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

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/*
Face-API
homepage: <https://github.com/vladmandic/face-api>
author: <https://github.com/vladmandic>'
*/
var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor,__markAsModule=target=>__defProp(target,"__esModule",{value:!0}),__commonJS=(callback,module2)=>()=>(module2||(module2={exports:{}},callback(module2.exports,module2)),module2.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module2,desc)=>{if(__markAsModule(target),module2&&typeof module2=="object"||typeof module2=="function")for(let key of __getOwnPropNames(module2))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module2[key],enumerable:!(desc=__getOwnPropDesc(module2,key))||desc.enumerable});return target},__toModule=module2=>module2&&module2.__esModule?module2:__exportStar(__defProp(module2!=null?__create(__getProtoOf(module2)):{},"default",{value:module2,enumerable:!0}),module2),require_tfjs_esm=__commonJS(exports2=>{var __create2=Object.create,__defProp2=Object.defineProperty,__getProtoOf2=Object.getPrototypeOf,__hasOwnProp2=Object.prototype.hasOwnProperty,__getOwnPropNames2=Object.getOwnPropertyNames,__getOwnPropDesc2=Object.getOwnPropertyDescriptor,__markAsModule2=target=>__defProp2(target,"__esModule",{value:!0}),__exportStar2=(target,module22,desc)=>{if(__markAsModule2(target),module22&&typeof module22=="object"||typeof module22=="function")for(let key of __getOwnPropNames2(module22))!__hasOwnProp2.call(target,key)&&key!=="default"&&__defProp2(target,key,{get:()=>module22[key],enumerable:!(desc=__getOwnPropDesc2(module22,key))||desc.enumerable});return target},__toModule2=module22=>module22&&module22.__esModule?module22:__exportStar2(__defProp2(module22!=null?__create2(__getProtoOf2(module22)):{},"default",{value:module22,enumerable:!0}),module22);__exportStar2(exports2,__toModule2(require("@tensorflow/tfjs-node")))}),require_isNodejs=__commonJS((exports2,module2)=>{__export(exports2,{isNodejs:()=>isNodejs2});function isNodejs2(){return typeof global=="object"&&!0&&typeof module2!="undefined"&&typeof process!="undefined"&&!!process.version}});__export(exports,{AgeGenderNet:()=>AgeGenderNet,BoundingBox:()=>BoundingBox,Box:()=>Box,ComposableTask:()=>ComposableTask,ComputeAllFaceDescriptorsTask:()=>ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase:()=>ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask:()=>ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask:()=>DetectAllFaceLandmarksTask,DetectAllFacesTask:()=>DetectAllFacesTask,DetectFaceLandmarksTaskBase:()=>DetectFaceLandmarksTaskBase,DetectFacesTaskBase:()=>DetectFacesTaskBase,DetectSingleFaceLandmarksTask:()=>DetectSingleFaceLandmarksTask,DetectSingleFaceTask:()=>DetectSingleFaceTask,Dimensions:()=>Dimensions,FACE_EXPRESSION_LABELS:()=>FACE_EXPRESSION_LABELS,FaceDetection:()=>FaceDetection,FaceDetectionNet:()=>FaceDetectionNet,FaceExpressionNet:()=>FaceExpressionNet,FaceExpressions:()=>FaceExpressions,FaceLandmark68Net:()=>FaceLandmark68Net,FaceLandmark68TinyNet:()=>FaceLandmark68TinyNet,FaceLandmarkNet:()=>FaceLandmarkNet,FaceLandmarks:()=>FaceLandmarks,FaceLandmarks5:()=>FaceLandmarks5,FaceLandmarks68:()=>FaceLandmarks68,FaceMatch:()=>FaceMatch,FaceMatcher:()=>FaceMatcher,FaceRecognitionNet:()=>FaceRecognitionNet,Gender:()=>Gender,LabeledBox:()=>LabeledBox,LabeledFaceDescriptors:()=>LabeledFaceDescriptors,NetInput:()=>NetInput,NeuralNetwork:()=>NeuralNetwork,ObjectDetection:()=>ObjectDetection,Point:()=>Point,PredictedBox:()=>PredictedBox,Rect:()=>Rect,SsdMobilenetv1:()=>SsdMobilenetv1,SsdMobilenetv1Options:()=>SsdMobilenetv1Options,TinyFaceDetector:()=>TinyFaceDetector,TinyFaceDetectorOptions:()=>TinyFaceDetectorOptions,TinyYolov2:()=>TinyYolov2,TinyYolov2Options:()=>TinyYolov2Options,allFaces:()=>allFaces,allFacesSsdMobilenetv1:()=>allFacesSsdMobilenetv1,allFacesTinyYolov2:()=>allFacesTinyYolov2,awaitMediaLoaded:()=>awaitMediaLoaded,bufferToImage:()=>bufferToImage,computeFaceDescriptor:()=>computeFaceDescriptor,createCanvas:()=>createCanvas,createCanvasFromMedia:()=>createCanvasFromMedia,createFaceDetectionNet:()=>createFaceDetectionNet,createFaceRecognitionNet:()=>createFaceRecognitionNet,createSsdMobilenetv1:()=>createSsdMobilenetv1,createTinyFaceDetector:()=>createTinyFaceDetector,createTinyYolov2:()=>createTinyYolov2,detectAllFaces:()=>detectAllFaces,detectFaceLandmarks:()=>detectFaceLandmarks,detectFaceLandmarksTiny:()=>detectFaceLandmarksTiny,detectLandmarks:()=>detectLandmarks,detectSingleFace:()=>detectSingleFace,draw:()=>draw_exports,env:()=>env,euclideanDistance:()=>euclideanDistance,extendWithAge:()=>extendWithAge,extendWithFaceDescriptor:()=>extendWithFaceDescriptor,extendWithFaceDetection:()=>extendWithFaceDetection,extendWithFaceExpressions:()=>extendWithFaceExpressions,extendWithFaceLandmarks:()=>extendWithFaceLandmarks,extendWithGender:()=>extendWithGender,extractFaceTensors:()=>extractFaceTensors,extractFaces:()=>extractFaces,fetchImage:()=>fetchImage,fetchJson:()=>fetchJson,fetchNetWeights:()=>fetchNetWeights,fetchOrThrow:()=>fetchOrThrow,getContext2dOrThrow:()=>getContext2dOrThrow,getMediaDimensions:()=>getMediaDimensions,imageTensorToCanvas:()=>imageTensorToCanvas,imageToSquare:()=>imageToSquare,inverseSigmoid:()=>inverseSigmoid,iou:()=>iou,isMediaElement:()=>isMediaElement,isMediaLoaded:()=>isMediaLoaded,isWithAge:()=>isWithAge,isWithFaceDetection:()=>isWithFaceDetection,isWithFaceExpressions:()=>isWithFaceExpressions,isWithFaceLandmarks:()=>isWithFaceLandmarks,isWithGender:()=>isWithGender,loadAgeGenderModel:()=>loadAgeGenderModel,loadFaceDetectionModel:()=>loadFaceDetectionModel,loadFaceExpressionModel:()=>loadFaceExpressionModel,loadFaceLandmarkModel:()=>loadFaceLandmarkModel,loadFaceLandmarkTinyModel:()=>loadFaceLandmarkTinyModel,loadFaceRecognitionModel:()=>loadFaceRecognitionModel,loadSsdMobilenetv1Model:()=>loadSsdMobilenetv1Model,loadTinyFaceDetectorModel:()=>loadTinyFaceDetectorModel,loadTinyYolov2Model:()=>loadTinyYolov2Model,loadWeightMap:()=>loadWeightMap,locateFaces:()=>locateFaces,matchDimensions:()=>matchDimensions,minBbox:()=>minBbox,nets:()=>nets,nonMaxSuppression:()=>nonMaxSuppression,normalize:()=>normalize,padToSquare:()=>padToSquare,predictAgeAndGender:()=>predictAgeAndGender,recognizeFaceExpressions:()=>recognizeFaceExpressions,resizeResults:()=>resizeResults,resolveInput:()=>resolveInput,shuffleArray:()=>shuffleArray,sigmoid:()=>sigmoid,ssdMobilenetv1:()=>ssdMobilenetv1,tf:()=>tf42,tinyFaceDetector:()=>tinyFaceDetector,tinyYolov2:()=>tinyYolov2,toNetInput:()=>toNetInput,utils:()=>utils_exports,validateConfig:()=>validateConfig,version:()=>version2});var tf42=__toModule(require_tfjs_esm()),draw_exports={};__export(draw_exports,{AnchorPosition:()=>AnchorPosition,DrawBox:()=>DrawBox,DrawBoxOptions:()=>DrawBoxOptions,DrawFaceLandmarks:()=>DrawFaceLandmarks,DrawFaceLandmarksOptions:()=>DrawFaceLandmarksOptions,DrawTextField:()=>DrawTextField,DrawTextFieldOptions:()=>DrawTextFieldOptions,drawContour:()=>drawContour,drawDetections:()=>drawDetections,drawFaceExpressions:()=>drawFaceExpressions,drawFaceLandmarks:()=>drawFaceLandmarks});function drawContour(ctx,points,isClosed=!1){if(ctx.beginPath(),points.slice(1).forEach(({x,y},prevIdx)=>{let from=points[prevIdx];ctx.moveTo(from.x,from.y),ctx.lineTo(x,y)}),isClosed){let from=points[points.length-1],to=points[0];if(!from||!to)return;ctx.moveTo(from.x,from.y),ctx.lineTo(to.x,to.y)}ctx.stroke()}var utils_exports={};__export(utils_exports,{computeReshapedDimensions:()=>computeReshapedDimensions,getCenterPoint:()=>getCenterPoint,isDimensions:()=>isDimensions,isEven:()=>isEven,isFloat:()=>isFloat,isTensor:()=>isTensor,isTensor1D:()=>isTensor1D,isTensor2D:()=>isTensor2D,isTensor3D:()=>isTensor3D,isTensor4D:()=>isTensor4D,isValidNumber:()=>isValidNumber,isValidProbablitiy:()=>isValidProbablitiy,range:()=>range,round:()=>round});var tf=__toModule(require_tfjs_esm()),Dimensions=class{constructor(width,height){if(!isValidNumber(width)||!isValidNumber(height))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width,height})}`);this._width=width,this._height=height}get width(){return this._width}get height(){return this._height}reverse(){return new Dimensions(1/this.width,1/this.height)}};function isTensor(tensor2,dim){return tensor2 instanceof tf.Tensor&&tensor2.shape.length===dim}function isTensor1D(tensor2){return isTensor(tensor2,1)}function isTensor2D(tensor2){return isTensor(tensor2,2)}function isTensor3D(tensor2){return isTensor(tensor2,3)}function isTensor4D(tensor2){return isTensor(tensor2,4)}function isFloat(num){return num%1!==0}function isEven(num){return num%2===0}function round(num,prec=2){let f=10**prec;return Math.floor(num*f)/f}function isDimensions(obj){return obj&&obj.width&&obj.height}function computeReshapedDimensions({width,height},inputSize){let scale2=inputSize/Math.max(height,width);return new Dimensions(Math.round(width*scale2),Math.round(height*scale2))}function getCenterPoint(pts){return pts.reduce((sum,pt)=>sum.add(pt),new Point(0,0)).div(new Point(pts.length,pts.length))}function range(num,start,step){return Array(num).fill(0).map((_,i)=>start+i*step)}function isValidNumber(num){return!!num&&num!==Infinity&&num!==-Infinity&&!Number.isNaN(num)||num===0}function isValidProbablitiy(num){return isValidNumber(num)&&num>=0&&num<=1}var Point=class{constructor(x,y){this._x=x,this._y=y}get x(){return this._x}get y(){return this._y}add(pt){return new Point(this.x+pt.x,this.y+pt.y)}sub(pt){return new Point(this.x-pt.x,this.y-pt.y)}mul(pt){return new Point(this.x*pt.x,this.y*pt.y)}div(pt){return new Point(this.x/pt.x,this.y/pt.y)}abs(){return new Point(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new Point(Math.floor(this.x),Math.floor(this.y))}},Box=class{static isRect(rect){return!!rect&&[rect.x,rect.y,rect.width,rect.height].every(isValidNumber)}static assertIsValidBox(box,callee,allowNegativeDimensions=!1){if(!Box.isRect(box))throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);if(!allowNegativeDimensions&&(box.width<0||box.height<0))throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)}constructor(_box,allowNegativeDimensions=!0){let box=_box||{},isBbox=[box.left,box.top,box.right,box.bottom].every(isValidNumber),isRect=[box.x,box.y,box.width,box.height].every(isValidNumber);if(!isRect&&!isBbox)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);let[x,y,width,height]=isRect?[box.x,box.y,box.width,box.height]:[box.left,box.top,box.right-box.left,box.bottom-box.top];Box.assertIsValidBox({x,y,width,height},"Box.constructor",allowNegativeDimensions),this._x=x,this._y=y,this._width=width,this._height=height}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new Point(this.left,this.top)}get topRight(){return new Point(this.right,this.top)}get bottomLeft(){return new Point(this.left,this.bottom)}get bottomRight(){return new Point(this.right,this.bottom)}round(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.round(val));return new Box({x,y,width,height})}floor(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.floor(val));return new Box({x,y,width,height})}toSquare(){let{x,y,width,height}=this,diff=Math.abs(width-height);return width<height&&(x-=diff/2,width+=diff),height<width&&(y-=diff/2,height+=diff),new Box({x,y,width,height})}rescale(s){let scaleX=isDimensions(s)?s.width:s,scaleY=isDimensions(s)?s.height:s;return new Box({x:this.x*scaleX,y:this.y*scaleY,width:this.width*scaleX,height:this.height*scaleY})}pad(padX,padY){let[x,y,width,height]=[this.x-padX/2,this.y-padY/2,this.width+padX,this.height+padY];return new Box({x,y,width,height})}clipAtImageBorders(imgWidth,imgHeight){let{x,y,right,bottom}=this,clippedX=Math.max(x,0),clippedY=Math.max(y,0),newWidth=right-clippedX,newHeight=bottom-clippedY,clippedWidth=Math.min(newWidth,imgWidth-clippedX),clippedHeight=Math.min(newHeight,imgHeight-clippedY);return new Box({x:clippedX,y:clippedY,width:clippedWidth,height:clippedHeight}).floor()}shift(sx,sy){let{width,height}=this,x=this.x+sx,y=this.y+sy;return new Box({x,y,width,height})}padAtBorders(imageHeight,imageWidth){let w=this.width+1,h=this.height+1,dx=1,dy=1,edx=w,edy=h,x=this.left,y=this.top,ex=this.right,ey=this.bottom;return ex>imageWidth&&(edx=-ex+imageWidth+w,ex=imageWidth),ey>imageHeight&&(edy=-ey+imageHeight+h,ey=imageHeight),x<1&&(edy=2-x,x=1),y<1&&(edy=2-y,y=1),{dy,edy,dx,edx,y,ey,x,ex,w,h}}calibrate(region){return new Box({left:this.left+region.left*this.width,top:this.top+region.top*this.height,right:this.right+region.right*this.width,bottom:this.bottom+region.bottom*this.height}).toSquare().round()}},BoundingBox=class extends Box{constructor(left,top,right,bottom,allowNegativeDimensions=!1){super({left,top,right,bottom},allowNegativeDimensions)}};var ObjectDetection=class{constructor(score,classScore,className,relativeBox,imageDims){this._imageDims=new Dimensions(imageDims.width,imageDims.height),this._score=score,this._classScore=classScore,this._className=className,this._box=new Box(relativeBox).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new Box(this._box).rescale(this.imageDims.reverse())}forSize(width,height){return new ObjectDetection(this.score,this.classScore,this.className,this.relativeBox,{width,height})}},FaceDetection=class extends ObjectDetection{constructor(score,relativeBox,imageDims){super(score,score,"",relativeBox,imageDims)}forSize(width,height){let{score,relativeBox,imageDims}=super.forSize(width,height);return new FaceDetection(score,relativeBox,imageDims)}};function iou(box1,box2,isIOU=!0){let width=Math.max(0,Math.min(box1.right,box2.right)-Math.max(box1.left,box2.left)),height=Math.max(0,Math.min(box1.bottom,box2.bottom)-Math.max(box1.top,box2.top)),interSection=width*height;return isIOU?interSection/(box1.area+box2.area-interSection):interSection/Math.min(box1.area,box2.area)}function minBbox(pts){let xs=pts.map(pt=>pt.x),ys=pts.map(pt=>pt.y),minX=xs.reduce((min,x)=>x<min?x:min,Infinity),minY=ys.reduce((min,y)=>y<min?y:min,Infinity),maxX=xs.reduce((max,x)=>max<x?x:max,0),maxY=ys.reduce((max,y)=>max<y?y:max,0);return new BoundingBox(minX,minY,maxX,maxY)}function nonMaxSuppression(boxes,scores,iouThreshold,isIOU=!0){let indicesSortedByScore=scores.map((score,boxIndex)=>({score,boxIndex})).sort((c1,c2)=>c1.score-c2.score).map(c=>c.boxIndex),pick=[];for(;indicesSortedByScore.length>0;){let curr=indicesSortedByScore.pop();pick.push(curr);let indices=indicesSortedByScore,outputs=[];for(let i=0;i<indices.length;i++){let idx=indices[i],currBox=boxes[curr],idxBox=boxes[idx];outputs.push(iou(currBox,idxBox,isIOU))}indicesSortedByScore=indicesSortedByScore.filter((_,j)=>outputs[j]<=iouThreshold)}return pick}var tf2=__toModule(require_tfjs_esm());function normalize(x,meanRgb){return tf2.tidy(()=>{let[r,g,b]=meanRgb,avg_r=tf2.fill([...x.shape.slice(0,3),1],r,"float32"),avg_g=tf2.fill([...x.shape.slice(0,3),1],g,"float32"),avg_b=tf2.fill([...x.shape.slice(0,3),1],b,"float32"),avg_rgb=tf2.concat([avg_r,avg_g,avg_b],3);return tf2.sub(x,avg_rgb)})}var tf3=__toModule(require_tfjs_esm());function padToSquare(imgTensor,isCenterImage=!1){return tf3.tidy(()=>{let[height,width]=imgTensor.shape.slice(1);if(height===width)return imgTensor;let dimDiff=Math.abs(height-width),paddingAmount=Math.round(dimDiff*(isCenterImage?.5:1)),paddingAxis=height>width?2:1,createPaddingTensor=paddingAmountLocal=>{let paddingTensorShape=imgTensor.shape.slice();return paddingTensorShape[paddingAxis]=paddingAmountLocal,tf3.fill(paddingTensorShape,0,"float32")},paddingTensorAppend=createPaddingTensor(paddingAmount),remainingPaddingAmount=dimDiff-paddingTensorAppend.shape[paddingAxis],tensorsToStack=[isCenterImage&&remainingPaddingAmount?createPaddingTensor(remainingPaddingAmount):null,imgTensor,paddingTensorAppend].filter(t=>!!t).map(t=>tf3.cast(t,"float32"));return tf3.concat(tensorsToStack,paddingAxis)})}function shuffleArray(inputArray){let array=inputArray.slice();for(let i=array.length-1;i>0;i--){let j=Math.floor(Math.random()*(i+1)),x=array[i];array[i]=array[j],array[j]=x}return array}function sigmoid(x){return 1/(1+Math.exp(-x))}function inverseSigmoid(x){return Math.log(x/(1-x))}var Rect=class extends Box{constructor(x,y,width,height,allowNegativeDimensions=!1){super({x,y,width,height},allowNegativeDimensions)}},relX=.5,relY=.43,relScale=.45,FaceLandmarks=class{constructor(relativeFaceLandmarkPositions,imgDims,shift=new Point(0,0)){let{width,height}=imgDims;this._imgDims=new Dimensions(width,height),this._shift=shift,this._positions=relativeFaceLandmarkPositions.map(pt=>pt.mul(new Point(width,height)).add(shift))}get shift(){return new Point(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(pt=>pt.sub(this._shift).div(new Point(this.imageWidth,this.imageHeight)))}forSize(width,height){return new this.constructor(this.relativePositions,{width,height})}shiftBy(x,y){return new this.constructor(this.relativePositions,this._imgDims,new Point(x,y))}shiftByPoint(pt){return this.shiftBy(pt.x,pt.y)}align(detection,options={}){if(detection){let box=detection instanceof FaceDetection?detection.box.floor():new Box(detection);return this.shiftBy(box.x,box.y).align(null,options)}let{useDlibAlignment,minBoxPadding}={useDlibAlignment:!1,minBoxPadding:.2,...options};return useDlibAlignment?this.alignDlib():this.alignMinBbox(minBoxPadding)}alignDlib(){let centers=this.getRefPointsForAlignment(),[leftEyeCenter,rightEyeCenter,mouthCenter]=centers,distToMouth=pt=>mouthCenter.sub(pt).magnitude(),eyeToMouthDist=(distToMouth(leftEyeCenter)+distToMouth(rightEyeCenter))/2,size=Math.floor(eyeToMouthDist/relScale),refPoint=getCenterPoint(centers),x=Math.floor(Math.max(0,refPoint.x-relX*size)),y=Math.floor(Math.max(0,refPoint.y-relY*size));return new Rect(x,y,Math.min(size,this.imageWidth+x),Math.min(size,this.imageHeight+y))}alignMinBbox(padding){let box=minBbox(this.positions);return box.pad(box.width*padding,box.height*padding)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var FaceLandmarks5=class extends FaceLandmarks{getRefPointsForAlignment(){let pts=this.positions;return[pts[0],pts[1],getCenterPoint([pts[3],pts[4]])]}};var FaceLandmarks68=class extends FaceLandmarks{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(getCenterPoint)}};var FaceMatch=class{constructor(label,distance){this._label=label,this._distance=distance}get label(){return this._label}get distance(){return this._distance}toString(withDistance=!0){return`${this.label}${withDistance?` (${round(this.distance)})`:""}`}};var LabeledBox=class extends Box{static assertIsValidLabeledBox(box,callee){if(Box.assertIsValidBox(box,callee),!isValidNumber(box.label))throw new Error(`${callee} - expected property label (${box.label}) to be a number`)}constructor(box,label){super(box);this._label=label}get label(){return this._label}};var LabeledFaceDescriptors=class{constructor(label,descriptors){if(typeof label!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(descriptors)||descriptors.some(desc=>!(desc instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=label,this._descriptors=descriptors}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(d=>Array.from(d))}}static fromJSON(json){let descriptors=json.descriptors.map(d=>new Float32Array(d));return new LabeledFaceDescriptors(json.label,descriptors)}};var PredictedBox=class extends LabeledBox{static assertIsValidPredictedBox(box,callee){if(LabeledBox.assertIsValidLabeledBox(box,callee),!isValidProbablitiy(box.score)||!isValidProbablitiy(box.classScore))throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)}constructor(box,label,score,classScore){super(box,label);this._score=score,this._classScore=classScore}get score(){return this._score}get classScore(){return this._classScore}};function isWithFaceDetection(obj){return obj.detection instanceof FaceDetection}function extendWithFaceDetection(sourceObj,detection){return{...sourceObj,...{detection}}}function createBrowserEnv(){let fetch=window.fetch;if(!fetch)throw new Error("fetch - missing fetch implementation for browser environment");let readFile=()=>{throw new Error("readFile - filesystem not available for browser environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch,readFile}}function createFileSystem(fs){let requireFsError="";if(!fs)try{fs=require("fs")}catch(err){requireFsError=err.toString()}return{readFile:fs?filePath=>new Promise((resolve,reject)=>{fs.readFile(filePath,(err,buffer)=>err?reject(err):resolve(buffer))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)}}}function createNodejsEnv(){let Canvas=global.Canvas||global.HTMLCanvasElement,Image=global.Image||global.HTMLImageElement,createCanvasElement=()=>{if(Canvas)return new Canvas;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},createImageElement=()=>{if(Image)return new Image;throw new Error("createImageElement - missing Image implementation for nodejs environment")},fetch=global.fetch,fileSystem=createFileSystem();return{Canvas:Canvas||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:Image||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement,createImageElement,fetch,...fileSystem}}function isBrowser(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var import_isNodejs=__toModule(require_isNodejs()),environment;function getEnv(){if(!environment)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return environment}function setEnv(env2){environment=env2}function initialize(){return isBrowser()?setEnv(createBrowserEnv()):import_isNodejs.isNodejs()?setEnv(createNodejsEnv()):null}function monkeyPatch(env2){if(environment||initialize(),!environment)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas=environment.Canvas,Image=environment.Image}=env2;environment.Canvas=Canvas,environment.Image=Image,environment.createCanvasElement=env2.createCanvasElement||(()=>new Canvas),environment.createImageElement=env2.createImageElement||(()=>new Image),environment.ImageData=env2.ImageData||environment.ImageData,environment.Video=env2.Video||environment.Video,environment.fetch=env2.fetch||environment.fetch,environment.readFile=env2.readFile||environment.readFile}var env={getEnv,setEnv,initialize,createBrowserEnv,createFileSystem,createNodejsEnv,monkeyPatch,isBrowser,isNodejs:import_isNodejs.isNodejs};initialize();function resolveInput(arg){return!env.isNodejs()&&typeof arg=="string"?document.getElementById(arg):arg}function getContext2dOrThrow(canvasArg){let{Canvas,CanvasRenderingContext2D:CanvasRenderingContext2D2}=env.getEnv();if(canvasArg instanceof CanvasRenderingContext2D2)return canvasArg;let canvas=resolveInput(canvasArg);if(!(canvas instanceof Canvas))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let ctx=canvas.getContext("2d");if(!ctx)throw new Error("resolveContext2d - canvas 2d context is null");return ctx}var AnchorPosition;(function(AnchorPosition2){AnchorPosition2.TOP_LEFT="TOP_LEFT",AnchorPosition2.TOP_RIGHT="TOP_RIGHT",AnchorPosition2.BOTTOM_LEFT="BOTTOM_LEFT",AnchorPosition2.BOTTOM_RIGHT="BOTTOM_RIGHT"})(AnchorPosition||(AnchorPosition={}));var DrawTextFieldOptions=class{constructor(options={}){let{anchorPosition,backgroundColor,fontColor,fontSize,fontStyle,padding}=options;this.anchorPosition=anchorPosition||AnchorPosition.TOP_LEFT,this.backgroundColor=backgroundColor||"rgba(0, 0, 0, 0.5)",this.fontColor=fontColor||"rgba(255, 255, 255, 1)",this.fontSize=fontSize||14,this.fontStyle=fontStyle||"Georgia",this.padding=padding||4}},DrawTextField=class{constructor(text,anchor,options={}){this.text=typeof text=="string"?[text]:text instanceof DrawTextField?text.text:text,this.anchor=anchor,this.options=new DrawTextFieldOptions(options)}measureWidth(ctx){let{padding}=this.options;return this.text.map(l=>ctx.measureText(l).width).reduce((w0,w1)=>w0<w1?w1:w0,0)+2*padding}measureHeight(){let{fontSize,padding}=this.options;return this.text.length*fontSize+2*padding}getUpperLeft(ctx,canvasDims){let{anchorPosition}=this.options,isShiftLeft=anchorPosition===AnchorPosition.BOTTOM_RIGHT||anchorPosition===AnchorPosition.TOP_RIGHT,isShiftTop=anchorPosition===AnchorPosition.BOTTOM_LEFT||anchorPosition===AnchorPosition.BOTTOM_RIGHT,textFieldWidth=this.measureWidth(ctx),textFieldHeight=this.measureHeight(),x=isShiftLeft?this.anchor.x-textFieldWidth:this.anchor.x,y=isShiftTop?this.anchor.y-textFieldHeight:this.anchor.y;if(canvasDims){let{width,height}=canvasDims,newX=Math.max(Math.min(x,width-textFieldWidth),0),newY=Math.max(Math.min(y,height-textFieldHeight),0);return{x:newX,y:newY}}return{x,y}}draw(canvasArg){let canvas=resolveInput(canvasArg),ctx=getContext2dOrThrow(canvas),{backgroundColor,fontColor,fontSize,fontStyle,padding}=this.options;ctx.font=`${fontSize}px ${fontStyle}`;let maxTextWidth=this.measureWidth(ctx),textHeight=this.measureHeight();ctx.fillStyle=backgroundColor;let upperLeft=this.getUpperLeft(ctx,canvas);ctx.fillRect(upperLeft.x,upperLeft.y,maxTextWidth,textHeight),ctx.fillStyle=fontColor,this.text.forEach((textLine,i)=>{let x=padding+upperLeft.x,y=padding+upperLeft.y+(i+1)*fontSize;ctx.fillText(textLine,x,y)})}},DrawBoxOptions=class{constructor(options={}){let{boxColor,lineWidth,label,drawLabelOptions}=options;this.boxColor=boxColor||"rgba(0, 0, 255, 1)",this.lineWidth=lineWidth||2,this.label=label;let defaultDrawLabelOptions={anchorPosition:AnchorPosition.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new DrawTextFieldOptions({...defaultDrawLabelOptions,...drawLabelOptions})}},DrawBox=class{constructor(box,options={}){this.box=new Box(box),this.options=new DrawBoxOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{boxColor,lineWidth}=this.options,{x,y,width,height}=this.box;ctx.strokeStyle=boxColor,ctx.lineWidth=lineWidth,ctx.strokeRect(x,y,width,height);let{label}=this.options;label&&new DrawTextField([label],{x:x-lineWidth/2,y},this.options.drawLabelOptions).draw(canvasArg)}};function drawDetections(canvasArg,detections){(Array.isArray(detections)?detections:[detections]).forEach(det=>{let score=det instanceof FaceDetection?det.score:isWithFaceDetection(det)?det.detection.score:void 0,box=det instanceof FaceDetection?det.box:isWithFaceDetection(det)?det.detection.box:new Box(det),label=score?`${round(score)}`:void 0;new DrawBox(box,{label}).draw(canvasArg)})}var tf18=__toModule(require_tfjs_esm());function isMediaLoaded(media){let{Image,Video}=env.getEnv();return media instanceof Image&&media.complete||media instanceof Video&&media.readyState>=3}function awaitMediaLoaded(media){return new Promise((resolve,reject)=>{if(media instanceof env.getEnv().Canvas||isMediaLoaded(media))return resolve(null);function onError(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),reject(e))}function onLoad(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),resolve(e))}media.addEventListener("load",onLoad),media.addEventListener("error",onError)})}function bufferToImage(buf){return new Promise((resolve,reject)=>{buf instanceof Blob||reject(new Error("bufferToImage - expected buf to be of type: Blob"));let reader=new FileReader;reader.onload=()=>{typeof reader.result!="string"&&reject(new Error("bufferToImage - expected reader.result to be a string, in onload"));let img=env.getEnv().createImageElement();img.onload=()=>resolve(img),img.onerror=reject,img.src=reader.result},reader.onerror=reject,reader.readAsDataURL(buf)})}function getMediaDimensions(input){let{Image,Video}=env.getEnv();return input instanceof Image?new Dimensions(input.naturalWidth,input.naturalHeight):input instanceof Video?new Dimensions(input.videoWidth,input.videoHeight):new Dimensions(input.width,input.height)}function createCanvas({width,height}){let{createCanvasElement}=env.getEnv(),canvas=createCanvasElement();return canvas.width=width,canvas.height=height,canvas}function createCanvasFromMedia(media,dims){let{ImageData:ImageData2}=env.getEnv();if(!(media instanceof ImageData2)&&!isMediaLoaded(media))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width,height}=dims||getMediaDimensions(media),canvas=createCanvas({width,height});return media instanceof ImageData2?getContext2dOrThrow(canvas).putImageData(media,0,0):getContext2dOrThrow(canvas).drawImage(media,0,0,width,height),canvas}var tf4=__toModule(require_tfjs_esm());async function imageTensorToCanvas(imgTensor,canvas){let targetCanvas=canvas||env.getEnv().createCanvasElement(),[height,width,numChannels]=imgTensor.shape.slice(isTensor4D(imgTensor)?1:0),imgTensor3D=tf4.tidy(()=>imgTensor.as3D(height,width,numChannels).toInt());return await tf4.browser.toPixels(imgTensor3D,targetCanvas),imgTensor3D.dispose(),targetCanvas}function isMediaElement(input){let{Image,Canvas,Video}=env.getEnv();return input instanceof Image||input instanceof Canvas||input instanceof Video}var tf5=__toModule(require_tfjs_esm());function imageToSquare(input,inputSize,centerImage=!1){let{Image,Canvas}=env.getEnv();if(!(input instanceof Image||input instanceof Canvas))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");let dims=getMediaDimensions(input),scale2=inputSize/Math.max(dims.height,dims.width),width=scale2*dims.width,height=scale2*dims.height,targetCanvas=createCanvas({width:inputSize,height:inputSize}),inputCanvas=input instanceof Canvas?input:createCanvasFromMedia(input),offset=Math.abs(width-height)/2,dx=centerImage&&width<height?offset:0,dy=centerImage&&height<width?offset:0;return getContext2dOrThrow(targetCanvas).drawImage(inputCanvas,dx,dy,width,height),targetCanvas}var NetInput=class{constructor(inputs,treatAsBatchInput=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(inputs))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);this._treatAsBatchInput=treatAsBatchInput,this._batchSize=inputs.length,inputs.forEach((input,idx)=>{if(isTensor3D(input)){this._imageTensors[idx]=input,this._inputDimensions[idx]=input.shape;return}if(isTensor4D(input)){let batchSize=input.shape[0];if(batchSize!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);this._imageTensors[idx]=input,this._inputDimensions[idx]=input.shape.slice(1);return}let canvas=input instanceof env.getEnv().Canvas?input:createCanvasFromMedia(input);this._canvases[idx]=canvas,this._inputDimensions[idx]=[canvas.height,canvas.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return range(this.batchSize,0,1).map((_,batchIdx)=>this.getReshapedInputDimensions(batchIdx))}getInput(batchIdx){return this.canvases[batchIdx]||this.imageTensors[batchIdx]}getInputDimensions(batchIdx){return this._inputDimensions[batchIdx]}getInputHeight(batchIdx){return this._inputDimensions[batchIdx][0]}getInputWidth(batchIdx){return this._inputDimensions[batchIdx][1]}getReshapedInputDimensions(batchIdx){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let width=this.getInputWidth(batchIdx),height=this.getInputHeight(batchIdx);return computeReshapedDimensions({width,height},this.inputSize)}toBatchTensor(inputSize,isCenterInputs=!0){return this._inputSize=inputSize,tf5.tidy(()=>{let inputTensors=range(this.batchSize,0,1).map(batchIdx=>{let input=this.getInput(batchIdx);if(input instanceof tf5.Tensor){let imgTensor=isTensor4D(input)?input:input.expandDims();return imgTensor=padToSquare(imgTensor,isCenterInputs),(imgTensor.shape[1]!==inputSize||imgTensor.shape[2]!==inputSize)&&(imgTensor=tf5.image.resizeBilinear(imgTensor,[inputSize,inputSize])),imgTensor.as3D(inputSize,inputSize,3)}if(input instanceof env.getEnv().Canvas)return tf5.browser.fromPixels(imageToSquare(input,inputSize,isCenterInputs));throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)});return tf5.stack(inputTensors.map(t=>tf5.cast(t,"float32"))).as4D(this.batchSize,inputSize,inputSize,3)})}};async function toNetInput(inputs){if(inputs instanceof NetInput)return inputs;let inputArgArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArgArray.length)throw new Error("toNetInput - empty array passed as input");let getIdxHint=idx=>Array.isArray(inputs)?` at input index ${idx}:`:"",inputArray=inputArgArray.map(resolveInput);return inputArray.forEach((input,i)=>{if(!isMediaElement(input)&&!isTensor3D(input)&&!isTensor4D(input))throw typeof inputArgArray[i]=="string"?new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`):new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(isTensor4D(input)){let batchSize=input.shape[0];if(batchSize!==1)throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)}}),await Promise.all(inputArray.map(input=>isMediaElement(input)&&awaitMediaLoaded(input))),new NetInput(inputArray,Array.isArray(inputs))}async function extractFaces(input,detections){let{Canvas}=env.getEnv(),canvas=input;if(!(input instanceof Canvas)){let netInput=await toNetInput(input);if(netInput.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let tensorOrCanvas=netInput.getInput(0);canvas=tensorOrCanvas instanceof Canvas?tensorOrCanvas:await imageTensorToCanvas(tensorOrCanvas)}let ctx=getContext2dOrThrow(canvas);return detections.map(det=>det instanceof FaceDetection?det.forSize(canvas.width,canvas.height).box.floor():det).map(box=>box.clipAtImageBorders(canvas.width,canvas.height)).map(({x,y,width,height})=>{let faceImg=createCanvas({width,height});return getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x,y,width,height),0,0),faceImg})}var tf6=__toModule(require_tfjs_esm());async function extractFaceTensors(imageTensor,detections){if(!isTensor3D(imageTensor)&&!isTensor4D(imageTensor))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(isTensor4D(imageTensor)&&imageTensor.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return tf6.tidy(()=>{let[imgHeight,imgWidth,numChannels]=imageTensor.shape.slice(isTensor4D(imageTensor)?1:0);return detections.map(det=>det instanceof FaceDetection?det.forSize(imgWidth,imgHeight).box:det).map(box=>box.clipAtImageBorders(imgWidth,imgHeight)).map(({x,y,width,height})=>tf6.slice3d(imageTensor.as3D(imgHeight,imgWidth,numChannels),[y,x,0],[height,width,numChannels]))})}async function fetchOrThrow(url,init){let{fetch}=env.getEnv(),res=await fetch(url,init);if(!(res.status<400))throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);return res}async function fetchImage(uri){let res=await fetchOrThrow(uri),blob=await res.blob();if(!blob.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);return bufferToImage(blob)}async function fetchJson(uri){return(await fetchOrThrow(uri)).json()}async function fetchNetWeights(uri){return new Float32Array(await(await fetchOrThrow(uri)).arrayBuffer())}var tf7=__toModule(require_tfjs_esm());function getModelUris(uri,defaultModelName){let defaultManifestFilename=`${defaultModelName}-weights_manifest.json`;if(!uri)return{modelBaseUri:"",manifestUri:defaultManifestFilename};if(uri==="/")return{modelBaseUri:"/",manifestUri:`/${defaultManifestFilename}`};let protocol=uri.startsWith("http://")?"http://":uri.startsWith("https://")?"https://":"";uri=uri.replace(protocol,"");let parts=uri.split("/").filter(s=>s),manifestFile=uri.endsWith(".json")?parts[parts.length-1]:defaultManifestFilename,modelBaseUri=protocol+(uri.endsWith(".json")?parts.slice(0,parts.length-1):parts).join("/");return modelBaseUri=uri.startsWith("/")?`/${modelBaseUri}`:modelBaseUri,{modelBaseUri,manifestUri:modelBaseUri==="/"?`/${manifestFile}`:`${modelBaseUri}/${manifestFile}`}}async function loadWeightMap(uri,defaultModelName){let{manifestUri,modelBaseUri}=getModelUris(uri,defaultModelName),manifest=await fetchJson(manifestUri);return tf7.io.loadWeights(manifest,modelBaseUri)}function matchDimensions(input,reference,useMediaDimensions=!1){let{width,height}=useMediaDimensions?getMediaDimensions(reference):reference;return input.width=width,input.height=height,{width,height}}var tf15=__toModule(require_tfjs_esm()),tf8=__toModule(require_tfjs_esm()),NeuralNetwork=class{constructor(){this._params=void 0;this._paramMappings=[]}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(paramPath){let{obj,objProp}=this.traversePropertyPath(paramPath);return obj[objProp]}reassignParamFromPath(paramPath,tensor2){let{obj,objProp}=this.traversePropertyPath(paramPath);obj[objProp].dispose(),obj[objProp]=tensor2}getParamList(){return this._paramMappings.map(({paramPath})=>({path:paramPath,tensor:this.getParamFromPath(paramPath)}))}getTrainableParams(){return this.getParamList().filter(param=>param.tensor instanceof tf8.Variable)}getFrozenParams(){return this.getParamList().filter(param=>!(param.tensor instanceof tf8.Variable))}variable(){this.getFrozenParams().forEach(({path,tensor:tensor2})=>{this.reassignParamFromPath(path,tensor2.variable())})}freeze(){this.getTrainableParams().forEach(({path,tensor:variable})=>{let tensor2=tf8.tensor(variable.dataSync());variable.dispose(),this.reassignParamFromPath(path,tensor2)})}dispose(throwOnRedispose=!0){this.getParamList().forEach(param=>{if(throwOnRedispose&&param.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${param.path}`);param.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:tensor2})=>Array.from(tensor2.dataSync())).reduce((flat,arr)=>flat.concat(arr)))}async load(weightsOrUrl){if(weightsOrUrl instanceof Float32Array){this.extractWeights(weightsOrUrl);return}await this.loadFromUri(weightsOrUrl)}async loadFromUri(uri){if(uri&&typeof uri!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let weightMap=await loadWeightMap(uri,this.getDefaultModelName());this.loadFromWeightMap(weightMap)}async loadFromDisk(filePath){if(filePath&&typeof filePath!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile}=env.getEnv(),{manifestUri,modelBaseUri}=getModelUris(filePath,this.getDefaultModelName()),fetchWeightsFromDisk=filePaths=>Promise.all(filePaths.map(fp=>readFile(fp).then(buf=>buf.buffer))),loadWeights=tf8.io.weightsLoaderFactory(fetchWeightsFromDisk),manifest=JSON.parse((await readFile(manifestUri)).toString()),weightMap=await loadWeights(manifest,modelBaseUri);this.loadFromWeightMap(weightMap)}loadFromWeightMap(weightMap){let{paramMappings,params}=this.extractParamsFromWeigthMap(weightMap);this._paramMappings=paramMappings,this._params=params}extractWeights(weights){let{paramMappings,params}=this.extractParams(weights);this._paramMappings=paramMappings,this._params=params}traversePropertyPath(paramPath){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let result=paramPath.split("/").reduce((res,objProp2)=>{if(!res.nextObj.hasOwnProperty(objProp2))throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`);return{obj:res.nextObj,objProp:objProp2,nextObj:res.nextObj[objProp2]}},{nextObj:this.params}),{obj,objProp}=result;if(!obj||!objProp||!(obj[objProp]instanceof tf8.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);return{obj,objProp}}},tf10=__toModule(require_tfjs_esm()),tf9=__toModule(require_tfjs_esm());function depthwiseSeparableConv(x,params,stride){return tf9.tidy(()=>{let out=tf9.separableConv2d(x,params.depthwise_filter,params.pointwise_filter,stride,"same");return out=tf9.add(out,params.bias),out})}function denseBlock3(x,denseBlockParams,isFirstLayer=!1){return tf10.tidy(()=>{let out1=tf10.relu(isFirstLayer?tf10.add(tf10.conv2d(x,denseBlockParams.conv0.filters,[2,2],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,[2,2])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=tf10.relu(tf10.add(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]);return tf10.relu(tf10.add(out1,tf10.add(out2,out3)))})}function denseBlock4(x,denseBlockParams,isFirstLayer=!1,isScaleDown=!0){return tf10.tidy(()=>{let out1=tf10.relu(isFirstLayer?tf10.add(tf10.conv2d(x,denseBlockParams.conv0.filters,isScaleDown?[2,2]:[1,1],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,isScaleDown?[2,2]:[1,1])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=tf10.relu(tf10.add(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]),in4=tf10.relu(tf10.add(out1,tf10.add(out2,out3))),out4=depthwiseSeparableConv(in4,denseBlockParams.conv3,[1,1]);return tf10.relu(tf10.add(out1,tf10.add(out2,tf10.add(out3,out4))))})}var tf11=__toModule(require_tfjs_esm());function convLayer(x,params,padding="same",withRelu=!1){return tf11.tidy(()=>{let out=tf11.add(tf11.conv2d(x,params.filters,[1,1],padding),params.bias);return withRelu?tf11.relu(out):out})}function disposeUnusedWeightTensors(weightMap,paramMappings){Object.keys(weightMap).forEach(path=>{paramMappings.some(pm=>pm.originalPath===path)||weightMap[path].dispose()})}var tf12=__toModule(require_tfjs_esm());function extractConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,filterSize,mappedPrefix)=>{let filters=tf12.tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tf12.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}}var tf13=__toModule(require_tfjs_esm());function extractFCParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let fc_weights=tf13.tensor2d(extractWeights(channelsIn*channelsOut),[channelsIn,channelsOut]),fc_bias=tf13.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/bias`}),{weights:fc_weights,bias:fc_bias}}}var tf14=__toModule(require_tfjs_esm()),SeparableConvParams=class{constructor(depthwise_filter,pointwise_filter,bias){this.depthwise_filter=depthwise_filter;this.pointwise_filter=pointwise_filter;this.bias=bias}};function extractSeparableConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let depthwise_filter=tf14.tensor4d(extractWeights(3*3*channelsIn),[3,3,channelsIn,1]),pointwise_filter=tf14.tensor4d(extractWeights(channelsIn*channelsOut),[1,1,channelsIn,channelsOut]),bias=tf14.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/depthwise_filter`},{paramPath:`${mappedPrefix}/pointwise_filter`},{paramPath:`${mappedPrefix}/bias`}),new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function loadSeparableConvParamsFactory(extractWeightEntry){return prefix=>{let depthwise_filter=extractWeightEntry(`${prefix}/depthwise_filter`,4),pointwise_filter=extractWeightEntry(`${prefix}/pointwise_filter`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function extractWeightEntryFactory(weightMap,paramMappings){return(originalPath,paramRank,mappedPath)=>{let tensor2=weightMap[originalPath];if(!isTensor(tensor2,paramRank))throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);return paramMappings.push({originalPath,paramPath:mappedPath||originalPath}),tensor2}}function extractWeightsFactory(weights){let remainingWeights=weights;function extractWeights(numWeights){let ret=remainingWeights.slice(0,numWeights);return remainingWeights=remainingWeights.slice(numWeights),ret}function getRemainingWeights(){return remainingWeights}return{extractWeights,getRemainingWeights}}function extractorsFactory(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv0`):extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/conv0`),conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv1`),conv22=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let{conv0,conv1,conv2:conv22}=extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer),conv3=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParams(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock4Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock4Params(3,32,"dense0",!0),dense1=extractDenseBlock4Params(32,64,"dense1"),dense2=extractDenseBlock4Params(64,128,"dense2"),dense3=extractDenseBlock4Params(128,256,"dense3");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2,dense3}}}function loadConvParamsFactory(extractWeightEntry){return prefix=>{let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}}function loadParamsFactory(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractDenseBlock3Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`),conv3=extractSeparableConvParams(`${prefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParamsFromWeigthMap(weightMap){let paramMappings=[],{extractDenseBlock4Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock4Params("dense0",!0),dense1:extractDenseBlock4Params("dense1"),dense2:extractDenseBlock4Params("dense2"),dense3:extractDenseBlock4Params("dense3")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var FaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("FaceFeatureExtractor")}forwardInput(input){let{params}=this;if(!params)throw new Error("FaceFeatureExtractor - load model before inference");return tf15.tidy(()=>{let batchTensor=tf15.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf15.scalar(255)),out=denseBlock4(normalized,params.dense0,!0);return out=denseBlock4(out,params.dense1),out=denseBlock4(out,params.dense2),out=denseBlock4(out,params.dense3),out=tf15.avgPool(out,[7,7],[2,2],"valid"),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap(weightMap)}extractParams(weights){return extractParams(weights)}},tf17=__toModule(require_tfjs_esm()),tf16=__toModule(require_tfjs_esm());function fullyConnectedLayer(x,params){return tf16.tidy(()=>tf16.add(tf16.matMul(x,params.weights),params.bias))}function extractParams2(weights,channelsIn,channelsOut){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),fc=extractFCParamsFactory(extractWeights,paramMappings)(channelsIn,channelsOut,"fc");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc}}}function extractParamsFromWeigthMap2(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:extractFcParams("fc")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function seperateWeightMaps(weightMap){let featureExtractorMap={},classifierMap={};return Object.keys(weightMap).forEach(key=>{let map=key.startsWith("fc")?classifierMap:featureExtractorMap;map[key]=weightMap[key]}),{featureExtractorMap,classifierMap}}var FaceProcessor=class extends NeuralNetwork{constructor(_name,faceFeatureExtractor){super(_name);this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tf17.tidy(()=>{let bottleneckFeatures=input instanceof NetInput?this.faceFeatureExtractor.forwardInput(input):input;return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0],-1),params.fc)})}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams2(weights,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap2(classifierMap)}extractParams(weights){let cIn=this.getClassifierChannelsIn(),cOut=this.getClassifierChannelsOut(),classifierWeightSize=cOut*cIn+cOut,featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}},FACE_EXPRESSION_LABELS=["neutral","happy","sad","angry","fearful","disgusted","surprised"],FaceExpressions=class{constructor(probabilities){if(probabilities.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);FACE_EXPRESSION_LABELS.forEach((expression,idx)=>{this[expression]=probabilities[idx]})}asSortedArray(){return FACE_EXPRESSION_LABELS.map(expression=>({expression,probability:this[expression]})).sort((e0,e1)=>e1.probability-e0.probability)}},FaceExpressionNet=class extends FaceProcessor{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceExpressionNet",faceFeatureExtractor)}forwardInput(input){return tf18.tidy(()=>tf18.softmax(this.runNet(input)))}async forward(input){return this.forwardInput(await toNetInput(input))}async predictExpressions(input){let netInput=await toNetInput(input),out=await this.forwardInput(netInput),probabilitesByBatch=await Promise.all(tf18.unstack(out).map(async t=>{let data=await t.data();return t.dispose(),data}));out.dispose();let predictionsByBatch=probabilitesByBatch.map(probabilites=>new FaceExpressions(probabilites));return netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function isWithFaceExpressions(obj){return obj.expressions instanceof FaceExpressions}function extendWithFaceExpressions(sourceObj,expressions){return{...sourceObj,...{expressions}}}function drawFaceExpressions(canvasArg,faceExpressions,minConfidence=.1,textFieldAnchor){(Array.isArray(faceExpressions)?faceExpressions:[faceExpressions]).forEach(e=>{let expr=e instanceof FaceExpressions?e:isWithFaceExpressions(e)?e.expressions:void 0;if(!expr)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let resultsToDisplay=expr.asSortedArray().filter(exprLocal=>exprLocal.probability>minConfidence),anchor=isWithFaceDetection(e)?e.detection.box.bottomLeft:textFieldAnchor||new Point(0,0);new DrawTextField(resultsToDisplay.map(exprLocal=>`${exprLocal.expression} (${round(exprLocal.probability)})`),anchor).draw(canvasArg)})}function isWithFaceLandmarks(obj){return isWithFaceDetection(obj)&&obj.landmarks instanceof FaceLandmarks&&obj.unshiftedLandmarks instanceof FaceLandmarks&&obj.alignedRect instanceof FaceDetection}function extendWithFaceLandmarks(sourceObj,unshiftedLandmarks){let{box:shift}=sourceObj.detection,landmarks=unshiftedLandmarks.shiftBy(shift.x,shift.y),rect=landmarks.align(),{imageDims}=sourceObj.detection,alignedRect=new FaceDetection(sourceObj.detection.score,rect.rescale(imageDims.reverse()),imageDims);return{...sourceObj,...{landmarks,unshiftedLandmarks,alignedRect}}}var DrawFaceLandmarksOptions=class{constructor(options={}){let{drawLines=!0,drawPoints=!0,lineWidth,lineColor,pointSize,pointColor}=options;this.drawLines=drawLines,this.drawPoints=drawPoints,this.lineWidth=lineWidth||1,this.pointSize=pointSize||2,this.lineColor=lineColor||"rgba(0, 255, 255, 1)",this.pointColor=pointColor||"rgba(255, 0, 255, 1)"}},DrawFaceLandmarks=class{constructor(faceLandmarks,options={}){this.faceLandmarks=faceLandmarks,this.options=new DrawFaceLandmarksOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{drawLines,drawPoints,lineWidth,lineColor,pointSize,pointColor}=this.options;if(drawLines&&this.faceLandmarks instanceof FaceLandmarks68&&(ctx.strokeStyle=lineColor,ctx.lineWidth=lineWidth,drawContour(ctx,this.faceLandmarks.getJawOutline()),drawContour(ctx,this.faceLandmarks.getLeftEyeBrow()),drawContour(ctx,this.faceLandmarks.getRightEyeBrow()),drawContour(ctx,this.faceLandmarks.getNose()),drawContour(ctx,this.faceLandmarks.getLeftEye(),!0),drawContour(ctx,this.faceLandmarks.getRightEye(),!0),drawContour(ctx,this.faceLandmarks.getMouth(),!0)),drawPoints){ctx.strokeStyle=pointColor,ctx.fillStyle=pointColor;let drawPoint=pt=>{ctx.beginPath(),ctx.arc(pt.x,pt.y,pointSize,0,2*Math.PI),ctx.fill()};this.faceLandmarks.positions.forEach(drawPoint)}}};function drawFaceLandmarks(canvasArg,faceLandmarks){(Array.isArray(faceLandmarks)?faceLandmarks:[faceLandmarks]).forEach(f=>{let landmarks=f instanceof FaceLandmarks?f:isWithFaceLandmarks(f)?f.landmarks:void 0;if(!landmarks)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new DrawFaceLandmarks(landmarks).draw(canvasArg)})}var version="0.10.1",tf20=__toModule(require_tfjs_esm()),tf19=__toModule(require_tfjs_esm());function extractorsFactory2(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractReductionBlockParams(channelsIn,channelsOut,mappedPrefix){let separable_conv0=extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(channels,mappedPrefix){let separable_conv0=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParams3(weights,numMainBlocks){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=extractorsFactory2(extractWeights,paramMappings),entry_flow_conv_in=extractConvParams(3,32,3,"entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams(32,64,"entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams(64,128,"entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(128,`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams(128,256,"exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams(256,512,"exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{entry_flow,middle_flow,exit_flow}}}function loadParamsFactory2(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractReductionBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParamsFromWeigthMap3(weightMap,numMainBlocks){let paramMappings=[],{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=loadParamsFactory2(weightMap,paramMappings),entry_flow_conv_in=extractConvParams("entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams("entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams("entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams("exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams("exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};return disposeUnusedWeightTensors(weightMap,paramMappings),{params:{entry_flow,middle_flow,exit_flow},paramMappings}}function conv(x,params,stride){return tf19.add(tf19.conv2d(x,params.filters,stride,"same"),params.bias)}function reductionBlock(x,params,isActivateInput=!0){let out=isActivateInput?tf19.relu(x):x;return out=depthwiseSeparableConv(out,params.separable_conv0,[1,1]),out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv1,[1,1]),out=tf19.maxPool(out,[3,3],[2,2],"same"),out=tf19.add(out,conv(x,params.expansion_conv,[2,2])),out}function mainBlock(x,params){let out=depthwiseSeparableConv(tf19.relu(x),params.separable_conv0,[1,1]);return out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv1,[1,1]),out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv2,[1,1]),out=tf19.add(out,x),out}var TinyXception=class extends NeuralNetwork{constructor(numMainBlocks){super("TinyXception");this._numMainBlocks=numMainBlocks}forwardInput(input){let{params}=this;if(!params)throw new Error("TinyXception - load model before inference");return tf19.tidy(()=>{let batchTensor=tf19.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf19.scalar(256)),out=tf19.relu(conv(normalized,params.entry_flow.conv_in,[2,2]));return out=reductionBlock(out,params.entry_flow.reduction_block_0,!1),out=reductionBlock(out,params.entry_flow.reduction_block_1),range(this._numMainBlocks,0,1).forEach(idx=>{out=mainBlock(out,params.middle_flow[`main_block_${idx}`])}),out=reductionBlock(out,params.exit_flow.reduction_block),out=tf19.relu(depthwiseSeparableConv(out,params.exit_flow.separable_conv,[1,1])),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap3(weightMap,this._numMainBlocks)}extractParams(weights){return extractParams3(weights,this._numMainBlocks)}};function extractParams4(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),age=extractFCParams(512,1,"fc/age"),gender=extractFCParams(512,2,"fc/gender");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc:{age,gender}}}}function extractParamsFromWeigthMap4(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:{age:extractFcParams("fc/age"),gender:extractFcParams("fc/gender")}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var Gender;(function(Gender2){Gender2.FEMALE="female",Gender2.MALE="male"})(Gender||(Gender={}));var AgeGenderNet=class extends NeuralNetwork{constructor(faceFeatureExtractor=new TinyXception(2)){super("AgeGenderNet");this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tf20.tidy(()=>{let bottleneckFeatures=input instanceof NetInput?this.faceFeatureExtractor.forwardInput(input):input,pooled=tf20.avgPool(bottleneckFeatures,[7,7],[2,2],"valid").as2D(bottleneckFeatures.shape[0],-1),age=fullyConnectedLayer(pooled,params.fc.age).as1D(),gender=fullyConnectedLayer(pooled,params.fc.gender);return{age,gender}})}forwardInput(input){return tf20.tidy(()=>{let{age,gender}=this.runNet(input);return{age,gender:tf20.softmax(gender)}})}async forward(input){return this.forwardInput(await toNetInput(input))}async predictAgeAndGender(input){let netInput=await toNetInput(input),out=await this.forwardInput(netInput),ages=tf20.unstack(out.age),genders=tf20.unstack(out.gender),ageAndGenderTensors=ages.map((ageTensor,i)=>({ageTensor,genderTensor:genders[i]})),predictionsByBatch=await Promise.all(ageAndGenderTensors.map(async({ageTensor,genderTensor})=>{let age=(await ageTensor.data())[0],probMale=(await genderTensor.data())[0],isMale=probMale>.5,gender=isMale?Gender.MALE:Gender.FEMALE,genderProbability=isMale?probMale:1-probMale;return ageTensor.dispose(),genderTensor.dispose(),{age,gender,genderProbability}}));return out.age.dispose(),out.gender.dispose(),netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"age_gender_model"}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams4(weights)}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap4(classifierMap)}extractParams(weights){let classifierWeightSize=512*1+1+(512*2+2),featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var tf21=__toModule(require_tfjs_esm()),FaceLandmark68NetBase=class extends FaceProcessor{postProcess(output,inputSize,originalDimensions){let inputDimensions=originalDimensions.map(({width,height})=>{let scale2=inputSize/Math.max(height,width);return{width:width*scale2,height:height*scale2}}),batchSize=inputDimensions.length;return tf21.tidy(()=>{let createInterleavedTensor=(fillX,fillY)=>tf21.stack([tf21.fill([68],fillX,"float32"),tf21.fill([68],fillY,"float32")],1).as2D(1,136).as1D(),getPadding=(batchIdx,cond)=>{let{width,height}=inputDimensions[batchIdx];return cond(width,height)?Math.abs(width-height)/2:0},getPaddingX=batchIdx=>getPadding(batchIdx,(w,h)=>w<h),getPaddingY=batchIdx=>getPadding(batchIdx,(w,h)=>h<w);return output.mul(tf21.fill([batchSize,136],inputSize,"float32")).sub(tf21.stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(getPaddingX(batchIdx),getPaddingY(batchIdx))))).div(tf21.stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(inputDimensions[batchIdx].width,inputDimensions[batchIdx].height))))})}forwardInput(input){return tf21.tidy(()=>{let out=this.runNet(input);return this.postProcess(out,input.inputSize,input.inputDimensions.map(([height,width])=>({height,width})))})}async forward(input){return this.forwardInput(await toNetInput(input))}async detectLandmarks(input){let netInput=await toNetInput(input),landmarkTensors=tf21.tidy(()=>tf21.unstack(this.forwardInput(netInput))),landmarksForBatch=await Promise.all(landmarkTensors.map(async(landmarkTensor,batchIdx)=>{let landmarksArray=Array.from(await landmarkTensor.data()),xCoords=landmarksArray.filter((_,i)=>isEven(i)),yCoords=landmarksArray.filter((_,i)=>!isEven(i));return new FaceLandmarks68(Array(68).fill(0).map((_,i)=>new Point(xCoords[i],yCoords[i])),{height:netInput.getInputHeight(batchIdx),width:netInput.getInputWidth(batchIdx)})}));return landmarkTensors.forEach(t=>t.dispose()),netInput.isBatchInput?landmarksForBatch:landmarksForBatch[0]}getClassifierChannelsOut(){return 136}},FaceLandmark68Net=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceLandmark68Net",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var tf22=__toModule(require_tfjs_esm());function extractParamsFromWeigthMapTiny(weightMap){let paramMappings=[],{extractDenseBlock3Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock3Params("dense0",!0),dense1:extractDenseBlock3Params("dense1"),dense2:extractDenseBlock3Params("dense2")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function extractParamsTiny(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock3Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock3Params(3,32,"dense0",!0),dense1=extractDenseBlock3Params(32,64,"dense1"),dense2=extractDenseBlock3Params(64,128,"dense2");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2}}}var TinyFaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(input){let{params}=this;if(!params)throw new Error("TinyFaceFeatureExtractor - load model before inference");return tf22.tidy(()=>{let batchTensor=tf22.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf22.scalar(255)),out=denseBlock3(normalized,params.dense0,!0);return out=denseBlock3(out,params.dense1),out=denseBlock3(out,params.dense2),out=tf22.avgPool(out,[14,14],[2,2],"valid"),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMapTiny(weightMap)}extractParams(weights){return extractParamsTiny(weights)}},FaceLandmark68TinyNet=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new TinyFaceFeatureExtractor){super("FaceLandmark68TinyNet",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}},FaceLandmarkNet=class extends FaceLandmark68Net{};var tf27=__toModule(require_tfjs_esm()),tf24=__toModule(require_tfjs_esm()),tf23=__toModule(require_tfjs_esm());function scale(x,params){return tf23.add(tf23.mul(x,params.weights),params.biases)}function convLayer2(x,params,strides,withRelu,padding="same"){let{filters,bias}=params.conv,out=tf24.conv2d(x,filters,strides,padding);return out=tf24.add(out,bias),out=scale(out,params.scale),withRelu?tf24.relu(out):out}function conv2(x,params){return convLayer2(x,params,[1,1],!0)}function convNoRelu(x,params){return convLayer2(x,params,[1,1],!1)}function convDown(x,params){return convLayer2(x,params,[2,2],!0,"valid")}var tf25=__toModule(require_tfjs_esm());function extractorsFactory3(extractWeights,paramMappings){function extractFilterValues(numFilterValues,numFilters,filterSize){let weights=extractWeights(numFilterValues),depth=weights.length/(numFilters*filterSize*filterSize);if(isFloat(depth))throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);return tf25.tidy(()=>tf25.transpose(tf25.tensor4d(weights,[numFilters,depth,filterSize,filterSize]),[2,3,1,0]))}function extractConvParams(numFilterValues,numFilters,filterSize,mappedPrefix){let filters=extractFilterValues(numFilterValues,numFilters,filterSize),bias=tf25.tensor1d(extractWeights(numFilters));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}function extractScaleLayerParams(numWeights,mappedPrefix){let weights=tf25.tensor1d(extractWeights(numWeights)),biases=tf25.tensor1d(extractWeights(numWeights));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/biases`}),{weights,biases}}function extractConvLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix){let conv3=extractConvParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv`),scale2=extractScaleLayerParams(numFilters,`${mappedPrefix}/scale`);return{conv:conv3,scale:scale2}}function extractResidualLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix,isDown=!1){let conv1=extractConvLayerParams((isDown?.5:1)*numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv1`),conv22=extractConvLayerParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv2`);return{conv1,conv2:conv22}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParams5(weights){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory3(extractWeights,paramMappings),conv32_down=extractConvLayerParams(4704,32,7,"conv32_down"),conv32_1=extractResidualLayerParams(9216,32,3,"conv32_1"),conv32_2=extractResidualLayerParams(9216,32,3,"conv32_2"),conv32_3=extractResidualLayerParams(9216,32,3,"conv32_3"),conv64_down=extractResidualLayerParams(36864,64,3,"conv64_down",!0),conv64_1=extractResidualLayerParams(36864,64,3,"conv64_1"),conv64_2=extractResidualLayerParams(36864,64,3,"conv64_2"),conv64_3=extractResidualLayerParams(36864,64,3,"conv64_3"),conv128_down=extractResidualLayerParams(147456,128,3,"conv128_down",!0),conv128_1=extractResidualLayerParams(147456,128,3,"conv128_1"),conv128_2=extractResidualLayerParams(147456,128,3,"conv128_2"),conv256_down=extractResidualLayerParams(589824,256,3,"conv256_down",!0),conv256_1=extractResidualLayerParams(589824,256,3,"conv256_1"),conv256_2=extractResidualLayerParams(589824,256,3,"conv256_2"),conv256_down_out=extractResidualLayerParams(589824,256,3,"conv256_down_out"),fc=tf25.tidy(()=>tf25.transpose(tf25.tensor2d(extractWeights(256*128),[128,256]),[1,0]));if(paramMappings.push({paramPath:"fc"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc},paramMappings}}function extractorsFactory4(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractScaleLayerParams(prefix){let weights=extractWeightEntry(`${prefix}/scale/weights`,1),biases=extractWeightEntry(`${prefix}/scale/biases`,1);return{weights,biases}}function extractConvLayerParams(prefix){let filters=extractWeightEntry(`${prefix}/conv/filters`,4),bias=extractWeightEntry(`${prefix}/conv/bias`,1),scale2=extractScaleLayerParams(prefix);return{conv:{filters,bias},scale:scale2}}function extractResidualLayerParams(prefix){return{conv1:extractConvLayerParams(`${prefix}/conv1`),conv2:extractConvLayerParams(`${prefix}/conv2`)}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParamsFromWeigthMap5(weightMap){let paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory4(weightMap,paramMappings),conv32_down=extractConvLayerParams("conv32_down"),conv32_1=extractResidualLayerParams("conv32_1"),conv32_2=extractResidualLayerParams("conv32_2"),conv32_3=extractResidualLayerParams("conv32_3"),conv64_down=extractResidualLayerParams("conv64_down"),conv64_1=extractResidualLayerParams("conv64_1"),conv64_2=extractResidualLayerParams("conv64_2"),conv64_3=extractResidualLayerParams("conv64_3"),conv128_down=extractResidualLayerParams("conv128_down"),conv128_1=extractResidualLayerParams("conv128_1"),conv128_2=extractResidualLayerParams("conv128_2"),conv256_down=extractResidualLayerParams("conv256_down"),conv256_1=extractResidualLayerParams("conv256_1"),conv256_2=extractResidualLayerParams("conv256_2"),conv256_down_out=extractResidualLayerParams("conv256_down_out"),{fc}=weightMap;if(paramMappings.push({originalPath:"fc",paramPath:"fc"}),!isTensor2D(fc))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);let params={conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var tf26=__toModule(require_tfjs_esm());function residual(x,params){let out=conv2(x,params.conv1);return out=convNoRelu(out,params.conv2),out=tf26.add(out,x),out=tf26.relu(out),out}function residualDown(x,params){let out=convDown(x,params.conv1);out=convNoRelu(out,params.conv2);let pooled=tf26.avgPool(x,2,2,"valid"),zeros2=tf26.zeros(pooled.shape),isPad=pooled.shape[3]!==out.shape[3];if(pooled.shape[1]!==out.shape[1]||pooled.shape[2]!==out.shape[2]){let padShapeX=[...out.shape];padShapeX[1]=1;let zerosW=tf26.zeros(padShapeX);out=tf26.concat([out,zerosW],1);let padShapeY=[...out.shape];padShapeY[2]=1;let zerosH=tf26.zeros(padShapeY);out=tf26.concat([out,zerosH],2)}return pooled=isPad?tf26.concat([pooled,zeros2],3):pooled,out=tf26.add(pooled,out),out=tf26.relu(out),out}var FaceRecognitionNet=class extends NeuralNetwork{constructor(){super("FaceRecognitionNet")}forwardInput(input){let{params}=this;if(!params)throw new Error("FaceRecognitionNet - load model before inference");return tf27.tidy(()=>{let batchTensor=tf27.cast(input.toBatchTensor(150,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf27.scalar(256)),out=convDown(normalized,params.conv32_down);out=tf27.maxPool(out,3,2,"valid"),out=residual(out,params.conv32_1),out=residual(out,params.conv32_2),out=residual(out,params.conv32_3),out=residualDown(out,params.conv64_down),out=residual(out,params.conv64_1),out=residual(out,params.conv64_2),out=residual(out,params.conv64_3),out=residualDown(out,params.conv128_down),out=residual(out,params.conv128_1),out=residual(out,params.conv128_2),out=residualDown(out,params.conv256_down),out=residual(out,params.conv256_1),out=residual(out,params.conv256_2),out=residualDown(out,params.conv256_down_out);let globalAvg=out.mean([1,2]);return tf27.matMul(globalAvg,params.fc)})}async forward(input){return this.forwardInput(await toNetInput(input))}async computeFaceDescriptor(input){let netInput=await toNetInput(input),faceDescriptorTensors=tf27.tidy(()=>tf27.unstack(this.forwardInput(netInput))),faceDescriptorsForBatch=await Promise.all(faceDescriptorTensors.map(t=>t.data()));return faceDescriptorTensors.forEach(t=>t.dispose()),netInput.isBatchInput?faceDescriptorsForBatch:faceDescriptorsForBatch[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap5(weightMap)}extractParams(weights){return extractParams5(weights)}};function createFaceRecognitionNet(weights){let net=new FaceRecognitionNet;return net.extractWeights(weights),net}function extendWithFaceDescriptor(sourceObj,descriptor){return{...sourceObj,...{descriptor}}}function isWithAge(obj){return typeof obj.age=="number"}function extendWithAge(sourceObj,age){return{...sourceObj,...{age}}}function isWithGender(obj){return(obj.gender===Gender.MALE||obj.gender===Gender.FEMALE)&&isValidProbablitiy(obj.genderProbability)}function extendWithGender(sourceObj,gender,genderProbability){return{...sourceObj,...{gender,genderProbability}}}var tf34=__toModule(require_tfjs_esm()),tf28=__toModule(require_tfjs_esm());function extractorsFactory5(extractWeights,paramMappings){function extractDepthwiseConvParams(numChannels,mappedPrefix){let filters=tf28.tensor4d(extractWeights(3*3*numChannels),[3,3,numChannels,1]),batch_norm_scale=tf28.tensor1d(extractWeights(numChannels)),batch_norm_offset=tf28.tensor1d(extractWeights(numChannels)),batch_norm_mean=tf28.tensor1d(extractWeights(numChannels)),batch_norm_variance=tf28.tensor1d(extractWeights(numChannels));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/batch_norm_scale`},{paramPath:`${mappedPrefix}/batch_norm_offset`},{paramPath:`${mappedPrefix}/batch_norm_mean`},{paramPath:`${mappedPrefix}/batch_norm_variance`}),{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance}}function extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,isPointwiseConv){let filters=tf28.tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tf28.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/${isPointwiseConv?"batch_norm_offset":"bias"}`}),{filters,bias}}function extractPointwiseConvParams(channelsIn,channelsOut,filterSize,mappedPrefix){let{filters,bias}=extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,!0);return{filters,batch_norm_offset:bias}}function extractConvPairParams(channelsIn,channelsOut,mappedPrefix){let depthwise_conv=extractDepthwiseConvParams(channelsIn,`${mappedPrefix}/depthwise_conv`),pointwise_conv=extractPointwiseConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/pointwise_conv`);return{depthwise_conv,pointwise_conv}}function extractMobilenetV1Params(){let conv_0=extractPointwiseConvParams(3,32,3,"mobilenetv1/conv_0"),conv_1=extractConvPairParams(32,64,"mobilenetv1/conv_1"),conv_2=extractConvPairParams(64,128,"mobilenetv1/conv_2"),conv_3=extractConvPairParams(128,128,"mobilenetv1/conv_3"),conv_4=extractConvPairParams(128,256,"mobilenetv1/conv_4"),conv_5=extractConvPairParams(256,256,"mobilenetv1/conv_5"),conv_6=extractConvPairParams(256,512,"mobilenetv1/conv_6"),conv_7=extractConvPairParams(512,512,"mobilenetv1/conv_7"),conv_8=extractConvPairParams(512,512,"mobilenetv1/conv_8"),conv_9=extractConvPairParams(512,512,"mobilenetv1/conv_9"),conv_10=extractConvPairParams(512,512,"mobilenetv1/conv_10"),conv_11=extractConvPairParams(512,512,"mobilenetv1/conv_11"),conv_12=extractConvPairParams(512,1024,"mobilenetv1/conv_12"),conv_13=extractConvPairParams(1024,1024,"mobilenetv1/conv_13");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,conv_8,conv_9,conv_10,conv_11,conv_12,conv_13}}function extractPredictionLayerParams(){let conv_0=extractPointwiseConvParams(1024,256,1,"prediction_layer/conv_0"),conv_1=extractPointwiseConvParams(256,512,3,"prediction_layer/conv_1"),conv_2=extractPointwiseConvParams(512,128,1,"prediction_layer/conv_2"),conv_3=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_3"),conv_4=extractPointwiseConvParams(256,128,1,"prediction_layer/conv_4"),conv_5=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_5"),conv_6=extractPointwiseConvParams(256,64,1,"prediction_layer/conv_6"),conv_7=extractPointwiseConvParams(64,128,3,"prediction_layer/conv_7"),box_encoding_0_predictor=extractConvParams(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),class_predictor_0=extractConvParams(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),box_encoding_1_predictor=extractConvParams(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),class_predictor_1=extractConvParams(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),box_encoding_2_predictor=extractConvParams(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),class_predictor_2=extractConvParams(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),box_encoding_3_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),class_predictor_3=extractConvParams(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),box_encoding_4_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),class_predictor_4=extractConvParams(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),box_encoding_5_predictor=extractConvParams(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),class_predictor_5=extractConvParams(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,box_predictor_0:{box_encoding_predictor:box_encoding_0_predictor,class_predictor:class_predictor_0},box_predictor_1:{box_encoding_predictor:box_encoding_1_predictor,class_predictor:class_predictor_1},box_predictor_2:{box_encoding_predictor:box_encoding_2_predictor,class_predictor:class_predictor_2},box_predictor_3:{box_encoding_predictor:box_encoding_3_predictor,class_predictor:class_predictor_3},box_predictor_4:{box_encoding_predictor:box_encoding_4_predictor,class_predictor:class_predictor_4},box_predictor_5:{box_encoding_predictor:box_encoding_5_predictor,class_predictor:class_predictor_5}}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParams6(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory5(extractWeights,paramMappings),mobilenetv1=extractMobilenetV1Params(),prediction_layer=extractPredictionLayerParams(),output_layer={extra_dim:tf28.tensor3d(extractWeights(5118*4),[1,5118,4])};if(paramMappings.push({paramPath:"output_layer/extra_dim"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{mobilenetv1,prediction_layer,output_layer},paramMappings}}function extractorsFactory6(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractPointwiseConvParams(prefix,idx,mappedPrefix){let filters=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`,4,`${mappedPrefix}/filters`),batch_norm_offset=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`,1,`${mappedPrefix}/batch_norm_offset`);return{filters,batch_norm_offset}}function extractConvPairParams(idx){let mappedPrefix=`mobilenetv1/conv_${idx}`,prefixDepthwiseConv=`MobilenetV1/Conv2d_${idx}_depthwise`,mappedPrefixDepthwiseConv=`${mappedPrefix}/depthwise_conv`,mappedPrefixPointwiseConv=`${mappedPrefix}/pointwise_conv`,filters=extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`,4,`${mappedPrefixDepthwiseConv}/filters`),batch_norm_scale=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`,1,`${mappedPrefixDepthwiseConv}/batch_norm_scale`),batch_norm_offset=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`,1,`${mappedPrefixDepthwiseConv}/batch_norm_offset`),batch_norm_mean=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`,1,`${mappedPrefixDepthwiseConv}/batch_norm_mean`),batch_norm_variance=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`,1,`${mappedPrefixDepthwiseConv}/batch_norm_variance`);return{depthwise_conv:{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance},pointwise_conv:extractPointwiseConvParams("MobilenetV1",idx,mappedPrefixPointwiseConv)}}function extractMobilenetV1Params(){return{conv_0:extractPointwiseConvParams("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:extractConvPairParams(1),conv_2:extractConvPairParams(2),conv_3:extractConvPairParams(3),conv_4:extractConvPairParams(4),conv_5:extractConvPairParams(5),conv_6:extractConvPairParams(6),conv_7:extractConvPairParams(7),conv_8:extractConvPairParams(8),conv_9:extractConvPairParams(9),conv_10:extractConvPairParams(10),conv_11:extractConvPairParams(11),conv_12:extractConvPairParams(12),conv_13:extractConvPairParams(13)}}function extractConvParams(prefix,mappedPrefix){let filters=extractWeightEntry(`${prefix}/weights`,4,`${mappedPrefix}/filters`),bias=extractWeightEntry(`${prefix}/biases`,1,`${mappedPrefix}/bias`);return{filters,bias}}function extractBoxPredictorParams(idx){let box_encoding_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${idx}/box_encoding_predictor`),class_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/ClassPredictor`,`prediction_layer/box_predictor_${idx}/class_predictor`);return{box_encoding_predictor,class_predictor}}function extractPredictionLayerParams(){return{conv_0:extractPointwiseConvParams("Prediction",0,"prediction_layer/conv_0"),conv_1:extractPointwiseConvParams("Prediction",1,"prediction_layer/conv_1"),conv_2:extractPointwiseConvParams("Prediction",2,"prediction_layer/conv_2"),conv_3:extractPointwiseConvParams("Prediction",3,"prediction_layer/conv_3"),conv_4:extractPointwiseConvParams("Prediction",4,"prediction_layer/conv_4"),conv_5:extractPointwiseConvParams("Prediction",5,"prediction_layer/conv_5"),conv_6:extractPointwiseConvParams("Prediction",6,"prediction_layer/conv_6"),conv_7:extractPointwiseConvParams("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:extractBoxPredictorParams(0),box_predictor_1:extractBoxPredictorParams(1),box_predictor_2:extractBoxPredictorParams(2),box_predictor_3:extractBoxPredictorParams(3),box_predictor_4:extractBoxPredictorParams(4),box_predictor_5:extractBoxPredictorParams(5)}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParamsFromWeigthMap6(weightMap){let paramMappings=[],{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory6(weightMap,paramMappings),extra_dim=weightMap["Output/extra_dim"];if(paramMappings.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!isTensor3D(extra_dim))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);let params={mobilenetv1:extractMobilenetV1Params(),prediction_layer:extractPredictionLayerParams(),output_layer:{extra_dim}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var tf30=__toModule(require_tfjs_esm()),tf29=__toModule(require_tfjs_esm());function pointwiseConvLayer(x,params,strides){return tf29.tidy(()=>{let out=tf29.conv2d(x,params.filters,strides,"same");return out=tf29.add(out,params.batch_norm_offset),tf29.clipByValue(out,0,6)})}var epsilon=.0010000000474974513;function depthwiseConvLayer(x,params,strides){return tf30.tidy(()=>{let out=tf30.depthwiseConv2d(x,params.filters,strides,"same");return out=tf30.batchNorm(out,params.batch_norm_mean,params.batch_norm_variance,params.batch_norm_offset,params.batch_norm_scale,epsilon),tf30.clipByValue(out,0,6)})}function getStridesForLayerIdx(layerIdx){return[2,4,6,12].some(idx=>idx===layerIdx)?[2,2]:[1,1]}function mobileNetV1(x,params){return tf30.tidy(()=>{let conv11,out=pointwiseConvLayer(x,params.conv_0,[2,2]);if([params.conv_1,params.conv_2,params.conv_3,params.conv_4,params.conv_5,params.conv_6,params.conv_7,params.conv_8,params.conv_9,params.conv_10,params.conv_11,params.conv_12,params.conv_13].forEach((param,i)=>{let layerIdx=i+1,depthwiseConvStrides=getStridesForLayerIdx(layerIdx);out=depthwiseConvLayer(out,param.depthwise_conv,depthwiseConvStrides),out=pointwiseConvLayer(out,param.pointwise_conv,[1,1]),layerIdx===11&&(conv11=out)}),conv11===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out,conv11}})}function IOU(boxes,i,j){let boxesData=boxes.arraySync(),yminI=Math.min(boxesData[i][0],boxesData[i][2]),xminI=Math.min(boxesData[i][1],boxesData[i][3]),ymaxI=Math.max(boxesData[i][0],boxesData[i][2]),xmaxI=Math.max(boxesData[i][1],boxesData[i][3]),yminJ=Math.min(boxesData[j][0],boxesData[j][2]),xminJ=Math.min(boxesData[j][1],boxesData[j][3]),ymaxJ=Math.max(boxesData[j][0],boxesData[j][2]),xmaxJ=Math.max(boxesData[j][1],boxesData[j][3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function nonMaxSuppression2(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){let numBoxes=boxes.shape[0],outputSize=Math.min(maxOutputSize,numBoxes),candidates=scores.map((score,boxIndex)=>({score,boxIndex})).filter(c=>c.score>scoreThreshold).sort((c1,c2)=>c2.score-c1.score),suppressFunc=x=>x<=iouThreshold?1:0,selected=[];return candidates.forEach(c=>{if(!(selected.length>=outputSize)){let originalScore=c.score;for(let j=selected.length-1;j>=0;--j){let iou2=IOU(boxes,c.boxIndex,selected[j]);if(iou2!==0&&(c.score*=suppressFunc(iou2),c.score<=scoreThreshold))break}originalScore===c.score&&selected.push(c.boxIndex)}}),selected}var tf31=__toModule(require_tfjs_esm());function getCenterCoordinatesAndSizesLayer(x){let vec=tf31.unstack(tf31.transpose(x,[1,0])),sizes=[tf31.sub(vec[2],vec[0]),tf31.sub(vec[3],vec[1])],centers=[tf31.add(vec[0],tf31.div(sizes[0],tf31.scalar(2))),tf31.add(vec[1],tf31.div(sizes[1],tf31.scalar(2)))];return{sizes,centers}}function decodeBoxesLayer(x0,x1){let{sizes,centers}=getCenterCoordinatesAndSizesLayer(x0),vec=tf31.unstack(tf31.transpose(x1,[1,0])),div0_out=tf31.div(tf31.mul(tf31.exp(tf31.div(vec[2],tf31.scalar(5))),sizes[0]),tf31.scalar(2)),add0_out=tf31.add(tf31.mul(tf31.div(vec[0],tf31.scalar(10)),sizes[0]),centers[0]),div1_out=tf31.div(tf31.mul(tf31.exp(tf31.div(vec[3],tf31.scalar(5))),sizes[1]),tf31.scalar(2)),add1_out=tf31.add(tf31.mul(tf31.div(vec[1],tf31.scalar(10)),sizes[1]),centers[1]);return tf31.transpose(tf31.stack([tf31.sub(add0_out,div0_out),tf31.sub(add1_out,div1_out),tf31.add(add0_out,div0_out),tf31.add(add1_out,div1_out)]),[1,0])}function outputLayer(boxPredictions,classPredictions,params){return tf31.tidy(()=>{let batchSize=boxPredictions.shape[0],boxes=decodeBoxesLayer(tf31.reshape(tf31.tile(params.extra_dim,[batchSize,1,1]),[-1,4]),tf31.reshape(boxPredictions,[-1,4]));boxes=tf31.reshape(boxes,[batchSize,boxes.shape[0]/batchSize,4]);let scoresAndClasses=tf31.sigmoid(tf31.slice(classPredictions,[0,0,1],[-1,-1,-1])),scores=tf31.slice(scoresAndClasses,[0,0,0],[-1,-1,1]);scores=tf31.reshape(scores,[batchSize,scores.shape[1]]);let boxesByBatch=tf31.unstack(boxes),scoresByBatch=tf31.unstack(scores);return{boxes:boxesByBatch,scores:scoresByBatch}})}var tf33=__toModule(require_tfjs_esm()),tf32=__toModule(require_tfjs_esm());function boxPredictionLayer(x,params){return tf32.tidy(()=>{let batchSize=x.shape[0],boxPredictionEncoding=tf32.reshape(convLayer(x,params.box_encoding_predictor),[batchSize,-1,1,4]),classPrediction=tf32.reshape(convLayer(x,params.class_predictor),[batchSize,-1,3]);return{boxPredictionEncoding,classPrediction}})}function predictionLayer(x,conv11,params){return tf33.tidy(()=>{let conv0=pointwiseConvLayer(x,params.conv_0,[1,1]),conv1=pointwiseConvLayer(conv0,params.conv_1,[2,2]),conv22=pointwiseConvLayer(conv1,params.conv_2,[1,1]),conv3=pointwiseConvLayer(conv22,params.conv_3,[2,2]),conv4=pointwiseConvLayer(conv3,params.conv_4,[1,1]),conv5=pointwiseConvLayer(conv4,params.conv_5,[2,2]),conv6=pointwiseConvLayer(conv5,params.conv_6,[1,1]),conv7=pointwiseConvLayer(conv6,params.conv_7,[2,2]),boxPrediction0=boxPredictionLayer(conv11,params.box_predictor_0),boxPrediction1=boxPredictionLayer(x,params.box_predictor_1),boxPrediction2=boxPredictionLayer(conv1,params.box_predictor_2),boxPrediction3=boxPredictionLayer(conv3,params.box_predictor_3),boxPrediction4=boxPredictionLayer(conv5,params.box_predictor_4),boxPrediction5=boxPredictionLayer(conv7,params.box_predictor_5),boxPredictions=tf33.concat([boxPrediction0.boxPredictionEncoding,boxPrediction1.boxPredictionEncoding,boxPrediction2.boxPredictionEncoding,boxPrediction3.boxPredictionEncoding,boxPrediction4.boxPredictionEncoding,boxPrediction5.boxPredictionEncoding],1),classPredictions=tf33.concat([boxPrediction0.classPrediction,boxPrediction1.classPrediction,boxPrediction2.classPrediction,boxPrediction3.classPrediction,boxPrediction4.classPrediction,boxPrediction5.classPrediction],1);return{boxPredictions,classPredictions}})}var SsdMobilenetv1Options=class{constructor({minConfidence,maxResults}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=minConfidence||.5,this._maxResults=maxResults||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}},SsdMobilenetv1=class extends NeuralNetwork{constructor(){super("SsdMobilenetv1")}forwardInput(input){let{params}=this;if(!params)throw new Error("SsdMobilenetv1 - load model before inference");return tf34.tidy(()=>{let batchTensor=tf34.cast(input.toBatchTensor(512,!1),"float32"),x=tf34.sub(tf34.mul(batchTensor,tf34.scalar(.007843137718737125)),tf34.scalar(1)),features=mobileNetV1(x,params.mobilenetv1),{boxPredictions,classPredictions}=predictionLayer(features.out,features.conv11,params.prediction_layer);return outputLayer(boxPredictions,classPredictions,params.output_layer)})}async forward(input){return this.forwardInput(await toNetInput(input))}async locateFaces(input,options={}){let{maxResults,minConfidence}=new SsdMobilenetv1Options(options),netInput=await toNetInput(input),{boxes:_boxes,scores:_scores}=this.forwardInput(netInput),boxes=_boxes[0],scores=_scores[0];for(let i=1;i<_boxes.length;i++)_boxes[i].dispose(),_scores[i].dispose();let scoresData=Array.from(await scores.data()),indices=nonMaxSuppression2(boxes,scoresData,maxResults,.5,minConfidence),reshapedDims=netInput.getReshapedInputDimensions(0),inputSize=netInput.inputSize,padX=inputSize/reshapedDims.width,padY=inputSize/reshapedDims.height,boxesData=boxes.arraySync(),results=indices.map(idx=>{let[top,bottom]=[Math.max(0,boxesData[idx][0]),Math.min(1,boxesData[idx][2])].map(val=>val*padY),[left,right]=[Math.max(0,boxesData[idx][1]),Math.min(1,boxesData[idx][3])].map(val=>val*padX);return new FaceDetection(scoresData[idx],new Rect(left,top,right-left,bottom-top),{height:netInput.getInputHeight(0),width:netInput.getInputWidth(0)})});return boxes.dispose(),scores.dispose(),results}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap6(weightMap)}extractParams(weights){return extractParams6(weights)}};function createSsdMobilenetv1(weights){let net=new SsdMobilenetv1;return net.extractWeights(weights),net}function createFaceDetectionNet(weights){return createSsdMobilenetv1(weights)}var FaceDetectionNet=class extends SsdMobilenetv1{},IOU_THRESHOLD=.4,BOX_ANCHORS=[new Point(.738768,.874946),new Point(2.42204,2.65704),new Point(4.30971,7.04493),new Point(10.246,4.59428),new Point(12.6868,11.8741)],BOX_ANCHORS_SEPARABLE=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB_SEPARABLE=[117.001,114.697,97.404],DEFAULT_MODEL_NAME="tiny_yolov2_model",DEFAULT_MODEL_NAME_SEPARABLE_CONV="tiny_yolov2_separable_conv_model",tf39=__toModule(require_tfjs_esm()),isNumber=arg=>typeof arg=="number";function validateConfig(config){if(!config)throw new Error(`invalid config: ${config}`);if(typeof config.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);if(!isNumber(config.iouThreshold)||config.iouThreshold<0||config.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);if(!Array.isArray(config.classes)||!config.classes.length||!config.classes.every(c=>typeof c=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);if(!Array.isArray(config.anchors)||!config.anchors.length||!config.anchors.map(a=>a||{}).every(a=>isNumber(a.x)&&isNumber(a.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);if(config.meanRgb&&(!Array.isArray(config.meanRgb)||config.meanRgb.length!==3||!config.meanRgb.every(isNumber)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)}var tf36=__toModule(require_tfjs_esm()),tf35=__toModule(require_tfjs_esm());function leaky(x){return tf35.tidy(()=>{let min=tf35.mul(x,tf35.scalar(.10000000149011612));return tf35.add(tf35.relu(tf35.sub(x,min)),min)})}function convWithBatchNorm(x,params){return tf36.tidy(()=>{let out=tf36.pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=tf36.conv2d(out,params.conv.filters,[1,1],"valid"),out=tf36.sub(out,params.bn.sub),out=tf36.mul(out,params.bn.truediv),out=tf36.add(out,params.conv.bias),leaky(out)})}var tf37=__toModule(require_tfjs_esm());function depthwiseSeparableConv2(x,params){return tf37.tidy(()=>{let out=tf37.pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=tf37.separableConv2d(out,params.depthwise_filter,params.pointwise_filter,[1,1],"valid"),out=tf37.add(out,params.bias),leaky(out)})}var tf38=__toModule(require_tfjs_esm());function extractorsFactory7(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings);function extractBatchNormParams(size,mappedPrefix){let sub6=tf38.tensor1d(extractWeights(size)),truediv=tf38.tensor1d(extractWeights(size));return paramMappings.push({paramPath:`${mappedPrefix}/sub`},{paramPath:`${mappedPrefix}/truediv`}),{sub:sub6,truediv}}function extractConvWithBatchNormParams(channelsIn,channelsOut,mappedPrefix){let conv3=extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv`),bn=extractBatchNormParams(channelsOut,`${mappedPrefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParams7(weights,config,boxEncodingSize,filterSizes){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory7(extractWeights,paramMappings),params;if(config.withSeparableConvs){let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=config.isFirstLayerConv2d?extractConvParams(s0,s1,3,"conv0"):extractSeparableConvParams(s0,s1,"conv0"),conv1=extractSeparableConvParams(s1,s2,"conv1"),conv22=extractSeparableConvParams(s2,s3,"conv2"),conv3=extractSeparableConvParams(s3,s4,"conv3"),conv4=extractSeparableConvParams(s4,s5,"conv4"),conv5=extractSeparableConvParams(s5,s6,"conv5"),conv6=s7?extractSeparableConvParams(s6,s7,"conv6"):void 0,conv7=s8?extractSeparableConvParams(s7,s8,"conv7"):void 0,conv8=extractConvParams(s8||s7||s6,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}else{let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=extractConvWithBatchNormParams(s0,s1,"conv0"),conv1=extractConvWithBatchNormParams(s1,s2,"conv1"),conv22=extractConvWithBatchNormParams(s2,s3,"conv2"),conv3=extractConvWithBatchNormParams(s3,s4,"conv3"),conv4=extractConvWithBatchNormParams(s4,s5,"conv4"),conv5=extractConvWithBatchNormParams(s5,s6,"conv5"),conv6=extractConvWithBatchNormParams(s6,s7,"conv6"),conv7=extractConvWithBatchNormParams(s7,s8,"conv7"),conv8=extractConvParams(s8,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params,paramMappings}}function extractorsFactory8(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractBatchNormParams(prefix){let sub6=extractWeightEntry(`${prefix}/sub`,1),truediv=extractWeightEntry(`${prefix}/truediv`,1);return{sub:sub6,truediv}}function extractConvParams(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}function extractConvWithBatchNormParams(prefix){let conv3=extractConvParams(`${prefix}/conv`),bn=extractBatchNormParams(`${prefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParamsFromWeigthMap7(weightMap,config){let paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory8(weightMap,paramMappings),params;if(config.withSeparableConvs){let numFilters=config.filterSizes&&config.filterSizes.length||9;params={conv0:config.isFirstLayerConv2d?extractConvParams("conv0"):extractSeparableConvParams("conv0"),conv1:extractSeparableConvParams("conv1"),conv2:extractSeparableConvParams("conv2"),conv3:extractSeparableConvParams("conv3"),conv4:extractSeparableConvParams("conv4"),conv5:extractSeparableConvParams("conv5"),conv6:numFilters>7?extractSeparableConvParams("conv6"):void 0,conv7:numFilters>8?extractSeparableConvParams("conv7"):void 0,conv8:extractConvParams("conv8")}}else params={conv0:extractConvWithBatchNormParams("conv0"),conv1:extractConvWithBatchNormParams("conv1"),conv2:extractConvWithBatchNormParams("conv2"),conv3:extractConvWithBatchNormParams("conv3"),conv4:extractConvWithBatchNormParams("conv4"),conv5:extractConvWithBatchNormParams("conv5"),conv6:extractConvWithBatchNormParams("conv6"),conv7:extractConvWithBatchNormParams("conv7"),conv8:extractConvParams("conv8")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var TinyYolov2Options=class{constructor({inputSize,scoreThreshold}={}){this._name="TinyYolov2Options";if(this._inputSize=inputSize||416,this._scoreThreshold=scoreThreshold||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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}},TinyYolov2Base2=class extends NeuralNetwork{constructor(config){super("TinyYolov2");validateConfig(config),this._config=config}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(x,params){let out=convWithBatchNorm(x,params.conv0);return out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv1),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv2),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv3),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv4),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv5),out=tf39.maxPool(out,[2,2],[1,1],"same"),out=convWithBatchNorm(out,params.conv6),out=convWithBatchNorm(out,params.conv7),convLayer(out,params.conv8,"valid",!1)}runMobilenet(x,params){let out=this.config.isFirstLayerConv2d?leaky(convLayer(x,params.conv0,"valid",!1)):depthwiseSeparableConv2(x,params.conv0);return out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv1),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv2),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv3),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv4),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv5),out=tf39.maxPool(out,[2,2],[1,1],"same"),out=params.conv6?depthwiseSeparableConv2(out,params.conv6):out,out=params.conv7?depthwiseSeparableConv2(out,params.conv7):out,convLayer(out,params.conv8,"valid",!1)}forwardInput(input,inputSize){let{params}=this;if(!params)throw new Error("TinyYolov2 - load model before inference");return tf39.tidy(()=>{let batchTensor=tf39.cast(input.toBatchTensor(inputSize,!1),"float32");return batchTensor=this.config.meanRgb?normalize(batchTensor,this.config.meanRgb):batchTensor,batchTensor=batchTensor.div(tf39.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(batchTensor,params):this.runTinyYolov2(batchTensor,params)})}async forward(input,inputSize){return this.forwardInput(await toNetInput(input),inputSize)}async detect(input,forwardParams={}){let{inputSize,scoreThreshold}=new TinyYolov2Options(forwardParams),netInput=await toNetInput(input),out=await this.forwardInput(netInput,inputSize),out0=tf39.tidy(()=>tf39.unstack(out)[0].expandDims()),inputDimensions={width:netInput.getInputWidth(0),height:netInput.getInputHeight(0)},results=await this.extractBoxes(out0,netInput.getReshapedInputDimensions(0),scoreThreshold);out.dispose(),out0.dispose();let boxes=results.map(res=>res.box),scores=results.map(res=>res.score),classScores=results.map(res=>res.classScore),classNames=results.map(res=>this.config.classes[res.label]);return nonMaxSuppression(boxes.map(box=>box.rescale(inputSize)),scores,this.config.iouThreshold,!0).map(idx=>new ObjectDetection(scores[idx],classScores[idx],classNames[idx],boxes[idx],inputDimensions))}getDefaultModelName(){return""}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap7(weightMap,this.config)}extractParams(weights){let filterSizes=this.config.filterSizes||TinyYolov2Base2.DEFAULT_FILTER_SIZES,numFilters=filterSizes?filterSizes.length:void 0;if(numFilters!==7&&numFilters!==8&&numFilters!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);return extractParams7(weights,this.config,this.boxEncodingSize,filterSizes)}async extractBoxes(outputTensor,inputBlobDimensions,scoreThreshold){let{width,height}=inputBlobDimensions,inputSize=Math.max(width,height),correctionFactorX=inputSize/width,correctionFactorY=inputSize/height,numCells=outputTensor.shape[1],numBoxes=this.config.anchors.length,[boxesTensor,scoresTensor,classScoresTensor]=tf39.tidy(()=>{let reshaped=outputTensor.reshape([numCells,numCells,numBoxes,this.boxEncodingSize]),boxes=reshaped.slice([0,0,0,0],[numCells,numCells,numBoxes,4]),scores=reshaped.slice([0,0,0,4],[numCells,numCells,numBoxes,1]),classScores=this.withClassScores?tf39.softmax(reshaped.slice([0,0,0,5],[numCells,numCells,numBoxes,this.config.classes.length]),3):tf39.scalar(0);return[boxes,scores,classScores]}),results=[],scoresData=await scoresTensor.array(),boxesData=await boxesTensor.array();for(let row=0;row<numCells;row++)for(let col=0;col<numCells;col++)for(let anchor=0;anchor<numBoxes;anchor++){let score=sigmoid(scoresData[row][col][anchor][0]);if(!scoreThreshold||score>scoreThreshold){let ctX=(col+sigmoid(boxesData[row][col][anchor][0]))/numCells*correctionFactorX,ctY=(row+sigmoid(boxesData[row][col][anchor][1]))/numCells*correctionFactorY,widthLocal=Math.exp(boxesData[row][col][anchor][2])*this.config.anchors[anchor].x/numCells*correctionFactorX,heightLocal=Math.exp(boxesData[row][col][anchor][3])*this.config.anchors[anchor].y/numCells*correctionFactorY,x=ctX-widthLocal/2,y=ctY-heightLocal/2,pos={row,col,anchor},{classScore,label}=this.withClassScores?await this.extractPredictedClass(classScoresTensor,pos):{classScore:1,label:0};results.push({box:new BoundingBox(x,y,x+widthLocal,y+heightLocal),score,classScore:score*classScore,label,...pos})}}return boxesTensor.dispose(),scoresTensor.dispose(),classScoresTensor.dispose(),results}async extractPredictedClass(classesTensor,pos){let{row,col,anchor}=pos,classesData=await classesTensor.array();return Array(this.config.classes.length).fill(0).map((_,i)=>classesData[row][col][anchor][i]).map((classScore,label)=>({classScore,label})).reduce((max,curr)=>max.classScore>curr.classScore?max:curr)}},TinyYolov2Base=TinyYolov2Base2;TinyYolov2Base.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var TinyYolov2=class extends TinyYolov2Base{constructor(withSeparableConvs=!0){let config={withSeparableConvs,iouThreshold:IOU_THRESHOLD,classes:["face"],...withSeparableConvs?{anchors:BOX_ANCHORS_SEPARABLE,meanRgb:MEAN_RGB_SEPARABLE}:{anchors:BOX_ANCHORS,withClassScores:!0}};super(config)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(input,forwardParams){return(await this.detect(input,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?DEFAULT_MODEL_NAME_SEPARABLE_CONV:DEFAULT_MODEL_NAME}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}};function createTinyYolov2(weights,withSeparableConvs=!0){let net=new TinyYolov2(withSeparableConvs);return net.extractWeights(weights),net}var TinyFaceDetectorOptions=class extends TinyYolov2Options{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}},ComposableTask=class{async then(onfulfilled){return onfulfilled(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}},tf41=__toModule(require_tfjs_esm()),tf40=__toModule(require_tfjs_esm());async function extractAllFacesAndComputeResults(parentResults,input,computeResults,extractedFaces,getRectForAlignment=({alignedRect})=>alignedRect){let faceBoxes=parentResults.map(parentResult=>isWithFaceLandmarks(parentResult)?getRectForAlignment(parentResult):parentResult.detection),faces=extractedFaces||(input instanceof tf40.Tensor?await extractFaceTensors(input,faceBoxes):await extractFaces(input,faceBoxes)),results=await computeResults(faces);return faces.forEach(f=>f instanceof tf40.Tensor&&f.dispose()),results}async function extractSingleFaceAndComputeResult(parentResult,input,computeResult,extractedFaces,getRectForAlignment){return extractAllFacesAndComputeResults([parentResult],input,async faces=>computeResult(faces[0]),extractedFaces,getRectForAlignment)}var IOU_THRESHOLD2=.4,BOX_ANCHORS2=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB=[117.001,114.697,97.404],TinyFaceDetector=class extends TinyYolov2Base{constructor(){let config={withSeparableConvs:!0,iouThreshold:IOU_THRESHOLD2,classes:["face"],anchors:BOX_ANCHORS2,meanRgb:MEAN_RGB,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(config)}get anchors(){return this.config.anchors}async locateFaces(input,forwardParams){return(await this.detect(input,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}},nets={ssdMobilenetv1:new SsdMobilenetv1,tinyFaceDetector:new TinyFaceDetector,tinyYolov2:new TinyYolov2,faceLandmark68Net:new FaceLandmark68Net,faceLandmark68TinyNet:new FaceLandmark68TinyNet,faceRecognitionNet:new FaceRecognitionNet,faceExpressionNet:new FaceExpressionNet,ageGenderNet:new AgeGenderNet},ssdMobilenetv1=(input,options)=>nets.ssdMobilenetv1.locateFaces(input,options),tinyFaceDetector=(input,options)=>nets.tinyFaceDetector.locateFaces(input,options),tinyYolov2=(input,options)=>nets.tinyYolov2.locateFaces(input,options),detectFaceLandmarks=input=>nets.faceLandmark68Net.detectLandmarks(input),detectFaceLandmarksTiny=input=>nets.faceLandmark68TinyNet.detectLandmarks(input),computeFaceDescriptor=input=>nets.faceRecognitionNet.computeFaceDescriptor(input),recognizeFaceExpressions=input=>nets.faceExpressionNet.predictExpressions(input),predictAgeAndGender=input=>nets.ageGenderNet.predictAgeAndGender(input),loadSsdMobilenetv1Model=url=>nets.ssdMobilenetv1.load(url),loadTinyFaceDetectorModel=url=>nets.tinyFaceDetector.load(url),loadTinyYolov2Model=url=>nets.tinyYolov2.load(url),loadFaceLandmarkModel=url=>nets.faceLandmark68Net.load(url),loadFaceLandmarkTinyModel=url=>nets.faceLandmark68TinyNet.load(url),loadFaceRecognitionModel=url=>nets.faceRecognitionNet.load(url),loadFaceExpressionModel=url=>nets.faceExpressionNet.load(url),loadAgeGenderModel=url=>nets.ageGenderNet.load(url),loadFaceDetectionModel=loadSsdMobilenetv1Model,locateFaces=ssdMobilenetv1,detectLandmarks=detectFaceLandmarks,PredictFaceExpressionsTaskBase=class extends ComposableTask{constructor(parentTask,input,extractedFaces){super();this.parentTask=parentTask;this.input=input;this.extractedFaces=extractedFaces}},PredictAllFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResults=await this.parentTask,faceExpressionsByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.faceExpressionNet.predictExpressions(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>extendWithFaceExpressions(parentResult,faceExpressionsByFace[i]))}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this,this.input)}},PredictSingleFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let faceExpressions=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceExpressionNet.predictExpressions(face),this.extractedFaces);return extendWithFaceExpressions(parentResult,faceExpressions)}}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this,this.input)}},PredictAllFaceExpressionsWithFaceAlignmentTask=class extends PredictAllFaceExpressionsTask{withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleFaceExpressionsWithFaceAlignmentTask=class extends PredictSingleFaceExpressionsTask{withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},PredictAgeAndGenderTaskBase=class extends ComposableTask{constructor(parentTask,input,extractedFaces){super();this.parentTask=parentTask;this.input=input;this.extractedFaces=extractedFaces}},PredictAllAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResults=await this.parentTask,ageAndGenderByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.ageGenderNet.predictAgeAndGender(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>{let{age,gender,genderProbability}=ageAndGenderByFace[i];return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)})}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this,this.input)}},PredictSingleAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let{age,gender,genderProbability}=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.ageGenderNet.predictAgeAndGender(face),this.extractedFaces);return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)}}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this,this.input)}},PredictAllAgeAndGenderWithFaceAlignmentTask=class extends PredictAllAgeAndGenderTask{withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleAgeAndGenderWithFaceAlignmentTask=class extends PredictSingleAgeAndGenderTask{withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},ComputeFaceDescriptorsTaskBase=class extends ComposableTask{constructor(parentTask,input){super();this.parentTask=parentTask;this.input=input}},ComputeAllFaceDescriptorsTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResults=await this.parentTask;return(await extractAllFacesAndComputeResults(parentResults,this.input,faces=>Promise.all(faces.map(face=>nets.faceRecognitionNet.computeFaceDescriptor(face))),null,parentResult=>parentResult.landmarks.align(null,{useDlibAlignment:!0}))).map((descriptor,i)=>extendWithFaceDescriptor(parentResults[i],descriptor))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}},ComputeSingleFaceDescriptorTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let descriptor=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceRecognitionNet.computeFaceDescriptor(face),null,parentResult2=>parentResult2.landmarks.align(null,{useDlibAlignment:!0}));return extendWithFaceDescriptor(parentResult,descriptor)}}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}},DetectFaceLandmarksTaskBase=class extends ComposableTask{constructor(parentTask,input,useTinyLandmarkNet){super();this.parentTask=parentTask;this.input=input;this.useTinyLandmarkNet=useTinyLandmarkNet}get landmarkNet(){return this.useTinyLandmarkNet?nets.faceLandmark68TinyNet:nets.faceLandmark68Net}},DetectAllFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResults=await this.parentTask,detections=parentResults.map(res=>res.detection),faces=this.input instanceof tf41.Tensor?await extractFaceTensors(this.input,detections):await extractFaces(this.input,detections),faceLandmarksByFace=await Promise.all(faces.map(face=>this.landmarkNet.detectLandmarks(face)));return faces.forEach(f=>f instanceof tf41.Tensor&&f.dispose()),parentResults.map((parentResult,i)=>extendWithFaceLandmarks(parentResult,faceLandmarksByFace[i]))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},DetectSingleFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let{detection}=parentResult,faces=this.input instanceof tf41.Tensor?await extractFaceTensors(this.input,[detection]):await extractFaces(this.input,[detection]),landmarks=await this.landmarkNet.detectLandmarks(faces[0]);return faces.forEach(f=>f instanceof tf41.Tensor&&f.dispose()),extendWithFaceLandmarks(parentResult,landmarks)}}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},DetectFacesTaskBase=class extends ComposableTask{constructor(input,options=new SsdMobilenetv1Options){super();this.input=input;this.options=options}},DetectAllFacesTask=class extends DetectFacesTaskBase{async run(){let{input,options}=this,faceDetectionFunction=options instanceof TinyFaceDetectorOptions?input2=>nets.tinyFaceDetector.locateFaces(input2,options):options instanceof SsdMobilenetv1Options?input2=>nets.ssdMobilenetv1.locateFaces(input2,options):options instanceof TinyYolov2Options?input2=>nets.tinyYolov2.locateFaces(input2,options):null;if(!faceDetectionFunction)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return faceDetectionFunction(input)}runAndExtendWithFaceDetections(){return new Promise(async resolve=>{let detections=await this.run();resolve(detections.map(detection=>extendWithFaceDetection({},detection)))})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(),this.input)}},DetectSingleFaceTask=class extends DetectFacesTaskBase{async run(){let faceDetections=await new DetectAllFacesTask(this.input,this.options),faceDetectionWithHighestScore=faceDetections[0];return faceDetections.forEach(faceDetection=>{faceDetection.score>faceDetectionWithHighestScore.score&&(faceDetectionWithHighestScore=faceDetection)}),faceDetectionWithHighestScore}runAndExtendWithFaceDetection(){return new Promise(async resolve=>{let detection=await this.run();resolve(detection?extendWithFaceDetection({},detection):void 0)})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(),this.input)}};function detectSingleFace(input,options=new SsdMobilenetv1Options){return new DetectSingleFaceTask(input,options)}function detectAllFaces(input,options=new SsdMobilenetv1Options){return new DetectAllFacesTask(input,options)}async function allFacesSsdMobilenetv1(input,minConfidence){return detectAllFaces(input,new SsdMobilenetv1Options(minConfidence?{minConfidence}:{})).withFaceLandmarks().withFaceDescriptors()}async function allFacesTinyYolov2(input,forwardParams={}){return detectAllFaces(input,new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors()}var allFaces=allFacesSsdMobilenetv1;function euclideanDistance(arr1,arr2){if(arr1.length!==arr2.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let desc1=Array.from(arr1),desc2=Array.from(arr2);return Math.sqrt(desc1.map((val,i)=>val-desc2[i]).reduce((res,diff)=>res+diff**2,0))}var FaceMatcher=class{constructor(inputs,distanceThreshold=.6){this._distanceThreshold=distanceThreshold;let inputArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArray.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let count=1,createUniqueLabel=()=>`person ${count++}`;this._labeledDescriptors=inputArray.map(desc=>{if(desc instanceof LabeledFaceDescriptors)return desc;if(desc instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc]);if(desc.descriptor&&desc.descriptor instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc.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(queryDescriptor,descriptors){return descriptors.map(d=>euclideanDistance(d,queryDescriptor)).reduce((d1,d2)=>d1+d2,0)/(descriptors.length||1)}matchDescriptor(queryDescriptor){return this.labeledDescriptors.map(({descriptors,label})=>new FaceMatch(label,this.computeMeanDistance(queryDescriptor,descriptors))).reduce((best,curr)=>best.distance<curr.distance?best:curr)}findBestMatch(queryDescriptor){let bestMatch=this.matchDescriptor(queryDescriptor);return bestMatch.distance<this.distanceThreshold?bestMatch:new FaceMatch("unknown",bestMatch.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(ld=>ld.toJSON())}}static fromJSON(json){let labeledDescriptors=json.labeledDescriptors.map(ld=>LabeledFaceDescriptors.fromJSON(ld));return new FaceMatcher(labeledDescriptors,json.distanceThreshold)}};function createTinyFaceDetector(weights){let net=new TinyFaceDetector;return net.extractWeights(weights),net}function resizeResults(results,dimensions){let{width,height}=new Dimensions(dimensions.width,dimensions.height);if(width<=0||height<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width,height})}`);if(Array.isArray(results))return results.map(obj=>resizeResults(obj,{width,height}));if(isWithFaceLandmarks(results)){let resizedDetection=results.detection.forSize(width,height),resizedLandmarks=results.unshiftedLandmarks.forSize(resizedDetection.box.width,resizedDetection.box.height);return extendWithFaceLandmarks(extendWithFaceDetection(results,resizedDetection),resizedLandmarks)}return isWithFaceDetection(results)?extendWithFaceDetection(results,results.detection.forSize(width,height)):results instanceof FaceLandmarks||results instanceof FaceDetection?results.forSize(width,height):results}var node=typeof process!="undefined",browser3=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",version2={faceapi:version,node,browser:browser3};
//# sourceMappingURL=face-api.node.js.map
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