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

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

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Jr=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Ct=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Jr.forEach((e,r)=>{this[e]=t[r]})}asSortedArray(){return Jr.map(t=>({expression:t,probability:this[t]})).sort((t,e)=>e.probability-t.probability)}};var ir=class extends We{constructor(t=new Se){super("FaceExpressionNet",t)}forwardInput(t){return de.tidy(()=>de.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(de.unstack(r).map(async s=>{let i=s.dataSync();return s.dispose(),i}));r.dispose();let a=n.map(s=>new Ct(s));return e.isBatchInput?a:a[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function qr(o){return o.expressions instanceof Ct}function 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predictAgeAndGender(t){let e=await E(t),r=await this.forwardInput(e),n=dt.unstack(r.age),a=dt.unstack(r.gender),s=n.map((c,m)=>({ageTensor:c,genderTensor:a[m]})),i=await Promise.all(s.map(async({ageTensor:c,genderTensor:m})=>{let p=c.dataSync()[0],d=m.dataSync()[0],u=d>.5,l=u?gt.MALE:gt.FEMALE,g=u?d:1-d;return c.dispose(),m.dispose(),{age:p,gender:l,genderProbability:g}}));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 Io(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=sr(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Lo(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 Y=y(b());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 Y.tidy(()=>{let s=(d,u)=>Y.stack([Y.fill([68],d,"float32"),Y.fill([68],u,"float32")],1).as2D(1,136).as1D(),i=(d,u)=>{let{width:l,height:g}=n[d];return u(l,g)?Math.abs(l-g)/2:0},c=d=>i(d,(u,l)=>u<l),m=d=>i(d,(u,l)=>l<u);return t.mul(Y.fill([a,136],e,"float32")).sub(Y.stack(Array.from(Array(a),(d,u)=>s(c(u),m(u))))).div(Y.stack(Array.from(Array(a),(d,u)=>s(n[u].width,n[u].height))))})}forwardInput(t){return Y.tidy(()=>{let e=this.runNet(t);return this.postProcess(e,t.inputSize,t.inputDimensions.map(([r,n])=>({height:r,width:n})))})}async forward(t){return this.forwardInput(await E(t))}async detectLandmarks(t){let e=await E(t),r=Y.tidy(()=>Y.unstack(this.forwardInput(e))),n=await Promise.all(r.map(async(a,s)=>{let i=Array.from(a.dataSync()),c=i.filter((p,d)=>Ge(d)),m=i.filter((p,d)=>!Ge(d));return new re(Array(68).fill(0).map((p,d)=>new x(c[d],m[d])),{height:e.getInputHeight(s),width:e.getInputWidth(s)})}));return r.forEach(a=>a.dispose()),e.isBatchInput?n:n[0]}getClassifierChannelsOut(){return 136}};var le=class extends Be{constructor(t=new Se){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var fe=y(b());function ko(o){let t=[],{extractDenseBlock3Params:e}=ar(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2")};return W(o,t),{params:r,paramMappings:t}}function So(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),{extractDenseBlock3Params:n}=or(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i}}}var eo=class extends S{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyFaceFeatureExtractor - load model before inference");return fe.tidy(()=>{let r=fe.cast(t.toBatchTensor(112,!0),"float32"),a=rt(r,[122.782,117.001,104.298]).div(255),s=Ke(a,e.dense0,!0);return s=Ke(s,e.dense1),s=Ke(s,e.dense2),s=fe.avgPool(s,[14,14],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return ko(t)}extractParams(t){return So(t)}};var pr=class extends Be{constructor(t=new eo){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var Ao=class extends le{};var nt=y(b());var he=y(b());var dr=y(b());function Wo(o,t){return dr.add(dr.mul(o,t.weights),t.biases)}function ro(o,t,e,r,n="same"){let{filters:a,bias:s}=t.conv,i=he.conv2d(o,a,e,n);return 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forward(t){return this.forwardInput(await E(t))}async computeFaceDescriptor(t){var a;if((a=t==null?void 0:t.shape)==null?void 0:a.some(s=>s<=0))return new Float32Array(128);let e=await E(t),r=nt.tidy(()=>nt.unstack(this.forwardInput(e))),n=await Promise.all(r.map(s=>s.data()));return r.forEach(s=>s.dispose()),e.isBatchInput?n:n[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return $o(t)}extractParams(t){return Ro(t)}};function Gn(o){let t=new xe;return t.extractWeights(o),t}function lr(o,t){return{...o,...{descriptor:t}}}function zn(o){return typeof o.age=="number"}function fr(o,t){return{...o,...{age:t}}}function Vn(o){return(o.gender===gt.MALE||o.gender===gt.FEMALE)&&Qt(o.genderProbability)}function hr(o,t,e){return{...o,...{gender:t,genderProbability:e}}}var It=y(b());var at=y(b());function Un(o,t){function e(c,m){let p=at.tensor4d(o(3*3*c),[3,3,c,1]),d=at.tensor1d(o(c)),u=at.tensor1d(o(c)),l=at.tensor1d(o(c)),g=at.tensor1d(o(c));return 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s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var ut=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var so=class extends S{constructor(t){super("TinyYolov2");no(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=yt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=yt(r,e.conv6),r=yt(r,e.conv7),Yt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(Yt(t,e.conv0,"valid",!1)):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=e.conv6?Ft(r,e.conv6):r,r=e.conv7?Ft(r,e.conv7):r,Yt(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?rt(n,this.config.meanRgb):n,n=n.div(255),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 ut(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 Sr(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new wt(d[h],u[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return en(t,this.config)}extractParams(t){let e=this.config.filterSizes||so.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,l]=N.tidy(()=>{let v=t.reshape([m,m,p,this.boxEncodingSize]),T=v.slice([0,0,0,0],[m,m,p,4]),F=v.slice([0,0,0,4],[m,m,p,1]),I=this.withClassScores?N.softmax(v.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):N.scalar(0);return[T,F,I]}),g=[],P=await u.array(),h=await d.array();for(let v=0;v<m;v++)for(let T=0;T<m;T++)for(let F=0;F<p;F++){let I=De(P[v][T][F][0]);if(!r||I>r){let z=(T+De(h[v][T][F][0]))/m*i,tt=(v+De(h[v][T][F][1]))/m*c,st=Math.exp(h[v][T][F][2])*this.config.anchors[F].x/m*i,X=Math.exp(h[v][T][F][3])*this.config.anchors[F].y/m*c,Tt=z-st/2,_t=tt-X/2,Pt={row:v,col:T,anchor:F},{classScore:Kt,label:uo}=this.withClassScores?await this.extractPredictedClass(l,Pt):{classScore:1,label:0};g.push({box:new te(Tt,_t,Tt+st,_t+X),score:I,classScore:I*Kt,label:uo,...Pt})}}return d.dispose(),u.dispose(),l.dispose(),g}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},ge=so;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: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 na(o,t=!0){let e=new ve(t);return e.extractWeights(o),e}var br=class extends ut{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Q=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var je=y(b());var io=y(b());async function Ut(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Gt(c)?n(c):c.detection),s=r||(t instanceof io.Tensor?await ne(t,a):await oe(t,a)),i=await e(s);return s.forEach(c=>c instanceof io.Tensor&&c.dispose()),i}async function ye(o,t,e,r,n){return Ut([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 Fe=class extends ge{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 _={ssdMobilenetv1:new Vt,tinyFaceDetector:new Fe,tinyYolov2:new ve,faceLandmark68Net:new le,faceLandmark68TinyNet:new pr,faceRecognitionNet:new xe,faceExpressionNet:new ir,ageGenderNet:new mr},an=(o,t)=>_.ssdMobilenetv1.locateFaces(o,t),aa=(o,t)=>_.tinyFaceDetector.locateFaces(o,t),sa=(o,t)=>_.tinyYolov2.locateFaces(o,t),sn=o=>_.faceLandmark68Net.detectLandmarks(o),ia=o=>_.faceLandmark68TinyNet.detectLandmarks(o),ca=o=>_.faceRecognitionNet.computeFaceDescriptor(o),ma=o=>_.faceExpressionNet.predictExpressions(o),pa=o=>_.ageGenderNet.predictAgeAndGender(o),cn=o=>_.ssdMobilenetv1.load(o),da=o=>_.tinyFaceDetector.load(o),ua=o=>_.tinyYolov2.load(o),la=o=>_.faceLandmark68Net.load(o),fa=o=>_.faceLandmark68TinyNet.load(o),ha=o=>_.faceRecognitionNet.load(o),xa=o=>_.faceExpressionNet.load(o),ba=o=>_.ageGenderNet.load(o),ga=cn,va=an,ya=sn;var co=class extends Q{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Pe=class extends co{async run(){let t=await this.parentTask,e=await Ut(t,this.input,async r=>Promise.all(r.map(n=>_.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>cr(r,e[n]))}withAgeAndGender(){return new Te(this,this.input)}},we=class extends co{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>_.faceExpressionNet.predictExpressions(r),this.extractedFaces);return cr(t,e)}withAgeAndGender(){return new _e(this,this.input)}},qt=class extends Pe{withAgeAndGender(){return new Xt(this,this.input)}withFaceDescriptors(){return new kt(this,this.input)}},Zt=class extends we{withAgeAndGender(){return new Jt(this,this.input)}withFaceDescriptor(){return new St(this,this.input)}};var mo=class extends Q{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Te=class extends mo{async run(){let t=await this.parentTask,e=await Ut(t,this.input,async r=>Promise.all(r.map(n=>_.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return fr(hr(r,s,i),a)})}withFaceExpressions(){return new Pe(this,this.input)}},_e=class extends mo{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await ye(t,this.input,a=>_.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return fr(hr(t,r,n),e)}withFaceExpressions(){return new we(this,this.input)}},Xt=class extends Te{withFaceExpressions(){return new qt(this,this.input)}withFaceDescriptors(){return new kt(this,this.input)}},Jt=class extends _e{withFaceExpressions(){return new Zt(this,this.input)}withFaceDescriptor(){return new St(this,this.input)}};var gr=class extends Q{constructor(t,e){super();this.parentTask=t;this.input=e}},kt=class extends gr{async run(){let t=await this.parentTask;return(await Ut(t,this.input,r=>Promise.all(r.map(n=>_.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 Xt(this,this.input)}},St=class extends gr{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>_.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return lr(t,e)}withFaceExpressions(){return new Zt(this,this.input)}withAgeAndGender(){return new Jt(this,this.input)}};var vr=class extends Q{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?_.faceLandmark68TinyNet:_.faceLandmark68Net}},yr=class extends vr{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof je.Tensor?await ne(this.input,e):await oe(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)=>ue(a,n[s]))}withFaceExpressions(){return new qt(this,this.input)}withAgeAndGender(){return new Xt(this,this.input)}withFaceDescriptors(){return new kt(this,this.input)}},Fr=class extends vr{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof je.Tensor?await ne(this.input,[e]):await oe(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),ue(t,n)}withFaceExpressions(){return new Zt(this,this.input)}withAgeAndGender(){return new Jt(this,this.input)}withFaceDescriptor(){return new St(this,this.input)}};var Tr=class extends Q{constructor(t,e=new q){super();this.input=t;this.options=e}},He=class extends Tr{async run(){let{input:t,options:e}=this,r;if(e instanceof br)r=_.tinyFaceDetector.locateFaces(t,e);else if(e instanceof q)r=_.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof ut)r=_.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>Rt({},r)))})}withFaceLandmarks(t=!1){return new yr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Pe(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Te(this.runAndExtendWithFaceDetections(),this.input)}},_r=class extends Tr{async run(){let t=await new He(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?Rt({},e):void 0)})}withFaceLandmarks(t=!1){return new Fr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new we(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new _e(this.runAndExtendWithFaceDetection(),this.input)}};function Fa(o,t=new q){return new _r(o,t)}function Pr(o,t=new q){return new He(o,t)}async function mn(o,t){return Pr(o,new q(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Ta(o,t={}){return Pr(o,new ut(t)).withFaceLandmarks().withFaceDescriptors()}var _a=mn;function po(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,a)=>n-r[a]).reduce((n,a)=>n+a**2,0))}var wr=class{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,a=()=>`person ${n++}`;this._labeledDescriptors=r.map(s=>{if(s instanceof ht)return s;if(s instanceof Float32Array)return new ht(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new ht(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=>po(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=>ht.fromJSON(r));return new wr(e,t.distanceThreshold)}};function Pa(o){let t=new Fe;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(Gt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return ue(Rt(o,n),a)}return mt(o)?Rt(o,o.detection.forSize(e,r)):o instanceof U||o instanceof M?o.forSize(e,r):o}var Da=typeof process!="undefined",Ea=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",Ma={faceapi:Eo,node:Da,browser:Ea};0&&(module.exports={AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf,tinyFaceDetector,tinyYolov2,toNetInput,utils,validateConfig,version});
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