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

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

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N(o,e),{params:i,paramMappings:e}}var et=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 Ke=class extends I{constructor(t){super("TinyYolov2");eo(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=Q(t,e.conv0);return r=n.maxPool(r,[2,2],[2,2],"same"),r=Q(r,e.conv1),r=n.maxPool(r,[2,2],[2,2],"same"),r=Q(r,e.conv2),r=n.maxPool(r,[2,2],[2,2],"same"),r=Q(r,e.conv3),r=n.maxPool(r,[2,2],[2,2],"same"),r=Q(r,e.conv4),r=n.maxPool(r,[2,2],[2,2],"same"),r=Q(r,e.conv5),r=n.maxPool(r,[2,2],[1,1],"same"),r=Q(r,e.conv6),r=Q(r,e.conv7),lt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?At(lt(t,e.conv0,"valid",!1)):tt(t,e.conv0);return r=n.maxPool(r,[2,2],[2,2],"same"),r=tt(r,e.conv1),r=n.maxPool(r,[2,2],[2,2],"same"),r=tt(r,e.conv2),r=n.maxPool(r,[2,2],[2,2],"same"),r=tt(r,e.conv3),r=n.maxPool(r,[2,2],[2,2],"same"),r=tt(r,e.conv4),r=n.maxPool(r,[2,2],[2,2],"same"),r=tt(r,e.conv5),r=n.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?tt(r,e.conv6):r,r=e.conv7?tt(r,e.conv7):r,lt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return n.tidy(()=>{let a=n.cast(t.toBatchTensor(e,!1),"float32");return a=this.config.meanRgb?J(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,r):this.runTinyYolov2(a,r)})}async forward(t,e){return this.forwardInput(await D(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:a}=new et(e),s=await D(t),i=await this.forwardInput(s,r),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),d=p.map(h=>this.config.classes[h.label]);return hr(u.map(h=>h.rescale(r)),f,this.config.iouThreshold,!0).map(h=>new ft(f[h],l[h],d[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return oo(t,this.config)}extractParams(t){let e=this.config.filterSizes||Ke.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return ro(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:a,height:s}=e,i=Math.max(a,s),c=i/a,m=i/s,p=t.shape[1],u=this.config.anchors.length,[f,l,d]=n.tidy(()=>{let x=t.reshape([p,p,u,this.boxEncodingSize]),T=x.slice([0,0,0,0],[p,p,u,4]),v=x.slice([0,0,0,4],[p,p,u,1]),M=this.withClassScores?n.softmax(x.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):n.scalar(0);return[T,v,M]}),g=[],_=await l.array(),h=await f.array();for(let x=0;x<p;x++)for(let T=0;T<p;T++)for(let v=0;v<u;v++){let M=ie(_[x][T][v][0]);if(!r||M>r){let B=(T+ie(h[x][T][v][0]))/p*c,z=(x+ie(h[x][T][v][1]))/p*m,U=Math.exp(h[x][T][v][2])*this.config.anchors[v].x/p*c,$=Math.exp(h[x][T][v][3])*this.config.anchors[v].y/p*m,rt=B-U/2,ot=z-$/2,nt={row:x,col:T,anchor:v},{classScore:Pt,label:pr}=this.withClassScores?await this.extractPredictedClass(d,nt):{classScore:1,label:0};g.push({box:new Ht(rt,ot,rt+U,ot+$),score:M,classScore:M*Pt,label:pr,...nt})}}return f.dispose(),l.dispose(),d.dispose(),g}async extractPredictedClass(t,e){let{row:r,col:a,anchor:s}=e,i=await t.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[r][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},kt=Ke;kt.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var oe=class extends kt{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Jr,classes:["face"],...t?{anchors:Zr,meanRgb:Kr}:{anchors:qr,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?to:Qr}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function nd(o,t=!0){let e=new oe(t);return e.extractWeights(o),e}var Qe=class extends et{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")}};async function bt(o,t,e,r,a=({alignedRect:s})=>s){let s=o.map(m=>Lt(m)?a(m):m.detection),i=r||(t instanceof n.Tensor?await Ut(t,s):await jt(t,s)),c=await e(i);return i.forEach(m=>m instanceof n.Tensor&&m.dispose()),c}async function Wt(o,t,e,r,a){return bt([o],t,async s=>e(s[0]),r,a)}var no=.4,ao=[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)],so=[117.001,114.697,97.404];var ne=class extends kt{constructor(){let t={withSeparableConvs:!0,iouThreshold:no,classes:["face"],anchors:ao,meanRgb:so,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 w={ssdMobilenetv1:new St,tinyFaceDetector:new ne,tinyYolov2:new oe,faceLandmark68Net:new te,faceLandmark68TinyNet:new je,faceRecognitionNet:new re,faceExpressionNet:new Be,ageGenderNet:new Ge},tn=(o,t)=>w.ssdMobilenetv1.locateFaces(o,t),Ad=(o,t)=>w.tinyFaceDetector.locateFaces(o,t),kd=(o,t)=>w.tinyYolov2.locateFaces(o,t),en=o=>w.faceLandmark68Net.detectLandmarks(o),Wd=o=>w.faceLandmark68TinyNet.detectLandmarks(o),Bd=o=>w.faceRecognitionNet.computeFaceDescriptor(o),Rd=o=>w.faceExpressionNet.predictExpressions(o),$d=o=>w.ageGenderNet.predictAgeAndGender(o),rn=o=>w.ssdMobilenetv1.load(o),Od=o=>w.tinyFaceDetector.load(o),Hd=o=>w.tinyYolov2.load(o),zd=o=>w.faceLandmark68Net.load(o),Yd=o=>w.faceLandmark68TinyNet.load(o),Gd=o=>w.faceRecognitionNet.load(o),Vd=o=>w.faceExpressionNet.load(o),jd=o=>w.ageGenderNet.load(o),Ud=rn,Xd=tn,Jd=en;var tr=class extends j{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Bt=class extends tr{async run(){let t=await this.parentTask,e=await bt(t,this.input,async r=>Promise.all(r.map(a=>w.faceExpressionNet.predictExpressions(a))),this.extractedFaces);return t.map((r,a)=>Re(r,e[a]))}withAgeAndGender(){return new $t(this,this.input)}},Rt=class extends tr{async run(){let t=await this.parentTask;if(!t)return;let e=await Wt(t,this.input,r=>w.faceExpressionNet.predictExpressions(r),this.extractedFaces);return Re(t,e)}withAgeAndGender(){return new Ot(this,this.input)}},gt=class extends Bt{withAgeAndGender(){return new vt(this,this.input)}withFaceDescriptors(){return new Tt(this,this.input)}},xt=class extends Rt{withAgeAndGender(){return new yt(this,this.input)}withFaceDescriptor(){return new _t(this,this.input)}};var er=class extends j{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},$t=class extends er{async run(){let t=await this.parentTask,e=await bt(t,this.input,async r=>Promise.all(r.map(a=>w.ageGenderNet.predictAgeAndGender(a))),this.extractedFaces);return t.map((r,a)=>{let{age:s,gender:i,genderProbability:c}=e[a];return qe(Ze(r,i,c),s)})}withFaceExpressions(){return new Bt(this,this.input)}},Ot=class extends er{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:a}=await Wt(t,this.input,s=>w.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return qe(Ze(t,r,a),e)}withFaceExpressions(){return new Rt(this,this.input)}},vt=class extends $t{withFaceExpressions(){return new gt(this,this.input)}withFaceDescriptors(){return new Tt(this,this.input)}},yt=class extends Ot{withFaceExpressions(){return new xt(this,this.input)}withFaceDescriptor(){return new _t(this,this.input)}};var rr=class extends j{constructor(t,e){super();this.parentTask=t;this.input=e}},Tt=class extends rr{async run(){let t=await this.parentTask;return(await bt(t,this.input,r=>Promise.all(r.map(a=>w.faceRecognitionNet.computeFaceDescriptor(a))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,a)=>Je(t[a],r))}withFaceExpressions(){return new gt(this,this.input)}withAgeAndGender(){return new vt(this,this.input)}},_t=class extends rr{async run(){let t=await this.parentTask;if(!t)return;let e=await Wt(t,this.input,r=>w.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Je(t,e)}withFaceExpressions(){return new xt(this,this.input)}withAgeAndGender(){return new yt(this,this.input)}};var or=class extends j{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?w.faceLandmark68TinyNet:w.faceLandmark68Net}},nr=class extends or{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof n.Tensor?await Ut(this.input,e):await jt(this.input,e),a=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof n.Tensor&&s.dispose()),t.map((s,i)=>Kt(s,a[i]))}withFaceExpressions(){return new gt(this,this.input)}withAgeAndGender(){return new vt(this,this.input)}withFaceDescriptors(){return new Tt(this,this.input)}},ar=class extends or{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof n.Tensor?await Ut(this.input,[e]):await jt(this.input,[e]),a=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof n.Tensor&&s.dispose()),Kt(t,a)}withFaceExpressions(){return new xt(this,this.input)}withAgeAndGender(){return new yt(this,this.input)}withFaceDescriptor(){return new _t(this,this.input)}};var sr=class extends j{constructor(t,e=new V){super();this.input=t;this.options=e}},we=class extends sr{async run(){let{input:t,options:e}=this,r;if(e instanceof Qe)r=w.tinyFaceDetector.locateFaces(t,e);else if(e instanceof V)r=w.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof et)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(a=>Ft({},a)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new nr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Bt(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new $t(this.runAndExtendWithFaceDetections(),this.input)}},ir=class extends sr{async run(){let t=await new we(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?Ft({},e):void 0)})}withFaceLandmarks(t=!1){return new ar(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Rt(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Ot(this.runAndExtendWithFaceDetection(),this.input)}};function jh(o,t=new V){return new ir(o,t)}function cr(o,t=new V){return new we(o,t)}async function on(o,t){return cr(o,new V(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Kh(o,t={}){return cr(o,new et(t)).withFaceLandmarks().withFaceDescriptors()}var Qh=on;function io(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**2,0))}var mr=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 at)return i;if(i instanceof Float32Array)return new at(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new at(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=>io(r,t)).reduce((r,a)=>r+a,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new ce(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 ce("unknown",e.distance)}toJSON(){return{distanceThreshold:this._distanceThreshold,labeledDescriptors:this._labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>at.fromJSON(r));return new mr(e,t.distanceThreshold)}};function gb(o){let t=new ne;return t.extractWeights(o),t}function nn(o,t){let{width:e,height:r}=new S(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(a=>nn(a,{width:e,height:r}));if(Lt(o)){let a=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(a.box.width,a.box.height);return Kt(Ft(o,a),s)}return K(o)?Ft(o,o.detection.forSize(e,r)):o instanceof O||o instanceof E?o.forSize(e,r):o}var Mb=Nr;export{Ge as AgeGenderNet,Ht as BoundingBox,F as Box,j as ComposableTask,Tt as ComputeAllFaceDescriptorsTask,rr as ComputeFaceDescriptorsTaskBase,_t as ComputeSingleFaceDescriptorTask,nr as DetectAllFaceLandmarksTask,we as DetectAllFacesTask,or as DetectFaceLandmarksTaskBase,sr as DetectFacesTaskBase,ar as DetectSingleFaceLandmarksTask,ir as DetectSingleFaceTask,S as Dimensions,Mr as FACE_EXPRESSION_LABELS,E as FaceDetection,Zo as FaceDetectionNet,Be as FaceExpressionNet,dt as FaceExpressions,te as FaceLandmark68Net,je as FaceLandmark68TinyNet,$o as FaceLandmarkNet,O as FaceLandmarks,Do as FaceLandmarks5,Yt as FaceLandmarks68,ce as FaceMatch,mr as FaceMatcher,re as FaceRecognitionNet,Ye as Gender,me as LabeledBox,at as LabeledFaceDescriptors,it as NetInput,I as NeuralNetwork,ft as ObjectDetection,b as Point,Eo as PredictedBox,zt as Rect,St as SsdMobilenetv1,V as SsdMobilenetv1Options,ne as TinyFaceDetector,Qe as TinyFaceDetectorOptions,oe as TinyYolov2,et as TinyYolov2Options,Qh as allFaces,on as allFacesSsdMobilenetv1,Kh as allFacesTinyYolov2,gr as awaitMediaLoaded,xr as bufferToImage,Bd as computeFaceDescriptor,Mt as createCanvas,le as createCanvasFromMedia,ll as createFaceDetectionNet,lf as createFaceRecognitionNet,qo as createSsdMobilenetv1,gb as createTinyFaceDetector,nd as createTinyYolov2,cr as detectAllFaces,en as detectFaceLandmarks,Wd as detectFaceLandmarksTiny,Jd as detectLandmarks,jh as detectSingleFace,Ir as draw,P as env,io as euclideanDistance,qe as extendWithAge,Je as extendWithFaceDescriptor,Ft as extendWithFaceDetection,Re as extendWithFaceExpressions,Kt as extendWithFaceLandmarks,Ze as extendWithGender,Ut as extractFaceTensors,jt as extractFaces,_i as fetchImage,Tr as fetchJson,Ei as fetchNetWeights,ct as fetchOrThrow,Si as fetchVideo,k as getContext2dOrThrow,Et as getMediaDimensions,vr as imageTensorToCanvas,yr as imageToSquare,zn as inverseSigmoid,lr as iou,We as isMediaElement,fe as isMediaLoaded,gf as isWithAge,K as isWithFaceDetection,Cr as isWithFaceExpressions,Lt as isWithFaceLandmarks,Tf as isWithGender,jd as loadAgeGenderModel,Ud as loadFaceDetectionModel,Vd as loadFaceExpressionModel,zd as loadFaceLandmarkModel,Yd as loadFaceLandmarkTinyModel,Gd as loadFaceRecognitionModel,rn as loadSsdMobilenetv1Model,Od as loadTinyFaceDetectorModel,Hd as loadTinyYolov2Model,Pr as loadWeightMap,Xd as locateFaces,Oi as matchDimensions,dr as minBbox,w as nets,hr as nonMaxSuppression,J as normalize,br as padToSquare,$d as predictAgeAndGender,Rd as recognizeFaceExpressions,nn as resizeResults,Dt as resolveInput,On as shuffleArray,ie as sigmoid,tn as ssdMobilenetv1,n as tf,Ad as tinyFaceDetector,kd as tinyYolov2,D as toNetInput,fr as utils,eo as validateConfig,Mb as version};