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face
Commit 6db98cd7 by vincent
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provide an allFaces helper function + global api

parent 9012be3d
Showing with 404 additions and 112 deletions
build/FullFaceDescription.d.ts 0 → 100644
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { FaceLandmarks } from './faceLandmarkNet/FaceLandmarks';
export declare class FullFaceDescription {
private _detection;
private _landmarks;
private _descriptor;
constructor(_detection: FaceDetection, _landmarks: FaceLandmarks, _descriptor: Float32Array);
readonly detection: FaceDetection;
readonly landmarks: FaceLandmarks;
readonly descriptor: Float32Array;
forSize(width: number, height: number): FullFaceDescription;
}
build/FullFaceDescription.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var FullFaceDescription = /** @class */ (function () {
function FullFaceDescription(_detection, _landmarks, _descriptor) {
this._detection = _detection;
this._landmarks = _landmarks;
this._descriptor = _descriptor;
}
Object.defineProperty(FullFaceDescription.prototype, "detection", {
get: function () {
return this._detection;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FullFaceDescription.prototype, "landmarks", {
get: function () {
return this._landmarks;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FullFaceDescription.prototype, "descriptor", {
get: function () {
return this._descriptor;
},
enumerable: true,
configurable: true
});
FullFaceDescription.prototype.forSize = function (width, height) {
return new FullFaceDescription(this._detection.forSize(width, height), this._landmarks.forSize(width, height), this._descriptor);
};
return FullFaceDescription;
}());
exports.FullFaceDescription = FullFaceDescription;
//# sourceMappingURL=FullFaceDescription.js.map
\ No newline at end of file
build/FullFaceDescription.js.map 0 → 100644
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\ No newline at end of file
build/NetInput.js.map
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\ No newline at end of file \ No newline at end of file
build/allFacesFactory.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { FullFaceDescription } from './FullFaceDescription';
import { NetInput } from './NetInput';
export declare function allFacesFactory(detectionNet: FaceDetectionNet, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput, minConfidence: number) => Promise<FullFaceDescription[]>;
build/allFacesFactory.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var extractFaceTensors_1 = require("./extractFaceTensors");
var FullFaceDescription_1 = require("./FullFaceDescription");
function allFacesFactory(detectionNet, landmarkNet, recognitionNet) {
return function (input, minConfidence) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var detections, faceTensors, faceLandmarksByFace, alignedFaceBoxes, alignedFaceTensors, descriptors;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, detectionNet.locateFaces(input, minConfidence)];
case 1:
detections = _a.sent();
return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, detections)];
case 2:
faceTensors = _a.sent();
return [4 /*yield*/, Promise.all(faceTensors.map(function (faceTensor) { return landmarkNet.detectLandmarks(faceTensor); }))];
case 3:
faceLandmarksByFace = _a.sent();
faceTensors.forEach(function (t) { return t.dispose(); });
return [4 /*yield*/, Promise.all(faceLandmarksByFace.map(function (landmarks, i) { return landmarks.align(detections[i].getBox()); }))];
case 4:
alignedFaceBoxes = _a.sent();
return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, alignedFaceBoxes)];
case 5:
alignedFaceTensors = (_a.sent());
return [4 /*yield*/, Promise.all(alignedFaceTensors.map(function (faceTensor) { return recognitionNet.computeFaceDescriptor(faceTensor); }))];
case 6:
descriptors = _a.sent();
alignedFaceTensors.forEach(function (t) { return t.dispose(); });
return [2 /*return*/, detections.map(function (detection, i) {
return new FullFaceDescription_1.FullFaceDescription(detection, faceLandmarksByFace[i].shiftByPoint(detection.getBox()), descriptors[i]);
})];
}
});
});
};
}
exports.allFacesFactory = allFacesFactory;
//# sourceMappingURL=allFacesFactory.js.map
\ No newline at end of file
build/allFacesFactory.js.map 0 → 100644
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build/faceDetectionNet/index.d.ts
import { FaceDetectionNet } from './FaceDetectionNet'; import { FaceDetectionNet } from './FaceDetectionNet';
export * from './FaceDetectionNet'; export * from './FaceDetectionNet';
export * from './FaceDetection';
export declare function faceDetectionNet(weights: Float32Array): FaceDetectionNet; export declare function faceDetectionNet(weights: Float32Array): FaceDetectionNet;
build/faceDetectionNet/index.js
...@@ -3,6 +3,7 @@ Object.defineProperty(exports, "__esModule", { value: true }); ...@@ -3,6 +3,7 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib"); var tslib_1 = require("tslib");
var FaceDetectionNet_1 = require("./FaceDetectionNet"); var FaceDetectionNet_1 = require("./FaceDetectionNet");
tslib_1.__exportStar(require("./FaceDetectionNet"), exports); tslib_1.__exportStar(require("./FaceDetectionNet"), exports);
tslib_1.__exportStar(require("./FaceDetection"), exports);
function faceDetectionNet(weights) { function faceDetectionNet(weights) {
var net = new FaceDetectionNet_1.FaceDetectionNet(); var net = new FaceDetectionNet_1.FaceDetectionNet();
net.extractWeights(weights); net.extractWeights(weights);
......
build/faceDetectionNet/index.js.map
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\ No newline at end of file \ No newline at end of file
build/faceLandmarkNet/FaceLandmarks.d.ts
import { Point } from '../Point'; import { IPoint, Point } from '../Point';
import { Rect } from '../Rect'; import { Rect } from '../Rect';
import { Dimensions } from '../types'; import { Dimensions } from '../types';
export declare class FaceLandmarks { export declare class FaceLandmarks {
...@@ -21,6 +21,7 @@ export declare class FaceLandmarks { ...@@ -21,6 +21,7 @@ export declare class FaceLandmarks {
getMouth(): Point[]; getMouth(): Point[];
forSize(width: number, height: number): FaceLandmarks; forSize(width: number, height: number): FaceLandmarks;
shift(x: number, y: number): FaceLandmarks; shift(x: number, y: number): FaceLandmarks;
shiftByPoint(pt: IPoint): FaceLandmarks;
/** /**
* Aligns the face landmarks after face detection from the relative positions of the faces * Aligns the face landmarks after face detection from the relative positions of the faces
* bounding box, or it's current shift. This function should be used to align the face images * bounding box, or it's current shift. This function should be used to align the face images
......
build/faceLandmarkNet/FaceLandmarks.js
...@@ -60,6 +60,9 @@ var FaceLandmarks = /** @class */ (function () { ...@@ -60,6 +60,9 @@ var FaceLandmarks = /** @class */ (function () {
FaceLandmarks.prototype.shift = function (x, y) { FaceLandmarks.prototype.shift = function (x, y) {
return new FaceLandmarks(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new Point_1.Point(x, y)); return new FaceLandmarks(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new Point_1.Point(x, y));
}; };
FaceLandmarks.prototype.shiftByPoint = function (pt) {
return this.shift(pt.x, pt.y);
};
/** /**
* Aligns the face landmarks after face detection from the relative positions of the faces * Aligns the face landmarks after face detection from the relative positions of the faces
* bounding box, or it's current shift. This function should be used to align the face images * bounding box, or it's current shift. This function should be used to align the face images
......
build/faceLandmarkNet/FaceLandmarks.js.map
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\ No newline at end of file \ No newline at end of file
build/faceLandmarkNet/index.d.ts
import { FaceLandmarkNet } from './FaceLandmarkNet'; import { FaceLandmarkNet } from './FaceLandmarkNet';
export * from './FaceLandmarkNet'; export * from './FaceLandmarkNet';
export * from './FaceLandmarks';
export declare function faceLandmarkNet(weights: Float32Array): FaceLandmarkNet; export declare function faceLandmarkNet(weights: Float32Array): FaceLandmarkNet;
build/faceLandmarkNet/index.js
...@@ -3,6 +3,7 @@ Object.defineProperty(exports, "__esModule", { value: true }); ...@@ -3,6 +3,7 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib"); var tslib_1 = require("tslib");
var FaceLandmarkNet_1 = require("./FaceLandmarkNet"); var FaceLandmarkNet_1 = require("./FaceLandmarkNet");
tslib_1.__exportStar(require("./FaceLandmarkNet"), exports); tslib_1.__exportStar(require("./FaceLandmarkNet"), exports);
tslib_1.__exportStar(require("./FaceLandmarks"), exports);
function faceLandmarkNet(weights) { function faceLandmarkNet(weights) {
var net = new FaceLandmarkNet_1.FaceLandmarkNet(); var net = new FaceLandmarkNet_1.FaceLandmarkNet();
net.extractWeights(weights); net.extractWeights(weights);
......
build/faceLandmarkNet/index.js.map
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\ No newline at end of file \ No newline at end of file
build/faceRecognitionNet/FaceRecognitionNet.d.ts
...@@ -6,6 +6,6 @@ export declare class FaceRecognitionNet { ...@@ -6,6 +6,6 @@ export declare class FaceRecognitionNet {
load(weightsOrUrl: Float32Array | string | undefined): Promise<void>; load(weightsOrUrl: Float32Array | string | undefined): Promise<void>;
extractWeights(weights: Float32Array): void; extractWeights(weights: Float32Array): void;
forward(input: tf.Tensor | NetInput | TNetInput): tf.Tensor<tf.Rank.R2>; forward(input: tf.Tensor | NetInput | TNetInput): tf.Tensor<tf.Rank.R2>;
computeFaceDescriptor(input: tf.Tensor | NetInput | TNetInput): Promise<Int32Array | Uint8Array | Float32Array>; computeFaceDescriptor(input: tf.Tensor | NetInput | TNetInput): Promise<Float32Array>;
computeFaceDescriptorSync(input: tf.Tensor | NetInput | TNetInput): Promise<Int32Array | Uint8Array | Float32Array>; computeFaceDescriptorSync(input: tf.Tensor | NetInput | TNetInput): Promise<Float32Array>;
} }
build/faceRecognitionNet/FaceRecognitionNet.js.map
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\ No newline at end of file \ No newline at end of file
build/globalApi.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
import { FaceLandmarks } from './faceLandmarkNet/FaceLandmarks';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { NetInput } from './NetInput';
import { TNetInput } from './types';
import { FullFaceDescription } from './FullFaceDescription';
export declare const detectionNet: FaceDetectionNet;
export declare const landmarkNet: FaceLandmarkNet;
export declare const recognitionNet: FaceRecognitionNet;
export declare function loadFaceDetectionModel(url: string): Promise<void>;
export declare function loadFaceLandmarkModel(url: string): Promise<void>;
export declare function loadFaceRecognitionModel(url: string): Promise<void>;
export declare function loadModels(url: string): Promise<[void, void, void]>;
export declare function locateFaces(input: tf.Tensor | NetInput | TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
export declare function detectLandmarks(input: tf.Tensor | NetInput | TNetInput): Promise<FaceLandmarks>;
export declare function computeFaceDescriptor(input: tf.Tensor | NetInput | TNetInput): Promise<Float32Array>;
export declare const allFaces: (input: tf.Tensor | NetInput | TNetInput, minConfidence: number) => Promise<FullFaceDescription[]>;
build/globalApi.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var allFacesFactory_1 = require("./allFacesFactory");
var FaceDetectionNet_1 = require("./faceDetectionNet/FaceDetectionNet");
var FaceLandmarkNet_1 = require("./faceLandmarkNet/FaceLandmarkNet");
var FaceRecognitionNet_1 = require("./faceRecognitionNet/FaceRecognitionNet");
exports.detectionNet = new FaceDetectionNet_1.FaceDetectionNet();
exports.landmarkNet = new FaceLandmarkNet_1.FaceLandmarkNet();
exports.recognitionNet = new FaceRecognitionNet_1.FaceRecognitionNet();
function loadFaceDetectionModel(url) {
return exports.detectionNet.load(url);
}
exports.loadFaceDetectionModel = loadFaceDetectionModel;
function loadFaceLandmarkModel(url) {
return exports.landmarkNet.load(url);
}
exports.loadFaceLandmarkModel = loadFaceLandmarkModel;
function loadFaceRecognitionModel(url) {
return exports.recognitionNet.load(url);
}
exports.loadFaceRecognitionModel = loadFaceRecognitionModel;
function loadModels(url) {
return Promise.all([
loadFaceDetectionModel(url),
loadFaceLandmarkModel(url),
loadFaceRecognitionModel(url)
]);
}
exports.loadModels = loadModels;
function locateFaces(input, minConfidence, maxResults) {
return exports.detectionNet.locateFaces(input, minConfidence, maxResults);
}
exports.locateFaces = locateFaces;
function detectLandmarks(input) {
return exports.landmarkNet.detectLandmarks(input);
}
exports.detectLandmarks = detectLandmarks;
function computeFaceDescriptor(input) {
return exports.recognitionNet.computeFaceDescriptor(input);
}
exports.computeFaceDescriptor = computeFaceDescriptor;
exports.allFaces = allFacesFactory_1.allFacesFactory(exports.detectionNet, exports.landmarkNet, exports.recognitionNet);
//# sourceMappingURL=globalApi.js.map
\ No newline at end of file
build/globalApi.js.map 0 → 100644
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\ No newline at end of file
build/index.d.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { euclideanDistance } from './euclideanDistance'; export { tf };
import { NetInput } from './NetInput'; export * from './FullFaceDescription';
import { padToSquare } from './padToSquare'; export * from './NetInput';
export { euclideanDistance, NetInput, tf, padToSquare }; export * from './Point';
export * from './Rect';
export * from './euclideanDistance';
export * from './extractFaces'; export * from './extractFaces';
export * from './extractFaceTensors'; export * from './extractFaceTensors';
export * from './faceDetectionNet'; export * from './faceDetectionNet';
export * from './faceLandmarkNet'; export * from './faceLandmarkNet';
export * from './faceRecognitionNet'; export * from './faceRecognitionNet';
export * from './globalApi';
export * from './padToSquare';
export * from './utils'; export * from './utils';
build/index.js
...@@ -3,16 +3,17 @@ Object.defineProperty(exports, "__esModule", { value: true }); ...@@ -3,16 +3,17 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib"); var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core"); var tf = require("@tensorflow/tfjs-core");
exports.tf = tf; exports.tf = tf;
var euclideanDistance_1 = require("./euclideanDistance"); tslib_1.__exportStar(require("./FullFaceDescription"), exports);
exports.euclideanDistance = euclideanDistance_1.euclideanDistance; tslib_1.__exportStar(require("./NetInput"), exports);
var NetInput_1 = require("./NetInput"); tslib_1.__exportStar(require("./Point"), exports);
exports.NetInput = NetInput_1.NetInput; tslib_1.__exportStar(require("./Rect"), exports);
var padToSquare_1 = require("./padToSquare"); tslib_1.__exportStar(require("./euclideanDistance"), exports);
exports.padToSquare = padToSquare_1.padToSquare;
tslib_1.__exportStar(require("./extractFaces"), exports); tslib_1.__exportStar(require("./extractFaces"), exports);
tslib_1.__exportStar(require("./extractFaceTensors"), exports); tslib_1.__exportStar(require("./extractFaceTensors"), exports);
tslib_1.__exportStar(require("./faceDetectionNet"), exports); tslib_1.__exportStar(require("./faceDetectionNet"), exports);
tslib_1.__exportStar(require("./faceLandmarkNet"), exports); tslib_1.__exportStar(require("./faceLandmarkNet"), exports);
tslib_1.__exportStar(require("./faceRecognitionNet"), exports); tslib_1.__exportStar(require("./faceRecognitionNet"), exports);
tslib_1.__exportStar(require("./globalApi"), exports);
tslib_1.__exportStar(require("./padToSquare"), exports);
tslib_1.__exportStar(require("./utils"), exports); tslib_1.__exportStar(require("./utils"), exports);
//# sourceMappingURL=index.js.map //# sourceMappingURL=index.js.map
\ No newline at end of file
build/index.js.map
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\ No newline at end of file \ No newline at end of file
dist/face-api.js
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dist/face-api.min.js
This source diff could not be displayed because it is too large. You can view the blob instead.
examples/views/detectAndDrawFaces.html
...@@ -54,7 +54,6 @@ ...@@ -54,7 +54,6 @@
<script> <script>
let minConfidence = 0.7 let minConfidence = 0.7
let net
function onIncreaseThreshold() { function onIncreaseThreshold() {
minConfidence = Math.min(faceapi.round(minConfidence + 0.1), 1.0) minConfidence = Math.min(faceapi.round(minConfidence + 0.1), 1.0)
...@@ -82,7 +81,7 @@ ...@@ -82,7 +81,7 @@
canvas.height = height canvas.height = height
const input = new faceapi.NetInput(inputImgEl) const input = new faceapi.NetInput(inputImgEl)
const detections = await net.locateFaces(input, minConfidence) const detections = await faceapi.locateFaces(input, minConfidence)
faceapi.drawDetection('overlay', detections.map(det => det.forSize(width, height))) faceapi.drawDetection('overlay', detections.map(det => det.forSize(width, height)))
const faceImages = await faceapi.extractFaces(input.canvases[0], detections) const faceImages = await faceapi.extractFaces(input.canvases[0], detections)
...@@ -97,8 +96,7 @@ ...@@ -97,8 +96,7 @@
} }
async function run() { async function run() {
net = new faceapi.FaceDetectionNet() await faceapi.loadFaceDetectionModel('/')
await net.load('/')
$('#loader').hide() $('#loader').hide()
onSelectionChanged($('#selectList select').val()) onSelectionChanged($('#selectList select').val())
} }
......
examples/views/detectAndDrawLandmarks.html
...@@ -54,7 +54,6 @@ ...@@ -54,7 +54,6 @@
<script> <script>
let minConfidence = 0.7 let minConfidence = 0.7
let drawLines = true let drawLines = true
let detectionNet, landmarkNet
function onIncreaseMinConfidence() { function onIncreaseMinConfidence() {
minConfidence = Math.min(faceapi.round(minConfidence + 0.1), 1.0) minConfidence = Math.min(faceapi.round(minConfidence + 0.1), 1.0)
...@@ -88,10 +87,10 @@ ...@@ -88,10 +87,10 @@
canvas.height = height canvas.height = height
const input = new faceapi.NetInput(inputImgEl) const input = new faceapi.NetInput(inputImgEl)
const locations = await detectionNet.locateFaces(input, minConfidence) const locations = await faceapi.locateFaces(input, minConfidence)
const faceTensors = (await faceapi.extractFaceTensors(input, locations)) const faceTensors = (await faceapi.extractFaceTensors(input, locations))
landmarksByFace = await Promise.all(faceTensors.map(t => landmarkNet.detectLandmarks(t))) landmarksByFace = await Promise.all(faceTensors.map(t => faceapi.detectLandmarks(t)))
// free memory for face image tensors after we computed their descriptors // free memory for face image tensors after we computed their descriptors
faceTensors.forEach(t => t.dispose()) faceTensors.forEach(t => t.dispose())
...@@ -106,10 +105,8 @@ ...@@ -106,10 +105,8 @@
} }
async function run() { async function run() {
detectionNet = new faceapi.FaceDetectionNet() await faceapi.loadFaceDetectionModel('/')
await detectionNet.load('/') await faceapi.loadFaceLandmarkModel('/')
landmarkNet = new faceapi.FaceLandmarkNet()
await landmarkNet.load('/')
$('#loader').hide() $('#loader').hide()
onSelectionChanged($('#selectList select').val()) onSelectionChanged($('#selectList select').val())
} }
......
examples/views/detectAndRecognizeFaces.html
...@@ -111,38 +111,14 @@ ...@@ -111,38 +111,14 @@
canvas.height = height canvas.height = height
const input = new faceapi.NetInput(inputImgEl) const input = new faceapi.NetInput(inputImgEl)
const detections = await detectionNet.locateFaces(input, minConfidence) const fullFaceDescriptions = (await faceapi.allFaces(input, minConfidence))
const faceTensors = (await faceapi.extractFaceTensors(input, detections)) .map(fd => fd.forSize(width, height))
// detect landmarks and get the aligned face image bounding boxes fullFaceDescriptions.forEach(({ detection, descriptor }) => {
const alignedFaceBoxes = await Promise.all(faceTensors.map( faceapi.drawDetection('overlay', [detection], { withScore: false })
async (faceTensor, i) => {
const faceLandmarks = await landmarkNet.detectLandmarks(faceTensor)
return faceLandmarks.align(detections[i])
}
))
// free memory for face image tensors after we detected the face landmarks
faceTensors.forEach(t => t.dispose())
const alignedFaceTensors = (await faceapi.extractFaceTensors(input, alignedFaceBoxes))
const descriptors = await Promise.all(alignedFaceTensors.map(
faceTensor => recognitionNet.computeFaceDescriptor(faceTensor)
))
// free memory for face image tensors after we computed their descriptors
alignedFaceTensors.forEach(t => t.dispose())
// draw detections
const detectionsForSize = detections.map(det => det.forSize(width, height))
faceapi.drawDetection('overlay', detectionsForSize, { withScore: false })
// draw the recognition results
descriptors.forEach((descriptor, i) => {
const bestMatch = getBestMatch(trainDescriptorsByClass, descriptor) const bestMatch = getBestMatch(trainDescriptorsByClass, descriptor)
const text = `${bestMatch.distance < maxDistance ? bestMatch.className : 'unkown'} (${bestMatch.distance})` const text = `${bestMatch.distance < maxDistance ? bestMatch.className : 'unkown'} (${bestMatch.distance})`
const { x, y, height: boxHeight } = detectionsForSize[i].getBox() const { x, y, height: boxHeight } = detection.getBox()
faceapi.drawText( faceapi.drawText(
canvas.getContext('2d'), canvas.getContext('2d'),
x, x,
...@@ -160,13 +136,8 @@ ...@@ -160,13 +136,8 @@
} }
async function run() { async function run() {
detectionNet = new faceapi.FaceDetectionNet() await faceapi.loadModels('/')
await detectionNet.load('/') trainDescriptorsByClass = await initTrainDescriptorsByClass(faceapi.recognitionNet, 1)
landmarkNet = new faceapi.FaceLandmarkNet()
await landmarkNet.load('/')
recognitionNet = new faceapi.FaceRecognitionNet()
await recognitionNet.load('/')
trainDescriptorsByClass = await initTrainDescriptorsByClass(recognitionNet, 1)
$('#loader').hide() $('#loader').hide()
onSelectionChanged($('#selectList select').val()) onSelectionChanged($('#selectList select').val())
} }
......
examples/views/faceAlignment.html
...@@ -55,7 +55,6 @@ ...@@ -55,7 +55,6 @@
<script> <script>
let minConfidence = 0.7 let minConfidence = 0.7
let drawLines = true let drawLines = true
let detectionNet, landmarkNet
function onIncreaseMinConfidence() { function onIncreaseMinConfidence() {
minConfidence = Math.min(faceapi.round(minConfidence + 0.1), 1.0) minConfidence = Math.min(faceapi.round(minConfidence + 0.1), 1.0)
...@@ -83,14 +82,14 @@ ...@@ -83,14 +82,14 @@
canvas.height = height canvas.height = height
const input = new faceapi.NetInput(inputImgEl) const input = new faceapi.NetInput(inputImgEl)
const locations = await detectionNet.locateFaces(input, minConfidence) const locations = await faceapi.locateFaces(input, minConfidence)
const faceImages = await faceapi.extractFaces(input.canvases[0], locations) const faceImages = await faceapi.extractFaces(input.canvases[0], locations)
// detect landmarks and get the aligned face image bounding boxes // detect landmarks and get the aligned face image bounding boxes
const alignedFaceBoxes = await Promise.all(faceImages.map( const alignedFaceBoxes = await Promise.all(faceImages.map(
async (faceCanvas, i) => { async (faceCanvas, i) => {
const faceLandmarks = await landmarkNet.detectLandmarks(faceCanvas) const faceLandmarks = await faceapi.detectLandmarks(faceCanvas)
return faceLandmarks.align(locations[i]) return faceLandmarks.align(locations[i])
} }
)) ))
...@@ -110,10 +109,8 @@ ...@@ -110,10 +109,8 @@
} }
async function run() { async function run() {
detectionNet = new faceapi.FaceDetectionNet() await faceapi.loadFaceDetectionModel('/')
await detectionNet.load('/') await faceapi.loadFaceLandmarkModel('/')
landmarkNet = new faceapi.FaceLandmarkNet()
await landmarkNet.load('/')
$('#loader').hide() $('#loader').hide()
onSelectionChanged($('#selectList select').val()) onSelectionChanged($('#selectList select').val())
} }
......
examples/views/faceDetection.html
...@@ -53,7 +53,7 @@ ...@@ -53,7 +53,7 @@
<script> <script>
let minConfidence = 0.7 let minConfidence = 0.7
let net, result let result
function onIncreaseThreshold() { function onIncreaseThreshold() {
minConfidence = Math.min(faceapi.round(minConfidence + 0.1), 1.0) minConfidence = Math.min(faceapi.round(minConfidence + 0.1), 1.0)
...@@ -81,7 +81,7 @@ ...@@ -81,7 +81,7 @@
canvas.height = height canvas.height = height
const input = new faceapi.NetInput(inputImgEl) const input = new faceapi.NetInput(inputImgEl)
result = await net.locateFaces(input, minConfidence) result = await faceapi.locateFaces(input, minConfidence)
faceapi.drawDetection('overlay', result.map(det => det.forSize(width, height))) faceapi.drawDetection('overlay', result.map(det => det.forSize(width, height)))
} }
...@@ -92,8 +92,7 @@ ...@@ -92,8 +92,7 @@
} }
async function run() { async function run() {
net = new faceapi.FaceDetectionNet() await faceapi.loadFaceDetectionModel('/')
await net.load('/')
$('#loader').hide() $('#loader').hide()
onSelectionChanged($('#selectList select').val()) onSelectionChanged($('#selectList select').val())
} }
......
examples/views/faceDetectionVideo.html
...@@ -50,7 +50,7 @@ ...@@ -50,7 +50,7 @@
<script> <script>
let minConfidence = 0.7 let minConfidence = 0.7
let net, result let result
function onIncreaseThreshold() { function onIncreaseThreshold() {
minConfidence = Math.min(faceapi.round(minConfidence + 0.1), 1.0) minConfidence = Math.min(faceapi.round(minConfidence + 0.1), 1.0)
...@@ -78,7 +78,7 @@ ...@@ -78,7 +78,7 @@
canvas.height = height canvas.height = height
const ts = Date.now() const ts = Date.now()
result = await net.locateFaces(input, minConfidence) result = await faceapi.locateFaces(input, minConfidence)
displayTimeStats(Date.now() - ts) displayTimeStats(Date.now() - ts)
faceapi.drawDetection('overlay', result.map(det => det.forSize(width, height))) faceapi.drawDetection('overlay', result.map(det => det.forSize(width, height)))
...@@ -86,8 +86,7 @@ ...@@ -86,8 +86,7 @@
} }
async function run() { async function run() {
net = new faceapi.FaceDetectionNet() await faceapi.loadFaceDetectionModel('/')
await net.load('/')
$('#loader').hide() $('#loader').hide()
} }
......
examples/views/faceLandmarks.html
...@@ -29,7 +29,6 @@ ...@@ -29,7 +29,6 @@
</div> </div>
<script> <script>
let net
let drawLines = true let drawLines = true
let landmarks let landmarks
let currentImg let currentImg
...@@ -49,13 +48,12 @@ ...@@ -49,13 +48,12 @@
async function onSelectionChanged(uri) { async function onSelectionChanged(uri) {
const imgBuf = await fetchImage(uri) const imgBuf = await fetchImage(uri)
currentImg = await faceapi.bufferToImage(imgBuf) currentImg = await faceapi.bufferToImage(imgBuf)
landmarks = await net.detectLandmarks(currentImg) landmarks = await faceapi.detectLandmarks(currentImg)
redraw() redraw()
} }
async function run() { async function run() {
net = new faceapi.FaceLandmarkNet() await faceapi.loadFaceLandmarkModel('/')
await net.load('/')
$('#loader').hide() $('#loader').hide()
await onSelectionChanged($('#selectList select').val()) await onSelectionChanged($('#selectList select').val())
} }
......
examples/views/faceRecognition.html
...@@ -69,7 +69,6 @@ ...@@ -69,7 +69,6 @@
let isStop = false let isStop = false
let trainDescriptorsByClass = [] let trainDescriptorsByClass = []
let net
let currImageIdx = 2, currClassIdx = 0 let currImageIdx = 2, currClassIdx = 0
let to = null let to = null
...@@ -114,7 +113,7 @@ ...@@ -114,7 +113,7 @@
imgEl.src = input.src imgEl.src = input.src
const ts = Date.now() const ts = Date.now()
const descriptor = await net.computeFaceDescriptor(input) const descriptor = await faceapi.computeFaceDescriptor(input)
displayTimeStats(Date.now() - ts) displayTimeStats(Date.now() - ts)
const bestMatch = getBestMatch(trainDescriptorsByClass, descriptor) const bestMatch = getBestMatch(trainDescriptorsByClass, descriptor)
...@@ -135,12 +134,11 @@ ...@@ -135,12 +134,11 @@
try { try {
setStatusText('loading model file...') setStatusText('loading model file...')
net = new faceapi.FaceRecognitionNet() await faceapi.loadFaceRecognitionModel('/')
await net.load('/')
setStatusText('computing initial descriptors...') setStatusText('computing initial descriptors...')
trainDescriptorsByClass = await initTrainDescriptorsByClass(net) trainDescriptorsByClass = await initTrainDescriptorsByClass(faceapi.recognitionNet)
$('#loader').hide() $('#loader').hide()
runFaceRecognition() runFaceRecognition()
......
examples/views/faceSimilarity.html
...@@ -35,7 +35,7 @@ ...@@ -35,7 +35,7 @@
<script> <script>
const threshold = 0.6 const threshold = 0.6
let net, descriptors = { desc1: null, desc2: null } let descriptors = { desc1: null, desc2: null }
function updateResult() { function updateResult() {
const distance = faceapi.round( const distance = faceapi.round(
...@@ -56,12 +56,11 @@ ...@@ -56,12 +56,11 @@
const input = await faceapi.bufferToImage(imgBuf) const input = await faceapi.bufferToImage(imgBuf)
const imgEl = $(`#face${which}`).get(0) const imgEl = $(`#face${which}`).get(0)
imgEl.src = input.src imgEl.src = input.src
descriptors[`desc${which}`] = await net.computeFaceDescriptor(input) descriptors[`desc${which}`] = await faceapi.computeFaceDescriptor(input)
} }
async function run() { async function run() {
net = new faceapi.FaceRecognitionNet() await faceapi.loadFaceRecognitionModel()
await net.load('/')
$('#loader').hide() $('#loader').hide()
await onSelectionChanged(1, $('#selectList1 select').val()) await onSelectionChanged(1, $('#selectList1 select').val())
await onSelectionChanged(2, $('#selectList2 select').val()) await onSelectionChanged(2, $('#selectList2 select').val())
......
src/FullFaceDescription.ts 0 → 100644
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { FaceLandmarks } from './faceLandmarkNet/FaceLandmarks';
export class FullFaceDescription {
constructor(
private _detection: FaceDetection,
private _landmarks: FaceLandmarks,
private _descriptor: Float32Array
) {}
public get detection(): FaceDetection {
return this._detection
}
public get landmarks(): FaceLandmarks {
return this._landmarks
}
public get descriptor(): Float32Array {
return this._descriptor
}
public forSize(width: number, height: number): FullFaceDescription {
return new FullFaceDescription(
this._detection.forSize(width, height),
this._landmarks.forSize(width, height),
this._descriptor
)
}
}
\ No newline at end of file
src/NetInput.ts
import { Dimensions, TMediaElement, TNetInput } from './types'; import { Dimensions, TMediaElement, TNetInput } from './types';
import { createCanvasFromMedia, getContext2dOrThrow, getElement, getMediaDimensions } from './utils'; import { createCanvasFromMedia, getElement } from './utils';
export class NetInput { export class NetInput {
private _canvases: HTMLCanvasElement[] private _canvases: HTMLCanvasElement[]
......
src/allFacesFactory.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { extractFaceTensors } from './extractFaceTensors';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { FullFaceDescription } from './FullFaceDescription';
import { NetInput } from './NetInput';
import { TNetInput } from './types';
export function allFacesFactory(
detectionNet: FaceDetectionNet,
landmarkNet: FaceLandmarkNet,
recognitionNet: FaceRecognitionNet
) {
return async function(
input: tf.Tensor | NetInput | TNetInput,
minConfidence: number
): Promise<FullFaceDescription[]> {
const detections = await detectionNet.locateFaces(input, minConfidence)
const faceTensors = await extractFaceTensors(input, detections)
const faceLandmarksByFace = await Promise.all(faceTensors.map(
faceTensor => landmarkNet.detectLandmarks(faceTensor)
))
faceTensors.forEach(t => t.dispose())
const alignedFaceBoxes = await Promise.all(faceLandmarksByFace.map(
(landmarks, i) => landmarks.align(detections[i].getBox())
))
const alignedFaceTensors = (await extractFaceTensors(input, alignedFaceBoxes))
const descriptors = await Promise.all(alignedFaceTensors.map(
faceTensor => recognitionNet.computeFaceDescriptor(faceTensor)
))
alignedFaceTensors.forEach(t => t.dispose())
return detections.map((detection, i) =>
new FullFaceDescription(
detection,
faceLandmarksByFace[i].shiftByPoint(detection.getBox()),
descriptors[i]
)
)
}
}
\ No newline at end of file
src/faceDetectionNet/index.ts
import { FaceDetectionNet } from './FaceDetectionNet'; import { FaceDetectionNet } from './FaceDetectionNet';
export * from './FaceDetectionNet'; export * from './FaceDetectionNet';
export * from './FaceDetection';
export function faceDetectionNet(weights: Float32Array) { export function faceDetectionNet(weights: Float32Array) {
const net = new FaceDetectionNet() const net = new FaceDetectionNet()
......
src/faceLandmarkNet/FaceLandmarks.ts
import { getCenterPoint } from '../commons/getCenterPoint'; import { getCenterPoint } from '../commons/getCenterPoint';
import { FaceDetection } from '../faceDetectionNet/FaceDetection'; import { FaceDetection } from '../faceDetectionNet/FaceDetection';
import { Point } from '../Point'; import { IPoint, Point } from '../Point';
import { Rect } from '../Rect'; import { Rect } from '../Rect';
import { Dimensions } from '../types'; import { Dimensions } from '../types';
...@@ -94,6 +94,10 @@ export class FaceLandmarks { ...@@ -94,6 +94,10 @@ export class FaceLandmarks {
) )
} }
public shiftByPoint(pt: IPoint): FaceLandmarks {
return this.shift(pt.x, pt.y)
}
/** /**
* Aligns the face landmarks after face detection from the relative positions of the faces * Aligns the face landmarks after face detection from the relative positions of the faces
* bounding box, or it's current shift. This function should be used to align the face images * bounding box, or it's current shift. This function should be used to align the face images
......
src/faceLandmarkNet/index.ts
import { FaceLandmarkNet } from './FaceLandmarkNet'; import { FaceLandmarkNet } from './FaceLandmarkNet';
export * from './FaceLandmarkNet'; export * from './FaceLandmarkNet';
export * from './FaceLandmarks';
export function faceLandmarkNet(weights: Float32Array) { export function faceLandmarkNet(weights: Float32Array) {
const net = new FaceLandmarkNet() const net = new FaceLandmarkNet()
......
src/faceRecognitionNet/FaceRecognitionNet.ts
...@@ -77,13 +77,13 @@ export class FaceRecognitionNet { ...@@ -77,13 +77,13 @@ export class FaceRecognitionNet {
const result = this.forward(input) const result = this.forward(input)
const data = await result.data() const data = await result.data()
result.dispose() result.dispose()
return data return data as Float32Array
} }
public async computeFaceDescriptorSync(input: tf.Tensor | NetInput | TNetInput) { public async computeFaceDescriptorSync(input: tf.Tensor | NetInput | TNetInput) {
const result = this.forward(input) const result = this.forward(input)
const data = result.dataSync() const data = result.dataSync()
result.dispose() result.dispose()
return data return data as Float32Array
} }
} }
\ No newline at end of file
src/globalApi.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { allFacesFactory } from './allFacesFactory';
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
import { FaceLandmarks } from './faceLandmarkNet/FaceLandmarks';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { NetInput } from './NetInput';
import { TNetInput } from './types';
import { FullFaceDescription } from './FullFaceDescription';
export const detectionNet = new FaceDetectionNet()
export const landmarkNet = new FaceLandmarkNet()
export const recognitionNet = new FaceRecognitionNet()
export function loadFaceDetectionModel(url: string) {
return detectionNet.load(url)
}
export function loadFaceLandmarkModel(url: string) {
return landmarkNet.load(url)
}
export function loadFaceRecognitionModel(url: string) {
return recognitionNet.load(url)
}
export function loadModels(url: string) {
return Promise.all([
loadFaceDetectionModel(url),
loadFaceLandmarkModel(url),
loadFaceRecognitionModel(url)
])
}
export function locateFaces(
input: tf.Tensor | NetInput | TNetInput,
minConfidence?: number,
maxResults?: number
): Promise<FaceDetection[]> {
return detectionNet.locateFaces(input, minConfidence, maxResults)
}
export function detectLandmarks(
input: tf.Tensor | NetInput | TNetInput
): Promise<FaceLandmarks> {
return landmarkNet.detectLandmarks(input)
}
export function computeFaceDescriptor(
input: tf.Tensor | NetInput | TNetInput
): Promise<Float32Array> {
return recognitionNet.computeFaceDescriptor(input)
}
export const allFaces: (
input: tf.Tensor | NetInput | TNetInput,
minConfidence: number
) => Promise<FullFaceDescription[]> = allFacesFactory(
detectionNet,
landmarkNet,
recognitionNet
)
\ No newline at end of file
src/index.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { euclideanDistance } from './euclideanDistance';
import { NetInput } from './NetInput';
import { padToSquare } from './padToSquare';
export { export {
euclideanDistance, tf
NetInput,
tf,
padToSquare
} }
export * from './FullFaceDescription';
export * from './NetInput';
export * from './Point';
export * from './Rect';
export * from './euclideanDistance';
export * from './extractFaces' export * from './extractFaces'
export * from './extractFaceTensors' export * from './extractFaceTensors'
export * from './faceDetectionNet'; export * from './faceDetectionNet';
export * from './faceLandmarkNet'; export * from './faceLandmarkNet';
export * from './faceRecognitionNet'; export * from './faceRecognitionNet';
export * from './globalApi';
export * from './padToSquare';
export * from './utils' export * from './utils'
\ No newline at end of file
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