check in latest build

build/BoundingBox.d.ts

-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

-"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

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\ No newline at end of file

build/FaceDetection.d.ts

-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

-"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

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build/FaceLandmarks.js.map

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build/FullFaceDescription.js.map

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build/NetInput.d.ts

-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

-"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

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\ No newline at end of file

build/Point.d.ts

-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

-"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

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\ No newline at end of file

build/Rect.d.ts

-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

-"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

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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

+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

+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

+{"version":3,"file":"FaceDetection.js","sourceRoot":"","sources":["../../src/classes/FaceDetection.ts"],"names":[],"mappings":";AAAA,OAAO,EAAc,eAAe,EAAQ,MAAM,6BAA6B,CAAC;AAEhF;IAAmC,yCAAe;IAChD,uBACE,KAAa,EACb,WAAiB,EACjB,SAAqB;eAErB,kBAAM,KAAK,EAAE,KAAK,EAAE,EAAE,EAAE,WAAW,EAAE,SAAS,CAAC;IACjD,CAAC;IACH,oBAAC;AAAD,CAAC,AARD,CAAmC,eAAe,GAQjD"}
\ 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

+{"version":3,"file":"FaceLandmarks.js","sourceRoot":"","sources":["../../src/classes/FaceLandmarks.ts"],"names":[],"mappings":"AAAA,OAAO,EAAc,cAAc,EAAE,KAAK,EAAE,IAAI,EAAE,MAAM,6BAA6B,CAAC;AAEtF,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAEhD,2BAA2B;AAC3B,IAAM,IAAI,GAAG,GAAG,CAAA;AAChB,IAAM,IAAI,GAAG,IAAI,CAAA;AACjB,IAAM,QAAQ,GAAG,IAAI,CAAA;AAErB;IAME,uBACE,6BAAsC,EACtC,SAAqB,EACrB,KAA8B;QAA9B,sBAAA,EAAA,YAAmB,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC;QAEtB,IAAA,uBAAK,EAAE,yBAAM,CAAc;QACnC,IAAI,CAAC,WAAW,GAAG,KAAK,CAAA;QACxB,IAAI,CAAC,YAAY,GAAG,MAAM,CAAA;QAC1B,IAAI,CAAC,MAAM,GAAG,KAAK,CAAA;QACnB,IAAI,CAAC,cAAc,GAAG,6BAA6B,CAAC,GAAG,CACrD,UAAA,EAAE,IAAI,OAAA,EAAE,CAAC,GAAG,CAAC,IAAI,KAAK,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC,CAAC,GAAG,CAAC,KAAK,CAAC,EAA3C,CAA2C,CAClD,CAAA;IACH,CAAC;IAEM,gCAAQ,GAAf;QACE,OAAO,IAAI,KAAK,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC,EAAE,IAAI,CAAC,MAAM,CAAC,CAAC,CAAC,CAAA;IAChD,CAAC;IAEM,qCAAa,GAApB;QACE,OAAO,IAAI,CAAC,WAAW,CAAA;IACzB,CAAC;IAEM,sCAAc,GAArB;QACE,OAAO,IAAI,CAAC,YAAY,CAAA;IAC1B,CAAC;IAEM,oCAAY,GAAnB;QACE,OAAO,IAAI,CAAC,cAAc,CAAA;IAC5B,CAAC;IAEM,4CAAoB,GAA3B;QAAA,iBAIC;QAHC,OAAO,IAAI,CAAC,cAAc,CAAC,GAAG,CAC5B,UAAA,EAAE,IAAI,OAAA,EAAE,CAAC,GAAG,CAAC,KAAI,CAAC,MAAM,CAAC,CAAC,GAAG,CAAC,IAAI,KAAK,CAAC,KAAI,CAAC,WAAW,EAAE,KAAI,CAAC,YAAY,CAAC,CAAC,EAAvE,CAAuE,CAC9E,CAAA;IACH,CAAC;IAEM,+BAAO,GAAd,UAAwC,KAAa,EAAE,MAAc;QACnE,OAAO,IAAK,IAAI,CAAC,WAAmB,CAClC,IAAI,CAAC,oBAAoB,EAAE,EAC3B,EAAE,KAAK,OAAA,EAAE,MAAM,QAAA,EAAE,CAClB,CAAA;IACH,CAAC;IAEM,6BAAK,GAAZ,UAAsC,CAAS,EAAE,CAAS;QACxD,OAAO,IAAK,IAAI,CAAC,WAAmB,CAClC,IAAI,CAAC,oBAAoB,EAAE,EAC3B,EAAE,KAAK,EAAE,IAAI,CAAC,WAAW,EAAE,MAAM,EAAE,IAAI,CAAC,YAAY,EAAE,EACtD,IAAI,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,CAChB,CAAA;IACH,CAAC;IAEM,oCAAY,GAAnB,UAA6C,EAAS;QACpD,OAAO,IAAI,CAAC,KAAK,CAAC,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC,CAAA;IAC/B,CAAC;IAED;;;;;;;;;;OAUG;IACI,6BAAK,GAAZ,UACE,SAAgC;QAEhC,IAAI,SAAS,EAAE;YACb,IAAM,GAAG,GAAG,SAAS,YAAY,aAAa;gBAC5C,CAAC,CAAC,SAAS,CAAC,MAAM,EAAE,CAAC,KAAK,EAAE;gBAC5B,CAAC,CAAC,SAAS,CAAA;YAEb,OAAO,IAAI,CAAC,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC,CAAC,KAAK,EAAE,CAAA;SACxC;QAED,IAAM,OAAO,GAAG,IAAI,CAAC,wBAAwB,EAAE,CAAA;QAExC,IAAA,0BAAa,EAAE,2BAAc,EAAE,wBAAW,CAAW;QAC5D,IAAM,WAAW,GAAG,UAAC,EAAS,IAAK,OAAA,WAAW,CAAC,GAAG,CAAC,EAAE,CAAC,CAAC,SAAS,EAAE,EAA/B,CAA+B,CAAA;QAClE,IAAM,cAAc,GAAG,CAAC,WAAW,CAAC,aAAa,CAAC,GAAG,WAAW,CAAC,cAAc,CAAC,CAAC,GAAG,CAAC,CAAA;QAErF,IAAM,IAAI,GAAG,IAAI,CAAC,KAAK,CAAC,cAAc,GAAG,QAAQ,CAAC,CAAA;QAElD,IAAM,QAAQ,GAAG,cAAc,CAAC,OAAO,CAAC,CAAA;QACxC,qDAAqD;QACrD,IAAM,CAAC,GAAG,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,QAAQ,CAAC,CAAC,GAAG,CAAC,IAAI,GAAG,IAAI,CAAC,CAAC,CAAC,CAAA;QAC7D,IAAM,CAAC,GAAG,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,QAAQ,CAAC,CAAC,GAAG,CAAC,IAAI,GAAG,IAAI,CAAC,CAAC,CAAC,CAAA;QAE7D,OAAO,IAAI,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,IAAI,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,WAAW,GAAG,CAAC,CAAC,EAAE,IAAI,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,YAAY,GAAG,CAAC,CAAC,CAAC,CAAA;IACpG,CAAC;IAES,gDAAwB,GAAlC;QACE,MAAM,IAAI,KAAK,CAAC,wDAAwD,CAAC,CAAA;IAC3E,CAAC;IACH,oBAAC;AAAD,CAAC,AAtGD,IAsGC"}
\ 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

+{"version":3,"file":"FaceLandmarks5.js","sourceRoot":"","sources":["../../src/classes/FaceLandmarks5.ts"],"names":[],"mappings":";AAAA,OAAO,EAAE,cAAc,EAAS,MAAM,6BAA6B,CAAC;AAEpE,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAEhD;IAAoC,0CAAa;IAAjD;;IAUA,CAAC;IARW,iDAAwB,GAAlC;QACE,IAAM,GAAG,GAAG,IAAI,CAAC,YAAY,EAAE,CAAA;QAC/B,OAAO;YACL,GAAG,CAAC,CAAC,CAAC;YACN,GAAG,CAAC,CAAC,CAAC;YACN,cAAc,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC;SACjC,CAAA;IACH,CAAC;IACH,qBAAC;AAAD,CAAC,AAVD,CAAoC,aAAa,GAUhD"}
\ 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

+{"version":3,"file":"FaceLandmarks68.js","sourceRoot":"","sources":["../../src/classes/FaceLandmarks68.ts"],"names":[],"mappings":";AAAA,OAAO,EAAE,cAAc,EAAS,MAAM,6BAA6B,CAAC;AAEpE,OAAO,EAAE,aAAa,EAAE,MAAM,0BAA0B,CAAC;AAGzD;IAAqC,2CAAa;IAAlD;;IAoCA,CAAC;IAnCQ,uCAAa,GAApB;QACE,OAAO,IAAI,CAAC,cAAc,CAAC,KAAK,CAAC,CAAC,EAAE,EAAE,CAAC,CAAA;IACzC,CAAC;IAEM,wCAAc,GAArB;QACE,OAAO,IAAI,CAAC,cAAc,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IAC1C,CAAC;IAEM,yCAAe,GAAtB;QACE,OAAO,IAAI,CAAC,cAAc,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IAC1C,CAAC;IAEM,iCAAO,GAAd;QACE,OAAO,IAAI,CAAC,cAAc,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IAC1C,CAAC;IAEM,oCAAU,GAAjB;QACE,OAAO,IAAI,CAAC,cAAc,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IAC1C,CAAC;IAEM,qCAAW,GAAlB;QACE,OAAO,IAAI,CAAC,cAAc,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IAC1C,CAAC;IAEM,kCAAQ,GAAf;QACE,OAAO,IAAI,CAAC,cAAc,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IAC1C,CAAC;IAES,kDAAwB,GAAlC;QACE,OAAO;YACL,IAAI,CAAC,UAAU,EAAE;YACjB,IAAI,CAAC,WAAW,EAAE;YAClB,IAAI,CAAC,QAAQ,EAAE;SAChB,CAAC,GAAG,CAAC,cAAc,CAAC,CAAA;IACvB,CAAC;IACH,sBAAC;AAAD,CAAC,AApCD,CAAqC,aAAa,GAoCjD"}
\ 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

+{"version":3,"file":"FullFaceDescription.js","sourceRoot":"","sources":["../../src/classes/FullFaceDescription.ts"],"names":[],"mappings":"AAGA;IACE,6BACU,UAAyB,EACzB,UAAyB,EACzB,WAAyB;QAFzB,eAAU,GAAV,UAAU,CAAe;QACzB,eAAU,GAAV,UAAU,CAAe;QACzB,gBAAW,GAAX,WAAW,CAAc;IAChC,CAAC;IAEJ,sBAAW,0CAAS;aAApB;YACE,OAAO,IAAI,CAAC,UAAU,CAAA;QACxB,CAAC;;;OAAA;IAED,sBAAW,0CAAS;aAApB;YACE,OAAO,IAAI,CAAC,UAAU,CAAA;QACxB,CAAC;;;OAAA;IAED,sBAAW,2CAAU;aAArB;YACE,OAAO,IAAI,CAAC,WAAW,CAAA;QACzB,CAAC;;;OAAA;IAEM,qCAAO,GAAd,UAAe,KAAa,EAAE,MAAc;QAC1C,OAAO,IAAI,mBAAmB,CAC5B,IAAI,CAAC,UAAU,CAAC,OAAO,CAAC,KAAK,EAAE,MAAM,CAAC,EACtC,IAAI,CAAC,UAAU,CAAC,OAAO,CAAC,KAAK,EAAE,MAAM,CAAC,EACtC,IAAI,CAAC,WAAW,CACjB,CAAA;IACH,CAAC;IACH,0BAAC;AAAD,CAAC,AA1BD,IA0BC"}
\ No newline at end of file

build/classes/index.d.ts

+export * from './FaceDetection';
+export * from './FaceLandmarks';
+export * from './FaceLandmarks5';
+export * from './FaceLandmarks68';
+export * from './FullFaceDescription';

build/classes/index.js

+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

+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/classes/index.ts"],"names":[],"mappings":"AAAA,cAAc,iBAAiB,CAAC;AAChC,cAAc,iBAAiB,CAAC;AAChC,cAAc,kBAAkB,CAAC;AACjC,cAAc,mBAAmB,CAAC;AAClC,cAAc,uBAAuB,CAAC"}
\ No newline at end of file

build/commons/NeuralNetwork.d.ts

-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

-"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
\ No newline at end of file

build/commons/NeuralNetwork.js.map

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\ No newline at end of file

build/commons/convLayer.d.ts

-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

-"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
\ No newline at end of file

build/commons/convLayer.js.map

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\ No newline at end of file

build/commons/disposeUnusedWeightTensors.d.ts

-import { ParamMapping } from './types';
-export declare function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]): void;

build/commons/disposeUnusedWeightTensors.js

-"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
\ No newline at end of file

build/commons/disposeUnusedWeightTensors.js.map

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\ No newline at end of file

build/commons/extractConvParamsFactory.d.ts

-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

-"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
\ No newline at end of file

build/commons/extractConvParamsFactory.js.map

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\ No newline at end of file

build/commons/extractFCParamsFactory.d.ts

-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

-"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
\ No newline at end of file

build/commons/extractFCParamsFactory.js.map

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\ No newline at end of file

build/commons/extractWeightEntry.d.ts

-export declare function extractWeightEntry(weightMap: any, path: string, paramRank: number): {
-    path: string;
-    tensor: any;
-};

build/commons/extractWeightEntry.js

-"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
\ No newline at end of file

build/commons/extractWeightEntry.js.map

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\ No newline at end of file

build/commons/extractWeightEntryFactory.d.ts

-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

-"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
\ No newline at end of file

build/commons/extractWeightEntryFactory.js.map

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\ No newline at end of file

build/commons/extractWeightsFactory.d.ts

-export declare function extractWeightsFactory(weights: Float32Array): {
-    extractWeights: (numWeights: number) => Float32Array;
-    getRemainingWeights: () => Float32Array;
-};

build/commons/extractWeightsFactory.js

-"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
\ No newline at end of file

build/commons/extractWeightsFactory.js.map

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\ No newline at end of file

build/commons/getCenterPoint.d.ts

-import { Point } from '../Point';
-export declare function getCenterPoint(pts: Point[]): Point;

build/commons/getCenterPoint.js

-"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
\ No newline at end of file

build/commons/getCenterPoint.js.map

-{"version":3,"file":"getCenterPoint.js","sourceRoot":"","sources":["../../src/commons/getCenterPoint.ts"],"names":[],"mappings":";;AAAA,kCAAiC;AAEjC,wBAA+B,GAAY;IACzC,OAAO,GAAG,CAAC,MAAM,CAAC,UAAC,GAAG,EAAE,EAAE,IAAK,OAAA,GAAG,CAAC,GAAG,CAAC,EAAE,CAAC,EAAX,CAAW,EAAE,IAAI,aAAK,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;SACzD,GAAG,CAAC,IAAI,aAAK,CAAC,GAAG,CAAC,MAAM,EAAE,GAAG,CAAC,MAAM,CAAC,CAAC,CAAA;AAC3C,CAAC;AAHD,wCAGC"}
\ No newline at end of file

build/commons/iou.d.ts

-import { BoundingBox } from '../BoundingBox';
-export declare function iou(box1: BoundingBox, box2: BoundingBox, isIOU?: boolean): number;

build/commons/iou.js

-"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
\ No newline at end of file

build/commons/iou.js.map

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\ No newline at end of file

build/commons/isMediaElement.d.ts

-export declare function isMediaElement(input: any): boolean;

build/commons/isMediaElement.js

-"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

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\ No newline at end of file

build/commons/isTensor.d.ts

-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

-"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

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\ No newline at end of file

build/commons/loadWeightMap.d.ts

-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

-"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

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\ No newline at end of file

build/commons/nonMaxSuppression.d.ts

-import { BoundingBox } from '../BoundingBox';
-export declare function nonMaxSuppression(boxes: BoundingBox[], scores: number[], iouThreshold: number, isIOU?: boolean): number[];

build/commons/nonMaxSuppression.js

-"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

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\ No newline at end of file

build/commons/normalize.d.ts

-import * as tf from '@tensorflow/tfjs-core';
-export declare function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D;

build/commons/normalize.js

-"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
\ No newline at end of file

build/commons/normalize.js.map

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\ No newline at end of file

build/commons/types.d.ts

-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

-{"version":3,"file":"types.js","sourceRoot":"","sources":["../../src/commons/types.ts"],"names":[],"mappings":""}
\ No newline at end of file

build/dom/drawContour.d.ts

+import { Point } from 'tfjs-image-recognition-base';
+export declare function drawContour(ctx: CanvasRenderingContext2D, points: Point[], isClosed?: boolean): void;

build/dom/drawContour.js

+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

+{"version":3,"file":"drawContour.js","sourceRoot":"","sources":["../../src/dom/drawContour.ts"],"names":[],"mappings":"AAEA,MAAM,sBACJ,GAA6B,EAC7B,MAAe,EACf,QAAyB;IAAzB,yBAAA,EAAA,gBAAyB;IAEzB,GAAG,CAAC,SAAS,EAAE,CAAA;IAEf,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,UAAC,EAAQ,EAAE,OAAO;YAAf,QAAC,EAAE,QAAC;QAC7B,IAAM,IAAI,GAAG,MAAM,CAAC,OAAO,CAAC,CAAA;QAC5B,GAAG,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC,CAAC,CAAA;QAC1B,GAAG,CAAC,MAAM,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;IAClB,CAAC,CAAC,CAAA;IAEF,IAAI,QAAQ,EAAE;QACZ,IAAM,IAAI,GAAG,MAAM,CAAC,MAAM,CAAC,MAAM,GAAG,CAAC,CAAC,CAAA;QACtC,IAAM,EAAE,GAAG,MAAM,CAAC,CAAC,CAAC,CAAA;QACpB,IAAI,CAAC,IAAI,IAAI,CAAC,EAAE,EAAE;YAChB,OAAM;SACP;QAED,GAAG,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC,CAAC,CAAA;QAC1B,GAAG,CAAC,MAAM,CAAC,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC,CAAA;KACvB;IAED,GAAG,CAAC,MAAM,EAAE,CAAA;AACd,CAAC"}
\ No newline at end of file

build/dom/drawLandmarks.d.ts

+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

+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

+{"version":3,"file":"drawLandmarks.js","sourceRoot":"","sources":["../../src/dom/drawLandmarks.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,mBAAmB,EAAE,qBAAqB,EAAE,YAAY,EAAE,MAAM,6BAA6B,CAAC;AAGvG,OAAO,EAAE,eAAe,EAAE,MAAM,4BAA4B,CAAC;AAC7D,OAAO,EAAE,WAAW,EAAE,MAAM,eAAe,CAAC;AAG5C,MAAM,wBACJ,SAAqC,EACrC,aAA8C,EAC9C,OAA8B;IAE9B,IAAM,MAAM,GAAG,YAAY,CAAC,SAAS,CAAC,CAAA;IACtC,IAAI,CAAC,CAAC,MAAM,YAAY,iBAAiB,CAAC,EAAE;QAC1C,MAAM,IAAI,KAAK,CAAC,kEAAkE,CAAC,CAAA;KACpF;IAED,IAAM,WAAW,GAAG,MAAM,CAAC,MAAM,CAC/B,qBAAqB,CAAC,OAAO,CAAC,EAC9B,CAAC,OAAO,IAAI,EAAE,CAAC,CAChB,CAAA;IAEO,IAAA,0EAAS,CAAyD;IAE1E,IAAM,GAAG,GAAG,mBAAmB,CAAC,MAAM,CAAC,CAAA;IAC/B,IAAA,iCAAS,EAAE,sBAAc,EAAd,mCAAc,CAAgB;IAEjD,IAAM,kBAAkB,GAAG,KAAK,CAAC,OAAO,CAAC,aAAa,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,CAAA;IAEzF,kBAAkB,CAAC,OAAO,CAAC,UAAA,SAAS;QAClC,IAAI,SAAS,IAAI,SAAS,YAAY,eAAe,EAAE;YACrD,GAAG,CAAC,WAAW,GAAG,KAAK,CAAA;YACvB,GAAG,CAAC,SAAS,GAAG,SAAS,CAAA;YACzB,WAAW,CAAC,GAAG,EAAE,SAAS,CAAC,aAAa,EAAE,CAAC,CAAA;YAC3C,WAAW,CAAC,GAAG,EAAE,SAAS,CAAC,cAAc,EAAE,CAAC,CAAA;YAC5C,WAAW,CAAC,GAAG,EAAE,SAAS,CAAC,eAAe,EAAE,CAAC,CAAA;YAC7C,WAAW,CAAC,GAAG,EAAE,SAAS,CAAC,OAAO,EAAE,CAAC,CAAA;YACrC,WAAW,CAAC,GAAG,EAAE,SAAS,CAAC,UAAU,EAAE,EAAE,IAAI,CAAC,CAAA;YAC9C,WAAW,CAAC,GAAG,EAAE,SAAS,CAAC,WAAW,EAAE,EAAE,IAAI,CAAC,CAAA;YAC/C,WAAW,CAAC,GAAG,EAAE,SAAS,CAAC,QAAQ,EAAE,EAAE,IAAI,CAAC,CAAA;YAC5C,OAAM;SACP;QAED,mBAAmB;QACnB,IAAM,QAAQ,GAAG,SAAS,GAAG,CAAC,CAAA;QAC9B,GAAG,CAAC,SAAS,GAAG,KAAK,CAAA;QACrB,SAAS,CAAC,YAAY,EAAE,CAAC,OAAO,CAAC,UAAA,EAAE,IAAI,OAAA,GAAG,CAAC,QAAQ,CAAC,EAAE,CAAC,CAAC,GAAG,QAAQ,EAAE,EAAE,CAAC,CAAC,GAAG,QAAQ,EAAE,SAAS,EAAE,SAAS,CAAC,EAApE,CAAoE,CAAC,CAAA;IAC9G,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ 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

+{"version":3,"file":"extractFaceTensors.js","sourceRoot":"","sources":["../../src/dom/extractFaceTensors.ts"],"names":[],"mappings":";AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAC5C,OAAO,EAAmB,UAAU,EAAE,MAAM,6BAA6B,CAAC;AAE1E,OAAO,EAAE,aAAa,EAAE,MAAM,0BAA0B,CAAC;AAEzD;;;;;;;;;GASG;AACH,MAAM,6BACJ,KAAgB,EAChB,UAAuC;;;;;wBAGtB,qBAAM,UAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;;oBAAxC,QAAQ,GAAG,SAA6B;oBAE9C,IAAI,QAAQ,CAAC,SAAS,GAAG,CAAC,EAAE;wBAC1B,IAAI,QAAQ,CAAC,SAAS,EAAE;4BACtB,QAAQ,CAAC,OAAO,EAAE,CAAA;yBACnB;wBACD,MAAM,IAAI,KAAK,CAAC,kDAAkD,CAAC,CAAA;qBACpE;oBAED,sBAAO,EAAE,CAAC,IAAI,CAAC;4BACb,IAAM,SAAS,GAAG,QAAQ,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,UAAU,EAAE,CAAC,OAAO,EAAiB,CAAA;4BAEpE,IAAA,6BAA6D,EAA5D,iBAAS,EAAE,gBAAQ,EAAE,mBAAW,CAA4B;4BAEnE,IAAM,KAAK,GAAG,UAAU,CAAC,GAAG,CAC1B,UAAA,GAAG,IAAI,OAAA,GAAG,YAAY,aAAa;gCACjC,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,QAAQ,EAAE,SAAS,CAAC,CAAC,MAAM,EAAE;gCAC3C,CAAC,CAAC,GAAG,EAFA,CAEA,CACR;iCACE,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,CAAC,kBAAkB,CAAC,QAAQ,EAAE,SAAS,CAAC,EAA3C,CAA2C,CAAC,CAAA;4BAE1D,IAAM,WAAW,GAAG,KAAK,CAAC,GAAG,CAAC,UAAC,EAAuB;oCAArB,QAAC,EAAE,QAAC,EAAE,gBAAK,EAAE,kBAAM;gCAClD,OAAA,EAAE,CAAC,OAAO,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,MAAM,EAAE,KAAK,EAAE,WAAW,CAAC,CAAC;4BAApE,CAAoE,CACrE,CAAA;4BAED,IAAI,QAAQ,CAAC,SAAS,EAAE;gCACtB,QAAQ,CAAC,OAAO,EAAE,CAAA;6BACnB;4BACD,OAAO,WAAW,CAAA;wBACpB,CAAC,CAAC,EAAA;;;;CACH"}
\ 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

+{"version":3,"file":"extractFaces.js","sourceRoot":"","sources":["../../src/dom/extractFaces.ts"],"names":[],"mappings":";AAAA,OAAO,EACL,YAAY,EACZ,mBAAmB,EACnB,mBAAmB,EAGnB,UAAU,GACX,MAAM,6BAA6B,CAAC;AAErC,OAAO,EAAE,aAAa,EAAE,MAAM,0BAA0B,CAAC;AAEzD;;;;;;GAMG;AACH,MAAM,uBACJ,KAAgB,EAChB,UAAuC;;;;;;oBAGnC,MAAM,GAAG,KAA0B,CAAA;yBAEnC,CAAC,CAAC,KAAK,YAAY,iBAAiB,CAAC,EAArC,wBAAqC;oBACtB,qBAAM,UAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;;oBAAxC,QAAQ,GAAG,SAA6B;oBAE9C,IAAI,QAAQ,CAAC,SAAS,GAAG,CAAC,EAAE;wBAC1B,IAAI,QAAQ,CAAC,SAAS,EAAE;4BACtB,QAAQ,CAAC,OAAO,EAAE,CAAA;yBACnB;wBACD,MAAM,IAAI,KAAK,CAAC,4CAA4C,CAAC,CAAA;qBAC9D;oBAEQ,qBAAM,mBAAmB,CAAC,QAAQ,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,EAAA;;oBAAtD,MAAM,GAAG,SAA6C,CAAA;oBAEtD,IAAI,QAAQ,CAAC,SAAS,EAAE;wBACtB,QAAQ,CAAC,OAAO,EAAE,CAAA;qBACnB;;;oBAGG,GAAG,GAAG,mBAAmB,CAAC,MAAM,CAAC,CAAA;oBAEjC,KAAK,GAAG,UAAU,CAAC,GAAG,CAC1B,UAAA,GAAG,IAAI,OAAA,GAAG,YAAY,aAAa;wBACjC,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,MAAM,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,CAAC,CAAC,MAAM,EAAE,CAAC,KAAK,EAAE;wBAC3D,CAAC,CAAC,GAAG,EAFA,CAEA,CACR;yBACE,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,CAAC,kBAAkB,CAAC,MAAM,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,CAAC,EAAnD,CAAmD,CAAC,CAAA;oBAElE,sBAAO,KAAK,CAAC,GAAG,CAAC,UAAC,EAAuB;gCAArB,QAAC,EAAE,QAAC,EAAE,gBAAK,EAAE,kBAAM;4BACrC,IAAM,OAAO,GAAG,YAAY,CAAC,EAAE,KAAK,OAAA,EAAE,MAAM,QAAA,EAAE,CAAC,CAAA;4BAC/C,mBAAmB,CAAC,OAAO,CAAC;iCACzB,YAAY,CAAC,GAAG,CAAC,YAAY,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,MAAM,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;4BAC5D,OAAO,OAAO,CAAA;wBAChB,CAAC,CAAC,EAAA;;;;CACH"}
\ No newline at end of file

build/dom/index.d.ts

+export * from './drawContour';
+export * from './drawLandmarks';
+export * from './extractFaces';
+export * from './extractFaceTensors';
+export * from './types';

build/dom/index.js

+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

+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/dom/index.ts"],"names":[],"mappings":"AAAA,cAAc,eAAe,CAAA;AAC7B,cAAc,iBAAiB,CAAA;AAC/B,cAAc,gBAAgB,CAAA;AAC9B,cAAc,sBAAsB,CAAA"}
\ No newline at end of file

build/dom/types.d.ts

+export declare type DrawLandmarksOptions = {
+    lineWidth?: number;
+    color?: string;
+    drawLines?: boolean;
+};

build/commons/types.js → build/dom/types.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
 //# sourceMappingURL=types.js.map
\ No newline at end of file

build/dom/types.js.map

+{"version":3,"file":"types.js","sourceRoot":"","sources":["../../src/dom/types.ts"],"names":[],"mappings":""}
\ No newline at end of file

build/drawing/index.d.ts

-import { FaceDetection } from '../FaceDetection';
-import { FaceLandmarks } from '../FaceLandmarks';
-import { DrawBoxOptions, DrawLandmarksOptions, DrawOptions, DrawTextOptions } from './types';
-export declare function getDefaultDrawOptions(): DrawOptions;
-export declare function drawBox(ctx: CanvasRenderingContext2D, x: number, y: number, w: number, h: number, options: DrawBoxOptions): void;
-export declare function drawText(ctx: CanvasRenderingContext2D, x: number, y: number, text: string, options: DrawTextOptions): void;
-export declare function drawDetection(canvasArg: string | HTMLCanvasElement, detection: FaceDetection | FaceDetection[], options?: DrawBoxOptions & DrawTextOptions & {
-    withScore: boolean;
-}): void;
-export declare function drawLandmarks(canvasArg: string | HTMLCanvasElement, faceLandmarks: FaceLandmarks | FaceLandmarks[], options?: DrawLandmarksOptions & {
-    drawLines: boolean;
-}): void;

build/drawing/index.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var faceLandmarkNet_1 = require("../faceLandmarkNet");
-var utils_1 = require("../utils");
-function getDefaultDrawOptions() {
-    return {
-        color: 'blue',
-        lineWidth: 2,
-        fontSize: 20,
-        fontStyle: 'Georgia'
-    };
-}
-exports.getDefaultDrawOptions = getDefaultDrawOptions;
-function drawBox(ctx, x, y, w, h, options) {
-    var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
-    ctx.strokeStyle = drawOptions.color;
-    ctx.lineWidth = drawOptions.lineWidth;
-    ctx.strokeRect(x, y, w, h);
-}
-exports.drawBox = drawBox;
-function drawText(ctx, x, y, text, options) {
-    var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
-    var padText = 2 + drawOptions.lineWidth;
-    ctx.fillStyle = drawOptions.color;
-    ctx.font = drawOptions.fontSize + "px " + drawOptions.fontStyle;
-    ctx.fillText(text, x + padText, y + padText + (drawOptions.fontSize * 0.6));
-}
-exports.drawText = drawText;
-function drawDetection(canvasArg, detection, options) {
-    var canvas = utils_1.resolveInput(canvasArg);
-    if (!(canvas instanceof HTMLCanvasElement)) {
-        throw new Error('drawBox - expected canvas to be of type: HTMLCanvasElement');
-    }
-    var detectionArray = Array.isArray(detection)
-        ? detection
-        : [detection];
-    detectionArray.forEach(function (det) {
-        var _a = det.getBox(), x = _a.x, y = _a.y, width = _a.width, height = _a.height;
-        var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
-        var withScore = Object.assign({ withScore: true }, (options || {})).withScore;
-        var ctx = utils_1.getContext2dOrThrow(canvas);
-        drawBox(ctx, x, y, width, height, drawOptions);
-        if (withScore) {
-            drawText(ctx, x, y, "" + utils_1.round(det.getScore()), drawOptions);
-        }
-    });
-}
-exports.drawDetection = drawDetection;
-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();
-}
-function drawLandmarks(canvasArg, faceLandmarks, options) {
-    var canvas = utils_1.resolveInput(canvasArg);
-    if (!(canvas instanceof HTMLCanvasElement)) {
-        throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement');
-    }
-    var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
-    var drawLines = Object.assign({ drawLines: false }, (options || {})).drawLines;
-    var ctx = utils_1.getContext2dOrThrow(canvas);
-    var lineWidth = drawOptions.lineWidth, color = drawOptions.color;
-    var faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];
-    faceLandmarksArray.forEach(function (landmarks) {
-        if (drawLines && landmarks instanceof faceLandmarkNet_1.FaceLandmarks68) {
-            ctx.strokeStyle = color;
-            ctx.lineWidth = lineWidth;
-            drawContour(ctx, landmarks.getJawOutline());
-            drawContour(ctx, landmarks.getLeftEyeBrow());
-            drawContour(ctx, landmarks.getRightEyeBrow());
-            drawContour(ctx, landmarks.getNose());
-            drawContour(ctx, landmarks.getLeftEye(), true);
-            drawContour(ctx, landmarks.getRightEye(), true);
-            drawContour(ctx, landmarks.getMouth(), true);
-            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); });
-    });
-}
-exports.drawLandmarks = drawLandmarks;
-//# sourceMappingURL=index.js.map
\ No newline at end of file

build/drawing/index.js.map

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\ No newline at end of file

build/drawing/types.d.ts

-export declare type DrawBoxOptions = {
-    lineWidth?: number;
-    color?: string;
-};
-export declare type DrawTextOptions = {
-    lineWidth?: number;
-    fontSize?: number;
-    fontStyle?: string;
-    color?: string;
-};
-export declare type DrawLandmarksOptions = {
-    lineWidth?: number;
-    color?: string;
-};
-export declare type DrawOptions = {
-    lineWidth: number;
-    fontSize: number;
-    fontStyle: string;
-    color: string;
-};

build/drawing/types.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-//# sourceMappingURL=types.js.map
\ No newline at end of file

build/drawing/types.js.map

-{"version":3,"file":"types.js","sourceRoot":"","sources":["../../src/drawing/types.ts"],"names":[],"mappings":""}
\ No newline at end of file

build/euclideanDistance.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-function euclideanDistance(arr1, arr2) {
+export function euclideanDistance(arr1, arr2) {
     if (arr1.length !== arr2.length)
         throw new Error('euclideanDistance: arr1.length !== arr2.length');
     var desc1 = Array.from(arr1);
@@ -9,5 +7,4 @@ function euclideanDistance(arr1, arr2) {
         .map(function (val, i) { return val - desc2[i]; })
         .reduce(function (res, diff) { return res + Math.pow(diff, 2); }, 0));
 }
-exports.euclideanDistance = euclideanDistance;
 //# sourceMappingURL=euclideanDistance.js.map
\ No newline at end of file

build/euclideanDistance.js.map

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+{"version":3,"file":"euclideanDistance.js","sourceRoot":"","sources":["../src/euclideanDistance.ts"],"names":[],"mappings":"AAAA,MAAM,4BAA4B,IAA6B,EAAE,IAA6B;IAC5F,IAAI,IAAI,CAAC,MAAM,KAAK,IAAI,CAAC,MAAM;QAC7B,MAAM,IAAI,KAAK,CAAC,gDAAgD,CAAC,CAAA;IAEnE,IAAM,KAAK,GAAG,KAAK,CAAC,IAAI,CAAC,IAAI,CAAC,CAAA;IAC9B,IAAM,KAAK,GAAG,KAAK,CAAC,IAAI,CAAC,IAAI,CAAC,CAAA;IAE9B,OAAO,IAAI,CAAC,IAAI,CACd,KAAK;SACF,GAAG,CAAC,UAAC,GAAG,EAAE,CAAC,IAAK,OAAA,GAAG,GAAG,KAAK,CAAC,CAAC,CAAC,EAAd,CAAc,CAAC;SAC/B,MAAM,CAAC,UAAC,GAAG,EAAE,IAAI,IAAK,OAAA,GAAG,GAAG,IAAI,CAAC,GAAG,CAAC,IAAI,EAAE,CAAC,CAAC,EAAvB,CAAuB,EAAE,CAAC,CAAC,CACrD,CAAA;AACH,CAAC"}
\ No newline at end of file

build/extractFaceTensors.js.map

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\ No newline at end of file

build/extractFaces.js.map

-{"version":3,"file":"extractFaces.js","sourceRoot":"","sources":["../src/extractFaces.ts"],"names":[],"mappings":";;;AAAA,iDAAgD;AAEhD,2CAA0C;AAE1C,iCAAiF;AAEjF;;;;;;GAMG;AACH,sBACE,KAAgB,EAChB,UAAuC;;;;;;oBAGnC,MAAM,GAAG,KAA0B,CAAA;yBAEnC,CAAC,CAAC,KAAK,YAAY,iBAAiB,CAAC,EAArC,wBAAqC;oBACtB,qBAAM,uBAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;;oBAAxC,QAAQ,GAAG,SAA6B;oBAE9C,IAAI,QAAQ,CAAC,SAAS,GAAG,CAAC,EAAE;wBAC1B,IAAI,QAAQ,CAAC,SAAS,EAAE;4BACtB,QAAQ,CAAC,OAAO,EAAE,CAAA;yBACnB;wBACD,MAAM,IAAI,KAAK,CAAC,4CAA4C,CAAC,CAAA;qBAC9D;oBAEQ,qBAAM,2BAAmB,CAAC,QAAQ,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,EAAA;;oBAAtD,MAAM,GAAG,SAA6C,CAAA;oBAEtD,IAAI,QAAQ,CAAC,SAAS,EAAE;wBACtB,QAAQ,CAAC,OAAO,EAAE,CAAA;qBACnB;;;oBAGG,GAAG,GAAG,2BAAmB,CAAC,MAAM,CAAC,CAAA;oBAEjC,KAAK,GAAG,UAAU,CAAC,GAAG,CAC1B,UAAA,GAAG,IAAI,OAAA,GAAG,YAAY,6BAAa;wBACjC,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,MAAM,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,CAAC,CAAC,MAAM,EAAE,CAAC,KAAK,EAAE;wBAC3D,CAAC,CAAC,GAAG,EAFA,CAEA,CACR;yBACE,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,CAAC,kBAAkB,CAAC,MAAM,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,CAAC,EAAnD,CAAmD,CAAC,CAAA;oBAElE,sBAAO,KAAK,CAAC,GAAG,CAAC,UAAC,EAAuB;gCAArB,QAAC,EAAE,QAAC,EAAE,gBAAK,EAAE,kBAAM;4BACrC,IAAM,OAAO,GAAG,oBAAY,CAAC,EAAE,KAAK,OAAA,EAAE,MAAM,QAAA,EAAE,CAAC,CAAA;4BAC/C,2BAAmB,CAAC,OAAO,CAAC;iCACzB,YAAY,CAAC,GAAG,CAAC,YAAY,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,MAAM,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;4BAC5D,OAAO,OAAO,CAAA;wBAChB,CAAC,CAAC,EAAA;;;;CACH;AAvCD,oCAuCC"}
\ No newline at end of file

build/faceDetectionNet/FaceDetection.d.ts

-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);
-    getScore(): number;
-    getBox(): Rect;
-    getImageWidth(): number;
-    getImageHeight(): number;
-    getRelativeBox(): Rect;
-    forSize(width: number, height: number): FaceDetection;
-}

build/faceDetectionNet/FaceDetection.js

-"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);
-    }
-    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 new Rect_1.Rect(this._box.x / this._imageWidth, this._box.y / this._imageHeight, this._box.width / this._imageWidth, this._box.height / this._imageHeight);
-    };
-    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/faceDetectionNet/FaceDetection.js.map

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\ No newline at end of file

build/faceDetectionNet/FaceDetectionNet.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { NeuralNetwork } from '../commons/NeuralNetwork';
-import { FaceDetection } from '../FaceDetection';
-import { NetInput } from '../NetInput';
-import { TNetInput } from '../types';
+import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
+import { FaceDetection } from '../classes/FaceDetection';
 import { NetParams } from './types';
 export declare class FaceDetectionNet extends NeuralNetwork<NetParams> {
     constructor();

build/faceDetectionNet/FaceDetectionNet.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var NeuralNetwork_1 = require("../commons/NeuralNetwork");
-var FaceDetection_1 = require("../FaceDetection");
-var Rect_1 = require("../Rect");
-var toNetInput_1 = require("../toNetInput");
-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");
+import * as tslib_1 from "tslib";
+import * as tf from '@tensorflow/tfjs-core';
+import { NeuralNetwork, Rect, toNetInput } from 'tfjs-image-recognition-base';
+import { FaceDetection } from '../classes/FaceDetection';
+import { extractParams } from './extractParams';
+import { loadQuantizedParams } from './loadQuantizedParams';
+import { mobileNetV1 } from './mobileNetV1';
+import { nonMaxSuppression } from './nonMaxSuppression';
+import { outputLayer } from './outputLayer';
+import { predictionLayer } from './predictionLayer';
 var FaceDetectionNet = /** @class */ (function (_super) {
     tslib_1.__extends(FaceDetectionNet, _super);
     function FaceDetectionNet() {
@@ -25,9 +21,9 @@ var FaceDetectionNet = /** @class */ (function (_super) {
         return tf.tidy(function () {
             var batchTensor = input.toBatchTensor(512, false);
             var x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1));
-            var features = mobileNetV1_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);
+            var features = mobileNetV1(x, params.mobilenetv1);
+            var _a = predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
+            return outputLayer(boxPredictions, classPredictions, params.output_layer);
         });
     };
     FaceDetectionNet.prototype.forward = function (input) {
@@ -37,7 +33,7 @@ var FaceDetectionNet = /** @class */ (function (_super) {
                 switch (_b.label) {
                     case 0:
                         _a = this.forwardInput;
-                        return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
+                        return [4 /*yield*/, toNetInput(input, true)];
                     case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
                 }
             });
@@ -50,7 +46,7 @@ var FaceDetectionNet = /** @class */ (function (_super) {
             var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, paddings, results;
             return tslib_1.__generator(this, function (_d) {
                 switch (_d.label) {
-                    case 0: return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
+                    case 0: return [4 /*yield*/, toNetInput(input, true)];
                     case 1:
                         netInput = _d.sent();
                         _a = this.forwardInput(netInput), _boxes = _a.boxes, _scores = _a.scores;
@@ -65,7 +61,7 @@ var FaceDetectionNet = /** @class */ (function (_super) {
                     case 2:
                         scoresData = _c.apply(_b, [_d.sent()]);
                         iouThreshold = 0.5;
-                        indices = nonMaxSuppression_1.nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
+                        indices = nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
                         paddings = netInput.getRelativePaddings(0);
                         results = indices
                             .map(function (idx) {
@@ -77,7 +73,7 @@ var FaceDetectionNet = /** @class */ (function (_super) {
                                 Math.max(0, boxes.get(idx, 1)),
                                 Math.min(1.0, boxes.get(idx, 3))
                             ].map(function (val) { return val * paddings.x; }), left = _b[0], right = _b[1];
-                            return new FaceDetection_1.FaceDetection(scoresData[idx], new Rect_1.Rect(left, top, right - left, bottom - top), {
+                            return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), {
                                 height: netInput.getInputHeight(0),
                                 width: netInput.getInputWidth(0)
                             });
@@ -90,12 +86,12 @@ var FaceDetectionNet = /** @class */ (function (_super) {
         });
     };
     FaceDetectionNet.prototype.loadQuantizedParams = function (uri) {
-        return loadQuantizedParams_1.loadQuantizedParams(uri);
+        return loadQuantizedParams(uri);
     };
     FaceDetectionNet.prototype.extractParams = function (weights) {
-        return extractParams_1.extractParams(weights);
+        return extractParams(weights);
     };
     return FaceDetectionNet;
-}(NeuralNetwork_1.NeuralNetwork));
-exports.FaceDetectionNet = FaceDetectionNet;
+}(NeuralNetwork));
+export { FaceDetectionNet };
 //# sourceMappingURL=FaceDetectionNet.js.map
\ No newline at end of file

build/faceDetectionNet/FaceDetectionNet.js.map

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\ No newline at end of file

build/faceDetectionNet/boxPredictionLayer.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var convLayer_1 = require("../commons/convLayer");
-function boxPredictionLayer(x, params) {
+import * as tf from '@tensorflow/tfjs-core';
+import { convLayer } from 'tfjs-tiny-yolov2';
+export function boxPredictionLayer(x, params) {
     return tf.tidy(function () {
         var batchSize = x.shape[0];
-        var boxPredictionEncoding = tf.reshape(convLayer_1.convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
-        var classPrediction = tf.reshape(convLayer_1.convLayer(x, params.class_predictor), [batchSize, -1, 3]);
+        var boxPredictionEncoding = tf.reshape(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
+        var classPrediction = tf.reshape(convLayer(x, params.class_predictor), [batchSize, -1, 3]);
         return {
             boxPredictionEncoding: boxPredictionEncoding,
             classPrediction: classPrediction
         };
     });
 }
-exports.boxPredictionLayer = boxPredictionLayer;
 //# sourceMappingURL=boxPredictionLayer.js.map
\ No newline at end of file

build/faceDetectionNet/boxPredictionLayer.js.map

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+{"version":3,"file":"boxPredictionLayer.js","sourceRoot":"","sources":["../../src/faceDetectionNet/boxPredictionLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAC5C,OAAO,EAAE,SAAS,EAAE,MAAM,kBAAkB,CAAC;AAK7C,MAAM,6BACJ,CAAc,EACd,MAA2B;IAE3B,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAM,SAAS,GAAG,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;QAE5B,IAAM,qBAAqB,GAAG,EAAE,CAAC,OAAO,CACtC,SAAS,CAAC,CAAC,EAAE,MAAM,CAAC,sBAAsB,CAAC,EAC3C,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CACtB,CAAA;QACD,IAAM,eAAe,GAAG,EAAE,CAAC,OAAO,CAChC,SAAS,CAAC,CAAC,EAAE,MAAM,CAAC,eAAe,CAAC,EACpC,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CACnB,CAAA;QAED,OAAO;YACL,qBAAqB,uBAAA;YACrB,eAAe,iBAAA;SAChB,CAAA;IACH,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file

build/faceDetectionNet/extractParams.d.ts

-import { ParamMapping } from '../commons/types';
+import { ParamMapping } from 'tfjs-image-recognition-base';
 import { NetParams } from './types';
 export declare function extractParams(weights: Float32Array): {
     params: NetParams;

build/faceDetectionNet/extractParams.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
+import * as tf from '@tensorflow/tfjs-core';
+import { extractWeightsFactory } from 'tfjs-image-recognition-base';
 function extractorsFactory(extractWeights, paramMappings) {
     function extractDepthwiseConvParams(numChannels, mappedPrefix) {
         var filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
@@ -135,9 +133,9 @@ function extractorsFactory(extractWeights, paramMappings) {
         extractPredictionLayerParams: extractPredictionLayerParams
     };
 }
-function extractParams(weights) {
+export function extractParams(weights) {
     var paramMappings = [];
-    var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
+    var _a = 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();
@@ -158,5 +156,4 @@ function extractParams(weights) {
         paramMappings: paramMappings
     };
 }
-exports.extractParams = extractParams;
 //# sourceMappingURL=extractParams.js.map
\ No newline at end of file

build/faceDetectionNet/extractParams.js.map

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\ No newline at end of file

build/faceDetectionNet/index.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var FaceDetectionNet_1 = require("./FaceDetectionNet");
-tslib_1.__exportStar(require("./FaceDetectionNet"), exports);
-function createFaceDetectionNet(weights) {
-    var net = new FaceDetectionNet_1.FaceDetectionNet();
+import { FaceDetectionNet } from './FaceDetectionNet';
+export * from './FaceDetectionNet';
+export function createFaceDetectionNet(weights) {
+    var net = new FaceDetectionNet();
     net.extractWeights(weights);
     return net;
 }
-exports.createFaceDetectionNet = createFaceDetectionNet;
-function faceDetectionNet(weights) {
+export function faceDetectionNet(weights) {
     console.warn('faceDetectionNet(weights: Float32Array) will be deprecated in future, use createFaceDetectionNet instead');
     return createFaceDetectionNet(weights);
 }
-exports.faceDetectionNet = faceDetectionNet;
 //# sourceMappingURL=index.js.map
\ No newline at end of file

build/faceDetectionNet/index.js.map

-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/faceDetectionNet/index.ts"],"names":[],"mappings":";;;AAAA,uDAAsD;AAEtD,6DAAmC;AAEnC,gCAAuC,OAAqB;IAC1D,IAAM,GAAG,GAAG,IAAI,mCAAgB,EAAE,CAAA;IAClC,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAJD,wDAIC;AAED,0BAAiC,OAAqB;IACpD,OAAO,CAAC,IAAI,CAAC,0GAA0G,CAAC,CAAA;IACxH,OAAO,sBAAsB,CAAC,OAAO,CAAC,CAAA;AACxC,CAAC;AAHD,4CAGC"}
+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/faceDetectionNet/index.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,gBAAgB,EAAE,MAAM,oBAAoB,CAAC;AAEtD,cAAc,oBAAoB,CAAC;AAEnC,MAAM,iCAAiC,OAAqB;IAC1D,IAAM,GAAG,GAAG,IAAI,gBAAgB,EAAE,CAAA;IAClC,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAED,MAAM,2BAA2B,OAAqB;IACpD,OAAO,CAAC,IAAI,CAAC,0GAA0G,CAAC,CAAA;IACxH,OAAO,sBAAsB,CAAC,OAAO,CAAC,CAAA;AACxC,CAAC"}
\ No newline at end of file

build/faceDetectionNet/loadQuantizedParams.d.ts

-import { ParamMapping } from '../commons/types';
+import { ParamMapping } from 'tfjs-image-recognition-base';
 import { NetParams } from './types';
 export declare function loadQuantizedParams(uri: string | undefined): Promise<{
     params: NetParams;

build/faceDetectionNet/loadQuantizedParams.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var disposeUnusedWeightTensors_1 = require("../commons/disposeUnusedWeightTensors");
-var extractWeightEntryFactory_1 = require("../commons/extractWeightEntryFactory");
-var isTensor_1 = require("../commons/isTensor");
-var loadWeightMap_1 = require("../commons/loadWeightMap");
+import * as tslib_1 from "tslib";
+import { disposeUnusedWeightTensors, extractWeightEntryFactory, isTensor3D, loadWeightMap, } from 'tfjs-image-recognition-base';
 var DEFAULT_MODEL_NAME = 'ssd_mobilenetv1_model';
 function extractorsFactory(weightMap, paramMappings) {
-    var extractWeightEntry = extractWeightEntryFactory_1.extractWeightEntryFactory(weightMap, paramMappings);
+    var extractWeightEntry = 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");
@@ -85,19 +80,19 @@ function extractorsFactory(weightMap, paramMappings) {
         extractPredictionLayerParams: extractPredictionLayerParams
     };
 }
-function loadQuantizedParams(uri) {
+export function loadQuantizedParams(uri) {
     return tslib_1.__awaiter(this, void 0, void 0, function () {
         var weightMap, paramMappings, _a, extractMobilenetV1Params, extractPredictionLayerParams, extra_dim, params;
         return tslib_1.__generator(this, function (_b) {
             switch (_b.label) {
-                case 0: return [4 /*yield*/, loadWeightMap_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
+                case 0: return [4 /*yield*/, 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 (!isTensor_1.isTensor3D(extra_dim)) {
+                    if (!isTensor3D(extra_dim)) {
                         throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have " + extra_dim);
                     }
                     params = {
@@ -107,11 +102,10 @@ function loadQuantizedParams(uri) {
                             extra_dim: extra_dim
                         }
                     };
-                    disposeUnusedWeightTensors_1.disposeUnusedWeightTensors(weightMap, paramMappings);
+                    disposeUnusedWeightTensors(weightMap, paramMappings);
                     return [2 /*return*/, { params: params, paramMappings: paramMappings }];
             }
         });
     });
 }
-exports.loadQuantizedParams = loadQuantizedParams;
 //# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file

build/faceDetectionNet/loadQuantizedParams.js.map

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\ No newline at end of file

build/faceDetectionNet/mobileNetV1.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var pointwiseConvLayer_1 = require("./pointwiseConvLayer");
+import * as tf from '@tensorflow/tfjs-core';
+import { pointwiseConvLayer } from './pointwiseConvLayer';
 var epsilon = 0.0010000000474974513;
 function depthwiseConvLayer(x, params, strides) {
     return tf.tidy(function () {
@@ -13,10 +11,10 @@ function depthwiseConvLayer(x, params, strides) {
 function getStridesForLayerIdx(layerIdx) {
     return [2, 4, 6, 12].some(function (idx) { return idx === layerIdx; }) ? [2, 2] : [1, 1];
 }
-function mobileNetV1(x, params) {
+export function mobileNetV1(x, params) {
     return tf.tidy(function () {
         var conv11 = null;
-        var out = pointwiseConvLayer_1.pointwiseConvLayer(x, params.conv_0, [2, 2]);
+        var out = pointwiseConvLayer(x, params.conv_0, [2, 2]);
         var convPairParams = [
             params.conv_1,
             params.conv_2,
@@ -36,7 +34,7 @@ function mobileNetV1(x, params) {
             var layerIdx = i + 1;
             var depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
             out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);
-            out = pointwiseConvLayer_1.pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);
+            out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);
             if (layerIdx === 11) {
                 conv11 = out;
             }
@@ -50,5 +48,4 @@ function mobileNetV1(x, params) {
         };
     });
 }
-exports.mobileNetV1 = mobileNetV1;
 //# sourceMappingURL=mobileNetV1.js.map
\ No newline at end of file

build/faceDetectionNet/mobileNetV1.js.map

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\ No newline at end of file

build/faceDetectionNet/nonMaxSuppression.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+export function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
     var numBoxes = boxes.shape[0];
     var outputSize = Math.min(maxOutputSize, numBoxes);
     var candidates = scores
@@ -30,7 +28,6 @@ function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThre
     });
     return selected;
 }
-exports.nonMaxSuppression = nonMaxSuppression;
 function IOU(boxes, i, j) {
     var yminI = Math.min(boxes.get(i, 0), boxes.get(i, 2));
     var xminI = Math.min(boxes.get(i, 1), boxes.get(i, 3));

build/faceDetectionNet/nonMaxSuppression.js.map

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\ No newline at end of file

build/faceDetectionNet/outputLayer.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
+import * as tf from '@tensorflow/tfjs-core';
 function getCenterCoordinatesAndSizesLayer(x) {
     var vec = tf.unstack(tf.transpose(x, [1, 0]));
     var sizes = [
@@ -30,7 +28,7 @@ function decodeBoxesLayer(x0, x1) {
         tf.add(add1_out, div1_out)
     ]), [1, 0]);
 }
-function outputLayer(boxPredictions, classPredictions, params) {
+export function outputLayer(boxPredictions, classPredictions, params) {
     return tf.tidy(function () {
         var batchSize = boxPredictions.shape[0];
         var boxes = decodeBoxesLayer(tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), tf.reshape(boxPredictions, [-1, 4]));
@@ -46,5 +44,4 @@ function outputLayer(boxPredictions, classPredictions, params) {
         };
     });
 }
-exports.outputLayer = outputLayer;
 //# sourceMappingURL=outputLayer.js.map
\ No newline at end of file

build/faceDetectionNet/outputLayer.js.map

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+{"version":3,"file":"outputLayer.js","sourceRoot":"","sources":["../../src/faceDetectionNet/outputLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAK5C,2CAA2C,CAAc;IACvD,IAAM,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,SAAS,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;IAE/C,IAAM,KAAK,GAAG;QACZ,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC,CAAC;QACtB,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC,CAAC;KACvB,CAAA;IAED,IAAM,OAAO,GAAG;QACd,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;QAC9C,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;KAC/C,CAAA;IAED,OAAO;QACL,KAAK,OAAA;QACL,OAAO,SAAA;KACR,CAAA;AACH,CAAC;AAED,0BAA0B,EAAe,EAAE,EAAe;IAClD,IAAA,0CAGmC,EAFvC,gBAAK,EACL,oBAAO,CACgC;IAEzC,IAAM,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,SAAS,CAAC,EAAE,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;IAEhD,IAAM,QAAQ,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAA;IAC7F,IAAM,QAAQ,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,CAAA;IAEpF,IAAM,QAAQ,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAA;IAC7F,IAAM,QAAQ,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,CAAA;IAEpF,OAAO,EAAE,CAAC,SAAS,CACjB,EAAE,CAAC,KAAK,CAAC;QACP,EAAE,CAAC,GAAG,CAAC,QAAQ,EAAE,QAAQ,CAAC;QAC1B,EAAE,CAAC,GAAG,CAAC,QAAQ,EAAE,QAAQ,CAAC;QAC1B,EAAE,CAAC,GAAG,CAAC,QAAQ,EAAE,QAAQ,CAAC;QAC1B,EAAE,CAAC,GAAG,CAAC,QAAQ,EAAE,QAAQ,CAAC;KAC3B,CAAC,EACF,CAAC,CAAC,EAAE,CAAC,CAAC,CACP,CAAA;AACH,CAAC;AAED,MAAM,sBACJ,cAA2B,EAC3B,gBAA6B,EAC7B,MAAyB;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAM,SAAS,GAAG,cAAc,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;QAEzC,IAAI,KAAK,GAAG,gBAAgB,CAC1B,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,IAAI,CAAC,MAAM,CAAC,SAAS,EAAE,CAAC,SAAS,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAgB,EAChF,EAAE,CAAC,OAAO,CAAC,cAAc,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAgB,CACnD,CAAA;QACD,KAAK,GAAG,EAAE,CAAC,OAAO,CAChB,KAAK,EACL,CAAC,SAAS,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,SAAS,CAAC,EAAE,CAAC,CAAC,CAC7C,CAAA;QAED,IAAM,gBAAgB,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,KAAK,CAAC,gBAAgB,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;QACxF,IAAI,MAAM,GAAG,EAAE,CAAC,KAAK,CAAC,gBAAgB,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAc,CAAA;QAE5E,MAAM,GAAG,EAAE,CAAC,OAAO,CACjB,MAAM,EACN,CAAC,SAAS,EAAE,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAC7B,CAAA;QAED,IAAM,YAAY,GAAG,EAAE,CAAC,OAAO,CAAC,KAAK,CAAkB,CAAA;QACvD,IAAM,aAAa,GAAG,EAAE,CAAC,OAAO,CAAC,MAAM,CAAkB,CAAA;QAEzD,OAAO;YACL,KAAK,EAAE,YAAY;YACnB,MAAM,EAAE,aAAa;SACtB,CAAA;IAEH,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file

build/faceDetectionNet/pointwiseConvLayer.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-function pointwiseConvLayer(x, params, strides) {
+import * as tf from '@tensorflow/tfjs-core';
+export function pointwiseConvLayer(x, params, strides) {
     return tf.tidy(function () {
         var out = tf.conv2d(x, params.filters, strides, 'same');
         out = tf.add(out, params.batch_norm_offset);
         return tf.clipByValue(out, 0, 6);
     });
 }
-exports.pointwiseConvLayer = pointwiseConvLayer;
 //# sourceMappingURL=pointwiseConvLayer.js.map
\ No newline at end of file

build/faceDetectionNet/pointwiseConvLayer.js.map

-{"version":3,"file":"pointwiseConvLayer.js","sourceRoot":"","sources":["../../src/faceDetectionNet/pointwiseConvLayer.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAI5C,4BACE,CAAc,EACd,MAA2B,EAC3B,OAAyB;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAI,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,OAAO,EAAE,MAAM,CAAC,CAAA;QACvD,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,iBAAiB,CAAC,CAAA;QAC3C,OAAO,EAAE,CAAC,WAAW,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;IAElC,CAAC,CAAC,CAAA;AACJ,CAAC;AAZD,gDAYC"}
+{"version":3,"file":"pointwiseConvLayer.js","sourceRoot":"","sources":["../../src/faceDetectionNet/pointwiseConvLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAI5C,MAAM,6BACJ,CAAc,EACd,MAA2B,EAC3B,OAAyB;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAI,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,OAAO,EAAE,MAAM,CAAC,CAAA;QACvD,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,iBAAiB,CAAC,CAAA;QAC3C,OAAO,EAAE,CAAC,WAAW,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;IAElC,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file

build/faceDetectionNet/predictionLayer.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var boxPredictionLayer_1 = require("./boxPredictionLayer");
-var pointwiseConvLayer_1 = require("./pointwiseConvLayer");
-function predictionLayer(x, conv11, params) {
+import * as tf from '@tensorflow/tfjs-core';
+import { boxPredictionLayer } from './boxPredictionLayer';
+import { pointwiseConvLayer } from './pointwiseConvLayer';
+export function predictionLayer(x, conv11, params) {
     return tf.tidy(function () {
-        var conv0 = pointwiseConvLayer_1.pointwiseConvLayer(x, params.conv_0, [1, 1]);
-        var conv1 = pointwiseConvLayer_1.pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
-        var conv2 = pointwiseConvLayer_1.pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
-        var conv3 = pointwiseConvLayer_1.pointwiseConvLayer(conv2, params.conv_3, [2, 2]);
-        var conv4 = pointwiseConvLayer_1.pointwiseConvLayer(conv3, params.conv_4, [1, 1]);
-        var conv5 = pointwiseConvLayer_1.pointwiseConvLayer(conv4, params.conv_5, [2, 2]);
-        var conv6 = pointwiseConvLayer_1.pointwiseConvLayer(conv5, params.conv_6, [1, 1]);
-        var conv7 = pointwiseConvLayer_1.pointwiseConvLayer(conv6, params.conv_7, [2, 2]);
-        var boxPrediction0 = boxPredictionLayer_1.boxPredictionLayer(conv11, params.box_predictor_0);
-        var boxPrediction1 = boxPredictionLayer_1.boxPredictionLayer(x, params.box_predictor_1);
-        var boxPrediction2 = boxPredictionLayer_1.boxPredictionLayer(conv1, params.box_predictor_2);
-        var boxPrediction3 = boxPredictionLayer_1.boxPredictionLayer(conv3, params.box_predictor_3);
-        var boxPrediction4 = boxPredictionLayer_1.boxPredictionLayer(conv5, params.box_predictor_4);
-        var boxPrediction5 = boxPredictionLayer_1.boxPredictionLayer(conv7, params.box_predictor_5);
+        var conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);
+        var conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
+        var conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
+        var conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);
+        var conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);
+        var conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);
+        var conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);
+        var conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);
+        var boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);
+        var boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);
+        var boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);
+        var boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);
+        var boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);
+        var boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);
         var boxPredictions = tf.concat([
             boxPrediction0.boxPredictionEncoding,
             boxPrediction1.boxPredictionEncoding,
@@ -41,5 +39,4 @@ function predictionLayer(x, conv11, params) {
         };
     });
 }
-exports.predictionLayer = predictionLayer;
 //# sourceMappingURL=predictionLayer.js.map
\ No newline at end of file

build/faceDetectionNet/predictionLayer.js.map

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\ No newline at end of file

build/faceDetectionNet/types.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { ConvParams } from '../commons/types';
+import { ConvParams } from 'tfjs-tiny-yolov2';
 export declare type PointwiseConvParams = {
     filters: tf.Tensor4D;
     batch_norm_offset: tf.Tensor1D;

build/faceDetectionNet/types.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
 //# sourceMappingURL=types.js.map
\ No newline at end of file

build/faceLandmarkNet/FaceLandmarkNet.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { NeuralNetwork } from '../commons/NeuralNetwork';
-import { NetInput } from '../NetInput';
-import { TNetInput } from '../types';
-import { FaceLandmarks68 } from './FaceLandmarks68';
+import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
+import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
 import { NetParams } from './types';
 export declare class FaceLandmarkNet extends NeuralNetwork<NetParams> {
     constructor();

build/faceLandmarkNet/FaceLandmarkNet.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var convLayer_1 = require("../commons/convLayer");
-var NeuralNetwork_1 = require("../commons/NeuralNetwork");
-var Point_1 = require("../Point");
-var toNetInput_1 = require("../toNetInput");
-var utils_1 = require("../utils");
-var extractParams_1 = require("./extractParams");
-var FaceLandmarks68_1 = require("./FaceLandmarks68");
-var fullyConnectedLayer_1 = require("./fullyConnectedLayer");
-var loadQuantizedParams_1 = require("./loadQuantizedParams");
+import * as tslib_1 from "tslib";
+import * as tf from '@tensorflow/tfjs-core';
+import { isEven, NeuralNetwork, Point, toNetInput } from 'tfjs-image-recognition-base';
+import { convLayer } from 'tfjs-tiny-yolov2';
+import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
+import { extractParams } from './extractParams';
+import { fullyConnectedLayer } from './fullyConnectedLayer';
+import { loadQuantizedParams } from './loadQuantizedParams';
 function conv(x, params) {
-    return convLayer_1.convLayer(x, params, 'valid', true);
+    return convLayer(x, params, 'valid', true);
 }
 function maxPool(x, strides) {
     if (strides === void 0) { strides = [2, 2]; }
@@ -42,8 +37,8 @@ var FaceLandmarkNet = /** @class */ (function (_super) {
             out = conv(out, params.conv6);
             out = maxPool(out, [1, 1]);
             out = conv(out, params.conv7);
-            var fc0 = tf.relu(fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0));
-            var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(fc0, params.fc1);
+            var fc0 = tf.relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0));
+            var fc1 = fullyConnectedLayer(fc0, params.fc1);
             var createInterleavedTensor = function (fillX, fillY) {
                 return tf.stack([
                     tf.fill([68], fillX),
@@ -74,7 +69,7 @@ var FaceLandmarkNet = /** @class */ (function (_super) {
                 switch (_b.label) {
                     case 0:
                         _a = this.forwardInput;
-                        return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
+                        return [4 /*yield*/, toNetInput(input, true)];
                     case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
                 }
             });
@@ -86,7 +81,7 @@ var FaceLandmarkNet = /** @class */ (function (_super) {
             var netInput, landmarkTensors, landmarksForBatch;
             return tslib_1.__generator(this, function (_a) {
                 switch (_a.label) {
-                    case 0: return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
+                    case 0: return [4 /*yield*/, toNetInput(input, true)];
                     case 1:
                         netInput = _a.sent();
                         landmarkTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });
@@ -99,9 +94,9 @@ var FaceLandmarkNet = /** @class */ (function (_super) {
                                             return [4 /*yield*/, landmarkTensor.data()];
                                         case 1:
                                             landmarksArray = _b.apply(_a, [_c.sent()]);
-                                            xCoords = landmarksArray.filter(function (_, i) { return utils_1.isEven(i); });
-                                            yCoords = landmarksArray.filter(function (_, i) { return !utils_1.isEven(i); });
-                                            return [2 /*return*/, new FaceLandmarks68_1.FaceLandmarks68(Array(68).fill(0).map(function (_, i) { return new Point_1.Point(xCoords[i], yCoords[i]); }), {
+                                            xCoords = landmarksArray.filter(function (_, i) { return isEven(i); });
+                                            yCoords = landmarksArray.filter(function (_, i) { return !isEven(i); });
+                                            return [2 /*return*/, new FaceLandmarks68(Array(68).fill(0).map(function (_, i) { return new Point(xCoords[i], yCoords[i]); }), {
                                                     height: netInput.getInputHeight(batchIdx),
                                                     width: netInput.getInputWidth(batchIdx),
                                                 })];
@@ -119,12 +114,12 @@ var FaceLandmarkNet = /** @class */ (function (_super) {
         });
     };
     FaceLandmarkNet.prototype.loadQuantizedParams = function (uri) {
-        return loadQuantizedParams_1.loadQuantizedParams(uri);
+        return loadQuantizedParams(uri);
     };
     FaceLandmarkNet.prototype.extractParams = function (weights) {
-        return extractParams_1.extractParams(weights);
+        return extractParams(weights);
     };
     return FaceLandmarkNet;
-}(NeuralNetwork_1.NeuralNetwork));
-exports.FaceLandmarkNet = FaceLandmarkNet;
+}(NeuralNetwork));
+export { FaceLandmarkNet };
 //# sourceMappingURL=FaceLandmarkNet.js.map
\ No newline at end of file

build/faceLandmarkNet/FaceLandmarkNet.js.map

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\ No newline at end of file

build/faceLandmarkNet/FaceLandmarks.d.ts

-import { IPoint, Point } from '../Point';
-import { Rect } from '../Rect';
-import { Dimensions } from '../types';
-export declare class FaceLandmarks {
-    private _imageWidth;
-    private _imageHeight;
-    private _shift;
-    private _faceLandmarks;
-    constructor(relativeFaceLandmarkPositions: Point[], imageDims: Dimensions, shift?: Point);
-    getShift(): Point;
-    getImageWidth(): number;
-    getImageHeight(): number;
-    getPositions(): Point[];
-    getRelativePositions(): Point[];
-    getJawOutline(): Point[];
-    getLeftEyeBrow(): Point[];
-    getRightEyeBrow(): Point[];
-    getNose(): Point[];
-    getLeftEye(): Point[];
-    getRightEye(): Point[];
-    getMouth(): Point[];
-    forSize(width: number, height: number): FaceLandmarks;
-    shift(x: number, y: number): FaceLandmarks;
-    shiftByPoint(pt: IPoint): FaceLandmarks;
-    /**
-     * 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
-     * after face detection has been performed, before they are passed to the face recognition net.
-     * This will make the computed face descriptor more accurate.
-     *
-     * @param detection (optional) The bounding box of the face or the face detection result. If
-     * no argument was passed the position of the face landmarks are assumed to be relative to
-     * it's current shift.
-     * @returns The bounding box of the aligned face.
-     */
-    align(detection?: Rect): Rect;
-}

build/faceLandmarkNet/FaceLandmarks.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var getCenterPoint_1 = require("../commons/getCenterPoint");
-var FaceDetection_1 = require("../faceDetectionNet/FaceDetection");
-var Point_1 = require("../Point");
-var Rect_1 = require("../Rect");
-// 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); }
-        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); });
-    }
-    FaceLandmarks.prototype.getShift = function () {
-        return new Point_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 Point_1.Point(_this._imageWidth, _this._imageHeight)); });
-    };
-    FaceLandmarks.prototype.getJawOutline = function () {
-        return this._faceLandmarks.slice(0, 17);
-    };
-    FaceLandmarks.prototype.getLeftEyeBrow = function () {
-        return this._faceLandmarks.slice(17, 22);
-    };
-    FaceLandmarks.prototype.getRightEyeBrow = function () {
-        return this._faceLandmarks.slice(22, 27);
-    };
-    FaceLandmarks.prototype.getNose = function () {
-        return this._faceLandmarks.slice(27, 36);
-    };
-    FaceLandmarks.prototype.getLeftEye = function () {
-        return this._faceLandmarks.slice(36, 42);
-    };
-    FaceLandmarks.prototype.getRightEye = function () {
-        return this._faceLandmarks.slice(42, 48);
-    };
-    FaceLandmarks.prototype.getMouth = function () {
-        return this._faceLandmarks.slice(48, 68);
-    };
-    FaceLandmarks.prototype.forSize = function (width, height) {
-        return new FaceLandmarks(this.getRelativePositions(), { width: width, height: height });
-    };
-    FaceLandmarks.prototype.shift = function (x, y) {
-        return new FaceLandmarks(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new Point_1.Point(x, y));
-    };
-    FaceLandmarks.prototype.shiftByPoint = function (pt) {
-        return this.shift(pt.x, pt.y);
-    };
-    /**
-     * 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
-     * after face detection has been performed, before they are passed to the face recognition net.
-     * This will make the computed face descriptor more accurate.
-     *
-     * @param detection (optional) The bounding box of the face or the face detection result. If
-     * no argument was passed the position of the face landmarks are assumed to be relative to
-     * it's current shift.
-     * @returns The bounding box of the aligned face.
-     */
-    FaceLandmarks.prototype.align = function (detection) {
-        if (detection) {
-            var box = detection instanceof FaceDetection_1.FaceDetection
-                ? detection.getBox().floor()
-                : detection;
-            return this.shift(box.x, box.y).align();
-        }
-        var centers = [
-            this.getLeftEye(),
-            this.getRightEye(),
-            this.getMouth()
-        ].map(getCenterPoint_1.getCenterPoint);
-        var leftEyeCenter = centers[0], rightEyeCenter = centers[1], mouthCenter = centers[2];
-        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);
-        // 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, size, size);
-    };
-    return FaceLandmarks;
-}());
-exports.FaceLandmarks = FaceLandmarks;
-//# sourceMappingURL=FaceLandmarks.js.map
\ No newline at end of file

build/faceLandmarkNet/FaceLandmarks.js.map

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\ No newline at end of file

build/faceLandmarkNet/FaceLandmarks68.js.map

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\ No newline at end of file

build/faceLandmarkNet/extractParams.d.ts

-import { ParamMapping } from '../commons/types';
+import { ParamMapping } from 'tfjs-image-recognition-base';
 import { NetParams } from './types';
 export declare function extractParams(weights: Float32Array): {
     params: NetParams;

build/faceLandmarkNet/extractParams.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var extractConvParamsFactory_1 = require("../commons/extractConvParamsFactory");
-var extractFCParamsFactory_1 = require("../commons/extractFCParamsFactory");
-var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
-function extractParams(weights) {
+import { extractWeightsFactory } from 'tfjs-image-recognition-base';
+import { extractConvParamsFactory, extractFCParamsFactory } from 'tfjs-tiny-yolov2';
+export function extractParams(weights) {
     var paramMappings = [];
-    var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
-    var extractConvParams = extractConvParamsFactory_1.extractConvParamsFactory(extractWeights, paramMappings);
-    var extractFCParams = extractFCParamsFactory_1.extractFCParamsFactory(extractWeights, paramMappings);
+    var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
+    var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
+    var extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);
     var conv0 = extractConvParams(3, 32, 3, 'conv0');
     var conv1 = extractConvParams(32, 64, 3, 'conv1');
     var conv2 = extractConvParams(64, 64, 3, 'conv2');
@@ -37,5 +34,4 @@ function extractParams(weights) {
         }
     };
 }
-exports.extractParams = extractParams;
 //# sourceMappingURL=extractParams.js.map
\ No newline at end of file

build/faceLandmarkNet/extractParams.js.map

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\ No newline at end of file

build/faceLandmarkNet/fullyConnectedLayer.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { FCParams } from '../commons/types';
+import { FCParams } from 'tfjs-tiny-yolov2';
 export declare function fullyConnectedLayer(x: tf.Tensor2D, params: FCParams): tf.Tensor2D;

build/faceLandmarkNet/fullyConnectedLayer.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-function fullyConnectedLayer(x, params) {
+import * as tf from '@tensorflow/tfjs-core';
+export function fullyConnectedLayer(x, params) {
     return tf.tidy(function () {
         return tf.add(tf.matMul(x, params.weights), params.bias);
     });
 }
-exports.fullyConnectedLayer = fullyConnectedLayer;
 //# sourceMappingURL=fullyConnectedLayer.js.map
\ No newline at end of file

build/faceLandmarkNet/fullyConnectedLayer.js.map

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\ No newline at end of file

build/faceLandmarkNet/index.d.ts

 import { FaceLandmarkNet } from './FaceLandmarkNet';
 export * from './FaceLandmarkNet';
-export * from './FaceLandmarks68';
 export declare function createFaceLandmarkNet(weights: Float32Array): FaceLandmarkNet;
 export declare function faceLandmarkNet(weights: Float32Array): FaceLandmarkNet;

build/faceLandmarkNet/index.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var FaceLandmarkNet_1 = require("./FaceLandmarkNet");
-tslib_1.__exportStar(require("./FaceLandmarkNet"), exports);
-tslib_1.__exportStar(require("./FaceLandmarks68"), exports);
-function createFaceLandmarkNet(weights) {
-    var net = new FaceLandmarkNet_1.FaceLandmarkNet();
+import { FaceLandmarkNet } from './FaceLandmarkNet';
+export * from './FaceLandmarkNet';
+export function createFaceLandmarkNet(weights) {
+    var net = new FaceLandmarkNet();
     net.extractWeights(weights);
     return net;
 }
-exports.createFaceLandmarkNet = createFaceLandmarkNet;
-function faceLandmarkNet(weights) {
+export function faceLandmarkNet(weights) {
     console.warn('faceLandmarkNet(weights: Float32Array) will be deprecated in future, use createFaceLandmarkNet instead');
     return createFaceLandmarkNet(weights);
 }
-exports.faceLandmarkNet = faceLandmarkNet;
 //# sourceMappingURL=index.js.map
\ No newline at end of file

build/faceLandmarkNet/index.js.map

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\ No newline at end of file

build/faceLandmarkNet/loadQuantizedParams.d.ts

-import { ParamMapping } from '../commons/types';
+import { ParamMapping } from 'tfjs-image-recognition-base';
 import { NetParams } from './types';
 export declare function loadQuantizedParams(uri: string | undefined): Promise<{
     params: NetParams;

build/faceLandmarkNet/loadQuantizedParams.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var disposeUnusedWeightTensors_1 = require("../commons/disposeUnusedWeightTensors");
-var extractWeightEntryFactory_1 = require("../commons/extractWeightEntryFactory");
-var loadWeightMap_1 = require("../commons/loadWeightMap");
+import * as tslib_1 from "tslib";
+import { disposeUnusedWeightTensors, extractWeightEntryFactory, loadWeightMap, } from 'tfjs-image-recognition-base';
 var DEFAULT_MODEL_NAME = 'face_landmark_68_model';
 function extractorsFactory(weightMap, paramMappings) {
-    var extractWeightEntry = extractWeightEntryFactory_1.extractWeightEntryFactory(weightMap, paramMappings);
+    var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
     function extractConvParams(prefix, mappedPrefix) {
         var filters = extractWeightEntry(prefix + "/kernel", 4, mappedPrefix + "/filters");
         var bias = extractWeightEntry(prefix + "/bias", 1, mappedPrefix + "/bias");
@@ -22,12 +18,12 @@ function extractorsFactory(weightMap, paramMappings) {
         extractFcParams: extractFcParams
     };
 }
-function loadQuantizedParams(uri) {
+export function loadQuantizedParams(uri) {
     return tslib_1.__awaiter(this, void 0, void 0, function () {
         var weightMap, paramMappings, _a, extractConvParams, extractFcParams, params;
         return tslib_1.__generator(this, function (_b) {
             switch (_b.label) {
-                case 0: return [4 /*yield*/, loadWeightMap_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
+                case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME)];
                 case 1:
                     weightMap = _b.sent();
                     paramMappings = [];
@@ -44,11 +40,10 @@ function loadQuantizedParams(uri) {
                         fc0: extractFcParams('dense', 'fc0'),
                         fc1: extractFcParams('logits', 'fc1')
                     };
-                    disposeUnusedWeightTensors_1.disposeUnusedWeightTensors(weightMap, paramMappings);
+                    disposeUnusedWeightTensors(weightMap, paramMappings);
                     return [2 /*return*/, { params: params, paramMappings: paramMappings }];
             }
         });
     });
 }
-exports.loadQuantizedParams = loadQuantizedParams;
 //# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file

build/faceLandmarkNet/loadQuantizedParams.js.map

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+{"version":3,"file":"loadQuantizedParams.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/loadQuantizedParams.ts"],"names":[],"mappings":";AACA,OAAO,EACL,0BAA0B,EAC1B,yBAAyB,EACzB,aAAa,GAEd,MAAM,6BAA6B,CAAC;AAKrC,IAAM,kBAAkB,GAAG,wBAAwB,CAAA;AAEnD,2BAA2B,SAAc,EAAE,aAA6B;IAEtE,IAAM,kBAAkB,GAAG,yBAAyB,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAE9E,2BAA2B,MAAc,EAAE,YAAoB;QAC7D,IAAM,OAAO,GAAG,kBAAkB,CAAiB,MAAM,YAAS,EAAE,CAAC,EAAK,YAAY,aAAU,CAAC,CAAA;QACjG,IAAM,IAAI,GAAG,kBAAkB,CAAiB,MAAM,UAAO,EAAE,CAAC,EAAK,YAAY,UAAO,CAAC,CAAA;QAEzF,OAAO,EAAE,OAAO,SAAA,EAAE,IAAI,MAAA,EAAE,CAAA;IAC1B,CAAC;IAED,yBAAyB,MAAc,EAAE,YAAoB;QAC3D,IAAM,OAAO,GAAG,kBAAkB,CAAiB,MAAM,YAAS,EAAE,CAAC,EAAK,YAAY,aAAU,CAAC,CAAA;QACjG,IAAM,IAAI,GAAG,kBAAkB,CAAiB,MAAM,UAAO,EAAE,CAAC,EAAK,YAAY,UAAO,CAAC,CAAA;QAEzF,OAAO,EAAE,OAAO,SAAA,EAAE,IAAI,MAAA,EAAE,CAAA;IAC1B,CAAC;IAED,OAAO;QACL,iBAAiB,mBAAA;QACjB,eAAe,iBAAA;KAChB,CAAA;AACH,CAAC;AAED,MAAM,8BACJ,GAAuB;;;;;wBAGL,qBAAM,aAAa,CAAC,GAAG,EAAE,kBAAkB,CAAC,EAAA;;oBAAxD,SAAS,GAAG,SAA4C;oBACxD,aAAa,GAAmB,EAAE,CAAA;oBAElC,KAGF,iBAAiB,CAAC,SAAS,EAAE,aAAa,CAAC,EAF7C,iBAAiB,uBAAA,EACjB,eAAe,qBAAA,CAC8B;oBAEzC,MAAM,GAAG;wBACb,KAAK,EAAE,iBAAiB,CAAC,UAAU,EAAE,OAAO,CAAC;wBAC7C,KAAK,EAAE,iBAAiB,CAAC,UAAU,EAAE,OAAO,CAAC;wBAC7C,KAAK,EAAE,iBAAiB,CAAC,UAAU,EAAE,OAAO,CAAC;wBAC7C,KAAK,EAAE,iBAAiB,CAAC,UAAU,EAAE,OAAO,CAAC;wBAC7C,KAAK,EAAE,iBAAiB,CAAC,UAAU,EAAE,OAAO,CAAC;wBAC7C,KAAK,EAAE,iBAAiB,CAAC,UAAU,EAAE,OAAO,CAAC;wBAC7C,KAAK,EAAE,iBAAiB,CAAC,UAAU,EAAE,OAAO,CAAC;wBAC7C,KAAK,EAAE,iBAAiB,CAAC,UAAU,EAAE,OAAO,CAAC;wBAC7C,GAAG,EAAE,eAAe,CAAC,OAAO,EAAE,KAAK,CAAC;wBACpC,GAAG,EAAE,eAAe,CAAC,QAAQ,EAAE,KAAK,CAAC;qBACtC,CAAA;oBAED,0BAA0B,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;oBAEpD,sBAAO,EAAE,MAAM,QAAA,EAAE,aAAa,eAAA,EAAE,EAAA;;;;CACjC"}
\ No newline at end of file

build/faceLandmarkNet/types.d.ts

-import { ConvParams, FCParams } from '../commons/types';
+import { ConvParams, FCParams } from 'tfjs-tiny-yolov2';
 export declare type NetParams = {
     conv0: ConvParams;
     conv1: ConvParams;

build/faceLandmarkNet/types.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
 //# sourceMappingURL=types.js.map
\ No newline at end of file

build/faceRecognitionNet/FaceRecognitionNet.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { NeuralNetwork } from '../commons/NeuralNetwork';
-import { NetInput } from '../NetInput';
-import { TNetInput } from '../types';
+import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
 import { NetParams } from './types';
 export declare class FaceRecognitionNet extends NeuralNetwork<NetParams> {
     constructor();

build/faceRecognitionNet/FaceRecognitionNet.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var NeuralNetwork_1 = require("../commons/NeuralNetwork");
-var normalize_1 = require("../commons/normalize");
-var toNetInput_1 = require("../toNetInput");
-var convLayer_1 = require("./convLayer");
-var extractParams_1 = require("./extractParams");
-var loadQuantizedParams_1 = require("./loadQuantizedParams");
-var residualLayer_1 = require("./residualLayer");
+import * as tslib_1 from "tslib";
+import * as tf from '@tensorflow/tfjs-core';
+import { NeuralNetwork, normalize, toNetInput } from 'tfjs-image-recognition-base';
+import { convDown } from './convLayer';
+import { extractParams } from './extractParams';
+import { loadQuantizedParams } from './loadQuantizedParams';
+import { residual, residualDown } from './residualLayer';
 var FaceRecognitionNet = /** @class */ (function (_super) {
     tslib_1.__extends(FaceRecognitionNet, _super);
     function FaceRecognitionNet() {
@@ -22,23 +18,23 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
         return tf.tidy(function () {
             var batchTensor = input.toBatchTensor(150, true);
             var meanRgb = [122.782, 117.001, 104.298];
-            var normalized = normalize_1.normalize(batchTensor, meanRgb).div(tf.scalar(256));
-            var out = convLayer_1.convDown(normalized, params.conv32_down);
+            var normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256));
+            var out = convDown(normalized, params.conv32_down);
             out = tf.maxPool(out, 3, 2, 'valid');
-            out = residualLayer_1.residual(out, params.conv32_1);
-            out = residualLayer_1.residual(out, params.conv32_2);
-            out = residualLayer_1.residual(out, params.conv32_3);
-            out = residualLayer_1.residualDown(out, params.conv64_down);
-            out = residualLayer_1.residual(out, params.conv64_1);
-            out = residualLayer_1.residual(out, params.conv64_2);
-            out = residualLayer_1.residual(out, params.conv64_3);
-            out = residualLayer_1.residualDown(out, params.conv128_down);
-            out = residualLayer_1.residual(out, params.conv128_1);
-            out = residualLayer_1.residual(out, params.conv128_2);
-            out = residualLayer_1.residualDown(out, params.conv256_down);
-            out = residualLayer_1.residual(out, params.conv256_1);
-            out = residualLayer_1.residual(out, params.conv256_2);
-            out = residualLayer_1.residualDown(out, params.conv256_down_out);
+            out = residual(out, params.conv32_1);
+            out = residual(out, params.conv32_2);
+            out = residual(out, params.conv32_3);
+            out = residualDown(out, params.conv64_down);
+            out = residual(out, params.conv64_1);
+            out = residual(out, params.conv64_2);
+            out = residual(out, params.conv64_3);
+            out = residualDown(out, params.conv128_down);
+            out = residual(out, params.conv128_1);
+            out = residual(out, params.conv128_2);
+            out = residualDown(out, params.conv256_down);
+            out = residual(out, params.conv256_1);
+            out = residual(out, params.conv256_2);
+            out = residualDown(out, params.conv256_down_out);
             var globalAvg = out.mean([1, 2]);
             var fullyConnected = tf.matMul(globalAvg, params.fc);
             return fullyConnected;
@@ -51,7 +47,7 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
                 switch (_b.label) {
                     case 0:
                         _a = this.forwardInput;
-                        return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
+                        return [4 /*yield*/, toNetInput(input, true)];
                     case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
                 }
             });
@@ -63,7 +59,7 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
             var netInput, faceDescriptorTensors, faceDescriptorsForBatch;
             return tslib_1.__generator(this, function (_a) {
                 switch (_a.label) {
-                    case 0: return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
+                    case 0: return [4 /*yield*/, toNetInput(input, true)];
                     case 1:
                         netInput = _a.sent();
                         faceDescriptorTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });
@@ -79,12 +75,12 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
         });
     };
     FaceRecognitionNet.prototype.loadQuantizedParams = function (uri) {
-        return loadQuantizedParams_1.loadQuantizedParams(uri);
+        return loadQuantizedParams(uri);
     };
     FaceRecognitionNet.prototype.extractParams = function (weights) {
-        return extractParams_1.extractParams(weights);
+        return extractParams(weights);
     };
     return FaceRecognitionNet;
-}(NeuralNetwork_1.NeuralNetwork));
-exports.FaceRecognitionNet = FaceRecognitionNet;
+}(NeuralNetwork));
+export { FaceRecognitionNet };
 //# sourceMappingURL=FaceRecognitionNet.js.map
\ No newline at end of file

build/faceRecognitionNet/FaceRecognitionNet.js.map

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\ No newline at end of file

build/faceRecognitionNet/convLayer.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var scaleLayer_1 = require("./scaleLayer");
+import * as tf from '@tensorflow/tfjs-core';
+import { scale } from './scaleLayer';
 function convLayer(x, params, strides, withRelu, padding) {
     if (padding === void 0) { padding = 'same'; }
     var _a = params.conv, filters = _a.filters, bias = _a.bias;
     var out = tf.conv2d(x, filters, strides, padding);
     out = tf.add(out, bias);
-    out = scaleLayer_1.scale(out, params.scale);
+    out = scale(out, params.scale);
     return withRelu ? tf.relu(out) : out;
 }
-function conv(x, params) {
+export function conv(x, params) {
     return convLayer(x, params, [1, 1], true);
 }
-exports.conv = conv;
-function convNoRelu(x, params) {
+export function convNoRelu(x, params) {
     return convLayer(x, params, [1, 1], false);
 }
-exports.convNoRelu = convNoRelu;
-function convDown(x, params) {
+export function convDown(x, params) {
     return convLayer(x, params, [2, 2], true, 'valid');
 }
-exports.convDown = convDown;
 //# sourceMappingURL=convLayer.js.map
\ No newline at end of file

build/faceRecognitionNet/convLayer.js.map

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+{"version":3,"file":"convLayer.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/convLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,KAAK,EAAE,MAAM,cAAc,CAAC;AAIrC,mBACE,CAAc,EACd,MAAuB,EACvB,OAAyB,EACzB,QAAiB,EACjB,OAAkC;IAAlC,wBAAA,EAAA,gBAAkC;IAE5B,IAAA,gBAA+B,EAA7B,oBAAO,EAAE,cAAI,CAAgB;IAErC,IAAI,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;IACjD,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;IACvB,GAAG,GAAG,KAAK,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IAC9B,OAAO,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAA;AACtC,CAAC;AAED,MAAM,eAAe,CAAc,EAAE,MAAuB;IAC1D,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,IAAI,CAAC,CAAA;AAC3C,CAAC;AAED,MAAM,qBAAqB,CAAc,EAAE,MAAuB;IAChE,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,CAAA;AAC5C,CAAC;AAED,MAAM,mBAAmB,CAAc,EAAE,MAAuB;IAC9D,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,IAAI,EAAE,OAAO,CAAC,CAAA;AACpD,CAAC"}
\ No newline at end of file

build/faceRecognitionNet/extractParams.d.ts

-import { ParamMapping } from '../commons/types';
+import { ParamMapping } from 'tfjs-image-recognition-base';
 import { NetParams } from './types';
 export declare function extractParams(weights: Float32Array): {
     params: NetParams;

build/faceRecognitionNet/extractParams.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
-var utils_1 = require("../utils");
+import * as tf from '@tensorflow/tfjs-core';
+import { extractWeightsFactory, isFloat } from 'tfjs-image-recognition-base';
 function extractorsFactory(extractWeights, paramMappings) {
     function extractFilterValues(numFilterValues, numFilters, filterSize) {
         var weights = extractWeights(numFilterValues);
         var depth = weights.length / (numFilters * filterSize * filterSize);
-        if (utils_1.isFloat(depth)) {
+        if (isFloat(depth)) {
             throw new Error("depth has to be an integer: " + depth + ", weights.length: " + weights.length + ", numFilters: " + numFilters + ", filterSize: " + filterSize);
         }
         return tf.tidy(function () { return tf.transpose(tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]); });
@@ -43,8 +40,8 @@ function extractorsFactory(extractWeights, paramMappings) {
         extractResidualLayerParams: extractResidualLayerParams
     };
 }
-function extractParams(weights) {
-    var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
+export function extractParams(weights) {
+    var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
     var paramMappings = [];
     var _b = extractorsFactory(extractWeights, paramMappings), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams;
     var conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');
@@ -87,5 +84,4 @@ function extractParams(weights) {
     };
     return { params: params, paramMappings: paramMappings };
 }
-exports.extractParams = extractParams;
 //# sourceMappingURL=extractParams.js.map
\ No newline at end of file

build/faceRecognitionNet/extractParams.js.map

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+{"version":3,"file":"extractParams.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/extractParams.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAC5C,OAAO,EAAE,qBAAqB,EAA0B,OAAO,EAAgB,MAAM,6BAA6B,CAAC;AAKnH,2BAA2B,cAAsC,EAAE,aAA6B;IAE9F,6BAA6B,eAAuB,EAAE,UAAkB,EAAE,UAAkB;QAC1F,IAAM,OAAO,GAAG,cAAc,CAAC,eAAe,CAAC,CAAA;QAC/C,IAAM,KAAK,GAAG,OAAO,CAAC,MAAM,GAAG,CAAC,UAAU,GAAG,UAAU,GAAG,UAAU,CAAC,CAAA;QAErE,IAAI,OAAO,CAAC,KAAK,CAAC,EAAE;YAClB,MAAM,IAAI,KAAK,CAAC,iCAA+B,KAAK,0BAAqB,OAAO,CAAC,MAAM,sBAAiB,UAAU,sBAAiB,UAAY,CAAC,CAAA;SACjJ;QAED,OAAO,EAAE,CAAC,IAAI,CACZ,cAAM,OAAA,EAAE,CAAC,SAAS,CAChB,EAAE,CAAC,QAAQ,CAAC,OAAO,EAAE,CAAC,UAAU,EAAE,KAAK,EAAE,UAAU,EAAE,UAAU,CAAC,CAAC,EACjE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CACb,EAHK,CAGL,CACF,CAAA;IACH,CAAC;IAED,2BACE,eAAuB,EACvB,UAAkB,EAClB,UAAkB,EAClB,YAAoB;QAGpB,IAAM,OAAO,GAAG,mBAAmB,CAAC,eAAe,EAAE,UAAU,EAAE,UAAU,CAAC,CAAA;QAC5E,IAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,CAAC,CAAC,CAAA;QAEpD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAK,YAAY,aAAU,EAAE,EACxC,EAAE,SAAS,EAAK,YAAY,UAAO,EAAE,CACtC,CAAA;QAED,OAAO,EAAE,OAAO,SAAA,EAAE,IAAI,MAAA,EAAE,CAAA;IAC1B,CAAC;IAED,iCAAiC,UAAkB,EAAE,YAAoB;QAEvE,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,CAAC,CAAC,CAAA;QACvD,IAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,CAAC,CAAC,CAAA;QAEtD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAK,YAAY,aAAU,EAAE,EACxC,EAAE,SAAS,EAAK,YAAY,YAAS,EAAE,CACxC,CAAA;QAED,OAAO;YACL,OAAO,SAAA;YACP,MAAM,QAAA;SACP,CAAA;IACH,CAAC;IAED,gCACE,eAAuB,EACvB,UAAkB,EAClB,UAAkB,EAClB,YAAoB;QAGpB,IAAM,IAAI,GAAG,iBAAiB,CAAC,eAAe,EAAE,UAAU,EAAE,UAAU,EAAK,YAAY,UAAO,CAAC,CAAA;QAC/F,IAAM,KAAK,GAAG,uBAAuB,CAAC,UAAU,EAAK,YAAY,WAAQ,CAAC,CAAA;QAE1E,OAAO,EAAE,IAAI,MAAA,EAAE,KAAK,OAAA,EAAE,CAAA;IACxB,CAAC;IAED,oCACE,eAAuB,EACvB,UAAkB,EAClB,UAAkB,EAClB,YAAoB,EACpB,MAAuB;QAAvB,uBAAA,EAAA,cAAuB;QAGvB,IAAM,KAAK,GAAG,sBAAsB,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,eAAe,EAAE,UAAU,EAAE,UAAU,EAAK,YAAY,WAAQ,CAAC,CAAA;QAC3H,IAAM,KAAK,GAAG,sBAAsB,CAAC,eAAe,EAAE,UAAU,EAAE,UAAU,EAAK,YAAY,WAAQ,CAAC,CAAA;QAEtG,OAAO,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,CAAA;IACzB,CAAC;IAED,OAAO;QACL,sBAAsB,wBAAA;QACtB,0BAA0B,4BAAA;KAC3B,CAAA;AAEH,CAAC;AAED,MAAM,wBAAwB,OAAqB;IAE3C,IAAA,mCAG4B,EAFhC,kCAAc,EACd,4CAAmB,CACa;IAElC,IAAM,aAAa,GAAmB,EAAE,CAAA;IAElC,IAAA,qDAG8C,EAFlD,kDAAsB,EACtB,0DAA0B,CACwB;IAEpD,IAAM,WAAW,GAAG,sBAAsB,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,EAAE,aAAa,CAAC,CAAA;IACtE,IAAM,QAAQ,GAAG,0BAA0B,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,EAAE,UAAU,CAAC,CAAA;IACpE,IAAM,QAAQ,GAAG,0BAA0B,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,EAAE,UAAU,CAAC,CAAA;IACpE,IAAM,QAAQ,GAAG,0BAA0B,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,EAAE,UAAU,CAAC,CAAA;IAEpE,IAAM,WAAW,GAAG,0BAA0B,CAAC,KAAK,EAAE,EAAE,EAAE,CAAC,EAAE,aAAa,EAAE,IAAI,CAAC,CAAA;IACjF,IAAM,QAAQ,GAAG,0BAA0B,CAAC,KAAK,EAAE,EAAE,EAAE,CAAC,EAAE,UAAU,CAAC,CAAA;IACrE,IAAM,QAAQ,GAAG,0BAA0B,CAAC,KAAK,EAAE,EAAE,EAAE,CAAC,EAAE,UAAU,CAAC,CAAA;IACrE,IAAM,QAAQ,GAAG,0BAA0B,CAAC,KAAK,EAAE,EAAE,EAAE,CAAC,EAAE,UAAU,CAAC,CAAA;IAErE,IAAM,YAAY,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,cAAc,EAAE,IAAI,CAAC,CAAA;IACrF,IAAM,SAAS,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,WAAW,CAAC,CAAA;IACzE,IAAM,SAAS,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,WAAW,CAAC,CAAA;IAEzE,IAAM,YAAY,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,cAAc,EAAE,IAAI,CAAC,CAAA;IACrF,IAAM,SAAS,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,WAAW,CAAC,CAAA;IACzE,IAAM,SAAS,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,WAAW,CAAC,CAAA;IACzE,IAAM,gBAAgB,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,kBAAkB,CAAC,CAAA;IAEvF,IAAM,EAAE,GAAG,EAAE,CAAC,IAAI,CAChB,cAAM,OAAA,EAAE,CAAC,SAAS,CAAC,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,GAAG,GAAG,GAAG,CAAC,EAAE,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAxE,CAAwE,CAC/E,CAAA;IACD,aAAa,CAAC,IAAI,CAAC,EAAE,SAAS,EAAE,IAAI,EAAE,CAAC,CAAA;IAEvC,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,oCAAkC,mBAAmB,EAAE,CAAC,MAAQ,CAAC,CAAA;KAClF;IAED,IAAM,MAAM,GAAG;QACb,WAAW,aAAA;QACX,QAAQ,UAAA;QACR,QAAQ,UAAA;QACR,QAAQ,UAAA;QACR,WAAW,aAAA;QACX,QAAQ,UAAA;QACR,QAAQ,UAAA;QACR,QAAQ,UAAA;QACR,YAAY,cAAA;QACZ,SAAS,WAAA;QACT,SAAS,WAAA;QACT,YAAY,cAAA;QACZ,SAAS,WAAA;QACT,SAAS,WAAA;QACT,gBAAgB,kBAAA;QAChB,EAAE,IAAA;KACH,CAAA;IAED,OAAO,EAAE,MAAM,QAAA,EAAE,aAAa,eAAA,EAAE,CAAA;AAClC,CAAC"}
\ No newline at end of file

build/faceRecognitionNet/index.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var FaceRecognitionNet_1 = require("./FaceRecognitionNet");
-tslib_1.__exportStar(require("./FaceRecognitionNet"), exports);
-function createFaceRecognitionNet(weights) {
-    var net = new FaceRecognitionNet_1.FaceRecognitionNet();
+import { FaceRecognitionNet } from './FaceRecognitionNet';
+export * from './FaceRecognitionNet';
+export function createFaceRecognitionNet(weights) {
+    var net = new FaceRecognitionNet();
     net.extractWeights(weights);
     return net;
 }
-exports.createFaceRecognitionNet = createFaceRecognitionNet;
-function faceRecognitionNet(weights) {
+export function faceRecognitionNet(weights) {
     console.warn('faceRecognitionNet(weights: Float32Array) will be deprecated in future, use createFaceRecognitionNet instead');
     return createFaceRecognitionNet(weights);
 }
-exports.faceRecognitionNet = faceRecognitionNet;
 //# sourceMappingURL=index.js.map
\ No newline at end of file

build/faceRecognitionNet/index.js.map

-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/index.ts"],"names":[],"mappings":";;;AAAA,2DAA0D;AAE1D,+DAAqC;AAErC,kCAAyC,OAAqB;IAC5D,IAAM,GAAG,GAAG,IAAI,uCAAkB,EAAE,CAAA;IACpC,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAJD,4DAIC;AAED,4BAAmC,OAAqB;IACtD,OAAO,CAAC,IAAI,CAAC,8GAA8G,CAAC,CAAA;IAC5H,OAAO,wBAAwB,CAAC,OAAO,CAAC,CAAA;AAC1C,CAAC;AAHD,gDAGC"}
+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/index.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,kBAAkB,EAAE,MAAM,sBAAsB,CAAC;AAE1D,cAAc,sBAAsB,CAAC;AAErC,MAAM,mCAAmC,OAAqB;IAC5D,IAAM,GAAG,GAAG,IAAI,kBAAkB,EAAE,CAAA;IACpC,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAED,MAAM,6BAA6B,OAAqB;IACtD,OAAO,CAAC,IAAI,CAAC,8GAA8G,CAAC,CAAA;IAC5H,OAAO,wBAAwB,CAAC,OAAO,CAAC,CAAA;AAC1C,CAAC"}
\ No newline at end of file

build/faceRecognitionNet/loadQuantizedParams.d.ts

-import { ParamMapping } from '../commons/types';
+import { ParamMapping } from 'tfjs-image-recognition-base';
 import { NetParams } from './types';
 export declare function loadQuantizedParams(uri: string | undefined): Promise<{
     params: NetParams;

build/faceRecognitionNet/loadQuantizedParams.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var disposeUnusedWeightTensors_1 = require("../commons/disposeUnusedWeightTensors");
-var extractWeightEntryFactory_1 = require("../commons/extractWeightEntryFactory");
-var isTensor_1 = require("../commons/isTensor");
-var loadWeightMap_1 = require("../commons/loadWeightMap");
+import * as tslib_1 from "tslib";
+import { disposeUnusedWeightTensors, extractWeightEntryFactory, isTensor2D, loadWeightMap, } from 'tfjs-image-recognition-base';
 var DEFAULT_MODEL_NAME = 'face_recognition_model';
 function extractorsFactory(weightMap, paramMappings) {
-    var extractWeightEntry = extractWeightEntryFactory_1.extractWeightEntryFactory(weightMap, paramMappings);
+    var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
     function extractScaleLayerParams(prefix) {
         var weights = extractWeightEntry(prefix + "/scale/weights", 1);
         var biases = extractWeightEntry(prefix + "/scale/biases", 1);
@@ -30,12 +25,12 @@ function extractorsFactory(weightMap, paramMappings) {
         extractResidualLayerParams: extractResidualLayerParams
     };
 }
-function loadQuantizedParams(uri) {
+export function loadQuantizedParams(uri) {
     return tslib_1.__awaiter(this, void 0, void 0, function () {
         var weightMap, paramMappings, _a, extractConvLayerParams, extractResidualLayerParams, conv32_down, conv32_1, conv32_2, conv32_3, conv64_down, conv64_1, conv64_2, conv64_3, conv128_down, conv128_1, conv128_2, conv256_down, conv256_1, conv256_2, conv256_down_out, fc, params;
         return tslib_1.__generator(this, function (_b) {
             switch (_b.label) {
-                case 0: return [4 /*yield*/, loadWeightMap_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
+                case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME)];
                 case 1:
                     weightMap = _b.sent();
                     paramMappings = [];
@@ -57,7 +52,7 @@ function loadQuantizedParams(uri) {
                     conv256_down_out = extractResidualLayerParams('conv256_down_out');
                     fc = weightMap['fc'];
                     paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });
-                    if (!isTensor_1.isTensor2D(fc)) {
+                    if (!isTensor2D(fc)) {
                         throw new Error("expected weightMap[fc] to be a Tensor2D, instead have " + fc);
                     }
                     params = {
@@ -78,11 +73,10 @@ function loadQuantizedParams(uri) {
                         conv256_down_out: conv256_down_out,
                         fc: fc
                     };
-                    disposeUnusedWeightTensors_1.disposeUnusedWeightTensors(weightMap, paramMappings);
+                    disposeUnusedWeightTensors(weightMap, paramMappings);
                     return [2 /*return*/, { params: params, paramMappings: paramMappings }];
             }
         });
     });
 }
-exports.loadQuantizedParams = loadQuantizedParams;
 //# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file

build/faceRecognitionNet/loadQuantizedParams.js.map

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\ No newline at end of file

build/faceRecognitionNet/normalize.d.ts

-import * as tf from '@tensorflow/tfjs-core';
-export declare function normalize(x: tf.Tensor4D): tf.Tensor4D;

build/faceRecognitionNet/normalize.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-function normalize(x) {
-    return tf.tidy(function () {
-        var avg_r = tf.fill(x.shape.slice(0, 3).concat([1]), 122.782);
-        var avg_g = tf.fill(x.shape.slice(0, 3).concat([1]), 117.001);
-        var avg_b = tf.fill(x.shape.slice(0, 3).concat([1]), 104.298);
-        var avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);
-        return tf.div(tf.sub(x, avg_rgb), tf.scalar(256));
-    });
-}
-exports.normalize = normalize;
-//# sourceMappingURL=normalize.js.map
\ No newline at end of file

build/faceRecognitionNet/normalize.js.map

-{"version":3,"file":"normalize.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/normalize.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,mBAA0B,CAAc;IACtC,OAAO,EAAE,CAAC,IAAI,CAAC;QACb,IAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAK,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,SAAE,CAAC,IAAG,OAAO,CAAC,CAAC;QAC5D,IAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAK,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,SAAE,CAAC,IAAG,OAAO,CAAC,CAAC;QAC5D,IAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAK,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,SAAE,CAAC,IAAG,OAAO,CAAC,CAAC;QAC5D,IAAM,OAAO,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,KAAK,EAAE,KAAK,EAAE,KAAK,CAAC,EAAE,CAAC,CAAC,CAAA;QAEnD,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,OAAO,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAC,CAAA;IACnD,CAAC,CAAC,CAAA;AACJ,CAAC;AATD,8BASC"}
\ No newline at end of file

build/faceRecognitionNet/residualLayer.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var convLayer_1 = require("./convLayer");
-function residual(x, params) {
-    var out = convLayer_1.conv(x, params.conv1);
-    out = convLayer_1.convNoRelu(out, params.conv2);
+import * as tf from '@tensorflow/tfjs-core';
+import { conv, convDown, convNoRelu } from './convLayer';
+export function residual(x, params) {
+    var out = conv(x, params.conv1);
+    out = convNoRelu(out, params.conv2);
     out = tf.add(out, x);
     out = tf.relu(out);
     return out;
 }
-exports.residual = residual;
-function residualDown(x, params) {
-    var out = convLayer_1.convDown(x, params.conv1);
-    out = convLayer_1.convNoRelu(out, params.conv2);
+export function residualDown(x, params) {
+    var out = convDown(x, params.conv1);
+    out = convNoRelu(out, params.conv2);
     var pooled = tf.avgPool(x, 2, 2, 'valid');
     var zeros = tf.zeros(pooled.shape);
     var isPad = pooled.shape[3] !== out.shape[3];
@@ -32,5 +29,4 @@ function residualDown(x, params) {
     out = tf.relu(out);
     return out;
 }
-exports.residualDown = residualDown;
 //# sourceMappingURL=residualLayer.js.map
\ No newline at end of file

build/faceRecognitionNet/residualLayer.js.map

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\ No newline at end of file

build/faceRecognitionNet/scaleLayer.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-function scale(x, params) {
+import * as tf from '@tensorflow/tfjs-core';
+export function scale(x, params) {
     return tf.add(tf.mul(x, params.weights), params.biases);
 }
-exports.scale = scale;
 //# sourceMappingURL=scaleLayer.js.map
\ No newline at end of file

build/faceRecognitionNet/scaleLayer.js.map

-{"version":3,"file":"scaleLayer.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/scaleLayer.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAI5C,eAAsB,CAAc,EAAE,MAAwB;IAC5D,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,CAAC,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;AACzD,CAAC;AAFD,sBAEC"}
+{"version":3,"file":"scaleLayer.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/scaleLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAI5C,MAAM,gBAAgB,CAAc,EAAE,MAAwB;IAC5D,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,CAAC,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;AACzD,CAAC"}
\ No newline at end of file

build/faceRecognitionNet/types.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { ConvParams } from '../commons/types';
+import { ConvParams } from 'tfjs-tiny-yolov2';
 export declare type ScaleLayerParams = {
     weights: tf.Tensor1D;
     biases: tf.Tensor1D;

build/faceRecognitionNet/types.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
 //# sourceMappingURL=types.js.map
\ No newline at end of file

build/globalApi.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { FaceDetection } from './FaceDetection';
+import { NetInput, TNetInput } from 'tfjs-image-recognition-base';
+import { TinyYolov2Types } from 'tfjs-tiny-yolov2';
+import { FaceDetection } from './classes/FaceDetection';
+import { FaceLandmarks68 } from './classes/FaceLandmarks68';
+import { FullFaceDescription } from './classes/FullFaceDescription';
 import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
 import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
-import { FaceLandmarks68 } from './faceLandmarkNet/FaceLandmarks68';
 import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
-import { FullFaceDescription } from './FullFaceDescription';
 import { Mtcnn } from './mtcnn/Mtcnn';
 import { MtcnnForwardParams, MtcnnResult } from './mtcnn/types';
-import { NetInput } from './NetInput';
 import { TinyYolov2 } from './tinyYolov2/TinyYolov2';
-import { TinyYolov2ForwardParams } from './tinyYolov2/types';
-import { TNetInput } from './types';
 export declare const detectionNet: FaceDetectionNet;
 export declare const landmarkNet: FaceLandmarkNet;
 export declare const recognitionNet: FaceRecognitionNet;
@@ -32,10 +31,10 @@ export declare function locateFaces(input: TNetInput, minConfidence?: number, ma
 export declare function detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;
 export declare function computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
 export declare function mtcnn(input: TNetInput, forwardParams: MtcnnForwardParams): Promise<MtcnnResult[]>;
-export declare function tinyYolov2(input: TNetInput, forwardParams: TinyYolov2ForwardParams): Promise<FaceDetection[]>;
+export declare function tinyYolov2(input: TNetInput, forwardParams: TinyYolov2Types.TinyYolov2ForwardParams): Promise<FaceDetection[]>;
 export declare type allFacesSsdMobilenetv1Function = (input: tf.Tensor | NetInput | TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
 export declare const allFacesSsdMobilenetv1: allFacesSsdMobilenetv1Function;
-export declare type allFacesTinyYolov2Function = (input: tf.Tensor | NetInput | TNetInput, forwardParams?: TinyYolov2ForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
+export declare type allFacesTinyYolov2Function = (input: tf.Tensor | NetInput | TNetInput, forwardParams?: TinyYolov2Types.TinyYolov2ForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
 export declare const allFacesTinyYolov2: allFacesTinyYolov2Function;
 export declare type allFacesMtcnnFunction = (input: tf.Tensor | NetInput | TNetInput, mtcnnForwardParams?: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
 export declare const allFacesMtcnn: allFacesMtcnnFunction;

build/globalApi.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var allFacesFactory_1 = require("./allFacesFactory");
-var FaceDetectionNet_1 = require("./faceDetectionNet/FaceDetectionNet");
-var FaceLandmarkNet_1 = require("./faceLandmarkNet/FaceLandmarkNet");
-var FaceRecognitionNet_1 = require("./faceRecognitionNet/FaceRecognitionNet");
-var Mtcnn_1 = require("./mtcnn/Mtcnn");
-var TinyYolov2_1 = require("./tinyYolov2/TinyYolov2");
-exports.detectionNet = new FaceDetectionNet_1.FaceDetectionNet();
-exports.landmarkNet = new FaceLandmarkNet_1.FaceLandmarkNet();
-exports.recognitionNet = new FaceRecognitionNet_1.FaceRecognitionNet();
+import { allFacesMtcnnFactory, allFacesSsdMobilenetv1Factory, allFacesTinyYolov2Factory } from './allFacesFactory';
+import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
+import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
+import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
+import { Mtcnn } from './mtcnn/Mtcnn';
+import { TinyYolov2 } from './tinyYolov2/TinyYolov2';
+export var detectionNet = new FaceDetectionNet();
+export var landmarkNet = new FaceLandmarkNet();
+export var recognitionNet = new FaceRecognitionNet();
 // nets need more specific names, to avoid ambiguity in future
 // when alternative net implementations are provided
-exports.nets = {
-    ssdMobilenetv1: exports.detectionNet,
-    faceLandmark68Net: exports.landmarkNet,
-    faceRecognitionNet: exports.recognitionNet,
-    mtcnn: new Mtcnn_1.Mtcnn(),
-    tinyYolov2: new TinyYolov2_1.TinyYolov2()
+export var nets = {
+    ssdMobilenetv1: detectionNet,
+    faceLandmark68Net: landmarkNet,
+    faceRecognitionNet: recognitionNet,
+    mtcnn: new Mtcnn(),
+    tinyYolov2: new TinyYolov2()
 };
-function loadSsdMobilenetv1Model(url) {
-    return exports.nets.ssdMobilenetv1.load(url);
+export function loadSsdMobilenetv1Model(url) {
+    return nets.ssdMobilenetv1.load(url);
 }
-exports.loadSsdMobilenetv1Model = loadSsdMobilenetv1Model;
-function loadFaceLandmarkModel(url) {
-    return exports.nets.faceLandmark68Net.load(url);
+export function loadFaceLandmarkModel(url) {
+    return nets.faceLandmark68Net.load(url);
 }
-exports.loadFaceLandmarkModel = loadFaceLandmarkModel;
-function loadFaceRecognitionModel(url) {
-    return exports.nets.faceRecognitionNet.load(url);
+export function loadFaceRecognitionModel(url) {
+    return nets.faceRecognitionNet.load(url);
 }
-exports.loadFaceRecognitionModel = loadFaceRecognitionModel;
-function loadMtcnnModel(url) {
-    return exports.nets.mtcnn.load(url);
+export function loadMtcnnModel(url) {
+    return nets.mtcnn.load(url);
 }
-exports.loadMtcnnModel = loadMtcnnModel;
-function loadTinyYolov2Model(url) {
-    return exports.nets.tinyYolov2.load(url);
+export function loadTinyYolov2Model(url) {
+    return nets.tinyYolov2.load(url);
 }
-exports.loadTinyYolov2Model = loadTinyYolov2Model;
-function loadFaceDetectionModel(url) {
+export function loadFaceDetectionModel(url) {
     return loadSsdMobilenetv1Model(url);
 }
-exports.loadFaceDetectionModel = loadFaceDetectionModel;
-function loadModels(url) {
+export function loadModels(url) {
     return Promise.all([
         loadSsdMobilenetv1Model(url),
         loadFaceLandmarkModel(url),
@@ -51,29 +43,23 @@ function loadModels(url) {
         loadTinyYolov2Model(url)
     ]);
 }
-exports.loadModels = loadModels;
-function locateFaces(input, minConfidence, maxResults) {
-    return exports.nets.ssdMobilenetv1.locateFaces(input, minConfidence, maxResults);
+export function locateFaces(input, minConfidence, maxResults) {
+    return nets.ssdMobilenetv1.locateFaces(input, minConfidence, maxResults);
 }
-exports.locateFaces = locateFaces;
-function detectLandmarks(input) {
-    return exports.nets.faceLandmark68Net.detectLandmarks(input);
+export function detectLandmarks(input) {
+    return nets.faceLandmark68Net.detectLandmarks(input);
 }
-exports.detectLandmarks = detectLandmarks;
-function computeFaceDescriptor(input) {
-    return exports.nets.faceRecognitionNet.computeFaceDescriptor(input);
+export function computeFaceDescriptor(input) {
+    return nets.faceRecognitionNet.computeFaceDescriptor(input);
 }
-exports.computeFaceDescriptor = computeFaceDescriptor;
-function mtcnn(input, forwardParams) {
-    return exports.nets.mtcnn.forward(input, forwardParams);
+export function mtcnn(input, forwardParams) {
+    return nets.mtcnn.forward(input, forwardParams);
 }
-exports.mtcnn = mtcnn;
-function tinyYolov2(input, forwardParams) {
-    return exports.nets.tinyYolov2.locateFaces(input, forwardParams);
+export function tinyYolov2(input, forwardParams) {
+    return nets.tinyYolov2.locateFaces(input, forwardParams);
 }
-exports.tinyYolov2 = tinyYolov2;
-exports.allFacesSsdMobilenetv1 = allFacesFactory_1.allFacesSsdMobilenetv1Factory(exports.nets.ssdMobilenetv1, exports.nets.faceLandmark68Net, exports.nets.faceRecognitionNet);
-exports.allFacesTinyYolov2 = allFacesFactory_1.allFacesTinyYolov2Factory(exports.nets.tinyYolov2, exports.nets.faceLandmark68Net, exports.nets.faceRecognitionNet);
-exports.allFacesMtcnn = allFacesFactory_1.allFacesMtcnnFactory(exports.nets.mtcnn, exports.nets.faceRecognitionNet);
-exports.allFaces = exports.allFacesSsdMobilenetv1;
+export var allFacesSsdMobilenetv1 = allFacesSsdMobilenetv1Factory(nets.ssdMobilenetv1, nets.faceLandmark68Net, nets.faceRecognitionNet);
+export var allFacesTinyYolov2 = allFacesTinyYolov2Factory(nets.tinyYolov2, nets.faceLandmark68Net, nets.faceRecognitionNet);
+export var allFacesMtcnn = allFacesMtcnnFactory(nets.mtcnn, nets.faceRecognitionNet);
+export var allFaces = allFacesSsdMobilenetv1;
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build/globalApi.js.map

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\ No newline at end of file

build/index.d.ts

 import * as tf from '@tensorflow/tfjs-core';
 export { tf };
-export * from './BoundingBox';
-export * from './FaceDetection';
-export * from './FullFaceDescription';
-export * from './NetInput';
-export * from './Point';
-export * from './Rect';
-export * from './drawing';
+export * from 'tfjs-image-recognition-base';
+export * from './classes';
+export * from './dom';
 export * from './euclideanDistance';
-export * from './extractFaces';
-export * from './extractFaceTensors';
 export * from './faceDetectionNet';
 export * from './faceLandmarkNet';
 export * from './faceRecognitionNet';
 export * from './globalApi';
-export * from './iou';
 export * from './mtcnn';
-export * from './padToSquare';
 export * from './tinyYolov2';
-export * from './toNetInput';
-export * from './utils';
-export * from './types';

build/index.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-exports.tf = tf;
-tslib_1.__exportStar(require("./BoundingBox"), exports);
-tslib_1.__exportStar(require("./FaceDetection"), exports);
-tslib_1.__exportStar(require("./FullFaceDescription"), exports);
-tslib_1.__exportStar(require("./NetInput"), exports);
-tslib_1.__exportStar(require("./Point"), exports);
-tslib_1.__exportStar(require("./Rect"), exports);
-tslib_1.__exportStar(require("./drawing"), exports);
-tslib_1.__exportStar(require("./euclideanDistance"), exports);
-tslib_1.__exportStar(require("./extractFaces"), exports);
-tslib_1.__exportStar(require("./extractFaceTensors"), 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("./iou"), exports);
-tslib_1.__exportStar(require("./mtcnn"), exports);
-tslib_1.__exportStar(require("./padToSquare"), exports);
-tslib_1.__exportStar(require("./tinyYolov2"), exports);
-tslib_1.__exportStar(require("./toNetInput"), exports);
-tslib_1.__exportStar(require("./utils"), exports);
+import * as tf from '@tensorflow/tfjs-core';
+export { tf };
+export * from 'tfjs-image-recognition-base';
+export * from './classes';
+export * from './dom';
+export * from './euclideanDistance';
+export * from './faceDetectionNet';
+export * from './faceLandmarkNet';
+export * from './faceRecognitionNet';
+export * from './globalApi';
+export * from './mtcnn';
+export * from './tinyYolov2';
 //# sourceMappingURL=index.js.map
\ No newline at end of file

build/index.js.map

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\ No newline at end of file

build/iou.d.ts

-import { BoundingBox } from './BoundingBox';
-export declare function iou(box1: BoundingBox, box2: BoundingBox, isIOU?: boolean): number;

build/iou.js

-"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
\ No newline at end of file

build/iou.js.map

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build/mtcnn/BoundingBox.d.ts

-import { Rect } from '../Rect';
-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;
-    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;
-    toRect(): Rect;
-}

build/mtcnn/BoundingBox.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var Rect_1 = require("../Rect");
-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
-    });
-    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.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/mtcnn/BoundingBox.js.map

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\ No newline at end of file

build/mtcnn/FaceLandmarks5.js.map

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\ No newline at end of file

build/mtcnn/Mtcnn.d.ts

-import { NeuralNetwork } from '../commons/NeuralNetwork';
-import { NetInput } from '../NetInput';
-import { TNetInput } from '../types';
+import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
 import { MtcnnForwardParams, MtcnnResult, NetParams } from './types';
 export declare class Mtcnn extends NeuralNetwork<NetParams> {
     constructor();

build/mtcnn/Mtcnn.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var NeuralNetwork_1 = require("../commons/NeuralNetwork");
-var FaceDetection_1 = require("../FaceDetection");
-var Point_1 = require("../Point");
-var Rect_1 = require("../Rect");
-var toNetInput_1 = require("../toNetInput");
-var bgrToRgbTensor_1 = require("./bgrToRgbTensor");
-var config_1 = require("./config");
-var extractParams_1 = require("./extractParams");
-var FaceLandmarks5_1 = require("./FaceLandmarks5");
-var getDefaultMtcnnForwardParams_1 = require("./getDefaultMtcnnForwardParams");
-var getSizesForScale_1 = require("./getSizesForScale");
-var loadQuantizedParams_1 = require("./loadQuantizedParams");
-var pyramidDown_1 = require("./pyramidDown");
-var stage1_1 = require("./stage1");
-var stage2_1 = require("./stage2");
-var stage3_1 = require("./stage3");
+import * as tslib_1 from "tslib";
+import * as tf from '@tensorflow/tfjs-core';
+import { NeuralNetwork, Point, Rect, toNetInput } from 'tfjs-image-recognition-base';
+import { FaceDetection } from '../classes/FaceDetection';
+import { FaceLandmarks5 } from '../classes/FaceLandmarks5';
+import { bgrToRgbTensor } from './bgrToRgbTensor';
+import { CELL_SIZE } from './config';
+import { extractParams } from './extractParams';
+import { getDefaultMtcnnForwardParams } from './getDefaultMtcnnForwardParams';
+import { getSizesForScale } from './getSizesForScale';
+import { loadQuantizedParams } from './loadQuantizedParams';
+import { pyramidDown } from './pyramidDown';
+import { stage1 } from './stage1';
+import { stage2 } from './stage2';
+import { stage3 } from './stage3';
 var Mtcnn = /** @class */ (function (_super) {
     tslib_1.__extends(Mtcnn, _super);
     function Mtcnn() {
@@ -42,7 +37,7 @@ var Mtcnn = /** @class */ (function (_super) {
                         stats = {};
                         tsTotal = Date.now();
                         imgTensor = tf.tidy(function () {
-                            return bgrToRgbTensor_1.bgrToRgbTensor(tf.expandDims(inputTensor).toFloat());
+                            return bgrToRgbTensor(tf.expandDims(inputTensor).toFloat());
                         });
                         onReturn = function (results) {
                             // dispose tensors on return
@@ -52,17 +47,17 @@ 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;
-                        scales = (scaleSteps || pyramidDown_1.pyramidDown(minFaceSize, scaleFactor, [height, width]))
+                        _b = Object.assign({}, getDefaultMtcnnForwardParams(), forwardParams), minFaceSize = _b.minFaceSize, scaleFactor = _b.scaleFactor, maxNumScales = _b.maxNumScales, scoreThresholds = _b.scoreThresholds, scaleSteps = _b.scaleSteps;
+                        scales = (scaleSteps || pyramidDown(minFaceSize, scaleFactor, [height, width]))
                             .filter(function (scale) {
-                            var sizes = getSizesForScale_1.getSizesForScale(scale, [height, width]);
-                            return Math.min(sizes.width, sizes.height) > config_1.CELL_SIZE;
+                            var sizes = getSizesForScale(scale, [height, width]);
+                            return Math.min(sizes.width, sizes.height) > CELL_SIZE;
                         })
                             .slice(0, maxNumScales);
                         stats.scales = scales;
-                        stats.pyramid = scales.map(function (scale) { return getSizesForScale_1.getSizesForScale(scale, [height, width]); });
+                        stats.pyramid = scales.map(function (scale) { return getSizesForScale(scale, [height, width]); });
                         ts = Date.now();
-                        return [4 /*yield*/, stage1_1.stage1(imgTensor, scales, scoreThresholds[0], params.pnet, stats)];
+                        return [4 /*yield*/, stage1(imgTensor, scales, scoreThresholds[0], params.pnet, stats)];
                     case 1:
                         out1 = _c.sent();
                         stats.total_stage1 = Date.now() - ts;
@@ -73,7 +68,7 @@ var Mtcnn = /** @class */ (function (_super) {
                         // using the inputCanvas to extract and resize the image patches, since it is faster
                         // than doing this on the gpu
                         ts = Date.now();
-                        return [4 /*yield*/, stage2_1.stage2(inputCanvas, out1.boxes, scoreThresholds[1], params.rnet, stats)];
+                        return [4 /*yield*/, stage2(inputCanvas, out1.boxes, scoreThresholds[1], params.rnet, stats)];
                     case 2:
                         out2 = _c.sent();
                         stats.total_stage2 = Date.now() - ts;
@@ -82,16 +77,16 @@ var Mtcnn = /** @class */ (function (_super) {
                         }
                         stats.stage3_numInputBoxes = out2.boxes.length;
                         ts = Date.now();
-                        return [4 /*yield*/, stage3_1.stage3(inputCanvas, out2.boxes, scoreThresholds[2], params.onet, stats)];
+                        return [4 /*yield*/, stage3(inputCanvas, out2.boxes, scoreThresholds[2], params.onet, stats)];
                     case 3:
                         out3 = _c.sent();
                         stats.total_stage3 = Date.now() - ts;
                         results = out3.boxes.map(function (box, idx) { return ({
-                            faceDetection: new FaceDetection_1.FaceDetection(out3.scores[idx], new Rect_1.Rect(box.left / width, box.top / height, box.width / width, box.height / height), {
+                            faceDetection: new FaceDetection(out3.scores[idx], new 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 Point_1.Point(width, height)); }), { width: width, height: height })
+                            faceLandmarks: new FaceLandmarks5(out3.points[idx].map(function (pt) { return pt.div(new Point(width, height)); }), { width: width, height: height })
                         }); });
                         return [2 /*return*/, onReturn({ results: results, stats: stats })];
                 }
@@ -106,7 +101,7 @@ var Mtcnn = /** @class */ (function (_super) {
                 switch (_b.label) {
                     case 0:
                         _a = this.forwardInput;
-                        return [4 /*yield*/, toNetInput_1.toNetInput(input, true, true)];
+                        return [4 /*yield*/, toNetInput(input, true, true)];
                     case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(),
                             forwardParams])];
                     case 2: return [2 /*return*/, (_b.sent()).results];
@@ -122,7 +117,7 @@ var Mtcnn = /** @class */ (function (_super) {
                 switch (_b.label) {
                     case 0:
                         _a = this.forwardInput;
-                        return [4 /*yield*/, toNetInput_1.toNetInput(input, true, true)];
+                        return [4 /*yield*/, toNetInput(input, true, true)];
                     case 1: return [2 /*return*/, _a.apply(this, [_b.sent(),
                             forwardParams])];
                 }
@@ -131,12 +126,12 @@ var Mtcnn = /** @class */ (function (_super) {
     };
     // none of the param tensors are quantized yet
     Mtcnn.prototype.loadQuantizedParams = function (uri) {
-        return loadQuantizedParams_1.loadQuantizedParams(uri);
+        return loadQuantizedParams(uri);
     };
     Mtcnn.prototype.extractParams = function (weights) {
-        return extractParams_1.extractParams(weights);
+        return extractParams(weights);
     };
     return Mtcnn;
-}(NeuralNetwork_1.NeuralNetwork));
-exports.Mtcnn = Mtcnn;
+}(NeuralNetwork));
+export { Mtcnn };
 //# sourceMappingURL=Mtcnn.js.map
\ No newline at end of file

build/mtcnn/Mtcnn.js.map

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\ No newline at end of file

build/mtcnn/ONet.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var convLayer_1 = require("../commons/convLayer");
-var fullyConnectedLayer_1 = require("../faceLandmarkNet/fullyConnectedLayer");
-var prelu_1 = require("./prelu");
-var sharedLayers_1 = require("./sharedLayers");
-function ONet(x, params) {
+import * as tf from '@tensorflow/tfjs-core';
+import { convLayer } from 'tfjs-tiny-yolov2';
+import { fullyConnectedLayer } from '../faceLandmarkNet/fullyConnectedLayer';
+import { prelu } from './prelu';
+import { sharedLayer } from './sharedLayers';
+export function ONet(x, params) {
     return tf.tidy(function () {
-        var out = sharedLayers_1.sharedLayer(x, params);
+        var out = sharedLayer(x, params);
         out = tf.maxPool(out, [2, 2], [2, 2], 'same');
-        out = convLayer_1.convLayer(out, params.conv4, 'valid');
-        out = prelu_1.prelu(out, params.prelu4_alpha);
+        out = convLayer(out, params.conv4, 'valid');
+        out = prelu(out, params.prelu4_alpha);
         var vectorized = tf.reshape(out, [out.shape[0], params.fc1.weights.shape[0]]);
-        var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(vectorized, params.fc1);
-        var prelu5 = prelu_1.prelu(fc1, params.prelu5_alpha);
-        var fc2_1 = fullyConnectedLayer_1.fullyConnectedLayer(prelu5, params.fc2_1);
+        var fc1 = fullyConnectedLayer(vectorized, params.fc1);
+        var prelu5 = prelu(fc1, params.prelu5_alpha);
+        var fc2_1 = fullyConnectedLayer(prelu5, params.fc2_1);
         var max = tf.expandDims(tf.max(fc2_1, 1), 1);
         var prob = tf.softmax(tf.sub(fc2_1, max), 1);
-        var regions = fullyConnectedLayer_1.fullyConnectedLayer(prelu5, params.fc2_2);
-        var points = fullyConnectedLayer_1.fullyConnectedLayer(prelu5, params.fc2_3);
+        var regions = fullyConnectedLayer(prelu5, params.fc2_2);
+        var points = fullyConnectedLayer(prelu5, params.fc2_3);
         var scores = tf.unstack(prob, 1)[1];
         return { scores: scores, regions: regions, points: points };
     });
 }
-exports.ONet = ONet;
 //# sourceMappingURL=ONet.js.map
\ No newline at end of file

build/mtcnn/ONet.js.map

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\ No newline at end of file

build/mtcnn/PNet.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var convLayer_1 = require("../commons/convLayer");
-var sharedLayers_1 = require("./sharedLayers");
-function PNet(x, params) {
+import * as tf from '@tensorflow/tfjs-core';
+import { convLayer } from 'tfjs-tiny-yolov2';
+import { sharedLayer } from './sharedLayers';
+export function PNet(x, params) {
     return tf.tidy(function () {
-        var out = sharedLayers_1.sharedLayer(x, params, true);
-        var conv = convLayer_1.convLayer(out, params.conv4_1, 'valid');
+        var out = sharedLayer(x, params, true);
+        var conv = convLayer(out, params.conv4_1, 'valid');
         var max = tf.expandDims(tf.max(conv, 3), 3);
         var prob = tf.softmax(tf.sub(conv, max), 3);
-        var regions = convLayer_1.convLayer(out, params.conv4_2, 'valid');
+        var regions = convLayer(out, params.conv4_2, 'valid');
         return { prob: prob, regions: regions };
     });
 }
-exports.PNet = PNet;
 //# sourceMappingURL=PNet.js.map
\ No newline at end of file

build/mtcnn/PNet.js.map

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\ No newline at end of file

build/mtcnn/RNet.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var fullyConnectedLayer_1 = require("../faceLandmarkNet/fullyConnectedLayer");
-var prelu_1 = require("./prelu");
-var sharedLayers_1 = require("./sharedLayers");
-function RNet(x, params) {
+import * as tf from '@tensorflow/tfjs-core';
+import { fullyConnectedLayer } from '../faceLandmarkNet/fullyConnectedLayer';
+import { prelu } from './prelu';
+import { sharedLayer } from './sharedLayers';
+export function RNet(x, params) {
     return tf.tidy(function () {
-        var convOut = sharedLayers_1.sharedLayer(x, params);
+        var convOut = sharedLayer(x, params);
         var vectorized = tf.reshape(convOut, [convOut.shape[0], params.fc1.weights.shape[0]]);
-        var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(vectorized, params.fc1);
-        var prelu4 = prelu_1.prelu(fc1, params.prelu4_alpha);
-        var fc2_1 = fullyConnectedLayer_1.fullyConnectedLayer(prelu4, params.fc2_1);
+        var fc1 = fullyConnectedLayer(vectorized, params.fc1);
+        var prelu4 = prelu(fc1, params.prelu4_alpha);
+        var fc2_1 = fullyConnectedLayer(prelu4, params.fc2_1);
         var max = tf.expandDims(tf.max(fc2_1, 1), 1);
         var prob = tf.softmax(tf.sub(fc2_1, max), 1);
-        var regions = fullyConnectedLayer_1.fullyConnectedLayer(prelu4, params.fc2_2);
+        var regions = fullyConnectedLayer(prelu4, params.fc2_2);
         var scores = tf.unstack(prob, 1)[1];
         return { scores: scores, regions: regions };
     });
 }
-exports.RNet = RNet;
 //# sourceMappingURL=RNet.js.map
\ No newline at end of file

build/mtcnn/RNet.js.map

-{"version":3,"file":"RNet.js","sourceRoot":"","sources":["../../src/mtcnn/RNet.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,8EAA6E;AAC7E,iCAAgC;AAChC,+CAA6C;AAG7C,cAAqB,CAAc,EAAE,MAAkB;IACrD,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAM,OAAO,GAAG,0BAAW,CAAC,CAAC,EAAE,MAAM,CAAC,CAAA;QACtC,IAAM,UAAU,GAAG,EAAE,CAAC,OAAO,CAAC,OAAO,EAAE,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,GAAG,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAgB,CAAA;QACtG,IAAM,GAAG,GAAG,yCAAmB,CAAC,UAAU,EAAE,MAAM,CAAC,GAAG,CAAC,CAAA;QACvD,IAAM,MAAM,GAAG,aAAK,CAAc,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;QAC3D,IAAM,KAAK,GAAG,yCAAmB,CAAC,MAAM,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;QACvD,IAAM,GAAG,GAAG,EAAE,CAAC,UAAU,CAAC,EAAE,CAAC,GAAG,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;QAC9C,IAAM,IAAI,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,GAAG,CAAC,KAAK,EAAE,GAAG,CAAC,EAAE,CAAC,CAAgB,CAAA;QAC7D,IAAM,OAAO,GAAG,yCAAmB,CAAC,MAAM,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;QAEzD,IAAM,MAAM,GAAG,EAAE,CAAC,OAAO,CAAC,IAAI,EAAE,CAAC,CAAC,CAAC,CAAC,CAAgB,CAAA;QACpD,OAAO,EAAE,MAAM,QAAA,EAAE,OAAO,SAAA,EAAE,CAAA;IAC5B,CAAC,CAAC,CAAA;AACJ,CAAC;AAfD,oBAeC"}
+{"version":3,"file":"RNet.js","sourceRoot":"","sources":["../../src/mtcnn/RNet.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,mBAAmB,EAAE,MAAM,wCAAwC,CAAC;AAC7E,OAAO,EAAE,KAAK,EAAE,MAAM,SAAS,CAAC;AAChC,OAAO,EAAE,WAAW,EAAE,MAAM,gBAAgB,CAAC;AAG7C,MAAM,eAAe,CAAc,EAAE,MAAkB;IACrD,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAM,OAAO,GAAG,WAAW,CAAC,CAAC,EAAE,MAAM,CAAC,CAAA;QACtC,IAAM,UAAU,GAAG,EAAE,CAAC,OAAO,CAAC,OAAO,EAAE,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,GAAG,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAgB,CAAA;QACtG,IAAM,GAAG,GAAG,mBAAmB,CAAC,UAAU,EAAE,MAAM,CAAC,GAAG,CAAC,CAAA;QACvD,IAAM,MAAM,GAAG,KAAK,CAAc,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;QAC3D,IAAM,KAAK,GAAG,mBAAmB,CAAC,MAAM,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;QACvD,IAAM,GAAG,GAAG,EAAE,CAAC,UAAU,CAAC,EAAE,CAAC,GAAG,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;QAC9C,IAAM,IAAI,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,GAAG,CAAC,KAAK,EAAE,GAAG,CAAC,EAAE,CAAC,CAAgB,CAAA;QAC7D,IAAM,OAAO,GAAG,mBAAmB,CAAC,MAAM,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;QAEzD,IAAM,MAAM,GAAG,EAAE,CAAC,OAAO,CAAC,IAAI,EAAE,CAAC,CAAC,CAAC,CAAC,CAAgB,CAAA;QACpD,OAAO,EAAE,MAAM,QAAA,EAAE,OAAO,SAAA,EAAE,CAAA;IAC5B,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file

build/mtcnn/bgrToRgbTensor.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-function bgrToRgbTensor(tensor) {
+import * as tf from '@tensorflow/tfjs-core';
+export function bgrToRgbTensor(tensor) {
     return tf.tidy(function () { return tf.stack(tf.unstack(tensor, 3).reverse(), 3); });
 }
-exports.bgrToRgbTensor = bgrToRgbTensor;
 //# sourceMappingURL=bgrToRgbTensor.js.map
\ No newline at end of file

build/mtcnn/bgrToRgbTensor.js.map

-{"version":3,"file":"bgrToRgbTensor.js","sourceRoot":"","sources":["../../src/mtcnn/bgrToRgbTensor.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,wBAA+B,MAAmB;IAChD,OAAO,EAAE,CAAC,IAAI,CACZ,cAAM,OAAA,EAAE,CAAC,KAAK,CAAC,EAAE,CAAC,OAAO,CAAC,MAAM,EAAE,CAAC,CAAC,CAAC,OAAO,EAAE,EAAE,CAAC,CAAC,EAA5C,CAA4C,CACpC,CAAA;AAClB,CAAC;AAJD,wCAIC"}
+{"version":3,"file":"bgrToRgbTensor.js","sourceRoot":"","sources":["../../src/mtcnn/bgrToRgbTensor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,MAAM,yBAAyB,MAAmB;IAChD,OAAO,EAAE,CAAC,IAAI,CACZ,cAAM,OAAA,EAAE,CAAC,KAAK,CAAC,EAAE,CAAC,OAAO,CAAC,MAAM,EAAE,CAAC,CAAC,CAAC,OAAO,EAAE,EAAE,CAAC,CAAC,EAA5C,CAA4C,CACpC,CAAA;AAClB,CAAC"}
\ No newline at end of file

build/mtcnn/calculateScaleSteps.d.ts

-export declare function calculateScaleSteps(numScales: number, minFaceSize: number, dims: number[]): number[];

build/mtcnn/calculateScaleSteps.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-function calculateScaleSteps(numScales, minFaceSize, dims) {
-    var height = dims[0], width = dims[1];
-    var maxDim = Math.max(height, width);
-    if (Math.min(height, width) < minFaceSize) {
-        throw new Error("Image dimensions: " + height + " x " + width + " are smaller than specified minimum face size of " + minFaceSize);
-    }
-    var scaleStep = (maxDim - minFaceSize) / numScales;
-    return Array(numScales).fill(0).map(function (_, i) { return ((i + 1) * scaleStep + minFaceSize) / maxDim; }).reverse();
-}
-exports.calculateScaleSteps = calculateScaleSteps;
-//# sourceMappingURL=calculateScaleSteps.js.map
\ No newline at end of file

build/mtcnn/calculateScaleSteps.js.map

-{"version":3,"file":"calculateScaleSteps.js","sourceRoot":"","sources":["../../src/mtcnn/calculateScaleSteps.ts"],"names":[],"mappings":";;AAAA,6BAAoC,SAAiB,EAAE,WAAmB,EAAE,IAAc;IAEjF,IAAA,gBAAM,EAAE,eAAK,CAAQ;IAC5B,IAAM,MAAM,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,EAAE,KAAK,CAAC,CAAA;IAEtC,IAAI,IAAI,CAAC,GAAG,CAAC,MAAM,EAAE,KAAK,CAAC,GAAG,WAAW,EAAE;QACzC,MAAM,IAAI,KAAK,CAAC,uBAAqB,MAAM,WAAM,KAAK,yDAAoD,WAAa,CAAC,CAAA;KACzH;IAED,IAAM,SAAS,GAAG,CAAC,MAAM,GAAG,WAAW,CAAC,GAAG,SAAS,CAAA;IAEpD,OAAO,KAAK,CAAC,SAAS,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,SAAS,GAAG,WAAW,CAAC,GAAG,MAAM,EAA5C,CAA4C,CAAC,CAAC,OAAO,EAAE,CAAA;AACvG,CAAC;AAZD,kDAYC"}
\ No newline at end of file

build/mtcnn/config.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-exports.CELL_STRIDE = 2;
-exports.CELL_SIZE = 12;
+export var CELL_STRIDE = 2;
+export var CELL_SIZE = 12;
 //# sourceMappingURL=config.js.map
\ No newline at end of file

build/mtcnn/config.js.map

-{"version":3,"file":"config.js","sourceRoot":"","sources":["../../src/mtcnn/config.ts"],"names":[],"mappings":";;AAAa,QAAA,WAAW,GAAG,CAAC,CAAA;AACf,QAAA,SAAS,GAAG,EAAE,CAAA"}
+{"version":3,"file":"config.js","sourceRoot":"","sources":["../../src/mtcnn/config.ts"],"names":[],"mappings":"AAAA,MAAM,CAAC,IAAM,WAAW,GAAG,CAAC,CAAA;AAC5B,MAAM,CAAC,IAAM,SAAS,GAAG,EAAE,CAAA"}
\ No newline at end of file

build/mtcnn/extractImagePatches.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { BoundingBox } from '../BoundingBox';
-import { Dimensions } from '../types';
+import { BoundingBox, Dimensions } from 'tfjs-image-recognition-base';
 export declare function extractImagePatches(img: HTMLCanvasElement, boxes: BoundingBox[], {width, height}: Dimensions): Promise<tf.Tensor4D[]>;

build/mtcnn/extractImagePatches.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var utils_1 = require("../utils");
-var normalize_1 = require("./normalize");
-function extractImagePatches(img, boxes, _a) {
+import * as tslib_1 from "tslib";
+import * as tf from '@tensorflow/tfjs-core';
+import { normalize } from './normalize';
+import { getContext2dOrThrow, createCanvas } from 'tfjs-image-recognition-base';
+export function extractImagePatches(img, boxes, _a) {
     var width = _a.width, height = _a.height;
     return tslib_1.__awaiter(this, void 0, void 0, function () {
         var _this = this;
@@ -12,7 +10,7 @@ function extractImagePatches(img, boxes, _a) {
         return tslib_1.__generator(this, function (_b) {
             switch (_b.label) {
                 case 0:
-                    imgCtx = utils_1.getContext2dOrThrow(img);
+                    imgCtx = getContext2dOrThrow(img);
                     return [4 /*yield*/, Promise.all(boxes.map(function (box) { return tslib_1.__awaiter(_this, void 0, void 0, function () {
                             var _a, y, ey, x, ex, fromX, fromY, imgData;
                             return tslib_1.__generator(this, function (_b) {
@@ -27,8 +25,8 @@ function extractImagePatches(img, boxes, _a) {
                     bitmaps = _b.sent();
                     imagePatchesDatas = [];
                     bitmaps.forEach(function (bmp) {
-                        var patch = utils_1.createCanvas({ width: width, height: height });
-                        var patchCtx = utils_1.getContext2dOrThrow(patch);
+                        var patch = createCanvas({ width: width, height: height });
+                        var patchCtx = getContext2dOrThrow(patch);
                         patchCtx.drawImage(bmp, 0, 0, width, height);
                         var data = patchCtx.getImageData(0, 0, width, height).data;
                         var currData = [];
@@ -43,7 +41,7 @@ function extractImagePatches(img, boxes, _a) {
                     return [2 /*return*/, imagePatchesDatas.map(function (data) {
                             var t = tf.tidy(function () {
                                 var imagePatchTensor = tf.transpose(tf.tensor4d(data, [1, width, height, 3]), [0, 2, 1, 3]).toFloat();
-                                return normalize_1.normalize(imagePatchTensor);
+                                return normalize(imagePatchTensor);
                             });
                             return t;
                         })];
@@ -51,5 +49,4 @@ function extractImagePatches(img, boxes, _a) {
         });
     });
 }
-exports.extractImagePatches = extractImagePatches;
 //# sourceMappingURL=extractImagePatches.js.map
\ No newline at end of file

build/mtcnn/extractImagePatches.js.map

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\ No newline at end of file

build/mtcnn/extractParams.d.ts

-import { ParamMapping } from '../commons/types';
+import { ParamMapping } from 'tfjs-image-recognition-base';
 import { NetParams } from './types';
 export declare function extractParams(weights: Float32Array): {
     params: NetParams;

build/mtcnn/extractParams.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var extractConvParamsFactory_1 = require("../commons/extractConvParamsFactory");
-var extractFCParamsFactory_1 = require("../commons/extractFCParamsFactory");
-var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
+import * as tslib_1 from "tslib";
+import * as tf from '@tensorflow/tfjs-core';
+import { extractWeightsFactory } from 'tfjs-image-recognition-base';
+import { extractConvParamsFactory, extractFCParamsFactory } from 'tfjs-tiny-yolov2';
 function extractorsFactory(extractWeights, paramMappings) {
-    var extractConvParams = extractConvParamsFactory_1.extractConvParamsFactory(extractWeights, paramMappings);
-    var extractFCParams = extractFCParamsFactory_1.extractFCParamsFactory(extractWeights, paramMappings);
+    var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
+    var extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);
     function extractPReluParams(size, paramPath) {
         var alpha = tf.tensor1d(extractWeights(size));
         paramMappings.push({ paramPath: paramPath });
@@ -54,8 +51,8 @@ function extractorsFactory(extractWeights, paramMappings) {
         extractONetParams: extractONetParams
     };
 }
-function extractParams(weights) {
-    var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
+export function extractParams(weights) {
+    var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
     var paramMappings = [];
     var _b = extractorsFactory(extractWeights, paramMappings), extractPNetParams = _b.extractPNetParams, extractRNetParams = _b.extractRNetParams, extractONetParams = _b.extractONetParams;
     var pnet = extractPNetParams();
@@ -66,5 +63,4 @@ function extractParams(weights) {
     }
     return { params: { pnet: pnet, rnet: rnet, onet: onet }, paramMappings: paramMappings };
 }
-exports.extractParams = extractParams;
 //# sourceMappingURL=extractParams.js.map
\ No newline at end of file

build/mtcnn/extractParams.js.map

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+{"version":3,"file":"extractParams.js","sourceRoot":"","sources":["../../src/mtcnn/extractParams.ts"],"names":[],"mappings":";AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAC5C,OAAO,EAAE,qBAAqB,EAAwC,MAAM,6BAA6B,CAAC;AAC1G,OAAO,EAAE,wBAAwB,EAAE,sBAAsB,EAAE,MAAM,kBAAkB,CAAC;AAIpF,2BAA2B,cAAsC,EAAE,aAA6B;IAE9F,IAAM,iBAAiB,GAAG,wBAAwB,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IACjF,IAAM,eAAe,GAAG,sBAAsB,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAE7E,4BAA4B,IAAY,EAAE,SAAiB;QACzD,IAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,IAAI,CAAC,CAAC,CAAA;QAC/C,aAAa,CAAC,IAAI,CAAC,EAAE,SAAS,WAAA,EAAE,CAAC,CAAA;QACjC,OAAO,KAAK,CAAA;IACd,CAAC;IAED,6BAA6B,UAAoB,EAAE,YAAoB,EAAE,MAAuB;QAAvB,uBAAA,EAAA,cAAuB;QAE9F,IAAM,KAAK,GAAG,iBAAiB,CAAC,UAAU,CAAC,CAAC,CAAC,EAAE,UAAU,CAAC,CAAC,CAAC,EAAE,CAAC,EAAK,YAAY,WAAQ,CAAC,CAAA;QACzF,IAAM,YAAY,GAAG,kBAAkB,CAAC,UAAU,CAAC,CAAC,CAAC,EAAK,YAAY,kBAAe,CAAC,CAAA;QACtF,IAAM,KAAK,GAAG,iBAAiB,CAAC,UAAU,CAAC,CAAC,CAAC,EAAE,UAAU,CAAC,CAAC,CAAC,EAAE,CAAC,EAAK,YAAY,WAAQ,CAAC,CAAA;QACzF,IAAM,YAAY,GAAG,kBAAkB,CAAC,UAAU,CAAC,CAAC,CAAC,EAAK,YAAY,kBAAe,CAAC,CAAA;QACtF,IAAM,KAAK,GAAG,iBAAiB,CAAC,UAAU,CAAC,CAAC,CAAC,EAAE,UAAU,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAK,YAAY,WAAQ,CAAC,CAAA;QACtG,IAAM,YAAY,GAAG,kBAAkB,CAAC,UAAU,CAAC,CAAC,CAAC,EAAK,YAAY,kBAAe,CAAC,CAAA;QAEtF,OAAO,EAAE,KAAK,OAAA,EAAE,YAAY,cAAA,EAAE,KAAK,OAAA,EAAE,YAAY,cAAA,EAAE,KAAK,OAAA,EAAE,YAAY,cAAA,EAAE,CAAA;IAC1E,CAAC;IAED;QAEE,IAAM,YAAY,GAAG,mBAAmB,CAAC,CAAC,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,MAAM,CAAC,CAAA;QACjE,IAAM,OAAO,GAAG,iBAAiB,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,EAAE,cAAc,CAAC,CAAA;QAC3D,IAAM,OAAO,GAAG,iBAAiB,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,EAAE,cAAc,CAAC,CAAA;QAE3D,4BAAY,YAAY,IAAE,OAAO,SAAA,EAAE,OAAO,SAAA,IAAE;IAC9C,CAAC;IAED;QAEE,IAAM,YAAY,GAAG,mBAAmB,CAAC,CAAC,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,MAAM,EAAE,IAAI,CAAC,CAAA;QACvE,IAAM,GAAG,GAAG,eAAe,CAAC,GAAG,EAAE,GAAG,EAAE,UAAU,CAAC,CAAA;QACjD,IAAM,YAAY,GAAG,kBAAkB,CAAC,GAAG,EAAE,mBAAmB,CAAC,CAAA;QACjE,IAAM,KAAK,GAAG,eAAe,CAAC,GAAG,EAAE,CAAC,EAAE,YAAY,CAAC,CAAA;QACnD,IAAM,KAAK,GAAG,eAAe,CAAC,GAAG,EAAE,CAAC,EAAE,YAAY,CAAC,CAAA;QAEnD,4BAAY,YAAY,IAAE,GAAG,KAAA,EAAE,YAAY,cAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,IAAE;IAC7D,CAAC;IAED;QAEE,IAAM,YAAY,GAAG,mBAAmB,CAAC,CAAC,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,MAAM,CAAC,CAAA;QACjE,IAAM,KAAK,GAAG,iBAAiB,CAAC,EAAE,EAAE,GAAG,EAAE,CAAC,EAAE,YAAY,CAAC,CAAA;QACzD,IAAM,YAAY,GAAG,kBAAkB,CAAC,GAAG,EAAE,mBAAmB,CAAC,CAAA;QACjE,IAAM,GAAG,GAAG,eAAe,CAAC,IAAI,EAAE,GAAG,EAAE,UAAU,CAAC,CAAA;QAClD,IAAM,YAAY,GAAG,kBAAkB,CAAC,GAAG,EAAE,mBAAmB,CAAC,CAAA;QACjE,IAAM,KAAK,GAAG,eAAe,CAAC,GAAG,EAAE,CAAC,EAAE,YAAY,CAAC,CAAA;QACnD,IAAM,KAAK,GAAG,eAAe,CAAC,GAAG,EAAE,CAAC,EAAE,YAAY,CAAC,CAAA;QACnD,IAAM,KAAK,GAAG,eAAe,CAAC,GAAG,EAAE,EAAE,EAAE,YAAY,CAAC,CAAA;QAEpD,4BAAY,YAAY,IAAE,KAAK,OAAA,EAAE,YAAY,cAAA,EAAE,GAAG,KAAA,EAAE,YAAY,cAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,IAAE;IACzF,CAAC;IAED,OAAO;QACL,iBAAiB,mBAAA;QACjB,iBAAiB,mBAAA;QACjB,iBAAiB,mBAAA;KAClB,CAAA;AAEH,CAAC;AAED,MAAM,wBAAwB,OAAqB;IAE3C,IAAA,mCAG4B,EAFhC,kCAAc,EACd,4CAAmB,CACa;IAElC,IAAM,aAAa,GAAmB,EAAE,CAAA;IAElC,IAAA,qDAI8C,EAHlD,wCAAiB,EACjB,wCAAiB,EACjB,wCAAiB,CACiC;IAEpD,IAAM,IAAI,GAAG,iBAAiB,EAAE,CAAA;IAChC,IAAM,IAAI,GAAG,iBAAiB,EAAE,CAAA;IAChC,IAAM,IAAI,GAAG,iBAAiB,EAAE,CAAA;IAEhC,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,oCAAkC,mBAAmB,EAAE,CAAC,MAAQ,CAAC,CAAA;KAClF;IAED,OAAO,EAAE,MAAM,EAAE,EAAE,IAAI,MAAA,EAAE,IAAI,MAAA,EAAE,IAAI,MAAA,EAAE,EAAE,aAAa,eAAA,EAAE,CAAA;AACxD,CAAC"}
\ No newline at end of file

build/mtcnn/getDefaultMtcnnForwardParams.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-function getDefaultMtcnnForwardParams() {
+export function getDefaultMtcnnForwardParams() {
     return {
         minFaceSize: 20,
         scaleFactor: 0.709,
@@ -8,5 +6,4 @@ function getDefaultMtcnnForwardParams() {
         scoreThresholds: [0.6, 0.7, 0.7]
     };
 }
-exports.getDefaultMtcnnForwardParams = getDefaultMtcnnForwardParams;
 //# sourceMappingURL=getDefaultMtcnnForwardParams.js.map
\ No newline at end of file

build/mtcnn/getDefaultMtcnnForwardParams.js.map

-{"version":3,"file":"getDefaultMtcnnForwardParams.js","sourceRoot":"","sources":["../../src/mtcnn/getDefaultMtcnnForwardParams.ts"],"names":[],"mappings":";;AAAA;IACE,OAAO;QACL,WAAW,EAAE,EAAE;QACf,WAAW,EAAE,KAAK;QAClB,YAAY,EAAE,EAAE;QAChB,eAAe,EAAE,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC;KACjC,CAAA;AACH,CAAC;AAPD,oEAOC"}
+{"version":3,"file":"getDefaultMtcnnForwardParams.js","sourceRoot":"","sources":["../../src/mtcnn/getDefaultMtcnnForwardParams.ts"],"names":[],"mappings":"AAAA,MAAM;IACJ,OAAO;QACL,WAAW,EAAE,EAAE;QACf,WAAW,EAAE,KAAK;QAClB,YAAY,EAAE,EAAE;QAChB,eAAe,EAAE,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC;KACjC,CAAA;AACH,CAAC"}
\ No newline at end of file

build/mtcnn/getSizesForScale.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-function getSizesForScale(scale, _a) {
+export function getSizesForScale(scale, _a) {
     var height = _a[0], width = _a[1];
     return {
         height: Math.floor(height * scale),
         width: Math.floor(width * scale)
     };
 }
-exports.getSizesForScale = getSizesForScale;
 //# sourceMappingURL=getSizesForScale.js.map
\ No newline at end of file

build/mtcnn/getSizesForScale.js.map

-{"version":3,"file":"getSizesForScale.js","sourceRoot":"","sources":["../../src/mtcnn/getSizesForScale.ts"],"names":[],"mappings":";;AAAA,0BAAiC,KAAa,EAAE,EAAyB;QAAxB,cAAM,EAAE,aAAK;IAC5D,OAAO;QACL,MAAM,EAAE,IAAI,CAAC,KAAK,CAAC,MAAM,GAAG,KAAK,CAAC;QAClC,KAAK,EAAE,IAAI,CAAC,KAAK,CAAC,KAAK,GAAG,KAAK,CAAC;KACjC,CAAA;AACH,CAAC;AALD,4CAKC"}
+{"version":3,"file":"getSizesForScale.js","sourceRoot":"","sources":["../../src/mtcnn/getSizesForScale.ts"],"names":[],"mappings":"AAAA,MAAM,2BAA2B,KAAa,EAAE,EAAyB;QAAxB,cAAM,EAAE,aAAK;IAC5D,OAAO;QACL,MAAM,EAAE,IAAI,CAAC,KAAK,CAAC,MAAM,GAAG,KAAK,CAAC;QAClC,KAAK,EAAE,IAAI,CAAC,KAAK,CAAC,KAAK,GAAG,KAAK,CAAC;KACjC,CAAA;AACH,CAAC"}
\ No newline at end of file

build/mtcnn/index.d.ts

 import { Mtcnn } from './Mtcnn';
 export * from './Mtcnn';
-export * from './FaceLandmarks5';
 export declare function createMtcnn(weights: Float32Array): Mtcnn;

build/mtcnn/index.js

-"use strict";
-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("./FaceLandmarks5"), exports);
-function createMtcnn(weights) {
-    var net = new Mtcnn_1.Mtcnn();
+import { Mtcnn } from './Mtcnn';
+export * from './Mtcnn';
+export function createMtcnn(weights) {
+    var net = new Mtcnn();
     net.extractWeights(weights);
     return net;
 }
-exports.createMtcnn = createMtcnn;
 //# sourceMappingURL=index.js.map
\ No newline at end of file

build/mtcnn/index.js.map

-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/mtcnn/index.ts"],"names":[],"mappings":";;;AAAA,iCAAgC;AAEhC,kDAAwB;AACxB,2DAAiC;AAEjC,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"}
+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/mtcnn/index.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,KAAK,EAAE,MAAM,SAAS,CAAC;AAEhC,cAAc,SAAS,CAAC;AAExB,MAAM,sBAAsB,OAAqB;IAC/C,IAAM,GAAG,GAAG,IAAI,KAAK,EAAE,CAAA;IACvB,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC"}
\ No newline at end of file

build/mtcnn/loadQuantizedParams.d.ts

-import { ParamMapping } from '../commons/types';
+import { ParamMapping } from 'tfjs-image-recognition-base';
 import { NetParams } from './types';
 export declare function loadQuantizedParams(uri: string | undefined): Promise<{
     params: NetParams;

build/mtcnn/loadQuantizedParams.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var disposeUnusedWeightTensors_1 = require("../commons/disposeUnusedWeightTensors");
-var extractWeightEntryFactory_1 = require("../commons/extractWeightEntryFactory");
-var loadWeightMap_1 = require("../commons/loadWeightMap");
+import * as tslib_1 from "tslib";
+import { disposeUnusedWeightTensors, extractWeightEntryFactory, loadWeightMap, } from 'tfjs-image-recognition-base';
 var DEFAULT_MODEL_NAME = 'mtcnn_model';
 function extractorsFactory(weightMap, paramMappings) {
-    var extractWeightEntry = extractWeightEntryFactory_1.extractWeightEntryFactory(weightMap, paramMappings);
+    var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
     function extractConvParams(prefix) {
         var filters = extractWeightEntry(prefix + "/weights", 4, prefix + "/filters");
         var bias = extractWeightEntry(prefix + "/bias", 1);
@@ -60,12 +56,12 @@ function extractorsFactory(weightMap, paramMappings) {
         extractONetParams: extractONetParams
     };
 }
-function loadQuantizedParams(uri) {
+export function loadQuantizedParams(uri) {
     return tslib_1.__awaiter(this, void 0, void 0, function () {
         var weightMap, paramMappings, _a, extractPNetParams, extractRNetParams, extractONetParams, pnet, rnet, onet;
         return tslib_1.__generator(this, function (_b) {
             switch (_b.label) {
-                case 0: return [4 /*yield*/, loadWeightMap_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
+                case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME)];
                 case 1:
                     weightMap = _b.sent();
                     paramMappings = [];
@@ -73,11 +69,10 @@ function loadQuantizedParams(uri) {
                     pnet = extractPNetParams();
                     rnet = extractRNetParams();
                     onet = extractONetParams();
-                    disposeUnusedWeightTensors_1.disposeUnusedWeightTensors(weightMap, paramMappings);
+                    disposeUnusedWeightTensors(weightMap, paramMappings);
                     return [2 /*return*/, { params: { pnet: pnet, rnet: rnet, onet: onet }, paramMappings: paramMappings }];
             }
         });
     });
 }
-exports.loadQuantizedParams = loadQuantizedParams;
 //# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file

build/mtcnn/loadQuantizedParams.js.map

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\ No newline at end of file

build/mtcnn/nms.d.ts

-import { BoundingBox } from './BoundingBox';
-export declare function nms(boxes: BoundingBox[], scores: number[], iouThreshold: number, isIOU?: boolean): number[];

build/mtcnn/nms.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-function nms(boxes, scores, iouThreshold, isIOU) {
-    if (isIOU === void 0) { isIOU = true; }
-    var areas = boxes.map(function (box) { return (box.width + 1) * (box.height + 1); });
-    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];
-            var width = Math.max(0.0, Math.min(currBox.right, idxBox.right) - Math.max(currBox.left, idxBox.left) + 1);
-            var height = Math.max(0.0, Math.min(currBox.bottom, idxBox.bottom) - Math.max(currBox.top, idxBox.top) + 1);
-            var interSection = width * height;
-            var out = isIOU
-                ? interSection / (areas[curr] + areas[idx] - interSection)
-                : interSection / Math.min(areas[curr], areas[idx]);
-            outputs.push(out);
-        }
-        indicesSortedByScore = indicesSortedByScore.filter(function (_, j) { return outputs[j] <= iouThreshold; });
-    };
-    while (indicesSortedByScore.length > 0) {
-        _loop_1();
-    }
-    return pick;
-}
-exports.nms = nms;
-//# sourceMappingURL=nms.js.map
\ No newline at end of file

build/mtcnn/nms.js.map

-{"version":3,"file":"nms.js","sourceRoot":"","sources":["../../src/mtcnn/nms.ts"],"names":[],"mappings":";;AAEA,aACE,KAAoB,EACpB,MAAgB,EAChB,YAAoB,EACpB,KAAqB;IAArB,sBAAA,EAAA,YAAqB;IAGrB,IAAM,KAAK,GAAG,KAAK,CAAC,GAAG,CACrB,UAAA,GAAG,IAAI,OAAA,CAAC,GAAG,CAAC,KAAK,GAAG,CAAC,CAAC,GAAG,CAAC,GAAG,CAAC,MAAM,GAAG,CAAC,CAAC,EAAlC,CAAkC,CAC1C,CAAA;IAED,IAAI,oBAAoB,GAAG,MAAM;SAC9B,GAAG,CAAC,UAAC,KAAK,EAAE,QAAQ,IAAK,OAAA,CAAC,EAAE,KAAK,OAAA,EAAE,QAAQ,UAAA,EAAE,CAAC,EAArB,CAAqB,CAAC;SAC/C,IAAI,CAAC,UAAC,EAAE,EAAE,EAAE,IAAK,OAAA,EAAE,CAAC,KAAK,GAAG,EAAE,CAAC,KAAK,EAAnB,CAAmB,CAAC;SACrC,GAAG,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,QAAQ,EAAV,CAAU,CAAC,CAAA;IAEvB,IAAM,IAAI,GAAa,EAAE,CAAA;;QAGvB,IAAM,IAAI,GAAG,oBAAoB,CAAC,GAAG,EAAY,CAAA;QACjD,IAAI,CAAC,IAAI,CAAC,IAAI,CAAC,CAAA;QAEf,IAAM,OAAO,GAAG,oBAAoB,CAAA;QAEpC,IAAM,OAAO,GAAa,EAAE,CAAA;QAC5B,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,OAAO,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;YACvC,IAAM,GAAG,GAAG,OAAO,CAAC,CAAC,CAAC,CAAA;YAEtB,IAAM,OAAO,GAAG,KAAK,CAAC,IAAI,CAAC,CAAA;YAC3B,IAAM,MAAM,GAAG,KAAK,CAAC,GAAG,CAAC,CAAA;YAEzB,IAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,GAAG,EAAE,IAAI,CAAC,GAAG,CAAC,OAAO,CAAC,KAAK,EAAE,MAAM,CAAC,KAAK,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,OAAO,CAAC,IAAI,EAAE,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAA;YAC5G,IAAM,MAAM,GAAG,IAAI,CAAC,GAAG,CAAC,GAAG,EAAE,IAAI,CAAC,GAAG,CAAC,OAAO,CAAC,MAAM,EAAE,MAAM,CAAC,MAAM,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,OAAO,CAAC,GAAG,EAAE,MAAM,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAA;YAC7G,IAAM,YAAY,GAAG,KAAK,GAAG,MAAM,CAAA;YAEnC,IAAM,GAAG,GAAG,KAAK;gBACf,CAAC,CAAC,YAAY,GAAG,CAAC,KAAK,CAAC,IAAI,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,GAAG,YAAY,CAAC;gBAC1D,CAAC,CAAC,YAAY,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,IAAI,CAAC,EAAE,KAAK,CAAC,GAAG,CAAC,CAAC,CAAA;YAEpD,OAAO,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;SAClB;QAED,oBAAoB,GAAG,oBAAoB,CAAC,MAAM,CAChD,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,OAAO,CAAC,CAAC,CAAC,IAAI,YAAY,EAA1B,CAA0B,CACrC,CAAA;IACH,CAAC;IA3BD,OAAM,oBAAoB,CAAC,MAAM,GAAG,CAAC;;KA2BpC;IAED,OAAO,IAAI,CAAA;AAEb,CAAC;AAjDD,kBAiDC"}
\ No newline at end of file

build/mtcnn/normalize.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-function normalize(x) {
+import * as tf from '@tensorflow/tfjs-core';
+export function normalize(x) {
     return tf.tidy(function () { return tf.mul(tf.sub(x, tf.scalar(127.5)), tf.scalar(0.0078125)); });
 }
-exports.normalize = normalize;
 //# sourceMappingURL=normalize.js.map
\ No newline at end of file

build/mtcnn/normalize.js.map

-{"version":3,"file":"normalize.js","sourceRoot":"","sources":["../../src/mtcnn/normalize.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,mBAA0B,CAAc;IACtC,OAAO,EAAE,CAAC,IAAI,CACZ,cAAM,OAAA,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,SAAS,CAAC,CAAC,EAAzD,CAAyD,CAChE,CAAA;AACH,CAAC;AAJD,8BAIC"}
+{"version":3,"file":"normalize.js","sourceRoot":"","sources":["../../src/mtcnn/normalize.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,MAAM,oBAAoB,CAAc;IACtC,OAAO,EAAE,CAAC,IAAI,CACZ,cAAM,OAAA,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,SAAS,CAAC,CAAC,EAAzD,CAAyD,CAChE,CAAA;AACH,CAAC"}
\ No newline at end of file

build/mtcnn/prelu.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-function prelu(x, alpha) {
+import * as tf from '@tensorflow/tfjs-core';
+export function prelu(x, alpha) {
     return tf.tidy(function () {
         return tf.add(tf.relu(x), tf.mul(alpha, tf.neg(tf.relu(tf.neg(x)))));
     });
 }
-exports.prelu = prelu;
 //# sourceMappingURL=prelu.js.map
\ No newline at end of file

build/mtcnn/prelu.js.map

-{"version":3,"file":"prelu.js","sourceRoot":"","sources":["../../src/mtcnn/prelu.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,eAA2C,CAAI,EAAE,KAAkB;IACjE,OAAO,EAAE,CAAC,IAAI,CAAC;QACb,OAAA,EAAE,CAAC,GAAG,CACJ,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,EACV,EAAE,CAAC,GAAG,CAAC,KAAK,EAAE,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAC1C;IAHD,CAGC,CACF,CAAA;AACH,CAAC;AAPD,sBAOC"}
+{"version":3,"file":"prelu.js","sourceRoot":"","sources":["../../src/mtcnn/prelu.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,MAAM,gBAAqC,CAAI,EAAE,KAAkB;IACjE,OAAO,EAAE,CAAC,IAAI,CAAC;QACb,OAAA,EAAE,CAAC,GAAG,CACJ,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,EACV,EAAE,CAAC,GAAG,CAAC,KAAK,EAAE,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAC1C;IAHD,CAGC,CACF,CAAA;AACH,CAAC"}
\ No newline at end of file

build/mtcnn/pyramidDown.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var config_1 = require("./config");
-function pyramidDown(minFaceSize, scaleFactor, dims) {
+import { CELL_SIZE } from './config';
+export function pyramidDown(minFaceSize, scaleFactor, dims) {
     var height = dims[0], width = dims[1];
-    var m = config_1.CELL_SIZE / minFaceSize;
+    var m = CELL_SIZE / minFaceSize;
     var scales = [];
     var minLayer = Math.min(height, width) * m;
     var exp = 0;
@@ -14,5 +12,4 @@ function pyramidDown(minFaceSize, scaleFactor, dims) {
     }
     return scales;
 }
-exports.pyramidDown = pyramidDown;
 //# sourceMappingURL=pyramidDown.js.map
\ No newline at end of file

build/mtcnn/pyramidDown.js.map

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build/mtcnn/sharedLayers.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var convLayer_1 = require("../commons/convLayer");
-var prelu_1 = require("./prelu");
-function sharedLayer(x, params, isPnet) {
+import * as tf from '@tensorflow/tfjs-core';
+import { convLayer } from 'tfjs-tiny-yolov2';
+import { prelu } from './prelu';
+export function sharedLayer(x, params, isPnet) {
     if (isPnet === void 0) { isPnet = false; }
     return tf.tidy(function () {
-        var out = convLayer_1.convLayer(x, params.conv1, 'valid');
-        out = prelu_1.prelu(out, params.prelu1_alpha);
+        var out = convLayer(x, params.conv1, 'valid');
+        out = prelu(out, params.prelu1_alpha);
         out = tf.maxPool(out, isPnet ? [2, 2] : [3, 3], [2, 2], 'same');
-        out = convLayer_1.convLayer(out, params.conv2, 'valid');
-        out = prelu_1.prelu(out, params.prelu2_alpha);
+        out = convLayer(out, params.conv2, 'valid');
+        out = prelu(out, params.prelu2_alpha);
         out = isPnet ? out : tf.maxPool(out, [3, 3], [2, 2], 'valid');
-        out = convLayer_1.convLayer(out, params.conv3, 'valid');
-        out = prelu_1.prelu(out, params.prelu3_alpha);
+        out = convLayer(out, params.conv3, 'valid');
+        out = prelu(out, params.prelu3_alpha);
         return out;
     });
 }
-exports.sharedLayer = sharedLayer;
 //# sourceMappingURL=sharedLayers.js.map
\ No newline at end of file

build/mtcnn/sharedLayers.js.map

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\ No newline at end of file

build/mtcnn/stage1.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { BoundingBox } from '../BoundingBox';
+import { BoundingBox } from 'tfjs-image-recognition-base';
 import { PNetParams } from './types';
 export declare function stage1(imgTensor: tf.Tensor4D, scales: number[], scoreThreshold: number, params: PNetParams, stats: any): {
     boxes: BoundingBox[];

build/mtcnn/stage1.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var BoundingBox_1 = require("../BoundingBox");
-var nonMaxSuppression_1 = require("../commons/nonMaxSuppression");
-var Point_1 = require("../Point");
-var config_1 = require("./config");
-var getSizesForScale_1 = require("./getSizesForScale");
-var normalize_1 = require("./normalize");
-var PNet_1 = require("./PNet");
+import * as tf from '@tensorflow/tfjs-core';
+import { BoundingBox, nonMaxSuppression, Point } from 'tfjs-image-recognition-base';
+import { CELL_SIZE, CELL_STRIDE } from './config';
+import { getSizesForScale } from './getSizesForScale';
+import { normalize } from './normalize';
+import { PNet } from './PNet';
 function rescaleAndNormalize(x, scale) {
     return tf.tidy(function () {
-        var _a = getSizesForScale_1.getSizesForScale(scale, x.shape.slice(1)), height = _a.height, width = _a.width;
+        var _a = getSizesForScale(scale, x.shape.slice(1)), height = _a.height, width = _a.width;
         var resized = tf.image.resizeBilinear(x, [height, width]);
-        var normalized = normalize_1.normalize(resized);
+        var normalized = normalize(resized);
         return tf.transpose(normalized, [0, 2, 1, 3]);
     });
 }
@@ -22,14 +18,14 @@ function extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold
     for (var y = 0; y < scoresTensor.shape[0]; y++) {
         for (var x = 0; x < scoresTensor.shape[1]; x++) {
             if (scoresTensor.get(y, x) >= scoreThreshold) {
-                indices.push(new Point_1.Point(x, y));
+                indices.push(new Point(x, y));
             }
         }
     }
     var boundingBoxes = indices.map(function (idx) {
-        var cell = new BoundingBox_1.BoundingBox(Math.round((idx.y * config_1.CELL_STRIDE + 1) / scale), Math.round((idx.x * config_1.CELL_STRIDE + 1) / scale), Math.round((idx.y * config_1.CELL_STRIDE + config_1.CELL_SIZE) / scale), Math.round((idx.x * config_1.CELL_STRIDE + config_1.CELL_SIZE) / scale));
+        var cell = new BoundingBox(Math.round((idx.y * CELL_STRIDE + 1) / scale), Math.round((idx.x * CELL_STRIDE + 1) / scale), Math.round((idx.y * CELL_STRIDE + CELL_SIZE) / scale), Math.round((idx.x * CELL_STRIDE + CELL_SIZE) / scale));
         var score = scoresTensor.get(idx.y, idx.x);
-        var region = new BoundingBox_1.BoundingBox(regionsTensor.get(idx.y, idx.x, 0), regionsTensor.get(idx.y, idx.x, 1), regionsTensor.get(idx.y, idx.x, 2), regionsTensor.get(idx.y, idx.x, 3));
+        var region = new BoundingBox(regionsTensor.get(idx.y, idx.x, 0), regionsTensor.get(idx.y, idx.x, 1), regionsTensor.get(idx.y, idx.x, 2), regionsTensor.get(idx.y, idx.x, 3));
         return {
             cell: cell,
             score: score,
@@ -38,13 +34,13 @@ function extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold
     });
     return boundingBoxes;
 }
-function stage1(imgTensor, scales, scoreThreshold, params, stats) {
+export function stage1(imgTensor, scales, scoreThreshold, params, stats) {
     stats.stage1 = [];
     var pnetOutputs = scales.map(function (scale) { return tf.tidy(function () {
         var statsForScale = { scale: scale };
         var resized = rescaleAndNormalize(imgTensor, scale);
         var ts = Date.now();
-        var _a = PNet_1.PNet(resized, params), prob = _a.prob, regions = _a.regions;
+        var _a = PNet(resized, params), prob = _a.prob, regions = _a.regions;
         statsForScale.pnet = Date.now() - ts;
         var scoresTensor = tf.unstack(tf.unstack(prob, 3)[1])[0];
         var regionsTensor = tf.unstack(regions)[0];
@@ -65,7 +61,7 @@ function stage1(imgTensor, scales, scoreThreshold, params, stats) {
             return [];
         }
         var ts = Date.now();
-        var indices = nonMaxSuppression_1.nonMaxSuppression(boundingBoxes.map(function (bbox) { return bbox.cell; }), boundingBoxes.map(function (bbox) { return bbox.score; }), 0.5);
+        var indices = nonMaxSuppression(boundingBoxes.map(function (bbox) { return bbox.cell; }), boundingBoxes.map(function (bbox) { return bbox.score; }), 0.5);
         statsForScale.nms = Date.now() - ts;
         statsForScale.numBoxes = indices.length;
         stats.stage1.push(statsForScale);
@@ -76,14 +72,14 @@ function stage1(imgTensor, scales, scoreThreshold, params, stats) {
     var finalScores = [];
     if (allBoxes.length > 0) {
         var ts = Date.now();
-        var indices = nonMaxSuppression_1.nonMaxSuppression(allBoxes.map(function (bbox) { return bbox.cell; }), allBoxes.map(function (bbox) { return bbox.score; }), 0.7);
+        var indices = nonMaxSuppression(allBoxes.map(function (bbox) { return bbox.cell; }), allBoxes.map(function (bbox) { return bbox.score; }), 0.7);
         stats.stage1_nms = Date.now() - ts;
         finalScores = indices.map(function (idx) { return allBoxes[idx].score; });
         finalBoxes = indices
             .map(function (idx) { return allBoxes[idx]; })
             .map(function (_a) {
             var cell = _a.cell, region = _a.region;
-            return new BoundingBox_1.BoundingBox(cell.left + (region.left * cell.width), cell.top + (region.top * cell.height), cell.right + (region.right * cell.width), cell.bottom + (region.bottom * cell.height)).toSquare().round();
+            return new BoundingBox(cell.left + (region.left * cell.width), cell.top + (region.top * cell.height), cell.right + (region.right * cell.width), cell.bottom + (region.bottom * cell.height)).toSquare().round();
         });
     }
     return {
@@ -91,5 +87,4 @@ function stage1(imgTensor, scales, scoreThreshold, params, stats) {
         scores: finalScores
     };
 }
-exports.stage1 = stage1;
 //# sourceMappingURL=stage1.js.map
\ No newline at end of file

build/mtcnn/stage1.js.map

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\ No newline at end of file

build/mtcnn/stage2.d.ts

-import { BoundingBox } from '../BoundingBox';
+import { BoundingBox } from 'tfjs-image-recognition-base';
 import { RNetParams } from './types';
 export declare function stage2(img: HTMLCanvasElement, inputBoxes: BoundingBox[], scoreThreshold: number, params: RNetParams, stats: any): Promise<{
     boxes: BoundingBox[];

build/mtcnn/stage2.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var BoundingBox_1 = require("../BoundingBox");
-var nonMaxSuppression_1 = require("../commons/nonMaxSuppression");
-var extractImagePatches_1 = require("./extractImagePatches");
-var RNet_1 = require("./RNet");
-function stage2(img, inputBoxes, scoreThreshold, params, stats) {
+import * as tslib_1 from "tslib";
+import * as tf from '@tensorflow/tfjs-core';
+import { BoundingBox, nonMaxSuppression } from 'tfjs-image-recognition-base';
+import { extractImagePatches } from './extractImagePatches';
+import { RNet } from './RNet';
+export function stage2(img, inputBoxes, scoreThreshold, params, stats) {
     return tslib_1.__awaiter(this, void 0, void 0, function () {
         var ts, rnetInputs, rnetOuts, scoresTensor, scores, _a, _b, indices, filteredBoxes, filteredScores, finalBoxes, finalScores, indicesNms, regions_1;
         return tslib_1.__generator(this, function (_c) {
             switch (_c.label) {
                 case 0:
                     ts = Date.now();
-                    return [4 /*yield*/, extractImagePatches_1.extractImagePatches(img, inputBoxes, { width: 24, height: 24 })];
+                    return [4 /*yield*/, extractImagePatches(img, inputBoxes, { width: 24, height: 24 })];
                 case 1:
                     rnetInputs = _c.sent();
                     stats.stage2_extractImagePatches = Date.now() - ts;
                     ts = Date.now();
                     rnetOuts = rnetInputs.map(function (rnetInput) {
-                        var out = RNet_1.RNet(rnetInput, params);
+                        var out = RNet(rnetInput, params);
                         rnetInput.dispose();
                         return out;
                     });
@@ -45,10 +42,10 @@ function stage2(img, inputBoxes, scoreThreshold, params, stats) {
                     finalScores = [];
                     if (filteredBoxes.length > 0) {
                         ts = Date.now();
-                        indicesNms = nonMaxSuppression_1.nonMaxSuppression(filteredBoxes, filteredScores, 0.7);
+                        indicesNms = nonMaxSuppression(filteredBoxes, filteredScores, 0.7);
                         stats.stage2_nms = Date.now() - ts;
                         regions_1 = indicesNms.map(function (idx) {
-                            return new BoundingBox_1.BoundingBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3));
+                            return new BoundingBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3));
                         });
                         finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
                         finalBoxes = indicesNms.map(function (idx, i) { return filteredBoxes[idx].calibrate(regions_1[i]); });
@@ -65,5 +62,4 @@ function stage2(img, inputBoxes, scoreThreshold, params, stats) {
         });
     });
 }
-exports.stage2 = stage2;
 //# sourceMappingURL=stage2.js.map
\ No newline at end of file

build/mtcnn/stage2.js.map

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build/mtcnn/stage3.1.d.ts

-import { Point } from '../Point';
-import { BoundingBox } from './BoundingBox';
-import { ONetParams } from './types';
-export declare function stage3(img: HTMLCanvasElement, inputBoxes: BoundingBox[], scoreThreshold: number, params: ONetParams, stats: any): Promise<{
-    boxes: BoundingBox[];
-    scores: number[];
-    points: Point[][];
-}>;

build/mtcnn/stage3.1.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var Point_1 = require("../Point");
-var BoundingBox_1 = require("./BoundingBox");
-var extractImagePatches_1 = require("./extractImagePatches");
-var nms_1 = require("./nms");
-var ONet_1 = require("./ONet");
-var tf = require("@tensorflow/tfjs-core");
-function stage3(img, inputBoxes, scoreThreshold, params, stats) {
-    return tslib_1.__awaiter(this, void 0, void 0, function () {
-        var ts, onetInputs, onetOuts, scoresTensor, scores, _a, _b, indices, filteredRegions, filteredBoxes, filteredScores, finalBoxes, finalScores, points, indicesNms;
-        return tslib_1.__generator(this, function (_c) {
-            switch (_c.label) {
-                case 0:
-                    ts = Date.now();
-                    return [4 /*yield*/, extractImagePatches_1.extractImagePatches(img, inputBoxes, { width: 48, height: 48 })];
-                case 1:
-                    onetInputs = _c.sent();
-                    stats.stage3_extractImagePatches = Date.now() - ts;
-                    ts = Date.now();
-                    onetOuts = onetInputs.map(function (onetInput) {
-                        var out = ONet_1.ONet(onetInput, params);
-                        onetInput.dispose();
-                        return out;
-                    });
-                    stats.stage3_onet = Date.now() - ts;
-                    console.time('scoreDatas2');
-                    scoresTensor = onetOuts.length > 1
-                        ? tf.tidy(function () { return tf.concat(onetOuts.map(function (out) { return out.scores; })); })
-                        : onetOuts[0].scores;
-                    _b = (_a = Array).from;
-                    return [4 /*yield*/, scoresTensor.data()];
-                case 2:
-                    scores = _b.apply(_a, [_c.sent()]);
-                    scoresTensor.dispose();
-                    console.timeEnd('scoreDatas2');
-                    indices = scores
-                        .map(function (score, idx) { return ({ score: score, idx: idx }); })
-                        .filter(function (c) { return c.score > scoreThreshold; })
-                        .map(function (_a) {
-                        var idx = _a.idx;
-                        return idx;
-                    });
-                    filteredRegions = indices.map(function (idx) { return new BoundingBox_1.BoundingBox(onetOuts[idx].regions.get(0, 0), onetOuts[idx].regions.get(0, 1), onetOuts[idx].regions.get(0, 2), onetOuts[idx].regions.get(0, 3)); });
-                    filteredBoxes = indices
-                        .map(function (idx, i) { return inputBoxes[idx].calibrate(filteredRegions[i]); });
-                    filteredScores = indices.map(function (idx) { return scores[idx]; });
-                    finalBoxes = [];
-                    finalScores = [];
-                    points = [];
-                    if (filteredBoxes.length > 0) {
-                        ts = Date.now();
-                        indicesNms = nms_1.nms(filteredBoxes, filteredScores, 0.7, false);
-                        stats.stage3_nms = Date.now() - ts;
-                        finalBoxes = indicesNms.map(function (idx) { return filteredBoxes[idx]; });
-                        finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
-                        points = indicesNms.map(function (idx, i) {
-                            return Array(5).fill(0).map(function (_, ptIdx) {
-                                return new Point_1.Point(((onetOuts[idx].points.get(0, ptIdx) * (finalBoxes[i].width + 1)) + finalBoxes[i].left), ((onetOuts[idx].points.get(0, ptIdx + 5) * (finalBoxes[i].height + 1)) + finalBoxes[i].top));
-                            });
-                        });
-                    }
-                    onetOuts.forEach(function (t) {
-                        t.regions.dispose();
-                        t.scores.dispose();
-                        t.points.dispose();
-                    });
-                    return [2 /*return*/, {
-                            boxes: finalBoxes,
-                            scores: finalScores,
-                            points: points
-                        }];
-            }
-        });
-    });
-}
-exports.stage3 = stage3;
-//# sourceMappingURL=stage3.1.js.map
\ No newline at end of file

build/mtcnn/stage3.1.js.map

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\ No newline at end of file

build/mtcnn/stage3.d.ts

-import { BoundingBox } from '../BoundingBox';
-import { Point } from '../Point';
+import { BoundingBox, Point } from 'tfjs-image-recognition-base';
 import { ONetParams } from './types';
 export declare function stage3(img: HTMLCanvasElement, inputBoxes: BoundingBox[], scoreThreshold: number, params: ONetParams, stats: any): Promise<{
     boxes: BoundingBox[];

build/mtcnn/stage3.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var BoundingBox_1 = require("../BoundingBox");
-var nonMaxSuppression_1 = require("../commons/nonMaxSuppression");
-var Point_1 = require("../Point");
-var extractImagePatches_1 = require("./extractImagePatches");
-var ONet_1 = require("./ONet");
-function stage3(img, inputBoxes, scoreThreshold, params, stats) {
+import * as tslib_1 from "tslib";
+import * as tf from '@tensorflow/tfjs-core';
+import { BoundingBox, nonMaxSuppression, Point } from 'tfjs-image-recognition-base';
+import { extractImagePatches } from './extractImagePatches';
+import { ONet } from './ONet';
+export function stage3(img, inputBoxes, scoreThreshold, params, stats) {
     return tslib_1.__awaiter(this, void 0, void 0, function () {
         var ts, onetInputs, onetOuts, scoresTensor, scores, _a, _b, indices, filteredRegions, filteredBoxes, filteredScores, finalBoxes, finalScores, points, indicesNms;
         return tslib_1.__generator(this, function (_c) {
             switch (_c.label) {
                 case 0:
                     ts = Date.now();
-                    return [4 /*yield*/, extractImagePatches_1.extractImagePatches(img, inputBoxes, { width: 48, height: 48 })];
+                    return [4 /*yield*/, extractImagePatches(img, inputBoxes, { width: 48, height: 48 })];
                 case 1:
                     onetInputs = _c.sent();
                     stats.stage3_extractImagePatches = Date.now() - ts;
                     ts = Date.now();
                     onetOuts = onetInputs.map(function (onetInput) {
-                        var out = ONet_1.ONet(onetInput, params);
+                        var out = ONet(onetInput, params);
                         onetInput.dispose();
                         return out;
                     });
@@ -40,7 +36,7 @@ function stage3(img, inputBoxes, scoreThreshold, params, stats) {
                         var idx = _a.idx;
                         return idx;
                     });
-                    filteredRegions = indices.map(function (idx) { return new BoundingBox_1.BoundingBox(onetOuts[idx].regions.get(0, 0), onetOuts[idx].regions.get(0, 1), onetOuts[idx].regions.get(0, 2), onetOuts[idx].regions.get(0, 3)); });
+                    filteredRegions = indices.map(function (idx) { return new BoundingBox(onetOuts[idx].regions.get(0, 0), onetOuts[idx].regions.get(0, 1), onetOuts[idx].regions.get(0, 2), onetOuts[idx].regions.get(0, 3)); });
                     filteredBoxes = indices
                         .map(function (idx, i) { return inputBoxes[idx].calibrate(filteredRegions[i]); });
                     filteredScores = indices.map(function (idx) { return scores[idx]; });
@@ -49,13 +45,13 @@ function stage3(img, inputBoxes, scoreThreshold, params, stats) {
                     points = [];
                     if (filteredBoxes.length > 0) {
                         ts = Date.now();
-                        indicesNms = nonMaxSuppression_1.nonMaxSuppression(filteredBoxes, filteredScores, 0.7, false);
+                        indicesNms = nonMaxSuppression(filteredBoxes, filteredScores, 0.7, false);
                         stats.stage3_nms = Date.now() - ts;
                         finalBoxes = indicesNms.map(function (idx) { return filteredBoxes[idx]; });
                         finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
                         points = indicesNms.map(function (idx, i) {
                             return Array(5).fill(0).map(function (_, ptIdx) {
-                                return new Point_1.Point(((onetOuts[idx].points.get(0, ptIdx) * (finalBoxes[i].width + 1)) + finalBoxes[i].left), ((onetOuts[idx].points.get(0, ptIdx + 5) * (finalBoxes[i].height + 1)) + finalBoxes[i].top));
+                                return new Point(((onetOuts[idx].points.get(0, ptIdx) * (finalBoxes[i].width + 1)) + finalBoxes[i].left), ((onetOuts[idx].points.get(0, ptIdx + 5) * (finalBoxes[i].height + 1)) + finalBoxes[i].top));
                             });
                         });
                     }
@@ -73,5 +69,4 @@ function stage3(img, inputBoxes, scoreThreshold, params, stats) {
         });
     });
 }
-exports.stage3 = stage3;
 //# sourceMappingURL=stage3.js.map
\ No newline at end of file

build/mtcnn/stage3.js.map

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\ No newline at end of file

build/mtcnn/types.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { ConvParams, FCParams } from '../commons/types';
-import { FaceDetection } from '../FaceDetection';
-import { FaceLandmarks5 } from './FaceLandmarks5';
+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;

build/mtcnn/types.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
 //# sourceMappingURL=types.js.map
\ No newline at end of file

build/padToSquare.d.ts

-import * as tf from '@tensorflow/tfjs-core';
-/**
- * Pads the smaller dimension of an image tensor with zeros, such that width === height.
- *
- * @param imgTensor The image tensor.
- * @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image.
- * @returns The padded tensor with width === height.
- */
-export declare function padToSquare(imgTensor: tf.Tensor4D, isCenterImage?: boolean): tf.Tensor4D;

build/padToSquare.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-/**
- * Pads the smaller dimension of an image tensor with zeros, such that width === height.
- *
- * @param imgTensor The image tensor.
- * @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image.
- * @returns The padded tensor with width === height.
- */
-function padToSquare(imgTensor, isCenterImage) {
-    if (isCenterImage === void 0) { isCenterImage = false; }
-    return tf.tidy(function () {
-        var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
-        if (height === width) {
-            return imgTensor;
-        }
-        var dimDiff = Math.abs(height - width);
-        var paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));
-        var paddingAxis = height > width ? 2 : 1;
-        var createPaddingTensor = function (paddingAmount) {
-            var paddingTensorShape = imgTensor.shape.slice();
-            paddingTensorShape[paddingAxis] = paddingAmount;
-            return tf.fill(paddingTensorShape, 0);
-        };
-        var paddingTensorAppend = createPaddingTensor(paddingAmount);
-        var remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
-        var paddingTensorPrepend = isCenterImage && remainingPaddingAmount
-            ? createPaddingTensor(remainingPaddingAmount)
-            : null;
-        var tensorsToStack = [
-            paddingTensorPrepend,
-            imgTensor,
-            paddingTensorAppend
-        ]
-            .filter(function (t) { return t !== null; });
-        return tf.concat(tensorsToStack, paddingAxis);
-    });
-}
-exports.padToSquare = padToSquare;
-//# sourceMappingURL=padToSquare.js.map
\ No newline at end of file

build/padToSquare.js.map

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\ No newline at end of file

build/tinyYolov2/TinyYolov2.d.ts

-import * as tf from '@tensorflow/tfjs-core';
-import { BoundingBox } from '../BoundingBox';
-import { NeuralNetwork } from '../commons/NeuralNetwork';
-import { FaceDetection } from '../FaceDetection';
-import { NetInput } from '../NetInput';
-import { Point } from '../Point';
-import { TNetInput } from '../types';
-import { NetParams, PostProcessingParams, TinyYolov2ForwardParams } from './types';
-export declare class TinyYolov2 extends NeuralNetwork<NetParams> {
-    private _withSeparableConvs;
-    private _anchors;
+import { Point, TNetInput } from 'tfjs-image-recognition-base';
+import { TinyYolov2 as TinyYolov2Base, TinyYolov2Types } from 'tfjs-tiny-yolov2';
+import { FaceDetection } from '../classes';
+export declare class TinyYolov2 extends TinyYolov2Base {
     constructor(withSeparableConvs?: boolean);
     readonly withSeparableConvs: boolean;
     readonly anchors: Point[];
-    forwardInput(input: NetInput, inputSize: number): tf.Tensor4D;
-    forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D>;
-    locateFaces(input: TNetInput, forwardParams?: TinyYolov2ForwardParams): Promise<FaceDetection[]>;
-    postProcess(outputTensor: tf.Tensor4D, {scoreThreshold, paddings}: PostProcessingParams): {
-        box: BoundingBox;
-        score: number;
-        row: number;
-        col: number;
-        anchor: number;
-    }[];
-    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;
-        }[];
-    };
+    locateFaces(input: TNetInput, forwardParams: TinyYolov2Types.TinyYolov2ForwardParams): Promise<FaceDetection[]>;
+    protected loadQuantizedParams(modelUri: string | undefined): any;
 }

build/tinyYolov2/TinyYolov2.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var BoundingBox_1 = require("../BoundingBox");
-var convLayer_1 = require("../commons/convLayer");
-var NeuralNetwork_1 = require("../commons/NeuralNetwork");
-var nonMaxSuppression_1 = require("../commons/nonMaxSuppression");
-var normalize_1 = require("../commons/normalize");
-var FaceDetection_1 = require("../FaceDetection");
-var toNetInput_1 = require("../toNetInput");
-var utils_1 = require("../utils");
-var config_1 = require("./config");
-var convWithBatchNorm_1 = require("./convWithBatchNorm");
-var extractParams_1 = require("./extractParams");
-var getDefaultParams_1 = require("./getDefaultParams");
-var loadQuantizedParams_1 = require("./loadQuantizedParams");
+import * as tslib_1 from "tslib";
+import { TinyYolov2 as TinyYolov2Base } from 'tfjs-tiny-yolov2';
+import { FaceDetection } from '../classes';
+import { BOX_ANCHORS, BOX_ANCHORS_SEPARABLE, DEFAULT_MODEL_NAME, DEFAULT_MODEL_NAME_SEPARABLE_CONV, IOU_THRESHOLD, MEAN_RGB_SEPARABLE, } from './const';
 var TinyYolov2 = /** @class */ (function (_super) {
     tslib_1.__extends(TinyYolov2, _super);
     function TinyYolov2(withSeparableConvs) {
         if (withSeparableConvs === void 0) { withSeparableConvs = true; }
-        var _this = _super.call(this, 'TinyYolov2') || this;
-        _this._withSeparableConvs = withSeparableConvs;
-        _this._anchors = withSeparableConvs ? config_1.BOX_ANCHORS_SEPARABLE : config_1.BOX_ANCHORS;
+        var _this = this;
+        var config = Object.assign({}, {
+            withSeparableConvs: withSeparableConvs,
+            iouThreshold: IOU_THRESHOLD,
+            classes: ['face']
+        }, withSeparableConvs
+            ? {
+                anchors: BOX_ANCHORS_SEPARABLE,
+                meanRgb: MEAN_RGB_SEPARABLE
+            }
+            : {
+                anchors: BOX_ANCHORS,
+                withClassScores: true
+            });
+        _this = _super.call(this, config) || this;
         return _this;
     }
     Object.defineProperty(TinyYolov2.prototype, "withSeparableConvs", {
         get: function () {
-            return this._withSeparableConvs;
+            return this.config.withSeparableConvs;
         },
         enumerable: true,
         configurable: true
     });
     Object.defineProperty(TinyYolov2.prototype, "anchors", {
         get: function () {
-            return this._anchors;
+            return this.config.anchors;
         },
         enumerable: true,
         configurable: true
     });
-    TinyYolov2.prototype.forwardInput = function (input, inputSize) {
-        var _this = this;
-        var params = this.params;
-        if (!params) {
-            throw new Error('TinyYolov2 - load model before inference');
-        }
-        var out = tf.tidy(function () {
-            var batchTensor = input.toBatchTensor(inputSize, false);
-            batchTensor = _this.withSeparableConvs
-                ? normalize_1.normalize(batchTensor, config_1.MEAN_RGB)
-                : batchTensor;
-            batchTensor = batchTensor.div(tf.scalar(256));
-            var out = convWithBatchNorm_1.convWithBatchNorm(batchTensor, params.conv0);
-            out = tf.maxPool(out, [2, 2], [2, 2], 'same');
-            out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv1);
-            out = tf.maxPool(out, [2, 2], [2, 2], 'same');
-            out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv2);
-            out = tf.maxPool(out, [2, 2], [2, 2], 'same');
-            out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv3);
-            out = tf.maxPool(out, [2, 2], [2, 2], 'same');
-            out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv4);
-            out = tf.maxPool(out, [2, 2], [2, 2], 'same');
-            out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv5);
-            out = tf.maxPool(out, [2, 2], [1, 1], 'same');
-            out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv6);
-            out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv7);
-            out = convLayer_1.convLayer(out, params.conv8, 'valid', false);
-            return out;
-        });
-        return out;
-    };
-    TinyYolov2.prototype.forward = function (input, inputSize) {
-        return tslib_1.__awaiter(this, void 0, void 0, function () {
-            var _a;
-            return tslib_1.__generator(this, function (_b) {
-                switch (_b.label) {
-                    case 0:
-                        _a = this.forwardInput;
-                        return [4 /*yield*/, toNetInput_1.toNetInput(input, true, true)];
-                    case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(), inputSize])];
-                    case 2: return [2 /*return*/, _b.sent()];
-                }
-            });
-        });
-    };
     TinyYolov2.prototype.locateFaces = function (input, forwardParams) {
-        if (forwardParams === void 0) { forwardParams = {}; }
         return tslib_1.__awaiter(this, void 0, void 0, function () {
-            var _a, _inputSize, scoreThreshold, inputSize, netInput, out, out0, inputDimensions, paddings, results, boxes, scores, indices, detections;
-            return tslib_1.__generator(this, function (_b) {
-                switch (_b.label) {
-                    case 0:
-                        _a = getDefaultParams_1.getDefaultParams(forwardParams), _inputSize = _a.inputSize, scoreThreshold = _a.scoreThreshold;
-                        inputSize = typeof _inputSize === 'string'
-                            ? config_1.INPUT_SIZES[_inputSize]
-                            : _inputSize;
-                        if (typeof inputSize !== 'number') {
-                            throw new Error("TinyYolov2 - unknown inputSize: " + inputSize + ", expected number or one of xs | sm | md | lg");
-                        }
-                        return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
+            var objectDetections;
+            return tslib_1.__generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4 /*yield*/, this.detect(input, forwardParams)];
                     case 1:
-                        netInput = _b.sent();
-                        return [4 /*yield*/, this.forwardInput(netInput, inputSize)];
-                    case 2:
-                        out = _b.sent();
-                        out0 = tf.tidy(function () { return tf.unstack(out)[0].expandDims(); });
-                        inputDimensions = {
-                            width: netInput.getInputWidth(0),
-                            height: netInput.getInputHeight(0)
-                        };
-                        paddings = netInput.getRelativePaddings(0);
-                        results = this.postProcess(out0, { scoreThreshold: scoreThreshold, paddings: paddings });
-                        boxes = results.map(function (res) { return res.box; });
-                        scores = results.map(function (res) { return res.score; });
-                        out.dispose();
-                        out0.dispose();
-                        indices = nonMaxSuppression_1.nonMaxSuppression(boxes.map(function (box) { return box.rescale(inputSize); }), scores, config_1.IOU_THRESHOLD, true);
-                        detections = indices.map(function (idx) {
-                            return new FaceDetection_1.FaceDetection(scores[idx], boxes[idx].toRect(), inputDimensions);
-                        });
-                        return [2 /*return*/, detections];
+                        objectDetections = _a.sent();
+                        return [2 /*return*/, objectDetections.map(function (det) { return new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }); })];
                 }
             });
         });
     };
-    TinyYolov2.prototype.postProcess = function (outputTensor, _a) {
-        var _this = this;
-        var scoreThreshold = _a.scoreThreshold, paddings = _a.paddings;
-        var numCells = outputTensor.shape[1];
-        var _b = tf.tidy(function () {
-            var reshaped = outputTensor.reshape([numCells, numCells, config_1.NUM_BOXES, _this.withSeparableConvs ? 5 : 6]);
-            var boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, config_1.NUM_BOXES, 4]);
-            var scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, config_1.NUM_BOXES, 1]);
-            return [boxes, scores];
-        }), boxesTensor = _b[0], scoresTensor = _b[1];
-        var results = [];
-        for (var row = 0; row < numCells; row++) {
-            for (var col = 0; col < numCells; col++) {
-                for (var anchor = 0; anchor < config_1.NUM_BOXES; anchor++) {
-                    var score = utils_1.sigmoid(scoresTensor.get(row, col, anchor, 0));
-                    if (!scoreThreshold || score > scoreThreshold) {
-                        var ctX = ((col + utils_1.sigmoid(boxesTensor.get(row, col, anchor, 0))) / numCells) * paddings.x;
-                        var ctY = ((row + utils_1.sigmoid(boxesTensor.get(row, col, anchor, 1))) / numCells) * paddings.y;
-                        var width = ((Math.exp(boxesTensor.get(row, col, anchor, 2)) * this.anchors[anchor].x) / numCells) * paddings.x;
-                        var height = ((Math.exp(boxesTensor.get(row, col, anchor, 3)) * this.anchors[anchor].y) / numCells) * paddings.y;
-                        var x = (ctX - (width / 2));
-                        var y = (ctY - (height / 2));
-                        results.push({
-                            box: new BoundingBox_1.BoundingBox(x, y, x + width, y + height),
-                            score: score,
-                            row: row,
-                            col: col,
-                            anchor: anchor
-                        });
-                    }
-                }
-            }
-        }
-        boxesTensor.dispose();
-        scoresTensor.dispose();
-        return results;
-    };
-    TinyYolov2.prototype.loadQuantizedParams = function (uri) {
-        return loadQuantizedParams_1.loadQuantizedParams(uri, this.withSeparableConvs);
-    };
-    TinyYolov2.prototype.extractParams = function (weights) {
-        return extractParams_1.extractParams(weights, this.withSeparableConvs);
+    TinyYolov2.prototype.loadQuantizedParams = function (modelUri) {
+        var defaultModelName = this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;
+        return _super.prototype.loadQuantizedParams.call(this, modelUri, defaultModelName);
     };
     return TinyYolov2;
-}(NeuralNetwork_1.NeuralNetwork));
-exports.TinyYolov2 = TinyYolov2;
+}(TinyYolov2Base));
+export { TinyYolov2 };
 //# sourceMappingURL=TinyYolov2.js.map
\ No newline at end of file

build/tinyYolov2/TinyYolov2.js.map

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\ No newline at end of file

build/tinyYolov2/config.d.ts

-import { Point } from '../Point';
-export declare const INPUT_SIZES: {
-    xs: number;
-    sm: number;
-    md: number;
-    lg: number;
-};
-export declare const NUM_BOXES = 5;
-export declare const IOU_THRESHOLD = 0.4;
-export declare const BOX_ANCHORS: Point[];
-export declare const BOX_ANCHORS_SEPARABLE: Point[];
-export declare const MEAN_RGB: number[];

build/tinyYolov2/config.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var Point_1 = require("../Point");
-exports.INPUT_SIZES = { xs: 224, sm: 320, md: 416, lg: 608 };
-exports.NUM_BOXES = 5;
-exports.IOU_THRESHOLD = 0.4;
-exports.BOX_ANCHORS = [
-    new Point_1.Point(0.738768, 0.874946),
-    new Point_1.Point(2.42204, 2.65704),
-    new Point_1.Point(4.30971, 7.04493),
-    new Point_1.Point(10.246, 4.59428),
-    new Point_1.Point(12.6868, 11.8741)
-];
-exports.BOX_ANCHORS_SEPARABLE = [
-    new Point_1.Point(1.603231, 2.094468),
-    new Point_1.Point(6.041143, 7.080126),
-    new Point_1.Point(2.882459, 3.518061),
-    new Point_1.Point(4.266906, 5.178857),
-    new Point_1.Point(9.041765, 10.66308)
-];
-exports.MEAN_RGB = [117.001, 114.697, 97.404];
-//# sourceMappingURL=config.js.map
\ No newline at end of file

build/tinyYolov2/config.js.map

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\ No newline at end of file

build/tinyYolov2/const.d.ts

+import { Point } from 'tfjs-image-recognition-base';
+export declare const IOU_THRESHOLD = 0.4;
+export declare const BOX_ANCHORS: Point[];
+export declare const BOX_ANCHORS_SEPARABLE: Point[];
+export declare const MEAN_RGB_SEPARABLE: [number, number, number];
+export declare const DEFAULT_MODEL_NAME = "tiny_yolov2_model";
+export declare const DEFAULT_MODEL_NAME_SEPARABLE_CONV = "tiny_yolov2_separable_conv_model";

build/tinyYolov2/const.js

+import { Point } from 'tfjs-image-recognition-base';
+export var IOU_THRESHOLD = 0.4;
+export var BOX_ANCHORS = [
+    new Point(0.738768, 0.874946),
+    new Point(2.42204, 2.65704),
+    new Point(4.30971, 7.04493),
+    new Point(10.246, 4.59428),
+    new Point(12.6868, 11.8741)
+];
+export var BOX_ANCHORS_SEPARABLE = [
+    new Point(1.603231, 2.094468),
+    new Point(6.041143, 7.080126),
+    new Point(2.882459, 3.518061),
+    new Point(4.266906, 5.178857),
+    new Point(9.041765, 10.66308)
+];
+export var MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404];
+export var DEFAULT_MODEL_NAME = 'tiny_yolov2_model';
+export var DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';
+//# sourceMappingURL=const.js.map
\ No newline at end of file

build/tinyYolov2/const.js.map

+{"version":3,"file":"const.js","sourceRoot":"","sources":["../../src/tinyYolov2/const.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,KAAK,EAAE,MAAM,6BAA6B,CAAC;AAEpD,MAAM,CAAC,IAAM,aAAa,GAAG,GAAG,CAAA;AAEhC,MAAM,CAAC,IAAM,WAAW,GAAG;IACzB,IAAI,KAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,KAAK,CAAC,OAAO,EAAE,OAAO,CAAC;IAC3B,IAAI,KAAK,CAAC,OAAO,EAAE,OAAO,CAAC;IAC3B,IAAI,KAAK,CAAC,MAAM,EAAE,OAAO,CAAC;IAC1B,IAAI,KAAK,CAAC,OAAO,EAAE,OAAO,CAAC;CAC5B,CAAA;AAED,MAAM,CAAC,IAAM,qBAAqB,GAAG;IACnC,IAAI,KAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,KAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,KAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,KAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,KAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;CAC9B,CAAA;AAED,MAAM,CAAC,IAAM,kBAAkB,GAA6B,CAAC,OAAO,EAAE,OAAO,EAAE,MAAM,CAAC,CAAA;AAEtF,MAAM,CAAC,IAAM,kBAAkB,GAAG,mBAAmB,CAAA;AACrD,MAAM,CAAC,IAAM,iCAAiC,GAAG,kCAAkC,CAAA"}
\ No newline at end of file

build/tinyYolov2/convWithBatchNorm.d.ts

-import * as tf from '@tensorflow/tfjs-core';
-import { ConvWithBatchNorm, SeparableConvParams } from './types';
-export declare function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm | SeparableConvParams): tf.Tensor4D;

build/tinyYolov2/convWithBatchNorm.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var leaky_1 = require("./leaky");
-var types_1 = require("./types");
-function convWithBatchNorm(x, params) {
-    return tf.tidy(function () {
-        var out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-        if (params instanceof types_1.SeparableConvParams) {
-            out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');
-            out = tf.add(out, params.bias);
-        }
-        else {
-            out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');
-            out = tf.sub(out, params.bn.sub);
-            out = tf.mul(out, params.bn.truediv);
-            out = tf.add(out, params.conv.bias);
-        }
-        return leaky_1.leaky(out);
-    });
-}
-exports.convWithBatchNorm = convWithBatchNorm;
-//# sourceMappingURL=convWithBatchNorm.js.map
\ No newline at end of file

build/tinyYolov2/convWithBatchNorm.js.map

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build/tinyYolov2/extractParams.d.ts

-import { ParamMapping } from '../commons/types';
-import { NetParams } from './types';
-export declare function extractParams(weights: Float32Array, withSeparableConvs: boolean): {
-    params: NetParams;
-    paramMappings: ParamMapping[];
-};

build/tinyYolov2/extractParams.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var extractConvParamsFactory_1 = require("../commons/extractConvParamsFactory");
-var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
-var types_1 = require("./types");
-function extractorsFactory(extractWeights, paramMappings) {
-    var extractConvParams = extractConvParamsFactory_1.extractConvParamsFactory(extractWeights, paramMappings);
-    function extractBatchNormParams(size, mappedPrefix) {
-        var sub = tf.tensor1d(extractWeights(size));
-        var truediv = tf.tensor1d(extractWeights(size));
-        paramMappings.push({ paramPath: mappedPrefix + "/sub" }, { paramPath: mappedPrefix + "/truediv" });
-        return { sub: sub, truediv: truediv };
-    }
-    function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) {
-        var conv = extractConvParams(channelsIn, channelsOut, 3, mappedPrefix + "/conv");
-        var bn = extractBatchNormParams(channelsOut, mappedPrefix + "/bn");
-        return { conv: conv, bn: bn };
-    }
-    function extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix) {
-        var depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
-        var pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
-        var bias = tf.tensor1d(extractWeights(channelsOut));
-        paramMappings.push({ paramPath: mappedPrefix + "/depthwise_filter" }, { paramPath: mappedPrefix + "/pointwise_filter" }, { paramPath: mappedPrefix + "/bias" });
-        return new types_1.SeparableConvParams(depthwise_filter, pointwise_filter, bias);
-    }
-    return {
-        extractConvParams: extractConvParams,
-        extractConvWithBatchNormParams: extractConvWithBatchNormParams,
-        extractSeparableConvParams: extractSeparableConvParams
-    };
-}
-function extractParams(weights, withSeparableConvs) {
-    var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
-    var paramMappings = [];
-    var _b = extractorsFactory(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractConvWithBatchNormParams = _b.extractConvWithBatchNormParams, extractSeparableConvParams = _b.extractSeparableConvParams;
-    var extractConvFn = withSeparableConvs ? extractSeparableConvParams : extractConvWithBatchNormParams;
-    var numAnchorEncodings = withSeparableConvs ? 5 : 6;
-    var conv0 = extractConvFn(3, 16, 'conv0');
-    var conv1 = extractConvFn(16, 32, 'conv1');
-    var conv2 = extractConvFn(32, 64, 'conv2');
-    var conv3 = extractConvFn(64, 128, 'conv3');
-    var conv4 = extractConvFn(128, 256, 'conv4');
-    var conv5 = extractConvFn(256, 512, 'conv5');
-    var conv6 = extractConvFn(512, 1024, 'conv6');
-    var conv7 = extractConvFn(1024, 1024, 'conv7');
-    var conv8 = extractConvParams(1024, 5 * numAnchorEncodings, 1, 'conv8');
-    if (getRemainingWeights().length !== 0) {
-        throw new Error("weights remaing after extract: " + getRemainingWeights().length);
-    }
-    var params = { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3, conv4: conv4, conv5: conv5, conv6: conv6, conv7: conv7, conv8: conv8 };
-    return { params: params, paramMappings: paramMappings };
-}
-exports.extractParams = extractParams;
-//# sourceMappingURL=extractParams.js.map
\ No newline at end of file

build/tinyYolov2/extractParams.js.map

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\ No newline at end of file

build/tinyYolov2/getDefaultParams.d.ts

-import { SizeType, TinyYolov2ForwardParams } from './types';
-export declare function getDefaultParams(params: TinyYolov2ForwardParams): {
-    inputSize: SizeType;
-    scoreThreshold: number;
-} & TinyYolov2ForwardParams;

build/tinyYolov2/getDefaultParams.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var types_1 = require("./types");
-function getDefaultParams(params) {
-    return Object.assign({}, {
-        inputSize: types_1.SizeType.MD,
-        scoreThreshold: 0.5
-    }, params);
-}
-exports.getDefaultParams = getDefaultParams;
-//# sourceMappingURL=getDefaultParams.js.map
\ No newline at end of file

build/tinyYolov2/getDefaultParams.js.map

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\ No newline at end of file

build/tinyYolov2/index.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var TinyYolov2_1 = require("./TinyYolov2");
-tslib_1.__exportStar(require("./TinyYolov2"), exports);
-function createTinyYolov2(weights, withSeparableConvs) {
+import { TinyYolov2 } from './TinyYolov2';
+export * from './TinyYolov2';
+export function createTinyYolov2(weights, withSeparableConvs) {
     if (withSeparableConvs === void 0) { withSeparableConvs = true; }
-    var net = new TinyYolov2_1.TinyYolov2(withSeparableConvs);
+    var net = new TinyYolov2(withSeparableConvs);
     net.extractWeights(weights);
     return net;
 }
-exports.createTinyYolov2 = createTinyYolov2;
 //# sourceMappingURL=index.js.map
\ No newline at end of file

build/tinyYolov2/index.js.map

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\ No newline at end of file

build/tinyYolov2/leaky.d.ts

-import * as tf from '@tensorflow/tfjs-core';
-export declare function leaky(x: tf.Tensor4D): tf.Tensor4D;

build/tinyYolov2/leaky.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-function leaky(x) {
-    return tf.tidy(function () {
-        var min = tf.mul(x, tf.scalar(0.10000000149011612));
-        return tf.add(tf.relu(tf.sub(x, min)), min);
-        //return tf.maximum(x, min)
-    });
-}
-exports.leaky = leaky;
-//# sourceMappingURL=leaky.js.map
\ No newline at end of file

build/tinyYolov2/leaky.js.map

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\ No newline at end of file

build/tinyYolov2/loadQuantizedParams.d.ts

-import { ParamMapping } from '../commons/types';
-import { NetParams } from './types';
-export declare function loadQuantizedParams(uri: string | undefined, withSeparableConvs: boolean): Promise<{
-    params: NetParams;
-    paramMappings: ParamMapping[];
-}>;

build/tinyYolov2/loadQuantizedParams.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var disposeUnusedWeightTensors_1 = require("../commons/disposeUnusedWeightTensors");
-var extractWeightEntryFactory_1 = require("../commons/extractWeightEntryFactory");
-var loadWeightMap_1 = require("../commons/loadWeightMap");
-var types_1 = require("./types");
-var DEFAULT_MODEL_NAME = 'tiny_yolov2_model';
-var DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';
-function extractorsFactory(weightMap, paramMappings) {
-    var extractWeightEntry = extractWeightEntryFactory_1.extractWeightEntryFactory(weightMap, paramMappings);
-    function extractBatchNormParams(prefix) {
-        var sub = extractWeightEntry(prefix + "/sub", 1);
-        var truediv = extractWeightEntry(prefix + "/truediv", 1);
-        return { sub: sub, truediv: truediv };
-    }
-    function extractConvParams(prefix) {
-        var filters = extractWeightEntry(prefix + "/filters", 4);
-        var bias = extractWeightEntry(prefix + "/bias", 1);
-        return { filters: filters, bias: bias };
-    }
-    function extractConvWithBatchNormParams(prefix) {
-        var conv = extractConvParams(prefix + "/conv");
-        var bn = extractBatchNormParams(prefix + "/bn");
-        return { conv: conv, bn: bn };
-    }
-    function extractSeparableConvParams(prefix) {
-        var depthwise_filter = extractWeightEntry(prefix + "/depthwise_filter", 4);
-        var pointwise_filter = extractWeightEntry(prefix + "/pointwise_filter", 4);
-        var bias = extractWeightEntry(prefix + "/bias", 1);
-        return new types_1.SeparableConvParams(depthwise_filter, pointwise_filter, bias);
-    }
-    return {
-        extractConvParams: extractConvParams,
-        extractConvWithBatchNormParams: extractConvWithBatchNormParams,
-        extractSeparableConvParams: extractSeparableConvParams
-    };
-}
-function loadQuantizedParams(uri, withSeparableConvs) {
-    return tslib_1.__awaiter(this, void 0, void 0, function () {
-        var weightMap, paramMappings, _a, extractConvParams, extractConvWithBatchNormParams, extractSeparableConvParams, extractConvFn, params;
-        return tslib_1.__generator(this, function (_b) {
-            switch (_b.label) {
-                case 0: return [4 /*yield*/, loadWeightMap_1.loadWeightMap(uri, withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME)];
-                case 1:
-                    weightMap = _b.sent();
-                    paramMappings = [];
-                    _a = extractorsFactory(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractConvWithBatchNormParams = _a.extractConvWithBatchNormParams, extractSeparableConvParams = _a.extractSeparableConvParams;
-                    extractConvFn = withSeparableConvs ? extractSeparableConvParams : extractConvWithBatchNormParams;
-                    params = {
-                        conv0: extractConvFn('conv0'),
-                        conv1: extractConvFn('conv1'),
-                        conv2: extractConvFn('conv2'),
-                        conv3: extractConvFn('conv3'),
-                        conv4: extractConvFn('conv4'),
-                        conv5: extractConvFn('conv5'),
-                        conv6: extractConvFn('conv6'),
-                        conv7: extractConvFn('conv7'),
-                        conv8: extractConvParams('conv8')
-                    };
-                    disposeUnusedWeightTensors_1.disposeUnusedWeightTensors(weightMap, paramMappings);
-                    return [2 /*return*/, { params: params, paramMappings: paramMappings }];
-            }
-        });
-    });
-}
-exports.loadQuantizedParams = loadQuantizedParams;
-//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file

build/tinyYolov2/loadQuantizedParams.js.map

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\ No newline at end of file

build/tinyYolov2/normalize.d.ts

-import * as tf from '@tensorflow/tfjs-core';
-export declare function normalize(x: tf.Tensor4D): tf.Tensor4D;

build/tinyYolov2/normalize.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-function normalize(x) {
-    return tf.tidy(function () {
-        var avg_r = tf.fill(x.shape.slice(0, 3).concat([1]), 117.001);
-        var avg_g = tf.fill(x.shape.slice(0, 3).concat([1]), 114.697);
-        var avg_b = tf.fill(x.shape.slice(0, 3).concat([1]), 97.404);
-        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
\ No newline at end of file

build/tinyYolov2/normalize.js.map

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build/tinyYolov2/types.d.ts

-import * as tf from '@tensorflow/tfjs-core';
-import { ConvParams } from '../commons/types';
-import { Point } from '../Point';
-export declare type BatchNorm = {
-    sub: tf.Tensor1D;
-    truediv: tf.Tensor1D;
-};
-export declare class SeparableConvParams {
-    depthwise_filter: tf.Tensor4D;
-    pointwise_filter: tf.Tensor4D;
-    bias: tf.Tensor1D;
-    constructor(depthwise_filter: tf.Tensor4D, pointwise_filter: tf.Tensor4D, bias: tf.Tensor1D);
-}
-export declare type ConvWithBatchNorm = {
-    conv: ConvParams;
-    bn: BatchNorm;
-};
-export declare type NetParams = {
-    conv0: ConvWithBatchNorm | SeparableConvParams;
-    conv1: ConvWithBatchNorm | SeparableConvParams;
-    conv2: ConvWithBatchNorm | SeparableConvParams;
-    conv3: ConvWithBatchNorm | SeparableConvParams;
-    conv4: ConvWithBatchNorm | SeparableConvParams;
-    conv5: ConvWithBatchNorm | SeparableConvParams;
-    conv6: ConvWithBatchNorm | SeparableConvParams;
-    conv7: ConvWithBatchNorm | SeparableConvParams;
-    conv8: ConvParams;
-};
-export declare enum SizeType {
-    XS = "xs",
-    SM = "sm",
-    MD = "md",
-    LG = "lg",
-}
-export declare type TinyYolov2ForwardParams = {
-    inputSize?: SizeType | number;
-    scoreThreshold?: number;
-};
-export declare type PostProcessingParams = {
-    scoreThreshold?: number;
-    paddings: Point;
-};

build/tinyYolov2/types.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var SeparableConvParams = /** @class */ (function () {
-    function SeparableConvParams(depthwise_filter, pointwise_filter, bias) {
-        this.depthwise_filter = depthwise_filter;
-        this.pointwise_filter = pointwise_filter;
-        this.bias = bias;
-    }
-    return SeparableConvParams;
-}());
-exports.SeparableConvParams = SeparableConvParams;
-var SizeType;
-(function (SizeType) {
-    SizeType["XS"] = "xs";
-    SizeType["SM"] = "sm";
-    SizeType["MD"] = "md";
-    SizeType["LG"] = "lg";
-})(SizeType = exports.SizeType || (exports.SizeType = {}));
-//# sourceMappingURL=types.js.map
\ No newline at end of file

build/tinyYolov2/types.js.map

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\ No newline at end of file

build/toNetInput.d.ts

-import { NetInput } from './NetInput';
-import { TNetInput } from './types';
-/**
- * Validates the input to make sure, they are valid net inputs and awaits all media elements
- * to be finished loading.
- *
- * @param input The input, which can be a media element or an array of different media elements.
- * @param manageCreatedInput If a new NetInput instance is created from the inputs, this flag
- * determines, whether to set the NetInput as managed or not.
- * @returns A NetInput instance, which can be passed into one of the neural networks.
- */
-export declare function toNetInput(inputs: TNetInput, manageCreatedInput?: boolean, keepCanvases?: boolean): Promise<NetInput>;

build/toNetInput.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var isMediaElement_1 = require("./commons/isMediaElement");
-var isTensor_1 = require("./commons/isTensor");
-var NetInput_1 = require("./NetInput");
-var utils_1 = require("./utils");
-/**
- * Validates the input to make sure, they are valid net inputs and awaits all media elements
- * to be finished loading.
- *
- * @param input The input, which can be a media element or an array of different media elements.
- * @param manageCreatedInput If a new NetInput instance is created from the inputs, this flag
- * determines, whether to set the NetInput as managed or not.
- * @returns A NetInput instance, which can be passed into one of the neural networks.
- */
-function toNetInput(inputs, manageCreatedInput, keepCanvases) {
-    if (manageCreatedInput === void 0) { manageCreatedInput = false; }
-    if (keepCanvases === void 0) { keepCanvases = false; }
-    return tslib_1.__awaiter(this, void 0, void 0, function () {
-        var afterCreate, inputArgArray, getIdxHint, inputArray;
-        return tslib_1.__generator(this, function (_a) {
-            switch (_a.label) {
-                case 0:
-                    if (inputs instanceof NetInput_1.NetInput) {
-                        return [2 /*return*/, inputs];
-                    }
-                    afterCreate = function (netInput) { return manageCreatedInput
-                        ? netInput.managed()
-                        : netInput; };
-                    if (isTensor_1.isTensor4D(inputs)) {
-                        return [2 /*return*/, afterCreate(new NetInput_1.NetInput(inputs))];
-                    }
-                    inputArgArray = Array.isArray(inputs)
-                        ? inputs
-                        : [inputs];
-                    if (!inputArgArray.length) {
-                        throw new Error('toNetInput - empty array passed as input');
-                    }
-                    getIdxHint = function (idx) { return Array.isArray(inputs) ? " at input index " + idx + ":" : ''; };
-                    inputArray = inputArgArray.map(utils_1.resolveInput);
-                    inputArray.forEach(function (input, i) {
-                        if (!isMediaElement_1.isMediaElement(input) && !isTensor_1.isTensor3D(input) && !isTensor_1.isTensor4D(input)) {
-                            if (typeof inputArgArray[i] === 'string') {
-                                throw new Error("toNetInput -" + getIdxHint(i) + " string passed, but could not resolve HTMLElement for element id " + inputArgArray[i]);
-                            }
-                            throw new Error("toNetInput -" + getIdxHint(i) + " expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id");
-                        }
-                        if (isTensor_1.isTensor4D(input)) {
-                            // if tf.Tensor4D is passed in the input array, the batch size has to be 1
-                            var batchSize = input.shape[0];
-                            if (batchSize !== 1) {
-                                throw new Error("toNetInput -" + getIdxHint(i) + " tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array");
-                            }
-                        }
-                    });
-                    // wait for all media elements being loaded
-                    return [4 /*yield*/, Promise.all(inputArray.map(function (input) { return isMediaElement_1.isMediaElement(input) && utils_1.awaitMediaLoaded(input); }))];
-                case 1:
-                    // wait for all media elements being loaded
-                    _a.sent();
-                    return [2 /*return*/, afterCreate(new NetInput_1.NetInput(inputArray, Array.isArray(inputs), keepCanvases))];
-            }
-        });
-    });
-}
-exports.toNetInput = toNetInput;
-//# sourceMappingURL=toNetInput.js.map
\ No newline at end of file

build/toNetInput.js.map

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\ No newline at end of file

build/types.d.ts

-import * as tf from '@tensorflow/tfjs-core';
-import { NetInput } from './NetInput';
-export declare type TMediaElement = HTMLImageElement | HTMLVideoElement | HTMLCanvasElement;
-export declare type TResolvedNetInput = TMediaElement | tf.Tensor3D | tf.Tensor4D;
-export declare type TNetInputArg = string | TResolvedNetInput;
-export declare type TNetInput = TNetInputArg | Array<TNetInputArg> | NetInput | tf.Tensor4D;
-export declare type Dimensions = {
-    width: number;
-    height: number;
-};

build/types.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-//# sourceMappingURL=types.js.map
\ No newline at end of file

build/types.js.map

-{"version":3,"file":"types.js","sourceRoot":"","sources":["../src/types.ts"],"names":[],"mappings":""}
\ No newline at end of file

build/utils.d.ts

-import * as tf from '@tensorflow/tfjs-core';
-import { Dimensions } from './types';
-export declare function isFloat(num: number): boolean;
-export declare function isEven(num: number): boolean;
-export declare function round(num: number): number;
-export declare function sigmoid(x: number): number;
-export declare function isDimensions(obj: any): boolean;
-export declare function resolveInput(arg: string | any): any;
-export declare function isLoaded(media: HTMLImageElement | HTMLVideoElement): boolean;
-export declare function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement): Promise<{}>;
-export declare function getContext2dOrThrow(canvas: HTMLCanvasElement): CanvasRenderingContext2D;
-export declare function createCanvas({width, height}: Dimensions): HTMLCanvasElement;
-export declare function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement, dims?: Dimensions): HTMLCanvasElement;
-export declare function getMediaDimensions(media: HTMLImageElement | HTMLVideoElement): {
-    width: number;
-    height: number;
-};
-export declare function bufferToImage(buf: Blob): Promise<HTMLImageElement>;
-export declare function imageTensorToCanvas(imgTensor: tf.Tensor, canvas?: HTMLCanvasElement): Promise<HTMLCanvasElement>;

build/utils.js

-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var isTensor_1 = require("./commons/isTensor");
-function isFloat(num) {
-    return num % 1 !== 0;
-}
-exports.isFloat = isFloat;
-function isEven(num) {
-    return num % 2 === 0;
-}
-exports.isEven = isEven;
-function round(num) {
-    return Math.floor(num * 100) / 100;
-}
-exports.round = round;
-function sigmoid(x) {
-    return 1 / (1 + Math.exp(-x));
-}
-exports.sigmoid = sigmoid;
-function isDimensions(obj) {
-    return obj && obj.width && obj.height;
-}
-exports.isDimensions = isDimensions;
-function resolveInput(arg) {
-    if (typeof arg === 'string') {
-        return document.getElementById(arg);
-    }
-    return arg;
-}
-exports.resolveInput = resolveInput;
-function isLoaded(media) {
-    return (media instanceof HTMLImageElement && media.complete)
-        || (media instanceof HTMLVideoElement && media.readyState >= 3);
-}
-exports.isLoaded = isLoaded;
-function awaitMediaLoaded(media) {
-    return new Promise(function (resolve, reject) {
-        if (media instanceof HTMLCanvasElement || isLoaded(media)) {
-            return resolve();
-        }
-        function onLoad(e) {
-            if (!e.currentTarget)
-                return;
-            e.currentTarget.removeEventListener('load', onLoad);
-            e.currentTarget.removeEventListener('error', onError);
-            resolve(e);
-        }
-        function onError(e) {
-            if (!e.currentTarget)
-                return;
-            e.currentTarget.removeEventListener('load', onLoad);
-            e.currentTarget.removeEventListener('error', onError);
-            reject(e);
-        }
-        media.addEventListener('load', onLoad);
-        media.addEventListener('error', onError);
-    });
-}
-exports.awaitMediaLoaded = awaitMediaLoaded;
-function getContext2dOrThrow(canvas) {
-    var ctx = canvas.getContext('2d');
-    if (!ctx) {
-        throw new Error('canvas 2d context is null');
-    }
-    return ctx;
-}
-exports.getContext2dOrThrow = getContext2dOrThrow;
-function createCanvas(_a) {
-    var width = _a.width, height = _a.height;
-    var canvas = document.createElement('canvas');
-    canvas.width = width;
-    canvas.height = height;
-    return canvas;
-}
-exports.createCanvas = createCanvas;
-function createCanvasFromMedia(media, dims) {
-    if (!isLoaded(media)) {
-        throw new Error('createCanvasFromMedia - media has not finished loading yet');
-    }
-    var _a = dims || getMediaDimensions(media), width = _a.width, height = _a.height;
-    var canvas = createCanvas({ width: width, height: height });
-    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
-    return canvas;
-}
-exports.createCanvasFromMedia = createCanvasFromMedia;
-function getMediaDimensions(media) {
-    if (media instanceof HTMLImageElement) {
-        return { width: media.naturalWidth, height: media.naturalHeight };
-    }
-    if (media instanceof HTMLVideoElement) {
-        return { width: media.videoWidth, height: media.videoHeight };
-    }
-    return media;
-}
-exports.getMediaDimensions = getMediaDimensions;
-function bufferToImage(buf) {
-    return new Promise(function (resolve, reject) {
-        if (!(buf instanceof Blob)) {
-            return reject('bufferToImage - expected buf to be of type: Blob');
-        }
-        var reader = new FileReader();
-        reader.onload = function () {
-            var img = new Image();
-            img.onload = function () { return resolve(img); };
-            img.onerror = reject;
-            img.src = reader.result;
-        };
-        reader.onerror = reject;
-        reader.readAsDataURL(buf);
-    });
-}
-exports.bufferToImage = bufferToImage;
-function imageTensorToCanvas(imgTensor, canvas) {
-    return tslib_1.__awaiter(this, void 0, void 0, function () {
-        var targetCanvas, _a, height, width, numChannels, imgTensor3D;
-        return tslib_1.__generator(this, function (_b) {
-            switch (_b.label) {
-                case 0:
-                    targetCanvas = canvas || document.createElement('canvas');
-                    _a = imgTensor.shape.slice(isTensor_1.isTensor4D(imgTensor) ? 1 : 0), height = _a[0], width = _a[1], numChannels = _a[2];
-                    imgTensor3D = tf.tidy(function () { return imgTensor.as3D(height, width, numChannels).toInt(); });
-                    return [4 /*yield*/, tf.toPixels(imgTensor3D, targetCanvas)];
-                case 1:
-                    _b.sent();
-                    imgTensor3D.dispose();
-                    return [2 /*return*/, targetCanvas];
-            }
-        });
-    });
-}
-exports.imageTensorToCanvas = imageTensorToCanvas;
-//# sourceMappingURL=utils.js.map
\ No newline at end of file

build/utils.js.map

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\ No newline at end of file