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face
Commit bc555c5c by vincent
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build commonjs instead of es6 modules

parent 50d388a2
Showing with 706 additions and 488 deletions
build/allFacesFactory.js
import * as tslib_1 from "tslib"; "use strict";
import { Point } from 'tfjs-image-recognition-base'; Object.defineProperty(exports, "__esModule", { value: true });
import { FullFaceDescription } from './classes/FullFaceDescription'; var tslib_1 = require("tslib");
import { extractFaceTensors } from './dom'; 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) { function computeDescriptorsFactory(recognitionNet) {
return function (input, alignedFaceBoxes, useBatchProcessing) { return function (input, alignedFaceBoxes, useBatchProcessing) {
return tslib_1.__awaiter(this, void 0, void 0, function () { return tslib_1.__awaiter(this, void 0, void 0, function () {
var alignedFaceTensors, descriptors, _a; var alignedFaceTensors, descriptors, _a;
return tslib_1.__generator(this, function (_b) { return tslib_1.__generator(this, function (_b) {
switch (_b.label) { switch (_b.label) {
case 0: return [4 /*yield*/, extractFaceTensors(input, alignedFaceBoxes)]; case 0: return [4 /*yield*/, dom_1.extractFaceTensors(input, alignedFaceBoxes)];
case 1: case 1:
alignedFaceTensors = _b.sent(); alignedFaceTensors = _b.sent();
if (!useBatchProcessing) return [3 /*break*/, 3]; if (!useBatchProcessing) return [3 /*break*/, 3];
...@@ -40,7 +42,7 @@ function allFacesFactory(detectFaces, landmarkNet, recognitionNet) { ...@@ -40,7 +42,7 @@ function allFacesFactory(detectFaces, landmarkNet, recognitionNet) {
case 0: return [4 /*yield*/, detectFaces(input)]; case 0: return [4 /*yield*/, detectFaces(input)];
case 1: case 1:
detections = _b.sent(); detections = _b.sent();
return [4 /*yield*/, extractFaceTensors(input, detections)]; return [4 /*yield*/, dom_1.extractFaceTensors(input, detections)];
case 2: case 2:
faceTensors = _b.sent(); faceTensors = _b.sent();
if (!useBatchProcessing) return [3 /*break*/, 4]; if (!useBatchProcessing) return [3 /*break*/, 4];
...@@ -60,14 +62,14 @@ function allFacesFactory(detectFaces, landmarkNet, recognitionNet) { ...@@ -60,14 +62,14 @@ function allFacesFactory(detectFaces, landmarkNet, recognitionNet) {
case 7: case 7:
descriptors = _b.sent(); descriptors = _b.sent();
return [2 /*return*/, detections.map(function (detection, i) { 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) { return function (input, minConfidence, useBatchProcessing) {
if (minConfidence === void 0) { minConfidence = 0.8; } if (minConfidence === void 0) { minConfidence = 0.8; }
if (useBatchProcessing === void 0) { useBatchProcessing = false; } if (useBatchProcessing === void 0) { useBatchProcessing = false; }
...@@ -81,7 +83,8 @@ export function allFacesSsdMobilenetv1Factory(ssdMobilenetv1, landmarkNet, recog ...@@ -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) { return function (input, forwardParams, useBatchProcessing) {
if (forwardParams === void 0) { forwardParams = {}; } if (forwardParams === void 0) { forwardParams = {}; }
if (useBatchProcessing === void 0) { useBatchProcessing = false; } if (useBatchProcessing === void 0) { useBatchProcessing = false; }
...@@ -95,7 +98,8 @@ export function allFacesTinyYolov2Factory(tinyYolov2, landmarkNet, recognitionNe ...@@ -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); var computeDescriptors = computeDescriptorsFactory(recognitionNet);
return function (input, mtcnnForwardParams, useBatchProcessing) { return function (input, mtcnnForwardParams, useBatchProcessing) {
if (mtcnnForwardParams === void 0) { mtcnnForwardParams = {}; } if (mtcnnForwardParams === void 0) { mtcnnForwardParams = {}; }
...@@ -116,11 +120,12 @@ export function allFacesMtcnnFactory(mtcnn, recognitionNet) { ...@@ -116,11 +120,12 @@ export function allFacesMtcnnFactory(mtcnn, recognitionNet) {
descriptors = _a.sent(); descriptors = _a.sent();
return [2 /*return*/, results.map(function (_a, i) { return [2 /*return*/, results.map(function (_a, i) {
var faceDetection = _a.faceDetection, faceLandmarks = _a.faceLandmarks; 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 //# sourceMappingURL=allFacesFactory.js.map
\ No newline at end of file
build/allFacesFactory.js.map
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\ No newline at end of file \ No newline at end of file
build/classes/FaceDetection.js
import * as tslib_1 from "tslib"; "use strict";
import { ObjectDetection } from 'tfjs-image-recognition-base'; 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) { var FaceDetection = /** @class */ (function (_super) {
tslib_1.__extends(FaceDetection, _super); tslib_1.__extends(FaceDetection, _super);
function FaceDetection(score, relativeBox, imageDims) { function FaceDetection(score, relativeBox, imageDims) {
return _super.call(this, score, score, '', relativeBox, imageDims) || this; return _super.call(this, score, score, '', relativeBox, imageDims) || this;
} }
return FaceDetection; return FaceDetection;
}(ObjectDetection)); }(tfjs_image_recognition_base_1.ObjectDetection));
export { FaceDetection }; exports.FaceDetection = FaceDetection;
//# sourceMappingURL=FaceDetection.js.map //# 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 \ No newline at end of file
build/classes/FaceLandmarks.js
import { getCenterPoint, Point, Rect } from 'tfjs-image-recognition-base'; "use strict";
import { FaceDetection } from './FaceDetection'; 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 // face alignment constants
var relX = 0.5; var relX = 0.5;
var relY = 0.43; var relY = 0.43;
var relScale = 0.45; var relScale = 0.45;
var FaceLandmarks = /** @class */ (function () { var FaceLandmarks = /** @class */ (function () {
function FaceLandmarks(relativeFaceLandmarkPositions, imageDims, shift) { 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; var width = imageDims.width, height = imageDims.height;
this._imageWidth = width; this._imageWidth = width;
this._imageHeight = height; this._imageHeight = height;
this._shift = shift; 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 () { 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 () { FaceLandmarks.prototype.getImageWidth = function () {
return this._imageWidth; return this._imageWidth;
...@@ -27,13 +29,13 @@ var FaceLandmarks = /** @class */ (function () { ...@@ -27,13 +29,13 @@ var FaceLandmarks = /** @class */ (function () {
}; };
FaceLandmarks.prototype.getRelativePositions = function () { FaceLandmarks.prototype.getRelativePositions = function () {
var _this = this; 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) { FaceLandmarks.prototype.forSize = function (width, height) {
return new this.constructor(this.getRelativePositions(), { width: width, height: height }); return new this.constructor(this.getRelativePositions(), { width: width, height: height });
}; };
FaceLandmarks.prototype.shift = function (x, y) { 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) { FaceLandmarks.prototype.shiftByPoint = function (pt) {
return this.shift(pt.x, pt.y); return this.shift(pt.x, pt.y);
...@@ -51,7 +53,7 @@ var FaceLandmarks = /** @class */ (function () { ...@@ -51,7 +53,7 @@ var FaceLandmarks = /** @class */ (function () {
*/ */
FaceLandmarks.prototype.align = function (detection) { FaceLandmarks.prototype.align = function (detection) {
if (detection) { if (detection) {
var box = detection instanceof FaceDetection var box = detection instanceof FaceDetection_1.FaceDetection
? detection.getBox().floor() ? detection.getBox().floor()
: detection; : detection;
return this.shift(box.x, box.y).align(); return this.shift(box.x, box.y).align();
...@@ -61,16 +63,16 @@ var FaceLandmarks = /** @class */ (function () { ...@@ -61,16 +63,16 @@ var FaceLandmarks = /** @class */ (function () {
var distToMouth = function (pt) { return mouthCenter.sub(pt).magnitude(); }; var distToMouth = function (pt) { return mouthCenter.sub(pt).magnitude(); };
var eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2; var eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;
var size = Math.floor(eyeToMouthDist / relScale); 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 // TODO: pad in case rectangle is out of image bounds
var x = Math.floor(Math.max(0, refPoint.x - (relX * size))); var x = Math.floor(Math.max(0, refPoint.x - (relX * size)));
var y = Math.floor(Math.max(0, refPoint.y - (relY * 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 () { FaceLandmarks.prototype.getRefPointsForAlignment = function () {
throw new Error('getRefPointsForAlignment not implemented by base class'); throw new Error('getRefPointsForAlignment not implemented by base class');
}; };
return FaceLandmarks; return FaceLandmarks;
}()); }());
export { FaceLandmarks }; exports.FaceLandmarks = FaceLandmarks;
//# sourceMappingURL=FaceLandmarks.js.map //# 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 \ No newline at end of file
build/classes/FaceLandmarks5.js
import * as tslib_1 from "tslib"; "use strict";
import { getCenterPoint } from 'tfjs-image-recognition-base'; Object.defineProperty(exports, "__esModule", { value: true });
import { FaceLandmarks } from './FaceLandmarks'; 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) { var FaceLandmarks5 = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmarks5, _super); tslib_1.__extends(FaceLandmarks5, _super);
function FaceLandmarks5() { function FaceLandmarks5() {
...@@ -11,10 +13,10 @@ var FaceLandmarks5 = /** @class */ (function (_super) { ...@@ -11,10 +13,10 @@ var FaceLandmarks5 = /** @class */ (function (_super) {
return [ return [
pts[0], pts[0],
pts[1], pts[1],
getCenterPoint([pts[3], pts[4]]) tfjs_image_recognition_base_1.getCenterPoint([pts[3], pts[4]])
]; ];
}; };
return FaceLandmarks5; return FaceLandmarks5;
}(FaceLandmarks)); }(FaceLandmarks_1.FaceLandmarks));
export { FaceLandmarks5 }; exports.FaceLandmarks5 = FaceLandmarks5;
//# sourceMappingURL=FaceLandmarks5.js.map //# 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 \ No newline at end of file
build/classes/FaceLandmarks68.js
import * as tslib_1 from "tslib"; "use strict";
import { getCenterPoint } from 'tfjs-image-recognition-base'; Object.defineProperty(exports, "__esModule", { value: true });
import { FaceLandmarks } from '../classes/FaceLandmarks'; 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) { var FaceLandmarks68 = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmarks68, _super); tslib_1.__extends(FaceLandmarks68, _super);
function FaceLandmarks68() { function FaceLandmarks68() {
...@@ -32,9 +34,9 @@ var FaceLandmarks68 = /** @class */ (function (_super) { ...@@ -32,9 +34,9 @@ var FaceLandmarks68 = /** @class */ (function (_super) {
this.getLeftEye(), this.getLeftEye(),
this.getRightEye(), this.getRightEye(),
this.getMouth() this.getMouth()
].map(getCenterPoint); ].map(tfjs_image_recognition_base_1.getCenterPoint);
}; };
return FaceLandmarks68; return FaceLandmarks68;
}(FaceLandmarks)); }(FaceLandmarks_1.FaceLandmarks));
export { FaceLandmarks68 }; exports.FaceLandmarks68 = FaceLandmarks68;
//# sourceMappingURL=FaceLandmarks68.js.map //# 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 \ No newline at end of file
build/classes/FullFaceDescription.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var FullFaceDescription = /** @class */ (function () { var FullFaceDescription = /** @class */ (function () {
function FullFaceDescription(_detection, _landmarks, _descriptor) { function FullFaceDescription(_detection, _landmarks, _descriptor) {
this._detection = _detection; this._detection = _detection;
...@@ -30,5 +32,5 @@ var FullFaceDescription = /** @class */ (function () { ...@@ -30,5 +32,5 @@ var FullFaceDescription = /** @class */ (function () {
}; };
return FullFaceDescription; return FullFaceDescription;
}()); }());
export { FullFaceDescription }; exports.FullFaceDescription = FullFaceDescription;
//# sourceMappingURL=FullFaceDescription.js.map //# 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 \ No newline at end of file
build/classes/index.js
export * from './FaceDetection'; "use strict";
export * from './FaceLandmarks'; Object.defineProperty(exports, "__esModule", { value: true });
export * from './FaceLandmarks5'; var tslib_1 = require("tslib");
export * from './FaceLandmarks68'; tslib_1.__exportStar(require("./FaceDetection"), exports);
export * from './FullFaceDescription'; 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 //# 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 \ 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; } if (isClosed === void 0) { isClosed = false; }
ctx.beginPath(); ctx.beginPath();
points.slice(1).forEach(function (_a, prevIdx) { points.slice(1).forEach(function (_a, prevIdx) {
...@@ -18,4 +20,5 @@ export function drawContour(ctx, points, isClosed) { ...@@ -18,4 +20,5 @@ export function drawContour(ctx, points, isClosed) {
} }
ctx.stroke(); ctx.stroke();
} }
exports.drawContour = drawContour;
//# sourceMappingURL=drawContour.js.map //# 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 \ No newline at end of file
build/dom/drawLandmarks.js
import { getContext2dOrThrow, getDefaultDrawOptions, resolveInput } from 'tfjs-image-recognition-base'; "use strict";
import { FaceLandmarks68 } from '../classes/FaceLandmarks68'; Object.defineProperty(exports, "__esModule", { value: true });
import { drawContour } from './drawContour'; var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
export function drawLandmarks(canvasArg, faceLandmarks, options) { var FaceLandmarks68_1 = require("../classes/FaceLandmarks68");
var canvas = resolveInput(canvasArg); var drawContour_1 = require("./drawContour");
function drawLandmarks(canvasArg, faceLandmarks, options) {
var canvas = tfjs_image_recognition_base_1.resolveInput(canvasArg);
if (!(canvas instanceof HTMLCanvasElement)) { if (!(canvas instanceof HTMLCanvasElement)) {
throw new Error('drawLandmarks - expected canvas to be of type: 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 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 lineWidth = drawOptions.lineWidth, _a = drawOptions.color, color = _a === void 0 ? 'blue' : _a;
var faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]; var faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];
faceLandmarksArray.forEach(function (landmarks) { faceLandmarksArray.forEach(function (landmarks) {
if (drawLines && landmarks instanceof FaceLandmarks68) { if (drawLines && landmarks instanceof FaceLandmarks68_1.FaceLandmarks68) {
ctx.strokeStyle = color; ctx.strokeStyle = color;
ctx.lineWidth = lineWidth; ctx.lineWidth = lineWidth;
drawContour(ctx, landmarks.getJawOutline()); drawContour_1.drawContour(ctx, landmarks.getJawOutline());
drawContour(ctx, landmarks.getLeftEyeBrow()); drawContour_1.drawContour(ctx, landmarks.getLeftEyeBrow());
drawContour(ctx, landmarks.getRightEyeBrow()); drawContour_1.drawContour(ctx, landmarks.getRightEyeBrow());
drawContour(ctx, landmarks.getNose()); drawContour_1.drawContour(ctx, landmarks.getNose());
drawContour(ctx, landmarks.getLeftEye(), true); drawContour_1.drawContour(ctx, landmarks.getLeftEye(), true);
drawContour(ctx, landmarks.getRightEye(), true); drawContour_1.drawContour(ctx, landmarks.getRightEye(), true);
drawContour(ctx, landmarks.getMouth(), true); drawContour_1.drawContour(ctx, landmarks.getMouth(), true);
return; return;
} }
// else draw points // else draw points
...@@ -30,4 +32,5 @@ export function drawLandmarks(canvasArg, faceLandmarks, options) { ...@@ -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); }); landmarks.getPositions().forEach(function (pt) { return ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth); });
}); });
} }
exports.drawLandmarks = drawLandmarks;
//# sourceMappingURL=drawLandmarks.js.map //# 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 \ No newline at end of file
build/dom/extractFaceTensors.js
import * as tslib_1 from "tslib"; "use strict";
import * as tf from '@tensorflow/tfjs-core'; Object.defineProperty(exports, "__esModule", { value: true });
import { toNetInput } from 'tfjs-image-recognition-base'; var tslib_1 = require("tslib");
import { FaceDetection } from '../classes/FaceDetection'; 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. * Extracts the tensors of the image regions containing the detected faces.
* Useful if you want to compute the face descriptors for the face images. * Useful if you want to compute the face descriptors for the face images.
...@@ -12,12 +14,12 @@ import { FaceDetection } from '../classes/FaceDetection'; ...@@ -12,12 +14,12 @@ import { FaceDetection } from '../classes/FaceDetection';
* @param detections The face detection results or face bounding boxes for that image. * @param detections The face detection results or face bounding boxes for that image.
* @returns Tensors of the corresponding image region for each detected face. * @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 () { return tslib_1.__awaiter(this, void 0, void 0, function () {
var netInput; var netInput;
return tslib_1.__generator(this, function (_a) { return tslib_1.__generator(this, function (_a) {
switch (_a.label) { 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: case 1:
netInput = _a.sent(); netInput = _a.sent();
if (netInput.batchSize > 1) { if (netInput.batchSize > 1) {
...@@ -29,7 +31,7 @@ export function extractFaceTensors(input, detections) { ...@@ -29,7 +31,7 @@ export function extractFaceTensors(input, detections) {
return [2 /*return*/, tf.tidy(function () { return [2 /*return*/, tf.tidy(function () {
var imgTensor = netInput.inputs[0].expandDims().toFloat(); var imgTensor = netInput.inputs[0].expandDims().toFloat();
var _a = imgTensor.shape.slice(1), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2]; 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.forSize(imgWidth, imgHeight).getBox()
: det; }) : det; })
.map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); }); .map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); });
...@@ -46,4 +48,5 @@ export function extractFaceTensors(input, detections) { ...@@ -46,4 +48,5 @@ export function extractFaceTensors(input, detections) {
}); });
}); });
} }
exports.extractFaceTensors = extractFaceTensors;
//# sourceMappingURL=extractFaceTensors.js.map //# 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 \ No newline at end of file
build/dom/extractFaces.js
import * as tslib_1 from "tslib"; "use strict";
import { createCanvas, getContext2dOrThrow, imageTensorToCanvas, toNetInput, } from 'tfjs-image-recognition-base'; Object.defineProperty(exports, "__esModule", { value: true });
import { FaceDetection } from '../classes/FaceDetection'; 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. * Extracts the image regions containing the detected faces.
* *
...@@ -8,7 +10,7 @@ import { FaceDetection } from '../classes/FaceDetection'; ...@@ -8,7 +10,7 @@ import { FaceDetection } from '../classes/FaceDetection';
* @param detections The face detection results or face bounding boxes for that image. * @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. * @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 () { return tslib_1.__awaiter(this, void 0, void 0, function () {
var canvas, netInput, ctx, boxes; var canvas, netInput, ctx, boxes;
return tslib_1.__generator(this, function (_a) { return tslib_1.__generator(this, function (_a) {
...@@ -16,7 +18,7 @@ export function extractFaces(input, detections) { ...@@ -16,7 +18,7 @@ export function extractFaces(input, detections) {
case 0: case 0:
canvas = input; canvas = input;
if (!!(input instanceof HTMLCanvasElement)) return [3 /*break*/, 3]; 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: case 1:
netInput = _a.sent(); netInput = _a.sent();
if (netInput.batchSize > 1) { if (netInput.batchSize > 1) {
...@@ -25,7 +27,7 @@ export function extractFaces(input, detections) { ...@@ -25,7 +27,7 @@ export function extractFaces(input, detections) {
} }
throw new Error('extractFaces - batchSize > 1 not supported'); 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: case 2:
canvas = _a.sent(); canvas = _a.sent();
if (netInput.isManaged) { if (netInput.isManaged) {
...@@ -33,15 +35,15 @@ export function extractFaces(input, detections) { ...@@ -33,15 +35,15 @@ export function extractFaces(input, detections) {
} }
_a.label = 3; _a.label = 3;
case 3: case 3:
ctx = getContext2dOrThrow(canvas); ctx = tfjs_image_recognition_base_1.getContext2dOrThrow(canvas);
boxes = detections.map(function (det) { return det instanceof FaceDetection boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
? det.forSize(canvas.width, canvas.height).getBox().floor() ? det.forSize(canvas.width, canvas.height).getBox().floor()
: det; }) : det; })
.map(function (box) { return box.clipAtImageBorders(canvas.width, canvas.height); }); .map(function (box) { return box.clipAtImageBorders(canvas.width, canvas.height); });
return [2 /*return*/, boxes.map(function (_a) { return [2 /*return*/, boxes.map(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height; var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var faceImg = createCanvas({ width: width, height: height }); var faceImg = tfjs_image_recognition_base_1.createCanvas({ width: width, height: height });
getContext2dOrThrow(faceImg) tfjs_image_recognition_base_1.getContext2dOrThrow(faceImg)
.putImageData(ctx.getImageData(x, y, width, height), 0, 0); .putImageData(ctx.getImageData(x, y, width, height), 0, 0);
return faceImg; return faceImg;
})]; })];
...@@ -49,4 +51,5 @@ export function extractFaces(input, detections) { ...@@ -49,4 +51,5 @@ export function extractFaces(input, detections) {
}); });
}); });
} }
exports.extractFaces = extractFaces;
//# sourceMappingURL=extractFaces.js.map //# 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 \ No newline at end of file
build/dom/index.js
export * from './drawContour'; "use strict";
export * from './drawLandmarks'; Object.defineProperty(exports, "__esModule", { value: true });
export * from './extractFaces'; var tslib_1 = require("tslib");
export * from './extractFaceTensors'; 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 //# 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 \ No newline at end of file
build/dom/types.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
//# sourceMappingURL=types.js.map //# 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) if (arr1.length !== arr2.length)
throw new Error('euclideanDistance: arr1.length !== arr2.length'); throw new Error('euclideanDistance: arr1.length !== arr2.length');
var desc1 = Array.from(arr1); var desc1 = Array.from(arr1);
...@@ -7,4 +9,5 @@ export function euclideanDistance(arr1, arr2) { ...@@ -7,4 +9,5 @@ export function euclideanDistance(arr1, arr2) {
.map(function (val, i) { return val - desc2[i]; }) .map(function (val, i) { return val - desc2[i]; })
.reduce(function (res, diff) { return res + Math.pow(diff, 2); }, 0)); .reduce(function (res, diff) { return res + Math.pow(diff, 2); }, 0));
} }
exports.euclideanDistance = euclideanDistance;
//# sourceMappingURL=euclideanDistance.js.map //# sourceMappingURL=euclideanDistance.js.map
\ No newline at end of file
build/euclideanDistance.js.map
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\ No newline at end of file \ No newline at end of file
build/faceDetectionNet/FaceDetectionNet.js
import * as tslib_1 from "tslib"; "use strict";
import * as tf from '@tensorflow/tfjs-core'; Object.defineProperty(exports, "__esModule", { value: true });
import { NeuralNetwork, Rect, toNetInput } from 'tfjs-image-recognition-base'; var tslib_1 = require("tslib");
import { FaceDetection } from '../classes/FaceDetection'; var tf = require("@tensorflow/tfjs-core");
import { extractParams } from './extractParams'; var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
import { loadQuantizedParams } from './loadQuantizedParams'; var FaceDetection_1 = require("../classes/FaceDetection");
import { mobileNetV1 } from './mobileNetV1'; var extractParams_1 = require("./extractParams");
import { nonMaxSuppression } from './nonMaxSuppression'; var loadQuantizedParams_1 = require("./loadQuantizedParams");
import { outputLayer } from './outputLayer'; var mobileNetV1_1 = require("./mobileNetV1");
import { predictionLayer } from './predictionLayer'; var nonMaxSuppression_1 = require("./nonMaxSuppression");
var outputLayer_1 = require("./outputLayer");
var predictionLayer_1 = require("./predictionLayer");
var FaceDetectionNet = /** @class */ (function (_super) { var FaceDetectionNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceDetectionNet, _super); tslib_1.__extends(FaceDetectionNet, _super);
function FaceDetectionNet() { function FaceDetectionNet() {
...@@ -21,9 +23,9 @@ var FaceDetectionNet = /** @class */ (function (_super) { ...@@ -21,9 +23,9 @@ var FaceDetectionNet = /** @class */ (function (_super) {
return tf.tidy(function () { return tf.tidy(function () {
var batchTensor = input.toBatchTensor(512, false); var batchTensor = input.toBatchTensor(512, false);
var x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)); var x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1));
var features = mobileNetV1(x, params.mobilenetv1); var features = mobileNetV1_1.mobileNetV1(x, params.mobilenetv1);
var _a = predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions; var _a = predictionLayer_1.predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
return outputLayer(boxPredictions, classPredictions, params.output_layer); return outputLayer_1.outputLayer(boxPredictions, classPredictions, params.output_layer);
}); });
}; };
FaceDetectionNet.prototype.forward = function (input) { FaceDetectionNet.prototype.forward = function (input) {
...@@ -33,7 +35,7 @@ var FaceDetectionNet = /** @class */ (function (_super) { ...@@ -33,7 +35,7 @@ var FaceDetectionNet = /** @class */ (function (_super) {
switch (_b.label) { switch (_b.label) {
case 0: case 0:
_a = this.forwardInput; _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()])]; case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
} }
}); });
...@@ -46,7 +48,7 @@ var FaceDetectionNet = /** @class */ (function (_super) { ...@@ -46,7 +48,7 @@ var FaceDetectionNet = /** @class */ (function (_super) {
var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, paddings, results; var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, paddings, results;
return tslib_1.__generator(this, function (_d) { return tslib_1.__generator(this, function (_d) {
switch (_d.label) { 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: case 1:
netInput = _d.sent(); netInput = _d.sent();
_a = this.forwardInput(netInput), _boxes = _a.boxes, _scores = _a.scores; _a = this.forwardInput(netInput), _boxes = _a.boxes, _scores = _a.scores;
...@@ -61,7 +63,7 @@ var FaceDetectionNet = /** @class */ (function (_super) { ...@@ -61,7 +63,7 @@ var FaceDetectionNet = /** @class */ (function (_super) {
case 2: case 2:
scoresData = _c.apply(_b, [_d.sent()]); scoresData = _c.apply(_b, [_d.sent()]);
iouThreshold = 0.5; iouThreshold = 0.5;
indices = nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence); indices = nonMaxSuppression_1.nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
paddings = netInput.getRelativePaddings(0); paddings = netInput.getRelativePaddings(0);
results = indices results = indices
.map(function (idx) { .map(function (idx) {
...@@ -73,7 +75,7 @@ var FaceDetectionNet = /** @class */ (function (_super) { ...@@ -73,7 +75,7 @@ var FaceDetectionNet = /** @class */ (function (_super) {
Math.max(0, boxes.get(idx, 1)), Math.max(0, boxes.get(idx, 1)),
Math.min(1.0, boxes.get(idx, 3)) Math.min(1.0, boxes.get(idx, 3))
].map(function (val) { return val * paddings.x; }), left = _b[0], right = _b[1]; ].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), height: netInput.getInputHeight(0),
width: netInput.getInputWidth(0) width: netInput.getInputWidth(0)
}); });
...@@ -86,12 +88,12 @@ var FaceDetectionNet = /** @class */ (function (_super) { ...@@ -86,12 +88,12 @@ var FaceDetectionNet = /** @class */ (function (_super) {
}); });
}; };
FaceDetectionNet.prototype.loadQuantizedParams = function (uri) { FaceDetectionNet.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams(uri); return loadQuantizedParams_1.loadQuantizedParams(uri);
}; };
FaceDetectionNet.prototype.extractParams = function (weights) { FaceDetectionNet.prototype.extractParams = function (weights) {
return extractParams(weights); return extractParams_1.extractParams(weights);
}; };
return FaceDetectionNet; return FaceDetectionNet;
}(NeuralNetwork)); }(tfjs_image_recognition_base_1.NeuralNetwork));
export { FaceDetectionNet }; exports.FaceDetectionNet = FaceDetectionNet;
//# sourceMappingURL=FaceDetectionNet.js.map //# sourceMappingURL=FaceDetectionNet.js.map
\ No newline at end of file
build/faceDetectionNet/FaceDetectionNet.js.map
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build/faceDetectionNet/boxPredictionLayer.js
import * as tf from '@tensorflow/tfjs-core'; "use strict";
import { convLayer } from 'tfjs-tiny-yolov2'; Object.defineProperty(exports, "__esModule", { value: true });
export function boxPredictionLayer(x, params) { var tf = require("@tensorflow/tfjs-core");
var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
function boxPredictionLayer(x, params) {
return tf.tidy(function () { return tf.tidy(function () {
var batchSize = x.shape[0]; var batchSize = x.shape[0];
var boxPredictionEncoding = tf.reshape(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]); var boxPredictionEncoding = tf.reshape(tfjs_tiny_yolov2_1.convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
var classPrediction = tf.reshape(convLayer(x, params.class_predictor), [batchSize, -1, 3]); var classPrediction = tf.reshape(tfjs_tiny_yolov2_1.convLayer(x, params.class_predictor), [batchSize, -1, 3]);
return { return {
boxPredictionEncoding: boxPredictionEncoding, boxPredictionEncoding: boxPredictionEncoding,
classPrediction: classPrediction classPrediction: classPrediction
}; };
}); });
} }
exports.boxPredictionLayer = boxPredictionLayer;
//# sourceMappingURL=boxPredictionLayer.js.map //# 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 \ No newline at end of file
build/faceDetectionNet/extractParams.js
import * as tf from '@tensorflow/tfjs-core'; "use strict";
import { extractWeightsFactory } from 'tfjs-image-recognition-base'; 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 extractorsFactory(extractWeights, paramMappings) {
function extractDepthwiseConvParams(numChannels, mappedPrefix) { function extractDepthwiseConvParams(numChannels, mappedPrefix) {
var filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]); var filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
...@@ -133,9 +135,9 @@ function extractorsFactory(extractWeights, paramMappings) { ...@@ -133,9 +135,9 @@ function extractorsFactory(extractWeights, paramMappings) {
extractPredictionLayerParams: extractPredictionLayerParams extractPredictionLayerParams: extractPredictionLayerParams
}; };
} }
export function extractParams(weights) { function extractParams(weights) {
var paramMappings = []; 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 _b = extractorsFactory(extractWeights, paramMappings), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;
var mobilenetv1 = extractMobilenetV1Params(); var mobilenetv1 = extractMobilenetV1Params();
var prediction_layer = extractPredictionLayerParams(); var prediction_layer = extractPredictionLayerParams();
...@@ -156,4 +158,5 @@ export function extractParams(weights) { ...@@ -156,4 +158,5 @@ export function extractParams(weights) {
paramMappings: paramMappings paramMappings: paramMappings
}; };
} }
exports.extractParams = extractParams;
//# sourceMappingURL=extractParams.js.map //# sourceMappingURL=extractParams.js.map
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build/faceDetectionNet/index.js
import { FaceDetectionNet } from './FaceDetectionNet'; "use strict";
export * from './FaceDetectionNet'; Object.defineProperty(exports, "__esModule", { value: true });
export function createFaceDetectionNet(weights) { var tslib_1 = require("tslib");
var net = new FaceDetectionNet(); var FaceDetectionNet_1 = require("./FaceDetectionNet");
tslib_1.__exportStar(require("./FaceDetectionNet"), exports);
function createFaceDetectionNet(weights) {
var net = new FaceDetectionNet_1.FaceDetectionNet();
net.extractWeights(weights); net.extractWeights(weights);
return net; 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'); console.warn('faceDetectionNet(weights: Float32Array) will be deprecated in future, use createFaceDetectionNet instead');
return createFaceDetectionNet(weights); return createFaceDetectionNet(weights);
} }
exports.faceDetectionNet = faceDetectionNet;
//# sourceMappingURL=index.js.map //# sourceMappingURL=index.js.map
\ No newline at end of file
build/faceDetectionNet/index.js.map
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\ No newline at end of file \ No newline at end of file
build/faceDetectionNet/loadQuantizedParams.js
import * as tslib_1 from "tslib"; "use strict";
import { disposeUnusedWeightTensors, extractWeightEntryFactory, isTensor3D, loadWeightMap, } from 'tfjs-image-recognition-base'; 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'; var DEFAULT_MODEL_NAME = 'ssd_mobilenetv1_model';
function extractorsFactory(weightMap, paramMappings) { function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); var extractWeightEntry = tfjs_image_recognition_base_1.extractWeightEntryFactory(weightMap, paramMappings);
function extractPointwiseConvParams(prefix, idx, mappedPrefix) { function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
var filters = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/weights", 4, mappedPrefix + "/filters"); 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"); 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) { ...@@ -80,19 +82,19 @@ function extractorsFactory(weightMap, paramMappings) {
extractPredictionLayerParams: extractPredictionLayerParams extractPredictionLayerParams: extractPredictionLayerParams
}; };
} }
export function loadQuantizedParams(uri) { function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () { return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractMobilenetV1Params, extractPredictionLayerParams, extra_dim, params; var weightMap, paramMappings, _a, extractMobilenetV1Params, extractPredictionLayerParams, extra_dim, params;
return tslib_1.__generator(this, function (_b) { return tslib_1.__generator(this, function (_b) {
switch (_b.label) { 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: case 1:
weightMap = _b.sent(); weightMap = _b.sent();
paramMappings = []; paramMappings = [];
_a = extractorsFactory(weightMap, paramMappings), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams; _a = extractorsFactory(weightMap, paramMappings), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
extra_dim = weightMap['Output/extra_dim']; extra_dim = weightMap['Output/extra_dim'];
paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/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); throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have " + extra_dim);
} }
params = { params = {
...@@ -102,10 +104,11 @@ export function loadQuantizedParams(uri) { ...@@ -102,10 +104,11 @@ export function loadQuantizedParams(uri) {
extra_dim: extra_dim extra_dim: extra_dim
} }
}; };
disposeUnusedWeightTensors(weightMap, paramMappings); tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }]; return [2 /*return*/, { params: params, paramMappings: paramMappings }];
} }
}); });
}); });
} }
exports.loadQuantizedParams = loadQuantizedParams;
//# sourceMappingURL=loadQuantizedParams.js.map //# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
build/faceDetectionNet/loadQuantizedParams.js.map
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\ No newline at end of file \ No newline at end of file
build/faceDetectionNet/mobileNetV1.js
import * as tf from '@tensorflow/tfjs-core'; "use strict";
import { pointwiseConvLayer } from './pointwiseConvLayer'; Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var pointwiseConvLayer_1 = require("./pointwiseConvLayer");
var epsilon = 0.0010000000474974513; var epsilon = 0.0010000000474974513;
function depthwiseConvLayer(x, params, strides) { function depthwiseConvLayer(x, params, strides) {
return tf.tidy(function () { return tf.tidy(function () {
...@@ -11,10 +13,10 @@ function depthwiseConvLayer(x, params, strides) { ...@@ -11,10 +13,10 @@ function depthwiseConvLayer(x, params, strides) {
function getStridesForLayerIdx(layerIdx) { function getStridesForLayerIdx(layerIdx) {
return [2, 4, 6, 12].some(function (idx) { return idx === layerIdx; }) ? [2, 2] : [1, 1]; 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 () { return tf.tidy(function () {
var conv11 = null; 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 = [ var convPairParams = [
params.conv_1, params.conv_1,
params.conv_2, params.conv_2,
...@@ -34,7 +36,7 @@ export function mobileNetV1(x, params) { ...@@ -34,7 +36,7 @@ export function mobileNetV1(x, params) {
var layerIdx = i + 1; var layerIdx = i + 1;
var depthwiseConvStrides = getStridesForLayerIdx(layerIdx); var depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides); 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) { if (layerIdx === 11) {
conv11 = out; conv11 = out;
} }
...@@ -48,4 +50,5 @@ export function mobileNetV1(x, params) { ...@@ -48,4 +50,5 @@ export function mobileNetV1(x, params) {
}; };
}); });
} }
exports.mobileNetV1 = mobileNetV1;
//# sourceMappingURL=mobileNetV1.js.map //# 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 \ 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 numBoxes = boxes.shape[0];
var outputSize = Math.min(maxOutputSize, numBoxes); var outputSize = Math.min(maxOutputSize, numBoxes);
var candidates = scores var candidates = scores
...@@ -28,6 +30,7 @@ export function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, sc ...@@ -28,6 +30,7 @@ export function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, sc
}); });
return selected; return selected;
} }
exports.nonMaxSuppression = nonMaxSuppression;
function IOU(boxes, i, j) { function IOU(boxes, i, j) {
var yminI = Math.min(boxes.get(i, 0), boxes.get(i, 2)); var yminI = Math.min(boxes.get(i, 0), boxes.get(i, 2));
var xminI = Math.min(boxes.get(i, 1), boxes.get(i, 3)); 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 \ 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) { function getCenterCoordinatesAndSizesLayer(x) {
var vec = tf.unstack(tf.transpose(x, [1, 0])); var vec = tf.unstack(tf.transpose(x, [1, 0]));
var sizes = [ var sizes = [
...@@ -28,7 +30,7 @@ function decodeBoxesLayer(x0, x1) { ...@@ -28,7 +30,7 @@ function decodeBoxesLayer(x0, x1) {
tf.add(add1_out, div1_out) tf.add(add1_out, div1_out)
]), [1, 0]); ]), [1, 0]);
} }
export function outputLayer(boxPredictions, classPredictions, params) { function outputLayer(boxPredictions, classPredictions, params) {
return tf.tidy(function () { return tf.tidy(function () {
var batchSize = boxPredictions.shape[0]; 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])); 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) { ...@@ -44,4 +46,5 @@ export function outputLayer(boxPredictions, classPredictions, params) {
}; };
}); });
} }
exports.outputLayer = outputLayer;
//# sourceMappingURL=outputLayer.js.map //# 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 \ No newline at end of file
build/faceDetectionNet/pointwiseConvLayer.js
import * as tf from '@tensorflow/tfjs-core'; "use strict";
export function pointwiseConvLayer(x, params, strides) { Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function pointwiseConvLayer(x, params, strides) {
return tf.tidy(function () { return tf.tidy(function () {
var out = tf.conv2d(x, params.filters, strides, 'same'); var out = tf.conv2d(x, params.filters, strides, 'same');
out = tf.add(out, params.batch_norm_offset); out = tf.add(out, params.batch_norm_offset);
return tf.clipByValue(out, 0, 6); return tf.clipByValue(out, 0, 6);
}); });
} }
exports.pointwiseConvLayer = pointwiseConvLayer;
//# sourceMappingURL=pointwiseConvLayer.js.map //# sourceMappingURL=pointwiseConvLayer.js.map
\ No newline at end of file
build/faceDetectionNet/pointwiseConvLayer.js.map
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\ No newline at end of file \ No newline at end of file
build/faceDetectionNet/predictionLayer.js
import * as tf from '@tensorflow/tfjs-core'; "use strict";
import { boxPredictionLayer } from './boxPredictionLayer'; Object.defineProperty(exports, "__esModule", { value: true });
import { pointwiseConvLayer } from './pointwiseConvLayer'; var tf = require("@tensorflow/tfjs-core");
export function predictionLayer(x, conv11, params) { var boxPredictionLayer_1 = require("./boxPredictionLayer");
var pointwiseConvLayer_1 = require("./pointwiseConvLayer");
function predictionLayer(x, conv11, params) {
return tf.tidy(function () { return tf.tidy(function () {
var conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]); var conv0 = pointwiseConvLayer_1.pointwiseConvLayer(x, params.conv_0, [1, 1]);
var conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]); var conv1 = pointwiseConvLayer_1.pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
var conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]); var conv2 = pointwiseConvLayer_1.pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
var conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]); var conv3 = pointwiseConvLayer_1.pointwiseConvLayer(conv2, params.conv_3, [2, 2]);
var conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]); var conv4 = pointwiseConvLayer_1.pointwiseConvLayer(conv3, params.conv_4, [1, 1]);
var conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]); var conv5 = pointwiseConvLayer_1.pointwiseConvLayer(conv4, params.conv_5, [2, 2]);
var conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]); var conv6 = pointwiseConvLayer_1.pointwiseConvLayer(conv5, params.conv_6, [1, 1]);
var conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]); var conv7 = pointwiseConvLayer_1.pointwiseConvLayer(conv6, params.conv_7, [2, 2]);
var boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0); var boxPrediction0 = boxPredictionLayer_1.boxPredictionLayer(conv11, params.box_predictor_0);
var boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1); var boxPrediction1 = boxPredictionLayer_1.boxPredictionLayer(x, params.box_predictor_1);
var boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2); var boxPrediction2 = boxPredictionLayer_1.boxPredictionLayer(conv1, params.box_predictor_2);
var boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3); var boxPrediction3 = boxPredictionLayer_1.boxPredictionLayer(conv3, params.box_predictor_3);
var boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4); var boxPrediction4 = boxPredictionLayer_1.boxPredictionLayer(conv5, params.box_predictor_4);
var boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5); var boxPrediction5 = boxPredictionLayer_1.boxPredictionLayer(conv7, params.box_predictor_5);
var boxPredictions = tf.concat([ var boxPredictions = tf.concat([
boxPrediction0.boxPredictionEncoding, boxPrediction0.boxPredictionEncoding,
boxPrediction1.boxPredictionEncoding, boxPrediction1.boxPredictionEncoding,
...@@ -39,4 +41,5 @@ export function predictionLayer(x, conv11, params) { ...@@ -39,4 +41,5 @@ export function predictionLayer(x, conv11, params) {
}; };
}); });
} }
exports.predictionLayer = predictionLayer;
//# sourceMappingURL=predictionLayer.js.map //# sourceMappingURL=predictionLayer.js.map
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build/faceDetectionNet/predictionLayer.js.map
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build/faceDetectionNet/types.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
//# sourceMappingURL=types.js.map //# sourceMappingURL=types.js.map
\ No newline at end of file
build/faceLandmarkNet/FaceLandmarkNet.js
import * as tslib_1 from "tslib"; "use strict";
import * as tf from '@tensorflow/tfjs-core'; Object.defineProperty(exports, "__esModule", { value: true });
import { isEven, NeuralNetwork, Point, toNetInput } from 'tfjs-image-recognition-base'; var tslib_1 = require("tslib");
import { convLayer } from 'tfjs-tiny-yolov2'; var tf = require("@tensorflow/tfjs-core");
import { FaceLandmarks68 } from '../classes/FaceLandmarks68'; var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
import { extractParams } from './extractParams'; var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
import { fullyConnectedLayer } from './fullyConnectedLayer'; var FaceLandmarks68_1 = require("../classes/FaceLandmarks68");
import { loadQuantizedParams } from './loadQuantizedParams'; var extractParams_1 = require("./extractParams");
var fullyConnectedLayer_1 = require("./fullyConnectedLayer");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
function conv(x, params) { function conv(x, params) {
return convLayer(x, params, 'valid', true); return tfjs_tiny_yolov2_1.convLayer(x, params, 'valid', true);
} }
function maxPool(x, strides) { function maxPool(x, strides) {
if (strides === void 0) { strides = [2, 2]; } if (strides === void 0) { strides = [2, 2]; }
...@@ -37,8 +39,8 @@ var FaceLandmarkNet = /** @class */ (function (_super) { ...@@ -37,8 +39,8 @@ var FaceLandmarkNet = /** @class */ (function (_super) {
out = conv(out, params.conv6); out = conv(out, params.conv6);
out = maxPool(out, [1, 1]); out = maxPool(out, [1, 1]);
out = conv(out, params.conv7); out = conv(out, params.conv7);
var fc0 = tf.relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0)); var fc0 = tf.relu(fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0));
var fc1 = fullyConnectedLayer(fc0, params.fc1); var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(fc0, params.fc1);
var createInterleavedTensor = function (fillX, fillY) { var createInterleavedTensor = function (fillX, fillY) {
return tf.stack([ return tf.stack([
tf.fill([68], fillX), tf.fill([68], fillX),
...@@ -69,7 +71,7 @@ var FaceLandmarkNet = /** @class */ (function (_super) { ...@@ -69,7 +71,7 @@ var FaceLandmarkNet = /** @class */ (function (_super) {
switch (_b.label) { switch (_b.label) {
case 0: case 0:
_a = this.forwardInput; _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()])]; case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
} }
}); });
...@@ -81,7 +83,7 @@ var FaceLandmarkNet = /** @class */ (function (_super) { ...@@ -81,7 +83,7 @@ var FaceLandmarkNet = /** @class */ (function (_super) {
var netInput, landmarkTensors, landmarksForBatch; var netInput, landmarkTensors, landmarksForBatch;
return tslib_1.__generator(this, function (_a) { return tslib_1.__generator(this, function (_a) {
switch (_a.label) { 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: case 1:
netInput = _a.sent(); netInput = _a.sent();
landmarkTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); }); landmarkTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });
...@@ -94,9 +96,9 @@ var FaceLandmarkNet = /** @class */ (function (_super) { ...@@ -94,9 +96,9 @@ var FaceLandmarkNet = /** @class */ (function (_super) {
return [4 /*yield*/, landmarkTensor.data()]; return [4 /*yield*/, landmarkTensor.data()];
case 1: case 1:
landmarksArray = _b.apply(_a, [_c.sent()]); landmarksArray = _b.apply(_a, [_c.sent()]);
xCoords = landmarksArray.filter(function (_, i) { return isEven(i); }); xCoords = landmarksArray.filter(function (_, i) { return tfjs_image_recognition_base_1.isEven(i); });
yCoords = landmarksArray.filter(function (_, i) { return !isEven(i); }); yCoords = landmarksArray.filter(function (_, i) { return !tfjs_image_recognition_base_1.isEven(i); });
return [2 /*return*/, new FaceLandmarks68(Array(68).fill(0).map(function (_, i) { return new Point(xCoords[i], yCoords[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), height: netInput.getInputHeight(batchIdx),
width: netInput.getInputWidth(batchIdx), width: netInput.getInputWidth(batchIdx),
})]; })];
...@@ -114,12 +116,12 @@ var FaceLandmarkNet = /** @class */ (function (_super) { ...@@ -114,12 +116,12 @@ var FaceLandmarkNet = /** @class */ (function (_super) {
}); });
}; };
FaceLandmarkNet.prototype.loadQuantizedParams = function (uri) { FaceLandmarkNet.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams(uri); return loadQuantizedParams_1.loadQuantizedParams(uri);
}; };
FaceLandmarkNet.prototype.extractParams = function (weights) { FaceLandmarkNet.prototype.extractParams = function (weights) {
return extractParams(weights); return extractParams_1.extractParams(weights);
}; };
return FaceLandmarkNet; return FaceLandmarkNet;
}(NeuralNetwork)); }(tfjs_image_recognition_base_1.NeuralNetwork));
export { FaceLandmarkNet }; exports.FaceLandmarkNet = FaceLandmarkNet;
//# sourceMappingURL=FaceLandmarkNet.js.map //# sourceMappingURL=FaceLandmarkNet.js.map
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build/faceLandmarkNet/FaceLandmarkNet.js.map
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\ No newline at end of file \ No newline at end of file
build/faceLandmarkNet/extractParams.js
import { extractWeightsFactory } from 'tfjs-image-recognition-base'; "use strict";
import { extractConvParamsFactory, extractFCParamsFactory } from 'tfjs-tiny-yolov2'; Object.defineProperty(exports, "__esModule", { value: true });
export function extractParams(weights) { 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 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 extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); var extractConvParams = tfjs_tiny_yolov2_1.extractConvParamsFactory(extractWeights, paramMappings);
var extractFCParams = extractFCParamsFactory(extractWeights, paramMappings); var extractFCParams = tfjs_tiny_yolov2_1.extractFCParamsFactory(extractWeights, paramMappings);
var conv0 = extractConvParams(3, 32, 3, 'conv0'); var conv0 = extractConvParams(3, 32, 3, 'conv0');
var conv1 = extractConvParams(32, 64, 3, 'conv1'); var conv1 = extractConvParams(32, 64, 3, 'conv1');
var conv2 = extractConvParams(64, 64, 3, 'conv2'); var conv2 = extractConvParams(64, 64, 3, 'conv2');
...@@ -34,4 +36,5 @@ export function extractParams(weights) { ...@@ -34,4 +36,5 @@ export function extractParams(weights) {
} }
}; };
} }
exports.extractParams = extractParams;
//# sourceMappingURL=extractParams.js.map //# sourceMappingURL=extractParams.js.map
\ No newline at end of file
build/faceLandmarkNet/extractParams.js.map
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build/faceLandmarkNet/fullyConnectedLayer.js
import * as tf from '@tensorflow/tfjs-core'; "use strict";
export function fullyConnectedLayer(x, params) { Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function fullyConnectedLayer(x, params) {
return tf.tidy(function () { return tf.tidy(function () {
return tf.add(tf.matMul(x, params.weights), params.bias); return tf.add(tf.matMul(x, params.weights), params.bias);
}); });
} }
exports.fullyConnectedLayer = fullyConnectedLayer;
//# sourceMappingURL=fullyConnectedLayer.js.map //# sourceMappingURL=fullyConnectedLayer.js.map
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build/faceLandmarkNet/fullyConnectedLayer.js.map
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build/faceLandmarkNet/index.js
import { FaceLandmarkNet } from './FaceLandmarkNet'; "use strict";
export * from './FaceLandmarkNet'; Object.defineProperty(exports, "__esModule", { value: true });
export function createFaceLandmarkNet(weights) { var tslib_1 = require("tslib");
var net = new FaceLandmarkNet(); var FaceLandmarkNet_1 = require("./FaceLandmarkNet");
tslib_1.__exportStar(require("./FaceLandmarkNet"), exports);
function createFaceLandmarkNet(weights) {
var net = new FaceLandmarkNet_1.FaceLandmarkNet();
net.extractWeights(weights); net.extractWeights(weights);
return net; 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'); console.warn('faceLandmarkNet(weights: Float32Array) will be deprecated in future, use createFaceLandmarkNet instead');
return createFaceLandmarkNet(weights); return createFaceLandmarkNet(weights);
} }
exports.faceLandmarkNet = faceLandmarkNet;
//# sourceMappingURL=index.js.map //# 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 \ No newline at end of file
build/faceLandmarkNet/loadQuantizedParams.js
import * as tslib_1 from "tslib"; "use strict";
import { disposeUnusedWeightTensors, extractWeightEntryFactory, loadWeightMap, } from 'tfjs-image-recognition-base'; 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'; var DEFAULT_MODEL_NAME = 'face_landmark_68_model';
function extractorsFactory(weightMap, paramMappings) { function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); var extractWeightEntry = tfjs_image_recognition_base_1.extractWeightEntryFactory(weightMap, paramMappings);
function extractConvParams(prefix, mappedPrefix) { function extractConvParams(prefix, mappedPrefix) {
var filters = extractWeightEntry(prefix + "/kernel", 4, mappedPrefix + "/filters"); var filters = extractWeightEntry(prefix + "/kernel", 4, mappedPrefix + "/filters");
var bias = extractWeightEntry(prefix + "/bias", 1, mappedPrefix + "/bias"); var bias = extractWeightEntry(prefix + "/bias", 1, mappedPrefix + "/bias");
...@@ -18,12 +20,12 @@ function extractorsFactory(weightMap, paramMappings) { ...@@ -18,12 +20,12 @@ function extractorsFactory(weightMap, paramMappings) {
extractFcParams: extractFcParams extractFcParams: extractFcParams
}; };
} }
export function loadQuantizedParams(uri) { function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () { return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractConvParams, extractFcParams, params; var weightMap, paramMappings, _a, extractConvParams, extractFcParams, params;
return tslib_1.__generator(this, function (_b) { return tslib_1.__generator(this, function (_b) {
switch (_b.label) { 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: case 1:
weightMap = _b.sent(); weightMap = _b.sent();
paramMappings = []; paramMappings = [];
...@@ -40,10 +42,11 @@ export function loadQuantizedParams(uri) { ...@@ -40,10 +42,11 @@ export function loadQuantizedParams(uri) {
fc0: extractFcParams('dense', 'fc0'), fc0: extractFcParams('dense', 'fc0'),
fc1: extractFcParams('logits', 'fc1') fc1: extractFcParams('logits', 'fc1')
}; };
disposeUnusedWeightTensors(weightMap, paramMappings); tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }]; return [2 /*return*/, { params: params, paramMappings: paramMappings }];
} }
}); });
}); });
} }
exports.loadQuantizedParams = loadQuantizedParams;
//# sourceMappingURL=loadQuantizedParams.js.map //# sourceMappingURL=loadQuantizedParams.js.map
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build/faceLandmarkNet/loadQuantizedParams.js.map
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build/faceLandmarkNet/types.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
//# sourceMappingURL=types.js.map //# sourceMappingURL=types.js.map
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build/faceRecognitionNet/FaceRecognitionNet.js
import * as tslib_1 from "tslib"; "use strict";
import * as tf from '@tensorflow/tfjs-core'; Object.defineProperty(exports, "__esModule", { value: true });
import { NeuralNetwork, normalize, toNetInput } from 'tfjs-image-recognition-base'; var tslib_1 = require("tslib");
import { convDown } from './convLayer'; var tf = require("@tensorflow/tfjs-core");
import { extractParams } from './extractParams'; var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
import { loadQuantizedParams } from './loadQuantizedParams'; var convLayer_1 = require("./convLayer");
import { residual, residualDown } from './residualLayer'; var extractParams_1 = require("./extractParams");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
var residualLayer_1 = require("./residualLayer");
var FaceRecognitionNet = /** @class */ (function (_super) { var FaceRecognitionNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceRecognitionNet, _super); tslib_1.__extends(FaceRecognitionNet, _super);
function FaceRecognitionNet() { function FaceRecognitionNet() {
...@@ -18,23 +20,23 @@ var FaceRecognitionNet = /** @class */ (function (_super) { ...@@ -18,23 +20,23 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
return tf.tidy(function () { return tf.tidy(function () {
var batchTensor = input.toBatchTensor(150, true); var batchTensor = input.toBatchTensor(150, true);
var meanRgb = [122.782, 117.001, 104.298]; var meanRgb = [122.782, 117.001, 104.298];
var normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)); var normalized = tfjs_image_recognition_base_1.normalize(batchTensor, meanRgb).div(tf.scalar(256));
var out = convDown(normalized, params.conv32_down); var out = convLayer_1.convDown(normalized, params.conv32_down);
out = tf.maxPool(out, 3, 2, 'valid'); out = tf.maxPool(out, 3, 2, 'valid');
out = residual(out, params.conv32_1); out = residualLayer_1.residual(out, params.conv32_1);
out = residual(out, params.conv32_2); out = residualLayer_1.residual(out, params.conv32_2);
out = residual(out, params.conv32_3); out = residualLayer_1.residual(out, params.conv32_3);
out = residualDown(out, params.conv64_down); out = residualLayer_1.residualDown(out, params.conv64_down);
out = residual(out, params.conv64_1); out = residualLayer_1.residual(out, params.conv64_1);
out = residual(out, params.conv64_2); out = residualLayer_1.residual(out, params.conv64_2);
out = residual(out, params.conv64_3); out = residualLayer_1.residual(out, params.conv64_3);
out = residualDown(out, params.conv128_down); out = residualLayer_1.residualDown(out, params.conv128_down);
out = residual(out, params.conv128_1); out = residualLayer_1.residual(out, params.conv128_1);
out = residual(out, params.conv128_2); out = residualLayer_1.residual(out, params.conv128_2);
out = residualDown(out, params.conv256_down); out = residualLayer_1.residualDown(out, params.conv256_down);
out = residual(out, params.conv256_1); out = residualLayer_1.residual(out, params.conv256_1);
out = residual(out, params.conv256_2); out = residualLayer_1.residual(out, params.conv256_2);
out = residualDown(out, params.conv256_down_out); out = residualLayer_1.residualDown(out, params.conv256_down_out);
var globalAvg = out.mean([1, 2]); var globalAvg = out.mean([1, 2]);
var fullyConnected = tf.matMul(globalAvg, params.fc); var fullyConnected = tf.matMul(globalAvg, params.fc);
return fullyConnected; return fullyConnected;
...@@ -47,7 +49,7 @@ var FaceRecognitionNet = /** @class */ (function (_super) { ...@@ -47,7 +49,7 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
switch (_b.label) { switch (_b.label) {
case 0: case 0:
_a = this.forwardInput; _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()])]; case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
} }
}); });
...@@ -59,7 +61,7 @@ var FaceRecognitionNet = /** @class */ (function (_super) { ...@@ -59,7 +61,7 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
var netInput, faceDescriptorTensors, faceDescriptorsForBatch; var netInput, faceDescriptorTensors, faceDescriptorsForBatch;
return tslib_1.__generator(this, function (_a) { return tslib_1.__generator(this, function (_a) {
switch (_a.label) { 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: case 1:
netInput = _a.sent(); netInput = _a.sent();
faceDescriptorTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); }); faceDescriptorTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });
...@@ -75,12 +77,12 @@ var FaceRecognitionNet = /** @class */ (function (_super) { ...@@ -75,12 +77,12 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
}); });
}; };
FaceRecognitionNet.prototype.loadQuantizedParams = function (uri) { FaceRecognitionNet.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams(uri); return loadQuantizedParams_1.loadQuantizedParams(uri);
}; };
FaceRecognitionNet.prototype.extractParams = function (weights) { FaceRecognitionNet.prototype.extractParams = function (weights) {
return extractParams(weights); return extractParams_1.extractParams(weights);
}; };
return FaceRecognitionNet; return FaceRecognitionNet;
}(NeuralNetwork)); }(tfjs_image_recognition_base_1.NeuralNetwork));
export { FaceRecognitionNet }; exports.FaceRecognitionNet = FaceRecognitionNet;
//# sourceMappingURL=FaceRecognitionNet.js.map //# 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 \ No newline at end of file
build/faceRecognitionNet/convLayer.js
import * as tf from '@tensorflow/tfjs-core'; "use strict";
import { scale } from './scaleLayer'; Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var scaleLayer_1 = require("./scaleLayer");
function convLayer(x, params, strides, withRelu, padding) { function convLayer(x, params, strides, withRelu, padding) {
if (padding === void 0) { padding = 'same'; } if (padding === void 0) { padding = 'same'; }
var _a = params.conv, filters = _a.filters, bias = _a.bias; var _a = params.conv, filters = _a.filters, bias = _a.bias;
var out = tf.conv2d(x, filters, strides, padding); var out = tf.conv2d(x, filters, strides, padding);
out = tf.add(out, bias); out = tf.add(out, bias);
out = scale(out, params.scale); out = scaleLayer_1.scale(out, params.scale);
return withRelu ? tf.relu(out) : out; return withRelu ? tf.relu(out) : out;
} }
export function conv(x, params) { function conv(x, params) {
return convLayer(x, params, [1, 1], true); 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); 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'); return convLayer(x, params, [2, 2], true, 'valid');
} }
exports.convDown = convDown;
//# sourceMappingURL=convLayer.js.map //# 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 \ No newline at end of file
build/faceRecognitionNet/extractParams.js
import * as tf from '@tensorflow/tfjs-core'; "use strict";
import { extractWeightsFactory, isFloat } from 'tfjs-image-recognition-base'; 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 extractorsFactory(extractWeights, paramMappings) {
function extractFilterValues(numFilterValues, numFilters, filterSize) { function extractFilterValues(numFilterValues, numFilters, filterSize) {
var weights = extractWeights(numFilterValues); var weights = extractWeights(numFilterValues);
var depth = weights.length / (numFilters * filterSize * filterSize); 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); 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]); }); 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) { ...@@ -40,8 +42,8 @@ function extractorsFactory(extractWeights, paramMappings) {
extractResidualLayerParams: extractResidualLayerParams extractResidualLayerParams: extractResidualLayerParams
}; };
} }
export function extractParams(weights) { function extractParams(weights) {
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 paramMappings = []; var paramMappings = [];
var _b = extractorsFactory(extractWeights, paramMappings), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams; var _b = extractorsFactory(extractWeights, paramMappings), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams;
var conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down'); var conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');
...@@ -84,4 +86,5 @@ export function extractParams(weights) { ...@@ -84,4 +86,5 @@ export function extractParams(weights) {
}; };
return { params: params, paramMappings: paramMappings }; return { params: params, paramMappings: paramMappings };
} }
exports.extractParams = extractParams;
//# sourceMappingURL=extractParams.js.map //# 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 \ No newline at end of file
build/faceRecognitionNet/index.js
import { FaceRecognitionNet } from './FaceRecognitionNet'; "use strict";
export * from './FaceRecognitionNet'; Object.defineProperty(exports, "__esModule", { value: true });
export function createFaceRecognitionNet(weights) { var tslib_1 = require("tslib");
var net = new FaceRecognitionNet(); var FaceRecognitionNet_1 = require("./FaceRecognitionNet");
tslib_1.__exportStar(require("./FaceRecognitionNet"), exports);
function createFaceRecognitionNet(weights) {
var net = new FaceRecognitionNet_1.FaceRecognitionNet();
net.extractWeights(weights); net.extractWeights(weights);
return net; 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'); console.warn('faceRecognitionNet(weights: Float32Array) will be deprecated in future, use createFaceRecognitionNet instead');
return createFaceRecognitionNet(weights); return createFaceRecognitionNet(weights);
} }
exports.faceRecognitionNet = faceRecognitionNet;
//# sourceMappingURL=index.js.map //# 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 \ No newline at end of file
build/faceRecognitionNet/loadQuantizedParams.js
import * as tslib_1 from "tslib"; "use strict";
import { disposeUnusedWeightTensors, extractWeightEntryFactory, isTensor2D, loadWeightMap, } from 'tfjs-image-recognition-base'; 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'; var DEFAULT_MODEL_NAME = 'face_recognition_model';
function extractorsFactory(weightMap, paramMappings) { function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); var extractWeightEntry = tfjs_image_recognition_base_1.extractWeightEntryFactory(weightMap, paramMappings);
function extractScaleLayerParams(prefix) { function extractScaleLayerParams(prefix) {
var weights = extractWeightEntry(prefix + "/scale/weights", 1); var weights = extractWeightEntry(prefix + "/scale/weights", 1);
var biases = extractWeightEntry(prefix + "/scale/biases", 1); var biases = extractWeightEntry(prefix + "/scale/biases", 1);
...@@ -25,12 +27,12 @@ function extractorsFactory(weightMap, paramMappings) { ...@@ -25,12 +27,12 @@ function extractorsFactory(weightMap, paramMappings) {
extractResidualLayerParams: extractResidualLayerParams extractResidualLayerParams: extractResidualLayerParams
}; };
} }
export function loadQuantizedParams(uri) { function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () { 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; 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) { return tslib_1.__generator(this, function (_b) {
switch (_b.label) { 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: case 1:
weightMap = _b.sent(); weightMap = _b.sent();
paramMappings = []; paramMappings = [];
...@@ -52,7 +54,7 @@ export function loadQuantizedParams(uri) { ...@@ -52,7 +54,7 @@ export function loadQuantizedParams(uri) {
conv256_down_out = extractResidualLayerParams('conv256_down_out'); conv256_down_out = extractResidualLayerParams('conv256_down_out');
fc = weightMap['fc']; fc = weightMap['fc'];
paramMappings.push({ originalPath: 'fc', paramPath: '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); throw new Error("expected weightMap[fc] to be a Tensor2D, instead have " + fc);
} }
params = { params = {
...@@ -73,10 +75,11 @@ export function loadQuantizedParams(uri) { ...@@ -73,10 +75,11 @@ export function loadQuantizedParams(uri) {
conv256_down_out: conv256_down_out, conv256_down_out: conv256_down_out,
fc: fc fc: fc
}; };
disposeUnusedWeightTensors(weightMap, paramMappings); tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }]; return [2 /*return*/, { params: params, paramMappings: paramMappings }];
} }
}); });
}); });
} }
exports.loadQuantizedParams = loadQuantizedParams;
//# sourceMappingURL=loadQuantizedParams.js.map //# 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 \ No newline at end of file
build/faceRecognitionNet/residualLayer.js
import * as tf from '@tensorflow/tfjs-core'; "use strict";
import { conv, convDown, convNoRelu } from './convLayer'; Object.defineProperty(exports, "__esModule", { value: true });
export function residual(x, params) { var tf = require("@tensorflow/tfjs-core");
var out = conv(x, params.conv1); var convLayer_1 = require("./convLayer");
out = convNoRelu(out, params.conv2); 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.add(out, x);
out = tf.relu(out); out = tf.relu(out);
return out; return out;
} }
export function residualDown(x, params) { exports.residual = residual;
var out = convDown(x, params.conv1); function residualDown(x, params) {
out = convNoRelu(out, params.conv2); var out = convLayer_1.convDown(x, params.conv1);
out = convLayer_1.convNoRelu(out, params.conv2);
var pooled = tf.avgPool(x, 2, 2, 'valid'); var pooled = tf.avgPool(x, 2, 2, 'valid');
var zeros = tf.zeros(pooled.shape); var zeros = tf.zeros(pooled.shape);
var isPad = pooled.shape[3] !== out.shape[3]; var isPad = pooled.shape[3] !== out.shape[3];
...@@ -29,4 +32,5 @@ export function residualDown(x, params) { ...@@ -29,4 +32,5 @@ export function residualDown(x, params) {
out = tf.relu(out); out = tf.relu(out);
return out; return out;
} }
exports.residualDown = residualDown;
//# sourceMappingURL=residualLayer.js.map //# sourceMappingURL=residualLayer.js.map
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build/faceRecognitionNet/residualLayer.js.map
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build/faceRecognitionNet/scaleLayer.js
import * as tf from '@tensorflow/tfjs-core'; "use strict";
export function scale(x, params) { 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); return tf.add(tf.mul(x, params.weights), params.biases);
} }
exports.scale = scale;
//# sourceMappingURL=scaleLayer.js.map //# sourceMappingURL=scaleLayer.js.map
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build/faceRecognitionNet/scaleLayer.js.map
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\ No newline at end of file \ No newline at end of file
build/faceRecognitionNet/types.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
//# sourceMappingURL=types.js.map //# sourceMappingURL=types.js.map
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build/globalApi.d.ts
...@@ -28,6 +28,7 @@ export declare function loadTinyYolov2Model(url: string): Promise<void>; ...@@ -28,6 +28,7 @@ export declare function loadTinyYolov2Model(url: string): Promise<void>;
export declare function loadFaceDetectionModel(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 loadModels(url: string): Promise<[void, void, void, void, void]>;
export declare function locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>; 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 detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;
export declare function computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>; export declare function computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
export declare function mtcnn(input: TNetInput, forwardParams: MtcnnForwardParams): Promise<MtcnnResult[]>; export declare function mtcnn(input: TNetInput, forwardParams: MtcnnForwardParams): Promise<MtcnnResult[]>;
......
build/globalApi.js
import { allFacesMtcnnFactory, allFacesSsdMobilenetv1Factory, allFacesTinyYolov2Factory } from './allFacesFactory'; "use strict";
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet'; Object.defineProperty(exports, "__esModule", { value: true });
import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet'; var allFacesFactory_1 = require("./allFacesFactory");
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet'; var FaceDetectionNet_1 = require("./faceDetectionNet/FaceDetectionNet");
import { Mtcnn } from './mtcnn/Mtcnn'; var FaceLandmarkNet_1 = require("./faceLandmarkNet/FaceLandmarkNet");
import { TinyYolov2 } from './tinyYolov2/TinyYolov2'; var FaceRecognitionNet_1 = require("./faceRecognitionNet/FaceRecognitionNet");
export var detectionNet = new FaceDetectionNet(); var Mtcnn_1 = require("./mtcnn/Mtcnn");
export var landmarkNet = new FaceLandmarkNet(); var TinyYolov2_1 = require("./tinyYolov2/TinyYolov2");
export var recognitionNet = new FaceRecognitionNet(); 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 // nets need more specific names, to avoid ambiguity in future
// when alternative net implementations are provided // when alternative net implementations are provided
export var nets = { exports.nets = {
ssdMobilenetv1: detectionNet, ssdMobilenetv1: exports.detectionNet,
faceLandmark68Net: landmarkNet, faceLandmark68Net: exports.landmarkNet,
faceRecognitionNet: recognitionNet, faceRecognitionNet: exports.recognitionNet,
mtcnn: new Mtcnn(), mtcnn: new Mtcnn_1.Mtcnn(),
tinyYolov2: new TinyYolov2() tinyYolov2: new TinyYolov2_1.TinyYolov2()
}; };
export function loadSsdMobilenetv1Model(url) { function loadSsdMobilenetv1Model(url) {
return nets.ssdMobilenetv1.load(url); return exports.nets.ssdMobilenetv1.load(url);
} }
export function loadFaceLandmarkModel(url) { exports.loadSsdMobilenetv1Model = loadSsdMobilenetv1Model;
return nets.faceLandmark68Net.load(url); function loadFaceLandmarkModel(url) {
return exports.nets.faceLandmark68Net.load(url);
} }
export function loadFaceRecognitionModel(url) { exports.loadFaceLandmarkModel = loadFaceLandmarkModel;
return nets.faceRecognitionNet.load(url); function loadFaceRecognitionModel(url) {
return exports.nets.faceRecognitionNet.load(url);
} }
export function loadMtcnnModel(url) { exports.loadFaceRecognitionModel = loadFaceRecognitionModel;
return nets.mtcnn.load(url); function loadMtcnnModel(url) {
return exports.nets.mtcnn.load(url);
} }
export function loadTinyYolov2Model(url) { exports.loadMtcnnModel = loadMtcnnModel;
return nets.tinyYolov2.load(url); function loadTinyYolov2Model(url) {
return exports.nets.tinyYolov2.load(url);
} }
export function loadFaceDetectionModel(url) { exports.loadTinyYolov2Model = loadTinyYolov2Model;
function loadFaceDetectionModel(url) {
return loadSsdMobilenetv1Model(url); return loadSsdMobilenetv1Model(url);
} }
export function loadModels(url) { exports.loadFaceDetectionModel = loadFaceDetectionModel;
function loadModels(url) {
return Promise.all([ return Promise.all([
loadSsdMobilenetv1Model(url), loadSsdMobilenetv1Model(url),
loadFaceLandmarkModel(url), loadFaceLandmarkModel(url),
...@@ -43,23 +51,30 @@ export function loadModels(url) { ...@@ -43,23 +51,30 @@ export function loadModels(url) {
loadTinyYolov2Model(url) loadTinyYolov2Model(url)
]); ]);
} }
export function locateFaces(input, minConfidence, maxResults) { exports.loadModels = loadModels;
return nets.ssdMobilenetv1.locateFaces(input, minConfidence, maxResults); function locateFaces(input, minConfidence, maxResults) {
return exports.nets.ssdMobilenetv1.locateFaces(input, minConfidence, maxResults);
} }
export function detectLandmarks(input) { exports.locateFaces = locateFaces;
return nets.faceLandmark68Net.detectLandmarks(input); exports.ssdMobilenetv1 = locateFaces;
function detectLandmarks(input) {
return exports.nets.faceLandmark68Net.detectLandmarks(input);
} }
export function computeFaceDescriptor(input) { exports.detectLandmarks = detectLandmarks;
return nets.faceRecognitionNet.computeFaceDescriptor(input); function computeFaceDescriptor(input) {
return exports.nets.faceRecognitionNet.computeFaceDescriptor(input);
} }
export function mtcnn(input, forwardParams) { exports.computeFaceDescriptor = computeFaceDescriptor;
return nets.mtcnn.forward(input, forwardParams); function mtcnn(input, forwardParams) {
return exports.nets.mtcnn.forward(input, forwardParams);
} }
export function tinyYolov2(input, forwardParams) { exports.mtcnn = mtcnn;
return nets.tinyYolov2.locateFaces(input, forwardParams); function tinyYolov2(input, forwardParams) {
return exports.nets.tinyYolov2.locateFaces(input, forwardParams);
} }
export var allFacesSsdMobilenetv1 = allFacesSsdMobilenetv1Factory(nets.ssdMobilenetv1, nets.faceLandmark68Net, nets.faceRecognitionNet); exports.tinyYolov2 = tinyYolov2;
export var allFacesTinyYolov2 = allFacesTinyYolov2Factory(nets.tinyYolov2, nets.faceLandmark68Net, nets.faceRecognitionNet); exports.allFacesSsdMobilenetv1 = allFacesFactory_1.allFacesSsdMobilenetv1Factory(exports.nets.ssdMobilenetv1, exports.nets.faceLandmark68Net, exports.nets.faceRecognitionNet);
export var allFacesMtcnn = allFacesMtcnnFactory(nets.mtcnn, nets.faceRecognitionNet); exports.allFacesTinyYolov2 = allFacesFactory_1.allFacesTinyYolov2Factory(exports.nets.tinyYolov2, exports.nets.faceLandmark68Net, exports.nets.faceRecognitionNet);
export var allFaces = allFacesSsdMobilenetv1; 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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build/index.js
import * as tf from '@tensorflow/tfjs-core'; "use strict";
export { tf }; Object.defineProperty(exports, "__esModule", { value: true });
export * from 'tfjs-image-recognition-base'; var tslib_1 = require("tslib");
export * from './classes'; var tf = require("@tensorflow/tfjs-core");
export * from './dom'; exports.tf = tf;
export * from './euclideanDistance'; tslib_1.__exportStar(require("tfjs-image-recognition-base"), exports);
export * from './faceDetectionNet'; tslib_1.__exportStar(require("./classes"), exports);
export * from './faceLandmarkNet'; tslib_1.__exportStar(require("./dom"), exports);
export * from './faceRecognitionNet'; tslib_1.__exportStar(require("./euclideanDistance"), exports);
export * from './globalApi'; tslib_1.__exportStar(require("./faceDetectionNet"), exports);
export * from './mtcnn'; tslib_1.__exportStar(require("./faceLandmarkNet"), exports);
export * from './tinyYolov2'; 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 //# sourceMappingURL=index.js.map
\ No newline at end of file
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\ No newline at end of file \ No newline at end of file
build/mtcnn/Mtcnn.js
import * as tslib_1 from "tslib"; "use strict";
import * as tf from '@tensorflow/tfjs-core'; Object.defineProperty(exports, "__esModule", { value: true });
import { NeuralNetwork, Point, Rect, toNetInput } from 'tfjs-image-recognition-base'; var tslib_1 = require("tslib");
import { FaceDetection } from '../classes/FaceDetection'; var tf = require("@tensorflow/tfjs-core");
import { FaceLandmarks5 } from '../classes/FaceLandmarks5'; var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
import { bgrToRgbTensor } from './bgrToRgbTensor'; var FaceDetection_1 = require("../classes/FaceDetection");
import { CELL_SIZE } from './config'; var FaceLandmarks5_1 = require("../classes/FaceLandmarks5");
import { extractParams } from './extractParams'; var bgrToRgbTensor_1 = require("./bgrToRgbTensor");
import { getDefaultMtcnnForwardParams } from './getDefaultMtcnnForwardParams'; var config_1 = require("./config");
import { getSizesForScale } from './getSizesForScale'; var extractParams_1 = require("./extractParams");
import { loadQuantizedParams } from './loadQuantizedParams'; var getDefaultMtcnnForwardParams_1 = require("./getDefaultMtcnnForwardParams");
import { pyramidDown } from './pyramidDown'; var getSizesForScale_1 = require("./getSizesForScale");
import { stage1 } from './stage1'; var loadQuantizedParams_1 = require("./loadQuantizedParams");
import { stage2 } from './stage2'; var pyramidDown_1 = require("./pyramidDown");
import { stage3 } from './stage3'; var stage1_1 = require("./stage1");
var stage2_1 = require("./stage2");
var stage3_1 = require("./stage3");
var Mtcnn = /** @class */ (function (_super) { var Mtcnn = /** @class */ (function (_super) {
tslib_1.__extends(Mtcnn, _super); tslib_1.__extends(Mtcnn, _super);
function Mtcnn() { function Mtcnn() {
...@@ -37,7 +39,7 @@ var Mtcnn = /** @class */ (function (_super) { ...@@ -37,7 +39,7 @@ var Mtcnn = /** @class */ (function (_super) {
stats = {}; stats = {};
tsTotal = Date.now(); tsTotal = Date.now();
imgTensor = tf.tidy(function () { imgTensor = tf.tidy(function () {
return bgrToRgbTensor(tf.expandDims(inputTensor).toFloat()); return bgrToRgbTensor_1.bgrToRgbTensor(tf.expandDims(inputTensor).toFloat());
}); });
onReturn = function (results) { onReturn = function (results) {
// dispose tensors on return // dispose tensors on return
...@@ -47,17 +49,17 @@ var Mtcnn = /** @class */ (function (_super) { ...@@ -47,17 +49,17 @@ var Mtcnn = /** @class */ (function (_super) {
return results; return results;
}; };
_a = imgTensor.shape.slice(1), height = _a[0], width = _a[1]; _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; _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(minFaceSize, scaleFactor, [height, width])) scales = (scaleSteps || pyramidDown_1.pyramidDown(minFaceSize, scaleFactor, [height, width]))
.filter(function (scale) { .filter(function (scale) {
var sizes = getSizesForScale(scale, [height, width]); var sizes = getSizesForScale_1.getSizesForScale(scale, [height, width]);
return Math.min(sizes.width, sizes.height) > CELL_SIZE; return Math.min(sizes.width, sizes.height) > config_1.CELL_SIZE;
}) })
.slice(0, maxNumScales); .slice(0, maxNumScales);
stats.scales = scales; 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(); 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: case 1:
out1 = _c.sent(); out1 = _c.sent();
stats.total_stage1 = Date.now() - ts; stats.total_stage1 = Date.now() - ts;
...@@ -68,7 +70,7 @@ var Mtcnn = /** @class */ (function (_super) { ...@@ -68,7 +70,7 @@ var Mtcnn = /** @class */ (function (_super) {
// using the inputCanvas to extract and resize the image patches, since it is faster // using the inputCanvas to extract and resize the image patches, since it is faster
// than doing this on the gpu // than doing this on the gpu
ts = Date.now(); 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: case 2:
out2 = _c.sent(); out2 = _c.sent();
stats.total_stage2 = Date.now() - ts; stats.total_stage2 = Date.now() - ts;
...@@ -77,16 +79,16 @@ var Mtcnn = /** @class */ (function (_super) { ...@@ -77,16 +79,16 @@ var Mtcnn = /** @class */ (function (_super) {
} }
stats.stage3_numInputBoxes = out2.boxes.length; stats.stage3_numInputBoxes = out2.boxes.length;
ts = Date.now(); 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: case 3:
out3 = _c.sent(); out3 = _c.sent();
stats.total_stage3 = Date.now() - ts; stats.total_stage3 = Date.now() - ts;
results = out3.boxes.map(function (box, idx) { return ({ 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, height: height,
width: width 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 })]; return [2 /*return*/, onReturn({ results: results, stats: stats })];
} }
...@@ -101,7 +103,7 @@ var Mtcnn = /** @class */ (function (_super) { ...@@ -101,7 +103,7 @@ var Mtcnn = /** @class */ (function (_super) {
switch (_b.label) { switch (_b.label) {
case 0: case 0:
_a = this.forwardInput; _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(), case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(),
forwardParams])]; forwardParams])];
case 2: return [2 /*return*/, (_b.sent()).results]; case 2: return [2 /*return*/, (_b.sent()).results];
...@@ -117,7 +119,7 @@ var Mtcnn = /** @class */ (function (_super) { ...@@ -117,7 +119,7 @@ var Mtcnn = /** @class */ (function (_super) {
switch (_b.label) { switch (_b.label) {
case 0: case 0:
_a = this.forwardInput; _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(), case 1: return [2 /*return*/, _a.apply(this, [_b.sent(),
forwardParams])]; forwardParams])];
} }
...@@ -126,12 +128,12 @@ var Mtcnn = /** @class */ (function (_super) { ...@@ -126,12 +128,12 @@ var Mtcnn = /** @class */ (function (_super) {
}; };
// none of the param tensors are quantized yet // none of the param tensors are quantized yet
Mtcnn.prototype.loadQuantizedParams = function (uri) { Mtcnn.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams(uri); return loadQuantizedParams_1.loadQuantizedParams(uri);
}; };
Mtcnn.prototype.extractParams = function (weights) { Mtcnn.prototype.extractParams = function (weights) {
return extractParams(weights); return extractParams_1.extractParams(weights);
}; };
return Mtcnn; return Mtcnn;
}(NeuralNetwork)); }(tfjs_image_recognition_base_1.NeuralNetwork));
export { Mtcnn }; exports.Mtcnn = Mtcnn;
//# sourceMappingURL=Mtcnn.js.map //# 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 \ No newline at end of file
build/mtcnn/ONet.js
import * as tf from '@tensorflow/tfjs-core'; "use strict";
import { convLayer } from 'tfjs-tiny-yolov2'; Object.defineProperty(exports, "__esModule", { value: true });
import { fullyConnectedLayer } from '../faceLandmarkNet/fullyConnectedLayer'; var tf = require("@tensorflow/tfjs-core");
import { prelu } from './prelu'; var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
import { sharedLayer } from './sharedLayers'; var fullyConnectedLayer_1 = require("../faceLandmarkNet/fullyConnectedLayer");
export function ONet(x, params) { var prelu_1 = require("./prelu");
var sharedLayers_1 = require("./sharedLayers");
function ONet(x, params) {
return tf.tidy(function () { 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 = tf.maxPool(out, [2, 2], [2, 2], 'same');
out = convLayer(out, params.conv4, 'valid'); out = tfjs_tiny_yolov2_1.convLayer(out, params.conv4, 'valid');
out = prelu(out, params.prelu4_alpha); out = prelu_1.prelu(out, params.prelu4_alpha);
var vectorized = tf.reshape(out, [out.shape[0], params.fc1.weights.shape[0]]); var vectorized = tf.reshape(out, [out.shape[0], params.fc1.weights.shape[0]]);
var fc1 = fullyConnectedLayer(vectorized, params.fc1); var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(vectorized, params.fc1);
var prelu5 = prelu(fc1, params.prelu5_alpha); var prelu5 = prelu_1.prelu(fc1, params.prelu5_alpha);
var fc2_1 = fullyConnectedLayer(prelu5, params.fc2_1); var fc2_1 = fullyConnectedLayer_1.fullyConnectedLayer(prelu5, params.fc2_1);
var max = tf.expandDims(tf.max(fc2_1, 1), 1); var max = tf.expandDims(tf.max(fc2_1, 1), 1);
var prob = tf.softmax(tf.sub(fc2_1, max), 1); var prob = tf.softmax(tf.sub(fc2_1, max), 1);
var regions = fullyConnectedLayer(prelu5, params.fc2_2); var regions = fullyConnectedLayer_1.fullyConnectedLayer(prelu5, params.fc2_2);
var points = fullyConnectedLayer(prelu5, params.fc2_3); var points = fullyConnectedLayer_1.fullyConnectedLayer(prelu5, params.fc2_3);
var scores = tf.unstack(prob, 1)[1]; var scores = tf.unstack(prob, 1)[1];
return { scores: scores, regions: regions, points: points }; return { scores: scores, regions: regions, points: points };
}); });
} }
exports.ONet = ONet;
//# sourceMappingURL=ONet.js.map //# 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'; "use strict";
import { convLayer } from 'tfjs-tiny-yolov2'; Object.defineProperty(exports, "__esModule", { value: true });
import { sharedLayer } from './sharedLayers'; var tf = require("@tensorflow/tfjs-core");
export function PNet(x, params) { var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
var sharedLayers_1 = require("./sharedLayers");
function PNet(x, params) {
return tf.tidy(function () { return tf.tidy(function () {
var out = sharedLayer(x, params, true); var out = sharedLayers_1.sharedLayer(x, params, true);
var conv = convLayer(out, params.conv4_1, 'valid'); var conv = tfjs_tiny_yolov2_1.convLayer(out, params.conv4_1, 'valid');
var max = tf.expandDims(tf.max(conv, 3), 3); var max = tf.expandDims(tf.max(conv, 3), 3);
var prob = tf.softmax(tf.sub(conv, max), 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 }; return { prob: prob, regions: regions };
}); });
} }
exports.PNet = PNet;
//# sourceMappingURL=PNet.js.map //# 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'; "use strict";
import { fullyConnectedLayer } from '../faceLandmarkNet/fullyConnectedLayer'; Object.defineProperty(exports, "__esModule", { value: true });
import { prelu } from './prelu'; var tf = require("@tensorflow/tfjs-core");
import { sharedLayer } from './sharedLayers'; var fullyConnectedLayer_1 = require("../faceLandmarkNet/fullyConnectedLayer");
export function RNet(x, params) { var prelu_1 = require("./prelu");
var sharedLayers_1 = require("./sharedLayers");
function RNet(x, params) {
return tf.tidy(function () { 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 vectorized = tf.reshape(convOut, [convOut.shape[0], params.fc1.weights.shape[0]]);
var fc1 = fullyConnectedLayer(vectorized, params.fc1); var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(vectorized, params.fc1);
var prelu4 = prelu(fc1, params.prelu4_alpha); var prelu4 = prelu_1.prelu(fc1, params.prelu4_alpha);
var fc2_1 = fullyConnectedLayer(prelu4, params.fc2_1); var fc2_1 = fullyConnectedLayer_1.fullyConnectedLayer(prelu4, params.fc2_1);
var max = tf.expandDims(tf.max(fc2_1, 1), 1); var max = tf.expandDims(tf.max(fc2_1, 1), 1);
var prob = tf.softmax(tf.sub(fc2_1, max), 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]; var scores = tf.unstack(prob, 1)[1];
return { scores: scores, regions: regions }; return { scores: scores, regions: regions };
}); });
} }
exports.RNet = RNet;
//# sourceMappingURL=RNet.js.map //# 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'; "use strict";
export function bgrToRgbTensor(tensor) { 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); }); return tf.tidy(function () { return tf.stack(tf.unstack(tensor, 3).reverse(), 3); });
} }
exports.bgrToRgbTensor = bgrToRgbTensor;
//# sourceMappingURL=bgrToRgbTensor.js.map //# sourceMappingURL=bgrToRgbTensor.js.map
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build/mtcnn/bgrToRgbTensor.js.map
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build/mtcnn/config.js
export var CELL_STRIDE = 2; "use strict";
export var CELL_SIZE = 12; Object.defineProperty(exports, "__esModule", { value: true });
exports.CELL_STRIDE = 2;
exports.CELL_SIZE = 12;
//# sourceMappingURL=config.js.map //# sourceMappingURL=config.js.map
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build/mtcnn/config.js.map
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build/mtcnn/extractImagePatches.js
import * as tslib_1 from "tslib"; "use strict";
import * as tf from '@tensorflow/tfjs-core'; Object.defineProperty(exports, "__esModule", { value: true });
import { normalize } from './normalize'; var tslib_1 = require("tslib");
import { getContext2dOrThrow, createCanvas } from 'tfjs-image-recognition-base'; var tf = require("@tensorflow/tfjs-core");
export function extractImagePatches(img, boxes, _a) { 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; var width = _a.width, height = _a.height;
return tslib_1.__awaiter(this, void 0, void 0, function () { return tslib_1.__awaiter(this, void 0, void 0, function () {
var _this = this; var _this = this;
...@@ -10,7 +12,7 @@ export function extractImagePatches(img, boxes, _a) { ...@@ -10,7 +12,7 @@ export function extractImagePatches(img, boxes, _a) {
return tslib_1.__generator(this, function (_b) { return tslib_1.__generator(this, function (_b) {
switch (_b.label) { switch (_b.label) {
case 0: 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 () { 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; var _a, y, ey, x, ex, fromX, fromY, imgData;
return tslib_1.__generator(this, function (_b) { return tslib_1.__generator(this, function (_b) {
...@@ -25,8 +27,8 @@ export function extractImagePatches(img, boxes, _a) { ...@@ -25,8 +27,8 @@ export function extractImagePatches(img, boxes, _a) {
bitmaps = _b.sent(); bitmaps = _b.sent();
imagePatchesDatas = []; imagePatchesDatas = [];
bitmaps.forEach(function (bmp) { bitmaps.forEach(function (bmp) {
var patch = createCanvas({ width: width, height: height }); var patch = tfjs_image_recognition_base_1.createCanvas({ width: width, height: height });
var patchCtx = getContext2dOrThrow(patch); var patchCtx = tfjs_image_recognition_base_1.getContext2dOrThrow(patch);
patchCtx.drawImage(bmp, 0, 0, width, height); patchCtx.drawImage(bmp, 0, 0, width, height);
var data = patchCtx.getImageData(0, 0, width, height).data; var data = patchCtx.getImageData(0, 0, width, height).data;
var currData = []; var currData = [];
...@@ -41,7 +43,7 @@ export function extractImagePatches(img, boxes, _a) { ...@@ -41,7 +43,7 @@ export function extractImagePatches(img, boxes, _a) {
return [2 /*return*/, imagePatchesDatas.map(function (data) { return [2 /*return*/, imagePatchesDatas.map(function (data) {
var t = tf.tidy(function () { var t = tf.tidy(function () {
var imagePatchTensor = tf.transpose(tf.tensor4d(data, [1, width, height, 3]), [0, 2, 1, 3]).toFloat(); 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; return t;
})]; })];
...@@ -49,4 +51,5 @@ export function extractImagePatches(img, boxes, _a) { ...@@ -49,4 +51,5 @@ export function extractImagePatches(img, boxes, _a) {
}); });
}); });
} }
exports.extractImagePatches = extractImagePatches;
//# sourceMappingURL=extractImagePatches.js.map //# sourceMappingURL=extractImagePatches.js.map
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build/mtcnn/extractImagePatches.js.map
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build/mtcnn/extractParams.js
import * as tslib_1 from "tslib"; "use strict";
import * as tf from '@tensorflow/tfjs-core'; Object.defineProperty(exports, "__esModule", { value: true });
import { extractWeightsFactory } from 'tfjs-image-recognition-base'; var tslib_1 = require("tslib");
import { extractConvParamsFactory, extractFCParamsFactory } from 'tfjs-tiny-yolov2'; 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) { function extractorsFactory(extractWeights, paramMappings) {
var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); var extractConvParams = tfjs_tiny_yolov2_1.extractConvParamsFactory(extractWeights, paramMappings);
var extractFCParams = extractFCParamsFactory(extractWeights, paramMappings); var extractFCParams = tfjs_tiny_yolov2_1.extractFCParamsFactory(extractWeights, paramMappings);
function extractPReluParams(size, paramPath) { function extractPReluParams(size, paramPath) {
var alpha = tf.tensor1d(extractWeights(size)); var alpha = tf.tensor1d(extractWeights(size));
paramMappings.push({ paramPath: paramPath }); paramMappings.push({ paramPath: paramPath });
...@@ -51,8 +53,8 @@ function extractorsFactory(extractWeights, paramMappings) { ...@@ -51,8 +53,8 @@ function extractorsFactory(extractWeights, paramMappings) {
extractONetParams: extractONetParams extractONetParams: extractONetParams
}; };
} }
export function extractParams(weights) { function extractParams(weights) {
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 paramMappings = []; var paramMappings = [];
var _b = extractorsFactory(extractWeights, paramMappings), extractPNetParams = _b.extractPNetParams, extractRNetParams = _b.extractRNetParams, extractONetParams = _b.extractONetParams; var _b = extractorsFactory(extractWeights, paramMappings), extractPNetParams = _b.extractPNetParams, extractRNetParams = _b.extractRNetParams, extractONetParams = _b.extractONetParams;
var pnet = extractPNetParams(); var pnet = extractPNetParams();
...@@ -63,4 +65,5 @@ export function extractParams(weights) { ...@@ -63,4 +65,5 @@ export function extractParams(weights) {
} }
return { params: { pnet: pnet, rnet: rnet, onet: onet }, paramMappings: paramMappings }; return { params: { pnet: pnet, rnet: rnet, onet: onet }, paramMappings: paramMappings };
} }
exports.extractParams = extractParams;
//# sourceMappingURL=extractParams.js.map //# sourceMappingURL=extractParams.js.map
\ No newline at end of file
build/mtcnn/extractParams.js.map
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build/mtcnn/getDefaultMtcnnForwardParams.js
export function getDefaultMtcnnForwardParams() { "use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function getDefaultMtcnnForwardParams() {
return { return {
minFaceSize: 20, minFaceSize: 20,
scaleFactor: 0.709, scaleFactor: 0.709,
...@@ -6,4 +8,5 @@ export function getDefaultMtcnnForwardParams() { ...@@ -6,4 +8,5 @@ export function getDefaultMtcnnForwardParams() {
scoreThresholds: [0.6, 0.7, 0.7] scoreThresholds: [0.6, 0.7, 0.7]
}; };
} }
exports.getDefaultMtcnnForwardParams = getDefaultMtcnnForwardParams;
//# sourceMappingURL=getDefaultMtcnnForwardParams.js.map //# 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]; var height = _a[0], width = _a[1];
return { return {
height: Math.floor(height * scale), height: Math.floor(height * scale),
width: Math.floor(width * scale) width: Math.floor(width * scale)
}; };
} }
exports.getSizesForScale = getSizesForScale;
//# sourceMappingURL=getSizesForScale.js.map //# sourceMappingURL=getSizesForScale.js.map
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build/mtcnn/getSizesForScale.js.map
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\ No newline at end of file \ No newline at end of file
build/mtcnn/index.js
import { Mtcnn } from './Mtcnn'; "use strict";
export * from './Mtcnn'; Object.defineProperty(exports, "__esModule", { value: true });
export function createMtcnn(weights) { var tslib_1 = require("tslib");
var net = new Mtcnn(); var Mtcnn_1 = require("./Mtcnn");
tslib_1.__exportStar(require("./Mtcnn"), exports);
function createMtcnn(weights) {
var net = new Mtcnn_1.Mtcnn();
net.extractWeights(weights); net.extractWeights(weights);
return net; return net;
} }
exports.createMtcnn = createMtcnn;
//# sourceMappingURL=index.js.map //# sourceMappingURL=index.js.map
\ No newline at end of file
dist/face-api.js.map
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dist/face-api.min.js
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