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

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/*
Face-API
homepage: <https://github.com/vladmandic/face-api>
author: <https://github.com/vladmandic>'
*/
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L(o,e),{params:i,paramMappings:e}}var J=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 cr=class extends I{constructor(e){super("TinyYolov2");co(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 a=ot(e,r.conv0);return a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv1),a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv2),a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv3),a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv4),a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv5),a=n.maxPool(a,[2,2],[1,1],"same"),a=ot(a,r.conv6),a=ot(a,r.conv7),_t(a,r.conv8,"valid",!1)}runMobilenet(e,r){let a=this.config.isFirstLayerConv2d?Xt(_t(e,r.conv0,"valid",!1)):nt(e,r.conv0);return a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv1),a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv2),a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv3),a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv4),a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv5),a=n.maxPool(a,[2,2],[1,1],"same"),a=r.conv6?nt(a,r.conv6):a,a=r.conv7?nt(a,r.conv7):a,_t(a,r.conv8,"valid",!1)}forwardInput(e,r){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return n.tidy(()=>{let s=n.cast(e.toBatchTensor(r,!1),"float32");return s=this.config.meanRgb?X(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,a):this.runTinyYolov2(s,a)})}async forward(e,r){return this.forwardInput(await M(e),r)}async detect(e,r={}){let{inputSize:a,scoreThreshold:s}=new J(r),i=await M(e),c=await this.forwardInput(i,a),m=n.tidy(()=>n.unstack(c)[0].expandDims()),p={width:i.getInputWidth(0),height:i.getInputHeight(0)},u=await this.extractBoxes(m,i.getReshapedInputDimensions(0),s);c.dispose(),m.dispose();let f=u.map(h=>h.box),l=u.map(h=>h.score),d=u.map(h=>h.classScore),b=u.map(h=>this.config.classes[h.label]);return xr(f.map(h=>h.rescale(a)),l,this.config.iouThreshold,!0).map(h=>new ct(l[h],d[h],b[h],f[h],p))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return po(e,this.config)}extractParams(e){let r=this.config.filterSizes||cr.DEFAULT_FILTER_SIZES,a=r?r.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return mo(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,a){let{width:s,height:i}=r,c=Math.max(s,i),m=c/s,p=c/i,u=e.shape[1],f=this.config.anchors.length,[l,d,b]=n.tidy(()=>{let y=e.reshape([u,u,f,this.boxEncodingSize]),v=y.slice([0,0,0,0],[u,u,f,4]),D=y.slice([0,0,0,4],[u,u,f,1]),N=this.withClassScores?n.softmax(y.slice([0,0,0,5],[u,u,f,this.config.classes.length]),3):n.scalar(0);return[v,D,N]}),x=[],w=await d.array(),h=await l.array();for(let y=0;y<u;y++)for(let v=0;v<u;v++)for(let D=0;D<f;D++){let N=fe(w[y][v][D][0]);if(!a||N>a){let Y=(v+fe(h[y][v][D][0]))/u*m,q=(y+fe(h[y][v][D][1]))/u*p,O=Math.exp(h[y][v][D][2])*this.config.anchors[D].x/u*m,at=Math.exp(h[y][v][D][3])*this.config.anchors[D].y/u*p,st=Y-O/2,it=q-at/2,gt={row:y,col:v,anchor:D},{classScore:pr,label:ur}=this.withClassScores?await this.extractPredictedClass(b,gt):{classScore:1,label:0};x.push({box:new Nt(st,it,st+O,it+at),score:N,classScore:N*pr,label:ur,...gt})}}return l.dispose(),d.dispose(),b.dispose(),x}async extractPredictedClass(e,r){let{row:a,col:s,anchor:i}=r,c=await e.array();return Array(this.config.classes.length).fill(0).map((m,p)=>c[a][s][i][p]).map((m,p)=>({classScore:m,label:p})).reduce((m,p)=>m.classScore>p.classScore?m:p)}},Pt=cr;Pt.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Jt=class extends Pt{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:ro,classes:["face"],...t?{anchors:no,meanRgb:ao}:{anchors:oo,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(a=>new E(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?io:so}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function id(o,t=!0){let e=new Jt(t);return e.extractWeights(o),e}var Le=class extends J{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var V=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function Ft(o,t,e,r,a=({alignedRect:s})=>s){let s=o.map(m=>Yt(m)?a(m):m.detection),i=r||(t instanceof n.Tensor?await oe(t,s):await re(t,s)),c=await e(i);return i.forEach(m=>m instanceof n.Tensor&&m.dispose()),c}async function qt(o,t,e,r,a){return Ft([o],t,async s=>e(s[0]),r,a)}var uo=.4,fo=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],lo=[117.001,114.697,97.404];var Zt=class extends Pt{constructor(){let t={withSeparableConvs:!0,iouThreshold:uo,classes:["face"],anchors:fo,meanRgb:lo,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(a=>new E(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var T={ssdMobilenetv1:new wt,tinyFaceDetector:new Zt,tinyYolov2:new Jt,faceLandmark68Net:new jt,faceLandmark68TinyNet:new Ie,faceRecognitionNet:new Ut,faceExpressionNet:new Pe,ageGenderNet:new Me},Qo=(o,t)=>T.ssdMobilenetv1.locateFaces(o,t),Bd=(o,t)=>T.tinyFaceDetector.locateFaces(o,t),Rd=(o,t)=>T.tinyYolov2.locateFaces(o,t),tn=o=>T.faceLandmark68Net.detectLandmarks(o),$d=o=>T.faceLandmark68TinyNet.detectLandmarks(o),Od=o=>T.faceRecognitionNet.computeFaceDescriptor(o),Hd=o=>T.faceExpressionNet.predictExpressions(o),zd=o=>T.ageGenderNet.predictAgeAndGender(o),en=o=>T.ssdMobilenetv1.load(o),Vd=o=>T.tinyFaceDetector.load(o),Yd=o=>T.tinyYolov2.load(o),Gd=o=>T.faceLandmark68Net.load(o),jd=o=>T.faceLandmark68TinyNet.load(o),Ud=o=>T.faceRecognitionNet.load(o),Xd=o=>T.faceExpressionNet.load(o),Jd=o=>T.ageGenderNet.load(o),qd=en,Zd=Qo,Kd=tn;var Ae=class extends V{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.extractedFaces=a}},Dt=class extends Ae{async run(){let t=await this.parentTask,e=await Ft(t,this.input,async r=>Promise.all(r.map(a=>T.faceExpressionNet.predictExpressions(a))),this.extractedFaces);return t.map((r,a)=>tr(r,e[a]))}withAgeAndGender(){return new Mt(this,this.input)}},Et=class extends Ae{async run(){let t=await this.parentTask;if(!t)return;let e=await qt(t,this.input,r=>T.faceExpressionNet.predictExpressions(r),this.extractedFaces);return tr(t,e)}withAgeAndGender(){return new Ct(this,this.input)}},ut=class extends Dt{withAgeAndGender(){return new lt(this,this.input)}withFaceDescriptors(){return new ht(this,this.input)}},ft=class extends Et{withAgeAndGender(){return new dt(this,this.input)}withFaceDescriptor(){return new bt(this,this.input)}};var We=class extends V{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.extractedFaces=a}},Mt=class extends We{async run(){let t=await this.parentTask,e=await Ft(t,this.input,async r=>Promise.all(r.map(a=>T.ageGenderNet.predictAgeAndGender(a))),this.extractedFaces);return t.map((r,a)=>{let{age:s,gender:i,genderProbability:c}=e[a];return sr(ir(r,i,c),s)})}withFaceExpressions(){return new Dt(this,this.input)}},Ct=class extends We{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:a}=await qt(t,this.input,s=>T.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return sr(ir(t,r,a),e)}withFaceExpressions(){return new Et(this,this.input)}},lt=class extends Mt{withFaceExpressions(){return new ut(this,this.input)}withFaceDescriptors(){return new ht(this,this.input)}},dt=class extends Ct{withFaceExpressions(){return new ft(this,this.input)}withFaceDescriptor(){return new bt(this,this.input)}};var ke=class extends V{constructor(e,r){super();this.parentTask=e;this.input=r}},ht=class extends ke{async run(){let t=await this.parentTask;return(await Ft(t,this.input,r=>Promise.all(r.map(a=>T.faceRecognitionNet.computeFaceDescriptor(a))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,a)=>ar(t[a],r))}withFaceExpressions(){return new ut(this,this.input)}withAgeAndGender(){return new lt(this,this.input)}},bt=class extends ke{async run(){let t=await this.parentTask;if(!t)return;let e=await qt(t,this.input,r=>T.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return ar(t,e)}withFaceExpressions(){return new ft(this,this.input)}withAgeAndGender(){return new dt(this,this.input)}};var Be=class extends V{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?T.faceLandmark68TinyNet:T.faceLandmark68Net}},Re=class extends Be{async run(){let t=await this.parentTask,e=t.map(i=>i.detection),r=this.input instanceof n.Tensor?await oe(this.input,e):await re(this.input,e),a=await Promise.all(r.map(i=>this.landmarkNet.detectLandmarks(i)));return r.forEach(i=>i instanceof n.Tensor&&i.dispose()),t.filter((i,c)=>a[c]).map((i,c)=>ie(i,a[c]))}withFaceExpressions(){return new ut(this,this.input)}withAgeAndGender(){return new lt(this,this.input)}withFaceDescriptors(){return new ht(this,this.input)}},$e=class extends Be{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof n.Tensor?await oe(this.input,[e]):await re(this.input,[e]),a=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof n.Tensor&&s.dispose()),ie(t,a)}withFaceExpressions(){return new ft(this,this.input)}withAgeAndGender(){return new dt(this,this.input)}withFaceDescriptor(){return new bt(this,this.input)}};var Oe=class extends V{constructor(e,r=new z){super();this.input=e;this.options=r}},me=class extends Oe{async run(){let{input:t,options:e}=this,r;if(e instanceof Le)r=T.tinyFaceDetector.locateFaces(t,e);else if(e instanceof z)r=T.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof J)r=T.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(a=>At({},a)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new Re(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Dt(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Mt(this.runAndExtendWithFaceDetections(),this.input)}},He=class extends Oe{async run(){let t=await new me(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?At({},e):void 0)})}withFaceLandmarks(t=!1){return new $e(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Et(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Ct(this.runAndExtendWithFaceDetection(),this.input)}};function Jh(o,t=new z){return new He(o,t)}function mr(o,t=new z){return new me(o,t)}async function rn(o,t){return mr(o,new z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function eb(o,t={}){return mr(o,new J(t)).withFaceLandmarks().withFaceDescriptors()}var rb=rn;function ho(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((a,s)=>a-r[s]).reduce((a,s)=>a+s*s,0))}var ze=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 a=1,s=()=>`person ${a++}`;this._labeledDescriptors=r.map(i=>{if(i instanceof Q)return i;if(i instanceof Float32Array)return new Q(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Q(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>ho(r,t)).reduce((r,a)=>r+a,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Kt(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distance<r.distance?e:r)}findBestMatch(t){let e=this.matchDescriptor(t);return e.distance<this._distanceThreshold?e:new Kt("unknown",e.distance)}toJSON(){return{distanceThreshold:this._distanceThreshold,labeledDescriptors:this._labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>Q.fromJSON(r));return new ze(e,t.distanceThreshold)}};function yb(o){let t=new Zt;return t.extractWeights(o),t}function on(o,t){let{width:e,height:r}=new S(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(a=>on(a,{width:e,height:r}));if(Yt(o)){let a=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(a.box.width,a.box.height);return ie(At(o,a),s)}return tt(o)?At(o,o.detection.forSize(e,r)):o instanceof $||o instanceof E?o.forSize(e,r):o}var Nb=kr;export{Me as AgeGenderNet,Nt as BoundingBox,F as Box,V as ComposableTask,ht as ComputeAllFaceDescriptorsTask,ke as ComputeFaceDescriptorsTaskBase,bt as ComputeSingleFaceDescriptorTask,Re as DetectAllFaceLandmarksTask,me as DetectAllFacesTask,Be as DetectFaceLandmarksTaskBase,Oe as DetectFacesTaskBase,$e as DetectSingleFaceLandmarksTask,He as DetectSingleFaceTask,S as Dimensions,Lr as FACE_EXPRESSION_LABELS,E as FaceDetection,eo as FaceDetectionNet,Pe as FaceExpressionNet,pt as FaceExpressions,jt as FaceLandmark68Net,Ie as FaceLandmark68TinyNet,Yr as FaceLandmarkNet,$ as FaceLandmarks,yr as FaceLandmarks5,Lt as FaceLandmarks68,Kt as FaceMatch,ze as FaceMatcher,Ut as FaceRecognitionNet,rr as Gender,Qt as LabeledBox,Q as LabeledFaceDescriptors,rt as NetInput,I as NeuralNetwork,ct as ObjectDetection,g as Point,_r as PredictedBox,St as Rect,wt as SsdMobilenetv1,z as SsdMobilenetv1Options,Zt as TinyFaceDetector,Le as TinyFaceDetectorOptions,Jt as TinyYolov2,J as TinyYolov2Options,rb as allFaces,rn as allFacesSsdMobilenetv1,eb as allFacesTinyYolov2,Tr as awaitMediaLoaded,wr as bufferToImage,Od as computeFaceDescriptor,Rt as createCanvas,be as createCanvasFromMedia,bl as createFaceDetectionNet,bf as createFaceRecognitionNet,qo as createSsdMobilenetv1,yb as createTinyFaceDetector,id as createTinyYolov2,mr as detectAllFaces,tn as detectFaceLandmarks,$d as detectFaceLandmarksTiny,Kd as detectLandmarks,Jh as detectSingleFace,Wr as draw,_ as env,ho as euclideanDistance,sr as extendWithAge,ar as extendWithFaceDescriptor,At as extendWithFaceDetection,tr as extendWithFaceExpressions,ie as extendWithFaceLandmarks,ir as extendWithGender,oe as extractFaceTensors,re as extractFaces,Fi as fetchImage,Dr as fetchJson,Ii as fetchNetWeights,mt as fetchOrThrow,ki as fetchVideo,k as getContext2dOrThrow,Bt as getMediaDimensions,Pr as imageTensorToCanvas,Fr as imageToSquare,On as inverseSigmoid,br as iou,Qe as isMediaElement,he as isMediaLoaded,yf as isWithAge,tt as isWithFaceDetection,Ar as isWithFaceExpressions,Yt as isWithFaceLandmarks,Pf as isWithGender,Jd as loadAgeGenderModel,qd as loadFaceDetectionModel,Xd as loadFaceExpressionModel,Gd as loadFaceLandmarkModel,jd as loadFaceLandmarkTinyModel,Ud as loadFaceRecognitionModel,en as loadSsdMobilenetv1Model,Vd as loadTinyFaceDetectorModel,Yd as loadTinyYolov2Model,Mr as loadWeightMap,Zd as locateFaces,Vi as matchDimensions,gr as minBbox,T as nets,xr as nonMaxSuppression,X as normalize,vr as padToSquare,zd as predictAgeAndGender,Hd as recognizeFaceExpressions,on as resizeResults,Wt as resolveInput,Rn as shuffleArray,fe as sigmoid,Qo as ssdMobilenetv1,n as tf,Bd as tinyFaceDetector,Rd as tinyYolov2,M as toNetInput,hr as utils,co as validateConfig,Nb as version};
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