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