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Commit f05a5f23 by vincent
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use async toNetInput instead creating NetInput directly, which awaits all media…

use async toNetInput instead creating NetInput directly, which awaits all media elements of the input to be finished loading
parent b9ca0b9b
Showing with 411 additions and 144 deletions
build/NetInput.d.ts
import { Dimensions, TNetInput } from './types';
import { Dimensions, TMediaElement } from './types';
export declare class NetInput {
private _canvases;
constructor(mediaArg: TNetInput, dims?: Dimensions);
constructor(medias: Array<TMediaElement>, dims?: Dimensions);
private initCanvas(media, dims?);
readonly canvases: HTMLCanvasElement[];
readonly width: number;
......
build/NetInput.js
......@@ -2,24 +2,8 @@
Object.defineProperty(exports, "__esModule", { value: true });
var utils_1 = require("./utils");
var NetInput = /** @class */ (function () {
function NetInput(mediaArg, dims) {
function NetInput(medias, dims) {
var _this = this;
var mediaArgArray = Array.isArray(mediaArg)
? mediaArg
: [mediaArg];
if (!mediaArgArray.length) {
throw new Error('NetInput - empty array passed as input');
}
var medias = mediaArgArray.map(utils_1.getElement);
medias.forEach(function (media, i) {
if (!(media instanceof HTMLImageElement || media instanceof HTMLVideoElement || media instanceof HTMLCanvasElement)) {
var idxHint = Array.isArray(mediaArg) ? " at input index " + i + ":" : '';
if (typeof mediaArgArray[i] === 'string') {
throw new Error("NetInput -" + idxHint + " string passed, but could not resolve HTMLElement for element id");
}
throw new Error("NetInput -" + idxHint + " expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement, or to be an element id");
}
});
this._canvases = [];
medias.forEach(function (m) { return _this.initCanvas(m, dims); });
}
......
build/NetInput.js.map
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\ No newline at end of file
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\ No newline at end of file
build/allFacesFactory.js
......@@ -24,7 +24,7 @@ function allFacesFactory(detectionNet, landmarkNet, recognitionNet) {
alignedFaceBoxes = _a.sent();
return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, alignedFaceBoxes)];
case 5:
alignedFaceTensors = (_a.sent());
alignedFaceTensors = _a.sent();
return [4 /*yield*/, Promise.all(alignedFaceTensors.map(function (faceTensor) { return recognitionNet.computeFaceDescriptor(faceTensor); }))];
case 6:
descriptors = _a.sent();
......
build/allFacesFactory.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commons/getImageTensor.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from '../NetInput';
export declare function getImageTensor(input: tf.Tensor | NetInput): tf.Tensor4D;
build/commons/getImageTensor.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var NetInput_1 = require("../NetInput");
function getImageTensor(input) {
return tf.tidy(function () {
if (input instanceof tf.Tensor) {
var rank = input.shape.length;
if (rank !== 3 && rank !== 4) {
throw new Error('input tensor must be of rank 3 or 4');
}
return (rank === 3 ? input.expandDims(0) : input).toFloat();
}
if (!(input instanceof NetInput_1.NetInput)) {
throw new Error('getImageTensor - expected input to be a tensor or an instance of NetInput');
}
return tf.concat(input.canvases.map(function (canvas) {
return tf.fromPixels(canvas).expandDims(0).toFloat();
}));
});
}
exports.getImageTensor = getImageTensor;
//# sourceMappingURL=getImageTensor.js.map
\ No newline at end of file
build/commons/getImageTensor.js.map 0 → 100644
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\ No newline at end of file
build/extractFaceTensors.d.ts
......@@ -13,4 +13,4 @@ import { TNetInput } from './types';
* @param detections The face detection results or face bounding boxes for that image.
* @returns Tensors of the corresponding image region for each detected face.
*/
export declare function extractFaceTensors(image: tf.Tensor | NetInput | TNetInput, detections: Array<FaceDetection | Rect>): tf.Tensor4D[];
export declare function extractFaceTensors(input: tf.Tensor | NetInput | TNetInput, detections: Array<FaceDetection | Rect>): Promise<tf.Tensor4D[]>;
build/extractFaceTensors.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var getImageTensor_1 = require("./commons/getImageTensor");
var FaceDetection_1 = require("./faceDetectionNet/FaceDetection");
var getImageTensor_1 = require("./getImageTensor");
var toNetInput_1 = require("./toNetInput");
/**
* Extracts the tensors of the image regions containing the detected faces.
* Useful if you want to compute the face descriptors for the face images.
......@@ -13,8 +15,22 @@ var getImageTensor_1 = require("./getImageTensor");
* @param detections The face detection results or face bounding boxes for that image.
* @returns Tensors of the corresponding image region for each detected face.
*/
function extractFaceTensors(image, detections) {
return tf.tidy(function () {
function extractFaceTensors(input, detections) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var image, _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
if (!(input instanceof tf.Tensor)) return [3 /*break*/, 1];
_a = input;
return [3 /*break*/, 3];
case 1: return [4 /*yield*/, toNetInput_1.toNetInput(input)];
case 2:
_a = _b.sent();
_b.label = 3;
case 3:
image = _a;
return [2 /*return*/, tf.tidy(function () {
var imgTensor = getImageTensor_1.getImageTensor(image);
// TODO handle batches
var _a = imgTensor.shape, batchSize = _a[0], imgHeight = _a[1], imgWidth = _a[2], numChannels = _a[3];
......@@ -26,6 +42,9 @@ function extractFaceTensors(image, detections) {
return tf.slice(imgTensor, [0, y, x, 0], [1, height, width, numChannels]);
});
return faceTensors;
})];
}
});
});
}
exports.extractFaceTensors = extractFaceTensors;
......
build/extractFaceTensors.js.map
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\ No newline at end of file
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\ No newline at end of file
build/faceDetectionNet/FaceDetectionNet.d.ts
......@@ -7,9 +7,9 @@ export declare class FaceDetectionNet {
load(weightsOrUrl?: Float32Array | string): Promise<void>;
extractWeights(weights: Float32Array): void;
private forwardTensor(imgTensor);
forward(input: tf.Tensor | NetInput | TNetInput): {
forward(input: tf.Tensor | NetInput | TNetInput): Promise<{
boxes: tf.Tensor<tf.Rank.R2>[];
scores: tf.Tensor<tf.Rank.R1>[];
};
}>;
locateFaces(input: tf.Tensor | NetInput | TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
}
build/faceDetectionNet/FaceDetectionNet.js
......@@ -2,9 +2,10 @@
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var getImageTensor_1 = require("../getImageTensor");
var getImageTensor_1 = require("../commons/getImageTensor");
var padToSquare_1 = require("../padToSquare");
var Rect_1 = require("../Rect");
var toNetInput_1 = require("../toNetInput");
var extractParams_1 = require("./extractParams");
var FaceDetection_1 = require("./FaceDetection");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
......@@ -54,38 +55,66 @@ var FaceDetectionNet = /** @class */ (function () {
});
};
FaceDetectionNet.prototype.forward = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _this = this;
return tf.tidy(function () { return _this.forwardTensor(padToSquare_1.padToSquare(getImageTensor_1.getImageTensor(input))); });
var netInput, _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
if (!(input instanceof tf.Tensor)) return [3 /*break*/, 1];
_a = input;
return [3 /*break*/, 3];
case 1: return [4 /*yield*/, toNetInput_1.toNetInput(input)];
case 2:
_a = _b.sent();
_b.label = 3;
case 3:
netInput = _a;
return [2 /*return*/, tf.tidy(function () {
return _this.forwardTensor(padToSquare_1.padToSquare(getImageTensor_1.getImageTensor(netInput)));
})];
}
});
});
};
FaceDetectionNet.prototype.locateFaces = function (input, minConfidence, maxResults) {
if (minConfidence === void 0) { minConfidence = 0.8; }
if (maxResults === void 0) { maxResults = 100; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _this = this;
var paddedHeightRelative, paddedWidthRelative, imageDimensions, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, results;
return tslib_1.__generator(this, function (_d) {
switch (_d.label) {
var netInput, _a, paddedHeightRelative, paddedWidthRelative, imageDimensions, _b, _boxes, _scores, boxes, scores, i, scoresData, _c, _d, iouThreshold, indices, results;
return tslib_1.__generator(this, function (_e) {
switch (_e.label) {
case 0:
if (!(input instanceof tf.Tensor)) return [3 /*break*/, 1];
_a = input;
return [3 /*break*/, 3];
case 1: return [4 /*yield*/, toNetInput_1.toNetInput(input)];
case 2:
_a = _e.sent();
_e.label = 3;
case 3:
netInput = _a;
paddedHeightRelative = 1, paddedWidthRelative = 1;
_a = tf.tidy(function () {
var imgTensor = getImageTensor_1.getImageTensor(input);
_b = tf.tidy(function () {
var imgTensor = getImageTensor_1.getImageTensor(netInput);
var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
imageDimensions = { width: width, height: height };
imgTensor = padToSquare_1.padToSquare(imgTensor);
paddedHeightRelative = imgTensor.shape[1] / height;
paddedWidthRelative = imgTensor.shape[2] / width;
return _this.forwardTensor(imgTensor);
}), _boxes = _a.boxes, _scores = _a.scores;
}), _boxes = _b.boxes, _scores = _b.scores;
boxes = _boxes[0];
scores = _scores[0];
for (i = 1; i < _boxes.length; i++) {
_boxes[i].dispose();
_scores[i].dispose();
}
_c = (_b = Array).from;
_d = (_c = Array).from;
return [4 /*yield*/, scores.data()];
case 1:
scoresData = _c.apply(_b, [_d.sent()]);
case 4:
scoresData = _d.apply(_c, [_e.sent()]);
iouThreshold = 0.5;
indices = nonMaxSuppression_1.nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
results = indices
......
build/faceDetectionNet/FaceDetectionNet.js.map
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\ No newline at end of file
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\ No newline at end of file
build/faceLandmarkNet/FaceLandmarkNet.js
......@@ -3,8 +3,9 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var convLayer_1 = require("../commons/convLayer");
var getImageTensor_1 = require("../getImageTensor");
var getImageTensor_1 = require("../commons/getImageTensor");
var Point_1 = require("../Point");
var toNetInput_1 = require("../toNetInput");
var extractParams_1 = require("./extractParams");
var FaceLandmarks_1 = require("./FaceLandmarks");
var fullyConnectedLayer_1 = require("./fullyConnectedLayer");
......@@ -47,16 +48,25 @@ var FaceLandmarkNet = /** @class */ (function () {
FaceLandmarkNet.prototype.detectLandmarks = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _this = this;
var imageDimensions, outTensor, faceLandmarksArray, _a, _b, xCoords, yCoords;
return tslib_1.__generator(this, function (_c) {
switch (_c.label) {
var netInput, _a, imageDimensions, outTensor, faceLandmarksArray, _b, _c, xCoords, yCoords;
return tslib_1.__generator(this, function (_d) {
switch (_d.label) {
case 0:
if (!this._params) {
throw new Error('FaceLandmarkNet - load model before inference');
}
if (!(input instanceof tf.Tensor)) return [3 /*break*/, 1];
_a = input;
return [3 /*break*/, 3];
case 1: return [4 /*yield*/, toNetInput_1.toNetInput(input)];
case 2:
_a = _d.sent();
_d.label = 3;
case 3:
netInput = _a;
outTensor = tf.tidy(function () {
var params = _this._params;
var imgTensor = getImageTensor_1.getImageTensor(input);
var imgTensor = getImageTensor_1.getImageTensor(netInput);
var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
imageDimensions = { width: width, height: height };
// work with 128 x 128 sized face images
......@@ -79,10 +89,10 @@ var FaceLandmarkNet = /** @class */ (function () {
var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(fc0, params.fc1_params);
return fc1;
});
_b = (_a = Array).from;
_c = (_b = Array).from;
return [4 /*yield*/, outTensor.data()];
case 1:
faceLandmarksArray = _b.apply(_a, [_c.sent()]);
case 4:
faceLandmarksArray = _c.apply(_b, [_d.sent()]);
outTensor.dispose();
xCoords = faceLandmarksArray.filter(function (c, i) { return (i - 1) % 2; });
yCoords = faceLandmarksArray.filter(function (c, i) { return i % 2; });
......
build/faceLandmarkNet/FaceLandmarkNet.js.map
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\ No newline at end of file
build/faceRecognitionNet/FaceRecognitionNet.d.ts
......@@ -5,7 +5,6 @@ export declare class FaceRecognitionNet {
private _params;
load(weightsOrUrl: Float32Array | string | undefined): Promise<void>;
extractWeights(weights: Float32Array): void;
forward(input: tf.Tensor | NetInput | TNetInput): tf.Tensor<tf.Rank.R2>;
forward(input: tf.Tensor | NetInput | TNetInput): Promise<tf.Tensor2D>;
computeFaceDescriptor(input: tf.Tensor | NetInput | TNetInput): Promise<Float32Array>;
computeFaceDescriptorSync(input: tf.Tensor | NetInput | TNetInput): Promise<Float32Array>;
}
build/faceRecognitionNet/FaceRecognitionNet.js
......@@ -2,8 +2,9 @@
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var getImageTensor_1 = require("../getImageTensor");
var getImageTensor_1 = require("../commons/getImageTensor");
var padToSquare_1 = require("../padToSquare");
var toNetInput_1 = require("../toNetInput");
var convLayer_1 = require("./convLayer");
var extractParams_1 = require("./extractParams");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
......@@ -38,12 +39,26 @@ var FaceRecognitionNet = /** @class */ (function () {
this._params = extractParams_1.extractParams(weights);
};
FaceRecognitionNet.prototype.forward = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _this = this;
var netInput, _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
if (!this._params) {
throw new Error('FaceRecognitionNet - load model before inference');
}
return tf.tidy(function () {
var x = padToSquare_1.padToSquare(getImageTensor_1.getImageTensor(input), true);
if (!(input instanceof tf.Tensor)) return [3 /*break*/, 1];
_a = input;
return [3 /*break*/, 3];
case 1: return [4 /*yield*/, toNetInput_1.toNetInput(input)];
case 2:
_a = _b.sent();
_b.label = 3;
case 3:
netInput = _a;
return [2 /*return*/, tf.tidy(function () {
var x = padToSquare_1.padToSquare(getImageTensor_1.getImageTensor(netInput), true);
// work with 150 x 150 sized face images
if (x.shape[1] !== 150 || x.shape[2] !== 150) {
x = tf.image.resizeBilinear(x, [150, 150]);
......@@ -68,35 +83,38 @@ var FaceRecognitionNet = /** @class */ (function () {
var globalAvg = out.mean([1, 2]);
var fullyConnected = tf.matMul(globalAvg, _this._params.fc);
return fullyConnected;
})];
}
});
});
};
FaceRecognitionNet.prototype.computeFaceDescriptor = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var result, data;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
var netInput, _a, result, data;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
result = this.forward(input);
if (!(input instanceof tf.Tensor)) return [3 /*break*/, 1];
_a = input;
return [3 /*break*/, 3];
case 1: return [4 /*yield*/, toNetInput_1.toNetInput(input)];
case 2:
_a = _b.sent();
_b.label = 3;
case 3:
netInput = _a;
return [4 /*yield*/, this.forward(netInput)];
case 4:
result = _b.sent();
return [4 /*yield*/, result.data()];
case 1:
data = _a.sent();
case 5:
data = _b.sent();
result.dispose();
return [2 /*return*/, data];
}
});
});
};
FaceRecognitionNet.prototype.computeFaceDescriptorSync = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var result, data;
return tslib_1.__generator(this, function (_a) {
result = this.forward(input);
data = result.dataSync();
result.dispose();
return [2 /*return*/, data];
});
});
};
return FaceRecognitionNet;
}());
exports.FaceRecognitionNet = FaceRecognitionNet;
......
build/faceRecognitionNet/FaceRecognitionNet.js.map
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\ No newline at end of file
build/index.d.ts
import * as tf from '@tensorflow/tfjs-core';
export { tf };
export * from './FullFaceDescription';
export * from './NetInput';
export * from './Point';
export * from './Rect';
export * from './euclideanDistance';
......@@ -12,4 +11,5 @@ export * from './faceLandmarkNet';
export * from './faceRecognitionNet';
export * from './globalApi';
export * from './padToSquare';
export * from './toNetInput';
export * from './utils';
build/index.js
......@@ -4,7 +4,6 @@ var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
exports.tf = tf;
tslib_1.__exportStar(require("./FullFaceDescription"), exports);
tslib_1.__exportStar(require("./NetInput"), exports);
tslib_1.__exportStar(require("./Point"), exports);
tslib_1.__exportStar(require("./Rect"), exports);
tslib_1.__exportStar(require("./euclideanDistance"), exports);
......@@ -15,5 +14,6 @@ tslib_1.__exportStar(require("./faceLandmarkNet"), exports);
tslib_1.__exportStar(require("./faceRecognitionNet"), exports);
tslib_1.__exportStar(require("./globalApi"), exports);
tslib_1.__exportStar(require("./padToSquare"), exports);
tslib_1.__exportStar(require("./toNetInput"), exports);
tslib_1.__exportStar(require("./utils"), exports);
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/index.js.map
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\ No newline at end of file
build/toNetInput.d.ts 0 → 100644
import { NetInput } from './NetInput';
import { TNetInput } from './types';
/**
* Validates the input to make sure, they are valid net inputs and awaits all media elements
* to be finished loading.
*
* @param input The input, which can be a media element or an array of different media elements.
* @returns A NetInput instance, which can be passed into one of the neural networks.
*/
export declare function toNetInput(input: NetInput | TNetInput): Promise<NetInput>;
build/toNetInput.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var NetInput_1 = require("./NetInput");
var utils_1 = require("./utils");
/**
* Validates the input to make sure, they are valid net inputs and awaits all media elements
* to be finished loading.
*
* @param input The input, which can be a media element or an array of different media elements.
* @returns A NetInput instance, which can be passed into one of the neural networks.
*/
function toNetInput(input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var mediaArgArray, medias;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0:
if (input instanceof NetInput_1.NetInput) {
return [2 /*return*/, input];
}
mediaArgArray = Array.isArray(input)
? input
: [input];
if (!mediaArgArray.length) {
throw new Error('toNetInput - empty array passed as input');
}
medias = mediaArgArray.map(utils_1.getElement);
medias.forEach(function (media, i) {
if (!(media instanceof HTMLImageElement || media instanceof HTMLVideoElement || media instanceof HTMLCanvasElement)) {
var idxHint = Array.isArray(input) ? " at input index " + i + ":" : '';
if (typeof mediaArgArray[i] === 'string') {
throw new Error("toNetInput -" + idxHint + " string passed, but could not resolve HTMLElement for element id");
}
throw new Error("toNetInput -" + idxHint + " expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement, or to be an element id");
}
});
// wait for all media elements being loaded
return [4 /*yield*/, Promise.all(medias.map(function (media) { return utils_1.awaitMediaLoaded(media); }))];
case 1:
// wait for all media elements being loaded
_a.sent();
return [2 /*return*/, new NetInput_1.NetInput(medias)];
}
});
});
}
exports.toNetInput = toNetInput;
//# sourceMappingURL=toNetInput.js.map
\ No newline at end of file
build/toNetInput.js.map 0 → 100644
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\ No newline at end of file
build/utils.d.ts
......@@ -6,6 +6,8 @@ export declare function isFloat(num: number): boolean;
export declare function isEven(num: number): boolean;
export declare function round(num: number): number;
export declare function getElement(arg: string | any): any;
export declare function isLoaded(media: HTMLImageElement | HTMLVideoElement): boolean;
export declare function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement): Promise<{}>;
export declare function getContext2dOrThrow(canvas: HTMLCanvasElement): CanvasRenderingContext2D;
export declare function createCanvas({width, height}: Dimensions): HTMLCanvasElement;
export declare function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement, dims?: Dimensions): HTMLCanvasElement;
......
build/utils.js
......@@ -21,6 +21,35 @@ function getElement(arg) {
return arg;
}
exports.getElement = getElement;
function isLoaded(media) {
return media instanceof HTMLImageElement && media.complete
|| media instanceof HTMLVideoElement && media.readyState >= 3;
}
exports.isLoaded = isLoaded;
function awaitMediaLoaded(media) {
return new Promise(function (resolve, reject) {
if (media instanceof HTMLCanvasElement || isLoaded(media)) {
return resolve();
}
function onLoad(e) {
if (!e.currentTarget)
return;
e.currentTarget.removeEventListener('load', onLoad);
e.currentTarget.removeEventListener('error', onError);
resolve();
}
function onError(e) {
if (!e.currentTarget)
return;
e.currentTarget.removeEventListener('load', onLoad);
e.currentTarget.removeEventListener('error', onError);
reject();
}
media.addEventListener('load', onLoad);
media.addEventListener('error', onError);
});
}
exports.awaitMediaLoaded = awaitMediaLoaded;
function getContext2dOrThrow(canvas) {
var ctx = canvas.getContext('2d');
if (!ctx) {
......@@ -38,6 +67,9 @@ function createCanvas(_a) {
}
exports.createCanvas = createCanvas;
function createCanvasFromMedia(media, dims) {
if (!isLoaded(media)) {
throw new Error('createCanvasFromMedia - media has not finished loading yet');
}
var _a = dims || getMediaDimensions(media), width = _a.width, height = _a.height;
var canvas = createCanvas({ width: width, height: height });
getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
......
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
......@@ -80,7 +80,7 @@
canvas.width = width
canvas.height = height
const input = new faceapi.NetInput(inputImgEl)
const input = await faceapi.toNetInput(inputImgEl)
const detections = await faceapi.locateFaces(input, minConfidence)
faceapi.drawDetection('overlay', detections.map(det => det.forSize(width, height)))
......
examples/views/detectAndDrawLandmarks.html
......@@ -86,7 +86,7 @@
canvas.width = width
canvas.height = height
const input = new faceapi.NetInput(inputImgEl)
const input = await faceapi.toNetInput(inputImgEl)
const locations = await faceapi.locateFaces(input, minConfidence)
const faceTensors = (await faceapi.extractFaceTensors(input, locations))
......
examples/views/detectAndRecognizeFaces.html
......@@ -110,8 +110,7 @@
canvas.width = width
canvas.height = height
const input = new faceapi.NetInput(inputImgEl)
const fullFaceDescriptions = (await faceapi.allFaces(input, minConfidence))
const fullFaceDescriptions = (await faceapi.allFaces(inputImgEl, minConfidence))
.map(fd => fd.forSize(width, height))
fullFaceDescriptions.forEach(({ detection, descriptor }) => {
......
examples/views/faceAlignment.html
......@@ -81,7 +81,7 @@
canvas.width = width
canvas.height = height
const input = new faceapi.NetInput(inputImgEl)
const input = await faceapi.toNetInput(inputImgEl)
const locations = await faceapi.locateFaces(input, minConfidence)
const faceImages = await faceapi.extractFaces(input.canvases[0], locations)
......
examples/views/faceDetection.html
......@@ -80,8 +80,7 @@
canvas.width = width
canvas.height = height
const input = new faceapi.NetInput(inputImgEl)
result = await faceapi.locateFaces(input, minConfidence)
result = await faceapi.locateFaces(inputImgEl, minConfidence)
faceapi.drawDetection('overlay', result.map(det => det.forSize(width, height)))
}
......
examples/views/faceDetectionVideo.html
......@@ -71,7 +71,7 @@
if(videoEl.paused || videoEl.ended)
return false
const input = new faceapi.NetInput(videoEl)
const input = await faceapi.toNetInput(videoEl)
const { width, height } = input
const canvas = $('#overlay').get(0)
canvas.width = width
......
src/NetInput.ts
import { Dimensions, TMediaElement, TNetInput } from './types';
import { createCanvasFromMedia, getElement } from './utils';
import { Dimensions, TMediaElement } from './types';
import { createCanvasFromMedia } from './utils';
export class NetInput {
private _canvases: HTMLCanvasElement[]
constructor(
mediaArg: TNetInput,
medias: Array<TMediaElement>,
dims?: Dimensions
) {
const mediaArgArray = Array.isArray(mediaArg)
? mediaArg
: [mediaArg]
if (!mediaArgArray.length) {
throw new Error('NetInput - empty array passed as input')
}
const medias = mediaArgArray.map(getElement)
medias.forEach((media, i) => {
if (!(media instanceof HTMLImageElement || media instanceof HTMLVideoElement || media instanceof HTMLCanvasElement)) {
const idxHint = Array.isArray(mediaArg) ? ` at input index ${i}:` : ''
if (typeof mediaArgArray[i] === 'string') {
throw new Error(`NetInput -${idxHint} string passed, but could not resolve HTMLElement for element id`)
}
throw new Error(`NetInput -${idxHint} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement, or to be an element id`)
}
})
this._canvases = []
medias.forEach(m => this.initCanvas(m, dims))
}
......
src/allFacesFactory.ts
......@@ -29,7 +29,7 @@ export function allFacesFactory(
const alignedFaceBoxes = await Promise.all(faceLandmarksByFace.map(
(landmarks, i) => landmarks.align(detections[i].getBox())
))
const alignedFaceTensors = (await extractFaceTensors(input, alignedFaceBoxes))
const alignedFaceTensors = await extractFaceTensors(input, alignedFaceBoxes)
const descriptors = await Promise.all(alignedFaceTensors.map(
faceTensor => recognitionNet.computeFaceDescriptor(faceTensor)
......
src/getImageTensor.ts → src/commons/getImageTensor.ts
import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from './NetInput';
import { TNetInput } from './types';
import { NetInput } from '../NetInput';
export function getImageTensor(input: tf.Tensor | NetInput | TNetInput): tf.Tensor4D {
export function getImageTensor(input: tf.Tensor | NetInput): tf.Tensor4D {
return tf.tidy(() => {
if (input instanceof tf.Tensor) {
const rank = input.shape.length
......@@ -14,9 +13,12 @@ export function getImageTensor(input: tf.Tensor | NetInput | TNetInput): tf.Tens
return (rank === 3 ? input.expandDims(0) : input).toFloat() as tf.Tensor4D
}
const netInput = input instanceof NetInput ? input : new NetInput(input)
if (!(input instanceof NetInput)) {
throw new Error('getImageTensor - expected input to be a tensor or an instance of NetInput')
}
return tf.concat(
netInput.canvases.map(canvas =>
input.canvases.map(canvas =>
tf.fromPixels(canvas).expandDims(0).toFloat()
)
) as tf.Tensor4D
......
src/extractFaceTensors.ts
import * as tf from '@tensorflow/tfjs-core';
import { getImageTensor } from './commons/getImageTensor';
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { getImageTensor } from './getImageTensor';
import { NetInput } from './NetInput';
import { Rect } from './Rect';
import { TNetInput } from './types';
import { toNetInput } from './toNetInput';
/**
* Extracts the tensors of the image regions containing the detected faces.
......@@ -16,10 +17,15 @@ import { TNetInput } from './types';
* @param detections The face detection results or face bounding boxes for that image.
* @returns Tensors of the corresponding image region for each detected face.
*/
export function extractFaceTensors(
image: tf.Tensor | NetInput | TNetInput,
export async function extractFaceTensors(
input: tf.Tensor | NetInput | TNetInput,
detections: Array<FaceDetection|Rect>
): tf.Tensor4D[] {
): Promise<tf.Tensor4D[]> {
const image = input instanceof tf.Tensor
? input
: await toNetInput(input)
return tf.tidy(() => {
const imgTensor = getImageTensor(image)
......
src/faceDetectionNet/FaceDetectionNet.ts
import * as tf from '@tensorflow/tfjs-core';
import { getImageTensor } from '../getImageTensor';
import { getImageTensor } from '../commons/getImageTensor';
import { NetInput } from '../NetInput';
import { padToSquare } from '../padToSquare';
import { Rect } from '../Rect';
import { toNetInput } from '../toNetInput';
import { Dimensions, TNetInput } from '../types';
import { extractParams } from './extractParams';
import { FaceDetection } from './FaceDetection';
......@@ -54,9 +55,13 @@ export class FaceDetectionNet {
})
}
public forward(input: tf.Tensor | NetInput | TNetInput) {
return tf.tidy(
() => this.forwardTensor(padToSquare(getImageTensor(input)))
public async forward(input: tf.Tensor | NetInput | TNetInput) {
const netInput = input instanceof tf.Tensor
? input
: await toNetInput(input)
return tf.tidy(() =>
this.forwardTensor(padToSquare(getImageTensor(netInput)))
)
}
......@@ -66,6 +71,10 @@ export class FaceDetectionNet {
maxResults: number = 100,
): Promise<FaceDetection[]> {
const netInput = input instanceof tf.Tensor
? input
: await toNetInput(input)
let paddedHeightRelative = 1, paddedWidthRelative = 1
let imageDimensions: Dimensions | undefined
......@@ -74,7 +83,7 @@ export class FaceDetectionNet {
scores: _scores
} = tf.tidy(() => {
let imgTensor = getImageTensor(input)
let imgTensor = getImageTensor(netInput)
const [height, width] = imgTensor.shape.slice(1)
imageDimensions = { width, height }
......
src/faceLandmarkNet/FaceLandmarkNet.ts
import * as tf from '@tensorflow/tfjs-core';
import { convLayer } from '../commons/convLayer';
import { getImageTensor } from '../commons/getImageTensor';
import { ConvParams } from '../commons/types';
import { getImageTensor } from '../getImageTensor';
import { NetInput } from '../NetInput';
import { Point } from '../Point';
import { toNetInput } from '../toNetInput';
import { Dimensions, TNetInput } from '../types';
import { extractParams } from './extractParams';
import { FaceLandmarks } from './FaceLandmarks';
......@@ -45,12 +46,16 @@ export class FaceLandmarkNet {
throw new Error('FaceLandmarkNet - load model before inference')
}
const netInput = input instanceof tf.Tensor
? input
: await toNetInput(input)
let imageDimensions: Dimensions | undefined
const outTensor = tf.tidy(() => {
const params = this._params
let imgTensor = getImageTensor(input)
let imgTensor = getImageTensor(netInput)
const [height, width] = imgTensor.shape.slice(1)
imageDimensions = { width, height }
......
src/faceRecognitionNet/FaceRecognitionNet.ts
import * as tf from '@tensorflow/tfjs-core';
import { getImageTensor } from '../getImageTensor';
import { getImageTensor } from '../commons/getImageTensor';
import { NetInput } from '../NetInput';
import { padToSquare } from '../padToSquare';
import { toNetInput } from '../toNetInput';
import { TNetInput } from '../types';
import { convDown } from './convLayer';
import { extractParams } from './extractParams';
......@@ -31,14 +32,18 @@ export class FaceRecognitionNet {
this._params = extractParams(weights)
}
public forward(input: tf.Tensor | NetInput | TNetInput) {
public async forward(input: tf.Tensor | NetInput | TNetInput): Promise<tf.Tensor2D> {
if (!this._params) {
throw new Error('FaceRecognitionNet - load model before inference')
}
const netInput = input instanceof tf.Tensor
? input
: await toNetInput(input)
return tf.tidy(() => {
let x = padToSquare(getImageTensor(input), true)
let x = padToSquare(getImageTensor(netInput), true)
// work with 150 x 150 sized face images
if (x.shape[1] !== 150 || x.shape[2] !== 150) {
x = tf.image.resizeBilinear(x, [150, 150])
......@@ -74,15 +79,12 @@ export class FaceRecognitionNet {
}
public async computeFaceDescriptor(input: tf.Tensor | NetInput | TNetInput) {
const result = this.forward(input)
const data = await result.data()
result.dispose()
return data as Float32Array
}
const netInput = input instanceof tf.Tensor
? input
: await toNetInput(input)
public async computeFaceDescriptorSync(input: tf.Tensor | NetInput | TNetInput) {
const result = this.forward(input)
const data = result.dataSync()
const result = await this.forward(netInput)
const data = await result.data()
result.dispose()
return data as Float32Array
}
......
src/index.ts
......@@ -5,7 +5,6 @@ export {
}
export * from './FullFaceDescription';
export * from './NetInput';
export * from './Point';
export * from './Rect';
......@@ -17,4 +16,5 @@ export * from './faceLandmarkNet';
export * from './faceRecognitionNet';
export * from './globalApi';
export * from './padToSquare';
export * from './toNetInput';
export * from './utils'
\ No newline at end of file
src/toNetInput.ts 0 → 100644
import { NetInput } from './NetInput';
import { TNetInput } from './types';
import { awaitMediaLoaded, getElement } from './utils';
/**
* Validates the input to make sure, they are valid net inputs and awaits all media elements
* to be finished loading.
*
* @param input The input, which can be a media element or an array of different media elements.
* @returns A NetInput instance, which can be passed into one of the neural networks.
*/
export async function toNetInput(
input: NetInput | TNetInput
): Promise<NetInput> {
if (input instanceof NetInput) {
return input
}
const mediaArgArray = Array.isArray(input)
? input
: [input]
if (!mediaArgArray.length) {
throw new Error('toNetInput - empty array passed as input')
}
const medias = mediaArgArray.map(getElement)
medias.forEach((media, i) => {
if (!(media instanceof HTMLImageElement || media instanceof HTMLVideoElement || media instanceof HTMLCanvasElement)) {
const idxHint = Array.isArray(input) ? ` at input index ${i}:` : ''
if (typeof mediaArgArray[i] === 'string') {
throw new Error(`toNetInput -${idxHint} string passed, but could not resolve HTMLElement for element id`)
}
throw new Error(`toNetInput -${idxHint} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement, or to be an element id`)
}
})
// wait for all media elements being loaded
await Promise.all(
medias.map(media => awaitMediaLoaded(media))
)
return new NetInput(medias)
}
\ No newline at end of file
src/utils.ts
......@@ -24,6 +24,36 @@ export function getElement(arg: string | any) {
return arg
}
export function isLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {
return media instanceof HTMLImageElement && media.complete
|| media instanceof HTMLVideoElement && media.readyState >= 3
}
export function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {
return new Promise((resolve, reject) => {
if (media instanceof HTMLCanvasElement || isLoaded(media)) {
return resolve()
}
function onLoad(e: Event) {
if (!e.currentTarget) return
e.currentTarget.removeEventListener('load', onLoad)
e.currentTarget.removeEventListener('error', onError)
resolve()
}
function onError(e: Event) {
if (!e.currentTarget) return
e.currentTarget.removeEventListener('load', onLoad)
e.currentTarget.removeEventListener('error', onError)
reject()
}
media.addEventListener('load', onLoad)
media.addEventListener('error', onError)
})
}
export function getContext2dOrThrow(canvas: HTMLCanvasElement): CanvasRenderingContext2D {
const ctx = canvas.getContext('2d')
if (!ctx) {
......@@ -40,6 +70,10 @@ export function createCanvas({ width, height }: Dimensions): HTMLCanvasElement {
}
export function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement, dims?: Dimensions): HTMLCanvasElement {
if (!isLoaded(media)) {
throw new Error('createCanvasFromMedia - media has not finished loading yet')
}
const { width, height } = dims || getMediaDimensions(media)
const canvas = createCanvas({ width, height })
getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)
......
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