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

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

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vt;(function(o){o.FEMALE="female",o.MALE="male"})(vt||(vt={}));var mr=class extends L{constructor(t=new Qr(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return dt.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t,n=dt.avgPool(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),a=Ae(n,e.fc.age).as1D(),s=Ae(n,e.fc.gender);return{age:a,gender:s}})}forwardInput(t){return dt.tidy(()=>{let{age:e,gender:r}=this.runNet(t);return{age:e,gender:dt.softmax(r)}})}async forward(t){return this.forwardInput(await D(t))}async predictAgeAndGender(t){let e=await D(t),r=await this.forwardInput(e),n=dt.unstack(r.age),a=dt.unstack(r.gender),s=n.map((c,m)=>({ageTensor:c,genderTensor:a[m]})),i=await Promise.all(s.map(async({ageTensor:c,genderTensor:m})=>{let p=(await c.data())[0],d=(await 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s=Ke(s,e.dense1),s=Ke(s,e.dense2),s=It.avgPool(s,[14,14],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await D(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return Io(t)}extractParams(t){return Lo(t)}};var pr=class extends Be{constructor(t=new to){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var So=class extends fe{};var V=b();var he=b();var dr=b();function Ao(o,t){return dr.add(dr.mul(o,t.weights),t.biases)}function eo(o,t,e,r,n="same"){let{filters:a,bias:s}=t.conv,i=he.conv2d(o,a,e,n);return i=he.add(i,s),i=Ao(i,t.scale),r?he.relu(i):i}function Wo(o,t){return eo(o,t,[1,1],!0)}function ro(o,t){return eo(o,t,[1,1],!1)}function ur(o,t){return eo(o,t,[2,2],!0,"valid")}var H=b();function Bn(o,t){function e(i,c,m){let p=o(i),d=p.length/(c*m*m);if(Cr(d))throw new Error(`depth has to be an integer: ${d}, weights.length: ${p.length}, 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c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function tn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=Kn(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return A(o,e),{params:s,paramMappings:e}}var ut=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var ao=class extends L{constructor(t){super("TinyYolov2");oo(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=Ft(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=Ft(r,e.conv6),r=Ft(r,e.conv7),zt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(zt(t,e.conv0,"valid",!1)):Tt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Tt(r,e.conv6):r,r=e.conv7?Tt(r,e.conv7):r,zt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return C.tidy(()=>{let n=C.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?rt(n,this.config.meanRgb):n,n=n.div(C.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await D(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new ut(e),a=await D(t),s=await this.forwardInput(a,r),i=C.tidy(()=>C.unstack(s)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},m=await this.extractBoxes(i,a.getReshapedInputDimensions(0),n);s.dispose(),i.dispose();let p=m.map(h=>h.box),d=m.map(h=>h.score),u=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Lr(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 tn(t,this.config)}extractParams(t){let e=this.config.filterSizes||ao.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return Qo(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,l]=C.tidy(()=>{let v=t.reshape([m,m,p,this.boxEncodingSize]),F=v.slice([0,0,0,0],[m,m,p,4]),y=v.slice([0,0,0,4],[m,m,p,1]),k=this.withClassScores?C.softmax(v.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):C.scalar(0);return[F,y,k]}),g=[],_=await u.array(),h=await d.array();for(let v=0;v<m;v++)for(let F=0;F<m;F++)for(let y=0;y<p;y++){let k=De(_[v][F][y][0]);if(!r||k>r){let Y=(F+De(h[v][F][y][0]))/m*i,tt=(v+De(h[v][F][y][1]))/m*c,st=Math.exp(h[v][F][y][2])*this.config.anchors[y].x/m*i,U=Math.exp(h[v][F][y][3])*this.config.anchors[y].y/m*c,_t=Y-st/2,wt=tt-U/2,Pt={row:v,col:F,anchor:y},{classScore:te,label:po}=this.withClassScores?await this.extractPredictedClass(l,Pt):{classScore:1,label:0};g.push({box:new re(_t,wt,_t+st,wt+U),score:k,classScore:k*te,label:po,...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=ao;ge.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ve=class extends ge{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Uo,classes:["face"],...t?{anchors:Jo,meanRgb:qo}:{anchors:Xo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new E(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?Ko:Zo}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Qn(o,t=!0){let e=new ve(t);return e.extractWeights(o),e}var br=class extends ut{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Q=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var je=b();var so=b();async function Jt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Vt(c)?n(c):c.detection),s=r||(t instanceof so.Tensor?await se(t,a):await ae(t,a)),i=await e(s);return s.forEach(c=>c instanceof so.Tensor&&c.dispose()),i}async function ye(o,t,e,r,n){return Jt([o],t,async a=>e(a[0]),r,n)}var en=.4,rn=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],on=[117.001,114.697,97.404];var Fe=class extends ge{constructor(){let t={withSeparableConvs:!0,iouThreshold:en,classes:["face"],anchors:rn,meanRgb:on,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new E(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var T={ssdMobilenetv1:new Xt,tinyFaceDetector:new Fe,tinyYolov2:new ve,faceLandmark68Net:new fe,faceLandmark68TinyNet:new pr,faceRecognitionNet:new xe,faceExpressionNet:new ir,ageGenderNet:new mr},nn=(o,t)=>T.ssdMobilenetv1.locateFaces(o,t),ta=(o,t)=>T.tinyFaceDetector.locateFaces(o,t),ea=(o,t)=>T.tinyYolov2.locateFaces(o,t),an=o=>T.faceLandmark68Net.detectLandmarks(o),ra=o=>T.faceLandmark68TinyNet.detectLandmarks(o),oa=o=>T.faceRecognitionNet.computeFaceDescriptor(o),na=o=>T.faceExpressionNet.predictExpressions(o),aa=o=>T.ageGenderNet.predictAgeAndGender(o),sn=o=>T.ssdMobilenetv1.load(o),sa=o=>T.tinyFaceDetector.load(o),ia=o=>T.tinyYolov2.load(o),ca=o=>T.faceLandmark68Net.load(o),ma=o=>T.faceLandmark68TinyNet.load(o),pa=o=>T.faceRecognitionNet.load(o),da=o=>T.faceExpressionNet.load(o),ua=o=>T.ageGenderNet.load(o),la=sn,fa=nn,ha=an;var io=class extends Q{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},we=class extends io{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>T.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>cr(r,e[n]))}withAgeAndGender(){return new Te(this,this.input)}},Pe=class extends io{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>T.faceExpressionNet.predictExpressions(r),this.extractedFaces);return cr(t,e)}withAgeAndGender(){return new _e(this,this.input)}},Kt=class extends we{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Qt=class extends Pe{withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var co=class extends Q{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Te=class extends co{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>T.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return fr(hr(r,s,i),a)})}withFaceExpressions(){return new we(this,this.input)}},_e=class extends co{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await ye(t,this.input,a=>T.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return fr(hr(t,r,n),e)}withFaceExpressions(){return new Pe(this,this.input)}},qt=class extends Te{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Zt=class extends _e{withFaceExpressions(){return new Qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var gr=class extends Q{constructor(t,e){super();this.parentTask=t;this.input=e}},At=class extends gr{async run(){let t=await this.parentTask;return(await Jt(t,this.input,r=>Promise.all(r.map(n=>T.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>lr(t[n],r))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}},Wt=class extends gr{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>T.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return lr(t,e)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}};var vr=class extends Q{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?T.faceLandmark68TinyNet:T.faceLandmark68Net}},yr=class extends vr{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof je.Tensor?await se(this.input,e):await ae(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),t.map((a,s)=>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)}},Fr=class extends vr{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof je.Tensor?await se(this.input,[e]):await ae(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),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 Tr=class extends Q{constructor(t,e=new q){super();this.input=t;this.options=e}},He=class extends Tr{async run(){let{input:t,options:e}=this,r;if(e instanceof br)r=T.tinyFaceDetector.locateFaces(t,e);else if(e instanceof q)r=T.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof ut)r=T.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>Ot({},r)))})}withFaceLandmarks(t=!1){return new yr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new we(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?Ot({},e):void 0)})}withFaceLandmarks(t=!1){return new Fr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Pe(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new _e(this.runAndExtendWithFaceDetection(),this.input)}};function xa(o,t=new q){return new _r(o,t)}function wr(o,t=new q){return new He(o,t)}async function cn(o,t){return wr(o,new q(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function ba(o,t={}){return wr(o,new ut(t)).withFaceLandmarks().withFaceDescriptors()}var ga=cn;function mo(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 Pr=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=>mo(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 Pr(e,t.distanceThreshold)}};function va(o){let t=new Fe;return t.extractWeights(o),t}function mn(o,t){let{width:e,height:r}=new S(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>mn(n,{width:e,height:r}));if(Vt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return le(Ot(o,n),a)}return mt(o)?Ot(o,o.detection.forSize(e,r)):o instanceof z||o instanceof E?o.forSize(e,r):o}var Fa=typeof process!="undefined",Ta=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",_a={faceapi:Do,node:Fa,browser:Ta};export{mr as AgeGenderNet,re as BoundingBox,P as Box,Q as ComposableTask,At as ComputeAllFaceDescriptorsTask,gr as ComputeFaceDescriptorsTaskBase,Wt as ComputeSingleFaceDescriptorTask,yr as DetectAllFaceLandmarksTask,He as DetectAllFacesTask,vr as DetectFaceLandmarksTaskBase,Tr as DetectFacesTaskBase,Fr as DetectSingleFaceLandmarksTask,_r as DetectSingleFaceTask,S as Dimensions,Xr as FACE_EXPRESSION_LABELS,E as FaceDetection,Vo as FaceDetectionNet,ir as FaceExpressionNet,kt as FaceExpressions,fe as FaceLandmark68Net,pr as FaceLandmark68TinyNet,So as FaceLandmarkNet,z as FaceLandmarks,ho as FaceLandmarks5,ne as FaceLandmarks68,Ee as FaceMatch,Pr as FaceMatcher,xe as FaceRecognitionNet,vt as Gender,Me as LabeledBox,xt as LabeledFaceDescriptors,bt as NetInput,L as NeuralNetwork,Dt as ObjectDetection,x as Point,xo as PredictedBox,oe as Rect,Xt as SsdMobilenetv1,q as SsdMobilenetv1Options,Fe as TinyFaceDetector,br as TinyFaceDetectorOptions,ve as TinyYolov2,ut as TinyYolov2Options,ga as allFaces,cn as allFacesSsdMobilenetv1,ba as allFacesTinyYolov2,Hr as awaitMediaLoaded,Yr as bufferToImage,oa as computeFaceDescriptor,Yt as createCanvas,ke as createCanvasFromMedia,qn as createFaceDetectionNet,$n as createFaceRecognitionNet,zo as createSsdMobilenetv1,va as createTinyFaceDetector,Qn as createTinyYolov2,wr as detectAllFaces,an as detectFaceLandmarks,ra as detectFaceLandmarksTiny,ha as detectLandmarks,xa as detectSingleFace,Po as draw,w as env,mo as euclideanDistance,fr as extendWithAge,lr as extendWithFaceDescriptor,Ot as extendWithFaceDetection,cr as extendWithFaceExpressions,le as extendWithFaceLandmarks,hr as extendWithGender,se as extractFaceTensors,ae as extractFaces,Mn as fetchImage,Vr as fetchJson,Cn as fetchNetWeights,Gt as fetchOrThrow,R as getContext2dOrThrow,Ht as getMediaDimensions,Gr as imageTensorToCanvas,zr as imageToSquare,vn as inverseSigmoid,kr as iou,Je as isMediaElement,Ne as isMediaLoaded,On as isWithAge,mt as isWithFaceDetection,Jr as isWithFaceExpressions,Vt as isWithFaceLandmarks,jn as isWithGender,ua as loadAgeGenderModel,la as loadFaceDetectionModel,da as loadFaceExpressionModel,ca as loadFaceLandmarkModel,ma as loadFaceLandmarkTinyModel,pa as loadFaceRecognitionModel,sn as loadSsdMobilenetv1Model,sa as loadTinyFaceDetectorModel,ia as loadTinyYolov2Model,Ur as loadWeightMap,fa as locateFaces,Nn as matchDimensions,Ir as minBbox,T as nets,Lr as nonMaxSuppression,rt as normalize,Sr as padToSquare,aa as predictAgeAndGender,na as recognizeFaceExpressions,mn as resizeResults,jt as resolveInput,gn as shuffleArray,De as sigmoid,nn as ssdMobilenetv1,ya as tf,ta as tinyFaceDetector,ea as tinyYolov2,D as toNetInput,lo as utils,oo as validateConfig,_a as version};
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