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Unverified Commit 287080b9 by justadudewhohacks Committed by GitHub
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Merge pull request #78 from justadudewhohacks/cleanup

consume shared codebase from tfjs-tiny-yolov2 and tfjs-image-recognition-base
parents 5add150b 8c2d4602
Showing with 195 additions and 1221 deletions
build/BoundingBox.d.ts deleted 100644 → 0
import { Rect } from './Rect';
import { Dimensions } from './types';
export declare class BoundingBox {
private _left;
private _top;
private _right;
private _bottom;
constructor(_left: number, _top: number, _right: number, _bottom: number);
readonly left: number;
readonly top: number;
readonly right: number;
readonly bottom: number;
readonly width: number;
readonly height: number;
readonly area: number;
toSquare(): BoundingBox;
round(): BoundingBox;
padAtBorders(imageHeight: number, imageWidth: number): {
dy: number;
edy: number;
dx: number;
edx: number;
y: number;
ey: number;
x: number;
ex: number;
w: number;
h: number;
};
calibrate(region: BoundingBox): BoundingBox;
rescale(s: Dimensions | number): BoundingBox;
toRect(): Rect;
}
build/BoundingBox.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var Rect_1 = require("./Rect");
var utils_1 = require("./utils");
var BoundingBox = /** @class */ (function () {
function BoundingBox(_left, _top, _right, _bottom) {
this._left = _left;
this._top = _top;
this._right = _right;
this._bottom = _bottom;
}
Object.defineProperty(BoundingBox.prototype, "left", {
get: function () {
return this._left;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "top", {
get: function () {
return this._top;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "right", {
get: function () {
return this._right;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "bottom", {
get: function () {
return this._bottom;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "width", {
get: function () {
return this.right - this.left;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "height", {
get: function () {
return this.bottom - this.top;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "area", {
get: function () {
return this.width * this.height;
},
enumerable: true,
configurable: true
});
BoundingBox.prototype.toSquare = function () {
var _a = this, left = _a.left, top = _a.top, right = _a.right, bottom = _a.bottom;
var off = (Math.abs(this.width - this.height) / 2);
if (this.width < this.height) {
left -= off;
right += off;
}
if (this.height < this.width) {
top -= off;
bottom += off;
}
return new BoundingBox(left, top, right, bottom);
};
BoundingBox.prototype.round = function () {
return new BoundingBox(Math.round(this.left), Math.round(this.top), Math.round(this.right), Math.round(this.bottom));
};
BoundingBox.prototype.padAtBorders = function (imageHeight, imageWidth) {
var w = this.width + 1;
var h = this.height + 1;
var dx = 1;
var dy = 1;
var edx = w;
var edy = h;
var x = this.left;
var y = this.top;
var ex = this.right;
var ey = this.bottom;
if (ex > imageWidth) {
edx = -ex + imageWidth + w;
ex = imageWidth;
}
if (ey > imageHeight) {
edy = -ey + imageHeight + h;
ey = imageHeight;
}
if (x < 1) {
edy = 2 - x;
x = 1;
}
if (y < 1) {
edy = 2 - y;
y = 1;
}
return { dy: dy, edy: edy, dx: dx, edx: edx, y: y, ey: ey, x: x, ex: ex, w: w, h: h };
};
BoundingBox.prototype.calibrate = function (region) {
return new BoundingBox(this.left + (region.left * this.width), this.top + (region.top * this.height), this.right + (region.right * this.width), this.bottom + (region.bottom * this.height)).toSquare().round();
};
BoundingBox.prototype.rescale = function (s) {
var scaleX = utils_1.isDimensions(s) ? s.width : s;
var scaleY = utils_1.isDimensions(s) ? s.height : s;
return new BoundingBox(this.left * scaleX, this.top * scaleY, this.right * scaleX, this.bottom * scaleY);
};
BoundingBox.prototype.toRect = function () {
return new Rect_1.Rect(this.left, this.top, this.width, this.height);
};
return BoundingBox;
}());
exports.BoundingBox = BoundingBox;
//# sourceMappingURL=BoundingBox.js.map
\ No newline at end of file
build/BoundingBox.js.map deleted 100644 → 0
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\ No newline at end of file
build/FaceDetection.d.ts deleted 100644 → 0
import { Rect } from './Rect';
import { Dimensions } from './types';
export declare class FaceDetection {
private _score;
private _box;
private _imageWidth;
private _imageHeight;
constructor(score: number, relativeBox: Rect, imageDims: Dimensions);
readonly score: number;
readonly box: Rect;
readonly imageWidth: number;
readonly imageHeight: number;
readonly relativeBox: Rect;
getScore(): number;
getBox(): Rect;
getImageWidth(): number;
getImageHeight(): number;
getRelativeBox(): Rect;
forSize(width: number, height: number): FaceDetection;
}
build/FaceDetection.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var Rect_1 = require("./Rect");
var FaceDetection = /** @class */ (function () {
function FaceDetection(score, relativeBox, imageDims) {
var width = imageDims.width, height = imageDims.height;
this._imageWidth = width;
this._imageHeight = height;
this._score = score;
this._box = new Rect_1.Rect(relativeBox.x * width, relativeBox.y * height, relativeBox.width * width, relativeBox.height * height);
}
Object.defineProperty(FaceDetection.prototype, "score", {
get: function () {
return this._score;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetection.prototype, "box", {
get: function () {
return this._box;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetection.prototype, "imageWidth", {
get: function () {
return this._imageWidth;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetection.prototype, "imageHeight", {
get: function () {
return this._imageHeight;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetection.prototype, "relativeBox", {
get: function () {
return new Rect_1.Rect(this._box.x / this._imageWidth, this._box.y / this._imageHeight, this._box.width / this._imageWidth, this._box.height / this._imageHeight);
},
enumerable: true,
configurable: true
});
FaceDetection.prototype.getScore = function () {
return this.score;
};
FaceDetection.prototype.getBox = function () {
return this.box;
};
FaceDetection.prototype.getImageWidth = function () {
return this.imageWidth;
};
FaceDetection.prototype.getImageHeight = function () {
return this.imageHeight;
};
FaceDetection.prototype.getRelativeBox = function () {
return this.relativeBox;
};
FaceDetection.prototype.forSize = function (width, height) {
return new FaceDetection(this._score, this.getRelativeBox(), { width: width, height: height });
};
return FaceDetection;
}());
exports.FaceDetection = FaceDetection;
//# sourceMappingURL=FaceDetection.js.map
\ No newline at end of file
build/FaceDetection.js.map deleted 100644 → 0
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\ No newline at end of file
build/FaceLandmarks.js.map deleted 100644 → 0
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\ No newline at end of file
build/FullFaceDescription.js.map deleted 100644 → 0
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\ No newline at end of file
build/NetInput.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { Point } from './Point';
import { TResolvedNetInput, Dimensions } from './types';
export declare class NetInput {
private _inputs;
private _canvases;
private _isManaged;
private _isBatchInput;
private _inputDimensions;
private _paddings;
private _inputSize;
constructor(inputs: tf.Tensor4D | Array<TResolvedNetInput>, isBatchInput?: boolean, keepCanvases?: boolean);
readonly inputs: tf.Tensor3D[];
readonly canvases: HTMLCanvasElement[];
readonly isManaged: boolean;
readonly isBatchInput: boolean;
readonly batchSize: number;
readonly inputDimensions: number[][];
readonly paddings: Point[];
readonly inputSize: number;
readonly relativePaddings: Point[];
readonly reshapedInputDimensions: Dimensions[];
getInputDimensions(batchIdx: number): number[];
getInputHeight(batchIdx: number): number;
getInputWidth(batchIdx: number): number;
getPaddings(batchIdx: number): Point;
getRelativePaddings(batchIdx: number): Point;
getReshapedInputDimensions(batchIdx: number): Dimensions;
toBatchTensor(inputSize: number, isCenterInputs?: boolean): tf.Tensor4D;
/**
* By setting the isManaged flag, all newly created tensors will be
* automatically disposed after the batch tensor has been created
*/
managed(): this;
dispose(): void;
}
build/NetInput.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var isTensor_1 = require("./commons/isTensor");
var padToSquare_1 = require("./padToSquare");
var Point_1 = require("./Point");
var utils_1 = require("./utils");
var NetInput = /** @class */ (function () {
function NetInput(inputs, isBatchInput, keepCanvases) {
if (isBatchInput === void 0) { isBatchInput = false; }
if (keepCanvases === void 0) { keepCanvases = false; }
var _this = this;
this._inputs = [];
this._canvases = [];
this._isManaged = false;
this._isBatchInput = false;
this._inputDimensions = [];
this._paddings = [];
this._inputSize = 0;
if (isTensor_1.isTensor4D(inputs)) {
this._inputs = tf.unstack(inputs);
}
if (Array.isArray(inputs)) {
this._inputs = inputs.map(function (input, idx) {
if (isTensor_1.isTensor3D(input)) {
// TODO: make sure not to dispose original tensors passed in by the user
return tf.clone(input);
}
if (isTensor_1.isTensor4D(input)) {
var shape = input.shape;
var batchSize = shape[0];
if (batchSize !== 1) {
throw new Error("NetInput - tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array");
}
return input.reshape(shape.slice(1));
}
var canvas = input instanceof HTMLCanvasElement ? input : utils_1.createCanvasFromMedia(input);
if (keepCanvases) {
_this._canvases[idx] = canvas;
}
return tf.fromPixels(canvas);
});
}
this._isBatchInput = this.batchSize > 1 || isBatchInput;
this._inputDimensions = this._inputs.map(function (t) { return t.shape; });
}
Object.defineProperty(NetInput.prototype, "inputs", {
get: function () {
return this._inputs;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "canvases", {
get: function () {
return this._canvases;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "isManaged", {
get: function () {
return this._isManaged;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "isBatchInput", {
get: function () {
return this._isBatchInput;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "batchSize", {
get: function () {
return this._inputs.length;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "inputDimensions", {
get: function () {
return this._inputDimensions;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "paddings", {
get: function () {
return this._paddings;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "inputSize", {
get: function () {
return this._inputSize;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "relativePaddings", {
get: function () {
var _this = this;
return Array(this.inputs.length).fill(0).map(function (_, batchIdx) { return _this.getRelativePaddings(batchIdx); });
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "reshapedInputDimensions", {
get: function () {
var _this = this;
return Array(this.inputs.length).fill(0).map(function (_, batchIdx) { return _this.getReshapedInputDimensions(batchIdx); });
},
enumerable: true,
configurable: true
});
NetInput.prototype.getInputDimensions = function (batchIdx) {
return this._inputDimensions[batchIdx];
};
NetInput.prototype.getInputHeight = function (batchIdx) {
return this._inputDimensions[batchIdx][0];
};
NetInput.prototype.getInputWidth = function (batchIdx) {
return this._inputDimensions[batchIdx][1];
};
NetInput.prototype.getPaddings = function (batchIdx) {
return this._paddings[batchIdx];
};
NetInput.prototype.getRelativePaddings = function (batchIdx) {
return new Point_1.Point((this.getPaddings(batchIdx).x + this.getInputWidth(batchIdx)) / this.getInputWidth(batchIdx), (this.getPaddings(batchIdx).y + this.getInputHeight(batchIdx)) / this.getInputHeight(batchIdx));
};
NetInput.prototype.getReshapedInputDimensions = function (batchIdx) {
var _a = [this.getInputHeight(batchIdx), this.getInputWidth(batchIdx)], h = _a[0], w = _a[1];
var f = this.inputSize / Math.max(h, w);
return {
height: Math.floor(h * f),
width: Math.floor(w * f)
};
};
NetInput.prototype.toBatchTensor = function (inputSize, isCenterInputs) {
var _this = this;
if (isCenterInputs === void 0) { isCenterInputs = true; }
this._inputSize = inputSize;
return tf.tidy(function () {
var inputTensors = _this._inputs.map(function (inputTensor) {
var _a = inputTensor.shape, originalHeight = _a[0], originalWidth = _a[1];
var imgTensor = inputTensor.expandDims().toFloat();
imgTensor = padToSquare_1.padToSquare(imgTensor, isCenterInputs);
var _b = imgTensor.shape.slice(1), heightAfterPadding = _b[0], widthAfterPadding = _b[1];
if (heightAfterPadding !== inputSize || widthAfterPadding !== inputSize) {
imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);
}
_this._paddings.push(new Point_1.Point(widthAfterPadding - originalWidth, heightAfterPadding - originalHeight));
return imgTensor;
});
var batchTensor = tf.stack(inputTensors).as4D(_this.batchSize, inputSize, inputSize, 3);
if (_this.isManaged) {
_this.dispose();
}
return batchTensor;
});
};
/**
* By setting the isManaged flag, all newly created tensors will be
* automatically disposed after the batch tensor has been created
*/
NetInput.prototype.managed = function () {
this._isManaged = true;
return this;
};
NetInput.prototype.dispose = function () {
this._inputs.forEach(function (t) { return t.dispose(); });
};
return NetInput;
}());
exports.NetInput = NetInput;
//# sourceMappingURL=NetInput.js.map
\ No newline at end of file
build/NetInput.js.map deleted 100644 → 0
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\ No newline at end of file
build/Point.d.ts deleted 100644 → 0
export interface IPoint {
x: number;
y: number;
}
export declare class Point implements IPoint {
x: number;
y: number;
constructor(x: number, y: number);
add(pt: IPoint): Point;
sub(pt: IPoint): Point;
mul(pt: IPoint): Point;
div(pt: IPoint): Point;
abs(): Point;
magnitude(): number;
floor(): Point;
}
build/Point.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var Point = /** @class */ (function () {
function Point(x, y) {
this.x = x;
this.y = y;
}
Point.prototype.add = function (pt) {
return new Point(this.x + pt.x, this.y + pt.y);
};
Point.prototype.sub = function (pt) {
return new Point(this.x - pt.x, this.y - pt.y);
};
Point.prototype.mul = function (pt) {
return new Point(this.x * pt.x, this.y * pt.y);
};
Point.prototype.div = function (pt) {
return new Point(this.x / pt.x, this.y / pt.y);
};
Point.prototype.abs = function () {
return new Point(Math.abs(this.x), Math.abs(this.y));
};
Point.prototype.magnitude = function () {
return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2));
};
Point.prototype.floor = function () {
return new Point(Math.floor(this.x), Math.floor(this.y));
};
return Point;
}());
exports.Point = Point;
//# sourceMappingURL=Point.js.map
\ No newline at end of file
build/Point.js.map deleted 100644 → 0
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\ No newline at end of file
build/Rect.d.ts deleted 100644 → 0
import { BoundingBox } from './BoundingBox';
export interface IRect {
x: number;
y: number;
width: number;
height: number;
}
export declare class Rect implements IRect {
x: number;
y: number;
width: number;
height: number;
constructor(x: number, y: number, width: number, height: number);
readonly right: number;
readonly bottom: number;
toSquare(): Rect;
pad(padX: number, padY: number): Rect;
floor(): Rect;
toBoundingBox(): BoundingBox;
clipAtImageBorders(imgWidth: number, imgHeight: number): Rect;
}
build/Rect.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var BoundingBox_1 = require("./BoundingBox");
var Rect = /** @class */ (function () {
function Rect(x, y, width, height) {
this.x = x;
this.y = y;
this.width = width;
this.height = height;
}
Object.defineProperty(Rect.prototype, "right", {
get: function () {
return this.x + this.width;
},
enumerable: true,
configurable: true
});
Object.defineProperty(Rect.prototype, "bottom", {
get: function () {
return this.y + this.height;
},
enumerable: true,
configurable: true
});
Rect.prototype.toSquare = function () {
var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var diff = Math.abs(width - height);
if (width < height) {
x -= (diff / 2);
width += diff;
}
if (height < width) {
y -= (diff / 2);
height += diff;
}
return new Rect(x, y, width, height);
};
Rect.prototype.pad = function (padX, padY) {
var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
return new Rect(x - (padX / 2), y - (padY / 2), width + padX, height + padY);
};
Rect.prototype.floor = function () {
return new Rect(Math.floor(this.x), Math.floor(this.y), Math.floor(this.width), Math.floor(this.height));
};
Rect.prototype.toBoundingBox = function () {
return new BoundingBox_1.BoundingBox(this.x, this.y, this.x + this.width, this.y + this.height);
};
Rect.prototype.clipAtImageBorders = function (imgWidth, imgHeight) {
var _a = this, x = _a.x, y = _a.y, right = _a.right, bottom = _a.bottom;
var clippedX = Math.max(x, 0);
var clippedY = Math.max(y, 0);
var newWidth = right - clippedX;
var newHeight = bottom - clippedY;
var clippedWidth = Math.min(newWidth, imgWidth - clippedX);
var clippedHeight = Math.min(newHeight, imgHeight - clippedY);
return (new Rect(clippedX, clippedY, clippedWidth, clippedHeight)).floor();
};
return Rect;
}());
exports.Rect = Rect;
//# sourceMappingURL=Rect.js.map
\ No newline at end of file
build/Rect.js.map deleted 100644 → 0
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\ No newline at end of file
build/allFacesFactory.d.ts
import { TNetInput } from 'tfjs-image-recognition-base';
import { TinyYolov2Types } from 'tfjs-tiny-yolov2';
import { TinyYolov2 } from '.';
import { FullFaceDescription } from './classes/FullFaceDescription';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { FullFaceDescription } from './FullFaceDescription';
import { Mtcnn } from './mtcnn/Mtcnn';
import { MtcnnForwardParams } from './mtcnn/types';
import { TinyYolov2ForwardParams } from './tinyYolov2/types';
import { TNetInput } from './types';
export declare function allFacesSsdMobilenetv1Factory(ssdMobilenetv1: FaceDetectionNet, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare function allFacesTinyYolov2Factory(tinyYolov2: TinyYolov2, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, forwardParams?: TinyYolov2ForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare function allFacesTinyYolov2Factory(tinyYolov2: TinyYolov2, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, forwardParams?: TinyYolov2Types.TinyYolov2ForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare function allFacesMtcnnFactory(mtcnn: Mtcnn, recognitionNet: FaceRecognitionNet): (input: TNetInput, mtcnnForwardParams?: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
build/allFacesFactory.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var extractFaceTensors_1 = require("./extractFaceTensors");
var FullFaceDescription_1 = require("./FullFaceDescription");
import * as tslib_1 from "tslib";
import { Point } from 'tfjs-image-recognition-base';
import { FullFaceDescription } from './classes/FullFaceDescription';
import { extractFaceTensors } from './dom';
function computeDescriptorsFactory(recognitionNet) {
return function (input, alignedFaceBoxes, useBatchProcessing) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var alignedFaceTensors, descriptors, _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, alignedFaceBoxes)];
case 0: return [4 /*yield*/, extractFaceTensors(input, alignedFaceBoxes)];
case 1:
alignedFaceTensors = _b.sent();
if (!useBatchProcessing) return [3 /*break*/, 3];
......@@ -41,7 +40,7 @@ function allFacesFactory(detectFaces, landmarkNet, recognitionNet) {
case 0: return [4 /*yield*/, detectFaces(input)];
case 1:
detections = _b.sent();
return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, detections)];
return [4 /*yield*/, extractFaceTensors(input, detections)];
case 2:
faceTensors = _b.sent();
if (!useBatchProcessing) return [3 /*break*/, 4];
......@@ -61,14 +60,14 @@ function allFacesFactory(detectFaces, landmarkNet, recognitionNet) {
case 7:
descriptors = _b.sent();
return [2 /*return*/, detections.map(function (detection, i) {
return new FullFaceDescription_1.FullFaceDescription(detection, faceLandmarksByFace[i].shiftByPoint(detection.getBox()), descriptors[i]);
return new FullFaceDescription(detection, faceLandmarksByFace[i].shiftByPoint(new Point(detection.box.x, detection.box.y)), descriptors[i]);
})];
}
});
});
};
}
function allFacesSsdMobilenetv1Factory(ssdMobilenetv1, landmarkNet, recognitionNet) {
export function allFacesSsdMobilenetv1Factory(ssdMobilenetv1, landmarkNet, recognitionNet) {
return function (input, minConfidence, useBatchProcessing) {
if (minConfidence === void 0) { minConfidence = 0.8; }
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
......@@ -82,8 +81,7 @@ function allFacesSsdMobilenetv1Factory(ssdMobilenetv1, landmarkNet, recognitionN
});
};
}
exports.allFacesSsdMobilenetv1Factory = allFacesSsdMobilenetv1Factory;
function allFacesTinyYolov2Factory(tinyYolov2, landmarkNet, recognitionNet) {
export function allFacesTinyYolov2Factory(tinyYolov2, landmarkNet, recognitionNet) {
return function (input, forwardParams, useBatchProcessing) {
if (forwardParams === void 0) { forwardParams = {}; }
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
......@@ -97,8 +95,7 @@ function allFacesTinyYolov2Factory(tinyYolov2, landmarkNet, recognitionNet) {
});
};
}
exports.allFacesTinyYolov2Factory = allFacesTinyYolov2Factory;
function allFacesMtcnnFactory(mtcnn, recognitionNet) {
export function allFacesMtcnnFactory(mtcnn, recognitionNet) {
var computeDescriptors = computeDescriptorsFactory(recognitionNet);
return function (input, mtcnnForwardParams, useBatchProcessing) {
if (mtcnnForwardParams === void 0) { mtcnnForwardParams = {}; }
......@@ -119,12 +116,11 @@ function allFacesMtcnnFactory(mtcnn, recognitionNet) {
descriptors = _a.sent();
return [2 /*return*/, results.map(function (_a, i) {
var faceDetection = _a.faceDetection, faceLandmarks = _a.faceLandmarks;
return new FullFaceDescription_1.FullFaceDescription(faceDetection, faceLandmarks, descriptors[i]);
return new FullFaceDescription(faceDetection, faceLandmarks, descriptors[i]);
})];
}
});
});
};
}
exports.allFacesMtcnnFactory = allFacesMtcnnFactory;
//# sourceMappingURL=allFacesFactory.js.map
\ No newline at end of file
build/allFacesFactory.js.map
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\ No newline at end of file
build/classes/FaceDetection.d.ts 0 → 100644
import { Dimensions, ObjectDetection, Rect } from 'tfjs-image-recognition-base';
export declare class FaceDetection extends ObjectDetection {
constructor(score: number, relativeBox: Rect, imageDims: Dimensions);
}
build/classes/FaceDetection.js 0 → 100644
import * as tslib_1 from "tslib";
import { ObjectDetection } from 'tfjs-image-recognition-base';
var FaceDetection = /** @class */ (function (_super) {
tslib_1.__extends(FaceDetection, _super);
function FaceDetection(score, relativeBox, imageDims) {
return _super.call(this, score, score, '', relativeBox, imageDims) || this;
}
return FaceDetection;
}(ObjectDetection));
export { FaceDetection };
//# sourceMappingURL=FaceDetection.js.map
\ No newline at end of file
build/classes/FaceDetection.js.map 0 → 100644
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\ No newline at end of file
build/FaceLandmarks.d.ts → build/classes/FaceLandmarks.d.ts
import { Dimensions, Point, Rect } from 'tfjs-image-recognition-base';
import { FaceDetection } from './FaceDetection';
import { IPoint, Point } from './Point';
import { Rect } from './Rect';
import { Dimensions } from './types';
export declare class FaceLandmarks {
protected _imageWidth: number;
protected _imageHeight: number;
......@@ -15,7 +13,7 @@ export declare class FaceLandmarks {
getRelativePositions(): Point[];
forSize<T extends FaceLandmarks>(width: number, height: number): T;
shift<T extends FaceLandmarks>(x: number, y: number): T;
shiftByPoint<T extends FaceLandmarks>(pt: IPoint): T;
shiftByPoint<T extends FaceLandmarks>(pt: Point): T;
/**
* Aligns the face landmarks after face detection from the relative positions of the faces
* bounding box, or it's current shift. This function should be used to align the face images
......
build/FaceLandmarks.js → build/classes/FaceLandmarks.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var getCenterPoint_1 = require("./commons/getCenterPoint");
var FaceDetection_1 = require("./FaceDetection");
var Point_1 = require("./Point");
var Rect_1 = require("./Rect");
import { getCenterPoint, Point, Rect } from 'tfjs-image-recognition-base';
import { FaceDetection } from './FaceDetection';
// face alignment constants
var relX = 0.5;
var relY = 0.43;
var relScale = 0.45;
var FaceLandmarks = /** @class */ (function () {
function FaceLandmarks(relativeFaceLandmarkPositions, imageDims, shift) {
if (shift === void 0) { shift = new Point_1.Point(0, 0); }
if (shift === void 0) { shift = new Point(0, 0); }
var width = imageDims.width, height = imageDims.height;
this._imageWidth = width;
this._imageHeight = height;
this._shift = shift;
this._faceLandmarks = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new Point_1.Point(width, height)).add(shift); });
this._faceLandmarks = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new Point(width, height)).add(shift); });
}
FaceLandmarks.prototype.getShift = function () {
return new Point_1.Point(this._shift.x, this._shift.y);
return new Point(this._shift.x, this._shift.y);
};
FaceLandmarks.prototype.getImageWidth = function () {
return this._imageWidth;
......@@ -31,13 +27,13 @@ var FaceLandmarks = /** @class */ (function () {
};
FaceLandmarks.prototype.getRelativePositions = function () {
var _this = this;
return this._faceLandmarks.map(function (pt) { return pt.sub(_this._shift).div(new Point_1.Point(_this._imageWidth, _this._imageHeight)); });
return this._faceLandmarks.map(function (pt) { return pt.sub(_this._shift).div(new Point(_this._imageWidth, _this._imageHeight)); });
};
FaceLandmarks.prototype.forSize = function (width, height) {
return new this.constructor(this.getRelativePositions(), { width: width, height: height });
};
FaceLandmarks.prototype.shift = function (x, y) {
return new this.constructor(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new Point_1.Point(x, y));
return new this.constructor(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new Point(x, y));
};
FaceLandmarks.prototype.shiftByPoint = function (pt) {
return this.shift(pt.x, pt.y);
......@@ -55,7 +51,7 @@ var FaceLandmarks = /** @class */ (function () {
*/
FaceLandmarks.prototype.align = function (detection) {
if (detection) {
var box = detection instanceof FaceDetection_1.FaceDetection
var box = detection instanceof FaceDetection
? detection.getBox().floor()
: detection;
return this.shift(box.x, box.y).align();
......@@ -65,16 +61,16 @@ var FaceLandmarks = /** @class */ (function () {
var distToMouth = function (pt) { return mouthCenter.sub(pt).magnitude(); };
var eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;
var size = Math.floor(eyeToMouthDist / relScale);
var refPoint = getCenterPoint_1.getCenterPoint(centers);
var refPoint = getCenterPoint(centers);
// TODO: pad in case rectangle is out of image bounds
var x = Math.floor(Math.max(0, refPoint.x - (relX * size)));
var y = Math.floor(Math.max(0, refPoint.y - (relY * size)));
return new Rect_1.Rect(x, y, Math.min(size, this._imageWidth + x), Math.min(size, this._imageHeight + y));
return new Rect(x, y, Math.min(size, this._imageWidth + x), Math.min(size, this._imageHeight + y));
};
FaceLandmarks.prototype.getRefPointsForAlignment = function () {
throw new Error('getRefPointsForAlignment not implemented by base class');
};
return FaceLandmarks;
}());
exports.FaceLandmarks = FaceLandmarks;
export { FaceLandmarks };
//# sourceMappingURL=FaceLandmarks.js.map
\ No newline at end of file
build/classes/FaceLandmarks.js.map 0 → 100644
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\ No newline at end of file
build/mtcnn/FaceLandmarks5.d.ts → build/classes/FaceLandmarks5.d.ts
import { FaceLandmarks } from '../FaceLandmarks';
import { Point } from '../Point';
import { Point } from 'tfjs-image-recognition-base';
import { FaceLandmarks } from './FaceLandmarks';
export declare class FaceLandmarks5 extends FaceLandmarks {
protected getRefPointsForAlignment(): Point[];
}
build/mtcnn/FaceLandmarks5.js → build/classes/FaceLandmarks5.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var getCenterPoint_1 = require("../commons/getCenterPoint");
var FaceLandmarks_1 = require("../FaceLandmarks");
import * as tslib_1 from "tslib";
import { getCenterPoint } from 'tfjs-image-recognition-base';
import { FaceLandmarks } from './FaceLandmarks';
var FaceLandmarks5 = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmarks5, _super);
function FaceLandmarks5() {
......@@ -13,10 +11,10 @@ var FaceLandmarks5 = /** @class */ (function (_super) {
return [
pts[0],
pts[1],
getCenterPoint_1.getCenterPoint([pts[3], pts[4]])
getCenterPoint([pts[3], pts[4]])
];
};
return FaceLandmarks5;
}(FaceLandmarks_1.FaceLandmarks));
exports.FaceLandmarks5 = FaceLandmarks5;
}(FaceLandmarks));
export { FaceLandmarks5 };
//# sourceMappingURL=FaceLandmarks5.js.map
\ No newline at end of file
build/classes/FaceLandmarks5.js.map 0 → 100644
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\ No newline at end of file
build/faceLandmarkNet/FaceLandmarks68.d.ts → build/classes/FaceLandmarks68.d.ts
import { FaceLandmarks } from '../FaceLandmarks';
import { Point } from '../Point';
import { Point } from 'tfjs-image-recognition-base';
import { FaceLandmarks } from '../classes/FaceLandmarks';
export declare class FaceLandmarks68 extends FaceLandmarks {
getJawOutline(): Point[];
getLeftEyeBrow(): Point[];
......
build/faceLandmarkNet/FaceLandmarks68.js → build/classes/FaceLandmarks68.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var getCenterPoint_1 = require("../commons/getCenterPoint");
var FaceLandmarks_1 = require("../FaceLandmarks");
import * as tslib_1 from "tslib";
import { getCenterPoint } from 'tfjs-image-recognition-base';
import { FaceLandmarks } from '../classes/FaceLandmarks';
var FaceLandmarks68 = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmarks68, _super);
function FaceLandmarks68() {
......@@ -34,9 +32,9 @@ var FaceLandmarks68 = /** @class */ (function (_super) {
this.getLeftEye(),
this.getRightEye(),
this.getMouth()
].map(getCenterPoint_1.getCenterPoint);
].map(getCenterPoint);
};
return FaceLandmarks68;
}(FaceLandmarks_1.FaceLandmarks));
exports.FaceLandmarks68 = FaceLandmarks68;
}(FaceLandmarks));
export { FaceLandmarks68 };
//# sourceMappingURL=FaceLandmarks68.js.map
\ No newline at end of file
build/classes/FaceLandmarks68.js.map 0 → 100644
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\ No newline at end of file
build/FullFaceDescription.js → build/classes/FullFaceDescription.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var FullFaceDescription = /** @class */ (function () {
function FullFaceDescription(_detection, _landmarks, _descriptor) {
this._detection = _detection;
......@@ -32,5 +30,5 @@ var FullFaceDescription = /** @class */ (function () {
};
return FullFaceDescription;
}());
exports.FullFaceDescription = FullFaceDescription;
export { FullFaceDescription };
//# sourceMappingURL=FullFaceDescription.js.map
\ No newline at end of file
build/classes/FullFaceDescription.js.map 0 → 100644
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\ No newline at end of file
build/classes/index.d.ts 0 → 100644
export * from './FaceDetection';
export * from './FaceLandmarks';
export * from './FaceLandmarks5';
export * from './FaceLandmarks68';
export * from './FullFaceDescription';
build/classes/index.js 0 → 100644
export * from './FaceDetection';
export * from './FaceLandmarks';
export * from './FaceLandmarks5';
export * from './FaceLandmarks68';
export * from './FullFaceDescription';
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/classes/index.js.map 0 → 100644
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\ No newline at end of file
build/commons/NeuralNetwork.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { ParamMapping } from './types';
export declare class NeuralNetwork<TNetParams> {
private _name;
protected _params: TNetParams | undefined;
protected _paramMappings: ParamMapping[];
constructor(_name: string);
readonly params: TNetParams | undefined;
readonly paramMappings: ParamMapping[];
getParamFromPath(paramPath: string): tf.Tensor;
reassignParamFromPath(paramPath: string, tensor: tf.Tensor): void;
getParamList(): {
path: string;
tensor: tf.Tensor<tf.Rank>;
}[];
getTrainableParams(): {
path: string;
tensor: tf.Tensor<tf.Rank>;
}[];
getFrozenParams(): {
path: string;
tensor: tf.Tensor<tf.Rank>;
}[];
variable(): void;
freeze(): void;
dispose(throwOnRedispose?: boolean): void;
load(weightsOrUrl: Float32Array | string | undefined): Promise<void>;
extractWeights(weights: Float32Array): void;
private traversePropertyPath(paramPath);
protected loadQuantizedParams(_: any): Promise<{
params: TNetParams;
paramMappings: ParamMapping[];
}>;
protected extractParams(_: any): {
params: TNetParams;
paramMappings: ParamMapping[];
};
}
build/commons/NeuralNetwork.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var NeuralNetwork = /** @class */ (function () {
function NeuralNetwork(_name) {
this._name = _name;
this._params = undefined;
this._paramMappings = [];
}
Object.defineProperty(NeuralNetwork.prototype, "params", {
get: function () {
return this._params;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NeuralNetwork.prototype, "paramMappings", {
get: function () {
return this._paramMappings;
},
enumerable: true,
configurable: true
});
NeuralNetwork.prototype.getParamFromPath = function (paramPath) {
var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;
return obj[objProp];
};
NeuralNetwork.prototype.reassignParamFromPath = function (paramPath, tensor) {
var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;
obj[objProp].dispose();
obj[objProp] = tensor;
};
NeuralNetwork.prototype.getParamList = function () {
var _this = this;
return this._paramMappings.map(function (_a) {
var paramPath = _a.paramPath;
return ({
path: paramPath,
tensor: _this.getParamFromPath(paramPath)
});
});
};
NeuralNetwork.prototype.getTrainableParams = function () {
return this.getParamList().filter(function (param) { return param.tensor instanceof tf.Variable; });
};
NeuralNetwork.prototype.getFrozenParams = function () {
return this.getParamList().filter(function (param) { return !(param.tensor instanceof tf.Variable); });
};
NeuralNetwork.prototype.variable = function () {
var _this = this;
this.getFrozenParams().forEach(function (_a) {
var path = _a.path, tensor = _a.tensor;
_this.reassignParamFromPath(path, tf.variable(tensor));
});
};
NeuralNetwork.prototype.freeze = function () {
var _this = this;
this.getTrainableParams().forEach(function (_a) {
var path = _a.path, tensor = _a.tensor;
_this.reassignParamFromPath(path, tf.tensor(tensor));
});
};
NeuralNetwork.prototype.dispose = function (throwOnRedispose) {
if (throwOnRedispose === void 0) { throwOnRedispose = true; }
this.getParamList().forEach(function (param) {
if (throwOnRedispose && param.tensor.isDisposed) {
throw new Error("param tensor has already been disposed for path " + param.path);
}
param.tensor.dispose();
});
this._params = undefined;
};
NeuralNetwork.prototype.load = function (weightsOrUrl) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a, paramMappings, params;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
if (weightsOrUrl instanceof Float32Array) {
this.extractWeights(weightsOrUrl);
return [2 /*return*/];
}
if (weightsOrUrl && typeof weightsOrUrl !== 'string') {
throw new Error(this._name + ".load - expected model uri, or weights as Float32Array");
}
return [4 /*yield*/, this.loadQuantizedParams(weightsOrUrl)];
case 1:
_a = _b.sent(), paramMappings = _a.paramMappings, params = _a.params;
this._paramMappings = paramMappings;
this._params = params;
return [2 /*return*/];
}
});
});
};
NeuralNetwork.prototype.extractWeights = function (weights) {
var _a = this.extractParams(weights), paramMappings = _a.paramMappings, params = _a.params;
this._paramMappings = paramMappings;
this._params = params;
};
NeuralNetwork.prototype.traversePropertyPath = function (paramPath) {
if (!this.params) {
throw new Error("traversePropertyPath - model has no loaded params");
}
var result = paramPath.split('/').reduce(function (res, objProp) {
if (!res.nextObj.hasOwnProperty(objProp)) {
throw new Error("traversePropertyPath - object does not have property " + objProp + ", for path " + paramPath);
}
return { obj: res.nextObj, objProp: objProp, nextObj: res.nextObj[objProp] };
}, { nextObj: this.params });
var obj = result.obj, objProp = result.objProp;
if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {
throw new Error("traversePropertyPath - parameter is not a tensor, for path " + paramPath);
}
return { obj: obj, objProp: objProp };
};
NeuralNetwork.prototype.loadQuantizedParams = function (_) {
throw new Error(this._name + ".loadQuantizedParams - not implemented");
};
NeuralNetwork.prototype.extractParams = function (_) {
throw new Error(this._name + ".extractParams - not implemented");
};
return NeuralNetwork;
}());
exports.NeuralNetwork = NeuralNetwork;
//# sourceMappingURL=NeuralNetwork.js.map
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build/commons/NeuralNetwork.js.map deleted 100644 → 0
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build/commons/convLayer.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from './types';
export declare function convLayer(x: tf.Tensor4D, params: ConvParams, padding?: 'valid' | 'same', withRelu?: boolean): tf.Tensor4D;
build/commons/convLayer.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function convLayer(x, params, padding, withRelu) {
if (padding === void 0) { padding = 'same'; }
if (withRelu === void 0) { withRelu = false; }
return tf.tidy(function () {
var out = tf.add(tf.conv2d(x, params.filters, [1, 1], padding), params.bias);
return withRelu ? tf.relu(out) : out;
});
}
exports.convLayer = convLayer;
//# sourceMappingURL=convLayer.js.map
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build/commons/convLayer.js.map deleted 100644 → 0
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build/commons/disposeUnusedWeightTensors.d.ts deleted 100644 → 0
import { ParamMapping } from './types';
export declare function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]): void;
build/commons/disposeUnusedWeightTensors.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function disposeUnusedWeightTensors(weightMap, paramMappings) {
Object.keys(weightMap).forEach(function (path) {
if (!paramMappings.some(function (pm) { return pm.originalPath === path; })) {
weightMap[path].dispose();
}
});
}
exports.disposeUnusedWeightTensors = disposeUnusedWeightTensors;
//# sourceMappingURL=disposeUnusedWeightTensors.js.map
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build/commons/disposeUnusedWeightTensors.js.map deleted 100644 → 0
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build/commons/extractConvParamsFactory.d.ts deleted 100644 → 0
import { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';
export declare function extractConvParamsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]): (channelsIn: number, channelsOut: number, filterSize: number, mappedPrefix: string) => ConvParams;
build/commons/extractConvParamsFactory.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function extractConvParamsFactory(extractWeights, paramMappings) {
return function (channelsIn, channelsOut, filterSize, mappedPrefix) {
var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
var bias = tf.tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
return { filters: filters, bias: bias };
};
}
exports.extractConvParamsFactory = extractConvParamsFactory;
//# sourceMappingURL=extractConvParamsFactory.js.map
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build/commons/extractConvParamsFactory.js.map deleted 100644 → 0
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build/commons/extractFCParamsFactory.d.ts deleted 100644 → 0
import { ExtractWeightsFunction, FCParams, ParamMapping } from './types';
export declare function extractFCParamsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]): (channelsIn: number, channelsOut: number, mappedPrefix: string) => FCParams;
build/commons/extractFCParamsFactory.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function extractFCParamsFactory(extractWeights, paramMappings) {
return function (channelsIn, channelsOut, mappedPrefix) {
var fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
var fc_bias = tf.tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/weights" }, { paramPath: mappedPrefix + "/bias" });
return {
weights: fc_weights,
bias: fc_bias
};
};
}
exports.extractFCParamsFactory = extractFCParamsFactory;
//# sourceMappingURL=extractFCParamsFactory.js.map
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build/commons/extractWeightEntry.d.ts deleted 100644 → 0
export declare function extractWeightEntry(weightMap: any, path: string, paramRank: number): {
path: string;
tensor: any;
};
build/commons/extractWeightEntry.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var isTensor_1 = require("./isTensor");
function extractWeightEntry(weightMap, path, paramRank) {
var tensor = weightMap[path];
if (!isTensor_1.isTensor(tensor, paramRank)) {
throw new Error("expected weightMap[" + path + "] to be a Tensor" + paramRank + "D, instead have " + tensor);
}
return { path: path, tensor: tensor };
}
exports.extractWeightEntry = extractWeightEntry;
//# sourceMappingURL=extractWeightEntry.js.map
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build/commons/extractWeightEntry.js.map deleted 100644 → 0
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build/commons/extractWeightEntryFactory.d.ts deleted 100644 → 0
import { ParamMapping } from './types';
export declare function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]): <T>(originalPath: string, paramRank: number, mappedPath?: string | undefined) => T;
build/commons/extractWeightEntryFactory.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var isTensor_1 = require("./isTensor");
function extractWeightEntryFactory(weightMap, paramMappings) {
return function (originalPath, paramRank, mappedPath) {
var tensor = weightMap[originalPath];
if (!isTensor_1.isTensor(tensor, paramRank)) {
throw new Error("expected weightMap[" + originalPath + "] to be a Tensor" + paramRank + "D, instead have " + tensor);
}
paramMappings.push({ originalPath: originalPath, paramPath: mappedPath || originalPath });
return tensor;
};
}
exports.extractWeightEntryFactory = extractWeightEntryFactory;
//# sourceMappingURL=extractWeightEntryFactory.js.map
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build/commons/extractWeightEntryFactory.js.map deleted 100644 → 0
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build/commons/extractWeightsFactory.d.ts deleted 100644 → 0
export declare function extractWeightsFactory(weights: Float32Array): {
extractWeights: (numWeights: number) => Float32Array;
getRemainingWeights: () => Float32Array;
};
build/commons/extractWeightsFactory.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function extractWeightsFactory(weights) {
var remainingWeights = weights;
function extractWeights(numWeights) {
var ret = remainingWeights.slice(0, numWeights);
remainingWeights = remainingWeights.slice(numWeights);
return ret;
}
function getRemainingWeights() {
return remainingWeights;
}
return {
extractWeights: extractWeights,
getRemainingWeights: getRemainingWeights
};
}
exports.extractWeightsFactory = extractWeightsFactory;
//# sourceMappingURL=extractWeightsFactory.js.map
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build/commons/extractWeightsFactory.js.map deleted 100644 → 0
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build/commons/getCenterPoint.d.ts deleted 100644 → 0
import { Point } from '../Point';
export declare function getCenterPoint(pts: Point[]): Point;
build/commons/getCenterPoint.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var Point_1 = require("../Point");
function getCenterPoint(pts) {
return pts.reduce(function (sum, pt) { return sum.add(pt); }, new Point_1.Point(0, 0))
.div(new Point_1.Point(pts.length, pts.length));
}
exports.getCenterPoint = getCenterPoint;
//# sourceMappingURL=getCenterPoint.js.map
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build/commons/getCenterPoint.js.map deleted 100644 → 0
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build/commons/iou.d.ts deleted 100644 → 0
import { BoundingBox } from '../BoundingBox';
export declare function iou(box1: BoundingBox, box2: BoundingBox, isIOU?: boolean): number;
build/commons/iou.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function iou(box1, box2, isIOU) {
if (isIOU === void 0) { isIOU = true; }
var width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left) + 1);
var height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top) + 1);
var interSection = width * height;
return isIOU
? interSection / (box1.area + box2.area - interSection)
: interSection / Math.min(box1.area, box2.area);
}
exports.iou = iou;
//# sourceMappingURL=iou.js.map
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build/commons/iou.js.map deleted 100644 → 0
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build/commons/isMediaElement.d.ts deleted 100644 → 0
export declare function isMediaElement(input: any): boolean;
build/commons/isMediaElement.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function isMediaElement(input) {
return input instanceof HTMLImageElement
|| input instanceof HTMLVideoElement
|| input instanceof HTMLCanvasElement;
}
exports.isMediaElement = isMediaElement;
//# sourceMappingURL=isMediaElement.js.map
\ No newline at end of file
build/commons/isMediaElement.js.map deleted 100644 → 0
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build/commons/isTensor.d.ts deleted 100644 → 0
export declare function isTensor(tensor: any, dim: number): boolean;
export declare function isTensor1D(tensor: any): boolean;
export declare function isTensor2D(tensor: any): boolean;
export declare function isTensor3D(tensor: any): boolean;
export declare function isTensor4D(tensor: any): boolean;
build/commons/isTensor.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function isTensor(tensor, dim) {
return tensor instanceof tf.Tensor && tensor.shape.length === dim;
}
exports.isTensor = isTensor;
function isTensor1D(tensor) {
return isTensor(tensor, 1);
}
exports.isTensor1D = isTensor1D;
function isTensor2D(tensor) {
return isTensor(tensor, 2);
}
exports.isTensor2D = isTensor2D;
function isTensor3D(tensor) {
return isTensor(tensor, 3);
}
exports.isTensor3D = isTensor3D;
function isTensor4D(tensor) {
return isTensor(tensor, 4);
}
exports.isTensor4D = isTensor4D;
//# sourceMappingURL=isTensor.js.map
\ No newline at end of file
build/commons/isTensor.js.map deleted 100644 → 0
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build/commons/loadWeightMap.d.ts deleted 100644 → 0
export declare function getModelUris(uri: string | undefined, defaultModelName: string): {
modelBaseUri: string;
manifestUri: string;
};
export declare function loadWeightMap(uri: string | undefined, defaultModelName: string): Promise<any>;
build/commons/loadWeightMap.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
function getModelUris(uri, defaultModelName) {
var defaultManifestFilename = defaultModelName + "-weights_manifest.json";
if (!uri) {
return {
modelBaseUri: '',
manifestUri: defaultManifestFilename
};
}
if (uri === '/') {
return {
modelBaseUri: '/',
manifestUri: "/" + defaultManifestFilename
};
}
var protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';
uri = uri.replace(protocol, '');
var parts = uri.split('/').filter(function (s) { return s; });
var manifestFile = uri.endsWith('.json')
? parts[parts.length - 1]
: defaultManifestFilename;
var modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');
modelBaseUri = uri.startsWith('/') ? "/" + modelBaseUri : modelBaseUri;
return {
modelBaseUri: modelBaseUri,
manifestUri: modelBaseUri === '/' ? "/" + manifestFile : modelBaseUri + "/" + manifestFile
};
}
exports.getModelUris = getModelUris;
function loadWeightMap(uri, defaultModelName) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a, manifestUri, modelBaseUri, manifest;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = getModelUris(uri, defaultModelName), manifestUri = _a.manifestUri, modelBaseUri = _a.modelBaseUri;
return [4 /*yield*/, fetch(manifestUri)];
case 1: return [4 /*yield*/, (_b.sent()).json()];
case 2:
manifest = _b.sent();
return [2 /*return*/, tf.io.loadWeights(manifest, modelBaseUri)];
}
});
});
}
exports.loadWeightMap = loadWeightMap;
//# sourceMappingURL=loadWeightMap.js.map
\ No newline at end of file
build/commons/loadWeightMap.js.map deleted 100644 → 0
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build/commons/nonMaxSuppression.d.ts deleted 100644 → 0
import { BoundingBox } from '../BoundingBox';
export declare function nonMaxSuppression(boxes: BoundingBox[], scores: number[], iouThreshold: number, isIOU?: boolean): number[];
build/commons/nonMaxSuppression.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var iou_1 = require("../iou");
function nonMaxSuppression(boxes, scores, iouThreshold, isIOU) {
if (isIOU === void 0) { isIOU = true; }
var indicesSortedByScore = scores
.map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })
.sort(function (c1, c2) { return c1.score - c2.score; })
.map(function (c) { return c.boxIndex; });
var pick = [];
var _loop_1 = function () {
var curr = indicesSortedByScore.pop();
pick.push(curr);
var indices = indicesSortedByScore;
var outputs = [];
for (var i = 0; i < indices.length; i++) {
var idx = indices[i];
var currBox = boxes[curr];
var idxBox = boxes[idx];
outputs.push(iou_1.iou(currBox, idxBox, isIOU));
}
indicesSortedByScore = indicesSortedByScore.filter(function (_, j) { return outputs[j] <= iouThreshold; });
};
while (indicesSortedByScore.length > 0) {
_loop_1();
}
return pick;
}
exports.nonMaxSuppression = nonMaxSuppression;
//# sourceMappingURL=nonMaxSuppression.js.map
\ No newline at end of file
build/commons/nonMaxSuppression.js.map deleted 100644 → 0
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build/commons/normalize.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
export declare function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D;
build/commons/normalize.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function normalize(x, meanRgb) {
return tf.tidy(function () {
var r = meanRgb[0], g = meanRgb[1], b = meanRgb[2];
var avg_r = tf.fill(x.shape.slice(0, 3).concat([1]), r);
var avg_g = tf.fill(x.shape.slice(0, 3).concat([1]), g);
var avg_b = tf.fill(x.shape.slice(0, 3).concat([1]), b);
var avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);
return tf.sub(x, avg_rgb);
});
}
exports.normalize = normalize;
//# sourceMappingURL=normalize.js.map
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build/commons/normalize.js.map deleted 100644 → 0
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build/commons/types.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
export declare type ConvParams = {
filters: tf.Tensor4D;
bias: tf.Tensor1D;
};
export declare type FCParams = {
weights: tf.Tensor2D;
bias: tf.Tensor1D;
};
export declare type ExtractWeightsFunction = (numWeights: number) => Float32Array;
export declare type BatchReshapeInfo = {
originalWidth: number;
originalHeight: number;
paddingX: number;
paddingY: number;
};
export declare type ParamMapping = {
originalPath?: string;
paramPath: string;
};
build/commons/types.js.map deleted 100644 → 0
{"version":3,"file":"types.js","sourceRoot":"","sources":["../../src/commons/types.ts"],"names":[],"mappings":""}
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build/dom/drawContour.d.ts 0 → 100644
import { Point } from 'tfjs-image-recognition-base';
export declare function drawContour(ctx: CanvasRenderingContext2D, points: Point[], isClosed?: boolean): void;
build/dom/drawContour.js 0 → 100644
export function drawContour(ctx, points, isClosed) {
if (isClosed === void 0) { isClosed = false; }
ctx.beginPath();
points.slice(1).forEach(function (_a, prevIdx) {
var x = _a.x, y = _a.y;
var from = points[prevIdx];
ctx.moveTo(from.x, from.y);
ctx.lineTo(x, y);
});
if (isClosed) {
var from = points[points.length - 1];
var to = points[0];
if (!from || !to) {
return;
}
ctx.moveTo(from.x, from.y);
ctx.lineTo(to.x, to.y);
}
ctx.stroke();
}
//# sourceMappingURL=drawContour.js.map
\ No newline at end of file
build/dom/drawContour.js.map 0 → 100644
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build/dom/drawLandmarks.d.ts 0 → 100644
import { FaceLandmarks } from '../classes/FaceLandmarks';
import { DrawLandmarksOptions } from './types';
export declare function drawLandmarks(canvasArg: string | HTMLCanvasElement, faceLandmarks: FaceLandmarks | FaceLandmarks[], options?: DrawLandmarksOptions): void;
build/dom/drawLandmarks.js 0 → 100644
import { getContext2dOrThrow, getDefaultDrawOptions, resolveInput } from 'tfjs-image-recognition-base';
import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
import { drawContour } from './drawContour';
export function drawLandmarks(canvasArg, faceLandmarks, options) {
var canvas = resolveInput(canvasArg);
if (!(canvas instanceof HTMLCanvasElement)) {
throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement');
}
var drawOptions = Object.assign(getDefaultDrawOptions(options), (options || {}));
var drawLines = Object.assign({ drawLines: false }, (options || {})).drawLines;
var ctx = getContext2dOrThrow(canvas);
var lineWidth = drawOptions.lineWidth, _a = drawOptions.color, color = _a === void 0 ? 'blue' : _a;
var faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];
faceLandmarksArray.forEach(function (landmarks) {
if (drawLines && landmarks instanceof FaceLandmarks68) {
ctx.strokeStyle = color;
ctx.lineWidth = lineWidth;
drawContour(ctx, landmarks.getJawOutline());
drawContour(ctx, landmarks.getLeftEyeBrow());
drawContour(ctx, landmarks.getRightEyeBrow());
drawContour(ctx, landmarks.getNose());
drawContour(ctx, landmarks.getLeftEye(), true);
drawContour(ctx, landmarks.getRightEye(), true);
drawContour(ctx, landmarks.getMouth(), true);
return;
}
// 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); });
});
}
//# sourceMappingURL=drawLandmarks.js.map
\ No newline at end of file
build/dom/drawLandmarks.js.map 0 → 100644
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\ No newline at end of file
build/extractFaceTensors.d.ts → build/dom/extractFaceTensors.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { FaceDetection } from './FaceDetection';
import { Rect } from './Rect';
import { TNetInput } from './types';
import { Rect, TNetInput } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection';
/**
* Extracts the tensors of the image regions containing the detected faces.
* Useful if you want to compute the face descriptors for the face images.
......
build/extractFaceTensors.js → build/dom/extractFaceTensors.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var FaceDetection_1 = require("./FaceDetection");
var toNetInput_1 = require("./toNetInput");
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { toNetInput } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection';
/**
* Extracts the tensors of the image regions containing the detected faces.
* Useful if you want to compute the face descriptors for the face images.
......@@ -14,12 +12,12 @@ var toNetInput_1 = require("./toNetInput");
* @param detections The face detection results or face bounding boxes for that image.
* @returns Tensors of the corresponding image region for each detected face.
*/
function extractFaceTensors(input, detections) {
export function extractFaceTensors(input, detections) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var netInput;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
case 0: return [4 /*yield*/, toNetInput(input, true)];
case 1:
netInput = _a.sent();
if (netInput.batchSize > 1) {
......@@ -31,13 +29,13 @@ function extractFaceTensors(input, detections) {
return [2 /*return*/, tf.tidy(function () {
var imgTensor = netInput.inputs[0].expandDims().toFloat();
var _a = imgTensor.shape.slice(1), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];
var boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
var boxes = detections.map(function (det) { return det instanceof FaceDetection
? det.forSize(imgWidth, imgHeight).getBox()
: det; })
.map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); });
var faceTensors = boxes.map(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
return tf.slice(imgTensor, [0, y, x, 0], [1, height, width, numChannels]);
return tf.slice4d(imgTensor, [0, y, x, 0], [1, height, width, numChannels]);
});
if (netInput.isManaged) {
netInput.dispose();
......@@ -48,5 +46,4 @@ function extractFaceTensors(input, detections) {
});
});
}
exports.extractFaceTensors = extractFaceTensors;
//# sourceMappingURL=extractFaceTensors.js.map
\ No newline at end of file
build/dom/extractFaceTensors.js.map 0 → 100644
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\ No newline at end of file
build/extractFaces.d.ts → build/dom/extractFaces.d.ts
import { FaceDetection } from './FaceDetection';
import { Rect } from './Rect';
import { TNetInput } from './types';
import { Rect, TNetInput } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection';
/**
* Extracts the image regions containing the detected faces.
*
......
build/extractFaces.js → build/dom/extractFaces.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var FaceDetection_1 = require("./FaceDetection");
var toNetInput_1 = require("./toNetInput");
var utils_1 = require("./utils");
import * as tslib_1 from "tslib";
import { createCanvas, getContext2dOrThrow, imageTensorToCanvas, toNetInput, } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection';
/**
* Extracts the image regions containing the detected faces.
*
......@@ -11,7 +8,7 @@ var utils_1 = require("./utils");
* @param detections The face detection results or face bounding boxes for that image.
* @returns The Canvases of the corresponding image region for each detected face.
*/
function extractFaces(input, detections) {
export function extractFaces(input, detections) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var canvas, netInput, ctx, boxes;
return tslib_1.__generator(this, function (_a) {
......@@ -19,7 +16,7 @@ function extractFaces(input, detections) {
case 0:
canvas = input;
if (!!(input instanceof HTMLCanvasElement)) return [3 /*break*/, 3];
return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
return [4 /*yield*/, toNetInput(input, true)];
case 1:
netInput = _a.sent();
if (netInput.batchSize > 1) {
......@@ -28,7 +25,7 @@ function extractFaces(input, detections) {
}
throw new Error('extractFaces - batchSize > 1 not supported');
}
return [4 /*yield*/, utils_1.imageTensorToCanvas(netInput.inputs[0])];
return [4 /*yield*/, imageTensorToCanvas(netInput.inputs[0])];
case 2:
canvas = _a.sent();
if (netInput.isManaged) {
......@@ -36,15 +33,15 @@ function extractFaces(input, detections) {
}
_a.label = 3;
case 3:
ctx = utils_1.getContext2dOrThrow(canvas);
boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
ctx = getContext2dOrThrow(canvas);
boxes = detections.map(function (det) { return det instanceof FaceDetection
? det.forSize(canvas.width, canvas.height).getBox().floor()
: det; })
.map(function (box) { return box.clipAtImageBorders(canvas.width, canvas.height); });
return [2 /*return*/, boxes.map(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var faceImg = utils_1.createCanvas({ width: width, height: height });
utils_1.getContext2dOrThrow(faceImg)
var faceImg = createCanvas({ width: width, height: height });
getContext2dOrThrow(faceImg)
.putImageData(ctx.getImageData(x, y, width, height), 0, 0);
return faceImg;
})];
......@@ -52,5 +49,4 @@ function extractFaces(input, detections) {
});
});
}
exports.extractFaces = extractFaces;
//# sourceMappingURL=extractFaces.js.map
\ No newline at end of file
build/dom/extractFaces.js.map 0 → 100644
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\ No newline at end of file
build/dom/index.d.ts 0 → 100644
export * from './drawContour';
export * from './drawLandmarks';
export * from './extractFaces';
export * from './extractFaceTensors';
export * from './types';
build/dom/index.js 0 → 100644
export * from './drawContour';
export * from './drawLandmarks';
export * from './extractFaces';
export * from './extractFaceTensors';
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/dom/index.js.map 0 → 100644
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\ No newline at end of file
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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package-lock.json
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