diff --git a/CHANGELOG.md b/CHANGELOG.md index e141724..41b1b5d 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,7 +9,7 @@ ## Changelog -### **HEAD -> master** 2022/07/26 mandic00@live.com +### **HEAD -> master** 2022/08/16 mandic00@live.com ### **release: 1.7.1** 2022/07/25 mandic00@live.com diff --git a/demo/webcam.js b/demo/webcam.js index ae00d54..9c8aa27 100644 --- a/demo/webcam.js +++ b/demo/webcam.js @@ -180,7 +180,7 @@ async function main() { log('FaceAPI WebCam Test'); // if you want to use wasm backend location for wasm binaries must be specified - // await faceapi.tf.setWasmPaths('../node_modules/@tensorflow/tfjs-backend-wasm/dist/'); + // await faceapi.tf.setWasmPaths(`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${faceapi.tf.version_core}/dist/`); // await faceapi.tf.setBackend('wasm'); // default is webgl backend diff --git a/dist/face-api.js b/dist/face-api.js index 382dd1b..b568252 100644 --- a/dist/face-api.js +++ b/dist/face-api.js @@ -1,10 +1,10 @@ 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st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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 ho=class extends A{constructor(e){super("TinyYolov2");fo(e),this._config=e}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(e,r){let n=Tt(e,r.conv0);return 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a=this.config.meanRgb?rt(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,n):this.runTinyYolov2(a,n)})}async forward(e,r){return this.forwardInput(await C(e),r)}async detect(e,r={}){let{inputSize:n,scoreThreshold:a}=new st(r),s=await C(e),i=await this.forwardInput(s,n),c=N.tidy(()=>N.unstack(i)[0].expandDims()),m={width:s.getInputWidth(0),height:s.getInputHeight(0)},p=await this.extractBoxes(c,s.getReshapedInputDimensions(0),a);i.dispose(),c.dispose();let u=p.map(h=>h.box),f=p.map(h=>h.score),l=p.map(h=>h.classScore),g=p.map(h=>this.config.classes[h.label]);return zr(u.map(h=>h.rescale(n)),f,this.config.iouThreshold,!0).map(h=>new bt(f[h],l[h],g[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return on(e,this.config)}extractParams(e){let r=this.config.filterSizes||ho.DEFAULT_FILTER_SIZES,n=r?r.length:void 0;if(n!==7&&n!==8&&n!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${n} filterSizes in config`);return rn(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,n){let{width:a,height:s}=r,i=Math.max(a,s),c=i/a,m=i/s,p=e.shape[1],u=this.config.anchors.length,[f,l,g]=N.tidy(()=>{let _=e.reshape([p,p,u,this.boxEncodingSize]),y=_.slice([0,0,0,0],[p,p,u,4]),E=_.slice([0,0,0,4],[p,p,u,1]),W=this.withClassScores?N.softmax(_.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):N.scalar(0);return[y,E,W]}),T=[],D=await l.array(),h=await f.array();for(let _=0;_n){let tt=(y+Ne(h[_][y][E][0]))/p*c,lt=(_+Ne(h[_][y][E][1]))/p*m,q=Math.exp(h[_][y][E][2])*this.config.anchors[E].x/p*c,Dt=Math.exp(h[_][y][E][3])*this.config.anchors[E].y/p*m,Et=tt-q/2,Mt=lt-Dt/2,$t={row:_,col:y,anchor:E},{classScore:xo,label:vo}=this.withClassScores?await this.extractPredictedClass(g,$t):{classScore:1,label:0};T.push({box:new Vt(Et,Mt,Et+q,Mt+Dt),score:W,classScore:W*xo,label:vo,...$t})}}return f.dispose(),l.dispose(),g.dispose(),T}async extractPredictedClass(e,r){let{row:n,col:a,anchor:s}=r,i=await e.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[n][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},ee=ho;ee.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var re=class extends ee{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:qo,classes:["face"],...t?{anchors:Ko,meanRgb:Qo}:{anchors:Zo,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?en:tn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ca(o,t=!0){let e=new re(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var bo=v(x());async function oe(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Zt(c)?n(c):c.detection),s=r||(t instanceof bo.Tensor?await de(t,a):await le(t,a)),i=await e(s);return s.forEach(c=>c instanceof bo.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return oe([o],t,async a=>e(a[0]),r,n)}var nn=.4,an=[new b(1.603231,2.094468),new b(6.041143,7.080126),new b(2.882459,3.518061),new b(4.266906,5.178857),new b(9.041765,10.66308)],sn=[117.001,114.697,97.404];var ne=class extends ee{constructor(){let t={withSeparableConvs:!0,iouThreshold:nn,classes:["face"],anchors:an,meanRgb:sn,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 w={ssdMobilenetv1:new St,tinyFaceDetector:new ne,tinyYolov2:new re,faceLandmark68Net:new Kt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Qt,faceExpressionNet:new Oe,ageGenderNet:new He},cn=(o,t)=>w.ssdMobilenetv1.locateFaces(o,t),ma=(o,t)=>w.tinyFaceDetector.locateFaces(o,t),pa=(o,t)=>w.tinyYolov2.locateFaces(o,t),mn=o=>w.faceLandmark68Net.detectLandmarks(o),ua=o=>w.faceLandmark68TinyNet.detectLandmarks(o),fa=o=>w.faceRecognitionNet.computeFaceDescriptor(o),la=o=>w.faceExpressionNet.predictExpressions(o),da=o=>w.ageGenderNet.predictAgeAndGender(o),pn=o=>w.ssdMobilenetv1.load(o),ha=o=>w.tinyFaceDetector.load(o),ba=o=>w.tinyYolov2.load(o),ga=o=>w.faceLandmark68Net.load(o),xa=o=>w.faceLandmark68TinyNet.load(o),va=o=>w.faceRecognitionNet.load(o),ya=o=>w.faceExpressionNet.load(o),_a=o=>w.ageGenderNet.load(o),Ta=pn,Pa=cn,wa=mn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Ir{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>w.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>xr(r,e[n]))}withAgeAndGender(){return new ie(this,this.input)}},se=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>w.faceExpressionNet.predictExpressions(r),this.extractedFaces);return xr(t,e)}withAgeAndGender(){return new ce(this,this.input)}},Wt=class extends ae{withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new wt(this,this.input)}},kt=class extends se{withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ie=class extends Nr{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>w.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return Er(Mr(r,s,i),a)})}withFaceExpressions(){return new ae(this,this.input)}},ce=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,a=>w.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Er(Mr(t,r,n),e)}withFaceExpressions(){return new se(this,this.input)}},Bt=class extends ie{withFaceExpressions(){return new Wt(this,this.input)}withFaceDescriptors(){return new wt(this,this.input)}},Rt=class extends ce{withFaceExpressions(){return new kt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},wt=class extends Ue{async run(){let t=await this.parentTask;return(await oe(t,this.input,r=>Promise.all(r.map(n=>w.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Dr(t[n],r))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}},Ft=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>w.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Dr(t,e)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?w.faceLandmark68TinyNet:w.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof Xe.Tensor?await de(this.input,e):await le(this.input,e),n=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),t.filter((s,i)=>n[i]).map((s,i)=>we(s,n[i]))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new wt(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await de(this.input,[e]):await le(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),we(t,n)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=w.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=w.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=w.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>jt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ie(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(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 Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new se(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ce(this.runAndExtendWithFaceDetection(),this.input)}};function Fa(o,t=new X){return new Qe(o,t)}function Sr(o,t=new X){return new Ie(o,t)}async function un(o,t){return Sr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Da(o,t={}){return Sr(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Ea=un;function go(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 tr=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 mt)return s;if(s instanceof Float32Array)return new mt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new mt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>go(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new pe(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>mt.fromJSON(r));return new tr(e,t.distanceThreshold)}};function Ma(o){let t=new ne;return t.extractWeights(o),t}function fn(o,t){let{width:e,height:r}=new k(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=>fn(n,{width:e,height:r}));if(Zt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return we(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof z||o instanceof M?o.forSize(e,r):o}var Ia=No;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,fetchVideo,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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N=v(x());var Cr=o=>typeof o=="number";function fo(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(!Cr(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=>Cr(t.x)&&Cr(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(Cr)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: 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st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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 ho=class extends A{constructor(e){super("TinyYolov2");fo(e),this._config=e}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(e,r){let n=Tt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=Tt(n,r.conv6),n=Tt(n,r.conv7),qt(n,r.conv8,"valid",!1)}runMobilenet(e,r){let n=this.config.isFirstLayerConv2d?Me(qt(e,r.conv0,"valid",!1)):Pt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=r.conv6?Pt(n,r.conv6):n,n=r.conv7?Pt(n,r.conv7):n,qt(n,r.conv8,"valid",!1)}forwardInput(e,r){let{params:n}=this;if(!n)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let a=N.cast(e.toBatchTensor(r,!1),"float32");return 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filterSizes in config`);return rn(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,n){let{width:a,height:s}=r,i=Math.max(a,s),c=i/a,m=i/s,p=e.shape[1],u=this.config.anchors.length,[f,l,g]=N.tidy(()=>{let _=e.reshape([p,p,u,this.boxEncodingSize]),y=_.slice([0,0,0,0],[p,p,u,4]),E=_.slice([0,0,0,4],[p,p,u,1]),W=this.withClassScores?N.softmax(_.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):N.scalar(0);return[y,E,W]}),T=[],D=await l.array(),h=await f.array();for(let _=0;_n){let tt=(y+Ne(h[_][y][E][0]))/p*c,lt=(_+Ne(h[_][y][E][1]))/p*m,q=Math.exp(h[_][y][E][2])*this.config.anchors[E].x/p*c,Dt=Math.exp(h[_][y][E][3])*this.config.anchors[E].y/p*m,Et=tt-q/2,Mt=lt-Dt/2,$t={row:_,col:y,anchor:E},{classScore:xo,label:vo}=this.withClassScores?await this.extractPredictedClass(g,$t):{classScore:1,label:0};T.push({box:new Vt(Et,Mt,Et+q,Mt+Dt),score:W,classScore:W*xo,label:vo,...$t})}}return f.dispose(),l.dispose(),g.dispose(),T}async extractPredictedClass(e,r){let{row:n,col:a,anchor:s}=r,i=await e.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[n][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},ee=ho;ee.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var re=class extends ee{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:qo,classes:["face"],...t?{anchors:Ko,meanRgb:Qo}:{anchors:Zo,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?en:tn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ca(o,t=!0){let e=new re(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var bo=v(x());async function oe(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Zt(c)?n(c):c.detection),s=r||(t instanceof bo.Tensor?await de(t,a):await le(t,a)),i=await e(s);return s.forEach(c=>c instanceof bo.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return oe([o],t,async a=>e(a[0]),r,n)}var nn=.4,an=[new b(1.603231,2.094468),new b(6.041143,7.080126),new b(2.882459,3.518061),new b(4.266906,5.178857),new b(9.041765,10.66308)],sn=[117.001,114.697,97.404];var ne=class extends ee{constructor(){let t={withSeparableConvs:!0,iouThreshold:nn,classes:["face"],anchors:an,meanRgb:sn,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 w={ssdMobilenetv1:new St,tinyFaceDetector:new ne,tinyYolov2:new re,faceLandmark68Net:new Kt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Qt,faceExpressionNet:new Oe,ageGenderNet:new He},cn=(o,t)=>w.ssdMobilenetv1.locateFaces(o,t),ma=(o,t)=>w.tinyFaceDetector.locateFaces(o,t),pa=(o,t)=>w.tinyYolov2.locateFaces(o,t),mn=o=>w.faceLandmark68Net.detectLandmarks(o),ua=o=>w.faceLandmark68TinyNet.detectLandmarks(o),fa=o=>w.faceRecognitionNet.computeFaceDescriptor(o),la=o=>w.faceExpressionNet.predictExpressions(o),da=o=>w.ageGenderNet.predictAgeAndGender(o),pn=o=>w.ssdMobilenetv1.load(o),ha=o=>w.tinyFaceDetector.load(o),ba=o=>w.tinyYolov2.load(o),ga=o=>w.faceLandmark68Net.load(o),xa=o=>w.faceLandmark68TinyNet.load(o),va=o=>w.faceRecognitionNet.load(o),ya=o=>w.faceExpressionNet.load(o),_a=o=>w.ageGenderNet.load(o),Ta=pn,Pa=cn,wa=mn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Ir{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>w.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>xr(r,e[n]))}withAgeAndGender(){return new ie(this,this.input)}},se=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>w.faceExpressionNet.predictExpressions(r),this.extractedFaces);return xr(t,e)}withAgeAndGender(){return new ce(this,this.input)}},Wt=class extends ae{withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new wt(this,this.input)}},kt=class extends se{withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ie=class extends Nr{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>w.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return Er(Mr(r,s,i),a)})}withFaceExpressions(){return new ae(this,this.input)}},ce=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,a=>w.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Er(Mr(t,r,n),e)}withFaceExpressions(){return new se(this,this.input)}},Bt=class extends ie{withFaceExpressions(){return new Wt(this,this.input)}withFaceDescriptors(){return new wt(this,this.input)}},Rt=class extends ce{withFaceExpressions(){return new kt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},wt=class extends Ue{async run(){let t=await this.parentTask;return(await oe(t,this.input,r=>Promise.all(r.map(n=>w.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Dr(t[n],r))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}},Ft=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>w.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Dr(t,e)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?w.faceLandmark68TinyNet:w.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof Xe.Tensor?await de(this.input,e):await le(this.input,e),n=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),t.filter((s,i)=>n[i]).map((s,i)=>we(s,n[i]))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new wt(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await de(this.input,[e]):await le(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),we(t,n)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=w.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=w.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=w.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>jt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ie(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(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 Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new se(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ce(this.runAndExtendWithFaceDetection(),this.input)}};function Fa(o,t=new X){return new Qe(o,t)}function Sr(o,t=new X){return new Ie(o,t)}async function un(o,t){return Sr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Da(o,t={}){return Sr(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Ea=un;function go(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 tr=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 mt)return s;if(s instanceof Float32Array)return new mt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new mt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>go(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new pe(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>mt.fromJSON(r));return new tr(e,t.distanceThreshold)}};function Ma(o){let t=new ne;return t.extractWeights(o),t}function fn(o,t){let{width:e,height:r}=new k(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=>fn(n,{width:e,height:r}));if(Zt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return we(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof z||o instanceof M?o.forSize(e,r):o}var Ia=No;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,fetchVideo,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}); diff --git a/dist/face-api.node-wasm.js b/dist/face-api.node-wasm.js index 20a4ab0..de274cd 100644 --- a/dist/face-api.node-wasm.js +++ b/dist/face-api.node-wasm.js @@ -1,7 +1,7 @@ -"use strict";/* +/* Face-API homepage: author: ' */ -var dn=Object.create;var er=Object.defineProperty;var hn=Object.getOwnPropertyDescriptor;var bn=Object.getOwnPropertyNames;var gn=Object.getPrototypeOf,xn=Object.prototype.hasOwnProperty;var vn=(o,t)=>()=>(t||o((t={exports:{}}).exports,t),t.exports),Ar=(o,t)=>{for(var e in t)er(o,e,{get:t[e],enumerable:!0})},_o=(o,t,e,r)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of bn(t))!xn.call(o,n)&&n!==e&&er(o,n,{get:()=>t[n],enumerable:!(r=hn(t,n))||r.enumerable});return o};var v=(o,t,e)=>(e=o!=null?dn(gn(o)):{},_o(t||!o||!o.__esModule?er(e,"default",{value:o,enumerable:!0}):e,o)),yn=o=>_o(er({},"__esModule",{value:!0}),o);var x=vn((ka,rr)=>{"use strict";var To=Object.defineProperty,_n=Object.getOwnPropertyDescriptor,Tn=Object.getOwnPropertyNames,Pn=Object.prototype.hasOwnProperty,Wr=(o,t,e,r)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of Tn(t))!Pn.call(o,n)&&n!==e&&To(o,n,{get:()=>t[n],enumerable:!(r=_n(t,n))||r.enumerable});return o},Po=(o,t,e)=>(Wr(o,t,"default"),e&&Wr(e,t,"default")),wn=o=>Wr(To({},"__esModule",{value:!0}),o),kr={};rr.exports=wn(kr);Po(kr,require("@tensorflow/tfjs"),rr.exports);Po(kr,require("@tensorflow/tfjs-backend-wasm"),rr.exports)});var Na={};Ar(Na,{AgeGenderNet:()=>He,BoundingBox:()=>Vt,Box:()=>F,ComposableTask:()=>J,ComputeAllFaceDescriptorsTask:()=>wt,ComputeFaceDescriptorsTaskBase:()=>Ue,ComputeSingleFaceDescriptorTask:()=>Ft,DetectAllFaceLandmarksTask:()=>qe,DetectAllFacesTask:()=>Ie,DetectFaceLandmarksTaskBase:()=>Je,DetectFacesTaskBase:()=>Ke,DetectSingleFaceLandmarksTask:()=>Ze,DetectSingleFaceTask:()=>Qe,Dimensions:()=>k,FACE_EXPRESSION_LABELS:()=>ao,FaceDetection:()=>M,FaceDetectionNet:()=>fo,FaceExpressionNet:()=>Oe,FaceExpressions:()=>yt,FaceLandmark68Net:()=>Kt,FaceLandmark68TinyNet:()=>ze,FaceLandmarkNet:()=>mo,FaceLandmarks:()=>z,FaceLandmarks5:()=>Gr,FaceLandmarks68:()=>Gt,FaceMatch:()=>pe,FaceMatcher:()=>tr,FaceRecognitionNet:()=>Qt,Gender:()=>Pr,LabeledBox:()=>ue,LabeledFaceDescriptors:()=>mt,NetInput:()=>ut,NeuralNetwork:()=>A,ObjectDetection:()=>bt,Point:()=>b,PredictedBox:()=>jr,Rect:()=>Yt,SsdMobilenetv1:()=>St,SsdMobilenetv1Options:()=>X,TinyFaceDetector:()=>ne,TinyFaceDetectorOptions:()=>je,TinyYolov2:()=>re,TinyYolov2Options:()=>st,allFaces:()=>Ea,allFacesSsdMobilenetv1:()=>fn,allFacesTinyYolov2:()=>Da,awaitMediaLoaded:()=>Qr,bufferToImage:()=>to,computeFaceDescriptor:()=>fa,createCanvas:()=>Jt,createCanvasFromMedia:()=>We,createFaceDetectionNet:()=>aa,createFaceRecognitionNet:()=>Xn,createSsdMobilenetv1:()=>qo,createTinyFaceDetector:()=>Ma,createTinyYolov2:()=>ca,detectAllFaces:()=>Lr,detectFaceLandmarks:()=>pn,detectFaceLandmarksTiny:()=>ua,detectLandmarks:()=>wa,detectSingleFace:()=>Fa,draw:()=>io,env:()=>P,euclideanDistance:()=>xo,extendWithAge:()=>Mr,extendWithFaceDescriptor:()=>Er,extendWithFaceDetection:()=>jt,extendWithFaceExpressions:()=>vr,extendWithFaceLandmarks:()=>we,extendWithGender:()=>Cr,extractFaceTensors:()=>de,extractFaces:()=>le,fetchImage:()=>An,fetchJson:()=>oo,fetchNetWeights:()=>Wn,fetchOrThrow:()=>xt,fetchVideo:()=>kn,getContext2dOrThrow:()=>O,getMediaDimensions:()=>Xt,imageTensorToCanvas:()=>eo,imageToSquare:()=>ro,inverseSigmoid:()=>En,iou:()=>Hr,isMediaElement:()=>cr,isMediaLoaded:()=>Ae,isWithAge:()=>Jn,isWithFaceDetection:()=>pt,isWithFaceExpressions:()=>so,isWithFaceLandmarks:()=>Zt,isWithGender:()=>qn,loadAgeGenderModel:()=>_a,loadFaceDetectionModel:()=>Ta,loadFaceExpressionModel:()=>ya,loadFaceLandmarkModel:()=>ga,loadFaceLandmarkTinyModel:()=>xa,loadFaceRecognitionModel:()=>va,loadSsdMobilenetv1Model:()=>un,loadTinyFaceDetectorModel:()=>ha,loadTinyYolov2Model:()=>ba,loadWeightMap:()=>no,locateFaces:()=>Pa,matchDimensions:()=>Bn,minBbox:()=>zr,nets:()=>w,nonMaxSuppression:()=>Vr,normalize:()=>rt,padToSquare:()=>Yr,predictAgeAndGender:()=>da,recognizeFaceExpressions:()=>la,resizeResults:()=>ln,resolveInput:()=>Ut,shuffleArray:()=>Dn,sigmoid:()=>Ne,ssdMobilenetv1:()=>mn,tf:()=>Ca,tinyFaceDetector:()=>ma,tinyYolov2:()=>pa,toNetInput:()=>C,utils:()=>Or,validateConfig:()=>lo,version:()=>Ia});module.exports=yn(Na);var 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N=v(x());var Ir=o=>typeof o=="number";function lo(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(!Ir(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=>Ir(t.x)&&Ir(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(Ir)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: 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c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=xe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=ia(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return B(o,e),{params:s,paramMappings:e}}var st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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 bo=class extends A{constructor(e){super("TinyYolov2");lo(e),this._config=e}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(e,r){let n=Tt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=Tt(n,r.conv6),n=Tt(n,r.conv7),qt(n,r.conv8,"valid",!1)}runMobilenet(e,r){let n=this.config.isFirstLayerConv2d?Me(qt(e,r.conv0,"valid",!1)):Pt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=r.conv6?Pt(n,r.conv6):n,n=r.conv7?Pt(n,r.conv7):n,qt(n,r.conv8,"valid",!1)}forwardInput(e,r){let{params:n}=this;if(!n)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let a=N.cast(e.toBatchTensor(r,!1),"float32");return 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filterSizes in config`);return on(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,n){let{width:a,height:s}=r,i=Math.max(a,s),c=i/a,m=i/s,p=e.shape[1],u=this.config.anchors.length,[f,l,g]=N.tidy(()=>{let _=e.reshape([p,p,u,this.boxEncodingSize]),y=_.slice([0,0,0,0],[p,p,u,4]),E=_.slice([0,0,0,4],[p,p,u,1]),W=this.withClassScores?N.softmax(_.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):N.scalar(0);return[y,E,W]}),T=[],D=await l.array(),h=await f.array();for(let _=0;_n){let tt=(y+Ne(h[_][y][E][0]))/p*c,lt=(_+Ne(h[_][y][E][1]))/p*m,q=Math.exp(h[_][y][E][2])*this.config.anchors[E].x/p*c,Dt=Math.exp(h[_][y][E][3])*this.config.anchors[E].y/p*m,Et=tt-q/2,Mt=lt-Dt/2,$t={row:_,col:y,anchor:E},{classScore:vo,label:yo}=this.withClassScores?await this.extractPredictedClass(g,$t):{classScore:1,label:0};T.push({box:new Vt(Et,Mt,Et+q,Mt+Dt),score:W,classScore:W*vo,label:yo,...$t})}}return f.dispose(),l.dispose(),g.dispose(),T}async extractPredictedClass(e,r){let{row:n,col:a,anchor:s}=r,i=await e.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[n][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},ee=bo;ee.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var re=class extends ee{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ca(o,t=!0){let e=new re(t);return e.extractWeights(o),e}var je=class extends 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M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var w={ssdMobilenetv1:new St,tinyFaceDetector:new ne,tinyYolov2:new re,faceLandmark68Net:new Kt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Qt,faceExpressionNet:new Oe,ageGenderNet:new He},mn=(o,t)=>w.ssdMobilenetv1.locateFaces(o,t),ma=(o,t)=>w.tinyFaceDetector.locateFaces(o,t),pa=(o,t)=>w.tinyYolov2.locateFaces(o,t),pn=o=>w.faceLandmark68Net.detectLandmarks(o),ua=o=>w.faceLandmark68TinyNet.detectLandmarks(o),fa=o=>w.faceRecognitionNet.computeFaceDescriptor(o),la=o=>w.faceExpressionNet.predictExpressions(o),da=o=>w.ageGenderNet.predictAgeAndGender(o),un=o=>w.ssdMobilenetv1.load(o),ha=o=>w.tinyFaceDetector.load(o),ba=o=>w.tinyYolov2.load(o),ga=o=>w.faceLandmark68Net.load(o),xa=o=>w.faceLandmark68TinyNet.load(o),va=o=>w.faceRecognitionNet.load(o),ya=o=>w.faceExpressionNet.load(o),_a=o=>w.ageGenderNet.load(o),Ta=un,Pa=mn,wa=pn;var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Nr{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>w.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>vr(r,e[n]))}withAgeAndGender(){return new ie(this,this.input)}},se=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>w.faceExpressionNet.predictExpressions(r),this.extractedFaces);return vr(t,e)}withAgeAndGender(){return new ce(this,this.input)}},Wt=class extends ae{withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new wt(this,this.input)}},kt=class extends se{withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Sr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ie=class extends Sr{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>w.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return Mr(Cr(r,s,i),a)})}withFaceExpressions(){return new ae(this,this.input)}},ce=class extends Sr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,a=>w.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Mr(Cr(t,r,n),e)}withFaceExpressions(){return new se(this,this.input)}},Bt=class extends ie{withFaceExpressions(){return new Wt(this,this.input)}withFaceDescriptors(){return new wt(this,this.input)}},Rt=class extends ce{withFaceExpressions(){return new kt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},wt=class extends Ue{async run(){let t=await this.parentTask;return(await oe(t,this.input,r=>Promise.all(r.map(n=>w.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Er(t[n],r))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}},Ft=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>w.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Er(t,e)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?w.faceLandmark68TinyNet:w.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof Xe.Tensor?await de(this.input,e):await le(this.input,e),n=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),t.filter((s,i)=>n[i]).map((s,i)=>we(s,n[i]))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new wt(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await de(this.input,[e]):await le(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),we(t,n)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=w.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=w.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=w.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>jt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ie(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(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 Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new se(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ce(this.runAndExtendWithFaceDetection(),this.input)}};function Fa(o,t=new X){return new Qe(o,t)}function Lr(o,t=new X){return new Ie(o,t)}async function fn(o,t){return Lr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Da(o,t={}){return Lr(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Ea=fn;function xo(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 tr=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 mt)return s;if(s instanceof Float32Array)return new mt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new mt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>xo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new pe(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>mt.fromJSON(r));return new tr(e,t.distanceThreshold)}};function Ma(o){let t=new ne;return t.extractWeights(o),t}function ln(o,t){let{width:e,height:r}=new k(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(Zt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return we(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof z||o instanceof M?o.forSize(e,r):o}var Ia=So;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,fetchVideo,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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N=v(x());var Ir=o=>typeof o=="number";function lo(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(!Ir(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=>Ir(t.x)&&Ir(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(Ir)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: 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c=e(`${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(`${i}/bn`);return{conv:c,bn:m}}let s=xe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=ia(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return B(o,e),{params:s,paramMappings:e}}var st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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 bo=class extends A{constructor(e){super("TinyYolov2");lo(e),this._config=e}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(e,r){let n=Tt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=Tt(n,r.conv6),n=Tt(n,r.conv7),qt(n,r.conv8,"valid",!1)}runMobilenet(e,r){let n=this.config.isFirstLayerConv2d?Me(qt(e,r.conv0,"valid",!1)):Pt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=r.conv6?Pt(n,r.conv6):n,n=r.conv7?Pt(n,r.conv7):n,qt(n,r.conv8,"valid",!1)}forwardInput(e,r){let{params:n}=this;if(!n)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let a=N.cast(e.toBatchTensor(r,!1),"float32");return a=this.config.meanRgb?rt(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,n):this.runTinyYolov2(a,n)})}async forward(e,r){return this.forwardInput(await C(e),r)}async detect(e,r={}){let{inputSize:n,scoreThreshold:a}=new st(r),s=await C(e),i=await this.forwardInput(s,n),c=N.tidy(()=>N.unstack(i)[0].expandDims()),m={width:s.getInputWidth(0),height:s.getInputHeight(0)},p=await this.extractBoxes(c,s.getReshapedInputDimensions(0),a);i.dispose(),c.dispose();let u=p.map(h=>h.box),f=p.map(h=>h.score),l=p.map(h=>h.classScore),g=p.map(h=>this.config.classes[h.label]);return Vr(u.map(h=>h.rescale(n)),f,this.config.iouThreshold,!0).map(h=>new bt(f[h],l[h],g[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return nn(e,this.config)}extractParams(e){let r=this.config.filterSizes||bo.DEFAULT_FILTER_SIZES,n=r?r.length:void 0;if(n!==7&&n!==8&&n!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${n} filterSizes in config`);return on(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,n){let{width:a,height:s}=r,i=Math.max(a,s),c=i/a,m=i/s,p=e.shape[1],u=this.config.anchors.length,[f,l,g]=N.tidy(()=>{let _=e.reshape([p,p,u,this.boxEncodingSize]),y=_.slice([0,0,0,0],[p,p,u,4]),E=_.slice([0,0,0,4],[p,p,u,1]),W=this.withClassScores?N.softmax(_.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):N.scalar(0);return[y,E,W]}),T=[],D=await l.array(),h=await f.array();for(let _=0;_n){let tt=(y+Ne(h[_][y][E][0]))/p*c,lt=(_+Ne(h[_][y][E][1]))/p*m,q=Math.exp(h[_][y][E][2])*this.config.anchors[E].x/p*c,Dt=Math.exp(h[_][y][E][3])*this.config.anchors[E].y/p*m,Et=tt-q/2,Mt=lt-Dt/2,$t={row:_,col:y,anchor:E},{classScore:vo,label:yo}=this.withClassScores?await this.extractPredictedClass(g,$t):{classScore:1,label:0};T.push({box:new Vt(Et,Mt,Et+q,Mt+Dt),score:W,classScore:W*vo,label:yo,...$t})}}return f.dispose(),l.dispose(),g.dispose(),T}async extractPredictedClass(e,r){let{row:n,col:a,anchor:s}=r,i=await e.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[n][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},ee=bo;ee.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var re=class extends ee{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ca(o,t=!0){let e=new re(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var go=v(x());async function oe(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Zt(c)?n(c):c.detection),s=r||(t instanceof go.Tensor?await de(t,a):await le(t,a)),i=await e(s);return s.forEach(c=>c instanceof go.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return oe([o],t,async a=>e(a[0]),r,n)}var an=.4,sn=[new b(1.603231,2.094468),new b(6.041143,7.080126),new b(2.882459,3.518061),new b(4.266906,5.178857),new b(9.041765,10.66308)],cn=[117.001,114.697,97.404];var ne=class extends ee{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 w={ssdMobilenetv1:new St,tinyFaceDetector:new ne,tinyYolov2:new re,faceLandmark68Net:new Kt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Qt,faceExpressionNet:new Oe,ageGenderNet:new He},mn=(o,t)=>w.ssdMobilenetv1.locateFaces(o,t),ma=(o,t)=>w.tinyFaceDetector.locateFaces(o,t),pa=(o,t)=>w.tinyYolov2.locateFaces(o,t),pn=o=>w.faceLandmark68Net.detectLandmarks(o),ua=o=>w.faceLandmark68TinyNet.detectLandmarks(o),fa=o=>w.faceRecognitionNet.computeFaceDescriptor(o),la=o=>w.faceExpressionNet.predictExpressions(o),da=o=>w.ageGenderNet.predictAgeAndGender(o),un=o=>w.ssdMobilenetv1.load(o),ha=o=>w.tinyFaceDetector.load(o),ba=o=>w.tinyYolov2.load(o),ga=o=>w.faceLandmark68Net.load(o),xa=o=>w.faceLandmark68TinyNet.load(o),va=o=>w.faceRecognitionNet.load(o),ya=o=>w.faceExpressionNet.load(o),_a=o=>w.ageGenderNet.load(o),Ta=un,Pa=mn,wa=pn;var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Nr{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>w.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>vr(r,e[n]))}withAgeAndGender(){return new ie(this,this.input)}},se=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>w.faceExpressionNet.predictExpressions(r),this.extractedFaces);return vr(t,e)}withAgeAndGender(){return new ce(this,this.input)}},Wt=class extends ae{withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new wt(this,this.input)}},kt=class extends se{withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Sr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ie=class extends Sr{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>w.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return Mr(Cr(r,s,i),a)})}withFaceExpressions(){return new ae(this,this.input)}},ce=class extends Sr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,a=>w.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Mr(Cr(t,r,n),e)}withFaceExpressions(){return new se(this,this.input)}},Bt=class extends ie{withFaceExpressions(){return new Wt(this,this.input)}withFaceDescriptors(){return new wt(this,this.input)}},Rt=class extends ce{withFaceExpressions(){return new kt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},wt=class extends Ue{async run(){let t=await this.parentTask;return(await oe(t,this.input,r=>Promise.all(r.map(n=>w.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Er(t[n],r))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}},Ft=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>w.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Er(t,e)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?w.faceLandmark68TinyNet:w.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof Xe.Tensor?await de(this.input,e):await le(this.input,e),n=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),t.filter((s,i)=>n[i]).map((s,i)=>we(s,n[i]))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new wt(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await de(this.input,[e]):await le(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),we(t,n)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=w.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=w.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=w.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>jt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ie(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(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 Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new se(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ce(this.runAndExtendWithFaceDetection(),this.input)}};function Fa(o,t=new X){return new Qe(o,t)}function Lr(o,t=new X){return new Ie(o,t)}async function fn(o,t){return Lr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Da(o,t={}){return Lr(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Ea=fn;function xo(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 tr=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 mt)return s;if(s instanceof Float32Array)return new mt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new mt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>xo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new pe(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>mt.fromJSON(r));return new tr(e,t.distanceThreshold)}};function Ma(o){let t=new ne;return t.extractWeights(o),t}function ln(o,t){let{width:e,height:r}=new k(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(Zt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return we(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof z||o instanceof M?o.forSize(e,r):o}var Ia=So;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,fetchVideo,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}); diff --git a/dist/face-api.node.js b/dist/face-api.node.js index d6c5cd8..aa28585 100644 --- a/dist/face-api.node.js +++ b/dist/face-api.node.js @@ -1,7 +1,7 @@ -"use strict";/* +/* Face-API homepage: author: ' */ -var ln=Object.create;var er=Object.defineProperty;var dn=Object.getOwnPropertyDescriptor;var hn=Object.getOwnPropertyNames;var bn=Object.getPrototypeOf,gn=Object.prototype.hasOwnProperty;var xn=(o,t)=>()=>(t||o((t={exports:{}}).exports,t),t.exports),Lr=(o,t)=>{for(var e in t)er(o,e,{get:t[e],enumerable:!0})},yo=(o,t,e,r)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of hn(t))!gn.call(o,n)&&n!==e&&er(o,n,{get:()=>t[n],enumerable:!(r=dn(t,n))||r.enumerable});return o};var v=(o,t,e)=>(e=o!=null?ln(bn(o)):{},yo(t||!o||!o.__esModule?er(e,"default",{value:o,enumerable:!0}):e,o)),vn=o=>yo(er({},"__esModule",{value:!0}),o);var x=xn((ka,Wr)=>{"use strict";var _o=Object.defineProperty,yn=Object.getOwnPropertyDescriptor,_n=Object.getOwnPropertyNames,Tn=Object.prototype.hasOwnProperty,Ar=(o,t,e,r)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of _n(t))!Tn.call(o,n)&&n!==e&&_o(o,n,{get:()=>t[n],enumerable:!(r=yn(t,n))||r.enumerable});return o},Pn=(o,t,e)=>(Ar(o,t,"default"),e&&Ar(e,t,"default")),wn=o=>Ar(_o({},"__esModule",{value:!0}),o),To={};Wr.exports=wn(To);Pn(To,require("@tensorflow/tfjs-node"),Wr.exports)});var Na={};Lr(Na,{AgeGenderNet:()=>He,BoundingBox:()=>Vt,Box:()=>F,ComposableTask:()=>J,ComputeAllFaceDescriptorsTask:()=>wt,ComputeFaceDescriptorsTaskBase:()=>Ue,ComputeSingleFaceDescriptorTask:()=>Ft,DetectAllFaceLandmarksTask:()=>qe,DetectAllFacesTask:()=>Ie,DetectFaceLandmarksTaskBase:()=>Je,DetectFacesTaskBase:()=>Ke,DetectSingleFaceLandmarksTask:()=>Ze,DetectSingleFaceTask:()=>Qe,Dimensions:()=>k,FACE_EXPRESSION_LABELS:()=>no,FaceDetection:()=>M,FaceDetectionNet:()=>uo,FaceExpressionNet:()=>Oe,FaceExpressions:()=>yt,FaceLandmark68Net:()=>Kt,FaceLandmark68TinyNet:()=>ze,FaceLandmarkNet:()=>co,FaceLandmarks:()=>z,FaceLandmarks5:()=>Yr,FaceLandmarks68:()=>Gt,FaceMatch:()=>pe,FaceMatcher:()=>tr,FaceRecognitionNet:()=>Qt,Gender:()=>Tr,LabeledBox:()=>ue,LabeledFaceDescriptors:()=>mt,NetInput:()=>ut,NeuralNetwork:()=>A,ObjectDetection:()=>bt,Point:()=>b,PredictedBox:()=>Gr,Rect:()=>Yt,SsdMobilenetv1:()=>St,SsdMobilenetv1Options:()=>X,TinyFaceDetector:()=>ne,TinyFaceDetectorOptions:()=>je,TinyYolov2:()=>re,TinyYolov2Options:()=>st,allFaces:()=>Ea,allFacesSsdMobilenetv1:()=>un,allFacesTinyYolov2:()=>Da,awaitMediaLoaded:()=>Kr,bufferToImage:()=>Qr,computeFaceDescriptor:()=>fa,createCanvas:()=>Jt,createCanvasFromMedia:()=>We,createFaceDetectionNet:()=>aa,createFaceRecognitionNet:()=>Xn,createSsdMobilenetv1:()=>Jo,createTinyFaceDetector:()=>Ma,createTinyYolov2:()=>ca,detectAllFaces:()=>Sr,detectFaceLandmarks:()=>mn,detectFaceLandmarksTiny:()=>ua,detectLandmarks:()=>wa,detectSingleFace:()=>Fa,draw:()=>so,env:()=>P,euclideanDistance:()=>go,extendWithAge:()=>Er,extendWithFaceDescriptor:()=>Dr,extendWithFaceDetection:()=>jt,extendWithFaceExpressions:()=>xr,extendWithFaceLandmarks:()=>we,extendWithGender:()=>Mr,extractFaceTensors:()=>de,extractFaces:()=>le,fetchImage:()=>An,fetchJson:()=>ro,fetchNetWeights:()=>Wn,fetchOrThrow:()=>xt,fetchVideo:()=>kn,getContext2dOrThrow:()=>O,getMediaDimensions:()=>Xt,imageTensorToCanvas:()=>to,imageToSquare:()=>eo,inverseSigmoid:()=>En,iou:()=>Or,isMediaElement:()=>ir,isMediaLoaded:()=>Ae,isWithAge:()=>Jn,isWithFaceDetection:()=>pt,isWithFaceExpressions:()=>ao,isWithFaceLandmarks:()=>Zt,isWithGender:()=>qn,loadAgeGenderModel:()=>_a,loadFaceDetectionModel:()=>Ta,loadFaceExpressionModel:()=>ya,loadFaceLandmarkModel:()=>ga,loadFaceLandmarkTinyModel:()=>xa,loadFaceRecognitionModel:()=>va,loadSsdMobilenetv1Model:()=>pn,loadTinyFaceDetectorModel:()=>ha,loadTinyYolov2Model:()=>ba,loadWeightMap:()=>oo,locateFaces:()=>Pa,matchDimensions:()=>Bn,minBbox:()=>Hr,nets:()=>w,nonMaxSuppression:()=>zr,normalize:()=>rt,padToSquare:()=>Vr,predictAgeAndGender:()=>da,recognizeFaceExpressions:()=>la,resizeResults:()=>fn,resolveInput:()=>Ut,shuffleArray:()=>Dn,sigmoid:()=>Ne,ssdMobilenetv1:()=>cn,tf:()=>Ca,tinyFaceDetector:()=>ma,tinyYolov2:()=>pa,toNetInput:()=>C,utils:()=>$r,validateConfig:()=>fo,version:()=>Ia});module.exports=vn(Na);var 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N=v(x());var Cr=o=>typeof o=="number";function fo(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(!Cr(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=>Cr(t.x)&&Cr(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(Cr)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: 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st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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 ho=class extends A{constructor(e){super("TinyYolov2");fo(e),this._config=e}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(e,r){let n=Tt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=Tt(n,r.conv6),n=Tt(n,r.conv7),qt(n,r.conv8,"valid",!1)}runMobilenet(e,r){let n=this.config.isFirstLayerConv2d?Me(qt(e,r.conv0,"valid",!1)):Pt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=r.conv6?Pt(n,r.conv6):n,n=r.conv7?Pt(n,r.conv7):n,qt(n,r.conv8,"valid",!1)}forwardInput(e,r){let{params:n}=this;if(!n)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let a=N.cast(e.toBatchTensor(r,!1),"float32");return 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extractPredictedClass(e,r){let{row:n,col:a,anchor:s}=r,i=await e.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[n][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},ee=ho;ee.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var re=class extends ee{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:qo,classes:["face"],...t?{anchors:Ko,meanRgb:Qo}:{anchors:Zo,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?en:tn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ca(o,t=!0){let e=new re(t);return e.extractWeights(o),e}var je=class extends 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ie(this,this.input)}},se=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>w.faceExpressionNet.predictExpressions(r),this.extractedFaces);return xr(t,e)}withAgeAndGender(){return new ce(this,this.input)}},Wt=class extends ae{withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new wt(this,this.input)}},kt=class extends se{withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ie=class extends Nr{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>w.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return Er(Mr(r,s,i),a)})}withFaceExpressions(){return new ae(this,this.input)}},ce=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,a=>w.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Er(Mr(t,r,n),e)}withFaceExpressions(){return new se(this,this.input)}},Bt=class extends ie{withFaceExpressions(){return new Wt(this,this.input)}withFaceDescriptors(){return new wt(this,this.input)}},Rt=class extends ce{withFaceExpressions(){return new kt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},wt=class extends Ue{async run(){let t=await this.parentTask;return(await oe(t,this.input,r=>Promise.all(r.map(n=>w.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Dr(t[n],r))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}},Ft=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await 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this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await de(this.input,[e]):await le(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),we(t,n)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=w.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=w.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=w.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>jt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ie(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(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 Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new se(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ce(this.runAndExtendWithFaceDetection(),this.input)}};function Fa(o,t=new X){return new Qe(o,t)}function Sr(o,t=new X){return new Ie(o,t)}async function un(o,t){return Sr(o,new 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N=v(x());var Cr=o=>typeof o=="number";function fo(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(!Cr(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=>Cr(t.x)&&Cr(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(Cr)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: 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st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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 ho=class extends A{constructor(e){super("TinyYolov2");fo(e),this._config=e}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(e,r){let n=Tt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=Tt(n,r.conv6),n=Tt(n,r.conv7),qt(n,r.conv8,"valid",!1)}runMobilenet(e,r){let n=this.config.isFirstLayerConv2d?Me(qt(e,r.conv0,"valid",!1)):Pt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Pt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=r.conv6?Pt(n,r.conv6):n,n=r.conv7?Pt(n,r.conv7):n,qt(n,r.conv8,"valid",!1)}forwardInput(e,r){let{params:n}=this;if(!n)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let a=N.cast(e.toBatchTensor(r,!1),"float32");return a=this.config.meanRgb?rt(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,n):this.runTinyYolov2(a,n)})}async forward(e,r){return this.forwardInput(await C(e),r)}async detect(e,r={}){let{inputSize:n,scoreThreshold:a}=new st(r),s=await C(e),i=await this.forwardInput(s,n),c=N.tidy(()=>N.unstack(i)[0].expandDims()),m={width:s.getInputWidth(0),height:s.getInputHeight(0)},p=await this.extractBoxes(c,s.getReshapedInputDimensions(0),a);i.dispose(),c.dispose();let u=p.map(h=>h.box),f=p.map(h=>h.score),l=p.map(h=>h.classScore),g=p.map(h=>this.config.classes[h.label]);return zr(u.map(h=>h.rescale(n)),f,this.config.iouThreshold,!0).map(h=>new bt(f[h],l[h],g[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return on(e,this.config)}extractParams(e){let r=this.config.filterSizes||ho.DEFAULT_FILTER_SIZES,n=r?r.length:void 0;if(n!==7&&n!==8&&n!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${n} filterSizes in config`);return rn(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,n){let{width:a,height:s}=r,i=Math.max(a,s),c=i/a,m=i/s,p=e.shape[1],u=this.config.anchors.length,[f,l,g]=N.tidy(()=>{let _=e.reshape([p,p,u,this.boxEncodingSize]),y=_.slice([0,0,0,0],[p,p,u,4]),E=_.slice([0,0,0,4],[p,p,u,1]),W=this.withClassScores?N.softmax(_.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):N.scalar(0);return[y,E,W]}),T=[],D=await l.array(),h=await f.array();for(let _=0;_n){let tt=(y+Ne(h[_][y][E][0]))/p*c,lt=(_+Ne(h[_][y][E][1]))/p*m,q=Math.exp(h[_][y][E][2])*this.config.anchors[E].x/p*c,Dt=Math.exp(h[_][y][E][3])*this.config.anchors[E].y/p*m,Et=tt-q/2,Mt=lt-Dt/2,$t={row:_,col:y,anchor:E},{classScore:xo,label:vo}=this.withClassScores?await this.extractPredictedClass(g,$t):{classScore:1,label:0};T.push({box:new Vt(Et,Mt,Et+q,Mt+Dt),score:W,classScore:W*xo,label:vo,...$t})}}return f.dispose(),l.dispose(),g.dispose(),T}async extractPredictedClass(e,r){let{row:n,col:a,anchor:s}=r,i=await e.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[n][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},ee=ho;ee.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var re=class extends ee{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:qo,classes:["face"],...t?{anchors:Ko,meanRgb:Qo}:{anchors:Zo,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?en:tn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ca(o,t=!0){let e=new re(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var bo=v(x());async function oe(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Zt(c)?n(c):c.detection),s=r||(t instanceof bo.Tensor?await de(t,a):await le(t,a)),i=await e(s);return s.forEach(c=>c instanceof bo.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return oe([o],t,async a=>e(a[0]),r,n)}var nn=.4,an=[new b(1.603231,2.094468),new b(6.041143,7.080126),new b(2.882459,3.518061),new b(4.266906,5.178857),new b(9.041765,10.66308)],sn=[117.001,114.697,97.404];var ne=class extends ee{constructor(){let t={withSeparableConvs:!0,iouThreshold:nn,classes:["face"],anchors:an,meanRgb:sn,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 w={ssdMobilenetv1:new St,tinyFaceDetector:new ne,tinyYolov2:new re,faceLandmark68Net:new Kt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Qt,faceExpressionNet:new Oe,ageGenderNet:new He},cn=(o,t)=>w.ssdMobilenetv1.locateFaces(o,t),ma=(o,t)=>w.tinyFaceDetector.locateFaces(o,t),pa=(o,t)=>w.tinyYolov2.locateFaces(o,t),mn=o=>w.faceLandmark68Net.detectLandmarks(o),ua=o=>w.faceLandmark68TinyNet.detectLandmarks(o),fa=o=>w.faceRecognitionNet.computeFaceDescriptor(o),la=o=>w.faceExpressionNet.predictExpressions(o),da=o=>w.ageGenderNet.predictAgeAndGender(o),pn=o=>w.ssdMobilenetv1.load(o),ha=o=>w.tinyFaceDetector.load(o),ba=o=>w.tinyYolov2.load(o),ga=o=>w.faceLandmark68Net.load(o),xa=o=>w.faceLandmark68TinyNet.load(o),va=o=>w.faceRecognitionNet.load(o),ya=o=>w.faceExpressionNet.load(o),_a=o=>w.ageGenderNet.load(o),Ta=pn,Pa=cn,wa=mn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Ir{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>w.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>xr(r,e[n]))}withAgeAndGender(){return new ie(this,this.input)}},se=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>w.faceExpressionNet.predictExpressions(r),this.extractedFaces);return xr(t,e)}withAgeAndGender(){return new ce(this,this.input)}},Wt=class extends ae{withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new wt(this,this.input)}},kt=class extends se{withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ie=class extends Nr{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>w.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return Er(Mr(r,s,i),a)})}withFaceExpressions(){return new ae(this,this.input)}},ce=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,a=>w.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Er(Mr(t,r,n),e)}withFaceExpressions(){return new se(this,this.input)}},Bt=class extends ie{withFaceExpressions(){return new Wt(this,this.input)}withFaceDescriptors(){return new wt(this,this.input)}},Rt=class extends ce{withFaceExpressions(){return new kt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},wt=class extends Ue{async run(){let t=await this.parentTask;return(await oe(t,this.input,r=>Promise.all(r.map(n=>w.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Dr(t[n],r))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}},Ft=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>w.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Dr(t,e)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?w.faceLandmark68TinyNet:w.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof Xe.Tensor?await de(this.input,e):await le(this.input,e),n=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),t.filter((s,i)=>n[i]).map((s,i)=>we(s,n[i]))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new wt(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await de(this.input,[e]):await le(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),we(t,n)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=w.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=w.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=w.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>jt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ie(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(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 Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new se(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ce(this.runAndExtendWithFaceDetection(),this.input)}};function Fa(o,t=new X){return new Qe(o,t)}function Sr(o,t=new X){return new Ie(o,t)}async function un(o,t){return Sr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Da(o,t={}){return Sr(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Ea=un;function go(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 tr=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 mt)return s;if(s instanceof Float32Array)return new mt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new mt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>go(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new pe(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>mt.fromJSON(r));return new tr(e,t.distanceThreshold)}};function Ma(o){let t=new ne;return t.extractWeights(o),t}function fn(o,t){let{width:e,height:r}=new k(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=>fn(n,{width:e,height:r}));if(Zt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return we(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof z||o instanceof M?o.forSize(e,r):o}var Ia=No;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,fetchVideo,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}); diff --git a/package.json b/package.json index c266926..0b63a02 100644 --- a/package.json +++ b/package.json @@ -44,10 +44,10 @@ "optionalDependencies": { "@canvas/image": "^1.0.1", "canvas": "^2.9.3", - "node-fetch": "^3.2.9" + "node-fetch": "^3.2.10" }, "devDependencies": { - "@microsoft/api-extractor": "^7.28.6", + "@microsoft/api-extractor": "^7.29.3", "@tensorflow/tfjs": "^3.19.0", "@tensorflow/tfjs-backend-cpu": "^3.19.0", "@tensorflow/tfjs-backend-wasm": "^3.19.0", @@ -59,15 +59,15 @@ "@tensorflow/tfjs-layers": "^3.19.0", "@tensorflow/tfjs-node": "^3.19.0", "@tensorflow/tfjs-node-gpu": "^3.19.0", - "@types/node": "^18.6.2", + "@types/node": "^18.7.9", "@types/offscreencanvas": "^2019.7.0", - "@typescript-eslint/eslint-plugin": "^5.31.0", - "@typescript-eslint/parser": "^5.31.0", - "@vladmandic/build": "^0.7.8", - "@vladmandic/pilogger": "^0.4.5", + "@typescript-eslint/eslint-plugin": "^5.33.1", + "@typescript-eslint/parser": "^5.33.1", + "@vladmandic/build": "^0.7.10", + "@vladmandic/pilogger": "^0.4.6", "@vladmandic/tfjs": "github:vladmandic/tfjs", - "esbuild": "^0.14.51", - "eslint": "^8.20.0", + "esbuild": "^0.15.5", + "eslint": "^8.22.0", "eslint-config-airbnb-base": "^15.0.0", "eslint-plugin-import": "^2.26.0", "eslint-plugin-json": "^3.1.0", @@ -76,7 +76,7 @@ "rimraf": "^3.0.2", "seedrandom": "^3.0.5", "tslib": "^2.4.0", - "typedoc": "^0.23.9", + "typedoc": "^0.23.10", "typescript": "4.7.4" } } diff --git a/typedoc/classes/draw.DrawBoxOptions.html b/typedoc/classes/draw.DrawBoxOptions.html index 7ce7ee8..b8fd93b 100644 --- a/typedoc/classes/draw.DrawBoxOptions.html +++ b/typedoc/classes/draw.DrawBoxOptions.html @@ -33,7 +33,7 @@

Properties

boxColor drawLabelOptions -label +label? lineWidth
@@ -100,6 +100,6 @@
  • constructor
  • boxColor
  • drawLabelOptions
    • -
  • label
    • +
  • label?
  • lineWidth
    • \ No newline at end of file diff --git a/typedoc/interfaces/ISsdMobilenetv1Options.html b/typedoc/interfaces/ISsdMobilenetv1Options.html index 54cb5fb..bb3543b 100644 --- a/typedoc/interfaces/ISsdMobilenetv1Options.html +++ b/typedoc/interfaces/ISsdMobilenetv1Options.html @@ -26,8 +26,8 @@

    Properties

    -
    maxResults -minConfidence +
    maxResults? +minConfidence?

    Properties

    @@ -65,6 +65,6 @@ +
  • maxResults?
    • +
  • minConfidence?
    • \ No newline at end of file diff --git a/typedoc/interfaces/ITinyYolov2Options.html b/typedoc/interfaces/ITinyYolov2Options.html index 251ea28..120b9a7 100644 --- a/typedoc/interfaces/ITinyYolov2Options.html +++ b/typedoc/interfaces/ITinyYolov2Options.html @@ -26,8 +26,8 @@

    Properties

    -
    inputSize -scoreThreshold +
    inputSize? +scoreThreshold?

    Properties

    @@ -65,6 +65,6 @@ +
  • inputSize?
    • +
  • scoreThreshold?
    • \ No newline at end of file diff --git a/typedoc/interfaces/draw.IDrawBoxOptions.html b/typedoc/interfaces/draw.IDrawBoxOptions.html index 3e9153d..e784297 100644 --- a/typedoc/interfaces/draw.IDrawBoxOptions.html +++ b/typedoc/interfaces/draw.IDrawBoxOptions.html @@ -27,10 +27,10 @@

    Properties

    -
    boxColor -drawLabelOptions -label -lineWidth +
    boxColor? +drawLabelOptions? +label? +lineWidth?

    Properties

    @@ -78,8 +78,8 @@ +
  • boxColor?
    • +
  • drawLabelOptions?
    • +
  • label?
    • +
  • lineWidth?
    • \ No newline at end of file diff --git a/typedoc/interfaces/draw.IDrawFaceLandmarksOptions.html b/typedoc/interfaces/draw.IDrawFaceLandmarksOptions.html index 76c8e95..c525747 100644 --- a/typedoc/interfaces/draw.IDrawFaceLandmarksOptions.html +++ b/typedoc/interfaces/draw.IDrawFaceLandmarksOptions.html @@ -27,12 +27,12 @@

    Properties

    -
    drawLines -drawPoints -lineColor -lineWidth -pointColor -pointSize +
    drawLines? +drawPoints? +lineColor? +lineWidth? +pointColor? +pointSize?

    Properties

    @@ -90,10 +90,10 @@ +
  • drawLines?
    • +
  • drawPoints?
    • +
  • lineColor?
    • +
  • lineWidth?
    • +
  • pointColor?
    • +
  • pointSize?
    • \ No newline at end of file diff --git a/typedoc/interfaces/draw.IDrawTextFieldOptions.html b/typedoc/interfaces/draw.IDrawTextFieldOptions.html index ba2cceb..7b091c5 100644 --- a/typedoc/interfaces/draw.IDrawTextFieldOptions.html +++ b/typedoc/interfaces/draw.IDrawTextFieldOptions.html @@ -31,12 +31,12 @@

    Properties

    -
    anchorPosition -backgroundColor -fontColor -fontSize -fontStyle -padding +
    anchorPosition? +backgroundColor? +fontColor? +fontSize? +fontStyle? +padding?

    Properties

    @@ -94,10 +94,10 @@ +
  • anchorPosition?
    • +
  • backgroundColor?
    • +
  • fontColor?
    • +
  • fontSize?
    • +
  • fontStyle?
    • +
  • padding?
    • \ No newline at end of file