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Unverified Commit c9a1e5f7 by justadudewhohacks Committed by GitHub
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Merge pull request #23 from justadudewhohacks/await-media-loaded

Await media loaded
parents b9ca0b9b 26c7fb41
Showing with 877 additions and 480 deletions
README.md
......@@ -171,7 +171,7 @@ faceapi.drawDetection(canvas, detectionsForSize, { withScore: false })
You can also obtain the tensors of the unfiltered bounding boxes and scores for each image in the batch (tensors have to be disposed manually):
``` javascript
const { boxes, scores } = net.forward('myImg')
const { boxes, scores } = await net.forward('myImg')
```
<a name="usage-face-recognition"></a>
......@@ -195,7 +195,7 @@ else
Or simply obtain the tensor (tensor has to be disposed manually):
``` javascript
const t = net.forward('myImg')
const t = await net.forward('myImg')
```
<a name="usage-face-landmark-detection"></a>
......
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/drawing/index.d.ts 0 → 100644
import { FaceDetection } from '../faceDetectionNet/FaceDetection';
import { FaceLandmarks } from '../faceLandmarkNet/FaceLandmarks';
import { DrawBoxOptions, DrawLandmarksOptions, DrawOptions, DrawTextOptions } from './types';
export declare function getDefaultDrawOptions(): DrawOptions;
export declare function drawBox(ctx: CanvasRenderingContext2D, x: number, y: number, w: number, h: number, options: DrawBoxOptions): void;
export declare function drawText(ctx: CanvasRenderingContext2D, x: number, y: number, text: string, options: DrawTextOptions): void;
export declare function drawDetection(canvasArg: string | HTMLCanvasElement, detection: FaceDetection | FaceDetection[], options?: DrawBoxOptions & DrawTextOptions & {
withScore: boolean;
}): void;
export declare function drawLandmarks(canvasArg: string | HTMLCanvasElement, faceLandmarks: FaceLandmarks | FaceLandmarks[], options?: DrawLandmarksOptions & {
drawLines: boolean;
}): void;
build/drawing/index.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var utils_1 = require("../utils");
function getDefaultDrawOptions() {
return {
color: 'blue',
lineWidth: 2,
fontSize: 20,
fontStyle: 'Georgia'
};
}
exports.getDefaultDrawOptions = getDefaultDrawOptions;
function drawBox(ctx, x, y, w, h, options) {
var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
ctx.strokeStyle = drawOptions.color;
ctx.lineWidth = drawOptions.lineWidth;
ctx.strokeRect(x, y, w, h);
}
exports.drawBox = drawBox;
function drawText(ctx, x, y, text, options) {
var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
var padText = 2 + drawOptions.lineWidth;
ctx.fillStyle = drawOptions.color;
ctx.font = drawOptions.fontSize + "px " + drawOptions.fontStyle;
ctx.fillText(text, x + padText, y + padText + (drawOptions.fontSize * 0.6));
}
exports.drawText = drawText;
function drawDetection(canvasArg, detection, options) {
var canvas = utils_1.getElement(canvasArg);
if (!(canvas instanceof HTMLCanvasElement)) {
throw new Error('drawBox - expected canvas to be of type: HTMLCanvasElement');
}
var detectionArray = Array.isArray(detection)
? detection
: [detection];
detectionArray.forEach(function (det) {
var _a = det.getBox(), x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
var withScore = Object.assign({ withScore: true }, (options || {})).withScore;
var ctx = utils_1.getContext2dOrThrow(canvas);
drawBox(ctx, x, y, width, height, drawOptions);
if (withScore) {
drawText(ctx, x, y, "" + utils_1.round(det.getScore()), drawOptions);
}
});
}
exports.drawDetection = drawDetection;
function drawContour(ctx, points, isClosed) {
if (isClosed === void 0) { isClosed = false; }
ctx.beginPath();
points.slice(1).forEach(function (_a, prevIdx) {
var x = _a.x, y = _a.y;
var from = points[prevIdx];
ctx.moveTo(from.x, from.y);
ctx.lineTo(x, y);
});
if (isClosed) {
var from = points[points.length - 1];
var to = points[0];
if (!from || !to) {
return;
}
ctx.moveTo(from.x, from.y);
ctx.lineTo(to.x, to.y);
}
ctx.stroke();
}
function drawLandmarks(canvasArg, faceLandmarks, options) {
var canvas = utils_1.getElement(canvasArg);
if (!(canvas instanceof HTMLCanvasElement)) {
throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement');
}
var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
var drawLines = Object.assign({ drawLines: false }, (options || {})).drawLines;
var ctx = utils_1.getContext2dOrThrow(canvas);
var lineWidth = drawOptions.lineWidth, color = drawOptions.color;
var faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];
faceLandmarksArray.forEach(function (landmarks) {
if (drawLines) {
ctx.strokeStyle = color;
ctx.lineWidth = lineWidth;
drawContour(ctx, landmarks.getJawOutline());
drawContour(ctx, landmarks.getLeftEyeBrow());
drawContour(ctx, landmarks.getRightEyeBrow());
drawContour(ctx, landmarks.getNose());
drawContour(ctx, landmarks.getLeftEye(), true);
drawContour(ctx, landmarks.getRightEye(), true);
drawContour(ctx, landmarks.getMouth(), true);
return;
}
// else draw points
var ptOffset = lineWidth / 2;
ctx.fillStyle = color;
landmarks.getPositions().forEach(function (pt) { return ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth); });
});
}
exports.drawLandmarks = drawLandmarks;
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/drawing/index.js.map 0 → 100644
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\ No newline at end of file
build/drawing/types.d.ts 0 → 100644
export declare type DrawBoxOptions = {
lineWidth?: number;
color?: string;
};
export declare type DrawTextOptions = {
lineWidth?: number;
fontSize?: number;
fontStyle?: string;
color?: string;
};
export declare type DrawLandmarksOptions = {
lineWidth?: number;
color?: string;
};
export declare type DrawOptions = {
lineWidth: number;
fontSize: number;
fontStyle: string;
color: string;
};
build/drawing/types.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
//# sourceMappingURL=types.js.map
\ No newline at end of file
build/drawing/types.js.map 0 → 100644
{"version":3,"file":"types.js","sourceRoot":"","sources":["../../src/drawing/types.ts"],"names":[],"mappings":""}
\ 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
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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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
......@@ -4,6 +4,7 @@ export * from './FullFaceDescription';
export * from './NetInput';
export * from './Point';
export * from './Rect';
export * from './drawing';
export * from './euclideanDistance';
export * from './extractFaces';
export * from './extractFaceTensors';
......@@ -12,4 +13,5 @@ export * from './faceLandmarkNet';
export * from './faceRecognitionNet';
export * from './globalApi';
export * from './padToSquare';
export * from './toNetInput';
export * from './utils';
build/index.js
......@@ -7,6 +7,7 @@ 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("./drawing"), exports);
tslib_1.__exportStar(require("./euclideanDistance"), exports);
tslib_1.__exportStar(require("./extractFaces"), exports);
tslib_1.__exportStar(require("./extractFaceTensors"), exports);
......@@ -15,5 +16,6 @@ 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/types.d.ts
......@@ -5,23 +5,3 @@ export declare type Dimensions = {
width: number;
height: number;
};
export declare type DrawBoxOptions = {
lineWidth?: number;
color?: string;
};
export declare type DrawTextOptions = {
lineWidth?: number;
fontSize?: number;
fontStyle?: string;
color?: string;
};
export declare type DrawLandmarksOptions = {
lineWidth?: number;
color?: string;
};
export declare type DrawOptions = {
lineWidth: number;
fontSize: number;
fontStyle: string;
color: string;
};
build/utils.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { FaceLandmarks } from './faceLandmarkNet/FaceLandmarks';
import { Dimensions, DrawBoxOptions, DrawLandmarksOptions, DrawOptions, DrawTextOptions } from './types';
import { Dimensions } from './types';
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;
......@@ -15,12 +15,3 @@ export declare function getMediaDimensions(media: HTMLImageElement | HTMLVideoEl
};
export declare function bufferToImage(buf: Blob): Promise<HTMLImageElement>;
export declare function imageTensorToCanvas(imgTensor: tf.Tensor4D, canvas?: HTMLCanvasElement): Promise<HTMLCanvasElement>;
export declare function getDefaultDrawOptions(): DrawOptions;
export declare function drawBox(ctx: CanvasRenderingContext2D, x: number, y: number, w: number, h: number, options: DrawBoxOptions): void;
export declare function drawText(ctx: CanvasRenderingContext2D, x: number, y: number, text: string, options: DrawTextOptions): void;
export declare function drawDetection(canvasArg: string | HTMLCanvasElement, detection: FaceDetection | FaceDetection[], options?: DrawBoxOptions & DrawTextOptions & {
withScore: boolean;
}): void;
export declare function drawLandmarks(canvasArg: string | HTMLCanvasElement, faceLandmarks: FaceLandmarks | FaceLandmarks[], options?: DrawLandmarksOptions & {
drawLines: boolean;
}): void;
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);
......@@ -88,98 +120,4 @@ function imageTensorToCanvas(imgTensor, canvas) {
});
}
exports.imageTensorToCanvas = imageTensorToCanvas;
function getDefaultDrawOptions() {
return {
color: 'blue',
lineWidth: 2,
fontSize: 20,
fontStyle: 'Georgia'
};
}
exports.getDefaultDrawOptions = getDefaultDrawOptions;
function drawBox(ctx, x, y, w, h, options) {
var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
ctx.strokeStyle = drawOptions.color;
ctx.lineWidth = drawOptions.lineWidth;
ctx.strokeRect(x, y, w, h);
}
exports.drawBox = drawBox;
function drawText(ctx, x, y, text, options) {
var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
var padText = 2 + drawOptions.lineWidth;
ctx.fillStyle = drawOptions.color;
ctx.font = drawOptions.fontSize + "px " + drawOptions.fontStyle;
ctx.fillText(text, x + padText, y + padText + (drawOptions.fontSize * 0.6));
}
exports.drawText = drawText;
function drawDetection(canvasArg, detection, options) {
var canvas = getElement(canvasArg);
if (!(canvas instanceof HTMLCanvasElement)) {
throw new Error('drawBox - expected canvas to be of type: HTMLCanvasElement');
}
var detectionArray = Array.isArray(detection)
? detection
: [detection];
detectionArray.forEach(function (det) {
var _a = det.getBox(), x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
var withScore = Object.assign({ withScore: true }, (options || {})).withScore;
var ctx = getContext2dOrThrow(canvas);
drawBox(ctx, x, y, width, height, drawOptions);
if (withScore) {
drawText(ctx, x, y, "" + round(det.getScore()), drawOptions);
}
});
}
exports.drawDetection = drawDetection;
function drawContour(ctx, points, isClosed) {
if (isClosed === void 0) { isClosed = false; }
ctx.beginPath();
points.slice(1).forEach(function (_a, prevIdx) {
var x = _a.x, y = _a.y;
var from = points[prevIdx];
ctx.moveTo(from.x, from.y);
ctx.lineTo(x, y);
});
if (isClosed) {
var from = points[points.length - 1];
var to = points[0];
if (!from || !to) {
return;
}
ctx.moveTo(from.x, from.y);
ctx.lineTo(to.x, to.y);
}
ctx.stroke();
}
function drawLandmarks(canvasArg, faceLandmarks, options) {
var canvas = getElement(canvasArg);
if (!(canvas instanceof HTMLCanvasElement)) {
throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement');
}
var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
var drawLines = Object.assign({ drawLines: false }, (options || {})).drawLines;
var ctx = getContext2dOrThrow(canvas);
var lineWidth = drawOptions.lineWidth, color = drawOptions.color;
var faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];
faceLandmarksArray.forEach(function (landmarks) {
if (drawLines) {
ctx.strokeStyle = color;
ctx.lineWidth = lineWidth;
drawContour(ctx, landmarks.getJawOutline());
drawContour(ctx, landmarks.getLeftEyeBrow());
drawContour(ctx, landmarks.getRightEyeBrow());
drawContour(ctx, landmarks.getNose());
drawContour(ctx, landmarks.getLeftEye(), true);
drawContour(ctx, landmarks.getRightEye(), true);
drawContour(ctx, landmarks.getMouth(), true);
return;
}
// else draw points
var ptOffset = lineWidth / 2;
ctx.fillStyle = color;
landmarks.getPositions().forEach(function (pt) { return ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth); });
});
}
exports.drawLandmarks = drawLandmarks;
//# sourceMappingURL=utils.js.map
\ No newline at end of file
build/utils.js.map
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\ No newline at end of file
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/drawing/index.ts 0 → 100644
import { FaceDetection } from '../faceDetectionNet/FaceDetection';
import { FaceLandmarks } from '../faceLandmarkNet/FaceLandmarks';
import { Point } from '../Point';
import { getContext2dOrThrow, getElement, round } from '../utils';
import { DrawBoxOptions, DrawLandmarksOptions, DrawOptions, DrawTextOptions } from './types';
export function getDefaultDrawOptions(): DrawOptions {
return {
color: 'blue',
lineWidth: 2,
fontSize: 20,
fontStyle: 'Georgia'
}
}
export function drawBox(
ctx: CanvasRenderingContext2D,
x: number,
y: number,
w: number,
h: number,
options: DrawBoxOptions
) {
const drawOptions = Object.assign(
getDefaultDrawOptions(),
(options || {})
)
ctx.strokeStyle = drawOptions.color
ctx.lineWidth = drawOptions.lineWidth
ctx.strokeRect(x, y, w, h)
}
export function drawText(
ctx: CanvasRenderingContext2D,
x: number,
y: number,
text: string,
options: DrawTextOptions
) {
const drawOptions = Object.assign(
getDefaultDrawOptions(),
(options || {})
)
const padText = 2 + drawOptions.lineWidth
ctx.fillStyle = drawOptions.color
ctx.font = `${drawOptions.fontSize}px ${drawOptions.fontStyle}`
ctx.fillText(text, x + padText, y + padText + (drawOptions.fontSize * 0.6))
}
export function drawDetection(
canvasArg: string | HTMLCanvasElement,
detection: FaceDetection | FaceDetection[],
options?: DrawBoxOptions & DrawTextOptions & { withScore: boolean }
) {
const canvas = getElement(canvasArg)
if (!(canvas instanceof HTMLCanvasElement)) {
throw new Error('drawBox - expected canvas to be of type: HTMLCanvasElement')
}
const detectionArray = Array.isArray(detection)
? detection
: [detection]
detectionArray.forEach((det) => {
const {
x,
y,
width,
height
} = det.getBox()
const drawOptions = Object.assign(
getDefaultDrawOptions(),
(options || {})
)
const { withScore } = Object.assign({ withScore: true }, (options || {}))
const ctx = getContext2dOrThrow(canvas)
drawBox(
ctx,
x,
y,
width,
height,
drawOptions
)
if (withScore) {
drawText(
ctx,
x,
y,
`${round(det.getScore())}`,
drawOptions
)
}
})
}
function drawContour(
ctx: CanvasRenderingContext2D,
points: Point[],
isClosed: boolean = false
) {
ctx.beginPath()
points.slice(1).forEach(({ x, y }, prevIdx) => {
const from = points[prevIdx]
ctx.moveTo(from.x, from.y)
ctx.lineTo(x, y)
})
if (isClosed) {
const from = points[points.length - 1]
const to = points[0]
if (!from || !to) {
return
}
ctx.moveTo(from.x, from.y)
ctx.lineTo(to.x, to.y)
}
ctx.stroke()
}
export function drawLandmarks(
canvasArg: string | HTMLCanvasElement,
faceLandmarks: FaceLandmarks | FaceLandmarks[],
options?: DrawLandmarksOptions & { drawLines: boolean }
) {
const canvas = getElement(canvasArg)
if (!(canvas instanceof HTMLCanvasElement)) {
throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement')
}
const drawOptions = Object.assign(
getDefaultDrawOptions(),
(options || {})
)
const { drawLines } = Object.assign({ drawLines: false }, (options || {}))
const ctx = getContext2dOrThrow(canvas)
const { lineWidth, color } = drawOptions
const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]
faceLandmarksArray.forEach(landmarks => {
if (drawLines) {
ctx.strokeStyle = color
ctx.lineWidth = lineWidth
drawContour(ctx, landmarks.getJawOutline())
drawContour(ctx, landmarks.getLeftEyeBrow())
drawContour(ctx, landmarks.getRightEyeBrow())
drawContour(ctx, landmarks.getNose())
drawContour(ctx, landmarks.getLeftEye(), true)
drawContour(ctx, landmarks.getRightEye(), true)
drawContour(ctx, landmarks.getMouth(), true)
return
}
// else draw points
const ptOffset = lineWidth / 2
ctx.fillStyle = color
landmarks.getPositions().forEach(pt => ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth))
})
}
\ No newline at end of file
src/drawing/types.ts 0 → 100644
export type DrawBoxOptions = {
lineWidth?: number
color?: string
}
export type DrawTextOptions = {
lineWidth?: number
fontSize?: number
fontStyle?: string
color?: string
}
export type DrawLandmarksOptions = {
lineWidth?: number
color?: string
}
export type DrawOptions = {
lineWidth: number
fontSize: number
fontStyle: string
color: string
}
\ No newline at end of file
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
......@@ -9,6 +9,7 @@ export * from './NetInput';
export * from './Point';
export * from './Rect';
export * from './drawing';
export * from './euclideanDistance';
export * from './extractFaces'
export * from './extractFaceTensors'
......@@ -17,4 +18,5 @@ 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/types.ts
......@@ -8,27 +8,3 @@ export type Dimensions = {
width: number
height: number
}
\ No newline at end of file
export type DrawBoxOptions = {
lineWidth?: number
color?: string
}
export type DrawTextOptions = {
lineWidth?: number
fontSize?: number
fontStyle?: string
color?: string
}
export type DrawLandmarksOptions = {
lineWidth?: number
color?: string
}
export type DrawOptions = {
lineWidth: number
fontSize: number
fontStyle: string
color: string
}
\ No newline at end of file
src/utils.ts
import * as tf from '@tensorflow/tfjs-core';
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { FaceLandmarks } from './faceLandmarkNet/FaceLandmarks';
import { Point } from './Point';
import { Dimensions, DrawBoxOptions, DrawLandmarksOptions, DrawOptions, DrawTextOptions } from './types';
import { Dimensions } from './types';
export function isFloat(num: number) {
return num % 1 !== 0
......@@ -24,6 +21,36 @@ 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 +67,10 @@ 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)
......@@ -85,169 +116,3 @@ export async function imageTensorToCanvas(
return targetCanvas
}
\ No newline at end of file
export function getDefaultDrawOptions(): DrawOptions {
return {
color: 'blue',
lineWidth: 2,
fontSize: 20,
fontStyle: 'Georgia'
}
}
export function drawBox(
ctx: CanvasRenderingContext2D,
x: number,
y: number,
w: number,
h: number,
options: DrawBoxOptions
) {
const drawOptions = Object.assign(
getDefaultDrawOptions(),
(options || {})
)
ctx.strokeStyle = drawOptions.color
ctx.lineWidth = drawOptions.lineWidth
ctx.strokeRect(x, y, w, h)
}
export function drawText(
ctx: CanvasRenderingContext2D,
x: number,
y: number,
text: string,
options: DrawTextOptions
) {
const drawOptions = Object.assign(
getDefaultDrawOptions(),
(options || {})
)
const padText = 2 + drawOptions.lineWidth
ctx.fillStyle = drawOptions.color
ctx.font = `${drawOptions.fontSize}px ${drawOptions.fontStyle}`
ctx.fillText(text, x + padText, y + padText + (drawOptions.fontSize * 0.6))
}
export function drawDetection(
canvasArg: string | HTMLCanvasElement,
detection: FaceDetection | FaceDetection[],
options?: DrawBoxOptions & DrawTextOptions & { withScore: boolean }
) {
const canvas = getElement(canvasArg)
if (!(canvas instanceof HTMLCanvasElement)) {
throw new Error('drawBox - expected canvas to be of type: HTMLCanvasElement')
}
const detectionArray = Array.isArray(detection)
? detection
: [detection]
detectionArray.forEach((det) => {
const {
x,
y,
width,
height
} = det.getBox()
const drawOptions = Object.assign(
getDefaultDrawOptions(),
(options || {})
)
const { withScore } = Object.assign({ withScore: true }, (options || {}))
const ctx = getContext2dOrThrow(canvas)
drawBox(
ctx,
x,
y,
width,
height,
drawOptions
)
if (withScore) {
drawText(
ctx,
x,
y,
`${round(det.getScore())}`,
drawOptions
)
}
})
}
function drawContour(
ctx: CanvasRenderingContext2D,
points: Point[],
isClosed: boolean = false
) {
ctx.beginPath()
points.slice(1).forEach(({ x, y }, prevIdx) => {
const from = points[prevIdx]
ctx.moveTo(from.x, from.y)
ctx.lineTo(x, y)
})
if (isClosed) {
const from = points[points.length - 1]
const to = points[0]
if (!from || !to) {
return
}
ctx.moveTo(from.x, from.y)
ctx.lineTo(to.x, to.y)
}
ctx.stroke()
}
export function drawLandmarks(
canvasArg: string | HTMLCanvasElement,
faceLandmarks: FaceLandmarks | FaceLandmarks[],
options?: DrawLandmarksOptions & { drawLines: boolean }
) {
const canvas = getElement(canvasArg)
if (!(canvas instanceof HTMLCanvasElement)) {
throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement')
}
const drawOptions = Object.assign(
getDefaultDrawOptions(),
(options || {})
)
const { drawLines } = Object.assign({ drawLines: false }, (options || {}))
const ctx = getContext2dOrThrow(canvas)
const { lineWidth, color } = drawOptions
const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]
faceLandmarksArray.forEach(landmarks => {
if (drawLines) {
ctx.strokeStyle = color
ctx.lineWidth = lineWidth
drawContour(ctx, landmarks.getJawOutline())
drawContour(ctx, landmarks.getLeftEyeBrow())
drawContour(ctx, landmarks.getRightEyeBrow())
drawContour(ctx, landmarks.getNose())
drawContour(ctx, landmarks.getLeftEye(), true)
drawContour(ctx, landmarks.getRightEye(), true)
drawContour(ctx, landmarks.getMouth(), true)
return
}
// else draw points
const ptOffset = lineWidth / 2
ctx.fillStyle = color
landmarks.getPositions().forEach(pt => ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth))
})
}
\ No newline at end of file
test/loadWeightMap.test.ts → test/tests/commons/loadWeightMap.test.ts
import { getModelUris } from '../src/commons/loadWeightMap';
import { getModelUris } from '../../../src/commons/loadWeightMap';
const FAKE_DEFAULT_MODEL_NAME = 'default_model_name'
......
test/e2e/faceDetectionNet.test.ts → test/tests/e2e/faceDetectionNet.test.ts
import * as faceapi from '../../src';
import { FaceDetection } from '../../src/faceDetectionNet/FaceDetection';
import { IRect } from '../../src/Rect';
import { expectMaxDelta } from '../utils';
import * as faceapi from '../../../src';
import { FaceDetection } from '../../../src/faceDetectionNet/FaceDetection';
import { IRect } from '../../../src/Rect';
import { expectMaxDelta } from '../../utils';
function expectRectClose(
result: IRect,
......
test/e2e/faceLandmarkNet.test.ts → test/tests/e2e/faceLandmarkNet.test.ts
import * as faceapi from '../../src';
import { FaceLandmarks } from '../../src/faceLandmarkNet/FaceLandmarks';
import { Point } from '../../src/Point';
import { expectMaxDelta } from '../utils';
import * as faceapi from '../../../src';
import { FaceLandmarks } from '../../../src/faceLandmarkNet/FaceLandmarks';
import { Point } from '../../../src/Point';
import { expectMaxDelta } from '../../utils';
describe('faceLandmarkNet', () => {
......
test/e2e/faceRecognitionNet.test.ts → test/tests/e2e/faceRecognitionNet.test.ts
import * as faceapi from '../../src';
import * as faceapi from '../../../src';
describe('faceRecognitionNet', () => {
......
test/padToSquare.test.ts → test/tests/padToSquare.test.ts
import * as tf from '@tensorflow/tfjs-core';
import { padToSquare } from '../src/padToSquare';
import { ones, zeros } from './utils';
import { padToSquare } from '../../src/padToSquare';
import { ones, zeros } from '../utils';
describe('padToSquare', () => {
......
test/tests/toNetInput.test.ts 0 → 100644
import { NetInput } from '../../src/NetInput';
import { toNetInput } from '../../src/toNetInput';
describe('toNetInput', () => {
describe('valid args', () => {
it('from HTMLImageElement', async () => {
const netInput = await toNetInput(document.createElement('img'))
expect(netInput instanceof NetInput).toBe(true)
expect(netInput.canvases.length).toEqual(1)
})
it('from HTMLVideoElement', async () => {
const videoEl = document.createElement('video')
spyOnProperty(videoEl, 'readyState', 'get').and.returnValue(4)
const netInput = await toNetInput(videoEl)
expect(netInput instanceof NetInput).toBe(true)
expect(netInput.canvases.length).toEqual(1)
})
it('from HTMLCanvasElement', async () => {
const netInput = await toNetInput(document.createElement('canvas'))
expect(netInput instanceof NetInput).toBe(true)
expect(netInput.canvases.length).toEqual(1)
})
it('from HTMLImageElement array', async () => {
const netInput = await toNetInput([
document.createElement('img'),
document.createElement('img')
])
expect(netInput instanceof NetInput).toBe(true)
expect(netInput.canvases.length).toEqual(2)
})
it('from HTMLVideoElement array', async () => {
const videoElements = [
document.createElement('video'),
document.createElement('video')
]
videoElements.forEach(videoEl =>
spyOnProperty(videoEl, 'readyState', 'get').and.returnValue(4)
)
const netInput = await toNetInput(videoElements)
expect(netInput instanceof NetInput).toBe(true)
expect(netInput.canvases.length).toEqual(2)
})
it('from HTMLCanvasElement array', async () => {
const netInput = await toNetInput([
document.createElement('canvas'),
document.createElement('canvas')
])
expect(netInput instanceof NetInput).toBe(true)
expect(netInput.canvases.length).toEqual(2)
})
it('from mixed media array', async () => {
const videoEl = document.createElement('video')
spyOnProperty(videoEl, 'readyState', 'get').and.returnValue(4)
const netInput = await toNetInput([
document.createElement('img'),
document.createElement('canvas'),
videoEl
])
expect(netInput instanceof NetInput).toBe(true)
expect(netInput.canvases.length).toEqual(3)
})
})
describe('invalid args', () => {
it('undefined', async () => {
let errorMessage
try {
await toNetInput(undefined as any)
} catch (error) {
errorMessage = error.message;
}
expect(errorMessage).toBe('toNetInput - expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement, or to be an element id')
})
it('empty array', async () => {
let errorMessage
try {
await toNetInput([])
} catch (error) {
errorMessage = error.message;
}
expect(errorMessage).toBe('toNetInput - empty array passed as input')
})
it('undefined at input index 1', async () => {
let errorMessage
try {
await toNetInput([document.createElement('img'), undefined] as any)
} catch (error) {
errorMessage = error.message;
}
expect(errorMessage).toBe('toNetInput - at input index 1: expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement, or to be an element id')
})
})
})
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