agent-app/rag-server/node_modules/@vladmandic/face-api/dist/face-api.esm-nobundle.js

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

var ur=Object.defineProperty;var ho=Object.getOwnPropertyDescriptor;var bo=Object.getOwnPropertyNames;var go=Object.prototype.hasOwnProperty;var xo=(o=>typeof require!="undefined"?require:typeof Proxy!="undefined"?new Proxy(o,{get:(t,e)=>(typeof require!="undefined"?require:t)[e]}):o)(function(o){if(typeof require!="undefined")return require.apply(this,arguments);throw Error('Dynamic require of "'+o+'" is not supported')});var Ye=(o,t)=>{for(var e in t)ur(o,e,{get:t[e],enumerable:!0})},pr=(o,t,e,r)=>{if(t&&typeof t=="object"||typeof t=="function")for(let a of bo(t))!go.call(o,a)&&a!==e&&ur(o,a,{get:()=>t[a],enumerable:!(r=ho(t,a))||r.enumerable});return o},w=(o,t,e)=>(pr(o,t,"default"),e&&pr(e,t,"default"));var n={};Ye(n,{version:()=>Po});w(n,mn);w(n,pn);w(n,un);import*as mn from"@tensorflow/tfjs/dist/index.js";import*as pn from"@tensorflow/tfjs-backend-webgl/dist/index.js";import*as un from"@tensorflow/tfjs-backend-wasm/dist/index.js";var fr="4.21.0",vo="4.21.0",yo="4.21.0",To="4.21.0",_o="4.21.0",Po={tfjs:fr,"tfjs-core":fr,"tfjs-converter":vo,"tfjs-backend-cpu":yo,"tfjs-backend-webgl":To,"tfjs-backend-wasm":_o};var Sr={};Ye(Sr,{AnchorPosition:()=>Ke,DrawBox:()=>ee,DrawBoxOptions:()=>de,DrawFaceLandmarks:()=>De,DrawFaceLandmarksOptions:()=>Fe,DrawTextField:()=>xt,DrawTextFieldOptions:()=>Wt,drawContour:()=>Z,drawDetections:()=>Io,drawFaceExpressions:()=>No,drawFaceLandmarks:()=>So});function Z(o,t,e=!1){if(o.beginPath(),t.slice(1).forEach(({x:r,y:a},i)=>{let s=t[i];o.moveTo(s.x,s.y),o.lineTo(r,a)}),e){let r=t[t.length-1],a=t[0];if(!r||!a)return;o.moveTo(r.x,r.y),o.lineTo(a.x,a.y)}o.stroke()}var lr={};Ye(lr,{computeReshapedDimensions:()=>je,getCenterPoint:()=>bt,isDimensions:()=>ue,isEven:()=>pe,isFloat:()=>Ge,isTensor:()=>dt,isTensor1D:()=>wo,isTensor2D:()=>Ve,isTensor3D:()=>K,isTensor4D:()=>R,isValidNumber:()=>G,isValidProbablitiy:()=>Mt,range:()=>X,round:()=>ht});var A=class o{constructor(t,e){if(!G(t)||!G(e))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width:t,height:e})}`);this._width=t,this._height=e}get width(){return this._width}get height(){return this._height}reverse(){return new o(1/this.width,1/this.height)}};function dt(o,t){return o instanceof n.Tensor&&o.shape.length===t}function wo(o){return dt(o,1)}function Ve(o){return dt(o,2)}function K(o){return dt(o,3)}function R(o){return dt(o,4)}function Ge(o){return o%1!==0}function pe(o){return o%2===0}function ht(o,t=2){let e=10**t;return Math.floor(o*e)/e}function ue(o){return o&&o.width&&o.height}function je({width:o,height:t},e){let r=e/Math.max(t,o);return new A(Math.round(o*r),Math.round(t*r))}function bt(o){return o.reduce((t,e)=>t.add(e),new v(0,0)).div(new v(o.length,o.length))}function X(o,t,e){return Array(o).fill(0).map((r,a)=>t+a*e)}function G(o){return!!o&&o!==1/0&&o!==-1/0&&!Number.isNaN(o)||o===0}function Mt(o){return G(o)&&o>=0&&o<=1}var v=class o{constructor(t,e){this._x=t,this._y=e}get x(){return this._x}get y(){return this._y}add(t){return new o(this.x+t.x,this.y+t.y)}sub(t){return new o(this.x-t.x,this.y-t.y)}mul(t){return new o(this.x*t.x,this.y*t.y)}div(t){return new o(this.x/t.x,this.y/t.y)}abs(){return new o(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new o(Math.floor(this.x),Math.floor(this.y))}};var C=class o{static isRect(t){return!!t&&[t.x,t.y,t.width,t.height].every(G)}static assertIsValidBox(t,e,r=!1){if(!o.isRect(t))throw new Error(`${e} - invalid box: ${JSON.stringify(t)}, expected object with properties x, y, width, height`);if(!r&&(t.width<0||t.height<0))throw new Error(`${e} - width (${t.width}) and height (${t.height}) must be positive numbers`)}constructor(t,e=!0){let r=t||{},a=[r.left,r.top,r.right,r.bottom].every(G),i=[r.x,r.y,r.width,r.height].every(G);if(!i&&!a)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(r)}`);let[s,c,m,p]=i?[r.x,r.y,r.width,r.height]:[r.left,r.top,r.right-r.left,r.bottom-r.top];o.assertIsValidBox({x:s,y:c,width:m,height:p},"Box.constructor",e),this._x=s,this._y=c,this._width=m,this._height=p}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 v(this.left,this.top)}get topRight(){return new v(this.right,this.top)}get bottomLeft(){return new v(this.left,this.bottom)}get bottomRight(){return new v(this.right,this.bottom)}round(){let[t,e,r,a]=[this.x,this.y,this.width,this.height].map(i=>Math.round(i));return new o({x:t,y:e,width:r,height:a})}floor(){let[t,e,r,a]=[this.x,this.y,this.width,this.height].map(i=>Math.floor(i));return new o({x:t,y:e,width:r,height:a})}toSquare(){let{x:t,y:e,width:r,height:a}=this,i=Math.abs(r-a);return r<a&&(t-=i/2,r+=i),a<r&&(e-=i/2,a+=i),new o({x:t,y:e,width:r,height:a})}rescale(t){let e=ue(t)?t.width:t,r=ue(t)?t.height:t;return new o({x:this.x*e,y:this.y*r,width:this.width*e,height:this.height*r})}pad(t,e){let[r,a,i,s]=[this.x-t/2,this.y-e/2,this.width+t,this.height+e];return new o({x:r,y:a,width:i,height:s})}clipAtImageBorders(t,e){let{x:r,y:a,right:i,bottom:s}=this,c=Math.max(r,0),m=Math.max(a,0),p=i-c,u=s-m,f=Math.min(p,t-c),l=Math.min(u,e-m);return new o({x:c,y:m,width:f,height:l}).floor()}shift(t,e){let{width:r,height:a}=this,i=this.x+t,s=this.y+e;return new o({x:i,y:s,width:r,height:a})}padAtBorders(t,e){let r=this.width+1,a=this.height+1,i=1,s=1,c=r,m=a,p=this.left,u=this.top,f=this.right,l=this.bottom;return f>e&&(c=-f+e+r,f=e),l>t&&(m=-l+t+a,l=t),p<1&&(m=2-p,p=1),u<1&&(m=2-u,u=1),{dy:s,edy:m,dx:i,edx:c,y:u,ey:l,x:p,ex:f,w:r,h:a}}calibrate(t){return new o({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var Ct=class extends C{constructor(t,e,r,a,i=!1){super({left:t,top:e,right:r,bottom:a},i)}};var It=class o{constructor(t,e,r,a,i){this._imageDims=new A(i.width,i.height),this._score=t,this._classScore=e,this._className=r,this._box=new C(a).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 C(this._box).rescale(this.imageDims.reverse())}forSize(t,e){return new o(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:e})}};var D=class o extends It{constructor(t,e,r){super(t,t,"",e,r)}forSize(t,e){let{score:r,relativeBox:a,imageDims:i}=super.forSize(t,e);return new o(r,a,i)}};function dr(o,t,e=!0){let r=Math.max(0,Math.min(o.right,t.right)-Math.max(o.left,t.left)),a=Math.max(0,Math.min(o.bottom,t.bottom)-Math.max(o.top,t.top)),i=r*a;return e?i/(o.area+t.area-i):i/Math.min(o.area,t.area)}function hr(o){let t=o.map(c=>c.x),e=o.map(c=>c.y),r=t.reduce((c,m)=>m<c?m:c,1/0),a=e.reduce((c,m)=>m<c?m:c,1/0),i=t.reduce((c,m)=>c<m?m:c,0),s=e.reduce((c,m)=>c<m?m:c,0);return new Ct(r,a,i,s)}function br(o,t,e,r=!0){let a=t.map((s,c)=>({score:s,boxIndex:c})).sort((s,c)=>s.score-c.score).map(s=>s.boxIndex),i=[];for(;a.length>0;){let s=a.pop();i.push(s);let c=a,m=[];for(let p=0;p<c.length;p++){let u=c[p],f=o[s],l=o[u];m.push(dr(f,l,r))}a=a.filter((p,u)=>m[u]<=e)}return i}function J(o,t){return n.tidy(()=>{let[e,r,a]=t,i=n.fill([...o.shape.slice(0,3),1],e,"float32"),s=n.fill([...o.shape.slice(0,3),1],r,"float32"),c=n.fill([...o.shape.slice(0,3),1],a,"float32"),m=n.concat([i,s,c],3);return n.sub(o,m)})}function gr(o,t=!1){return n.tidy(()=>{let[e,r]=o.shape.slice(1);if(e===r)return o;let a=Math.abs(e-r),i=Math.round(a*(t?.5:1)),s=e>r?2:1,c=l=>{let d=o.shape.slice();return d[s]=l,n.fill(d,0,"float32")},m=c(i),p=a-m.shape[s],f=[t&&p?c(p):null,o,m].filter(l=>!!l).map(l=>n.cast(l,"float32"));return n.concat(f,s)})}function Rn(o){let t=o.slice();for(let e=t.length-1;e>0;e--){let r=Math.floor(Math.random()*(e+1)),a=t[e];t[e]=t[r],t[r]=a}return t}function fe(o){return 1/(1+Math.exp(-o))}function On(o){return Math.log(o/(1-o))}var Nt=class extends C{constructor(t,e,r,a,i=!1){super({x:t,y:e,width:r,height:a},i)}};var Fo=.5,Do=.43,Eo=.45,$=class{constructor(t,e,r=new v(0,0)){let{width:a,height:i}=e;this._imgDims=new A(a,i),this._shift=r,this._positions=t.map(s=>s.mul(new v(a,i)).add(r))}get shift(){return new v(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(t=>t.sub(this._shift).div(new v(this.imageWidth,this.imageHeight)))}forSize(t,e){return new this.constructor(this.relativePositions,{width:t,height:e})}shiftBy(t,e){return new this.constructor(this.relativePositions,this._imgDims,new v(t,e))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,e={}){if(t){let i=t instanceof D?t.box.floor():new C(t);return this.shiftBy(i.x,i.y).align(null,e)}let{useDlibAlignment:r,minBoxPadding:a}={useDlibAlignment:!1,minBoxPadding:.2,...e};return r?this.alignDlib():this.alignMinBbox(a)}alignDlib(){let t=this.getRefPointsForAlignment(),[e,r,a]=t,i=f=>a.sub(f).magnitude(),s=(i(e)+i(r))/2,c=Math.floor(s/Eo),m=bt(t),p=Math.floor(Math.max(0,m.x-Fo*c)),u=Math.floor(Math.max(0,m.y-Do*c));return new Nt(p,u,Math.min(c,this.imageWidth+p),Math.min(c,this.imageHeight+u))}alignMinBbox(t){let e=hr(this.positions);return e.pad(e.width*t,e.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var xr=class extends ${getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],bt([t[3],t[4]])]}};var Lt=class extends ${getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(bt)}};var Kt=class{constructor(t,e){this._label=t,this._distance=e}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${ht(this.distance)})`:""}`}};var Qt=class extends C{static assertIsValidLabeledBox(t,e){if(C.assertIsValidBox(t,e),!G(t.label))throw new Error(`${e} - expected property label (${t.label}) to be a number`)}constructor(t,e){super(t),this._label=e}get label(){return this._label}};var gt=class o{constructor(t,e){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(e)||e.some(r=>!(r instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=e}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let e=t.descriptors.map(r=>new Float32Array(r));return new o(t.label,e)}};var vr=class extends Qt{static assertIsValidPredictedBox(t,e){if(Qt.assertIsValidLabeledBox(t,e),!Mt(t.score)||!Mt(t.classScore))throw new Error(`${e} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,e,r,a){super(t,e),this._score=r,this._classScore=a}get score(){return this._score}get classScore(){return this._classScore}};function Q(o){return o.detection instanceof D}function St(o,t){return{...o,...{detection:t}}}function Ue(){let o=window.fetch;if(!o)throw new Error("fetch - missing fetch implementation for browser environment");return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),createVideoElement:()=>document.createElement("video"),fetch:o,readFile:()=>{throw new Error("readFile - filesystem not available for browser environment")}}}function te(){return typeof global=="object"&&typeof process!="undefined"&&process.versions!=null&&process.versions.node!=null}function le(o){let t="";if(!o&&te())try{o=xo("fs")}catch(r){t=r.toString()}return{readFile:o?r=>new Promise((a,i)=>{o.readFile(r,(s,c)=>s?i(s):a(c))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)}}}function Xe(){let o=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,e=global.Video||global.HTMLVideoElement,r=()=>{if(o)return new o;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},a=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},i=()=>{if(e)return new e;throw new Error("createVideoElement - missing Video implementation for nodejs environment")},s=global.fetch,c=le();return{Canvas:o||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:r,createImageElement:a,createVideoElement:i,fetch:s,...c}}function Je(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var M;function Mo(){if(!M)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return M}function qe(o){M=o}function Ze(){return Je()?qe(Ue()):te()?qe(Xe()):null}function Co(o){if(M||Ze(),!M)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=M.Canvas,Image:e=M.Image}=o;M.Canvas=t,M.Image=e,M.createCanvasElement=o.createCanvasElement||(()=>new t),M.createImageElement=o.createImageElement||(()=>new e),M.ImageData=o.ImageData||M.ImageData,M.Video=o.Video||M.Video,M.fetch=o.fetch||M.fetch,M.readFile=o.readFile||M.readFile}var _={getEnv:Mo,setEnv:qe,initialize:Ze,createBrowserEnv:Ue,createFileSystem:le,createNodejsEnv:Xe,monkeyPatch:Co,isBrowser:Je,isNodejs:te};Ze();function At(o){return!_.isNodejs()&&typeof o=="string"?document.getElementById(o):o}function W(o){let{Canvas:t,CanvasRenderingContext2D:e}=_.getEnv();if(o instanceof e)return o;let r=At(o);if(!(r instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let a=r.getContext("2d",{willReadFrequently:!0});if(!a)throw new Error("resolveContext2d - canvas 2d context is null");return a}var Ke=(a=>(a.TOP_LEFT="TOP_LEFT",a.TOP_RIGHT="TOP_RIGHT",a.BOTTOM_LEFT="BOTTOM_LEFT",a.BOTTOM_RIGHT="BOTTOM_RIGHT",a))(Ke||{}),Wt=class{constructor(t={}){let{anchorPosition:e,backgroundColor:r,fontColor:a,fontSize:i,fontStyle:s,padding:c}=t;this.anchorPosition=e||"TOP_LEFT",this.backgroundColor=r||"rgba(0, 0, 0, 0.5)",this.fontColor=a||"rgba(255, 255, 255, 1)",this.fontSize=i||14,this.fontStyle=s||"Georgia",this.padding=c||4}},xt=class o{constructor(t,e,r={}){this.text=typeof t=="string"?[t]:t instanceof o?t.text:t,this.anchor=e,this.options=new Wt(r)}measureWidth(t){let{padding:e}=this.options;return this.text.map(r=>t.measureText(r).width).reduce((r,a)=>r<a?a:r,0)+2*e}measureHeight(){let{fontSize:t,padding:e}=this.options;return this.text.length*t+2*e}getUpperLeft(t,e){let{anchorPosition:r}=this.options,a=r==="BOTTOM_RIGHT"||r==="TOP_RIGHT",i=r==="BOTTOM_LEFT"||r==="BOTTOM_RIGHT",s=this.measureWidth(t),c=this.measureHeight(),m=a?this.anchor.x-s:this.anchor.x,p=i?this.anchor.y-c:this.anchor.y;if(e){let{width:u,height:f}=e,l=Math.max(Math.min(m,u-s),0),d=Math.max(Math.min(p,f-c),0);return{x:l,y:d}}return{x:m,y:p}}draw(t){let e=At(t),r=W(e),{backgroundColor:a,fontColor:i,fontSize:s,fontStyle:c,padding:m}=this.options;r.font=`${s}px ${c}`;let p=this.measureWidth(r),u=this.measureHeight();r.fillStyle=a;let f=this.getUpperLeft(r,e);r.fillRect(f.x,f.y,p,u),r.fillStyle=i,this.text.forEach((l,d)=>{let b=m+f.x,y=m+f.y+(d+1)*s;r.fillText(l,b,y)})}};var de=class{constructor(t={}){let{boxColor:e,lineWidth:r,label:a,drawLabelOptions:i}=t;this.boxColor=e||"rgba(0, 0, 255, 1)",this.lineWidth=r||2,this.label=a;let s={anchorPosition:"BOTTOM_LEFT",backgroundColor:this.boxColor};this.drawLabelOptions=new Wt({...s,...i})}},ee=class{constructor(t,e={}){this.box=new C(t),this.options=new de(e)}draw(t){let e=W(t),{boxColor:r,lineWidth:a}=this.options,{x:i,y:s,width:c,height:m}=this.box;e.strokeStyle=r,e.lineWidth=a,e.strokeRect(i,s,c,m);let{label:p}=this.options;p&&new xt([p],{x:i-a/2,y:s},this.options.drawLabelOptions).draw(t)}};function Io(o,t){(Array.isArray(t)?t:[t]).forEach(r=>{let a=r instanceof D?r.score:Q(r)?r.detection.score:void 0,i=r instanceof D?r.box:Q(r)?r.detection.box:new C(r),s=a?`${ht(a)}`:void 0;new ee(i,{label:s}).draw(o)})}function he(o){let{Image:t,Video:e}=_.getEnv();return o instanceof t&&o.complete||o instanceof e&&o.readyState>=3}function yr(o){return new Promise((t,e)=>{if(o instanceof _.getEnv().Canvas||he(o)){t(null);return}function r(i){i.currentTarget&&(i.currentTarget.removeEventListener("load",a),i.currentTarget.removeEventListener("error",r),e(i))}function a(i){i.currentTarget&&(i.currentTarget.removeEventListener("load",a),i.currentTarget.removeEventListener("error",r),t(i))}o.addEventListener("load",a),o.addEventListener("error",r)})}function Tr(o){return new Promise((t,e)=>{o instanceof Blob||e(new Error("bufferToImage - expected buf to be of type: Blob"));let r=new FileReader;r.onload=()=>{typeof r.result!="string"&&e(new Error("bufferToImage - expected reader.result to be a string, in onload"));let a=_.getEnv().createImageElement();a.onload=()=>t(a),a.onerror=e,a.src=r.result},r.onerror=e,r.readAsDataURL(o)})}function kt(o){let{Image:t,Video:e}=_.getEnv();return o instanceof t?new A(o.naturalWidth,o.naturalHeight):o instanceof e?new A(o.videoWidth,o.videoHeight):new A(o.width,o.height)}function Bt({width:o,height:t}){let{createCanvasElement:e}=_.getEnv(),r=e();return r.width=o,r.height=t,r}function be(o,t){let{ImageData:e}=_.getEnv();if(!(o instanceof e)&&!he(o))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:r,height:a}=t||kt(o),i=Bt({width:r,height:a});return o instanceof e?W(i).putImageData(o,0,0):W(i).drawImage(o,0,0,r,a),i}async function _r(o,t){let e=t||_.getEnv().createCanvasElement(),[r,a,i]=o.shape.slice(R(o)?1:0),s=n.tidy(()=>o.as3D(r,a,i).toInt());return await n.browser.toPixels(s,e),s.dispose(),e}function Qe(o){let{Image:t,Canvas:e,Video:r}=_.getEnv();return o instanceof t||o instanceof e||o instanceof r}function Pr(o,t,e=!1){let{Image:r,Canvas:a}=_.getEnv();if(!(o instanceof r||o instanceof a))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return Bt({width:1,height:1});let i=kt(o),s=t/Math.max(i.height,i.width),c=s*i.width,m=s*i.height,p=Bt({width:t,height:t}),u=o instanceof a?o:be(o),f=Math.abs(c-m)/2,l=e&&c<m?f:0,d=e&&m<c?f:0;return u.width>0&&u.height>0&&W(p).drawImage(u,l,d,c,m),p}var tt=class{constructor(t,e=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];this._inputSize=0;if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=e,this._batchSize=t.length,t.forEach((r,a)=>{if(K(r)){this._imageTensors[a]=r,this._inputDimensions[a]=r.shape;return}if(R(r)){let s=r.shape[0];if(s!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${s} passed, but not supported in input array`);this._imageTensors[a]=r,this._inputDimensions[a]=r.shape.slice(1);return}let i=r instanceof _.getEnv().Canvas?r:be(r);this._canvases[a]=i,this._inputDimensions[a]=[i.height,i.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 X(this.batchSize,0,1).map((t,e)=>this.getReshapedInputDimensions(e))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let e=this.getInputWidth(t),r=this.getInputHeight(t);return je({width:e,height:r},this.inputSize)}toBatchTensor(t,e=!0){return this._inputSize=t,n.tidy(()=>{let r=X(this.batchSize,0,1).map(i=>{let s=this.getInput(i);if(s instanceof n.Tensor){let c=R(s)?s:n.expandDims(s);return c=gr(c,e),(c.shape[1]!==t||c.shape[2]!==t)&&(c=n.image.resizeBilinear(c,[t,t],!1,!1)),c.as3D(t,t,3)}if(s instanceof _.getEnv().Canvas)return n.browser.fromPixels(Pr(s,t,e));throw new Error(`toBatchTensor - at batchIdx ${i}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${s}`)});return n.stack(r.map(i=>n.cast(i,"float32"))).as4D(this.batchSize,t,t,3)})}};async function F(o){if(o instanceof tt)return o;let t=Array.isArray(o)?o:[o];if(!t.length)throw new Error("toNetInput - empty array passed as input");let e=a=>Array.isArray(o)?` at input index ${a}:`:"",r=t.map(At);return r.forEach((a,i)=>{if(!Qe(a)&&!K(a)&&!R(a))throw typeof t[i]=="string"?new Error(`toNetInput -${e(i)} string passed, but could not resolve HTMLElement for element id ${t[i]}`):new Error(`toNetInput -${e(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(R(a)){let s=a.shape[0];if(s!==1)throw new Error(`toNetInput -${e(i)} tf.Tensor4D with batchSize ${s} passed, but not supported in input array`)}}),await Promise.all(r.map(a=>Qe(a)&&yr(a))),new tt(r,Array.isArray(o))}async function re(o,t){let{Canvas:e}=_.getEnv(),r=o;if(!(o instanceof e)){let s=await F(o);if(s.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let c=s.getInput(0);r=c instanceof e?c:await _r(c)}let a=W(r);return t.map(s=>s instanceof D?s.forSize(r.width,r.height).box.floor():s).map(s=>s.clipAtImageBorders(r.width,r.height)).map(({x:s,y:c,width:m,height:p})=>{let u=Bt({width:m,height:p});return m>0&&p>0&&W(u).putImageData(a.getImageData(s,c,m,p),0,0),u})}async function oe(o,t){if(!K(o)&&!R(o))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(R(o)&&o.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return n.tidy(()=>{let[e,r,a]=o.shape.slice(R(o)?1:0);return t.map(c=>c instanceof D?c.forSize(r,e).box:c).map(c=>c.clipAtImageBorders(r,e)).filter(c=>c.width>0&&c.height>0).map(({x:c,y:m,width:p,height:u})=>n.slice3d(o.as3D(e,r,a),[m,c,0],[u,p,a]))})}async function st(o,t){let{fetch:e}=_.getEnv(),r=await e(o,t);if(!(r.status<400))throw new Error(`failed to fetch: (${r.status}) ${r.statusText}, from url: ${r.url}`);return r}async function Fi(o){let t=await st(o),e=await t.blob();if(!e.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${e.type}, for url: ${t.url}`);return Tr(e)}async function wr(o){return(await st(o)).json()}async function Ii(o){return new Float32Array(await(await st(o)).arrayBuffer())}function Fr(o){return new Promise((t,e)=>{o instanceof Blob||e(new Error("bufferToVideo - expected buf to be of type: Blob"));let r=_.getEnv().createVideoElement();r.oncanplay=()=>t(r),r.onerror=e,r.playsInline=!0,r.muted=!0,r.src=URL.createObjectURL(o),r.play()})}async function ki(o){let t=await st(o),e=await t.blob();if(!e.type.startsWith("video/"))throw new Error(`fetchVideo - expected blob type to be of type video/*, instead have: ${e.type}, for url: ${t.url}`);return Fr(e)}function ge(o,t){let e=`${t}-weights_manifest.json`;if(!o)return{modelBaseUri:"",manifestUri:e};if(o==="/")return{modelBaseUri:"/",manifestUri:`/${e}`};let r=o.startsWith("http://")?"http://":o.startsWith("https://")?"https://":"";o=o.replace(r,"");let a=o.split("/").filter(c=>c),i=o.endsWith(".json")?a[a.length-1]:e,s=r+(o.endsWith(".json")?a.slice(0,a.length-1):a).join("/");return s=o.startsWith("/")?`/${s}`:s,{modelBaseUri:s,manifestUri:s==="/"?`/${i}`:`${s}/${i}`}}async function Dr(o,t){let{manifestUri:e,modelBaseUri:r}=ge(o,t),a=await wr(e);return n.io.loadWeights(a,r)}function Yi(o,t,e=!1){let{width:r,height:a}=e?kt(t):t;return o.width=r,o.height=a,{width:r,height:a}}var I=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:e,objProp:r}=this.traversePropertyPath(t);return e[r]}reassignParamFromPath(t,e){let{obj:r,objProp:a}=this.traversePropertyPath(t);r[a].dispose(),r[a]=e}getParamList(){return this._paramMappings.map(({paramPath:t})=>({path:t,tensor:this.getParamFromPath(t)}))}getTrainableParams(){return this.getParamList().filter(t=>t.tensor instanceof n.Variable)}getFrozenParams(){return this.getParamList().filter(t=>!(t.tensor instanceof n.Variable))}variable(){this.getFrozenParams().forEach(({path:t,tensor:e})=>{this.reassignParamFromPath(t,e.variable())})}freeze(){this.getTrainableParams().forEach(({path:t,tensor:e})=>{let r=n.tensor(e.dataSync());e.dispose(),this.reassignParamFromPath(t,r)})}dispose(t=!0){this.getParamList().forEach(e=>{if(t&&e.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${e.path}`);e.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:t})=>Array.from(t.dataSync())).reduce((t,e)=>t.concat(e)))}async load(t){if(t instanceof Float32Array){this.extractWeights(t);return}await this.loadFromUri(t)}async loadFromUri(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let e=await Dr(t,this.getDefaultModelName());this.loadFromWeightMap(e)}async loadFromDisk(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile:e}=_.getEnv(),{manifestUri:r,modelBaseUri:a}=ge(t,this.getDefaultModelName()),i=p=>Promise.all(p.map(u=>e(u).then(f=>typeof f=="string"?Buffer.from(f):f.buffer))),s=n.io.weightsLoaderFactory(i),c=JSON.parse((await e(r)).toString()),m=await s(c,a);this.loadFromWeightMap(m)}loadFromWeightMap(t){let{paramMappings:e,params:r}=this.extractParamsFromWeightMap(t);this._paramMappings=e,this._params=r}extractWeights(t){let{paramMappings:e,params:r}=this.extractParams(t);this._paramMappings=e,this._params=r}traversePropertyPath(t){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let e=t.split("/").reduce((i,s)=>{if(!i.nextObj.hasOwnProperty(s))throw new Error(`traversePropertyPath - object does not have property ${s}, for path ${t}`);return{obj:i.nextObj,objProp:s,nextObj:i.nextObj[s]}},{nextObj:this.params}),{obj:r,objProp:a}=e;if(!r||!a||!(r[a]instanceof n.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${t}`);return{obj:r,objProp:a}}};function S(o,t,e){return n.tidy(()=>{let r=n.separableConv2d(o,t.depthwise_filter,t.pointwise_filter,e,"same");return r=n.add(r,t.bias),r})}function xe(o,t,e=!1){return n.tidy(()=>{let r=n.relu(e?n.add(n.conv2d(o,t.conv0.filters,[2,2],"same"),t.conv0.bias):S(o,t.conv0,[2,2])),a=S(r,t.conv1,[1,1]),i=n.relu(n.add(r,a)),s=S(i,t.conv2,[1,1]);return n.relu(n.add(r,n.add(a,s)))})}function ne(o,t,e=!1,r=!0){return n.tidy(()=>{let a=n.relu(e?n.add(n.conv2d(o,t.conv0.filters,r?[2,2]:[1,1],"same"),t.conv0.bias):S(o,t.conv0,r?[2,2]:[1,1])),i=S(a,t.conv1,[1,1]),s=n.relu(n.add(a,i)),c=S(s,t.conv2,[1,1]),m=n.relu(n.add(a,n.add(i,c))),p=S(m,t.conv3,[1,1]);return n.relu(n.add(a,n.add(i,n.add(c,p))))})}function vt(o,t,e="same",r=!1){return n.tidy(()=>{let a=n.add(n.conv2d(o,t.filters,[1,1],e),t.bias);return r?n.relu(a):a})}function N(o,t){Object.keys(o).forEach(e=>{t.some(r=>r.originalPath===e)||o[e].dispose()})}function Rt(o,t){return(e,r,a,i)=>{let s=n.tensor4d(o(e*r*a*a),[a,a,e,r]),c=n.tensor1d(o(r));return t.push({paramPath:`${i}/filters`},{paramPath:`${i}/bias`}),{filters:s,bias:c}}}function ve(o,t){return(e,r,a)=>{let i=n.tensor2d(o(e*r),[e,r]),s=n.tensor1d(o(r));return t.push({paramPath:`${a}/weights`},{paramPath:`${a}/bias`}),{weights:i,bias:s}}}var ae=class{constructor(t,e,r){this.depthwise_filter=t;this.pointwise_filter=e;this.bias=r}};function $t(o,t){return(e,r,a)=>{let i=n.tensor4d(o(9*e),[3,3,e,1]),s=n.tensor4d(o(e*r),[1,1,e,r]),c=n.tensor1d(o(r));return t.push({paramPath:`${a}/depthwise_filter`},{paramPath:`${a}/pointwise_filter`},{paramPath:`${a}/bias`}),new ae(i,s,c)}}function Ot(o){return t=>{let e=o(`${t}/depthwise_filter`,4),r=o(`${t}/pointwise_filter`,4),a=o(`${t}/bias`,1);return new ae(e,r,a)}}function k(o,t){return(e,r,a)=>{let i=o[e];if(!dt(i,r))throw new Error(`expected weightMap[${e}] to be a Tensor${r}D, instead have ${i}`);return t.push({originalPath:e,paramPath:a||e}),i}}function L(o){let t=o;function e(a){let i=t.slice(0,a);return t=t.slice(a),i}function r(){return t}return{extractWeights:e,getRemainingWeights:r}}function ye(o,t){let e=Rt(o,t),r=$t(o,t);function a(s,c,m,p=!1){let u=p?e(s,c,3,`${m}/conv0`):r(s,c,`${m}/conv0`),f=r(c,c,`${m}/conv1`),l=r(c,c,`${m}/conv2`);return{conv0:u,conv1:f,conv2:l}}function i(s,c,m,p=!1){let{conv0:u,conv1:f,conv2:l}=a(s,c,m,p),d=r(c,c,`${m}/conv3`);return{conv0:u,conv1:f,conv2:l,conv3:d}}return{extractDenseBlock3Params:a,extractDenseBlock4Params:i}}function Er(o){let t=[],{extractWeights:e,getRemainingWeights:r}=L(o),{extractDenseBlock4Params:a}=ye(e,t),i=a(3,32,"dense0",!0),s=a(32,64,"dense1"),c=a(64,128,"dense2"),m=a(128,256,"dense3");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:i,dense1:s,dense2:c,dense3:m}}}function Te(o){return t=>{let e=o(`${t}/filters`,4),r=o(`${t}/bias`,1);return{filters:e,bias:r}}}function _e(o,t){let e=k(o,t),r=Te(e),a=Ot(e);function i(c,m=!1){let p=m?r(`${c}/conv0`):a(`${c}/conv0`),u=a(`${c}/conv1`),f=a(`${c}/conv2`);return{conv0:p,conv1:u,conv2:f}}function s(c,m=!1){let p=m?r(`${c}/conv0`):a(`${c}/conv0`),u=a(`${c}/conv1`),f=a(`${c}/conv2`),l=a(`${c}/conv3`);return{conv0:p,conv1:u,conv2:f,conv3:l}}return{extractDenseBlock3Params:i,extractDenseBlock4Params:s}}function Mr(o){let t=[],{extractDenseBlock4Params:e}=_e(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2"),dense3:e("dense3")};return N(o,t),{params:r,paramMappings:t}}var Ht=class extends I{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceFeatureExtractor - load model before inference");return n.tidy(()=>{let r=n.cast(t.toBatchTensor(112,!0),"float32"),i=J(r,[122.782,117.001,104.298]).div(255),s=ne(i,e.dense0,!0);return s=ne(s,e.dense1),s=ne(s,e.dense2),s=ne(s,e.dense3),s=n.avgPool(s,[7,7],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await F(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return Mr(t)}extractParams(t){return Er(t)}};function se(o,t){return n.tidy(()=>n.add(n.matMul(o,t.weights),t.bias))}function Cr(o,t,e){let r=[],{extractWeights:a,getRemainingWeights:i}=L(o),c=ve(a,r)(t,e,"fc");if(i().length!==0)throw new Error(`weights remaing after extract: ${i().length}`);return{paramMappings:r,params:{fc:c}}}function Ir(o){let t=[],e=k(o,t);function r(i){let s=e(`${i}/weights`,2),c=e(`${i}/bias`,1);return{weights:s,bias:c}}let a={fc:r("fc")};return N(o,t),{params:a,paramMappings:t}}function Pe(o){let t={},e={};return Object.keys(o).forEach(r=>{let a=r.startsWith("fc")?e:t;a[r]=o[r]}),{featureExtractorMap:t,classifierMap:e}}var zt=class extends I{constructor(t,e){super(t),this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return n.tidy(()=>{let r=t instanceof tt?this.faceFeatureExtractor.forwardInput(t):t;return se(r.as2D(r.shape[0],-1),e.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return Cr(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=Pe(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Ir(r)}extractParams(t){let e=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),a=r*e+r,i=t.slice(0,t.length-a),s=t.slice(t.length-a);return this.faceFeatureExtractor.extractWeights(i),this.extractClassifierParams(s)}};var Nr=["neutral","happy","sad","angry","fearful","disgusted","surprised"],it=class{constructor(t){this.neutral=0;this.happy=0;this.sad=0;this.angry=0;this.fearful=0;this.disgusted=0;this.surprised=0;if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Nr.forEach((e,r)=>{this[e]=t[r]})}asSortedArray(){return Nr.map(t=>({expression:t,probability:this[t]})).sort((t,e)=>e.probability-t.probability)}};var we=class extends zt{constructor(t=new Ht){super("FaceExpressionNet",t)}forwardInput(t){return n.tidy(()=>n.softmax(this.runNet(t)))}async forward(t){return this.forwardInput(await F(t))}async predictExpressions(t){let e=await F(t),r=await this.forwardInput(e),a=await Promise.all(n.unstack(r).map(async s=>{let c=s.dataSync();return s.dispose(),c}));r.dispose();let i=a.map(s=>new it(s));return e.isBatchInput?i:i[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Lr(o){return o.expressions instanceof it}function tr(o,t){return{...o,...{expressions:t}}}function No(o,t,e=.1,r){(Array.isArray(t)?t:[t]).forEach(i=>{let s=i instanceof it?i:Lr(i)?i.expressions:void 0;if(!s)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let m=s.asSortedArray().filter(f=>f.probability>e),p=Q(i)?i.detection.box.bottomLeft:r||new v(0,0);new xt(m.map(f=>`${f.expression} (${ht(f.probability)})`),p).draw(o)})}function Yt(o){return Q(o)&&o.landmarks instanceof $&&o.unshiftedLandmarks instanceof $&&o.alignedRect instanceof D}function Lo(o){let t=m=>m*180/Math.PI,e=(m,p)=>Math.sqrt((m.x-p.x)**2+(m.y-p.y)**2),r={roll:void 0,pitch:void 0,yaw:void 0},a=(m,p,u)=>{let f=Math.floor(m.x-p.x),l=Math.floor(p.x-u.x);return f-l},i=(m,p)=>{let u=Math.hypot(p.x-m.x,p.y-m.y),f=p.y-m.y,l=Math.asin(f/u),d=t(l),b=Math.floor(90-d),y=p.x-m.x<0?-1:1;return b*y},s=(m,p,u)=>{let f=e(m,u),l=new v((m.x+u.x)/2,(m.y+u.y)/2),d=e(p,l),b=Math.atan(d/f),y=Math.floor(t(b)),h=l.y-p.y<0?-1:1;return y*h};if(!o||!o.positions||o.positions.length!==68)return r;let c=o.positions;return r.roll=i(c[27],c[66]),r.pitch=s(c[14],c[30],c[2]),r.yaw=a(c[14],c[33],c[2]),r}function ie(o,t){let{box:e}=o.detection,r=t.shiftBy(e.x,e.y),a=r.align(),{imageDims:i}=o.detection,s=new D(o.detection.score,a.rescale(i.reverse()),i),c=Lo(t);return{...o,...{landmarks:r,unshiftedLandmarks:t,alignedRect:s,angle:c}}}var Fe=class{constructor(t={}){let{drawLines:e=!0,drawPoints:r=!0,lineWidth:a,lineColor:i,pointSize:s,pointColor:c}=t;this.drawLines=e,this.drawPoints=r,this.lineWidth=a||1,this.pointSize=s||2,this.lineColor=i||"rgba(0, 255, 255, 1)",this.pointColor=c||"rgba(255, 0, 255, 1)"}},De=class{constructor(t,e={}){this.faceLandmarks=t,this.options=new Fe(e)}draw(t){let e=W(t),{drawLines:r,drawPoints:a,lineWidth:i,lineColor:s,pointSize:c,pointColor:m}=this.options;if(r&&this.faceLandmarks instanceof Lt&&(e.strokeStyle=s,e.lineWidth=i,Z(e,this.faceLandmarks.getJawOutline()),Z(e,this.faceLandmarks.getLeftEyeBrow()),Z(e,this.faceLandmarks.getRightEyeBrow()),Z(e,this.faceLandmarks.getNose()),Z(e,this.faceLandmarks.getLeftEye(),!0),Z(e,this.faceLandmarks.getRightEye(),!0),Z(e,this.faceLandmarks.getMouth(),!0)),a){e.strokeStyle=m,e.fillStyle=m;let p=u=>{e.beginPath(),e.arc(u.x,u.y,c,0,2*Math.PI),e.fill()};this.faceLandmarks.positions.forEach(p)}}};function So(o,t){(Array.isArray(t)?t:[t]).forEach(r=>{let a=r instanceof $?r:Yt(r)?r.landmarks:void 0;if(!a)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new De(a).draw(o)})}var Ar="1.7.14";function ko(o,t){let e=Rt(o,t),r=$t(o,t);function a(s,c,m){let p=r(s,c,`${m}/separable_conv0`),u=r(c,c,`${m}/separable_conv1`),f=e(s,c,1,`${m}/expansion_conv`);return{separable_conv0:p,separable_conv1:u,expansion_conv:f}}function i(s,c){let m=r(s,s,`${c}/separable_conv0`),p=r(s,s,`${c}/separable_conv1`),u=r(s,s,`${c}/separable_conv2`);return{separable_conv0:m,separable_conv1:p,separable_conv2:u}}return{extractConvParams:e,extractSeparableConvParams:r,extractReductionBlockParams:a,extractMainBlockParams:i}}function Wr(o,t){let e=[],{extractWeights:r,getRemainingWeights:a}=L(o),{extractConvParams:i,extractSeparableConvParams:s,extractReductionBlockParams:c,extractMainBlockParams:m}=ko(r,e),p=i(3,32,3,"entry_flow/conv_in"),u=c(32,64,"entry_flow/reduction_block_0"),f=c(64,128,"entry_flow/reduction_block_1"),l={conv_in:p,reduction_block_0:u,reduction_block_1:f},d={};X(t,0,1).forEach(g=>{d[`main_block_${g}`]=m(128,`middle_flow/main_block_${g}`)});let b=c(128,256,"exit_flow/reduction_block"),y=s(256,512,"exit_flow/separable_conv"),h={reduction_block:b,separable_conv:y};if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:e,params:{entry_flow:l,middle_flow:d,exit_flow:h}}}function Bo(o,t){let e=k(o,t),r=Te(e),a=Ot(e);function i(c){let m=a(`${c}/separable_conv0`),p=a(`${c}/separable_conv1`),u=r(`${c}/expansion_conv`);return{separable_conv0:m,separable_conv1:p,expansion_conv:u}}function s(c){let m=a(`${c}/separable_conv0`),p=a(`${c}/separable_conv1`),u=a(`${c}/separable_conv2`);return{separable_conv0:m,separable_conv1:p,separable_conv2:u}}return{extractConvParams:r,extractSeparableConvParams:a,extractReductionBlockParams:i,extractMainBlockParams:s}}function kr(o,t){let e=[],{extractConvParams:r,extractSeparableConvParams:a,extractReductionBlockParams:i,extractMainBlockParams:s}=Bo(o,e),c=r("entry_flow/conv_in"),m=i("entry_flow/reduction_block_0"),p=i("entry_flow/reduction_block_1"),u={conv_in:c,reduction_block_0:m,reduction_block_1:p},f={};X(t,0,1).forEach(y=>{f[`main_block_${y}`]=s(`middle_flow/main_block_${y}`)});let l=i("exit_flow/reduction_block"),d=a("exit_flow/separable_conv"),b={reduction_block:l,separable_conv:d};return N(o,e),{params:{entry_flow:u,middle_flow:f,exit_flow:b},paramMappings:e}}function Br(o,t,e){return n.add(n.conv2d(o,t.filters,e,"same"),t.bias)}function er(o,t,e=!0){let r=e?n.relu(o):o;return r=S(r,t.separable_conv0,[1,1]),r=S(n.relu(r),t.separable_conv1,[1,1]),r=n.maxPool(r,[3,3],[2,2],"same"),r=n.add(r,Br(o,t.expansion_conv,[2,2])),r}function Ro(o,t){let e=S(n.relu(o),t.separable_conv0,[1,1]);return e=S(n.relu(e),t.separable_conv1,[1,1]),e=S(n.relu(e),t.separable_conv2,[1,1]),e=n.add(e,o),e}var Ee=class extends I{constructor(t){super("TinyXception"),this._numMainBlocks=t}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyXception - load model before inference");return n.tidy(()=>{let r=n.cast(t.toBatchTensor(112,!0),"float32"),i=J(r,[122.782,117.001,104.298]).div(255),s=n.relu(Br(i,e.entry_flow.conv_in,[2,2]));return s=er(s,e.entry_flow.reduction_block_0,!1),s=er(s,e.entry_flow.reduction_block_1),X(this._numMainBlocks,0,1).forEach(c=>{s=Ro(s,e.middle_flow[`main_block_${c}`])}),s=er(s,e.exit_flow.reduction_block),s=n.relu(S(s,e.exit_flow.separable_conv,[1,1])),s})}async forward(t){return this.forwardInput(await F(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return kr(t,this._numMainBlocks)}extractParams(t){return Wr(t,this._numMainBlocks)}};function Rr(o){let t=[],{extractWeights:e,getRemainingWeights:r}=L(o),a=ve(e,t),i=a(512,1,"fc/age"),s=a(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:i,gender:s}}}}function $r(o){let t=[],e=k(o,t);function r(i){let s=e(`${i}/weights`,2),c=e(`${i}/bias`,1);return{weights:s,bias:c}}let a={fc:{age:r("fc/age"),gender:r("fc/gender")}};return N(o,t),{params:a,paramMappings:t}}var rr=(e=>(e.FEMALE="female",e.MALE="male",e))(rr||{});var Me=class extends I{constructor(t=new Ee(2)){super("AgeGenderNet"),this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return n.tidy(()=>{let r=t instanceof tt?this.faceFeatureExtractor.forwardInput(t):t,a=n.avgPool(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),i=se(a,e.fc.age).as1D(),s=se(a,e.fc.gender);return{age:i,gender:s}})}forwardInput(t){return n.tidy(()=>{let{age:e,gender:r}=this.runNet(t);return{age:e,gender:n.softmax(r)}})}async forward(t){return this.forwardInput(await F(t))}async predictAgeAndGender(t){let e=await F(t),r=await this.forwardInput(e),a=n.unstack(r.age),i=n.unstack(r.gender),s=a.map((m,p)=>({ageTensor:m,genderTensor:i[p]})),c=await Promise.all(s.map(async({ageTensor:m,genderTensor:p})=>{let u=m.dataSync()[0],f=p.dataSync()[0],l=f>.5,d=l?"male":"female",b=l?f:1-f;return m.dispose(),p.dispose(),{age:u,gender:d,genderProbability:b}}));return r.age.dispose(),r.gender.dispose(),e.isBatchInput?c:c[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return Rr(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=Pe(t);return this.faceFeatureExtractor.loadFromWeightMap(e),$r(r)}extractParams(t){let r=t.slice(0,t.length-1539),a=t.slice(t.length-1539);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(a)}};var Vt=class extends zt{postProcess(t,e,r){let a=r.map(({width:s,height:c})=>{let m=e/Math.max(c,s);return{width:s*m,height:c*m}}),i=a.length;return n.tidy(()=>{let s=(f,l)=>n.stack([n.fill([68],f,"float32"),n.fill([68],l,"float32")],1).as2D(1,136).as1D(),c=(f,l)=>{let{width:d,height:b}=a[f];return l(d,b)?Math.abs(d-b)/2:0},m=f=>c(f,(l,d)=>l<d),p=f=>c(f,(l,d)=>d<l);return t.mul(n.fill([i,136],e,"float32")).sub(n.stack(Array.from(Array(i),(f,l)=>s(m(l),p(l))))).div(n.stack(Array.from(Array(i),(f,l)=>s(a[l].width,a[l].height))))})}forwardInput(t){return n.tidy(()=>{let e=this.runNet(t);return this.postProcess(e,t.inputSize,t.inputDimensions.map(([r,a])=>({height:r,width:a})))})}async forward(t){return this.forwardInput(await F(t))}async detectLandmarks(t){let e=await F(t),r=n.tidy(()=>n.unstack(this.forwardInput(e))),a=await Promise.all(r.map(async(i,s)=>{let c=Array.from(i.dataSync()),m=c.filter((u,f)=>pe(f)),p=c.filter((u,f)=>!pe(f));return new Lt(Array(68).fill(0).map((u,f)=>new v(m[f],p[f])),{height:e.getInputHeight(s),width:e.getInputWidth(s)})}));return r.forEach(i=>i.dispose()),e.isBatchInput?a:a[0]}getClassifierChannelsOut(){return 136}};var Gt=class extends Vt{constructor(t=new Ht){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function Or(o){let t=[],{extractDenseBlock3Params:e}=_e(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2")};return N(o,t),{params:r,paramMappings:t}}function Hr(o){let t=[],{extractWeights:e,getRemainingWeights:r}=L(o),{extractDenseBlock3Params:a}=ye(e,t),i=a(3,32,"dense0",!0),s=a(32,64,"dense1"),c=a(64,128,"dense2");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:i,dense1:s,dense2:c}}}var Ce=class extends I{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyFaceFeatureExtractor - load model before inference");return n.tidy(()=>{let r=n.cast(t.toBatchTensor(112,!0),"float32"),i=J(r,[122.782,117.001,104.298]).div(255),s=xe(i,e.dense0,!0);return s=xe(s,e.dense1),s=xe(s,e.dense2),s=n.avgPool(s,[14,14],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await F(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return Or(t)}extractParams(t){return Hr(t)}};var Ie=class extends Vt{constructor(t=new Ce){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var zr=class extends Gt{};function Yr(o,t){return n.add(n.mul(o,t.weights),t.biases)}function or(o,t,e,r,a="same"){let{filters:i,bias:s}=t.conv,c=n.conv2d(o,i,e,a);return c=n.add(c,s),c=Yr(c,t.scale),r?n.relu(c):c}function Vr(o,t){return or(o,t,[1,1],!0)}function nr(o,t){return or(o,t,[1,1],!1)}function Ne(o,t){return or(o,t,[2,2],!0,"valid")}function $o(o,t){function e(c,m,p){let u=o(c),f=u.length/(m*p*p);if(Ge(f))throw new Error(`depth has to be an integer: ${f}, weights.length: ${u.length}, numFilters: ${m}, filterSize: ${p}`);return n.tidy(()=>n.transpose(n.tensor4d(u,[m,f,p,p]),[2,3,1,0]))}function r(c,m,p,u){let f=e(c,m,p),l=n.tensor1d(o(m));return t.push({paramPath:`${u}/filters`},{paramPath:`${u}/bias`}),{filters:f,bias:l}}function a(c,m){let p=n.tensor1d(o(c)),u=n.tensor1d(o(c));return t.push({paramPath:`${m}/weights`},{paramPath:`${m}/biases`}),{weights:p,biases:u}}function i(c,m,p,u){let f=r(c,m,p,`${u}/conv`),l=a(m,`${u}/scale`);return{conv:f,scale:l}}function s(c,m,p,u,f=!1){let l=i((f?.5:1)*c,m,p,`${u}/conv1`),d=i(c,m,p,`${u}/conv2`);return{conv1:l,conv2:d}}return{extractConvLayerParams:i,extractResidualLayerParams:s}}function Gr(o){let{extractWeights:t,getRemainingWeights:e}=L(o),r=[],{extractConvLayerParams:a,extractResidualLayerParams:i}=$o(t,r),s=a(4704,32,7,"conv32_down"),c=i(9216,32,3,"conv32_1"),m=i(9216,32,3,"conv32_2"),p=i(9216,32,3,"conv32_3"),u=i(36864,64,3,"conv64_down",!0),f=i(36864,64,3,"conv64_1"),l=i(36864,64,3,"conv64_2"),d=i(36864,64,3,"conv64_3"),b=i(147456,128,3,"conv128_down",!0),y=i(147456,128,3,"conv128_1"),h=i(147456,128,3,"conv128_2"),g=i(589824,256,3,"conv256_down",!0),T=i(589824,256,3,"conv256_1"),x=i(589824,256,3,"conv256_2"),E=i(589824,256,3,"conv256_down_out"),B=n.tidy(()=>n.transpose(n.tensor2d(t(256*128),[128,256]),[1,0]));if(r.push({paramPath:"fc"}),e().length!==0)throw new Error(`weights remaing after extract: ${e().length}`);return{params:{conv32_down:s,conv32_1:c,conv32_2:m,conv32_3:p,conv64_down:u,conv64_1:f,conv64_2:l,conv64_3:d,conv128_down:b,conv128_1:y,conv128_2:h,conv256_down:g,conv256_1:T,conv256_2:x,conv256_down_out:E,fc:B},paramMappings:r}}function Oo(o,t){let e=k(o,t);function r(s){let c=e(`${s}/scale/weights`,1),m=e(`${s}/scale/biases`,1);return{weights:c,biases:m}}function a(s){let c=e(`${s}/conv/filters`,4),m=e(`${s}/conv/bias`,1),p=r(s);return{conv:{filters:c,bias:m},scale:p}}function i(s){return{conv1:a(`${s}/conv1`),conv2:a(`${s}/conv2`)}}return{extractConvLayerParams:a,extractResidualLayerParams:i}}function jr(o){let t=[],{extractConvLayerParams:e,extractResidualLayerParams:r}=Oo(o,t),a=e("conv32_down"),i=r("conv32_1"),s=r("conv32_2"),c=r("conv32_3"),m=r("conv64_down"),p=r("conv64_1"),u=r("conv64_2"),f=r("conv64_3"),l=r("conv128_down"),d=r("conv128_1"),b=r("conv128_2"),y=r("conv256_down"),h=r("conv256_1"),g=r("conv256_2"),T=r("conv256_down_out"),{fc:x}=o;if(t.push({originalPath:"fc",paramPath:"fc"}),!Ve(x))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${x}`);let E={conv32_down:a,conv32_1:i,conv32_2:s,conv32_3:c,conv64_down:m,conv64_1:p,conv64_2:u,conv64_3:f,conv128_down:l,conv128_1:d,conv128_2:b,conv256_down:y,conv256_1:h,conv256_2:g,conv256_down_out:T,fc:x};return N(o,t),{params:E,paramMappings:t}}function j(o,t){let e=Vr(o,t.conv1);return e=nr(e,t.conv2),e=n.add(e,o),e=n.relu(e),e}function ce(o,t){let e=Ne(o,t.conv1);e=nr(e,t.conv2);let r=n.avgPool(o,2,2,"valid"),a=n.zeros(r.shape),i=r.shape[3]!==e.shape[3];if(r.shape[1]!==e.shape[1]||r.shape[2]!==e.shape[2]){let c=[...e.shape];c[1]=1;let m=n.zeros(c);e=n.concat([e,m],1);let p=[...e.shape];p[2]=1;let u=n.zeros(p);e=n.concat([e,u],2)}return r=i?n.concat([r,a],3):r,e=n.add(r,e),e=n.relu(e),e}var jt=class extends I{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceRecognitionNet - load model before inference");return n.tidy(()=>{let r=n.cast(t.toBatchTensor(150,!0),"float32"),i=J(r,[122.782,117.001,104.298]).div(255),s=Ne(i,e.conv32_down);s=n.maxPool(s,3,2,"valid"),s=j(s,e.conv32_1),s=j(s,e.conv32_2),s=j(s,e.conv32_3),s=ce(s,e.conv64_down),s=j(s,e.conv64_1),s=j(s,e.conv64_2),s=j(s,e.conv64_3),s=ce(s,e.conv128_down),s=j(s,e.conv128_1),s=j(s,e.conv128_2),s=ce(s,e.conv256_down),s=j(s,e.conv256_1),s=j(s,e.conv256_2),s=ce(s,e.conv256_down_out);let c=s.mean([1,2]);return n.matMul(c,e.fc)})}async forward(t){return this.forwardInput(await F(t))}async computeFaceDescriptor(t){var i;if((i=t==null?void 0:t.shape)!=null&&i.some(s=>s<=0))return new Float32Array(128);let e=await F(t),r=n.tidy(()=>n.unstack(this.forwardInput(e))),a=await Promise.all(r.map(s=>s.data()));return r.forEach(s=>s.dispose()),e.isBatchInput?a:a[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return jr(t)}extractParams(t){return Gr(t)}};function gf(o){let t=new jt;return t.extractWeights(o),t}function ar(o,t){return{...o,...{descriptor:t}}}function Tf(o){return typeof o.age=="number"}function sr(o,t){return{...o,...{age:t}}}function Ff(o){return(o.gender==="male"||o.gender==="female")&&Mt(o.genderProbability)}function ir(o,t,e){return{...o,...{gender:t,genderProbability:e}}}function Ho(o,t){function e(m,p){let u=n.tensor4d(o(9*m),[3,3,m,1]),f=n.tensor1d(o(m)),l=n.tensor1d(o(m)),d=n.tensor1d(o(m)),b=n.tensor1d(o(m));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/batch_norm_scale`},{paramPath:`${p}/batch_norm_offset`},{paramPath:`${p}/batch_norm_mean`},{paramPath:`${p}/batch_norm_variance`}),{filters:u,batch_norm_scale:f,batch_norm_offset:l,batch_norm_mean:d,batch_norm_variance:b}}function r(m,p,u,f,l){let d=n.tensor4d(o(m*p*u*u),[u,u,m,p]),b=n.tensor1d(o(p));return t.push({paramPath:`${f}/filters`},{paramPath:`${f}/${l?"batch_norm_offset":"bias"}`}),{filters:d,bias:b}}function a(m,p,u,f){let{filters:l,bias:d}=r(m,p,u,f,!0);return{filters:l,batch_norm_offset:d}}function i(m,p,u){let f=e(m,`${u}/depthwise_conv`),l=a(m,p,1,`${u}/pointwise_conv`);return{depthwise_conv:f,pointwise_conv:l}}function s(){let m=a(3,32,3,"mobilenetv1/conv_0"),p=i(32,64,"mobilenetv1/conv_1"),u=i(64,128,"mobilenetv1/conv_2"),f=i(128,128,"mobilenetv1/conv_3"),l=i(128,256,"mobilenetv1/conv_4"),d=i(256,256,"mobilenetv1/conv_5"),b=i(256,512,"mobilenetv1/conv_6"),y=i(512,512,"mobilenetv1/conv_7"),h=i(512,512,"mobilenetv1/conv_8"),g=i(512,512,"mobilenetv1/conv_9"),T=i(512,512,"mobilenetv1/conv_10"),x=i(512,512,"mobilenetv1/conv_11"),E=i(512,1024,"mobilenetv1/conv_12"),B=i(1024,1024,"mobilenetv1/conv_13");return{conv_0:m,conv_1:p,conv_2:u,conv_3:f,conv_4:l,conv_5:d,conv_6:b,conv_7:y,conv_8:h,conv_9:g,conv_10:T,conv_11:x,conv_12:E,conv_13:B}}function c(){let m=a(1024,256,1,"prediction_layer/conv_0"),p=a(256,512,3,"prediction_layer/conv_1"),u=a(512,128,1,"prediction_layer/conv_2"),f=a(128,256,3,"prediction_layer/conv_3"),l=a(256,128,1,"prediction_layer/conv_4"),d=a(128,256,3,"prediction_layer/conv_5"),b=a(256,64,1,"prediction_layer/conv_6"),y=a(64,128,3,"prediction_layer/conv_7"),h=r(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),g=r(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),T=r(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),x=r(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),E=r(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),B=r(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),V=r(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),U=r(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),O=r(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),ot=r(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),nt=r(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),at=r(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:m,conv_1:p,conv_2:u,conv_3:f,conv_4:l,conv_5:d,conv_6:b,conv_7:y,box_predictor_0:{box_encoding_predictor:h,class_predictor:g},box_predictor_1:{box_encoding_predictor:T,class_predictor:x},box_predictor_2:{box_encoding_predictor:E,class_predictor:B},box_predictor_3:{box_encoding_predictor:V,class_predictor:U},box_predictor_4:{box_encoding_predictor:O,class_predictor:ot},box_predictor_5:{box_encoding_predictor:nt,class_predictor:at}}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:c}}function Ur(o){let t=[],{extractWeights:e,getRemainingWeights:r}=L(o),{extractMobilenetV1Params:a,extractPredictionLayerParams:i}=Ho(e,t),s=a(),c=i(),p={extra_dim:n.tensor3d(e(5118*4),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{params:{mobilenetv1:s,prediction_layer:c,output_layer:p},paramMappings:t}}function zo(o,t){let e=k(o,t);function r(p,u,f){let l=e(`${p}/Conv2d_${u}_pointwise/weights`,4,`${f}/filters`),d=e(`${p}/Conv2d_${u}_pointwise/convolution_bn_offset`,1,`${f}/batch_norm_offset`);return{filters:l,batch_norm_offset:d}}function a(p){let u=`mobilenetv1/conv_${p}`,f=`MobilenetV1/Conv2d_${p}_depthwise`,l=`${u}/depthwise_conv`,d=`${u}/pointwise_conv`,b=e(`${f}/depthwise_weights`,4,`${l}/filters`),y=e(`${f}/BatchNorm/gamma`,1,`${l}/batch_norm_scale`),h=e(`${f}/BatchNorm/beta`,1,`${l}/batch_norm_offset`),g=e(`${f}/BatchNorm/moving_mean`,1,`${l}/batch_norm_mean`),T=e(`${f}/BatchNorm/moving_variance`,1,`${l}/batch_norm_variance`);return{depthwise_conv:{filters:b,batch_norm_scale:y,batch_norm_offset:h,batch_norm_mean:g,batch_norm_variance:T},pointwise_conv:r("MobilenetV1",p,d)}}function i(){return{conv_0:r("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:a(1),conv_2:a(2),conv_3:a(3),conv_4:a(4),conv_5:a(5),conv_6:a(6),conv_7:a(7),conv_8:a(8),conv_9:a(9),conv_10:a(10),conv_11:a(11),conv_12:a(12),conv_13:a(13)}}function s(p,u){let f=e(`${p}/weights`,4,`${u}/filters`),l=e(`${p}/biases`,1,`${u}/bias`);return{filters:f,bias:l}}function c(p){let u=s(`Prediction/BoxPredictor_${p}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${p}/box_encoding_predictor`),f=s(`Prediction/BoxPredictor_${p}/ClassPredictor`,`prediction_layer/box_predictor_${p}/class_predictor`);return{box_encoding_predictor:u,class_predictor:f}}function m(){return{conv_0:r("Prediction",0,"prediction_layer/conv_0"),conv_1:r("Prediction",1,"prediction_layer/conv_1"),conv_2:r("Prediction",2,"prediction_layer/conv_2"),conv_3:r("Prediction",3,"prediction_layer/conv_3"),conv_4:r("Prediction",4,"prediction_layer/conv_4"),conv_5:r("Prediction",5,"prediction_layer/conv_5"),conv_6:r("Prediction",6,"prediction_layer/conv_6"),conv_7:r("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:c(0),box_predictor_1:c(1),box_predictor_2:c(2),box_predictor_3:c(3),box_predictor_4:c(4),box_predictor_5:c(5)}}return{extractMobilenetV1Params:i,extractPredictionLayerParams:m}}function Xr(o){let t=[],{extractMobilenetV1Params:e,extractPredictionLayerParams:r}=zo(o,t),a=o["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!K(a))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${a}`);let i={mobilenetv1:e(),prediction_layer:r(),output_layer:{extra_dim:a}};return N(o,t),{params:i,paramMappings:t}}function H(o,t,e){return n.tidy(()=>{let r=n.conv2d(o,t.filters,e,"same");return r=n.add(r,t.batch_norm_offset),n.clipByValue(r,0,6)})}var Yo=.0010000000474974513;function Vo(o,t,e){return n.tidy(()=>{let r=n.depthwiseConv2d(o,t.filters,e,"same");return r=n.batchNorm(r,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,Yo),n.clipByValue(r,0,6)})}function Go(o){return[2,4,6,12].some(t=>t===o)?[2,2]:[1,1]}function Jr(o,t){return n.tidy(()=>{let e,r=H(o,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((i,s)=>{let c=s+1,m=Go(c);r=Vo(r,i.depthwise_conv,m),r=H(r,i.pointwise_conv,[1,1]),c===11&&(e=r)}),e===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:r,conv11:e}})}function jo(o,t,e){let r=o.arraySync(),a=Math.min(r[t][0],r[t][2]),i=Math.min(r[t][1],r[t][3]),s=Math.max(r[t][0],r[t][2]),c=Math.max(r[t][1],r[t][3]),m=Math.min(r[e][0],r[e][2]),p=Math.min(r[e][1],r[e][3]),u=Math.max(r[e][0],r[e][2]),f=Math.max(r[e][1],r[e][3]),l=(s-a)*(c-i),d=(u-m)*(f-p);if(l<=0||d<=0)return 0;let b=Math.max(a,m),y=Math.max(i,p),h=Math.min(s,u),g=Math.min(c,f),T=Math.max(h-b,0)*Math.max(g-y,0);return T/(l+d-T)}function qr(o,t,e,r,a){let i=o.shape[0],s=Math.min(e,i),c=t.map((u,f)=>({score:u,boxIndex:f})).filter(u=>u.score>a).sort((u,f)=>f.score-u.score),m=u=>u<=r?1:0,p=[];return c.forEach(u=>{if(p.length>=s)return;let f=u.score;for(let l=p.length-1;l>=0;--l){let d=jo(o,u.boxIndex,p[l]);if(d!==0&&(u.score*=m(d),u.score<=a))break}f===u.score&&p.push(u.boxIndex)}),p}function Uo(o){let t=n.unstack(n.transpose(o,[1,0])),e=[n.sub(t[2],t[0]),n.sub(t[3],t[1])],r=[n.add(t[0],n.div(e[0],2)),n.add(t[1],n.div(e[1],2))];return{sizes:e,centers:r}}function Xo(o,t){let{sizes:e,centers:r}=Uo(o),a=n.unstack(n.transpose(t,[1,0])),i=n.div(n.mul(n.exp(n.div(a[2],5)),e[0]),2),s=n.add(n.mul(n.div(a[0],10),e[0]),r[0]),c=n.div(n.mul(n.exp(n.div(a[3],5)),e[1]),2),m=n.add(n.mul(n.div(a[1],10),e[1]),r[1]);return n.transpose(n.stack([n.sub(s,i),n.sub(m,c),n.add(s,i),n.add(m,c)]),[1,0])}function Zr(o,t,e){return n.tidy(()=>{let r=o.shape[0],a=Xo(n.reshape(n.tile(e.extra_dim,[r,1,1]),[-1,4]),n.reshape(o,[-1,4]));a=n.reshape(a,[r,a.shape[0]/r,4]);let i=n.sigmoid(n.slice(t,[0,0,1],[-1,-1,-1])),s=n.slice(i,[0,0,0],[-1,-1,1]);s=n.reshape(s,[r,s.shape[1]]);let c=n.unstack(a),m=n.unstack(s);return{boxes:c,scores:m}})}function yt(o,t){return n.tidy(()=>{let e=o.shape[0],r=n.reshape(vt(o,t.box_encoding_predictor),[e,-1,1,4]),a=n.reshape(vt(o,t.class_predictor),[e,-1,3]);return{boxPredictionEncoding:r,classPrediction:a}})}function Kr(o,t,e){return n.tidy(()=>{let r=H(o,e.conv_0,[1,1]),a=H(r,e.conv_1,[2,2]),i=H(a,e.conv_2,[1,1]),s=H(i,e.conv_3,[2,2]),c=H(s,e.conv_4,[1,1]),m=H(c,e.conv_5,[2,2]),p=H(m,e.conv_6,[1,1]),u=H(p,e.conv_7,[2,2]),f=yt(t,e.box_predictor_0),l=yt(o,e.box_predictor_1),d=yt(a,e.box_predictor_2),b=yt(s,e.box_predictor_3),y=yt(m,e.box_predictor_4),h=yt(u,e.box_predictor_5),g=n.concat([f.boxPredictionEncoding,l.boxPredictionEncoding,d.boxPredictionEncoding,b.boxPredictionEncoding,y.boxPredictionEncoding,h.boxPredictionEncoding],1),T=n.concat([f.classPrediction,l.classPrediction,d.classPrediction,b.classPrediction,y.classPrediction,h.classPrediction],1);return{boxPredictions:g,classPredictions:T}})}var z=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||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}};var Tt=class extends I{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return n.tidy(()=>{let r=n.cast(t.toBatchTensor(512,!1),"float32"),a=n.sub(n.div(r,127.5),1),i=Jr(a,e.mobilenetv1),{boxPredictions:s,classPredictions:c}=Kr(i.out,i.conv11,e.prediction_layer);return Zr(s,c,e.output_layer)})}async forward(t){return this.forwardInput(await F(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:a}=new z(e),i=await F(t),{boxes:s,scores:c}=this.forwardInput(i),m=s[0],p=c[0];for(let x=1;x<s.length;x++)s[x].dispose(),c[x].dispose();let u=Array.from(p.dataSync()),l=qr(m,u,r,.5,a),d=i.getReshapedInputDimensions(0),b=i.inputSize,y=b/d.width,h=b/d.height,g=m.arraySync(),T=l.map(x=>{let[E,B]=[Math.max(0,g[x][0]),Math.min(1,g[x][2])].map(O=>O*h),[V,U]=[Math.max(0,g[x][1]),Math.min(1,g[x][3])].map(O=>O*y);return new D(u[x],new Nt(V,E,U-V,B-E),{height:i.getInputHeight(0),width:i.getInputWidth(0)})});return m.dispose(),p.dispose(),T}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return Xr(t)}extractParams(t){return Ur(t)}};function Jo(o){let t=new Tt;return t.extractWeights(o),t}function gl(o){return Jo(o)}var Qr=class extends Tt{};var to=.4,eo=[new v(.738768,.874946),new v(2.42204,2.65704),new v(4.30971,7.04493),new v(10.246,4.59428),new v(12.6868,11.8741)],ro=[new v(1.603231,2.094468),new v(6.041143,7.080126),new v(2.882459,3.518061),new v(4.266906,5.178857),new v(9.041765,10.66308)],oo=[117.001,114.697,97.404],no="tiny_yolov2_model",ao="tiny_yolov2_separable_conv_model";var Le=o=>typeof o=="number";function so(o){if(!o)throw new Error(`invalid config: ${o}`);if(typeof o.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${o.withSeparableConvs}`);if(!Le(o.iouThreshold)||o.iouThreshold<0||o.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${o.iouThreshold}`);if(!Array.isArray(o.classes)||!o.classes.length||!o.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>Le(t.x)&&Le(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(Le)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(o.meanRgb)}`)}function Ut(o){return n.tidy(()=>{let t=n.mul(o,n.scalar(.10000000149011612));return n.add(n.relu(n.sub(o,t)),t)})}function et(o,t){return n.tidy(()=>{let e=n.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=n.conv2d(e,t.conv.filters,[1,1],"valid"),e=n.sub(e,t.bn.sub),e=n.mul(e,t.bn.truediv),e=n.add(e,t.conv.bias),Ut(e)})}function rt(o,t){return n.tidy(()=>{let e=n.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=n.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=n.add(e,t.bias),Ut(e)})}function qo(o,t){let e=Rt(o,t);function r(s,c){let m=n.tensor1d(o(s)),p=n.tensor1d(o(s));return t.push({paramPath:`${c}/sub`},{paramPath:`${c}/truediv`}),{sub:m,truediv:p}}function a(s,c,m){let p=e(s,c,3,`${m}/conv`),u=r(c,`${m}/bn`);return{conv:p,bn:u}}let i=$t(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:a,extractSeparableConvParams:i}}function io(o,t,e,r){let{extractWeights:a,getRemainingWeights:i}=L(o),s=[],{extractConvParams:c,extractConvWithBatchNormParams:m,extractSeparableConvParams:p}=qo(a,s),u;if(t.withSeparableConvs){let[f,l,d,b,y,h,g,T,x]=r,E=t.isFirstLayerConv2d?c(f,l,3,"conv0"):p(f,l,"conv0"),B=p(l,d,"conv1"),V=p(d,b,"conv2"),U=p(b,y,"conv3"),O=p(y,h,"conv4"),ot=p(h,g,"conv5"),nt=T?p(g,T,"conv6"):void 0,at=x?p(T,x,"conv7"):void 0,Et=c(x||T||g,5*e,1,"conv8");u={conv0:E,conv1:B,conv2:V,conv3:U,conv4:O,conv5:ot,conv6:nt,conv7:at,conv8:Et}}else{let[f,l,d,b,y,h,g,T,x]=r,E=m(f,l,"conv0"),B=m(l,d,"conv1"),V=m(d,b,"conv2"),U=m(b,y,"conv3"),O=m(y,h,"conv4"),ot=m(h,g,"conv5"),nt=m(g,T,"conv6"),at=m(T,x,"conv7"),Et=c(x,5*e,1,"conv8");u={conv0:E,conv1:B,conv2:V,conv3:U,conv4:O,conv5:ot,conv6:nt,conv7:at,conv8:Et}}if(i().length!==0)throw new Error(`weights remaing after extract: ${i().length}`);return{params:u,paramMappings:s}}function Zo(o,t){let e=k(o,t);function r(c){let m=e(`${c}/sub`,1),p=e(`${c}/truediv`,1);return{sub:m,truediv:p}}function a(c){let m=e(`${c}/filters`,4),p=e(`${c}/bias`,1);return{filters:m,bias:p}}function i(c){let m=a(`${c}/conv`),p=r(`${c}/bn`);return{conv:m,bn:p}}let s=Ot(e);return{extractConvParams:a,extractConvWithBatchNormParams:i,extractSeparableConvParams:s}}function co(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:a,extractSeparableConvParams:i}=Zo(o,e),s;if(t.withSeparableConvs){let c=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):i("conv0"),conv1:i("conv1"),conv2:i("conv2"),conv3:i("conv3"),conv4:i("conv4"),conv5:i("conv5"),conv6:c>7?i("conv6"):void 0,conv7:c>8?i("conv7"):void 0,conv8:r("conv8")}}else s={conv0:a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:a("conv6"),conv7:a("conv7"),conv8:r("conv8")};return N(o,e),{params:s,paramMappings:e}}var q=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Se=class Se extends I{constructor(t){super("TinyYolov2"),so(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=et(t,e.conv0);return r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv1),r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv2),r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv3),r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv4),r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv5),r=n.maxPool(r,[2,2],[1,1],"same"),r=et(r,e.conv6),r=et(r,e.conv7),vt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?Ut(vt(t,e.conv0,"valid",!1)):rt(t,e.conv0);return r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv1),r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv2),r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv3),r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv4),r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv5),r=n.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?rt(r,e.conv6):r,r=e.conv7?rt(r,e.conv7):r,vt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return n.tidy(()=>{let a=n.cast(t.toBatchTensor(e,!1),"float32");return a=this.config.meanRgb?J(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,r):this.runTinyYolov2(a,r)})}async forward(t,e){return this.forwardInput(await F(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:a}=new q(e),i=await F(t),s=await this.forwardInput(i,r),c=n.tidy(()=>n.unstack(s)[0].expandDims()),m={width:i.getInputWidth(0),height:i.getInputHeight(0)},p=await this.extractBoxes(c,i.getReshapedInputDimensions(0),a);s.dispose(),c.dispose();let u=p.map(h=>h.box),f=p.map(h=>h.score),l=p.map(h=>h.classScore),d=p.map(h=>this.config.classes[h.label]);return br(u.map(h=>h.rescale(r)),f,this.config.iouThreshold,!0).map(h=>new It(f[h],l[h],d[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return co(t,this.config)}extractParams(t){let e=this.config.filterSizes||Se.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return io(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:a,height:i}=e,s=Math.max(a,i),c=s/a,m=s/i,p=t.shape[1],u=this.config.anchors.length,[f,l,d]=n.tidy(()=>{let g=t.reshape([p,p,u,this.boxEncodingSize]),T=g.slice([0,0,0,0],[p,p,u,4]),x=g.slice([0,0,0,4],[p,p,u,1]),E=this.withClassScores?n.softmax(g.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):n.scalar(0);return[T,x,E]}),b=[],y=await l.array(),h=await f.array();for(let g=0;g<p;g++)for(let T=0;T<p;T++)for(let x=0;x<u;x++){let E=fe(y[g][T][x][0]);if(!r||E>r){let B=(T+fe(h[g][T][x][0]))/p*c,V=(g+fe(h[g][T][x][1]))/p*m,U=Math.exp(h[g][T][x][2])*this.config.anchors[x].x/p*c,O=Math.exp(h[g][T][x][3])*this.config.anchors[x].y/p*m,ot=B-U/2,nt=V-O/2,at={row:g,col:T,anchor:x},{classScore:Et,label:mr}=this.withClassScores?await this.extractPredictedClass(d,at):{classScore:1,label:0};b.push({box:new Ct(ot,nt,ot+U,nt+O),score:E,classScore:E*Et,label:mr,...at})}}return f.dispose(),l.dispose(),d.dispose(),b}async extractPredictedClass(t,e){let{row:r,col:a,anchor:i}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((c,m)=>s[r][a][i][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}};Se.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Xt=Se;var Jt=class extends Xt{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:to,classes:["face"],...t?{anchors:ro,meanRgb:oo}:{anchors:eo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(a=>new D(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?ao:no}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function cd(o,t=!0){let e=new Jt(t);return e.extractWeights(o),e}var Ae=class extends q{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Y=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function _t(o,t,e,r,a=({alignedRect:i})=>i){let i=o.map(m=>Yt(m)?a(m):m.detection),s=r||(t instanceof n.Tensor?await oe(t,i):await re(t,i)),c=await e(s);return s.forEach(m=>m instanceof n.Tensor&&m.dispose()),c}async function qt(o,t,e,r,a){return _t([o],t,async i=>e(i[0]),r,a)}var mo=.4,po=[new v(1.603231,2.094468),new v(6.041143,7.080126),new v(2.882459,3.518061),new v(4.266906,5.178857),new v(9.041765,10.66308)],uo=[117.001,114.697,97.404];var Zt=class extends Xt{constructor(){let t={withSeparableConvs:!0,iouThreshold:mo,classes:["face"],anchors:po,meanRgb:uo,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(a=>new D(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var P={ssdMobilenetv1:new Tt,tinyFaceDetector:new Zt,tinyYolov2:new Jt,faceLandmark68Net:new Gt,faceLandmark68TinyNet:new Ie,faceRecognitionNet:new jt,faceExpressionNet:new we,ageGenderNet:new Me},Ko=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),Rd=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),$d=(o,t)=>P.tinyYolov2.locateFaces(o,t),Qo=o=>P.faceLandmark68Net.detectLandmarks(o),Od=o=>P.faceLandmark68TinyNet.detectLandmarks(o),Hd=o=>P.faceRecognitionNet.computeFaceDescriptor(o),zd=o=>P.faceExpressionNet.predictExpressions(o),Yd=o=>P.ageGenderNet.predictAgeAndGender(o),tn=o=>P.ssdMobilenetv1.load(o),Vd=o=>P.tinyFaceDetector.load(o),Gd=o=>P.tinyYolov2.load(o),jd=o=>P.faceLandmark68Net.load(o),Ud=o=>P.faceLandmark68TinyNet.load(o),Xd=o=>P.faceRecognitionNet.load(o),Jd=o=>P.faceExpressionNet.load(o),qd=o=>P.ageGenderNet.load(o),Zd=tn,Kd=Ko,Qd=Qo;var We=class extends Y{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.extractedFaces=a}},Pt=class extends We{async run(){let t=await this.parentTask,e=await _t(t,this.input,async r=>Promise.all(r.map(a=>P.faceExpressionNet.predictExpressions(a))),this.extractedFaces);return t.map((r,a)=>tr(r,e[a]))}withAgeAndGender(){return new Ft(this,this.input)}},wt=class extends We{async run(){let t=await this.parentTask;if(!t)return;let e=await qt(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return tr(t,e)}withAgeAndGender(){return new Dt(this,this.input)}},ct=class extends Pt{withAgeAndGender(){return new pt(this,this.input)}withFaceDescriptors(){return new ft(this,this.input)}},mt=class extends wt{withAgeAndGender(){return new ut(this,this.input)}withFaceDescriptor(){return new lt(this,this.input)}};var ke=class extends Y{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.extractedFaces=a}},Ft=class extends ke{async run(){let t=await this.parentTask,e=await _t(t,this.input,async r=>Promise.all(r.map(a=>P.ageGenderNet.predictAgeAndGender(a))),this.extractedFaces);return t.map((r,a)=>{let{age:i,gender:s,genderProbability:c}=e[a];return sr(ir(r,s,c),i)})}withFaceExpressions(){return new Pt(this,this.input)}},Dt=class extends ke{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:a}=await qt(t,this.input,i=>P.ageGenderNet.predictAgeAndGender(i),this.extractedFaces);return sr(ir(t,r,a),e)}withFaceExpressions(){return new wt(this,this.input)}},pt=class extends Ft{withFaceExpressions(){return new ct(this,this.input)}withFaceDescriptors(){return new ft(this,this.input)}},ut=class extends Dt{withFaceExpressions(){return new mt(this,this.input)}withFaceDescriptor(){return new lt(this,this.input)}};var Be=class extends Y{constructor(e,r){super();this.parentTask=e;this.input=r}},ft=class extends Be{async run(){let t=await this.parentTask;return(await _t(t,this.input,r=>Promise.all(r.map(a=>P.faceRecognitionNet.computeFaceDescriptor(a))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,a)=>ar(t[a],r))}withFaceExpressions(){return new ct(this,this.input)}withAgeAndGender(){return new pt(this,this.input)}},lt=class extends Be{async run(){let t=await this.parentTask;if(!t)return;let e=await qt(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return ar(t,e)}withFaceExpressions(){return new mt(this,this.input)}withAgeAndGender(){return new ut(this,this.input)}};var Re=class extends Y{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},$e=class extends Re{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof n.Tensor?await oe(this.input,e):await re(this.input,e),a=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof n.Tensor&&s.dispose()),t.filter((s,c)=>a[c]).map((s,c)=>ie(s,a[c]))}withFaceExpressions(){return new ct(this,this.input)}withAgeAndGender(){return new pt(this,this.input)}withFaceDescriptors(){return new ft(this,this.input)}},Oe=class extends Re{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof n.Tensor?await oe(this.input,[e]):await re(this.input,[e]),a=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(i=>i instanceof n.Tensor&&i.dispose()),ie(t,a)}withFaceExpressions(){return new mt(this,this.input)}withAgeAndGender(){return new ut(this,this.input)}withFaceDescriptor(){return new lt(this,this.input)}};var He=class extends Y{constructor(e,r=new z){super();this.input=e;this.options=r}},me=class extends He{async run(){let{input:t,options:e}=this,r;if(e instanceof Ae)r=P.tinyFaceDetector.locateFaces(t,e);else if(e instanceof z)r=P.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof q)r=P.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(a=>St({},a)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new $e(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Pt(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Ft(this.runAndExtendWithFaceDetections(),this.input)}},ze=class extends He{async run(){let t=await new me(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?St({},e):void 0)})}withFaceLandmarks(t=!1){return new Oe(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new wt(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Dt(this.runAndExtendWithFaceDetection(),this.input)}};function qh(o,t=new z){return new ze(o,t)}function cr(o,t=new z){return new me(o,t)}async function en(o,t){return cr(o,new z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function rb(o,t={}){return cr(o,new q(t)).withFaceLandmarks().withFaceDescriptors()}var ob=en;function fo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((a,i)=>a-r[i]).reduce((a,i)=>a+i*i,0))}var lo=class o{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let a=1,i=()=>`person ${a++}`;this._labeledDescriptors=r.map(s=>{if(s instanceof gt)return s;if(s instanceof Float32Array)return new gt(i(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new gt(i(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>fo(r,t)).reduce((r,a)=>r+a,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Kt(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distance<r.distance?e:r)}findBestMatch(t){let e=this.matchDescriptor(t);return e.distance<this._distanceThreshold?e:new Kt("unknown",e.distance)}toJSON(){return{distanceThreshold:this._distanceThreshold,labeledDescriptors:this._labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>gt.fromJSON(r));return new o(e,t.distanceThreshold)}};function Tb(o){let t=new Zt;return t.extractWeights(o),t}function rn(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(a=>rn(a,{width:e,height:r}));if(Yt(o)){let a=o.detection.forSize(e,r),i=o.unshiftedLandmarks.forSize(a.box.width,a.box.height);return ie(St(o,a),i)}return Q(o)?St(o,o.detection.forSize(e,r)):o instanceof $||o instanceof D?o.forSize(e,r):o}var Lb=Ar;export{Me as AgeGenderNet,Ct as BoundingBox,C as Box,Y as ComposableTask,ft as ComputeAllFaceDescriptorsTask,Be as ComputeFaceDescriptorsTaskBase,lt as ComputeSingleFaceDescriptorTask,$e as DetectAllFaceLandmarksTask,me as DetectAllFacesTask,Re as DetectFaceLandmarksTaskBase,He as DetectFacesTaskBase,Oe as DetectSingleFaceLandmarksTask,ze as DetectSingleFaceTask,A as Dimensions,Nr as FACE_EXPRESSION_LABELS,D as FaceDetection,Qr as FaceDetectionNet,we as FaceExpressionNet,it as FaceExpressions,Gt as FaceLandmark68Net,Ie as FaceLandmark68TinyNet,zr as FaceLandmarkNet,$ as FaceLandmarks,xr as FaceLandmarks5,Lt as FaceLandmarks68,Kt as FaceMatch,lo as FaceMatcher,jt as FaceRecognitionNet,rr as Gender,Qt as LabeledBox,gt as LabeledFaceDescriptors,tt as NetInput,I as NeuralNetwork,It as ObjectDetection,v as Point,vr as PredictedBox,Nt as Rect,Tt as SsdMobilenetv1,z as SsdMobilenetv1Options,Zt as TinyFaceDetector,Ae as TinyFaceDetectorOptions,Jt as TinyYolov2,q as TinyYolov2Options,ob as allFaces,en as allFacesSsdMobilenetv1,rb as allFacesTinyYolov2,yr as awaitMediaLoaded,Tr as bufferToImage,Hd as computeFaceDescriptor,Bt as createCanvas,be as createCanvasFromMedia,gl as createFaceDetectionNet,gf as createFaceRecognitionNet,Jo as createSsdMobilenetv1,Tb as createTinyFaceDetector,cd as createTinyYolov2,cr as detectAllFaces,Qo as detectFaceLandmarks,Od as detectFaceLandmarksTiny,Qd as detectLandmarks,qh as detectSingleFace,Sr as draw,_ as env,fo as euclideanDistance,sr as extendWithAge,ar as extendWithFaceDescriptor,St as extendWithFaceDetection,tr as extendWithFaceExpressions,ie as extendWithFaceLandmarks,ir as extendWithGender,oe as extractFaceTensors,re as extractFaces,Fi as fetchImage,wr as fetchJson,Ii as fetchNetWeights,st as fetchOrThrow,ki as fetchVideo,W as getContext2dOrThrow,kt as getMediaDimensions,_r as imageTensorToCanvas,Pr as imageToSquare,On as inverseSigmoid,dr as iou,Qe as isMediaElement,he as isMediaLoaded,Tf as isWithAge,Q as isWithFaceDetection,Lr as isWithFaceExpressions,Yt as isWithFaceLandmarks,Ff as isWithGender,qd as loadAgeGenderModel,Zd as loadFaceDetectionModel,Jd as loadFaceExpressionModel,jd as loadFaceLandmarkModel,Ud as loadFaceLandmarkTinyModel,Xd as loadFaceRecognitionModel,tn as loadSsdMobilenetv1Model,Vd as loadTinyFaceDetectorModel,Gd as loadTinyYolov2Model,Dr as loadWeightMap,Kd as locateFaces,Yi as matchDimensions,hr as minBbox,P as nets,br as nonMaxSuppression,J as normalize,gr as padToSquare,Yd as predictAgeAndGender,zd as recognizeFaceExpressions,rn as resizeResults,At as resolveInput,Rn as shuffleArray,fe as sigmoid,Ko as ssdMobilenetv1,n as tf,Rd as tinyFaceDetector,$d as tinyYolov2,F as toNetInput,lr as utils,so as validateConfig,Lb as version};