diff --git a/CHANGELOG.md b/CHANGELOG.md index 19b26eb..481399d 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,7 +9,10 @@ ## Changelog -### **HEAD -> master** 2021/12/14 mandic00@live.com +### **1.6.2** 2022/01/01 mandic00@live.com + + +### **origin/master** 2021/12/27 mandic00@live.com ### **1.6.1** 2021/12/09 mandic00@live.com diff --git a/demo/node-canvas.js b/demo/node-canvas.js index 073cea2..a8604cd 100644 --- a/demo/node-canvas.js +++ b/demo/node-canvas.js @@ -1,15 +1,13 @@ -// @ts-nocheck - const fs = require('fs'); -const process = require('process'); const path = require('path'); -// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require +const process = require('process'); const log = require('@vladmandic/pilogger'); -// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require, no-unused-vars, @typescript-eslint/no-unused-vars -const tf = require('@tensorflow/tfjs-node'); -// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require const canvas = require('canvas'); -const faceapi = require('../dist/face-api.node.js'); // this is equivalent to '@vladmandic/faceapi' + +// eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars +const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before face-api +// const faceapi = require('@vladmandic/face-api'); // use this when face-api is installed as module (majority of use cases) +const faceapi = require('../dist/face-api.node.js'); // use this when using face-api in dev mode const modelPathRoot = '../model'; const imgPathRoot = './demo'; // modify to include your sample images @@ -80,7 +78,7 @@ async function main() { for (const face of result) print(face); } const t1 = process.hrtime.bigint(); - log.info('Processed', numImages, 'images in', Math.trunc(parseInt(t1 - t0) / 1000 / 1000), 'ms'); + log.info('Processed', numImages, 'images in', Math.trunc(Number((t1 - t0).toString()) / 1000 / 1000), 'ms'); } else { const param = process.argv[2]; if (fs.existsSync(param) || param.startsWith('http:') || param.startsWith('https:')) { diff --git a/demo/node-image.js b/demo/node-image.js index ae804b0..7114a0e 100644 --- a/demo/node-image.js +++ b/demo/node-image.js @@ -1,11 +1,12 @@ const fs = require('fs'); // eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require -const tf = require('@tensorflow/tfjs-node'); -// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require const image = require('@canvas/image'); // @canvas/image can decode jpeg, png, webp -// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require const log = require('@vladmandic/pilogger'); -const faceapi = require('../dist/face-api.node.js'); // this is equivalent to '@vladmandic/faceapi' + +// eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars +const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before face-api +// const faceapi = require('@vladmandic/face-api'); // use this when face-api is installed as module (majority of use cases) +const faceapi = require('../dist/face-api.node.js'); // use this when using face-api in dev mode const modelPath = 'model/'; const imageFile = 'demo/sample1.jpg'; diff --git a/demo/node-match.js b/demo/node-match.js index 2961d58..6629df1 100644 --- a/demo/node-match.js +++ b/demo/node-match.js @@ -1,8 +1,11 @@ const fs = require('fs'); const path = require('path'); -const tf = require('@tensorflow/tfjs-node'); const log = require('@vladmandic/pilogger'); -const faceapi = require('../dist/face-api.node.js'); + +// eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars +const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before face-api +// const faceapi = require('@vladmandic/face-api'); // use this when face-api is installed as module (majority of use cases) +const faceapi = require('../dist/face-api.node.js'); // use this when using face-api in dev mode let optionsSSDMobileNet; const minConfidence = 0.1; @@ -13,6 +16,7 @@ const labeledFaceDescriptors = []; async function initFaceAPI() { await faceapi.nets.ssdMobilenetv1.loadFromDisk(modelPath); await faceapi.nets.faceLandmark68Net.loadFromDisk(modelPath); + await faceapi.nets.faceExpressionNet.loadFromDisk(modelPath); await faceapi.nets.faceRecognitionNet.loadFromDisk(modelPath); optionsSSDMobileNet = new faceapi.SsdMobilenetv1Options({ minConfidence, maxResults: 1 }); } @@ -22,6 +26,7 @@ async function getDescriptors(imageFile) { const tensor = tf.node.decodeImage(buffer, 3); const faces = await faceapi.detectAllFaces(tensor, optionsSSDMobileNet) .withFaceLandmarks() + .withFaceExpressions() .withFaceDescriptors(); tf.dispose(tensor); return faces.map((face) => face.descriptor); diff --git a/demo/node-multiprocess-worker.js b/demo/node-multiprocess-worker.js index e84f071..0837686 100644 --- a/demo/node-multiprocess-worker.js +++ b/demo/node-multiprocess-worker.js @@ -2,13 +2,13 @@ const fs = require('fs'); const path = require('path'); -// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require const log = require('@vladmandic/pilogger'); // workers actual import tfjs and faceapi modules -// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require -const tf = require('@tensorflow/tfjs-node'); -const faceapi = require('../dist/face-api.node.js'); // this is equivalent to '@vladmandic/faceapi' +// eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars +const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before face-api +// const faceapi = require('@vladmandic/face-api'); // use this when face-api is installed as module (majority of use cases) +const faceapi = require('../dist/face-api.node.js'); // use this when using face-api in dev mode // options used by faceapi const modelPathRoot = '../model'; diff --git a/demo/node-multiprocess.js b/demo/node-multiprocess.js index f2a7419..a8e2924 100644 --- a/demo/node-multiprocess.js +++ b/demo/node-multiprocess.js @@ -1,11 +1,8 @@ -// @ts-nocheck - const fs = require('fs'); const path = require('path'); -// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require const log = require('@vladmandic/pilogger'); // this is my simple logger with few extra features const child_process = require('child_process'); -// note that main process import faceapi or tfjs at all +// note that main process does not need to import faceapi or tfjs at all as processing is done in a worker process const imgPathRoot = './demo'; // modify to include your sample images const numWorkers = 4; // how many workers will be started @@ -33,14 +30,14 @@ function waitCompletion() { if (activeWorkers > 0) setImmediate(() => waitCompletion()); else { t[1] = process.hrtime.bigint(); - log.info('Processed:', numImages, 'images in', 'total:', Math.trunc(parseInt(t[1] - t[0]) / 1000000), 'ms', 'working:', Math.trunc(parseInt(t[1] - t[2]) / 1000000), 'ms', 'average:', Math.trunc(parseInt(t[1] - t[2]) / numImages / 1000000), 'ms'); + log.info('Processed:', numImages, 'images in', 'total:', Math.trunc(Number(t[1] - t[0]) / 1000000), 'ms', 'working:', Math.trunc(Number(t[1] - t[2]) / 1000000), 'ms', 'average:', Math.trunc(Number(t[1] - t[2]) / numImages / 1000000), 'ms'); } } function measureLatency() { t[3] = process.hrtime.bigint(); - const latencyInitialization = Math.trunc(parseInt(t[2] - t[0]) / 1000 / 1000); - const latencyRoundTrip = Math.trunc(parseInt(t[3] - t[2]) / 1000 / 1000); + const latencyInitialization = Math.trunc(Number(t[2] - t[0]) / 1000 / 1000); + const latencyRoundTrip = Math.trunc(Number(t[3] - t[2]) / 1000 / 1000); log.info('Latency: worker initializtion: ', latencyInitialization, 'message round trip:', latencyRoundTrip); } diff --git a/demo/node-simple.js b/demo/node-simple.js new file mode 100644 index 0000000..b9591d2 --- /dev/null +++ b/demo/node-simple.js @@ -0,0 +1,26 @@ +const fs = require('fs'); + +// const faceapi = require('@vladmandic/face-api'); // use this when face-api is installed as module (majority of use cases) +const faceapi = require('../dist/face-api.node.js'); // use this when using face-api in dev mode + +async function main() { + await faceapi.nets.ssdMobilenetv1.loadFromDisk('model'); // load models from a specific patch + await faceapi.nets.faceLandmark68Net.loadFromDisk('model'); + await faceapi.nets.ageGenderNet.loadFromDisk('model'); + await faceapi.nets.faceRecognitionNet.loadFromDisk('model'); + await faceapi.nets.faceExpressionNet.loadFromDisk('model'); + const options = new faceapi.SsdMobilenetv1Options({ minConfidence: 0.1, maxResults: 10 }); // set model options + const buffer = fs.readFileSync('demo/sample1.jpg'); // load jpg image as binary + const decodeT = faceapi.tf.node.decodeImage(buffer, 3); // decode binary buffer to rgb tensor + const expandT = faceapi.tf.expandDims(decodeT, 0); // add batch dimension to tensor + const result = await faceapi.detectAllFaces(expandT, options) // run detection + .withFaceLandmarks() + .withFaceExpressions() + .withFaceDescriptors() + .withAgeAndGender(); + faceapi.tf.dispose([decodeT, expandT]); // dispose tensors to avoid memory leaks + // eslint-disable-next-line no-console + console.log({ result }); // print results +} + +main(); diff --git a/demo/node.js b/demo/node.js index 8a05b17..099f86b 100644 --- a/demo/node.js +++ b/demo/node.js @@ -1,13 +1,12 @@ -// @ts-nocheck - const fs = require('fs'); const process = require('process'); const path = require('path'); -// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require const log = require('@vladmandic/pilogger'); -// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require -const tf = require('@tensorflow/tfjs-node'); -const faceapi = require('../dist/face-api.node.js'); // this is equivalent to '@vladmandic/faceapi' + +// eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars +const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before face-api +// const faceapi = require('@vladmandic/face-api'); // use this when face-api is installed as module (majority of use cases) +const faceapi = require('../dist/face-api.node.js'); // use this when using face-api in dev mode const modelPathRoot = '../model'; const imgPathRoot = './demo'; // modify to include your sample images @@ -118,7 +117,7 @@ async function main() { tensor.dispose(); } const t1 = process.hrtime.bigint(); - log.info('Processed', dir.length, 'images in', Math.trunc(parseInt(t1 - t0) / 1000 / 1000), 'ms'); + log.info('Processed', dir.length, 'images in', Math.trunc(Number((t1 - t0)) / 1000 / 1000), 'ms'); } else { const param = process.argv[2]; if (fs.existsSync(param) || param.startsWith('http:') || param.startsWith('https:')) { diff --git a/dist/face-api.esm-nobundle.js b/dist/face-api.esm-nobundle.js index df9eabb..88b3f18 100644 --- a/dist/face-api.esm-nobundle.js +++ b/dist/face-api.esm-nobundle.js @@ -2135,7 +2135,7 @@ function drawFaceLandmarks(canvasArg, faceLandmarks) { } // package.json -var version10 = "1.6.1"; +var version10 = "1.6.2"; // src/xception/extractParams.ts function extractorsFactory2(extractWeights, paramMappings) { diff --git a/dist/face-api.esm.js b/dist/face-api.esm.js index 30792e2..ababfa9 100644 --- a/dist/face-api.esm.js +++ b/dist/face-api.esm.js @@ -65931,7 +65931,7 @@ function drawFaceLandmarks(canvasArg, faceLandmarks) { } // package.json -var version5 = "1.6.1"; +var version5 = "1.6.2"; // src/xception/extractParams.ts function extractorsFactory2(extractWeights, paramMappings) { diff --git a/dist/face-api.js b/dist/face-api.js index 8fe40fc..91c250e 100644 --- a/dist/face-api.js +++ b/dist/face-api.js @@ -4616,7 +4616,7 @@ return a / b;`,bte=` } `}};function tse(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,segmentIds:a}=t,{numSegments:o}=r,i=s.shape.length,c=[],l=0,u=_.getAxesPermutation([l],i),d=s;u!=null&&(d=Tn({inputs:{x:s},backend:n,attrs:{perm:u}}),c.push(d),l=_.getInnerMostAxes(1,i)[0]);let p=_.segment_util.computeOutShape(d.shape,l,o),h=k.sizeFromShape([d.shape[l]]),f=ge({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});c.push(f);let m=Mh(s.dtype),g=(x,w,T,C,D)=>{let F=x.shape[0],O=x.shape[1],$=_.segment_util.segOpComputeOptimalWindowSize(O,D),R={windowSize:$,inSize:O,batchSize:F,numSegments:D},N=new 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Bse={kernelName:oc,backendName:"wasm",kernelFunc:Lse};function Zd(e){let{inputs:{x:t},attrs:{dtype:n},backend:r}=e,s=r.makeOutput(t.shape,n),a=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(s).set(a),s}var zse={kernelName:Ua,backendName:"wasm",kernelFunc:Zd},Wse=un(Ga),$_;function Vse(e){$_=e.wasm.cwrap(Js,null,["number","number","number","number"])}function Use(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{clipValueMin:a,clipValueMax:o}=r,i=n.dataIdMap.get(s.dataId).id,c=n.makeOutput(s.shape,s.dtype),l=n.dataIdMap.get(c.dataId).id;return $_(i,a,o,l),c}var Gse={kernelName:Js,backendName:"wasm",setupFunc:Vse,kernelFunc:Use};function R_(e){let{inputs:t,backend:n}=e,r=k.parseAxisParam(e.attrs.axis,t[0].shape)[0],s=_.computeOutShape(t.map(h=>h.shape),r),a=t.filter(h=>k.sizeFromShape(h.shape)>0);if(a.length===1)return Pm({inputs:{x:a[0]},backend:n});let o=n.makeOutput(s,t[0].dtype);if(k.sizeFromShape(s)===0)return o;let i=a.map(h=>h.shape);if(_.assertParamsConsistent(i,r),a[0].dtype==="string"){let h=a.map(v=>{let x=k.sizeFromShape(v.shape.slice(r));return Vn({inputs:{x:v},backend:n,attrs:{shape:[-1,x]}})}),f=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape}));s=_.computeOutShape(h.map(v=>v.shape),1);let m=h[0].shape[0]===1,g=_w(f,s,t[0].dtype,m),b=_.computeOutShape(a.map(v=>v.shape),r);o.shape=b;let y=n.dataIdMap.get(o.dataId);return y.stringBytes=_.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),o}let c=k.sizeFromShape(a[0].shape.slice(0,r)),l=0,u=a.map(h=>{let f=k.sizeFromShape(h.shape.slice(r));return l+=f,f}),d=a.map(h=>n.typedArrayFromHeap(h)),p=n.typedArrayFromHeap(o);for(let h=0;h`cumsum does not support ${s.dtype} tensors in the WASM backend`);let l=_.getAxesPermutation([a],c),u=s;l!==null&&(u=Bu({inputs:{x:s},attrs:{perm:l},backend:n}));let d=_.getInnerMostAxes(1,c)[0];_.assertAxesAreInnerMostDims("cumsum",[d],c);let p=n.makeOutput(u.shape,u.dtype),h=u.shape[d],f=n.dataIdMap.get(u.dataId).id,m=n.dataIdMap.get(p.dataId).id;L_(f,o?1:0,i?1:0,h,m,$t[s.dtype]);let g=p;if(l!==null){let b=_.getUndoAxesPermutation(l);g=Bu({inputs:{x:p},attrs:{perm:b},backend:n}),n.disposeData(u.dataId),n.disposeData(p.dataId)}return g}var aae={kernelName:Xa,backendName:"wasm",setupFunc:rae,kernelFunc:sae},B_;function oae(e){B_=e.wasm.cwrap(uc,null,["number","number","number","array","number","array","array","number","number"])}function iae(e){let{backend:t,inputs:n,attrs:r}=e,{x:s}=n,{blockSize:a,dataFormat:o}=r,i=s.shape[0],c=o==="NHWC"?s.shape[1]:s.shape[2],l=o==="NHWC"?s.shape[2]:s.shape[3],u=o==="NHWC"?s.shape[3]:s.shape[1],d=c*a,p=l*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=t.makeOutput(f,"float32"),b=t.dataIdMap.get(s.dataId).id,y=new Uint8Array(new Int32Array(k.computeStrides(s.shape)).buffer),v=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(f)).buffer),w=t.dataIdMap.get(m.dataId).id;return B_(b,a,o==="NHWC"?1:0,y,s.shape.length-1,v,x,f.length,w),m}var cae={kernelName:uc,backendName:"wasm",setupFunc:oae,kernelFunc:iae},z_;function uae(e){z_=e.wasm.cwrap(Ya,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function lae(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a}=t,o=r.dataIdMap.get(s.dataId).id,i=r.dataIdMap.get(a.dataId).id,{strides:c,dilations:l,pad:u,dimRoundingMode:d}=n,p=l==null?[1,1]:l,h=_.computeConv2DInfo(s.shape,a.shape,c,p,u,d,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,w=h.dilationWidth,T=h.strideHeight,C=h.strideWidth,D=h.inChannels,F=h.outChannels,O=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. 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poe={kernelName:po,backendName:"wasm",setupFunc:loe,kernelFunc:doe},hoe=!1,foe=Cn(ho,hoe),c1;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(c1||(c1={}));var Q_;function moe(e){Q_=e.wasm.cwrap(fo,null,["number","array","number","number","array","array","number","number"])}function goe(e){let{inputs:{x:t},backend:n,attrs:{paddings:r,mode:s}}=e,a=r.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),c=n.dataIdMap.get(i.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),u=r.map(f=>f[0]),d=r.map(f=>f[1]),p=new Uint8Array(new Int32Array(u).buffer),h=new Uint8Array(new Int32Array(d).buffer);return Q_(o,l,t.shape.length,$t[t.dtype],p,h,c1[s],c),i}var boe={kernelName:fo,backendName:"wasm",kernelFunc:goe,setupFunc:moe},yoe=!0,voe=Cn(mo,yoe),xoe=un(Cc);function u1(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),r=n[0],s=n[1],a=n[2],o=n[3];return 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ap({...o,...a})}},zm=class{constructor(t,n={}){this.box=new ut(t),this.options=new N1(n)}draw(t){let n=Un(t),{boxColor:r,lineWidth:s}=this.options,{x:a,y:o,width:i,height:c}=this.box;n.strokeStyle=r,n.lineWidth=s,n.strokeRect(a,o,i,c);let{label:l}=this.options;l&&new _a([l],{x:a-s/2,y:o},this.options.drawLabelOptions).draw(t)}};function Hce(e,t){(Array.isArray(t)?t:[t]).forEach(r=>{let s=r instanceof xt?r.score:hs(r)?r.detection.score:void 0,a=r instanceof xt?r.box:hs(r)?r.detection.box:new ut(r),o=s?`${ki(s)}`:void 0;new zm(a,{label:o}).draw(e)})}function op(e){let{Image:t,Video:n}=tt.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function _1(e){return new Promise((t,n)=>{(e instanceof tt.getEnv().Canvas||op(e))&&t(null);function r(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",s),a.currentTarget.removeEventListener("error",r),n(a))}function 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Ps=class{constructor(t,n=!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 ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((r,s)=>{if($s(r)){this._imageTensors[s]=r,this._inputDimensions[s]=r.shape;return}if(br(r)){let o=r.shape[0];if(o!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${o} passed, but not supported in input array`);this._imageTensors[s]=r,this._inputDimensions[s]=r.shape.slice(1);return}let a=r instanceof tt.getEnv().Canvas?r:ip(r);this._canvases[s]=a,this._inputDimensions[s]=[a.height,a.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 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r=ce(t.toBatchTensor(112,!0),"float32"),a=Xr(r,[122.782,117.001,104.298]).div(255),o=cp(a,n.dense0,!0);return o=cp(o,n.dense1),o=cp(o,n.dense2),o=cp(o,n.dense3),o=lr(o,[7,7],[2,2],"valid"),o})}async forward(t){return this.forwardInput(await bt(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return RE(t)}extractParams(t){return $E(t)}};function lp(e,t){return M(()=>Y(De(e,t.weights),t.bias))}function PE(e,t,n){let r=[],{extractWeights:s,getRemainingWeights:a}=En(e),i=Gm(s,r)(t,n,"fc");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:r,params:{fc:i}}}function OE(e){let t=[],n=ar(e,t);function r(a){let o=n(`${a}/weights`,2),i=n(`${a}/bias`,1);return{weights:o,bias:i}}let s={fc:r("fc")};return _n(e,t),{params:s,paramMappings:t}}function Xm(e){let t={},n={};return Object.keys(e).forEach(r=>{let s=r.startsWith("fc")?n:t;s[r]=e[r]}),{featureExtractorMap:t,classifierMap:n}}var dp=class extends ln{constructor(t,n){super(t);this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return M(()=>{let r=t instanceof Ps?this.faceFeatureExtractor.forwardInput(t):t;return lp(r.as2D(r.shape[0],-1),n.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:r}=this.extractClassifierParams(t);this._params=n,this._paramMappings=r}extractClassifierParams(t){return PE(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:r}=Xm(t);return this.faceFeatureExtractor.loadFromWeightMap(n),OE(r)}extractParams(t){let n=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),s=r*n+r,a=t.slice(0,t.length-s),o=t.slice(t.length-s);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(o)}};var 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7}};function P1(e){return e.expressions instanceof Ea}function Zm(e,t){return{...e,...{expressions:t}}}function Yce(e,t,n=.1,r){(Array.isArray(t)?t:[t]).forEach(a=>{let o=a instanceof Ea?a:P1(a)?a.expressions:void 0;if(!o)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let c=o.asSortedArray().filter(d=>d.probability>n),l=hs(a)?a.detection.box.bottomLeft:r||new Pe(0,0);new _a(c.map(d=>`${d.expression} (${ki(d.probability)})`),l).draw(e)})}function Ei(e){return hs(e)&&e.landmarks instanceof yr&&e.unshiftedLandmarks instanceof yr&&e.alignedRect instanceof xt}function Zce(e){let t=(i,c,l,u)=>Math.atan2(u-c,l-i)%Math.PI,n=i=>i*180/Math.PI,r={roll:void 0,pitch:void 0,yaw:void 0};if(!e||!e._positions||e._positions.length!==68)return r;let s=e._positions;r.roll=-t(s[36]._x,s[36]._y,s[45]._x,s[45]._y),r.pitch=t(0,Math.abs(s[0]._x-s[30]._x)/s[30]._x,Math.PI,Math.abs(s[16]._x-s[30]._x)/s[30]._x);let 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p=a("exit_flow/reduction_block"),h=s("exit_flow/separable_conv"),f={reduction_block:p,separable_conv:h};return _n(e,n),{params:{entry_flow:u,middle_flow:d,exit_flow:f},paramMappings:n}}function zE(e,t,n){return Y(Rt(e,t.filters,n,"same"),t.bias)}function B1(e,t,n=!0){let r=n?qe(e):e;return r=Gn(r,t.separable_conv0,[1,1]),r=Gn(qe(r),t.separable_conv1,[1,1]),r=Pt(r,[3,3],[2,2],"same"),r=Y(r,zE(e,t.expansion_conv,[2,2])),r}function nue(e,t){let n=Gn(qe(e),t.separable_conv0,[1,1]);return n=Gn(qe(n),t.separable_conv1,[1,1]),n=Gn(qe(n),t.separable_conv2,[1,1]),n=Y(n,e),n}var z1=class extends ln{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return M(()=>{let r=ce(t.toBatchTensor(112,!0),"float32"),a=Xr(r,[122.782,117.001,104.298]).div(255),o=qe(zE(a,n.entry_flow.conv_in,[2,2]));return o=B1(o,n.entry_flow.reduction_block_0,!1),o=B1(o,n.entry_flow.reduction_block_1),ps(this._numMainBlocks,0,1).forEach(i=>{o=nue(o,n.middle_flow[`main_block_${i}`])}),o=B1(o,n.exit_flow.reduction_block),o=qe(Gn(o,n.exit_flow.separable_conv,[1,1])),o})}async forward(t){return this.forwardInput(await bt(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return BE(t,this._numMainBlocks)}extractParams(t){return LE(t,this._numMainBlocks)}};function WE(e){let t=[],{extractWeights:n,getRemainingWeights:r}=En(e),s=Gm(n,t),a=s(512,1,"fc/age"),o=s(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:o}}}}function VE(e){let t=[],n=ar(e,t);function r(a){let o=n(`${a}/weights`,2),i=n(`${a}/bias`,1);return{weights:o,bias:i}}let s={fc:{age:r("fc/age"),gender:r("fc/gender")}};return _n(e,t),{params:s,paramMappings:t}}var Ms;(function(n){n.FEMALE="female",n.MALE="male"})(Ms||(Ms={}));var Jm=class extends ln{constructor(t=new z1(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return M(()=>{let r=t instanceof Ps?this.faceFeatureExtractor.forwardInput(t):t,s=lr(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),a=lp(s,n.fc.age).as1D(),o=lp(s,n.fc.gender);return{age:a,gender:o}})}forwardInput(t){return M(()=>{let{age:n,gender:r}=this.runNet(t);return{age:n,gender:Mr(r)}})}async forward(t){return this.forwardInput(await bt(t))}async predictAgeAndGender(t){let n=await bt(t),r=await this.forwardInput(n),s=ht(r.age),a=ht(r.gender),o=s.map((c,l)=>({ageTensor:c,genderTensor:a[l]})),i=await Promise.all(o.map(async({ageTensor:c,genderTensor:l})=>{let u=c.dataSync()[0],d=l.dataSync()[0],p=d>.5,h=p?Ms.MALE:Ms.FEMALE,f=p?d:1-d;return c.dispose(),l.dispose(),{age:u,gender:h,genderProbability:f}}));return r.age.dispose(),r.gender.dispose(),n.isBatchInput?i:i[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:r}=this.extractClassifierParams(t);this._params=n,this._paramMappings=r}extractClassifierParams(t){return WE(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:r}=Xm(t);return this.faceFeatureExtractor.loadFromWeightMap(n),VE(r)}extractParams(t){let n=512*1+1+(512*2+2),r=t.slice(0,t.length-n),s=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(s)}};var pp=class extends dp{postProcess(t,n,r){let s=r.map(({width:o,height:i})=>{let c=n/Math.max(i,o);return{width:o*c,height:i*c}}),a=s.length;return M(()=>{let o=(d,p)=>Ot([xn([68],d,"float32"),xn([68],p,"float32")],1).as2D(1,136).as1D(),i=(d,p)=>{let{width:h,height:f}=s[d];return 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c.dispose(),l.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return ZE(t)}extractParams(t){return YE(t)}};function nA(e){let t=new Di;return t.extractWeights(e),t}function gue(e){return nA(e)}var rA=class extends Di{};var sA=.4,aA=[new Pe(.738768,.874946),new Pe(2.42204,2.65704),new Pe(4.30971,7.04493),new Pe(10.246,4.59428),new Pe(12.6868,11.8741)],oA=[new Pe(1.603231,2.094468),new Pe(6.041143,7.080126),new Pe(2.882459,3.518061),new Pe(4.266906,5.178857),new Pe(9.041765,10.66308)],iA=[117.001,114.697,97.404],cA="tiny_yolov2_model",uA="tiny_yolov2_separable_conv_model";var sg=e=>typeof e=="number";function G1(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!sg(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>sg(t.x)&&sg(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(sg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Ju(e){return M(()=>{let t=V(e,Ie(.10000000149011612));return Y(qe(fe(e,t)),t)})}function Ls(e,t){return M(()=>{let n=pr(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Rt(n,t.conv.filters,[1,1],"valid"),n=fe(n,t.bn.sub),n=V(n,t.bn.truediv),n=Y(n,t.conv.bias),Ju(n)})}function Bs(e,t){return M(()=>{let n=pr(e,[[0,0],[1,1],[1,1],[0,0]]);return n=ei(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=Y(n,t.bias),Ju(n)})}function bue(e,t){let n=ju(e,t);function r(o,i){let c=He(e(o)),l=He(e(o));return t.push({paramPath:`${i}/sub`},{paramPath:`${i}/truediv`}),{sub:c,truediv:l}}function s(o,i,c){let l=n(o,i,3,`${c}/conv`),u=r(i,`${c}/bn`);return{conv:l,bn:u}}let a=qu(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:s,extractSeparableConvParams:a}}function lA(e,t,n,r){let{extractWeights:s,getRemainingWeights:a}=En(e),o=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:l}=bue(s,o),u;if(t.withSeparableConvs){let[d,p,h,f,m,g,b,y,v]=r,x=t.isFirstLayerConv2d?i(d,p,3,"conv0"):l(d,p,"conv0"),w=l(p,h,"conv1"),T=l(h,f,"conv2"),C=l(f,m,"conv3"),D=l(m,g,"conv4"),F=l(g,b,"conv5"),O=y?l(b,y,"conv6"):void 0,$=v?l(y,v,"conv7"):void 0,R=i(v||y||b,5*n,1,"conv8");u={conv0:x,conv1:w,conv2:T,conv3:C,conv4:D,conv5:F,conv6:O,conv7:$,conv8:R}}else{let[d,p,h,f,m,g,b,y,v]=r,x=c(d,p,"conv0"),w=c(p,h,"conv1"),T=c(h,f,"conv2"),C=c(f,m,"conv3"),D=c(m,g,"conv4"),F=c(g,b,"conv5"),O=c(b,y,"conv6"),$=c(y,v,"conv7"),R=i(v,5*n,1,"conv8");u={conv0:x,conv1:w,conv2:T,conv3:C,conv4:D,conv5:F,conv6:O,conv7:$,conv8:R}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:u,paramMappings:o}}function yue(e,t){let n=ar(e,t);function r(i){let c=n(`${i}/sub`,1),l=n(`${i}/truediv`,1);return{sub:c,truediv:l}}function s(i){let c=n(`${i}/filters`,4),l=n(`${i}/bias`,1);return{filters:c,bias:l}}function a(i){let c=s(`${i}/conv`),l=r(`${i}/bn`);return{conv:c,bn:l}}let o=Ku(n);return{extractConvParams:s,extractConvWithBatchNormParams:a,extractSeparableConvParams:o}}function dA(e,t){let n=[],{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:a}=yue(e,n),o;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;o={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else o={conv0:s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:s("conv6"),conv7:s("conv7"),conv8:r("conv8")};return _n(e,n),{params:o,paramMappings:n}}var ms=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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 H1=class extends ln{constructor(t){super("TinyYolov2");G1(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,n){let r=Ls(t,n.conv0);return r=Pt(r,[2,2],[2,2],"same"),r=Ls(r,n.conv1),r=Pt(r,[2,2],[2,2],"same"),r=Ls(r,n.conv2),r=Pt(r,[2,2],[2,2],"same"),r=Ls(r,n.conv3),r=Pt(r,[2,2],[2,2],"same"),r=Ls(r,n.conv4),r=Pt(r,[2,2],[2,2],"same"),r=Ls(r,n.conv5),r=Pt(r,[2,2],[1,1],"same"),r=Ls(r,n.conv6),r=Ls(r,n.conv7),_i(r,n.conv8,"valid",!1)}runMobilenet(t,n){let r=this.config.isFirstLayerConv2d?Ju(_i(t,n.conv0,"valid",!1)):Bs(t,n.conv0);return r=Pt(r,[2,2],[2,2],"same"),r=Bs(r,n.conv1),r=Pt(r,[2,2],[2,2],"same"),r=Bs(r,n.conv2),r=Pt(r,[2,2],[2,2],"same"),r=Bs(r,n.conv3),r=Pt(r,[2,2],[2,2],"same"),r=Bs(r,n.conv4),r=Pt(r,[2,2],[2,2],"same"),r=Bs(r,n.conv5),r=Pt(r,[2,2],[1,1],"same"),r=n.conv6?Bs(r,n.conv6):r,r=n.conv7?Bs(r,n.conv7):r,_i(r,n.conv8,"valid",!1)}forwardInput(t,n){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return M(()=>{let s=ce(t.toBatchTensor(n,!1),"float32");return s=this.config.meanRgb?Xr(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,r):this.runTinyYolov2(s,r)})}async forward(t,n){return this.forwardInput(await bt(t),n)}async detect(t,n={}){let{inputSize:r,scoreThreshold:s}=new ms(n),a=await bt(t),o=await this.forwardInput(a,r),i=M(()=>ht(o)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},l=await this.extractBoxes(i,a.getReshapedInputDimensions(0),s);o.dispose(),i.dispose();let u=l.map(g=>g.box),d=l.map(g=>g.score),p=l.map(g=>g.classScore),h=l.map(g=>this.config.classes[g.label]);return x1(u.map(g=>g.rescale(r)),d,this.config.iouThreshold,!0).map(g=>new Na(d[g],p[g],h[g],u[g],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return dA(t,this.config)}extractParams(t){let n=this.config.filterSizes||H1.DEFAULT_FILTER_SIZES,r=n?n.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return lA(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,r){let{width:s,height:a}=n,o=Math.max(s,a),i=o/s,c=o/a,l=t.shape[1],u=this.config.anchors.length,[d,p,h]=M(()=>{let b=t.reshape([l,l,u,this.boxEncodingSize]),y=b.slice([0,0,0,0],[l,l,u,4]),v=b.slice([0,0,0,4],[l,l,u,1]),x=this.withClassScores?Mr(b.slice([0,0,0,5],[l,l,u,this.config.classes.length]),3):Ie(0);return[y,v,x]}),f=[],m=await p.array(),g=await d.array();for(let b=0;br){let w=(y+tp(g[b][y][v][0]))/l*i,T=(b+tp(g[b][y][v][1]))/l*c,C=Math.exp(g[b][y][v][2])*this.config.anchors[v].x/l*i,D=Math.exp(g[b][y][v][3])*this.config.anchors[v].y/l*c,F=w-C/2,O=T-D/2,$={row:b,col:y,anchor:v},{classScore:R,label:N}=this.withClassScores?await this.extractPredictedClass(h,$):{classScore:1,label:0};f.push({box:new Wu(F,O,F+C,O+D),score:x,classScore:x*R,label:N,...$})}}return d.dispose(),p.dispose(),h.dispose(),f}async extractPredictedClass(t,n){let{row:r,col:s,anchor:a}=n,o=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>o[r][s][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},Qu=H1;Qu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var el=class extends Qu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:sA,classes:["face"],...t?{anchors:oA,meanRgb:iA}:{anchors:aA,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(s=>new xt(s.score,s.relativeBox,{width:s.imageWidth,height:s.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?uA:cA}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function vue(e,t=!0){let n=new el(t);return n.extractWeights(e),n}var ag=class extends ms{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Rr=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function Fi(e,t,n,r,s=({alignedRect:a})=>a){let a=e.map(c=>Ei(c)?s(c):c.detection),o=r||(t instanceof Ee?await Hu(t,a):await Gu(t,a)),i=await n(o);return o.forEach(c=>c instanceof Ee&&c.dispose()),i}async function tl(e,t,n,r,s){return Fi([e],t,async a=>n(a[0]),r,s)}var pA=.4,hA=[new Pe(1.603231,2.094468),new Pe(6.041143,7.080126),new Pe(2.882459,3.518061),new Pe(4.266906,5.178857),new Pe(9.041765,10.66308)],fA=[117.001,114.697,97.404];var nl=class extends Qu{constructor(){let t={withSeparableConvs:!0,iouThreshold:pA,classes:["face"],anchors:hA,meanRgb:fA,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(s=>new xt(s.score,s.relativeBox,{width:s.imageWidth,height:s.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var nt={ssdMobilenetv1:new Di,tinyFaceDetector:new nl,tinyYolov2:new el,faceLandmark68Net:new Yu,faceLandmark68TinyNet:new Qm,faceRecognitionNet:new Zu,faceExpressionNet:new Ym,ageGenderNet:new Jm},mA=(e,t)=>nt.ssdMobilenetv1.locateFaces(e,t),xue=(e,t)=>nt.tinyFaceDetector.locateFaces(e,t),wue=(e,t)=>nt.tinyYolov2.locateFaces(e,t),gA=e=>nt.faceLandmark68Net.detectLandmarks(e),kue=e=>nt.faceLandmark68TinyNet.detectLandmarks(e),Iue=e=>nt.faceRecognitionNet.computeFaceDescriptor(e),Sue=e=>nt.faceExpressionNet.predictExpressions(e),Tue=e=>nt.ageGenderNet.predictAgeAndGender(e),bA=e=>nt.ssdMobilenetv1.load(e),Cue=e=>nt.tinyFaceDetector.load(e),Nue=e=>nt.tinyYolov2.load(e),_ue=e=>nt.faceLandmark68Net.load(e),Eue=e=>nt.faceLandmark68TinyNet.load(e),Aue=e=>nt.faceRecognitionNet.load(e),Due=e=>nt.faceExpressionNet.load(e),Fue=e=>nt.ageGenderNet.load(e),$ue=bA,Rue=mA,Pue=gA;var j1=class extends Rr{constructor(t,n,r){super();this.parentTask=t;this.input=n;this.extractedFaces=r}},rl=class extends j1{async run(){let t=await this.parentTask,n=await Fi(t,this.input,async r=>Promise.all(r.map(s=>nt.faceExpressionNet.predictExpressions(s))),this.extractedFaces);return t.map((r,s)=>Zm(r,n[s]))}withAgeAndGender(){return new al(this,this.input)}},sl=class extends j1{async run(){let t=await this.parentTask;if(!t)return;let n=await tl(t,this.input,r=>nt.faceExpressionNet.predictExpressions(r),this.extractedFaces);return Zm(t,n)}withAgeAndGender(){return new ol(this,this.input)}},$i=class extends rl{withAgeAndGender(){return new Pi(this,this.input)}withFaceDescriptors(){return new Aa(this,this.input)}},Ri=class extends sl{withAgeAndGender(){return new Oi(this,this.input)}withFaceDescriptor(){return new Da(this,this.input)}};var q1=class extends Rr{constructor(t,n,r){super();this.parentTask=t;this.input=n;this.extractedFaces=r}},al=class extends q1{async run(){let t=await this.parentTask,n=await Fi(t,this.input,async r=>Promise.all(r.map(s=>nt.ageGenderNet.predictAgeAndGender(s))),this.extractedFaces);return t.map((r,s)=>{let{age:a,gender:o,genderProbability:i}=n[s];return ng(rg(r,o,i),a)})}withFaceExpressions(){return new rl(this,this.input)}},ol=class extends q1{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:r,genderProbability:s}=await tl(t,this.input,a=>nt.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return ng(rg(t,r,s),n)}withFaceExpressions(){return new sl(this,this.input)}},Pi=class extends al{withFaceExpressions(){return new $i(this,this.input)}withFaceDescriptors(){return new Aa(this,this.input)}},Oi=class extends ol{withFaceExpressions(){return new Ri(this,this.input)}withFaceDescriptor(){return new Da(this,this.input)}};var og=class extends Rr{constructor(t,n){super();this.parentTask=t;this.input=n}},Aa=class extends og{async run(){let t=await this.parentTask;return(await Fi(t,this.input,r=>Promise.all(r.map(s=>nt.faceRecognitionNet.computeFaceDescriptor(s))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,s)=>tg(t[s],r))}withFaceExpressions(){return new $i(this,this.input)}withAgeAndGender(){return new Pi(this,this.input)}},Da=class extends og{async run(){let t=await this.parentTask;if(!t)return;let n=await tl(t,this.input,r=>nt.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return tg(t,n)}withFaceExpressions(){return new Ri(this,this.input)}withAgeAndGender(){return new Oi(this,this.input)}};var ig=class extends Rr{constructor(t,n,r){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?nt.faceLandmark68TinyNet:nt.faceLandmark68Net}},cg=class extends ig{async run(){let t=await this.parentTask,n=t.map(a=>a.detection),r=this.input instanceof Ee?await Hu(this.input,n):await Gu(this.input,n),s=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Ee&&a.dispose()),t.map((a,o)=>Xu(a,s[o]))}withFaceExpressions(){return new $i(this,this.input)}withAgeAndGender(){return new Pi(this,this.input)}withFaceDescriptors(){return new Aa(this,this.input)}},ug=class extends ig{async run(){let t=await 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ln{constructor(t,n){super(t);this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return M(()=>{let r=t instanceof Ps?this.faceFeatureExtractor.forwardInput(t):t;return lp(r.as2D(r.shape[0],-1),n.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:r}=this.extractClassifierParams(t);this._params=n,this._paramMappings=r}extractClassifierParams(t){return PE(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:r}=Xm(t);return this.faceFeatureExtractor.loadFromWeightMap(n),OE(r)}extractParams(t){let n=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),s=r*n+r,a=t.slice(0,t.length-s),o=t.slice(t.length-s);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(o)}};var R1=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Ea=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: ${t.length}`);R1.forEach((n,r)=>{this[n]=t[r]})}asSortedArray(){return R1.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var Ym=class extends dp{constructor(t=new up){super("FaceExpressionNet",t)}forwardInput(t){return M(()=>Mr(this.runNet(t)))}async forward(t){return this.forwardInput(await bt(t))}async predictExpressions(t){let n=await bt(t),r=await this.forwardInput(n),s=await Promise.all(ht(r).map(async o=>{let i=o.dataSync();return o.dispose(),i}));r.dispose();let a=s.map(o=>new Ea(o));return n.isBatchInput?a:a[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 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Ms;(function(n){n.FEMALE="female",n.MALE="male"})(Ms||(Ms={}));var Jm=class extends ln{constructor(t=new z1(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return M(()=>{let r=t instanceof Ps?this.faceFeatureExtractor.forwardInput(t):t,s=lr(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),a=lp(s,n.fc.age).as1D(),o=lp(s,n.fc.gender);return{age:a,gender:o}})}forwardInput(t){return M(()=>{let{age:n,gender:r}=this.runNet(t);return{age:n,gender:Mr(r)}})}async forward(t){return this.forwardInput(await bt(t))}async predictAgeAndGender(t){let n=await bt(t),r=await this.forwardInput(n),s=ht(r.age),a=ht(r.gender),o=s.map((c,l)=>({ageTensor:c,genderTensor:a[l]})),i=await Promise.all(o.map(async({ageTensor:c,genderTensor:l})=>{let u=c.dataSync()[0],d=l.dataSync()[0],p=d>.5,h=p?Ms.MALE:Ms.FEMALE,f=p?d:1-d;return 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n=[],{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:a}=yue(e,n),o;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;o={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else o={conv0:s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:s("conv6"),conv7:s("conv7"),conv8:r("conv8")};return _n(e,n),{params:o,paramMappings:n}}var ms=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.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 H1=class extends ln{constructor(t){super("TinyYolov2");G1(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,n){let r=Ls(t,n.conv0);return r=Pt(r,[2,2],[2,2],"same"),r=Ls(r,n.conv1),r=Pt(r,[2,2],[2,2],"same"),r=Ls(r,n.conv2),r=Pt(r,[2,2],[2,2],"same"),r=Ls(r,n.conv3),r=Pt(r,[2,2],[2,2],"same"),r=Ls(r,n.conv4),r=Pt(r,[2,2],[2,2],"same"),r=Ls(r,n.conv5),r=Pt(r,[2,2],[1,1],"same"),r=Ls(r,n.conv6),r=Ls(r,n.conv7),_i(r,n.conv8,"valid",!1)}runMobilenet(t,n){let r=this.config.isFirstLayerConv2d?Ju(_i(t,n.conv0,"valid",!1)):Bs(t,n.conv0);return r=Pt(r,[2,2],[2,2],"same"),r=Bs(r,n.conv1),r=Pt(r,[2,2],[2,2],"same"),r=Bs(r,n.conv2),r=Pt(r,[2,2],[2,2],"same"),r=Bs(r,n.conv3),r=Pt(r,[2,2],[2,2],"same"),r=Bs(r,n.conv4),r=Pt(r,[2,2],[2,2],"same"),r=Bs(r,n.conv5),r=Pt(r,[2,2],[1,1],"same"),r=n.conv6?Bs(r,n.conv6):r,r=n.conv7?Bs(r,n.conv7):r,_i(r,n.conv8,"valid",!1)}forwardInput(t,n){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return M(()=>{let s=ce(t.toBatchTensor(n,!1),"float32");return s=this.config.meanRgb?Xr(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,r):this.runTinyYolov2(s,r)})}async forward(t,n){return this.forwardInput(await bt(t),n)}async detect(t,n={}){let{inputSize:r,scoreThreshold:s}=new ms(n),a=await bt(t),o=await this.forwardInput(a,r),i=M(()=>ht(o)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},l=await this.extractBoxes(i,a.getReshapedInputDimensions(0),s);o.dispose(),i.dispose();let u=l.map(g=>g.box),d=l.map(g=>g.score),p=l.map(g=>g.classScore),h=l.map(g=>this.config.classes[g.label]);return x1(u.map(g=>g.rescale(r)),d,this.config.iouThreshold,!0).map(g=>new Na(d[g],p[g],h[g],u[g],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return dA(t,this.config)}extractParams(t){let n=this.config.filterSizes||H1.DEFAULT_FILTER_SIZES,r=n?n.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return lA(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,r){let{width:s,height:a}=n,o=Math.max(s,a),i=o/s,c=o/a,l=t.shape[1],u=this.config.anchors.length,[d,p,h]=M(()=>{let b=t.reshape([l,l,u,this.boxEncodingSize]),y=b.slice([0,0,0,0],[l,l,u,4]),v=b.slice([0,0,0,4],[l,l,u,1]),x=this.withClassScores?Mr(b.slice([0,0,0,5],[l,l,u,this.config.classes.length]),3):Ie(0);return[y,v,x]}),f=[],m=await p.array(),g=await d.array();for(let b=0;br){let w=(y+tp(g[b][y][v][0]))/l*i,T=(b+tp(g[b][y][v][1]))/l*c,C=Math.exp(g[b][y][v][2])*this.config.anchors[v].x/l*i,D=Math.exp(g[b][y][v][3])*this.config.anchors[v].y/l*c,F=w-C/2,O=T-D/2,$={row:b,col:y,anchor:v},{classScore:R,label:N}=this.withClassScores?await this.extractPredictedClass(h,$):{classScore:1,label:0};f.push({box:new Wu(F,O,F+C,O+D),score:x,classScore:x*R,label:N,...$})}}return d.dispose(),p.dispose(),h.dispose(),f}async extractPredictedClass(t,n){let{row:r,col:s,anchor:a}=n,o=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>o[r][s][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},Qu=H1;Qu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var el=class extends Qu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:sA,classes:["face"],...t?{anchors:oA,meanRgb:iA}:{anchors:aA,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(s=>new xt(s.score,s.relativeBox,{width:s.imageWidth,height:s.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?uA:cA}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function vue(e,t=!0){let n=new el(t);return n.extractWeights(e),n}var ag=class extends ms{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Rr=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function Fi(e,t,n,r,s=({alignedRect:a})=>a){let a=e.map(c=>Ei(c)?s(c):c.detection),o=r||(t instanceof Ee?await Hu(t,a):await Gu(t,a)),i=await n(o);return o.forEach(c=>c instanceof Ee&&c.dispose()),i}async function tl(e,t,n,r,s){return Fi([e],t,async a=>n(a[0]),r,s)}var pA=.4,hA=[new Pe(1.603231,2.094468),new Pe(6.041143,7.080126),new Pe(2.882459,3.518061),new Pe(4.266906,5.178857),new Pe(9.041765,10.66308)],fA=[117.001,114.697,97.404];var nl=class extends Qu{constructor(){let t={withSeparableConvs:!0,iouThreshold:pA,classes:["face"],anchors:hA,meanRgb:fA,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(s=>new xt(s.score,s.relativeBox,{width:s.imageWidth,height:s.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var nt={ssdMobilenetv1:new Di,tinyFaceDetector:new nl,tinyYolov2:new el,faceLandmark68Net:new Yu,faceLandmark68TinyNet:new Qm,faceRecognitionNet:new Zu,faceExpressionNet:new Ym,ageGenderNet:new Jm},mA=(e,t)=>nt.ssdMobilenetv1.locateFaces(e,t),xue=(e,t)=>nt.tinyFaceDetector.locateFaces(e,t),wue=(e,t)=>nt.tinyYolov2.locateFaces(e,t),gA=e=>nt.faceLandmark68Net.detectLandmarks(e),kue=e=>nt.faceLandmark68TinyNet.detectLandmarks(e),Iue=e=>nt.faceRecognitionNet.computeFaceDescriptor(e),Sue=e=>nt.faceExpressionNet.predictExpressions(e),Tue=e=>nt.ageGenderNet.predictAgeAndGender(e),bA=e=>nt.ssdMobilenetv1.load(e),Cue=e=>nt.tinyFaceDetector.load(e),Nue=e=>nt.tinyYolov2.load(e),_ue=e=>nt.faceLandmark68Net.load(e),Eue=e=>nt.faceLandmark68TinyNet.load(e),Aue=e=>nt.faceRecognitionNet.load(e),Due=e=>nt.faceExpressionNet.load(e),Fue=e=>nt.ageGenderNet.load(e),$ue=bA,Rue=mA,Pue=gA;var j1=class extends Rr{constructor(t,n,r){super();this.parentTask=t;this.input=n;this.extractedFaces=r}},rl=class extends j1{async run(){let t=await this.parentTask,n=await Fi(t,this.input,async r=>Promise.all(r.map(s=>nt.faceExpressionNet.predictExpressions(s))),this.extractedFaces);return t.map((r,s)=>Zm(r,n[s]))}withAgeAndGender(){return new al(this,this.input)}},sl=class extends j1{async run(){let t=await this.parentTask;if(!t)return;let n=await tl(t,this.input,r=>nt.faceExpressionNet.predictExpressions(r),this.extractedFaces);return Zm(t,n)}withAgeAndGender(){return new ol(this,this.input)}},$i=class extends rl{withAgeAndGender(){return new Pi(this,this.input)}withFaceDescriptors(){return new Aa(this,this.input)}},Ri=class extends sl{withAgeAndGender(){return new Oi(this,this.input)}withFaceDescriptor(){return new Da(this,this.input)}};var q1=class extends Rr{constructor(t,n,r){super();this.parentTask=t;this.input=n;this.extractedFaces=r}},al=class extends q1{async run(){let t=await this.parentTask,n=await Fi(t,this.input,async r=>Promise.all(r.map(s=>nt.ageGenderNet.predictAgeAndGender(s))),this.extractedFaces);return t.map((r,s)=>{let{age:a,gender:o,genderProbability:i}=n[s];return ng(rg(r,o,i),a)})}withFaceExpressions(){return new rl(this,this.input)}},ol=class extends q1{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:r,genderProbability:s}=await tl(t,this.input,a=>nt.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return ng(rg(t,r,s),n)}withFaceExpressions(){return new sl(this,this.input)}},Pi=class extends al{withFaceExpressions(){return new $i(this,this.input)}withFaceDescriptors(){return new Aa(this,this.input)}},Oi=class extends ol{withFaceExpressions(){return new Ri(this,this.input)}withFaceDescriptor(){return new Da(this,this.input)}};var og=class extends Rr{constructor(t,n){super();this.parentTask=t;this.input=n}},Aa=class extends og{async run(){let t=await this.parentTask;return(await Fi(t,this.input,r=>Promise.all(r.map(s=>nt.faceRecognitionNet.computeFaceDescriptor(s))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,s)=>tg(t[s],r))}withFaceExpressions(){return new $i(this,this.input)}withAgeAndGender(){return new Pi(this,this.input)}},Da=class extends og{async run(){let t=await this.parentTask;if(!t)return;let n=await tl(t,this.input,r=>nt.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return tg(t,n)}withFaceExpressions(){return new Ri(this,this.input)}withAgeAndGender(){return new Oi(this,this.input)}};var ig=class extends Rr{constructor(t,n,r){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?nt.faceLandmark68TinyNet:nt.faceLandmark68Net}},cg=class extends ig{async run(){let t=await this.parentTask,n=t.map(a=>a.detection),r=this.input instanceof Ee?await Hu(this.input,n):await Gu(this.input,n),s=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Ee&&a.dispose()),t.map((a,o)=>Xu(a,s[o]))}withFaceExpressions(){return new $i(this,this.input)}withAgeAndGender(){return new Pi(this,this.input)}withFaceDescriptors(){return new Aa(this,this.input)}},ug=class extends ig{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,r=this.input instanceof Ee?await Hu(this.input,[n]):await Gu(this.input,[n]),s=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Ee&&a.dispose()),Xu(t,s)}withFaceExpressions(){return new Ri(this,this.input)}withAgeAndGender(){return new Oi(this,this.input)}withFaceDescriptor(){return new Da(this,this.input)}};var lg=class extends Rr{constructor(t,n=new $r){super();this.input=t;this.options=n}},fp=class extends lg{async run(){let{input:t,options:n}=this,r;if(n instanceof ag)r=nt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof $r)r=nt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof ms)r=nt.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(r=>t(r.map(s=>Si({},s)))).catch(r=>n(r))})}withFaceLandmarks(t=!1){return new cg(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new rl(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new al(this.runAndExtendWithFaceDetections(),this.input)}},dg=class extends lg{async run(){let t=await new fp(this.input,this.options),n=t[0];return t.forEach(r=>{r.score>n.score&&(n=r)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?Si({},n):void 0)})}withFaceLandmarks(t=!1){return new ug(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new sl(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ol(this.runAndExtendWithFaceDetection(),this.input)}};function Oue(e,t=new $r){return new dg(e,t)}function pg(e,t=new $r){return new fp(e,t)}async function yA(e,t){return pg(e,new $r(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Mue(e,t={}){return pg(e,new ms(t)).withFaceLandmarks().withFaceDescriptors()}var Lue=yA;function K1(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),r=Array.from(t);return Math.sqrt(n.map((s,a)=>s-r[a]).reduce((s,a)=>s+a**2,0))}var hg=class{constructor(t,n=.6){this._distanceThreshold=n;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let s=1,a=()=>`person ${s++}`;this._labeledDescriptors=r.map(o=>{if(o instanceof Rs)return o;if(o instanceof Float32Array)return new Rs(a(),[o]);if(o.descriptor&&o.descriptor instanceof Float32Array)return new Rs(a(),[o.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(r=>K1(r,t)).reduce((r,s)=>r+s,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:r})=>new np(r,this.computeMeanDistance(t,n))).reduce((n,r)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(r=>Rs.fromJSON(r));return new hg(n,t.distanceThreshold)}};function Bue(e){let t=new nl;return t.extractWeights(e),t}function vA(e,t){let{width:n,height:r}=new Nn(t.width,t.height);if(n<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:r})}`);if(Array.isArray(e))return e.map(s=>vA(s,{width:n,height:r}));if(Ei(e)){let s=e.detection.forSize(n,r),a=e.unshiftedLandmarks.forSize(s.box.width,s.box.height);return Xu(Si(e,s),a)}return hs(e)?Si(e,e.detection.forSize(n,r)):e instanceof yr||e instanceof xt?e.forSize(n,r):e}var zue=ME;return Wue;})(); /** * @license * Copyright 2017 Google LLC. All Rights Reserved. diff --git a/dist/face-api.node-gpu.js b/dist/face-api.node-gpu.js index 405a82a..77e8375 100644 --- a/dist/face-api.node-gpu.js +++ b/dist/face-api.node-gpu.js @@ -2290,7 +2290,7 @@ function drawFaceLandmarks(canvasArg, faceLandmarks) { } // package.json -var version = "1.6.1"; +var version = "1.6.2"; // src/ageGenderNet/AgeGenderNet.ts var tf20 = __toModule(require_tfjs_esm()); diff --git a/dist/face-api.node.js b/dist/face-api.node.js index 8e4fd85..af42017 100644 --- a/dist/face-api.node.js +++ b/dist/face-api.node.js @@ -2290,7 +2290,7 @@ function drawFaceLandmarks(canvasArg, faceLandmarks) { } // package.json -var version = "1.6.1"; +var version = "1.6.2"; // src/ageGenderNet/AgeGenderNet.ts var tf20 = __toModule(require_tfjs_esm()); diff --git a/typedoc/classes/AgeGenderNet.html b/typedoc/classes/AgeGenderNet.html index e4955d9..05526f6 100644 --- a/typedoc/classes/AgeGenderNet.html +++ b/typedoc/classes/AgeGenderNet.html @@ -1 +1 @@ -AgeGenderNet | @vladmandic/face-api - v1.6.1
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Class AgeGenderNet

Hierarchy

Index

Constructors

Properties

Accessors

Methods

Constructors

constructor

  • new AgeGenderNet(faceFeatureExtractor?: TinyXception): AgeGenderNet

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): TinyXception

isLoaded

  • get isLoaded(): boolean

paramMappings

  • get paramMappings(): ParamMapping[]

params

  • get params(): undefined | TNetParams

  • Returns undefined | TNetParams

Methods

dispose

  • dispose(throwOnRedispose?: boolean): void

extractClassifierParams

  • extractClassifierParams(weights: Float32Array): { paramMappings: ParamMapping[]; params: NetParams }

extractWeights

  • extractWeights(weights: Float32Array): void

forward

forwardInput

freeze

  • freeze(): void

getFrozenParams

  • getFrozenParams(): { path: string; tensor: Tensor }[]

getParamFromPath

  • getParamFromPath(paramPath: string): Tensor

getParamList

  • getParamList(): { path: string; tensor: Tensor }[]

getTrainableParams

  • getTrainableParams(): { path: string; tensor: Tensor }[]

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

loadClassifierParams

  • loadClassifierParams(weights: Float32Array): void

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

loadFromWeightMap

  • loadFromWeightMap(weightMap: NamedTensorMap): void

predictAgeAndGender

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: Tensor): void

runNet

serializeParams

  • serializeParams(): Float32Array

variable

  • variable(): void

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  • Inherited constructor
  • Inherited property
  • Inherited method
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  • Static method

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\ No newline at end of file +AgeGenderNet | @vladmandic/face-api - v1.6.2
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Class AgeGenderNet

Hierarchy

Index

Constructors

Properties

Accessors

Methods

Constructors

constructor

  • new AgeGenderNet(faceFeatureExtractor?: TinyXception): AgeGenderNet

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): TinyXception

isLoaded

  • get isLoaded(): boolean

paramMappings

  • get paramMappings(): ParamMapping[]

params

  • get params(): undefined | TNetParams

  • Returns undefined | TNetParams

Methods

dispose

  • dispose(throwOnRedispose?: boolean): void

extractClassifierParams

  • extractClassifierParams(weights: Float32Array): { paramMappings: ParamMapping[]; params: NetParams }

extractWeights

  • extractWeights(weights: Float32Array): void

forward

forwardInput

freeze

  • freeze(): void

getFrozenParams

  • getFrozenParams(): { path: string; tensor: Tensor }[]

getParamFromPath

  • getParamFromPath(paramPath: string): Tensor

getParamList

  • getParamList(): { path: string; tensor: Tensor }[]

getTrainableParams

  • getTrainableParams(): { path: string; tensor: Tensor }[]

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

loadClassifierParams

  • loadClassifierParams(weights: Float32Array): void

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

loadFromWeightMap

  • loadFromWeightMap(weightMap: NamedTensorMap): void

predictAgeAndGender

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: Tensor): void

runNet

serializeParams

  • serializeParams(): Float32Array

variable

  • variable(): void

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  • Inherited constructor
  • Inherited property
  • Inherited method
  • Property
  • Static method

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\ No newline at end of file diff --git a/typedoc/classes/BoundingBox.html b/typedoc/classes/BoundingBox.html index da02fbf..e1f7bd9 100644 --- a/typedoc/classes/BoundingBox.html +++ b/typedoc/classes/BoundingBox.html @@ -1 +1 @@ -BoundingBox | @vladmandic/face-api - v1.6.1
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Class BoundingBox

Hierarchy

Implements

Index

Constructors

Accessors

Methods

Constructors

constructor

  • new BoundingBox(left: number, top: number, right: number, bottom: number, allowNegativeDimensions?: boolean): BoundingBox

Accessors

area

  • get area(): number

bottom

  • get bottom(): number

bottomLeft

bottomRight

height

  • get height(): number

left

  • get left(): number

right

  • get right(): number

top

  • get top(): number

topLeft

topRight

width

  • get width(): number

x

  • get x(): number

y

  • get y(): number

Methods

calibrate

  • calibrate(region: Box<any>): Box<any>

clipAtImageBorders

  • clipAtImageBorders(imgWidth: number, imgHeight: number): Box<any>

floor

  • floor(): Box<any>

pad

  • pad(padX: number, padY: number): Box<any>

padAtBorders

  • padAtBorders(imageHeight: number, imageWidth: number): { dx: number; dy: number; edx: number; edy: number; ex: number; ey: number; h: number; w: number; x: number; y: number }

  • Parameters

    • imageHeight: number
    • imageWidth: number

    Returns { dx: number; dy: number; edx: number; edy: number; ex: number; ey: number; h: number; w: number; x: number; y: number }

    • dx: number
    • dy: number
    • edx: number
    • edy: number
    • ex: number
    • ey: number
    • h: number
    • w: number
    • x: number
    • y: number

rescale

round

  • round(): Box<any>

shift

  • shift(sx: number, sy: number): Box<any>

toSquare

  • toSquare(): Box<any>

Static assertIsValidBox

  • assertIsValidBox(box: any, callee: string, allowNegativeDimensions?: boolean): void

Static isRect

  • isRect(rect: any): boolean

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  • Inherited method
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\ No newline at end of file +BoundingBox | @vladmandic/face-api - v1.6.2
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Class BoundingBox

Hierarchy

Implements

Index

Constructors

Accessors

Methods

Constructors

constructor

  • new BoundingBox(left: number, top: number, right: number, bottom: number, allowNegativeDimensions?: boolean): BoundingBox

Accessors

area

  • get area(): number

bottom

  • get bottom(): number

bottomLeft

bottomRight

height

  • get height(): number

left

  • get left(): number

right

  • get right(): number

top

  • get top(): number

topLeft

topRight

width

  • get width(): number

x

  • get x(): number

y

  • get y(): number

Methods

calibrate

  • calibrate(region: Box<any>): Box<any>

clipAtImageBorders

  • clipAtImageBorders(imgWidth: number, imgHeight: number): Box<any>

floor

  • floor(): Box<any>

pad

  • pad(padX: number, padY: number): Box<any>

padAtBorders

  • padAtBorders(imageHeight: number, imageWidth: number): { dx: number; dy: number; edx: number; edy: number; ex: number; ey: number; h: number; w: number; x: number; y: number }

  • Parameters

    • imageHeight: number
    • imageWidth: number

    Returns { dx: number; dy: number; edx: number; edy: number; ex: number; ey: number; h: number; w: number; x: number; y: number }

    • dx: number
    • dy: number
    • edx: number
    • edy: number
    • ex: number
    • ey: number
    • h: number
    • w: number
    • x: number
    • y: number

rescale

round

  • round(): Box<any>

shift

  • shift(sx: number, sy: number): Box<any>

toSquare

  • toSquare(): Box<any>

Static assertIsValidBox

  • assertIsValidBox(box: any, callee: string, allowNegativeDimensions?: boolean): void

Static isRect

  • isRect(rect: any): boolean

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\ No newline at end of file diff --git a/typedoc/classes/Box.html b/typedoc/classes/Box.html index 3cbaa47..521ee53 100644 --- a/typedoc/classes/Box.html +++ b/typedoc/classes/Box.html @@ -1 +1 @@ -Box | @vladmandic/face-api - v1.6.1
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Class Box<BoxType>

Type parameters

  • BoxType = any

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Index

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Constructors

constructor

Accessors

area

  • get area(): number

bottom

  • get bottom(): number

bottomLeft

bottomRight

height

  • get height(): number

left

  • get left(): number

right

  • get right(): number

top

  • get top(): number

topLeft

topRight

width

  • get width(): number

x

  • get x(): number

y

  • get y(): number

Methods

calibrate

  • calibrate(region: Box<any>): Box<any>

clipAtImageBorders

  • clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType>

floor

  • floor(): Box<BoxType>

pad

  • pad(padX: number, padY: number): Box<BoxType>

padAtBorders

  • padAtBorders(imageHeight: number, imageWidth: number): { dx: number; dy: number; edx: number; edy: number; ex: number; ey: number; h: number; w: number; x: number; y: number }

  • Parameters

    • imageHeight: number
    • imageWidth: number

    Returns { dx: number; dy: number; edx: number; edy: number; ex: number; ey: number; h: number; w: number; x: number; y: number }

    • dx: number
    • dy: number
    • edx: number
    • edy: number
    • ex: number
    • ey: number
    • h: number
    • w: number
    • x: number
    • y: number

rescale

round

  • round(): Box<BoxType>

shift

  • shift(sx: number, sy: number): Box<BoxType>

toSquare

  • toSquare(): Box<BoxType>

Static assertIsValidBox

  • assertIsValidBox(box: any, callee: string, allowNegativeDimensions?: boolean): void

  • Parameters

    • box: any
    • callee: string
    • allowNegativeDimensions: boolean = false

    Returns void

Static isRect

  • isRect(rect: any): boolean

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\ No newline at end of file +Box | @vladmandic/face-api - v1.6.2
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Class Box<BoxType>

Type parameters

  • BoxType = any

Hierarchy

Implements

Index

Constructors

Accessors

Methods

Constructors

constructor

Accessors

area

  • get area(): number

bottom

  • get bottom(): number

bottomLeft

bottomRight

height

  • get height(): number

left

  • get left(): number

right

  • get right(): number

top

  • get top(): number

topLeft

topRight

width

  • get width(): number

x

  • get x(): number

y

  • get y(): number

Methods

calibrate

  • calibrate(region: Box<any>): Box<any>

clipAtImageBorders

  • clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType>

floor

  • floor(): Box<BoxType>

pad

  • pad(padX: number, padY: number): Box<BoxType>

padAtBorders

  • padAtBorders(imageHeight: number, imageWidth: number): { dx: number; dy: number; edx: number; edy: number; ex: number; ey: number; h: number; w: number; x: number; y: number }

  • Parameters

    • imageHeight: number
    • imageWidth: number

    Returns { dx: number; dy: number; edx: number; edy: number; ex: number; ey: number; h: number; w: number; x: number; y: number }

    • dx: number
    • dy: number
    • edx: number
    • edy: number
    • ex: number
    • ey: number
    • h: number
    • w: number
    • x: number
    • y: number

rescale

round

  • round(): Box<BoxType>

shift

  • shift(sx: number, sy: number): Box<BoxType>

toSquare

  • toSquare(): Box<BoxType>

Static assertIsValidBox

  • assertIsValidBox(box: any, callee: string, allowNegativeDimensions?: boolean): void

  • Parameters

    • box: any
    • callee: string
    • allowNegativeDimensions: boolean = false

    Returns void

Static isRect

  • isRect(rect: any): boolean

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\ No newline at end of file diff --git a/typedoc/classes/ComposableTask.html b/typedoc/classes/ComposableTask.html index 1d82a30..86cb2da 100644 --- a/typedoc/classes/ComposableTask.html +++ b/typedoc/classes/ComposableTask.html @@ -1 +1 @@ -ComposableTask | @vladmandic/face-api - v1.6.1
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Class ComposableTask<T>

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  • run(): Promise<T>

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    Returns Promise<T>

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\ No newline at end of file +ComposableTask | @vladmandic/face-api - v1.6.2
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Class ComposableTask<T>

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run

  • run(): Promise<T>

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    Returns Promise<T>

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\ No newline at end of file diff --git a/typedoc/classes/ComputeAllFaceDescriptorsTask.html b/typedoc/classes/ComputeAllFaceDescriptorsTask.html index fb38421..50d99d4 100644 --- a/typedoc/classes/ComputeAllFaceDescriptorsTask.html +++ b/typedoc/classes/ComputeAllFaceDescriptorsTask.html @@ -1 +1 @@ -ComputeAllFaceDescriptorsTask | @vladmandic/face-api - v1.6.1
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Class ComputeAllFaceDescriptorsTask<TSource>

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withAgeAndGender

  • withAgeAndGender(): PredictAllAgeAndGenderWithFaceAlignmentTask<WithFaceDescriptor<TSource>>

withFaceExpressions

  • withFaceExpressions(): PredictAllFaceExpressionsWithFaceAlignmentTask<WithFaceDescriptor<TSource>>

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\ No newline at end of file +ComputeAllFaceDescriptorsTask | @vladmandic/face-api - v1.6.2
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Class ComputeAllFaceDescriptorsTask<TSource>

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withAgeAndGender

  • withAgeAndGender(): PredictAllAgeAndGenderWithFaceAlignmentTask<WithFaceDescriptor<TSource>>

withFaceExpressions

  • withFaceExpressions(): PredictAllFaceExpressionsWithFaceAlignmentTask<WithFaceDescriptor<TSource>>

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\ No newline at end of file diff --git a/typedoc/classes/ComputeFaceDescriptorsTaskBase.html b/typedoc/classes/ComputeFaceDescriptorsTaskBase.html index b604efb..e02a67b 100644 --- a/typedoc/classes/ComputeFaceDescriptorsTaskBase.html +++ b/typedoc/classes/ComputeFaceDescriptorsTaskBase.html @@ -1 +1 @@ -ComputeFaceDescriptorsTaskBase | @vladmandic/face-api - v1.6.1
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Class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn>

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  • TParentReturn

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  • run(): Promise<TReturn>

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  • then(onfulfilled: (value: TReturn) => TReturn | PromiseLike<TReturn>): Promise<TReturn>

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    • onfulfilled: (value: TReturn) => TReturn | PromiseLike<TReturn>
        • (value: TReturn): TReturn | PromiseLike<TReturn>

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          • value: TReturn

          Returns TReturn | PromiseLike<TReturn>

    Returns Promise<TReturn>

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\ No newline at end of file +ComputeFaceDescriptorsTaskBase | @vladmandic/face-api - v1.6.2
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Class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn>

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  • TReturn

  • TParentReturn

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  • run(): Promise<TReturn>

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  • then(onfulfilled: (value: TReturn) => TReturn | PromiseLike<TReturn>): Promise<TReturn>

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    • onfulfilled: (value: TReturn) => TReturn | PromiseLike<TReturn>
        • (value: TReturn): TReturn | PromiseLike<TReturn>

        • Parameters

          • value: TReturn

          Returns TReturn | PromiseLike<TReturn>

    Returns Promise<TReturn>

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\ No newline at end of file diff --git a/typedoc/classes/ComputeSingleFaceDescriptorTask.html b/typedoc/classes/ComputeSingleFaceDescriptorTask.html index a569075..4e35f5a 100644 --- a/typedoc/classes/ComputeSingleFaceDescriptorTask.html +++ b/typedoc/classes/ComputeSingleFaceDescriptorTask.html @@ -1 +1 @@ -ComputeSingleFaceDescriptorTask | @vladmandic/face-api - v1.6.1
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Class ComputeSingleFaceDescriptorTask<TSource>

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withAgeAndGender

  • withAgeAndGender(): PredictSingleAgeAndGenderWithFaceAlignmentTask<WithFaceDescriptor<TSource>>

withFaceExpressions

  • withFaceExpressions(): PredictSingleFaceExpressionsWithFaceAlignmentTask<WithFaceDescriptor<TSource>>

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\ No newline at end of file +ComputeSingleFaceDescriptorTask | @vladmandic/face-api - v1.6.2
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Class ComputeSingleFaceDescriptorTask<TSource>

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withAgeAndGender

  • withAgeAndGender(): PredictSingleAgeAndGenderWithFaceAlignmentTask<WithFaceDescriptor<TSource>>

withFaceExpressions

  • withFaceExpressions(): PredictSingleFaceExpressionsWithFaceAlignmentTask<WithFaceDescriptor<TSource>>

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\ No newline at end of file diff --git a/typedoc/classes/DetectAllFaceLandmarksTask.html b/typedoc/classes/DetectAllFaceLandmarksTask.html index d16a3be..8607cfc 100644 --- a/typedoc/classes/DetectAllFaceLandmarksTask.html +++ b/typedoc/classes/DetectAllFaceLandmarksTask.html @@ -1 +1 @@ -DetectAllFaceLandmarksTask | @vladmandic/face-api - v1.6.1
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Class DetectAllFaceLandmarksTask<TSource>

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withAgeAndGender

withFaceDescriptors

withFaceExpressions

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\ No newline at end of file +DetectAllFaceLandmarksTask | @vladmandic/face-api - v1.6.2
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Class DetectAllFaceLandmarksTask<TSource>

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withAgeAndGender

withFaceDescriptors

withFaceExpressions

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\ No newline at end of file diff --git a/typedoc/classes/DetectAllFacesTask.html b/typedoc/classes/DetectAllFacesTask.html index 5f4c003..6aa8aa0 100644 --- a/typedoc/classes/DetectAllFacesTask.html +++ b/typedoc/classes/DetectAllFacesTask.html @@ -1 +1 @@ -DetectAllFacesTask | @vladmandic/face-api - v1.6.1
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Class DetectAllFacesTask

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withAgeAndGender

  • withAgeAndGender(): PredictAllAgeAndGenderTask<{ detection: FaceDetection }>

withFaceExpressions

  • withFaceExpressions(): PredictAllFaceExpressionsTask<{ detection: FaceDetection }>

withFaceLandmarks

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\ No newline at end of file +DetectAllFacesTask | @vladmandic/face-api - v1.6.2
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Class DetectAllFacesTask

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withAgeAndGender

  • withAgeAndGender(): PredictAllAgeAndGenderTask<{ detection: FaceDetection }>

withFaceExpressions

  • withFaceExpressions(): PredictAllFaceExpressionsTask<{ detection: FaceDetection }>

withFaceLandmarks

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\ No newline at end of file diff --git a/typedoc/classes/DetectFaceLandmarksTaskBase.html b/typedoc/classes/DetectFaceLandmarksTaskBase.html index f19ae9f..e120de0 100644 --- a/typedoc/classes/DetectFaceLandmarksTaskBase.html +++ b/typedoc/classes/DetectFaceLandmarksTaskBase.html @@ -1 +1 @@ -DetectFaceLandmarksTaskBase | @vladmandic/face-api - v1.6.1
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Class DetectFaceLandmarksTaskBase<TReturn, TParentReturn>

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  • TParentReturn

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constructor

  • new DetectFaceLandmarksTaskBase<TReturn, TParentReturn>(parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>, input: any, useTinyLandmarkNet: boolean): DetectFaceLandmarksTaskBase<TReturn, TParentReturn>

Methods

run

  • run(): Promise<TReturn>

then

  • then(onfulfilled: (value: TReturn) => TReturn | PromiseLike<TReturn>): Promise<TReturn>

  • Parameters

    • onfulfilled: (value: TReturn) => TReturn | PromiseLike<TReturn>
        • (value: TReturn): TReturn | PromiseLike<TReturn>

        • Parameters

          • value: TReturn

          Returns TReturn | PromiseLike<TReturn>

    Returns Promise<TReturn>

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\ No newline at end of file +DetectFaceLandmarksTaskBase | @vladmandic/face-api - v1.6.2
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Class DetectFaceLandmarksTaskBase<TReturn, TParentReturn>

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  • TParentReturn

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constructor

  • new DetectFaceLandmarksTaskBase<TReturn, TParentReturn>(parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>, input: any, useTinyLandmarkNet: boolean): DetectFaceLandmarksTaskBase<TReturn, TParentReturn>

Methods

run

  • run(): Promise<TReturn>

then

  • then(onfulfilled: (value: TReturn) => TReturn | PromiseLike<TReturn>): Promise<TReturn>

  • Parameters

    • onfulfilled: (value: TReturn) => TReturn | PromiseLike<TReturn>
        • (value: TReturn): TReturn | PromiseLike<TReturn>

        • Parameters

          • value: TReturn

          Returns TReturn | PromiseLike<TReturn>

    Returns Promise<TReturn>

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\ No newline at end of file diff --git a/typedoc/classes/DetectFacesTaskBase.html b/typedoc/classes/DetectFacesTaskBase.html index 8225617..6a591d5 100644 --- a/typedoc/classes/DetectFacesTaskBase.html +++ b/typedoc/classes/DetectFacesTaskBase.html @@ -1 +1 @@ -DetectFacesTaskBase | @vladmandic/face-api - v1.6.1
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Class DetectFacesTaskBase<TReturn>

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  • run(): Promise<TReturn>

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  • then(onfulfilled: (value: TReturn) => TReturn | PromiseLike<TReturn>): Promise<TReturn>

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          • value: TReturn

          Returns TReturn | PromiseLike<TReturn>

    Returns Promise<TReturn>

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\ No newline at end of file +DetectFacesTaskBase | @vladmandic/face-api - v1.6.2
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Class DetectFacesTaskBase<TReturn>

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  • run(): Promise<TReturn>

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  • then(onfulfilled: (value: TReturn) => TReturn | PromiseLike<TReturn>): Promise<TReturn>

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    • onfulfilled: (value: TReturn) => TReturn | PromiseLike<TReturn>
        • (value: TReturn): TReturn | PromiseLike<TReturn>

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          • value: TReturn

          Returns TReturn | PromiseLike<TReturn>

    Returns Promise<TReturn>

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\ No newline at end of file diff --git a/typedoc/classes/DetectSingleFaceLandmarksTask.html b/typedoc/classes/DetectSingleFaceLandmarksTask.html index 0619251..70c7b08 100644 --- a/typedoc/classes/DetectSingleFaceLandmarksTask.html +++ b/typedoc/classes/DetectSingleFaceLandmarksTask.html @@ -1 +1 @@ -DetectSingleFaceLandmarksTask | @vladmandic/face-api - v1.6.1
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Class DetectSingleFaceLandmarksTask<TSource>

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withAgeAndGender

withFaceDescriptor

withFaceExpressions

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\ No newline at end of file +DetectSingleFaceLandmarksTask | @vladmandic/face-api - v1.6.2
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Class DetectSingleFaceLandmarksTask<TSource>

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withAgeAndGender

withFaceDescriptor

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\ No newline at end of file diff --git a/typedoc/classes/DetectSingleFaceTask.html b/typedoc/classes/DetectSingleFaceTask.html index 29fa59f..5eb4518 100644 --- a/typedoc/classes/DetectSingleFaceTask.html +++ b/typedoc/classes/DetectSingleFaceTask.html @@ -1 +1 @@ -DetectSingleFaceTask | @vladmandic/face-api - v1.6.1
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Class DetectSingleFaceTask

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withAgeAndGender

  • withAgeAndGender(): PredictSingleAgeAndGenderTask<{ detection: FaceDetection }>

withFaceExpressions

  • withFaceExpressions(): PredictSingleFaceExpressionsTask<{ detection: FaceDetection }>

withFaceLandmarks

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\ No newline at end of file +DetectSingleFaceTask | @vladmandic/face-api - v1.6.2
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Class DetectSingleFaceTask

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withAgeAndGender

  • withAgeAndGender(): PredictSingleAgeAndGenderTask<{ detection: FaceDetection }>

withFaceExpressions

  • withFaceExpressions(): PredictSingleFaceExpressionsTask<{ detection: FaceDetection }>

withFaceLandmarks

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\ No newline at end of file diff --git a/typedoc/classes/Dimensions.html b/typedoc/classes/Dimensions.html index ec44f77..717abd1 100644 --- a/typedoc/classes/Dimensions.html +++ b/typedoc/classes/Dimensions.html @@ -1 +1 @@ -Dimensions | @vladmandic/face-api - v1.6.1
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Class Dimensions

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  • Dimensions

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Accessors

Methods

Constructors

constructor

  • new Dimensions(width: number, height: number): Dimensions

Accessors

height

  • get height(): number

width

  • get width(): number

Methods

reverse

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\ No newline at end of file +Dimensions | @vladmandic/face-api - v1.6.2
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Class Dimensions

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  • Dimensions

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Constructors

Accessors

Methods

Constructors

constructor

  • new Dimensions(width: number, height: number): Dimensions

Accessors

height

  • get height(): number

width

  • get width(): number

Methods

reverse

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\ No newline at end of file diff --git a/typedoc/classes/FaceDetection.html b/typedoc/classes/FaceDetection.html index b5d3db3..8e49c53 100644 --- a/typedoc/classes/FaceDetection.html +++ b/typedoc/classes/FaceDetection.html @@ -1 +1 @@ -FaceDetection | @vladmandic/face-api - v1.6.1
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Class FaceDetection

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Methods

Constructors

constructor

Accessors

box

  • get box(): Box<any>

className

  • get className(): string

classScore

  • get classScore(): number

imageDims

imageHeight

  • get imageHeight(): number

imageWidth

  • get imageWidth(): number

relativeBox

  • get relativeBox(): Box<any>

score

  • get score(): number

Methods

forSize

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\ No newline at end of file +FaceDetection | @vladmandic/face-api - v1.6.2
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Class FaceDetection

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Accessors

Methods

Constructors

constructor

Accessors

box

  • get box(): Box<any>

className

  • get className(): string

classScore

  • get classScore(): number

imageDims

imageHeight

  • get imageHeight(): number

imageWidth

  • get imageWidth(): number

relativeBox

  • get relativeBox(): Box<any>

score

  • get score(): number

Methods

forSize

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\ No newline at end of file diff --git a/typedoc/classes/FaceDetectionNet.html b/typedoc/classes/FaceDetectionNet.html index db8460e..3e139f5 100644 --- a/typedoc/classes/FaceDetectionNet.html +++ b/typedoc/classes/FaceDetectionNet.html @@ -1 +1 @@ -FaceDetectionNet | @vladmandic/face-api - v1.6.1
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Class FaceDetectionNet

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Constructors

Properties

Accessors

Methods

Constructors

constructor

Properties

_name

_name: any

Accessors

isLoaded

  • get isLoaded(): boolean

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

  • get params(): undefined | TNetParams

  • Returns undefined | TNetParams

Methods

dispose

  • dispose(throwOnRedispose?: boolean): void

extractWeights

  • extractWeights(weights: Float32Array): void

forward

  • forward(input: any): Promise<any>

forwardInput

freeze

  • freeze(): void

getFrozenParams

  • getFrozenParams(): { path: string; tensor: Tensor }[]

getParamFromPath

  • getParamFromPath(paramPath: string): Tensor

getParamList

  • getParamList(): { path: string; tensor: Tensor }[]

getTrainableParams

  • getTrainableParams(): { path: string; tensor: Tensor }[]

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

loadFromWeightMap

  • loadFromWeightMap(weightMap: NamedTensorMap): void

locateFaces

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: Tensor): void

serializeParams

  • serializeParams(): Float32Array

variable

  • variable(): void

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\ No newline at end of file +FaceDetectionNet | @vladmandic/face-api - v1.6.2
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Class FaceDetectionNet

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Constructors

Properties

Accessors

Methods

Constructors

constructor

Properties

_name

_name: any

Accessors

isLoaded

  • get isLoaded(): boolean

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

  • get params(): undefined | TNetParams

  • Returns undefined | TNetParams

Methods

dispose

  • dispose(throwOnRedispose?: boolean): void

extractWeights

  • extractWeights(weights: Float32Array): void

forward

  • forward(input: any): Promise<any>

forwardInput

freeze

  • freeze(): void

getFrozenParams

  • getFrozenParams(): { path: string; tensor: Tensor }[]

getParamFromPath

  • getParamFromPath(paramPath: string): Tensor

getParamList

  • getParamList(): { path: string; tensor: Tensor }[]

getTrainableParams

  • getTrainableParams(): { path: string; tensor: Tensor }[]

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

loadFromWeightMap

  • loadFromWeightMap(weightMap: NamedTensorMap): void

locateFaces

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: Tensor): void

serializeParams

  • serializeParams(): Float32Array

variable

  • variable(): void

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\ No newline at end of file diff --git a/typedoc/classes/FaceExpressionNet.html b/typedoc/classes/FaceExpressionNet.html index 0a1b749..6f669e0 100644 --- a/typedoc/classes/FaceExpressionNet.html +++ b/typedoc/classes/FaceExpressionNet.html @@ -1 +1 @@ -FaceExpressionNet | @vladmandic/face-api - v1.6.1
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Class FaceExpressionNet

Hierarchy

  • FaceProcessor<FaceFeatureExtractorParams>
    • FaceExpressionNet

Index

Constructors

Properties

Accessors

Methods

Constructors

constructor

  • new FaceExpressionNet(faceFeatureExtractor?: FaceFeatureExtractor): FaceExpressionNet

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

isLoaded

  • get isLoaded(): boolean

paramMappings

  • get paramMappings(): ParamMapping[]

params

  • get params(): undefined | TNetParams

  • Returns undefined | TNetParams

Methods

dispose

  • dispose(throwOnRedispose?: boolean): void

extractClassifierParams

  • extractClassifierParams(weights: Float32Array): { paramMappings: ParamMapping[]; params: NetParams }

  • Parameters

    • weights: Float32Array

    Returns { paramMappings: ParamMapping[]; params: NetParams }

    • paramMappings: ParamMapping[]
    • params: NetParams

extractWeights

  • extractWeights(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

  • forward(input: any): Promise<Tensor2D>

forwardInput

  • forwardInput(input: any): Tensor2D

freeze

  • freeze(): void

getFrozenParams

  • getFrozenParams(): { path: string; tensor: Tensor }[]

  • Returns { path: string; tensor: Tensor }[]

getParamFromPath

  • getParamFromPath(paramPath: string): Tensor

  • Parameters

    • paramPath: string

    Returns Tensor

getParamList

  • getParamList(): { path: string; tensor: Tensor }[]

  • Returns { path: string; tensor: Tensor }[]

getTrainableParams

  • getTrainableParams(): { path: string; tensor: Tensor }[]

  • Returns { path: string; tensor: Tensor }[]

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

  • loadClassifierParams(weights: Float32Array): void

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

  • loadFromWeightMap(weightMap: NamedTensorMap): void

  • Parameters

    • weightMap: NamedTensorMap

    Returns void

predictExpressions

  • predictExpressions(input: any): Promise<any>

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: Tensor): void

  • Parameters

    • paramPath: string
    • tensor: Tensor

    Returns void

runNet

  • runNet(input: any): Tensor2D

serializeParams

  • serializeParams(): Float32Array

variable

  • variable(): void

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\ No newline at end of file +FaceExpressionNet | @vladmandic/face-api - v1.6.2
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Class FaceExpressionNet

Hierarchy

  • FaceProcessor<FaceFeatureExtractorParams>
    • FaceExpressionNet

Index

Constructors

Properties

Accessors

Methods

Constructors

constructor

  • new FaceExpressionNet(faceFeatureExtractor?: FaceFeatureExtractor): FaceExpressionNet

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

isLoaded

  • get isLoaded(): boolean

paramMappings

  • get paramMappings(): ParamMapping[]

params

  • get params(): undefined | TNetParams

  • Returns undefined | TNetParams

Methods

dispose

  • dispose(throwOnRedispose?: boolean): void

extractClassifierParams

  • extractClassifierParams(weights: Float32Array): { paramMappings: ParamMapping[]; params: NetParams }

  • Parameters

    • weights: Float32Array

    Returns { paramMappings: ParamMapping[]; params: NetParams }

    • paramMappings: ParamMapping[]
    • params: NetParams

extractWeights

  • extractWeights(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

  • forward(input: any): Promise<Tensor2D>

forwardInput

  • forwardInput(input: any): Tensor2D

freeze

  • freeze(): void

getFrozenParams

  • getFrozenParams(): { path: string; tensor: Tensor }[]

  • Returns { path: string; tensor: Tensor }[]

getParamFromPath

  • getParamFromPath(paramPath: string): Tensor

  • Parameters

    • paramPath: string

    Returns Tensor

getParamList

  • getParamList(): { path: string; tensor: Tensor }[]

  • Returns { path: string; tensor: Tensor }[]

getTrainableParams

  • getTrainableParams(): { path: string; tensor: Tensor }[]

  • Returns { path: string; tensor: Tensor }[]

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

  • loadClassifierParams(weights: Float32Array): void

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

  • loadFromWeightMap(weightMap: NamedTensorMap): void

  • Parameters

    • weightMap: NamedTensorMap

    Returns void

predictExpressions

  • predictExpressions(input: any): Promise<any>

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: Tensor): void

  • Parameters

    • paramPath: string
    • tensor: Tensor

    Returns void

runNet

  • runNet(input: any): Tensor2D

serializeParams

  • serializeParams(): Float32Array

variable

  • variable(): void

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\ No newline at end of file diff --git a/typedoc/classes/FaceExpressions.html b/typedoc/classes/FaceExpressions.html index b25152d..843e6f9 100644 --- a/typedoc/classes/FaceExpressions.html +++ b/typedoc/classes/FaceExpressions.html @@ -1 +1 @@ -FaceExpressions | @vladmandic/face-api - v1.6.1
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Class FaceExpressions

Hierarchy

  • FaceExpressions

Index

Constructors

Properties

Methods

Constructors

constructor

  • new FaceExpressions(probabilities: number[] | Float32Array): FaceExpressions

Properties

angry

angry: number = 0

disgusted

disgusted: number = 0

fearful

fearful: number = 0

happy

happy: number = 0

neutral

neutral: number = 0

sad

sad: number = 0

surprised

surprised: number = 0

Methods

asSortedArray

  • asSortedArray(): { expression: string; probability: number }[]

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\ No newline at end of file +FaceExpressions | @vladmandic/face-api - v1.6.2
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Class FaceExpressions

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  • FaceExpressions

Index

Constructors

Properties

Methods

Constructors

constructor

  • new FaceExpressions(probabilities: number[] | Float32Array): FaceExpressions

Properties

angry

angry: number = 0

disgusted

disgusted: number = 0

fearful

fearful: number = 0

happy

happy: number = 0

neutral

neutral: number = 0

sad

sad: number = 0

surprised

surprised: number = 0

Methods

asSortedArray

  • asSortedArray(): { expression: string; probability: number }[]

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\ No newline at end of file diff --git a/typedoc/classes/FaceLandmark68Net.html b/typedoc/classes/FaceLandmark68Net.html index 6a74c9a..867e1a4 100644 --- a/typedoc/classes/FaceLandmark68Net.html +++ b/typedoc/classes/FaceLandmark68Net.html @@ -1 +1 @@ -FaceLandmark68Net | @vladmandic/face-api - v1.6.1
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Class FaceLandmark68Net

Hierarchy

  • FaceLandmark68NetBase<FaceFeatureExtractorParams>

Index

Constructors

Properties

Accessors

Methods

Constructors

constructor

  • new FaceLandmark68Net(faceFeatureExtractor?: FaceFeatureExtractor): FaceLandmark68Net

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

isLoaded

  • get isLoaded(): boolean

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

  • get params(): undefined | TNetParams

  • Returns undefined | TNetParams

Methods

detectLandmarks

dispose

  • dispose(throwOnRedispose?: boolean): void

extractClassifierParams

  • extractClassifierParams(weights: Float32Array): { paramMappings: ParamMapping[]; params: NetParams }

  • Parameters

    • weights: Float32Array

    Returns { paramMappings: ParamMapping[]; params: NetParams }

    • paramMappings: ParamMapping[]
    • params: NetParams

extractWeights

  • extractWeights(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

  • forward(input: any): Promise<Tensor2D>

forwardInput

freeze

  • freeze(): void

getFrozenParams

  • getFrozenParams(): { path: string; tensor: Tensor }[]

  • Returns { path: string; tensor: Tensor }[]

getParamFromPath

  • getParamFromPath(paramPath: string): Tensor

  • Parameters

    • paramPath: string

    Returns Tensor

getParamList

  • getParamList(): { path: string; tensor: Tensor }[]

  • Returns { path: string; tensor: Tensor }[]

getTrainableParams

  • getTrainableParams(): { path: string; tensor: Tensor }[]

  • Returns { path: string; tensor: Tensor }[]

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

  • loadClassifierParams(weights: Float32Array): void

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

  • loadFromWeightMap(weightMap: NamedTensorMap): void

  • Parameters

    • weightMap: NamedTensorMap

    Returns void

postProcess

  • postProcess(output: Tensor2D, inputSize: number, originalDimensions: IDimensions[]): Tensor2D

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: Tensor): void

  • Parameters

    • paramPath: string
    • tensor: Tensor

    Returns void

runNet

  • runNet(input: any): Tensor2D

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

  • variable(): void

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\ No newline at end of file +FaceLandmark68Net | @vladmandic/face-api - v1.6.2
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Class FaceLandmark68Net

Hierarchy

  • FaceLandmark68NetBase<FaceFeatureExtractorParams>

Index

Constructors

Properties

Accessors

Methods

Constructors

constructor

  • new FaceLandmark68Net(faceFeatureExtractor?: FaceFeatureExtractor): FaceLandmark68Net

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

isLoaded

  • get isLoaded(): boolean

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

  • get params(): undefined | TNetParams

  • Returns undefined | TNetParams

Methods

detectLandmarks

dispose

  • dispose(throwOnRedispose?: boolean): void

extractClassifierParams

  • extractClassifierParams(weights: Float32Array): { paramMappings: ParamMapping[]; params: NetParams }

  • Parameters

    • weights: Float32Array

    Returns { paramMappings: ParamMapping[]; params: NetParams }

    • paramMappings: ParamMapping[]
    • params: NetParams

extractWeights

  • extractWeights(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

  • forward(input: any): Promise<Tensor2D>

forwardInput

freeze

  • freeze(): void

getFrozenParams

  • getFrozenParams(): { path: string; tensor: Tensor }[]

  • Returns { path: string; tensor: Tensor }[]

getParamFromPath

  • getParamFromPath(paramPath: string): Tensor

  • Parameters

    • paramPath: string

    Returns Tensor

getParamList

  • getParamList(): { path: string; tensor: Tensor }[]

  • Returns { path: string; tensor: Tensor }[]

getTrainableParams

  • getTrainableParams(): { path: string; tensor: Tensor }[]

  • Returns { path: string; tensor: Tensor }[]

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

  • loadClassifierParams(weights: Float32Array): void

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

  • loadFromWeightMap(weightMap: NamedTensorMap): void

  • Parameters

    • weightMap: NamedTensorMap

    Returns void

postProcess

  • postProcess(output: Tensor2D, inputSize: number, originalDimensions: IDimensions[]): Tensor2D

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: Tensor): void

  • Parameters

    • paramPath: string
    • tensor: Tensor

    Returns void

runNet

  • runNet(input: any): Tensor2D

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

  • variable(): void

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\ No newline at end of file diff --git a/typedoc/classes/FaceLandmark68TinyNet.html b/typedoc/classes/FaceLandmark68TinyNet.html index 72ff643..3f44e4c 100644 --- a/typedoc/classes/FaceLandmark68TinyNet.html +++ b/typedoc/classes/FaceLandmark68TinyNet.html @@ -1 +1 @@ -FaceLandmark68TinyNet | @vladmandic/face-api - v1.6.1
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Class FaceLandmark68TinyNet

Hierarchy

  • FaceLandmark68NetBase<TinyFaceFeatureExtractorParams>
    • FaceLandmark68TinyNet

Index

Constructors

Properties

Accessors

Methods

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

isLoaded

  • get isLoaded(): boolean

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

  • get params(): undefined | TNetParams

  • Returns undefined | TNetParams

Methods

detectLandmarks

dispose

  • dispose(throwOnRedispose?: boolean): void

extractClassifierParams

  • extractClassifierParams(weights: Float32Array): { paramMappings: ParamMapping[]; params: NetParams }

  • Parameters

    • weights: Float32Array

    Returns { paramMappings: ParamMapping[]; params: NetParams }

    • paramMappings: ParamMapping[]
    • params: NetParams

extractWeights

  • extractWeights(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

  • forward(input: any): Promise<Tensor2D>

forwardInput

freeze

  • freeze(): void

getFrozenParams

  • getFrozenParams(): { path: string; tensor: Tensor }[]

  • Returns { path: string; tensor: Tensor }[]

getParamFromPath

  • getParamFromPath(paramPath: string): Tensor

  • Parameters

    • paramPath: string

    Returns Tensor

getParamList

  • getParamList(): { path: string; tensor: Tensor }[]

  • Returns { path: string; tensor: Tensor }[]

getTrainableParams

  • getTrainableParams(): { path: string; tensor: Tensor }[]

  • Returns { path: string; tensor: Tensor }[]

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

  • loadClassifierParams(weights: Float32Array): void

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

  • loadFromWeightMap(weightMap: NamedTensorMap): void

  • Parameters

    • weightMap: NamedTensorMap

    Returns void

postProcess

  • postProcess(output: Tensor2D, inputSize: number, originalDimensions: IDimensions[]): Tensor2D

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: Tensor): void

  • Parameters

    • paramPath: string
    • tensor: Tensor

    Returns void

runNet

  • runNet(input: any): Tensor2D

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

  • variable(): void

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  • Property
  • Method
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\ No newline at end of file +FaceLandmark68TinyNet | @vladmandic/face-api - v1.6.2
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Class FaceLandmark68TinyNet

Hierarchy

  • FaceLandmark68NetBase<TinyFaceFeatureExtractorParams>
    • FaceLandmark68TinyNet

Index

Constructors

Properties

Accessors

Methods

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

isLoaded

  • get isLoaded(): boolean

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

  • get params(): undefined | TNetParams

  • Returns undefined | TNetParams

Methods

detectLandmarks

dispose

  • dispose(throwOnRedispose?: boolean): void

extractClassifierParams

  • extractClassifierParams(weights: Float32Array): { paramMappings: ParamMapping[]; params: NetParams }

  • Parameters

    • weights: Float32Array

    Returns { paramMappings: ParamMapping[]; params: NetParams }

    • paramMappings: ParamMapping[]
    • params: NetParams

extractWeights

  • extractWeights(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

  • forward(input: any): Promise<Tensor2D>

forwardInput

freeze

  • freeze(): void

getFrozenParams

  • getFrozenParams(): { path: string; tensor: Tensor }[]

  • Returns { path: string; tensor: Tensor }[]

getParamFromPath

  • getParamFromPath(paramPath: string): Tensor

  • Parameters

    • paramPath: string

    Returns Tensor

getParamList

  • getParamList(): { path: string; tensor: Tensor }[]

  • Returns { path: string; tensor: Tensor }[]

getTrainableParams

  • getTrainableParams(): { path: string; tensor: Tensor }[]

  • Returns { path: string; tensor: Tensor }[]

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

  • loadClassifierParams(weights: Float32Array): void

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

  • loadFromWeightMap(weightMap: NamedTensorMap): void

  • Parameters

    • weightMap: NamedTensorMap

    Returns void

postProcess

  • postProcess(output: Tensor2D, inputSize: number, originalDimensions: IDimensions[]): Tensor2D

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: Tensor): void

  • Parameters

    • paramPath: string
    • tensor: Tensor

    Returns void

runNet

  • runNet(input: any): Tensor2D

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

  • variable(): void

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  • Inherited method
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\ No newline at end of file diff --git a/typedoc/classes/FaceLandmarkNet.html b/typedoc/classes/FaceLandmarkNet.html index e565148..609bd80 100644 --- a/typedoc/classes/FaceLandmarkNet.html +++ b/typedoc/classes/FaceLandmarkNet.html @@ -1 +1 @@ -FaceLandmarkNet | @vladmandic/face-api - v1.6.1
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Class FaceLandmarkNet

Hierarchy

Index

Constructors

Properties

Accessors

Methods

Constructors

constructor

  • new FaceLandmarkNet(faceFeatureExtractor?: FaceFeatureExtractor): FaceLandmarkNet

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

isLoaded

  • get isLoaded(): boolean

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

  • get params(): undefined | TNetParams

  • Returns undefined | TNetParams

Methods

detectLandmarks

dispose

  • dispose(throwOnRedispose?: boolean): void

extractClassifierParams

  • extractClassifierParams(weights: Float32Array): { paramMappings: ParamMapping[]; params: NetParams }

extractWeights

  • extractWeights(weights: Float32Array): void

forward

  • forward(input: any): Promise<Tensor2D>

forwardInput

freeze

  • freeze(): void

getFrozenParams

  • getFrozenParams(): { path: string; tensor: Tensor }[]

getParamFromPath

  • getParamFromPath(paramPath: string): Tensor

getParamList

  • getParamList(): { path: string; tensor: Tensor }[]

getTrainableParams

  • getTrainableParams(): { path: string; tensor: Tensor }[]

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

loadClassifierParams

  • loadClassifierParams(weights: Float32Array): void

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

loadFromWeightMap

  • loadFromWeightMap(weightMap: NamedTensorMap): void

postProcess

  • postProcess(output: Tensor2D, inputSize: number, originalDimensions: IDimensions[]): Tensor2D

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: Tensor): void

runNet

  • runNet(input: any): Tensor2D

serializeParams

  • serializeParams(): Float32Array

variable

  • variable(): void

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\ No newline at end of file +FaceLandmarkNet | @vladmandic/face-api - v1.6.2
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Class FaceLandmarkNet

Hierarchy

Index

Constructors

Properties

Accessors

Methods

Constructors

constructor

  • new FaceLandmarkNet(faceFeatureExtractor?: FaceFeatureExtractor): FaceLandmarkNet

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

isLoaded

  • get isLoaded(): boolean

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

  • get params(): undefined | TNetParams

  • Returns undefined | TNetParams

Methods

detectLandmarks

dispose

  • dispose(throwOnRedispose?: boolean): void

extractClassifierParams

  • extractClassifierParams(weights: Float32Array): { paramMappings: ParamMapping[]; params: NetParams }

extractWeights

  • extractWeights(weights: Float32Array): void

forward

  • forward(input: any): Promise<Tensor2D>

forwardInput

freeze

  • freeze(): void

getFrozenParams

  • getFrozenParams(): { path: string; tensor: Tensor }[]

getParamFromPath

  • getParamFromPath(paramPath: string): Tensor

getParamList

  • getParamList(): { path: string; tensor: Tensor }[]

getTrainableParams

  • getTrainableParams(): { path: string; tensor: Tensor }[]

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

loadClassifierParams

  • loadClassifierParams(weights: Float32Array): void

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

loadFromWeightMap

  • loadFromWeightMap(weightMap: NamedTensorMap): void

postProcess

  • postProcess(output: Tensor2D, inputSize: number, originalDimensions: IDimensions[]): Tensor2D

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: Tensor): void

runNet

  • runNet(input: any): Tensor2D

serializeParams

  • serializeParams(): Float32Array

variable

  • variable(): void

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\ No newline at end of file diff --git a/typedoc/classes/FaceLandmarks.html b/typedoc/classes/FaceLandmarks.html index 3792b93..de1ae03 100644 --- a/typedoc/classes/FaceLandmarks.html +++ b/typedoc/classes/FaceLandmarks.html @@ -1,4 +1,4 @@ -FaceLandmarks | @vladmandic/face-api - v1.6.1
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Class FaceLandmarks

Hierarchy

Implements

Index

Constructors

Accessors

Methods

Constructors

constructor

Accessors

imageHeight

  • get imageHeight(): number

imageWidth

  • get imageWidth(): number

positions

relativePositions

  • get relativePositions(): Point[]

shift

Methods

align