],o,f,g,y,b,v,x,B,N,T,C,F,$,P,G),W}var vte={kernelName:zs,backendName:"wasm",setupFunc:bte,kernelFunc:xte},y2;function wte(e){y2=e.wasm.cwrap(Bs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function kte(e){let{backend:t,inputs:n,attrs:a}=e,{dy:r,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,inputShape:u}=a,p=1,d=_.convertConv2DDataFormat(l),h=_.computeConv2DInfo(u,s.shape,i,p,o,c,!1,d),{batchSize:m,filterHeight:f,filterWidth:g,inChannels:y,inHeight:b,inWidth:v,outChannels:x,outHeight:N,outWidth:T,strideHeight:C,strideWidth:F}=h,$=f-1-h.padInfo.top,P=g-1-h.padInfo.left,B=h.dataFormat==="channelsLast",W=k.computeStrides(h.inShape),G=k.computeStrides(r.shape),[q,j,X]=k.computeStrides(s.shape),K=W[0],ne=B?W[1]:W[2],J=B?W[2]:1,se=B?1:W[1],Q=G[0],oe=B?G[1]:G[2],ae=B?G[2]:1,pe=B?1:G[1],ue=t.makeOutput(h.inShape,"float32"),ge=t.dataIdMap.get(ue.dataId).id,ye=t.dataIdMap.get(r.dataId).id,ke=t.dataIdMap.get(s.dataId).id;return y2(ye,ke,m,f,g,b,v,y,N,T,x,C,F,$,P,q,j,X,K,ne,J,se,Q,oe,ae,pe,ge),ue}var Ite={kernelName:Bs,backendName:"wasm",setupFunc:wte,kernelFunc:kte},Nte=Cn(Ws),hw;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(hw||(hw={}));var b2;function Tte(e){b2=e.wasm.cwrap(Xo,null,["number","number","number","number","array","number","number","number","number","number"])}function Ste(e){let{backend:t,inputs:n,attrs:a}=e,{method:r,extrapolationValue:s,cropSize:i}=a,{image:o,boxes:l,boxInd:c}=n,u=l.shape[0],[p,d]=i,h=[u,p,d,o.shape[3]],m=t.dataIdMap.get(o.dataId),f;o.dtype!=="float32"&&(f=bf({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(f.dataId));let g=m.id,y=t.dataIdMap.get(l.dataId).id,b=t.dataIdMap.get(c.dataId).id,v=t.makeOutput(h,"float32"),x=t.dataIdMap.get(v.dataId).id,N=new Uint8Array(new Int32Array(o.shape).buffer);return b2(g,y,b,u,N,p,d,hw[r],s,x),f!=null&&t.disposeData(f.dataId),v}var Cte={kernelName:Xo,backendName:"wasm",setupFunc:Tte,kernelFunc:Ste},x2;function _te(e){x2=e.wasm.cwrap(Vs,null,["number","number","number","number","number","number"])}function Ete(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;k.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let c=_.getAxesPermutation([s],l),u=r;c!==null&&(u=yf({inputs:{x:r},attrs:{perm:c},backend:n}));let p=_.getInnerMostAxes(1,l)[0];_.assertAxesAreInnerMostDims("cumsum",[p],l);let d=n.makeOutput(u.shape,u.dtype),h=u.shape[p],m=n.dataIdMap.get(u.dataId).id,f=n.dataIdMap.get(d.dataId).id;x2(m,i?1:0,o?1:0,h,f,Vn[r.dtype]);let g=d;if(c!==null){let y=_.getUndoAxesPermutation(c);g=yf({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(u.dataId),n.disposeData(d.dataId)}return g}var Ate={kernelName:Vs,backendName:"wasm",setupFunc:_te,kernelFunc:Ete},v2;function Fte(e){v2=e.wasm.cwrap(Yo,null,["number","number","number","array","number","array","array","number","number"])}function $te(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],c=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=c*s,h=u/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),v=new Uint8Array(new Int32Array(k.computeStrides(m)).buffer),x=t.dataIdMap.get(f.dataId).id;return v2(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,v,m.length,x),f}var Dte={kernelName:Yo,backendName:"wasm",setupFunc:Fte,kernelFunc:$te},w2;function Rte(e){w2=e.wasm.cwrap(Us,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Mte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:p}=n,d=c==null?[1,1]:c,h=_.computeConv2DInfo(r.shape,s.shape,l,d,u,p,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,N=h.dilationWidth,T=h.strideHeight,C=h.strideWidth,F=h.inChannels,$=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let B=a.makeOutput(h.outShape,"float32"),W=a.dataIdMap.get(B.dataId).id;return w2(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,v,P,x,N,T,C,F,$,W),B}var Pte={kernelName:Us,backendName:"wasm",setupFunc:Rte,kernelFunc:Mte},Ote=!1,Lte=gn(Qo,Ote,"bool"),zte=Cn(Hs);function mw(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Oa({inputs:{x:r},backend:a,attrs:{shape:o}})}var Bte={kernelName:el,backendName:"wasm",kernelFunc:mw};function Wte(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var Vte={kernelName:vc,backendName:"wasm",kernelFunc:Wte},k2;function Ute(e){k2=e.wasm.cwrap(nl,null,["number","number","number","number","number","number"])}function Gte(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,c,u]=a.shape;return k2(s,o,l,c,u,i),r}var Hte={kernelName:nl,backendName:"wasm",kernelFunc:Gte,setupFunc:Ute},jte=Cn(js),qte=!1,Kte=gn(qs,qte),I2;function Xte(e){I2=e.wasm.cwrap(Ks,null,["number","number","number","number","number","number","number"])}function Yte(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=c!=null?t.dataIdMap.get(c.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return I2(u,p,d,h,m,r,g),f}var Zte={kernelName:Ks,backendName:"wasm",setupFunc:Xte,kernelFunc:Yte},N2;function Jte(e){N2=e.wasm.cwrap(Si,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Qte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d),g=Dp[h];if(g==null)throw new Error(`${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,v=f.outChannels,x=0;if(i!=null){let ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,F=f.padInfo.right,$=f.padInfo.bottom,P=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,K=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return N2(y,K,ne,J,b,N,T,x,C,F,$,P,X,B,W,G,q,j,v,g,oe,m||0,Q),se}var ene={kernelName:Si,backendName:"wasm",setupFunc:Jte,kernelFunc:Qte},T2;function tne(e){T2=e.wasm.cwrap(Ci,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 nne(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=Dp[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,v=f.outChannels,x=0;if(i!=null){let ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,F=f.padInfo.right,$=f.padInfo.bottom,P=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,K=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return T2(y,K,ne,J,b,N,T,x,C,F,$,P,X,B,W,G,q,j,v,g,oe,m||0,Q),se}var ane={kernelName:Ci,backendName:"wasm",setupFunc:tne,kernelFunc:nne},S2;function rne(e){S2=e.wasm.cwrap(rl,null,["number","number","number","number","number","number","array","number"])}function sne(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Ry.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 S2(d,Vn[a.dtype],h,i,p,o,m,f),c}var ine={kernelName:rl,backendName:"wasm",setupFunc:rne,kernelFunc:sne},C2;function one(e){C2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function lne(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=Oa({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),d=Oa({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(k.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,v=new Uint8Array(new Int32Array(k.computeStrides(u.shape)).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer);return C2(g,Vn[r.dtype],v,f,y,c.batchSize,x,b),t.disposeData(u.dataId),t.disposeData(d.dataId),m.shape=c.outputShape,m}var une={kernelName:al,backendName:"wasm",setupFunc:one,kernelFunc:lne},cne=!1,pne=gn(sl,cne,"bool"),dne=!1,hne=gn(Xs,dne,"bool"),_2;function mne(e){_2=e.wasm.cwrap(Zs,null,["number","number","number"])}function fne(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(k.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;_2(r,n,i)}return s}var gne={kernelName:Zs,backendName:"wasm",setupFunc:mne,kernelFunc:fne},yne=!1,bne=gn(ul,yne,"bool"),xne=!1,vne=gn(cl,xne,"bool"),wne=Cn(Js),kne=!1,Ine=gn(dl,kne,"bool"),E2;function Nne(e){E2=e.wasm.cwrap(Qs,null,["number, number, number"])}function Tne(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}=Cu(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=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;E2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=_.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var Sne={kernelName:Qs,backendName:"wasm",setupFunc:Nne,kernelFunc:Tne},Cne=!1,_ne=gn(ei,Cne),A2;function Ene(e){A2=e.wasm.cwrap(ti,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ane(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,v=u.strideHeight,x=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 C=a.makeOutput(u.outShape,"float32"),F=a.dataIdMap.get(C.dataId).id;return A2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,v,x,N,T,F),C}var Fne={kernelName:ti,backendName:"wasm",setupFunc:Ene,kernelFunc:Ane},F2;function $ne(e){F2=e.wasm.cwrap(ni,null,["number, number, number"])}function Dne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=Cu(i,r,t),m=p;if(h){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x,m=_.getInnerMostAxes(m.length,c.shape.length))}_.assertAxesAreInnerMostDims("mean",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=c;c.dtype!=="float32"&&(b=bf({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let v=t.makeOutput(f,"float32");if(k.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(v.dataId).id;F2(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(v.shape,d);v.shape=x}return c.dtype!=="float32"&&t.disposeData(b.dataId),v}var Rne={kernelName:ni,backendName:"wasm",setupFunc:$ne,kernelFunc:Dne},$2;function Mne(e){$2=e.wasm.cwrap(ai,null,["number, number, number"])}function Pne(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}=Cu(i,r,t);if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v)}let m=c.shape.length;_.assertAxesAreInnerMostDims("min",p,m);let[f,g]=_.computeOutAndReduceShapes(c.shape,p),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;$2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var One={kernelName:ai,backendName:"wasm",setupFunc:Mne,kernelFunc:Pne},Lne=!1,zne=gn(ri,Lne),Bne=!0,Wne=gn(si,Bne),Vne=Cn(ml);function fw(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 D2;function Une(e){D2=e.wasm.cwrap(gl,"number",["number","number","number","number","number"])}function Gne(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=D2(c,u,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=fw(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var Hne={kernelName:gl,backendName:"wasm",setupFunc:Une,kernelFunc:Gne},R2;function jne(e){R2=e.wasm.cwrap(yl,"number",["number","number","number","number","number","bool"])}function qne(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=R2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=fw(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var Kne={kernelName:yl,backendName:"wasm",setupFunc:jne,kernelFunc:qne},M2;function Xne(e){M2=e.wasm.cwrap(bl,"number",["number","number","number","number","number","number"])}function Yne(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=M2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=fw(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var Zne={kernelName:bl,backendName:"wasm",setupFunc:Xne,kernelFunc:Yne},Jne=!1,Qne=gn(fl,Jne,"bool"),P2;function eae(e){P2=e.wasm.cwrap(ii,null,["number","number","number","number","number"])}function tae(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 P2(u,s,i,o,c),l}var nae={kernelName:ii,backendName:"wasm",setupFunc:eae,kernelFunc:tae};function aae(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var rae={kernelName:xl,backendName:"wasm",kernelFunc:aae};function sae(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return mw({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(l=>{k.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===l.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=t.map(l=>mw({inputs:{input:l},backend:n,attrs:{dim:r}}));return f2({inputs:o,backend:n,attrs:{axis:r}})}var iae={kernelName:vl,backendName:"wasm",kernelFunc:sae},O2;function oae(e){O2=e.wasm.cwrap(oi,null,["number","array","number","number","array","array","number","number"])}function lae(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 O2(i,c,t.shape.length,Vn[t.dtype],d,h,r,l),o}var uae={kernelName:oi,backendName:"wasm",kernelFunc:lae,setupFunc:oae},cae=!1,pae=gn(li,cae),L2;function dae(e){L2=e.wasm.cwrap(ui,null,["number","number","number"])}function hae(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 L2(s,i,l),o}var mae={kernelName:ui,backendName:"wasm",setupFunc:dae,kernelFunc:hae},z2;function fae(e){z2=e.wasm.cwrap(wl,null,["number","number","number","number"])}function gae(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}=Cu(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("prod",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;z2(l,y,Vn[b.dtype],v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var yae={kernelName:wl,backendName:"wasm",setupFunc:fae,kernelFunc:gae},bae=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=zv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},xae={kernelName:Sc,backendName:"wasm",kernelFunc:bae},vae=!0,wae=gn(Gs,vae),kae=Cn(ci),Iae=Cn(di),B2;function Nae(e){B2=e.wasm.cwrap(pi,null,["number","number","number","number","number","number","number","number","number","number"])}function Tae(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=bf({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(k.sizeFromShape(r.shape)===0)return b;let v=t.dataIdMap.get(b.dataId).id;return B2(y,u,p,d,h,l,c,s?1:0,i?1:0,v),g!=null&&t.disposeData(g.dataId),b}var Sae={kernelName:pi,backendName:"wasm",setupFunc:Nae,kernelFunc:Tae},W2;function Cae(e){W2=e.wasm.cwrap(hi,null,["number","array","number","array","number","number"])}function _ae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=k.parseAxisParam(s,r.shape);if(r.shape.length===0)return gf({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);W2(l,u,i.length,p,r.shape.length,c);let d=Oa({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),d}var Eae={kernelName:hi,backendName:"wasm",kernelFunc:_ae,setupFunc:Cae},V2;function Aae(e){V2=e.wasm.cwrap(Ol,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Fae(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,v=typeof i=="number"?[i,i,i,y?0:b]:[...i,b],x=new Uint8Array(new Int32Array(v).buffer);return V2(c,p,d,h,m,s,f,g,x,v.length,u),l}var $ae={kernelName:Ol,backendName:"wasm",kernelFunc:Fae,setupFunc:Aae},Dae=Cn(mi),Rae=Cn(fi),U2;function Mae(e){U2=e.wasm.cwrap(Nl,null,["number","number","number","number","number","number","array","number","number"])}function Pae(e){let{backend:t,inputs:n,attrs:a}=e,{indices:r,updates:s}=n,{shape:i}=a,o=t.makeOutput(i,s.dtype);if(k.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:c,sliceSize:u,strides:p,outputSize:d}=My.calculateShapes(s,r,i),h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(p).buffer),g=t.dataIdMap.get(o.dataId).id;return 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u=i(`Prediction/BoxPredictor_${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 _C(e){let t=[],{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=ese(e,t),r=e["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Ar(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 ka(e,t,n){return D(()=>{let a=At(e,t.filters,n,"same");return a=Z(a,t.batch_norm_offset),qt(a,0,6)})}var tse=.0010000000474974513;function nse(e,t,n){return D(()=>{let a=es(e,t.filters,n,"same");return a=xr(a,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,tse),qt(a,0,6)})}function ase(e){return[2,4,6,12].some(t=>t===e)?[2,2]:[1,1]}function EC(e,t){return D(()=>{let n,a=ka(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=ase(o);a=nse(a,s.depthwise_conv,l),a=ka(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 rse(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 AC(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=rse(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 sse(e){let t=dt(Ue(e,[1,0])),n=[fe(t[2],t[0]),fe(t[3],t[1])],a=[Z(t[0],ve(n[0],de(2))),Z(t[1],ve(n[1],de(2)))];return{sizes:n,centers:a}}function ise(e,t){let{sizes:n,centers:a}=sse(e),r=dt(Ue(t,[1,0])),s=ve(L(dn(ve(r[2],de(5))),n[0]),de(2)),i=Z(L(ve(r[0],de(10)),n[0]),a[0]),o=ve(L(dn(ve(r[3],de(5))),n[1]),de(2)),l=Z(L(ve(r[1],de(10)),n[1]),a[1]);return Ue($t([fe(i,s),fe(l,o),Z(i,s),Z(l,o)]),[1,0])}function FC(e,t,n){return D(()=>{let a=e.shape[0],r=ise(H(Xa(n.extra_dim,[a,1,1]),[-1,4]),H(e,[-1,4]));r=H(r,[a,r.shape[0]/a,4]);let s=da(We(t,[0,0,1],[-1,-1,-1])),i=We(s,[0,0,0],[-1,-1,1]);i=H(i,[a,i.shape[1]]);let o=dt(r),l=dt(i);return{boxes:o,scores:l}})}function fo(e,t){return D(()=>{let n=e.shape[0],a=H(co(e,t.box_encoding_predictor),[n,-1,1,4]),r=H(co(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function $C(e,t,n){return D(()=>{let a=ka(e,n.conv_0,[1,1]),r=ka(a,n.conv_1,[2,2]),s=ka(r,n.conv_2,[1,1]),i=ka(s,n.conv_3,[2,2]),o=ka(i,n.conv_4,[1,1]),l=ka(o,n.conv_5,[2,2]),c=ka(l,n.conv_6,[1,1]),u=ka(c,n.conv_7,[2,2]),p=fo(t,n.box_predictor_0),d=fo(e,n.box_predictor_1),h=fo(r,n.box_predictor_2),m=fo(i,n.box_predictor_3),f=fo(l,n.box_predictor_4),g=fo(u,n.box_predictor_5),y=Qe([p.boxPredictionEncoding,d.boxPredictionEncoding,h.boxPredictionEncoding,m.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding],1),b=Qe([p.classPrediction,d.classPrediction,h.classPrediction,m.classPrediction,f.classPrediction,g.classPrediction],1);return{boxPredictions:y,classPredictions:b}})}var na=class{constructor({minConfidence:t,maxResults:n}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=n||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Is=class extends Qt{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return D(()=>{let a=ce(t.toBatchTensor(512,!1),"float32"),r=fe(L(a,de(.007843137718737125)),de(1)),s=EC(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=$C(s.out,s.conv11,n.prediction_layer);return FC(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await gt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new na(n),s=await gt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],c=o[0];for(let v=1;v<i.length;v++)i[v].dispose(),o[v].dispose();let u=Array.from(await c.data()),d=AC(l,u,a,.5,r),h=s.getReshapedInputDimensions(0),m=s.inputSize,f=m/h.width,g=m/h.height,y=l.arraySync(),b=d.map(v=>{let[x,N]=[Math.max(0,y[v][0]),Math.min(1,y[v][2])].map(F=>F*g),[T,C]=[Math.max(0,y[v][1]),Math.min(1,y[v][3])].map(F=>F*f);return new yt(u[v],new io(T,x,C-T,N-x),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),c.dispose(),b}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return _C(t)}extractParams(t){return CC(t)}};function Lw(e){let t=new Is;return t.extractWeights(e),t}function DC(e){return Lw(e)}var zw=class extends Is{};var RC=.4,MC=[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)],PC=[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)],OC=[117.001,114.697,97.404],LC="tiny_yolov2_model",zC="tiny_yolov2_separable_conv_model";var Kf=e=>typeof e=="number";function Xf(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(!Kf(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=>Kf(t.x)&&Kf(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(Kf)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Ou(e){return D(()=>{let t=L(e,de(.10000000149011612));return Z(Ye(fe(e,t)),t)})}function Dr(e,t){return D(()=>{let n=Jn(e,[[0,0],[1,1],[1,1],[0,0]]);return n=At(n,t.conv.filters,[1,1],"valid"),n=fe(n,t.bn.sub),n=L(n,t.bn.truediv),n=Z(n,t.conv.bias),Ou(n)})}function Rr(e,t){return D(()=>{let n=Jn(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Li(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=Z(n,t.bias),Ou(n)})}function ose(e,t){let n=Ru(e,t);function a(i,o){let l=nt(e(i)),c=nt(e(i));return t.push({paramPath:`${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,`${l}/bn`);return{conv:c,bn:u}}let s=Mu(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function BC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=xn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=ose(r,i),u;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,v]=a,x=t.isFirstLayerConv2d?o(p,d,3,"conv0"):c(p,d,"conv0"),N=c(d,h,"conv1"),T=c(h,m,"conv2"),C=c(m,f,"conv3"),F=c(f,g,"conv4"),$=c(g,y,"conv5"),P=b?c(y,b,"conv6"):void 0,B=v?c(b,v,"conv7"):void 0,W=o(v||b||y,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:F,conv5:$,conv6:P,conv7:B,conv8:W}}else{let[p,d,h,m,f,g,y,b,v]=a,x=l(p,d,"conv0"),N=l(d,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),F=l(f,g,"conv4"),$=l(g,y,"conv5"),P=l(y,b,"conv6"),B=l(b,v,"conv7"),W=o(v,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:F,conv5:$,conv6:P,conv7:B,conv8:W}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function lse(e,t){let n=Gn(e,t);function a(o){let l=n(`${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(`${o}/bn`);return{conv:l,bn:c}}let i=Pu(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function WC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=lse(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return bn(e,n),{params:i,paramMappings:n}}var Wa=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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 Bw=class extends Qt{constructor(t){super("TinyYolov2");Xf(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=Dr(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=Dr(a,n.conv6),a=Dr(a,n.conv7),co(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Ou(co(t,n.conv0,"valid",!1)):Rr(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=n.conv6?Rr(a,n.conv6):a,a=n.conv7?Rr(a,n.conv7):a,co(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=ce(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?wa(r,this.config.meanRgb):r,r=r.div(de(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await gt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Wa(n),s=await gt(t),i=await this.forwardInput(s,a),o=D(()=>dt(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 Sf(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 WC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Bw.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 ${a} filterSizes in config`);return BC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),v=y.slice([0,0,0,4],[c,c,u,1]),x=this.withClassScores?Sa(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):de(0);return[b,v,x]}),m=[],f=await d.array(),g=await p.array();for(let y=0;y<c;y++)for(let b=0;b<c;b++)for(let v=0;v<u;v++){let x=Eu(f[y][b][v][0]);if(!a||x>a){let N=(b+Eu(g[y][b][v][0]))/c*o,T=(y+Eu(g[y][b][v][1]))/c*l,C=Math.exp(g[y][b][v][2])*this.config.anchors[v].x/c*o,F=Math.exp(g[y][b][v][3])*this.config.anchors[v].y/c*l,$=N-C/2,P=T-F/2,B={row:y,col:b,anchor:v},{classScore:W,label:G}=this.withClassScores?await this.extractPredictedClass(h,B):{classScore:1,label:0};m.push({box:new so($,P,$+C,P+F),score:x,classScore:x*W,label:G,...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)}},Lu=Bw;Lu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var go=class extends Lu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:RC,classes:["face"],...t?{anchors:PC,meanRgb:OC}:{anchors:MC,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 yt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?zC:LC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function VC(e,t=!0){let n=new go(t);return n.extractWeights(e),n}var Jp=class extends Wa{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var aa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function yo(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>ks(l)?r(l):l.detection),i=a||(t instanceof Ae?await uo(t,s):await lo(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ae&&l.dispose()),o}async function zu(e,t,n,a,r){return yo([e],t,async s=>n(s[0]),a,r)}var UC=.4,GC=[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)],HC=[117.001,114.697,97.404];var bo=class extends Lu{constructor(){let t={withSeparableConvs:!0,iouThreshold:UC,classes:["face"],anchors:GC,meanRgb:HC,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 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Hp},Ww=(e,t)=>et.ssdMobilenetv1.locateFaces(e,t),jC=(e,t)=>et.tinyFaceDetector.locateFaces(e,t),qC=(e,t)=>et.tinyYolov2.locateFaces(e,t),Vw=e=>et.faceLandmark68Net.detectLandmarks(e),KC=e=>et.faceLandmark68TinyNet.detectLandmarks(e),XC=e=>et.faceRecognitionNet.computeFaceDescriptor(e),YC=e=>et.faceExpressionNet.predictExpressions(e),ZC=e=>et.ageGenderNet.predictAgeAndGender(e),Uw=e=>et.ssdMobilenetv1.load(e),JC=e=>et.tinyFaceDetector.load(e),QC=e=>et.tinyYolov2.load(e),e_=e=>et.faceLandmark68Net.load(e),t_=e=>et.faceLandmark68TinyNet.load(e),n_=e=>et.faceRecognitionNet.load(e),a_=e=>et.faceExpressionNet.load(e),r_=e=>et.ageGenderNet.load(e),s_=Uw,i_=Ww,o_=Vw;var Gw=class extends aa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Vu=class extends Gw{async run(){let t=await this.parentTask,n=await yo(t,this.input,async a=>Promise.all(a.map(r=>et.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>Gp(a,n[r]))}withAgeAndGender(){return new Bu(this,this.input)}},Uu=class extends Gw{async run(){let t=await this.parentTask;if(!t)return;let n=await zu(t,this.input,a=>et.faceExpressionNet.predictExpressions(a),this.extractedFaces);return Gp(t,n)}withAgeAndGender(){return new Wu(this,this.input)}},wo=class extends Vu{withAgeAndGender(){return new xo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},ko=class extends Uu{withAgeAndGender(){return new vo(this,this.input)}withFaceDescriptor(){return new Pr(this,this.input)}};var Hw=class extends aa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Bu=class extends Hw{async run(){let t=await this.parentTask,n=await yo(t,this.input,async a=>Promise.all(a.map(r=>et.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Yp(Zp(a,i,o),s)})}withFaceExpressions(){return new Vu(this,this.input)}},Wu=class extends Hw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await zu(t,this.input,s=>et.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Yp(Zp(t,a,r),n)}withFaceExpressions(){return new Uu(this,this.input)}},xo=class extends Bu{withFaceExpressions(){return new wo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},vo=class extends Wu{withFaceExpressions(){return new ko(this,this.input)}withFaceDescriptor(){return new Pr(this,this.input)}};var Qp=class extends aa{constructor(t,n){super();this.parentTask=t;this.input=n}},Mr=class extends Qp{async run(){let t=await this.parentTask;return(await yo(t,this.input,a=>Promise.all(a.map(r=>et.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>Xp(t[r],a))}withFaceExpressions(){return new wo(this,this.input)}withAgeAndGender(){return new xo(this,this.input)}},Pr=class extends Qp{async run(){let t=await this.parentTask;if(!t)return;let n=await zu(t,this.input,a=>et.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return Xp(t,n)}withFaceExpressions(){return new ko(this,this.input)}withAgeAndGender(){return new vo(this,this.input)}};var ed=class extends aa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?et.faceLandmark68TinyNet:et.faceLandmark68Net}},td=class extends ed{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Ae?await uo(this.input,n):await lo(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Ae&&s.dispose()),t.map((s,i)=>po(s,r[i]))}withFaceExpressions(){return new wo(this,this.input)}withAgeAndGender(){return new xo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},nd=class extends ed{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ae?await uo(this.input,[n]):await lo(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ae&&s.dispose()),po(t,r)}withFaceExpressions(){return new ko(this,this.input)}withAgeAndGender(){return new vo(this,this.input)}withFaceDescriptor(){return new Pr(this,this.input)}};var ad=class extends aa{constructor(t,n=new na){super();this.input=t;this.options=n}},Gu=class extends ad{async run(){let{input:t,options:n}=this,a=n instanceof Jp?r=>et.tinyFaceDetector.locateFaces(r,n):n instanceof na?r=>et.ssdMobilenetv1.locateFaces(r,n):n instanceof Wa?r=>et.tinyYolov2.locateFaces(r,n):null;if(!a)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return a(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>gs({},a)))})}withFaceLandmarks(t=!1){return new td(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Vu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Bu(this.runAndExtendWithFaceDetections(),this.input)}},rd=class extends ad{async run(){let t=await new Gu(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?gs({},n):void 0)})}withFaceLandmarks(t=!1){return new nd(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Uu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Wu(this.runAndExtendWithFaceDetection(),this.input)}};function l_(e,t=new na){return new rd(e,t)}function sd(e,t=new na){return new Gu(e,t)}async function jw(e,t){return sd(e,new na(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async 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distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>Yf(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Au(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distance<a.distance?n:a)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this.distanceThreshold?n:new Au("unknown",n.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>ur.fromJSON(a));return new id(n,t.distanceThreshold)}};function p_(e){let t=new bo;return t.extractWeights(e),t}function qw(e,t){let{width:n,height:a}=new ln(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>qw(r,{width:n,height:a}));if(ks(e)){let 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ite(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=_.computePool2DInfo(r.shape,i,o,1,l,c),p=u.filterHeight,d=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,g=u.padInfo.left,y=u.strideHeight,b=u.strideWidth,v=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. 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New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var lte={kernelName:Il,backendName:"wasm",kernelFunc:Oa},h2;function ute(e){h2=e.wasm.cwrap(Ps,null,["number","array","number","number","array","number","number","number","number"])}function cte(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,c=s.shape.length,u=i?r.shape[l-2]:r.shape[l-1],p=o?s.shape[c-1]:s.shape[c-2],d=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[c-2]:s.shape[c-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=k.sizeFromShape(m),y=k.sizeFromShape(f),b=g===y||g===1||y===1;k.assert(l>=2&&c>=2&&b,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. 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Please use 'channelsLast'.`);let B=a.makeOutput(h.outShape,"float32"),W=a.dataIdMap.get(B.dataId).id;return w2(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,v,P,x,N,T,C,F,$,W),B}var Pte={kernelName:Us,backendName:"wasm",setupFunc:Rte,kernelFunc:Mte},Ote=!1,Lte=gn(Qo,Ote,"bool"),zte=Cn(Hs);function mw(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Oa({inputs:{x:r},backend:a,attrs:{shape:o}})}var Bte={kernelName:el,backendName:"wasm",kernelFunc:mw};function Wte(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var Vte={kernelName:vc,backendName:"wasm",kernelFunc:Wte},k2;function Ute(e){k2=e.wasm.cwrap(nl,null,["number","number","number","number","number","number"])}function Gte(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,c,u]=a.shape;return k2(s,o,l,c,u,i),r}var Hte={kernelName:nl,backendName:"wasm",kernelFunc:Gte,setupFunc:Ute},jte=Cn(js),qte=!1,Kte=gn(qs,qte),I2;function Xte(e){I2=e.wasm.cwrap(Ks,null,["number","number","number","number","number","number","number"])}function Yte(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=c!=null?t.dataIdMap.get(c.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return I2(u,p,d,h,m,r,g),f}var Zte={kernelName:Ks,backendName:"wasm",setupFunc:Xte,kernelFunc:Yte},N2;function Jte(e){N2=e.wasm.cwrap(Si,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Qte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d),g=Dp[h];if(g==null)throw new Error(`${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,v=f.outChannels,x=0;if(i!=null){let ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,F=f.padInfo.right,$=f.padInfo.bottom,P=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,K=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return N2(y,K,ne,J,b,N,T,x,C,F,$,P,X,B,W,G,q,j,v,g,oe,m||0,Q),se}var ene={kernelName:Si,backendName:"wasm",setupFunc:Jte,kernelFunc:Qte},T2;function tne(e){T2=e.wasm.cwrap(Ci,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 nne(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=Dp[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,v=f.outChannels,x=0;if(i!=null){let ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,F=f.padInfo.right,$=f.padInfo.bottom,P=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,K=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return T2(y,K,ne,J,b,N,T,x,C,F,$,P,X,B,W,G,q,j,v,g,oe,m||0,Q),se}var ane={kernelName:Ci,backendName:"wasm",setupFunc:tne,kernelFunc:nne},S2;function rne(e){S2=e.wasm.cwrap(rl,null,["number","number","number","number","number","number","array","number"])}function sne(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Ry.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 S2(d,Vn[a.dtype],h,i,p,o,m,f),c}var ine={kernelName:rl,backendName:"wasm",setupFunc:rne,kernelFunc:sne},C2;function one(e){C2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function lne(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=Oa({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),d=Oa({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(k.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,v=new Uint8Array(new Int32Array(k.computeStrides(u.shape)).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer);return C2(g,Vn[r.dtype],v,f,y,c.batchSize,x,b),t.disposeData(u.dataId),t.disposeData(d.dataId),m.shape=c.outputShape,m}var une={kernelName:al,backendName:"wasm",setupFunc:one,kernelFunc:lne},cne=!1,pne=gn(sl,cne,"bool"),dne=!1,hne=gn(Xs,dne,"bool"),_2;function mne(e){_2=e.wasm.cwrap(Zs,null,["number","number","number"])}function fne(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(k.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;_2(r,n,i)}return s}var gne={kernelName:Zs,backendName:"wasm",setupFunc:mne,kernelFunc:fne},yne=!1,bne=gn(ul,yne,"bool"),xne=!1,vne=gn(cl,xne,"bool"),wne=Cn(Js),kne=!1,Ine=gn(dl,kne,"bool"),E2;function Nne(e){E2=e.wasm.cwrap(Qs,null,["number, number, number"])}function Tne(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}=Cu(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=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;E2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=_.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var Sne={kernelName:Qs,backendName:"wasm",setupFunc:Nne,kernelFunc:Tne},Cne=!1,_ne=gn(ei,Cne),A2;function Ene(e){A2=e.wasm.cwrap(ti,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ane(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,v=u.strideHeight,x=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 C=a.makeOutput(u.outShape,"float32"),F=a.dataIdMap.get(C.dataId).id;return A2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,v,x,N,T,F),C}var Fne={kernelName:ti,backendName:"wasm",setupFunc:Ene,kernelFunc:Ane},F2;function $ne(e){F2=e.wasm.cwrap(ni,null,["number, number, number"])}function Dne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=Cu(i,r,t),m=p;if(h){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x,m=_.getInnerMostAxes(m.length,c.shape.length))}_.assertAxesAreInnerMostDims("mean",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=c;c.dtype!=="float32"&&(b=bf({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let v=t.makeOutput(f,"float32");if(k.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(v.dataId).id;F2(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(v.shape,d);v.shape=x}return c.dtype!=="float32"&&t.disposeData(b.dataId),v}var Rne={kernelName:ni,backendName:"wasm",setupFunc:$ne,kernelFunc:Dne},$2;function Mne(e){$2=e.wasm.cwrap(ai,null,["number, number, number"])}function Pne(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}=Cu(i,r,t);if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v)}let m=c.shape.length;_.assertAxesAreInnerMostDims("min",p,m);let[f,g]=_.computeOutAndReduceShapes(c.shape,p),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;$2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var One={kernelName:ai,backendName:"wasm",setupFunc:Mne,kernelFunc:Pne},Lne=!1,zne=gn(ri,Lne),Bne=!0,Wne=gn(si,Bne),Vne=Cn(ml);function fw(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 D2;function Une(e){D2=e.wasm.cwrap(gl,"number",["number","number","number","number","number"])}function Gne(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=D2(c,u,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=fw(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var Hne={kernelName:gl,backendName:"wasm",setupFunc:Une,kernelFunc:Gne},R2;function jne(e){R2=e.wasm.cwrap(yl,"number",["number","number","number","number","number","bool"])}function qne(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=R2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=fw(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var Kne={kernelName:yl,backendName:"wasm",setupFunc:jne,kernelFunc:qne},M2;function Xne(e){M2=e.wasm.cwrap(bl,"number",["number","number","number","number","number","number"])}function Yne(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=M2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=fw(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var Zne={kernelName:bl,backendName:"wasm",setupFunc:Xne,kernelFunc:Yne},Jne=!1,Qne=gn(fl,Jne,"bool"),P2;function eae(e){P2=e.wasm.cwrap(ii,null,["number","number","number","number","number"])}function tae(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 P2(u,s,i,o,c),l}var nae={kernelName:ii,backendName:"wasm",setupFunc:eae,kernelFunc:tae};function aae(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var rae={kernelName:xl,backendName:"wasm",kernelFunc:aae};function sae(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return mw({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(l=>{k.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===l.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=t.map(l=>mw({inputs:{input:l},backend:n,attrs:{dim:r}}));return f2({inputs:o,backend:n,attrs:{axis:r}})}var iae={kernelName:vl,backendName:"wasm",kernelFunc:sae},O2;function oae(e){O2=e.wasm.cwrap(oi,null,["number","array","number","number","array","array","number","number"])}function lae(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 O2(i,c,t.shape.length,Vn[t.dtype],d,h,r,l),o}var uae={kernelName:oi,backendName:"wasm",kernelFunc:lae,setupFunc:oae},cae=!1,pae=gn(li,cae),L2;function dae(e){L2=e.wasm.cwrap(ui,null,["number","number","number"])}function hae(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 L2(s,i,l),o}var mae={kernelName:ui,backendName:"wasm",setupFunc:dae,kernelFunc:hae},z2;function fae(e){z2=e.wasm.cwrap(wl,null,["number","number","number","number"])}function gae(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}=Cu(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("prod",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;z2(l,y,Vn[b.dtype],v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var yae={kernelName:wl,backendName:"wasm",setupFunc:fae,kernelFunc:gae},bae=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=zv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},xae={kernelName:Sc,backendName:"wasm",kernelFunc:bae},vae=!0,wae=gn(Gs,vae),kae=Cn(ci),Iae=Cn(di),B2;function Nae(e){B2=e.wasm.cwrap(pi,null,["number","number","number","number","number","number","number","number","number","number"])}function Tae(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=bf({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(k.sizeFromShape(r.shape)===0)return b;let v=t.dataIdMap.get(b.dataId).id;return B2(y,u,p,d,h,l,c,s?1:0,i?1:0,v),g!=null&&t.disposeData(g.dataId),b}var Sae={kernelName:pi,backendName:"wasm",setupFunc:Nae,kernelFunc:Tae},W2;function Cae(e){W2=e.wasm.cwrap(hi,null,["number","array","number","array","number","number"])}function _ae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=k.parseAxisParam(s,r.shape);if(r.shape.length===0)return gf({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);W2(l,u,i.length,p,r.shape.length,c);let d=Oa({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),d}var Eae={kernelName:hi,backendName:"wasm",kernelFunc:_ae,setupFunc:Cae},V2;function Aae(e){V2=e.wasm.cwrap(Ol,null,["number","number","number","number","number","number","number","number","array","number","number"])}function 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zf=class{constructor(t,n,a){this.depthwise_filter=t;this.pointwise_filter=n;this.bias=a}};function Mu(e,t){return(n,a,r)=>{let s=_a(e(3*3*n),[3,3,n,1]),i=_a(e(n*a),[1,1,n,a]),o=nt(e(a));return t.push({paramPath:`${r}/depthwise_filter`},{paramPath:`${r}/pointwise_filter`},{paramPath:`${r}/bias`}),new zf(s,i,o)}}function Pu(e){return t=>{let n=e(`${t}/depthwise_filter`,4),a=e(`${t}/pointwise_filter`,4),r=e(`${t}/bias`,1);return new zf(n,a,r)}}function Gn(e,t){return(n,a,r)=>{let s=e[n];if(!no(s,a))throw new Error(`expected weightMap[${n}] to be a Tensor${a}D, instead have ${s}`);return t.push({originalPath:n,paramPath:r||n}),s}}function xn(e){let t=e;function n(r){let s=t.slice(0,r);return t=t.slice(r),s}function a(){return t}return{extractWeights:n,getRemainingWeights:a}}function Bf(e,t){let n=Ru(e,t),a=Mu(e,t);function r(i,o,l,c=!1){let u=c?n(i,o,3,`${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 uC(e){let t=[],{extractDenseBlock4Params:n}=Vf(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 Bp=class extends Qt{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceFeatureExtractor - load model before inference");return D(()=>{let a=ce(t.toBatchTensor(112,!0),"float32"),s=wa(a,[122.782,117.001,104.298]).div(de(255)),i=zp(s,n.dense0,!0);return i=zp(i,n.dense1),i=zp(i,n.dense2),i=zp(i,n.dense3),i=Yn(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await gt(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return uC(t)}extractParams(t){return lC(t)}};function Wp(e,t){return D(()=>Z(ze(e,t.weights),t.bias))}function cC(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=xn(e),o=Lf(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function pC(e){let t=[],n=Gn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return bn(e,t),{params:r,paramMappings:t}}function Uf(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var Vp=class extends Qt{constructor(t,n){super(t);this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof pr?this.faceFeatureExtractor.forwardInput(t):t;return Wp(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 cC(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=Uf(t);return this.faceFeatureExtractor.loadFromWeightMap(n),pC(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 Gf=["neutral","happy","sad","angry","fearful","disgusted","surprised"],$r=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Gf.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return 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dr;(function(e){e.FEMALE="female",e.MALE="male"})(dr||(dr={}));var Hp=class extends Qt{constructor(t=new Dw(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof pr?this.faceFeatureExtractor.forwardInput(t):t,r=Yn(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Wp(r,n.fc.age).as1D(),i=Wp(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:Sa(a)}})}async forward(t){return this.forwardInput(await gt(t))}async predictAgeAndGender(t){let n=await gt(t),a=await this.forwardInput(n),r=dt(a.age),s=dt(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?dr.MALE:dr.FEMALE,m=d?p:1-p;return 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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=Pu(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function WC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=lse(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 Wa=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 Bw=class extends Qt{constructor(t){super("TinyYolov2");Xf(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=Dr(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=Dr(a,n.conv6),a=Dr(a,n.conv7),co(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Ou(co(t,n.conv0,"valid",!1)):Rr(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=n.conv6?Rr(a,n.conv6):a,a=n.conv7?Rr(a,n.conv7):a,co(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=ce(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?wa(r,this.config.meanRgb):r,r=r.div(de(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await gt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Wa(n),s=await gt(t),i=await this.forwardInput(s,a),o=D(()=>dt(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 Sf(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 WC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Bw.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 ${a} filterSizes in config`);return BC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),v=y.slice([0,0,0,4],[c,c,u,1]),x=this.withClassScores?Sa(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):de(0);return[b,v,x]}),m=[],f=await d.array(),g=await p.array();for(let y=0;y<c;y++)for(let b=0;b<c;b++)for(let v=0;v<u;v++){let x=Eu(f[y][b][v][0]);if(!a||x>a){let N=(b+Eu(g[y][b][v][0]))/c*o,T=(y+Eu(g[y][b][v][1]))/c*l,C=Math.exp(g[y][b][v][2])*this.config.anchors[v].x/c*o,F=Math.exp(g[y][b][v][3])*this.config.anchors[v].y/c*l,$=N-C/2,P=T-F/2,B={row:y,col:b,anchor:v},{classScore:W,label:G}=this.withClassScores?await this.extractPredictedClass(h,B):{classScore:1,label:0};m.push({box:new so($,P,$+C,P+F),score:x,classScore:x*W,label:G,...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)}},Lu=Bw;Lu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var go=class extends Lu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:RC,classes:["face"],...t?{anchors:PC,meanRgb:OC}:{anchors:MC,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 yt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?zC:LC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function VC(e,t=!0){let n=new go(t);return n.extractWeights(e),n}var Jp=class extends Wa{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var aa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function yo(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>ks(l)?r(l):l.detection),i=a||(t instanceof Ae?await uo(t,s):await lo(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ae&&l.dispose()),o}async function zu(e,t,n,a,r){return yo([e],t,async s=>n(s[0]),a,r)}var UC=.4,GC=[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)],HC=[117.001,114.697,97.404];var bo=class extends Lu{constructor(){let t={withSeparableConvs:!0,iouThreshold:UC,classes:["face"],anchors:GC,meanRgb:HC,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 yt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var et={ssdMobilenetv1:new Is,tinyFaceDetector:new bo,tinyYolov2:new go,faceLandmark68Net:new ho,faceLandmark68TinyNet:new qp,faceRecognitionNet:new mo,faceExpressionNet:new Up,ageGenderNet:new Hp},Ww=(e,t)=>et.ssdMobilenetv1.locateFaces(e,t),jC=(e,t)=>et.tinyFaceDetector.locateFaces(e,t),qC=(e,t)=>et.tinyYolov2.locateFaces(e,t),Vw=e=>et.faceLandmark68Net.detectLandmarks(e),KC=e=>et.faceLandmark68TinyNet.detectLandmarks(e),XC=e=>et.faceRecognitionNet.computeFaceDescriptor(e),YC=e=>et.faceExpressionNet.predictExpressions(e),ZC=e=>et.ageGenderNet.predictAgeAndGender(e),Uw=e=>et.ssdMobilenetv1.load(e),JC=e=>et.tinyFaceDetector.load(e),QC=e=>et.tinyYolov2.load(e),e_=e=>et.faceLandmark68Net.load(e),t_=e=>et.faceLandmark68TinyNet.load(e),n_=e=>et.faceRecognitionNet.load(e),a_=e=>et.faceExpressionNet.load(e),r_=e=>et.ageGenderNet.load(e),s_=Uw,i_=Ww,o_=Vw;var Gw=class extends aa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Vu=class extends Gw{async run(){let t=await this.parentTask,n=await yo(t,this.input,async a=>Promise.all(a.map(r=>et.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>Gp(a,n[r]))}withAgeAndGender(){return new Bu(this,this.input)}},Uu=class extends Gw{async run(){let t=await this.parentTask;if(!t)return;let n=await zu(t,this.input,a=>et.faceExpressionNet.predictExpressions(a),this.extractedFaces);return Gp(t,n)}withAgeAndGender(){return new Wu(this,this.input)}},wo=class extends Vu{withAgeAndGender(){return new xo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},ko=class extends Uu{withAgeAndGender(){return new vo(this,this.input)}withFaceDescriptor(){return new Pr(this,this.input)}};var Hw=class extends aa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Bu=class extends Hw{async run(){let t=await this.parentTask,n=await yo(t,this.input,async a=>Promise.all(a.map(r=>et.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Yp(Zp(a,i,o),s)})}withFaceExpressions(){return new Vu(this,this.input)}},Wu=class extends Hw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await zu(t,this.input,s=>et.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Yp(Zp(t,a,r),n)}withFaceExpressions(){return new Uu(this,this.input)}},xo=class extends Bu{withFaceExpressions(){return new wo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},vo=class extends Wu{withFaceExpressions(){return new ko(this,this.input)}withFaceDescriptor(){return new Pr(this,this.input)}};var Qp=class extends aa{constructor(t,n){super();this.parentTask=t;this.input=n}},Mr=class extends Qp{async run(){let t=await this.parentTask;return(await yo(t,this.input,a=>Promise.all(a.map(r=>et.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>Xp(t[r],a))}withFaceExpressions(){return new wo(this,this.input)}withAgeAndGender(){return new xo(this,this.input)}},Pr=class extends Qp{async run(){let t=await this.parentTask;if(!t)return;let n=await zu(t,this.input,a=>et.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return Xp(t,n)}withFaceExpressions(){return new ko(this,this.input)}withAgeAndGender(){return new vo(this,this.input)}};var ed=class extends aa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?et.faceLandmark68TinyNet:et.faceLandmark68Net}},td=class extends ed{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Ae?await uo(this.input,n):await lo(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Ae&&s.dispose()),t.map((s,i)=>po(s,r[i]))}withFaceExpressions(){return new wo(this,this.input)}withAgeAndGender(){return new xo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},nd=class extends ed{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ae?await uo(this.input,[n]):await lo(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ae&&s.dispose()),po(t,r)}withFaceExpressions(){return new ko(this,this.input)}withAgeAndGender(){return new vo(this,this.input)}withFaceDescriptor(){return new Pr(this,this.input)}};var ad=class extends aa{constructor(t,n=new na){super();this.input=t;this.options=n}},Gu=class extends ad{async run(){let{input:t,options:n}=this,a=n instanceof Jp?r=>et.tinyFaceDetector.locateFaces(r,n):n instanceof na?r=>et.ssdMobilenetv1.locateFaces(r,n):n instanceof Wa?r=>et.tinyYolov2.locateFaces(r,n):null;if(!a)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return a(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>gs({},a)))})}withFaceLandmarks(t=!1){return new td(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Vu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Bu(this.runAndExtendWithFaceDetections(),this.input)}},rd=class extends ad{async run(){let t=await new Gu(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?gs({},n):void 0)})}withFaceLandmarks(t=!1){return new nd(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Uu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Wu(this.runAndExtendWithFaceDetection(),this.input)}};function l_(e,t=new na){return new rd(e,t)}function sd(e,t=new na){return new Gu(e,t)}async function jw(e,t){return sd(e,new na(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function u_(e,t={}){return sd(e,new Wa(t)).withFaceLandmarks().withFaceDescriptors()}var c_=jw;function Yf(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 id=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 ur)return i;if(i instanceof Float32Array)return new ur(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new ur(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>Yf(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Au(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distance<a.distance?n:a)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this.distanceThreshold?n:new Au("unknown",n.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>ur.fromJSON(a));return new id(n,t.distanceThreshold)}};function p_(e){let t=new bo;return t.extractWeights(e),t}function qw(e,t){let{width:n,height:a}=new ln(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>qw(r,{width:n,height:a}));if(ks(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return po(gs(e,r),s)}return za(e)?gs(e,e.detection.forSize(n,a)):e instanceof Un||e instanceof yt?e.forSize(n,a):e}var cse=typeof process!="undefined",pse=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",d_={faceapi:dC,node:cse,browser:pse};return use;})();
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/dist/face-api.node-cpu.js b/dist/face-api.node-cpu.js
new file mode 100644
index 0000000..950b4bc
--- /dev/null
+++ b/dist/face-api.node-cpu.js
@@ -0,0 +1,9 @@
+
+  /*
+  Face-API
+  homepage: <https://github.com/vladmandic/face-api>
+  author: <https://github.com/vladmandic>'
+  */
+
+var fn=Object.create,Ye=Object.defineProperty,hn=Object.getPrototypeOf,xn=Object.prototype.hasOwnProperty,bn=Object.getOwnPropertyNames,gn=Object.getOwnPropertyDescriptor;var Er=o=>Ye(o,"__esModule",{value:!0});var xo=(o,t)=>()=>(t||(t={exports:{}},o(t.exports,t)),t.exports),Ge=(o,t)=>{for(var e in t)Ye(o,e,{get:t[e],enumerable:!0})},vn=(o,t,e)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of bn(t))!xn.call(o,r)&&r!=="default"&&Ye(o,r,{get:()=>t[r],enumerable:!(e=gn(t,r))||e.enumerable});return o},b=o=>o&&o.__esModule?o:vn(Er(Ye(o!=null?fn(hn(o)):{},"default",{value:o,enumerable:!0})),o);var g=xo(Mr=>{var yn=Object.create,Cr=Object.defineProperty,Fn=Object.getPrototypeOf,Tn=Object.prototype.hasOwnProperty,Pn=Object.getOwnPropertyNames,_n=Object.getOwnPropertyDescriptor,bo=o=>Cr(o,"__esModule",{value:!0}),go=(o,t,e)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of 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tr=b(g());function ce(o,t){return(e,r,n,a)=>{let s=tr.tensor4d(o(e*r*n*n),[n,n,e,r]),i=tr.tensor1d(o(r));return t.push({paramPath:`${a}/filters`},{paramPath:`${a}/bias`}),{filters:s,bias:i}}}var er=b(g());function rr(o,t){return(e,r,n)=>{let a=er.tensor2d(o(e*r),[e,r]),s=er.tensor1d(o(r));return t.push({paramPath:`${n}/weights`},{paramPath:`${n}/bias`}),{weights:a,bias:s}}}var ke=b(g());var or=class{constructor(t,e,r){this.depthwise_filter=t;this.pointwise_filter=e;this.bias=r}};function me(o,t){return(e,r,n)=>{let a=ke.tensor4d(o(3*3*e),[3,3,e,1]),s=ke.tensor4d(o(e*r),[1,1,e,r]),i=ke.tensor1d(o(r));return t.push({paramPath:`${n}/depthwise_filter`},{paramPath:`${n}/pointwise_filter`},{paramPath:`${n}/bias`}),new or(a,s,i)}}function pe(o){return t=>{let e=o(`${t}/depthwise_filter`,4),r=o(`${t}/pointwise_filter`,4),n=o(`${t}/bias`,1);return new or(e,r,n)}}function j(o,t){return(e,r,n)=>{let a=o[e];if(!Bt(a,r))throw new Error(`expected weightMap[${e}] to be a Tensor${r}D, instead have 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e=o(`${t}/filters`,4),r=o(`${t}/bias`,1);return{filters:e,bias:r}}}function sr(o,t){let e=j(o,t),r=ar(e),n=pe(e);function a(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`);return{conv0:m,conv1:p,conv2:d}}function s(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`),u=n(`${i}/conv3`);return{conv0:m,conv1:p,conv2:d,conv3:u}}return{extractDenseBlock3Params:a,extractDenseBlock4Params:s}}function Do(o){let t=[],{extractDenseBlock4Params:e}=sr(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2"),dense3:e("dense3")};return W(o,t),{params:r,paramMappings:t}}var Se=class extends S{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceFeatureExtractor - load model before inference");return Nt.tidy(()=>{let r=Nt.cast(t.toBatchTensor(112,!0),"float32"),a=ot(r,[122.782,117.001,104.298]).div(Nt.scalar(255)),s=Le(a,e.dense0,!0);return s=Le(s,e.dense1),s=Le(s,e.dense2),s=Le(s,e.dense3),s=Nt.avgPool(s,[7,7],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return Do(t)}extractParams(t){return wo(t)}};var Co=b(g());var de=b(g());function Ae(o,t){return de.tidy(()=>de.add(de.matMul(o,t.weights),t.bias))}function Eo(o,t,e){let r=[],{extractWeights:n,getRemainingWeights:a}=B(o),i=rr(n,r)(t,e,"fc");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:r,params:{fc:i}}}function Mo(o){let t=[],e=j(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:r("fc")};return W(o,t),{params:n,paramMappings:t}}function ir(o){let t={},e={};return Object.keys(o).forEach(r=>{let n=r.startsWith("fc")?e:t;n[r]=o[r]}),{featureExtractorMap:t,classifierMap:e}}var We=class extends S{constructor(t,e){super(t);this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${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: ${t.length}`);Zr.forEach((e,r)=>{this[e]=t[r]})}asSortedArray(){return Zr.map(t=>({expression:t,probability:this[t]})).sort((t,e)=>e.probability-t.probability)}};var cr=class extends We{constructor(t=new Se){super("FaceExpressionNet",t)}forwardInput(t){return ue.tidy(()=>ue.softmax(this.runNet(t)))}async forward(t){return this.forwardInput(await E(t))}async predictExpressions(t){let e=await E(t),r=await this.forwardInput(e),n=await Promise.all(ue.unstack(r).map(async s=>{let i=await s.data();return s.dispose(),i}));r.dispose();let a=n.map(s=>new It(s));return e.isBatchInput?a:a[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Kr(o){return o.expressions instanceof It}function 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c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=sa(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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;y<m;y++)for(let T=0;T<m;T++)for(let F=0;F<p;F++){let L=De(_[y][T][F][0]);if(!r||L>r){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 ia(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),ca=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ma=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),pa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),da=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ua=o=>P.faceExpressionNet.predictExpressions(o),la=o=>P.ageGenderNet.predictAgeAndGender(o),dn=o=>P.ssdMobilenetv1.load(o),fa=o=>P.tinyFaceDetector.load(o),ha=o=>P.tinyYolov2.load(o),xa=o=>P.faceLandmark68Net.load(o),ba=o=>P.faceLandmark68TinyNet.load(o),ga=o=>P.faceRecognitionNet.load(o),va=o=>P.faceExpressionNet.load(o),ya=o=>P.ageGenderNet.load(o),Fa=dn,Ta=mn,Pa=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=e instanceof gr?n=>P.tinyFaceDetector.locateFaces(n,e):e instanceof Z?n=>P.ssdMobilenetv1.locateFaces(n,e):e instanceof lt?n=>P.tinyYolov2.locateFaces(n,e):null;if(!r)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return r(t)}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 _a(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 wa(o,t={}){return wr(o,new lt(t)).withFaceLandmarks().withFaceDescriptors()}var Da=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<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | 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.distance<r.distance?e:r)}findBestMatch(t){let e=this.matchDescriptor(t);return e.distance<this.distanceThreshold?e:new Ee("unknown",e.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>xt.fromJSON(r));return new Dr(e,t.distanceThreshold)}};function Ea(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 Ca=typeof process!="undefined",Na=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",Ia={faceapi:No,node:Ca,browser:Na};
+//# sourceMappingURL=face-api.node-cpu.js.map
diff --git a/dist/face-api.node-cpu.js.map b/dist/face-api.node-cpu.js.map
new file mode 100644
index 0000000..9b2b46b
--- /dev/null
+++ b/dist/face-api.node-cpu.js.map
@@ -0,0 +1,7 @@
+{
+  "version": 3,
+  "sources": ["../src/tfjs/tf-node-cpu.ts", "../src/env/isNodejs.ts", "../src/index.ts", "../src/draw/index.ts", "../src/draw/drawContour.ts", "../src/utils/index.ts", "../src/classes/Dimensions.ts", "../src/classes/Point.ts", "../src/classes/Box.ts", "../src/classes/BoundingBox.ts", "../src/classes/ObjectDetection.ts", "../src/classes/FaceDetection.ts", "../src/ops/iou.ts", "../src/ops/minBbox.ts", "../src/ops/nonMaxSuppression.ts", "../src/ops/normalize.ts", "../src/ops/padToSquare.ts", "../src/ops/shuffleArray.ts", "../src/ops/index.ts", "../src/classes/Rect.ts", "../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/NetInput.ts", "../src/dom/imageToSquare.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/dom/loadWeightMap.ts", "../src/common/getModelUris.ts", "../src/dom/matchDimensions.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/NeuralNetwork.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/depthwiseSeparableConv.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/types.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMap.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeightMap.ts", "../src/faceProcessor/util.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/xception/TinyXception.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeightMap.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeightMap.ts", "../src/ageGenderNet/types.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeightMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeightMap.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeightMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts"],
+  "sourcesContent": ["/* eslint-disable import/no-extraneous-dependencies */\n/* eslint-disable node/no-unpublished-import */\n\nexport * from '@tensorflow/tfjs';\n", "export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version as string, node, browser };\n", "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false,\n) {\n  ctx.beginPath();\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx];\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(x, y);\n  });\n\n  if (isClosed) {\n    const from = points[points.length - 1];\n    const to = points[0];\n    if (!from || !to) {\n      return;\n    }\n\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(to.x, to.y);\n  }\n\n  ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0;\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = 10 ** prec;\n  return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width);\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n  private _width: number\n\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n    }\n\n    this._width = width;\n    this._height = height;\n  }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height);\n  }\n}\n", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x;\n    this._y = y;\n  }\n\n  get x(): number { return this._x; }\n\n  get y(): number { return this._y; }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y);\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y);\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y);\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y);\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y));\n  }\n\n  public magnitude(): number {\n    return Math.sqrt((this.x ** 2) + (this.y ** 2));\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y));\n  }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n    }\n  }\n\n  private _x: number\n\n  private _y: number\n\n  private _width: number\n\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any;\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n    Box.assertIsValidBox({\n      x, y, width, height,\n    }, 'Box.constructor', allowNegativeDimensions);\n\n    this._x = x;\n    this._y = y;\n    this._width = width;\n    this._height = height;\n  }\n\n  public get x(): number { return this._x; }\n\n  public get y(): number { return this._y; }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public get left(): number { return this.x; }\n\n  public get top(): number { return this.y; }\n\n  public get right(): number { return this.x + this.width; }\n\n  public get bottom(): number { return this.y + this.height; }\n\n  public get area(): number { return this.width * this.height; }\n\n  public get topLeft(): Point { return new Point(this.left, this.top); }\n\n  public get topRight(): Point { return new Point(this.right, this.top); }\n\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n  public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.round(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.floor(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public toSquare(): Box<BoxType> {\n    let {\n      x, y, width, height,\n    } = this;\n    const diff = Math.abs(width - height);\n    if (width < height) {\n      x -= (diff / 2);\n      width += diff;\n    }\n    if (height < width) {\n      y -= (diff / 2);\n      height += diff;\n    }\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY,\n    });\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    const [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY,\n    ];\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const {\n      x, y, right, bottom,\n    } = this;\n    const clippedX = Math.max(x, 0);\n    const clippedY = Math.max(y, 0);\n\n    const newWidth = right - clippedX;\n    const newHeight = bottom - clippedY;\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n    return (new Box({\n      x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight,\n    })).floor();\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this;\n    const x = this.x + sx;\n    const y = this.y + sy;\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1;\n    const h = this.height + 1;\n\n    const dx = 1;\n    const dy = 1;\n    let edx = w;\n    let edy = h;\n\n    let x = this.left;\n    let y = this.top;\n    let ex = this.right;\n    let ey = this.bottom;\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w;\n      ex = imageWidth;\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h;\n      ey = imageHeight;\n    }\n    if (x < 1) {\n      edy = 2 - x;\n      x = 1;\n    }\n    if (y < 1) {\n      edy = 2 - y;\n      y = 1;\n    }\n\n    return {\n      dy, edy, dx, edx, y, ey, x, ex, w, h,\n    };\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height),\n    }).toSquare().round();\n  }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({\n      left, top, right, bottom,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n\n  private _classScore: number\n\n  private _className: string\n\n  private _box: Rect\n\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions,\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n    this._score = score;\n    this._classScore = classScore;\n    this._className = className;\n    this._box = new Box(relativeBox).rescale(this._imageDims);\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n\n  public get className(): string { return this._className; }\n\n  public get box(): Box { return this._box; }\n\n  public get imageDims(): Dimensions { return this._imageDims; }\n\n  public get imageWidth(): number { return this.imageDims.width; }\n\n  public get imageHeight(): number { return this.imageDims.height; }\n\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height },\n    );\n  }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions,\n  ) {\n    super(score, score, '', relativeBox, imageDims);\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height);\n    return new FaceDetection(score, relativeBox, imageDims);\n  }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n  const interSection = width * height;\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map((pt) => pt.x);\n  const ys = pts.map((pt) => pt.y);\n  const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n  const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n  const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n  const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n  return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true,\n): number[] {\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map((c) => c.boxIndex);\n\n  const pick: number[] = [];\n\n  while (indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number;\n    pick.push(curr);\n\n    const indices = indicesSortedByScore;\n\n    const outputs: number[] = [];\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i];\n\n      const currBox = boxes[curr];\n      const idxBox = boxes[idx];\n\n      outputs.push(iou(currBox, idxBox, isIOU));\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold,\n    );\n  }\n\n  return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb;\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n    return tf.sub(x, avg_rgb);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [height, width] = imgTensor.shape.slice(1);\n    if (height === width) {\n      return imgTensor;\n    }\n\n    const dimDiff = Math.abs(height - width);\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n    const paddingAxis = height > width ? 2 : 1;\n\n    const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice();\n      paddingTensorShape[paddingAxis] = paddingAmountLocal;\n      return tf.fill(paddingTensorShape, 0, 'float32');\n    };\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount);\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null;\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend,\n    ]\n      .filter((t) => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[];\n    return tf.concat(tensorsToStack, paddingAxis);\n  });\n}\n", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice();\n  for (let i = array.length - 1; i > 0; i--) {\n    const j = Math.floor(Math.random() * (i + 1));\n    const x = array[i];\n    array[i] = array[j];\n    array[j] = x;\n  }\n  return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({\n      x, y, width, height,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n\n  protected _positions: Point[]\n\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0),\n  ) {\n    const { width, height } = imgDims;\n    this._imgDims = new Dimensions(width, height);\n    this._shift = shift;\n    this._positions = relativeFaceLandmarkPositions.map(\n      (pt) => pt.mul(new Point(width, height)).add(shift),\n    );\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n  public get imageWidth(): number { return this._imgDims.width; }\n\n  public get imageHeight(): number { return this._imgDims.height; }\n\n  public get positions(): Point[] { return this._positions; }\n\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n    );\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height },\n    );\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y),\n    );\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y);\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection);\n\n      return this.shiftBy(box.x, box.y).align(null, options);\n    }\n\n    const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n    if (useDlibAlignment) {\n      return this.alignDlib();\n    }\n\n    return this.alignMinBbox(minBoxPadding);\n  }\n\n  private alignDlib(): Box {\n    const centers = this.getRefPointsForAlignment();\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n    const size = Math.floor(eyeToMouthDist / relScale);\n\n    const refPoint = getCenterPoint(centers);\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions);\n    return box.pad(box.width * padding, box.height * padding);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class');\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions;\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]]),\n    ];\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17);\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22);\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27);\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36);\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42);\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48);\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth(),\n    ].map(getCenterPoint);\n  }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label;\n    this._distance = distance;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get distance(): number { return this._distance; }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n  }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee);\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box);\n    this._label = label;\n  }\n\n  public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n  private _label: string\n\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n    }\n\n    this._label = label;\n    this._descriptors = descriptors;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get descriptors(): Float32Array[] { return this._descriptors; }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d)),\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee);\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n    }\n  }\n\n  private _score: number\n\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label);\n    this._score = score;\n    this._classScore = classScore;\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection<\n  TSource\n>(\n  sourceObj: TSource,\n  detection: FaceDetection,\n): WithFaceDetection<TSource> {\n  const extension = { detection };\n  return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n  const fetch = window.fetch;\n  if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n  const readFile = () => {\n    throw new Error('readFile - filesystem not available for browser environment');\n  };\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile,\n  };\n}\n", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n  let requireFsError = '';\n\n  if (!fs) {\n    try {\n      // eslint-disable-next-line global-require\n      fs = require('fs');\n    } catch (err) {\n      requireFsError = err.toString();\n    }\n  }\n\n  const readFile = fs\n    ? (filePath: string) => new Promise<Buffer>((resolve, reject) => {\n      fs.readFile(filePath, (err: any, buffer: Buffer) => (err ? reject(err) : resolve(buffer)));\n    })\n    : () => {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);\n    };\n\n  return {\n    readFile,\n  };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n  // eslint-disable-next-line dot-notation\n  const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n  const Image = global.Image || global.HTMLImageElement;\n\n  const createCanvasElement = () => {\n    if (Canvas) return new Canvas();\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n  };\n\n  const createImageElement = () => {\n    if (Image) return new Image();\n    throw new Error('createImageElement - missing Image implementation for nodejs environment');\n  };\n\n  const fetch = global.fetch;\n  // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n  const fileSystem = createFileSystem();\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n    Image: Image || class {},\n    ImageData: global.ImageData || class {},\n    Video: global.HTMLVideoElement || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem,\n  };\n}\n", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n  }\n  return environment;\n}\n\nfunction setEnv(env: Environment) {\n  environment = env;\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) return setEnv(createBrowserEnv());\n  if (isNodejs()) return setEnv(createNodejsEnv());\n  return null;\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize();\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env;\n  environment.Canvas = Canvas;\n  environment.Image = Image;\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n  environment.createImageElement = env.createImageElement || (() => new Image());\n\n  environment.ImageData = env.ImageData || environment.ImageData;\n  environment.Video = env.Video || environment.Video;\n  environment.fetch = env.fetch || environment.fetch;\n  environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg);\n  }\n  return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg;\n  }\n\n  const canvas = resolveInput(canvasArg);\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n  }\n\n  const ctx = canvas.getContext('2d');\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null');\n  }\n\n  return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n  // eslint-disable-next-line no-unused-vars\n  TOP_LEFT = 'TOP_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  TOP_RIGHT = 'TOP_RIGHT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n\n  public backgroundColor: string\n\n  public fontColor: string\n\n  public fontSize: number\n\n  public fontStyle: string\n\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const {\n      anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = options;\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n    this.fontSize = fontSize || 14;\n    this.fontStyle = fontStyle || 'Georgia';\n    this.padding = padding || 4;\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n\n  public anchor : IPoint\n\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {},\n  ) {\n    // eslint-disable-next-line no-nested-ternary\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text);\n    this.anchor = anchor;\n    this.options = new DrawTextFieldOptions(options);\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options;\n    return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options;\n    return this.text.length * fontSize + (2 * padding);\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options;\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n    const textFieldWidth = this.measureWidth(ctx);\n    const textFieldHeight = this.measureHeight();\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims;\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n      return { x: newX, y: newY };\n    }\n    return { x, y };\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg);\n    const ctx = getContext2dOrThrow(canvas);\n\n    const {\n      backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = this.options;\n\n    ctx.font = `${fontSize}px ${fontStyle}`;\n    const maxTextWidth = this.measureWidth(ctx);\n    const textHeight = this.measureHeight();\n\n    ctx.fillStyle = backgroundColor;\n    const upperLeft = this.getUpperLeft(ctx, canvas);\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x;\n      const y = padding + upperLeft.y + ((i + 1) * fontSize);\n      ctx.fillText(textLine, x, y);\n    });\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport {\n  AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions,\n} from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n\n  public lineWidth: number\n\n  public drawLabelOptions: DrawTextFieldOptions\n\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const {\n      boxColor, lineWidth, label, drawLabelOptions,\n    } = options;\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n    this.lineWidth = lineWidth || 2;\n    this.label = label;\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor,\n    };\n    this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {},\n  ) {\n    this.box = new Box(box);\n    this.options = new DrawBoxOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const { boxColor, lineWidth } = this.options;\n\n    const {\n      x, y, width, height,\n    } = this.box;\n    ctx.strokeStyle = boxColor;\n    ctx.lineWidth = lineWidth;\n    ctx.strokeRect(x, y, width, height);\n\n    const { label } = this.options;\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n    }\n  }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>,\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n  detectionsArray.forEach((det) => {\n    // eslint-disable-next-line no-nested-ternary\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n    // eslint-disable-next-line no-nested-ternary\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n    const label = score ? `${round(score)}` : undefined;\n    new DrawBox(box, { label }).draw(canvasArg);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor);\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)));\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n      const data = await t.data();\n      t.dispose();\n      return data;\n    }));\n    out.dispose();\n\n    const predictionsByBatch = probabilitesByBatch\n      .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7;\n  }\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n  const { Image, Video } = env.getEnv();\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n  // eslint-disable-next-line consistent-return\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null);\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return;\n      // eslint-disable-next-line no-use-before-define\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      reject(e);\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return;\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      resolve(e);\n    }\n\n    media.addEventListener('load', onLoad);\n    media.addEventListener('error', onError);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n    const reader = new FileReader();\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n      const img = env.getEnv().createImageElement();\n      img.onload = () => resolve(img);\n      img.onerror = reject;\n      img.src = reader.result as string;\n    };\n    reader.onerror = reject;\n    reader.readAsDataURL(buf);\n  });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n  const { Image, Video } = env.getEnv();\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight);\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight);\n  }\n  return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n  const { createCanvasElement } = env.getEnv();\n  const canvas = createCanvasElement();\n  canvas.width = width;\n  canvas.height = height;\n  return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n  const { ImageData } = env.getEnv();\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet');\n  }\n\n  const { width, height } = dims || getMediaDimensions(media);\n  const canvas = createCanvas({ width, height });\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n  }\n  return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement,\n): Promise<HTMLCanvasElement> {\n  const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n  await tf.browser.toPixels(imgTensor3D, targetCanvas);\n\n  imgTensor3D.dispose();\n\n  return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n  const { Image, Canvas, Video } = env.getEnv();\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport {\n  computeReshapedDimensions, isTensor3D, isTensor4D, range,\n} from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n\n  private _canvases: HTMLCanvasElement[] = []\n\n  private _batchSize: number\n\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false,\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput;\n    this._batchSize = inputs.length;\n\n    inputs.forEach((input, idx) => {\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = input.shape;\n        return;\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0];\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n        }\n\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = (input as any).shape.slice(1);\n        return;\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n      this._canvases[idx] = canvas;\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n    });\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors;\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases;\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput;\n  }\n\n  public get batchSize(): number {\n    return this._batchSize;\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions;\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize;\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n    );\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx];\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0];\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1];\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n    }\n\n    const width = this.getInputWidth(batchIdx);\n    const height = this.getInputHeight(batchIdx);\n    return computeReshapedDimensions({ width, height }, this.inputSize);\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n    this._inputSize = inputSize;\n\n    return tf.tidy(() => {\n      const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n        const input = this.getInput(batchIdx);\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>();\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs);\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3);\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n      });\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n\n      return batchTensor;\n    });\n  }\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n  const { Image, Canvas } = env.getEnv();\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n  }\n\n  if (inputSize <= 0) return createCanvas({ width: 1, height: 1 });\n  const dims = getMediaDimensions(input);\n  const scale = inputSize / Math.max(dims.height, dims.width);\n  const width = scale * dims.width;\n  const height = scale * dims.height;\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n  const offset = Math.abs(width - height) / 2;\n  const dx = centerImage && width < height ? offset : 0;\n  const dy = centerImage && height < width ? offset : 0;\n  if (inputCanvas.width > 0 && inputCanvas.height > 0) getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n  return targetCanvas;\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs;\n  }\n\n  const inputArgArray = Array.isArray(inputs)\n    ? inputs\n    : [inputs];\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input');\n  }\n\n  const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n\n  const inputArray = inputArgArray.map(resolveInput);\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0];\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n      }\n    }\n  });\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)),\n  );\n\n  return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>,\n): Promise<HTMLCanvasElement[]> {\n  const { Canvas } = env.getEnv();\n\n  let canvas = input as HTMLCanvasElement;\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input);\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported');\n    }\n\n    const tensorOrCanvas = netInput.getInput(0);\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas);\n  }\n\n  const ctx = getContext2dOrThrow(canvas);\n  const boxes = detections.map(\n    (det) => (det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det),\n  )\n    .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n\n  return boxes.map(({\n    x, y, width, height,\n  }) => {\n    const faceImg = createCanvas({ width, height });\n    if (width > 0 && height > 0) getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n    return faceImg;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>,\n): Promise<tf.Tensor3D[]> {\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported');\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n\n    const boxes = detections.map(\n      (det) => (det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det),\n    )\n      .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n\n    const faceTensors = boxes.map(({\n      x, y, width, height,\n    }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n\n    return faceTensors;\n  });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n  url: string,\n  // eslint-disable-next-line no-undef\n  init?: RequestInit,\n): Promise<Response> {\n  const { fetch } = env.getEnv();\n  const res = await fetch(url, init);\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n  }\n  return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri);\n  const blob = await (res).blob();\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n  }\n  return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n  const manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri);\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri);\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename,\n    };\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`,\n    };\n  }\n  // eslint-disable-next-line no-nested-ternary\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter((s) => s);\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename;\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n  };\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference;\n  input.width = width;\n  input.height = height;\n  return { width, height };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n  constructor() {\n    super('FaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock4(normalized, params.dense0, true);\n      out = denseBlock4(out, params.dense1);\n      out = denseBlock4(out, params.dense2);\n      out = denseBlock4(out, params.dense3);\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork<TNetParams> {\n  constructor(name: string) {\n    this._name = name;\n  }\n\n  protected _params: TNetParams | undefined = undefined\n\n  protected _paramMappings: ParamMapping[] = []\n\n  public _name: any;\n\n  public get params(): TNetParams | undefined { return this._params; }\n\n  public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n  public get isLoaded(): boolean { return !!this.params; }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    return obj[objProp];\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    obj[objProp].dispose();\n    obj[objProp] = tensor;\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath),\n    }));\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable());\n    });\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync());\n      variable.dispose();\n      this.reassignParamFromPath(path, tensor);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach((param) => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`);\n      }\n      param.tensor.dispose();\n    });\n    this._params = undefined;\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr)),\n    );\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl);\n      return;\n    }\n    await this.loadFromUri(weightsOrUrl);\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`);\n    }\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n    }\n    const { readFile } = env.getEnv();\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)));\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\n    const manifest = JSON.parse((await readFile(manifestUri)).toString());\n    const weightMap = await loadWeights(manifest, modelBaseUri);\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap);\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const { paramMappings, params } = this.extractParams(weights);\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error('traversePropertyPath - model has no loaded params');\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      // eslint-disable-next-line no-prototype-builtins\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n      }\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n    }, { nextObj: this.params });\n\n    const { obj, objProp } = result;\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n    }\n\n    return { obj, objProp };\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n  });\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number],\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n    out = tf.add(out, params.bias);\n    return out;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias,\n    ) as tf.Tensor4D;\n\n    return withRelu ? tf.relu(out) : out;\n  });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach((path) => {\n    if (!paramMappings.some((pm) => pm.originalPath === path)) {\n      weightMap[path].dispose();\n    }\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams => {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): FCParams => {\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias,\n    };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n\nexport function loadSeparableConvParamsFactory(\n  // eslint-disable-next-line no-unused-vars\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T,\n) {\n  return (prefix: string): SeparableConvParams => {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4);\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  // eslint-disable-next-line no-useless-constructor\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    public depthwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public pointwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public bias: tf.Tensor1D,\n  // eslint-disable-next-line no-empty-function\n  ) {}\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  return (originalPath: string, paramRank: number, mappedPath?: string) => {\n    const tensor = weightMap[originalPath];\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath },\n    );\n\n    return tensor;\n  };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights;\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights);\n    remainingWeights = remainingWeights.slice(numWeights);\n    return ret;\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights;\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights,\n  };\n}\n", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock4Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: {\n      dense0, dense1, dense2, dense3,\n    },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return (prefix: string): ConvParams => {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return { filters, bias };\n  };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock4Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name);\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor;\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  protected abstract getClassifierChannelsIn(): number\n\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeightMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const cIn = this.getClassifierChannelsIn();\n    const cOut = this.getClassifierChannelsOut();\n    const classifierWeightSize = (cOut * cIn) + cOut;\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams,\n): tf.Tensor2D {\n  return tf.tidy(() => tf.add(\n    tf.matMul(x, params.weights),\n    params.bias,\n  ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: extractFcParams('fc'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n  const featureExtractorMap: tf.NamedTensorMap = {};\n  const classifierMap: tf.NamedTensorMap = {};\n\n  Object.keys(weightMap).forEach((key) => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n    map[key] = weightMap[key];\n  });\n\n  return { featureExtractorMap, classifierMap };\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\n\nexport class FaceExpressions {\n  public neutral: number\n\n  public happy: number\n\n  public sad: number\n\n  public angry: number\n\n  public fearful: number\n\n  public disgusted: number\n\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx];\n    });\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map((expression) => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability);\n  }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n>(\n  sourceObj: TSource,\n  expressions: FaceExpressions,\n): WithFaceExpressions<TSource> {\n  const extension = { expressions };\n  return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint,\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n  faceExpressionsArray.forEach((e) => {\n    // eslint-disable-next-line no-nested-ternary\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined);\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n    }\n\n    const sorted = expr.asSortedArray();\n    const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0));\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n      anchor,\n    );\n    drawTextField.draw(canvasArg);\n  });\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 > = TSource & {\n    landmarks: TFaceLandmarks\n    unshiftedLandmarks: TFaceLandmarks\n    alignedRect: FaceDetection\n  }\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    // eslint-disable-next-line dot-notation\n    && obj['landmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['alignedRect'] instanceof FaceDetection;\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 >(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks<TSource, TFaceLandmarks> {\n  const { box: shift } = sourceObj.detection;\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y);\n\n  const rect = landmarks.align();\n  const { imageDims } = sourceObj.detection;\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect,\n  };\n\n  return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n\n  public drawPoints: boolean\n\n  public lineWidth: number\n\n  public pointSize: number\n\n  public lineColor: string\n\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const {\n      drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n    } = options;\n    this.drawLines = drawLines;\n    this.drawPoints = drawPoints;\n    this.lineWidth = lineWidth || 1;\n    this.pointSize = pointSize || 2;\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {},\n  ) {\n    this.faceLandmarks = faceLandmarks;\n    this.options = new DrawFaceLandmarksOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const {\n      drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n    } = this.options;\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor;\n      ctx.lineWidth = lineWidth;\n      drawContour(ctx, this.faceLandmarks.getJawOutline());\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getNose());\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n      drawContour(ctx, this.faceLandmarks.getMouth(), true);\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor;\n      ctx.fillStyle = pointColor;\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath();\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n        ctx.fill();\n      };\n      this.faceLandmarks.positions.forEach(drawPoint);\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>,\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n  faceLandmarksArray.forEach((f) => {\n    // eslint-disable-next-line no-nested-ternary\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof');\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport {\n  AgeAndGenderPrediction, Gender, NetOutput, NetParams,\n} from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet');\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor;\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n      const gender = fullyConnectedLayer(pooled, params.fc.gender);\n      return { age, gender };\n    });\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input);\n      return { age, gender: tf.softmax(gender) };\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n\n    const ages = tf.unstack(out.age);\n    const genders = tf.unstack(out.gender);\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i],\n    }));\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0];\n        const probMale = (await genderTensor.data())[0];\n        const isMale = probMale > 0.5;\n        const gender = isMale ? Gender.MALE : Gender.FEMALE;\n        const genderProbability = isMale ? probMale : (1 - probMale);\n\n        ageTensor.dispose();\n        genderTensor.dispose();\n        return { age, gender, genderProbability };\n      }),\n    );\n    out.age.dispose();\n    out.gender.dispose();\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model';\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeightMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x;\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n  out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n  return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n  out = tf.add(out, x);\n  return out;\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception');\n    this._numMainBlocks = numMainBlocks;\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n    if (!params) {\n      throw new Error('TinyXception - load model before inference');\n    }\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n      out = reductionBlock(out, params.entry_flow.reduction_block_1);\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n      });\n      out = reductionBlock(out, params.exit_flow.reduction_block);\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap, this._numMainBlocks);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks);\n  }\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const age = extractFCParams(512, 1, 'fc/age');\n  const gender = extractFCParams(512, 2, 'fc/gender');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender'),\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n  // eslint-disable-next-line no-unused-vars\n  FEMALE = 'female',\n  // eslint-disable-next-line no-unused-vars\n  MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width);\n      return {\n        width: width * scale,\n        height: height * scale,\n      };\n    });\n\n    const batchSize = inputDimensions.length;\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n      // eslint-disable-next-line no-unused-vars\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx];\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n      };\n\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          getPaddingX(batchIdx),\n          getPaddingY(batchIdx),\n        ))))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          inputDimensions[batchIdx].width,\n          inputDimensions[batchIdx].height,\n        ))));\n\n      return landmarkTensors as tf.Tensor2D;\n    });\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input);\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width })),\n      );\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input);\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data());\n        const xCoords = landmarksArray.filter((_, i) => isEven(i));\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width: netInput.getInputWidth(batchIdx),\n          },\n        );\n      },\n    ));\n\n    landmarkTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136;\n  }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeightMapTiny } from './extractParamsFromWeightMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n  constructor() {\n    super('TinyFaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock3(normalized, params.dense0, true);\n      out = denseBlock3(out, params.dense1);\n      out = denseBlock3(out, params.dense2);\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMapTiny(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMapTiny(\n  weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock3Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock3Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 },\n  };\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128;\n  }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('FaceRecognitionNet');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n      let out = convDown(normalized, params.conv32_down);\n      out = tf.maxPool(out, 3, 2, 'valid');\n\n      out = residual(out, params.conv32_1);\n      out = residual(out, params.conv32_2);\n      out = residual(out, params.conv32_3);\n\n      out = residualDown(out, params.conv64_down);\n      out = residual(out, params.conv64_1);\n      out = residual(out, params.conv64_2);\n      out = residual(out, params.conv64_3);\n\n      out = residualDown(out, params.conv128_down);\n      out = residual(out, params.conv128_1);\n      out = residual(out, params.conv128_2);\n\n      out = residualDown(out, params.conv256_down);\n      out = residual(out, params.conv256_1);\n      out = residual(out, params.conv256_2);\n      out = residualDown(out, params.conv256_down_out);\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n      const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n      return fullyConnected;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    if (input?.shape?.some((dim) => dim <= 0)) return new Float32Array(128);\n    const netInput = await toNetInput(input);\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())) as Float32Array[];\n    faceDescriptorTensors.forEach((t) => t.dispose());\n    return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n  const { filters, bias } = params.conv;\n\n  let out = tf.conv2d(x, filters, strides, padding);\n  out = tf.add(out, bias);\n  out = scale(out, params.scale);\n  return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping,\n} from '../common/index';\nimport { isFloat } from '../utils/index';\nimport {\n  ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues);\n    const depth = weights.length / (numFilters * filterSize * filterSize);\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0],\n      ),\n    );\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams {\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n    const bias = tf.tensor1d(extractWeights(numFilters));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n    const weights = tf.tensor1d(extractWeights(numWeights));\n    const biases = tf.tensor1d(extractWeights(numWeights));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` },\n    );\n\n    return {\n      weights,\n      biases,\n    };\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvLayerParams {\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n    return { conv, scale };\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false,\n  ): ResidualLayerParams {\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n    return { conv1, conv2 };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n  );\n  paramMappings.push({ paramPath: 'fc' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n    const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n    const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n    return { weights, biases };\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n    const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n    const scale = extractScaleLayerParams(prefix);\n\n    return { conv: { filters, bias }, scale };\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`),\n    };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const conv32_down = extractConvLayerParams('conv32_down');\n  const conv32_1 = extractResidualLayerParams('conv32_1');\n  const conv32_2 = extractResidualLayerParams('conv32_2');\n  const conv32_3 = extractResidualLayerParams('conv32_3');\n\n  const conv64_down = extractResidualLayerParams('conv64_down');\n  const conv64_1 = extractResidualLayerParams('conv64_1');\n  const conv64_2 = extractResidualLayerParams('conv64_2');\n  const conv64_3 = extractResidualLayerParams('conv64_3');\n\n  const conv128_down = extractResidualLayerParams('conv128_down');\n  const conv128_1 = extractResidualLayerParams('conv128_1');\n  const conv128_2 = extractResidualLayerParams('conv128_2');\n\n  const conv256_down = extractResidualLayerParams('conv256_down');\n  const conv256_1 = extractResidualLayerParams('conv256_1');\n  const conv256_2 = extractResidualLayerParams('conv256_2');\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n  const { fc } = weightMap;\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n  out = tf.add(out, x);\n  out = tf.relu(out);\n  return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape);\n  const isPad = pooled.shape[3] !== out.shape[3];\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number];\n    padShapeX[1] = 1;\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX);\n    out = tf.concat([out, zerosW], 1);\n\n    const padShapeY = [...out.shape] as [number, number, number, number];\n    padShapeY[2] = 1;\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY);\n    out = tf.concat([out, zerosH], 2);\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n  out = tf.add(pooled, out) as tf.Tensor4D;\n\n  out = tf.relu(out);\n  return out;\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet();\n  net.extractWeights(weights);\n  return net;\n}\n", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n>(\n  sourceObj: TSource,\n  descriptor: Float32Array,\n): WithFaceDescriptor<TSource> {\n  const extension = { descriptor };\n  return { ...sourceObj, ...extension };\n}\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n  TSource\n>(\n  sourceObj: TSource,\n  age: number,\n): WithAge<TSource> {\n  const extension = { age };\n  return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n    && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n  TSource\n>(\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number,\n): WithGender<TSource> {\n  const extension = { gender, genderProbability };\n  return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('SsdMobilenetv1');\n  }\n\n  public forwardInput(input: NetInput) {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D;\n      const features = mobileNetV1(x, params.mobilenetv1);\n\n      const {\n        boxPredictions,\n        classPredictions,\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer);\n    });\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {},\n  ): Promise<FaceDetection[]> {\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n\n    const netInput = await toNetInput(input);\n\n    const {\n      boxes: _boxes,\n      scores: _scores,\n    } = this.forwardInput(netInput);\n\n    const boxes = _boxes[0];\n    const scores = _scores[0];\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose();\n      _scores[i].dispose();\n    }\n\n    const scoresData = Array.from(await scores.data());\n    const iouThreshold = 0.5;\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence,\n    );\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0);\n    const inputSize = netInput.inputSize as number;\n    const padX = inputSize / reshapedDims.width;\n    const padY = inputSize / reshapedDims.height;\n\n    const boxesData = boxes.arraySync();\n    const results = indices\n      .map((idx) => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2]),\n        ].map((val) => val * padY);\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3]),\n        ].map((val) => val * padX);\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top,\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0),\n          },\n        );\n      });\n\n    boxes.dispose();\n    scores.dispose();\n    return results;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory,\n} from '../common/index';\nimport {\n  MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` },\n    );\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance,\n    };\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean,\n  ): ConvParams {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): PointwiseConvParams {\n    const {\n      filters,\n      bias,\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n    return {\n      filters,\n      batch_norm_offset: bias,\n    };\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): MobileNetV1.ConvPairParams {\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n    return { depthwise_conv, pointwise_conv };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13,\n    };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0,\n    };\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1,\n    };\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2,\n    };\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3,\n    };\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4,\n    };\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5,\n    };\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5,\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n  const mobilenetv1 = extractMobilenetV1Params();\n  const prediction_layer = extractPredictionLayerParams();\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4],\n  );\n  const output_layer = {\n    extra_dim,\n  };\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer,\n    },\n    paramMappings,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping,\n} from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport {\n  BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n    const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n    const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n    return { filters, batch_norm_offset };\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n    const mappedPrefix = `mobilenetv1/conv_${idx}`;\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n    const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n    const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n    const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n    const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n    const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance,\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n    };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13),\n    };\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n    const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n    return { filters, bias };\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n    );\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`,\n    );\n    return { box_encoding_predictor, class_predictor };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5),\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n  const extra_dim = weightMap['Output/extra_dim'];\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim,\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon,\n    );\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13,\n    ];\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1;\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n      if (layerIdx === 11) {\n        conv11 = out;\n      }\n    });\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null');\n    }\n\n    return {\n      out,\n      conv11: conv11 as any,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.conv2d(x, params.filters, strides, 'same');\n    out = tf.add(out, params.batch_norm_offset);\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync();\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0)\n      * Math.max(intersectionXmax - intersectionXmin, 0.0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number,\n): number[] {\n  const numBoxes = boxes.shape[0];\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes,\n  );\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter((c) => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score);\n\n  const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n  const selected: number[] = [];\n\n  candidates.forEach((c) => {\n    if (selected.length >= outputSize) return;\n    const originalScore = c.score;\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j]);\n      if (iou === 0.0) continue;\n      c.score *= suppressFunc(iou);\n      if (c.score <= scoreThreshold) break;\n    }\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex);\n    }\n  });\n  return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1]),\n  ];\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2))),\n  ];\n  return {\n    sizes,\n    centers,\n  };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers,\n  } = getCenterCoordinatesAndSizesLayer(x0);\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out),\n    ]),\n    [1, 0],\n  );\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = boxPredictions.shape[0];\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n    );\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4],\n    );\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number],\n    );\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams,\n) {\n  return tf.tidy(() => {\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding,\n    ], 1) as tf.Tensor4D;\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction,\n    ], 1) as tf.Tensor4D;\n\n    return {\n      boxPredictions,\n      classPredictions,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = x.shape[0];\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4],\n    );\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3],\n    );\n    return { boxPredictionEncoding, classPrediction };\n  });\n}\n", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5;\n    this._maxResults = maxResults || 100;\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`);\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence; }\n\n  get maxResults(): number { return this._maxResults; }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1();\n  net.extractWeights(weights);\n  return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n  public static DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024];\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2');\n    validateConfig(config);\n    this._config = config;\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config;\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1;\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0);\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n    let out = convWithBatchNorm(x, params.conv0);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = convWithBatchNorm(out, params.conv6);\n    out = convWithBatchNorm(out, params.conv7);\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor;\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D;\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n    });\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input), inputSize);\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput, inputSize);\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0),\n    };\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n    out.dispose();\n    out0.dispose();\n\n    const boxes = results.map((res) => res.box);\n    const scores = results.map((res) => res.score);\n    const classScores = results.map((res) => res.classScore);\n    const classNames = results.map((res) => this.config.classes[res.label]);\n\n    const indices = nonMaxSuppression(\n      boxes.map((box) => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true,\n    );\n\n    const detections = indices.map((idx) => new ObjectDetection(\n      scores[idx],\n      classScores[idx],\n      classNames[idx],\n      boxes[idx],\n      inputDimensions,\n    ));\n    return detections;\n  }\n\n  protected getDefaultModelName(): string {\n    return '';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap, this.config);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n    const numFilters = filterSizes ? filterSizes.length : undefined;\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number,\n  ) {\n    const { width, height } = inputBlobDimensions;\n    const inputSize = Math.max(width, height);\n    const correctionFactorX = inputSize / width;\n    const correctionFactorY = inputSize / height;\n\n    const numCells = outputTensor.shape[1];\n    const numBoxes = this.config.anchors.length;\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0);\n      return [boxes, scores, classScores];\n    });\n\n    const results = [] as any;\n    const scoresData = await scoresTensor.array();\n    const boxesData = await boxesTensor.array();\n    for (let row = 0; row < numCells; row++) {\n      for (let col = 0; col < numCells; col++) {\n        for (let anchor = 0; anchor < numBoxes; anchor++) {\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n            const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n            const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n            const x = (ctX - (widthLocal / 2));\n            const y = (ctY - (heightLocal / 2));\n            const pos = { row, col, anchor };\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 };\n            results.push({\n              box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n              score,\n              classScore: score * classScore,\n              label,\n              ...pos,\n            });\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose();\n    scoresTensor.dispose();\n    classScoresTensor.dispose();\n    return results;\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n    const { row, col, anchor } = pos;\n    const classesData = await classesTensor.array();\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label,\n      }))\n      .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n  }\n}\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`);\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n    out = tf.sub(out, params.bn.sub);\n    out = tf.mul(out, params.bn.truediv);\n    out = tf.add(out, params.conv.bias);\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612));\n    return tf.add(tf.relu(tf.sub(x, min)), min);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n    out = tf.add(out, params.bias);\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n    const sub = tf.tensor1d(extractWeights(size));\n    const truediv = tf.tensor1d(extractWeights(size));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` },\n    );\n    return { sub, truediv };\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n    return { conv, bn };\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0');\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  }\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry(`${prefix}/sub`, 1);\n    const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n    return { sub, truediv };\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { filters, bias };\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`);\n    const bn = extractBatchNormParams(`${prefix}/bn`);\n    return { conv, bn };\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    // eslint-disable-next-line no-mixed-operators\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8'),\n    };\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8'),\n    };\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n  return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416;\n    this._scoreThreshold = scoreThreshold || 0.5;\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n    }\n  }\n\n  get inputSize(): number { return this._inputSize; }\n\n  get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n  constructor(withSeparableConvs: boolean = true) {\n    const config = {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      ...(withSeparableConvs\n        ? {\n          anchors: BOX_ANCHORS_SEPARABLE,\n          meanRgb: MEAN_RGB_SEPARABLE,\n        }\n        : {\n          anchors: BOX_ANCHORS,\n          withClassScores: true,\n        }),\n    };\n\n    super(config);\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs;\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeightMap(weightMap);\n  }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\nexport * from './config';\nexport * from './types';\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs);\n  net.extractWeights(weights);\n  return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}\n", "export class ComposableTask<T> {\n  public async then(\n    // eslint-disable-next-line no-unused-vars\n    onfulfilled: (value: T) => T | PromiseLike<T>,\n  ): Promise<T> {\n    return onfulfilled(await this.run());\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented');\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected useTinyLandmarkNet: boolean,\n  ) {\n    super();\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net;\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n    const parentResults = await this.parentTask;\n    const detections = parentResults.map((res) => res.detection);\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections);\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      (face) => this.landmarkNet.detectLandmarks(face),\n    )) as FaceLandmarks68[];\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return parentResults.map((parentResult, i) => extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i]));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { detection } = parentResult;\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection]);\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n  const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n    ? getRectForAlignment(parentResult)\n    : parentResult.detection));\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  );\n\n  const results = await computeResults(faces);\n\n  faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n  return results;\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment?: (parentResultLocal: WithFaceLandmarks<TSource, any>) => FaceDetection,\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async (faces) => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment,\n  );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512],\n    };\n\n    super(config);\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeightMap(weightMap);\n  }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]> => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i]),\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces,\n    );\n\n    return extendWithFaceExpressions(parentResult, faceExpressions);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n    const parentResults = await this.parentTask;\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i];\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n    });\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces,\n    );\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n  ) {\n    super();\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>)),\n      null,\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      // eslint-disable-next-line no-shadow\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return extendWithFaceDescriptor(parentResult, descriptor);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n  ) {\n    super();\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n  public async run(): Promise<FaceDetection[]> {\n    const { input, options } = this;\n\n    // eslint-disable-next-line no-nested-ternary\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      // eslint-disable-next-line no-shadow\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        // eslint-disable-next-line no-nested-ternary\n        options instanceof SsdMobilenetv1Options\n          // eslint-disable-next-line no-shadow\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              // eslint-disable-next-line no-shadow\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      );\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options');\n    }\n\n    return faceDetectionFunction(input);\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}>[]>(async (resolve) => {\n      const detections = await this.run();\n      resolve(detections.map((detection) => extendWithFaceDetection({}, detection)));\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach((faceDetection) => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}> | undefined>(async (resolve) => {\n      const detection = await this.run();\n      resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number,\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {},\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n\n  const desc1 = Array.from(arr1);\n  const desc2 = Array.from(arr2);\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + (diff ** 2), 0),\n  );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6,\n  ) {\n    this._distanceThreshold = distanceThreshold;\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n\n    if (!inputArray.length) {\n      throw new Error('FaceRecognizer.constructor - expected atleast one input');\n    }\n\n    let count = 1;\n    const createUniqueLabel = () => `person ${count++}`;\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc;\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n      }\n\n      throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>');\n    });\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n  public get distanceThreshold(): number { return this._distanceThreshold; }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map((d) => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1);\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n        label,\n        this.computeMeanDistance(queryDescriptor, descriptors),\n      ))\n      .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor);\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance);\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON()),\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector();\n  net.extractWeights(weights);\n  return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height);\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height));\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height);\n  }\n\n  return results;\n}\n"],
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new file mode 100644
index 0000000..fbc3739
--- /dev/null
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diff --git a/dist/face-api.node-gpu.js b/dist/face-api.node-gpu.js
index 456e925..dba2baa 100644
--- a/dist/face-api.node-gpu.js
+++ b/dist/face-api.node-gpu.js
@@ -5,5 +5,5 @@
   author: <https://github.com/vladmandic>'
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mt.tidy(()=>{let[e,r,n]=t,a=mt.fill([...o.shape.slice(0,3),1],e,"float32"),s=mt.fill([...o.shape.slice(0,3),1],r,"float32"),i=mt.fill([...o.shape.slice(0,3),1],n,"float32"),c=mt.concat([a,s,i],3);return mt.sub(o,c)})}var Et=b(g());function Br(o,t=!1){return Et.tidy(()=>{let[e,r]=o.shape.slice(1);if(e===r)return o;let n=Math.abs(e-r),a=Math.round(n*(t?.5:1)),s=e>r?2:1,i=u=>{let l=o.shape.slice();return l[s]=u,Et.fill(l,0,"float32")},c=i(a),m=n-c.shape[s],d=[t&&m?i(m):null,o,c].filter(u=>!!u).map(u=>Et.cast(u,"float32"));return Et.concat(d,s)})}function En(o){let t=o.slice();for(let e=t.length-1;e>0;e--){let r=Math.floor(Math.random()*(e+1)),n=t[e];t[e]=t[r],t[r]=n}return t}function De(o){return 1/(1+Math.exp(-o))}function Mn(o){return Math.log(o/(1-o))}var oe=class extends D{constructor(t,e,r,n,a=!1){super({x:t,y:e,width:r,height:n},a)}};var Cn=.5,Nn=.43,In=.45,V=class{constructor(t,e,r=new x(0,0)){let{width:n,height:a}=e;this._imgDims=new 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this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let e=t.descriptors.map(r=>new Float32Array(r));return new xt(t.label,e)}};var Fo=class extends Me{static assertIsValidPredictedBox(t,e){if(Me.assertIsValidLabeledBox(t,e),!ee(t.score)||!ee(t.classScore))throw new Error(`${e} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,e,r,n){super(t,e);this._score=r,this._classScore=n}get score(){return this._score}get classScore(){return this._classScore}};function pt(o){return o.detection instanceof M}function $t(o,t){return{...o,...{detection:t}}}function Rr(){let o=window.fetch;if(!o)throw new Error("fetch - missing fetch implementation for browser 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Ce=class{constructor(t={}){let{anchorPosition:e,backgroundColor:r,fontColor:n,fontSize:a,fontStyle:s,padding:i}=t;this.anchorPosition=e||dt.TOP_LEFT,this.backgroundColor=r||"rgba(0, 0, 0, 0.5)",this.fontColor=n||"rgba(255, 255, 255, 1)",this.fontSize=a||14,this.fontStyle=s||"Georgia",this.padding=i||4}},Mt=class{constructor(t,e,r={}){this.text=typeof t=="string"?[t]:t instanceof Mt?t.text:t,this.anchor=e,this.options=new Ce(r)}measureWidth(t){let{padding:e}=this.options;return this.text.map(r=>t.measureText(r).width).reduce((r,n)=>r<n?n:r,0)+2*e}measureHeight(){let{fontSize:t,padding:e}=this.options;return 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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 ${s} passed, but not supported in input array`);this._imageTensors[n]=r,this._inputDimensions[n]=r.shape.slice(1);return}let a=r instanceof w.getEnv().Canvas?r:Ie(r);this._canvases[n]=a,this._inputDimensions[n]=[a.height,a.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get 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e=o(`${t}/filters`,4),r=o(`${t}/bias`,1);return{filters:e,bias:r}}}function sr(o,t){let e=j(o,t),r=ar(e),n=pe(e);function a(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`);return{conv0:m,conv1:p,conv2:d}}function s(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`),u=n(`${i}/conv3`);return{conv0:m,conv1:p,conv2:d,conv3:u}}return{extractDenseBlock3Params:a,extractDenseBlock4Params:s}}function Do(o){let t=[],{extractDenseBlock4Params:e}=sr(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2"),dense3:e("dense3")};return W(o,t),{params:r,paramMappings:t}}var Se=class extends S{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceFeatureExtractor - load model before inference");return Nt.tidy(()=>{let r=Nt.cast(t.toBatchTensor(112,!0),"float32"),a=ot(r,[122.782,117.001,104.298]).div(Nt.scalar(255)),s=Le(a,e.dense0,!0);return 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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 mr(o,t){return{...o,...{expressions:t}}}function $n(o,t,e=.1,r){(Array.isArray(t)?t:[t]).forEach(a=>{let s=a instanceof It?a:Kr(a)?a.expressions:void 0;if(!s)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let c=s.asSortedArray().filter(d=>d.probability>e),m=pt(a)?a.detection.box.bottomLeft:r||new x(0,0);new Mt(c.map(d=>`${d.expression} (${Rt(d.probability)})`),m).draw(o)})}function Vt(o){return pt(o)&&o.landmarks instanceof V&&o.unshiftedLandmarks instanceof V&&o.alignedRect instanceof M}function le(o,t){let{box:e}=o.detection,r=t.shiftBy(e.x,e.y),n=r.align(),{imageDims:a}=o.detection,s=new M(o.detection.score,n.rescale(a.reverse()),a);return{...o,...{landmarks:r,unshiftedLandmarks:t,alignedRect:s}}}var Qr=class{constructor(t={}){let{drawLines:e=!0,drawPoints:r=!0,lineWidth:n,lineColor:a,pointSize:s,pointColor:i}=t;this.drawLines=e,this.drawPoints=r,this.lineWidth=n||1,this.pointSize=s||2,this.lineColor=a||"rgba(0, 255, 255, 1)",this.pointColor=i||"rgba(255, 0, 255, 1)"}},to=class{constructor(t,e={}){this.faceLandmarks=t,this.options=new Qr(e)}draw(t){let e=O(t),{drawLines:r,drawPoints:n,lineWidth:a,lineColor:s,pointSize:i,pointColor:c}=this.options;if(r&&this.faceLandmarks instanceof ne&&(e.strokeStyle=s,e.lineWidth=a,ft(e,this.faceLandmarks.getJawOutline()),ft(e,this.faceLandmarks.getLeftEyeBrow()),ft(e,this.faceLandmarks.getRightEyeBrow()),ft(e,this.faceLandmarks.getNose()),ft(e,this.faceLandmarks.getLeftEye(),!0),ft(e,this.faceLandmarks.getRightEye(),!0),ft(e,this.faceLandmarks.getMouth(),!0)),n){e.strokeStyle=c,e.fillStyle=c;let m=p=>{e.beginPath(),e.arc(p.x,p.y,i,0,2*Math.PI),e.fill()};this.faceLandmarks.positions.forEach(m)}}};function jn(o,t){(Array.isArray(t)?t:[t]).forEach(r=>{let n=r instanceof V?r:Vt(r)?r.landmarks:void 0;if(!n)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new to(n).draw(o)})}var No="0.13.1";var ut=b(g());var I=b(g());function Hn(o,t){let e=ce(o,t),r=me(o,t);function n(s,i,c){let m=r(s,i,`${c}/separable_conv0`),p=r(i,i,`${c}/separable_conv1`),d=e(s,i,1,`${c}/expansion_conv`);return{separable_conv0:m,separable_conv1:p,expansion_conv:d}}function a(s,i){let c=r(s,s,`${i}/separable_conv0`),m=r(s,s,`${i}/separable_conv1`),p=r(s,s,`${i}/separable_conv2`);return{separable_conv0:c,separable_conv1:m,separable_conv2:p}}return{extractConvParams:e,extractSeparableConvParams:r,extractReductionBlockParams:n,extractMainBlockParams:a}}function Io(o,t){let e=[],{extractWeights:r,getRemainingWeights:n}=B(o),{extractConvParams:a,extractSeparableConvParams:s,extractReductionBlockParams:i,extractMainBlockParams:c}=Hn(r,e),m=a(3,32,3,"entry_flow/conv_in"),p=i(32,64,"entry_flow/reduction_block_0"),d=i(64,128,"entry_flow/reduction_block_1"),u={conv_in:m,reduction_block_0:p,reduction_block_1:d},l={};ct(t,0,1).forEach(y=>{l[`main_block_${y}`]=c(128,`middle_flow/main_block_${y}`)});let v=i(128,256,"exit_flow/reduction_block"),_=s(256,512,"exit_flow/separable_conv"),h={reduction_block:v,separable_conv:_};if(n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{paramMappings:e,params:{entry_flow:u,middle_flow:l,exit_flow:h}}}function Yn(o,t){let e=j(o,t),r=ar(e),n=pe(e);function a(i){let c=n(`${i}/separable_conv0`),m=n(`${i}/separable_conv1`),p=r(`${i}/expansion_conv`);return{separable_conv0:c,separable_conv1:m,expansion_conv:p}}function s(i){let c=n(`${i}/separable_conv0`),m=n(`${i}/separable_conv1`),p=n(`${i}/separable_conv2`);return{separable_conv0:c,separable_conv1:m,separable_conv2:p}}return{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:a,extractMainBlockParams:s}}function Lo(o,t){let e=[],{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:a,extractMainBlockParams:s}=Yn(o,e),i=r("entry_flow/conv_in"),c=a("entry_flow/reduction_block_0"),m=a("entry_flow/reduction_block_1"),p={conv_in:i,reduction_block_0:c,reduction_block_1:m},d={};ct(t,0,1).forEach(_=>{d[`main_block_${_}`]=s(`middle_flow/main_block_${_}`)});let 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 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E(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return Lo(t,this._numMainBlocks)}extractParams(t){return Io(t,this._numMainBlocks)}};function So(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),n=rr(e,t),a=n(512,1,"fc/age"),s=n(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:s}}}}function Ao(o){let t=[],e=j(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:{age:r("fc/age"),gender:r("fc/gender")}};return W(o,t),{params:n,paramMappings:t}}var vt;(function(o){o.FEMALE="female",o.MALE="male"})(vt||(vt={}));var pr=class extends S{constructor(t=new oo(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${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 m.data())[0],u=d>.5,l=u?vt.MALE:vt.FEMALE,v=u?d:1-d;return c.dispose(),m.dispose(),{age:p,gender:l,genderProbability:v}}));return 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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<s.length;F++)s[F].dispose(),i[F].dispose();let p=Array.from(await m.data()),u=Vo(c,p,r,.5,n),l=a.getReshapedInputDimensions(0),v=a.inputSize,_=v/l.width,h=v/l.height,y=c.arraySync(),T=u.map(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 na(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: ${JSON.stringify(o.meanRgb)}`)}var Q=b(g());var K=b(g());function be(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Ft(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),be(e)})}var St=b(g());function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),be(e)})}var co=b(g());function aa(o,t){let e=ce(o,t);function r(s,i){let c=co.tensor1d(o(s)),m=co.tensor1d(o(s));return t.push({paramPath:`${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}=aa(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: ${a().length}`);return{params:p,paramMappings:s}}function sa(o,t){let e=j(o,t);function r(i){let c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=sa(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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;y<m;y++)for(let T=0;T<m;T++)for(let F=0;F<p;F++){let L=De(_[y][T][F][0]);if(!r||L>r){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 ia(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),ca=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ma=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),pa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),da=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ua=o=>P.faceExpressionNet.predictExpressions(o),la=o=>P.ageGenderNet.predictAgeAndGender(o),dn=o=>P.ssdMobilenetv1.load(o),fa=o=>P.tinyFaceDetector.load(o),ha=o=>P.tinyYolov2.load(o),xa=o=>P.faceLandmark68Net.load(o),ba=o=>P.faceLandmark68TinyNet.load(o),ga=o=>P.faceRecognitionNet.load(o),va=o=>P.faceExpressionNet.load(o),ya=o=>P.ageGenderNet.load(o),Fa=dn,Ta=mn,Pa=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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Ce=class{constructor(t={}){let{anchorPosition:e,backgroundColor:r,fontColor:n,fontSize:a,fontStyle:s,padding:i}=t;this.anchorPosition=e||dt.TOP_LEFT,this.backgroundColor=r||"rgba(0, 0, 0, 0.5)",this.fontColor=n||"rgba(255, 255, 255, 1)",this.fontSize=a||14,this.fontStyle=s||"Georgia",this.padding=i||4}},Mt=class{constructor(t,e,r={}){this.text=typeof t=="string"?[t]:t instanceof Mt?t.text:t,this.anchor=e,this.options=new Ce(r)}measureWidth(t){let{padding:e}=this.options;return this.text.map(r=>t.measureText(r).width).reduce((r,n)=>r<n?n:r,0)+2*e}measureHeight(){let{fontSize:t,padding:e}=this.options;return this.text.length*t+2*e}getUpperLeft(t,e){let{anchorPosition:r}=this.options,n=r===dt.BOTTOM_RIGHT||r===dt.TOP_RIGHT,a=r===dt.BOTTOM_LEFT||r===dt.BOTTOM_RIGHT,s=this.measureWidth(t),i=this.measureHeight(),c=n?this.anchor.x-s:this.anchor.x,m=a?this.anchor.y-i:this.anchor.y;if(e){let{width:p,height:d}=e,u=Math.max(Math.min(c,p-s),0),l=Math.max(Math.min(m,d-i),0);return{x:u,y:l}}return{x:c,y:m}}draw(t){let e=jt(t),r=O(e),{backgroundColor:n,fontColor:a,fontSize:s,fontStyle:i,padding:c}=this.options;r.font=`${s}px ${i}`;let m=this.measureWidth(r),p=this.measureHeight();r.fillStyle=n;let d=this.getUpperLeft(r,e);r.fillRect(d.x,d.y,m,p),r.fillStyle=a,this.text.forEach((u,l)=>{let v=c+d.x,_=c+d.y+(l+1)*s;r.fillText(u,v,_)})}};var Gr=class{constructor(t={}){let{boxColor:e,lineWidth:r,label:n,drawLabelOptions:a}=t;this.boxColor=e||"rgba(0, 0, 255, 1)",this.lineWidth=r||2,this.label=n;let s={anchorPosition:dt.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=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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function ae(o,t){let{Canvas:e}=w.getEnv(),r=o;if(!(o instanceof e)){let s=await E(o);if(s.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let i=s.getInput(0);r=i instanceof e?i:await Ur(i)}let n=O(r);return t.map(s=>s instanceof M?s.forSize(r.width,r.height).box.floor():s).map(s=>s.clipAtImageBorders(r.width,r.height)).map(({x:s,y:i,width:c,height:m})=>{let p=Yt({width:c,height:m});return c>0&&m>0&&O(p).putImageData(n.getImageData(s,i,c,m),0,0),p})}var Ze=b(g());async function se(o,t){if(!ht(o)&&!z(o))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(z(o)&&o.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return Ze.tidy(()=>{let[e,r,n]=o.shape.slice(z(o)?1:0);return t.map(i=>i instanceof M?i.forSize(r,e).box:i).map(i=>i.clipAtImageBorders(r,e)).map(({x:i,y:c,width:m,height:p})=>Ze.slice3d(o.as3D(e,r,n),[c,i,0],[p,m,n]))})}async function Gt(o,t){let{fetch:e}=w.getEnv(),r=await 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qr(o,t){let{manifestUri:e,modelBaseUri:r}=Ke(o,t),n=await Jr(e);return _o.io.loadWeights(n,r)}function On(o,t,e=!1){let{width:r,height:n}=e?Ht(t):t;return o.width=r,o.height=n,{width:r,height:n}}var Nt=b(g());var gt=b(g());var S=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:e,objProp:r}=this.traversePropertyPath(t);return e[r]}reassignParamFromPath(t,e){let{obj:r,objProp:n}=this.traversePropertyPath(t);r[n].dispose(),r[n]=e}getParamList(){return this._paramMappings.map(({paramPath:t})=>({path:t,tensor:this.getParamFromPath(t)}))}getTrainableParams(){return this.getParamList().filter(t=>t.tensor instanceof gt.Variable)}getFrozenParams(){return this.getParamList().filter(t=>!(t.tensor instanceof 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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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E(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return Lo(t,this._numMainBlocks)}extractParams(t){return Io(t,this._numMainBlocks)}};function So(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),n=rr(e,t),a=n(512,1,"fc/age"),s=n(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:s}}}}function Ao(o){let t=[],e=j(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:{age:r("fc/age"),gender:r("fc/gender")}};return W(o,t),{params:n,paramMappings:t}}var vt;(function(o){o.FEMALE="female",o.MALE="male"})(vt||(vt={}));var pr=class extends S{constructor(t=new oo(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${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 m.data())[0],u=d>.5,l=u?vt.MALE:vt.FEMALE,v=u?d:1-d;return c.dispose(),m.dispose(),{age:p,gender:l,genderProbability:v}}));return r.age.dispose(),r.gender.dispose(),e.isBatchInput?i:i[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return So(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=ir(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Ao(r)}extractParams(t){let e=512*1+1+(512*2+2),r=t.slice(0,t.length-e),n=t.slice(t.length-e);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(n)}};var H=b(g());var Be=class extends We{postProcess(t,e,r){let n=r.map(({width:s,height:i})=>{let c=e/Math.max(i,s);return{width:s*c,height:i*c}}),a=n.length;return H.tidy(()=>{let s=(d,u)=>H.stack([H.fill([68],d,"float32"),H.fill([68],u,"float32")],1).as2D(1,136).as1D(),i=(d,u)=>{let{width:l,height:v}=n[d];return 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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 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m.data()),u=Vo(c,p,r,.5,n),l=a.getReshapedInputDimensions(0),v=a.inputSize,_=v/l.width,h=v/l.height,y=c.arraySync(),T=u.map(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 na(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: ${JSON.stringify(o.meanRgb)}`)}var Q=b(g());var K=b(g());function be(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Ft(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),be(e)})}var St=b(g());function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),be(e)})}var co=b(g());function aa(o,t){let e=ce(o,t);function r(s,i){let c=co.tensor1d(o(s)),m=co.tensor1d(o(s));return t.push({paramPath:`${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}=aa(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: ${a().length}`);return{params:p,paramMappings:s}}function sa(o,t){let e=j(o,t);function r(i){let c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=sa(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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;y<m;y++)for(let T=0;T<m;T++)for(let F=0;F<p;F++){let L=De(_[y][T][F][0]);if(!r||L>r){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 ia(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),ca=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ma=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),pa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),da=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ua=o=>P.faceExpressionNet.predictExpressions(o),la=o=>P.ageGenderNet.predictAgeAndGender(o),dn=o=>P.ssdMobilenetv1.load(o),fa=o=>P.tinyFaceDetector.load(o),ha=o=>P.tinyYolov2.load(o),xa=o=>P.faceLandmark68Net.load(o),ba=o=>P.faceLandmark68TinyNet.load(o),ga=o=>P.faceRecognitionNet.load(o),va=o=>P.faceExpressionNet.load(o),ya=o=>P.ageGenderNet.load(o),Fa=dn,Ta=mn,Pa=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=e instanceof gr?n=>P.tinyFaceDetector.locateFaces(n,e):e instanceof Z?n=>P.ssdMobilenetv1.locateFaces(n,e):e instanceof lt?n=>P.tinyYolov2.locateFaces(n,e):null;if(!r)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return r(t)}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 _a(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 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 //# sourceMappingURL=face-api.node-gpu.js.map
diff --git a/dist/face-api.node.js b/dist/face-api.node.js
index a4f2288..0aa1824 100644
--- a/dist/face-api.node.js
+++ b/dist/face-api.node.js
@@ -5,5 +5,5 @@
   author: <https://github.com/vladmandic>'
   */
 
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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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E(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return Lo(t,this._numMainBlocks)}extractParams(t){return Io(t,this._numMainBlocks)}};function So(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),n=rr(e,t),a=n(512,1,"fc/age"),s=n(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:s}}}}function Ao(o){let t=[],e=j(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:{age:r("fc/age"),gender:r("fc/gender")}};return W(o,t),{params:n,paramMappings:t}}var vt;(function(o){o.FEMALE="female",o.MALE="male"})(vt||(vt={}));var pr=class extends S{constructor(t=new oo(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${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 m.data())[0],u=d>.5,l=u?vt.MALE:vt.FEMALE,v=u?d:1-d;return c.dispose(),m.dispose(),{age:p,gender:l,genderProbability:v}}));return r.age.dispose(),r.gender.dispose(),e.isBatchInput?i:i[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return So(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=ir(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Ao(r)}extractParams(t){let e=512*1+1+(512*2+2),r=t.slice(0,t.length-e),n=t.slice(t.length-e);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(n)}};var H=b(g());var Be=class extends We{postProcess(t,e,r){let n=r.map(({width:s,height:i})=>{let c=e/Math.max(i,s);return{width:s*c,height:i*c}}),a=n.length;return H.tidy(()=>{let s=(d,u)=>H.stack([H.fill([68],d,"float32"),H.fill([68],u,"float32")],1).as2D(1,136).as1D(),i=(d,u)=>{let{width:l,height:v}=n[d];return 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m.data()),u=Vo(c,p,r,.5,n),l=a.getReshapedInputDimensions(0),v=a.inputSize,_=v/l.width,h=v/l.height,y=c.arraySync(),T=u.map(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 na(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 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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 aa(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}=aa(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: ${a().length}`);return{params:p,paramMappings:s}}function sa(o,t){let e=j(o,t);function r(i){let c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=sa(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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;y<m;y++)for(let T=0;T<m;T++)for(let F=0;F<p;F++){let L=De(_[y][T][F][0]);if(!r||L>r){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 ia(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),ca=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ma=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),pa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),da=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ua=o=>P.faceExpressionNet.predictExpressions(o),la=o=>P.ageGenderNet.predictAgeAndGender(o),dn=o=>P.ssdMobilenetv1.load(o),fa=o=>P.tinyFaceDetector.load(o),ha=o=>P.tinyYolov2.load(o),xa=o=>P.faceLandmark68Net.load(o),ba=o=>P.faceLandmark68TinyNet.load(o),ga=o=>P.faceRecognitionNet.load(o),va=o=>P.faceExpressionNet.load(o),ya=o=>P.ageGenderNet.load(o),Fa=dn,Ta=mn,Pa=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=e instanceof gr?n=>P.tinyFaceDetector.locateFaces(n,e):e instanceof Z?n=>P.ssdMobilenetv1.locateFaces(n,e):e instanceof lt?n=>P.tinyYolov2.locateFaces(n,e):null;if(!r)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return r(t)}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 _a(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 wa(o,t={}){return wr(o,new lt(t)).withFaceLandmarks().withFaceDescriptors()}var Da=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<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | 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.distance<r.distance?e:r)}findBestMatch(t){let e=this.matchDescriptor(t);return e.distance<this.distanceThreshold?e:new Ee("unknown",e.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>xt.fromJSON(r));return new Dr(e,t.distanceThreshold)}};function Ea(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: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>ln(n,{width:e,height:r}));if(Vt(o)){let 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tr=b(g());function ce(o,t){return(e,r,n,a)=>{let s=tr.tensor4d(o(e*r*n*n),[n,n,e,r]),i=tr.tensor1d(o(r));return t.push({paramPath:`${a}/filters`},{paramPath:`${a}/bias`}),{filters:s,bias:i}}}var er=b(g());function rr(o,t){return(e,r,n)=>{let a=er.tensor2d(o(e*r),[e,r]),s=er.tensor1d(o(r));return t.push({paramPath:`${n}/weights`},{paramPath:`${n}/bias`}),{weights:a,bias:s}}}var ke=b(g());var or=class{constructor(t,e,r){this.depthwise_filter=t;this.pointwise_filter=e;this.bias=r}};function me(o,t){return(e,r,n)=>{let a=ke.tensor4d(o(3*3*e),[3,3,e,1]),s=ke.tensor4d(o(e*r),[1,1,e,r]),i=ke.tensor1d(o(r));return t.push({paramPath:`${n}/depthwise_filter`},{paramPath:`${n}/pointwise_filter`},{paramPath:`${n}/bias`}),new or(a,s,i)}}function pe(o){return t=>{let e=o(`${t}/depthwise_filter`,4),r=o(`${t}/pointwise_filter`,4),n=o(`${t}/bias`,1);return new or(e,r,n)}}function j(o,t){return(e,r,n)=>{let a=o[e];if(!Bt(a,r))throw new Error(`expected weightMap[${e}] to be a Tensor${r}D, instead have 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e=o(`${t}/filters`,4),r=o(`${t}/bias`,1);return{filters:e,bias:r}}}function sr(o,t){let e=j(o,t),r=ar(e),n=pe(e);function a(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`);return{conv0:m,conv1:p,conv2:d}}function s(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`),u=n(`${i}/conv3`);return{conv0:m,conv1:p,conv2:d,conv3:u}}return{extractDenseBlock3Params:a,extractDenseBlock4Params:s}}function Do(o){let t=[],{extractDenseBlock4Params:e}=sr(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2"),dense3:e("dense3")};return W(o,t),{params:r,paramMappings:t}}var Se=class extends S{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceFeatureExtractor - load model before inference");return Nt.tidy(()=>{let r=Nt.cast(t.toBatchTensor(112,!0),"float32"),a=ot(r,[122.782,117.001,104.298]).div(Nt.scalar(255)),s=Le(a,e.dense0,!0);return 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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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E(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return Lo(t,this._numMainBlocks)}extractParams(t){return Io(t,this._numMainBlocks)}};function So(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),n=rr(e,t),a=n(512,1,"fc/age"),s=n(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:s}}}}function Ao(o){let t=[],e=j(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:{age:r("fc/age"),gender:r("fc/gender")}};return W(o,t),{params:n,paramMappings:t}}var vt;(function(o){o.FEMALE="female",o.MALE="male"})(vt||(vt={}));var pr=class extends S{constructor(t=new oo(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return ut.tidy(()=>{let r=t 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r.age.dispose(),r.gender.dispose(),e.isBatchInput?i:i[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return So(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=ir(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Ao(r)}extractParams(t){let e=512*1+1+(512*2+2),r=t.slice(0,t.length-e),n=t.slice(t.length-e);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(n)}};var H=b(g());var Be=class extends We{postProcess(t,e,r){let n=r.map(({width:s,height:i})=>{let c=e/Math.max(i,s);return{width:s*c,height:i*c}}),a=n.length;return H.tidy(()=>{let s=(d,u)=>H.stack([H.fill([68],d,"float32"),H.fill([68],u,"float32")],1).as2D(1,136).as1D(),i=(d,u)=>{let{width:l,height:v}=n[d];return 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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 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;y<m;y++)for(let T=0;T<m;T++)for(let F=0;F<p;F++){let L=De(_[y][T][F][0]);if(!r||L>r){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 ia(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),ca=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ma=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),pa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),da=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ua=o=>P.faceExpressionNet.predictExpressions(o),la=o=>P.ageGenderNet.predictAgeAndGender(o),dn=o=>P.ssdMobilenetv1.load(o),fa=o=>P.tinyFaceDetector.load(o),ha=o=>P.tinyYolov2.load(o),xa=o=>P.faceLandmark68Net.load(o),ba=o=>P.faceLandmark68TinyNet.load(o),ga=o=>P.faceRecognitionNet.load(o),va=o=>P.faceExpressionNet.load(o),ya=o=>P.ageGenderNet.load(o),Fa=dn,Ta=mn,Pa=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=e instanceof gr?n=>P.tinyFaceDetector.locateFaces(n,e):e instanceof Z?n=>P.ssdMobilenetv1.locateFaces(n,e):e instanceof lt?n=>P.tinyYolov2.locateFaces(n,e):null;if(!r)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return r(t)}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 _a(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 wa(o,t={}){return wr(o,new lt(t)).withFaceLandmarks().withFaceDescriptors()}var Da=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<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | 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.distance<r.distance?e:r)}findBestMatch(t){let e=this.matchDescriptor(t);return e.distance<this.distanceThreshold?e:new Ee("unknown",e.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>xt.fromJSON(r));return new Dr(e,t.distanceThreshold)}};function Ea(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 Ca=typeof process!="undefined",Na=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",Ia={faceapi:No,node:Ca,browser:Na};
 //# sourceMappingURL=face-api.node.js.map
diff --git a/package-lock.json b/package-lock.json
index 2e078a3..26ad334 100644
--- a/package-lock.json
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+      "dev": true,
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@@ -909,9 +914,9 @@
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diff --git a/package.json b/package.json
index 61704f4..a9f5b2e 100644
--- a/package.json
+++ b/package.json
@@ -49,7 +49,7 @@
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     "chokidar": "^3.5.1",
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+    "esbuild": "^0.8.50",
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diff --git a/src/tfjs/tf-node-cpu.ts b/src/tfjs/tf-node-cpu.ts
index 988803d..3e4088d 100644
--- a/src/tfjs/tf-node-cpu.ts
+++ b/src/tfjs/tf-node-cpu.ts
@@ -1,6 +1,4 @@
 /* eslint-disable import/no-extraneous-dependencies */
 /* eslint-disable node/no-unpublished-import */
 
-
 export * from '@tensorflow/tfjs';
-