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Commit 4caa0f1a by vincent
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check in latest build

parent cff49661
Showing with 832 additions and 610 deletions
build/NetInput.d.ts
import { Dimensions, TMediaElement } from './types';
import * as tf from '@tensorflow/tfjs-core';
import { Point } from './Point';
import { TResolvedNetInput } from './types';
export declare class NetInput {
private _canvases;
constructor(medias: Array<TMediaElement>, dims?: Dimensions);
private initCanvas(media, dims?);
readonly canvases: HTMLCanvasElement[];
readonly width: number;
readonly height: number;
readonly dims: Dimensions | null;
private _inputs;
private _isManaged;
private _inputDimensions;
private _paddings;
constructor(inputs: tf.Tensor4D | Array<TResolvedNetInput>);
readonly inputs: tf.Tensor3D[];
readonly isManaged: boolean;
readonly batchSize: number;
readonly inputDimensions: number[][];
readonly paddings: Point[];
getInputDimensions(batchIdx: number): number[];
getInputHeight(batchIdx: number): number;
getInputWidth(batchIdx: number): number;
getPaddings(batchIdx: number): Point;
toBatchTensor(inputSize: number, isCenterInputs?: boolean): tf.Tensor4D;
/**
* By setting the isManaged flag, all newly created tensors will be automatically
* automatically disposed after the batch tensor has been created
*/
managed(): this;
dispose(): void;
}
build/NetInput.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var isTensor_1 = require("./commons/isTensor");
var padToSquare_1 = require("./padToSquare");
var Point_1 = require("./Point");
var utils_1 = require("./utils");
var NetInput = /** @class */ (function () {
function NetInput(medias, dims) {
var _this = this;
this._canvases = [];
medias.forEach(function (m) { return _this.initCanvas(m, dims); });
function NetInput(inputs) {
this._inputs = [];
this._isManaged = false;
this._inputDimensions = [];
this._paddings = [];
if (isTensor_1.isTensor4D(inputs)) {
this._inputs = tf.unstack(inputs);
}
NetInput.prototype.initCanvas = function (media, dims) {
if (media instanceof HTMLCanvasElement) {
this._canvases.push(media);
return;
if (Array.isArray(inputs)) {
this._inputs = inputs.map(function (input) {
if (isTensor_1.isTensor3D(input)) {
// TODO: make sure not to dispose original tensors passed in by the user
return tf.clone(input);
}
// if input is batch type, make sure every canvas has the same dimensions
var canvasDims = this.dims || dims;
this._canvases.push(utils_1.createCanvasFromMedia(media, canvasDims));
};
Object.defineProperty(NetInput.prototype, "canvases", {
return tf.fromPixels(input instanceof HTMLCanvasElement ? input : utils_1.createCanvasFromMedia(input));
});
}
this._inputDimensions = this._inputs.map(function (t) { return t.shape; });
}
Object.defineProperty(NetInput.prototype, "inputs", {
get: function () {
return this._canvases;
return this._inputs;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "width", {
Object.defineProperty(NetInput.prototype, "isManaged", {
get: function () {
return (this._canvases[0] || {}).width;
return this._isManaged;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "height", {
Object.defineProperty(NetInput.prototype, "batchSize", {
get: function () {
return (this._canvases[0] || {}).height;
return this._inputs.length;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "dims", {
Object.defineProperty(NetInput.prototype, "inputDimensions", {
get: function () {
var _a = this, width = _a.width, height = _a.height;
return (width > 0 && height > 0) ? { width: width, height: height } : null;
return this._inputDimensions;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "paddings", {
get: function () {
return this._paddings;
},
enumerable: true,
configurable: true
});
NetInput.prototype.getInputDimensions = function (batchIdx) {
return this._inputDimensions[batchIdx];
};
NetInput.prototype.getInputHeight = function (batchIdx) {
return this._inputDimensions[batchIdx][0];
};
NetInput.prototype.getInputWidth = function (batchIdx) {
return this._inputDimensions[batchIdx][1];
};
NetInput.prototype.getPaddings = function (batchIdx) {
return this._paddings[batchIdx];
};
NetInput.prototype.toBatchTensor = function (inputSize, isCenterInputs) {
var _this = this;
if (isCenterInputs === void 0) { isCenterInputs = true; }
return tf.tidy(function () {
var inputTensors = _this._inputs.map(function (inputTensor) {
var _a = inputTensor.shape, originalHeight = _a[0], originalWidth = _a[1];
var imgTensor = inputTensor.expandDims().toFloat();
imgTensor = padToSquare_1.padToSquare(imgTensor, isCenterInputs);
var _b = imgTensor.shape.slice(1), heightAfterPadding = _b[0], widthAfterPadding = _b[1];
if (heightAfterPadding !== inputSize || widthAfterPadding !== inputSize) {
imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);
}
_this._paddings.push(new Point_1.Point(widthAfterPadding - originalWidth, heightAfterPadding - originalHeight));
return imgTensor;
});
var batchTensor = tf.stack(inputTensors).as4D(_this.batchSize, inputSize, inputSize, 3);
if (_this.isManaged) {
_this.dispose();
}
return batchTensor;
});
};
/**
* By setting the isManaged flag, all newly created tensors will be automatically
* automatically disposed after the batch tensor has been created
*/
NetInput.prototype.managed = function () {
this._isManaged = true;
return this;
};
NetInput.prototype.dispose = function () {
this._inputs.forEach(function (t) { return t.dispose(); });
};
return NetInput;
}());
exports.NetInput = NetInput;
......
build/NetInput.js.map
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\ No newline at end of file
build/allFacesFactory.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { FullFaceDescription } from './FullFaceDescription';
import { NetInput } from './NetInput';
export declare function allFacesFactory(detectionNet: FaceDetectionNet, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput, minConfidence: number) => Promise<FullFaceDescription[]>;
import { TNetInput } from './types';
export declare function allFacesFactory(detectionNet: FaceDetectionNet, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence: number) => Promise<FullFaceDescription[]>;
build/allFacesFactory.js
......@@ -12,21 +12,25 @@ function allFacesFactory(detectionNet, landmarkNet, recognitionNet) {
case 0: return [4 /*yield*/, detectionNet.locateFaces(input, minConfidence)];
case 1:
detections = _a.sent();
return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, detections)];
return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, detections)
/**
const faceLandmarksByFace = await Promise.all(faceTensors.map(
faceTensor => landmarkNet.detectLandmarks(faceTensor)
)) as FaceLandmarks[]
*/
];
case 2:
faceTensors = _a.sent();
return [4 /*yield*/, Promise.all(faceTensors.map(function (faceTensor) { return landmarkNet.detectLandmarks(faceTensor); }))];
return [4 /*yield*/, landmarkNet.detectLandmarks(faceTensors)];
case 3:
faceLandmarksByFace = _a.sent();
faceTensors.forEach(function (t) { return t.dispose(); });
return [4 /*yield*/, Promise.all(faceLandmarksByFace.map(function (landmarks, i) { return landmarks.align(detections[i].getBox()); }))];
case 4:
alignedFaceBoxes = _a.sent();
alignedFaceBoxes = faceLandmarksByFace.map(function (landmarks, i) { return landmarks.align(detections[i].getBox()); });
return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, alignedFaceBoxes)];
case 5:
case 4:
alignedFaceTensors = _a.sent();
return [4 /*yield*/, Promise.all(alignedFaceTensors.map(function (faceTensor) { return recognitionNet.computeFaceDescriptor(faceTensor); }))];
case 6:
case 5:
descriptors = _a.sent();
alignedFaceTensors.forEach(function (t) { return t.dispose(); });
return [2 /*return*/, detections.map(function (detection, i) {
......
build/allFacesFactory.js.map
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\ No newline at end of file
build/commons/getImageTensor.js
......@@ -2,21 +2,19 @@
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var NetInput_1 = require("../NetInput");
var tensorTo4D_1 = require("./tensorTo4D");
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();
return tensorTo4D_1.tensorTo4D(input);
}
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();
}));
if (input.canvases.length > 1) {
throw new Error('getImageTensor - batch input is not accepted here');
}
return tf.fromPixels(input.canvases[0]).expandDims(0).toFloat();
});
}
exports.getImageTensor = getImageTensor;
......
build/commons/getImageTensor.js.map
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\ No newline at end of file
build/commons/isMediaElement.d.ts 0 → 100644
export declare function isMediaElement(input: any): boolean;
build/commons/isMediaElement.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function isMediaElement(input) {
return input instanceof HTMLImageElement
|| input instanceof HTMLVideoElement
|| input instanceof HTMLCanvasElement;
}
exports.isMediaElement = isMediaElement;
//# sourceMappingURL=isMediaElement.js.map
\ No newline at end of file
build/commons/isMediaElement.js.map 0 → 100644
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\ No newline at end of file
build/commons/isTensor.d.ts
import * as tf from '@tensorflow/tfjs-core';
export declare function isTensor(tensor: tf.Tensor, dim: number): boolean;
export declare function isTensor1D(tensor: tf.Tensor): boolean;
export declare function isTensor2D(tensor: tf.Tensor): boolean;
export declare function isTensor3D(tensor: tf.Tensor): boolean;
export declare function isTensor4D(tensor: tf.Tensor): boolean;
export declare function isTensor(tensor: any, dim: number): boolean;
export declare function isTensor1D(tensor: any): boolean;
export declare function isTensor2D(tensor: any): boolean;
export declare function isTensor3D(tensor: any): boolean;
export declare function isTensor4D(tensor: any): boolean;
build/commons/isTensor.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commons/tensorTo4D.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
export declare function tensorTo4D(input: tf.Tensor): tf.Tensor4D;
build/commons/tensorTo4D.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function tensorTo4D(input) {
if (input.rank !== 3 && input.rank !== 4) {
throw new Error('tensorTo4D - input tensor must be of rank 3 or 4');
}
return tf.tidy(function () { return input.rank === 3 ? input.expandDims(0) : input; });
}
exports.tensorTo4D = tensorTo4D;
//# sourceMappingURL=tensorTo4D.js.map
\ No newline at end of file
build/commons/tensorTo4D.js.map 0 → 100644
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\ No newline at end of file
build/commons/toInputTensor.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from '../NetInput';
import { BatchReshapeInfo } from './types';
export declare function toInputTensor(input: tf.Tensor | tf.Tensor[] | NetInput, inputSize: number, center?: boolean): {
batchTensor: tf.Tensor4D;
batchInfo: BatchReshapeInfo[];
};
build/commons/toInputTensor.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var NetInput_1 = require("../NetInput");
var padToSquare_1 = require("../padToSquare");
var tensorTo4D_1 = require("./tensorTo4D");
function toInputTensor(input, inputSize, center) {
if (center === void 0) { center = true; }
if (!(input instanceof tf.Tensor) && !(input instanceof NetInput_1.NetInput)) {
throw new Error('toInputTensor - expected input to be a tensor of an instance of NetInput');
}
return tf.tidy(function () {
var inputTensors = input instanceof NetInput_1.NetInput
? input.canvases.map(function (c) { return tf.expandDims(tf.fromPixels(c)); })
: [tensorTo4D_1.tensorTo4D(input)];
var preprocessedTensors = [];
var batchInfo = [];
inputTensors.forEach(function (inputTensor) {
var _a = inputTensor.shape.slice(1), originalHeight = _a[0], originalWidth = _a[1];
var imgTensor = padToSquare_1.padToSquare(inputTensor.toFloat(), center);
var _b = imgTensor.shape.slice(1), heightAfterPadding = _b[0], widthAfterPadding = _b[1];
if (heightAfterPadding !== inputSize || widthAfterPadding !== inputSize) {
imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);
}
preprocessedTensors.push(imgTensor);
batchInfo.push({
originalWidth: originalWidth,
originalHeight: originalHeight,
paddingX: widthAfterPadding - originalWidth,
paddingY: heightAfterPadding - originalHeight
});
});
var batchSize = inputTensors.length;
return {
batchTensor: tf.stack(preprocessedTensors).as4D(batchSize, inputSize, inputSize, 3),
batchInfo: batchInfo
};
});
}
exports.toInputTensor = toInputTensor;
//# sourceMappingURL=toInputTensor.js.map
\ No newline at end of file
build/commons/toInputTensor.js.map 0 → 100644
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\ No newline at end of file
build/commons/types.d.ts
......@@ -4,3 +4,9 @@ export declare type ConvParams = {
bias: tf.Tensor1D;
};
export declare type ExtractWeightsFunction = (numWeights: number) => Float32Array;
export declare type BatchReshapeInfo = {
originalWidth: number;
originalHeight: number;
paddingX: number;
paddingY: number;
};
build/drawing/index.js
......@@ -26,7 +26,7 @@ function drawText(ctx, x, y, text, options) {
}
exports.drawText = drawText;
function drawDetection(canvasArg, detection, options) {
var canvas = utils_1.getElement(canvasArg);
var canvas = utils_1.resolveInput(canvasArg);
if (!(canvas instanceof HTMLCanvasElement)) {
throw new Error('drawBox - expected canvas to be of type: HTMLCanvasElement');
}
......@@ -66,7 +66,7 @@ function drawContour(ctx, points, isClosed) {
ctx.stroke();
}
function drawLandmarks(canvasArg, faceLandmarks, options) {
var canvas = utils_1.getElement(canvasArg);
var canvas = utils_1.resolveInput(canvasArg);
if (!(canvas instanceof HTMLCanvasElement)) {
throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement');
}
......
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 { NetInput } from './NetInput';
import { Rect } from './Rect';
import { TNetInput } from './types';
/**
......@@ -13,4 +12,4 @@ 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(input: tf.Tensor | NetInput | TNetInput, detections: Array<FaceDetection | Rect>): Promise<tf.Tensor4D[]>;
export declare function extractFaceTensors(input: TNetInput, detections: Array<FaceDetection | Rect>): Promise<tf.Tensor4D[]>;
build/extractFaceTensors.js
......@@ -2,7 +2,6 @@
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 toNetInput_1 = require("./toNetInput");
/**
......@@ -17,23 +16,21 @@ var toNetInput_1 = require("./toNetInput");
*/
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;
var netInput;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
case 1:
netInput = _a.sent();
if (netInput.batchSize > 1) {
if (netInput.isManaged) {
netInput.dispose();
}
throw new Error('extractFaceTensors - batchSize > 1 not supported');
}
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];
var imgTensor = netInput.inputs[0].expandDims().toFloat();
var _a = imgTensor.shape.slice(1), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];
var boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
? det.forSize(imgWidth, imgHeight).getBox().floor()
: det; });
......@@ -41,6 +38,9 @@ function extractFaceTensors(input, detections) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
return tf.slice(imgTensor, [0, y, x, 0], [1, height, width, numChannels]);
});
if (netInput.isManaged) {
netInput.dispose();
}
return faceTensors;
})];
}
......
build/extractFaceTensors.js.map
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\ No newline at end of file
build/extractFaces.d.ts
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { Rect } from './Rect';
import { TNetInput } from './types';
/**
* Extracts the image regions containing the detected faces.
*
......@@ -7,4 +8,4 @@ import { Rect } from './Rect';
* @param detections The face detection results or face bounding boxes for that image.
* @returns The Canvases of the corresponding image region for each detected face.
*/
export declare function extractFaces(image: HTMLCanvasElement, detections: Array<FaceDetection | Rect>): HTMLCanvasElement[];
export declare function extractFaces(input: TNetInput, detections: Array<FaceDetection | Rect>): Promise<HTMLCanvasElement[]>;
build/extractFaces.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var FaceDetection_1 = require("./faceDetectionNet/FaceDetection");
var toNetInput_1 = require("./toNetInput");
var utils_1 = require("./utils");
/**
* Extracts the image regions containing the detected faces.
......@@ -9,17 +11,41 @@ var utils_1 = require("./utils");
* @param detections The face detection results or face bounding boxes for that image.
* @returns The Canvases of the corresponding image region for each detected face.
*/
function extractFaces(image, detections) {
var ctx = utils_1.getContext2dOrThrow(image);
var boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
? det.forSize(image.width, image.height).getBox().floor()
function extractFaces(input, detections) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var canvas, netInput, ctx, boxes;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0:
canvas = input;
if (!!(input instanceof HTMLCanvasElement)) return [3 /*break*/, 3];
return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
case 1:
netInput = _a.sent();
if (netInput.batchSize > 1) {
if (netInput.isManaged) {
netInput.dispose();
}
throw new Error('extractFaces - batchSize > 1 not supported');
}
return [4 /*yield*/, utils_1.imageTensorToCanvas(netInput.inputs[0])];
case 2:
canvas = _a.sent();
_a.label = 3;
case 3:
ctx = utils_1.getContext2dOrThrow(canvas);
boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
? det.forSize(canvas.width, canvas.height).getBox().floor()
: det; });
return boxes.map(function (_a) {
return [2 /*return*/, boxes.map(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var faceImg = utils_1.createCanvas({ width: width, height: height });
utils_1.getContext2dOrThrow(faceImg)
.putImageData(ctx.getImageData(x, y, width, height), 0, 0);
return faceImg;
})];
}
});
});
}
exports.extractFaces = extractFaces;
......
build/extractFaces.js.map
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build/faceDetectionNet/FaceDetectionNet.d.ts
......@@ -6,10 +6,13 @@ export declare class FaceDetectionNet {
private _params;
load(weightsOrUrl?: Float32Array | string): Promise<void>;
extractWeights(weights: Float32Array): void;
private forwardTensor(imgTensor);
forward(input: tf.Tensor | NetInput | TNetInput): Promise<{
forwardInput(input: NetInput): {
boxes: tf.Tensor<tf.Rank.R2>[];
scores: tf.Tensor<tf.Rank.R1>[];
};
forward(input: 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[]>;
locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
}
build/faceDetectionNet/FaceDetectionNet.js
......@@ -2,8 +2,6 @@
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
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");
......@@ -13,7 +11,6 @@ var mobileNetV1_1 = require("./mobileNetV1");
var nonMaxSuppression_1 = require("./nonMaxSuppression");
var outputLayer_1 = require("./outputLayer");
var predictionLayer_1 = require("./predictionLayer");
var resizeLayer_1 = require("./resizeLayer");
var FaceDetectionNet = /** @class */ (function () {
function FaceDetectionNet() {
}
......@@ -42,37 +39,28 @@ var FaceDetectionNet = /** @class */ (function () {
FaceDetectionNet.prototype.extractWeights = function (weights) {
this._params = extractParams_1.extractParams(weights);
};
FaceDetectionNet.prototype.forwardTensor = function (imgTensor) {
FaceDetectionNet.prototype.forwardInput = function (input) {
var _this = this;
if (!this._params) {
throw new Error('FaceDetectionNet - load model before inference');
}
return tf.tidy(function () {
var resized = resizeLayer_1.resizeLayer(imgTensor);
var features = mobileNetV1_1.mobileNetV1(resized, _this._params.mobilenetv1_params);
var batchTensor = input.toBatchTensor(512, false);
var x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1));
var features = mobileNetV1_1.mobileNetV1(x, _this._params.mobilenetv1_params);
var _a = predictionLayer_1.predictionLayer(features.out, features.conv11, _this._params.prediction_layer_params), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
return outputLayer_1.outputLayer(boxPredictions, classPredictions, _this._params.output_layer_params);
});
};
FaceDetectionNet.prototype.forward = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _this = this;
var netInput, _a;
var _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)));
})];
_a = this.forwardInput;
return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
});
......@@ -81,42 +69,27 @@ var FaceDetectionNet = /** @class */ (function () {
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 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;
_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 = _b.boxes, _scores = _b.scores;
var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, paddedHeightRelative, paddedWidthRelative, results;
return tslib_1.__generator(this, function (_d) {
switch (_d.label) {
case 0: return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
case 1:
netInput = _d.sent();
_a = this.forwardInput(netInput), _boxes = _a.boxes, _scores = _a.scores;
boxes = _boxes[0];
scores = _scores[0];
for (i = 1; i < _boxes.length; i++) {
_boxes[i].dispose();
_scores[i].dispose();
}
_d = (_c = Array).from;
_c = (_b = Array).from;
return [4 /*yield*/, scores.data()];
case 4:
scoresData = _d.apply(_c, [_e.sent()]);
case 2:
scoresData = _c.apply(_b, [_d.sent()]);
iouThreshold = 0.5;
indices = nonMaxSuppression_1.nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
paddedHeightRelative = (netInput.getPaddings(0).y + netInput.getInputHeight(0)) / netInput.getInputHeight(0);
paddedWidthRelative = (netInput.getPaddings(0).x + netInput.getInputWidth(0)) / netInput.getInputWidth(0);
results = indices
.map(function (idx) {
var _a = [
......@@ -127,7 +100,10 @@ var FaceDetectionNet = /** @class */ (function () {
Math.max(0, boxes.get(idx, 1)),
Math.min(1.0, boxes.get(idx, 3))
].map(function (val) { return val * paddedWidthRelative; }), left = _b[0], right = _b[1];
return new FaceDetection_1.FaceDetection(scoresData[idx], new Rect_1.Rect(left, top, right - left, bottom - top), imageDimensions);
return new FaceDetection_1.FaceDetection(scoresData[idx], new Rect_1.Rect(left, top, right - left, bottom - top), {
height: netInput.getInputHeight(0),
width: netInput.getInputWidth(0)
});
});
boxes.dispose();
scores.dispose();
......
build/faceDetectionNet/FaceDetectionNet.js.map
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build/faceLandmarkNet/FaceLandmarkNet.d.ts
......@@ -6,7 +6,7 @@ export declare class FaceLandmarkNet {
private _params;
load(weightsOrUrl: Float32Array | string | undefined): Promise<void>;
extractWeights(weights: Float32Array): void;
forwardTensor(imgTensor: tf.Tensor4D): tf.Tensor2D;
forward(input: tf.Tensor | NetInput | TNetInput): Promise<tf.Tensor2D>;
detectLandmarks(input: tf.Tensor | NetInput | TNetInput): Promise<FaceLandmarks>;
forwardInput(input: NetInput): tf.Tensor2D;
forward(input: TNetInput): Promise<tf.Tensor2D>;
detectLandmarks(input: TNetInput): Promise<FaceLandmarks | FaceLandmarks[]>;
}
build/faceLandmarkNet/FaceLandmarkNet.js
......@@ -3,8 +3,6 @@ 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("../commons/getImageTensor");
var padToSquare_1 = require("../padToSquare");
var Point_1 = require("../Point");
var toNetInput_1 = require("../toNetInput");
var utils_1 = require("../utils");
......@@ -47,20 +45,14 @@ var FaceLandmarkNet = /** @class */ (function () {
FaceLandmarkNet.prototype.extractWeights = function (weights) {
this._params = extractParams_1.extractParams(weights);
};
FaceLandmarkNet.prototype.forwardTensor = function (imgTensor) {
FaceLandmarkNet.prototype.forwardInput = function (input) {
var params = this._params;
if (!params) {
throw new Error('FaceLandmarkNet - load model before inference');
}
return tf.tidy(function () {
var _a = imgTensor.shape.slice(), batchSize = _a[0], height = _a[1], width = _a[2];
var x = padToSquare_1.padToSquare(imgTensor, true);
var _b = x.shape.slice(1), heightAfterPadding = _b[0], widthAfterPadding = _b[1];
// work with 128 x 128 sized face images
if (heightAfterPadding !== 128 || widthAfterPadding !== 128) {
x = tf.image.resizeBilinear(x, [128, 128]);
}
var out = conv(x, params.conv0_params);
var batchTensor = input.toBatchTensor(128, true);
var out = conv(batchTensor, params.conv0_params);
out = maxPool(out);
out = conv(out, params.conv1_params);
out = conv(out, params.conv2_params);
......@@ -78,37 +70,34 @@ var FaceLandmarkNet = /** @class */ (function () {
return tf.stack([
tf.fill([68], fillX),
tf.fill([68], fillY)
], 1).as2D(batchSize, 136);
], 1).as2D(1, 136).as1D();
};
/* shift coordinates back, to undo centered padding
((x * widthAfterPadding) - shiftX) / width
((y * heightAfterPadding) - shiftY) / height
x = ((x * widthAfterPadding) - shiftX) / width
y = ((y * heightAfterPadding) - shiftY) / height
*/
var shiftX = Math.floor(Math.abs(widthAfterPadding - width) / 2);
var shiftY = Math.floor(Math.abs(heightAfterPadding - height) / 2);
var landmarkTensor = fc1
.mul(createInterleavedTensor(widthAfterPadding, heightAfterPadding))
.sub(createInterleavedTensor(shiftX, shiftY))
.div(createInterleavedTensor(width, height));
return landmarkTensor;
var landmarkTensors = fc1
.mul(tf.stack(Array.from(Array(input.batchSize), function (_, batchIdx) {
return createInterleavedTensor(input.getPaddings(batchIdx).x + input.getInputWidth(batchIdx), input.getPaddings(batchIdx).y + input.getInputHeight(batchIdx));
})))
.sub(tf.stack(Array.from(Array(input.batchSize), function (_, batchIdx) {
return createInterleavedTensor(Math.floor(input.getPaddings(batchIdx).x / 2), Math.floor(input.getPaddings(batchIdx).y / 2));
})))
.div(tf.stack(Array.from(Array(input.batchSize), function (_, batchIdx) {
return createInterleavedTensor(input.getInputWidth(batchIdx), input.getInputHeight(batchIdx));
})));
return landmarkTensors;
});
};
FaceLandmarkNet.prototype.forward = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var netInput, _a;
var _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*/, this.forwardTensor(getImageTensor_1.getImageTensor(netInput))];
_a = this.forwardInput;
return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
});
......@@ -116,33 +105,35 @@ var FaceLandmarkNet = /** @class */ (function () {
FaceLandmarkNet.prototype.detectLandmarks = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _this = this;
var netInput, _a, imageDimensions, outTensor, faceLandmarksArray, _b, _c, xCoords, yCoords;
return tslib_1.__generator(this, function (_d) {
switch (_d.label) {
var netInput, landmarkTensors, landmarksForBatch;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
case 1:
netInput = _a.sent();
landmarkTensors = tf.unstack(this.forwardInput(netInput));
return [4 /*yield*/, Promise.all(landmarkTensors.map(function (landmarkTensor, batchIdx) { return tslib_1.__awaiter(_this, void 0, void 0, function () {
var landmarksArray, _a, _b, xCoords, yCoords;
return tslib_1.__generator(this, function (_c) {
switch (_c.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 = _d.sent();
_d.label = 3;
case 3:
netInput = _a;
outTensor = 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 };
return _this.forwardTensor(imgTensor);
_b = (_a = Array).from;
return [4 /*yield*/, landmarkTensor.data()];
case 1:
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]); }), {
height: netInput.getInputHeight(batchIdx),
width: netInput.getInputWidth(batchIdx),
})];
}
});
_c = (_b = Array).from;
return [4 /*yield*/, outTensor.data()];
case 4:
faceLandmarksArray = _c.apply(_b, [_d.sent()]);
outTensor.dispose();
xCoords = faceLandmarksArray.filter(function (_, i) { return utils_1.isEven(i); });
yCoords = faceLandmarksArray.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]); }), imageDimensions)];
}); }))];
case 2:
landmarksForBatch = _a.sent();
landmarkTensors.forEach(function (t) { return t.dispose(); });
return [2 /*return*/, landmarksForBatch.length === 1 ? landmarksForBatch[0] : landmarksForBatch];
}
});
});
......
build/faceLandmarkNet/FaceLandmarkNet.js.map
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\ No newline at end of file
build/faceRecognitionNet/FaceRecognitionNet.d.ts
......@@ -5,6 +5,7 @@ export declare class FaceRecognitionNet {
private _params;
load(weightsOrUrl: Float32Array | string | undefined): Promise<void>;
extractWeights(weights: Float32Array): void;
forward(input: tf.Tensor | NetInput | TNetInput): Promise<tf.Tensor2D>;
computeFaceDescriptor(input: tf.Tensor | NetInput | TNetInput): Promise<Float32Array>;
forwardInput(input: NetInput): Promise<tf.Tensor2D>;
forward(input: TNetInput): Promise<tf.Tensor2D>;
computeFaceDescriptor(input: TNetInput): Promise<Float32Array>;
}
build/faceRecognitionNet/FaceRecognitionNet.js
......@@ -2,8 +2,6 @@
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
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");
......@@ -38,33 +36,17 @@ var FaceRecognitionNet = /** @class */ (function () {
FaceRecognitionNet.prototype.extractWeights = function (weights) {
this._params = extractParams_1.extractParams(weights);
};
FaceRecognitionNet.prototype.forward = function (input) {
FaceRecognitionNet.prototype.forwardInput = 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:
return tslib_1.__generator(this, function (_a) {
if (!this._params) {
throw new Error('FaceRecognitionNet - 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 = _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]);
}
x = normalize_1.normalize(x);
var out = convLayer_1.convDown(x, _this._params.conv32_down);
var batchTensor = input.toBatchTensor(150, true);
var normalized = normalize_1.normalize(batchTensor);
var out = convLayer_1.convDown(normalized, _this._params.conv32_down);
out = tf.maxPool(out, 3, 2, 'valid');
out = residualLayer_1.residual(out, _this._params.conv32_1);
out = residualLayer_1.residual(out, _this._params.conv32_2);
......@@ -84,30 +66,35 @@ var FaceRecognitionNet = /** @class */ (function () {
var fullyConnected = tf.matMul(globalAvg, _this._params.fc);
return fullyConnected;
})];
});
});
};
FaceRecognitionNet.prototype.forward = function (input) {
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)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
});
};
FaceRecognitionNet.prototype.computeFaceDescriptor = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var netInput, _a, result, data;
var result, _a, data;
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)];
_a = this.forward;
return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
case 1: return [4 /*yield*/, _a.apply(this, [_b.sent()])];
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 5:
case 3:
data = _b.sent();
result.dispose();
return [2 /*return*/, data];
......
build/faceRecognitionNet/FaceRecognitionNet.js.map
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\ No newline at end of file
build/globalApi.d.ts
......@@ -4,9 +4,9 @@ import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
import { FaceLandmarks } from './faceLandmarkNet/FaceLandmarks';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { FullFaceDescription } from './FullFaceDescription';
import { NetInput } from './NetInput';
import { TNetInput } from './types';
import { FullFaceDescription } from './FullFaceDescription';
export declare const detectionNet: FaceDetectionNet;
export declare const landmarkNet: FaceLandmarkNet;
export declare const recognitionNet: FaceRecognitionNet;
......@@ -14,7 +14,7 @@ export declare function loadFaceDetectionModel(url: string): Promise<void>;
export declare function loadFaceLandmarkModel(url: string): Promise<void>;
export declare function loadFaceRecognitionModel(url: string): Promise<void>;
export declare function loadModels(url: string): Promise<[void, void, void]>;
export declare function locateFaces(input: tf.Tensor | NetInput | TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
export declare function detectLandmarks(input: tf.Tensor | NetInput | TNetInput): Promise<FaceLandmarks>;
export declare function computeFaceDescriptor(input: tf.Tensor | NetInput | TNetInput): Promise<Float32Array>;
export declare function locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
export declare function detectLandmarks(input: TNetInput): Promise<FaceLandmarks | FaceLandmarks[]>;
export declare function computeFaceDescriptor(input: TNetInput): Promise<Float32Array>;
export declare const allFaces: (input: tf.Tensor | NetInput | TNetInput, minConfidence: number) => Promise<FullFaceDescription[]>;
build/globalApi.js.map
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\ No newline at end of file
build/padToSquare.js.map
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\ No newline at end of file
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\ No newline at end of file
build/toNetInput.d.ts
......@@ -5,6 +5,8 @@ import { TNetInput } from './types';
* to be finished loading.
*
* @param input The input, which can be a media element or an array of different media elements.
* @param manageCreatedInput If a new NetInput instance is created from the inputs, this flag
* determines, whether to set the NetInput as managed or not.
* @returns A NetInput instance, which can be passed into one of the neural networks.
*/
export declare function toNetInput(input: NetInput | TNetInput): Promise<NetInput>;
export declare function toNetInput(inputs: TNetInput, manageCreatedInput?: boolean): Promise<NetInput>;
build/toNetInput.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var isMediaElement_1 = require("./commons/isMediaElement");
var isTensor_1 = require("./commons/isTensor");
var NetInput_1 = require("./NetInput");
var utils_1 = require("./utils");
/**
......@@ -8,39 +10,61 @@ var utils_1 = require("./utils");
* to be finished loading.
*
* @param input The input, which can be a media element or an array of different media elements.
* @param manageCreatedInput If a new NetInput instance is created from the inputs, this flag
* determines, whether to set the NetInput as managed or not.
* @returns A NetInput instance, which can be passed into one of the neural networks.
*/
function toNetInput(input) {
function toNetInput(inputs, manageCreatedInput) {
if (manageCreatedInput === void 0) { manageCreatedInput = false; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var mediaArgArray, medias;
var afterCreate, inputArgArray, getIdxHint, inputArray;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0:
if (input instanceof NetInput_1.NetInput) {
return [2 /*return*/, input];
if (inputs instanceof NetInput_1.NetInput) {
return [2 /*return*/, inputs];
}
mediaArgArray = Array.isArray(input)
? input
: [input];
if (!mediaArgArray.length) {
afterCreate = function (netInput) { return manageCreatedInput
? netInput.managed()
: netInput; };
if (isTensor_1.isTensor4D(inputs)) {
return [2 /*return*/, afterCreate(new NetInput_1.NetInput(inputs))];
}
inputArgArray = Array.isArray(inputs)
? inputs
: [inputs];
if (!inputArgArray.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");
getIdxHint = function (idx) { return Array.isArray(inputs) ? " at input index " + idx + ":" : ''; };
inputArray = inputArgArray
.map(utils_1.resolveInput)
.map(function (input, i) {
if (isTensor_1.isTensor4D(input)) {
// if tf.Tensor4D is passed in the input array, the batch size has to be 1
var batchSize = input.shape[0];
if (batchSize !== 1) {
throw new Error("toNetInput -" + getIdxHint(i) + " tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array");
}
// to tf.Tensor3D
return input.reshape(input.shape.slice(1));
}
return input;
});
inputArray.forEach(function (input, i) {
if (!isMediaElement_1.isMediaElement(input) && !isTensor_1.isTensor3D(input)) {
if (typeof inputArgArray[i] === 'string') {
throw new Error("toNetInput -" + getIdxHint(i) + " string passed, but could not resolve HTMLElement for element id " + inputArgArray[i]);
}
throw new Error("toNetInput -" + idxHint + " expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement, or to be an element id");
throw new Error("toNetInput -" + getIdxHint(i) + " expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, 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); }))];
return [4 /*yield*/, Promise.all(inputArray.map(function (input) { return isMediaElement_1.isMediaElement(input) && utils_1.awaitMediaLoaded(input); }))];
case 1:
// wait for all media elements being loaded
_a.sent();
return [2 /*return*/, new NetInput_1.NetInput(medias)];
return [2 /*return*/, afterCreate(new NetInput_1.NetInput(inputArray))];
}
});
});
......
build/toNetInput.js.map
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\ No newline at end of file
build/types.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from './NetInput';
export declare type TMediaElement = HTMLImageElement | HTMLVideoElement | HTMLCanvasElement;
export declare type TNetInputArg = string | TMediaElement;
export declare type TNetInput = TNetInputArg | Array<TNetInputArg>;
export declare type TResolvedNetInput = TMediaElement | tf.Tensor3D | tf.Tensor4D;
export declare type TNetInputArg = string | TResolvedNetInput;
export declare type TNetInput = TNetInputArg | Array<TNetInputArg> | NetInput | tf.Tensor4D;
export declare type Dimensions = {
width: number;
height: number;
......
build/utils.d.ts
......@@ -3,7 +3,7 @@ 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 resolveInput(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;
......@@ -14,4 +14,4 @@ export declare function getMediaDimensions(media: HTMLImageElement | HTMLVideoEl
height: number;
};
export declare function bufferToImage(buf: Blob): Promise<HTMLImageElement>;
export declare function imageTensorToCanvas(imgTensor: tf.Tensor4D, canvas?: HTMLCanvasElement): Promise<HTMLCanvasElement>;
export declare function imageTensorToCanvas(imgTensor: tf.Tensor, canvas?: HTMLCanvasElement): Promise<HTMLCanvasElement>;
build/utils.js
......@@ -2,6 +2,7 @@
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var isTensor_1 = require("./commons/isTensor");
function isFloat(num) {
return num % 1 !== 0;
}
......@@ -14,13 +15,13 @@ function round(num) {
return Math.floor(num * 100) / 100;
}
exports.round = round;
function getElement(arg) {
function resolveInput(arg) {
if (typeof arg === 'string') {
return document.getElementById(arg);
}
return arg;
}
exports.getElement = getElement;
exports.resolveInput = resolveInput;
function isLoaded(media) {
return (media instanceof HTMLImageElement && media.complete)
|| (media instanceof HTMLVideoElement && media.readyState >= 3);
......@@ -105,12 +106,12 @@ function bufferToImage(buf) {
exports.bufferToImage = bufferToImage;
function imageTensorToCanvas(imgTensor, canvas) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var targetCanvas, _a, _, height, width, numChannels;
var targetCanvas, _a, height, width, numChannels;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
targetCanvas = canvas || document.createElement('canvas');
_a = imgTensor.shape, _ = _a[0], height = _a[1], width = _a[2], numChannels = _a[3];
_a = imgTensor.shape.slice(isTensor_1.isTensor4D(imgTensor) ? 1 : 0), height = _a[0], width = _a[1], numChannels = _a[2];
return [4 /*yield*/, tf.toPixels(imgTensor.as3D(height, width, numChannels).toInt(), targetCanvas)];
case 1:
_b.sent();
......
build/utils.js.map
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\ No newline at end of file
dist/face-api.js
......@@ -268,6 +268,88 @@
return FullFaceDescription;
}());
function isTensor(tensor$$1, dim) {
return tensor$$1 instanceof Tensor && tensor$$1.shape.length === dim;
}
function isTensor1D(tensor$$1) {
return isTensor(tensor$$1, 1);
}
function isTensor2D(tensor$$1) {
return isTensor(tensor$$1, 2);
}
function isTensor3D(tensor$$1) {
return isTensor(tensor$$1, 3);
}
function isTensor4D(tensor$$1) {
return isTensor(tensor$$1, 4);
}
/**
* Pads the smaller dimension of an image tensor with zeros, such that width === height.
*
* @param imgTensor The image tensor.
* @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image.
* @returns The padded tensor with width === height.
*/
function padToSquare(imgTensor, isCenterImage) {
if (isCenterImage === void 0) { isCenterImage = false; }
return tidy(function () {
var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
if (height === width) {
return imgTensor;
}
var dimDiff = Math.abs(height - width);
var paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));
var paddingAxis = height > width ? 2 : 1;
var createPaddingTensor = function (paddingAmount) {
var paddingTensorShape = imgTensor.shape.slice();
paddingTensorShape[paddingAxis] = paddingAmount;
return fill(paddingTensorShape, 0);
};
var paddingTensorAppend = createPaddingTensor(paddingAmount);
var remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
var paddingTensorPrepend = isCenterImage && remainingPaddingAmount
? createPaddingTensor(remainingPaddingAmount)
: null;
var tensorsToStack = [
paddingTensorPrepend,
imgTensor,
paddingTensorAppend
]
.filter(function (t) { return t !== null; });
return concat(tensorsToStack, paddingAxis);
});
}
var Point = /** @class */ (function () {
function Point(x, y) {
this.x = x;
this.y = y;
}
Point.prototype.add = function (pt) {
return new Point(this.x + pt.x, this.y + pt.y);
};
Point.prototype.sub = function (pt) {
return new Point(this.x - pt.x, this.y - pt.y);
};
Point.prototype.mul = function (pt) {
return new Point(this.x * pt.x, this.y * pt.y);
};
Point.prototype.div = function (pt) {
return new Point(this.x / pt.x, this.y / pt.y);
};
Point.prototype.abs = function () {
return new Point(Math.abs(this.x), Math.abs(this.y));
};
Point.prototype.magnitude = function () {
return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2));
};
Point.prototype.floor = function () {
return new Point(Math.floor(this.x), Math.floor(this.y));
};
return Point;
}());
/*! *****************************************************************************
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use
......@@ -329,7 +411,7 @@
function round$1(num) {
return Math.floor(num * 100) / 100;
}
function getElement(arg) {
function resolveInput(arg) {
if (typeof arg === 'string') {
return document.getElementById(arg);
}
......@@ -412,12 +494,12 @@
}
function imageTensorToCanvas(imgTensor, canvas) {
return __awaiter$1(this, void 0, void 0, function () {
var targetCanvas, _a, _, height, width, numChannels;
var targetCanvas, _a, height, width, numChannels;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0:
targetCanvas = canvas || document.createElement('canvas');
_a = imgTensor.shape, _ = _a[0], height = _a[1], width = _a[2], numChannels = _a[3];
_a = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0), height = _a[0], width = _a[1], numChannels = _a[2];
return [4 /*yield*/, toPixels(imgTensor.as3D(height, width, numChannels).toInt(), targetCanvas)];
case 1:
_b.sent();
......@@ -428,79 +510,106 @@
}
var NetInput = /** @class */ (function () {
function NetInput(medias, dims) {
var _this = this;
this._canvases = [];
medias.forEach(function (m) { return _this.initCanvas(m, dims); });
function NetInput(inputs) {
this._inputs = [];
this._isManaged = false;
this._inputDimensions = [];
this._paddings = [];
if (isTensor4D(inputs)) {
this._inputs = unstack(inputs);
}
NetInput.prototype.initCanvas = function (media, dims) {
if (media instanceof HTMLCanvasElement) {
this._canvases.push(media);
return;
if (Array.isArray(inputs)) {
this._inputs = inputs.map(function (input) {
if (isTensor3D(input)) {
// TODO: make sure not to dispose original tensors passed in by the user
return clone(input);
}
// if input is batch type, make sure every canvas has the same dimensions
var canvasDims = this.dims || dims;
this._canvases.push(createCanvasFromMedia(media, canvasDims));
};
Object.defineProperty(NetInput.prototype, "canvases", {
return fromPixels(input instanceof HTMLCanvasElement ? input : createCanvasFromMedia(input));
});
}
this._inputDimensions = this._inputs.map(function (t) { return t.shape; });
}
Object.defineProperty(NetInput.prototype, "inputs", {
get: function () {
return this._canvases;
return this._inputs;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "width", {
Object.defineProperty(NetInput.prototype, "isManaged", {
get: function () {
return (this._canvases[0] || {}).width;
return this._isManaged;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "height", {
Object.defineProperty(NetInput.prototype, "batchSize", {
get: function () {
return (this._canvases[0] || {}).height;
return this._inputs.length;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "dims", {
Object.defineProperty(NetInput.prototype, "inputDimensions", {
get: function () {
var _a = this, width = _a.width, height = _a.height;
return (width > 0 && height > 0) ? { width: width, height: height } : null;
return this._inputDimensions;
},
enumerable: true,
configurable: true
});
return NetInput;
}());
var Point = /** @class */ (function () {
function Point(x, y) {
this.x = x;
this.y = y;
}
Point.prototype.add = function (pt) {
return new Point(this.x + pt.x, this.y + pt.y);
Object.defineProperty(NetInput.prototype, "paddings", {
get: function () {
return this._paddings;
},
enumerable: true,
configurable: true
});
NetInput.prototype.getInputDimensions = function (batchIdx) {
return this._inputDimensions[batchIdx];
};
Point.prototype.sub = function (pt) {
return new Point(this.x - pt.x, this.y - pt.y);
NetInput.prototype.getInputHeight = function (batchIdx) {
return this._inputDimensions[batchIdx][0];
};
Point.prototype.mul = function (pt) {
return new Point(this.x * pt.x, this.y * pt.y);
NetInput.prototype.getInputWidth = function (batchIdx) {
return this._inputDimensions[batchIdx][1];
};
Point.prototype.div = function (pt) {
return new Point(this.x / pt.x, this.y / pt.y);
NetInput.prototype.getPaddings = function (batchIdx) {
return this._paddings[batchIdx];
};
Point.prototype.abs = function () {
return new Point(Math.abs(this.x), Math.abs(this.y));
NetInput.prototype.toBatchTensor = function (inputSize, isCenterInputs) {
var _this = this;
if (isCenterInputs === void 0) { isCenterInputs = true; }
return tidy(function () {
var inputTensors = _this._inputs.map(function (inputTensor) {
var _a = inputTensor.shape, originalHeight = _a[0], originalWidth = _a[1];
var imgTensor = inputTensor.expandDims().toFloat();
imgTensor = padToSquare(imgTensor, isCenterInputs);
var _b = imgTensor.shape.slice(1), heightAfterPadding = _b[0], widthAfterPadding = _b[1];
if (heightAfterPadding !== inputSize || widthAfterPadding !== inputSize) {
imgTensor = image.resizeBilinear(imgTensor, [inputSize, inputSize]);
}
_this._paddings.push(new Point(widthAfterPadding - originalWidth, heightAfterPadding - originalHeight));
return imgTensor;
});
var batchTensor = stack(inputTensors).as4D(_this.batchSize, inputSize, inputSize, 3);
if (_this.isManaged) {
_this.dispose();
}
return batchTensor;
});
};
Point.prototype.magnitude = function () {
return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2));
/**
* By setting the isManaged flag, all newly created tensors will be automatically
* automatically disposed after the batch tensor has been created
*/
NetInput.prototype.managed = function () {
this._isManaged = true;
return this;
};
Point.prototype.floor = function () {
return new Point(Math.floor(this.x), Math.floor(this.y));
NetInput.prototype.dispose = function () {
this._inputs.forEach(function (t) { return t.dispose(); });
};
return Point;
return NetInput;
}());
var Rect = /** @class */ (function () {
......@@ -551,7 +660,7 @@
ctx.fillText(text, x + padText, y + padText + (drawOptions.fontSize * 0.6));
}
function drawDetection(canvasArg, detection, options) {
var canvas = getElement(canvasArg);
var canvas = resolveInput(canvasArg);
if (!(canvas instanceof HTMLCanvasElement)) {
throw new Error('drawBox - expected canvas to be of type: HTMLCanvasElement');
}
......@@ -590,7 +699,7 @@
ctx.stroke();
}
function drawLandmarks(canvasArg, faceLandmarks, options) {
var canvas = getElement(canvasArg);
var canvas = resolveInput(canvasArg);
if (!(canvas instanceof HTMLCanvasElement)) {
throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement');
}
......@@ -658,43 +767,10 @@
return FaceDetection;
}());
/**
* Extracts the image regions containing the detected faces.
*
* @param input The image that face detection has been performed on.
* @param detections The face detection results or face bounding boxes for that image.
* @returns The Canvases of the corresponding image region for each detected face.
*/
function extractFaces(image, detections) {
var ctx = getContext2dOrThrow(image);
var boxes = detections.map(function (det) { return det instanceof FaceDetection
? det.forSize(image.width, image.height).getBox().floor()
: det; });
return boxes.map(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var faceImg = createCanvas({ width: width, height: height });
getContext2dOrThrow(faceImg)
.putImageData(ctx.getImageData(x, y, width, height), 0, 0);
return faceImg;
});
}
function getImageTensor(input) {
return tidy(function () {
if (input instanceof 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)) {
throw new Error('getImageTensor - expected input to be a tensor or an instance of NetInput');
}
return concat(input.canvases.map(function (canvas) {
return fromPixels(canvas).expandDims(0).toFloat();
}));
});
function isMediaElement(input) {
return input instanceof HTMLImageElement
|| input instanceof HTMLVideoElement
|| input instanceof HTMLCanvasElement;
}
/**
......@@ -702,39 +778,106 @@
* to be finished loading.
*
* @param input The input, which can be a media element or an array of different media elements.
* @param manageCreatedInput If a new NetInput instance is created from the inputs, this flag
* determines, whether to set the NetInput as managed or not.
* @returns A NetInput instance, which can be passed into one of the neural networks.
*/
function toNetInput(input) {
function toNetInput(inputs, manageCreatedInput) {
if (manageCreatedInput === void 0) { manageCreatedInput = false; }
return __awaiter$1(this, void 0, void 0, function () {
var mediaArgArray, medias;
var afterCreate, inputArgArray, getIdxHint, inputArray;
return __generator$1(this, function (_a) {
switch (_a.label) {
case 0:
if (input instanceof NetInput) {
return [2 /*return*/, input];
}
mediaArgArray = Array.isArray(input)
? input
: [input];
if (!mediaArgArray.length) {
if (inputs instanceof NetInput) {
return [2 /*return*/, inputs];
}
afterCreate = function (netInput) { return manageCreatedInput
? netInput.managed()
: netInput; };
if (isTensor4D(inputs)) {
return [2 /*return*/, afterCreate(new NetInput(inputs))];
}
inputArgArray = Array.isArray(inputs)
? inputs
: [inputs];
if (!inputArgArray.length) {
throw new Error('toNetInput - empty array passed as input');
}
medias = mediaArgArray.map(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");
getIdxHint = function (idx) { return Array.isArray(inputs) ? " at input index " + idx + ":" : ''; };
inputArray = inputArgArray
.map(resolveInput)
.map(function (input, i) {
if (isTensor4D(input)) {
// if tf.Tensor4D is passed in the input array, the batch size has to be 1
var batchSize = input.shape[0];
if (batchSize !== 1) {
throw new Error("toNetInput -" + getIdxHint(i) + " tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array");
}
// to tf.Tensor3D
return input.reshape(input.shape.slice(1));
}
return input;
});
inputArray.forEach(function (input, i) {
if (!isMediaElement(input) && !isTensor3D(input)) {
if (typeof inputArgArray[i] === 'string') {
throw new Error("toNetInput -" + getIdxHint(i) + " string passed, but could not resolve HTMLElement for element id " + inputArgArray[i]);
}
throw new Error("toNetInput -" + idxHint + " expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement, or to be an element id");
throw new Error("toNetInput -" + getIdxHint(i) + " expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id");
}
});
// wait for all media elements being loaded
return [4 /*yield*/, Promise.all(medias.map(function (media) { return awaitMediaLoaded(media); }))];
return [4 /*yield*/, Promise.all(inputArray.map(function (input) { return isMediaElement(input) && awaitMediaLoaded(input); }))];
case 1:
// wait for all media elements being loaded
_a.sent();
return [2 /*return*/, new NetInput(medias)];
return [2 /*return*/, afterCreate(new NetInput(inputArray))];
}
});
});
}
/**
* Extracts the image regions containing the detected faces.
*
* @param input The image that face detection has been performed on.
* @param detections The face detection results or face bounding boxes for that image.
* @returns The Canvases of the corresponding image region for each detected face.
*/
function extractFaces(input, detections) {
return __awaiter$1(this, void 0, void 0, function () {
var canvas, netInput, ctx, boxes;
return __generator$1(this, function (_a) {
switch (_a.label) {
case 0:
canvas = input;
if (!!(input instanceof HTMLCanvasElement)) return [3 /*break*/, 3];
return [4 /*yield*/, toNetInput(input, true)];
case 1:
netInput = _a.sent();
if (netInput.batchSize > 1) {
if (netInput.isManaged) {
netInput.dispose();
}
throw new Error('extractFaces - batchSize > 1 not supported');
}
return [4 /*yield*/, imageTensorToCanvas(netInput.inputs[0])];
case 2:
canvas = _a.sent();
_a.label = 3;
case 3:
ctx = getContext2dOrThrow(canvas);
boxes = detections.map(function (det) { return det instanceof FaceDetection
? det.forSize(canvas.width, canvas.height).getBox().floor()
: det; });
return [2 /*return*/, boxes.map(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var faceImg = createCanvas({ width: width, height: height });
getContext2dOrThrow(faceImg)
.putImageData(ctx.getImageData(x, y, width, height), 0, 0);
return faceImg;
})];
}
});
});
......@@ -752,23 +895,21 @@
*/
function extractFaceTensors(input, detections) {
return __awaiter$1(this, void 0, void 0, function () {
var image$$1, _a;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0:
if (!(input instanceof Tensor)) return [3 /*break*/, 1];
_a = input;
return [3 /*break*/, 3];
case 1: return [4 /*yield*/, toNetInput(input)];
case 2:
_a = _b.sent();
_b.label = 3;
case 3:
image$$1 = _a;
var netInput;
return __generator$1(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, toNetInput(input, true)];
case 1:
netInput = _a.sent();
if (netInput.batchSize > 1) {
if (netInput.isManaged) {
netInput.dispose();
}
throw new Error('extractFaceTensors - batchSize > 1 not supported');
}
return [2 /*return*/, tidy(function () {
var imgTensor = getImageTensor(image$$1);
// TODO handle batches
var _a = imgTensor.shape, batchSize = _a[0], imgHeight = _a[1], imgWidth = _a[2], numChannels = _a[3];
var imgTensor = netInput.inputs[0].expandDims().toFloat();
var _a = imgTensor.shape.slice(1), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];
var boxes = detections.map(function (det) { return det instanceof FaceDetection
? det.forSize(imgWidth, imgHeight).getBox().floor()
: det; });
......@@ -776,6 +917,9 @@
var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
return slice(imgTensor, [0, y, x, 0], [1, height, width, numChannels]);
});
if (netInput.isManaged) {
netInput.dispose();
}
return faceTensors;
})];
}
......@@ -783,43 +927,6 @@
});
}
/**
* Pads the smaller dimension of an image tensor with zeros, such that width === height.
*
* @param imgTensor The image tensor.
* @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image.
* @returns The padded tensor with width === height.
*/
function padToSquare(imgTensor, isCenterImage) {
if (isCenterImage === void 0) { isCenterImage = false; }
return tidy(function () {
var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
if (height === width) {
return imgTensor;
}
var dimDiff = Math.abs(height - width);
var paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));
var paddingAxis = height > width ? 2 : 1;
var createPaddingTensor = function (paddingAmount) {
var paddingTensorShape = imgTensor.shape.slice();
paddingTensorShape[paddingAxis] = paddingAmount;
return fill(paddingTensorShape, 0);
};
var paddingTensorAppend = createPaddingTensor(paddingAmount);
var remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
var paddingTensorPrepend = isCenterImage && remainingPaddingAmount
? createPaddingTensor(remainingPaddingAmount)
: null;
var tensorsToStack = [
paddingTensorPrepend,
imgTensor,
paddingTensorAppend
]
.filter(function (t) { return t !== null; });
return concat(tensorsToStack, paddingAxis);
});
}
function extractWeightsFactory(weights) {
var remainingWeights = weights;
function extractWeights(numWeights) {
......@@ -986,22 +1093,6 @@
};
}
function isTensor(tensor$$1, dim) {
return tensor$$1 instanceof Tensor && tensor$$1.shape.length === dim;
}
function isTensor1D(tensor$$1) {
return isTensor(tensor$$1, 1);
}
function isTensor2D(tensor$$1) {
return isTensor(tensor$$1, 2);
}
function isTensor3D(tensor$$1) {
return isTensor(tensor$$1, 3);
}
function isTensor4D(tensor$$1) {
return isTensor(tensor$$1, 4);
}
function getModelUris(uri, defaultModelName) {
var parts = (uri || '').split('/');
var modelBaseUri = ((uri || '').endsWith('.json')
......@@ -1355,16 +1446,6 @@
});
}
var resizedImageSize = [512, 512];
var weight = scalar(0.007843137718737125);
var bias = scalar(1);
function resizeLayer(x) {
return tidy(function () {
var resized = image.resizeBilinear(x, resizedImageSize, false);
return sub(mul(resized, weight), bias);
});
}
var FaceDetectionNet = /** @class */ (function () {
function FaceDetectionNet() {
}
......@@ -1393,37 +1474,28 @@
FaceDetectionNet.prototype.extractWeights = function (weights) {
this._params = extractParams(weights);
};
FaceDetectionNet.prototype.forwardTensor = function (imgTensor) {
FaceDetectionNet.prototype.forwardInput = function (input) {
var _this = this;
if (!this._params) {
throw new Error('FaceDetectionNet - load model before inference');
}
return tidy(function () {
var resized = resizeLayer(imgTensor);
var features = mobileNetV1(resized, _this._params.mobilenetv1_params);
var batchTensor = input.toBatchTensor(512, false);
var x = sub(mul(batchTensor, scalar(0.007843137718737125)), scalar(1));
var features = mobileNetV1(x, _this._params.mobilenetv1_params);
var _a = predictionLayer(features.out, features.conv11, _this._params.prediction_layer_params), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
return outputLayer(boxPredictions, classPredictions, _this._params.output_layer_params);
});
};
FaceDetectionNet.prototype.forward = function (input) {
return __awaiter$1(this, void 0, void 0, function () {
var _this = this;
var netInput, _a;
var _a;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0:
if (!(input instanceof Tensor)) return [3 /*break*/, 1];
_a = input;
return [3 /*break*/, 3];
case 1: return [4 /*yield*/, toNetInput(input)];
case 2:
_a = _b.sent();
_b.label = 3;
case 3:
netInput = _a;
return [2 /*return*/, tidy(function () {
return _this.forwardTensor(padToSquare(getImageTensor(netInput)));
})];
_a = this.forwardInput;
return [4 /*yield*/, toNetInput(input, true)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
});
......@@ -1432,42 +1504,27 @@
if (minConfidence === void 0) { minConfidence = 0.8; }
if (maxResults === void 0) { maxResults = 100; }
return __awaiter$1(this, void 0, void 0, function () {
var _this = this;
var netInput, _a, paddedHeightRelative, paddedWidthRelative, imageDimensions, _b, _boxes, _scores, boxes, scores, i, scoresData, _c, _d, iouThreshold, indices, results;
return __generator$1(this, function (_e) {
switch (_e.label) {
case 0:
if (!(input instanceof Tensor)) return [3 /*break*/, 1];
_a = input;
return [3 /*break*/, 3];
case 1: return [4 /*yield*/, toNetInput(input)];
case 2:
_a = _e.sent();
_e.label = 3;
case 3:
netInput = _a;
paddedHeightRelative = 1, paddedWidthRelative = 1;
_b = tidy(function () {
var imgTensor = getImageTensor(netInput);
var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
imageDimensions = { width: width, height: height };
imgTensor = padToSquare(imgTensor);
paddedHeightRelative = imgTensor.shape[1] / height;
paddedWidthRelative = imgTensor.shape[2] / width;
return _this.forwardTensor(imgTensor);
}), _boxes = _b.boxes, _scores = _b.scores;
var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, paddedHeightRelative, paddedWidthRelative, results;
return __generator$1(this, function (_d) {
switch (_d.label) {
case 0: return [4 /*yield*/, toNetInput(input, true)];
case 1:
netInput = _d.sent();
_a = this.forwardInput(netInput), _boxes = _a.boxes, _scores = _a.scores;
boxes = _boxes[0];
scores = _scores[0];
for (i = 1; i < _boxes.length; i++) {
_boxes[i].dispose();
_scores[i].dispose();
}
_d = (_c = Array).from;
_c = (_b = Array).from;
return [4 /*yield*/, scores.data()];
case 4:
scoresData = _d.apply(_c, [_e.sent()]);
case 2:
scoresData = _c.apply(_b, [_d.sent()]);
iouThreshold = 0.5;
indices = nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
paddedHeightRelative = (netInput.getPaddings(0).y + netInput.getInputHeight(0)) / netInput.getInputHeight(0);
paddedWidthRelative = (netInput.getPaddings(0).x + netInput.getInputWidth(0)) / netInput.getInputWidth(0);
results = indices
.map(function (idx) {
var _a = [
......@@ -1478,7 +1535,10 @@
Math.max(0, boxes.get(idx, 1)),
Math.min(1.0, boxes.get(idx, 3))
].map(function (val) { return val * paddedWidthRelative; }), left = _b[0], right = _b[1];
return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), imageDimensions);
return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), {
height: netInput.getInputHeight(0),
width: netInput.getInputWidth(0)
});
});
boxes.dispose();
scores.dispose();
......@@ -1745,20 +1805,14 @@
FaceLandmarkNet.prototype.extractWeights = function (weights) {
this._params = extractParams$1(weights);
};
FaceLandmarkNet.prototype.forwardTensor = function (imgTensor) {
FaceLandmarkNet.prototype.forwardInput = function (input) {
var params = this._params;
if (!params) {
throw new Error('FaceLandmarkNet - load model before inference');
}
return tidy(function () {
var _a = imgTensor.shape.slice(), batchSize = _a[0], height = _a[1], width = _a[2];
var x = padToSquare(imgTensor, true);
var _b = x.shape.slice(1), heightAfterPadding = _b[0], widthAfterPadding = _b[1];
// work with 128 x 128 sized face images
if (heightAfterPadding !== 128 || widthAfterPadding !== 128) {
x = image.resizeBilinear(x, [128, 128]);
}
var out = conv(x, params.conv0_params);
var batchTensor = input.toBatchTensor(128, true);
var out = conv(batchTensor, params.conv0_params);
out = maxPool$1(out);
out = conv(out, params.conv1_params);
out = conv(out, params.conv2_params);
......@@ -1776,37 +1830,34 @@
return stack([
fill([68], fillX),
fill([68], fillY)
], 1).as2D(batchSize, 136);
], 1).as2D(1, 136).as1D();
};
/* shift coordinates back, to undo centered padding
((x * widthAfterPadding) - shiftX) / width
((y * heightAfterPadding) - shiftY) / height
x = ((x * widthAfterPadding) - shiftX) / width
y = ((y * heightAfterPadding) - shiftY) / height
*/
var shiftX = Math.floor(Math.abs(widthAfterPadding - width) / 2);
var shiftY = Math.floor(Math.abs(heightAfterPadding - height) / 2);
var landmarkTensor = fc1
.mul(createInterleavedTensor(widthAfterPadding, heightAfterPadding))
.sub(createInterleavedTensor(shiftX, shiftY))
.div(createInterleavedTensor(width, height));
return landmarkTensor;
var landmarkTensors = fc1
.mul(stack(Array.from(Array(input.batchSize), function (_, batchIdx) {
return createInterleavedTensor(input.getPaddings(batchIdx).x + input.getInputWidth(batchIdx), input.getPaddings(batchIdx).y + input.getInputHeight(batchIdx));
})))
.sub(stack(Array.from(Array(input.batchSize), function (_, batchIdx) {
return createInterleavedTensor(Math.floor(input.getPaddings(batchIdx).x / 2), Math.floor(input.getPaddings(batchIdx).y / 2));
})))
.div(stack(Array.from(Array(input.batchSize), function (_, batchIdx) {
return createInterleavedTensor(input.getInputWidth(batchIdx), input.getInputHeight(batchIdx));
})));
return landmarkTensors;
});
};
FaceLandmarkNet.prototype.forward = function (input) {
return __awaiter$1(this, void 0, void 0, function () {
var netInput, _a;
var _a;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0:
if (!(input instanceof Tensor)) return [3 /*break*/, 1];
_a = input;
return [3 /*break*/, 3];
case 1: return [4 /*yield*/, toNetInput(input)];
case 2:
_a = _b.sent();
_b.label = 3;
case 3:
netInput = _a;
return [2 /*return*/, this.forwardTensor(getImageTensor(netInput))];
_a = this.forwardInput;
return [4 /*yield*/, toNetInput(input, true)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
});
......@@ -1814,33 +1865,35 @@
FaceLandmarkNet.prototype.detectLandmarks = function (input) {
return __awaiter$1(this, void 0, void 0, function () {
var _this = this;
var netInput, _a, imageDimensions, outTensor, faceLandmarksArray, _b, _c, xCoords, yCoords;
return __generator$1(this, function (_d) {
switch (_d.label) {
var netInput, landmarkTensors, landmarksForBatch;
return __generator$1(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, toNetInput(input, true)];
case 1:
netInput = _a.sent();
landmarkTensors = unstack(this.forwardInput(netInput));
return [4 /*yield*/, Promise.all(landmarkTensors.map(function (landmarkTensor, batchIdx) { return __awaiter$1(_this, void 0, void 0, function () {
var landmarksArray, _a, _b, xCoords, yCoords;
return __generator$1(this, function (_c) {
switch (_c.label) {
case 0:
if (!(input instanceof Tensor)) return [3 /*break*/, 1];
_a = input;
return [3 /*break*/, 3];
case 1: return [4 /*yield*/, toNetInput(input)];
case 2:
_a = _d.sent();
_d.label = 3;
case 3:
netInput = _a;
outTensor = tidy(function () {
var imgTensor = getImageTensor(netInput);
var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
imageDimensions = { width: width, height: height };
return _this.forwardTensor(imgTensor);
_b = (_a = Array).from;
return [4 /*yield*/, landmarkTensor.data()];
case 1:
landmarksArray = _b.apply(_a, [_c.sent()]);
xCoords = landmarksArray.filter(function (_, i) { return isEven(i); });
yCoords = landmarksArray.filter(function (_, i) { return !isEven(i); });
return [2 /*return*/, new FaceLandmarks(Array(68).fill(0).map(function (_, i) { return new Point(xCoords[i], yCoords[i]); }), {
height: netInput.getInputHeight(batchIdx),
width: netInput.getInputWidth(batchIdx),
})];
}
});
_c = (_b = Array).from;
return [4 /*yield*/, outTensor.data()];
case 4:
faceLandmarksArray = _c.apply(_b, [_d.sent()]);
outTensor.dispose();
xCoords = faceLandmarksArray.filter(function (_, i) { return isEven(i); });
yCoords = faceLandmarksArray.filter(function (_, i) { return !isEven(i); });
return [2 /*return*/, new FaceLandmarks(Array(68).fill(0).map(function (_, i) { return new Point(xCoords[i], yCoords[i]); }), imageDimensions)];
}); }))];
case 2:
landmarksForBatch = _a.sent();
landmarkTensors.forEach(function (t) { return t.dispose(); });
return [2 /*return*/, landmarksForBatch.length === 1 ? landmarksForBatch[0] : landmarksForBatch];
}
});
});
......@@ -2122,33 +2175,17 @@
FaceRecognitionNet.prototype.extractWeights = function (weights) {
this._params = extractParams$2(weights);
};
FaceRecognitionNet.prototype.forward = function (input) {
FaceRecognitionNet.prototype.forwardInput = function (input) {
return __awaiter$1(this, void 0, void 0, function () {
var _this = this;
var netInput, _a;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0:
return __generator$1(this, function (_a) {
if (!this._params) {
throw new Error('FaceRecognitionNet - load model before inference');
}
if (!(input instanceof Tensor)) return [3 /*break*/, 1];
_a = input;
return [3 /*break*/, 3];
case 1: return [4 /*yield*/, toNetInput(input)];
case 2:
_a = _b.sent();
_b.label = 3;
case 3:
netInput = _a;
return [2 /*return*/, tidy(function () {
var x = padToSquare(getImageTensor(netInput), true);
// work with 150 x 150 sized face images
if (x.shape[1] !== 150 || x.shape[2] !== 150) {
x = image.resizeBilinear(x, [150, 150]);
}
x = normalize(x);
var out = convDown(x, _this._params.conv32_down);
var batchTensor = input.toBatchTensor(150, true);
var normalized = normalize(batchTensor);
var out = convDown(normalized, _this._params.conv32_down);
out = maxPool(out, 3, 2, 'valid');
out = residual(out, _this._params.conv32_1);
out = residual(out, _this._params.conv32_2);
......@@ -2168,30 +2205,35 @@
var fullyConnected = matMul(globalAvg, _this._params.fc);
return fullyConnected;
})];
});
});
};
FaceRecognitionNet.prototype.forward = function (input) {
return __awaiter$1(this, void 0, void 0, function () {
var _a;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, toNetInput(input, true)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
});
};
FaceRecognitionNet.prototype.computeFaceDescriptor = function (input) {
return __awaiter$1(this, void 0, void 0, function () {
var netInput, _a, result, data;
var result, _a, data;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0:
if (!(input instanceof Tensor)) return [3 /*break*/, 1];
_a = input;
return [3 /*break*/, 3];
case 1: return [4 /*yield*/, toNetInput(input)];
_a = this.forward;
return [4 /*yield*/, toNetInput(input, true)];
case 1: return [4 /*yield*/, _a.apply(this, [_b.sent()])];
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 5:
case 3:
data = _b.sent();
result.dispose();
return [2 /*return*/, data];
......@@ -2217,21 +2259,25 @@
case 0: return [4 /*yield*/, detectionNet.locateFaces(input, minConfidence)];
case 1:
detections = _a.sent();
return [4 /*yield*/, extractFaceTensors(input, detections)];
return [4 /*yield*/, extractFaceTensors(input, detections)
/**
const faceLandmarksByFace = await Promise.all(faceTensors.map(
faceTensor => landmarkNet.detectLandmarks(faceTensor)
)) as FaceLandmarks[]
*/
];
case 2:
faceTensors = _a.sent();
return [4 /*yield*/, Promise.all(faceTensors.map(function (faceTensor) { return landmarkNet.detectLandmarks(faceTensor); }))];
return [4 /*yield*/, landmarkNet.detectLandmarks(faceTensors)];
case 3:
faceLandmarksByFace = _a.sent();
faceTensors.forEach(function (t) { return t.dispose(); });
return [4 /*yield*/, Promise.all(faceLandmarksByFace.map(function (landmarks, i) { return landmarks.align(detections[i].getBox()); }))];
case 4:
alignedFaceBoxes = _a.sent();
alignedFaceBoxes = faceLandmarksByFace.map(function (landmarks, i) { return landmarks.align(detections[i].getBox()); });
return [4 /*yield*/, extractFaceTensors(input, alignedFaceBoxes)];
case 5:
case 4:
alignedFaceTensors = _a.sent();
return [4 /*yield*/, Promise.all(alignedFaceTensors.map(function (faceTensor) { return recognitionNet.computeFaceDescriptor(faceTensor); }))];
case 6:
case 5:
descriptors = _a.sent();
alignedFaceTensors.forEach(function (t) { return t.dispose(); });
return [2 /*return*/, detections.map(function (detection, i) {
......@@ -2310,7 +2356,7 @@
exports.isFloat = isFloat;
exports.isEven = isEven;
exports.round = round$1;
exports.getElement = getElement;
exports.resolveInput = resolveInput;
exports.isLoaded = isLoaded;
exports.awaitMediaLoaded = awaitMediaLoaded;
exports.getContext2dOrThrow = getContext2dOrThrow;
......
dist/face-api.js.map
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dist/face-api.min.js
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