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face
Commit bc555c5c by vincent
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build commonjs instead of es6 modules

parent 50d388a2
Showing with 874 additions and 608 deletions
build/allFacesFactory.js
import * as tslib_1 from "tslib";
import { Point } from 'tfjs-image-recognition-base';
import { FullFaceDescription } from './classes/FullFaceDescription';
import { extractFaceTensors } from './dom';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FullFaceDescription_1 = require("./classes/FullFaceDescription");
var dom_1 = require("./dom");
function computeDescriptorsFactory(recognitionNet) {
return function (input, alignedFaceBoxes, useBatchProcessing) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var alignedFaceTensors, descriptors, _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, extractFaceTensors(input, alignedFaceBoxes)];
case 0: return [4 /*yield*/, dom_1.extractFaceTensors(input, alignedFaceBoxes)];
case 1:
alignedFaceTensors = _b.sent();
if (!useBatchProcessing) return [3 /*break*/, 3];
......@@ -40,7 +42,7 @@ function allFacesFactory(detectFaces, landmarkNet, recognitionNet) {
case 0: return [4 /*yield*/, detectFaces(input)];
case 1:
detections = _b.sent();
return [4 /*yield*/, extractFaceTensors(input, detections)];
return [4 /*yield*/, dom_1.extractFaceTensors(input, detections)];
case 2:
faceTensors = _b.sent();
if (!useBatchProcessing) return [3 /*break*/, 4];
......@@ -60,14 +62,14 @@ function allFacesFactory(detectFaces, landmarkNet, recognitionNet) {
case 7:
descriptors = _b.sent();
return [2 /*return*/, detections.map(function (detection, i) {
return new FullFaceDescription(detection, faceLandmarksByFace[i].shiftByPoint(new Point(detection.box.x, detection.box.y)), descriptors[i]);
return new FullFaceDescription_1.FullFaceDescription(detection, faceLandmarksByFace[i].shiftByPoint(new tfjs_image_recognition_base_1.Point(detection.box.x, detection.box.y)), descriptors[i]);
})];
}
});
});
};
}
export function allFacesSsdMobilenetv1Factory(ssdMobilenetv1, landmarkNet, recognitionNet) {
function allFacesSsdMobilenetv1Factory(ssdMobilenetv1, landmarkNet, recognitionNet) {
return function (input, minConfidence, useBatchProcessing) {
if (minConfidence === void 0) { minConfidence = 0.8; }
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
......@@ -81,7 +83,8 @@ export function allFacesSsdMobilenetv1Factory(ssdMobilenetv1, landmarkNet, recog
});
};
}
export function allFacesTinyYolov2Factory(tinyYolov2, landmarkNet, recognitionNet) {
exports.allFacesSsdMobilenetv1Factory = allFacesSsdMobilenetv1Factory;
function allFacesTinyYolov2Factory(tinyYolov2, landmarkNet, recognitionNet) {
return function (input, forwardParams, useBatchProcessing) {
if (forwardParams === void 0) { forwardParams = {}; }
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
......@@ -95,7 +98,8 @@ export function allFacesTinyYolov2Factory(tinyYolov2, landmarkNet, recognitionNe
});
};
}
export function allFacesMtcnnFactory(mtcnn, recognitionNet) {
exports.allFacesTinyYolov2Factory = allFacesTinyYolov2Factory;
function allFacesMtcnnFactory(mtcnn, recognitionNet) {
var computeDescriptors = computeDescriptorsFactory(recognitionNet);
return function (input, mtcnnForwardParams, useBatchProcessing) {
if (mtcnnForwardParams === void 0) { mtcnnForwardParams = {}; }
......@@ -116,11 +120,12 @@ export function allFacesMtcnnFactory(mtcnn, recognitionNet) {
descriptors = _a.sent();
return [2 /*return*/, results.map(function (_a, i) {
var faceDetection = _a.faceDetection, faceLandmarks = _a.faceLandmarks;
return new FullFaceDescription(faceDetection, faceLandmarks, descriptors[i]);
return new FullFaceDescription_1.FullFaceDescription(faceDetection, faceLandmarks, descriptors[i]);
})];
}
});
});
};
}
exports.allFacesMtcnnFactory = allFacesMtcnnFactory;
//# sourceMappingURL=allFacesFactory.js.map
\ No newline at end of file
build/allFacesFactory.js.map
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\ No newline at end of file
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\ No newline at end of file
build/classes/FaceDetection.js
import * as tslib_1 from "tslib";
import { ObjectDetection } from 'tfjs-image-recognition-base';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceDetection = /** @class */ (function (_super) {
tslib_1.__extends(FaceDetection, _super);
function FaceDetection(score, relativeBox, imageDims) {
return _super.call(this, score, score, '', relativeBox, imageDims) || this;
}
return FaceDetection;
}(ObjectDetection));
export { FaceDetection };
}(tfjs_image_recognition_base_1.ObjectDetection));
exports.FaceDetection = FaceDetection;
//# sourceMappingURL=FaceDetection.js.map
\ No newline at end of file
build/classes/FaceDetection.js.map
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\ No newline at end of file
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\ No newline at end of file
build/classes/FaceLandmarks.js
import { getCenterPoint, Point, Rect } from 'tfjs-image-recognition-base';
import { FaceDetection } from './FaceDetection';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceDetection_1 = require("./FaceDetection");
// face alignment constants
var relX = 0.5;
var relY = 0.43;
var relScale = 0.45;
var FaceLandmarks = /** @class */ (function () {
function FaceLandmarks(relativeFaceLandmarkPositions, imageDims, shift) {
if (shift === void 0) { shift = new Point(0, 0); }
if (shift === void 0) { shift = new tfjs_image_recognition_base_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(width, height)).add(shift); });
this._faceLandmarks = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new tfjs_image_recognition_base_1.Point(width, height)).add(shift); });
}
FaceLandmarks.prototype.getShift = function () {
return new Point(this._shift.x, this._shift.y);
return new tfjs_image_recognition_base_1.Point(this._shift.x, this._shift.y);
};
FaceLandmarks.prototype.getImageWidth = function () {
return this._imageWidth;
......@@ -27,13 +29,13 @@ var FaceLandmarks = /** @class */ (function () {
};
FaceLandmarks.prototype.getRelativePositions = function () {
var _this = this;
return this._faceLandmarks.map(function (pt) { return pt.sub(_this._shift).div(new Point(_this._imageWidth, _this._imageHeight)); });
return this._faceLandmarks.map(function (pt) { return pt.sub(_this._shift).div(new tfjs_image_recognition_base_1.Point(_this._imageWidth, _this._imageHeight)); });
};
FaceLandmarks.prototype.forSize = function (width, height) {
return new this.constructor(this.getRelativePositions(), { width: width, height: height });
};
FaceLandmarks.prototype.shift = function (x, y) {
return new this.constructor(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new Point(x, y));
return new this.constructor(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new tfjs_image_recognition_base_1.Point(x, y));
};
FaceLandmarks.prototype.shiftByPoint = function (pt) {
return this.shift(pt.x, pt.y);
......@@ -51,7 +53,7 @@ var FaceLandmarks = /** @class */ (function () {
*/
FaceLandmarks.prototype.align = function (detection) {
if (detection) {
var box = detection instanceof FaceDetection
var box = detection instanceof FaceDetection_1.FaceDetection
? detection.getBox().floor()
: detection;
return this.shift(box.x, box.y).align();
......@@ -61,16 +63,16 @@ var FaceLandmarks = /** @class */ (function () {
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(centers);
var refPoint = tfjs_image_recognition_base_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(x, y, Math.min(size, this._imageWidth + x), Math.min(size, this._imageHeight + y));
return new tfjs_image_recognition_base_1.Rect(x, y, Math.min(size, this._imageWidth + x), Math.min(size, this._imageHeight + y));
};
FaceLandmarks.prototype.getRefPointsForAlignment = function () {
throw new Error('getRefPointsForAlignment not implemented by base class');
};
return FaceLandmarks;
}());
export { FaceLandmarks };
exports.FaceLandmarks = FaceLandmarks;
//# sourceMappingURL=FaceLandmarks.js.map
\ No newline at end of file
build/classes/FaceLandmarks.js.map
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\ No newline at end of file
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\ No newline at end of file
build/classes/FaceLandmarks5.js
import * as tslib_1 from "tslib";
import { getCenterPoint } from 'tfjs-image-recognition-base';
import { FaceLandmarks } from './FaceLandmarks';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceLandmarks_1 = require("./FaceLandmarks");
var FaceLandmarks5 = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmarks5, _super);
function FaceLandmarks5() {
......@@ -11,10 +13,10 @@ var FaceLandmarks5 = /** @class */ (function (_super) {
return [
pts[0],
pts[1],
getCenterPoint([pts[3], pts[4]])
tfjs_image_recognition_base_1.getCenterPoint([pts[3], pts[4]])
];
};
return FaceLandmarks5;
}(FaceLandmarks));
export { FaceLandmarks5 };
}(FaceLandmarks_1.FaceLandmarks));
exports.FaceLandmarks5 = FaceLandmarks5;
//# sourceMappingURL=FaceLandmarks5.js.map
\ No newline at end of file
build/classes/FaceLandmarks5.js.map
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\ No newline at end of file
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\ No newline at end of file
build/classes/FaceLandmarks68.js
import * as tslib_1 from "tslib";
import { getCenterPoint } from 'tfjs-image-recognition-base';
import { FaceLandmarks } from '../classes/FaceLandmarks';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceLandmarks_1 = require("../classes/FaceLandmarks");
var FaceLandmarks68 = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmarks68, _super);
function FaceLandmarks68() {
......@@ -32,9 +34,9 @@ var FaceLandmarks68 = /** @class */ (function (_super) {
this.getLeftEye(),
this.getRightEye(),
this.getMouth()
].map(getCenterPoint);
].map(tfjs_image_recognition_base_1.getCenterPoint);
};
return FaceLandmarks68;
}(FaceLandmarks));
export { FaceLandmarks68 };
}(FaceLandmarks_1.FaceLandmarks));
exports.FaceLandmarks68 = FaceLandmarks68;
//# sourceMappingURL=FaceLandmarks68.js.map
\ No newline at end of file
build/classes/FaceLandmarks68.js.map
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\ No newline at end of file
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\ No newline at end of file
build/classes/FullFaceDescription.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var FullFaceDescription = /** @class */ (function () {
function FullFaceDescription(_detection, _landmarks, _descriptor) {
this._detection = _detection;
......@@ -30,5 +32,5 @@ var FullFaceDescription = /** @class */ (function () {
};
return FullFaceDescription;
}());
export { FullFaceDescription };
exports.FullFaceDescription = FullFaceDescription;
//# sourceMappingURL=FullFaceDescription.js.map
\ No newline at end of file
build/classes/FullFaceDescription.js.map
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\ No newline at end of file
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\ No newline at end of file
build/classes/index.js
export * from './FaceDetection';
export * from './FaceLandmarks';
export * from './FaceLandmarks5';
export * from './FaceLandmarks68';
export * from './FullFaceDescription';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
tslib_1.__exportStar(require("./FaceDetection"), exports);
tslib_1.__exportStar(require("./FaceLandmarks"), exports);
tslib_1.__exportStar(require("./FaceLandmarks5"), exports);
tslib_1.__exportStar(require("./FaceLandmarks68"), exports);
tslib_1.__exportStar(require("./FullFaceDescription"), exports);
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/classes/index.js.map
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\ No newline at end of file
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\ No newline at end of file
build/dom/drawContour.js
export function drawContour(ctx, points, isClosed) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function drawContour(ctx, points, isClosed) {
if (isClosed === void 0) { isClosed = false; }
ctx.beginPath();
points.slice(1).forEach(function (_a, prevIdx) {
......@@ -18,4 +20,5 @@ export function drawContour(ctx, points, isClosed) {
}
ctx.stroke();
}
exports.drawContour = drawContour;
//# sourceMappingURL=drawContour.js.map
\ No newline at end of file
build/dom/drawContour.js.map
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\ No newline at end of file
build/dom/drawLandmarks.js
import { getContext2dOrThrow, getDefaultDrawOptions, resolveInput } from 'tfjs-image-recognition-base';
import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
import { drawContour } from './drawContour';
export function drawLandmarks(canvasArg, faceLandmarks, options) {
var canvas = resolveInput(canvasArg);
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceLandmarks68_1 = require("../classes/FaceLandmarks68");
var drawContour_1 = require("./drawContour");
function drawLandmarks(canvasArg, faceLandmarks, options) {
var canvas = tfjs_image_recognition_base_1.resolveInput(canvasArg);
if (!(canvas instanceof HTMLCanvasElement)) {
throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement');
}
var drawOptions = Object.assign(getDefaultDrawOptions(options), (options || {}));
var drawOptions = Object.assign(tfjs_image_recognition_base_1.getDefaultDrawOptions(options), (options || {}));
var drawLines = Object.assign({ drawLines: false }, (options || {})).drawLines;
var ctx = getContext2dOrThrow(canvas);
var ctx = tfjs_image_recognition_base_1.getContext2dOrThrow(canvas);
var lineWidth = drawOptions.lineWidth, _a = drawOptions.color, color = _a === void 0 ? 'blue' : _a;
var faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];
faceLandmarksArray.forEach(function (landmarks) {
if (drawLines && landmarks instanceof FaceLandmarks68) {
if (drawLines && landmarks instanceof FaceLandmarks68_1.FaceLandmarks68) {
ctx.strokeStyle = color;
ctx.lineWidth = lineWidth;
drawContour(ctx, landmarks.getJawOutline());
drawContour(ctx, landmarks.getLeftEyeBrow());
drawContour(ctx, landmarks.getRightEyeBrow());
drawContour(ctx, landmarks.getNose());
drawContour(ctx, landmarks.getLeftEye(), true);
drawContour(ctx, landmarks.getRightEye(), true);
drawContour(ctx, landmarks.getMouth(), true);
drawContour_1.drawContour(ctx, landmarks.getJawOutline());
drawContour_1.drawContour(ctx, landmarks.getLeftEyeBrow());
drawContour_1.drawContour(ctx, landmarks.getRightEyeBrow());
drawContour_1.drawContour(ctx, landmarks.getNose());
drawContour_1.drawContour(ctx, landmarks.getLeftEye(), true);
drawContour_1.drawContour(ctx, landmarks.getRightEye(), true);
drawContour_1.drawContour(ctx, landmarks.getMouth(), true);
return;
}
// else draw points
......@@ -30,4 +32,5 @@ export function drawLandmarks(canvasArg, faceLandmarks, options) {
landmarks.getPositions().forEach(function (pt) { return ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth); });
});
}
exports.drawLandmarks = drawLandmarks;
//# sourceMappingURL=drawLandmarks.js.map
\ No newline at end of file
build/dom/drawLandmarks.js.map
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\ No newline at end of file
build/dom/extractFaceTensors.js
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { toNetInput } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceDetection_1 = require("../classes/FaceDetection");
/**
* Extracts the tensors of the image regions containing the detected faces.
* Useful if you want to compute the face descriptors for the face images.
......@@ -12,12 +14,12 @@ import { FaceDetection } from '../classes/FaceDetection';
* @param detections The face detection results or face bounding boxes for that image.
* @returns Tensors of the corresponding image region for each detected face.
*/
export function extractFaceTensors(input, detections) {
function extractFaceTensors(input, detections) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var netInput;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, toNetInput(input, true)];
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)];
case 1:
netInput = _a.sent();
if (netInput.batchSize > 1) {
......@@ -29,7 +31,7 @@ export function extractFaceTensors(input, detections) {
return [2 /*return*/, tf.tidy(function () {
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
var boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
? det.forSize(imgWidth, imgHeight).getBox()
: det; })
.map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); });
......@@ -46,4 +48,5 @@ export function extractFaceTensors(input, detections) {
});
});
}
exports.extractFaceTensors = extractFaceTensors;
//# sourceMappingURL=extractFaceTensors.js.map
\ No newline at end of file
build/dom/extractFaceTensors.js.map
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\ No newline at end of file
build/dom/extractFaces.js
import * as tslib_1 from "tslib";
import { createCanvas, getContext2dOrThrow, imageTensorToCanvas, toNetInput, } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceDetection_1 = require("../classes/FaceDetection");
/**
* Extracts the image regions containing the detected faces.
*
......@@ -8,7 +10,7 @@ import { FaceDetection } from '../classes/FaceDetection';
* @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 function extractFaces(input, detections) {
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) {
......@@ -16,7 +18,7 @@ export function extractFaces(input, detections) {
case 0:
canvas = input;
if (!!(input instanceof HTMLCanvasElement)) return [3 /*break*/, 3];
return [4 /*yield*/, toNetInput(input, true)];
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)];
case 1:
netInput = _a.sent();
if (netInput.batchSize > 1) {
......@@ -25,7 +27,7 @@ export function extractFaces(input, detections) {
}
throw new Error('extractFaces - batchSize > 1 not supported');
}
return [4 /*yield*/, imageTensorToCanvas(netInput.inputs[0])];
return [4 /*yield*/, tfjs_image_recognition_base_1.imageTensorToCanvas(netInput.inputs[0])];
case 2:
canvas = _a.sent();
if (netInput.isManaged) {
......@@ -33,15 +35,15 @@ export function extractFaces(input, detections) {
}
_a.label = 3;
case 3:
ctx = getContext2dOrThrow(canvas);
boxes = detections.map(function (det) { return det instanceof FaceDetection
ctx = tfjs_image_recognition_base_1.getContext2dOrThrow(canvas);
boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
? det.forSize(canvas.width, canvas.height).getBox().floor()
: det; })
.map(function (box) { return box.clipAtImageBorders(canvas.width, canvas.height); });
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)
var faceImg = tfjs_image_recognition_base_1.createCanvas({ width: width, height: height });
tfjs_image_recognition_base_1.getContext2dOrThrow(faceImg)
.putImageData(ctx.getImageData(x, y, width, height), 0, 0);
return faceImg;
})];
......@@ -49,4 +51,5 @@ export function extractFaces(input, detections) {
});
});
}
exports.extractFaces = extractFaces;
//# sourceMappingURL=extractFaces.js.map
\ No newline at end of file
build/dom/extractFaces.js.map
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\ No newline at end of file
build/dom/index.js
export * from './drawContour';
export * from './drawLandmarks';
export * from './extractFaces';
export * from './extractFaceTensors';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
tslib_1.__exportStar(require("./drawContour"), exports);
tslib_1.__exportStar(require("./drawLandmarks"), exports);
tslib_1.__exportStar(require("./extractFaces"), exports);
tslib_1.__exportStar(require("./extractFaceTensors"), exports);
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/dom/index.js.map
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\ No newline at end of file
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\ No newline at end of file
build/dom/types.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
//# sourceMappingURL=types.js.map
\ No newline at end of file
build/euclideanDistance.js
export function euclideanDistance(arr1, arr2) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function euclideanDistance(arr1, arr2) {
if (arr1.length !== arr2.length)
throw new Error('euclideanDistance: arr1.length !== arr2.length');
var desc1 = Array.from(arr1);
......@@ -7,4 +9,5 @@ export function euclideanDistance(arr1, arr2) {
.map(function (val, i) { return val - desc2[i]; })
.reduce(function (res, diff) { return res + Math.pow(diff, 2); }, 0));
}
exports.euclideanDistance = euclideanDistance;
//# sourceMappingURL=euclideanDistance.js.map
\ No newline at end of file
build/euclideanDistance.js.map
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\ No newline at end of file
build/faceDetectionNet/FaceDetectionNet.js
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { NeuralNetwork, Rect, toNetInput } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection';
import { extractParams } from './extractParams';
import { loadQuantizedParams } from './loadQuantizedParams';
import { mobileNetV1 } from './mobileNetV1';
import { nonMaxSuppression } from './nonMaxSuppression';
import { outputLayer } from './outputLayer';
import { predictionLayer } from './predictionLayer';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceDetection_1 = require("../classes/FaceDetection");
var extractParams_1 = require("./extractParams");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
var mobileNetV1_1 = require("./mobileNetV1");
var nonMaxSuppression_1 = require("./nonMaxSuppression");
var outputLayer_1 = require("./outputLayer");
var predictionLayer_1 = require("./predictionLayer");
var FaceDetectionNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceDetectionNet, _super);
function FaceDetectionNet() {
......@@ -21,9 +23,9 @@ var FaceDetectionNet = /** @class */ (function (_super) {
return tf.tidy(function () {
var batchTensor = input.toBatchTensor(512, false);
var x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1));
var features = mobileNetV1(x, params.mobilenetv1);
var _a = predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
return outputLayer(boxPredictions, classPredictions, params.output_layer);
var features = mobileNetV1_1.mobileNetV1(x, params.mobilenetv1);
var _a = predictionLayer_1.predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
return outputLayer_1.outputLayer(boxPredictions, classPredictions, params.output_layer);
});
};
FaceDetectionNet.prototype.forward = function (input) {
......@@ -33,7 +35,7 @@ var FaceDetectionNet = /** @class */ (function (_super) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, toNetInput(input, true)];
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
......@@ -46,7 +48,7 @@ var FaceDetectionNet = /** @class */ (function (_super) {
var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, paddings, results;
return tslib_1.__generator(this, function (_d) {
switch (_d.label) {
case 0: return [4 /*yield*/, toNetInput(input, true)];
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)];
case 1:
netInput = _d.sent();
_a = this.forwardInput(netInput), _boxes = _a.boxes, _scores = _a.scores;
......@@ -61,7 +63,7 @@ var FaceDetectionNet = /** @class */ (function (_super) {
case 2:
scoresData = _c.apply(_b, [_d.sent()]);
iouThreshold = 0.5;
indices = nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
indices = nonMaxSuppression_1.nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
paddings = netInput.getRelativePaddings(0);
results = indices
.map(function (idx) {
......@@ -73,7 +75,7 @@ var FaceDetectionNet = /** @class */ (function (_super) {
Math.max(0, boxes.get(idx, 1)),
Math.min(1.0, boxes.get(idx, 3))
].map(function (val) { return val * paddings.x; }), left = _b[0], right = _b[1];
return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), {
return new FaceDetection_1.FaceDetection(scoresData[idx], new tfjs_image_recognition_base_1.Rect(left, top, right - left, bottom - top), {
height: netInput.getInputHeight(0),
width: netInput.getInputWidth(0)
});
......@@ -86,12 +88,12 @@ var FaceDetectionNet = /** @class */ (function (_super) {
});
};
FaceDetectionNet.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams(uri);
return loadQuantizedParams_1.loadQuantizedParams(uri);
};
FaceDetectionNet.prototype.extractParams = function (weights) {
return extractParams(weights);
return extractParams_1.extractParams(weights);
};
return FaceDetectionNet;
}(NeuralNetwork));
export { FaceDetectionNet };
}(tfjs_image_recognition_base_1.NeuralNetwork));
exports.FaceDetectionNet = FaceDetectionNet;
//# sourceMappingURL=FaceDetectionNet.js.map
\ No newline at end of file
build/faceDetectionNet/FaceDetectionNet.js.map
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\ No newline at end of file
build/faceDetectionNet/boxPredictionLayer.js
import * as tf from '@tensorflow/tfjs-core';
import { convLayer } from 'tfjs-tiny-yolov2';
export function boxPredictionLayer(x, params) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
function boxPredictionLayer(x, params) {
return tf.tidy(function () {
var batchSize = x.shape[0];
var boxPredictionEncoding = tf.reshape(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
var classPrediction = tf.reshape(convLayer(x, params.class_predictor), [batchSize, -1, 3]);
var boxPredictionEncoding = tf.reshape(tfjs_tiny_yolov2_1.convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
var classPrediction = tf.reshape(tfjs_tiny_yolov2_1.convLayer(x, params.class_predictor), [batchSize, -1, 3]);
return {
boxPredictionEncoding: boxPredictionEncoding,
classPrediction: classPrediction
};
});
}
exports.boxPredictionLayer = boxPredictionLayer;
//# sourceMappingURL=boxPredictionLayer.js.map
\ No newline at end of file
build/faceDetectionNet/boxPredictionLayer.js.map
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\ No newline at end of file
{"version":3,"file":"boxPredictionLayer.js","sourceRoot":"","sources":["../../src/faceDetectionNet/boxPredictionLayer.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAC5C,qDAA6C;AAK7C,4BACE,CAAc,EACd,MAA2B;IAE3B,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAM,SAAS,GAAG,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;QAE5B,IAAM,qBAAqB,GAAG,EAAE,CAAC,OAAO,CACtC,4BAAS,CAAC,CAAC,EAAE,MAAM,CAAC,sBAAsB,CAAC,EAC3C,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CACtB,CAAA;QACD,IAAM,eAAe,GAAG,EAAE,CAAC,OAAO,CAChC,4BAAS,CAAC,CAAC,EAAE,MAAM,CAAC,eAAe,CAAC,EACpC,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CACnB,CAAA;QAED,OAAO;YACL,qBAAqB,uBAAA;YACrB,eAAe,iBAAA;SAChB,CAAA;IACH,CAAC,CAAC,CAAA;AACJ,CAAC;AAtBD,gDAsBC"}
\ No newline at end of file
build/faceDetectionNet/extractParams.js
import * as tf from '@tensorflow/tfjs-core';
import { extractWeightsFactory } from 'tfjs-image-recognition-base';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
function extractorsFactory(extractWeights, paramMappings) {
function extractDepthwiseConvParams(numChannels, mappedPrefix) {
var filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
......@@ -133,9 +135,9 @@ function extractorsFactory(extractWeights, paramMappings) {
extractPredictionLayerParams: extractPredictionLayerParams
};
}
export function extractParams(weights) {
function extractParams(weights) {
var paramMappings = [];
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var _a = tfjs_image_recognition_base_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var _b = extractorsFactory(extractWeights, paramMappings), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;
var mobilenetv1 = extractMobilenetV1Params();
var prediction_layer = extractPredictionLayerParams();
......@@ -156,4 +158,5 @@ export function extractParams(weights) {
paramMappings: paramMappings
};
}
exports.extractParams = extractParams;
//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
build/faceDetectionNet/extractParams.js.map
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build/faceDetectionNet/index.js
import { FaceDetectionNet } from './FaceDetectionNet';
export * from './FaceDetectionNet';
export function createFaceDetectionNet(weights) {
var net = new FaceDetectionNet();
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var FaceDetectionNet_1 = require("./FaceDetectionNet");
tslib_1.__exportStar(require("./FaceDetectionNet"), exports);
function createFaceDetectionNet(weights) {
var net = new FaceDetectionNet_1.FaceDetectionNet();
net.extractWeights(weights);
return net;
}
export function faceDetectionNet(weights) {
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
import * as tslib_1 from "tslib";
import { disposeUnusedWeightTensors, extractWeightEntryFactory, isTensor3D, loadWeightMap, } from 'tfjs-image-recognition-base';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var DEFAULT_MODEL_NAME = 'ssd_mobilenetv1_model';
function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
var extractWeightEntry = tfjs_image_recognition_base_1.extractWeightEntryFactory(weightMap, paramMappings);
function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
var filters = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/weights", 4, mappedPrefix + "/filters");
var batch_norm_offset = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset", 1, mappedPrefix + "/batch_norm_offset");
......@@ -80,19 +82,19 @@ function extractorsFactory(weightMap, paramMappings) {
extractPredictionLayerParams: extractPredictionLayerParams
};
}
export function loadQuantizedParams(uri) {
function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractMobilenetV1Params, extractPredictionLayerParams, extra_dim, params;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
paramMappings = [];
_a = extractorsFactory(weightMap, paramMappings), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
extra_dim = weightMap['Output/extra_dim'];
paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });
if (!isTensor3D(extra_dim)) {
if (!tfjs_image_recognition_base_1.isTensor3D(extra_dim)) {
throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have " + extra_dim);
}
params = {
......@@ -102,10 +104,11 @@ export function loadQuantizedParams(uri) {
extra_dim: extra_dim
}
};
disposeUnusedWeightTensors(weightMap, paramMappings);
tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
}
});
});
}
exports.loadQuantizedParams = loadQuantizedParams;
//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
build/faceDetectionNet/loadQuantizedParams.js.map
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build/faceDetectionNet/mobileNetV1.js
import * as tf from '@tensorflow/tfjs-core';
import { pointwiseConvLayer } from './pointwiseConvLayer';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var pointwiseConvLayer_1 = require("./pointwiseConvLayer");
var epsilon = 0.0010000000474974513;
function depthwiseConvLayer(x, params, strides) {
return tf.tidy(function () {
......@@ -11,10 +13,10 @@ function depthwiseConvLayer(x, params, strides) {
function getStridesForLayerIdx(layerIdx) {
return [2, 4, 6, 12].some(function (idx) { return idx === layerIdx; }) ? [2, 2] : [1, 1];
}
export function mobileNetV1(x, params) {
function mobileNetV1(x, params) {
return tf.tidy(function () {
var conv11 = null;
var out = pointwiseConvLayer(x, params.conv_0, [2, 2]);
var out = pointwiseConvLayer_1.pointwiseConvLayer(x, params.conv_0, [2, 2]);
var convPairParams = [
params.conv_1,
params.conv_2,
......@@ -34,7 +36,7 @@ export function mobileNetV1(x, params) {
var layerIdx = i + 1;
var depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);
out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);
out = pointwiseConvLayer_1.pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);
if (layerIdx === 11) {
conv11 = out;
}
......@@ -48,4 +50,5 @@ export function mobileNetV1(x, params) {
};
});
}
exports.mobileNetV1 = mobileNetV1;
//# sourceMappingURL=mobileNetV1.js.map
\ No newline at end of file
build/faceDetectionNet/mobileNetV1.js.map
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\ No newline at end of file
build/faceDetectionNet/nonMaxSuppression.js
export function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
var numBoxes = boxes.shape[0];
var outputSize = Math.min(maxOutputSize, numBoxes);
var candidates = scores
......@@ -28,6 +30,7 @@ export function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, sc
});
return selected;
}
exports.nonMaxSuppression = nonMaxSuppression;
function IOU(boxes, i, j) {
var yminI = Math.min(boxes.get(i, 0), boxes.get(i, 2));
var xminI = Math.min(boxes.get(i, 1), boxes.get(i, 3));
......
build/faceDetectionNet/nonMaxSuppression.js.map
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\ No newline at end of file
build/faceDetectionNet/outputLayer.js
import * as tf from '@tensorflow/tfjs-core';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function getCenterCoordinatesAndSizesLayer(x) {
var vec = tf.unstack(tf.transpose(x, [1, 0]));
var sizes = [
......@@ -28,7 +30,7 @@ function decodeBoxesLayer(x0, x1) {
tf.add(add1_out, div1_out)
]), [1, 0]);
}
export function outputLayer(boxPredictions, classPredictions, params) {
function outputLayer(boxPredictions, classPredictions, params) {
return tf.tidy(function () {
var batchSize = boxPredictions.shape[0];
var boxes = decodeBoxesLayer(tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), tf.reshape(boxPredictions, [-1, 4]));
......@@ -44,4 +46,5 @@ export function outputLayer(boxPredictions, classPredictions, params) {
};
});
}
exports.outputLayer = outputLayer;
//# sourceMappingURL=outputLayer.js.map
\ No newline at end of file
build/faceDetectionNet/outputLayer.js.map
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\ No newline at end of file
build/faceDetectionNet/pointwiseConvLayer.js
import * as tf from '@tensorflow/tfjs-core';
export function pointwiseConvLayer(x, params, strides) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function pointwiseConvLayer(x, params, strides) {
return tf.tidy(function () {
var out = tf.conv2d(x, params.filters, strides, 'same');
out = tf.add(out, params.batch_norm_offset);
return tf.clipByValue(out, 0, 6);
});
}
exports.pointwiseConvLayer = pointwiseConvLayer;
//# sourceMappingURL=pointwiseConvLayer.js.map
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build/faceDetectionNet/pointwiseConvLayer.js.map
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build/faceDetectionNet/predictionLayer.js
import * as tf from '@tensorflow/tfjs-core';
import { boxPredictionLayer } from './boxPredictionLayer';
import { pointwiseConvLayer } from './pointwiseConvLayer';
export function predictionLayer(x, conv11, params) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var boxPredictionLayer_1 = require("./boxPredictionLayer");
var pointwiseConvLayer_1 = require("./pointwiseConvLayer");
function predictionLayer(x, conv11, params) {
return tf.tidy(function () {
var conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);
var conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
var conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
var conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);
var conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);
var conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);
var conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);
var conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);
var boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);
var boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);
var boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);
var boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);
var boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);
var boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);
var conv0 = pointwiseConvLayer_1.pointwiseConvLayer(x, params.conv_0, [1, 1]);
var conv1 = pointwiseConvLayer_1.pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
var conv2 = pointwiseConvLayer_1.pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
var conv3 = pointwiseConvLayer_1.pointwiseConvLayer(conv2, params.conv_3, [2, 2]);
var conv4 = pointwiseConvLayer_1.pointwiseConvLayer(conv3, params.conv_4, [1, 1]);
var conv5 = pointwiseConvLayer_1.pointwiseConvLayer(conv4, params.conv_5, [2, 2]);
var conv6 = pointwiseConvLayer_1.pointwiseConvLayer(conv5, params.conv_6, [1, 1]);
var conv7 = pointwiseConvLayer_1.pointwiseConvLayer(conv6, params.conv_7, [2, 2]);
var boxPrediction0 = boxPredictionLayer_1.boxPredictionLayer(conv11, params.box_predictor_0);
var boxPrediction1 = boxPredictionLayer_1.boxPredictionLayer(x, params.box_predictor_1);
var boxPrediction2 = boxPredictionLayer_1.boxPredictionLayer(conv1, params.box_predictor_2);
var boxPrediction3 = boxPredictionLayer_1.boxPredictionLayer(conv3, params.box_predictor_3);
var boxPrediction4 = boxPredictionLayer_1.boxPredictionLayer(conv5, params.box_predictor_4);
var boxPrediction5 = boxPredictionLayer_1.boxPredictionLayer(conv7, params.box_predictor_5);
var boxPredictions = tf.concat([
boxPrediction0.boxPredictionEncoding,
boxPrediction1.boxPredictionEncoding,
......@@ -39,4 +41,5 @@ export function predictionLayer(x, conv11, params) {
};
});
}
exports.predictionLayer = predictionLayer;
//# sourceMappingURL=predictionLayer.js.map
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build/faceDetectionNet/predictionLayer.js.map
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\ No newline at end of file
build/faceDetectionNet/types.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
//# sourceMappingURL=types.js.map
\ No newline at end of file
build/faceLandmarkNet/FaceLandmarkNet.js
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { isEven, NeuralNetwork, Point, toNetInput } from 'tfjs-image-recognition-base';
import { convLayer } from 'tfjs-tiny-yolov2';
import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
import { extractParams } from './extractParams';
import { fullyConnectedLayer } from './fullyConnectedLayer';
import { loadQuantizedParams } from './loadQuantizedParams';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
var FaceLandmarks68_1 = require("../classes/FaceLandmarks68");
var extractParams_1 = require("./extractParams");
var fullyConnectedLayer_1 = require("./fullyConnectedLayer");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
function conv(x, params) {
return convLayer(x, params, 'valid', true);
return tfjs_tiny_yolov2_1.convLayer(x, params, 'valid', true);
}
function maxPool(x, strides) {
if (strides === void 0) { strides = [2, 2]; }
......@@ -37,8 +39,8 @@ var FaceLandmarkNet = /** @class */ (function (_super) {
out = conv(out, params.conv6);
out = maxPool(out, [1, 1]);
out = conv(out, params.conv7);
var fc0 = tf.relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0));
var fc1 = fullyConnectedLayer(fc0, params.fc1);
var fc0 = tf.relu(fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0));
var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(fc0, params.fc1);
var createInterleavedTensor = function (fillX, fillY) {
return tf.stack([
tf.fill([68], fillX),
......@@ -69,7 +71,7 @@ var FaceLandmarkNet = /** @class */ (function (_super) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, toNetInput(input, true)];
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
......@@ -81,7 +83,7 @@ var FaceLandmarkNet = /** @class */ (function (_super) {
var netInput, landmarkTensors, landmarksForBatch;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, toNetInput(input, true)];
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)];
case 1:
netInput = _a.sent();
landmarkTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });
......@@ -94,9 +96,9 @@ var FaceLandmarkNet = /** @class */ (function (_super) {
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 FaceLandmarks68(Array(68).fill(0).map(function (_, i) { return new Point(xCoords[i], yCoords[i]); }), {
xCoords = landmarksArray.filter(function (_, i) { return tfjs_image_recognition_base_1.isEven(i); });
yCoords = landmarksArray.filter(function (_, i) { return !tfjs_image_recognition_base_1.isEven(i); });
return [2 /*return*/, new FaceLandmarks68_1.FaceLandmarks68(Array(68).fill(0).map(function (_, i) { return new tfjs_image_recognition_base_1.Point(xCoords[i], yCoords[i]); }), {
height: netInput.getInputHeight(batchIdx),
width: netInput.getInputWidth(batchIdx),
})];
......@@ -114,12 +116,12 @@ var FaceLandmarkNet = /** @class */ (function (_super) {
});
};
FaceLandmarkNet.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams(uri);
return loadQuantizedParams_1.loadQuantizedParams(uri);
};
FaceLandmarkNet.prototype.extractParams = function (weights) {
return extractParams(weights);
return extractParams_1.extractParams(weights);
};
return FaceLandmarkNet;
}(NeuralNetwork));
export { FaceLandmarkNet };
}(tfjs_image_recognition_base_1.NeuralNetwork));
exports.FaceLandmarkNet = FaceLandmarkNet;
//# sourceMappingURL=FaceLandmarkNet.js.map
\ No newline at end of file
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/extractParams.js
import { extractWeightsFactory } from 'tfjs-image-recognition-base';
import { extractConvParamsFactory, extractFCParamsFactory } from 'tfjs-tiny-yolov2';
export function extractParams(weights) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
function extractParams(weights) {
var paramMappings = [];
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
var extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);
var _a = tfjs_image_recognition_base_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var extractConvParams = tfjs_tiny_yolov2_1.extractConvParamsFactory(extractWeights, paramMappings);
var extractFCParams = tfjs_tiny_yolov2_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');
......@@ -34,4 +36,5 @@ export function extractParams(weights) {
}
};
}
exports.extractParams = extractParams;
//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
build/faceLandmarkNet/extractParams.js.map
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\ No newline at end of file
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\ No newline at end of file
build/faceLandmarkNet/fullyConnectedLayer.js
import * as tf from '@tensorflow/tfjs-core';
export function fullyConnectedLayer(x, params) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function fullyConnectedLayer(x, params) {
return tf.tidy(function () {
return tf.add(tf.matMul(x, params.weights), params.bias);
});
}
exports.fullyConnectedLayer = fullyConnectedLayer;
//# sourceMappingURL=fullyConnectedLayer.js.map
\ No newline at end of file
build/faceLandmarkNet/fullyConnectedLayer.js.map
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\ No newline at end of file
build/faceLandmarkNet/index.js
import { FaceLandmarkNet } from './FaceLandmarkNet';
export * from './FaceLandmarkNet';
export function createFaceLandmarkNet(weights) {
var net = new FaceLandmarkNet();
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var FaceLandmarkNet_1 = require("./FaceLandmarkNet");
tslib_1.__exportStar(require("./FaceLandmarkNet"), exports);
function createFaceLandmarkNet(weights) {
var net = new FaceLandmarkNet_1.FaceLandmarkNet();
net.extractWeights(weights);
return net;
}
export function faceLandmarkNet(weights) {
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/loadQuantizedParams.js
import * as tslib_1 from "tslib";
import { disposeUnusedWeightTensors, extractWeightEntryFactory, loadWeightMap, } from 'tfjs-image-recognition-base';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var DEFAULT_MODEL_NAME = 'face_landmark_68_model';
function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
var extractWeightEntry = tfjs_image_recognition_base_1.extractWeightEntryFactory(weightMap, paramMappings);
function extractConvParams(prefix, mappedPrefix) {
var filters = extractWeightEntry(prefix + "/kernel", 4, mappedPrefix + "/filters");
var bias = extractWeightEntry(prefix + "/bias", 1, mappedPrefix + "/bias");
......@@ -18,12 +20,12 @@ function extractorsFactory(weightMap, paramMappings) {
extractFcParams: extractFcParams
};
}
export function loadQuantizedParams(uri) {
function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractConvParams, extractFcParams, params;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
paramMappings = [];
......@@ -40,10 +42,11 @@ export function loadQuantizedParams(uri) {
fc0: extractFcParams('dense', 'fc0'),
fc1: extractFcParams('logits', 'fc1')
};
disposeUnusedWeightTensors(weightMap, paramMappings);
tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
}
});
});
}
exports.loadQuantizedParams = loadQuantizedParams;
//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
build/faceLandmarkNet/loadQuantizedParams.js.map
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\ No newline at end of file
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\ No newline at end of file
build/faceLandmarkNet/types.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
//# sourceMappingURL=types.js.map
\ No newline at end of file
build/faceRecognitionNet/FaceRecognitionNet.js
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { NeuralNetwork, normalize, toNetInput } from 'tfjs-image-recognition-base';
import { convDown } from './convLayer';
import { extractParams } from './extractParams';
import { loadQuantizedParams } from './loadQuantizedParams';
import { residual, residualDown } from './residualLayer';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var convLayer_1 = require("./convLayer");
var extractParams_1 = require("./extractParams");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
var residualLayer_1 = require("./residualLayer");
var FaceRecognitionNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceRecognitionNet, _super);
function FaceRecognitionNet() {
......@@ -18,23 +20,23 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
return tf.tidy(function () {
var batchTensor = input.toBatchTensor(150, true);
var meanRgb = [122.782, 117.001, 104.298];
var normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256));
var out = convDown(normalized, params.conv32_down);
var normalized = tfjs_image_recognition_base_1.normalize(batchTensor, meanRgb).div(tf.scalar(256));
var out = convLayer_1.convDown(normalized, params.conv32_down);
out = tf.maxPool(out, 3, 2, 'valid');
out = residual(out, params.conv32_1);
out = residual(out, params.conv32_2);
out = residual(out, params.conv32_3);
out = residualDown(out, params.conv64_down);
out = residual(out, params.conv64_1);
out = residual(out, params.conv64_2);
out = residual(out, params.conv64_3);
out = residualDown(out, params.conv128_down);
out = residual(out, params.conv128_1);
out = residual(out, params.conv128_2);
out = residualDown(out, params.conv256_down);
out = residual(out, params.conv256_1);
out = residual(out, params.conv256_2);
out = residualDown(out, params.conv256_down_out);
out = residualLayer_1.residual(out, params.conv32_1);
out = residualLayer_1.residual(out, params.conv32_2);
out = residualLayer_1.residual(out, params.conv32_3);
out = residualLayer_1.residualDown(out, params.conv64_down);
out = residualLayer_1.residual(out, params.conv64_1);
out = residualLayer_1.residual(out, params.conv64_2);
out = residualLayer_1.residual(out, params.conv64_3);
out = residualLayer_1.residualDown(out, params.conv128_down);
out = residualLayer_1.residual(out, params.conv128_1);
out = residualLayer_1.residual(out, params.conv128_2);
out = residualLayer_1.residualDown(out, params.conv256_down);
out = residualLayer_1.residual(out, params.conv256_1);
out = residualLayer_1.residual(out, params.conv256_2);
out = residualLayer_1.residualDown(out, params.conv256_down_out);
var globalAvg = out.mean([1, 2]);
var fullyConnected = tf.matMul(globalAvg, params.fc);
return fullyConnected;
......@@ -47,7 +49,7 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, toNetInput(input, true)];
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
......@@ -59,7 +61,7 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
var netInput, faceDescriptorTensors, faceDescriptorsForBatch;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, toNetInput(input, true)];
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)];
case 1:
netInput = _a.sent();
faceDescriptorTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });
......@@ -75,12 +77,12 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
});
};
FaceRecognitionNet.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams(uri);
return loadQuantizedParams_1.loadQuantizedParams(uri);
};
FaceRecognitionNet.prototype.extractParams = function (weights) {
return extractParams(weights);
return extractParams_1.extractParams(weights);
};
return FaceRecognitionNet;
}(NeuralNetwork));
export { FaceRecognitionNet };
}(tfjs_image_recognition_base_1.NeuralNetwork));
exports.FaceRecognitionNet = FaceRecognitionNet;
//# sourceMappingURL=FaceRecognitionNet.js.map
\ No newline at end of file
build/faceRecognitionNet/FaceRecognitionNet.js.map
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\ No newline at end of file
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\ No newline at end of file
build/faceRecognitionNet/convLayer.js
import * as tf from '@tensorflow/tfjs-core';
import { scale } from './scaleLayer';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var scaleLayer_1 = require("./scaleLayer");
function convLayer(x, params, strides, withRelu, padding) {
if (padding === void 0) { padding = 'same'; }
var _a = params.conv, filters = _a.filters, bias = _a.bias;
var out = tf.conv2d(x, filters, strides, padding);
out = tf.add(out, bias);
out = scale(out, params.scale);
out = scaleLayer_1.scale(out, params.scale);
return withRelu ? tf.relu(out) : out;
}
export function conv(x, params) {
function conv(x, params) {
return convLayer(x, params, [1, 1], true);
}
export function convNoRelu(x, params) {
exports.conv = conv;
function convNoRelu(x, params) {
return convLayer(x, params, [1, 1], false);
}
export function convDown(x, params) {
exports.convNoRelu = convNoRelu;
function convDown(x, params) {
return convLayer(x, params, [2, 2], true, 'valid');
}
exports.convDown = convDown;
//# sourceMappingURL=convLayer.js.map
\ No newline at end of file
build/faceRecognitionNet/convLayer.js.map
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\ No newline at end of file
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\ No newline at end of file
build/faceRecognitionNet/extractParams.js
import * as tf from '@tensorflow/tfjs-core';
import { extractWeightsFactory, isFloat } from 'tfjs-image-recognition-base';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
function extractorsFactory(extractWeights, paramMappings) {
function extractFilterValues(numFilterValues, numFilters, filterSize) {
var weights = extractWeights(numFilterValues);
var depth = weights.length / (numFilters * filterSize * filterSize);
if (isFloat(depth)) {
if (tfjs_image_recognition_base_1.isFloat(depth)) {
throw new Error("depth has to be an integer: " + depth + ", weights.length: " + weights.length + ", numFilters: " + numFilters + ", filterSize: " + filterSize);
}
return tf.tidy(function () { return tf.transpose(tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]); });
......@@ -40,8 +42,8 @@ function extractorsFactory(extractWeights, paramMappings) {
extractResidualLayerParams: extractResidualLayerParams
};
}
export function extractParams(weights) {
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
function extractParams(weights) {
var _a = tfjs_image_recognition_base_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var paramMappings = [];
var _b = extractorsFactory(extractWeights, paramMappings), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams;
var conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');
......@@ -84,4 +86,5 @@ export function extractParams(weights) {
};
return { params: params, paramMappings: paramMappings };
}
exports.extractParams = extractParams;
//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
build/faceRecognitionNet/extractParams.js.map
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\ No newline at end of file
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\ No newline at end of file
build/faceRecognitionNet/index.js
import { FaceRecognitionNet } from './FaceRecognitionNet';
export * from './FaceRecognitionNet';
export function createFaceRecognitionNet(weights) {
var net = new FaceRecognitionNet();
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var FaceRecognitionNet_1 = require("./FaceRecognitionNet");
tslib_1.__exportStar(require("./FaceRecognitionNet"), exports);
function createFaceRecognitionNet(weights) {
var net = new FaceRecognitionNet_1.FaceRecognitionNet();
net.extractWeights(weights);
return net;
}
export function faceRecognitionNet(weights) {
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
build/faceRecognitionNet/loadQuantizedParams.js
import * as tslib_1 from "tslib";
import { disposeUnusedWeightTensors, extractWeightEntryFactory, isTensor2D, loadWeightMap, } from 'tfjs-image-recognition-base';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var DEFAULT_MODEL_NAME = 'face_recognition_model';
function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
var extractWeightEntry = tfjs_image_recognition_base_1.extractWeightEntryFactory(weightMap, paramMappings);
function extractScaleLayerParams(prefix) {
var weights = extractWeightEntry(prefix + "/scale/weights", 1);
var biases = extractWeightEntry(prefix + "/scale/biases", 1);
......@@ -25,12 +27,12 @@ function extractorsFactory(weightMap, paramMappings) {
extractResidualLayerParams: extractResidualLayerParams
};
}
export function loadQuantizedParams(uri) {
function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractConvLayerParams, extractResidualLayerParams, conv32_down, conv32_1, conv32_2, conv32_3, conv64_down, conv64_1, conv64_2, conv64_3, conv128_down, conv128_1, conv128_2, conv256_down, conv256_1, conv256_2, conv256_down_out, fc, params;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
paramMappings = [];
......@@ -52,7 +54,7 @@ export function loadQuantizedParams(uri) {
conv256_down_out = extractResidualLayerParams('conv256_down_out');
fc = weightMap['fc'];
paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });
if (!isTensor2D(fc)) {
if (!tfjs_image_recognition_base_1.isTensor2D(fc)) {
throw new Error("expected weightMap[fc] to be a Tensor2D, instead have " + fc);
}
params = {
......@@ -73,10 +75,11 @@ export function loadQuantizedParams(uri) {
conv256_down_out: conv256_down_out,
fc: fc
};
disposeUnusedWeightTensors(weightMap, paramMappings);
tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
}
});
});
}
exports.loadQuantizedParams = loadQuantizedParams;
//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
build/faceRecognitionNet/loadQuantizedParams.js.map
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\ No newline at end of file
build/faceRecognitionNet/residualLayer.js
import * as tf from '@tensorflow/tfjs-core';
import { conv, convDown, convNoRelu } from './convLayer';
export function residual(x, params) {
var out = conv(x, params.conv1);
out = convNoRelu(out, params.conv2);
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var convLayer_1 = require("./convLayer");
function residual(x, params) {
var out = convLayer_1.conv(x, params.conv1);
out = convLayer_1.convNoRelu(out, params.conv2);
out = tf.add(out, x);
out = tf.relu(out);
return out;
}
export function residualDown(x, params) {
var out = convDown(x, params.conv1);
out = convNoRelu(out, params.conv2);
exports.residual = residual;
function residualDown(x, params) {
var out = convLayer_1.convDown(x, params.conv1);
out = convLayer_1.convNoRelu(out, params.conv2);
var pooled = tf.avgPool(x, 2, 2, 'valid');
var zeros = tf.zeros(pooled.shape);
var isPad = pooled.shape[3] !== out.shape[3];
......@@ -29,4 +32,5 @@ export function residualDown(x, params) {
out = tf.relu(out);
return out;
}
exports.residualDown = residualDown;
//# sourceMappingURL=residualLayer.js.map
\ No newline at end of file
build/faceRecognitionNet/residualLayer.js.map
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\ No newline at end of file
build/faceRecognitionNet/scaleLayer.js
import * as tf from '@tensorflow/tfjs-core';
export function scale(x, params) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function scale(x, params) {
return tf.add(tf.mul(x, params.weights), params.biases);
}
exports.scale = scale;
//# sourceMappingURL=scaleLayer.js.map
\ No newline at end of file
build/faceRecognitionNet/scaleLayer.js.map
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\ No newline at end of file
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\ No newline at end of file
build/faceRecognitionNet/types.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
//# sourceMappingURL=types.js.map
\ No newline at end of file
build/globalApi.d.ts
......@@ -28,6 +28,7 @@ export declare function loadTinyYolov2Model(url: string): Promise<void>;
export declare function loadFaceDetectionModel(url: string): Promise<void>;
export declare function loadModels(url: string): Promise<[void, void, void, void, void]>;
export declare function locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
export declare const ssdMobilenetv1: typeof locateFaces;
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[]>;
......
build/globalApi.js
import { allFacesMtcnnFactory, allFacesSsdMobilenetv1Factory, allFacesTinyYolov2Factory } from './allFacesFactory';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { Mtcnn } from './mtcnn/Mtcnn';
import { TinyYolov2 } from './tinyYolov2/TinyYolov2';
export var detectionNet = new FaceDetectionNet();
export var landmarkNet = new FaceLandmarkNet();
export var recognitionNet = new FaceRecognitionNet();
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var allFacesFactory_1 = require("./allFacesFactory");
var FaceDetectionNet_1 = require("./faceDetectionNet/FaceDetectionNet");
var FaceLandmarkNet_1 = require("./faceLandmarkNet/FaceLandmarkNet");
var FaceRecognitionNet_1 = require("./faceRecognitionNet/FaceRecognitionNet");
var Mtcnn_1 = require("./mtcnn/Mtcnn");
var TinyYolov2_1 = require("./tinyYolov2/TinyYolov2");
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
export var nets = {
ssdMobilenetv1: detectionNet,
faceLandmark68Net: landmarkNet,
faceRecognitionNet: recognitionNet,
mtcnn: new Mtcnn(),
tinyYolov2: new TinyYolov2()
exports.nets = {
ssdMobilenetv1: exports.detectionNet,
faceLandmark68Net: exports.landmarkNet,
faceRecognitionNet: exports.recognitionNet,
mtcnn: new Mtcnn_1.Mtcnn(),
tinyYolov2: new TinyYolov2_1.TinyYolov2()
};
export function loadSsdMobilenetv1Model(url) {
return nets.ssdMobilenetv1.load(url);
function loadSsdMobilenetv1Model(url) {
return exports.nets.ssdMobilenetv1.load(url);
}
export function loadFaceLandmarkModel(url) {
return nets.faceLandmark68Net.load(url);
exports.loadSsdMobilenetv1Model = loadSsdMobilenetv1Model;
function loadFaceLandmarkModel(url) {
return exports.nets.faceLandmark68Net.load(url);
}
export function loadFaceRecognitionModel(url) {
return nets.faceRecognitionNet.load(url);
exports.loadFaceLandmarkModel = loadFaceLandmarkModel;
function loadFaceRecognitionModel(url) {
return exports.nets.faceRecognitionNet.load(url);
}
export function loadMtcnnModel(url) {
return nets.mtcnn.load(url);
exports.loadFaceRecognitionModel = loadFaceRecognitionModel;
function loadMtcnnModel(url) {
return exports.nets.mtcnn.load(url);
}
export function loadTinyYolov2Model(url) {
return nets.tinyYolov2.load(url);
exports.loadMtcnnModel = loadMtcnnModel;
function loadTinyYolov2Model(url) {
return exports.nets.tinyYolov2.load(url);
}
export function loadFaceDetectionModel(url) {
exports.loadTinyYolov2Model = loadTinyYolov2Model;
function loadFaceDetectionModel(url) {
return loadSsdMobilenetv1Model(url);
}
export function loadModels(url) {
exports.loadFaceDetectionModel = loadFaceDetectionModel;
function loadModels(url) {
return Promise.all([
loadSsdMobilenetv1Model(url),
loadFaceLandmarkModel(url),
......@@ -43,23 +51,30 @@ export function loadModels(url) {
loadTinyYolov2Model(url)
]);
}
export function locateFaces(input, minConfidence, maxResults) {
return nets.ssdMobilenetv1.locateFaces(input, minConfidence, maxResults);
exports.loadModels = loadModels;
function locateFaces(input, minConfidence, maxResults) {
return exports.nets.ssdMobilenetv1.locateFaces(input, minConfidence, maxResults);
}
export function detectLandmarks(input) {
return nets.faceLandmark68Net.detectLandmarks(input);
exports.locateFaces = locateFaces;
exports.ssdMobilenetv1 = locateFaces;
function detectLandmarks(input) {
return exports.nets.faceLandmark68Net.detectLandmarks(input);
}
export function computeFaceDescriptor(input) {
return nets.faceRecognitionNet.computeFaceDescriptor(input);
exports.detectLandmarks = detectLandmarks;
function computeFaceDescriptor(input) {
return exports.nets.faceRecognitionNet.computeFaceDescriptor(input);
}
export function mtcnn(input, forwardParams) {
return nets.mtcnn.forward(input, forwardParams);
exports.computeFaceDescriptor = computeFaceDescriptor;
function mtcnn(input, forwardParams) {
return exports.nets.mtcnn.forward(input, forwardParams);
}
export function tinyYolov2(input, forwardParams) {
return nets.tinyYolov2.locateFaces(input, forwardParams);
exports.mtcnn = mtcnn;
function tinyYolov2(input, forwardParams) {
return exports.nets.tinyYolov2.locateFaces(input, forwardParams);
}
export var allFacesSsdMobilenetv1 = allFacesSsdMobilenetv1Factory(nets.ssdMobilenetv1, nets.faceLandmark68Net, nets.faceRecognitionNet);
export var allFacesTinyYolov2 = allFacesTinyYolov2Factory(nets.tinyYolov2, nets.faceLandmark68Net, nets.faceRecognitionNet);
export var allFacesMtcnn = allFacesMtcnnFactory(nets.mtcnn, nets.faceRecognitionNet);
export var allFaces = allFacesSsdMobilenetv1;
exports.tinyYolov2 = tinyYolov2;
exports.allFacesSsdMobilenetv1 = allFacesFactory_1.allFacesSsdMobilenetv1Factory(exports.nets.ssdMobilenetv1, exports.nets.faceLandmark68Net, exports.nets.faceRecognitionNet);
exports.allFacesTinyYolov2 = allFacesFactory_1.allFacesTinyYolov2Factory(exports.nets.tinyYolov2, exports.nets.faceLandmark68Net, exports.nets.faceRecognitionNet);
exports.allFacesMtcnn = allFacesFactory_1.allFacesMtcnnFactory(exports.nets.mtcnn, exports.nets.faceRecognitionNet);
exports.allFaces = exports.allFacesSsdMobilenetv1;
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build/globalApi.js.map
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\ No newline at end of file
build/index.js
import * as tf from '@tensorflow/tfjs-core';
export { tf };
export * from 'tfjs-image-recognition-base';
export * from './classes';
export * from './dom';
export * from './euclideanDistance';
export * from './faceDetectionNet';
export * from './faceLandmarkNet';
export * from './faceRecognitionNet';
export * from './globalApi';
export * from './mtcnn';
export * from './tinyYolov2';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
exports.tf = tf;
tslib_1.__exportStar(require("tfjs-image-recognition-base"), exports);
tslib_1.__exportStar(require("./classes"), exports);
tslib_1.__exportStar(require("./dom"), exports);
tslib_1.__exportStar(require("./euclideanDistance"), exports);
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("./tinyYolov2"), exports);
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/index.js.map
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\ No newline at end of file
build/mtcnn/Mtcnn.js
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { NeuralNetwork, Point, Rect, toNetInput } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection';
import { FaceLandmarks5 } from '../classes/FaceLandmarks5';
import { bgrToRgbTensor } from './bgrToRgbTensor';
import { CELL_SIZE } from './config';
import { extractParams } from './extractParams';
import { getDefaultMtcnnForwardParams } from './getDefaultMtcnnForwardParams';
import { getSizesForScale } from './getSizesForScale';
import { loadQuantizedParams } from './loadQuantizedParams';
import { pyramidDown } from './pyramidDown';
import { stage1 } from './stage1';
import { stage2 } from './stage2';
import { stage3 } from './stage3';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceDetection_1 = require("../classes/FaceDetection");
var FaceLandmarks5_1 = require("../classes/FaceLandmarks5");
var bgrToRgbTensor_1 = require("./bgrToRgbTensor");
var config_1 = require("./config");
var extractParams_1 = require("./extractParams");
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() {
......@@ -37,7 +39,7 @@ var Mtcnn = /** @class */ (function (_super) {
stats = {};
tsTotal = Date.now();
imgTensor = tf.tidy(function () {
return bgrToRgbTensor(tf.expandDims(inputTensor).toFloat());
return bgrToRgbTensor_1.bgrToRgbTensor(tf.expandDims(inputTensor).toFloat());
});
onReturn = function (results) {
// dispose tensors on return
......@@ -47,17 +49,17 @@ var Mtcnn = /** @class */ (function (_super) {
return results;
};
_a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
_b = Object.assign({}, getDefaultMtcnnForwardParams(), forwardParams), minFaceSize = _b.minFaceSize, scaleFactor = _b.scaleFactor, maxNumScales = _b.maxNumScales, scoreThresholds = _b.scoreThresholds, scaleSteps = _b.scaleSteps;
scales = (scaleSteps || pyramidDown(minFaceSize, scaleFactor, [height, width]))
_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(scale, [height, width]);
return Math.min(sizes.width, sizes.height) > CELL_SIZE;
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(scale, [height, width]); });
stats.pyramid = scales.map(function (scale) { return getSizesForScale_1.getSizesForScale(scale, [height, width]); });
ts = Date.now();
return [4 /*yield*/, stage1(imgTensor, scales, scoreThresholds[0], params.pnet, stats)];
return [4 /*yield*/, stage1_1.stage1(imgTensor, scales, scoreThresholds[0], params.pnet, stats)];
case 1:
out1 = _c.sent();
stats.total_stage1 = Date.now() - ts;
......@@ -68,7 +70,7 @@ var Mtcnn = /** @class */ (function (_super) {
// 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(inputCanvas, out1.boxes, scoreThresholds[1], params.rnet, stats)];
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;
......@@ -77,16 +79,16 @@ var Mtcnn = /** @class */ (function (_super) {
}
stats.stage3_numInputBoxes = out2.boxes.length;
ts = Date.now();
return [4 /*yield*/, stage3(inputCanvas, out2.boxes, scoreThresholds[2], params.onet, stats)];
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(out3.scores[idx], new Rect(box.left / width, box.top / height, box.width / width, box.height / height), {
faceDetection: new FaceDetection_1.FaceDetection(out3.scores[idx], new tfjs_image_recognition_base_1.Rect(box.left / width, box.top / height, box.width / width, box.height / height), {
height: height,
width: width
}),
faceLandmarks: new FaceLandmarks5(out3.points[idx].map(function (pt) { return pt.div(new Point(width, height)); }), { width: width, height: height })
faceLandmarks: new FaceLandmarks5_1.FaceLandmarks5(out3.points[idx].map(function (pt) { return pt.div(new tfjs_image_recognition_base_1.Point(width, height)); }), { width: width, height: height })
}); });
return [2 /*return*/, onReturn({ results: results, stats: stats })];
}
......@@ -101,7 +103,7 @@ var Mtcnn = /** @class */ (function (_super) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, toNetInput(input, true, true)];
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true, true)];
case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(),
forwardParams])];
case 2: return [2 /*return*/, (_b.sent()).results];
......@@ -117,7 +119,7 @@ var Mtcnn = /** @class */ (function (_super) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, toNetInput(input, true, true)];
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true, true)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent(),
forwardParams])];
}
......@@ -126,12 +128,12 @@ var Mtcnn = /** @class */ (function (_super) {
};
// none of the param tensors are quantized yet
Mtcnn.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams(uri);
return loadQuantizedParams_1.loadQuantizedParams(uri);
};
Mtcnn.prototype.extractParams = function (weights) {
return extractParams(weights);
return extractParams_1.extractParams(weights);
};
return Mtcnn;
}(NeuralNetwork));
export { Mtcnn };
}(tfjs_image_recognition_base_1.NeuralNetwork));
exports.Mtcnn = Mtcnn;
//# sourceMappingURL=Mtcnn.js.map
\ No newline at end of file
build/mtcnn/Mtcnn.js.map
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\ No newline at end of file
build/mtcnn/ONet.js
import * as tf from '@tensorflow/tfjs-core';
import { convLayer } from 'tfjs-tiny-yolov2';
import { fullyConnectedLayer } from '../faceLandmarkNet/fullyConnectedLayer';
import { prelu } from './prelu';
import { sharedLayer } from './sharedLayers';
export function ONet(x, params) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
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 = sharedLayer(x, params);
var out = sharedLayers_1.sharedLayer(x, params);
out = tf.maxPool(out, [2, 2], [2, 2], 'same');
out = convLayer(out, params.conv4, 'valid');
out = prelu(out, params.prelu4_alpha);
out = tfjs_tiny_yolov2_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(vectorized, params.fc1);
var prelu5 = prelu(fc1, params.prelu5_alpha);
var fc2_1 = fullyConnectedLayer(prelu5, params.fc2_1);
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(prelu5, params.fc2_2);
var points = fullyConnectedLayer(prelu5, params.fc2_3);
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
\ No newline at end of file
build/mtcnn/ONet.js.map
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build/mtcnn/PNet.js
import * as tf from '@tensorflow/tfjs-core';
import { convLayer } from 'tfjs-tiny-yolov2';
import { sharedLayer } from './sharedLayers';
export function PNet(x, params) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
var sharedLayers_1 = require("./sharedLayers");
function PNet(x, params) {
return tf.tidy(function () {
var out = sharedLayer(x, params, true);
var conv = convLayer(out, params.conv4_1, 'valid');
var out = sharedLayers_1.sharedLayer(x, params, true);
var conv = tfjs_tiny_yolov2_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(out, params.conv4_2, 'valid');
var regions = tfjs_tiny_yolov2_1.convLayer(out, params.conv4_2, 'valid');
return { prob: prob, regions: regions };
});
}
exports.PNet = PNet;
//# sourceMappingURL=PNet.js.map
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build/mtcnn/PNet.js.map
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build/mtcnn/RNet.js
import * as tf from '@tensorflow/tfjs-core';
import { fullyConnectedLayer } from '../faceLandmarkNet/fullyConnectedLayer';
import { prelu } from './prelu';
import { sharedLayer } from './sharedLayers';
export function RNet(x, params) {
"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 = sharedLayer(x, params);
var convOut = sharedLayers_1.sharedLayer(x, params);
var vectorized = tf.reshape(convOut, [convOut.shape[0], params.fc1.weights.shape[0]]);
var fc1 = fullyConnectedLayer(vectorized, params.fc1);
var prelu4 = prelu(fc1, params.prelu4_alpha);
var fc2_1 = fullyConnectedLayer(prelu4, params.fc2_1);
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(prelu4, params.fc2_2);
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
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build/mtcnn/bgrToRgbTensor.js
import * as tf from '@tensorflow/tfjs-core';
export function bgrToRgbTensor(tensor) {
"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
\ No newline at end of file
build/mtcnn/bgrToRgbTensor.js.map
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\ No newline at end of file
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build/mtcnn/config.js
export var CELL_STRIDE = 2;
export var CELL_SIZE = 12;
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.CELL_STRIDE = 2;
exports.CELL_SIZE = 12;
//# sourceMappingURL=config.js.map
\ No newline at end of file
build/mtcnn/config.js.map
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\ No newline at end of file
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\ No newline at end of file
build/mtcnn/extractImagePatches.js
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { normalize } from './normalize';
import { getContext2dOrThrow, createCanvas } from 'tfjs-image-recognition-base';
export function extractImagePatches(img, boxes, _a) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var normalize_1 = require("./normalize");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
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;
......@@ -10,7 +12,7 @@ export function extractImagePatches(img, boxes, _a) {
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
imgCtx = getContext2dOrThrow(img);
imgCtx = tfjs_image_recognition_base_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) {
......@@ -25,8 +27,8 @@ export function extractImagePatches(img, boxes, _a) {
bitmaps = _b.sent();
imagePatchesDatas = [];
bitmaps.forEach(function (bmp) {
var patch = createCanvas({ width: width, height: height });
var patchCtx = getContext2dOrThrow(patch);
var patch = tfjs_image_recognition_base_1.createCanvas({ width: width, height: height });
var patchCtx = tfjs_image_recognition_base_1.getContext2dOrThrow(patch);
patchCtx.drawImage(bmp, 0, 0, width, height);
var data = patchCtx.getImageData(0, 0, width, height).data;
var currData = [];
......@@ -41,7 +43,7 @@ export function extractImagePatches(img, boxes, _a) {
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(imagePatchTensor);
return normalize_1.normalize(imagePatchTensor);
});
return t;
})];
......@@ -49,4 +51,5 @@ export function extractImagePatches(img, boxes, _a) {
});
});
}
exports.extractImagePatches = extractImagePatches;
//# sourceMappingURL=extractImagePatches.js.map
\ No newline at end of file
build/mtcnn/extractImagePatches.js.map
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\ No newline at end of file
build/mtcnn/extractParams.js
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { extractWeightsFactory } from 'tfjs-image-recognition-base';
import { extractConvParamsFactory, extractFCParamsFactory } from 'tfjs-tiny-yolov2';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
function extractorsFactory(extractWeights, paramMappings) {
var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
var extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);
var extractConvParams = tfjs_tiny_yolov2_1.extractConvParamsFactory(extractWeights, paramMappings);
var extractFCParams = tfjs_tiny_yolov2_1.extractFCParamsFactory(extractWeights, paramMappings);
function extractPReluParams(size, paramPath) {
var alpha = tf.tensor1d(extractWeights(size));
paramMappings.push({ paramPath: paramPath });
......@@ -51,8 +53,8 @@ function extractorsFactory(extractWeights, paramMappings) {
extractONetParams: extractONetParams
};
}
export function extractParams(weights) {
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
function extractParams(weights) {
var _a = tfjs_image_recognition_base_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();
......@@ -63,4 +65,5 @@ export function extractParams(weights) {
}
return { params: { pnet: pnet, rnet: rnet, onet: onet }, paramMappings: paramMappings };
}
exports.extractParams = extractParams;
//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
build/mtcnn/extractParams.js.map
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\ No newline at end of file
build/mtcnn/getDefaultMtcnnForwardParams.js
export function getDefaultMtcnnForwardParams() {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function getDefaultMtcnnForwardParams() {
return {
minFaceSize: 20,
scaleFactor: 0.709,
......@@ -6,4 +8,5 @@ export function getDefaultMtcnnForwardParams() {
scoreThresholds: [0.6, 0.7, 0.7]
};
}
exports.getDefaultMtcnnForwardParams = getDefaultMtcnnForwardParams;
//# sourceMappingURL=getDefaultMtcnnForwardParams.js.map
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build/mtcnn/getDefaultMtcnnForwardParams.js.map
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build/mtcnn/getSizesForScale.js
export function getSizesForScale(scale, _a) {
"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
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build/mtcnn/getSizesForScale.js.map
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build/mtcnn/index.js
import { Mtcnn } from './Mtcnn';
export * from './Mtcnn';
export function createMtcnn(weights) {
var net = new Mtcnn();
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var Mtcnn_1 = require("./Mtcnn");
tslib_1.__exportStar(require("./Mtcnn"), exports);
function createMtcnn(weights) {
var net = new Mtcnn_1.Mtcnn();
net.extractWeights(weights);
return net;
}
exports.createMtcnn = createMtcnn;
//# sourceMappingURL=index.js.map
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build/mtcnn/index.js.map
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build/mtcnn/loadQuantizedParams.js
import * as tslib_1 from "tslib";
import { disposeUnusedWeightTensors, extractWeightEntryFactory, loadWeightMap, } from 'tfjs-image-recognition-base';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var DEFAULT_MODEL_NAME = 'mtcnn_model';
function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
var extractWeightEntry = tfjs_image_recognition_base_1.extractWeightEntryFactory(weightMap, paramMappings);
function extractConvParams(prefix) {
var filters = extractWeightEntry(prefix + "/weights", 4, prefix + "/filters");
var bias = extractWeightEntry(prefix + "/bias", 1);
......@@ -56,12 +58,12 @@ function extractorsFactory(weightMap, paramMappings) {
extractONetParams: extractONetParams
};
}
export function loadQuantizedParams(uri) {
function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractPNetParams, extractRNetParams, extractONetParams, pnet, rnet, onet;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
paramMappings = [];
......@@ -69,10 +71,11 @@ export function loadQuantizedParams(uri) {
pnet = extractPNetParams();
rnet = extractRNetParams();
onet = extractONetParams();
disposeUnusedWeightTensors(weightMap, paramMappings);
tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: { pnet: pnet, rnet: rnet, onet: onet }, paramMappings: paramMappings }];
}
});
});
}
exports.loadQuantizedParams = loadQuantizedParams;
//# sourceMappingURL=loadQuantizedParams.js.map
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build/mtcnn/loadQuantizedParams.js.map
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build/mtcnn/normalize.js
import * as tf from '@tensorflow/tfjs-core';
export function normalize(x) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function normalize(x) {
return tf.tidy(function () { return tf.mul(tf.sub(x, tf.scalar(127.5)), tf.scalar(0.0078125)); });
}
exports.normalize = normalize;
//# sourceMappingURL=normalize.js.map
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build/mtcnn/normalize.js.map
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build/mtcnn/prelu.js
import * as tf from '@tensorflow/tfjs-core';
export function prelu(x, alpha) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function prelu(x, alpha) {
return tf.tidy(function () {
return tf.add(tf.relu(x), tf.mul(alpha, tf.neg(tf.relu(tf.neg(x)))));
});
}
exports.prelu = prelu;
//# sourceMappingURL=prelu.js.map
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build/mtcnn/prelu.js.map
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build/mtcnn/pyramidDown.js
import { CELL_SIZE } from './config';
export function pyramidDown(minFaceSize, scaleFactor, dims) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var config_1 = require("./config");
function pyramidDown(minFaceSize, scaleFactor, dims) {
var height = dims[0], width = dims[1];
var m = CELL_SIZE / minFaceSize;
var m = config_1.CELL_SIZE / minFaceSize;
var scales = [];
var minLayer = Math.min(height, width) * m;
var exp = 0;
......@@ -12,4 +14,5 @@ export function pyramidDown(minFaceSize, scaleFactor, dims) {
}
return scales;
}
exports.pyramidDown = pyramidDown;
//# sourceMappingURL=pyramidDown.js.map
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build/mtcnn/pyramidDown.js.map
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build/mtcnn/sharedLayers.js
import * as tf from '@tensorflow/tfjs-core';
import { convLayer } from 'tfjs-tiny-yolov2';
import { prelu } from './prelu';
export function sharedLayer(x, params, isPnet) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
var prelu_1 = require("./prelu");
function sharedLayer(x, params, isPnet) {
if (isPnet === void 0) { isPnet = false; }
return tf.tidy(function () {
var out = convLayer(x, params.conv1, 'valid');
out = prelu(out, params.prelu1_alpha);
var out = tfjs_tiny_yolov2_1.convLayer(x, params.conv1, 'valid');
out = prelu_1.prelu(out, params.prelu1_alpha);
out = tf.maxPool(out, isPnet ? [2, 2] : [3, 3], [2, 2], 'same');
out = convLayer(out, params.conv2, 'valid');
out = prelu(out, params.prelu2_alpha);
out = tfjs_tiny_yolov2_1.convLayer(out, params.conv2, 'valid');
out = prelu_1.prelu(out, params.prelu2_alpha);
out = isPnet ? out : tf.maxPool(out, [3, 3], [2, 2], 'valid');
out = convLayer(out, params.conv3, 'valid');
out = prelu(out, params.prelu3_alpha);
out = tfjs_tiny_yolov2_1.convLayer(out, params.conv3, 'valid');
out = prelu_1.prelu(out, params.prelu3_alpha);
return out;
});
}
exports.sharedLayer = sharedLayer;
//# sourceMappingURL=sharedLayers.js.map
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build/mtcnn/sharedLayers.js.map
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build/mtcnn/stage1.js
import * as tf from '@tensorflow/tfjs-core';
import { BoundingBox, nonMaxSuppression, Point } from 'tfjs-image-recognition-base';
import { CELL_SIZE, CELL_STRIDE } from './config';
import { getSizesForScale } from './getSizesForScale';
import { normalize } from './normalize';
import { PNet } from './PNet';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var config_1 = require("./config");
var getSizesForScale_1 = require("./getSizesForScale");
var normalize_1 = require("./normalize");
var PNet_1 = require("./PNet");
function rescaleAndNormalize(x, scale) {
return tf.tidy(function () {
var _a = getSizesForScale(scale, x.shape.slice(1)), height = _a.height, width = _a.width;
var _a = getSizesForScale_1.getSizesForScale(scale, x.shape.slice(1)), height = _a.height, width = _a.width;
var resized = tf.image.resizeBilinear(x, [height, width]);
var normalized = normalize(resized);
var normalized = normalize_1.normalize(resized);
return tf.transpose(normalized, [0, 2, 1, 3]);
});
}
......@@ -18,14 +20,14 @@ function extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold
for (var y = 0; y < scoresTensor.shape[0]; y++) {
for (var x = 0; x < scoresTensor.shape[1]; x++) {
if (scoresTensor.get(y, x) >= scoreThreshold) {
indices.push(new Point(x, y));
indices.push(new tfjs_image_recognition_base_1.Point(x, y));
}
}
}
var boundingBoxes = indices.map(function (idx) {
var cell = new BoundingBox(Math.round((idx.y * CELL_STRIDE + 1) / scale), Math.round((idx.x * CELL_STRIDE + 1) / scale), Math.round((idx.y * CELL_STRIDE + CELL_SIZE) / scale), Math.round((idx.x * CELL_STRIDE + CELL_SIZE) / scale));
var cell = new tfjs_image_recognition_base_1.BoundingBox(Math.round((idx.y * config_1.CELL_STRIDE + 1) / scale), Math.round((idx.x * config_1.CELL_STRIDE + 1) / scale), Math.round((idx.y * config_1.CELL_STRIDE + config_1.CELL_SIZE) / scale), Math.round((idx.x * config_1.CELL_STRIDE + config_1.CELL_SIZE) / scale));
var score = scoresTensor.get(idx.y, idx.x);
var region = new BoundingBox(regionsTensor.get(idx.y, idx.x, 0), regionsTensor.get(idx.y, idx.x, 1), regionsTensor.get(idx.y, idx.x, 2), regionsTensor.get(idx.y, idx.x, 3));
var region = new tfjs_image_recognition_base_1.BoundingBox(regionsTensor.get(idx.y, idx.x, 0), regionsTensor.get(idx.y, idx.x, 1), regionsTensor.get(idx.y, idx.x, 2), regionsTensor.get(idx.y, idx.x, 3));
return {
cell: cell,
score: score,
......@@ -34,13 +36,13 @@ function extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold
});
return boundingBoxes;
}
export function stage1(imgTensor, scales, scoreThreshold, params, stats) {
function stage1(imgTensor, scales, scoreThreshold, params, stats) {
stats.stage1 = [];
var pnetOutputs = scales.map(function (scale) { return tf.tidy(function () {
var statsForScale = { scale: scale };
var resized = rescaleAndNormalize(imgTensor, scale);
var ts = Date.now();
var _a = PNet(resized, params), prob = _a.prob, regions = _a.regions;
var _a = PNet_1.PNet(resized, params), prob = _a.prob, regions = _a.regions;
statsForScale.pnet = Date.now() - ts;
var scoresTensor = tf.unstack(tf.unstack(prob, 3)[1])[0];
var regionsTensor = tf.unstack(regions)[0];
......@@ -61,7 +63,7 @@ export function stage1(imgTensor, scales, scoreThreshold, params, stats) {
return [];
}
var ts = Date.now();
var indices = nonMaxSuppression(boundingBoxes.map(function (bbox) { return bbox.cell; }), boundingBoxes.map(function (bbox) { return bbox.score; }), 0.5);
var indices = tfjs_image_recognition_base_1.nonMaxSuppression(boundingBoxes.map(function (bbox) { return bbox.cell; }), boundingBoxes.map(function (bbox) { return bbox.score; }), 0.5);
statsForScale.nms = Date.now() - ts;
statsForScale.numBoxes = indices.length;
stats.stage1.push(statsForScale);
......@@ -72,14 +74,14 @@ export function stage1(imgTensor, scales, scoreThreshold, params, stats) {
var finalScores = [];
if (allBoxes.length > 0) {
var ts = Date.now();
var indices = nonMaxSuppression(allBoxes.map(function (bbox) { return bbox.cell; }), allBoxes.map(function (bbox) { return bbox.score; }), 0.7);
var indices = tfjs_image_recognition_base_1.nonMaxSuppression(allBoxes.map(function (bbox) { return bbox.cell; }), allBoxes.map(function (bbox) { return bbox.score; }), 0.7);
stats.stage1_nms = Date.now() - ts;
finalScores = indices.map(function (idx) { return allBoxes[idx].score; });
finalBoxes = indices
.map(function (idx) { return allBoxes[idx]; })
.map(function (_a) {
var cell = _a.cell, region = _a.region;
return new BoundingBox(cell.left + (region.left * cell.width), cell.top + (region.top * cell.height), cell.right + (region.right * cell.width), cell.bottom + (region.bottom * cell.height)).toSquare().round();
return new tfjs_image_recognition_base_1.BoundingBox(cell.left + (region.left * cell.width), cell.top + (region.top * cell.height), cell.right + (region.right * cell.width), cell.bottom + (region.bottom * cell.height)).toSquare().round();
});
}
return {
......@@ -87,4 +89,5 @@ export function stage1(imgTensor, scales, scoreThreshold, params, stats) {
scores: finalScores
};
}
exports.stage1 = stage1;
//# sourceMappingURL=stage1.js.map
\ No newline at end of file
build/mtcnn/stage1.js.map
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\ No newline at end of file
build/mtcnn/stage2.js
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { BoundingBox, nonMaxSuppression } from 'tfjs-image-recognition-base';
import { extractImagePatches } from './extractImagePatches';
import { RNet } from './RNet';
export function stage2(img, inputBoxes, scoreThreshold, params, stats) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var extractImagePatches_1 = require("./extractImagePatches");
var RNet_1 = require("./RNet");
function stage2(img, inputBoxes, scoreThreshold, params, stats) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var ts, rnetInputs, rnetOuts, scoresTensor, scores, _a, _b, indices, filteredBoxes, filteredScores, finalBoxes, finalScores, indicesNms, regions_1;
return tslib_1.__generator(this, function (_c) {
switch (_c.label) {
case 0:
ts = Date.now();
return [4 /*yield*/, extractImagePatches(img, inputBoxes, { width: 24, height: 24 })];
return [4 /*yield*/, extractImagePatches_1.extractImagePatches(img, inputBoxes, { width: 24, height: 24 })];
case 1:
rnetInputs = _c.sent();
stats.stage2_extractImagePatches = Date.now() - ts;
ts = Date.now();
rnetOuts = rnetInputs.map(function (rnetInput) {
var out = RNet(rnetInput, params);
var out = RNet_1.RNet(rnetInput, params);
rnetInput.dispose();
return out;
});
......@@ -42,10 +44,10 @@ export function stage2(img, inputBoxes, scoreThreshold, params, stats) {
finalScores = [];
if (filteredBoxes.length > 0) {
ts = Date.now();
indicesNms = nonMaxSuppression(filteredBoxes, filteredScores, 0.7);
indicesNms = tfjs_image_recognition_base_1.nonMaxSuppression(filteredBoxes, filteredScores, 0.7);
stats.stage2_nms = Date.now() - ts;
regions_1 = indicesNms.map(function (idx) {
return new BoundingBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3));
return new tfjs_image_recognition_base_1.BoundingBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3));
});
finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
finalBoxes = indicesNms.map(function (idx, i) { return filteredBoxes[idx].calibrate(regions_1[i]); });
......@@ -62,4 +64,5 @@ export function stage2(img, inputBoxes, scoreThreshold, params, stats) {
});
});
}
exports.stage2 = stage2;
//# sourceMappingURL=stage2.js.map
\ No newline at end of file
build/mtcnn/stage2.js.map
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build/mtcnn/stage3.js
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { BoundingBox, nonMaxSuppression, Point } from 'tfjs-image-recognition-base';
import { extractImagePatches } from './extractImagePatches';
import { ONet } from './ONet';
export function stage3(img, inputBoxes, scoreThreshold, params, stats) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var extractImagePatches_1 = require("./extractImagePatches");
var ONet_1 = require("./ONet");
function stage3(img, inputBoxes, scoreThreshold, params, stats) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var ts, onetInputs, onetOuts, scoresTensor, scores, _a, _b, indices, filteredRegions, filteredBoxes, filteredScores, finalBoxes, finalScores, points, indicesNms;
return tslib_1.__generator(this, function (_c) {
switch (_c.label) {
case 0:
ts = Date.now();
return [4 /*yield*/, extractImagePatches(img, inputBoxes, { width: 48, height: 48 })];
return [4 /*yield*/, extractImagePatches_1.extractImagePatches(img, inputBoxes, { width: 48, height: 48 })];
case 1:
onetInputs = _c.sent();
stats.stage3_extractImagePatches = Date.now() - ts;
ts = Date.now();
onetOuts = onetInputs.map(function (onetInput) {
var out = ONet(onetInput, params);
var out = ONet_1.ONet(onetInput, params);
onetInput.dispose();
return out;
});
......@@ -36,7 +38,7 @@ export function stage3(img, inputBoxes, scoreThreshold, params, stats) {
var idx = _a.idx;
return idx;
});
filteredRegions = indices.map(function (idx) { return new BoundingBox(onetOuts[idx].regions.get(0, 0), onetOuts[idx].regions.get(0, 1), onetOuts[idx].regions.get(0, 2), onetOuts[idx].regions.get(0, 3)); });
filteredRegions = indices.map(function (idx) { return new tfjs_image_recognition_base_1.BoundingBox(onetOuts[idx].regions.get(0, 0), onetOuts[idx].regions.get(0, 1), onetOuts[idx].regions.get(0, 2), onetOuts[idx].regions.get(0, 3)); });
filteredBoxes = indices
.map(function (idx, i) { return inputBoxes[idx].calibrate(filteredRegions[i]); });
filteredScores = indices.map(function (idx) { return scores[idx]; });
......@@ -45,13 +47,13 @@ export function stage3(img, inputBoxes, scoreThreshold, params, stats) {
points = [];
if (filteredBoxes.length > 0) {
ts = Date.now();
indicesNms = nonMaxSuppression(filteredBoxes, filteredScores, 0.7, false);
indicesNms = tfjs_image_recognition_base_1.nonMaxSuppression(filteredBoxes, filteredScores, 0.7, false);
stats.stage3_nms = Date.now() - ts;
finalBoxes = indicesNms.map(function (idx) { return filteredBoxes[idx]; });
finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
points = indicesNms.map(function (idx, i) {
return Array(5).fill(0).map(function (_, ptIdx) {
return new Point(((onetOuts[idx].points.get(0, ptIdx) * (finalBoxes[i].width + 1)) + finalBoxes[i].left), ((onetOuts[idx].points.get(0, ptIdx + 5) * (finalBoxes[i].height + 1)) + finalBoxes[i].top));
return new tfjs_image_recognition_base_1.Point(((onetOuts[idx].points.get(0, ptIdx) * (finalBoxes[i].width + 1)) + finalBoxes[i].left), ((onetOuts[idx].points.get(0, ptIdx + 5) * (finalBoxes[i].height + 1)) + finalBoxes[i].top));
});
});
}
......@@ -69,4 +71,5 @@ export function stage3(img, inputBoxes, scoreThreshold, params, stats) {
});
});
}
exports.stage3 = stage3;
//# sourceMappingURL=stage3.js.map
\ No newline at end of file
build/mtcnn/stage3.js.map
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\ No newline at end of file
build/mtcnn/types.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
//# sourceMappingURL=types.js.map
\ No newline at end of file
build/tinyYolov2/TinyYolov2.js
import * as tslib_1 from "tslib";
import { TinyYolov2 as TinyYolov2Base } from 'tfjs-tiny-yolov2';
import { FaceDetection } from '../classes';
import { BOX_ANCHORS, BOX_ANCHORS_SEPARABLE, DEFAULT_MODEL_NAME, DEFAULT_MODEL_NAME_SEPARABLE_CONV, IOU_THRESHOLD, MEAN_RGB_SEPARABLE, } from './const';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
var classes_1 = require("../classes");
var const_1 = require("./const");
var TinyYolov2 = /** @class */ (function (_super) {
tslib_1.__extends(TinyYolov2, _super);
function TinyYolov2(withSeparableConvs) {
......@@ -9,15 +11,15 @@ var TinyYolov2 = /** @class */ (function (_super) {
var _this = this;
var config = Object.assign({}, {
withSeparableConvs: withSeparableConvs,
iouThreshold: IOU_THRESHOLD,
iouThreshold: const_1.IOU_THRESHOLD,
classes: ['face']
}, withSeparableConvs
? {
anchors: BOX_ANCHORS_SEPARABLE,
meanRgb: MEAN_RGB_SEPARABLE
anchors: const_1.BOX_ANCHORS_SEPARABLE,
meanRgb: const_1.MEAN_RGB_SEPARABLE
}
: {
anchors: BOX_ANCHORS,
anchors: const_1.BOX_ANCHORS,
withClassScores: true
});
_this = _super.call(this, config) || this;
......@@ -45,16 +47,16 @@ var TinyYolov2 = /** @class */ (function (_super) {
case 0: return [4 /*yield*/, this.detect(input, forwardParams)];
case 1:
objectDetections = _a.sent();
return [2 /*return*/, objectDetections.map(function (det) { return new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }); })];
return [2 /*return*/, objectDetections.map(function (det) { return new classes_1.FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }); })];
}
});
});
};
TinyYolov2.prototype.loadQuantizedParams = function (modelUri) {
var defaultModelName = this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;
var defaultModelName = this.withSeparableConvs ? const_1.DEFAULT_MODEL_NAME_SEPARABLE_CONV : const_1.DEFAULT_MODEL_NAME;
return _super.prototype.loadQuantizedParams.call(this, modelUri, defaultModelName);
};
return TinyYolov2;
}(TinyYolov2Base));
export { TinyYolov2 };
}(tfjs_tiny_yolov2_1.TinyYolov2));
exports.TinyYolov2 = TinyYolov2;
//# sourceMappingURL=TinyYolov2.js.map
\ No newline at end of file
build/tinyYolov2/TinyYolov2.js.map
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\ No newline at end of file
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\ No newline at end of file
build/tinyYolov2/const.js
import { Point } from 'tfjs-image-recognition-base';
export var IOU_THRESHOLD = 0.4;
export var BOX_ANCHORS = [
new Point(0.738768, 0.874946),
new Point(2.42204, 2.65704),
new Point(4.30971, 7.04493),
new Point(10.246, 4.59428),
new Point(12.6868, 11.8741)
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
exports.IOU_THRESHOLD = 0.4;
exports.BOX_ANCHORS = [
new tfjs_image_recognition_base_1.Point(0.738768, 0.874946),
new tfjs_image_recognition_base_1.Point(2.42204, 2.65704),
new tfjs_image_recognition_base_1.Point(4.30971, 7.04493),
new tfjs_image_recognition_base_1.Point(10.246, 4.59428),
new tfjs_image_recognition_base_1.Point(12.6868, 11.8741)
];
export var BOX_ANCHORS_SEPARABLE = [
new Point(1.603231, 2.094468),
new Point(6.041143, 7.080126),
new Point(2.882459, 3.518061),
new Point(4.266906, 5.178857),
new Point(9.041765, 10.66308)
exports.BOX_ANCHORS_SEPARABLE = [
new tfjs_image_recognition_base_1.Point(1.603231, 2.094468),
new tfjs_image_recognition_base_1.Point(6.041143, 7.080126),
new tfjs_image_recognition_base_1.Point(2.882459, 3.518061),
new tfjs_image_recognition_base_1.Point(4.266906, 5.178857),
new tfjs_image_recognition_base_1.Point(9.041765, 10.66308)
];
export var MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404];
export var DEFAULT_MODEL_NAME = 'tiny_yolov2_model';
export var DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';
exports.MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404];
exports.DEFAULT_MODEL_NAME = 'tiny_yolov2_model';
exports.DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';
//# sourceMappingURL=const.js.map
\ No newline at end of file
build/tinyYolov2/const.js.map
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\ No newline at end of file
build/tinyYolov2/index.js
import { TinyYolov2 } from './TinyYolov2';
export * from './TinyYolov2';
export function createTinyYolov2(weights, withSeparableConvs) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var TinyYolov2_1 = require("./TinyYolov2");
tslib_1.__exportStar(require("./TinyYolov2"), exports);
function createTinyYolov2(weights, withSeparableConvs) {
if (withSeparableConvs === void 0) { withSeparableConvs = true; }
var net = new TinyYolov2(withSeparableConvs);
var net = new TinyYolov2_1.TinyYolov2(withSeparableConvs);
net.extractWeights(weights);
return net;
}
exports.createTinyYolov2 = createTinyYolov2;
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/tinyYolov2/index.js.map
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\ No newline at end of file
dist/face-api.js
......@@ -4462,6 +4462,7 @@
function locateFaces(input, minConfidence, maxResults) {
return nets.ssdMobilenetv1.locateFaces(input, minConfidence, maxResults);
}
var ssdMobilenetv1 = locateFaces;
function detectLandmarks(input) {
return nets.faceLandmark68Net.detectLandmarks(input);
}
......@@ -4569,6 +4570,7 @@
exports.loadFaceDetectionModel = loadFaceDetectionModel;
exports.loadModels = loadModels;
exports.locateFaces = locateFaces;
exports.ssdMobilenetv1 = ssdMobilenetv1;
exports.detectLandmarks = detectLandmarks;
exports.computeFaceDescriptor = computeFaceDescriptor;
exports.mtcnn = mtcnn;
......
dist/face-api.js.map
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dist/face-api.min.js
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tsconfig.json
......@@ -18,7 +18,7 @@
"suppressImplicitAnyIndexErrors": true,
"strictNullChecks": true,
"importHelpers": true,
"module": "es6",
"module": "commonjs",
"moduleResolution": "node",
"target": "es5",
"outDir": "build",
......
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