this.video.addEventListener(
    "play",
    function() {
      self.canvasFace = document.getElementById("c1");
      self.displaySize = {
        width: self.videoWidth,
        height: self.videoHeight
      };
      faceapi.matchDimensions(self.canvasFace, self.displaySize);
      self.canvasText = document.getElementById("c2");
      self.timerCallback();
    },
    false
  );

  var lenghtx = box.x + box.width;
  var lenghty = box.y + box.height;
  // see DrawBoxOptions below
  const drawOptions = {
    lineWidth: 2,
    boxColor: "#23b2be"
  };
  const drawBox = new faceapi.draw.DrawBox(box, drawOptions);
  drawBox.draw(this.canvasFace);
  let inputSize = 128;
  let scoreThresholds = 0.2;
  let self = this;
  faceapi
    .detectAllFaces(
      self.video,
      new faceapi.TinyFaceDetectorOptions({ inputSize, scoreThresholds })
    )
    .then(result => {
      const resizedDetections = faceapi.resizeResults(
        result,
        self.displaySize
      );

      if (resizedDetections[0]) {
        if (self.$q.screen.xs) {
          anchBoundBox = resizedDetections[0].box["width"] - 40;
          altBoundBox = resizedDetections[0].box["height"] - 35;
        } else {
          anchBoundBox = resizedDetections[0].box["width"] - 15;
          altBoundBox = resizedDetections[0].box["height"] - 15;
        }
        if (
          anchBoundBox + resizedDetections[0].box["x"] > lenghtx ||
          resizedDetections[0].box["x"] < xBox ||
          resizedDetections[0].box["y"] < yBox ||
          altBoundBox + resizedDetections[0].box["y"] > lenghty
        ) {
          self.validCentered = false;
          self.textAlert = "Centre su rostro";
          self.paintBoxAlert(self.textAlert);
          self.$emit("detect-centered", false);
        } else {
          self.validCentered = true;
          self.$emit("detect-centered", true);
        }
        if (resizedDetections[0].box.area > boxArea) {
          self.validProximity = true;
          self.$emit("detect-proximity", true);
        } else {
          self.textAlert = "Acerque su rostro";
          self.paintBoxAlert(self.textAlert);
          self.validProximity = false;
          self.$emit("detect-proximity", false);
        }
      } else {
        self.validProximity = false;
        self.validCentered = false;
      }
      if (self.validCentered && self.validProximity) {
        self.canvasText
          .getContext("2d")
          .clearRect(0, 0, self.canvasText.width, 20);
      }
      requestAnimationFrame(() => self.timerCallback());
      return true;
    })
    .catch(err => {
      console.error(`Detect Error: ${err}`);
      return false;
    });
  return false;
},

  async function analyzeFrame (frameData, index) {
    const tensor = await getTensorFromBuffer(frameData)
    const faces = await faceapi.detectAllFaces(tensor, optionsSSDMobileNet).withFaceLandmarks().withFaceDescriptors()
    tensor.dispose()
    faces.forEach(async (face) => {
      // console.log(face)
    })
  }

file:///home/ali/Desktop/Nodejs%20Projects/face-api-test2/node_modules/@vladmandic/face-api/dist/face-api.node.js:5
`;return b[b.length-1]=" "+b[b.length-1]+"]"+(o?"":L),b}function dh(e){const t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}class an{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=M(e),n!=null){const s=n.length;A(s===this.size,()=>`Length of values '${s}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||Is(t,this.size),this.strides=Ke(e)}set(e,...t){t.length===0&&(t=[0]),A(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);const n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(const s of e){if(s<0||s>=this.shape[t]){const i=`Requested out of range element at ${e}.   Buffer shape=${this.shape}`;throw new Error(i)}t++}let n=e[e.length-1];for(let s=0;s<e.length-1;++s)n+=this.strides[s]*e[s];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];const t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return vi().makeTensor(this.values,this.shape,this.dtype)}}let vi=null,za=null,kT=null;function zk(e){vi=e}function Vk(e){za=e}function Gk(e){kT=e}class te{constructor(e,t,n,s){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=M(e),this.strides=Ke(e),this.dataId=n,this.id=s,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){const e=await this.data();return za.buffer(this.shape,this.dtype,e)}bufferSync(){return za.buffer(this.shape,this.dtype,this.dataSync())}async array(){const e=await this.data();return xs(this.shape,e)}arraySync(){return xs(this.shape,this.dataSync())}async data(){this.throwIfDisposed();const e=vi().read(this.dataId);if(this.dtype==="string"){const t=await e;try{return t.map(n=>lh(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();const e=vi().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>lh(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();const e=await vi().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){if(this.isDisposed)return;vi().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return za.print(this,e)}clone(){return this.throwIfDisposed(),za.clone(this)}toString(e=!1){const t=this.dataSync();return Mk(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),za.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),vi().makeVariable(this,e,t,n)}}Object.defineProperty(te,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});class ph extends te{constructor(e,t,n,s){super(e.shape,e.dtype,e.dataId,s);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!ie(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);vi().disposeTensor(this),this.dataId=e.dataId,vi().incRef(this,null)}dispose(){vi().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(ph,Symbol.hasInstance,{value:e=>e instanceof te&&e.assign!=null&&e.assign instanceof Function});(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(r.Rank||(r.Rank={}));var Qy;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(Qy||(Qy={}));var eb;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(eb||(eb={}));var tb;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(tb||(tb={}));var nb;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(nb||(nb={}));const Yk={float32:tb,int32:Qy,bool:eb,complex64:nb};function Bn(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return Yk[e][t]}function np(e){return Bn(e,"int32")}function Gt(e,t){if(e.dtype===t.dtype)return[e,t];const n=Bn(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function FT(e,t){A(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function sp(e,t){return t.some(n=>n.id===e.id)}function Zi(e){const t=[],n=new Set;return _T(e,t,n),t}function _T(e,t,n){if(e==null)return;if(e instanceof te){t.push(e);return}if(!Hk(e))return;const s=e;for(const i in s){const o=s[i];n.has(o)||(n.add(o),_T(o,t,n))}}function Hk(e){return Array.isArray(e)||typeof e=="object"}var qk=Object.freeze({__proto__:null,makeTypesMatch:Gt,assertTypesMatch:FT,isTensorInList:sp,getTensorsInContainer:Zi});class WT{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class mh{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new WT}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],s=await this.initializeBackend(n).success;if(s){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){const{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;const{success:t,asyncInit:n}=this.initializeBackend(e),s=n?await t:t;if(!s)return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new _k(this.backendInstance),!0}setupRegisteredKernels(){const e=Zd(this.backendName);e.forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){const t=Zd(e);t.forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){const t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{const n=t.factory();if(n&&!(n instanceof f)&&typeof n.then=="function"){const s=++this.pendingBackendInitId,i=n.then(o=>s<this.pendingBackendInitId?!1:(this.registry[e]=o,this.pendingBackendInit=null,!0)).catch(o=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(o.stack||o.message)),!1));return this.pendingBackendInit=i,{success:i,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],{success:s,asyncInit:i}=this.initializeBackend(n);if(i||s)return{name:n,asyncInit:i}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const n=this.state.tensorInfo.get(t),s=n.backend,i=this.readSync(t);s.disposeData(t),n.backend=e,e.move(t,i,n.shape,n.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{const s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return mh.nextTensorId++}nextVariableId(){return mh.nextVariableId++}clone(e){const t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),n={x:e},s=o=>({x:()=>{const a="float32",c={x:o},h={dtype:a};return G.runKernelFunc(p=>p.cast(o,a),c,null,ka,h)}}),i=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,i,{}),t}runKernel(e,t,n,s,i){const o=null,a=null;return this.runKernelFunc(o,t,a,e,n,s,i)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){const s=this.backend.numDataIds();let i=0;n.forEach(c=>{i+=c.dtype==="complex64"?3:1});const o=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=s-t-i-o;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${e}'`)}runKernelFunc(e,t,n,s,i,o,a){let c,h=[];const p=this.isTapeOn();s==null&&(s=this.state.activeScope!=null?this.state.activeScope.name:"");const m=this.state.numBytes,y=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let b;const w=Ky(s,this.backendName);let L;if(w!=null)b=()=>{const v=this.backend.numDataIds();L=w.kernelFunc({inputs:t,attrs:i,backend:this.backend});const C=Array.isArray(L)?L:[L];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,v,C);const O=C.map(({dataId:D,shape:k,dtype:F})=>this.makeTensorFromDataId(D,k,F));if(p){let D=this.getTensorsForGradient(s,t,O);if(D==null){a==null&&(a=[]);const k=O.filter((F,B)=>a[B]);D=(o||[]).slice().concat(k)}h=this.saveTensorsForBackwardMode(D)}return O};else{const v=C=>{if(!p)return;h=C.map(O=>this.keep(this.clone(O)))};b=()=>{const C=this.backend.numDataIds();L=this.tidy(()=>e(this.backend,v));const O=Array.isArray(L)?L:[L];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,C,O),O}}let T;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?c=b():(T=this.profiler.profileKernel(s,t,()=>b()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(T),c=T.outputs)}),p&&this.addTapeNode(s,t,c,n,h,i),this.state.profiling&&this.state.activeProfile.kernels.push({name:s,bytesAdded:this.state.numBytes-m,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-y,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(v=>t[v]!=null?t[v].shape:null),outputShapes:c.map(v=>v.shape),kernelTimeMs:T.timeMs,extraInfo:T.extraInfo}),Array.isArray(L)?c:c[0]}saveTensorsForBackwardMode(e){const t=e.map(n=>this.keep(this.clone(n)));return t}getTensorsForGradient(e,t,n){const s=Xy(e);if(s!=null){const i=s.inputsToSave||[],o=s.outputsToSave||[];let a;s.saveAllInputs?(A(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(t).map(h=>t[h])):a=i.map(h=>t[h]);const c=n.filter((h,p)=>o[p]);return a.concat(c)}return null}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let i=e;n==="string"&&Ji(e[0])&&(i=e.map(c=>ep(c)));const o=s.write(i,t,n),a=new te(t,n,o,this.nextTensorId());if(this.incRef(a,s),n==="string"){const c=this.state.tensorInfo.get(o),h=Xx(i);this.state.numBytes+=h-c.bytes,c.bytes=h}return a}makeTensorFromDataId(e,t,n,s){n=n||"float32";const i=new te(t,n,e,this.nextTensorId());return this.incRef(i,s),i}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));const i=new ph(e,t,n,this.nextTensorId());if(this.state.registeredVariables[i.name]!=null)throw new Error(`Variable with name ${i.name} was already registered`);return this.state.registeredVariables[i.name]=i,this.incRef(i,this.backend),i}incRef(e,t){const n=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,n===0){this.state.numDataBuffers++;let s=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(s=e.size*iy(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:s,refCount:0}),this.state.numBytes+=s}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof ph||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;const t=this.state.tensorInfo.get(e.dataId),n=t.refCount;n<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):this.state.tensorInfo.get(e.dataId).refCount--}disposeVariables(){for(const e in this.state.registeredVariables){const t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){const e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;const t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(const s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,i,o){const a={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:i},c=Xy(e);c!=null&&(s=c.gradFunc),s!=null&&(a.gradient=h=>(h=h.map((p,m)=>{if(p==null){const y=n[m],b=Da(y.size,y.dtype);return this.makeTensor(b,y.shape,y.dtype)}return p}),s(h.length>1?h:h[0],i,o))),this.state.activeTape.push(a)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){const t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){const t=Zi(e),n=new Set(t.map(i=>i.id));for(let i=0;i<this.state.activeScope.track.length;i++){const o=this.state.activeScope.track[i];!o.kept&&!n.has(o.id)&&o.dispose()}const s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(i=>{!i.kept&&i.scopeId===s.id&&this.track(i)})}gradients(e,t,n,s=!1){if(A(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);const i=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));A(i instanceof te,()=>"The result y returned by f() must be a tensor.");const o=Uk(this.state.activeTape,t,i);if(!s&&o.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")}),Qi.registerFlag("IS_BROWSER",()=>sb()),Qi.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined"),Qi.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor)),Qi.registerFlag("PROD",()=>!1),Qi.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>Qi.getBool("DEBUG")),Qi.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0),Qi.registerFlag("IS_TEST",()=>!1);function Ni(e,t){let n=e;if(un(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];const s=[];for(;Array.isArray(n)||un(n)&&t!=="string";)s.push(n.length),n=n[0];return Array.isArray(e)&&ae().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&BT(e,s,[]),s}function BT(e,t,n){if(n=n||[],!Array.isArray(e)&&!un(e)){A(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}A(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),A(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);const s=t.slice(1);for(let i=0;i<e.length;++i)BT(e[i],s,n.concat(i))}function MT(e,t,n,s){if(e==null)return;if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${s}' must be ${e} tensor, but got ${t} tensor`)}function W(e,t,n,s="numeric"){if(e instanceof te)return MT(s,e.dtype,t,n),e;let i=Ea(e);if(i!=="string"&&["bool","int32","float32"].indexOf(s)>=0&&(i=s),MT(s,i,t,n),e==null||!un(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){const h=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${h}'`)}const o=Ni(e,i);!un(e)&&!Array.isArray(e)&&(e=[e]);const a=!0,c=i!=="string"?Ur(e,i):Q(e,[],a);return G.makeTensor(c,o,i)}function fh(e,t,n,s="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);const i=e;return i.map((o,a)=>W(o,`${t}[${a}]`,n),s)}const PT="__op";function z(e){const t=Object.keys(e);if(t.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. 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Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);const b=ib[y.dtype],w=e.slice(i,i+p*b),L=y.dtype==="uint8"?new Uint8Array(w):new Uint16Array(w);if(c==="float32")if(y.dtype==="uint8"||y.dtype==="uint16"){m=new Float32Array(L.length);for(let T=0;T<L.length;T++){const v=L[T];m[T]=v*y.scale+y.min}}else if(y.dtype==="float16")s===void 0&&(s=rF()),m=s(L);else throw new Error(`Unsupported quantization type ${y.dtype} for weight type float32.`);else if(c==="int32"){if(y.dtype!=="uint8"&&y.dtype!=="uint16")throw new Error(`Unsupported quantization type ${y.dtype} for weight type int32.`);m=new Int32Array(L.length);for(let T=0;T<L.length;T++){const v=L[T];m[T]=Math.round(v*y.scale+y.min)}}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=p*b}else if(c==="string"){const y=M(o.shape);m=[];for(let b=0;b<y;b++){const w=new Uint32Array(e.slice(i,i+ip))[0];i+=ip;const L=new Uint8Array(e.slice(i,i+w));m.push(L),i+=w}}else{const y=ib[c],b=e.slice(i,i+p*y);if(c==="float32")m=new Float32Array(b);else if(c==="int32")m=new Int32Array(b);else if(c==="bool")m=new Uint8Array(b);else if(c==="complex64"){m=new Float32Array(b);const w=new Float32Array(m.length/2),L=new Float32Array(m.length/2);for(let C=0;C<w.length;C++)w[C]=m[C*2],L[C]=m[C*2+1];const T=sn(w,h,"float32"),v=sn(L,h,"float32");n[a]=er(T,v),T.dispose(),v.dispose()}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=p*y}c!=="complex64"&&(n[a]=sn(m,h,c))}return n}function Qk(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0;const n=[];e.forEach(o=>{if(t+=o.byteLength,n.push(o.byteLength===o.buffer.byteLength?o:new o.constructor(o)),!(o instanceof Float32Array||o instanceof Int32Array||o instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${o.constructor.name}`)});const s=new Uint8Array(t);let i=0;return n.forEach(o=>{s.set(new Uint8Array(o.buffer),i),i+=o.byteLength}),s.buffer}const ob=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function zT(e){return ob?Buffer.byteLength(e):new Blob([e]).size}function eF(e){if(ob)return Buffer.from(e).toString("base64");const t=new Uint8Array(e);let n="";for(let s=0,i=t.length;s<i;s++)n+=String.fromCharCode(t[s]);return btoa(n)}function tF(e){if(ob){const s=Buffer.from(e,"base64");return s.buffer.slice(s.byteOffset,s.byteOffset+s.byteLength)}const t=atob(e),n=new Uint8Array(t.length);for(let s=0;s<t.length;++s)n.set([t.charCodeAt(s)],s);return n.buffer}function op(e){if(e.length===1)return e[0];let t=0;e.forEach(i=>{t+=i.byteLength});const n=new Uint8Array(t);let s=0;return e.forEach(i=>{n.set(new Uint8Array(i),s),s+=i.byteLength}),n.buffer}function VT(e){const t="/";for(e=e.trim();e.endsWith(t);)e=e.slice(0,e.length-1);const n=e.split(t);return n[n.length-1]}function gh(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, 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c=s[a],h=e[n[c>>10]+(c&1023)]+t[c>>10];o[a]=h}return new Float32Array(i)}}class en{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return en.instance==null&&(en.instance=new en),en.instance}static registerSaveRouter(e){en.getInstance().saveRouters.push(e)}static registerLoadRouter(e){en.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return en.getHandlers(e,"save")}static getLoadHandlers(e,t){return en.getHandlers(e,"load",t)}static getHandlers(e,t,n){const s=[],i=t==="load"?en.getInstance().loadRouters:en.getInstance().saveRouters;return i.forEach(o=>{const a=o(e,n);a!==null&&s.push(a)}),s}}const oF=e=>en.registerSaveRouter(e),aF=e=>en.registerLoadRouter(e),ab=e=>en.getSaveHandlers(e),cb=(e,t)=>en.getLoadHandlers(e,t);const ap="tensorflowjs",lb=1,Eo="models_store",Mr="model_info_store";async function dte(){const e=hb();return new Promise((t,n)=>{const s=e.deleteDatabase(ap);s.onsuccess=()=>t(),s.onerror=i=>n(i)})}function hb(){if(!ae().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");const e=typeof window=="undefined"?self:window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(t==null)throw new Error("The current browser does not appear to support IndexedDB.");return t}function ub(e){const t=e.result;t.createObjectStore(Eo,{keyPath:"modelPath"}),t.createObjectStore(Mr,{keyPath:"modelPath"})}class Do{constructor(e){if(this.indexedDB=hb(),e==null||!e)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=e}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,e)}async load(){return this.databaseAction(this.modelPath)}databaseAction(e,t){return new Promise((n,s)=>{const i=this.indexedDB.open(ap,lb);i.onupgradeneeded=()=>ub(i),i.onsuccess=()=>{const o=i.result;if(t==null){const a=o.transaction(Eo,"readonly"),c=a.objectStore(Eo),h=c.get(this.modelPath);h.onsuccess=()=>{if(h.result==null)return o.close(),s(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(h.result.modelArtifacts)},h.onerror=p=>(o.close(),s(h.error)),a.oncomplete=()=>o.close()}else{const a=gh(t),c=o.transaction(Mr,"readwrite");let h=c.objectStore(Mr);const p=h.put({modelPath:this.modelPath,modelArtifactsInfo:a});let m;p.onsuccess=()=>{m=o.transaction(Eo,"readwrite");const y=m.objectStore(Eo),b=y.put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:a});b.onsuccess=()=>n({modelArtifactsInfo:a}),b.onerror=w=>{h=c.objectStore(Mr);const L=h.delete(this.modelPath);L.onsuccess=()=>(o.close(),s(b.error)),L.onerror=T=>(o.close(),s(b.error))}},p.onerror=y=>(o.close(),s(p.error)),c.oncomplete=()=>{m==null?o.close():m.oncomplete=()=>o.close()}}},i.onerror=o=>s(i.error)})}}Do.URL_SCHEME="indexeddb://";const GT=e=>ae().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(Do.URL_SCHEME))?cF(e.slice(Do.URL_SCHEME.length)):null;en.registerSaveRouter(GT),en.registerLoadRouter(GT);function cF(e){return new Do(e)}function lF(e){return e.startsWith(Do.URL_SCHEME)?e.slice(Do.URL_SCHEME.length):e}class hF{constructor(){this.indexedDB=hb()}async listModels(){return new Promise((e,t)=>{const n=this.indexedDB.open(ap,lb);n.onupgradeneeded=()=>ub(n),n.onsuccess=()=>{const s=n.result,i=s.transaction(Mr,"readonly"),o=i.objectStore(Mr),a=o.getAll();a.onsuccess=()=>{const c={};for(const h of a.result)c[h.modelPath]=h.modelArtifactsInfo;e(c)},a.onerror=c=>(s.close(),t(a.error)),i.oncomplete=()=>s.close()},n.onerror=s=>t(n.error)})}async removeModel(e){return e=lF(e),new Promise((t,n)=>{const s=this.indexedDB.open(ap,lb);s.onupgradeneeded=()=>ub(s),s.onsuccess=()=>{const i=s.result,o=i.transaction(Mr,"readwrite"),a=o.objectStore(Mr),c=a.get(e);let h;c.onsuccess=()=>{if(c.result==null)return i.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{const p=a.delete(e),m=()=>{h=i.transaction(Eo,"readwrite");const y=h.objectStore(Eo),b=y.delete(e);b.onsuccess=()=>t(c.result.modelArtifactsInfo),b.onerror=w=>n(c.error)};p.onsuccess=m,p.onerror=y=>(m(),i.close(),n(c.error))}},c.onerror=p=>(i.close(),n(c.error)),o.oncomplete=()=>{h==null?i.close():h.oncomplete=()=>i.close()}},s.onerror=i=>n(s.error)})}}const Ci="/",ko="tensorflowjs_models",YT="info",uF="model_topology",dF="weight_specs",pF="weight_data",mF="model_metadata";function pte(){if(!ae().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("purgeLocalStorageModels() cannot proceed because local storage is unavailable in the current environment.");const e=window.localStorage,t=[];for(let n=0;n<e.length;++n){const s=e.key(n),i=ko+Ci;if(s.startsWith(i)&&s.length>i.length){e.removeItem(s);const o=qT(s);t.indexOf(o)===-1&&t.push(o)}}return t}function HT(e){return{info:[ko,e,YT].join(Ci),topology:[ko,e,uF].join(Ci),weightSpecs:[ko,e,dF].join(Ci),weightData:[ko,e,pF].join(Ci),modelMetadata:[ko,e,mF].join(Ci)}}function qT(e){const t=e.split(Ci);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(Ci)}function fF(e){return e.startsWith(Fo.URL_SCHEME)?e.slice(Fo.URL_SCHEME.length):e}class Fo{constructor(e){if(!ae().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=HT(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{const t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),s=gh(e);try{return this.LS.setItem(this.keys.info,JSON.stringify(s)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,eF(e.weightData)),this.LS.setItem(this.keys.modelMetadata,JSON.stringify({format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,userDefinedMetadata:e.userDefinedMetadata})),{modelArtifactsInfo:s}}catch(i){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${s.modelTopologyBytes}, weightSpecsBytes=${s.weightSpecsBytes}, weightDataBytes=${s.weightDataBytes}.`)}}}async load(){const e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(e.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");const t={},n=JSON.parse(this.LS.getItem(this.keys.topology));if(n==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);t.modelTopology=n;const s=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(s==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=s;const i=this.LS.getItem(this.keys.modelMetadata);if(i!=null){const a=JSON.parse(i);t.format=a.format,t.generatedBy=a.generatedBy,t.convertedBy=a.convertedBy,t.userDefinedMetadata=a.userDefinedMetadata}const o=this.LS.getItem(this.keys.weightData);if(o==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=tF(o),t}}Fo.URL_SCHEME="localstorage://";const jT=e=>ae().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(Fo.URL_SCHEME))?gF(e.slice(Fo.URL_SCHEME.length)):null;en.registerSaveRouter(jT),en.registerLoadRouter(jT);function gF(e){return new Fo(e)}class yF{constructor(){A(ae().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),A(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){const e={},t=ko+Ci,n=Ci+YT;for(let s=0;s<this.LS.length;++s){const i=this.LS.key(s);if(i.startsWith(t)&&i.endsWith(n)){const o=qT(i);e[o]=JSON.parse(this.LS.getItem(i))}}return e}async removeModel(e){e=fF(e);const t=HT(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);const n=JSON.parse(this.LS.getItem(t.info));return this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),n}}const Va="://";class Ts{constructor(){this.managers={}}static getInstance(){return Ts.instance==null&&(Ts.instance=new Ts),Ts.instance}static registerManager(e,t){A(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(Va)&&(e=e.slice(0,e.indexOf(Va))),A(e.length>0,()=>"scheme must not be an empty string.");const n=Ts.getInstance();A(n.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),n.managers[e]=t}static getManager(e){const t=this.getInstance().managers[e];if(t==null)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}}function cp(e){if(e.indexOf(Va)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Ts.getSchemes().join(",")}`);return{scheme:e.split(Va)[0],path:e.split(Va)[1]}}async function KT(e,t,n=!1){A(e!==t,()=>`Old path and new path are the same: '${e}'`);const s=en.getLoadHandlers(e);A(s.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),A(s.length<2,()=>`Copying failed because more than one (${s.length}) load handlers for source URL ${e}.`);const i=s[0],o=en.getSaveHandlers(t);A(o.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),A(o.length<2,()=>`Copying failed because more than one (${s.length}) save handlers for destination URL ${t}.`);const a=o[0],c=cp(e).scheme,h=cp(e).path,p=c===cp(e).scheme,m=await i.load();n&&p&&await Ts.getManager(c).removeModel(h);const y=await a.save(m);return n&&!p&&await Ts.getManager(c).removeModel(h),y.modelArtifactsInfo}async function bF(){const e=Ts.getSchemes(),t={};for(const n of e){const s=await Ts.getManager(n).listModels();for(const i in s){const o=n+Va+i;t[o]=s[i]}}return t}async function wF(e){const t=cp(e),n=Ts.getManager(t.scheme);return n.removeModel(t.path)}async function LF(e,t){const n=!1;return KT(e,t,n)}async function SF(e,t){const n=!0;return KT(e,t,n)}class IF{fetch(e,t){return fetch(e,t)}now(){return performance.now()}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${t}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(e)}decode(e,t){return new TextDecoder(t).decode(e)}}if(ae().get("IS_BROWSER")){ae().setPlatform("browser",new IF);try{Ts.registerManager(Fo.URL_SCHEME,new yF)}catch(e){}try{Ts.registerManager(Do.URL_SCHEME,new hF)}catch(e){}}const xF={importFetch:()=>F2()};let Ga;function mte(){Ga=null}function fte(e){Ga=e}function gte(){return Ga}class TF{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return ae().global.fetch!=null?ae().global.fetch(e,t):(Ga==null&&(Ga=xF.importFetch()),Ga(e,t))}now(){const e=process.hrtime();return e[0]*1e3+e[1]/1e6}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${t}`);return this.textEncoder.encode(e)}decode(e,t){return e.length===0?"":new this.util.TextDecoder(t).decode(e)}}ae().get("IS_NODE")&&ae().setPlatform("node",new TF);function wt(e,t="float32",n){return t=t||"float32",ay(e),new an(e,t,n)}function AF(e,t){const n=W(e,"x","cast");if(!Wr(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");const s={x:n},i={dtype:t};return G.runKernelFunc(o=>o.cast(n,t),s,null,ka,i)}const ve=z({cast_:AF});function vF(e){const t=W(e,"x","clone",null),n=()=>G.makeTensorFromDataId(t.dataId,t.shape,t.dtype),s={x:t};return G.runKernelFunc(n,s,null,$l)}const Pr=z({clone_:vF});function XT(e,t=!1){console.log(e.toString(t))}$T();const NF={buffer:wt,cast:ve,clone:Pr,print:XT};Vk(NF);const CF="model",RF=".json",OF=".weights.bin";function JT(e){return new Promise(t=>setTimeout(t)).then(e)}class Ya{constructor(e){if(!ae().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(Ya.URL_SCHEME)&&(e=e.slice(Ya.URL_SCHEME.length)),(e==null||e.length===0)&&(e=CF),this.modelTopologyFileName=e+RF,this.weightDataFileName=e+OF}async save(e){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");const t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{const n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],s={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n},i=window.URL.createObjectURL(new Blob([JSON.stringify(s)],{type:"application/json"})),o=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(o.download=this.modelTopologyFileName,o.href=i,await JT(()=>o.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){const a=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;a.download=this.weightDataFileName,a.href=t,await JT(()=>a.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:gh(e)}}}}Ya.URL_SCHEME="downloads://";class EF{constructor(e){if(e==null||e.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);this.files=e}async load(){const e=this.files[0],t=this.files.slice(1);return new Promise((n,s)=>{const i=new FileReader;i.onload=o=>{const a=JSON.parse(o.target.result),c=a.modelTopology;if(c==null){s(new Error(`modelTopology field is missing from file ${e.name}`));return}t.length===0&&n({modelTopology:c});const h=a.weightsManifest;if(h==null){s(new Error(`weightManifest field is missing from file ${e.name}`));return}let p;try{p=this.checkManifestAndWeightFiles(h,t)}catch(w){s(w);return}const m=[],y=[],b=[];h.forEach(w=>{w.paths.forEach(L=>{y.push(L),b.push(null)}),m.push(...w.weights)}),h.forEach(w=>{w.paths.forEach(L=>{const T=new FileReader;T.onload=v=>{const C=v.target.result,O=y.indexOf(L);b[O]=C,b.indexOf(null)===-1&&n({modelTopology:c,weightSpecs:m,weightData:op(b),format:a.format,generatedBy:a.generatedBy,convertedBy:a.convertedBy,userDefinedMetadata:a.userDefinedMetadata})},T.onerror=v=>s(`Failed to weights data from file of path '${L}'.`),T.readAsArrayBuffer(p[L])})})},i.onerror=o=>s(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),i.readAsText(e)})}checkManifestAndWeightFiles(e,t){const n=[],s=t.map(o=>VT(o.name)),i={};for(const o of e)o.paths.forEach(a=>{const c=VT(a);if(n.indexOf(c)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${c}'`);if(n.push(c),s.indexOf(c)===-1)throw new Error(`Weight file with basename '${c}' is not provided.`);i[a]=t[s.indexOf(c)]});if(n.length!==t.length)throw new Error(`Mismatch in the number of files in weights manifest (${n.length}) and the number of weight files provided (${t.length}).`);return i}}const DF=e=>ae().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(Ya.URL_SCHEME))?kF(e.slice(Ya.URL_SCHEME.length)):null;en.registerSaveRouter(DF);function kF(e="model"){return new Ya(e)}function FF(e){return new EF(e)}function ZT(e,t,n,s){a(e),n=n==null?0:n,s=s==null?1:s,c(n,s);let i=0;const o=h=>(h.then(p=>{const m=n+ ++i/e.length*(s-n);return t(m),p}),h);function a(h){A(h!=null&&Array.isArray(h)&&h.length>0,()=>"promises must be a none empty array")}function c(h,p){A(h>=0&&h<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${h}`),A(p>=0&&p<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${p}`),A(p>=h,()=>`startFraction must be no more than endFraction, but got startFraction ${h} and endFraction ${p}`)}return Promise.all(e.map(o))}async function QT(e,t){t==null&&(t={});const n=t.fetchFunc==null?ae().platform.fetch:t.fetchFunc,s=e.map(y=>n(y,t.requestInit,{isBinary:!0})),i=0,o=.5,a=t.onProgress==null?await Promise.all(s):await ZT(s,t.onProgress,i,o),c=a.map(y=>y.arrayBuffer()),h=.5,p=1,m=t.onProgress==null?await Promise.all(c):await ZT(c,t.onProgress,h,p);return m}async function eA(e,t="",n,s){const i=a=>QT(a,{requestInit:s}),o=tA(i);return o(e,t,n)}function tA(e){return async(t,n="",s)=>{const i=t.map(()=>!1),o={},a=s!=null?s.map(()=>!1):[],c=[];if(t.forEach((w,L)=>{let T=0;w.weights.forEach(v=>{const C="quantization"in v?v.quantization.dtype:v.dtype,O=ib[C]*M(v.shape),D=()=>{i[L]=!0,o[L]==null&&(o[L]=[]),o[L].push({manifestEntry:v,groupOffset:T,sizeBytes:O})};s!=null?s.forEach((k,F)=>{k===v.name&&(D(),a[F]=!0)}):D(),c.push(v.name),T+=O})}),!a.every(w=>w)){const w=s.filter((L,T)=>!a[T]);throw new Error(`Could not find weights in manifest with names: ${w.join(", ")}. 
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            

ReferenceError: require is not defined
    at new TF (file:///home/ali/Desktop/Nodejs%20Projects/face-api-test2/node_modules/@vladmandic/face-api/dist/face-api.node.js:5:43877)
    at file:///home/ali/Desktop/Nodejs%20Projects/face-api-test2/node_modules/@vladmandic/face-api/dist/face-api.node.js:5:44377
    at file:///home/ali/Desktop/Nodejs%20Projects/face-api-test2/node_modules/@vladmandic/face-api/dist/face-api.node.js:1:18641
    at file:///home/ali/Desktop/Nodejs%20Projects/face-api-test2/node_modules/@vladmandic/face-api/dist/face-api.node.js:1:18737
    at file:///home/ali/Desktop/Nodejs%20Projects/face-api-test2/node_modules/@vladmandic/face-api/dist/face-api.node.js:1:99
    at file:///home/ali/Desktop/Nodejs%20Projects/face-api-test2/node_modules/@vladmandic/face-api/dist/face-api.node.js:3957:9445
    at ModuleJob.run (internal/modules/esm/module_job.js:146:23)
    at async Loader.import (internal/modules/esm/loader.js:165:24)
    at async Object.loadESM (internal/process/esm_loader.js:68:5)

Process finished with exit code 1

{
  "type": "module",
  "name": "face-api-test",
  "version": "0.0.1",
  "description": "Face API test",
  "main": "index.mjs",
  "dependencies": {
    "@tensorflow/tfjs-node": "latest",
    "@tensorflow/tfjs": "latest",
    "@vladmandic/face-api": "latest",
    "canvas": "latest",
    "express": "latest",
    "formidable": "latest"
  },
  "scripts": {
    "start": "tsc && node dist/faceDetection.js",
    "test": "echo \"Error: no test specified\" && exit 1"
  },
  "author": "Ali Tafakkori",
  "license": "ISC"
}

import '@tensorflow/tfjs'
import * as canvas from 'canvas';
import * as faceapi from '@vladmandic/face-api/dist/face-api.esm.js';
import * as express from "express";
import * as formidable from "formidable";

import fs from 'fs';
import path from 'path';

const {IncomingForm} = formidable;
const app = express();
const {Canvas, Image, ImageData} = canvas;
faceapi.env.monkeyPatch({Canvas, Image, ImageData});

function saveFile(fileName, buf) {
    const baseDir = "./out";
    if (!fs.existsSync(baseDir)) {
        fs.mkdirSync(baseDir)
    }

    fs.writeFileSync(path.resolve(baseDir, fileName), buf)
}

const minConfidence = 0.5;

// TinyFaceDetectorOptions
const inputSize = 408;
const scoreThreshold = 0.5;

function getFaceDetectorOptions(net) {
    return net === faceapi.nets.ssdMobilenetv1
        ? new faceapi.SsdMobilenetv1Options({minConfidence})
        : new faceapi.TinyFaceDetectorOptions({inputSize, scoreThreshold})
}

async function run() {
    console.log(new Date().toISOString());
    const start = new Date().getTime();

    await faceapi.nets.ssdMobilenetv1.loadFromDisk('./weights');
    await faceapi.nets.faceRecognitionNet.loadFromDisk('./weights');
    await faceapi.nets.faceLandmark68Net.loadFromDisk('./weights');
    await faceapi.nets.faceLandmark68TinyNet.loadFromDisk('./weights');
    await faceapi.nets.ageGenderNet.loadFromDisk('./weights');
    await faceapi.nets.faceExpressionNet.loadFromDisk('./weights');

    //await faceRecognition();
    //await detectSingleFace();

    console.log(new Date().getTime() - start);
    console.log(new Date().toISOString());
}

run().then();

async function detectSingleFace(query) {
    const img = await canvas.loadImage(query);
    const res = await faceapi.detectSingleFace(img, getFaceDetectorOptions(faceapi.nets.ssdMobilenetv1))
        .withFaceLandmarks()
        .withAgeAndGender()
        .withFaceExpressions()
        .withFaceDescriptor();
    const out = faceapi.createCanvasFromMedia(img);
    //faceapi.draw.drawFaceLandmarks(out, res);
    faceapi.draw.drawDetections(out, res);
    //faceapi.draw.drawFaceExpressions(out, res);
    fs.writeFileSync('./out/faceDetection.jpg', out.toBuffer('image/jpeg'));
}

async function detectAllFaces(query) {
    const img = await canvas.loadImage(query);
    const results = await faceapi.detectAllFaces(img).withFaceLandmarks().withFaceDescriptors()
    const out = faceapi.createCanvasFromMedia(img);
    faceapi.draw.drawDetections(out, results);
    //faceapi.draw.drawFaceLandmarks(out, results);
    fs.writeFileSync(`./out/faces/faces.jpg`, out.toBuffer('image/jpeg'));
}

async function faceRecognition(queryImagePath, src) {

    const referenceImage = await canvas.loadImage(src);
    const queryImage = await canvas.loadImage(queryImagePath);

    const resultsRef = await faceapi.detectAllFaces(referenceImage, getFaceDetectorOptions(faceapi.nets.ssdMobilenetv1))
        .withFaceLandmarks()
        .withFaceDescriptors();

    const resultsQuery = await faceapi.detectAllFaces(queryImage, getFaceDetectorOptions(faceapi.nets.ssdMobilenetv1))
        .withFaceLandmarks()
        .withFaceDescriptors();

    const faceMatcher = new faceapi.FaceMatcher(resultsRef);

    const labels = faceMatcher.labeledDescriptors
        .map(ld => ld.label);
    const refDrawBoxes = resultsRef
        .map(res => res.detection.box)
        .map((box, i) => new faceapi.draw.DrawBox(box, {label: labels[i]}));
    const outRef = faceapi.createCanvasFromMedia(referenceImage);
    refDrawBoxes.forEach(drawBox => drawBox.draw(outRef));

    saveFile('referenceImage.jpg', outRef.toBuffer('image/jpeg'));

    const queryDrawBoxes = resultsQuery.map(res => {
        const bestMatch = faceMatcher.findBestMatch(res.descriptor);
        return new faceapi.draw.DrawBox(res.detection.box, {label: bestMatch.toString()});
    });
    const outQuery = faceapi.createCanvasFromMedia(queryImage);
    queryDrawBoxes.forEach(drawBox => drawBox.draw(outQuery));
    saveFile('queryImage.jpg', outQuery.toBuffer('image/jpeg'));
    console.log('done, saved results to out/queryImage.jpg');
}


app.post("/faceRecognition", function (req, res) {
    IncomingForm().parse(req, async function (err, fields, files) {
        if (err) {
            return res.status(503).send(err);
        } else {
            const src = files.src.path;
            const query = files.query.path;
            console.log(new Date().toISOString());
            const start = new Date().getTime();
            await faceRecognition(src, query);
            console.log(new Date().getTime() - start);
            console.log(new Date().toISOString());
            res.redirect("index.html");
        }
    });
});

app.post("/detectSingleFace", function (req, res) {
    IncomingForm().parse(req, async function (err, fields, files) {
        if (err) {
            return res.status(503).send(err);
        } else {
            const query = files.query.path;
            console.log(new Date().toISOString());
            const start = new Date().getTime();
            await detectSingleFace(query);
            console.log(new Date().getTime() - start);
            console.log(new Date().toISOString());
            res.redirect("index.html");
        }
    });
});

app.post("/detectAllFaces", function (req, res) {
    IncomingForm().parse(req, async function (err, fields, files) {
        if (err) {
            return res.status(503).send(err);
        } else {
            const query = files.query.path;
            console.log(new Date().toISOString());
            const start = new Date().getTime();
            await detectAllFaces(query);
            console.log(new Date().getTime() - start);
            console.log(new Date().toISOString());
            res.redirect("index.html");
        }
    });
});

app.use(express.static("./static"));

app.listen(7700, function () {
    console.log("Server ready on 7700 port");
});

 const ssdOptions = { minConfidence: 0.1, maxResults: 10 };
      const optionsSSDMobileNet = new faceapi.SsdMobilenetv1Options(ssdOptions);
      const faces = await faceapi
        .detectAllFaces(tensor, optionsSSDMobileNet)
        .withFaceLandmarks()
        .withFaceExpressions()
        .withFaceDescriptors()
        .withAgeAndGender();

         > node_modules/@tensorflow/tfjs-data/dist/dataset.js:19:28: error: Could not resolve "seedrandom" (mark it as external to exclude it from the bundle)
            19 │ import * as seedrandom from 'seedrandom';
               ╵                             ~~~~~~~~~~~~
        
         > node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js:19:28: error: Could not resolve "seedrandom" (mark it as external to exclude it from the bundle)
            19 │ import * as seedrandom from 'seedrandom';
               ╵                             ~~~~~~~~~~~~
        
        2021-03-16 09:26:04 STATE:  Monitoring: [ 'package.json', 'demo', 'src', [length]: 3 ]
        2021-03-16 09:26:04 ERROR:  Build error [
          {
            "location": {
              "column": 28,
              "file": "node_modules/@tensorflow/tfjs-data/dist/dataset.js",
              "length": 12,
              "line": 19,
              "lineText": "import * as seedrandom from 'seedrandom';",
              "namespace": ""
            },
            "notes": [],
            "text": "Could not resolve \"seedrandom\" (mark it as external to exclude it from the bundle)"
          },
          {
            "location": {
              "column": 28,
              "file": "node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js",
              "length": 12,
              "line": 19,
              "lineText": "import * as seedrandom from 'seedrandom';",
              "namespace": ""
            },
            "notes": [],
            "text": "Could not resolve \"seedrandom\" (mark it as external to exclude it from the bundle)"
          }
        ]

/node_modules/@vladmandic/face-api/build/index.js:1
import * as tf from '@tensorflow/tfjs-core';
^^^^^^

SyntaxError: Cannot use import statement outside a module
    at wrapSafe (internal/modules/cjs/loader.js:1053:16)
    at Module._compile (internal/modules/cjs/loader.js:1101:27)
    at Module._extensions..js (internal/modules/cjs/loader.js:1157:10)
    at Object.require.extensions.<computed> [as .js] (/home/martin/Trabajo/SevntV2/src/Monitoreo.Core.Server/node_modules/ts-node/src/index.ts:851:44)
    at Module.load (internal/modules/cjs/loader.js:985:32)
    at Function.Module._load (internal/modules/cjs/loader.js:878:14)
    at Module.require (internal/modules/cjs/loader.js:1025:19)
    at require (internal/modules/cjs/helpers.js:72:18)
    at Object.<anonymous> (/home/martin/Trabajo/SevntV2/src/Monitoreo.Core.Server/src/commons/env.ts:5:1)
    at Module._compile (internal/modules/cjs/loader.js:1137:30)
Waiting for the debugger to disconnect...

{
  "name": "monitoreo.core.server",
  "version": "1.0.0",
  "description": "Modulo Core de Reco Facial y Monitoreo Sevnt",
  "main": "dist/app.js",
  "scripts": {
    "test": "echo \"Error: no test specified\" && exit 1"
  },
  "author": "",
  "license": "ISC",
  "dependencies": {
    "@tensorflow/tfjs": "^2.3.0",
    "@tensorflow/tfjs-node-gpu": "^2.3.0",
    "@types/express": "^4.17.7",
    "@vladmandic/face-api": "^0.4.1",
    "dotenv": "^8.2.0",
    "event-loop-stats": "^1.3.0",
    "express": "^4.17.1",
    "microjob": "^0.7.0",
    "rtsp-ffmpeg": "0.0.14",
    "socket.io": "^2.3.0",
    "tslib": "^1.11.1"
  },
  "devDependencies": {
    "@types/jasmine": "^3.5.14",
    "@types/node": "^13.9.2",
    "@types/socket.io": "^2.1.6",
    "canvas": "2.6.1",
    "jasmine": "^3.5.0",
    "jasmine-core": "^3.5.0",
    "karma": "^4.4.1",
    "karma-chrome-launcher": "^3.1.0",
    "karma-jasmine": "^3.1.1",
    "karma-typescript": "^5.1.0",
    "rollup": "^2.26.8",
    "rollup-plugin-commonjs": "^10.1.0",
    "rollup-plugin-node-resolve": "^5.2.0",
    "rollup-plugin-typescript2": "^0.26.0",
    "rollup-plugin-uglify": "^6.0.4",
    "ts-node": "^8.7.0",
    "typescript": "^3.8.3"
  }
}

Could not find a declaration file for module '@vladmandic/face-api'.
'.../node_modules/@vladmandic/face-api/dist/face-api.node.js' implicitly has an 'any' type.
Try `npm i --save-dev @types/vladmandic__face-api`
 if it exists or add a new declaration (.d.ts) file containing `declare module '@vladmandic/face-api';`js(7016)

async backendWasm() {
  await faceapi.tf.setWasmPaths("../statics/");
  await faceapi.tf.setBackend("wasm");
},

Module not found: Error: Can't resolve '@vladmandic/face-api' in '/opt/whatsign/frontend/src/components/lib'
resolve '@vladmandic/face-api' in '/opt/whatsign/frontend/src/components/lib'
  Parsed request is a module
  using description file: /opt/whatsign/frontend/package.json (relative path: ./src/components/lib)
    Field 'browser' doesn't contain a valid alias configuration
    resolve as module
      /opt/whatsign/frontend/src/components/lib/node_modules doesn't exist or is not a directory
      /opt/whatsign/frontend/src/components/node_modules doesn't exist or is not a directory
      /opt/whatsign/frontend/src/node_modules doesn't exist or is not a directory
      /opt/whatsign/node_modules doesn't exist or is not a directory
      /opt/node_modules doesn't exist or is not a directory
      /node_modules doesn't exist or is not a directory
      looking for modules in /opt/whatsign/frontend/node_modules
        using description file: /opt/whatsign/frontend/package.json (relative path: ./node_modules)
          Field 'browser' doesn't contain a valid alias configuration
          using description file: /opt/whatsign/frontend/package.json (relative path: ./node_modules/@vladmandic/face-api)
            no extension
              Field 'browser' doesn't contain a valid alias configuration
              /opt/whatsign/frontend/node_modules/@vladmandic/face-api doesn't exist
            .mjs
              Field 'browser' doesn't contain a valid alias configuration
              /opt/whatsign/frontend/node_modules/@vladmandic/face-api.mjs doesn't exist
            .js
              Field 'browser' doesn't contain a valid alias configuration
              /opt/whatsign/frontend/node_modules/@vladmandic/face-api.js doesn't exist
            .vue
              Field 'browser' doesn't contain a valid alias configuration
              /opt/whatsign/frontend/node_modules/@vladmandic/face-api.vue doesn't exist
            .json
              Field 'browser' doesn't contain a valid alias configuration
              /opt/whatsign/frontend/node_modules/@vladmandic/face-api.json doesn't exist
            .wasm
              Field 'browser' doesn't contain a valid alias configuration
              /opt/whatsign/frontend/node_modules/@vladmandic/face-api.wasm doesn't exist
            as directory
              /opt/whatsign/frontend/node_modules/@vladmandic/face-api doesn't exist
      looking for modules in /opt/whatsign/frontend/node_modules
        using description file: /opt/whatsign/frontend/package.json (relative path: ./node_modules)
          Field 'browser' doesn't contain a valid alias configuration
          using description file: /opt/whatsign/frontend/package.json (relative path: ./node_modules/@vladmandic/face-api)
            no extension
              Field 'browser' doesn't contain a valid alias configuration
              /opt/whatsign/frontend/node_modules/@vladmandic/face-api doesn't exist
            .mjs
              Field 'browser' doesn't contain a valid alias configuration
              /opt/whatsign/frontend/node_modules/@vladmandic/face-api.mjs doesn't exist
            .js
              Field 'browser' doesn't contain a valid alias configuration
              /opt/whatsign/frontend/node_modules/@vladmandic/face-api.js doesn't exist
            .vue
              Field 'browser' doesn't contain a valid alias configuration
              /opt/whatsign/frontend/node_modules/@vladmandic/face-api.vue doesn't exist
            .json
              Field 'browser' doesn't contain a valid alias configuration
              /opt/whatsign/frontend/node_modules/@vladmandic/face-api.json doesn't exist
            .wasm
              Field 'browser' doesn't contain a valid alias configuration
              /opt/whatsign/frontend/node_modules/@vladmandic/face-api.wasm doesn't exist
            as directory
              /opt/whatsign/frontend/node_modules/@vladmandic/face-api doesn't exist
      looking for modules in /opt/whatsign/frontend/node_modules/@quasar/app/node_modules
        using description file: /opt/whatsign/frontend/node_modules/@quasar/app/package.json (relative path: ./node_modules)
          Field 'browser' doesn't contain a valid alias configuration
          using description file: /opt/whatsign/frontend/node_modules/@quasar/app/package.json (relative path: ./node_modules/@vladmandic/face-api)
            no extension
              Field 'browser' doesn't contain a valid alias configuration
              /opt/whatsign/frontend/node_modules/@quasar/app/node_modules/@vladmandic/face-api doesn't exist
            .mjs
              Field 'browser' doesn't contain a valid alias configuration
              /opt/whatsign/frontend/node_modules/@quasar/app/node_modules/@vladmandic/face-api.mjs doesn't exist
            .js
              Field 'browser' doesn't contain a valid alias configuration
              /opt/whatsign/frontend/node_modules/@quasar/app/node_modules/@vladmandic/face-api.js doesn't exist
            .vue
              Field 'browser' doesn't contain a valid alias configuration
              /opt/whatsign/frontend/node_modules/@quasar/app/node_modules/@vladmandic/face-api.vue doesn't exist
            .json
              Field 'browser' doesn't contain a valid alias configuration
              /opt/whatsign/frontend/node_modules/@quasar/app/node_modules/@vladmandic/face-api.json doesn't exist
            .wasm
              Field 'browser' doesn't contain a valid alias configuration
              /opt/whatsign/frontend/node_modules/@quasar/app/node_modules/@vladmandic/face-api.wasm doesn't exist
            as directory
              /opt/whatsign/frontend/node_modules/@quasar/app/node_modules/@vladmandic/face-api doesn't exist
[/opt/whatsign/frontend/src/components/lib/node_modules]
[/opt/whatsign/frontend/src/components/node_modules]
[/opt/whatsign/frontend/src/node_modules]
[/opt/whatsign/node_modules]
[/opt/node_modules]
[/node_modules]
[/opt/whatsign/frontend/node_modules/@vladmandic/face-api]
[/opt/whatsign/frontend/node_modules/@vladmandic/face-api.mjs]
[/opt/whatsign/frontend/node_modules/@vladmandic/face-api.js]
[/opt/whatsign/frontend/node_modules/@vladmandic/face-api.vue]
[/opt/whatsign/frontend/node_modules/@vladmandic/face-api.json]
[/opt/whatsign/frontend/node_modules/@vladmandic/face-api.wasm]
[/opt/whatsign/frontend/node_modules/@quasar/app/node_modules/@vladmandic/face-api]
[/opt/whatsign/frontend/node_modules/@quasar/app/node_modules/@vladmandic/face-api.mjs]
[/opt/whatsign/frontend/node_modules/@quasar/app/node_modules/@vladmandic/face-api.js]
[/opt/whatsign/frontend/node_modules/@quasar/app/node_modules/@vladmandic/face-api.vue]
[/opt/whatsign/frontend/node_modules/@quasar/app/node_modules/@vladmandic/face-api.json]
[/opt/whatsign/frontend/node_modules/@quasar/app/node_modules/@vladmandic/face-api.wasm]
 @ ./src/components/lib/faceapi.js 1:14-45
 @ ./node_modules/@quasar/app/lib/webpack/loader.transform-quasar-imports.js!./node_modules/babel-loader/lib??ref--2-0!./node_modules/@quasar/app/lib/webpack/loader.auto-import-client.js?kebab!./node_modules/vue-loader/lib??vue-loader-options!./src/components/Camare4.vue?vue&type=script&lang=js&
 @ ./src/components/Camare4.vue?vue&type=script&lang=js&
 @ ./src/components/Camare4.vue
 @ ./node_modules/@quasar/app/lib/webpack/loader.transform-quasar-imports.js!./node_modules/babel-loader/lib??ref--2-0!./node_modules/@quasar/app/lib/webpack/loader.auto-import-client.js?kebab!./node_modules/vue-loader/lib??vue-loader-options!./src/pages/signingProcess/selfiePhotoFaceApi.vue?vue&type=script&lang=js&
 @ ./src/pages/signingProcess/selfiePhotoFaceApi.vue?vue&type=script&lang=js&
 @ ./src/pages/signingProcess/selfiePhotoFaceApi.vue
 @ ./src/router/routes.js
 @ ./src/router/index.js
 @ ./.quasar/app.js
 @ ./.quasar/client-entry.js
 @ multi ./.quasar/client-entry.js

const resultsQuery = await faceapi
	.detectSingleFace(queryImage, faceDetectionOptions)
	.withFaceLandmarks()
	.withFaceDescriptor();
const labeledFaceDescriptors = await loadLabeledImages();
const faceMatcher = new faceapi.FaceMatcher(labeledFaceDescriptors);
if (resultsQuery) {
	const bestMatch = faceMatcher.findBestMatch(resultsQuery.descriptor);
	var match_percentage = 100 - Math.ceil(bestMatch._distance * 100);
	db.query(
		"update required_documents set _8applicant_image_verification_status = ? where application_id  = ?",
		[match_percentage, application_id],
		(err, result, field) => {
			if (err) throw err;
			if (result.affectedRows > 0) {
				console.log("match percentage added", match_percentage);
			} else {
				console.log("match percentage not added");
			}
		}
	);
}
const labels = faceMatcher.labeledDescriptors.map((ld) => ld.label);

function loadLabeledImages() {
	const labels = [application_id];
	return Promise.all(
		labels.map(async (label) => {
			const descriptions = [];
			//              let url = `../public/images/${application_id}/training_data`;

			//       const TrainingData = path.resolve(__dirname, dir2);
			for (let i = 1; i <= trainingDataLength; i++) {
				//   const REFERENCE_IMAGE = TrainingData + `/abc (${i}).jpg`;
				const referenceImage = await canvas.loadImage(dir2);
				const detections = await faceapi
					.detectSingleFace(referenceImage, faceDetectionOptions)
					.withFaceLandmarks()
					.withFaceDescriptor();
				descriptions.push(detections.descriptor);
			}

			return new faceapi.LabeledFaceDescriptors(label, descriptions);
		})
	);
}

const baseFaceTensor = await urlImageFace(imageUrl);
        let baseImageDetection = await FACE_API.detectSingleFace(baseFaceTensor);

        const faceTensor = await urlImageFace(imageUrl);
        let imageDetection = await FACE_API.detectSingleFace(faceTensor);

        console.log(`PARAMS`);
        console.log(`Base image detection : ${JSON.stringify(baseImageDetection)}`);
        console.log(`Image detection : ${JSON.stringify(imageDetection)}`);

        let faceMatcher = await FACE_API.getFaceMatcher(baseImageDetection, CX.AIMonitoringUtil.FACE_DISTANCE_THRESHOLD);
        let bestMatch = faceMatcher.findBestMatch((imageDetection as any).descriptor);
        console.log(`Best match : ${JSON.stringify(bestMatch)}`);

async function urlImageFace(url: string): Promise<Canvas> {
    const img = await canvas.loadImage(url);
    const c: Canvas = canvas.createCanvas(img.width, img.height);
    const ctx = c.getContext('2d');
    ctx.drawImage(img, 0, 0, img.width, img.height);
    return c;
}

Error: FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>
    at C:\Development\hemsor\functions\node_modules\@vladmandic\face-api\dist\face-api.node.js:4481:13
    at Array.map (<anonymous>)
    at new FaceMatcher (C:\Development\hemsor\functions\node_modules\@vladmandic\face-api\dist\face-api.node.js:4471:43)
    at Object.<anonymous> (C:\Development\hemsor\functions\lib\util\vlad-face-api.util.js:19:16)
    at Generator.next (<anonymous>)
    at C:\Development\hemsor\functions\lib\util\vlad-face-api.util.js:8:71
    at new Promise (<anonymous>)
    at __awaiter (C:\Development\hemsor\functions\lib\util\vlad-face-api.util.js:4:12)
    at Object.getFaceMatcher (C:\Development\hemsor\functions\lib\util\vlad-face-api.util.js:18:12)
    at C:\Development\hemsor\functions\lib\util\ai-util.js:109:71

        const detections = await faceapi
            .detectSingleFace (imageRef.current, faceapi.SsdMobilenetv1Options ())
            .withFaceLandmarks ()
            .withFaceDescriptor ();
        console.log ('fl detection', detections)

Uncaught (in promise) DOMException: Failed to execute 'drawImage' on 'CanvasRenderingContext2D': The image argument is a canvas element with a width or height of 0.
      Ur @ imageToSquare.ts:24
      (anonymous) @ NetInput.ts:147
      (anonymous) @ NetInput.ts:130
      (anonymous) @ engine.js:327
      scopedRun @ engine.js:337
      tidy @ engine.js:326
      tidy @ globals.ts:192
      toBatchTensor @ NetInput.ts:129
      (anonymous) @ FaceRecognitionNet.ts:25
      (anonymous) @ engine.js:327
      scopedRun @ engine.js:337
      tidy @ engine.js:326
      tidy @ globals.ts:192
      forwardInput @ FaceRecognitionNet.ts:24
      (anonymous) @ FaceRecognitionNet.ts:66
      (anonymous) @ engine.js:327
      scopedRun @ engine.js:337
      tidy @ engine.js:326
      tidy @ globals.ts:192
      computeFaceDescriptor @ FaceRecognitionNet.ts:65
      async function (async)
      then @ ComposableTask.ts:6
      setTimeout (async)
      tryFn @ util_base.js:289
      (anonymous) @ util_base.js:291
      repeatedTry2 @ util_base.js:276
      addItemToPoll @ gpgpu_context.js:391
      (anonymous) @ gpgpu_context.js:372
      pollFence @ gpgpu_context.js:371
      createAndWaitForFence @ gpgpu_context.js:163
      read @ backend_webgl.js:246
      read @ backend_webgl.js:223
      read @ engine.js:929
      data @ tensor.js:230
      detect @ modelDetect.js:135
      async function (async)
      detect @ modelDetect.js:106
      process2 @ processImage.js:235
      async function (async)
      process2 @ processImage.js:202
      processFiles @ process.js:67
      async function (async)
      processFiles @ process.js:39
      main @ process.js:121
      async function (async)
      main @ process.js:111
      load (async)
      (anonymous) @ process.js:124

faceapi = require("face-api.js");

const faceapi = require('@vladmandic/face-api');

[1] Error [ERR_REQUIRE_ESM]: Must use import to load ES Module: /Users/me/app/node_modules/@vladmandic/face-api/dist/face-api.esm.js
[1] require() of ES modules is not supported.
[1] require() of /Users/me/app/node_modules/@vladmandic/face-api/dist/face-api.esm.js from /Users/me/app/electron/face-extraction.js is an ES module file as it is a .js file whose nearest parent package.json contains "type": "module" which defines all .js files in that package scope as ES modules.
[1] Instead rename face-api.esm.js to end in .cjs, change the requiring code to use import(), or remove "type": "module" from /Users/me/app/node_modules/@vladmandic/face-api/package.json.
[1] 

faceapi.tf.setWasmPaths(
    "../node_modules/@tensorflow/tfjs-backend-wasm/dist/"
  );
  await faceapi.tf.setBackend("wasm");

Uncaught error: initialization of  backend wasm failed

 SSDMOBIlENETV1 : load model before inference

  const baseFaceTensor = await urlImageFace(baseImageUrl);
    console.log(`Base image url : ${baseImageUrl}`);
    let baseImageDetection = await FACE_API.detectSingleFace(baseFaceTensor);
    console.log(baseFaceTensor);
    console.log(`Base image result : ${JSON.stringify(baseImageDetection)}`);

    const faceTensor = await urlImageFace(imageUrl);
    console.log(`Image url : ${imageUrl}`);
    let imageDetection = await FACE_API.detectSingleFace(faceTensor);
    console.log(`Image result : ${JSON.stringify(imageDetection)}`);

import * as faceapi from '../dist/face-api.esm.js';


const modelPath = "../model";


await faceapi.tf.setBackend('webgl');
await faceapi.tf.enableProdMode();
await faceapi.tf.ENV.set('DEBUG', false);
await faceapi.tf.ready();

const minScore = 0.3; // minimum score
const maxResults = 10; // maximum number of results to return

const optionsSSDMobileNet = new faceapi.SsdMobilenetv1Options({
    minConfidence: minScore,
    maxResults
});

document.getElementById("run").addEventListener("click", run);

let imgElement;

async function run(){
        
    await faceapi.nets.ssdMobilenetv1.load(modelPath);
    await faceapi.nets.faceLandmark68Net.load(modelPath);
    await faceapi.nets.faceRecognitionNet.load(modelPath);

    console.log("Models loaded")

    const inputElement = document.getElementById("file");
    const file = inputElement.files[0];

    if(FileReader){ //file reader support
      let fr = new FileReader();
      fr.onload = async function () {

          imgElement = new Image();
          imgElement.onload = ImgOnLoad;
          imgElement.src = fr.result;


      }
      fr.readAsDataURL(file);
    }
    else{
      console.log("File reader not supported")
    }
    console.log(inputElement.files[0]);


}

async function ImgOnLoad(){
    let canvas = document.getElementById("canvas")
    canvas.width = imgElement.width;
    canvas.height = imgElement.height;
    let ctx = canvas.getContext("2d");
    ctx.drawImage(imgElement,0,0);



  	const encoding = await calculateEncoding(canvas);

 	console.log(encoding);
}


async function calculateEncoding(img){


      let result;

      for(let i = 0; i < 10; ++i){

          const startTime = performance.now();

          //We want to detect the best face in each file
          result = await faceapi.detectSingleFace(img, new faceapi.SsdMobilenetv1Options(optionsSSDMobileNet))
          .withFaceLandmarks()
          .withFaceDescriptor()

          const endTime = performance.now();

          console.log(`Time for run ${i + 1} in ms: ${endTime - startTime}`);

      }

      //only if face found
      if(result != undefined)
          return result.descriptor;
      else 
        return undefined;

}

<!DOCTYPE html>
<html>
<head>
    <script src="./TimeMultipleRuns.js" type="module"></script>

</head>
<body>

  <input type="file" id="file" name="file"/><br><br>

    <button id = "run">Run</button><br><br>
    <canvas id="canvas" width="640" height="480"></canvas>


</body>
</html>

Error [ERR_REQUIRE_ESM]: Must use import to load ES Module: /home/meeki/node-red-contrib-facial-recognition/node_modules/@vladmandic/face-api/dist/face-api.node.js
require() of ES modules is not supported.
require() of /home/meeki/node-red-contrib-facial-recognition/node_modules/@vladmandic/face-api/dist/face-api.node.js from /home/meeki/node-red-contrib-facial-recognition/facial-recognition.js is an ES module file as it is a .js file whose nearest parent package.json contains "type": "module" which defines all .js files in that package scope as ES modules.
Instead rename face-api.node.js to end in .cjs, change the requiring code to use import(), or remove "type": "module" from /home/meeki/node-red-contrib-facial-recognition/node_modules/@vladmandic/face-api/package.json.

// patch nodejs environment, we need to provide an implementation of
// HTMLCanvasElement and HTMLImageElement
const { Canvas, Image, ImageData } = canvas
faceapi.env.monkeyPatch({ Canvas, Image, ImageData })

const buffer = fs.readFileSync(image_path);
const decoded = tf.node.decodeImage(buffer);
const casted = decoded.toFloat();
const loadedImage = casted.expandDims(0);
decoded.dispose(); //release from memory
casted.dispose(); //release from memory

const result = await FaceAPI
                 .detectAllFaces(loadedImage, new FaceAPI.SsdMobilenetv1Options({ minConfidence: 0.5 }))
                .withFaceLandmarks()
                .withFaceDescriptors()

      const res = await faceapi
      .detectAllFaces(tensor, optionsSSDMobileNet)
      .withFaceLandmarks()
      .withFaceExpressions()
      .withFaceDescriptors()
      .withAgeAndGender();

    //   const faceMatcher = new faceapi.FaceMatcher(res)
      console.log('res:',res)

        throw Error("Size(" + size + ") must match the product of shape " + shape);
              ^
      Error: Size(1048576) must match the product of shape 512,512,3
          at util.inferFromImplicitShape (/home/thape/faceApi/node_modules/@tensorflow/tfjs-core/src/util_base.ts:317:13)
          at forward (/home/thape/faceApi/node_modules/@tensorflow/tfjs-core/src/ops/reshape.ts:64:13)
          at /home/thape/faceApi/node_modules/@tensorflow/tfjs-core/src/engine.ts:625:31
          at /home/thape/faceApi/node_modules/@tensorflow/tfjs-core/src/engine.ts:433:20
          at Engine.scopedRun (/home/thape/faceApi/node_modules/@tensorflow/tfjs-core/src/engine.ts:444:19)
          at Engine.tidy (/home/thape/faceApi/node_modules/@tensorflow/tfjs-core/src/engine.ts:431:17)
          at kernelFunc (/home/thape/faceApi/node_modules/@tensorflow/tfjs-core/src/engine.ts:625:20)
          at /home/thape/faceApi/node_modules/@tensorflow/tfjs-core/src/engine.ts:639:23
          at Engine.scopedRun (/home/thape/faceApi/node_modules/@tensorflow/tfjs-core/src/engine.ts:444:19)
at Engine.runKernelFunc (/home/thape/faceApi/node_modules/@tensorflow/tfjs-core/src/engine.ts:636:10)