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Commit f8a9c0ad by vincent
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check in latest build

parent 5a066131
Showing with 1710 additions and 166 deletions
build/commonjs/classes/FaceDetection.d.ts
import { Dimensions, ObjectDetection, Rect } from 'tfjs-image-recognition-base';
export declare class FaceDetection extends ObjectDetection {
constructor(score: number, relativeBox: Rect, imageDims: Dimensions);
import { Box, IDimensions, ObjectDetection, Rect } from 'tfjs-image-recognition-base';
export interface IFaceDetecion {
score: number;
box: Box;
}
export declare class FaceDetection extends ObjectDetection implements IFaceDetecion {
constructor(score: number, relativeBox: Rect, imageDims: IDimensions);
}
build/commonjs/classes/FaceDetection.js.map
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build/commonjs/classes/FaceDetectionWithLandmarks.d.ts 0 → 100644
import { FaceDetection } from './FaceDetection';
import { FaceLandmarks } from './FaceLandmarks';
import { FaceLandmarks68 } from './FaceLandmarks68';
export interface IFaceDetectionWithLandmarks<TFaceLandmarks extends FaceLandmarks = FaceLandmarks68> {
detection: FaceDetection;
landmarks: TFaceLandmarks;
}
export declare class FaceDetectionWithLandmarks<TFaceLandmarks extends FaceLandmarks = FaceLandmarks68> implements IFaceDetectionWithLandmarks<TFaceLandmarks> {
private _detection;
private _unshiftedLandmarks;
constructor(detection: FaceDetection, unshiftedLandmarks: TFaceLandmarks);
readonly detection: FaceDetection;
readonly unshiftedLandmarks: TFaceLandmarks;
readonly alignedRect: FaceDetection;
readonly landmarks: TFaceLandmarks;
readonly faceDetection: FaceDetection;
readonly faceLandmarks: TFaceLandmarks;
forSize(width: number, height: number): FaceDetectionWithLandmarks<TFaceLandmarks>;
}
build/commonjs/classes/FaceDetectionWithLandmarks.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var FaceDetection_1 = require("./FaceDetection");
var FaceDetectionWithLandmarks = /** @class */ (function () {
function FaceDetectionWithLandmarks(detection, unshiftedLandmarks) {
this._detection = detection;
this._unshiftedLandmarks = unshiftedLandmarks;
}
Object.defineProperty(FaceDetectionWithLandmarks.prototype, "detection", {
get: function () { return this._detection; },
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetectionWithLandmarks.prototype, "unshiftedLandmarks", {
get: function () { return this._unshiftedLandmarks; },
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetectionWithLandmarks.prototype, "alignedRect", {
get: function () {
var rect = this.landmarks.align();
var imageDims = this.detection.imageDims;
return new FaceDetection_1.FaceDetection(this._detection.score, rect.rescale(imageDims.reverse()), imageDims);
},
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetectionWithLandmarks.prototype, "landmarks", {
get: function () {
var _a = this.detection.box, x = _a.x, y = _a.y;
return this._unshiftedLandmarks.shiftBy(x, y);
},
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetectionWithLandmarks.prototype, "faceDetection", {
// aliases for backward compatibily
get: function () { return this.detection; },
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetectionWithLandmarks.prototype, "faceLandmarks", {
get: function () { return this.landmarks; },
enumerable: true,
configurable: true
});
FaceDetectionWithLandmarks.prototype.forSize = function (width, height) {
var resizedDetection = this._detection.forSize(width, height);
var resizedLandmarks = this._unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);
return new FaceDetectionWithLandmarks(resizedDetection, resizedLandmarks);
};
return FaceDetectionWithLandmarks;
}());
exports.FaceDetectionWithLandmarks = FaceDetectionWithLandmarks;
//# sourceMappingURL=FaceDetectionWithLandmarks.js.map
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build/commonjs/classes/FaceDetectionWithLandmarks.js.map 0 → 100644
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build/commonjs/classes/FaceLandmarks.d.ts
import { Dimensions, Point, Rect } from 'tfjs-image-recognition-base';
import { Dimensions, IDimensions, Point, Rect } from 'tfjs-image-recognition-base';
import { FaceDetection } from './FaceDetection';
export declare class FaceLandmarks {
protected _imageWidth: number;
protected _imageHeight: number;
export interface IFaceLandmarks {
positions: Point[];
shift: Point;
}
export declare class FaceLandmarks implements IFaceLandmarks {
protected _shift: Point;
protected _faceLandmarks: Point[];
constructor(relativeFaceLandmarkPositions: Point[], imageDims: Dimensions, shift?: Point);
getShift(): Point;
getImageWidth(): number;
getImageHeight(): number;
getPositions(): Point[];
getRelativePositions(): Point[];
protected _positions: Point[];
protected _imgDims: Dimensions;
constructor(relativeFaceLandmarkPositions: Point[], imgDims: IDimensions, shift?: Point);
readonly shift: Point;
readonly imageWidth: number;
readonly imageHeight: number;
readonly positions: Point[];
readonly relativePositions: Point[];
forSize<T extends FaceLandmarks>(width: number, height: number): T;
shift<T extends FaceLandmarks>(x: number, y: number): T;
shiftBy<T extends FaceLandmarks>(x: number, y: number): T;
shiftByPoint<T extends FaceLandmarks>(pt: Point): T;
/**
* Aligns the face landmarks after face detection from the relative positions of the faces
......
build/commonjs/classes/FaceLandmarks.js
......@@ -7,38 +7,49 @@ var relX = 0.5;
var relY = 0.43;
var relScale = 0.45;
var FaceLandmarks = /** @class */ (function () {
function FaceLandmarks(relativeFaceLandmarkPositions, imageDims, shift) {
function FaceLandmarks(relativeFaceLandmarkPositions, imgDims, shift) {
if (shift === void 0) { shift = new tfjs_image_recognition_base_1.Point(0, 0); }
var width = imageDims.width, height = imageDims.height;
this._imageWidth = width;
this._imageHeight = height;
var width = imgDims.width, height = imgDims.height;
this._imgDims = new tfjs_image_recognition_base_1.Dimensions(width, height);
this._shift = shift;
this._faceLandmarks = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new tfjs_image_recognition_base_1.Point(width, height)).add(shift); });
this._positions = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new tfjs_image_recognition_base_1.Point(width, height)).add(shift); });
}
FaceLandmarks.prototype.getShift = function () {
return new tfjs_image_recognition_base_1.Point(this._shift.x, this._shift.y);
};
FaceLandmarks.prototype.getImageWidth = function () {
return this._imageWidth;
};
FaceLandmarks.prototype.getImageHeight = function () {
return this._imageHeight;
};
FaceLandmarks.prototype.getPositions = function () {
return this._faceLandmarks;
};
FaceLandmarks.prototype.getRelativePositions = function () {
var _this = this;
return this._faceLandmarks.map(function (pt) { return pt.sub(_this._shift).div(new tfjs_image_recognition_base_1.Point(_this._imageWidth, _this._imageHeight)); });
};
Object.defineProperty(FaceLandmarks.prototype, "shift", {
get: function () { return new tfjs_image_recognition_base_1.Point(this._shift.x, this._shift.y); },
enumerable: true,
configurable: true
});
Object.defineProperty(FaceLandmarks.prototype, "imageWidth", {
get: function () { return this._imgDims.width; },
enumerable: true,
configurable: true
});
Object.defineProperty(FaceLandmarks.prototype, "imageHeight", {
get: function () { return this._imgDims.height; },
enumerable: true,
configurable: true
});
Object.defineProperty(FaceLandmarks.prototype, "positions", {
get: function () { return this._positions; },
enumerable: true,
configurable: true
});
Object.defineProperty(FaceLandmarks.prototype, "relativePositions", {
get: function () {
var _this = this;
return this._positions.map(function (pt) { return pt.sub(_this._shift).div(new tfjs_image_recognition_base_1.Point(_this.imageWidth, _this.imageHeight)); });
},
enumerable: true,
configurable: true
});
FaceLandmarks.prototype.forSize = function (width, height) {
return new this.constructor(this.getRelativePositions(), { width: width, height: height });
return new this.constructor(this.relativePositions, { width: width, height: height });
};
FaceLandmarks.prototype.shift = function (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.shiftBy = function (x, y) {
return new this.constructor(this.relativePositions, this._imgDims, new tfjs_image_recognition_base_1.Point(x, y));
};
FaceLandmarks.prototype.shiftByPoint = function (pt) {
return this.shift(pt.x, pt.y);
return this.shiftBy(pt.x, pt.y);
};
/**
* Aligns the face landmarks after face detection from the relative positions of the faces
......@@ -54,9 +65,9 @@ var FaceLandmarks = /** @class */ (function () {
FaceLandmarks.prototype.align = function (detection) {
if (detection) {
var box = detection instanceof FaceDetection_1.FaceDetection
? detection.getBox().floor()
? detection.box.floor()
: detection;
return this.shift(box.x, box.y).align();
return this.shiftBy(box.x, box.y).align();
}
var centers = this.getRefPointsForAlignment();
var leftEyeCenter = centers[0], rightEyeCenter = centers[1], mouthCenter = centers[2];
......@@ -67,7 +78,7 @@ var FaceLandmarks = /** @class */ (function () {
// TODO: pad in case rectangle is out of image bounds
var x = Math.floor(Math.max(0, refPoint.x - (relX * size)));
var y = Math.floor(Math.max(0, refPoint.y - (relY * size)));
return new tfjs_image_recognition_base_1.Rect(x, y, Math.min(size, this._imageWidth + x), Math.min(size, this._imageHeight + y));
return new tfjs_image_recognition_base_1.Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));
};
FaceLandmarks.prototype.getRefPointsForAlignment = function () {
throw new Error('getRefPointsForAlignment not implemented by base class');
......
build/commonjs/classes/FaceLandmarks.js.map
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\ No newline at end of file
build/commonjs/classes/FaceLandmarks5.js
......@@ -9,7 +9,7 @@ var FaceLandmarks5 = /** @class */ (function (_super) {
return _super !== null && _super.apply(this, arguments) || this;
}
FaceLandmarks5.prototype.getRefPointsForAlignment = function () {
var pts = this.getPositions();
var pts = this.positions;
return [
pts[0],
pts[1],
......
build/commonjs/classes/FaceLandmarks5.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/classes/FaceLandmarks68.js
......@@ -9,25 +9,25 @@ var FaceLandmarks68 = /** @class */ (function (_super) {
return _super !== null && _super.apply(this, arguments) || this;
}
FaceLandmarks68.prototype.getJawOutline = function () {
return this._faceLandmarks.slice(0, 17);
return this.positions.slice(0, 17);
};
FaceLandmarks68.prototype.getLeftEyeBrow = function () {
return this._faceLandmarks.slice(17, 22);
return this.positions.slice(17, 22);
};
FaceLandmarks68.prototype.getRightEyeBrow = function () {
return this._faceLandmarks.slice(22, 27);
return this.positions.slice(22, 27);
};
FaceLandmarks68.prototype.getNose = function () {
return this._faceLandmarks.slice(27, 36);
return this.positions.slice(27, 36);
};
FaceLandmarks68.prototype.getLeftEye = function () {
return this._faceLandmarks.slice(36, 42);
return this.positions.slice(36, 42);
};
FaceLandmarks68.prototype.getRightEye = function () {
return this._faceLandmarks.slice(42, 48);
return this.positions.slice(42, 48);
};
FaceLandmarks68.prototype.getMouth = function () {
return this._faceLandmarks.slice(48, 68);
return this.positions.slice(48, 68);
};
FaceLandmarks68.prototype.getRefPointsForAlignment = function () {
return [
......
build/commonjs/classes/FaceLandmarks68.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/classes/FaceMatch.d.ts 0 → 100644
export interface IFaceMatch {
label: string;
distance: number;
}
export declare class FaceMatch implements IFaceMatch {
private _label;
private _distance;
constructor(label: string, distance: number);
readonly label: string;
readonly distance: number;
toString(withDistance?: boolean): string;
}
build/commonjs/classes/FaceMatch.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceMatch = /** @class */ (function () {
function FaceMatch(label, distance) {
this._label = label;
this._distance = distance;
}
Object.defineProperty(FaceMatch.prototype, "label", {
get: function () { return this._label; },
enumerable: true,
configurable: true
});
Object.defineProperty(FaceMatch.prototype, "distance", {
get: function () { return this._distance; },
enumerable: true,
configurable: true
});
FaceMatch.prototype.toString = function (withDistance) {
if (withDistance === void 0) { withDistance = true; }
return "" + this.label + (withDistance ? " (" + tfjs_image_recognition_base_1.round(this.distance) + ")" : '');
};
return FaceMatch;
}());
exports.FaceMatch = FaceMatch;
//# sourceMappingURL=FaceMatch.js.map
\ No newline at end of file
build/commonjs/classes/FaceMatch.js.map 0 → 100644
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\ No newline at end of file
build/commonjs/classes/FullFaceDescription.d.ts
import { FaceDetection } from './FaceDetection';
import { FaceDetectionWithLandmarks, IFaceDetectionWithLandmarks } from './FaceDetectionWithLandmarks';
import { FaceLandmarks } from './FaceLandmarks';
export declare class FullFaceDescription {
private _detection;
private _landmarks;
import { FaceLandmarks68 } from './FaceLandmarks68';
export interface IFullFaceDescription<TFaceLandmarks extends FaceLandmarks = FaceLandmarks68> extends IFaceDetectionWithLandmarks<TFaceLandmarks> {
detection: FaceDetection;
landmarks: TFaceLandmarks;
descriptor: Float32Array;
}
export declare class FullFaceDescription<TFaceLandmarks extends FaceLandmarks = FaceLandmarks68> extends FaceDetectionWithLandmarks<TFaceLandmarks> implements IFullFaceDescription<TFaceLandmarks> {
private _descriptor;
constructor(_detection: FaceDetection, _landmarks: FaceLandmarks, _descriptor: Float32Array);
readonly detection: FaceDetection;
readonly landmarks: FaceLandmarks;
constructor(detection: FaceDetection, unshiftedLandmarks: TFaceLandmarks, descriptor: Float32Array);
readonly descriptor: Float32Array;
forSize(width: number, height: number): FullFaceDescription;
forSize(width: number, height: number): FullFaceDescription<TFaceLandmarks>;
}
build/commonjs/classes/FullFaceDescription.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var FullFaceDescription = /** @class */ (function () {
function FullFaceDescription(_detection, _landmarks, _descriptor) {
this._detection = _detection;
this._landmarks = _landmarks;
this._descriptor = _descriptor;
var tslib_1 = require("tslib");
var FaceDetectionWithLandmarks_1 = require("./FaceDetectionWithLandmarks");
var FullFaceDescription = /** @class */ (function (_super) {
tslib_1.__extends(FullFaceDescription, _super);
function FullFaceDescription(detection, unshiftedLandmarks, descriptor) {
var _this = _super.call(this, detection, unshiftedLandmarks) || this;
_this._descriptor = descriptor;
return _this;
}
Object.defineProperty(FullFaceDescription.prototype, "detection", {
get: function () {
return this._detection;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FullFaceDescription.prototype, "landmarks", {
get: function () {
return this._landmarks;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FullFaceDescription.prototype, "descriptor", {
get: function () {
return this._descriptor;
......@@ -28,9 +17,10 @@ var FullFaceDescription = /** @class */ (function () {
configurable: true
});
FullFaceDescription.prototype.forSize = function (width, height) {
return new FullFaceDescription(this._detection.forSize(width, height), this._landmarks.forSize(width, height), this._descriptor);
var _a = _super.prototype.forSize.call(this, width, height), detection = _a.detection, landmarks = _a.landmarks;
return new FullFaceDescription(detection, landmarks, this.descriptor);
};
return FullFaceDescription;
}());
}(FaceDetectionWithLandmarks_1.FaceDetectionWithLandmarks));
exports.FullFaceDescription = FullFaceDescription;
//# sourceMappingURL=FullFaceDescription.js.map
\ No newline at end of file
build/commonjs/classes/FullFaceDescription.js.map
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\ No newline at end of file
{"version":3,"file":"FullFaceDescription.js","sourceRoot":"","sources":["../../../src/classes/FullFaceDescription.ts"],"names":[],"mappings":";;;AACA,2EAAuG;AAYvG;IACU,+CAA0C;IAKlD,6BACE,SAAwB,EACxB,kBAAkC,EAClC,UAAwB;QAH1B,YAKE,kBAAM,SAAS,EAAE,kBAAkB,CAAC,SAErC;QADC,KAAI,CAAC,WAAW,GAAG,UAAU,CAAA;;IAC/B,CAAC;IAED,sBAAW,2CAAU;aAArB;YACE,OAAO,IAAI,CAAC,WAAW,CAAA;QACzB,CAAC;;;OAAA;IAEM,qCAAO,GAAd,UAAe,KAAa,EAAE,MAAc;QACpC,IAAA,uDAAuD,EAArD,wBAAS,EAAE,wBAAS,CAAiC;QAC7D,OAAO,IAAI,mBAAmB,CAAiB,SAAS,EAAE,SAAS,EAAE,IAAI,CAAC,UAAU,CAAC,CAAA;IACvF,CAAC;IACH,0BAAC;AAAD,CAAC,AAvBD,CACU,uDAA0B,GAsBnC;AAvBY,kDAAmB"}
\ No newline at end of file
build/commonjs/classes/LabeledFaceDescriptors.d.ts 0 → 100644
export declare class LabeledFaceDescriptors {
private _label;
private _descriptors;
constructor(label: string, descriptors: Float32Array[]);
readonly label: string;
readonly descriptors: Float32Array[];
}
build/commonjs/classes/LabeledFaceDescriptors.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var LabeledFaceDescriptors = /** @class */ (function () {
function LabeledFaceDescriptors(label, descriptors) {
if (!(typeof label === 'string')) {
throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');
}
if (!Array.isArray(descriptors) || descriptors.some(function (desc) { return !(desc instanceof Float32Array); })) {
throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');
}
this._label = label;
this._descriptors = descriptors;
}
Object.defineProperty(LabeledFaceDescriptors.prototype, "label", {
get: function () { return this._label; },
enumerable: true,
configurable: true
});
Object.defineProperty(LabeledFaceDescriptors.prototype, "descriptors", {
get: function () { return this._descriptors; },
enumerable: true,
configurable: true
});
return LabeledFaceDescriptors;
}());
exports.LabeledFaceDescriptors = LabeledFaceDescriptors;
//# sourceMappingURL=LabeledFaceDescriptors.js.map
\ No newline at end of file
build/commonjs/classes/LabeledFaceDescriptors.js.map 0 → 100644
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\ No newline at end of file
build/commonjs/classes/index.d.ts
export * from './FaceDetection';
export * from './FaceDetectionWithLandmarks';
export * from './FaceLandmarks';
export * from './FaceLandmarks5';
export * from './FaceLandmarks68';
export * from './FaceMatch';
export * from './FullFaceDescription';
export * from './LabeledFaceDescriptors';
build/commonjs/classes/index.js
......@@ -2,8 +2,11 @@
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
tslib_1.__exportStar(require("./FaceDetection"), exports);
tslib_1.__exportStar(require("./FaceDetectionWithLandmarks"), exports);
tslib_1.__exportStar(require("./FaceLandmarks"), exports);
tslib_1.__exportStar(require("./FaceLandmarks5"), exports);
tslib_1.__exportStar(require("./FaceLandmarks68"), exports);
tslib_1.__exportStar(require("./FaceMatch"), exports);
tslib_1.__exportStar(require("./FullFaceDescription"), exports);
tslib_1.__exportStar(require("./LabeledFaceDescriptors"), exports);
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/commonjs/classes/index.js.map
{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/classes/index.ts"],"names":[],"mappings":";;;AAAA,0DAAgC;AAChC,0DAAgC;AAChC,2DAAiC;AACjC,4DAAkC;AAClC,gEAAsC"}
\ No newline at end of file
{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/classes/index.ts"],"names":[],"mappings":";;;AAAA,0DAAgC;AAChC,uEAA6C;AAC7C,0DAAgC;AAChC,2DAAiC;AACjC,4DAAkC;AAClC,sDAA4B;AAC5B,gEAAsC;AACtC,mEAAyC"}
\ No newline at end of file
build/commonjs/dom/drawLandmarks.js
......@@ -29,7 +29,7 @@ function drawLandmarks(canvasArg, faceLandmarks, options) {
// else draw points
var ptOffset = lineWidth / 2;
ctx.fillStyle = color;
landmarks.getPositions().forEach(function (pt) { return ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth); });
landmarks.positions.forEach(function (pt) { return ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth); });
});
}
exports.drawLandmarks = drawLandmarks;
......
build/commonjs/dom/drawLandmarks.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/dom/extractFaceTensors.js
......@@ -23,7 +23,7 @@ function extractFaceTensors(imageTensor, detections) {
return [2 /*return*/, tf.tidy(function () {
var _a = imageTensor.shape.slice(tfjs_image_recognition_base_1.isTensor4D(imageTensor) ? 1 : 0), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];
var boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
? det.forSize(imgWidth, imgHeight).getBox()
? det.forSize(imgWidth, imgHeight).box
: det; })
.map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); });
var faceTensors = boxes.map(function (_a) {
......
build/commonjs/dom/extractFaceTensors.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/dom/extractFaces.js
......@@ -38,7 +38,7 @@ function extractFaces(input, detections) {
case 5:
ctx = tfjs_image_recognition_base_1.getContext2dOrThrow(canvas);
boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
? det.forSize(canvas.width, canvas.height).getBox().floor()
? det.forSize(canvas.width, canvas.height).box.floor()
: det; })
.map(function (box) { return box.clipAtImageBorders(canvas.width, canvas.height); });
return [2 /*return*/, boxes.map(function (_a) {
......
build/commonjs/dom/extractFaces.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/faceLandmarkNet/FaceLandmark68Net.js
......@@ -25,12 +25,12 @@ function denseBlock(x, denseBlockParams, isFirstLayer) {
var FaceLandmark68Net = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmark68Net, _super);
function FaceLandmark68Net() {
return _super.call(this, 'FaceLandmark68LargeNet') || this;
return _super.call(this, 'FaceLandmark68Net') || this;
}
FaceLandmark68Net.prototype.runNet = function (input) {
var params = this.params;
if (!params) {
throw new Error('FaceLandmark68LargeNet - load model before inference');
throw new Error('FaceLandmark68Net - load model before inference');
}
return tf.tidy(function () {
var batchTensor = input.toBatchTensor(112, true);
......
build/commonjs/faceLandmarkNet/FaceLandmark68Net.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/faceLandmarkNet/FaceLandmark68NetBase.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { NetInput, NeuralNetwork, TNetInput, Dimensions } from 'tfjs-image-recognition-base';
import { IDimensions, NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
export declare class FaceLandmark68NetBase<NetParams> extends NeuralNetwork<NetParams> {
private __name;
constructor(_name: string);
runNet(_: NetInput): tf.Tensor2D;
postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: Dimensions[]): tf.Tensor2D;
postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D;
forwardInput(input: NetInput): tf.Tensor2D;
forward(input: TNetInput): Promise<tf.Tensor2D>;
detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;
......
build/commonjs/faceLandmarkNet/FaceLandmark68NetBase.js.map
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\ No newline at end of file
build/commonjs/faceLandmarkNet/index.d.ts
......@@ -4,4 +4,3 @@ export * from './FaceLandmark68TinyNet';
export declare class FaceLandmarkNet extends FaceLandmark68Net {
}
export declare function createFaceLandmarkNet(weights: Float32Array): FaceLandmarkNet;
export declare function faceLandmarkNet(weights: Float32Array): FaceLandmarkNet;
build/commonjs/faceLandmarkNet/index.js
......@@ -18,9 +18,4 @@ function createFaceLandmarkNet(weights) {
return net;
}
exports.createFaceLandmarkNet = createFaceLandmarkNet;
function faceLandmarkNet(weights) {
console.warn('faceLandmarkNet(weights: Float32Array) will be deprecated in future, use createFaceLandmarkNet instead');
return createFaceLandmarkNet(weights);
}
exports.faceLandmarkNet = faceLandmarkNet;
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/commonjs/faceLandmarkNet/index.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/faceRecognitionNet/index.d.ts
import { FaceRecognitionNet } from './FaceRecognitionNet';
export * from './FaceRecognitionNet';
export declare function createFaceRecognitionNet(weights: Float32Array): FaceRecognitionNet;
export declare function faceRecognitionNet(weights: Float32Array): FaceRecognitionNet;
build/commonjs/faceRecognitionNet/index.js
......@@ -9,9 +9,4 @@ function createFaceRecognitionNet(weights) {
return net;
}
exports.createFaceRecognitionNet = createFaceRecognitionNet;
function faceRecognitionNet(weights) {
console.warn('faceRecognitionNet(weights: Float32Array) will be deprecated in future, use createFaceRecognitionNet instead');
return createFaceRecognitionNet(weights);
}
exports.faceRecognitionNet = faceRecognitionNet;
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/commonjs/faceRecognitionNet/index.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/globalApi/ComposableTask.d.ts 0 → 100644
export declare class ComposableTask<T> {
then(onfulfilled: (value: T) => T | PromiseLike<T>): Promise<T>;
run(): Promise<T>;
}
build/commonjs/globalApi/ComposableTask.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var ComposableTask = /** @class */ (function () {
function ComposableTask() {
}
ComposableTask.prototype.then = function (onfulfilled) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = onfulfilled;
return [4 /*yield*/, this.run()];
case 1: return [2 /*return*/, _a.apply(void 0, [_b.sent()])];
}
});
});
};
ComposableTask.prototype.run = function () {
return tslib_1.__awaiter(this, void 0, void 0, function () {
return tslib_1.__generator(this, function (_a) {
throw new Error('ComposableTask - run is not implemented');
});
});
};
return ComposableTask;
}());
exports.ComposableTask = ComposableTask;
//# sourceMappingURL=ComposableTask.js.map
\ No newline at end of file
build/commonjs/globalApi/ComposableTask.js.map 0 → 100644
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\ No newline at end of file
build/commonjs/globalApi/ComputeFaceDescriptorsTasks.d.ts 0 → 100644
import { TNetInput } from 'tfjs-image-recognition-base';
import { FaceDetectionWithLandmarks } from '../classes/FaceDetectionWithLandmarks';
import { FullFaceDescription } from '../classes/FullFaceDescription';
import { ComposableTask } from './ComposableTask';
export declare class ComputeFaceDescriptorsTaskBase<TReturn, DetectFaceLandmarksReturnType> extends ComposableTask<TReturn> {
protected detectFaceLandmarksTask: ComposableTask<DetectFaceLandmarksReturnType> | Promise<DetectFaceLandmarksReturnType>;
protected input: TNetInput;
constructor(detectFaceLandmarksTask: ComposableTask<DetectFaceLandmarksReturnType> | Promise<DetectFaceLandmarksReturnType>, input: TNetInput);
}
export declare class ComputeAllFaceDescriptorsTask extends ComputeFaceDescriptorsTaskBase<FullFaceDescription[], FaceDetectionWithLandmarks[]> {
run(): Promise<FullFaceDescription[]>;
}
export declare class ComputeSingleFaceDescriptorTask extends ComputeFaceDescriptorsTaskBase<FullFaceDescription | undefined, FaceDetectionWithLandmarks | undefined> {
run(): Promise<FullFaceDescription | undefined>;
}
build/commonjs/globalApi/ComputeFaceDescriptorsTasks.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var FullFaceDescription_1 = require("../classes/FullFaceDescription");
var dom_1 = require("../dom");
var ComposableTask_1 = require("./ComposableTask");
var nets_1 = require("./nets");
var ComputeFaceDescriptorsTaskBase = /** @class */ (function (_super) {
tslib_1.__extends(ComputeFaceDescriptorsTaskBase, _super);
function ComputeFaceDescriptorsTaskBase(detectFaceLandmarksTask, input) {
var _this = _super.call(this) || this;
_this.detectFaceLandmarksTask = detectFaceLandmarksTask;
_this.input = input;
return _this;
}
return ComputeFaceDescriptorsTaskBase;
}(ComposableTask_1.ComposableTask));
exports.ComputeFaceDescriptorsTaskBase = ComputeFaceDescriptorsTaskBase;
var ComputeAllFaceDescriptorsTask = /** @class */ (function (_super) {
tslib_1.__extends(ComputeAllFaceDescriptorsTask, _super);
function ComputeAllFaceDescriptorsTask() {
return _super !== null && _super.apply(this, arguments) || this;
}
ComputeAllFaceDescriptorsTask.prototype.run = function () {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _this = this;
var facesWithLandmarks, alignedFaceCanvases;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, this.detectFaceLandmarksTask];
case 1:
facesWithLandmarks = _a.sent();
return [4 /*yield*/, dom_1.extractFaces(this.input, facesWithLandmarks.map(function (_a) {
var landmarks = _a.landmarks;
return landmarks.align();
}))];
case 2:
alignedFaceCanvases = _a.sent();
return [4 /*yield*/, Promise.all(facesWithLandmarks.map(function (_a, i) {
var detection = _a.detection, landmarks = _a.landmarks;
return tslib_1.__awaiter(_this, void 0, void 0, function () {
var descriptor;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, nets_1.nets.faceRecognitionNet.computeFaceDescriptor(alignedFaceCanvases[i])];
case 1:
descriptor = _b.sent();
return [2 /*return*/, new FullFaceDescription_1.FullFaceDescription(detection, landmarks, descriptor)];
}
});
});
}))];
case 3: return [2 /*return*/, _a.sent()];
}
});
});
};
return ComputeAllFaceDescriptorsTask;
}(ComputeFaceDescriptorsTaskBase));
exports.ComputeAllFaceDescriptorsTask = ComputeAllFaceDescriptorsTask;
var ComputeSingleFaceDescriptorTask = /** @class */ (function (_super) {
tslib_1.__extends(ComputeSingleFaceDescriptorTask, _super);
function ComputeSingleFaceDescriptorTask() {
return _super !== null && _super.apply(this, arguments) || this;
}
ComputeSingleFaceDescriptorTask.prototype.run = function () {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var detectionWithLandmarks, detection, landmarks, alignedRect, alignedFaceCanvas, descriptor;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, this.detectFaceLandmarksTask];
case 1:
detectionWithLandmarks = _a.sent();
if (!detectionWithLandmarks) {
return [2 /*return*/];
}
detection = detectionWithLandmarks.detection, landmarks = detectionWithLandmarks.landmarks, alignedRect = detectionWithLandmarks.alignedRect;
return [4 /*yield*/, dom_1.extractFaces(this.input, [alignedRect])];
case 2:
alignedFaceCanvas = (_a.sent())[0];
return [4 /*yield*/, nets_1.nets.faceRecognitionNet.computeFaceDescriptor(alignedFaceCanvas)];
case 3:
descriptor = _a.sent();
return [2 /*return*/, new FullFaceDescription_1.FullFaceDescription(detection, landmarks, descriptor)];
}
});
});
};
return ComputeSingleFaceDescriptorTask;
}(ComputeFaceDescriptorsTaskBase));
exports.ComputeSingleFaceDescriptorTask = ComputeSingleFaceDescriptorTask;
//# sourceMappingURL=ComputeFaceDescriptorsTasks.js.map
\ No newline at end of file
build/commonjs/globalApi/ComputeFaceDescriptorsTasks.js.map 0 → 100644
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\ No newline at end of file
build/commonjs/globalApi/DetectFaceLandmarksTasks.d.ts 0 → 100644
import { TNetInput } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection';
import { FaceDetectionWithLandmarks } from '../classes/FaceDetectionWithLandmarks';
import { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';
import { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';
import { ComposableTask } from './ComposableTask';
import { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';
export declare class DetectFaceLandmarksTaskBase<ReturnType, DetectFacesReturnType> extends ComposableTask<ReturnType> {
protected detectFacesTask: ComposableTask<DetectFacesReturnType> | Promise<DetectFacesReturnType>;
protected input: TNetInput;
protected useTinyLandmarkNet: boolean;
constructor(detectFacesTask: ComposableTask<DetectFacesReturnType> | Promise<DetectFacesReturnType>, input: TNetInput, useTinyLandmarkNet: boolean);
protected readonly landmarkNet: FaceLandmark68Net | FaceLandmark68TinyNet;
}
export declare class DetectAllFaceLandmarksTask extends DetectFaceLandmarksTaskBase<FaceDetectionWithLandmarks[], FaceDetection[]> {
run(): Promise<FaceDetectionWithLandmarks[]>;
withFaceDescriptors(): ComputeAllFaceDescriptorsTask;
}
export declare class DetectSingleFaceLandmarksTask extends DetectFaceLandmarksTaskBase<FaceDetectionWithLandmarks | undefined, FaceDetection | undefined> {
run(): Promise<FaceDetectionWithLandmarks | undefined>;
withFaceDescriptor(): ComputeSingleFaceDescriptorTask;
}
build/commonjs/globalApi/DetectFaceLandmarksTasks.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var FaceDetectionWithLandmarks_1 = require("../classes/FaceDetectionWithLandmarks");
var dom_1 = require("../dom");
var ComposableTask_1 = require("./ComposableTask");
var ComputeFaceDescriptorsTasks_1 = require("./ComputeFaceDescriptorsTasks");
var nets_1 = require("./nets");
var DetectFaceLandmarksTaskBase = /** @class */ (function (_super) {
tslib_1.__extends(DetectFaceLandmarksTaskBase, _super);
function DetectFaceLandmarksTaskBase(detectFacesTask, input, useTinyLandmarkNet) {
var _this = _super.call(this) || this;
_this.detectFacesTask = detectFacesTask;
_this.input = input;
_this.useTinyLandmarkNet = useTinyLandmarkNet;
return _this;
}
Object.defineProperty(DetectFaceLandmarksTaskBase.prototype, "landmarkNet", {
get: function () {
return this.useTinyLandmarkNet
? nets_1.nets.faceLandmark68TinyNet
: nets_1.nets.faceLandmark68Net;
},
enumerable: true,
configurable: true
});
return DetectFaceLandmarksTaskBase;
}(ComposableTask_1.ComposableTask));
exports.DetectFaceLandmarksTaskBase = DetectFaceLandmarksTaskBase;
var DetectAllFaceLandmarksTask = /** @class */ (function (_super) {
tslib_1.__extends(DetectAllFaceLandmarksTask, _super);
function DetectAllFaceLandmarksTask() {
return _super !== null && _super.apply(this, arguments) || this;
}
DetectAllFaceLandmarksTask.prototype.run = function () {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _this = this;
var detections, faceCanvases, faceLandmarksByFace;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, this.detectFacesTask];
case 1:
detections = _a.sent();
return [4 /*yield*/, dom_1.extractFaces(this.input, detections)];
case 2:
faceCanvases = _a.sent();
return [4 /*yield*/, Promise.all(faceCanvases.map(function (canvas) { return _this.landmarkNet.detectLandmarks(canvas); }))];
case 3:
faceLandmarksByFace = _a.sent();
return [2 /*return*/, detections.map(function (detection, i) {
return new FaceDetectionWithLandmarks_1.FaceDetectionWithLandmarks(detection, faceLandmarksByFace[i]);
})];
}
});
});
};
DetectAllFaceLandmarksTask.prototype.withFaceDescriptors = function () {
return new ComputeFaceDescriptorsTasks_1.ComputeAllFaceDescriptorsTask(this, this.input);
};
return DetectAllFaceLandmarksTask;
}(DetectFaceLandmarksTaskBase));
exports.DetectAllFaceLandmarksTask = DetectAllFaceLandmarksTask;
var DetectSingleFaceLandmarksTask = /** @class */ (function (_super) {
tslib_1.__extends(DetectSingleFaceLandmarksTask, _super);
function DetectSingleFaceLandmarksTask() {
return _super !== null && _super.apply(this, arguments) || this;
}
DetectSingleFaceLandmarksTask.prototype.run = function () {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var detection, faceCanvas, _a, _b;
return tslib_1.__generator(this, function (_c) {
switch (_c.label) {
case 0: return [4 /*yield*/, this.detectFacesTask];
case 1:
detection = _c.sent();
if (!detection) {
return [2 /*return*/];
}
return [4 /*yield*/, dom_1.extractFaces(this.input, [detection])];
case 2:
faceCanvas = (_c.sent())[0];
_a = FaceDetectionWithLandmarks_1.FaceDetectionWithLandmarks.bind;
_b = [void 0, detection];
return [4 /*yield*/, this.landmarkNet.detectLandmarks(faceCanvas)];
case 3: return [2 /*return*/, new (_a.apply(FaceDetectionWithLandmarks_1.FaceDetectionWithLandmarks, _b.concat([_c.sent()])))()];
}
});
});
};
DetectSingleFaceLandmarksTask.prototype.withFaceDescriptor = function () {
return new ComputeFaceDescriptorsTasks_1.ComputeSingleFaceDescriptorTask(this, this.input);
};
return DetectSingleFaceLandmarksTask;
}(DetectFaceLandmarksTaskBase));
exports.DetectSingleFaceLandmarksTask = DetectSingleFaceLandmarksTask;
//# sourceMappingURL=DetectFaceLandmarksTasks.js.map
\ No newline at end of file
build/commonjs/globalApi/DetectFaceLandmarksTasks.js.map 0 → 100644
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\ No newline at end of file
build/commonjs/globalApi/DetectFacesTasks.d.ts 0 → 100644
import { TNetInput } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection';
import { ComposableTask } from './ComposableTask';
import { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';
import { FaceDetectionOptions } from './types';
export declare function detectSingleFace(input: TNetInput, options?: FaceDetectionOptions): DetectSingleFaceTask;
export declare function detectAllFaces(input: TNetInput, options?: FaceDetectionOptions): DetectAllFacesTask;
export declare class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {
protected input: TNetInput;
protected options: FaceDetectionOptions;
constructor(input: TNetInput, options?: FaceDetectionOptions);
}
export declare class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {
run(): Promise<FaceDetection[]>;
withFaceLandmarks(useTinyLandmarkNet?: boolean): DetectAllFaceLandmarksTask;
}
export declare class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {
run(): Promise<FaceDetection | undefined>;
withFaceLandmarks(useTinyLandmarkNet?: boolean): DetectSingleFaceLandmarksTask;
}
build/commonjs/globalApi/DetectFacesTasks.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
var MtcnnOptions_1 = require("../mtcnn/MtcnnOptions");
var SsdMobilenetv1Options_1 = require("../ssdMobilenetv1/SsdMobilenetv1Options");
var TinyFaceDetectorOptions_1 = require("../tinyFaceDetector/TinyFaceDetectorOptions");
var ComposableTask_1 = require("./ComposableTask");
var DetectFaceLandmarksTasks_1 = require("./DetectFaceLandmarksTasks");
var nets_1 = require("./nets");
function detectSingleFace(input, options) {
if (options === void 0) { options = new SsdMobilenetv1Options_1.SsdMobilenetv1Options(); }
return new DetectSingleFaceTask(input, options);
}
exports.detectSingleFace = detectSingleFace;
function detectAllFaces(input, options) {
if (options === void 0) { options = new SsdMobilenetv1Options_1.SsdMobilenetv1Options(); }
return new DetectAllFacesTask(input, options);
}
exports.detectAllFaces = detectAllFaces;
var DetectFacesTaskBase = /** @class */ (function (_super) {
tslib_1.__extends(DetectFacesTaskBase, _super);
function DetectFacesTaskBase(input, options) {
if (options === void 0) { options = new SsdMobilenetv1Options_1.SsdMobilenetv1Options(); }
var _this = _super.call(this) || this;
_this.input = input;
_this.options = options;
return _this;
}
return DetectFacesTaskBase;
}(ComposableTask_1.ComposableTask));
exports.DetectFacesTaskBase = DetectFacesTaskBase;
var DetectAllFacesTask = /** @class */ (function (_super) {
tslib_1.__extends(DetectAllFacesTask, _super);
function DetectAllFacesTask() {
return _super !== null && _super.apply(this, arguments) || this;
}
DetectAllFacesTask.prototype.run = function () {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a, input, options, faceDetectionFunction;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = this, input = _a.input, options = _a.options;
if (!(options instanceof MtcnnOptions_1.MtcnnOptions)) return [3 /*break*/, 2];
return [4 /*yield*/, nets_1.nets.mtcnn.forward(input, options)];
case 1: return [2 /*return*/, (_b.sent())
.map(function (result) { return result.faceDetection; })];
case 2:
faceDetectionFunction = options instanceof TinyFaceDetectorOptions_1.TinyFaceDetectorOptions
? function (input) { return nets_1.nets.tinyFaceDetector.locateFaces(input, options); }
: (options instanceof SsdMobilenetv1Options_1.SsdMobilenetv1Options
? function (input) { return nets_1.nets.ssdMobilenetv1.locateFaces(input, options); }
: (options instanceof tfjs_tiny_yolov2_1.TinyYolov2Options
? function (input) { return nets_1.nets.tinyYolov2.locateFaces(input, options); }
: null));
if (!faceDetectionFunction) {
throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options');
}
return [2 /*return*/, faceDetectionFunction(input)];
}
});
});
};
DetectAllFacesTask.prototype.withFaceLandmarks = function (useTinyLandmarkNet) {
if (useTinyLandmarkNet === void 0) { useTinyLandmarkNet = false; }
return new DetectFaceLandmarksTasks_1.DetectAllFaceLandmarksTask(this, this.input, useTinyLandmarkNet);
};
return DetectAllFacesTask;
}(DetectFacesTaskBase));
exports.DetectAllFacesTask = DetectAllFacesTask;
var DetectSingleFaceTask = /** @class */ (function (_super) {
tslib_1.__extends(DetectSingleFaceTask, _super);
function DetectSingleFaceTask() {
return _super !== null && _super.apply(this, arguments) || this;
}
DetectSingleFaceTask.prototype.run = function () {
return tslib_1.__awaiter(this, void 0, void 0, function () {
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, new DetectAllFacesTask(this.input, this.options)];
case 1: return [2 /*return*/, (_a.sent())
.sort(function (f1, f2) { return f1.score - f2.score; })[0]];
}
});
});
};
DetectSingleFaceTask.prototype.withFaceLandmarks = function (useTinyLandmarkNet) {
if (useTinyLandmarkNet === void 0) { useTinyLandmarkNet = false; }
return new DetectFaceLandmarksTasks_1.DetectSingleFaceLandmarksTask(this, this.input, useTinyLandmarkNet);
};
return DetectSingleFaceTask;
}(DetectFacesTaskBase));
exports.DetectSingleFaceTask = DetectSingleFaceTask;
//# sourceMappingURL=DetectFacesTasks.js.map
\ No newline at end of file
build/commonjs/globalApi/DetectFacesTasks.js.map 0 → 100644
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\ No newline at end of file
build/commonjs/globalApi/FaceMatcher.d.ts 0 → 100644
import { FaceMatch } from '../classes/FaceMatch';
import { FullFaceDescription } from '../classes/FullFaceDescription';
import { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';
export declare class FaceMatcher {
private _labeledDescriptors;
private _distanceThreshold;
constructor(inputs: LabeledFaceDescriptors | FullFaceDescription | Float32Array | Array<LabeledFaceDescriptors | FullFaceDescription | Float32Array>, distanceThreshold?: number);
readonly labeledDescriptors: LabeledFaceDescriptors[];
readonly distanceThreshold: number;
computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number;
matchDescriptor(queryDescriptor: Float32Array): FaceMatch;
findBestMatch(queryDescriptor: Float32Array): FaceMatch;
}
build/commonjs/globalApi/FaceMatcher.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var FaceMatch_1 = require("../classes/FaceMatch");
var FullFaceDescription_1 = require("../classes/FullFaceDescription");
var LabeledFaceDescriptors_1 = require("../classes/LabeledFaceDescriptors");
var euclideanDistance_1 = require("../euclideanDistance");
var FaceMatcher = /** @class */ (function () {
function FaceMatcher(inputs, distanceThreshold) {
if (distanceThreshold === void 0) { distanceThreshold = 0.6; }
this._distanceThreshold = distanceThreshold;
var inputArray = Array.isArray(inputs) ? inputs : [inputs];
if (!inputArray.length) {
throw new Error("FaceRecognizer.constructor - expected atleast one input");
}
var count = 1;
var createUniqueLabel = function () { return "person " + count++; };
this._labeledDescriptors = inputArray.map(function (desc) {
if (desc instanceof LabeledFaceDescriptors_1.LabeledFaceDescriptors) {
return desc;
}
if (desc instanceof FullFaceDescription_1.FullFaceDescription) {
return new LabeledFaceDescriptors_1.LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);
}
if (desc instanceof Float32Array) {
return new LabeledFaceDescriptors_1.LabeledFaceDescriptors(createUniqueLabel(), [desc]);
}
throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | FullFaceDescription | Float32Array | Array<LabeledFaceDescriptors | FullFaceDescription | Float32Array>");
});
}
Object.defineProperty(FaceMatcher.prototype, "labeledDescriptors", {
get: function () { return this._labeledDescriptors; },
enumerable: true,
configurable: true
});
Object.defineProperty(FaceMatcher.prototype, "distanceThreshold", {
get: function () { return this._distanceThreshold; },
enumerable: true,
configurable: true
});
FaceMatcher.prototype.computeMeanDistance = function (queryDescriptor, descriptors) {
return descriptors
.map(function (d) { return euclideanDistance_1.euclideanDistance(d, queryDescriptor); })
.reduce(function (d1, d2) { return d1 + d2; }, 0)
/ (descriptors.length || 1);
};
FaceMatcher.prototype.matchDescriptor = function (queryDescriptor) {
var _this = this;
return this.labeledDescriptors
.map(function (_a) {
var descriptors = _a.descriptors, label = _a.label;
return new FaceMatch_1.FaceMatch(label, _this.computeMeanDistance(queryDescriptor, descriptors));
})
.reduce(function (best, curr) { return best.distance < curr.distance ? best : curr; });
};
FaceMatcher.prototype.findBestMatch = function (queryDescriptor) {
var bestMatch = this.matchDescriptor(queryDescriptor);
return bestMatch.distance < this.distanceThreshold
? bestMatch
: new FaceMatch_1.FaceMatch('unknown', bestMatch.distance);
};
return FaceMatcher;
}());
exports.FaceMatcher = FaceMatcher;
//# sourceMappingURL=FaceMatcher.js.map
\ No newline at end of file
build/commonjs/globalApi/FaceMatcher.js.map 0 → 100644
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\ No newline at end of file
build/commonjs/globalApi/allFaces.d.ts 0 → 100644
import { TNetInput } from 'tfjs-image-recognition-base';
import { ITinyYolov2Options } from 'tfjs-tiny-yolov2';
import { FullFaceDescription } from '../classes';
import { IMtcnnOptions } from '../mtcnn/MtcnnOptions';
export declare function allFacesSsdMobilenetv1(input: TNetInput, minConfidence?: number): Promise<FullFaceDescription[]>;
export declare function allFacesTinyYolov2(input: TNetInput, forwardParams?: ITinyYolov2Options): Promise<FullFaceDescription[]>;
export declare function allFacesMtcnn(input: TNetInput, forwardParams?: IMtcnnOptions): Promise<FullFaceDescription[]>;
export declare const allFaces: typeof allFacesSsdMobilenetv1;
build/commonjs/globalApi/allFaces.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
var MtcnnOptions_1 = require("../mtcnn/MtcnnOptions");
var ssdMobilenetv1_1 = require("../ssdMobilenetv1");
var DetectFacesTasks_1 = require("./DetectFacesTasks");
// export allFaces API for backward compatibility
function allFacesSsdMobilenetv1(input, minConfidence) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, DetectFacesTasks_1.detectAllFaces(input, new ssdMobilenetv1_1.SsdMobilenetv1Options(minConfidence ? { minConfidence: minConfidence } : {}))
.withFaceLandmarks()
.withFaceDescriptors()];
case 1: return [2 /*return*/, _a.sent()];
}
});
});
}
exports.allFacesSsdMobilenetv1 = allFacesSsdMobilenetv1;
function allFacesTinyYolov2(input, forwardParams) {
if (forwardParams === void 0) { forwardParams = {}; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, DetectFacesTasks_1.detectAllFaces(input, new tfjs_tiny_yolov2_1.TinyYolov2Options(forwardParams))
.withFaceLandmarks()
.withFaceDescriptors()];
case 1: return [2 /*return*/, _a.sent()];
}
});
});
}
exports.allFacesTinyYolov2 = allFacesTinyYolov2;
function allFacesMtcnn(input, forwardParams) {
if (forwardParams === void 0) { forwardParams = {}; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, DetectFacesTasks_1.detectAllFaces(input, new MtcnnOptions_1.MtcnnOptions(forwardParams))
.withFaceLandmarks()
.withFaceDescriptors()];
case 1: return [2 /*return*/, _a.sent()];
}
});
});
}
exports.allFacesMtcnn = allFacesMtcnn;
exports.allFaces = allFacesSsdMobilenetv1;
//# sourceMappingURL=allFaces.js.map
\ No newline at end of file
build/commonjs/globalApi/allFaces.js.map 0 → 100644
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\ No newline at end of file
build/commonjs/globalApi/index.d.ts 0 → 100644
export * from './allFaces';
export * from './ComposableTask';
export * from './ComputeFaceDescriptorsTasks';
export * from './DetectFacesTasks';
export * from './DetectFaceLandmarksTasks';
export * from './FaceMatcher';
export * from './nets';
export * from './types';
build/commonjs/globalApi/index.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
tslib_1.__exportStar(require("./allFaces"), exports);
tslib_1.__exportStar(require("./ComposableTask"), exports);
tslib_1.__exportStar(require("./ComputeFaceDescriptorsTasks"), exports);
tslib_1.__exportStar(require("./DetectFacesTasks"), exports);
tslib_1.__exportStar(require("./DetectFaceLandmarksTasks"), exports);
tslib_1.__exportStar(require("./FaceMatcher"), exports);
tslib_1.__exportStar(require("./nets"), exports);
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/commonjs/globalApi/index.js.map 0 → 100644
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\ No newline at end of file
build/commonjs/globalApi/nets.d.ts 0 → 100644
import { TNetInput } from 'tfjs-image-recognition-base';
import { ITinyYolov2Options } from 'tfjs-tiny-yolov2';
import { FaceDetection } from '../classes/FaceDetection';
import { FaceDetectionWithLandmarks } from '../classes/FaceDetectionWithLandmarks';
import { FaceLandmarks5 } from '../classes/FaceLandmarks5';
import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
import { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';
import { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';
import { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';
import { Mtcnn } from '../mtcnn/Mtcnn';
import { MtcnnOptions } from '../mtcnn/MtcnnOptions';
import { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';
import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
import { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';
import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
import { TinyYolov2 } from '../tinyYolov2/TinyYolov2';
export declare const nets: {
ssdMobilenetv1: SsdMobilenetv1;
tinyFaceDetector: TinyFaceDetector;
tinyYolov2: TinyYolov2;
mtcnn: Mtcnn;
faceLandmark68Net: FaceLandmark68Net;
faceLandmark68TinyNet: FaceLandmark68TinyNet;
faceRecognitionNet: FaceRecognitionNet;
};
/**
* Attempts to detect all faces in an image using SSD Mobilenetv1 Network.
*
* @param input The input image.
* @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).
* @returns Bounding box of each face with score.
*/
export declare const ssdMobilenetv1: (input: TNetInput, options: SsdMobilenetv1Options) => Promise<FaceDetection[]>;
/**
* Attempts to detect all faces in an image using the Tiny Face Detector.
*
* @param input The input image.
* @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).
* @returns Bounding box of each face with score.
*/
export declare const tinyFaceDetector: (input: TNetInput, options: TinyFaceDetectorOptions) => Promise<FaceDetection[]>;
/**
* Attempts to detect all faces in an image using the Tiny Yolov2 Network.
*
* @param input The input image.
* @param options (optional, default: see TinyYolov2Options constructor for default parameters).
* @returns Bounding box of each face with score.
*/
export declare const tinyYolov2: (input: TNetInput, options: ITinyYolov2Options) => Promise<FaceDetection[]>;
/**
* Attempts to detect all faces in an image and the 5 point face landmarks
* of each detected face using the MTCNN Network.
*
* @param input The input image.
* @param options (optional, default: see MtcnnOptions constructor for default parameters).
* @returns Bounding box of each face with score and 5 point face landmarks.
*/
export declare const mtcnn: (input: TNetInput, options: MtcnnOptions) => Promise<FaceDetectionWithLandmarks<FaceLandmarks5>[]>;
/**
* Detects the 68 point face landmark positions of the face shown in an image.
*
* @param inputs The face image extracted from the bounding box of a face. Can
* also be an array of input images, which will be batch processed.
* @returns 68 point face landmarks or array thereof in case of batch input.
*/
export declare const detectFaceLandmarks: (input: TNetInput) => Promise<FaceLandmarks68 | FaceLandmarks68[]>;
/**
* Detects the 68 point face landmark positions of the face shown in an image
* using a tinier version of the 68 point face landmark model, which is slightly
* faster at inference, but also slightly less accurate.
*
* @param inputs The face image extracted from the bounding box of a face. Can
* also be an array of input images, which will be batch processed.
* @returns 68 point face landmarks or array thereof in case of batch input.
*/
export declare const detectFaceLandmarksTiny: (input: TNetInput) => Promise<FaceLandmarks68 | FaceLandmarks68[]>;
/**
* Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,
* which uniquely represents the features of that persons face. The computed face descriptor can
* be used to measure the similarity between faces, by computing the euclidean distance of two
* face descriptors.
*
* @param inputs The face image extracted from the aligned bounding box of a face. Can
* also be an array of input images, which will be batch processed.
* @returns Face descriptor with 128 entries or array thereof in case of batch input.
*/
export declare const computeFaceDescriptor: (input: TNetInput) => Promise<Float32Array | Float32Array[]>;
export declare const loadSsdMobilenetv1Model: (url: string) => Promise<void>;
export declare const loadTinyFaceDetectorModel: (url: string) => Promise<void>;
export declare const loadMtcnnModel: (url: string) => Promise<void>;
export declare const loadTinyYolov2Model: (url: string) => Promise<void>;
export declare const loadFaceLandmarkModel: (url: string) => Promise<void>;
export declare const loadFaceLandmarkTinyModel: (url: string) => Promise<void>;
export declare const loadFaceRecognitionModel: (url: string) => Promise<void>;
export declare const loadFaceDetectionModel: (url: string) => Promise<void>;
export declare const locateFaces: (input: TNetInput, options: SsdMobilenetv1Options) => Promise<FaceDetection[]>;
export declare const detectLandmarks: (input: TNetInput) => Promise<FaceLandmarks68 | FaceLandmarks68[]>;
build/commonjs/globalApi/nets.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var FaceLandmark68Net_1 = require("../faceLandmarkNet/FaceLandmark68Net");
var FaceLandmark68TinyNet_1 = require("../faceLandmarkNet/FaceLandmark68TinyNet");
var FaceRecognitionNet_1 = require("../faceRecognitionNet/FaceRecognitionNet");
var Mtcnn_1 = require("../mtcnn/Mtcnn");
var SsdMobilenetv1_1 = require("../ssdMobilenetv1/SsdMobilenetv1");
var TinyFaceDetector_1 = require("../tinyFaceDetector/TinyFaceDetector");
var TinyYolov2_1 = require("../tinyYolov2/TinyYolov2");
exports.nets = {
ssdMobilenetv1: new SsdMobilenetv1_1.SsdMobilenetv1(),
tinyFaceDetector: new TinyFaceDetector_1.TinyFaceDetector(),
tinyYolov2: new TinyYolov2_1.TinyYolov2(),
mtcnn: new Mtcnn_1.Mtcnn(),
faceLandmark68Net: new FaceLandmark68Net_1.FaceLandmark68Net(),
faceLandmark68TinyNet: new FaceLandmark68TinyNet_1.FaceLandmark68TinyNet(),
faceRecognitionNet: new FaceRecognitionNet_1.FaceRecognitionNet()
};
/**
* Attempts to detect all faces in an image using SSD Mobilenetv1 Network.
*
* @param input The input image.
* @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).
* @returns Bounding box of each face with score.
*/
exports.ssdMobilenetv1 = function (input, options) {
return exports.nets.ssdMobilenetv1.locateFaces(input, options);
};
/**
* Attempts to detect all faces in an image using the Tiny Face Detector.
*
* @param input The input image.
* @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).
* @returns Bounding box of each face with score.
*/
exports.tinyFaceDetector = function (input, options) {
return exports.nets.tinyFaceDetector.locateFaces(input, options);
};
/**
* Attempts to detect all faces in an image using the Tiny Yolov2 Network.
*
* @param input The input image.
* @param options (optional, default: see TinyYolov2Options constructor for default parameters).
* @returns Bounding box of each face with score.
*/
exports.tinyYolov2 = function (input, options) {
return exports.nets.tinyYolov2.locateFaces(input, options);
};
/**
* Attempts to detect all faces in an image and the 5 point face landmarks
* of each detected face using the MTCNN Network.
*
* @param input The input image.
* @param options (optional, default: see MtcnnOptions constructor for default parameters).
* @returns Bounding box of each face with score and 5 point face landmarks.
*/
exports.mtcnn = function (input, options) {
return exports.nets.mtcnn.forward(input, options);
};
/**
* Detects the 68 point face landmark positions of the face shown in an image.
*
* @param inputs The face image extracted from the bounding box of a face. Can
* also be an array of input images, which will be batch processed.
* @returns 68 point face landmarks or array thereof in case of batch input.
*/
exports.detectFaceLandmarks = function (input) {
return exports.nets.faceLandmark68Net.detectLandmarks(input);
};
/**
* Detects the 68 point face landmark positions of the face shown in an image
* using a tinier version of the 68 point face landmark model, which is slightly
* faster at inference, but also slightly less accurate.
*
* @param inputs The face image extracted from the bounding box of a face. Can
* also be an array of input images, which will be batch processed.
* @returns 68 point face landmarks or array thereof in case of batch input.
*/
exports.detectFaceLandmarksTiny = function (input) {
return exports.nets.faceLandmark68TinyNet.detectLandmarks(input);
};
/**
* Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,
* which uniquely represents the features of that persons face. The computed face descriptor can
* be used to measure the similarity between faces, by computing the euclidean distance of two
* face descriptors.
*
* @param inputs The face image extracted from the aligned bounding box of a face. Can
* also be an array of input images, which will be batch processed.
* @returns Face descriptor with 128 entries or array thereof in case of batch input.
*/
exports.computeFaceDescriptor = function (input) {
return exports.nets.faceRecognitionNet.computeFaceDescriptor(input);
};
exports.loadSsdMobilenetv1Model = function (url) { return exports.nets.ssdMobilenetv1.load(url); };
exports.loadTinyFaceDetectorModel = function (url) { return exports.nets.tinyFaceDetector.load(url); };
exports.loadMtcnnModel = function (url) { return exports.nets.mtcnn.load(url); };
exports.loadTinyYolov2Model = function (url) { return exports.nets.tinyYolov2.load(url); };
exports.loadFaceLandmarkModel = function (url) { return exports.nets.faceLandmark68Net.load(url); };
exports.loadFaceLandmarkTinyModel = function (url) { return exports.nets.faceLandmark68TinyNet.load(url); };
exports.loadFaceRecognitionModel = function (url) { return exports.nets.faceRecognitionNet.load(url); };
// backward compatibility
exports.loadFaceDetectionModel = exports.loadSsdMobilenetv1Model;
exports.locateFaces = exports.ssdMobilenetv1;
exports.detectLandmarks = exports.detectFaceLandmarks;
//# sourceMappingURL=nets.js.map
\ No newline at end of file
build/commonjs/globalApi/nets.js.map 0 → 100644
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\ No newline at end of file
build/commonjs/globalApi/types.d.ts 0 → 100644
import { TNetInput } from 'tfjs-image-recognition-base';
import { TinyYolov2Options } from 'tfjs-tiny-yolov2';
import { FaceDetection } from '../classes/FaceDetection';
import { MtcnnOptions } from '../mtcnn/MtcnnOptions';
import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
export declare type FaceDetectionOptions = TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options;
export declare type FaceDetectionFunction = (input: TNetInput) => Promise<FaceDetection[]>;
build/commonjs/globalApi/types.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
//# sourceMappingURL=types.js.map
\ No newline at end of file
build/commonjs/globalApi/types.js.map 0 → 100644
{"version":3,"file":"types.js","sourceRoot":"","sources":["../../../src/globalApi/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
build/commonjs/index.d.ts
......@@ -4,9 +4,10 @@ export * from 'tfjs-image-recognition-base';
export * from './classes';
export * from './dom';
export * from './euclideanDistance';
export * from './faceDetectionNet';
export * from './faceLandmarkNet';
export * from './faceRecognitionNet';
export * from './globalApi';
export * from './mtcnn';
export * from './ssdMobilenetv1';
export * from './tinyFaceDetector';
export * from './tinyYolov2';
build/commonjs/index.js
......@@ -7,10 +7,11 @@ tslib_1.__exportStar(require("tfjs-image-recognition-base"), exports);
tslib_1.__exportStar(require("./classes"), exports);
tslib_1.__exportStar(require("./dom"), exports);
tslib_1.__exportStar(require("./euclideanDistance"), exports);
tslib_1.__exportStar(require("./faceDetectionNet"), exports);
tslib_1.__exportStar(require("./faceLandmarkNet"), exports);
tslib_1.__exportStar(require("./faceRecognitionNet"), exports);
tslib_1.__exportStar(require("./globalApi"), exports);
tslib_1.__exportStar(require("./mtcnn"), exports);
tslib_1.__exportStar(require("./ssdMobilenetv1"), exports);
tslib_1.__exportStar(require("./tinyFaceDetector"), exports);
tslib_1.__exportStar(require("./tinyYolov2"), exports);
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/commonjs/index.js.map
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\ No newline at end of file
build/commonjs/mtcnn/Mtcnn.d.ts
import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
import { MtcnnForwardParams, MtcnnResult, NetParams } from './types';
import { FaceDetectionWithLandmarks } from '../classes/FaceDetectionWithLandmarks';
import { FaceLandmarks5 } from '../classes/FaceLandmarks5';
import { IMtcnnOptions } from './MtcnnOptions';
import { NetParams } from './types';
export declare class Mtcnn extends NeuralNetwork<NetParams> {
constructor();
forwardInput(input: NetInput, forwardParams?: MtcnnForwardParams): Promise<{
results: MtcnnResult[];
forwardInput(input: NetInput, forwardParams?: IMtcnnOptions): Promise<{
results: FaceDetectionWithLandmarks<FaceLandmarks5>[];
stats: any;
}>;
forward(input: TNetInput, forwardParams?: MtcnnForwardParams): Promise<MtcnnResult[]>;
forwardWithStats(input: TNetInput, forwardParams?: MtcnnForwardParams): Promise<{
results: MtcnnResult[];
forward(input: TNetInput, forwardParams?: IMtcnnOptions): Promise<FaceDetectionWithLandmarks<FaceLandmarks5>[]>;
forwardWithStats(input: TNetInput, forwardParams?: IMtcnnOptions): Promise<{
results: FaceDetectionWithLandmarks<FaceLandmarks5>[];
stats: any;
}>;
protected loadQuantizedParams(uri: string | undefined): Promise<{
......
build/commonjs/mtcnn/Mtcnn.js
......@@ -4,13 +4,14 @@ var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceDetection_1 = require("../classes/FaceDetection");
var FaceDetectionWithLandmarks_1 = require("../classes/FaceDetectionWithLandmarks");
var FaceLandmarks5_1 = require("../classes/FaceLandmarks5");
var bgrToRgbTensor_1 = require("./bgrToRgbTensor");
var config_1 = require("./config");
var extractParams_1 = require("./extractParams");
var getDefaultMtcnnForwardParams_1 = require("./getDefaultMtcnnForwardParams");
var getSizesForScale_1 = require("./getSizesForScale");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
var MtcnnOptions_1 = require("./MtcnnOptions");
var pyramidDown_1 = require("./pyramidDown");
var stage1_1 = require("./stage1");
var stage2_1 = require("./stage2");
......@@ -47,7 +48,7 @@ var Mtcnn = /** @class */ (function (_super) {
return results;
};
_a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
_b = Object.assign({}, getDefaultMtcnnForwardParams_1.getDefaultMtcnnForwardParams(), forwardParams), minFaceSize = _b.minFaceSize, scaleFactor = _b.scaleFactor, maxNumScales = _b.maxNumScales, scoreThresholds = _b.scoreThresholds, scaleSteps = _b.scaleSteps;
_b = new MtcnnOptions_1.MtcnnOptions(forwardParams), minFaceSize = _b.minFaceSize, scaleFactor = _b.scaleFactor, maxNumScales = _b.maxNumScales, scoreThresholds = _b.scoreThresholds, scaleSteps = _b.scaleSteps;
scales = (scaleSteps || pyramidDown_1.pyramidDown(minFaceSize, scaleFactor, [height, width]))
.filter(function (scale) {
var sizes = getSizesForScale_1.getSizesForScale(scale, [height, width]);
......@@ -81,13 +82,10 @@ var Mtcnn = /** @class */ (function (_super) {
case 3:
out3 = _c.sent();
stats.total_stage3 = Date.now() - ts;
results = out3.boxes.map(function (box, idx) { return ({
faceDetection: new FaceDetection_1.FaceDetection(out3.scores[idx], new tfjs_image_recognition_base_1.Rect(box.left / width, box.top / height, box.width / width, box.height / height), {
height: height,
width: width
}),
faceLandmarks: new FaceLandmarks5_1.FaceLandmarks5(out3.points[idx].map(function (pt) { return pt.div(new tfjs_image_recognition_base_1.Point(width, height)); }), { width: width, height: height })
}); });
results = out3.boxes.map(function (box, idx) { return new FaceDetectionWithLandmarks_1.FaceDetectionWithLandmarks(new FaceDetection_1.FaceDetection(out3.scores[idx], new tfjs_image_recognition_base_1.Rect(box.left / width, box.top / height, box.width / width, box.height / height), {
height: height,
width: width
}), new FaceLandmarks5_1.FaceLandmarks5(out3.points[idx].map(function (pt) { return pt.sub(new tfjs_image_recognition_base_1.Point(box.left, box.top)).div(new tfjs_image_recognition_base_1.Point(box.width, box.height)); }), { width: box.width, height: box.height })); });
return [2 /*return*/, onReturn({ results: results, stats: stats })];
}
});
......
build/commonjs/mtcnn/Mtcnn.js.map
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\ No newline at end of file
build/commonjs/mtcnn/MtcnnOptions.d.ts 0 → 100644
export interface IMtcnnOptions {
minFaceSize?: number;
scaleFactor?: number;
maxNumScales?: number;
scoreThresholds?: number[];
scaleSteps?: number[];
}
export declare class MtcnnOptions {
protected _name: string;
private _minFaceSize;
private _scaleFactor;
private _maxNumScales;
private _scoreThresholds;
private _scaleSteps;
constructor({minFaceSize, scaleFactor, maxNumScales, scoreThresholds, scaleSteps}?: IMtcnnOptions);
readonly minFaceSize: number;
readonly scaleFactor: number;
readonly maxNumScales: number;
readonly scoreThresholds: number[];
readonly scaleSteps: number[] | undefined;
}
build/commonjs/mtcnn/MtcnnOptions.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var MtcnnOptions = /** @class */ (function () {
function MtcnnOptions(_a) {
var _b = _a === void 0 ? {} : _a, minFaceSize = _b.minFaceSize, scaleFactor = _b.scaleFactor, maxNumScales = _b.maxNumScales, scoreThresholds = _b.scoreThresholds, scaleSteps = _b.scaleSteps;
this._name = 'MtcnnOptions';
this._minFaceSize = minFaceSize || 20;
this._scaleFactor = scaleFactor || 0.709;
this._maxNumScales = maxNumScales || 10;
this._scoreThresholds = scoreThresholds || [0.6, 0.7, 0.7];
this._scaleSteps = scaleSteps;
if (typeof this._minFaceSize !== 'number' || this._minFaceSize < 0) {
throw new Error(this._name + " - expected minFaceSize to be a number > 0");
}
if (typeof this._scaleFactor !== 'number' || this._scaleFactor <= 0 || this._scaleFactor >= 1) {
throw new Error(this._name + " - expected scaleFactor to be a number between 0 and 1");
}
if (typeof this._maxNumScales !== 'number' || this._maxNumScales < 0) {
throw new Error(this._name + " - expected maxNumScales to be a number > 0");
}
if (!Array.isArray(this._scoreThresholds)
|| this._scoreThresholds.length !== 3
|| this._scoreThresholds.some(function (th) { return typeof th !== 'number'; })) {
throw new Error(this._name + " - expected scoreThresholds to be an array of numbers of length 3");
}
if (this._scaleSteps
&& (!Array.isArray(this._scaleSteps) || this._scaleSteps.some(function (th) { return typeof th !== 'number'; }))) {
throw new Error(this._name + " - expected scaleSteps to be an array of numbers");
}
}
Object.defineProperty(MtcnnOptions.prototype, "minFaceSize", {
get: function () { return this._minFaceSize; },
enumerable: true,
configurable: true
});
Object.defineProperty(MtcnnOptions.prototype, "scaleFactor", {
get: function () { return this._scaleFactor; },
enumerable: true,
configurable: true
});
Object.defineProperty(MtcnnOptions.prototype, "maxNumScales", {
get: function () { return this._maxNumScales; },
enumerable: true,
configurable: true
});
Object.defineProperty(MtcnnOptions.prototype, "scoreThresholds", {
get: function () { return this._scoreThresholds; },
enumerable: true,
configurable: true
});
Object.defineProperty(MtcnnOptions.prototype, "scaleSteps", {
get: function () { return this._scaleSteps; },
enumerable: true,
configurable: true
});
return MtcnnOptions;
}());
exports.MtcnnOptions = MtcnnOptions;
//# sourceMappingURL=MtcnnOptions.js.map
\ No newline at end of file
build/commonjs/mtcnn/MtcnnOptions.js.map 0 → 100644
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\ No newline at end of file
build/commonjs/mtcnn/extractImagePatches.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { Box, Dimensions } from 'tfjs-image-recognition-base';
export declare function extractImagePatches(img: HTMLCanvasElement, boxes: Box[], {width, height}: Dimensions): Promise<tf.Tensor4D[]>;
import { Box, IDimensions } from 'tfjs-image-recognition-base';
export declare function extractImagePatches(img: HTMLCanvasElement, boxes: Box[], {width, height}: IDimensions): Promise<tf.Tensor4D[]>;
build/commonjs/mtcnn/extractImagePatches.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/mtcnn/index.d.ts
import { Mtcnn } from './Mtcnn';
export * from './Mtcnn';
export * from './MtcnnOptions';
export declare function createMtcnn(weights: Float32Array): Mtcnn;
build/commonjs/mtcnn/index.js
......@@ -3,6 +3,7 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var Mtcnn_1 = require("./Mtcnn");
tslib_1.__exportStar(require("./Mtcnn"), exports);
tslib_1.__exportStar(require("./MtcnnOptions"), exports);
function createMtcnn(weights) {
var net = new Mtcnn_1.Mtcnn();
net.extractWeights(weights);
......
build/commonjs/mtcnn/index.js.map
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\ No newline at end of file
{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/mtcnn/index.ts"],"names":[],"mappings":";;;AAAA,iCAAgC;AAEhC,kDAAwB;AACxB,yDAA+B;AAE/B,qBAA4B,OAAqB;IAC/C,IAAM,GAAG,GAAG,IAAI,aAAK,EAAE,CAAA;IACvB,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAJD,kCAIC"}
\ No newline at end of file
build/commonjs/mtcnn/types.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams, FCParams } from 'tfjs-tiny-yolov2';
import { FaceDetection } from '../classes/FaceDetection';
import { FaceLandmarks5 } from '../classes/FaceLandmarks5';
export declare type SharedParams = {
conv1: ConvParams;
prelu1_alpha: tf.Tensor1D;
......@@ -34,14 +32,3 @@ export declare type NetParams = {
rnet: RNetParams;
onet: ONetParams;
};
export declare type MtcnnResult = {
faceDetection: FaceDetection;
faceLandmarks: FaceLandmarks5;
};
export declare type MtcnnForwardParams = {
minFaceSize?: number;
scaleFactor?: number;
maxNumScales?: number;
scoreThresholds?: number[];
scaleSteps?: number[];
};
build/commonjs/ssdMobilenetv1/SsdMobilenetv1.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection';
import { ISsdMobilenetv1Options } from './SsdMobilenetv1Options';
import { NetParams } from './types';
export declare class SsdMobilenetv1 extends NeuralNetwork<NetParams> {
constructor();
forwardInput(input: NetInput): {
boxes: tf.Tensor<tf.Rank.R2>[];
scores: tf.Tensor<tf.Rank.R1>[];
};
forward(input: TNetInput): Promise<{
boxes: tf.Tensor<tf.Rank.R2>[];
scores: tf.Tensor<tf.Rank.R1>[];
}>;
locateFaces(input: TNetInput, options?: ISsdMobilenetv1Options): Promise<FaceDetection[]>;
protected loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
protected extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
};
}
build/commonjs/ssdMobilenetv1/SsdMobilenetv1.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceDetection_1 = require("../classes/FaceDetection");
var extractParams_1 = require("./extractParams");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
var mobileNetV1_1 = require("./mobileNetV1");
var nonMaxSuppression_1 = require("./nonMaxSuppression");
var outputLayer_1 = require("./outputLayer");
var predictionLayer_1 = require("./predictionLayer");
var SsdMobilenetv1Options_1 = require("./SsdMobilenetv1Options");
var SsdMobilenetv1 = /** @class */ (function (_super) {
tslib_1.__extends(SsdMobilenetv1, _super);
function SsdMobilenetv1() {
return _super.call(this, 'SsdMobilenetv1') || this;
}
SsdMobilenetv1.prototype.forwardInput = function (input) {
var params = this.params;
if (!params) {
throw new Error('SsdMobilenetv1 - load model before inference');
}
return tf.tidy(function () {
var batchTensor = input.toBatchTensor(512, false).toFloat();
var x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1));
var features = mobileNetV1_1.mobileNetV1(x, params.mobilenetv1);
var _a = predictionLayer_1.predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
return outputLayer_1.outputLayer(boxPredictions, classPredictions, params.output_layer);
});
};
SsdMobilenetv1.prototype.forward = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
});
};
SsdMobilenetv1.prototype.locateFaces = function (input, options) {
if (options === void 0) { options = {}; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a, maxResults, minConfidence, netInput, _b, _boxes, _scores, boxes, scores, i, scoresData, _c, _d, iouThreshold, indices, reshapedDims, inputSize, padX, padY, results;
return tslib_1.__generator(this, function (_e) {
switch (_e.label) {
case 0:
_a = new SsdMobilenetv1Options_1.SsdMobilenetv1Options(options), maxResults = _a.maxResults, minConfidence = _a.minConfidence;
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1:
netInput = _e.sent();
_b = this.forwardInput(netInput), _boxes = _b.boxes, _scores = _b.scores;
boxes = _boxes[0];
scores = _scores[0];
for (i = 1; i < _boxes.length; i++) {
_boxes[i].dispose();
_scores[i].dispose();
}
_d = (_c = Array).from;
return [4 /*yield*/, scores.data()];
case 2:
scoresData = _d.apply(_c, [_e.sent()]);
iouThreshold = 0.5;
indices = nonMaxSuppression_1.nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
reshapedDims = netInput.getReshapedInputDimensions(0);
inputSize = netInput.inputSize;
padX = inputSize / reshapedDims.width;
padY = inputSize / reshapedDims.height;
results = indices
.map(function (idx) {
var _a = [
Math.max(0, boxes.get(idx, 0)),
Math.min(1.0, boxes.get(idx, 2))
].map(function (val) { return val * padY; }), top = _a[0], bottom = _a[1];
var _b = [
Math.max(0, boxes.get(idx, 1)),
Math.min(1.0, boxes.get(idx, 3))
].map(function (val) { return val * padX; }), left = _b[0], right = _b[1];
return new FaceDetection_1.FaceDetection(scoresData[idx], new tfjs_image_recognition_base_1.Rect(left, top, right - left, bottom - top), {
height: netInput.getInputHeight(0),
width: netInput.getInputWidth(0)
});
});
boxes.dispose();
scores.dispose();
return [2 /*return*/, results];
}
});
});
};
SsdMobilenetv1.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams_1.loadQuantizedParams(uri);
};
SsdMobilenetv1.prototype.extractParams = function (weights) {
return extractParams_1.extractParams(weights);
};
return SsdMobilenetv1;
}(tfjs_image_recognition_base_1.NeuralNetwork));
exports.SsdMobilenetv1 = SsdMobilenetv1;
//# sourceMappingURL=SsdMobilenetv1.js.map
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build/commonjs/ssdMobilenetv1/SsdMobilenetv1.js.map 0 → 100644
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build/commonjs/ssdMobilenetv1/SsdMobilenetv1Options.d.ts 0 → 100644
export interface ISsdMobilenetv1Options {
minConfidence?: number;
maxResults?: number;
}
export declare class SsdMobilenetv1Options {
protected _name: string;
private _minConfidence;
private _maxResults;
constructor({minConfidence, maxResults}?: ISsdMobilenetv1Options);
readonly minConfidence: number;
readonly maxResults: number;
}
build/commonjs/ssdMobilenetv1/SsdMobilenetv1Options.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var SsdMobilenetv1Options = /** @class */ (function () {
function SsdMobilenetv1Options(_a) {
var _b = _a === void 0 ? {} : _a, minConfidence = _b.minConfidence, maxResults = _b.maxResults;
this._name = 'SsdMobilenetv1Options';
this._minConfidence = minConfidence || 0.5;
this._maxResults = maxResults || 100;
if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {
throw new Error(this._name + " - expected minConfidence to be a number between 0 and 1");
}
if (typeof this._maxResults !== 'number') {
throw new Error(this._name + " - expected maxResults to be a number");
}
}
Object.defineProperty(SsdMobilenetv1Options.prototype, "minConfidence", {
get: function () { return this._minConfidence; },
enumerable: true,
configurable: true
});
Object.defineProperty(SsdMobilenetv1Options.prototype, "maxResults", {
get: function () { return this._maxResults; },
enumerable: true,
configurable: true
});
return SsdMobilenetv1Options;
}());
exports.SsdMobilenetv1Options = SsdMobilenetv1Options;
//# sourceMappingURL=SsdMobilenetv1Options.js.map
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build/commonjs/ssdMobilenetv1/SsdMobilenetv1Options.js.map 0 → 100644
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build/commonjs/ssdMobilenetv1/boxPredictionLayer.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { BoxPredictionParams } from './types';
export declare function boxPredictionLayer(x: tf.Tensor4D, params: BoxPredictionParams): {
boxPredictionEncoding: tf.Tensor<tf.Rank>;
classPrediction: tf.Tensor<tf.Rank>;
};
build/commonjs/ssdMobilenetv1/boxPredictionLayer.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
function boxPredictionLayer(x, params) {
return tf.tidy(function () {
var batchSize = x.shape[0];
var boxPredictionEncoding = tf.reshape(tfjs_tiny_yolov2_1.convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
var classPrediction = tf.reshape(tfjs_tiny_yolov2_1.convLayer(x, params.class_predictor), [batchSize, -1, 3]);
return {
boxPredictionEncoding: boxPredictionEncoding,
classPrediction: classPrediction
};
});
}
exports.boxPredictionLayer = boxPredictionLayer;
//# sourceMappingURL=boxPredictionLayer.js.map
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build/commonjs/ssdMobilenetv1/boxPredictionLayer.js.map 0 → 100644
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build/commonjs/ssdMobilenetv1/extractParams.d.ts 0 → 100644
import { ParamMapping } from 'tfjs-image-recognition-base';
import { NetParams } from './types';
export declare function extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: ParamMapping[];
};
build/commonjs/ssdMobilenetv1/extractParams.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
function extractorsFactory(extractWeights, paramMappings) {
function extractDepthwiseConvParams(numChannels, mappedPrefix) {
var filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
var batch_norm_scale = tf.tensor1d(extractWeights(numChannels));
var batch_norm_offset = tf.tensor1d(extractWeights(numChannels));
var batch_norm_mean = tf.tensor1d(extractWeights(numChannels));
var batch_norm_variance = tf.tensor1d(extractWeights(numChannels));
paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/batch_norm_scale" }, { paramPath: mappedPrefix + "/batch_norm_offset" }, { paramPath: mappedPrefix + "/batch_norm_mean" }, { paramPath: mappedPrefix + "/batch_norm_variance" });
return {
filters: filters,
batch_norm_scale: batch_norm_scale,
batch_norm_offset: batch_norm_offset,
batch_norm_mean: batch_norm_mean,
batch_norm_variance: batch_norm_variance
};
}
function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
var bias = tf.tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/" + (isPointwiseConv ? 'batch_norm_offset' : 'bias') });
return { filters: filters, bias: bias };
}
function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
var _a = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true), filters = _a.filters, bias = _a.bias;
return {
filters: filters,
batch_norm_offset: bias
};
}
function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) {
var depthwise_conv = extractDepthwiseConvParams(channelsIn, mappedPrefix + "/depthwise_conv");
var pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, mappedPrefix + "/pointwise_conv");
return { depthwise_conv: depthwise_conv, pointwise_conv: pointwise_conv };
}
function extractMobilenetV1Params() {
var conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');
var conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');
var conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');
var conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');
var conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');
var conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');
var conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');
var conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');
var conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');
var conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');
var conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');
var conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');
var conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');
var conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');
return {
conv_0: conv_0,
conv_1: conv_1,
conv_2: conv_2,
conv_3: conv_3,
conv_4: conv_4,
conv_5: conv_5,
conv_6: conv_6,
conv_7: conv_7,
conv_8: conv_8,
conv_9: conv_9,
conv_10: conv_10,
conv_11: conv_11,
conv_12: conv_12,
conv_13: conv_13
};
}
function extractPredictionLayerParams() {
var conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');
var conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');
var conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');
var conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');
var conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');
var conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');
var conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');
var conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');
var box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');
var class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');
var box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');
var class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');
var box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');
var class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');
var box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');
var class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');
var box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');
var class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');
var box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');
var class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');
var box_predictor_0 = {
box_encoding_predictor: box_encoding_0_predictor,
class_predictor: class_predictor_0
};
var box_predictor_1 = {
box_encoding_predictor: box_encoding_1_predictor,
class_predictor: class_predictor_1
};
var box_predictor_2 = {
box_encoding_predictor: box_encoding_2_predictor,
class_predictor: class_predictor_2
};
var box_predictor_3 = {
box_encoding_predictor: box_encoding_3_predictor,
class_predictor: class_predictor_3
};
var box_predictor_4 = {
box_encoding_predictor: box_encoding_4_predictor,
class_predictor: class_predictor_4
};
var box_predictor_5 = {
box_encoding_predictor: box_encoding_5_predictor,
class_predictor: class_predictor_5
};
return {
conv_0: conv_0,
conv_1: conv_1,
conv_2: conv_2,
conv_3: conv_3,
conv_4: conv_4,
conv_5: conv_5,
conv_6: conv_6,
conv_7: conv_7,
box_predictor_0: box_predictor_0,
box_predictor_1: box_predictor_1,
box_predictor_2: box_predictor_2,
box_predictor_3: box_predictor_3,
box_predictor_4: box_predictor_4,
box_predictor_5: box_predictor_5
};
}
return {
extractMobilenetV1Params: extractMobilenetV1Params,
extractPredictionLayerParams: extractPredictionLayerParams
};
}
function extractParams(weights) {
var paramMappings = [];
var _a = tfjs_image_recognition_base_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var _b = extractorsFactory(extractWeights, paramMappings), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;
var mobilenetv1 = extractMobilenetV1Params();
var prediction_layer = extractPredictionLayerParams();
var extra_dim = tf.tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
var output_layer = {
extra_dim: extra_dim
};
paramMappings.push({ paramPath: 'output_layer/extra_dim' });
if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
}
return {
params: {
mobilenetv1: mobilenetv1,
prediction_layer: prediction_layer,
output_layer: output_layer
},
paramMappings: paramMappings
};
}
exports.extractParams = extractParams;
//# sourceMappingURL=extractParams.js.map
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build/commonjs/ssdMobilenetv1/extractParams.js.map 0 → 100644
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build/commonjs/ssdMobilenetv1/index.d.ts 0 → 100644
import { SsdMobilenetv1 } from './SsdMobilenetv1';
export * from './SsdMobilenetv1';
export * from './SsdMobilenetv1Options';
export declare function createSsdMobilenetv1(weights: Float32Array): SsdMobilenetv1;
export declare function createFaceDetectionNet(weights: Float32Array): SsdMobilenetv1;
export declare class FaceDetectionNet extends SsdMobilenetv1 {
}
build/commonjs/ssdMobilenetv1/index.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var SsdMobilenetv1_1 = require("./SsdMobilenetv1");
tslib_1.__exportStar(require("./SsdMobilenetv1"), exports);
tslib_1.__exportStar(require("./SsdMobilenetv1Options"), exports);
function createSsdMobilenetv1(weights) {
var net = new SsdMobilenetv1_1.SsdMobilenetv1();
net.extractWeights(weights);
return net;
}
exports.createSsdMobilenetv1 = createSsdMobilenetv1;
function createFaceDetectionNet(weights) {
return createSsdMobilenetv1(weights);
}
exports.createFaceDetectionNet = createFaceDetectionNet;
// alias for backward compatibily
var FaceDetectionNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceDetectionNet, _super);
function FaceDetectionNet() {
return _super !== null && _super.apply(this, arguments) || this;
}
return FaceDetectionNet;
}(SsdMobilenetv1_1.SsdMobilenetv1));
exports.FaceDetectionNet = FaceDetectionNet;
//# sourceMappingURL=index.js.map
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build/commonjs/ssdMobilenetv1/index.js.map 0 → 100644
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build/commonjs/ssdMobilenetv1/loadQuantizedParams.d.ts 0 → 100644
import { ParamMapping } from 'tfjs-image-recognition-base';
import { NetParams } from './types';
export declare function loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: ParamMapping[];
}>;
build/commonjs/ssdMobilenetv1/loadQuantizedParams.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var DEFAULT_MODEL_NAME = 'ssd_mobilenetv1_model';
function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = tfjs_image_recognition_base_1.extractWeightEntryFactory(weightMap, paramMappings);
function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
var filters = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/weights", 4, mappedPrefix + "/filters");
var batch_norm_offset = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset", 1, mappedPrefix + "/batch_norm_offset");
return { filters: filters, batch_norm_offset: batch_norm_offset };
}
function extractConvPairParams(idx) {
var mappedPrefix = "mobilenetv1/conv_" + idx;
var prefixDepthwiseConv = "MobilenetV1/Conv2d_" + idx + "_depthwise";
var mappedPrefixDepthwiseConv = mappedPrefix + "/depthwise_conv";
var mappedPrefixPointwiseConv = mappedPrefix + "/pointwise_conv";
var filters = extractWeightEntry(prefixDepthwiseConv + "/depthwise_weights", 4, mappedPrefixDepthwiseConv + "/filters");
var batch_norm_scale = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/gamma", 1, mappedPrefixDepthwiseConv + "/batch_norm_scale");
var batch_norm_offset = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/beta", 1, mappedPrefixDepthwiseConv + "/batch_norm_offset");
var batch_norm_mean = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_mean", 1, mappedPrefixDepthwiseConv + "/batch_norm_mean");
var batch_norm_variance = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_variance", 1, mappedPrefixDepthwiseConv + "/batch_norm_variance");
return {
depthwise_conv: {
filters: filters,
batch_norm_scale: batch_norm_scale,
batch_norm_offset: batch_norm_offset,
batch_norm_mean: batch_norm_mean,
batch_norm_variance: batch_norm_variance
},
pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)
};
}
function extractMobilenetV1Params() {
return {
conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),
conv_1: extractConvPairParams(1),
conv_2: extractConvPairParams(2),
conv_3: extractConvPairParams(3),
conv_4: extractConvPairParams(4),
conv_5: extractConvPairParams(5),
conv_6: extractConvPairParams(6),
conv_7: extractConvPairParams(7),
conv_8: extractConvPairParams(8),
conv_9: extractConvPairParams(9),
conv_10: extractConvPairParams(10),
conv_11: extractConvPairParams(11),
conv_12: extractConvPairParams(12),
conv_13: extractConvPairParams(13)
};
}
function extractConvParams(prefix, mappedPrefix) {
var filters = extractWeightEntry(prefix + "/weights", 4, mappedPrefix + "/filters");
var bias = extractWeightEntry(prefix + "/biases", 1, mappedPrefix + "/bias");
return { filters: filters, bias: bias };
}
function extractBoxPredictorParams(idx) {
var box_encoding_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor", "prediction_layer/box_predictor_" + idx + "/box_encoding_predictor");
var class_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/ClassPredictor", "prediction_layer/box_predictor_" + idx + "/class_predictor");
return { box_encoding_predictor: box_encoding_predictor, class_predictor: class_predictor };
}
function extractPredictionLayerParams() {
return {
conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),
conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),
conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),
conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),
conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),
conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),
conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),
conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),
box_predictor_0: extractBoxPredictorParams(0),
box_predictor_1: extractBoxPredictorParams(1),
box_predictor_2: extractBoxPredictorParams(2),
box_predictor_3: extractBoxPredictorParams(3),
box_predictor_4: extractBoxPredictorParams(4),
box_predictor_5: extractBoxPredictorParams(5)
};
}
return {
extractMobilenetV1Params: extractMobilenetV1Params,
extractPredictionLayerParams: extractPredictionLayerParams
};
}
function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractMobilenetV1Params, extractPredictionLayerParams, extra_dim, params;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
paramMappings = [];
_a = extractorsFactory(weightMap, paramMappings), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
extra_dim = weightMap['Output/extra_dim'];
paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });
if (!tfjs_image_recognition_base_1.isTensor3D(extra_dim)) {
throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have " + extra_dim);
}
params = {
mobilenetv1: extractMobilenetV1Params(),
prediction_layer: extractPredictionLayerParams(),
output_layer: {
extra_dim: extra_dim
}
};
tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
}
});
});
}
exports.loadQuantizedParams = loadQuantizedParams;
//# sourceMappingURL=loadQuantizedParams.js.map
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build/commonjs/ssdMobilenetv1/loadQuantizedParams.js.map 0 → 100644
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dist/face-api.js
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dist/face-api.js.map
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dist/face-api.min.js
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