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Commit ad62d766 by vincent
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check in latest build

parent b7c3c3b1
Showing with 1110 additions and 102 deletions
README.md
......@@ -16,7 +16,7 @@ Check out my article **[face-api.js — JavaScript API for Face Recognition
* **[Loading the Models](#usage-load-models)**
* **[Face Detection - SSD Mobilenet v1](#usage-face-detection-ssd)**
* **[Face Detection & 5 Point Face Landmarks - MTCNN](#usage-face-detection-mtcnn)**
* **[Face Recognition - FaceNet](#usage-face-recognition)**
* **[Face Recognition](#usage-face-recognition)**
* **[68 Point Face Landmark Detection](#usage-face-landmark-detection)**
* **[Full Face Detection and Recognition Pipeline](#usage-full-face-detection-and-recognition-pipeline)**
......@@ -86,7 +86,7 @@ MTCNN has been presented in the paper [Joint Face Detection and Alignment using
<a name="about-face-recognition"></a>
### Face Recognition - FaceNet
### Face Recognition
For face recognition, a ResNet-34 like architecture is implemented to compute a face descriptor (a feature vector with 128 values) from any given face image, which is used to describe the characteristics of a persons face. The model is **not** limited to the set of faces used for training, meaning you can use it for face recognition of any person, for example yourself. You can determine the similarity of two arbitrary faces by comparing their face descriptors, for example by computing the euclidean distance or using any other classifier of your choice.
......
build/FaceDetection.d.ts 0 → 100644
import { Rect } from './Rect';
import { Dimensions } from './types';
export declare class FaceDetection {
private _score;
private _box;
private _imageWidth;
private _imageHeight;
constructor(score: number, relativeBox: Rect, imageDims: Dimensions);
readonly score: number;
readonly box: Rect;
readonly imageWidth: number;
readonly imageHeight: number;
readonly relativeBox: Rect;
getScore(): number;
getBox(): Rect;
getImageWidth(): number;
getImageHeight(): number;
getRelativeBox(): Rect;
forSize(width: number, height: number): FaceDetection;
}
build/FaceDetection.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var Rect_1 = require("./Rect");
var FaceDetection = /** @class */ (function () {
function FaceDetection(score, relativeBox, imageDims) {
var width = imageDims.width, height = imageDims.height;
this._imageWidth = width;
this._imageHeight = height;
this._score = score;
this._box = new Rect_1.Rect(relativeBox.x * width, relativeBox.y * height, relativeBox.width * width, relativeBox.height * height);
}
Object.defineProperty(FaceDetection.prototype, "score", {
get: function () {
return this._score;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetection.prototype, "box", {
get: function () {
return this._box;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetection.prototype, "imageWidth", {
get: function () {
return this._imageWidth;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetection.prototype, "imageHeight", {
get: function () {
return this._imageHeight;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetection.prototype, "relativeBox", {
get: function () {
return new Rect_1.Rect(this._box.x / this._imageWidth, this._box.y / this._imageHeight, this._box.width / this._imageWidth, this._box.height / this._imageHeight);
},
enumerable: true,
configurable: true
});
FaceDetection.prototype.getScore = function () {
return this.score;
};
FaceDetection.prototype.getBox = function () {
return this.box;
};
FaceDetection.prototype.getImageWidth = function () {
return this.imageWidth;
};
FaceDetection.prototype.getImageHeight = function () {
return this.imageHeight;
};
FaceDetection.prototype.getRelativeBox = function () {
return this.relativeBox;
};
FaceDetection.prototype.forSize = function (width, height) {
return new FaceDetection(this._score, this.getRelativeBox(), { width: width, height: height });
};
return FaceDetection;
}());
exports.FaceDetection = FaceDetection;
//# sourceMappingURL=FaceDetection.js.map
\ No newline at end of file
build/FaceDetection.js.map 0 → 100644
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\ No newline at end of file
build/FaceLandmarks.d.ts 0 → 100644
import { Point } from './Point';
import { Dimensions } from './types';
export declare class FaceLandmarks {
protected _imageWidth: number;
protected _imageHeight: number;
protected _shift: Point;
protected _faceLandmarks: Point[];
constructor(relativeFaceLandmarkPositions: Point[], imageDims: Dimensions, shift?: Point);
getShift(): Point;
getImageWidth(): number;
getImageHeight(): number;
getPositions(): Point[];
getRelativePositions(): Point[];
}
build/FaceLandmarks.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var Point_1 = require("./Point");
var FaceLandmarks = /** @class */ (function () {
function FaceLandmarks(relativeFaceLandmarkPositions, imageDims, shift) {
if (shift === void 0) { shift = new Point_1.Point(0, 0); }
var width = imageDims.width, height = imageDims.height;
this._imageWidth = width;
this._imageHeight = height;
this._shift = shift;
this._faceLandmarks = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new Point_1.Point(width, height)).add(shift); });
}
FaceLandmarks.prototype.getShift = function () {
return new Point_1.Point(this._shift.x, this._shift.y);
};
FaceLandmarks.prototype.getImageWidth = function () {
return this._imageWidth;
};
FaceLandmarks.prototype.getImageHeight = function () {
return this._imageHeight;
};
FaceLandmarks.prototype.getPositions = function () {
return this._faceLandmarks;
};
FaceLandmarks.prototype.getRelativePositions = function () {
var _this = this;
return this._faceLandmarks.map(function (pt) { return pt.sub(_this._shift).div(new Point_1.Point(_this._imageWidth, _this._imageHeight)); });
};
return FaceLandmarks;
}());
exports.FaceLandmarks = FaceLandmarks;
//# sourceMappingURL=FaceLandmarks.js.map
\ No newline at end of file
build/FaceLandmarks.js.map 0 → 100644
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\ No newline at end of file
build/FullFaceDescription.d.ts
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { FaceLandmarks } from './faceLandmarkNet/FaceLandmarks';
import { FaceDetection } from './FaceDetection';
import { FaceLandmarks68 } from './faceLandmarkNet/FaceLandmarks68';
export declare class FullFaceDescription {
private _detection;
private _landmarks;
private _descriptor;
constructor(_detection: FaceDetection, _landmarks: FaceLandmarks, _descriptor: Float32Array);
constructor(_detection: FaceDetection, _landmarks: FaceLandmarks68, _descriptor: Float32Array);
readonly detection: FaceDetection;
readonly landmarks: FaceLandmarks;
readonly landmarks: FaceLandmarks68;
readonly descriptor: Float32Array;
forSize(width: number, height: number): FullFaceDescription;
}
build/FullFaceDescription.js.map
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\ No newline at end of file
build/NetInput.d.ts
......@@ -3,12 +3,14 @@ import { Point } from './Point';
import { TResolvedNetInput } from './types';
export declare class NetInput {
private _inputs;
private _canvases;
private _isManaged;
private _isBatchInput;
private _inputDimensions;
private _paddings;
constructor(inputs: tf.Tensor4D | Array<TResolvedNetInput>, isBatchInput?: boolean);
constructor(inputs: tf.Tensor4D | Array<TResolvedNetInput>, isBatchInput?: boolean, keepCanvases?: boolean);
readonly inputs: tf.Tensor3D[];
readonly canvases: HTMLCanvasElement[];
readonly isManaged: boolean;
readonly isBatchInput: boolean;
readonly batchSize: number;
......
build/NetInput.js
......@@ -6,9 +6,12 @@ var padToSquare_1 = require("./padToSquare");
var Point_1 = require("./Point");
var utils_1 = require("./utils");
var NetInput = /** @class */ (function () {
function NetInput(inputs, isBatchInput) {
function NetInput(inputs, isBatchInput, keepCanvases) {
if (isBatchInput === void 0) { isBatchInput = false; }
if (keepCanvases === void 0) { keepCanvases = false; }
var _this = this;
this._inputs = [];
this._canvases = [];
this._isManaged = false;
this._isBatchInput = false;
this._inputDimensions = [];
......@@ -17,7 +20,7 @@ var NetInput = /** @class */ (function () {
this._inputs = tf.unstack(inputs);
}
if (Array.isArray(inputs)) {
this._inputs = inputs.map(function (input) {
this._inputs = inputs.map(function (input, idx) {
if (isTensor_1.isTensor3D(input)) {
// TODO: make sure not to dispose original tensors passed in by the user
return tf.clone(input);
......@@ -30,7 +33,11 @@ var NetInput = /** @class */ (function () {
}
return input.reshape(shape.slice(1));
}
return tf.fromPixels(input instanceof HTMLCanvasElement ? input : utils_1.createCanvasFromMedia(input));
var canvas = input instanceof HTMLCanvasElement ? input : utils_1.createCanvasFromMedia(input);
if (keepCanvases) {
_this._canvases[idx] = canvas;
}
return tf.fromPixels(canvas);
});
}
this._isBatchInput = this.batchSize > 1 || isBatchInput;
......@@ -43,6 +50,13 @@ var NetInput = /** @class */ (function () {
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "canvases", {
get: function () {
return this._canvases;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "isManaged", {
get: function () {
return this._isManaged;
......
build/NetInput.js.map
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\ No newline at end of file
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build/allFacesFactory.js.map
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build/commons/extractConvParamsFactory.js
......@@ -6,10 +6,7 @@ function extractConvParamsFactory(extractWeights, paramMappings) {
var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
var bias = tf.tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
return {
filters: filters,
bias: bias
};
return { filters: filters, bias: bias };
};
}
exports.extractConvParamsFactory = extractConvParamsFactory;
......
build/commons/extractConvParamsFactory.js.map
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\ No newline at end of file
build/commons/extractFCParamsFactory.d.ts 0 → 100644
import { ExtractWeightsFunction, FCParams, ParamMapping } from './types';
export declare function extractFCParamsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]): (channelsIn: number, channelsOut: number, mappedPrefix: string) => FCParams;
build/commons/extractFCParamsFactory.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function extractFCParamsFactory(extractWeights, paramMappings) {
return function (channelsIn, channelsOut, mappedPrefix) {
var fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
var fc_bias = tf.tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/weights" }, { paramPath: mappedPrefix + "/bias" });
return {
weights: fc_weights,
bias: fc_bias
};
};
}
exports.extractFCParamsFactory = extractFCParamsFactory;
//# sourceMappingURL=extractFCParamsFactory.js.map
\ No newline at end of file
build/commons/extractFCParamsFactory.js.map 0 → 100644
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\ No newline at end of file
build/commons/types.d.ts
......@@ -3,6 +3,10 @@ export declare type ConvParams = {
filters: tf.Tensor4D;
bias: tf.Tensor1D;
};
export declare type FCParams = {
weights: tf.Tensor2D;
bias: tf.Tensor1D;
};
export declare type ExtractWeightsFunction = (numWeights: number) => Float32Array;
export declare type BatchReshapeInfo = {
originalWidth: number;
......
build/drawing/index.d.ts
import { FaceDetection } from '../faceDetectionNet/FaceDetection';
import { FaceLandmarks } from '../faceLandmarkNet/FaceLandmarks';
import { FaceDetection } from '../FaceDetection';
import { FaceLandmarks } from '../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;
......
build/drawing/index.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var faceLandmarkNet_1 = require("../faceLandmarkNet");
var utils_1 = require("../utils");
function getDefaultDrawOptions() {
return {
......@@ -76,7 +77,7 @@ function drawLandmarks(canvasArg, faceLandmarks, options) {
var lineWidth = drawOptions.lineWidth, color = drawOptions.color;
var faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];
faceLandmarksArray.forEach(function (landmarks) {
if (drawLines) {
if (drawLines && landmarks instanceof faceLandmarkNet_1.FaceLandmarks68) {
ctx.strokeStyle = color;
ctx.lineWidth = lineWidth;
drawContour(ctx, landmarks.getJawOutline());
......
build/drawing/index.js.map
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\ No newline at end of file
build/extractFaceTensors.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { FaceDetection } from './FaceDetection';
import { Rect } from './Rect';
import { TNetInput } from './types';
/**
......
build/extractFaceTensors.js
......@@ -2,7 +2,7 @@
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var FaceDetection_1 = require("./faceDetectionNet/FaceDetection");
var FaceDetection_1 = require("./FaceDetection");
var toNetInput_1 = require("./toNetInput");
/**
* Extracts the tensors of the image regions containing the detected faces.
......
build/extractFaceTensors.js.map
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\ No newline at end of file
build/extractFaces.d.ts
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { FaceDetection } from './FaceDetection';
import { Rect } from './Rect';
import { TNetInput } from './types';
/**
......
build/extractFaces.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var FaceDetection_1 = require("./faceDetectionNet/FaceDetection");
var FaceDetection_1 = require("./FaceDetection");
var toNetInput_1 = require("./toNetInput");
var utils_1 = require("./utils");
/**
......
build/extractFaces.js.map
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\ No newline at end of file
build/faceDetectionNet/FaceDetectionNet.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { NeuralNetwork } from '../commons/NeuralNetwork';
import { FaceDetection } from '../FaceDetection';
import { NetInput } from '../NetInput';
import { TNetInput } from '../types';
import { FaceDetection } from './FaceDetection';
import { NetParams } from './types';
export declare class FaceDetectionNet extends NeuralNetwork<NetParams> {
constructor();
......
build/faceDetectionNet/FaceDetectionNet.js
......@@ -3,10 +3,10 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var NeuralNetwork_1 = require("../commons/NeuralNetwork");
var FaceDetection_1 = require("../FaceDetection");
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");
var mobileNetV1_1 = require("./mobileNetV1");
var nonMaxSuppression_1 = require("./nonMaxSuppression");
......
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/faceDetectionNet/index.d.ts
import { FaceDetectionNet } from './FaceDetectionNet';
export * from './FaceDetectionNet';
export * from './FaceDetection';
export declare function createFaceDetectionNet(weights: Float32Array): FaceDetectionNet;
export declare function faceDetectionNet(weights: Float32Array): FaceDetectionNet;
build/faceDetectionNet/index.js
......@@ -3,11 +3,15 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var FaceDetectionNet_1 = require("./FaceDetectionNet");
tslib_1.__exportStar(require("./FaceDetectionNet"), exports);
tslib_1.__exportStar(require("./FaceDetection"), exports);
function faceDetectionNet(weights) {
function createFaceDetectionNet(weights) {
var net = new FaceDetectionNet_1.FaceDetectionNet();
net.extractWeights(weights);
return net;
}
exports.createFaceDetectionNet = createFaceDetectionNet;
function faceDetectionNet(weights) {
console.warn('faceDetectionNet(weights: Float32Array) will be deprecated in future, use createFaceDetectionNet instead');
return createFaceDetectionNet(weights);
}
exports.faceDetectionNet = faceDetectionNet;
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/faceDetectionNet/index.js.map
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build/faceDetectionNet/loadQuantizedParams.js.map
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build/faceLandmarkNet/FaceLandmarkNet.d.ts
......@@ -2,13 +2,13 @@ import * as tf from '@tensorflow/tfjs-core';
import { NeuralNetwork } from '../commons/NeuralNetwork';
import { NetInput } from '../NetInput';
import { TNetInput } from '../types';
import { FaceLandmarks } from './FaceLandmarks';
import { FaceLandmarks68 } from './FaceLandmarks68';
import { NetParams } from './types';
export declare class FaceLandmarkNet extends NeuralNetwork<NetParams> {
constructor();
forwardInput(input: NetInput): tf.Tensor2D;
forward(input: TNetInput): Promise<tf.Tensor2D>;
detectLandmarks(input: TNetInput): Promise<FaceLandmarks | FaceLandmarks[]>;
detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;
protected loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: {
......
build/faceLandmarkNet/FaceLandmarkNet.js
......@@ -8,7 +8,7 @@ var Point_1 = require("../Point");
var toNetInput_1 = require("../toNetInput");
var utils_1 = require("../utils");
var extractParams_1 = require("./extractParams");
var FaceLandmarks_1 = require("./FaceLandmarks");
var FaceLandmarks68_1 = require("./FaceLandmarks68");
var fullyConnectedLayer_1 = require("./fullyConnectedLayer");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
function conv(x, params) {
......@@ -101,7 +101,7 @@ var FaceLandmarkNet = /** @class */ (function (_super) {
landmarksArray = _b.apply(_a, [_c.sent()]);
xCoords = landmarksArray.filter(function (_, i) { return utils_1.isEven(i); });
yCoords = landmarksArray.filter(function (_, i) { return !utils_1.isEven(i); });
return [2 /*return*/, new FaceLandmarks_1.FaceLandmarks(Array(68).fill(0).map(function (_, i) { return new Point_1.Point(xCoords[i], yCoords[i]); }), {
return [2 /*return*/, new FaceLandmarks68_1.FaceLandmarks68(Array(68).fill(0).map(function (_, i) { return new Point_1.Point(xCoords[i], yCoords[i]); }), {
height: netInput.getInputHeight(batchIdx),
width: netInput.getInputWidth(batchIdx),
})];
......
build/faceLandmarkNet/FaceLandmarkNet.js.map
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\ No newline at end of file
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\ No newline at end of file
build/faceLandmarkNet/FaceLandmarks68.d.ts 0 → 100644
import { FaceDetection } from '../FaceDetection';
import { FaceLandmarks } from '../FaceLandmarks';
import { IPoint, Point } from '../Point';
import { Rect } from '../Rect';
export declare class FaceLandmarks68 extends FaceLandmarks {
getJawOutline(): Point[];
getLeftEyeBrow(): Point[];
getRightEyeBrow(): Point[];
getNose(): Point[];
getLeftEye(): Point[];
getRightEye(): Point[];
getMouth(): Point[];
forSize(width: number, height: number): FaceLandmarks68;
shift(x: number, y: number): FaceLandmarks68;
shiftByPoint(pt: IPoint): FaceLandmarks68;
/**
* Aligns the face landmarks after face detection from the relative positions of the faces
* bounding box, or it's current shift. This function should be used to align the face images
* after face detection has been performed, before they are passed to the face recognition net.
* This will make the computed face descriptor more accurate.
*
* @param detection (optional) The bounding box of the face or the face detection result. If
* no argument was passed the position of the face landmarks are assumed to be relative to
* it's current shift.
* @returns The bounding box of the aligned face.
*/
align(detection?: FaceDetection | Rect): Rect;
}
build/faceLandmarkNet/FaceLandmarks68.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var getCenterPoint_1 = require("../commons/getCenterPoint");
var FaceDetection_1 = require("../FaceDetection");
var FaceLandmarks_1 = require("../FaceLandmarks");
var Point_1 = require("../Point");
var Rect_1 = require("../Rect");
// face alignment constants
var relX = 0.5;
var relY = 0.43;
var relScale = 0.45;
var FaceLandmarks68 = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmarks68, _super);
function FaceLandmarks68() {
return _super !== null && _super.apply(this, arguments) || this;
}
FaceLandmarks68.prototype.getJawOutline = function () {
return this._faceLandmarks.slice(0, 17);
};
FaceLandmarks68.prototype.getLeftEyeBrow = function () {
return this._faceLandmarks.slice(17, 22);
};
FaceLandmarks68.prototype.getRightEyeBrow = function () {
return this._faceLandmarks.slice(22, 27);
};
FaceLandmarks68.prototype.getNose = function () {
return this._faceLandmarks.slice(27, 36);
};
FaceLandmarks68.prototype.getLeftEye = function () {
return this._faceLandmarks.slice(36, 42);
};
FaceLandmarks68.prototype.getRightEye = function () {
return this._faceLandmarks.slice(42, 48);
};
FaceLandmarks68.prototype.getMouth = function () {
return this._faceLandmarks.slice(48, 68);
};
FaceLandmarks68.prototype.forSize = function (width, height) {
return new FaceLandmarks68(this.getRelativePositions(), { width: width, height: height });
};
FaceLandmarks68.prototype.shift = function (x, y) {
return new FaceLandmarks68(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new Point_1.Point(x, y));
};
FaceLandmarks68.prototype.shiftByPoint = function (pt) {
return this.shift(pt.x, pt.y);
};
/**
* Aligns the face landmarks after face detection from the relative positions of the faces
* bounding box, or it's current shift. This function should be used to align the face images
* after face detection has been performed, before they are passed to the face recognition net.
* This will make the computed face descriptor more accurate.
*
* @param detection (optional) The bounding box of the face or the face detection result. If
* no argument was passed the position of the face landmarks are assumed to be relative to
* it's current shift.
* @returns The bounding box of the aligned face.
*/
FaceLandmarks68.prototype.align = function (detection) {
if (detection) {
var box = detection instanceof FaceDetection_1.FaceDetection
? detection.getBox().floor()
: detection;
return this.shift(box.x, box.y).align();
}
var centers = [
this.getLeftEye(),
this.getRightEye(),
this.getMouth()
].map(getCenterPoint_1.getCenterPoint);
var leftEyeCenter = centers[0], rightEyeCenter = centers[1], mouthCenter = centers[2];
var distToMouth = function (pt) { return mouthCenter.sub(pt).magnitude(); };
var eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;
var size = Math.floor(eyeToMouthDist / relScale);
var refPoint = getCenterPoint_1.getCenterPoint(centers);
// TODO: pad in case rectangle is out of image bounds
var x = Math.floor(Math.max(0, refPoint.x - (relX * size)));
var y = Math.floor(Math.max(0, refPoint.y - (relY * size)));
return new Rect_1.Rect(x, y, size, size);
};
return FaceLandmarks68;
}(FaceLandmarks_1.FaceLandmarks));
exports.FaceLandmarks68 = FaceLandmarks68;
//# sourceMappingURL=FaceLandmarks68.js.map
\ No newline at end of file
build/faceLandmarkNet/FaceLandmarks68.js.map 0 → 100644
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\ No newline at end of file
build/faceLandmarkNet/extractParams.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var extractConvParamsFactory_1 = require("../commons/extractConvParamsFactory");
var extractFCParamsFactory_1 = require("../commons/extractFCParamsFactory");
var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
function extractParams(weights) {
var paramMappings = [];
var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
var bias = tf.tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
return { filters: filters, bias: bias };
}
function extractFcParams(channelsIn, channelsOut, mappedPrefix) {
var fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
var fc_bias = tf.tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/weights" }, { paramPath: mappedPrefix + "/bias" });
return {
weights: fc_weights,
bias: fc_bias
};
}
var extractConvParams = extractConvParamsFactory_1.extractConvParamsFactory(extractWeights, paramMappings);
var extractFCParams = extractFCParamsFactory_1.extractFCParamsFactory(extractWeights, paramMappings);
var conv0 = extractConvParams(3, 32, 3, 'conv0');
var conv1 = extractConvParams(32, 64, 3, 'conv1');
var conv2 = extractConvParams(64, 64, 3, 'conv2');
......@@ -28,8 +16,8 @@ function extractParams(weights) {
var conv5 = extractConvParams(64, 128, 3, 'conv5');
var conv6 = extractConvParams(128, 128, 3, 'conv6');
var conv7 = extractConvParams(128, 256, 3, 'conv7');
var fc0 = extractFcParams(6400, 1024, 'fc0');
var fc1 = extractFcParams(1024, 136, 'fc1');
var fc0 = extractFCParams(6400, 1024, 'fc0');
var fc1 = extractFCParams(1024, 136, 'fc1');
if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
}
......
build/faceLandmarkNet/extractParams.js.map
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\ No newline at end of file
build/faceLandmarkNet/fullyConnectedLayer.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { FCParams } from './types';
import { FCParams } from '../commons/types';
export declare function fullyConnectedLayer(x: tf.Tensor2D, params: FCParams): tf.Tensor2D;
build/faceLandmarkNet/index.d.ts
import { FaceLandmarkNet } from './FaceLandmarkNet';
export * from './FaceLandmarkNet';
export * from './FaceLandmarks';
export * from './FaceLandmarks68';
export declare function createFaceLandmarkNet(weights: Float32Array): FaceLandmarkNet;
export declare function faceLandmarkNet(weights: Float32Array): FaceLandmarkNet;
build/faceLandmarkNet/index.js
......@@ -3,11 +3,16 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var FaceLandmarkNet_1 = require("./FaceLandmarkNet");
tslib_1.__exportStar(require("./FaceLandmarkNet"), exports);
tslib_1.__exportStar(require("./FaceLandmarks"), exports);
function faceLandmarkNet(weights) {
tslib_1.__exportStar(require("./FaceLandmarks68"), exports);
function createFaceLandmarkNet(weights) {
var net = new FaceLandmarkNet_1.FaceLandmarkNet();
net.extractWeights(weights);
return net;
}
exports.createFaceLandmarkNet = createFaceLandmarkNet;
function faceLandmarkNet(weights) {
console.warn('faceLandmarkNet(weights: Float32Array) will be deprecated in future, use createFaceLandmarkNet instead');
return createFaceLandmarkNet(weights);
}
exports.faceLandmarkNet = faceLandmarkNet;
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/faceLandmarkNet/index.js.map
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\ No newline at end of file
build/faceLandmarkNet/types.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from '../commons/types';
export declare type FCParams = {
weights: tf.Tensor2D;
bias: tf.Tensor1D;
};
import { ConvParams, FCParams } from '../commons/types';
export declare type NetParams = {
conv0: ConvParams;
conv1: ConvParams;
......
build/faceRecognitionNet/index.d.ts
import { FaceRecognitionNet } from './FaceRecognitionNet';
export * from './FaceRecognitionNet';
export declare function createFaceRecognitionNet(weights: Float32Array): FaceRecognitionNet;
export declare function faceRecognitionNet(weights: Float32Array): FaceRecognitionNet;
build/faceRecognitionNet/index.js
......@@ -3,10 +3,15 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var FaceRecognitionNet_1 = require("./FaceRecognitionNet");
tslib_1.__exportStar(require("./FaceRecognitionNet"), exports);
function faceRecognitionNet(weights) {
function createFaceRecognitionNet(weights) {
var net = new FaceRecognitionNet_1.FaceRecognitionNet();
net.extractWeights(weights);
return net;
}
exports.createFaceRecognitionNet = createFaceRecognitionNet;
function faceRecognitionNet(weights) {
console.warn('faceRecognitionNet(weights: Float32Array) will be deprecated in future, use createFaceRecognitionNet instead');
return createFaceRecognitionNet(weights);
}
exports.faceRecognitionNet = faceRecognitionNet;
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/faceRecognitionNet/index.js.map
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\ No newline at end of file
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\ No newline at end of file
build/globalApi.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { FaceDetection } from './FaceDetection';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
import { FaceLandmarks } from './faceLandmarkNet/FaceLandmarks';
import { FaceLandmarks68 } from './faceLandmarkNet/FaceLandmarks68';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { FullFaceDescription } from './FullFaceDescription';
import { Mtcnn } from './mtcnn/Mtcnn';
import { MtcnnForwardParams, MtcnnResult } from './mtcnn/types';
import { NetInput } from './NetInput';
import { TNetInput } from './types';
export declare const detectionNet: FaceDetectionNet;
export declare const landmarkNet: FaceLandmarkNet;
export declare const recognitionNet: FaceRecognitionNet;
export declare const nets: {
ssdMobilenet: FaceDetectionNet;
faceLandmark68Net: FaceLandmarkNet;
faceNet: FaceRecognitionNet;
mtcnn: Mtcnn;
};
export declare function loadFaceDetectionModel(url: string): Promise<void>;
export declare function loadFaceLandmarkModel(url: string): Promise<void>;
export declare function loadFaceRecognitionModel(url: string): Promise<void>;
export declare function loadModels(url: string): Promise<[void, void, void]>;
export declare function loadMtcnnModel(url: string): Promise<void>;
export declare function loadModels(url: string): Promise<[void, void, void, void]>;
export declare function locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
export declare function detectLandmarks(input: TNetInput): Promise<FaceLandmarks | FaceLandmarks[]>;
export declare function detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;
export declare function computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
export declare function mtcnn(input: TNetInput, forwardParams: MtcnnForwardParams): Promise<MtcnnResult[]>;
export declare const allFaces: (input: tf.Tensor | NetInput | TNetInput, minConfidence: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
build/globalApi.js
......@@ -4,40 +4,58 @@ var allFacesFactory_1 = require("./allFacesFactory");
var FaceDetectionNet_1 = require("./faceDetectionNet/FaceDetectionNet");
var FaceLandmarkNet_1 = require("./faceLandmarkNet/FaceLandmarkNet");
var FaceRecognitionNet_1 = require("./faceRecognitionNet/FaceRecognitionNet");
var Mtcnn_1 = require("./mtcnn/Mtcnn");
exports.detectionNet = new FaceDetectionNet_1.FaceDetectionNet();
exports.landmarkNet = new FaceLandmarkNet_1.FaceLandmarkNet();
exports.recognitionNet = new FaceRecognitionNet_1.FaceRecognitionNet();
// nets need more specific names, to avoid ambiguity in future
// when alternative net implementations are provided
exports.nets = {
ssdMobilenet: exports.detectionNet,
faceLandmark68Net: exports.landmarkNet,
faceNet: exports.recognitionNet,
mtcnn: new Mtcnn_1.Mtcnn()
};
function loadFaceDetectionModel(url) {
return exports.detectionNet.load(url);
return exports.nets.ssdMobilenet.load(url);
}
exports.loadFaceDetectionModel = loadFaceDetectionModel;
function loadFaceLandmarkModel(url) {
return exports.landmarkNet.load(url);
return exports.nets.faceLandmark68Net.load(url);
}
exports.loadFaceLandmarkModel = loadFaceLandmarkModel;
function loadFaceRecognitionModel(url) {
return exports.recognitionNet.load(url);
return exports.nets.faceNet.load(url);
}
exports.loadFaceRecognitionModel = loadFaceRecognitionModel;
function loadMtcnnModel(url) {
return exports.nets.mtcnn.load(url);
}
exports.loadMtcnnModel = loadMtcnnModel;
function loadModels(url) {
return Promise.all([
loadFaceDetectionModel(url),
loadFaceLandmarkModel(url),
loadFaceRecognitionModel(url)
loadFaceRecognitionModel(url),
loadMtcnnModel(url)
]);
}
exports.loadModels = loadModels;
function locateFaces(input, minConfidence, maxResults) {
return exports.detectionNet.locateFaces(input, minConfidence, maxResults);
return exports.nets.ssdMobilenet.locateFaces(input, minConfidence, maxResults);
}
exports.locateFaces = locateFaces;
function detectLandmarks(input) {
return exports.landmarkNet.detectLandmarks(input);
return exports.nets.faceLandmark68Net.detectLandmarks(input);
}
exports.detectLandmarks = detectLandmarks;
function computeFaceDescriptor(input) {
return exports.recognitionNet.computeFaceDescriptor(input);
return exports.nets.faceNet.computeFaceDescriptor(input);
}
exports.computeFaceDescriptor = computeFaceDescriptor;
function mtcnn(input, forwardParams) {
return exports.nets.mtcnn.forward(input, forwardParams);
}
exports.mtcnn = mtcnn;
exports.allFaces = allFacesFactory_1.allFacesFactory(exports.detectionNet, exports.landmarkNet, exports.recognitionNet);
//# sourceMappingURL=globalApi.js.map
\ No newline at end of file
build/globalApi.js.map
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\ No newline at end of file
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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 './FaceDetection';
export * from './FullFaceDescription';
export * from './NetInput';
export * from './Point';
......@@ -12,6 +13,7 @@ export * from './faceDetectionNet';
export * from './faceLandmarkNet';
export * from './faceRecognitionNet';
export * from './globalApi';
export * from './mtcnn';
export * from './padToSquare';
export * from './toNetInput';
export * from './utils';
build/index.js
......@@ -3,6 +3,7 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
exports.tf = tf;
tslib_1.__exportStar(require("./FaceDetection"), exports);
tslib_1.__exportStar(require("./FullFaceDescription"), exports);
tslib_1.__exportStar(require("./NetInput"), exports);
tslib_1.__exportStar(require("./Point"), exports);
......@@ -15,6 +16,7 @@ tslib_1.__exportStar(require("./faceDetectionNet"), exports);
tslib_1.__exportStar(require("./faceLandmarkNet"), exports);
tslib_1.__exportStar(require("./faceRecognitionNet"), exports);
tslib_1.__exportStar(require("./globalApi"), exports);
tslib_1.__exportStar(require("./mtcnn"), exports);
tslib_1.__exportStar(require("./padToSquare"), exports);
tslib_1.__exportStar(require("./toNetInput"), exports);
tslib_1.__exportStar(require("./utils"), exports);
......
build/index.js.map
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\ No newline at end of file
build/mtcnn/BoundingBox.d.ts 0 → 100644
export declare class BoundingBox {
private _left;
private _top;
private _right;
private _bottom;
constructor(_left: number, _top: number, _right: number, _bottom: number);
readonly left: number;
readonly top: number;
readonly right: number;
readonly bottom: number;
readonly width: number;
readonly height: number;
toSquare(): BoundingBox;
round(): BoundingBox;
padAtBorders(imageHeight: number, imageWidth: number): {
dy: number;
edy: number;
dx: number;
edx: number;
y: number;
ey: number;
x: number;
ex: number;
w: number;
h: number;
};
calibrate(region: BoundingBox): BoundingBox;
}
build/mtcnn/BoundingBox.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var BoundingBox = /** @class */ (function () {
function BoundingBox(_left, _top, _right, _bottom) {
this._left = _left;
this._top = _top;
this._right = _right;
this._bottom = _bottom;
}
Object.defineProperty(BoundingBox.prototype, "left", {
get: function () {
return this._left;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "top", {
get: function () {
return this._top;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "right", {
get: function () {
return this._right;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "bottom", {
get: function () {
return this._bottom;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "width", {
get: function () {
return this.right - this.left;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "height", {
get: function () {
return this.bottom - this.top;
},
enumerable: true,
configurable: true
});
BoundingBox.prototype.toSquare = function () {
var _a = this, left = _a.left, top = _a.top, right = _a.right, bottom = _a.bottom;
var off = (Math.abs(this.width - this.height) / 2);
if (this.width < this.height) {
left -= off;
right += off;
}
if (this.height < this.width) {
top -= off;
bottom += off;
}
return new BoundingBox(left, top, right, bottom);
};
BoundingBox.prototype.round = function () {
return new BoundingBox(Math.round(this.left), Math.round(this.top), Math.round(this.right), Math.round(this.bottom));
};
BoundingBox.prototype.padAtBorders = function (imageHeight, imageWidth) {
var w = this.width + 1;
var h = this.height + 1;
var dx = 1;
var dy = 1;
var edx = w;
var edy = h;
var x = this.left;
var y = this.top;
var ex = this.right;
var ey = this.bottom;
if (ex > imageWidth) {
edx = -ex + imageWidth + w;
ex = imageWidth;
}
if (ey > imageHeight) {
edy = -ey + imageHeight + h;
ey = imageHeight;
}
if (x < 1) {
edy = 2 - x;
x = 1;
}
if (y < 1) {
edy = 2 - y;
y = 1;
}
return { dy: dy, edy: edy, dx: dx, edx: edx, y: y, ey: ey, x: x, ex: ex, w: w, h: h };
};
BoundingBox.prototype.calibrate = function (region) {
return new BoundingBox(this.left + (region.left * this.width), this.top + (region.top * this.height), this.right + (region.right * this.width), this.bottom + (region.bottom * this.height)).toSquare().round();
};
return BoundingBox;
}());
exports.BoundingBox = BoundingBox;
//# sourceMappingURL=BoundingBox.js.map
\ No newline at end of file
build/mtcnn/BoundingBox.js.map 0 → 100644
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\ No newline at end of file
build/mtcnn/FaceLandmarks5.d.ts 0 → 100644
import { FaceLandmarks } from '../FaceLandmarks';
import { IPoint } from '../Point';
export declare class FaceLandmarks5 extends FaceLandmarks {
forSize(width: number, height: number): FaceLandmarks5;
shift(x: number, y: number): FaceLandmarks5;
shiftByPoint(pt: IPoint): FaceLandmarks5;
}
build/mtcnn/FaceLandmarks5.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var FaceLandmarks_1 = require("../FaceLandmarks");
var Point_1 = require("../Point");
var FaceLandmarks5 = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmarks5, _super);
function FaceLandmarks5() {
return _super !== null && _super.apply(this, arguments) || this;
}
FaceLandmarks5.prototype.forSize = function (width, height) {
return new FaceLandmarks5(this.getRelativePositions(), { width: width, height: height });
};
FaceLandmarks5.prototype.shift = function (x, y) {
return new FaceLandmarks5(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new Point_1.Point(x, y));
};
FaceLandmarks5.prototype.shiftByPoint = function (pt) {
return this.shift(pt.x, pt.y);
};
return FaceLandmarks5;
}(FaceLandmarks_1.FaceLandmarks));
exports.FaceLandmarks5 = FaceLandmarks5;
//# sourceMappingURL=FaceLandmarks5.js.map
\ No newline at end of file
build/mtcnn/FaceLandmarks5.js.map 0 → 100644
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\ No newline at end of file
build/mtcnn/Mtcnn.d.ts 0 → 100644
import { NeuralNetwork } from '../commons/NeuralNetwork';
import { NetInput } from '../NetInput';
import { TNetInput } from '../types';
import { MtcnnForwardParams, MtcnnResult, NetParams } from './types';
export declare class Mtcnn extends NeuralNetwork<NetParams> {
constructor();
forwardInput(input: NetInput, forwardParams: MtcnnForwardParams): Promise<{
results: MtcnnResult[];
stats: any;
}>;
forward(input: TNetInput, forwardParams: MtcnnForwardParams): Promise<MtcnnResult[]>;
forwardWithStats(input: TNetInput, forwardParams: MtcnnForwardParams): Promise<{
results: MtcnnResult[];
stats: any;
}>;
protected loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
protected extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
};
}
build/mtcnn/Mtcnn.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var NeuralNetwork_1 = require("../commons/NeuralNetwork");
var FaceDetection_1 = require("../FaceDetection");
var Point_1 = require("../Point");
var Rect_1 = require("../Rect");
var toNetInput_1 = require("../toNetInput");
var bgrToRgbTensor_1 = require("./bgrToRgbTensor");
var config_1 = require("./config");
var extractParams_1 = require("./extractParams");
var FaceLandmarks5_1 = require("./FaceLandmarks5");
var getDefaultMtcnnForwardParams_1 = require("./getDefaultMtcnnForwardParams");
var getSizesForScale_1 = require("./getSizesForScale");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
var pyramidDown_1 = require("./pyramidDown");
var stage1_1 = require("./stage1");
var stage2_1 = require("./stage2");
var stage3_1 = require("./stage3");
var Mtcnn = /** @class */ (function (_super) {
tslib_1.__extends(Mtcnn, _super);
function Mtcnn() {
return _super.call(this, 'Mtcnn') || this;
}
Mtcnn.prototype.forwardInput = function (input, forwardParams) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var params, inputTensor, inputCanvas, stats, tsTotal, imgTensor, onReturn, _a, height, width, _b, minFaceSize, scaleFactor, maxNumScales, scoreThresholds, scaleSteps, scales, ts, out1, out2, out3, results;
return tslib_1.__generator(this, function (_c) {
switch (_c.label) {
case 0:
params = this.params;
if (!params) {
throw new Error('Mtcnn - load model before inference');
}
inputTensor = input.inputs[0];
inputCanvas = input.canvases[0];
if (!inputCanvas) {
throw new Error('Mtcnn - inputCanvas is not defined, note that passing tensors into Mtcnn.forwardInput is not supported yet.');
}
stats = {};
tsTotal = Date.now();
imgTensor = tf.tidy(function () {
return bgrToRgbTensor_1.bgrToRgbTensor(tf.expandDims(inputTensor).toFloat());
});
onReturn = function (results) {
// dispose tensors on return
imgTensor.dispose();
input.dispose();
stats.total = Date.now() - tsTotal;
return results;
};
_a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
_b = Object.assign({}, getDefaultMtcnnForwardParams_1.getDefaultMtcnnForwardParams(), forwardParams), minFaceSize = _b.minFaceSize, scaleFactor = _b.scaleFactor, maxNumScales = _b.maxNumScales, scoreThresholds = _b.scoreThresholds, scaleSteps = _b.scaleSteps;
scales = (scaleSteps || pyramidDown_1.pyramidDown(minFaceSize, scaleFactor, [height, width]))
.filter(function (scale) {
var sizes = getSizesForScale_1.getSizesForScale(scale, [height, width]);
return Math.min(sizes.width, sizes.height) > config_1.CELL_SIZE;
})
.slice(0, maxNumScales);
stats.scales = scales;
stats.pyramid = scales.map(function (scale) { return getSizesForScale_1.getSizesForScale(scale, [height, width]); });
ts = Date.now();
return [4 /*yield*/, stage1_1.stage1(imgTensor, scales, scoreThresholds[0], params.pnet, stats)];
case 1:
out1 = _c.sent();
stats.total_stage1 = Date.now() - ts;
if (!out1.boxes.length) {
return [2 /*return*/, onReturn({ results: [], stats: stats })];
}
stats.stage2_numInputBoxes = out1.boxes.length;
// using the inputCanvas to extract and resize the image patches, since it is faster
// than doing this on the gpu
ts = Date.now();
return [4 /*yield*/, stage2_1.stage2(inputCanvas, out1.boxes, scoreThresholds[1], params.rnet, stats)];
case 2:
out2 = _c.sent();
stats.total_stage2 = Date.now() - ts;
if (!out2.boxes.length) {
return [2 /*return*/, onReturn({ results: [], stats: stats })];
}
stats.stage3_numInputBoxes = out2.boxes.length;
ts = Date.now();
return [4 /*yield*/, stage3_1.stage3(inputCanvas, out2.boxes, scoreThresholds[2], params.onet, stats)];
case 3:
out3 = _c.sent();
stats.total_stage3 = Date.now() - ts;
results = out3.boxes.map(function (box, idx) { return ({
faceDetection: new FaceDetection_1.FaceDetection(out3.scores[idx], new Rect_1.Rect(box.left / width, box.top / height, box.width / width, box.height / height), {
height: height,
width: width
}),
faceLandmarks: new FaceLandmarks5_1.FaceLandmarks5(out3.points[idx].map(function (pt) { return pt.div(new Point_1.Point(width, height)); }), { width: width, height: height })
}); });
return [2 /*return*/, onReturn({ results: results, stats: stats })];
}
});
});
};
Mtcnn.prototype.forward = function (input, forwardParams) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, toNetInput_1.toNetInput(input, true, true)];
case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(),
forwardParams])];
case 2: return [2 /*return*/, (_b.sent()).results];
}
});
});
};
Mtcnn.prototype.forwardWithStats = function (input, forwardParams) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, toNetInput_1.toNetInput(input, true, true)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent(),
forwardParams])];
}
});
});
};
// none of the param tensors are quantized yet
Mtcnn.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams_1.loadQuantizedParams(uri);
};
Mtcnn.prototype.extractParams = function (weights) {
return extractParams_1.extractParams(weights);
};
return Mtcnn;
}(NeuralNetwork_1.NeuralNetwork));
exports.Mtcnn = Mtcnn;
//# sourceMappingURL=Mtcnn.js.map
\ No newline at end of file
build/mtcnn/Mtcnn.js.map 0 → 100644
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\ No newline at end of file
build/mtcnn/ONet.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { ONetParams } from './types';
export declare function ONet(x: tf.Tensor4D, params: ONetParams): {
scores: tf.Tensor1D;
regions: tf.Tensor2D;
points: tf.Tensor2D;
};
build/mtcnn/ONet.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var convLayer_1 = require("../commons/convLayer");
var fullyConnectedLayer_1 = require("../faceLandmarkNet/fullyConnectedLayer");
var prelu_1 = require("./prelu");
var sharedLayers_1 = require("./sharedLayers");
function ONet(x, params) {
return tf.tidy(function () {
var out = sharedLayers_1.sharedLayer(x, params);
out = tf.maxPool(out, [2, 2], [2, 2], 'same');
out = convLayer_1.convLayer(out, params.conv4, 'valid');
out = prelu_1.prelu(out, params.prelu4_alpha);
var vectorized = tf.reshape(out, [out.shape[0], params.fc1.weights.shape[0]]);
var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(vectorized, params.fc1);
var prelu5 = prelu_1.prelu(fc1, params.prelu5_alpha);
var fc2_1 = fullyConnectedLayer_1.fullyConnectedLayer(prelu5, params.fc2_1);
var max = tf.expandDims(tf.max(fc2_1, 1), 1);
var prob = tf.softmax(tf.sub(fc2_1, max), 1);
var regions = fullyConnectedLayer_1.fullyConnectedLayer(prelu5, params.fc2_2);
var points = fullyConnectedLayer_1.fullyConnectedLayer(prelu5, params.fc2_3);
var scores = tf.unstack(prob, 1)[1];
return { scores: scores, regions: regions, points: points };
});
}
exports.ONet = ONet;
//# sourceMappingURL=ONet.js.map
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build/mtcnn/ONet.js.map 0 → 100644
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build/mtcnn/PNet.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { PNetParams } from './types';
export declare function PNet(x: tf.Tensor4D, params: PNetParams): {
prob: tf.Tensor4D;
regions: tf.Tensor4D;
};
build/mtcnn/PNet.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var convLayer_1 = require("../commons/convLayer");
var sharedLayers_1 = require("./sharedLayers");
function PNet(x, params) {
return tf.tidy(function () {
var out = sharedLayers_1.sharedLayer(x, params, true);
var conv = convLayer_1.convLayer(out, params.conv4_1, 'valid');
var max = tf.expandDims(tf.max(conv, 3), 3);
var prob = tf.softmax(tf.sub(conv, max), 3);
var regions = convLayer_1.convLayer(out, params.conv4_2, 'valid');
return { prob: prob, regions: regions };
});
}
exports.PNet = PNet;
//# sourceMappingURL=PNet.js.map
\ No newline at end of file
build/mtcnn/PNet.js.map 0 → 100644
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build/mtcnn/RNet.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { RNetParams } from './types';
export declare function RNet(x: tf.Tensor4D, params: RNetParams): {
scores: tf.Tensor1D;
regions: tf.Tensor2D;
};
build/mtcnn/RNet.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var fullyConnectedLayer_1 = require("../faceLandmarkNet/fullyConnectedLayer");
var prelu_1 = require("./prelu");
var sharedLayers_1 = require("./sharedLayers");
function RNet(x, params) {
return tf.tidy(function () {
var convOut = sharedLayers_1.sharedLayer(x, params);
var vectorized = tf.reshape(convOut, [convOut.shape[0], params.fc1.weights.shape[0]]);
var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(vectorized, params.fc1);
var prelu4 = prelu_1.prelu(fc1, params.prelu4_alpha);
var fc2_1 = fullyConnectedLayer_1.fullyConnectedLayer(prelu4, params.fc2_1);
var max = tf.expandDims(tf.max(fc2_1, 1), 1);
var prob = tf.softmax(tf.sub(fc2_1, max), 1);
var regions = fullyConnectedLayer_1.fullyConnectedLayer(prelu4, params.fc2_2);
var scores = tf.unstack(prob, 1)[1];
return { scores: scores, regions: regions };
});
}
exports.RNet = RNet;
//# sourceMappingURL=RNet.js.map
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build/mtcnn/RNet.js.map 0 → 100644
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build/mtcnn/bgrToRgbTensor.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
export declare function bgrToRgbTensor(tensor: tf.Tensor4D): tf.Tensor4D;
build/mtcnn/bgrToRgbTensor.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function bgrToRgbTensor(tensor) {
return tf.tidy(function () { return tf.stack(tf.unstack(tensor, 3).reverse(), 3); });
}
exports.bgrToRgbTensor = bgrToRgbTensor;
//# sourceMappingURL=bgrToRgbTensor.js.map
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build/mtcnn/bgrToRgbTensor.js.map 0 → 100644
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build/mtcnn/calculateScaleSteps.d.ts 0 → 100644
export declare function calculateScaleSteps(numScales: number, minFaceSize: number, dims: number[]): number[];
build/mtcnn/calculateScaleSteps.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function calculateScaleSteps(numScales, minFaceSize, dims) {
var height = dims[0], width = dims[1];
var maxDim = Math.max(height, width);
if (Math.min(height, width) < minFaceSize) {
throw new Error("Image dimensions: " + height + " x " + width + " are smaller than specified minimum face size of " + minFaceSize);
}
var scaleStep = (maxDim - minFaceSize) / numScales;
return Array(numScales).fill(0).map(function (_, i) { return ((i + 1) * scaleStep + minFaceSize) / maxDim; }).reverse();
}
exports.calculateScaleSteps = calculateScaleSteps;
//# sourceMappingURL=calculateScaleSteps.js.map
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build/mtcnn/calculateScaleSteps.js.map 0 → 100644
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build/mtcnn/config.d.ts 0 → 100644
export declare const CELL_STRIDE = 2;
export declare const CELL_SIZE = 12;
build/mtcnn/config.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.CELL_STRIDE = 2;
exports.CELL_SIZE = 12;
//# sourceMappingURL=config.js.map
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build/mtcnn/config.js.map 0 → 100644
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build/mtcnn/extractImagePatches.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { Dimensions } from '../types';
import { BoundingBox } from './BoundingBox';
export declare function extractImagePatches(img: HTMLCanvasElement, boxes: BoundingBox[], {width, height}: Dimensions): Promise<tf.Tensor4D[]>;
build/mtcnn/extractImagePatches.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var utils_1 = require("../utils");
var normalize_1 = require("./normalize");
function extractImagePatches(img, boxes, _a) {
var width = _a.width, height = _a.height;
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _this = this;
var imgCtx, bitmaps, imagePatchesDatas;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
imgCtx = utils_1.getContext2dOrThrow(img);
return [4 /*yield*/, Promise.all(boxes.map(function (box) { return tslib_1.__awaiter(_this, void 0, void 0, function () {
var _a, y, ey, x, ex, fromX, fromY, imgData;
return tslib_1.__generator(this, function (_b) {
_a = box.padAtBorders(img.height, img.width), y = _a.y, ey = _a.ey, x = _a.x, ex = _a.ex;
fromX = x - 1;
fromY = y - 1;
imgData = imgCtx.getImageData(fromX, fromY, (ex - fromX), (ey - fromY));
return [2 /*return*/, createImageBitmap(imgData)];
});
}); }))];
case 1:
bitmaps = _b.sent();
imagePatchesDatas = [];
bitmaps.forEach(function (bmp) {
var patch = utils_1.createCanvas({ width: width, height: height });
var patchCtx = utils_1.getContext2dOrThrow(patch);
patchCtx.drawImage(bmp, 0, 0, width, height);
var data = patchCtx.getImageData(0, 0, width, height).data;
var currData = [];
// RGBA -> BGR
for (var i = 0; i < data.length; i += 4) {
currData.push(data[i + 2]);
currData.push(data[i + 1]);
currData.push(data[i]);
}
imagePatchesDatas.push(currData);
});
return [2 /*return*/, imagePatchesDatas.map(function (data) {
var t = tf.tidy(function () {
var imagePatchTensor = tf.transpose(tf.tensor4d(data, [1, width, height, 3]), [0, 2, 1, 3]).toFloat();
return normalize_1.normalize(imagePatchTensor);
});
return t;
})];
}
});
});
}
exports.extractImagePatches = extractImagePatches;
//# sourceMappingURL=extractImagePatches.js.map
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build/mtcnn/extractImagePatches.js.map 0 → 100644
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build/mtcnn/extractParams.d.ts 0 → 100644
import { ParamMapping } from '../commons/types';
import { NetParams } from './types';
export declare function extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: ParamMapping[];
};
build/mtcnn/extractParams.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var extractConvParamsFactory_1 = require("../commons/extractConvParamsFactory");
var extractFCParamsFactory_1 = require("../commons/extractFCParamsFactory");
var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
function extractorsFactory(extractWeights, paramMappings) {
var extractConvParams = extractConvParamsFactory_1.extractConvParamsFactory(extractWeights, paramMappings);
var extractFCParams = extractFCParamsFactory_1.extractFCParamsFactory(extractWeights, paramMappings);
function extractPReluParams(size, paramPath) {
var alpha = tf.tensor1d(extractWeights(size));
paramMappings.push({ paramPath: paramPath });
return alpha;
}
function extractSharedParams(numFilters, mappedPrefix, isRnet) {
if (isRnet === void 0) { isRnet = false; }
var conv1 = extractConvParams(numFilters[0], numFilters[1], 3, mappedPrefix + "/conv1");
var prelu1_alpha = extractPReluParams(numFilters[1], mappedPrefix + "/prelu1_alpha");
var conv2 = extractConvParams(numFilters[1], numFilters[2], 3, mappedPrefix + "/conv2");
var prelu2_alpha = extractPReluParams(numFilters[2], mappedPrefix + "/prelu2_alpha");
var conv3 = extractConvParams(numFilters[2], numFilters[3], isRnet ? 2 : 3, mappedPrefix + "/conv3");
var prelu3_alpha = extractPReluParams(numFilters[3], mappedPrefix + "/prelu3_alpha");
return { conv1: conv1, prelu1_alpha: prelu1_alpha, conv2: conv2, prelu2_alpha: prelu2_alpha, conv3: conv3, prelu3_alpha: prelu3_alpha };
}
function extractPNetParams() {
var sharedParams = extractSharedParams([3, 10, 16, 32], 'pnet');
var conv4_1 = extractConvParams(32, 2, 1, 'pnet/conv4_1');
var conv4_2 = extractConvParams(32, 4, 1, 'pnet/conv4_2');
return tslib_1.__assign({}, sharedParams, { conv4_1: conv4_1, conv4_2: conv4_2 });
}
function extractRNetParams() {
var sharedParams = extractSharedParams([3, 28, 48, 64], 'rnet', true);
var fc1 = extractFCParams(576, 128, 'rnet/fc1');
var prelu4_alpha = extractPReluParams(128, 'rnet/prelu4_alpha');
var fc2_1 = extractFCParams(128, 2, 'rnet/fc2_1');
var fc2_2 = extractFCParams(128, 4, 'rnet/fc2_2');
return tslib_1.__assign({}, sharedParams, { fc1: fc1, prelu4_alpha: prelu4_alpha, fc2_1: fc2_1, fc2_2: fc2_2 });
}
function extractONetParams() {
var sharedParams = extractSharedParams([3, 32, 64, 64], 'onet');
var conv4 = extractConvParams(64, 128, 2, 'onet/conv4');
var prelu4_alpha = extractPReluParams(128, 'onet/prelu4_alpha');
var fc1 = extractFCParams(1152, 256, 'onet/fc1');
var prelu5_alpha = extractPReluParams(256, 'onet/prelu5_alpha');
var fc2_1 = extractFCParams(256, 2, 'onet/fc2_1');
var fc2_2 = extractFCParams(256, 4, 'onet/fc2_2');
var fc2_3 = extractFCParams(256, 10, 'onet/fc2_3');
return tslib_1.__assign({}, sharedParams, { conv4: conv4, prelu4_alpha: prelu4_alpha, fc1: fc1, prelu5_alpha: prelu5_alpha, fc2_1: fc2_1, fc2_2: fc2_2, fc2_3: fc2_3 });
}
return {
extractPNetParams: extractPNetParams,
extractRNetParams: extractRNetParams,
extractONetParams: extractONetParams
};
}
function extractParams(weights) {
var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var paramMappings = [];
var _b = extractorsFactory(extractWeights, paramMappings), extractPNetParams = _b.extractPNetParams, extractRNetParams = _b.extractRNetParams, extractONetParams = _b.extractONetParams;
var pnet = extractPNetParams();
var rnet = extractRNetParams();
var onet = extractONetParams();
if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
}
return { params: { pnet: pnet, rnet: rnet, onet: onet }, paramMappings: paramMappings };
}
exports.extractParams = extractParams;
//# sourceMappingURL=extractParams.js.map
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build/mtcnn/extractParams.js.map 0 → 100644
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build/mtcnn/getDefaultMtcnnForwardParams.d.ts 0 → 100644
export declare function getDefaultMtcnnForwardParams(): {
minFaceSize: number;
scaleFactor: number;
maxNumScales: number;
scoreThresholds: number[];
};
build/mtcnn/getDefaultMtcnnForwardParams.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function getDefaultMtcnnForwardParams() {
return {
minFaceSize: 20,
scaleFactor: 0.709,
maxNumScales: 10,
scoreThresholds: [0.6, 0.7, 0.7]
};
}
exports.getDefaultMtcnnForwardParams = getDefaultMtcnnForwardParams;
//# sourceMappingURL=getDefaultMtcnnForwardParams.js.map
\ No newline at end of file
build/mtcnn/getDefaultMtcnnForwardParams.js.map 0 → 100644
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build/mtcnn/getSizesForScale.d.ts 0 → 100644
export declare function getSizesForScale(scale: number, [height, width]: number[]): {
height: number;
width: number;
};
build/mtcnn/getSizesForScale.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function getSizesForScale(scale, _a) {
var height = _a[0], width = _a[1];
return {
height: Math.floor(height * scale),
width: Math.floor(width * scale)
};
}
exports.getSizesForScale = getSizesForScale;
//# sourceMappingURL=getSizesForScale.js.map
\ No newline at end of file
build/mtcnn/getSizesForScale.js.map 0 → 100644
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\ No newline at end of file
build/mtcnn/index.d.ts 0 → 100644
import { Mtcnn } from './Mtcnn';
export * from './Mtcnn';
export * from './FaceLandmarks5';
export declare function createMtcnn(weights: Float32Array): Mtcnn;
build/mtcnn/index.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var Mtcnn_1 = require("./Mtcnn");
tslib_1.__exportStar(require("./Mtcnn"), exports);
tslib_1.__exportStar(require("./FaceLandmarks5"), exports);
function createMtcnn(weights) {
var net = new Mtcnn_1.Mtcnn();
net.extractWeights(weights);
return net;
}
exports.createMtcnn = createMtcnn;
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/mtcnn/index.js.map 0 → 100644
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\ No newline at end of file
build/mtcnn/loadQuantizedParams.d.ts 0 → 100644
import { ParamMapping } from '../commons/types';
import { NetParams } from './types';
export declare function loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: ParamMapping[];
}>;
dist/face-api.js
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dist/face-api.js.map
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dist/face-api.min.js
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