check in latest build

build/commonjs/classes/FaceDetection.d.ts

-import { Dimensions, ObjectDetection, Rect } from 'tfjs-image-recognition-base';
-export declare class FaceDetection extends ObjectDetection {
-    constructor(score: number, relativeBox: Rect, imageDims: Dimensions);
+import { Box, IDimensions, ObjectDetection, Rect } from 'tfjs-image-recognition-base';
+export interface IFaceDetecion {
+    score: number;
+    box: Box;
+}
+export declare class FaceDetection extends ObjectDetection implements IFaceDetecion {
+    constructor(score: number, relativeBox: Rect, imageDims: IDimensions);
 }

build/commonjs/classes/FaceDetection.js.map

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\ No newline at end of file

build/commonjs/classes/FaceDetectionWithLandmarks.d.ts

+import { FaceDetection } from './FaceDetection';
+import { FaceLandmarks } from './FaceLandmarks';
+import { FaceLandmarks68 } from './FaceLandmarks68';
+export interface IFaceDetectionWithLandmarks<TFaceLandmarks extends FaceLandmarks = FaceLandmarks68> {
+    detection: FaceDetection;
+    landmarks: TFaceLandmarks;
+}
+export declare class FaceDetectionWithLandmarks<TFaceLandmarks extends FaceLandmarks = FaceLandmarks68> implements IFaceDetectionWithLandmarks<TFaceLandmarks> {
+    private _detection;
+    private _unshiftedLandmarks;
+    constructor(detection: FaceDetection, unshiftedLandmarks: TFaceLandmarks);
+    readonly detection: FaceDetection;
+    readonly unshiftedLandmarks: TFaceLandmarks;
+    readonly alignedRect: FaceDetection;
+    readonly landmarks: TFaceLandmarks;
+    readonly faceDetection: FaceDetection;
+    readonly faceLandmarks: TFaceLandmarks;
+    forSize(width: number, height: number): FaceDetectionWithLandmarks<TFaceLandmarks>;
+}

build/commonjs/classes/FaceDetectionWithLandmarks.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var FaceDetection_1 = require("./FaceDetection");
+var FaceDetectionWithLandmarks = /** @class */ (function () {
+    function FaceDetectionWithLandmarks(detection, unshiftedLandmarks) {
+        this._detection = detection;
+        this._unshiftedLandmarks = unshiftedLandmarks;
+    }
+    Object.defineProperty(FaceDetectionWithLandmarks.prototype, "detection", {
+        get: function () { return this._detection; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceDetectionWithLandmarks.prototype, "unshiftedLandmarks", {
+        get: function () { return this._unshiftedLandmarks; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceDetectionWithLandmarks.prototype, "alignedRect", {
+        get: function () {
+            var rect = this.landmarks.align();
+            var imageDims = this.detection.imageDims;
+            return new FaceDetection_1.FaceDetection(this._detection.score, rect.rescale(imageDims.reverse()), imageDims);
+        },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceDetectionWithLandmarks.prototype, "landmarks", {
+        get: function () {
+            var _a = this.detection.box, x = _a.x, y = _a.y;
+            return this._unshiftedLandmarks.shiftBy(x, y);
+        },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceDetectionWithLandmarks.prototype, "faceDetection", {
+        // aliases for backward compatibily
+        get: function () { return this.detection; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceDetectionWithLandmarks.prototype, "faceLandmarks", {
+        get: function () { return this.landmarks; },
+        enumerable: true,
+        configurable: true
+    });
+    FaceDetectionWithLandmarks.prototype.forSize = function (width, height) {
+        var resizedDetection = this._detection.forSize(width, height);
+        var resizedLandmarks = this._unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);
+        return new FaceDetectionWithLandmarks(resizedDetection, resizedLandmarks);
+    };
+    return FaceDetectionWithLandmarks;
+}());
+exports.FaceDetectionWithLandmarks = FaceDetectionWithLandmarks;
+//# sourceMappingURL=FaceDetectionWithLandmarks.js.map
\ No newline at end of file

build/commonjs/classes/FaceDetectionWithLandmarks.js.map

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build/commonjs/classes/FaceLandmarks.d.ts

-import { Dimensions, Point, Rect } from 'tfjs-image-recognition-base';
+import { Dimensions, IDimensions, Point, Rect } from 'tfjs-image-recognition-base';
 import { FaceDetection } from './FaceDetection';
-export declare class FaceLandmarks {
-    protected _imageWidth: number;
-    protected _imageHeight: number;
+export interface IFaceLandmarks {
+    positions: Point[];
+    shift: Point;
+}
+export declare class FaceLandmarks implements IFaceLandmarks {
     protected _shift: Point;
-    protected _faceLandmarks: Point[];
-    constructor(relativeFaceLandmarkPositions: Point[], imageDims: Dimensions, shift?: Point);
-    getShift(): Point;
-    getImageWidth(): number;
-    getImageHeight(): number;
-    getPositions(): Point[];
-    getRelativePositions(): Point[];
+    protected _positions: Point[];
+    protected _imgDims: Dimensions;
+    constructor(relativeFaceLandmarkPositions: Point[], imgDims: IDimensions, shift?: Point);
+    readonly shift: Point;
+    readonly imageWidth: number;
+    readonly imageHeight: number;
+    readonly positions: Point[];
+    readonly relativePositions: Point[];
     forSize<T extends FaceLandmarks>(width: number, height: number): T;
-    shift<T extends FaceLandmarks>(x: number, y: number): T;
+    shiftBy<T extends FaceLandmarks>(x: number, y: number): T;
     shiftByPoint<T extends FaceLandmarks>(pt: Point): T;
     /**
      * Aligns the face landmarks after face detection from the relative positions of the faces

build/commonjs/classes/FaceLandmarks.js

@@ -7,38 +7,49 @@ var relX = 0.5;
 var relY = 0.43;
 var relScale = 0.45;
 var FaceLandmarks = /** @class */ (function () {
-    function FaceLandmarks(relativeFaceLandmarkPositions, imageDims, shift) {
+    function FaceLandmarks(relativeFaceLandmarkPositions, imgDims, shift) {
         if (shift === void 0) { shift = new tfjs_image_recognition_base_1.Point(0, 0); }
-        var width = imageDims.width, height = imageDims.height;
-        this._imageWidth = width;
-        this._imageHeight = height;
+        var width = imgDims.width, height = imgDims.height;
+        this._imgDims = new tfjs_image_recognition_base_1.Dimensions(width, height);
         this._shift = shift;
-        this._faceLandmarks = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new tfjs_image_recognition_base_1.Point(width, height)).add(shift); });
+        this._positions = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new tfjs_image_recognition_base_1.Point(width, height)).add(shift); });
     }
-    FaceLandmarks.prototype.getShift = function () {
-        return new tfjs_image_recognition_base_1.Point(this._shift.x, this._shift.y);
-    };
-    FaceLandmarks.prototype.getImageWidth = function () {
-        return this._imageWidth;
-    };
-    FaceLandmarks.prototype.getImageHeight = function () {
-        return this._imageHeight;
-    };
-    FaceLandmarks.prototype.getPositions = function () {
-        return this._faceLandmarks;
-    };
-    FaceLandmarks.prototype.getRelativePositions = function () {
+    Object.defineProperty(FaceLandmarks.prototype, "shift", {
+        get: function () { return new tfjs_image_recognition_base_1.Point(this._shift.x, this._shift.y); },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceLandmarks.prototype, "imageWidth", {
+        get: function () { return this._imgDims.width; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceLandmarks.prototype, "imageHeight", {
+        get: function () { return this._imgDims.height; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceLandmarks.prototype, "positions", {
+        get: function () { return this._positions; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceLandmarks.prototype, "relativePositions", {
+        get: function () {
             var _this = this;
-        return this._faceLandmarks.map(function (pt) { return pt.sub(_this._shift).div(new tfjs_image_recognition_base_1.Point(_this._imageWidth, _this._imageHeight)); });
-    };
+            return this._positions.map(function (pt) { return pt.sub(_this._shift).div(new tfjs_image_recognition_base_1.Point(_this.imageWidth, _this.imageHeight)); });
+        },
+        enumerable: true,
+        configurable: true
+    });
     FaceLandmarks.prototype.forSize = function (width, height) {
-        return new this.constructor(this.getRelativePositions(), { width: width, height: height });
+        return new this.constructor(this.relativePositions, { width: width, height: height });
     };
-    FaceLandmarks.prototype.shift = function (x, y) {
-        return new this.constructor(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new tfjs_image_recognition_base_1.Point(x, y));
+    FaceLandmarks.prototype.shiftBy = function (x, y) {
+        return new this.constructor(this.relativePositions, this._imgDims, new tfjs_image_recognition_base_1.Point(x, y));
     };
     FaceLandmarks.prototype.shiftByPoint = function (pt) {
-        return this.shift(pt.x, pt.y);
+        return this.shiftBy(pt.x, pt.y);
     };
     /**
      * Aligns the face landmarks after face detection from the relative positions of the faces
@@ -54,9 +65,9 @@ var FaceLandmarks = /** @class */ (function () {
     FaceLandmarks.prototype.align = function (detection) {
         if (detection) {
             var box = detection instanceof FaceDetection_1.FaceDetection
-                ? detection.getBox().floor()
+                ? detection.box.floor()
                 : detection;
-            return this.shift(box.x, box.y).align();
+            return this.shiftBy(box.x, box.y).align();
         }
         var centers = this.getRefPointsForAlignment();
         var leftEyeCenter = centers[0], rightEyeCenter = centers[1], mouthCenter = centers[2];
@@ -67,7 +78,7 @@ var FaceLandmarks = /** @class */ (function () {
         // TODO: pad in case rectangle is out of image bounds
         var x = Math.floor(Math.max(0, refPoint.x - (relX * size)));
         var y = Math.floor(Math.max(0, refPoint.y - (relY * size)));
-        return new tfjs_image_recognition_base_1.Rect(x, y, Math.min(size, this._imageWidth + x), Math.min(size, this._imageHeight + y));
+        return new tfjs_image_recognition_base_1.Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));
     };
     FaceLandmarks.prototype.getRefPointsForAlignment = function () {
         throw new Error('getRefPointsForAlignment not implemented by base class');

build/commonjs/classes/FaceLandmarks.js.map

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\ No newline at end of file
+{"version":3,"file":"FaceLandmarks.js","sourceRoot":"","sources":["../../../src/classes/FaceLandmarks.ts"],"names":[],"mappings":";;AAAA,2EAAmG;AAEnG,iDAAgD;AAEhD,2BAA2B;AAC3B,IAAM,IAAI,GAAG,GAAG,CAAA;AAChB,IAAM,IAAI,GAAG,IAAI,CAAA;AACjB,IAAM,QAAQ,GAAG,IAAI,CAAA;AAOrB;IAKE,uBACE,6BAAsC,EACtC,OAAoB,EACpB,KAA8B;QAA9B,sBAAA,EAAA,YAAmB,mCAAK,CAAC,CAAC,EAAE,CAAC,CAAC;QAEtB,IAAA,qBAAK,EAAE,uBAAM,CAAY;QACjC,IAAI,CAAC,QAAQ,GAAG,IAAI,wCAAU,CAAC,KAAK,EAAE,MAAM,CAAC,CAAA;QAC7C,IAAI,CAAC,MAAM,GAAG,KAAK,CAAA;QACnB,IAAI,CAAC,UAAU,GAAG,6BAA6B,CAAC,GAAG,CACjD,UAAA,EAAE,IAAI,OAAA,EAAE,CAAC,GAAG,CAAC,IAAI,mCAAK,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC,CAAC,GAAG,CAAC,KAAK,CAAC,EAA3C,CAA2C,CAClD,CAAA;IACH,CAAC;IAED,sBAAW,gCAAK;aAAhB,cAA4B,OAAO,IAAI,mCAAK,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC,EAAE,IAAI,CAAC,MAAM,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC;;;OAAA;IAC5E,sBAAW,qCAAU;aAArB,cAAkC,OAAO,IAAI,CAAC,QAAQ,CAAC,KAAK,CAAA,CAAC,CAAC;;;OAAA;IAC9D,sBAAW,sCAAW;aAAtB,cAAmC,OAAO,IAAI,CAAC,QAAQ,CAAC,MAAM,CAAA,CAAC,CAAC;;;OAAA;IAChE,sBAAW,oCAAS;aAApB,cAAkC,OAAO,IAAI,CAAC,UAAU,CAAA,CAAC,CAAC;;;OAAA;IAC1D,sBAAW,4CAAiB;aAA5B;YAAA,iBAIC;YAHC,OAAO,IAAI,CAAC,UAAU,CAAC,GAAG,CACxB,UAAA,EAAE,IAAI,OAAA,EAAE,CAAC,GAAG,CAAC,KAAI,CAAC,MAAM,CAAC,CAAC,GAAG,CAAC,IAAI,mCAAK,CAAC,KAAI,CAAC,UAAU,EAAE,KAAI,CAAC,WAAW,CAAC,CAAC,EAArE,CAAqE,CAC5E,CAAA;QACH,CAAC;;;OAAA;IAEM,+BAAO,GAAd,UAAwC,KAAa,EAAE,MAAc;QACnE,OAAO,IAAK,IAAI,CAAC,WAAmB,CAClC,IAAI,CAAC,iBAAiB,EACtB,EAAE,KAAK,OAAA,EAAE,MAAM,QAAA,EAAE,CAClB,CAAA;IACH,CAAC;IAEM,+BAAO,GAAd,UAAwC,CAAS,EAAE,CAAS;QAC1D,OAAO,IAAK,IAAI,CAAC,WAAmB,CAClC,IAAI,CAAC,iBAAiB,EACtB,IAAI,CAAC,QAAQ,EACb,IAAI,mCAAK,CAAC,CAAC,EAAE,CAAC,CAAC,CAChB,CAAA;IACH,CAAC;IAEM,oCAAY,GAAnB,UAA6C,EAAS;QACpD,OAAO,IAAI,CAAC,OAAO,CAAC,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC,CAAA;IACjC,CAAC;IAED;;;;;;;;;;OAUG;IACI,6BAAK,GAAZ,UACE,SAAgC;QAEhC,IAAI,SAAS,EAAE;YACb,IAAM,GAAG,GAAG,SAAS,YAAY,6BAAa;gBAC5C,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,KAAK,EAAE;gBACvB,CAAC,CAAC,SAAS,CAAA;YAEb,OAAO,IAAI,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC,CAAC,KAAK,EAAE,CAAA;SAC1C;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,4CAAc,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,kCAAI,CAAC,CAAC,EAAE,CAAC,EAAE,IAAI,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,UAAU,GAAG,CAAC,CAAC,EAAE,IAAI,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,WAAW,GAAG,CAAC,CAAC,CAAC,CAAA;IAClG,CAAC;IAES,gDAAwB,GAAlC;QACE,MAAM,IAAI,KAAK,CAAC,wDAAwD,CAAC,CAAA;IAC3E,CAAC;IACH,oBAAC;AAAD,CAAC,AAxFD,IAwFC;AAxFY,sCAAa"}
\ No newline at end of file

build/commonjs/classes/FaceLandmarks5.js

@@ -9,7 +9,7 @@ var FaceLandmarks5 = /** @class */ (function (_super) {
         return _super !== null && _super.apply(this, arguments) || this;
     }
     FaceLandmarks5.prototype.getRefPointsForAlignment = function () {
-        var pts = this.getPositions();
+        var pts = this.positions;
         return [
             pts[0],
             pts[1],

build/commonjs/classes/FaceLandmarks5.js.map

-{"version":3,"file":"FaceLandmarks5.js","sourceRoot":"","sources":["../../../src/classes/FaceLandmarks5.ts"],"names":[],"mappings":";;;AAAA,2EAAoE;AAEpE,iDAAgD;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,4CAAc,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC;SACjC,CAAA;IACH,CAAC;IACH,qBAAC;AAAD,CAAC,AAVD,CAAoC,6BAAa,GAUhD;AAVY,wCAAc"}
\ No newline at end of file
+{"version":3,"file":"FaceLandmarks5.js","sourceRoot":"","sources":["../../../src/classes/FaceLandmarks5.ts"],"names":[],"mappings":";;;AAAA,2EAAoE;AAEpE,iDAAgD;AAEhD;IAAoC,0CAAa;IAAjD;;IAUA,CAAC;IARW,iDAAwB,GAAlC;QACE,IAAM,GAAG,GAAG,IAAI,CAAC,SAAS,CAAA;QAC1B,OAAO;YACL,GAAG,CAAC,CAAC,CAAC;YACN,GAAG,CAAC,CAAC,CAAC;YACN,4CAAc,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC;SACjC,CAAA;IACH,CAAC;IACH,qBAAC;AAAD,CAAC,AAVD,CAAoC,6BAAa,GAUhD;AAVY,wCAAc"}
\ No newline at end of file

build/commonjs/classes/FaceLandmarks68.js

@@ -9,25 +9,25 @@ var FaceLandmarks68 = /** @class */ (function (_super) {
         return _super !== null && _super.apply(this, arguments) || this;
     }
     FaceLandmarks68.prototype.getJawOutline = function () {
-        return this._faceLandmarks.slice(0, 17);
+        return this.positions.slice(0, 17);
     };
     FaceLandmarks68.prototype.getLeftEyeBrow = function () {
-        return this._faceLandmarks.slice(17, 22);
+        return this.positions.slice(17, 22);
     };
     FaceLandmarks68.prototype.getRightEyeBrow = function () {
-        return this._faceLandmarks.slice(22, 27);
+        return this.positions.slice(22, 27);
     };
     FaceLandmarks68.prototype.getNose = function () {
-        return this._faceLandmarks.slice(27, 36);
+        return this.positions.slice(27, 36);
     };
     FaceLandmarks68.prototype.getLeftEye = function () {
-        return this._faceLandmarks.slice(36, 42);
+        return this.positions.slice(36, 42);
     };
     FaceLandmarks68.prototype.getRightEye = function () {
-        return this._faceLandmarks.slice(42, 48);
+        return this.positions.slice(42, 48);
     };
     FaceLandmarks68.prototype.getMouth = function () {
-        return this._faceLandmarks.slice(48, 68);
+        return this.positions.slice(48, 68);
     };
     FaceLandmarks68.prototype.getRefPointsForAlignment = function () {
         return [

build/commonjs/classes/FaceLandmarks68.js.map

-{"version":3,"file":"FaceLandmarks68.js","sourceRoot":"","sources":["../../../src/classes/FaceLandmarks68.ts"],"names":[],"mappings":";;;AAAA,2EAAoE;AAEpE,0DAAyD;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,4CAAc,CAAC,CAAA;IACvB,CAAC;IACH,sBAAC;AAAD,CAAC,AApCD,CAAqC,6BAAa,GAoCjD;AApCY,0CAAe"}
\ No newline at end of file
+{"version":3,"file":"FaceLandmarks68.js","sourceRoot":"","sources":["../../../src/classes/FaceLandmarks68.ts"],"names":[],"mappings":";;;AAAA,2EAAoE;AAEpE,0DAAyD;AAEzD;IAAqC,2CAAa;IAAlD;;IAoCA,CAAC;IAnCQ,uCAAa,GAApB;QACE,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,CAAC,EAAE,EAAE,CAAC,CAAA;IACpC,CAAC;IAEM,wCAAc,GAArB;QACE,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IACrC,CAAC;IAEM,yCAAe,GAAtB;QACE,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IACrC,CAAC;IAEM,iCAAO,GAAd;QACE,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IACrC,CAAC;IAEM,oCAAU,GAAjB;QACE,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IACrC,CAAC;IAEM,qCAAW,GAAlB;QACE,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IACrC,CAAC;IAEM,kCAAQ,GAAf;QACE,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IACrC,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,4CAAc,CAAC,CAAA;IACvB,CAAC;IACH,sBAAC;AAAD,CAAC,AApCD,CAAqC,6BAAa,GAoCjD;AApCY,0CAAe"}
\ No newline at end of file

build/commonjs/classes/FaceMatch.d.ts

+export interface IFaceMatch {
+    label: string;
+    distance: number;
+}
+export declare class FaceMatch implements IFaceMatch {
+    private _label;
+    private _distance;
+    constructor(label: string, distance: number);
+    readonly label: string;
+    readonly distance: number;
+    toString(withDistance?: boolean): string;
+}

build/commonjs/classes/FaceMatch.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
+var FaceMatch = /** @class */ (function () {
+    function FaceMatch(label, distance) {
+        this._label = label;
+        this._distance = distance;
+    }
+    Object.defineProperty(FaceMatch.prototype, "label", {
+        get: function () { return this._label; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceMatch.prototype, "distance", {
+        get: function () { return this._distance; },
+        enumerable: true,
+        configurable: true
+    });
+    FaceMatch.prototype.toString = function (withDistance) {
+        if (withDistance === void 0) { withDistance = true; }
+        return "" + this.label + (withDistance ? " (" + tfjs_image_recognition_base_1.round(this.distance) + ")" : '');
+    };
+    return FaceMatch;
+}());
+exports.FaceMatch = FaceMatch;
+//# sourceMappingURL=FaceMatch.js.map
\ No newline at end of file

build/commonjs/classes/FaceMatch.js.map

+{"version":3,"file":"FaceMatch.js","sourceRoot":"","sources":["../../../src/classes/FaceMatch.ts"],"names":[],"mappings":";;AAAA,2EAAoD;AAOpD;IAIE,mBAAY,KAAa,EAAE,QAAgB;QACzC,IAAI,CAAC,MAAM,GAAG,KAAK,CAAA;QACnB,IAAI,CAAC,SAAS,GAAG,QAAQ,CAAA;IAC3B,CAAC;IAED,sBAAW,4BAAK;aAAhB,cAA6B,OAAO,IAAI,CAAC,MAAM,CAAA,CAAC,CAAC;;;OAAA;IACjD,sBAAW,+BAAQ;aAAnB,cAAgC,OAAO,IAAI,CAAC,SAAS,CAAA,CAAC,CAAC;;;OAAA;IAEhD,4BAAQ,GAAf,UAAgB,YAA4B;QAA5B,6BAAA,EAAA,mBAA4B;QAC1C,OAAO,KAAG,IAAI,CAAC,KAAK,IAAG,YAAY,CAAC,CAAC,CAAC,OAAK,mCAAK,CAAC,IAAI,CAAC,QAAQ,CAAC,MAAG,CAAC,CAAC,CAAC,EAAE,CAAE,CAAA;IAC3E,CAAC;IACH,gBAAC;AAAD,CAAC,AAfD,IAeC;AAfY,8BAAS"}
\ No newline at end of file

build/commonjs/classes/FullFaceDescription.d.ts

 import { FaceDetection } from './FaceDetection';
+import { FaceDetectionWithLandmarks, IFaceDetectionWithLandmarks } from './FaceDetectionWithLandmarks';
 import { FaceLandmarks } from './FaceLandmarks';
-export declare class FullFaceDescription {
-    private _detection;
-    private _landmarks;
+import { FaceLandmarks68 } from './FaceLandmarks68';
+export interface IFullFaceDescription<TFaceLandmarks extends FaceLandmarks = FaceLandmarks68> extends IFaceDetectionWithLandmarks<TFaceLandmarks> {
+    detection: FaceDetection;
+    landmarks: TFaceLandmarks;
+    descriptor: Float32Array;
+}
+export declare class FullFaceDescription<TFaceLandmarks extends FaceLandmarks = FaceLandmarks68> extends FaceDetectionWithLandmarks<TFaceLandmarks> implements IFullFaceDescription<TFaceLandmarks> {
     private _descriptor;
-    constructor(_detection: FaceDetection, _landmarks: FaceLandmarks, _descriptor: Float32Array);
-    readonly detection: FaceDetection;
-    readonly landmarks: FaceLandmarks;
+    constructor(detection: FaceDetection, unshiftedLandmarks: TFaceLandmarks, descriptor: Float32Array);
     readonly descriptor: Float32Array;
-    forSize(width: number, height: number): FullFaceDescription;
+    forSize(width: number, height: number): FullFaceDescription<TFaceLandmarks>;
 }

build/commonjs/classes/FullFaceDescription.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
-var FullFaceDescription = /** @class */ (function () {
-    function FullFaceDescription(_detection, _landmarks, _descriptor) {
-        this._detection = _detection;
-        this._landmarks = _landmarks;
-        this._descriptor = _descriptor;
+var tslib_1 = require("tslib");
+var FaceDetectionWithLandmarks_1 = require("./FaceDetectionWithLandmarks");
+var FullFaceDescription = /** @class */ (function (_super) {
+    tslib_1.__extends(FullFaceDescription, _super);
+    function FullFaceDescription(detection, unshiftedLandmarks, descriptor) {
+        var _this = _super.call(this, detection, unshiftedLandmarks) || this;
+        _this._descriptor = descriptor;
+        return _this;
     }
-    Object.defineProperty(FullFaceDescription.prototype, "detection", {
-        get: function () {
-            return this._detection;
-        },
-        enumerable: true,
-        configurable: true
-    });
-    Object.defineProperty(FullFaceDescription.prototype, "landmarks", {
-        get: function () {
-            return this._landmarks;
-        },
-        enumerable: true,
-        configurable: true
-    });
     Object.defineProperty(FullFaceDescription.prototype, "descriptor", {
         get: function () {
             return this._descriptor;
@@ -28,9 +17,10 @@ var FullFaceDescription = /** @class */ (function () {
         configurable: true
     });
     FullFaceDescription.prototype.forSize = function (width, height) {
-        return new FullFaceDescription(this._detection.forSize(width, height), this._landmarks.forSize(width, height), this._descriptor);
+        var _a = _super.prototype.forSize.call(this, width, height), detection = _a.detection, landmarks = _a.landmarks;
+        return new FullFaceDescription(detection, landmarks, this.descriptor);
     };
     return FullFaceDescription;
-}());
+}(FaceDetectionWithLandmarks_1.FaceDetectionWithLandmarks));
 exports.FullFaceDescription = FullFaceDescription;
 //# sourceMappingURL=FullFaceDescription.js.map
\ No newline at end of file

build/commonjs/classes/FullFaceDescription.js.map

-{"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;AA1BY,kDAAmB"}
\ No newline at end of file
+{"version":3,"file":"FullFaceDescription.js","sourceRoot":"","sources":["../../../src/classes/FullFaceDescription.ts"],"names":[],"mappings":";;;AACA,2EAAuG;AAYvG;IACU,+CAA0C;IAKlD,6BACE,SAAwB,EACxB,kBAAkC,EAClC,UAAwB;QAH1B,YAKE,kBAAM,SAAS,EAAE,kBAAkB,CAAC,SAErC;QADC,KAAI,CAAC,WAAW,GAAG,UAAU,CAAA;;IAC/B,CAAC;IAED,sBAAW,2CAAU;aAArB;YACE,OAAO,IAAI,CAAC,WAAW,CAAA;QACzB,CAAC;;;OAAA;IAEM,qCAAO,GAAd,UAAe,KAAa,EAAE,MAAc;QACpC,IAAA,uDAAuD,EAArD,wBAAS,EAAE,wBAAS,CAAiC;QAC7D,OAAO,IAAI,mBAAmB,CAAiB,SAAS,EAAE,SAAS,EAAE,IAAI,CAAC,UAAU,CAAC,CAAA;IACvF,CAAC;IACH,0BAAC;AAAD,CAAC,AAvBD,CACU,uDAA0B,GAsBnC;AAvBY,kDAAmB"}
\ No newline at end of file

build/commonjs/classes/LabeledFaceDescriptors.d.ts

+export declare class LabeledFaceDescriptors {
+    private _label;
+    private _descriptors;
+    constructor(label: string, descriptors: Float32Array[]);
+    readonly label: string;
+    readonly descriptors: Float32Array[];
+}

build/commonjs/classes/LabeledFaceDescriptors.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var LabeledFaceDescriptors = /** @class */ (function () {
+    function LabeledFaceDescriptors(label, descriptors) {
+        if (!(typeof label === 'string')) {
+            throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');
+        }
+        if (!Array.isArray(descriptors) || descriptors.some(function (desc) { return !(desc instanceof Float32Array); })) {
+            throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');
+        }
+        this._label = label;
+        this._descriptors = descriptors;
+    }
+    Object.defineProperty(LabeledFaceDescriptors.prototype, "label", {
+        get: function () { return this._label; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(LabeledFaceDescriptors.prototype, "descriptors", {
+        get: function () { return this._descriptors; },
+        enumerable: true,
+        configurable: true
+    });
+    return LabeledFaceDescriptors;
+}());
+exports.LabeledFaceDescriptors = LabeledFaceDescriptors;
+//# sourceMappingURL=LabeledFaceDescriptors.js.map
\ No newline at end of file

build/commonjs/classes/LabeledFaceDescriptors.js.map

+{"version":3,"file":"LabeledFaceDescriptors.js","sourceRoot":"","sources":["../../../src/classes/LabeledFaceDescriptors.ts"],"names":[],"mappings":";;AAAA;IAIE,gCAAY,KAAa,EAAE,WAA2B;QACpD,IAAI,CAAC,CAAC,OAAO,KAAK,KAAK,QAAQ,CAAC,EAAE;YAChC,MAAM,IAAI,KAAK,CAAC,oEAAoE,CAAC,CAAA;SACtF;QAED,IAAI,CAAC,KAAK,CAAC,OAAO,CAAC,WAAW,CAAC,IAAI,WAAW,CAAC,IAAI,CAAC,UAAA,IAAI,IAAI,OAAA,CAAC,CAAC,IAAI,YAAY,YAAY,CAAC,EAA/B,CAA+B,CAAC,EAAE;YAC5F,MAAM,IAAI,KAAK,CAAC,0FAA0F,CAAC,CAAA;SAC5G;QAED,IAAI,CAAC,MAAM,GAAG,KAAK,CAAA;QACnB,IAAI,CAAC,YAAY,GAAG,WAAW,CAAA;IACjC,CAAC;IAED,sBAAW,yCAAK;aAAhB,cAA6B,OAAO,IAAI,CAAC,MAAM,CAAA,CAAC,CAAC;;;OAAA;IACjD,sBAAW,+CAAW;aAAtB,cAA2C,OAAO,IAAI,CAAC,YAAY,CAAA,CAAC,CAAC;;;OAAA;IACvE,6BAAC;AAAD,CAAC,AAnBD,IAmBC;AAnBY,wDAAsB"}
\ No newline at end of file

build/commonjs/classes/index.d.ts

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

build/commonjs/classes/index.js

@@ -2,8 +2,11 @@
 Object.defineProperty(exports, "__esModule", { value: true });
 var tslib_1 = require("tslib");
 tslib_1.__exportStar(require("./FaceDetection"), exports);
+tslib_1.__exportStar(require("./FaceDetectionWithLandmarks"), exports);
 tslib_1.__exportStar(require("./FaceLandmarks"), exports);
 tslib_1.__exportStar(require("./FaceLandmarks5"), exports);
 tslib_1.__exportStar(require("./FaceLandmarks68"), exports);
+tslib_1.__exportStar(require("./FaceMatch"), exports);
 tslib_1.__exportStar(require("./FullFaceDescription"), exports);
+tslib_1.__exportStar(require("./LabeledFaceDescriptors"), exports);
 //# sourceMappingURL=index.js.map
\ No newline at end of file

build/commonjs/classes/index.js.map

-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/classes/index.ts"],"names":[],"mappings":";;;AAAA,0DAAgC;AAChC,0DAAgC;AAChC,2DAAiC;AACjC,4DAAkC;AAClC,gEAAsC"}
\ No newline at end of file
+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/classes/index.ts"],"names":[],"mappings":";;;AAAA,0DAAgC;AAChC,uEAA6C;AAC7C,0DAAgC;AAChC,2DAAiC;AACjC,4DAAkC;AAClC,sDAA4B;AAC5B,gEAAsC;AACtC,mEAAyC"}
\ No newline at end of file

build/commonjs/dom/drawLandmarks.js

@@ -29,7 +29,7 @@ function drawLandmarks(canvasArg, faceLandmarks, options) {
         // else draw points
         var ptOffset = lineWidth / 2;
         ctx.fillStyle = color;
-        landmarks.getPositions().forEach(function (pt) { return ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth); });
+        landmarks.positions.forEach(function (pt) { return ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth); });
     });
 }
 exports.drawLandmarks = drawLandmarks;

build/commonjs/dom/drawLandmarks.js.map

-{"version":3,"file":"drawLandmarks.js","sourceRoot":"","sources":["../../../src/dom/drawLandmarks.ts"],"names":[],"mappings":";;AAAA,2EAAuG;AAGvG,8DAA6D;AAC7D,6CAA4C;AAG5C,uBACE,SAAqC,EACrC,aAA8C,EAC9C,OAA8B;IAE9B,IAAM,MAAM,GAAG,0CAAY,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,mDAAqB,CAAC,OAAO,CAAC,EAC9B,CAAC,OAAO,IAAI,EAAE,CAAC,CAChB,CAAA;IAEO,IAAA,0EAAS,CAAyD;IAE1E,IAAM,GAAG,GAAG,iDAAmB,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,iCAAe,EAAE;YACrD,GAAG,CAAC,WAAW,GAAG,KAAK,CAAA;YACvB,GAAG,CAAC,SAAS,GAAG,SAAS,CAAA;YACzB,yBAAW,CAAC,GAAG,EAAE,SAAS,CAAC,aAAa,EAAE,CAAC,CAAA;YAC3C,yBAAW,CAAC,GAAG,EAAE,SAAS,CAAC,cAAc,EAAE,CAAC,CAAA;YAC5C,yBAAW,CAAC,GAAG,EAAE,SAAS,CAAC,eAAe,EAAE,CAAC,CAAA;YAC7C,yBAAW,CAAC,GAAG,EAAE,SAAS,CAAC,OAAO,EAAE,CAAC,CAAA;YACrC,yBAAW,CAAC,GAAG,EAAE,SAAS,CAAC,UAAU,EAAE,EAAE,IAAI,CAAC,CAAA;YAC9C,yBAAW,CAAC,GAAG,EAAE,SAAS,CAAC,WAAW,EAAE,EAAE,IAAI,CAAC,CAAA;YAC/C,yBAAW,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;AAzCD,sCAyCC"}
\ No newline at end of file
+{"version":3,"file":"drawLandmarks.js","sourceRoot":"","sources":["../../../src/dom/drawLandmarks.ts"],"names":[],"mappings":";;AAAA,2EAAuG;AAGvG,8DAA6D;AAC7D,6CAA4C;AAG5C,uBACE,SAAqC,EACrC,aAA8C,EAC9C,OAA8B;IAE9B,IAAM,MAAM,GAAG,0CAAY,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,mDAAqB,CAAC,OAAO,CAAC,EAC9B,CAAC,OAAO,IAAI,EAAE,CAAC,CAChB,CAAA;IAEO,IAAA,0EAAS,CAAyD;IAE1E,IAAM,GAAG,GAAG,iDAAmB,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,iCAAe,EAAE;YACrD,GAAG,CAAC,WAAW,GAAG,KAAK,CAAA;YACvB,GAAG,CAAC,SAAS,GAAG,SAAS,CAAA;YACzB,yBAAW,CAAC,GAAG,EAAE,SAAS,CAAC,aAAa,EAAE,CAAC,CAAA;YAC3C,yBAAW,CAAC,GAAG,EAAE,SAAS,CAAC,cAAc,EAAE,CAAC,CAAA;YAC5C,yBAAW,CAAC,GAAG,EAAE,SAAS,CAAC,eAAe,EAAE,CAAC,CAAA;YAC7C,yBAAW,CAAC,GAAG,EAAE,SAAS,CAAC,OAAO,EAAE,CAAC,CAAA;YACrC,yBAAW,CAAC,GAAG,EAAE,SAAS,CAAC,UAAU,EAAE,EAAE,IAAI,CAAC,CAAA;YAC9C,yBAAW,CAAC,GAAG,EAAE,SAAS,CAAC,WAAW,EAAE,EAAE,IAAI,CAAC,CAAA;YAC/C,yBAAW,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,SAAS,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;IACzG,CAAC,CAAC,CAAA;AACJ,CAAC;AAzCD,sCAyCC"}
\ No newline at end of file

build/commonjs/dom/extractFaceTensors.js

@@ -23,7 +23,7 @@ function extractFaceTensors(imageTensor, detections) {
             return [2 /*return*/, tf.tidy(function () {
                     var _a = imageTensor.shape.slice(tfjs_image_recognition_base_1.isTensor4D(imageTensor) ? 1 : 0), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];
                     var boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
-                        ? det.forSize(imgWidth, imgHeight).getBox()
+                        ? det.forSize(imgWidth, imgHeight).box
                         : det; })
                         .map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); });
                     var faceTensors = boxes.map(function (_a) {

build/commonjs/dom/extractFaceTensors.js.map

-{"version":3,"file":"extractFaceTensors.js","sourceRoot":"","sources":["../../../src/dom/extractFaceTensors.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAA+D;AAE/D,0DAAyD;AAEzD;;;;;;;;;GASG;AACH,4BACE,WAAsC,EACtC,UAAuC;;;YAGvC,IAAI,wCAAU,CAAC,WAAW,CAAC,IAAI,WAAW,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,CAAC,EAAE;gBACvD,MAAM,IAAI,KAAK,CAAC,kDAAkD,CAAC,CAAA;aACpE;YAED,sBAAO,EAAE,CAAC,IAAI,CAAC;oBACP,IAAA,2FAA6F,EAA5F,iBAAS,EAAE,gBAAQ,EAAE,mBAAW,CAA4D;oBAEnG,IAAM,KAAK,GAAG,UAAU,CAAC,GAAG,CAC1B,UAAA,GAAG,IAAI,OAAA,GAAG,YAAY,6BAAa;wBACjC,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,QAAQ,EAAE,SAAS,CAAC,CAAC,MAAM,EAAE;wBAC3C,CAAC,CAAC,GAAG,EAFA,CAEA,CACR;yBACE,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,CAAC,kBAAkB,CAAC,QAAQ,EAAE,SAAS,CAAC,EAA3C,CAA2C,CAAC,CAAA;oBAE1D,IAAM,WAAW,GAAG,KAAK,CAAC,GAAG,CAAC,UAAC,EAAuB;4BAArB,QAAC,EAAE,QAAC,EAAE,gBAAK,EAAE,kBAAM;wBAClD,OAAA,EAAE,CAAC,OAAO,CAAC,WAAW,CAAC,IAAI,CAAC,SAAS,EAAE,QAAQ,EAAE,WAAW,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,MAAM,EAAE,KAAK,EAAE,WAAW,CAAC,CAAC;oBAAvG,CAAuG,CACxG,CAAA;oBAED,OAAO,WAAW,CAAA;gBACpB,CAAC,CAAC,EAAA;;;CACH;AAzBD,gDAyBC"}
\ No newline at end of file
+{"version":3,"file":"extractFaceTensors.js","sourceRoot":"","sources":["../../../src/dom/extractFaceTensors.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAA+D;AAE/D,0DAAyD;AAEzD;;;;;;;;;GASG;AACH,4BACE,WAAsC,EACtC,UAAuC;;;YAGvC,IAAI,wCAAU,CAAC,WAAW,CAAC,IAAI,WAAW,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,CAAC,EAAE;gBACvD,MAAM,IAAI,KAAK,CAAC,kDAAkD,CAAC,CAAA;aACpE;YAED,sBAAO,EAAE,CAAC,IAAI,CAAC;oBACP,IAAA,2FAA6F,EAA5F,iBAAS,EAAE,gBAAQ,EAAE,mBAAW,CAA4D;oBAEnG,IAAM,KAAK,GAAG,UAAU,CAAC,GAAG,CAC1B,UAAA,GAAG,IAAI,OAAA,GAAG,YAAY,6BAAa;wBACjC,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,QAAQ,EAAE,SAAS,CAAC,CAAC,GAAG;wBACtC,CAAC,CAAC,GAAG,EAFA,CAEA,CACR;yBACE,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,CAAC,kBAAkB,CAAC,QAAQ,EAAE,SAAS,CAAC,EAA3C,CAA2C,CAAC,CAAA;oBAE1D,IAAM,WAAW,GAAG,KAAK,CAAC,GAAG,CAAC,UAAC,EAAuB;4BAArB,QAAC,EAAE,QAAC,EAAE,gBAAK,EAAE,kBAAM;wBAClD,OAAA,EAAE,CAAC,OAAO,CAAC,WAAW,CAAC,IAAI,CAAC,SAAS,EAAE,QAAQ,EAAE,WAAW,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,MAAM,EAAE,KAAK,EAAE,WAAW,CAAC,CAAC;oBAAvG,CAAuG,CACxG,CAAA;oBAED,OAAO,WAAW,CAAA;gBACpB,CAAC,CAAC,EAAA;;;CACH;AAzBD,gDAyBC"}
\ No newline at end of file

build/commonjs/dom/extractFaces.js

@@ -38,7 +38,7 @@ function extractFaces(input, detections) {
                 case 5:
                     ctx = tfjs_image_recognition_base_1.getContext2dOrThrow(canvas);
                     boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
-                        ? det.forSize(canvas.width, canvas.height).getBox().floor()
+                        ? det.forSize(canvas.width, canvas.height).box.floor()
                         : det; })
                         .map(function (box) { return box.clipAtImageBorders(canvas.width, canvas.height); });
                     return [2 /*return*/, boxes.map(function (_a) {

build/commonjs/dom/extractFaces.js.map

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+{"version":3,"file":"extractFaces.js","sourceRoot":"","sources":["../../../src/dom/extractFaces.ts"],"names":[],"mappings":";;;AAAA,2EAOqC;AAErC,0DAAyD;AAEzD;;;;;;GAMG;AACH,sBACE,KAAgB,EAChB,UAAuC;;;;;;oBAGnC,MAAM,GAAG,KAA0B,CAAA;yBAEnC,CAAC,CAAC,KAAK,YAAY,iBAAiB,CAAC,EAArC,wBAAqC;oBACtB,qBAAM,wCAAU,CAAC,KAAK,CAAC,EAAA;;oBAAlC,QAAQ,GAAG,SAAuB;oBAExC,IAAI,QAAQ,CAAC,SAAS,GAAG,CAAC,EAAE;wBAC1B,MAAM,IAAI,KAAK,CAAC,4CAA4C,CAAC,CAAA;qBAC9D;oBAEK,cAAc,GAAG,QAAQ,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAA;yBAClC,CAAA,cAAc,YAAY,iBAAiB,CAAA,EAA3C,wBAA2C;oBAChD,KAAA,cAAc,CAAA;;wBACd,qBAAM,iDAAmB,CAAC,cAAc,CAAC,EAAA;;oBAAzC,KAAA,SAAyC,CAAA;;;oBAF7C,MAAM,KAEuC,CAAA;;;oBAGzC,GAAG,GAAG,iDAAmB,CAAC,MAAM,CAAC,CAAA;oBACjC,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,GAAG,CAAC,KAAK,EAAE;wBACtD,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,0CAAY,CAAC,EAAE,KAAK,OAAA,EAAE,MAAM,QAAA,EAAE,CAAC,CAAA;4BAC/C,iDAAmB,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;AAlCD,oCAkCC"}
\ No newline at end of file

build/commonjs/faceLandmarkNet/FaceLandmark68Net.js

@@ -25,12 +25,12 @@ function denseBlock(x, denseBlockParams, isFirstLayer) {
 var FaceLandmark68Net = /** @class */ (function (_super) {
     tslib_1.__extends(FaceLandmark68Net, _super);
     function FaceLandmark68Net() {
-        return _super.call(this, 'FaceLandmark68LargeNet') || this;
+        return _super.call(this, 'FaceLandmark68Net') || this;
     }
     FaceLandmark68Net.prototype.runNet = function (input) {
         var params = this.params;
         if (!params) {
-            throw new Error('FaceLandmark68LargeNet - load model before inference');
+            throw new Error('FaceLandmark68Net - load model before inference');
         }
         return tf.tidy(function () {
             var batchTensor = input.toBatchTensor(112, true);

build/commonjs/faceLandmarkNet/FaceLandmark68Net.js.map

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\ No newline at end of file
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\ No newline at end of file

build/commonjs/faceLandmarkNet/FaceLandmark68NetBase.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { NetInput, NeuralNetwork, TNetInput, Dimensions } from 'tfjs-image-recognition-base';
+import { IDimensions, NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
 import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
 export declare class FaceLandmark68NetBase<NetParams> extends NeuralNetwork<NetParams> {
     private __name;
     constructor(_name: string);
     runNet(_: NetInput): tf.Tensor2D;
-    postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: Dimensions[]): tf.Tensor2D;
+    postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D;
     forwardInput(input: NetInput): tf.Tensor2D;
     forward(input: TNetInput): Promise<tf.Tensor2D>;
     detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;

build/commonjs/faceLandmarkNet/FaceLandmark68NetBase.js.map

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\ No newline at end of file
+{"version":3,"file":"FaceLandmark68NetBase.js","sourceRoot":"","sources":["../../../src/faceLandmarkNet/FaceLandmark68NetBase.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAAyH;AAEzH,8DAA6D;AAE7D;IAAsD,iDAAwB;IAK5E,+BAAY,KAAa;QAAzB,YACE,kBAAM,KAAK,CAAC,SAEb;QADC,KAAI,CAAC,MAAM,GAAG,KAAK,CAAA;;IACrB,CAAC;IAEM,sCAAM,GAAb,UAAc,CAAW;QACvB,MAAM,IAAI,KAAK,CAAI,IAAI,CAAC,MAAM,8BAA2B,CAAC,CAAA;IAC5D,CAAC;IAEM,2CAAW,GAAlB,UAAmB,MAAmB,EAAE,SAAiB,EAAE,kBAAiC;QAE1F,IAAM,eAAe,GAAG,kBAAkB,CAAC,GAAG,CAAC,UAAC,EAAiB;gBAAf,gBAAK,EAAE,kBAAM;YAC7D,IAAM,KAAK,GAAG,SAAS,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,EAAE,KAAK,CAAC,CAAA;YACjD,OAAO;gBACL,KAAK,EAAE,KAAK,GAAG,KAAK;gBACpB,MAAM,EAAE,MAAM,GAAG,KAAK;aACvB,CAAA;QACH,CAAC,CAAC,CAAA;QAEF,IAAM,SAAS,GAAG,eAAe,CAAC,MAAM,CAAA;QAExC,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,uBAAuB,GAAG,UAAC,KAAa,EAAE,KAAa;gBAC3D,OAAA,EAAE,CAAC,KAAK,CAAC;oBACP,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,CAAC;oBACpB,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,CAAC;iBACrB,EAAE,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,IAAI,EAAE;YAHzB,CAGyB,CAAA;YAE3B,IAAM,UAAU,GAAG,UAAC,QAAgB,EAAE,IAAuC;gBACrE,IAAA,8BAA6C,EAA3C,gBAAK,EAAE,kBAAM,CAA8B;gBACnD,OAAO,IAAI,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,KAAK,GAAG,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;YAC/D,CAAC,CAAA;YACD,IAAM,WAAW,GAAG,UAAC,QAAgB,IAAK,OAAA,UAAU,CAAC,QAAQ,EAAE,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,GAAG,CAAC,EAAL,CAAK,CAAC,EAArC,CAAqC,CAAA;YAC/E,IAAM,WAAW,GAAG,UAAC,QAAgB,IAAK,OAAA,UAAU,CAAC,QAAQ,EAAE,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,GAAG,CAAC,EAAL,CAAK,CAAC,EAArC,CAAqC,CAAA;YAE/E,IAAM,eAAe,GAAG,MAAM;iBAC3B,GAAG,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,SAAS,EAAE,GAAG,CAAC,EAAE,SAAS,CAAC,CAAC;iBACzC,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,SAAS,CAAC,EAAE,UAAC,CAAC,EAAE,QAAQ;gBACrD,OAAA,uBAAuB,CACrB,WAAW,CAAC,QAAQ,CAAC,EACrB,WAAW,CAAC,QAAQ,CAAC,CACtB;YAHD,CAGC,CACF,CAAC,CAAC;iBACF,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,SAAS,CAAC,EAAE,UAAC,CAAC,EAAE,QAAQ;gBACrD,OAAA,uBAAuB,CACrB,eAAe,CAAC,QAAQ,CAAC,CAAC,KAAK,EAC/B,eAAe,CAAC,QAAQ,CAAC,CAAC,MAAM,CACjC;YAHD,CAGC,CACF,CAAC,CAAC,CAAA;YAEL,OAAO,eAA8B,CAAA;QACvC,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,4CAAY,GAAnB,UAAoB,KAAe;QAAnC,iBASC;QARC,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,GAAG,GAAG,KAAI,CAAC,MAAM,CAAC,KAAK,CAAC,CAAA;YAC9B,OAAO,KAAI,CAAC,WAAW,CACrB,GAAG,EACH,KAAK,CAAC,SAAmB,EACzB,KAAK,CAAC,eAAe,CAAC,GAAG,CAAC,UAAC,EAAe;oBAAd,cAAM,EAAE,aAAK;gBAAM,OAAA,CAAC,EAAE,MAAM,QAAA,EAAE,KAAK,OAAA,EAAE,CAAC;YAAnB,CAAmB,CAAC,CACpE,CAAA;QACH,CAAC,CAAC,CAAA;IACJ,CAAC;IAEY,uCAAO,GAApB,UAAqB,KAAgB;;;;;;wBAC5B,KAAA,IAAI,CAAC,YAAY,CAAA;wBAAC,qBAAM,wCAAU,CAAC,KAAK,CAAC,EAAA;4BAAhD,sBAAO,SAAA,IAAI,GAAc,SAAuB,EAAC,EAAA;;;;KAClD;IAEY,+CAAe,GAA5B,UAA6B,KAAgB;;;;;;4BAC1B,qBAAM,wCAAU,CAAC,KAAK,CAAC,EAAA;;wBAAlC,QAAQ,GAAG,SAAuB;wBAClC,eAAe,GAAG,EAAE,CAAC,IAAI,CAC7B,cAAM,OAAA,EAAE,CAAC,OAAO,CAAC,KAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,EAAvC,CAAuC,CAC9C,CAAA;wBAEyB,qBAAM,OAAO,CAAC,GAAG,CAAC,eAAe,CAAC,GAAG,CAC7D,UAAO,cAAc,EAAE,QAAQ;;;;;4CACN,KAAA,CAAA,KAAA,KAAK,CAAA,CAAC,IAAI,CAAA;4CAAC,qBAAM,cAAc,CAAC,IAAI,EAAE,EAAA;;4CAAvD,cAAc,GAAG,cAAW,SAA2B,EAAC;4CACxD,OAAO,GAAG,cAAc,CAAC,MAAM,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,oCAAM,CAAC,CAAC,CAAC,EAAT,CAAS,CAAC,CAAA;4CACpD,OAAO,GAAG,cAAc,CAAC,MAAM,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,oCAAM,CAAC,CAAC,CAAC,EAAV,CAAU,CAAC,CAAA;4CAE3D,sBAAO,IAAI,iCAAe,CACxB,KAAK,CAAC,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,IAAI,mCAAK,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,EAAjC,CAAiC,CAAC,EAClE;oDACE,MAAM,EAAE,QAAQ,CAAC,cAAc,CAAC,QAAQ,CAAC;oDACzC,KAAK,EAAG,QAAQ,CAAC,aAAa,CAAC,QAAQ,CAAC;iDACzC,CACF,EAAA;;;iCACF,CACF,CAAC,EAAA;;wBAdI,iBAAiB,GAAG,SAcxB;wBAEF,eAAe,CAAC,OAAO,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,OAAO,EAAE,EAAX,CAAW,CAAC,CAAA;wBAEzC,sBAAO,QAAQ,CAAC,YAAY;gCAC1B,CAAC,CAAC,iBAAiB;gCACnB,CAAC,CAAC,iBAAiB,CAAC,CAAC,CAAC,EAAA;;;;KACzB;IACH,4BAAC;AAAD,CAAC,AAtGD,CAAsD,2CAAa,GAsGlE;AAtGY,sDAAqB"}
\ No newline at end of file

build/commonjs/faceLandmarkNet/index.d.ts

@@ -4,4 +4,3 @@ export * from './FaceLandmark68TinyNet';
 export declare class FaceLandmarkNet extends FaceLandmark68Net {
 }
 export declare function createFaceLandmarkNet(weights: Float32Array): FaceLandmarkNet;
-export declare function faceLandmarkNet(weights: Float32Array): FaceLandmarkNet;

build/commonjs/faceLandmarkNet/index.js

@@ -18,9 +18,4 @@ function createFaceLandmarkNet(weights) {
     return net;
 }
 exports.createFaceLandmarkNet = createFaceLandmarkNet;
-function faceLandmarkNet(weights) {
-    console.warn('faceLandmarkNet(weights: Float32Array) will be deprecated in future, use createFaceLandmarkNet instead');
-    return createFaceLandmarkNet(weights);
-}
-exports.faceLandmarkNet = faceLandmarkNet;
 //# sourceMappingURL=index.js.map
\ No newline at end of file

build/commonjs/faceLandmarkNet/index.js.map

-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/faceLandmarkNet/index.ts"],"names":[],"mappings":";;;AAAA,yDAAwD;AAExD,8DAAoC;AACpC,kEAAwC;AAExC;IAAqC,2CAAiB;IAAtD;;IAAwD,CAAC;IAAD,sBAAC;AAAD,CAAC,AAAzD,CAAqC,qCAAiB,GAAG;AAA5C,0CAAe;AAE5B,+BAAsC,OAAqB;IACzD,IAAM,GAAG,GAAG,IAAI,eAAe,EAAE,CAAA;IACjC,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAJD,sDAIC;AAED,yBAAgC,OAAqB;IACnD,OAAO,CAAC,IAAI,CAAC,wGAAwG,CAAC,CAAA;IACtH,OAAO,qBAAqB,CAAC,OAAO,CAAC,CAAA;AACvC,CAAC;AAHD,0CAGC"}
\ No newline at end of file
+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/faceLandmarkNet/index.ts"],"names":[],"mappings":";;;AAAA,yDAAwD;AAExD,8DAAoC;AACpC,kEAAwC;AAExC;IAAqC,2CAAiB;IAAtD;;IAAwD,CAAC;IAAD,sBAAC;AAAD,CAAC,AAAzD,CAAqC,qCAAiB,GAAG;AAA5C,0CAAe;AAE5B,+BAAsC,OAAqB;IACzD,IAAM,GAAG,GAAG,IAAI,eAAe,EAAE,CAAA;IACjC,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAJD,sDAIC"}
\ No newline at end of file

build/commonjs/faceRecognitionNet/index.d.ts

 import { FaceRecognitionNet } from './FaceRecognitionNet';
 export * from './FaceRecognitionNet';
 export declare function createFaceRecognitionNet(weights: Float32Array): FaceRecognitionNet;
-export declare function faceRecognitionNet(weights: Float32Array): FaceRecognitionNet;

build/commonjs/faceRecognitionNet/index.js

@@ -9,9 +9,4 @@ function createFaceRecognitionNet(weights) {
     return net;
 }
 exports.createFaceRecognitionNet = createFaceRecognitionNet;
-function faceRecognitionNet(weights) {
-    console.warn('faceRecognitionNet(weights: Float32Array) will be deprecated in future, use createFaceRecognitionNet instead');
-    return createFaceRecognitionNet(weights);
-}
-exports.faceRecognitionNet = faceRecognitionNet;
 //# sourceMappingURL=index.js.map
\ No newline at end of file

build/commonjs/faceRecognitionNet/index.js.map

-{"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"}
\ No newline at end of file
+{"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"}
\ No newline at end of file

build/commonjs/globalApi/ComposableTask.d.ts

+export declare class ComposableTask<T> {
+    then(onfulfilled: (value: T) => T | PromiseLike<T>): Promise<T>;
+    run(): Promise<T>;
+}

build/commonjs/globalApi/ComposableTask.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tslib_1 = require("tslib");
+var ComposableTask = /** @class */ (function () {
+    function ComposableTask() {
+    }
+    ComposableTask.prototype.then = function (onfulfilled) {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var _a;
+            return tslib_1.__generator(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        _a = onfulfilled;
+                        return [4 /*yield*/, this.run()];
+                    case 1: return [2 /*return*/, _a.apply(void 0, [_b.sent()])];
+                }
+            });
+        });
+    };
+    ComposableTask.prototype.run = function () {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            return tslib_1.__generator(this, function (_a) {
+                throw new Error('ComposableTask - run is not implemented');
+            });
+        });
+    };
+    return ComposableTask;
+}());
+exports.ComposableTask = ComposableTask;
+//# sourceMappingURL=ComposableTask.js.map
\ No newline at end of file

build/commonjs/globalApi/ComposableTask.js.map

+{"version":3,"file":"ComposableTask.js","sourceRoot":"","sources":["../../../src/globalApi/ComposableTask.ts"],"names":[],"mappings":";;;AAAA;IAAA;IAWA,CAAC;IATc,6BAAI,GAAjB,UACE,WAA6C;;;;;;wBAEtC,KAAA,WAAW,CAAA;wBAAC,qBAAM,IAAI,CAAC,GAAG,EAAE,EAAA;4BAAnC,sBAAO,kBAAY,SAAgB,EAAC,EAAA;;;;KACrC;IAEY,4BAAG,GAAhB;;;gBACE,MAAM,IAAI,KAAK,CAAC,yCAAyC,CAAC,CAAA;;;KAC3D;IACH,qBAAC;AAAD,CAAC,AAXD,IAWC;AAXY,wCAAc"}
\ No newline at end of file

build/commonjs/globalApi/ComputeFaceDescriptorsTasks.d.ts

+import { TNetInput } from 'tfjs-image-recognition-base';
+import { FaceDetectionWithLandmarks } from '../classes/FaceDetectionWithLandmarks';
+import { FullFaceDescription } from '../classes/FullFaceDescription';
+import { ComposableTask } from './ComposableTask';
+export declare class ComputeFaceDescriptorsTaskBase<TReturn, DetectFaceLandmarksReturnType> extends ComposableTask<TReturn> {
+    protected detectFaceLandmarksTask: ComposableTask<DetectFaceLandmarksReturnType> | Promise<DetectFaceLandmarksReturnType>;
+    protected input: TNetInput;
+    constructor(detectFaceLandmarksTask: ComposableTask<DetectFaceLandmarksReturnType> | Promise<DetectFaceLandmarksReturnType>, input: TNetInput);
+}
+export declare class ComputeAllFaceDescriptorsTask extends ComputeFaceDescriptorsTaskBase<FullFaceDescription[], FaceDetectionWithLandmarks[]> {
+    run(): Promise<FullFaceDescription[]>;
+}
+export declare class ComputeSingleFaceDescriptorTask extends ComputeFaceDescriptorsTaskBase<FullFaceDescription | undefined, FaceDetectionWithLandmarks | undefined> {
+    run(): Promise<FullFaceDescription | undefined>;
+}

build/commonjs/globalApi/ComputeFaceDescriptorsTasks.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tslib_1 = require("tslib");
+var FullFaceDescription_1 = require("../classes/FullFaceDescription");
+var dom_1 = require("../dom");
+var ComposableTask_1 = require("./ComposableTask");
+var nets_1 = require("./nets");
+var ComputeFaceDescriptorsTaskBase = /** @class */ (function (_super) {
+    tslib_1.__extends(ComputeFaceDescriptorsTaskBase, _super);
+    function ComputeFaceDescriptorsTaskBase(detectFaceLandmarksTask, input) {
+        var _this = _super.call(this) || this;
+        _this.detectFaceLandmarksTask = detectFaceLandmarksTask;
+        _this.input = input;
+        return _this;
+    }
+    return ComputeFaceDescriptorsTaskBase;
+}(ComposableTask_1.ComposableTask));
+exports.ComputeFaceDescriptorsTaskBase = ComputeFaceDescriptorsTaskBase;
+var ComputeAllFaceDescriptorsTask = /** @class */ (function (_super) {
+    tslib_1.__extends(ComputeAllFaceDescriptorsTask, _super);
+    function ComputeAllFaceDescriptorsTask() {
+        return _super !== null && _super.apply(this, arguments) || this;
+    }
+    ComputeAllFaceDescriptorsTask.prototype.run = function () {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var _this = this;
+            var facesWithLandmarks, alignedFaceCanvases;
+            return tslib_1.__generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4 /*yield*/, this.detectFaceLandmarksTask];
+                    case 1:
+                        facesWithLandmarks = _a.sent();
+                        return [4 /*yield*/, dom_1.extractFaces(this.input, facesWithLandmarks.map(function (_a) {
+                                var landmarks = _a.landmarks;
+                                return landmarks.align();
+                            }))];
+                    case 2:
+                        alignedFaceCanvases = _a.sent();
+                        return [4 /*yield*/, Promise.all(facesWithLandmarks.map(function (_a, i) {
+                                var detection = _a.detection, landmarks = _a.landmarks;
+                                return tslib_1.__awaiter(_this, void 0, void 0, function () {
+                                    var descriptor;
+                                    return tslib_1.__generator(this, function (_b) {
+                                        switch (_b.label) {
+                                            case 0: return [4 /*yield*/, nets_1.nets.faceRecognitionNet.computeFaceDescriptor(alignedFaceCanvases[i])];
+                                            case 1:
+                                                descriptor = _b.sent();
+                                                return [2 /*return*/, new FullFaceDescription_1.FullFaceDescription(detection, landmarks, descriptor)];
+                                        }
+                                    });
+                                });
+                            }))];
+                    case 3: return [2 /*return*/, _a.sent()];
+                }
+            });
+        });
+    };
+    return ComputeAllFaceDescriptorsTask;
+}(ComputeFaceDescriptorsTaskBase));
+exports.ComputeAllFaceDescriptorsTask = ComputeAllFaceDescriptorsTask;
+var ComputeSingleFaceDescriptorTask = /** @class */ (function (_super) {
+    tslib_1.__extends(ComputeSingleFaceDescriptorTask, _super);
+    function ComputeSingleFaceDescriptorTask() {
+        return _super !== null && _super.apply(this, arguments) || this;
+    }
+    ComputeSingleFaceDescriptorTask.prototype.run = function () {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var detectionWithLandmarks, detection, landmarks, alignedRect, alignedFaceCanvas, descriptor;
+            return tslib_1.__generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4 /*yield*/, this.detectFaceLandmarksTask];
+                    case 1:
+                        detectionWithLandmarks = _a.sent();
+                        if (!detectionWithLandmarks) {
+                            return [2 /*return*/];
+                        }
+                        detection = detectionWithLandmarks.detection, landmarks = detectionWithLandmarks.landmarks, alignedRect = detectionWithLandmarks.alignedRect;
+                        return [4 /*yield*/, dom_1.extractFaces(this.input, [alignedRect])];
+                    case 2:
+                        alignedFaceCanvas = (_a.sent())[0];
+                        return [4 /*yield*/, nets_1.nets.faceRecognitionNet.computeFaceDescriptor(alignedFaceCanvas)];
+                    case 3:
+                        descriptor = _a.sent();
+                        return [2 /*return*/, new FullFaceDescription_1.FullFaceDescription(detection, landmarks, descriptor)];
+                }
+            });
+        });
+    };
+    return ComputeSingleFaceDescriptorTask;
+}(ComputeFaceDescriptorsTaskBase));
+exports.ComputeSingleFaceDescriptorTask = ComputeSingleFaceDescriptorTask;
+//# sourceMappingURL=ComputeFaceDescriptorsTasks.js.map
\ No newline at end of file

build/commonjs/globalApi/ComputeFaceDescriptorsTasks.js.map

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\ No newline at end of file

build/commonjs/globalApi/DetectFaceLandmarksTasks.d.ts

+import { TNetInput } from 'tfjs-image-recognition-base';
+import { FaceDetection } from '../classes/FaceDetection';
+import { FaceDetectionWithLandmarks } from '../classes/FaceDetectionWithLandmarks';
+import { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';
+import { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';
+import { ComposableTask } from './ComposableTask';
+import { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';
+export declare class DetectFaceLandmarksTaskBase<ReturnType, DetectFacesReturnType> extends ComposableTask<ReturnType> {
+    protected detectFacesTask: ComposableTask<DetectFacesReturnType> | Promise<DetectFacesReturnType>;
+    protected input: TNetInput;
+    protected useTinyLandmarkNet: boolean;
+    constructor(detectFacesTask: ComposableTask<DetectFacesReturnType> | Promise<DetectFacesReturnType>, input: TNetInput, useTinyLandmarkNet: boolean);
+    protected readonly landmarkNet: FaceLandmark68Net | FaceLandmark68TinyNet;
+}
+export declare class DetectAllFaceLandmarksTask extends DetectFaceLandmarksTaskBase<FaceDetectionWithLandmarks[], FaceDetection[]> {
+    run(): Promise<FaceDetectionWithLandmarks[]>;
+    withFaceDescriptors(): ComputeAllFaceDescriptorsTask;
+}
+export declare class DetectSingleFaceLandmarksTask extends DetectFaceLandmarksTaskBase<FaceDetectionWithLandmarks | undefined, FaceDetection | undefined> {
+    run(): Promise<FaceDetectionWithLandmarks | undefined>;
+    withFaceDescriptor(): ComputeSingleFaceDescriptorTask;
+}

build/commonjs/globalApi/DetectFaceLandmarksTasks.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tslib_1 = require("tslib");
+var FaceDetectionWithLandmarks_1 = require("../classes/FaceDetectionWithLandmarks");
+var dom_1 = require("../dom");
+var ComposableTask_1 = require("./ComposableTask");
+var ComputeFaceDescriptorsTasks_1 = require("./ComputeFaceDescriptorsTasks");
+var nets_1 = require("./nets");
+var DetectFaceLandmarksTaskBase = /** @class */ (function (_super) {
+    tslib_1.__extends(DetectFaceLandmarksTaskBase, _super);
+    function DetectFaceLandmarksTaskBase(detectFacesTask, input, useTinyLandmarkNet) {
+        var _this = _super.call(this) || this;
+        _this.detectFacesTask = detectFacesTask;
+        _this.input = input;
+        _this.useTinyLandmarkNet = useTinyLandmarkNet;
+        return _this;
+    }
+    Object.defineProperty(DetectFaceLandmarksTaskBase.prototype, "landmarkNet", {
+        get: function () {
+            return this.useTinyLandmarkNet
+                ? nets_1.nets.faceLandmark68TinyNet
+                : nets_1.nets.faceLandmark68Net;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    return DetectFaceLandmarksTaskBase;
+}(ComposableTask_1.ComposableTask));
+exports.DetectFaceLandmarksTaskBase = DetectFaceLandmarksTaskBase;
+var DetectAllFaceLandmarksTask = /** @class */ (function (_super) {
+    tslib_1.__extends(DetectAllFaceLandmarksTask, _super);
+    function DetectAllFaceLandmarksTask() {
+        return _super !== null && _super.apply(this, arguments) || this;
+    }
+    DetectAllFaceLandmarksTask.prototype.run = function () {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var _this = this;
+            var detections, faceCanvases, faceLandmarksByFace;
+            return tslib_1.__generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4 /*yield*/, this.detectFacesTask];
+                    case 1:
+                        detections = _a.sent();
+                        return [4 /*yield*/, dom_1.extractFaces(this.input, detections)];
+                    case 2:
+                        faceCanvases = _a.sent();
+                        return [4 /*yield*/, Promise.all(faceCanvases.map(function (canvas) { return _this.landmarkNet.detectLandmarks(canvas); }))];
+                    case 3:
+                        faceLandmarksByFace = _a.sent();
+                        return [2 /*return*/, detections.map(function (detection, i) {
+                                return new FaceDetectionWithLandmarks_1.FaceDetectionWithLandmarks(detection, faceLandmarksByFace[i]);
+                            })];
+                }
+            });
+        });
+    };
+    DetectAllFaceLandmarksTask.prototype.withFaceDescriptors = function () {
+        return new ComputeFaceDescriptorsTasks_1.ComputeAllFaceDescriptorsTask(this, this.input);
+    };
+    return DetectAllFaceLandmarksTask;
+}(DetectFaceLandmarksTaskBase));
+exports.DetectAllFaceLandmarksTask = DetectAllFaceLandmarksTask;
+var DetectSingleFaceLandmarksTask = /** @class */ (function (_super) {
+    tslib_1.__extends(DetectSingleFaceLandmarksTask, _super);
+    function DetectSingleFaceLandmarksTask() {
+        return _super !== null && _super.apply(this, arguments) || this;
+    }
+    DetectSingleFaceLandmarksTask.prototype.run = function () {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var detection, faceCanvas, _a, _b;
+            return tslib_1.__generator(this, function (_c) {
+                switch (_c.label) {
+                    case 0: return [4 /*yield*/, this.detectFacesTask];
+                    case 1:
+                        detection = _c.sent();
+                        if (!detection) {
+                            return [2 /*return*/];
+                        }
+                        return [4 /*yield*/, dom_1.extractFaces(this.input, [detection])];
+                    case 2:
+                        faceCanvas = (_c.sent())[0];
+                        _a = FaceDetectionWithLandmarks_1.FaceDetectionWithLandmarks.bind;
+                        _b = [void 0, detection];
+                        return [4 /*yield*/, this.landmarkNet.detectLandmarks(faceCanvas)];
+                    case 3: return [2 /*return*/, new (_a.apply(FaceDetectionWithLandmarks_1.FaceDetectionWithLandmarks, _b.concat([_c.sent()])))()];
+                }
+            });
+        });
+    };
+    DetectSingleFaceLandmarksTask.prototype.withFaceDescriptor = function () {
+        return new ComputeFaceDescriptorsTasks_1.ComputeSingleFaceDescriptorTask(this, this.input);
+    };
+    return DetectSingleFaceLandmarksTask;
+}(DetectFaceLandmarksTaskBase));
+exports.DetectSingleFaceLandmarksTask = DetectSingleFaceLandmarksTask;
+//# sourceMappingURL=DetectFaceLandmarksTasks.js.map
\ No newline at end of file

build/commonjs/globalApi/DetectFaceLandmarksTasks.js.map

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\ No newline at end of file

build/commonjs/globalApi/DetectFacesTasks.d.ts

+import { TNetInput } from 'tfjs-image-recognition-base';
+import { FaceDetection } from '../classes/FaceDetection';
+import { ComposableTask } from './ComposableTask';
+import { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';
+import { FaceDetectionOptions } from './types';
+export declare function detectSingleFace(input: TNetInput, options?: FaceDetectionOptions): DetectSingleFaceTask;
+export declare function detectAllFaces(input: TNetInput, options?: FaceDetectionOptions): DetectAllFacesTask;
+export declare class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {
+    protected input: TNetInput;
+    protected options: FaceDetectionOptions;
+    constructor(input: TNetInput, options?: FaceDetectionOptions);
+}
+export declare class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {
+    run(): Promise<FaceDetection[]>;
+    withFaceLandmarks(useTinyLandmarkNet?: boolean): DetectAllFaceLandmarksTask;
+}
+export declare class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {
+    run(): Promise<FaceDetection | undefined>;
+    withFaceLandmarks(useTinyLandmarkNet?: boolean): DetectSingleFaceLandmarksTask;
+}

build/commonjs/globalApi/DetectFacesTasks.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tslib_1 = require("tslib");
+var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
+var MtcnnOptions_1 = require("../mtcnn/MtcnnOptions");
+var SsdMobilenetv1Options_1 = require("../ssdMobilenetv1/SsdMobilenetv1Options");
+var TinyFaceDetectorOptions_1 = require("../tinyFaceDetector/TinyFaceDetectorOptions");
+var ComposableTask_1 = require("./ComposableTask");
+var DetectFaceLandmarksTasks_1 = require("./DetectFaceLandmarksTasks");
+var nets_1 = require("./nets");
+function detectSingleFace(input, options) {
+    if (options === void 0) { options = new SsdMobilenetv1Options_1.SsdMobilenetv1Options(); }
+    return new DetectSingleFaceTask(input, options);
+}
+exports.detectSingleFace = detectSingleFace;
+function detectAllFaces(input, options) {
+    if (options === void 0) { options = new SsdMobilenetv1Options_1.SsdMobilenetv1Options(); }
+    return new DetectAllFacesTask(input, options);
+}
+exports.detectAllFaces = detectAllFaces;
+var DetectFacesTaskBase = /** @class */ (function (_super) {
+    tslib_1.__extends(DetectFacesTaskBase, _super);
+    function DetectFacesTaskBase(input, options) {
+        if (options === void 0) { options = new SsdMobilenetv1Options_1.SsdMobilenetv1Options(); }
+        var _this = _super.call(this) || this;
+        _this.input = input;
+        _this.options = options;
+        return _this;
+    }
+    return DetectFacesTaskBase;
+}(ComposableTask_1.ComposableTask));
+exports.DetectFacesTaskBase = DetectFacesTaskBase;
+var DetectAllFacesTask = /** @class */ (function (_super) {
+    tslib_1.__extends(DetectAllFacesTask, _super);
+    function DetectAllFacesTask() {
+        return _super !== null && _super.apply(this, arguments) || this;
+    }
+    DetectAllFacesTask.prototype.run = function () {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var _a, input, options, faceDetectionFunction;
+            return tslib_1.__generator(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        _a = this, input = _a.input, options = _a.options;
+                        if (!(options instanceof MtcnnOptions_1.MtcnnOptions)) return [3 /*break*/, 2];
+                        return [4 /*yield*/, nets_1.nets.mtcnn.forward(input, options)];
+                    case 1: return [2 /*return*/, (_b.sent())
+                            .map(function (result) { return result.faceDetection; })];
+                    case 2:
+                        faceDetectionFunction = options instanceof TinyFaceDetectorOptions_1.TinyFaceDetectorOptions
+                            ? function (input) { return nets_1.nets.tinyFaceDetector.locateFaces(input, options); }
+                            : (options instanceof SsdMobilenetv1Options_1.SsdMobilenetv1Options
+                                ? function (input) { return nets_1.nets.ssdMobilenetv1.locateFaces(input, options); }
+                                : (options instanceof tfjs_tiny_yolov2_1.TinyYolov2Options
+                                    ? function (input) { return nets_1.nets.tinyYolov2.locateFaces(input, options); }
+                                    : null));
+                        if (!faceDetectionFunction) {
+                            throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options');
+                        }
+                        return [2 /*return*/, faceDetectionFunction(input)];
+                }
+            });
+        });
+    };
+    DetectAllFacesTask.prototype.withFaceLandmarks = function (useTinyLandmarkNet) {
+        if (useTinyLandmarkNet === void 0) { useTinyLandmarkNet = false; }
+        return new DetectFaceLandmarksTasks_1.DetectAllFaceLandmarksTask(this, this.input, useTinyLandmarkNet);
+    };
+    return DetectAllFacesTask;
+}(DetectFacesTaskBase));
+exports.DetectAllFacesTask = DetectAllFacesTask;
+var DetectSingleFaceTask = /** @class */ (function (_super) {
+    tslib_1.__extends(DetectSingleFaceTask, _super);
+    function DetectSingleFaceTask() {
+        return _super !== null && _super.apply(this, arguments) || this;
+    }
+    DetectSingleFaceTask.prototype.run = function () {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            return tslib_1.__generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4 /*yield*/, new DetectAllFacesTask(this.input, this.options)];
+                    case 1: return [2 /*return*/, (_a.sent())
+                            .sort(function (f1, f2) { return f1.score - f2.score; })[0]];
+                }
+            });
+        });
+    };
+    DetectSingleFaceTask.prototype.withFaceLandmarks = function (useTinyLandmarkNet) {
+        if (useTinyLandmarkNet === void 0) { useTinyLandmarkNet = false; }
+        return new DetectFaceLandmarksTasks_1.DetectSingleFaceLandmarksTask(this, this.input, useTinyLandmarkNet);
+    };
+    return DetectSingleFaceTask;
+}(DetectFacesTaskBase));
+exports.DetectSingleFaceTask = DetectSingleFaceTask;
+//# sourceMappingURL=DetectFacesTasks.js.map
\ No newline at end of file

build/commonjs/globalApi/DetectFacesTasks.js.map

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\ No newline at end of file

build/commonjs/globalApi/FaceMatcher.d.ts

+import { FaceMatch } from '../classes/FaceMatch';
+import { FullFaceDescription } from '../classes/FullFaceDescription';
+import { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';
+export declare class FaceMatcher {
+    private _labeledDescriptors;
+    private _distanceThreshold;
+    constructor(inputs: LabeledFaceDescriptors | FullFaceDescription | Float32Array | Array<LabeledFaceDescriptors | FullFaceDescription | Float32Array>, distanceThreshold?: number);
+    readonly labeledDescriptors: LabeledFaceDescriptors[];
+    readonly distanceThreshold: number;
+    computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number;
+    matchDescriptor(queryDescriptor: Float32Array): FaceMatch;
+    findBestMatch(queryDescriptor: Float32Array): FaceMatch;
+}

build/commonjs/globalApi/FaceMatcher.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var FaceMatch_1 = require("../classes/FaceMatch");
+var FullFaceDescription_1 = require("../classes/FullFaceDescription");
+var LabeledFaceDescriptors_1 = require("../classes/LabeledFaceDescriptors");
+var euclideanDistance_1 = require("../euclideanDistance");
+var FaceMatcher = /** @class */ (function () {
+    function FaceMatcher(inputs, distanceThreshold) {
+        if (distanceThreshold === void 0) { distanceThreshold = 0.6; }
+        this._distanceThreshold = distanceThreshold;
+        var inputArray = Array.isArray(inputs) ? inputs : [inputs];
+        if (!inputArray.length) {
+            throw new Error("FaceRecognizer.constructor - expected atleast one input");
+        }
+        var count = 1;
+        var createUniqueLabel = function () { return "person " + count++; };
+        this._labeledDescriptors = inputArray.map(function (desc) {
+            if (desc instanceof LabeledFaceDescriptors_1.LabeledFaceDescriptors) {
+                return desc;
+            }
+            if (desc instanceof FullFaceDescription_1.FullFaceDescription) {
+                return new LabeledFaceDescriptors_1.LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);
+            }
+            if (desc instanceof Float32Array) {
+                return new LabeledFaceDescriptors_1.LabeledFaceDescriptors(createUniqueLabel(), [desc]);
+            }
+            throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | FullFaceDescription | Float32Array | Array<LabeledFaceDescriptors | FullFaceDescription | Float32Array>");
+        });
+    }
+    Object.defineProperty(FaceMatcher.prototype, "labeledDescriptors", {
+        get: function () { return this._labeledDescriptors; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceMatcher.prototype, "distanceThreshold", {
+        get: function () { return this._distanceThreshold; },
+        enumerable: true,
+        configurable: true
+    });
+    FaceMatcher.prototype.computeMeanDistance = function (queryDescriptor, descriptors) {
+        return descriptors
+            .map(function (d) { return euclideanDistance_1.euclideanDistance(d, queryDescriptor); })
+            .reduce(function (d1, d2) { return d1 + d2; }, 0)
+            / (descriptors.length || 1);
+    };
+    FaceMatcher.prototype.matchDescriptor = function (queryDescriptor) {
+        var _this = this;
+        return this.labeledDescriptors
+            .map(function (_a) {
+            var descriptors = _a.descriptors, label = _a.label;
+            return new FaceMatch_1.FaceMatch(label, _this.computeMeanDistance(queryDescriptor, descriptors));
+        })
+            .reduce(function (best, curr) { return best.distance < curr.distance ? best : curr; });
+    };
+    FaceMatcher.prototype.findBestMatch = function (queryDescriptor) {
+        var bestMatch = this.matchDescriptor(queryDescriptor);
+        return bestMatch.distance < this.distanceThreshold
+            ? bestMatch
+            : new FaceMatch_1.FaceMatch('unknown', bestMatch.distance);
+    };
+    return FaceMatcher;
+}());
+exports.FaceMatcher = FaceMatcher;
+//# sourceMappingURL=FaceMatcher.js.map
\ No newline at end of file

build/commonjs/globalApi/FaceMatcher.js.map

+{"version":3,"file":"FaceMatcher.js","sourceRoot":"","sources":["../../../src/globalApi/FaceMatcher.ts"],"names":[],"mappings":";;AAAA,kDAAiD;AACjD,sEAAqE;AACrE,4EAA2E;AAC3E,0DAAyD;AAEzD;IAKE,qBACE,MAAwI,EACxI,iBAA+B;QAA/B,kCAAA,EAAA,uBAA+B;QAG/B,IAAI,CAAC,kBAAkB,GAAG,iBAAiB,CAAA;QAE3C,IAAM,UAAU,GAAG,KAAK,CAAC,OAAO,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAA;QAE5D,IAAI,CAAC,UAAU,CAAC,MAAM,EAAE;YACtB,MAAM,IAAI,KAAK,CAAC,yDAAyD,CAAC,CAAA;SAC3E;QAED,IAAI,KAAK,GAAG,CAAC,CAAA;QACb,IAAM,iBAAiB,GAAG,cAAM,OAAA,YAAU,KAAK,EAAI,EAAnB,CAAmB,CAAA;QAEnD,IAAI,CAAC,mBAAmB,GAAG,UAAU,CAAC,GAAG,CAAC,UAAC,IAAI;YAC7C,IAAI,IAAI,YAAY,+CAAsB,EAAE;gBAC1C,OAAO,IAAI,CAAA;aACZ;YAED,IAAI,IAAI,YAAY,yCAAmB,EAAE;gBACvC,OAAO,IAAI,+CAAsB,CAAC,iBAAiB,EAAE,EAAE,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC,CAAA;aAC1E;YAED,IAAI,IAAI,YAAY,YAAY,EAAE;gBAChC,OAAO,IAAI,+CAAsB,CAAC,iBAAiB,EAAE,EAAE,CAAC,IAAI,CAAC,CAAC,CAAA;aAC/D;YAED,MAAM,IAAI,KAAK,CAAC,6LAA6L,CAAC,CAAA;QAChN,CAAC,CAAC,CAAA;IACJ,CAAC;IAED,sBAAW,2CAAkB;aAA7B,cAA4D,OAAO,IAAI,CAAC,mBAAmB,CAAA,CAAC,CAAC;;;OAAA;IAC7F,sBAAW,0CAAiB;aAA5B,cAAyC,OAAO,IAAI,CAAC,kBAAkB,CAAA,CAAC,CAAC;;;OAAA;IAElE,yCAAmB,GAA1B,UAA2B,eAA6B,EAAE,WAA2B;QACnF,OAAO,WAAW;aACf,GAAG,CAAC,UAAA,CAAC,IAAI,OAAA,qCAAiB,CAAC,CAAC,EAAE,eAAe,CAAC,EAArC,CAAqC,CAAC;aAC/C,MAAM,CAAC,UAAC,EAAE,EAAE,EAAE,IAAK,OAAA,EAAE,GAAG,EAAE,EAAP,CAAO,EAAE,CAAC,CAAC;cAC3B,CAAC,WAAW,CAAC,MAAM,IAAI,CAAC,CAAC,CAAA;IACjC,CAAC;IAEM,qCAAe,GAAtB,UAAuB,eAA6B;QAApD,iBAOC;QANC,OAAO,IAAI,CAAC,kBAAkB;aAC3B,GAAG,CAAC,UAAC,EAAsB;gBAApB,4BAAW,EAAE,gBAAK;YAAO,OAAA,IAAI,qBAAS,CAC1C,KAAK,EACL,KAAI,CAAC,mBAAmB,CAAC,eAAe,EAAE,WAAW,CAAC,CACzD;QAHgC,CAGhC,CAAC;aACD,MAAM,CAAC,UAAC,IAAI,EAAE,IAAI,IAAK,OAAA,IAAI,CAAC,QAAQ,GAAG,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,IAAI,EAA3C,CAA2C,CAAC,CAAA;IACxE,CAAC;IAEM,mCAAa,GAApB,UAAqB,eAA6B;QAChD,IAAM,SAAS,GAAG,IAAI,CAAC,eAAe,CAAC,eAAe,CAAC,CAAA;QACvD,OAAO,SAAS,CAAC,QAAQ,GAAG,IAAI,CAAC,iBAAiB;YAChD,CAAC,CAAC,SAAS;YACX,CAAC,CAAC,IAAI,qBAAS,CAAC,SAAS,EAAE,SAAS,CAAC,QAAQ,CAAC,CAAA;IAClD,CAAC;IAEH,kBAAC;AAAD,CAAC,AAhED,IAgEC;AAhEY,kCAAW"}
\ No newline at end of file

build/commonjs/globalApi/allFaces.d.ts

+import { TNetInput } from 'tfjs-image-recognition-base';
+import { ITinyYolov2Options } from 'tfjs-tiny-yolov2';
+import { FullFaceDescription } from '../classes';
+import { IMtcnnOptions } from '../mtcnn/MtcnnOptions';
+export declare function allFacesSsdMobilenetv1(input: TNetInput, minConfidence?: number): Promise<FullFaceDescription[]>;
+export declare function allFacesTinyYolov2(input: TNetInput, forwardParams?: ITinyYolov2Options): Promise<FullFaceDescription[]>;
+export declare function allFacesMtcnn(input: TNetInput, forwardParams?: IMtcnnOptions): Promise<FullFaceDescription[]>;
+export declare const allFaces: typeof allFacesSsdMobilenetv1;

build/commonjs/globalApi/allFaces.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tslib_1 = require("tslib");
+var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
+var MtcnnOptions_1 = require("../mtcnn/MtcnnOptions");
+var ssdMobilenetv1_1 = require("../ssdMobilenetv1");
+var DetectFacesTasks_1 = require("./DetectFacesTasks");
+// export allFaces API for backward compatibility
+function allFacesSsdMobilenetv1(input, minConfidence) {
+    return tslib_1.__awaiter(this, void 0, void 0, function () {
+        return tslib_1.__generator(this, function (_a) {
+            switch (_a.label) {
+                case 0: return [4 /*yield*/, DetectFacesTasks_1.detectAllFaces(input, new ssdMobilenetv1_1.SsdMobilenetv1Options(minConfidence ? { minConfidence: minConfidence } : {}))
+                        .withFaceLandmarks()
+                        .withFaceDescriptors()];
+                case 1: return [2 /*return*/, _a.sent()];
+            }
+        });
+    });
+}
+exports.allFacesSsdMobilenetv1 = allFacesSsdMobilenetv1;
+function allFacesTinyYolov2(input, forwardParams) {
+    if (forwardParams === void 0) { forwardParams = {}; }
+    return tslib_1.__awaiter(this, void 0, void 0, function () {
+        return tslib_1.__generator(this, function (_a) {
+            switch (_a.label) {
+                case 0: return [4 /*yield*/, DetectFacesTasks_1.detectAllFaces(input, new tfjs_tiny_yolov2_1.TinyYolov2Options(forwardParams))
+                        .withFaceLandmarks()
+                        .withFaceDescriptors()];
+                case 1: return [2 /*return*/, _a.sent()];
+            }
+        });
+    });
+}
+exports.allFacesTinyYolov2 = allFacesTinyYolov2;
+function allFacesMtcnn(input, forwardParams) {
+    if (forwardParams === void 0) { forwardParams = {}; }
+    return tslib_1.__awaiter(this, void 0, void 0, function () {
+        return tslib_1.__generator(this, function (_a) {
+            switch (_a.label) {
+                case 0: return [4 /*yield*/, DetectFacesTasks_1.detectAllFaces(input, new MtcnnOptions_1.MtcnnOptions(forwardParams))
+                        .withFaceLandmarks()
+                        .withFaceDescriptors()];
+                case 1: return [2 /*return*/, _a.sent()];
+            }
+        });
+    });
+}
+exports.allFacesMtcnn = allFacesMtcnn;
+exports.allFaces = allFacesSsdMobilenetv1;
+//# sourceMappingURL=allFaces.js.map
\ No newline at end of file

build/commonjs/globalApi/allFaces.js.map

+{"version":3,"file":"allFaces.js","sourceRoot":"","sources":["../../../src/globalApi/allFaces.ts"],"names":[],"mappings":";;;AACA,qDAAyE;AAGzE,sDAAoE;AACpE,oDAA0D;AAC1D,uDAAoD;AAEpD,iDAAiD;AAEjD,gCACE,KAAgB,EAChB,aAAsB;;;;wBAEf,qBAAM,iCAAc,CAAC,KAAK,EAAE,IAAI,sCAAqB,CAAC,aAAa,CAAC,CAAC,CAAC,EAAE,aAAa,eAAA,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC;yBAClG,iBAAiB,EAAE;yBACnB,mBAAmB,EAAE,EAAA;wBAFxB,sBAAO,SAEiB,EAAA;;;;CACzB;AAPD,wDAOC;AAED,4BACE,KAAgB,EAChB,aAAsC;IAAtC,8BAAA,EAAA,kBAAsC;;;;wBAE/B,qBAAM,iCAAc,CAAC,KAAK,EAAE,IAAI,oCAAiB,CAAC,aAAa,CAAC,CAAC;yBACrE,iBAAiB,EAAE;yBACnB,mBAAmB,EAAE,EAAA;wBAFxB,sBAAO,SAEiB,EAAA;;;;CACzB;AAPD,gDAOC;AAED,uBACE,KAAgB,EAChB,aAAiC;IAAjC,8BAAA,EAAA,kBAAiC;;;;wBAE1B,qBAAM,iCAAc,CAAC,KAAK,EAAE,IAAI,2BAAY,CAAC,aAAa,CAAC,CAAC;yBAChE,iBAAiB,EAAE;yBACnB,mBAAmB,EAAE,EAAA;wBAFxB,sBAAO,SAEiB,EAAA;;;;CACzB;AAPD,sCAOC;AAEY,QAAA,QAAQ,GAAG,sBAAsB,CAAA"}
\ No newline at end of file

build/commonjs/globalApi/index.d.ts

+export * from './allFaces';
+export * from './ComposableTask';
+export * from './ComputeFaceDescriptorsTasks';
+export * from './DetectFacesTasks';
+export * from './DetectFaceLandmarksTasks';
+export * from './FaceMatcher';
+export * from './nets';
+export * from './types';

build/commonjs/globalApi/index.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tslib_1 = require("tslib");
+tslib_1.__exportStar(require("./allFaces"), exports);
+tslib_1.__exportStar(require("./ComposableTask"), exports);
+tslib_1.__exportStar(require("./ComputeFaceDescriptorsTasks"), exports);
+tslib_1.__exportStar(require("./DetectFacesTasks"), exports);
+tslib_1.__exportStar(require("./DetectFaceLandmarksTasks"), exports);
+tslib_1.__exportStar(require("./FaceMatcher"), exports);
+tslib_1.__exportStar(require("./nets"), exports);
+//# sourceMappingURL=index.js.map
\ No newline at end of file

build/commonjs/globalApi/index.js.map

+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/globalApi/index.ts"],"names":[],"mappings":";;;AAAA,qDAA0B;AAC1B,2DAAgC;AAChC,wEAA6C;AAC7C,6DAAkC;AAClC,qEAA0C;AAC1C,wDAA6B;AAC7B,iDAAsB"}
\ No newline at end of file

build/commonjs/globalApi/nets.d.ts

+import { TNetInput } from 'tfjs-image-recognition-base';
+import { ITinyYolov2Options } from 'tfjs-tiny-yolov2';
+import { FaceDetection } from '../classes/FaceDetection';
+import { FaceDetectionWithLandmarks } from '../classes/FaceDetectionWithLandmarks';
+import { FaceLandmarks5 } from '../classes/FaceLandmarks5';
+import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
+import { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';
+import { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';
+import { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';
+import { Mtcnn } from '../mtcnn/Mtcnn';
+import { MtcnnOptions } from '../mtcnn/MtcnnOptions';
+import { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';
+import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
+import { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';
+import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
+import { TinyYolov2 } from '../tinyYolov2/TinyYolov2';
+export declare const nets: {
+    ssdMobilenetv1: SsdMobilenetv1;
+    tinyFaceDetector: TinyFaceDetector;
+    tinyYolov2: TinyYolov2;
+    mtcnn: Mtcnn;
+    faceLandmark68Net: FaceLandmark68Net;
+    faceLandmark68TinyNet: FaceLandmark68TinyNet;
+    faceRecognitionNet: FaceRecognitionNet;
+};
+/**
+ * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.
+ *
+ * @param input The input image.
+ * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).
+ * @returns Bounding box of each face with score.
+ */
+export declare const ssdMobilenetv1: (input: TNetInput, options: SsdMobilenetv1Options) => Promise<FaceDetection[]>;
+/**
+ * Attempts to detect all faces in an image using the Tiny Face Detector.
+ *
+ * @param input The input image.
+ * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).
+ * @returns Bounding box of each face with score.
+ */
+export declare const tinyFaceDetector: (input: TNetInput, options: TinyFaceDetectorOptions) => Promise<FaceDetection[]>;
+/**
+ * Attempts to detect all faces in an image using the Tiny Yolov2 Network.
+ *
+ * @param input The input image.
+ * @param options (optional, default: see TinyYolov2Options constructor for default parameters).
+ * @returns Bounding box of each face with score.
+ */
+export declare const tinyYolov2: (input: TNetInput, options: ITinyYolov2Options) => Promise<FaceDetection[]>;
+/**
+ * Attempts to detect all faces in an image and the 5 point face landmarks
+ * of each detected face using the MTCNN Network.
+ *
+ * @param input The input image.
+ * @param options (optional, default: see MtcnnOptions constructor for default parameters).
+ * @returns Bounding box of each face with score and 5 point face landmarks.
+ */
+export declare const mtcnn: (input: TNetInput, options: MtcnnOptions) => Promise<FaceDetectionWithLandmarks<FaceLandmarks5>[]>;
+/**
+ * Detects the 68 point face landmark positions of the face shown in an image.
+ *
+ * @param inputs The face image extracted from the bounding box of a face. Can
+ * also be an array of input images, which will be batch processed.
+ * @returns 68 point face landmarks or array thereof in case of batch input.
+ */
+export declare const detectFaceLandmarks: (input: TNetInput) => Promise<FaceLandmarks68 | FaceLandmarks68[]>;
+/**
+ * Detects the 68 point face landmark positions of the face shown in an image
+ * using a tinier version of the 68 point face landmark model, which is slightly
+ * faster at inference, but also slightly less accurate.
+ *
+ * @param inputs The face image extracted from the bounding box of a face. Can
+ * also be an array of input images, which will be batch processed.
+ * @returns 68 point face landmarks or array thereof in case of batch input.
+ */
+export declare const detectFaceLandmarksTiny: (input: TNetInput) => Promise<FaceLandmarks68 | FaceLandmarks68[]>;
+/**
+ * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,
+ * which uniquely represents the features of that persons face. The computed face descriptor can
+ * be used to measure the similarity between faces, by computing the euclidean distance of two
+ * face descriptors.
+ *
+ * @param inputs The face image extracted from the aligned bounding box of a face. Can
+ * also be an array of input images, which will be batch processed.
+ * @returns Face descriptor with 128 entries or array thereof in case of batch input.
+ */
+export declare const computeFaceDescriptor: (input: TNetInput) => Promise<Float32Array | Float32Array[]>;
+export declare const loadSsdMobilenetv1Model: (url: string) => Promise<void>;
+export declare const loadTinyFaceDetectorModel: (url: string) => Promise<void>;
+export declare const loadMtcnnModel: (url: string) => Promise<void>;
+export declare const loadTinyYolov2Model: (url: string) => Promise<void>;
+export declare const loadFaceLandmarkModel: (url: string) => Promise<void>;
+export declare const loadFaceLandmarkTinyModel: (url: string) => Promise<void>;
+export declare const loadFaceRecognitionModel: (url: string) => Promise<void>;
+export declare const loadFaceDetectionModel: (url: string) => Promise<void>;
+export declare const locateFaces: (input: TNetInput, options: SsdMobilenetv1Options) => Promise<FaceDetection[]>;
+export declare const detectLandmarks: (input: TNetInput) => Promise<FaceLandmarks68 | FaceLandmarks68[]>;

build/commonjs/globalApi/nets.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var FaceLandmark68Net_1 = require("../faceLandmarkNet/FaceLandmark68Net");
+var FaceLandmark68TinyNet_1 = require("../faceLandmarkNet/FaceLandmark68TinyNet");
+var FaceRecognitionNet_1 = require("../faceRecognitionNet/FaceRecognitionNet");
+var Mtcnn_1 = require("../mtcnn/Mtcnn");
+var SsdMobilenetv1_1 = require("../ssdMobilenetv1/SsdMobilenetv1");
+var TinyFaceDetector_1 = require("../tinyFaceDetector/TinyFaceDetector");
+var TinyYolov2_1 = require("../tinyYolov2/TinyYolov2");
+exports.nets = {
+    ssdMobilenetv1: new SsdMobilenetv1_1.SsdMobilenetv1(),
+    tinyFaceDetector: new TinyFaceDetector_1.TinyFaceDetector(),
+    tinyYolov2: new TinyYolov2_1.TinyYolov2(),
+    mtcnn: new Mtcnn_1.Mtcnn(),
+    faceLandmark68Net: new FaceLandmark68Net_1.FaceLandmark68Net(),
+    faceLandmark68TinyNet: new FaceLandmark68TinyNet_1.FaceLandmark68TinyNet(),
+    faceRecognitionNet: new FaceRecognitionNet_1.FaceRecognitionNet()
+};
+/**
+ * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.
+ *
+ * @param input The input image.
+ * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).
+ * @returns Bounding box of each face with score.
+ */
+exports.ssdMobilenetv1 = function (input, options) {
+    return exports.nets.ssdMobilenetv1.locateFaces(input, options);
+};
+/**
+ * Attempts to detect all faces in an image using the Tiny Face Detector.
+ *
+ * @param input The input image.
+ * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).
+ * @returns Bounding box of each face with score.
+ */
+exports.tinyFaceDetector = function (input, options) {
+    return exports.nets.tinyFaceDetector.locateFaces(input, options);
+};
+/**
+ * Attempts to detect all faces in an image using the Tiny Yolov2 Network.
+ *
+ * @param input The input image.
+ * @param options (optional, default: see TinyYolov2Options constructor for default parameters).
+ * @returns Bounding box of each face with score.
+ */
+exports.tinyYolov2 = function (input, options) {
+    return exports.nets.tinyYolov2.locateFaces(input, options);
+};
+/**
+ * Attempts to detect all faces in an image and the 5 point face landmarks
+ * of each detected face using the MTCNN Network.
+ *
+ * @param input The input image.
+ * @param options (optional, default: see MtcnnOptions constructor for default parameters).
+ * @returns Bounding box of each face with score and 5 point face landmarks.
+ */
+exports.mtcnn = function (input, options) {
+    return exports.nets.mtcnn.forward(input, options);
+};
+/**
+ * Detects the 68 point face landmark positions of the face shown in an image.
+ *
+ * @param inputs The face image extracted from the bounding box of a face. Can
+ * also be an array of input images, which will be batch processed.
+ * @returns 68 point face landmarks or array thereof in case of batch input.
+ */
+exports.detectFaceLandmarks = function (input) {
+    return exports.nets.faceLandmark68Net.detectLandmarks(input);
+};
+/**
+ * Detects the 68 point face landmark positions of the face shown in an image
+ * using a tinier version of the 68 point face landmark model, which is slightly
+ * faster at inference, but also slightly less accurate.
+ *
+ * @param inputs The face image extracted from the bounding box of a face. Can
+ * also be an array of input images, which will be batch processed.
+ * @returns 68 point face landmarks or array thereof in case of batch input.
+ */
+exports.detectFaceLandmarksTiny = function (input) {
+    return exports.nets.faceLandmark68TinyNet.detectLandmarks(input);
+};
+/**
+ * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,
+ * which uniquely represents the features of that persons face. The computed face descriptor can
+ * be used to measure the similarity between faces, by computing the euclidean distance of two
+ * face descriptors.
+ *
+ * @param inputs The face image extracted from the aligned bounding box of a face. Can
+ * also be an array of input images, which will be batch processed.
+ * @returns Face descriptor with 128 entries or array thereof in case of batch input.
+ */
+exports.computeFaceDescriptor = function (input) {
+    return exports.nets.faceRecognitionNet.computeFaceDescriptor(input);
+};
+exports.loadSsdMobilenetv1Model = function (url) { return exports.nets.ssdMobilenetv1.load(url); };
+exports.loadTinyFaceDetectorModel = function (url) { return exports.nets.tinyFaceDetector.load(url); };
+exports.loadMtcnnModel = function (url) { return exports.nets.mtcnn.load(url); };
+exports.loadTinyYolov2Model = function (url) { return exports.nets.tinyYolov2.load(url); };
+exports.loadFaceLandmarkModel = function (url) { return exports.nets.faceLandmark68Net.load(url); };
+exports.loadFaceLandmarkTinyModel = function (url) { return exports.nets.faceLandmark68TinyNet.load(url); };
+exports.loadFaceRecognitionModel = function (url) { return exports.nets.faceRecognitionNet.load(url); };
+// backward compatibility
+exports.loadFaceDetectionModel = exports.loadSsdMobilenetv1Model;
+exports.locateFaces = exports.ssdMobilenetv1;
+exports.detectLandmarks = exports.detectFaceLandmarks;
+//# sourceMappingURL=nets.js.map
\ No newline at end of file

build/commonjs/globalApi/nets.js.map

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\ No newline at end of file

build/commonjs/globalApi/types.d.ts

+import { TNetInput } from 'tfjs-image-recognition-base';
+import { TinyYolov2Options } from 'tfjs-tiny-yolov2';
+import { FaceDetection } from '../classes/FaceDetection';
+import { MtcnnOptions } from '../mtcnn/MtcnnOptions';
+import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
+import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
+export declare type FaceDetectionOptions = TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options;
+export declare type FaceDetectionFunction = (input: TNetInput) => Promise<FaceDetection[]>;

build/commonjs/globalApi/types.js

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

build/commonjs/globalApi/types.js.map

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

build/commonjs/index.d.ts

@@ -4,9 +4,10 @@ export * from 'tfjs-image-recognition-base';
 export * from './classes';
 export * from './dom';
 export * from './euclideanDistance';
-export * from './faceDetectionNet';
 export * from './faceLandmarkNet';
 export * from './faceRecognitionNet';
 export * from './globalApi';
 export * from './mtcnn';
+export * from './ssdMobilenetv1';
+export * from './tinyFaceDetector';
 export * from './tinyYolov2';

build/commonjs/index.js

@@ -7,10 +7,11 @@ tslib_1.__exportStar(require("tfjs-image-recognition-base"), exports);
 tslib_1.__exportStar(require("./classes"), exports);
 tslib_1.__exportStar(require("./dom"), exports);
 tslib_1.__exportStar(require("./euclideanDistance"), exports);
-tslib_1.__exportStar(require("./faceDetectionNet"), exports);
 tslib_1.__exportStar(require("./faceLandmarkNet"), exports);
 tslib_1.__exportStar(require("./faceRecognitionNet"), exports);
 tslib_1.__exportStar(require("./globalApi"), exports);
 tslib_1.__exportStar(require("./mtcnn"), exports);
+tslib_1.__exportStar(require("./ssdMobilenetv1"), exports);
+tslib_1.__exportStar(require("./tinyFaceDetector"), exports);
 tslib_1.__exportStar(require("./tinyYolov2"), exports);
 //# sourceMappingURL=index.js.map
\ No newline at end of file

build/commonjs/index.js.map

-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/index.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAG1C,gBAAE;AAGJ,sEAA4C;AAE5C,oDAA0B;AAE1B,gDAAqB;AACrB,8DAAoC;AACpC,6DAAmC;AACnC,4DAAkC;AAClC,+DAAqC;AACrC,sDAA4B;AAC5B,kDAAwB;AACxB,uDAA6B"}
\ No newline at end of file
+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/index.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAG1C,gBAAE;AAGJ,sEAA4C;AAE5C,oDAA0B;AAE1B,gDAAqB;AACrB,8DAAoC;AACpC,4DAAkC;AAClC,+DAAqC;AACrC,sDAA4B;AAC5B,kDAAwB;AACxB,2DAAiC;AACjC,6DAAmC;AACnC,uDAA6B"}
\ No newline at end of file

build/commonjs/mtcnn/Mtcnn.d.ts

 import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
-import { MtcnnForwardParams, MtcnnResult, NetParams } from './types';
+import { FaceDetectionWithLandmarks } from '../classes/FaceDetectionWithLandmarks';
+import { FaceLandmarks5 } from '../classes/FaceLandmarks5';
+import { IMtcnnOptions } from './MtcnnOptions';
+import { NetParams } from './types';
 export declare class Mtcnn extends NeuralNetwork<NetParams> {
     constructor();
-    forwardInput(input: NetInput, forwardParams?: MtcnnForwardParams): Promise<{
-        results: MtcnnResult[];
+    forwardInput(input: NetInput, forwardParams?: IMtcnnOptions): Promise<{
+        results: FaceDetectionWithLandmarks<FaceLandmarks5>[];
         stats: any;
     }>;
-    forward(input: TNetInput, forwardParams?: MtcnnForwardParams): Promise<MtcnnResult[]>;
-    forwardWithStats(input: TNetInput, forwardParams?: MtcnnForwardParams): Promise<{
-        results: MtcnnResult[];
+    forward(input: TNetInput, forwardParams?: IMtcnnOptions): Promise<FaceDetectionWithLandmarks<FaceLandmarks5>[]>;
+    forwardWithStats(input: TNetInput, forwardParams?: IMtcnnOptions): Promise<{
+        results: FaceDetectionWithLandmarks<FaceLandmarks5>[];
         stats: any;
     }>;
     protected loadQuantizedParams(uri: string | undefined): Promise<{

build/commonjs/mtcnn/Mtcnn.js

@@ -4,13 +4,14 @@ var tslib_1 = require("tslib");
 var tf = require("@tensorflow/tfjs-core");
 var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
 var FaceDetection_1 = require("../classes/FaceDetection");
+var FaceDetectionWithLandmarks_1 = require("../classes/FaceDetectionWithLandmarks");
 var FaceLandmarks5_1 = require("../classes/FaceLandmarks5");
 var bgrToRgbTensor_1 = require("./bgrToRgbTensor");
 var config_1 = require("./config");
 var extractParams_1 = require("./extractParams");
-var getDefaultMtcnnForwardParams_1 = require("./getDefaultMtcnnForwardParams");
 var getSizesForScale_1 = require("./getSizesForScale");
 var loadQuantizedParams_1 = require("./loadQuantizedParams");
+var MtcnnOptions_1 = require("./MtcnnOptions");
 var pyramidDown_1 = require("./pyramidDown");
 var stage1_1 = require("./stage1");
 var stage2_1 = require("./stage2");
@@ -47,7 +48,7 @@ var Mtcnn = /** @class */ (function (_super) {
                             return results;
                         };
                         _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
-                        _b = Object.assign({}, getDefaultMtcnnForwardParams_1.getDefaultMtcnnForwardParams(), forwardParams), minFaceSize = _b.minFaceSize, scaleFactor = _b.scaleFactor, maxNumScales = _b.maxNumScales, scoreThresholds = _b.scoreThresholds, scaleSteps = _b.scaleSteps;
+                        _b = new MtcnnOptions_1.MtcnnOptions(forwardParams), minFaceSize = _b.minFaceSize, scaleFactor = _b.scaleFactor, maxNumScales = _b.maxNumScales, scoreThresholds = _b.scoreThresholds, scaleSteps = _b.scaleSteps;
                         scales = (scaleSteps || pyramidDown_1.pyramidDown(minFaceSize, scaleFactor, [height, width]))
                             .filter(function (scale) {
                             var sizes = getSizesForScale_1.getSizesForScale(scale, [height, width]);
@@ -81,13 +82,10 @@ var Mtcnn = /** @class */ (function (_super) {
                     case 3:
                         out3 = _c.sent();
                         stats.total_stage3 = Date.now() - ts;
-                        results = out3.boxes.map(function (box, idx) { return ({
-                            faceDetection: new FaceDetection_1.FaceDetection(out3.scores[idx], new tfjs_image_recognition_base_1.Rect(box.left / width, box.top / height, box.width / width, box.height / height), {
+                        results = out3.boxes.map(function (box, idx) { return new FaceDetectionWithLandmarks_1.FaceDetectionWithLandmarks(new FaceDetection_1.FaceDetection(out3.scores[idx], new tfjs_image_recognition_base_1.Rect(box.left / width, box.top / height, box.width / width, box.height / height), {
                             height: height,
                             width: width
-                            }),
-                            faceLandmarks: new FaceLandmarks5_1.FaceLandmarks5(out3.points[idx].map(function (pt) { return pt.div(new tfjs_image_recognition_base_1.Point(width, height)); }), { width: width, height: height })
-                        }); });
+                        }), new FaceLandmarks5_1.FaceLandmarks5(out3.points[idx].map(function (pt) { return pt.sub(new tfjs_image_recognition_base_1.Point(box.left, box.top)).div(new tfjs_image_recognition_base_1.Point(box.width, box.height)); }), { width: box.width, height: box.height })); });
                         return [2 /*return*/, onReturn({ results: results, stats: stats })];
                 }
             });

build/commonjs/mtcnn/Mtcnn.js.map

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\ No newline at end of file
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\ No newline at end of file

build/commonjs/mtcnn/MtcnnOptions.d.ts

+export interface IMtcnnOptions {
+    minFaceSize?: number;
+    scaleFactor?: number;
+    maxNumScales?: number;
+    scoreThresholds?: number[];
+    scaleSteps?: number[];
+}
+export declare class MtcnnOptions {
+    protected _name: string;
+    private _minFaceSize;
+    private _scaleFactor;
+    private _maxNumScales;
+    private _scoreThresholds;
+    private _scaleSteps;
+    constructor({minFaceSize, scaleFactor, maxNumScales, scoreThresholds, scaleSteps}?: IMtcnnOptions);
+    readonly minFaceSize: number;
+    readonly scaleFactor: number;
+    readonly maxNumScales: number;
+    readonly scoreThresholds: number[];
+    readonly scaleSteps: number[] | undefined;
+}

build/commonjs/mtcnn/MtcnnOptions.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var MtcnnOptions = /** @class */ (function () {
+    function MtcnnOptions(_a) {
+        var _b = _a === void 0 ? {} : _a, minFaceSize = _b.minFaceSize, scaleFactor = _b.scaleFactor, maxNumScales = _b.maxNumScales, scoreThresholds = _b.scoreThresholds, scaleSteps = _b.scaleSteps;
+        this._name = 'MtcnnOptions';
+        this._minFaceSize = minFaceSize || 20;
+        this._scaleFactor = scaleFactor || 0.709;
+        this._maxNumScales = maxNumScales || 10;
+        this._scoreThresholds = scoreThresholds || [0.6, 0.7, 0.7];
+        this._scaleSteps = scaleSteps;
+        if (typeof this._minFaceSize !== 'number' || this._minFaceSize < 0) {
+            throw new Error(this._name + " - expected minFaceSize to be a number > 0");
+        }
+        if (typeof this._scaleFactor !== 'number' || this._scaleFactor <= 0 || this._scaleFactor >= 1) {
+            throw new Error(this._name + " - expected scaleFactor to be a number between 0 and 1");
+        }
+        if (typeof this._maxNumScales !== 'number' || this._maxNumScales < 0) {
+            throw new Error(this._name + " - expected maxNumScales to be a number > 0");
+        }
+        if (!Array.isArray(this._scoreThresholds)
+            || this._scoreThresholds.length !== 3
+            || this._scoreThresholds.some(function (th) { return typeof th !== 'number'; })) {
+            throw new Error(this._name + " - expected scoreThresholds to be an array of numbers of length 3");
+        }
+        if (this._scaleSteps
+            && (!Array.isArray(this._scaleSteps) || this._scaleSteps.some(function (th) { return typeof th !== 'number'; }))) {
+            throw new Error(this._name + " - expected scaleSteps to be an array of numbers");
+        }
+    }
+    Object.defineProperty(MtcnnOptions.prototype, "minFaceSize", {
+        get: function () { return this._minFaceSize; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(MtcnnOptions.prototype, "scaleFactor", {
+        get: function () { return this._scaleFactor; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(MtcnnOptions.prototype, "maxNumScales", {
+        get: function () { return this._maxNumScales; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(MtcnnOptions.prototype, "scoreThresholds", {
+        get: function () { return this._scoreThresholds; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(MtcnnOptions.prototype, "scaleSteps", {
+        get: function () { return this._scaleSteps; },
+        enumerable: true,
+        configurable: true
+    });
+    return MtcnnOptions;
+}());
+exports.MtcnnOptions = MtcnnOptions;
+//# sourceMappingURL=MtcnnOptions.js.map
\ No newline at end of file

build/commonjs/mtcnn/MtcnnOptions.js.map

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\ No newline at end of file

build/commonjs/mtcnn/extractImagePatches.d.ts

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

build/commonjs/mtcnn/extractImagePatches.js.map

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\ No newline at end of file
+{"version":3,"file":"extractImagePatches.js","sourceRoot":"","sources":["../../../src/mtcnn/extractImagePatches.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAAkG;AAElG,yCAAwC;AAExC,6BACE,GAAsB,EACtB,KAAY,EACZ,EAA8B;QAA5B,gBAAK,EAAE,kBAAM;;;;;;;oBAIT,MAAM,GAAG,iDAAmB,CAAC,GAAG,CAAC,CAAA;oBAEvB,qBAAM,OAAO,CAAC,GAAG,CAAC,KAAK,CAAC,GAAG,CAAC,UAAM,GAAG;;;gCAE7C,KAAmB,GAAG,CAAC,YAAY,CAAC,GAAG,CAAC,MAAM,EAAE,GAAG,CAAC,KAAK,CAAC,EAAxD,CAAC,OAAA,EAAE,EAAE,QAAA,EAAE,CAAC,OAAA,EAAE,EAAE,QAAA,CAA4C;gCAE1D,KAAK,GAAG,CAAC,GAAG,CAAC,CAAA;gCACb,KAAK,GAAG,CAAC,GAAG,CAAC,CAAA;gCACb,OAAO,GAAG,MAAM,CAAC,YAAY,CAAC,KAAK,EAAE,KAAK,EAAE,CAAC,EAAE,GAAG,KAAK,CAAC,EAAE,CAAC,EAAE,GAAG,KAAK,CAAC,CAAC,CAAA;gCAE7E,sBAAO,iBAAiB,CAAC,OAAO,CAAC,EAAA;;6BAClC,CAAC,CAAC,EAAA;;oBATG,OAAO,GAAG,SASb;oBAEG,iBAAiB,GAAe,EAAE,CAAA;oBAExC,OAAO,CAAC,OAAO,CAAC,UAAA,GAAG;wBACjB,IAAM,KAAK,GAAG,0CAAY,CAAC,EAAE,KAAK,OAAA,EAAE,MAAM,QAAA,EAAE,CAAC,CAAA;wBAC7C,IAAM,QAAQ,GAAG,iDAAmB,CAAC,KAAK,CAAC,CAAA;wBAC3C,QAAQ,CAAC,SAAS,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,MAAM,CAAC,CAAA;wBACpC,IAAA,sDAAI,CAA+C;wBAE3D,IAAM,QAAQ,GAAG,EAAE,CAAA;wBACnB,cAAc;wBACd,KAAI,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,CAAC,MAAM,EAAE,CAAC,IAAE,CAAC,EAAE;4BACpC,QAAQ,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA;4BAC1B,QAAQ,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA;4BAC1B,QAAQ,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAA;yBACvB;wBACD,iBAAiB,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAA;oBAClC,CAAC,CAAC,CAAA;oBAGF,sBAAO,iBAAiB,CAAC,GAAG,CAAC,UAAA,IAAI;4BAC/B,IAAM,CAAC,GAAG,EAAE,CAAC,IAAI,CAAC;gCAChB,IAAM,gBAAgB,GAAG,EAAE,CAAC,SAAS,CACnC,EAAE,CAAC,QAAQ,CAAC,IAAI,EAAE,CAAC,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,CAAC,CAAC,CAAC,EACxC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CACb,CAAC,OAAO,EAAiB,CAAA;gCAE1B,OAAO,qBAAS,CAAC,gBAAgB,CAAC,CAAA;4BACpC,CAAC,CAAC,CAAA;4BACF,OAAO,CAAC,CAAA;wBACV,CAAC,CAAC,EAAA;;;;CACH;AAlDD,kDAkDC"}
\ No newline at end of file

build/commonjs/mtcnn/index.d.ts

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

build/commonjs/mtcnn/index.js

@@ -3,6 +3,7 @@ Object.defineProperty(exports, "__esModule", { value: true });
 var tslib_1 = require("tslib");
 var Mtcnn_1 = require("./Mtcnn");
 tslib_1.__exportStar(require("./Mtcnn"), exports);
+tslib_1.__exportStar(require("./MtcnnOptions"), exports);
 function createMtcnn(weights) {
     var net = new Mtcnn_1.Mtcnn();
     net.extractWeights(weights);

build/commonjs/mtcnn/index.js.map

-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/mtcnn/index.ts"],"names":[],"mappings":";;;AAAA,iCAAgC;AAEhC,kDAAwB;AAExB,qBAA4B,OAAqB;IAC/C,IAAM,GAAG,GAAG,IAAI,aAAK,EAAE,CAAA;IACvB,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAJD,kCAIC"}
\ No newline at end of file
+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/mtcnn/index.ts"],"names":[],"mappings":";;;AAAA,iCAAgC;AAEhC,kDAAwB;AACxB,yDAA+B;AAE/B,qBAA4B,OAAqB;IAC/C,IAAM,GAAG,GAAG,IAAI,aAAK,EAAE,CAAA;IACvB,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAJD,kCAIC"}
\ No newline at end of file

build/commonjs/mtcnn/types.d.ts

 import * as tf from '@tensorflow/tfjs-core';
 import { ConvParams, FCParams } from 'tfjs-tiny-yolov2';
-import { FaceDetection } from '../classes/FaceDetection';
-import { FaceLandmarks5 } from '../classes/FaceLandmarks5';
 export declare type SharedParams = {
     conv1: ConvParams;
     prelu1_alpha: tf.Tensor1D;
@@ -34,14 +32,3 @@ export declare type NetParams = {
     rnet: RNetParams;
     onet: ONetParams;
 };
-export declare type MtcnnResult = {
-    faceDetection: FaceDetection;
-    faceLandmarks: FaceLandmarks5;
-};
-export declare type MtcnnForwardParams = {
-    minFaceSize?: number;
-    scaleFactor?: number;
-    maxNumScales?: number;
-    scoreThresholds?: number[];
-    scaleSteps?: number[];
-};

build/commonjs/ssdMobilenetv1/SsdMobilenetv1.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
+import { FaceDetection } from '../classes/FaceDetection';
+import { ISsdMobilenetv1Options } from './SsdMobilenetv1Options';
+import { NetParams } from './types';
+export declare class SsdMobilenetv1 extends NeuralNetwork<NetParams> {
+    constructor();
+    forwardInput(input: NetInput): {
+        boxes: tf.Tensor<tf.Rank.R2>[];
+        scores: tf.Tensor<tf.Rank.R1>[];
+    };
+    forward(input: TNetInput): Promise<{
+        boxes: tf.Tensor<tf.Rank.R2>[];
+        scores: tf.Tensor<tf.Rank.R1>[];
+    }>;
+    locateFaces(input: TNetInput, options?: ISsdMobilenetv1Options): Promise<FaceDetection[]>;
+    protected loadQuantizedParams(uri: string | undefined): Promise<{
+        params: NetParams;
+        paramMappings: {
+            originalPath?: string | undefined;
+            paramPath: string;
+        }[];
+    }>;
+    protected extractParams(weights: Float32Array): {
+        params: NetParams;
+        paramMappings: {
+            originalPath?: string | undefined;
+            paramPath: string;
+        }[];
+    };
+}

build/commonjs/ssdMobilenetv1/SsdMobilenetv1.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tslib_1 = require("tslib");
+var tf = require("@tensorflow/tfjs-core");
+var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
+var FaceDetection_1 = require("../classes/FaceDetection");
+var extractParams_1 = require("./extractParams");
+var loadQuantizedParams_1 = require("./loadQuantizedParams");
+var mobileNetV1_1 = require("./mobileNetV1");
+var nonMaxSuppression_1 = require("./nonMaxSuppression");
+var outputLayer_1 = require("./outputLayer");
+var predictionLayer_1 = require("./predictionLayer");
+var SsdMobilenetv1Options_1 = require("./SsdMobilenetv1Options");
+var SsdMobilenetv1 = /** @class */ (function (_super) {
+    tslib_1.__extends(SsdMobilenetv1, _super);
+    function SsdMobilenetv1() {
+        return _super.call(this, 'SsdMobilenetv1') || this;
+    }
+    SsdMobilenetv1.prototype.forwardInput = function (input) {
+        var params = this.params;
+        if (!params) {
+            throw new Error('SsdMobilenetv1 - load model before inference');
+        }
+        return tf.tidy(function () {
+            var batchTensor = input.toBatchTensor(512, false).toFloat();
+            var x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1));
+            var features = mobileNetV1_1.mobileNetV1(x, params.mobilenetv1);
+            var _a = predictionLayer_1.predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
+            return outputLayer_1.outputLayer(boxPredictions, classPredictions, params.output_layer);
+        });
+    };
+    SsdMobilenetv1.prototype.forward = function (input) {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var _a;
+            return tslib_1.__generator(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        _a = this.forwardInput;
+                        return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
+                    case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
+                }
+            });
+        });
+    };
+    SsdMobilenetv1.prototype.locateFaces = function (input, options) {
+        if (options === void 0) { options = {}; }
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var _a, maxResults, minConfidence, netInput, _b, _boxes, _scores, boxes, scores, i, scoresData, _c, _d, iouThreshold, indices, reshapedDims, inputSize, padX, padY, results;
+            return tslib_1.__generator(this, function (_e) {
+                switch (_e.label) {
+                    case 0:
+                        _a = new SsdMobilenetv1Options_1.SsdMobilenetv1Options(options), maxResults = _a.maxResults, minConfidence = _a.minConfidence;
+                        return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
+                    case 1:
+                        netInput = _e.sent();
+                        _b = this.forwardInput(netInput), _boxes = _b.boxes, _scores = _b.scores;
+                        boxes = _boxes[0];
+                        scores = _scores[0];
+                        for (i = 1; i < _boxes.length; i++) {
+                            _boxes[i].dispose();
+                            _scores[i].dispose();
+                        }
+                        _d = (_c = Array).from;
+                        return [4 /*yield*/, scores.data()];
+                    case 2:
+                        scoresData = _d.apply(_c, [_e.sent()]);
+                        iouThreshold = 0.5;
+                        indices = nonMaxSuppression_1.nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
+                        reshapedDims = netInput.getReshapedInputDimensions(0);
+                        inputSize = netInput.inputSize;
+                        padX = inputSize / reshapedDims.width;
+                        padY = inputSize / reshapedDims.height;
+                        results = indices
+                            .map(function (idx) {
+                            var _a = [
+                                Math.max(0, boxes.get(idx, 0)),
+                                Math.min(1.0, boxes.get(idx, 2))
+                            ].map(function (val) { return val * padY; }), top = _a[0], bottom = _a[1];
+                            var _b = [
+                                Math.max(0, boxes.get(idx, 1)),
+                                Math.min(1.0, boxes.get(idx, 3))
+                            ].map(function (val) { return val * padX; }), left = _b[0], right = _b[1];
+                            return new FaceDetection_1.FaceDetection(scoresData[idx], new tfjs_image_recognition_base_1.Rect(left, top, right - left, bottom - top), {
+                                height: netInput.getInputHeight(0),
+                                width: netInput.getInputWidth(0)
+                            });
+                        });
+                        boxes.dispose();
+                        scores.dispose();
+                        return [2 /*return*/, results];
+                }
+            });
+        });
+    };
+    SsdMobilenetv1.prototype.loadQuantizedParams = function (uri) {
+        return loadQuantizedParams_1.loadQuantizedParams(uri);
+    };
+    SsdMobilenetv1.prototype.extractParams = function (weights) {
+        return extractParams_1.extractParams(weights);
+    };
+    return SsdMobilenetv1;
+}(tfjs_image_recognition_base_1.NeuralNetwork));
+exports.SsdMobilenetv1 = SsdMobilenetv1;
+//# sourceMappingURL=SsdMobilenetv1.js.map
\ No newline at end of file

build/commonjs/ssdMobilenetv1/SsdMobilenetv1.js.map

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\ No newline at end of file

build/commonjs/ssdMobilenetv1/SsdMobilenetv1Options.d.ts

+export interface ISsdMobilenetv1Options {
+    minConfidence?: number;
+    maxResults?: number;
+}
+export declare class SsdMobilenetv1Options {
+    protected _name: string;
+    private _minConfidence;
+    private _maxResults;
+    constructor({minConfidence, maxResults}?: ISsdMobilenetv1Options);
+    readonly minConfidence: number;
+    readonly maxResults: number;
+}

build/commonjs/ssdMobilenetv1/SsdMobilenetv1Options.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var SsdMobilenetv1Options = /** @class */ (function () {
+    function SsdMobilenetv1Options(_a) {
+        var _b = _a === void 0 ? {} : _a, minConfidence = _b.minConfidence, maxResults = _b.maxResults;
+        this._name = 'SsdMobilenetv1Options';
+        this._minConfidence = minConfidence || 0.5;
+        this._maxResults = maxResults || 100;
+        if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {
+            throw new Error(this._name + " - expected minConfidence to be a number between 0 and 1");
+        }
+        if (typeof this._maxResults !== 'number') {
+            throw new Error(this._name + " - expected maxResults to be a number");
+        }
+    }
+    Object.defineProperty(SsdMobilenetv1Options.prototype, "minConfidence", {
+        get: function () { return this._minConfidence; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(SsdMobilenetv1Options.prototype, "maxResults", {
+        get: function () { return this._maxResults; },
+        enumerable: true,
+        configurable: true
+    });
+    return SsdMobilenetv1Options;
+}());
+exports.SsdMobilenetv1Options = SsdMobilenetv1Options;
+//# sourceMappingURL=SsdMobilenetv1Options.js.map
\ No newline at end of file

build/commonjs/ssdMobilenetv1/SsdMobilenetv1Options.js.map

+{"version":3,"file":"SsdMobilenetv1Options.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/SsdMobilenetv1Options.ts"],"names":[],"mappings":";;AAKA;IAME,+BAAY,EAA0D;YAA1D,4BAA0D,EAAxD,gCAAa,EAAE,0BAAU;QAL7B,UAAK,GAAW,uBAAuB,CAAA;QAM/C,IAAI,CAAC,cAAc,GAAG,aAAa,IAAI,GAAG,CAAA;QAC1C,IAAI,CAAC,WAAW,GAAG,UAAU,IAAI,GAAG,CAAA;QAEpC,IAAI,OAAO,IAAI,CAAC,cAAc,KAAK,QAAQ,IAAI,IAAI,CAAC,cAAc,IAAI,CAAC,IAAI,IAAI,CAAC,cAAc,IAAI,CAAC,EAAE;YACnG,MAAM,IAAI,KAAK,CAAI,IAAI,CAAC,KAAK,6DAA0D,CAAC,CAAA;SACzF;QAED,IAAI,OAAO,IAAI,CAAC,WAAW,KAAK,QAAQ,EAAE;YACxC,MAAM,IAAI,KAAK,CAAI,IAAI,CAAC,KAAK,0CAAuC,CAAC,CAAA;SACtE;IACH,CAAC;IAED,sBAAI,gDAAa;aAAjB,cAA8B,OAAO,IAAI,CAAC,cAAc,CAAA,CAAC,CAAC;;;OAAA;IAC1D,sBAAI,6CAAU;aAAd,cAA2B,OAAO,IAAI,CAAC,WAAW,CAAA,CAAC,CAAC;;;OAAA;IACtD,4BAAC;AAAD,CAAC,AArBD,IAqBC;AArBY,sDAAqB"}
\ No newline at end of file

build/commonjs/ssdMobilenetv1/boxPredictionLayer.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { BoxPredictionParams } from './types';
+export declare function boxPredictionLayer(x: tf.Tensor4D, params: BoxPredictionParams): {
+    boxPredictionEncoding: tf.Tensor<tf.Rank>;
+    classPrediction: tf.Tensor<tf.Rank>;
+};

build/commonjs/ssdMobilenetv1/boxPredictionLayer.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("@tensorflow/tfjs-core");
+var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
+function boxPredictionLayer(x, params) {
+    return tf.tidy(function () {
+        var batchSize = x.shape[0];
+        var boxPredictionEncoding = tf.reshape(tfjs_tiny_yolov2_1.convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
+        var classPrediction = tf.reshape(tfjs_tiny_yolov2_1.convLayer(x, params.class_predictor), [batchSize, -1, 3]);
+        return {
+            boxPredictionEncoding: boxPredictionEncoding,
+            classPrediction: classPrediction
+        };
+    });
+}
+exports.boxPredictionLayer = boxPredictionLayer;
+//# sourceMappingURL=boxPredictionLayer.js.map
\ No newline at end of file

build/commonjs/ssdMobilenetv1/boxPredictionLayer.js.map

+{"version":3,"file":"boxPredictionLayer.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/boxPredictionLayer.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAC5C,qDAA6C;AAK7C,4BACE,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,4BAAS,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,4BAAS,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;AAtBD,gDAsBC"}
\ No newline at end of file

build/commonjs/ssdMobilenetv1/extractParams.d.ts

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

build/commonjs/ssdMobilenetv1/extractParams.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("@tensorflow/tfjs-core");
+var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
+function extractorsFactory(extractWeights, paramMappings) {
+    function extractDepthwiseConvParams(numChannels, mappedPrefix) {
+        var filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
+        var batch_norm_scale = tf.tensor1d(extractWeights(numChannels));
+        var batch_norm_offset = tf.tensor1d(extractWeights(numChannels));
+        var batch_norm_mean = tf.tensor1d(extractWeights(numChannels));
+        var batch_norm_variance = tf.tensor1d(extractWeights(numChannels));
+        paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/batch_norm_scale" }, { paramPath: mappedPrefix + "/batch_norm_offset" }, { paramPath: mappedPrefix + "/batch_norm_mean" }, { paramPath: mappedPrefix + "/batch_norm_variance" });
+        return {
+            filters: filters,
+            batch_norm_scale: batch_norm_scale,
+            batch_norm_offset: batch_norm_offset,
+            batch_norm_mean: batch_norm_mean,
+            batch_norm_variance: batch_norm_variance
+        };
+    }
+    function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
+        var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
+        var bias = tf.tensor1d(extractWeights(channelsOut));
+        paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/" + (isPointwiseConv ? 'batch_norm_offset' : 'bias') });
+        return { filters: filters, bias: bias };
+    }
+    function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
+        var _a = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true), filters = _a.filters, bias = _a.bias;
+        return {
+            filters: filters,
+            batch_norm_offset: bias
+        };
+    }
+    function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) {
+        var depthwise_conv = extractDepthwiseConvParams(channelsIn, mappedPrefix + "/depthwise_conv");
+        var pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, mappedPrefix + "/pointwise_conv");
+        return { depthwise_conv: depthwise_conv, pointwise_conv: pointwise_conv };
+    }
+    function extractMobilenetV1Params() {
+        var conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');
+        var conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');
+        var conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');
+        var conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');
+        var conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');
+        var conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');
+        var conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');
+        var conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');
+        var conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');
+        var conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');
+        var conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');
+        var conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');
+        var conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');
+        var conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');
+        return {
+            conv_0: conv_0,
+            conv_1: conv_1,
+            conv_2: conv_2,
+            conv_3: conv_3,
+            conv_4: conv_4,
+            conv_5: conv_5,
+            conv_6: conv_6,
+            conv_7: conv_7,
+            conv_8: conv_8,
+            conv_9: conv_9,
+            conv_10: conv_10,
+            conv_11: conv_11,
+            conv_12: conv_12,
+            conv_13: conv_13
+        };
+    }
+    function extractPredictionLayerParams() {
+        var conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');
+        var conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');
+        var conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');
+        var conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');
+        var conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');
+        var conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');
+        var conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');
+        var conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');
+        var box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');
+        var class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');
+        var box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');
+        var class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');
+        var box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');
+        var class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');
+        var box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');
+        var class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');
+        var box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');
+        var class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');
+        var box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');
+        var class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');
+        var box_predictor_0 = {
+            box_encoding_predictor: box_encoding_0_predictor,
+            class_predictor: class_predictor_0
+        };
+        var box_predictor_1 = {
+            box_encoding_predictor: box_encoding_1_predictor,
+            class_predictor: class_predictor_1
+        };
+        var box_predictor_2 = {
+            box_encoding_predictor: box_encoding_2_predictor,
+            class_predictor: class_predictor_2
+        };
+        var box_predictor_3 = {
+            box_encoding_predictor: box_encoding_3_predictor,
+            class_predictor: class_predictor_3
+        };
+        var box_predictor_4 = {
+            box_encoding_predictor: box_encoding_4_predictor,
+            class_predictor: class_predictor_4
+        };
+        var box_predictor_5 = {
+            box_encoding_predictor: box_encoding_5_predictor,
+            class_predictor: class_predictor_5
+        };
+        return {
+            conv_0: conv_0,
+            conv_1: conv_1,
+            conv_2: conv_2,
+            conv_3: conv_3,
+            conv_4: conv_4,
+            conv_5: conv_5,
+            conv_6: conv_6,
+            conv_7: conv_7,
+            box_predictor_0: box_predictor_0,
+            box_predictor_1: box_predictor_1,
+            box_predictor_2: box_predictor_2,
+            box_predictor_3: box_predictor_3,
+            box_predictor_4: box_predictor_4,
+            box_predictor_5: box_predictor_5
+        };
+    }
+    return {
+        extractMobilenetV1Params: extractMobilenetV1Params,
+        extractPredictionLayerParams: extractPredictionLayerParams
+    };
+}
+function extractParams(weights) {
+    var paramMappings = [];
+    var _a = tfjs_image_recognition_base_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
+    var _b = extractorsFactory(extractWeights, paramMappings), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;
+    var mobilenetv1 = extractMobilenetV1Params();
+    var prediction_layer = extractPredictionLayerParams();
+    var extra_dim = tf.tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
+    var output_layer = {
+        extra_dim: extra_dim
+    };
+    paramMappings.push({ paramPath: 'output_layer/extra_dim' });
+    if (getRemainingWeights().length !== 0) {
+        throw new Error("weights remaing after extract: " + getRemainingWeights().length);
+    }
+    return {
+        params: {
+            mobilenetv1: mobilenetv1,
+            prediction_layer: prediction_layer,
+            output_layer: output_layer
+        },
+        paramMappings: paramMappings
+    };
+}
+exports.extractParams = extractParams;
+//# sourceMappingURL=extractParams.js.map
\ No newline at end of file

build/commonjs/ssdMobilenetv1/extractParams.js.map

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\ No newline at end of file

build/commonjs/ssdMobilenetv1/index.d.ts

+import { SsdMobilenetv1 } from './SsdMobilenetv1';
+export * from './SsdMobilenetv1';
+export * from './SsdMobilenetv1Options';
+export declare function createSsdMobilenetv1(weights: Float32Array): SsdMobilenetv1;
+export declare function createFaceDetectionNet(weights: Float32Array): SsdMobilenetv1;
+export declare class FaceDetectionNet extends SsdMobilenetv1 {
+}

build/commonjs/ssdMobilenetv1/index.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tslib_1 = require("tslib");
+var SsdMobilenetv1_1 = require("./SsdMobilenetv1");
+tslib_1.__exportStar(require("./SsdMobilenetv1"), exports);
+tslib_1.__exportStar(require("./SsdMobilenetv1Options"), exports);
+function createSsdMobilenetv1(weights) {
+    var net = new SsdMobilenetv1_1.SsdMobilenetv1();
+    net.extractWeights(weights);
+    return net;
+}
+exports.createSsdMobilenetv1 = createSsdMobilenetv1;
+function createFaceDetectionNet(weights) {
+    return createSsdMobilenetv1(weights);
+}
+exports.createFaceDetectionNet = createFaceDetectionNet;
+// alias for backward compatibily
+var FaceDetectionNet = /** @class */ (function (_super) {
+    tslib_1.__extends(FaceDetectionNet, _super);
+    function FaceDetectionNet() {
+        return _super !== null && _super.apply(this, arguments) || this;
+    }
+    return FaceDetectionNet;
+}(SsdMobilenetv1_1.SsdMobilenetv1));
+exports.FaceDetectionNet = FaceDetectionNet;
+//# sourceMappingURL=index.js.map
\ No newline at end of file

build/commonjs/ssdMobilenetv1/index.js.map

+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/index.ts"],"names":[],"mappings":";;;AAAA,mDAAkD;AAElD,2DAAiC;AACjC,kEAAwC;AAExC,8BAAqC,OAAqB;IACxD,IAAM,GAAG,GAAG,IAAI,+BAAc,EAAE,CAAA;IAChC,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAJD,oDAIC;AAED,gCAAuC,OAAqB;IAC1D,OAAO,oBAAoB,CAAC,OAAO,CAAC,CAAA;AACtC,CAAC;AAFD,wDAEC;AAED,iCAAiC;AACjC;IAAsC,4CAAc;IAApD;;IAAsD,CAAC;IAAD,uBAAC;AAAD,CAAC,AAAvD,CAAsC,+BAAc,GAAG;AAA1C,4CAAgB"}
\ No newline at end of file

build/commonjs/ssdMobilenetv1/loadQuantizedParams.d.ts

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

build/commonjs/ssdMobilenetv1/loadQuantizedParams.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tslib_1 = require("tslib");
+var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
+var DEFAULT_MODEL_NAME = 'ssd_mobilenetv1_model';
+function extractorsFactory(weightMap, paramMappings) {
+    var extractWeightEntry = tfjs_image_recognition_base_1.extractWeightEntryFactory(weightMap, paramMappings);
+    function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
+        var filters = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/weights", 4, mappedPrefix + "/filters");
+        var batch_norm_offset = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset", 1, mappedPrefix + "/batch_norm_offset");
+        return { filters: filters, batch_norm_offset: batch_norm_offset };
+    }
+    function extractConvPairParams(idx) {
+        var mappedPrefix = "mobilenetv1/conv_" + idx;
+        var prefixDepthwiseConv = "MobilenetV1/Conv2d_" + idx + "_depthwise";
+        var mappedPrefixDepthwiseConv = mappedPrefix + "/depthwise_conv";
+        var mappedPrefixPointwiseConv = mappedPrefix + "/pointwise_conv";
+        var filters = extractWeightEntry(prefixDepthwiseConv + "/depthwise_weights", 4, mappedPrefixDepthwiseConv + "/filters");
+        var batch_norm_scale = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/gamma", 1, mappedPrefixDepthwiseConv + "/batch_norm_scale");
+        var batch_norm_offset = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/beta", 1, mappedPrefixDepthwiseConv + "/batch_norm_offset");
+        var batch_norm_mean = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_mean", 1, mappedPrefixDepthwiseConv + "/batch_norm_mean");
+        var batch_norm_variance = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_variance", 1, mappedPrefixDepthwiseConv + "/batch_norm_variance");
+        return {
+            depthwise_conv: {
+                filters: filters,
+                batch_norm_scale: batch_norm_scale,
+                batch_norm_offset: batch_norm_offset,
+                batch_norm_mean: batch_norm_mean,
+                batch_norm_variance: batch_norm_variance
+            },
+            pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)
+        };
+    }
+    function extractMobilenetV1Params() {
+        return {
+            conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),
+            conv_1: extractConvPairParams(1),
+            conv_2: extractConvPairParams(2),
+            conv_3: extractConvPairParams(3),
+            conv_4: extractConvPairParams(4),
+            conv_5: extractConvPairParams(5),
+            conv_6: extractConvPairParams(6),
+            conv_7: extractConvPairParams(7),
+            conv_8: extractConvPairParams(8),
+            conv_9: extractConvPairParams(9),
+            conv_10: extractConvPairParams(10),
+            conv_11: extractConvPairParams(11),
+            conv_12: extractConvPairParams(12),
+            conv_13: extractConvPairParams(13)
+        };
+    }
+    function extractConvParams(prefix, mappedPrefix) {
+        var filters = extractWeightEntry(prefix + "/weights", 4, mappedPrefix + "/filters");
+        var bias = extractWeightEntry(prefix + "/biases", 1, mappedPrefix + "/bias");
+        return { filters: filters, bias: bias };
+    }
+    function extractBoxPredictorParams(idx) {
+        var box_encoding_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor", "prediction_layer/box_predictor_" + idx + "/box_encoding_predictor");
+        var class_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/ClassPredictor", "prediction_layer/box_predictor_" + idx + "/class_predictor");
+        return { box_encoding_predictor: box_encoding_predictor, class_predictor: class_predictor };
+    }
+    function extractPredictionLayerParams() {
+        return {
+            conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),
+            conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),
+            conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),
+            conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),
+            conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),
+            conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),
+            conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),
+            conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),
+            box_predictor_0: extractBoxPredictorParams(0),
+            box_predictor_1: extractBoxPredictorParams(1),
+            box_predictor_2: extractBoxPredictorParams(2),
+            box_predictor_3: extractBoxPredictorParams(3),
+            box_predictor_4: extractBoxPredictorParams(4),
+            box_predictor_5: extractBoxPredictorParams(5)
+        };
+    }
+    return {
+        extractMobilenetV1Params: extractMobilenetV1Params,
+        extractPredictionLayerParams: extractPredictionLayerParams
+    };
+}
+function loadQuantizedParams(uri) {
+    return tslib_1.__awaiter(this, void 0, void 0, function () {
+        var weightMap, paramMappings, _a, extractMobilenetV1Params, extractPredictionLayerParams, extra_dim, params;
+        return tslib_1.__generator(this, function (_b) {
+            switch (_b.label) {
+                case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
+                case 1:
+                    weightMap = _b.sent();
+                    paramMappings = [];
+                    _a = extractorsFactory(weightMap, paramMappings), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
+                    extra_dim = weightMap['Output/extra_dim'];
+                    paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });
+                    if (!tfjs_image_recognition_base_1.isTensor3D(extra_dim)) {
+                        throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have " + extra_dim);
+                    }
+                    params = {
+                        mobilenetv1: extractMobilenetV1Params(),
+                        prediction_layer: extractPredictionLayerParams(),
+                        output_layer: {
+                            extra_dim: extra_dim
+                        }
+                    };
+                    tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
+                    return [2 /*return*/, { params: params, paramMappings: paramMappings }];
+            }
+        });
+    });
+}
+exports.loadQuantizedParams = loadQuantizedParams;
+//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file

build/commonjs/ssdMobilenetv1/loadQuantizedParams.js.map

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build/commonjs/ssdMobilenetv1/mobileNetV1.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { MobileNetV1 } from './types';
+export declare function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params): {
+    out: tf.Tensor<tf.Rank.R4>;
+    conv11: any;
+};

build/commonjs/ssdMobilenetv1/mobileNetV1.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("@tensorflow/tfjs-core");
+var pointwiseConvLayer_1 = require("./pointwiseConvLayer");
+var epsilon = 0.0010000000474974513;
+function depthwiseConvLayer(x, params, strides) {
+    return tf.tidy(function () {
+        var out = tf.depthwiseConv2d(x, params.filters, strides, 'same');
+        out = tf.batchNormalization(out, params.batch_norm_mean, params.batch_norm_variance, epsilon, params.batch_norm_scale, params.batch_norm_offset);
+        return tf.clipByValue(out, 0, 6);
+    });
+}
+function getStridesForLayerIdx(layerIdx) {
+    return [2, 4, 6, 12].some(function (idx) { return idx === layerIdx; }) ? [2, 2] : [1, 1];
+}
+function mobileNetV1(x, params) {
+    return tf.tidy(function () {
+        var conv11 = null;
+        var out = pointwiseConvLayer_1.pointwiseConvLayer(x, params.conv_0, [2, 2]);
+        var convPairParams = [
+            params.conv_1,
+            params.conv_2,
+            params.conv_3,
+            params.conv_4,
+            params.conv_5,
+            params.conv_6,
+            params.conv_7,
+            params.conv_8,
+            params.conv_9,
+            params.conv_10,
+            params.conv_11,
+            params.conv_12,
+            params.conv_13
+        ];
+        convPairParams.forEach(function (param, i) {
+            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]);
+            if (layerIdx === 11) {
+                conv11 = out;
+            }
+        });
+        if (conv11 === null) {
+            throw new Error('mobileNetV1 - output of conv layer 11 is null');
+        }
+        return {
+            out: out,
+            conv11: conv11
+        };
+    });
+}
+exports.mobileNetV1 = mobileNetV1;
+//# sourceMappingURL=mobileNetV1.js.map
\ No newline at end of file

build/commonjs/ssdMobilenetv1/mobileNetV1.js.map

+{"version":3,"file":"mobileNetV1.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/mobileNetV1.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,2DAA0D;AAG1D,IAAM,OAAO,GAAG,qBAAqB,CAAA;AAErC,4BACE,CAAc,EACd,MAAuC,EACvC,OAAyB;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAI,GAAG,GAAG,EAAE,CAAC,eAAe,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,OAAO,EAAE,MAAM,CAAC,CAAA;QAChE,GAAG,GAAG,EAAE,CAAC,kBAAkB,CACzB,GAAG,EACH,MAAM,CAAC,eAAe,EACtB,MAAM,CAAC,mBAAmB,EAC1B,OAAO,EACP,MAAM,CAAC,gBAAgB,EACvB,MAAM,CAAC,iBAAiB,CACzB,CAAA;QACD,OAAO,EAAE,CAAC,WAAW,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;IAElC,CAAC,CAAC,CAAA;AACJ,CAAC;AAED,+BAA+B,QAAgB;IAC7C,OAAO,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,IAAI,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,KAAK,QAAQ,EAAhB,CAAgB,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;AACtE,CAAC;AAED,qBAA4B,CAAc,EAAE,MAA0B;IACpE,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAI,MAAM,GAAG,IAAI,CAAA;QACjB,IAAI,GAAG,GAAG,uCAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAEtD,IAAM,cAAc,GAAG;YACrB,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,OAAO;YACd,MAAM,CAAC,OAAO;YACd,MAAM,CAAC,OAAO;YACd,MAAM,CAAC,OAAO;SACf,CAAA;QAED,cAAc,CAAC,OAAO,CAAC,UAAC,KAAK,EAAE,CAAC;YAC9B,IAAM,QAAQ,GAAG,CAAC,GAAG,CAAC,CAAA;YACtB,IAAM,oBAAoB,GAAG,qBAAqB,CAAC,QAAQ,CAAC,CAAA;YAC5D,GAAG,GAAG,kBAAkB,CAAC,GAAG,EAAE,KAAK,CAAC,cAAc,EAAE,oBAAoB,CAAC,CAAA;YACzE,GAAG,GAAG,uCAAkB,CAAC,GAAG,EAAE,KAAK,CAAC,cAAc,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;YAC3D,IAAI,QAAQ,KAAK,EAAE,EAAE;gBACnB,MAAM,GAAG,GAAG,CAAA;aACb;QACH,CAAC,CAAC,CAAA;QAEF,IAAI,MAAM,KAAK,IAAI,EAAE;YACnB,MAAM,IAAI,KAAK,CAAC,+CAA+C,CAAC,CAAA;SACjE;QAED,OAAO;YACL,GAAG,KAAA;YACH,MAAM,EAAE,MAAa;SACtB,CAAA;IAEH,CAAC,CAAC,CAAA;AACJ,CAAC;AA1CD,kCA0CC"}
\ No newline at end of file

build/commonjs/ssdMobilenetv1/nonMaxSuppression.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+export declare function nonMaxSuppression(boxes: tf.Tensor2D, scores: number[], maxOutputSize: number, iouThreshold: number, scoreThreshold: number): number[];

build/commonjs/ssdMobilenetv1/nonMaxSuppression.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+    var numBoxes = boxes.shape[0];
+    var outputSize = Math.min(maxOutputSize, numBoxes);
+    var candidates = scores
+        .map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })
+        .filter(function (c) { return c.score > scoreThreshold; })
+        .sort(function (c1, c2) { return c2.score - c1.score; });
+    var suppressFunc = function (x) { return x <= iouThreshold ? 1 : 0; };
+    var selected = [];
+    candidates.forEach(function (c) {
+        if (selected.length >= outputSize) {
+            return;
+        }
+        var originalScore = c.score;
+        for (var j = selected.length - 1; j >= 0; --j) {
+            var iou = IOU(boxes, c.boxIndex, selected[j]);
+            if (iou === 0.0) {
+                continue;
+            }
+            c.score *= suppressFunc(iou);
+            if (c.score <= scoreThreshold) {
+                break;
+            }
+        }
+        if (originalScore === c.score) {
+            selected.push(c.boxIndex);
+        }
+    });
+    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));
+    var ymaxI = Math.max(boxes.get(i, 0), boxes.get(i, 2));
+    var xmaxI = Math.max(boxes.get(i, 1), boxes.get(i, 3));
+    var yminJ = Math.min(boxes.get(j, 0), boxes.get(j, 2));
+    var xminJ = Math.min(boxes.get(j, 1), boxes.get(j, 3));
+    var ymaxJ = Math.max(boxes.get(j, 0), boxes.get(j, 2));
+    var xmaxJ = Math.max(boxes.get(j, 1), boxes.get(j, 3));
+    var areaI = (ymaxI - yminI) * (xmaxI - xminI);
+    var areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
+    if (areaI <= 0 || areaJ <= 0) {
+        return 0.0;
+    }
+    var intersectionYmin = Math.max(yminI, yminJ);
+    var intersectionXmin = Math.max(xminI, xminJ);
+    var intersectionYmax = Math.min(ymaxI, ymaxJ);
+    var intersectionXmax = Math.min(xmaxI, xmaxJ);
+    var intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *
+        Math.max(intersectionXmax - intersectionXmin, 0.0);
+    return intersectionArea / (areaI + areaJ - intersectionArea);
+}
+//# sourceMappingURL=nonMaxSuppression.js.map
\ No newline at end of file

build/commonjs/ssdMobilenetv1/nonMaxSuppression.js.map

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build/commonjs/ssdMobilenetv1/outputLayer.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { OutputLayerParams } from './types';
+export declare function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams): {
+    boxes: tf.Tensor<tf.Rank.R2>[];
+    scores: tf.Tensor<tf.Rank.R1>[];
+};

build/commonjs/ssdMobilenetv1/outputLayer.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("@tensorflow/tfjs-core");
+function getCenterCoordinatesAndSizesLayer(x) {
+    var vec = tf.unstack(tf.transpose(x, [1, 0]));
+    var sizes = [
+        tf.sub(vec[2], vec[0]),
+        tf.sub(vec[3], vec[1])
+    ];
+    var centers = [
+        tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),
+        tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))
+    ];
+    return {
+        sizes: sizes,
+        centers: centers
+    };
+}
+function decodeBoxesLayer(x0, x1) {
+    var _a = getCenterCoordinatesAndSizesLayer(x0), sizes = _a.sizes, centers = _a.centers;
+    var vec = tf.unstack(tf.transpose(x1, [1, 0]));
+    var div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));
+    var add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);
+    var div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));
+    var add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);
+    return tf.transpose(tf.stack([
+        tf.sub(add0_out, div0_out),
+        tf.sub(add1_out, div1_out),
+        tf.add(add0_out, div0_out),
+        tf.add(add1_out, div1_out)
+    ]), [1, 0]);
+}
+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]));
+        boxes = tf.reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);
+        var scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
+        var scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]);
+        scores = tf.reshape(scores, [batchSize, scores.shape[1]]);
+        var boxesByBatch = tf.unstack(boxes);
+        var scoresByBatch = tf.unstack(scores);
+        return {
+            boxes: boxesByBatch,
+            scores: scoresByBatch
+        };
+    });
+}
+exports.outputLayer = outputLayer;
+//# sourceMappingURL=outputLayer.js.map
\ No newline at end of file

build/commonjs/ssdMobilenetv1/outputLayer.js.map

+{"version":3,"file":"outputLayer.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/outputLayer.ts"],"names":[],"mappings":";;AAAA,0CAA4C;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,qBACE,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;AAnCD,kCAmCC"}
\ No newline at end of file

build/commonjs/ssdMobilenetv1/pointwiseConvLayer.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { PointwiseConvParams } from './types';
+export declare function pointwiseConvLayer(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]): tf.Tensor<tf.Rank.R4>;

build/commonjs/ssdMobilenetv1/pointwiseConvLayer.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("@tensorflow/tfjs-core");
+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/commonjs/ssdMobilenetv1/pointwiseConvLayer.js.map

+{"version":3,"file":"pointwiseConvLayer.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/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"}
\ No newline at end of file

build/commonjs/ssdMobilenetv1/predictionLayer.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { PredictionLayerParams } from './types';
+export declare function predictionLayer(x: tf.Tensor4D, conv11: tf.Tensor4D, params: PredictionLayerParams): {
+    boxPredictions: tf.Tensor<tf.Rank.R4>;
+    classPredictions: tf.Tensor<tf.Rank.R4>;
+};

build/commonjs/ssdMobilenetv1/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) {
+    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 boxPredictions = tf.concat([
+            boxPrediction0.boxPredictionEncoding,
+            boxPrediction1.boxPredictionEncoding,
+            boxPrediction2.boxPredictionEncoding,
+            boxPrediction3.boxPredictionEncoding,
+            boxPrediction4.boxPredictionEncoding,
+            boxPrediction5.boxPredictionEncoding
+        ], 1);
+        var classPredictions = tf.concat([
+            boxPrediction0.classPrediction,
+            boxPrediction1.classPrediction,
+            boxPrediction2.classPrediction,
+            boxPrediction3.classPrediction,
+            boxPrediction4.classPrediction,
+            boxPrediction5.classPrediction
+        ], 1);
+        return {
+            boxPredictions: boxPredictions,
+            classPredictions: classPredictions
+        };
+    });
+}
+exports.predictionLayer = predictionLayer;
+//# sourceMappingURL=predictionLayer.js.map
\ No newline at end of file

build/commonjs/ssdMobilenetv1/predictionLayer.js.map

+{"version":3,"file":"predictionLayer.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/predictionLayer.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,2DAA0D;AAC1D,2DAA0D;AAG1D,yBACE,CAAc,EACd,MAAmB,EACnB,MAA6B;IAE7B,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAM,KAAK,GAAG,uCAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC1D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAE9D,IAAM,cAAc,GAAG,uCAAkB,CAAC,MAAM,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACzE,IAAM,cAAc,GAAG,uCAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACpE,IAAM,cAAc,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACxE,IAAM,cAAc,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACxE,IAAM,cAAc,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACxE,IAAM,cAAc,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QAExE,IAAM,cAAc,GAAG,EAAE,CAAC,MAAM,CAAC;YAC/B,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;SACrC,EAAE,CAAC,CAAgB,CAAA;QAEpB,IAAM,gBAAgB,GAAG,EAAE,CAAC,MAAM,CAAC;YACjC,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;SAC/B,EAAE,CAAC,CAAgB,CAAA;QAEpB,OAAO;YACL,cAAc,gBAAA;YACd,gBAAgB,kBAAA;SACjB,CAAA;IACH,CAAC,CAAC,CAAA;AACJ,CAAC;AA9CD,0CA8CC"}
\ No newline at end of file

build/commonjs/ssdMobilenetv1/types.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { ConvParams } from 'tfjs-tiny-yolov2';
+export declare type PointwiseConvParams = {
+    filters: tf.Tensor4D;
+    batch_norm_offset: tf.Tensor1D;
+};
+export declare namespace MobileNetV1 {
+    type DepthwiseConvParams = {
+        filters: tf.Tensor4D;
+        batch_norm_scale: tf.Tensor1D;
+        batch_norm_offset: tf.Tensor1D;
+        batch_norm_mean: tf.Tensor1D;
+        batch_norm_variance: tf.Tensor1D;
+    };
+    type ConvPairParams = {
+        depthwise_conv: DepthwiseConvParams;
+        pointwise_conv: PointwiseConvParams;
+    };
+    type Params = {
+        conv_0: PointwiseConvParams;
+        conv_1: ConvPairParams;
+        conv_2: ConvPairParams;
+        conv_3: ConvPairParams;
+        conv_4: ConvPairParams;
+        conv_5: ConvPairParams;
+        conv_6: ConvPairParams;
+        conv_7: ConvPairParams;
+        conv_8: ConvPairParams;
+        conv_9: ConvPairParams;
+        conv_10: ConvPairParams;
+        conv_11: ConvPairParams;
+        conv_12: ConvPairParams;
+        conv_13: ConvPairParams;
+    };
+}
+export declare type BoxPredictionParams = {
+    box_encoding_predictor: ConvParams;
+    class_predictor: ConvParams;
+};
+export declare type PredictionLayerParams = {
+    conv_0: PointwiseConvParams;
+    conv_1: PointwiseConvParams;
+    conv_2: PointwiseConvParams;
+    conv_3: PointwiseConvParams;
+    conv_4: PointwiseConvParams;
+    conv_5: PointwiseConvParams;
+    conv_6: PointwiseConvParams;
+    conv_7: PointwiseConvParams;
+    box_predictor_0: BoxPredictionParams;
+    box_predictor_1: BoxPredictionParams;
+    box_predictor_2: BoxPredictionParams;
+    box_predictor_3: BoxPredictionParams;
+    box_predictor_4: BoxPredictionParams;
+    box_predictor_5: BoxPredictionParams;
+};
+export declare type OutputLayerParams = {
+    extra_dim: tf.Tensor3D;
+};
+export declare type NetParams = {
+    mobilenetv1: MobileNetV1.Params;
+    prediction_layer: PredictionLayerParams;
+    output_layer: OutputLayerParams;
+};

build/commonjs/ssdMobilenetv1/types.js

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

build/commonjs/ssdMobilenetv1/types.js.map

+{"version":3,"file":"types.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/types.ts"],"names":[],"mappings":""}
\ No newline at end of file

build/commonjs/tinyFaceDetector/TinyFaceDetector.d.ts

+import { Point, TNetInput } from 'tfjs-image-recognition-base';
+import { TinyYolov2 as TinyYolov2Base, ITinyYolov2Options } from 'tfjs-tiny-yolov2';
+import { FaceDetection } from '../classes';
+export declare class TinyFaceDetector extends TinyYolov2Base {
+    constructor();
+    readonly anchors: Point[];
+    locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]>;
+    protected loadQuantizedParams(modelUri: string | undefined): any;
+}

build/commonjs/tinyFaceDetector/TinyFaceDetector.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tslib_1 = require("tslib");
+var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
+var classes_1 = require("../classes");
+var const_1 = require("./const");
+var TinyFaceDetector = /** @class */ (function (_super) {
+    tslib_1.__extends(TinyFaceDetector, _super);
+    function TinyFaceDetector() {
+        var _this = this;
+        var config = {
+            withSeparableConvs: true,
+            iouThreshold: const_1.IOU_THRESHOLD,
+            classes: ['face'],
+            anchors: const_1.BOX_ANCHORS,
+            meanRgb: const_1.MEAN_RGB,
+            isFirstLayerConv2d: true,
+            filterSizes: [3, 16, 32, 64, 128, 256, 512]
+        };
+        _this = _super.call(this, config) || this;
+        return _this;
+    }
+    Object.defineProperty(TinyFaceDetector.prototype, "anchors", {
+        get: function () {
+            return this.config.anchors;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    TinyFaceDetector.prototype.locateFaces = function (input, forwardParams) {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var objectDetections;
+            return tslib_1.__generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4 /*yield*/, this.detect(input, forwardParams)];
+                    case 1:
+                        objectDetections = _a.sent();
+                        return [2 /*return*/, objectDetections.map(function (det) { return new classes_1.FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }); })];
+                }
+            });
+        });
+    };
+    TinyFaceDetector.prototype.loadQuantizedParams = function (modelUri) {
+        var defaultModelName = const_1.DEFAULT_MODEL_NAME;
+        return _super.prototype.loadQuantizedParams.call(this, modelUri, defaultModelName);
+    };
+    return TinyFaceDetector;
+}(tfjs_tiny_yolov2_1.TinyYolov2));
+exports.TinyFaceDetector = TinyFaceDetector;
+//# sourceMappingURL=TinyFaceDetector.js.map
\ No newline at end of file

build/commonjs/tinyFaceDetector/TinyFaceDetector.js.map

+{"version":3,"file":"TinyFaceDetector.js","sourceRoot":"","sources":["../../../src/tinyFaceDetector/TinyFaceDetector.ts"],"names":[],"mappings":";;;AACA,qDAAoF;AAEpF,sCAA2C;AAC3C,iCAAmF;AAEnF;IAAsC,4CAAc;IAElD;QAAA,iBAYC;QAXC,IAAM,MAAM,GAAG;YACb,kBAAkB,EAAE,IAAI;YACxB,YAAY,EAAE,qBAAa;YAC3B,OAAO,EAAE,CAAC,MAAM,CAAC;YACjB,OAAO,EAAE,mBAAW;YACpB,OAAO,EAAE,gBAAQ;YACjB,kBAAkB,EAAE,IAAI;YACxB,WAAW,EAAE,CAAC,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC;SAC5C,CAAA;QAED,QAAA,kBAAM,MAAM,CAAC,SAAA;;IACf,CAAC;IAED,sBAAW,qCAAO;aAAlB;YACE,OAAO,IAAI,CAAC,MAAM,CAAC,OAAO,CAAA;QAC5B,CAAC;;;OAAA;IAEY,sCAAW,GAAxB,UAAyB,KAAgB,EAAE,aAAiC;;;;;4BACjD,qBAAM,IAAI,CAAC,MAAM,CAAC,KAAK,EAAE,aAAa,CAAC,EAAA;;wBAA1D,gBAAgB,GAAG,SAAuC;wBAChE,sBAAO,gBAAgB,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,IAAI,uBAAa,CAAC,GAAG,CAAC,KAAK,EAAE,GAAG,CAAC,WAAW,EAAE,EAAE,KAAK,EAAE,GAAG,CAAC,UAAU,EAAE,MAAM,EAAE,GAAG,CAAC,WAAW,EAAE,CAAC,EAAjG,CAAiG,CAAC,EAAA;;;;KACtI;IAES,8CAAmB,GAA7B,UAA8B,QAA4B;QACxD,IAAM,gBAAgB,GAAG,0BAAkB,CAAA;QAC3C,OAAO,iBAAM,mBAAmB,YAAC,QAAQ,EAAE,gBAAgB,CAAQ,CAAA;IACrE,CAAC;IACH,uBAAC;AAAD,CAAC,AA7BD,CAAsC,6BAAc,GA6BnD;AA7BY,4CAAgB"}
\ No newline at end of file

build/commonjs/tinyFaceDetector/TinyFaceDetectorOptions.d.ts

+import { ITinyYolov2Options, TinyYolov2Options } from 'tfjs-tiny-yolov2';
+export interface ITinyFaceDetectorOptions extends ITinyYolov2Options {
+}
+export declare class TinyFaceDetectorOptions extends TinyYolov2Options {
+    protected _name: string;
+}

build/commonjs/tinyFaceDetector/TinyFaceDetectorOptions.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tslib_1 = require("tslib");
+var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
+var TinyFaceDetectorOptions = /** @class */ (function (_super) {
+    tslib_1.__extends(TinyFaceDetectorOptions, _super);
+    function TinyFaceDetectorOptions() {
+        var _this = _super !== null && _super.apply(this, arguments) || this;
+        _this._name = 'TinyFaceDetectorOptions';
+        return _this;
+    }
+    return TinyFaceDetectorOptions;
+}(tfjs_tiny_yolov2_1.TinyYolov2Options));
+exports.TinyFaceDetectorOptions = TinyFaceDetectorOptions;
+//# sourceMappingURL=TinyFaceDetectorOptions.js.map
\ No newline at end of file

build/commonjs/tinyFaceDetector/TinyFaceDetectorOptions.js.map

+{"version":3,"file":"TinyFaceDetectorOptions.js","sourceRoot":"","sources":["../../../src/tinyFaceDetector/TinyFaceDetectorOptions.ts"],"names":[],"mappings":";;;AAAA,qDAAyE;AAIzE;IAA6C,mDAAiB;IAA9D;QAAA,qEAEC;QADW,WAAK,GAAW,yBAAyB,CAAA;;IACrD,CAAC;IAAD,8BAAC;AAAD,CAAC,AAFD,CAA6C,oCAAiB,GAE7D;AAFY,0DAAuB"}
\ No newline at end of file

build/commonjs/tinyFaceDetector/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 MEAN_RGB: [number, number, number];
+export declare const DEFAULT_MODEL_NAME = "tiny_face_detector_model";

build/commonjs/tinyFaceDetector/const.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
+exports.IOU_THRESHOLD = 0.4;
+exports.BOX_ANCHORS = [
+    new tfjs_image_recognition_base_1.Point(1.603231, 2.094468),
+    new tfjs_image_recognition_base_1.Point(6.041143, 7.080126),
+    new tfjs_image_recognition_base_1.Point(2.882459, 3.518061),
+    new tfjs_image_recognition_base_1.Point(4.266906, 5.178857),
+    new tfjs_image_recognition_base_1.Point(9.041765, 10.66308)
+];
+exports.MEAN_RGB = [117.001, 114.697, 97.404];
+exports.DEFAULT_MODEL_NAME = 'tiny_face_detector_model';
+//# sourceMappingURL=const.js.map
\ No newline at end of file

build/commonjs/tinyFaceDetector/const.js.map

+{"version":3,"file":"const.js","sourceRoot":"","sources":["../../../src/tinyFaceDetector/const.ts"],"names":[],"mappings":";;AAAA,2EAAoD;AAEvC,QAAA,aAAa,GAAG,GAAG,CAAA;AAEnB,QAAA,WAAW,GAAG;IACzB,IAAI,mCAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,mCAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,mCAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,mCAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,mCAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;CAC9B,CAAA;AAEY,QAAA,QAAQ,GAA6B,CAAC,OAAO,EAAE,OAAO,EAAE,MAAM,CAAC,CAAA;AAE/D,QAAA,kBAAkB,GAAG,0BAA0B,CAAA"}
\ No newline at end of file

build/commonjs/tinyFaceDetector/index.d.ts

+import { TinyFaceDetector } from './TinyFaceDetector';
+export * from './TinyFaceDetector';
+export * from './TinyFaceDetectorOptions';
+export declare function createTinyFaceDetector(weights: Float32Array): TinyFaceDetector;

build/commonjs/tinyFaceDetector/index.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tslib_1 = require("tslib");
+var TinyFaceDetector_1 = require("./TinyFaceDetector");
+tslib_1.__exportStar(require("./TinyFaceDetector"), exports);
+tslib_1.__exportStar(require("./TinyFaceDetectorOptions"), exports);
+function createTinyFaceDetector(weights) {
+    var net = new TinyFaceDetector_1.TinyFaceDetector();
+    net.extractWeights(weights);
+    return net;
+}
+exports.createTinyFaceDetector = createTinyFaceDetector;
+//# sourceMappingURL=index.js.map
\ No newline at end of file

build/commonjs/tinyFaceDetector/index.js.map

+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/tinyFaceDetector/index.ts"],"names":[],"mappings":";;;AAAA,uDAAsD;AAEtD,6DAAmC;AACnC,oEAA0C;AAE1C,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"}
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build/commonjs/tinyYolov2/TinyYolov2.d.ts

 import { Point, TNetInput } from 'tfjs-image-recognition-base';
-import { TinyYolov2 as TinyYolov2Base, TinyYolov2Types } from 'tfjs-tiny-yolov2';
+import { ITinyYolov2Options, TinyYolov2 as TinyYolov2Base } from 'tfjs-tiny-yolov2';
 import { FaceDetection } from '../classes';
 export declare class TinyYolov2 extends TinyYolov2Base {
     constructor(withSeparableConvs?: boolean);
     readonly withSeparableConvs: boolean;
     readonly anchors: Point[];
-    locateFaces(input: TNetInput, forwardParams: TinyYolov2Types.TinyYolov2ForwardParams): Promise<FaceDetection[]>;
+    locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]>;
     protected loadQuantizedParams(modelUri: string | undefined): any;
 }

build/commonjs/tinyYolov2/TinyYolov2.js.map

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\ No newline at end of file

build/es6/classes/FaceDetection.d.ts

-import { Dimensions, ObjectDetection, Rect } from 'tfjs-image-recognition-base';
-export declare class FaceDetection extends ObjectDetection {
-    constructor(score: number, relativeBox: Rect, imageDims: Dimensions);
+import { Box, IDimensions, ObjectDetection, Rect } from 'tfjs-image-recognition-base';
+export interface IFaceDetecion {
+    score: number;
+    box: Box;
+}
+export declare class FaceDetection extends ObjectDetection implements IFaceDetecion {
+    constructor(score: number, relativeBox: Rect, imageDims: IDimensions);
 }

build/es6/classes/FaceDetection.js.map

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build/es6/classes/FaceDetectionWithLandmarks.d.ts

+import { FaceDetection } from './FaceDetection';
+import { FaceLandmarks } from './FaceLandmarks';
+import { FaceLandmarks68 } from './FaceLandmarks68';
+export interface IFaceDetectionWithLandmarks<TFaceLandmarks extends FaceLandmarks = FaceLandmarks68> {
+    detection: FaceDetection;
+    landmarks: TFaceLandmarks;
+}
+export declare class FaceDetectionWithLandmarks<TFaceLandmarks extends FaceLandmarks = FaceLandmarks68> implements IFaceDetectionWithLandmarks<TFaceLandmarks> {
+    private _detection;
+    private _unshiftedLandmarks;
+    constructor(detection: FaceDetection, unshiftedLandmarks: TFaceLandmarks);
+    readonly detection: FaceDetection;
+    readonly unshiftedLandmarks: TFaceLandmarks;
+    readonly alignedRect: FaceDetection;
+    readonly landmarks: TFaceLandmarks;
+    readonly faceDetection: FaceDetection;
+    readonly faceLandmarks: TFaceLandmarks;
+    forSize(width: number, height: number): FaceDetectionWithLandmarks<TFaceLandmarks>;
+}

build/es6/classes/FaceDetectionWithLandmarks.js

+import { FaceDetection } from './FaceDetection';
+var FaceDetectionWithLandmarks = /** @class */ (function () {
+    function FaceDetectionWithLandmarks(detection, unshiftedLandmarks) {
+        this._detection = detection;
+        this._unshiftedLandmarks = unshiftedLandmarks;
+    }
+    Object.defineProperty(FaceDetectionWithLandmarks.prototype, "detection", {
+        get: function () { return this._detection; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceDetectionWithLandmarks.prototype, "unshiftedLandmarks", {
+        get: function () { return this._unshiftedLandmarks; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceDetectionWithLandmarks.prototype, "alignedRect", {
+        get: function () {
+            var rect = this.landmarks.align();
+            var imageDims = this.detection.imageDims;
+            return new FaceDetection(this._detection.score, rect.rescale(imageDims.reverse()), imageDims);
+        },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceDetectionWithLandmarks.prototype, "landmarks", {
+        get: function () {
+            var _a = this.detection.box, x = _a.x, y = _a.y;
+            return this._unshiftedLandmarks.shiftBy(x, y);
+        },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceDetectionWithLandmarks.prototype, "faceDetection", {
+        // aliases for backward compatibily
+        get: function () { return this.detection; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceDetectionWithLandmarks.prototype, "faceLandmarks", {
+        get: function () { return this.landmarks; },
+        enumerable: true,
+        configurable: true
+    });
+    FaceDetectionWithLandmarks.prototype.forSize = function (width, height) {
+        var resizedDetection = this._detection.forSize(width, height);
+        var resizedLandmarks = this._unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);
+        return new FaceDetectionWithLandmarks(resizedDetection, resizedLandmarks);
+    };
+    return FaceDetectionWithLandmarks;
+}());
+export { FaceDetectionWithLandmarks };
+//# sourceMappingURL=FaceDetectionWithLandmarks.js.map
\ No newline at end of file

build/es6/classes/FaceDetectionWithLandmarks.js.map

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build/es6/classes/FaceLandmarks.d.ts

-import { Dimensions, Point, Rect } from 'tfjs-image-recognition-base';
+import { Dimensions, IDimensions, Point, Rect } from 'tfjs-image-recognition-base';
 import { FaceDetection } from './FaceDetection';
-export declare class FaceLandmarks {
-    protected _imageWidth: number;
-    protected _imageHeight: number;
+export interface IFaceLandmarks {
+    positions: Point[];
+    shift: Point;
+}
+export declare class FaceLandmarks implements IFaceLandmarks {
     protected _shift: Point;
-    protected _faceLandmarks: Point[];
-    constructor(relativeFaceLandmarkPositions: Point[], imageDims: Dimensions, shift?: Point);
-    getShift(): Point;
-    getImageWidth(): number;
-    getImageHeight(): number;
-    getPositions(): Point[];
-    getRelativePositions(): Point[];
+    protected _positions: Point[];
+    protected _imgDims: Dimensions;
+    constructor(relativeFaceLandmarkPositions: Point[], imgDims: IDimensions, shift?: Point);
+    readonly shift: Point;
+    readonly imageWidth: number;
+    readonly imageHeight: number;
+    readonly positions: Point[];
+    readonly relativePositions: Point[];
     forSize<T extends FaceLandmarks>(width: number, height: number): T;
-    shift<T extends FaceLandmarks>(x: number, y: number): T;
+    shiftBy<T extends FaceLandmarks>(x: number, y: number): T;
     shiftByPoint<T extends FaceLandmarks>(pt: Point): T;
     /**
      * Aligns the face landmarks after face detection from the relative positions of the faces

build/es6/classes/FaceLandmarks.js

-import { getCenterPoint, Point, Rect } from 'tfjs-image-recognition-base';
+import { Dimensions, 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) {
+    function FaceLandmarks(relativeFaceLandmarkPositions, imgDims, shift) {
         if (shift === void 0) { shift = new Point(0, 0); }
-        var width = imageDims.width, height = imageDims.height;
-        this._imageWidth = width;
-        this._imageHeight = height;
+        var width = imgDims.width, height = imgDims.height;
+        this._imgDims = new Dimensions(width, height);
         this._shift = shift;
-        this._faceLandmarks = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new Point(width, height)).add(shift); });
+        this._positions = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new Point(width, height)).add(shift); });
     }
-    FaceLandmarks.prototype.getShift = function () {
-        return new 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 () {
+    Object.defineProperty(FaceLandmarks.prototype, "shift", {
+        get: function () { return new Point(this._shift.x, this._shift.y); },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceLandmarks.prototype, "imageWidth", {
+        get: function () { return this._imgDims.width; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceLandmarks.prototype, "imageHeight", {
+        get: function () { return this._imgDims.height; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceLandmarks.prototype, "positions", {
+        get: function () { return this._positions; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceLandmarks.prototype, "relativePositions", {
+        get: function () {
             var _this = this;
-        return this._faceLandmarks.map(function (pt) { return pt.sub(_this._shift).div(new Point(_this._imageWidth, _this._imageHeight)); });
-    };
+            return this._positions.map(function (pt) { return pt.sub(_this._shift).div(new Point(_this.imageWidth, _this.imageHeight)); });
+        },
+        enumerable: true,
+        configurable: true
+    });
     FaceLandmarks.prototype.forSize = function (width, height) {
-        return new this.constructor(this.getRelativePositions(), { width: width, height: height });
+        return new this.constructor(this.relativePositions, { width: width, height: height });
     };
-    FaceLandmarks.prototype.shift = function (x, y) {
-        return new this.constructor(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new Point(x, y));
+    FaceLandmarks.prototype.shiftBy = function (x, y) {
+        return new this.constructor(this.relativePositions, this._imgDims, new Point(x, y));
     };
     FaceLandmarks.prototype.shiftByPoint = function (pt) {
-        return this.shift(pt.x, pt.y);
+        return this.shiftBy(pt.x, pt.y);
     };
     /**
      * Aligns the face landmarks after face detection from the relative positions of the faces
@@ -52,9 +63,9 @@ var FaceLandmarks = /** @class */ (function () {
     FaceLandmarks.prototype.align = function (detection) {
         if (detection) {
             var box = detection instanceof FaceDetection
-                ? detection.getBox().floor()
+                ? detection.box.floor()
                 : detection;
-            return this.shift(box.x, box.y).align();
+            return this.shiftBy(box.x, box.y).align();
         }
         var centers = this.getRefPointsForAlignment();
         var leftEyeCenter = centers[0], rightEyeCenter = centers[1], mouthCenter = centers[2];
@@ -65,7 +76,7 @@ var FaceLandmarks = /** @class */ (function () {
         // TODO: pad in case rectangle is out of image bounds
         var x = Math.floor(Math.max(0, refPoint.x - (relX * size)));
         var y = Math.floor(Math.max(0, refPoint.y - (relY * size)));
-        return new 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');

build/es6/classes/FaceLandmarks.js.map

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\ No newline at end of file
+{"version":3,"file":"FaceLandmarks.js","sourceRoot":"","sources":["../../../src/classes/FaceLandmarks.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,UAAU,EAAE,cAAc,EAAe,KAAK,EAAE,IAAI,EAAE,MAAM,6BAA6B,CAAC;AAEnG,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;AAOrB;IAKE,uBACE,6BAAsC,EACtC,OAAoB,EACpB,KAA8B;QAA9B,sBAAA,EAAA,YAAmB,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC;QAEtB,IAAA,qBAAK,EAAE,uBAAM,CAAY;QACjC,IAAI,CAAC,QAAQ,GAAG,IAAI,UAAU,CAAC,KAAK,EAAE,MAAM,CAAC,CAAA;QAC7C,IAAI,CAAC,MAAM,GAAG,KAAK,CAAA;QACnB,IAAI,CAAC,UAAU,GAAG,6BAA6B,CAAC,GAAG,CACjD,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;IAED,sBAAW,gCAAK;aAAhB,cAA4B,OAAO,IAAI,KAAK,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC,EAAE,IAAI,CAAC,MAAM,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC;;;OAAA;IAC5E,sBAAW,qCAAU;aAArB,cAAkC,OAAO,IAAI,CAAC,QAAQ,CAAC,KAAK,CAAA,CAAC,CAAC;;;OAAA;IAC9D,sBAAW,sCAAW;aAAtB,cAAmC,OAAO,IAAI,CAAC,QAAQ,CAAC,MAAM,CAAA,CAAC,CAAC;;;OAAA;IAChE,sBAAW,oCAAS;aAApB,cAAkC,OAAO,IAAI,CAAC,UAAU,CAAA,CAAC,CAAC;;;OAAA;IAC1D,sBAAW,4CAAiB;aAA5B;YAAA,iBAIC;YAHC,OAAO,IAAI,CAAC,UAAU,CAAC,GAAG,CACxB,UAAA,EAAE,IAAI,OAAA,EAAE,CAAC,GAAG,CAAC,KAAI,CAAC,MAAM,CAAC,CAAC,GAAG,CAAC,IAAI,KAAK,CAAC,KAAI,CAAC,UAAU,EAAE,KAAI,CAAC,WAAW,CAAC,CAAC,EAArE,CAAqE,CAC5E,CAAA;QACH,CAAC;;;OAAA;IAEM,+BAAO,GAAd,UAAwC,KAAa,EAAE,MAAc;QACnE,OAAO,IAAK,IAAI,CAAC,WAAmB,CAClC,IAAI,CAAC,iBAAiB,EACtB,EAAE,KAAK,OAAA,EAAE,MAAM,QAAA,EAAE,CAClB,CAAA;IACH,CAAC;IAEM,+BAAO,GAAd,UAAwC,CAAS,EAAE,CAAS;QAC1D,OAAO,IAAK,IAAI,CAAC,WAAmB,CAClC,IAAI,CAAC,iBAAiB,EACtB,IAAI,CAAC,QAAQ,EACb,IAAI,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,CAChB,CAAA;IACH,CAAC;IAEM,oCAAY,GAAnB,UAA6C,EAAS;QACpD,OAAO,IAAI,CAAC,OAAO,CAAC,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC,CAAA;IACjC,CAAC;IAED;;;;;;;;;;OAUG;IACI,6BAAK,GAAZ,UACE,SAAgC;QAEhC,IAAI,SAAS,EAAE;YACb,IAAM,GAAG,GAAG,SAAS,YAAY,aAAa;gBAC5C,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,KAAK,EAAE;gBACvB,CAAC,CAAC,SAAS,CAAA;YAEb,OAAO,IAAI,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC,CAAC,KAAK,EAAE,CAAA;SAC1C;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,UAAU,GAAG,CAAC,CAAC,EAAE,IAAI,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,WAAW,GAAG,CAAC,CAAC,CAAC,CAAA;IAClG,CAAC;IAES,gDAAwB,GAAlC;QACE,MAAM,IAAI,KAAK,CAAC,wDAAwD,CAAC,CAAA;IAC3E,CAAC;IACH,oBAAC;AAAD,CAAC,AAxFD,IAwFC"}
\ No newline at end of file

build/es6/classes/FaceLandmarks5.js

@@ -7,7 +7,7 @@ var FaceLandmarks5 = /** @class */ (function (_super) {
         return _super !== null && _super.apply(this, arguments) || this;
     }
     FaceLandmarks5.prototype.getRefPointsForAlignment = function () {
-        var pts = this.getPositions();
+        var pts = this.positions;
         return [
             pts[0],
             pts[1],

build/es6/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
+{"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,SAAS,CAAA;QAC1B,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/es6/classes/FaceLandmarks68.js

@@ -7,25 +7,25 @@ var FaceLandmarks68 = /** @class */ (function (_super) {
         return _super !== null && _super.apply(this, arguments) || this;
     }
     FaceLandmarks68.prototype.getJawOutline = function () {
-        return this._faceLandmarks.slice(0, 17);
+        return this.positions.slice(0, 17);
     };
     FaceLandmarks68.prototype.getLeftEyeBrow = function () {
-        return this._faceLandmarks.slice(17, 22);
+        return this.positions.slice(17, 22);
     };
     FaceLandmarks68.prototype.getRightEyeBrow = function () {
-        return this._faceLandmarks.slice(22, 27);
+        return this.positions.slice(22, 27);
     };
     FaceLandmarks68.prototype.getNose = function () {
-        return this._faceLandmarks.slice(27, 36);
+        return this.positions.slice(27, 36);
     };
     FaceLandmarks68.prototype.getLeftEye = function () {
-        return this._faceLandmarks.slice(36, 42);
+        return this.positions.slice(36, 42);
     };
     FaceLandmarks68.prototype.getRightEye = function () {
-        return this._faceLandmarks.slice(42, 48);
+        return this.positions.slice(42, 48);
     };
     FaceLandmarks68.prototype.getMouth = function () {
-        return this._faceLandmarks.slice(48, 68);
+        return this.positions.slice(48, 68);
     };
     FaceLandmarks68.prototype.getRefPointsForAlignment = function () {
         return [

build/es6/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
+{"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;AAEzD;IAAqC,2CAAa;IAAlD;;IAoCA,CAAC;IAnCQ,uCAAa,GAApB;QACE,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,CAAC,EAAE,EAAE,CAAC,CAAA;IACpC,CAAC;IAEM,wCAAc,GAArB;QACE,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IACrC,CAAC;IAEM,yCAAe,GAAtB;QACE,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IACrC,CAAC;IAEM,iCAAO,GAAd;QACE,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IACrC,CAAC;IAEM,oCAAU,GAAjB;QACE,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IACrC,CAAC;IAEM,qCAAW,GAAlB;QACE,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IACrC,CAAC;IAEM,kCAAQ,GAAf;QACE,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IACrC,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/es6/classes/FaceMatch.d.ts

+export interface IFaceMatch {
+    label: string;
+    distance: number;
+}
+export declare class FaceMatch implements IFaceMatch {
+    private _label;
+    private _distance;
+    constructor(label: string, distance: number);
+    readonly label: string;
+    readonly distance: number;
+    toString(withDistance?: boolean): string;
+}

build/es6/classes/FaceMatch.js

+import { round } from 'tfjs-image-recognition-base';
+var FaceMatch = /** @class */ (function () {
+    function FaceMatch(label, distance) {
+        this._label = label;
+        this._distance = distance;
+    }
+    Object.defineProperty(FaceMatch.prototype, "label", {
+        get: function () { return this._label; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceMatch.prototype, "distance", {
+        get: function () { return this._distance; },
+        enumerable: true,
+        configurable: true
+    });
+    FaceMatch.prototype.toString = function (withDistance) {
+        if (withDistance === void 0) { withDistance = true; }
+        return "" + this.label + (withDistance ? " (" + round(this.distance) + ")" : '');
+    };
+    return FaceMatch;
+}());
+export { FaceMatch };
+//# sourceMappingURL=FaceMatch.js.map
\ No newline at end of file

build/es6/classes/FaceMatch.js.map

+{"version":3,"file":"FaceMatch.js","sourceRoot":"","sources":["../../../src/classes/FaceMatch.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,KAAK,EAAE,MAAM,6BAA6B,CAAC;AAOpD;IAIE,mBAAY,KAAa,EAAE,QAAgB;QACzC,IAAI,CAAC,MAAM,GAAG,KAAK,CAAA;QACnB,IAAI,CAAC,SAAS,GAAG,QAAQ,CAAA;IAC3B,CAAC;IAED,sBAAW,4BAAK;aAAhB,cAA6B,OAAO,IAAI,CAAC,MAAM,CAAA,CAAC,CAAC;;;OAAA;IACjD,sBAAW,+BAAQ;aAAnB,cAAgC,OAAO,IAAI,CAAC,SAAS,CAAA,CAAC,CAAC;;;OAAA;IAEhD,4BAAQ,GAAf,UAAgB,YAA4B;QAA5B,6BAAA,EAAA,mBAA4B;QAC1C,OAAO,KAAG,IAAI,CAAC,KAAK,IAAG,YAAY,CAAC,CAAC,CAAC,OAAK,KAAK,CAAC,IAAI,CAAC,QAAQ,CAAC,MAAG,CAAC,CAAC,CAAC,EAAE,CAAE,CAAA;IAC3E,CAAC;IACH,gBAAC;AAAD,CAAC,AAfD,IAeC"}
\ No newline at end of file

build/es6/classes/FullFaceDescription.d.ts

 import { FaceDetection } from './FaceDetection';
+import { FaceDetectionWithLandmarks, IFaceDetectionWithLandmarks } from './FaceDetectionWithLandmarks';
 import { FaceLandmarks } from './FaceLandmarks';
-export declare class FullFaceDescription {
-    private _detection;
-    private _landmarks;
+import { FaceLandmarks68 } from './FaceLandmarks68';
+export interface IFullFaceDescription<TFaceLandmarks extends FaceLandmarks = FaceLandmarks68> extends IFaceDetectionWithLandmarks<TFaceLandmarks> {
+    detection: FaceDetection;
+    landmarks: TFaceLandmarks;
+    descriptor: Float32Array;
+}
+export declare class FullFaceDescription<TFaceLandmarks extends FaceLandmarks = FaceLandmarks68> extends FaceDetectionWithLandmarks<TFaceLandmarks> implements IFullFaceDescription<TFaceLandmarks> {
     private _descriptor;
-    constructor(_detection: FaceDetection, _landmarks: FaceLandmarks, _descriptor: Float32Array);
-    readonly detection: FaceDetection;
-    readonly landmarks: FaceLandmarks;
+    constructor(detection: FaceDetection, unshiftedLandmarks: TFaceLandmarks, descriptor: Float32Array);
     readonly descriptor: Float32Array;
-    forSize(width: number, height: number): FullFaceDescription;
+    forSize(width: number, height: number): FullFaceDescription<TFaceLandmarks>;
 }

build/es6/classes/FullFaceDescription.js

-var FullFaceDescription = /** @class */ (function () {
-    function FullFaceDescription(_detection, _landmarks, _descriptor) {
-        this._detection = _detection;
-        this._landmarks = _landmarks;
-        this._descriptor = _descriptor;
+import * as tslib_1 from "tslib";
+import { FaceDetectionWithLandmarks } from './FaceDetectionWithLandmarks';
+var FullFaceDescription = /** @class */ (function (_super) {
+    tslib_1.__extends(FullFaceDescription, _super);
+    function FullFaceDescription(detection, unshiftedLandmarks, descriptor) {
+        var _this = _super.call(this, detection, unshiftedLandmarks) || this;
+        _this._descriptor = descriptor;
+        return _this;
     }
-    Object.defineProperty(FullFaceDescription.prototype, "detection", {
-        get: function () {
-            return this._detection;
-        },
-        enumerable: true,
-        configurable: true
-    });
-    Object.defineProperty(FullFaceDescription.prototype, "landmarks", {
-        get: function () {
-            return this._landmarks;
-        },
-        enumerable: true,
-        configurable: true
-    });
     Object.defineProperty(FullFaceDescription.prototype, "descriptor", {
         get: function () {
             return this._descriptor;
@@ -26,9 +15,10 @@ var FullFaceDescription = /** @class */ (function () {
         configurable: true
     });
     FullFaceDescription.prototype.forSize = function (width, height) {
-        return new FullFaceDescription(this._detection.forSize(width, height), this._landmarks.forSize(width, height), this._descriptor);
+        var _a = _super.prototype.forSize.call(this, width, height), detection = _a.detection, landmarks = _a.landmarks;
+        return new FullFaceDescription(detection, landmarks, this.descriptor);
     };
     return FullFaceDescription;
-}());
+}(FaceDetectionWithLandmarks));
 export { FullFaceDescription };
 //# sourceMappingURL=FullFaceDescription.js.map
\ No newline at end of file

build/es6/classes/FullFaceDescription.js.map

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\ No newline at end of file
+{"version":3,"file":"FullFaceDescription.js","sourceRoot":"","sources":["../../../src/classes/FullFaceDescription.ts"],"names":[],"mappings":";AACA,OAAO,EAAE,0BAA0B,EAA+B,MAAM,8BAA8B,CAAC;AAYvG;IACU,+CAA0C;IAKlD,6BACE,SAAwB,EACxB,kBAAkC,EAClC,UAAwB;QAH1B,YAKE,kBAAM,SAAS,EAAE,kBAAkB,CAAC,SAErC;QADC,KAAI,CAAC,WAAW,GAAG,UAAU,CAAA;;IAC/B,CAAC;IAED,sBAAW,2CAAU;aAArB;YACE,OAAO,IAAI,CAAC,WAAW,CAAA;QACzB,CAAC;;;OAAA;IAEM,qCAAO,GAAd,UAAe,KAAa,EAAE,MAAc;QACpC,IAAA,uDAAuD,EAArD,wBAAS,EAAE,wBAAS,CAAiC;QAC7D,OAAO,IAAI,mBAAmB,CAAiB,SAAS,EAAE,SAAS,EAAE,IAAI,CAAC,UAAU,CAAC,CAAA;IACvF,CAAC;IACH,0BAAC;AAAD,CAAC,AAvBD,CACU,0BAA0B,GAsBnC"}
\ No newline at end of file

build/es6/classes/LabeledFaceDescriptors.d.ts

+export declare class LabeledFaceDescriptors {
+    private _label;
+    private _descriptors;
+    constructor(label: string, descriptors: Float32Array[]);
+    readonly label: string;
+    readonly descriptors: Float32Array[];
+}

build/es6/classes/LabeledFaceDescriptors.js

+var LabeledFaceDescriptors = /** @class */ (function () {
+    function LabeledFaceDescriptors(label, descriptors) {
+        if (!(typeof label === 'string')) {
+            throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');
+        }
+        if (!Array.isArray(descriptors) || descriptors.some(function (desc) { return !(desc instanceof Float32Array); })) {
+            throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');
+        }
+        this._label = label;
+        this._descriptors = descriptors;
+    }
+    Object.defineProperty(LabeledFaceDescriptors.prototype, "label", {
+        get: function () { return this._label; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(LabeledFaceDescriptors.prototype, "descriptors", {
+        get: function () { return this._descriptors; },
+        enumerable: true,
+        configurable: true
+    });
+    return LabeledFaceDescriptors;
+}());
+export { LabeledFaceDescriptors };
+//# sourceMappingURL=LabeledFaceDescriptors.js.map
\ No newline at end of file

build/es6/classes/LabeledFaceDescriptors.js.map

+{"version":3,"file":"LabeledFaceDescriptors.js","sourceRoot":"","sources":["../../../src/classes/LabeledFaceDescriptors.ts"],"names":[],"mappings":"AAAA;IAIE,gCAAY,KAAa,EAAE,WAA2B;QACpD,IAAI,CAAC,CAAC,OAAO,KAAK,KAAK,QAAQ,CAAC,EAAE;YAChC,MAAM,IAAI,KAAK,CAAC,oEAAoE,CAAC,CAAA;SACtF;QAED,IAAI,CAAC,KAAK,CAAC,OAAO,CAAC,WAAW,CAAC,IAAI,WAAW,CAAC,IAAI,CAAC,UAAA,IAAI,IAAI,OAAA,CAAC,CAAC,IAAI,YAAY,YAAY,CAAC,EAA/B,CAA+B,CAAC,EAAE;YAC5F,MAAM,IAAI,KAAK,CAAC,0FAA0F,CAAC,CAAA;SAC5G;QAED,IAAI,CAAC,MAAM,GAAG,KAAK,CAAA;QACnB,IAAI,CAAC,YAAY,GAAG,WAAW,CAAA;IACjC,CAAC;IAED,sBAAW,yCAAK;aAAhB,cAA6B,OAAO,IAAI,CAAC,MAAM,CAAA,CAAC,CAAC;;;OAAA;IACjD,sBAAW,+CAAW;aAAtB,cAA2C,OAAO,IAAI,CAAC,YAAY,CAAA,CAAC,CAAC;;;OAAA;IACvE,6BAAC;AAAD,CAAC,AAnBD,IAmBC"}
\ No newline at end of file

build/es6/classes/index.d.ts

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

build/es6/classes/index.js

 export * from './FaceDetection';
+export * from './FaceDetectionWithLandmarks';
 export * from './FaceLandmarks';
 export * from './FaceLandmarks5';
 export * from './FaceLandmarks68';
+export * from './FaceMatch';
 export * from './FullFaceDescription';
+export * from './LabeledFaceDescriptors';
 //# sourceMappingURL=index.js.map
\ No newline at end of file

build/es6/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
+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/classes/index.ts"],"names":[],"mappings":"AAAA,cAAc,iBAAiB,CAAC;AAChC,cAAc,8BAA8B,CAAC;AAC7C,cAAc,iBAAiB,CAAC;AAChC,cAAc,kBAAkB,CAAC;AACjC,cAAc,mBAAmB,CAAC;AAClC,cAAc,aAAa,CAAC;AAC5B,cAAc,uBAAuB,CAAC;AACtC,cAAc,0BAA0B,CAAC"}
\ No newline at end of file

build/es6/dom/drawLandmarks.js

@@ -27,7 +27,7 @@ export function drawLandmarks(canvasArg, faceLandmarks, options) {
         // else draw points
         var ptOffset = lineWidth / 2;
         ctx.fillStyle = color;
-        landmarks.getPositions().forEach(function (pt) { return ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth); });
+        landmarks.positions.forEach(function (pt) { return ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth); });
     });
 }
 //# sourceMappingURL=drawLandmarks.js.map
\ No newline at end of file

build/es6/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
+{"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,SAAS,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;IACzG,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file

build/es6/dom/extractFaceTensors.js

@@ -21,7 +21,7 @@ export function extractFaceTensors(imageTensor, detections) {
             return [2 /*return*/, tf.tidy(function () {
                     var _a = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];
                     var boxes = detections.map(function (det) { return det instanceof FaceDetection
-                        ? det.forSize(imgWidth, imgHeight).getBox()
+                        ? det.forSize(imgWidth, imgHeight).box
                         : det; })
                         .map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); });
                     var faceTensors = boxes.map(function (_a) {

build/es6/dom/extractFaceTensors.js.map

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\ No newline at end of file
+{"version":3,"file":"extractFaceTensors.js","sourceRoot":"","sources":["../../../src/dom/extractFaceTensors.ts"],"names":[],"mappings":";AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAC5C,OAAO,EAAE,UAAU,EAAQ,MAAM,6BAA6B,CAAC;AAE/D,OAAO,EAAE,aAAa,EAAE,MAAM,0BAA0B,CAAC;AAEzD;;;;;;;;;GASG;AACH,MAAM,6BACJ,WAAsC,EACtC,UAAuC;;;YAGvC,IAAI,UAAU,CAAC,WAAW,CAAC,IAAI,WAAW,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,CAAC,EAAE;gBACvD,MAAM,IAAI,KAAK,CAAC,kDAAkD,CAAC,CAAA;aACpE;YAED,sBAAO,EAAE,CAAC,IAAI,CAAC;oBACP,IAAA,6DAA6F,EAA5F,iBAAS,EAAE,gBAAQ,EAAE,mBAAW,CAA4D;oBAEnG,IAAM,KAAK,GAAG,UAAU,CAAC,GAAG,CAC1B,UAAA,GAAG,IAAI,OAAA,GAAG,YAAY,aAAa;wBACjC,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,QAAQ,EAAE,SAAS,CAAC,CAAC,GAAG;wBACtC,CAAC,CAAC,GAAG,EAFA,CAEA,CACR;yBACE,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,CAAC,kBAAkB,CAAC,QAAQ,EAAE,SAAS,CAAC,EAA3C,CAA2C,CAAC,CAAA;oBAE1D,IAAM,WAAW,GAAG,KAAK,CAAC,GAAG,CAAC,UAAC,EAAuB;4BAArB,QAAC,EAAE,QAAC,EAAE,gBAAK,EAAE,kBAAM;wBAClD,OAAA,EAAE,CAAC,OAAO,CAAC,WAAW,CAAC,IAAI,CAAC,SAAS,EAAE,QAAQ,EAAE,WAAW,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,MAAM,EAAE,KAAK,EAAE,WAAW,CAAC,CAAC;oBAAvG,CAAuG,CACxG,CAAA;oBAED,OAAO,WAAW,CAAA;gBACpB,CAAC,CAAC,EAAA;;;CACH"}
\ No newline at end of file

build/es6/dom/extractFaces.js

@@ -36,7 +36,7 @@ export function extractFaces(input, detections) {
                 case 5:
                     ctx = getContext2dOrThrow(canvas);
                     boxes = detections.map(function (det) { return det instanceof FaceDetection
-                        ? det.forSize(canvas.width, canvas.height).getBox().floor()
+                        ? det.forSize(canvas.width, canvas.height).box.floor()
                         : det; })
                         .map(function (box) { return box.clipAtImageBorders(canvas.width, canvas.height); });
                     return [2 /*return*/, boxes.map(function (_a) {

build/es6/dom/extractFaces.js.map

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\ No newline at end of file
+{"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,CAAC,EAAA;;oBAAlC,QAAQ,GAAG,SAAuB;oBAExC,IAAI,QAAQ,CAAC,SAAS,GAAG,CAAC,EAAE;wBAC1B,MAAM,IAAI,KAAK,CAAC,4CAA4C,CAAC,CAAA;qBAC9D;oBAEK,cAAc,GAAG,QAAQ,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAA;yBAClC,CAAA,cAAc,YAAY,iBAAiB,CAAA,EAA3C,wBAA2C;oBAChD,KAAA,cAAc,CAAA;;wBACd,qBAAM,mBAAmB,CAAC,cAAc,CAAC,EAAA;;oBAAzC,KAAA,SAAyC,CAAA;;;oBAF7C,MAAM,KAEuC,CAAA;;;oBAGzC,GAAG,GAAG,mBAAmB,CAAC,MAAM,CAAC,CAAA;oBACjC,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,GAAG,CAAC,KAAK,EAAE;wBACtD,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/es6/faceLandmarkNet/FaceLandmark68Net.js

@@ -23,12 +23,12 @@ function denseBlock(x, denseBlockParams, isFirstLayer) {
 var FaceLandmark68Net = /** @class */ (function (_super) {
     tslib_1.__extends(FaceLandmark68Net, _super);
     function FaceLandmark68Net() {
-        return _super.call(this, 'FaceLandmark68LargeNet') || this;
+        return _super.call(this, 'FaceLandmark68Net') || this;
     }
     FaceLandmark68Net.prototype.runNet = function (input) {
         var params = this.params;
         if (!params) {
-            throw new Error('FaceLandmark68LargeNet - load model before inference');
+            throw new Error('FaceLandmark68Net - load model before inference');
         }
         return tf.tidy(function () {
             var batchTensor = input.toBatchTensor(112, true);

build/es6/faceLandmarkNet/FaceLandmark68Net.js.map

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\ No newline at end of file
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\ No newline at end of file

build/es6/faceLandmarkNet/FaceLandmark68NetBase.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { NetInput, NeuralNetwork, TNetInput, Dimensions } from 'tfjs-image-recognition-base';
+import { IDimensions, NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
 import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
 export declare class FaceLandmark68NetBase<NetParams> extends NeuralNetwork<NetParams> {
     private __name;
     constructor(_name: string);
     runNet(_: NetInput): tf.Tensor2D;
-    postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: Dimensions[]): tf.Tensor2D;
+    postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D;
     forwardInput(input: NetInput): tf.Tensor2D;
     forward(input: TNetInput): Promise<tf.Tensor2D>;
     detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;

build/es6/faceLandmarkNet/FaceLandmark68NetBase.js.map

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\ No newline at end of file
+{"version":3,"file":"FaceLandmark68NetBase.js","sourceRoot":"","sources":["../../../src/faceLandmarkNet/FaceLandmark68NetBase.ts"],"names":[],"mappings":";AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAC5C,OAAO,EAAe,MAAM,EAAY,aAAa,EAAE,KAAK,EAAa,UAAU,EAAE,MAAM,6BAA6B,CAAC;AAEzH,OAAO,EAAE,eAAe,EAAE,MAAM,4BAA4B,CAAC;AAE7D;IAAsD,iDAAwB;IAK5E,+BAAY,KAAa;QAAzB,YACE,kBAAM,KAAK,CAAC,SAEb;QADC,KAAI,CAAC,MAAM,GAAG,KAAK,CAAA;;IACrB,CAAC;IAEM,sCAAM,GAAb,UAAc,CAAW;QACvB,MAAM,IAAI,KAAK,CAAI,IAAI,CAAC,MAAM,8BAA2B,CAAC,CAAA;IAC5D,CAAC;IAEM,2CAAW,GAAlB,UAAmB,MAAmB,EAAE,SAAiB,EAAE,kBAAiC;QAE1F,IAAM,eAAe,GAAG,kBAAkB,CAAC,GAAG,CAAC,UAAC,EAAiB;gBAAf,gBAAK,EAAE,kBAAM;YAC7D,IAAM,KAAK,GAAG,SAAS,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,EAAE,KAAK,CAAC,CAAA;YACjD,OAAO;gBACL,KAAK,EAAE,KAAK,GAAG,KAAK;gBACpB,MAAM,EAAE,MAAM,GAAG,KAAK;aACvB,CAAA;QACH,CAAC,CAAC,CAAA;QAEF,IAAM,SAAS,GAAG,eAAe,CAAC,MAAM,CAAA;QAExC,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,uBAAuB,GAAG,UAAC,KAAa,EAAE,KAAa;gBAC3D,OAAA,EAAE,CAAC,KAAK,CAAC;oBACP,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,CAAC;oBACpB,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,CAAC;iBACrB,EAAE,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,IAAI,EAAE;YAHzB,CAGyB,CAAA;YAE3B,IAAM,UAAU,GAAG,UAAC,QAAgB,EAAE,IAAuC;gBACrE,IAAA,8BAA6C,EAA3C,gBAAK,EAAE,kBAAM,CAA8B;gBACnD,OAAO,IAAI,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,KAAK,GAAG,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;YAC/D,CAAC,CAAA;YACD,IAAM,WAAW,GAAG,UAAC,QAAgB,IAAK,OAAA,UAAU,CAAC,QAAQ,EAAE,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,GAAG,CAAC,EAAL,CAAK,CAAC,EAArC,CAAqC,CAAA;YAC/E,IAAM,WAAW,GAAG,UAAC,QAAgB,IAAK,OAAA,UAAU,CAAC,QAAQ,EAAE,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,GAAG,CAAC,EAAL,CAAK,CAAC,EAArC,CAAqC,CAAA;YAE/E,IAAM,eAAe,GAAG,MAAM;iBAC3B,GAAG,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,SAAS,EAAE,GAAG,CAAC,EAAE,SAAS,CAAC,CAAC;iBACzC,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,SAAS,CAAC,EAAE,UAAC,CAAC,EAAE,QAAQ;gBACrD,OAAA,uBAAuB,CACrB,WAAW,CAAC,QAAQ,CAAC,EACrB,WAAW,CAAC,QAAQ,CAAC,CACtB;YAHD,CAGC,CACF,CAAC,CAAC;iBACF,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,SAAS,CAAC,EAAE,UAAC,CAAC,EAAE,QAAQ;gBACrD,OAAA,uBAAuB,CACrB,eAAe,CAAC,QAAQ,CAAC,CAAC,KAAK,EAC/B,eAAe,CAAC,QAAQ,CAAC,CAAC,MAAM,CACjC;YAHD,CAGC,CACF,CAAC,CAAC,CAAA;YAEL,OAAO,eAA8B,CAAA;QACvC,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,4CAAY,GAAnB,UAAoB,KAAe;QAAnC,iBASC;QARC,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,GAAG,GAAG,KAAI,CAAC,MAAM,CAAC,KAAK,CAAC,CAAA;YAC9B,OAAO,KAAI,CAAC,WAAW,CACrB,GAAG,EACH,KAAK,CAAC,SAAmB,EACzB,KAAK,CAAC,eAAe,CAAC,GAAG,CAAC,UAAC,EAAe;oBAAd,cAAM,EAAE,aAAK;gBAAM,OAAA,CAAC,EAAE,MAAM,QAAA,EAAE,KAAK,OAAA,EAAE,CAAC;YAAnB,CAAmB,CAAC,CACpE,CAAA;QACH,CAAC,CAAC,CAAA;IACJ,CAAC;IAEY,uCAAO,GAApB,UAAqB,KAAgB;;;;;;wBAC5B,KAAA,IAAI,CAAC,YAAY,CAAA;wBAAC,qBAAM,UAAU,CAAC,KAAK,CAAC,EAAA;4BAAhD,sBAAO,SAAA,IAAI,GAAc,SAAuB,EAAC,EAAA;;;;KAClD;IAEY,+CAAe,GAA5B,UAA6B,KAAgB;;;;;;4BAC1B,qBAAM,UAAU,CAAC,KAAK,CAAC,EAAA;;wBAAlC,QAAQ,GAAG,SAAuB;wBAClC,eAAe,GAAG,EAAE,CAAC,IAAI,CAC7B,cAAM,OAAA,EAAE,CAAC,OAAO,CAAC,KAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,EAAvC,CAAuC,CAC9C,CAAA;wBAEyB,qBAAM,OAAO,CAAC,GAAG,CAAC,eAAe,CAAC,GAAG,CAC7D,UAAO,cAAc,EAAE,QAAQ;;;;;4CACN,KAAA,CAAA,KAAA,KAAK,CAAA,CAAC,IAAI,CAAA;4CAAC,qBAAM,cAAc,CAAC,IAAI,EAAE,EAAA;;4CAAvD,cAAc,GAAG,cAAW,SAA2B,EAAC;4CACxD,OAAO,GAAG,cAAc,CAAC,MAAM,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,MAAM,CAAC,CAAC,CAAC,EAAT,CAAS,CAAC,CAAA;4CACpD,OAAO,GAAG,cAAc,CAAC,MAAM,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,MAAM,CAAC,CAAC,CAAC,EAAV,CAAU,CAAC,CAAA;4CAE3D,sBAAO,IAAI,eAAe,CACxB,KAAK,CAAC,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,IAAI,KAAK,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,EAAjC,CAAiC,CAAC,EAClE;oDACE,MAAM,EAAE,QAAQ,CAAC,cAAc,CAAC,QAAQ,CAAC;oDACzC,KAAK,EAAG,QAAQ,CAAC,aAAa,CAAC,QAAQ,CAAC;iDACzC,CACF,EAAA;;;iCACF,CACF,CAAC,EAAA;;wBAdI,iBAAiB,GAAG,SAcxB;wBAEF,eAAe,CAAC,OAAO,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,OAAO,EAAE,EAAX,CAAW,CAAC,CAAA;wBAEzC,sBAAO,QAAQ,CAAC,YAAY;gCAC1B,CAAC,CAAC,iBAAiB;gCACnB,CAAC,CAAC,iBAAiB,CAAC,CAAC,CAAC,EAAA;;;;KACzB;IACH,4BAAC;AAAD,CAAC,AAtGD,CAAsD,aAAa,GAsGlE"}
\ No newline at end of file

build/es6/faceLandmarkNet/index.d.ts

@@ -4,4 +4,3 @@ export * from './FaceLandmark68TinyNet';
 export declare class FaceLandmarkNet extends FaceLandmark68Net {
 }
 export declare function createFaceLandmarkNet(weights: Float32Array): FaceLandmarkNet;
-export declare function faceLandmarkNet(weights: Float32Array): FaceLandmarkNet;

build/es6/faceLandmarkNet/index.js

@@ -15,8 +15,4 @@ export function createFaceLandmarkNet(weights) {
     net.extractWeights(weights);
     return net;
 }
-export function faceLandmarkNet(weights) {
-    console.warn('faceLandmarkNet(weights: Float32Array) will be deprecated in future, use createFaceLandmarkNet instead');
-    return createFaceLandmarkNet(weights);
-}
 //# sourceMappingURL=index.js.map
\ No newline at end of file

build/es6/faceLandmarkNet/index.js.map

-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/faceLandmarkNet/index.ts"],"names":[],"mappings":";AAAA,OAAO,EAAE,iBAAiB,EAAE,MAAM,qBAAqB,CAAC;AAExD,cAAc,qBAAqB,CAAC;AACpC,cAAc,yBAAyB,CAAC;AAExC;IAAqC,2CAAiB;IAAtD;;IAAwD,CAAC;IAAD,sBAAC;AAAD,CAAC,AAAzD,CAAqC,iBAAiB,GAAG;;AAEzD,MAAM,gCAAgC,OAAqB;IACzD,IAAM,GAAG,GAAG,IAAI,eAAe,EAAE,CAAA;IACjC,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAED,MAAM,0BAA0B,OAAqB;IACnD,OAAO,CAAC,IAAI,CAAC,wGAAwG,CAAC,CAAA;IACtH,OAAO,qBAAqB,CAAC,OAAO,CAAC,CAAA;AACvC,CAAC"}
\ No newline at end of file
+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/faceLandmarkNet/index.ts"],"names":[],"mappings":";AAAA,OAAO,EAAE,iBAAiB,EAAE,MAAM,qBAAqB,CAAC;AAExD,cAAc,qBAAqB,CAAC;AACpC,cAAc,yBAAyB,CAAC;AAExC;IAAqC,2CAAiB;IAAtD;;IAAwD,CAAC;IAAD,sBAAC;AAAD,CAAC,AAAzD,CAAqC,iBAAiB,GAAG;;AAEzD,MAAM,gCAAgC,OAAqB;IACzD,IAAM,GAAG,GAAG,IAAI,eAAe,EAAE,CAAA;IACjC,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC"}
\ No newline at end of file

build/es6/faceRecognitionNet/index.d.ts

 import { FaceRecognitionNet } from './FaceRecognitionNet';
 export * from './FaceRecognitionNet';
 export declare function createFaceRecognitionNet(weights: Float32Array): FaceRecognitionNet;
-export declare function faceRecognitionNet(weights: Float32Array): FaceRecognitionNet;

build/es6/faceRecognitionNet/index.js

@@ -5,8 +5,4 @@ export function createFaceRecognitionNet(weights) {
     net.extractWeights(weights);
     return net;
 }
-export function faceRecognitionNet(weights) {
-    console.warn('faceRecognitionNet(weights: Float32Array) will be deprecated in future, use createFaceRecognitionNet instead');
-    return createFaceRecognitionNet(weights);
-}
 //# sourceMappingURL=index.js.map
\ No newline at end of file

build/es6/faceRecognitionNet/index.js.map

-{"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
+{"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"}
\ No newline at end of file

build/es6/globalApi/ComposableTask.d.ts

+export declare class ComposableTask<T> {
+    then(onfulfilled: (value: T) => T | PromiseLike<T>): Promise<T>;
+    run(): Promise<T>;
+}

build/es6/globalApi/ComposableTask.js

+import * as tslib_1 from "tslib";
+var ComposableTask = /** @class */ (function () {
+    function ComposableTask() {
+    }
+    ComposableTask.prototype.then = function (onfulfilled) {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var _a;
+            return tslib_1.__generator(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        _a = onfulfilled;
+                        return [4 /*yield*/, this.run()];
+                    case 1: return [2 /*return*/, _a.apply(void 0, [_b.sent()])];
+                }
+            });
+        });
+    };
+    ComposableTask.prototype.run = function () {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            return tslib_1.__generator(this, function (_a) {
+                throw new Error('ComposableTask - run is not implemented');
+            });
+        });
+    };
+    return ComposableTask;
+}());
+export { ComposableTask };
+//# sourceMappingURL=ComposableTask.js.map
\ No newline at end of file

build/es6/globalApi/ComposableTask.js.map

+{"version":3,"file":"ComposableTask.js","sourceRoot":"","sources":["../../../src/globalApi/ComposableTask.ts"],"names":[],"mappings":";AAAA;IAAA;IAWA,CAAC;IATc,6BAAI,GAAjB,UACE,WAA6C;;;;;;wBAEtC,KAAA,WAAW,CAAA;wBAAC,qBAAM,IAAI,CAAC,GAAG,EAAE,EAAA;4BAAnC,sBAAO,kBAAY,SAAgB,EAAC,EAAA;;;;KACrC;IAEY,4BAAG,GAAhB;;;gBACE,MAAM,IAAI,KAAK,CAAC,yCAAyC,CAAC,CAAA;;;KAC3D;IACH,qBAAC;AAAD,CAAC,AAXD,IAWC"}
\ No newline at end of file

build/es6/globalApi/ComputeFaceDescriptorsTasks.d.ts

+import { TNetInput } from 'tfjs-image-recognition-base';
+import { FaceDetectionWithLandmarks } from '../classes/FaceDetectionWithLandmarks';
+import { FullFaceDescription } from '../classes/FullFaceDescription';
+import { ComposableTask } from './ComposableTask';
+export declare class ComputeFaceDescriptorsTaskBase<TReturn, DetectFaceLandmarksReturnType> extends ComposableTask<TReturn> {
+    protected detectFaceLandmarksTask: ComposableTask<DetectFaceLandmarksReturnType> | Promise<DetectFaceLandmarksReturnType>;
+    protected input: TNetInput;
+    constructor(detectFaceLandmarksTask: ComposableTask<DetectFaceLandmarksReturnType> | Promise<DetectFaceLandmarksReturnType>, input: TNetInput);
+}
+export declare class ComputeAllFaceDescriptorsTask extends ComputeFaceDescriptorsTaskBase<FullFaceDescription[], FaceDetectionWithLandmarks[]> {
+    run(): Promise<FullFaceDescription[]>;
+}
+export declare class ComputeSingleFaceDescriptorTask extends ComputeFaceDescriptorsTaskBase<FullFaceDescription | undefined, FaceDetectionWithLandmarks | undefined> {
+    run(): Promise<FullFaceDescription | undefined>;
+}

build/es6/globalApi/ComputeFaceDescriptorsTasks.js

+import * as tslib_1 from "tslib";
+import { FullFaceDescription } from '../classes/FullFaceDescription';
+import { extractFaces } from '../dom';
+import { ComposableTask } from './ComposableTask';
+import { nets } from './nets';
+var ComputeFaceDescriptorsTaskBase = /** @class */ (function (_super) {
+    tslib_1.__extends(ComputeFaceDescriptorsTaskBase, _super);
+    function ComputeFaceDescriptorsTaskBase(detectFaceLandmarksTask, input) {
+        var _this = _super.call(this) || this;
+        _this.detectFaceLandmarksTask = detectFaceLandmarksTask;
+        _this.input = input;
+        return _this;
+    }
+    return ComputeFaceDescriptorsTaskBase;
+}(ComposableTask));
+export { ComputeFaceDescriptorsTaskBase };
+var ComputeAllFaceDescriptorsTask = /** @class */ (function (_super) {
+    tslib_1.__extends(ComputeAllFaceDescriptorsTask, _super);
+    function ComputeAllFaceDescriptorsTask() {
+        return _super !== null && _super.apply(this, arguments) || this;
+    }
+    ComputeAllFaceDescriptorsTask.prototype.run = function () {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var _this = this;
+            var facesWithLandmarks, alignedFaceCanvases;
+            return tslib_1.__generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4 /*yield*/, this.detectFaceLandmarksTask];
+                    case 1:
+                        facesWithLandmarks = _a.sent();
+                        return [4 /*yield*/, extractFaces(this.input, facesWithLandmarks.map(function (_a) {
+                                var landmarks = _a.landmarks;
+                                return landmarks.align();
+                            }))];
+                    case 2:
+                        alignedFaceCanvases = _a.sent();
+                        return [4 /*yield*/, Promise.all(facesWithLandmarks.map(function (_a, i) {
+                                var detection = _a.detection, landmarks = _a.landmarks;
+                                return tslib_1.__awaiter(_this, void 0, void 0, function () {
+                                    var descriptor;
+                                    return tslib_1.__generator(this, function (_b) {
+                                        switch (_b.label) {
+                                            case 0: return [4 /*yield*/, nets.faceRecognitionNet.computeFaceDescriptor(alignedFaceCanvases[i])];
+                                            case 1:
+                                                descriptor = _b.sent();
+                                                return [2 /*return*/, new FullFaceDescription(detection, landmarks, descriptor)];
+                                        }
+                                    });
+                                });
+                            }))];
+                    case 3: return [2 /*return*/, _a.sent()];
+                }
+            });
+        });
+    };
+    return ComputeAllFaceDescriptorsTask;
+}(ComputeFaceDescriptorsTaskBase));
+export { ComputeAllFaceDescriptorsTask };
+var ComputeSingleFaceDescriptorTask = /** @class */ (function (_super) {
+    tslib_1.__extends(ComputeSingleFaceDescriptorTask, _super);
+    function ComputeSingleFaceDescriptorTask() {
+        return _super !== null && _super.apply(this, arguments) || this;
+    }
+    ComputeSingleFaceDescriptorTask.prototype.run = function () {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var detectionWithLandmarks, detection, landmarks, alignedRect, alignedFaceCanvas, descriptor;
+            return tslib_1.__generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4 /*yield*/, this.detectFaceLandmarksTask];
+                    case 1:
+                        detectionWithLandmarks = _a.sent();
+                        if (!detectionWithLandmarks) {
+                            return [2 /*return*/];
+                        }
+                        detection = detectionWithLandmarks.detection, landmarks = detectionWithLandmarks.landmarks, alignedRect = detectionWithLandmarks.alignedRect;
+                        return [4 /*yield*/, extractFaces(this.input, [alignedRect])];
+                    case 2:
+                        alignedFaceCanvas = (_a.sent())[0];
+                        return [4 /*yield*/, nets.faceRecognitionNet.computeFaceDescriptor(alignedFaceCanvas)];
+                    case 3:
+                        descriptor = _a.sent();
+                        return [2 /*return*/, new FullFaceDescription(detection, landmarks, descriptor)];
+                }
+            });
+        });
+    };
+    return ComputeSingleFaceDescriptorTask;
+}(ComputeFaceDescriptorsTaskBase));
+export { ComputeSingleFaceDescriptorTask };
+//# sourceMappingURL=ComputeFaceDescriptorsTasks.js.map
\ No newline at end of file

build/es6/globalApi/ComputeFaceDescriptorsTasks.js.map

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\ No newline at end of file

build/es6/globalApi/DetectFaceLandmarksTasks.d.ts

+import { TNetInput } from 'tfjs-image-recognition-base';
+import { FaceDetection } from '../classes/FaceDetection';
+import { FaceDetectionWithLandmarks } from '../classes/FaceDetectionWithLandmarks';
+import { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';
+import { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';
+import { ComposableTask } from './ComposableTask';
+import { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';
+export declare class DetectFaceLandmarksTaskBase<ReturnType, DetectFacesReturnType> extends ComposableTask<ReturnType> {
+    protected detectFacesTask: ComposableTask<DetectFacesReturnType> | Promise<DetectFacesReturnType>;
+    protected input: TNetInput;
+    protected useTinyLandmarkNet: boolean;
+    constructor(detectFacesTask: ComposableTask<DetectFacesReturnType> | Promise<DetectFacesReturnType>, input: TNetInput, useTinyLandmarkNet: boolean);
+    protected readonly landmarkNet: FaceLandmark68Net | FaceLandmark68TinyNet;
+}
+export declare class DetectAllFaceLandmarksTask extends DetectFaceLandmarksTaskBase<FaceDetectionWithLandmarks[], FaceDetection[]> {
+    run(): Promise<FaceDetectionWithLandmarks[]>;
+    withFaceDescriptors(): ComputeAllFaceDescriptorsTask;
+}
+export declare class DetectSingleFaceLandmarksTask extends DetectFaceLandmarksTaskBase<FaceDetectionWithLandmarks | undefined, FaceDetection | undefined> {
+    run(): Promise<FaceDetectionWithLandmarks | undefined>;
+    withFaceDescriptor(): ComputeSingleFaceDescriptorTask;
+}

build/es6/globalApi/DetectFaceLandmarksTasks.js

+import * as tslib_1 from "tslib";
+import { FaceDetectionWithLandmarks } from '../classes/FaceDetectionWithLandmarks';
+import { extractFaces } from '../dom';
+import { ComposableTask } from './ComposableTask';
+import { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';
+import { nets } from './nets';
+var DetectFaceLandmarksTaskBase = /** @class */ (function (_super) {
+    tslib_1.__extends(DetectFaceLandmarksTaskBase, _super);
+    function DetectFaceLandmarksTaskBase(detectFacesTask, input, useTinyLandmarkNet) {
+        var _this = _super.call(this) || this;
+        _this.detectFacesTask = detectFacesTask;
+        _this.input = input;
+        _this.useTinyLandmarkNet = useTinyLandmarkNet;
+        return _this;
+    }
+    Object.defineProperty(DetectFaceLandmarksTaskBase.prototype, "landmarkNet", {
+        get: function () {
+            return this.useTinyLandmarkNet
+                ? nets.faceLandmark68TinyNet
+                : nets.faceLandmark68Net;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    return DetectFaceLandmarksTaskBase;
+}(ComposableTask));
+export { DetectFaceLandmarksTaskBase };
+var DetectAllFaceLandmarksTask = /** @class */ (function (_super) {
+    tslib_1.__extends(DetectAllFaceLandmarksTask, _super);
+    function DetectAllFaceLandmarksTask() {
+        return _super !== null && _super.apply(this, arguments) || this;
+    }
+    DetectAllFaceLandmarksTask.prototype.run = function () {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var _this = this;
+            var detections, faceCanvases, faceLandmarksByFace;
+            return tslib_1.__generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4 /*yield*/, this.detectFacesTask];
+                    case 1:
+                        detections = _a.sent();
+                        return [4 /*yield*/, extractFaces(this.input, detections)];
+                    case 2:
+                        faceCanvases = _a.sent();
+                        return [4 /*yield*/, Promise.all(faceCanvases.map(function (canvas) { return _this.landmarkNet.detectLandmarks(canvas); }))];
+                    case 3:
+                        faceLandmarksByFace = _a.sent();
+                        return [2 /*return*/, detections.map(function (detection, i) {
+                                return new FaceDetectionWithLandmarks(detection, faceLandmarksByFace[i]);
+                            })];
+                }
+            });
+        });
+    };
+    DetectAllFaceLandmarksTask.prototype.withFaceDescriptors = function () {
+        return new ComputeAllFaceDescriptorsTask(this, this.input);
+    };
+    return DetectAllFaceLandmarksTask;
+}(DetectFaceLandmarksTaskBase));
+export { DetectAllFaceLandmarksTask };
+var DetectSingleFaceLandmarksTask = /** @class */ (function (_super) {
+    tslib_1.__extends(DetectSingleFaceLandmarksTask, _super);
+    function DetectSingleFaceLandmarksTask() {
+        return _super !== null && _super.apply(this, arguments) || this;
+    }
+    DetectSingleFaceLandmarksTask.prototype.run = function () {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var detection, faceCanvas, _a, _b;
+            return tslib_1.__generator(this, function (_c) {
+                switch (_c.label) {
+                    case 0: return [4 /*yield*/, this.detectFacesTask];
+                    case 1:
+                        detection = _c.sent();
+                        if (!detection) {
+                            return [2 /*return*/];
+                        }
+                        return [4 /*yield*/, extractFaces(this.input, [detection])];
+                    case 2:
+                        faceCanvas = (_c.sent())[0];
+                        _a = FaceDetectionWithLandmarks.bind;
+                        _b = [void 0, detection];
+                        return [4 /*yield*/, this.landmarkNet.detectLandmarks(faceCanvas)];
+                    case 3: return [2 /*return*/, new (_a.apply(FaceDetectionWithLandmarks, _b.concat([_c.sent()])))()];
+                }
+            });
+        });
+    };
+    DetectSingleFaceLandmarksTask.prototype.withFaceDescriptor = function () {
+        return new ComputeSingleFaceDescriptorTask(this, this.input);
+    };
+    return DetectSingleFaceLandmarksTask;
+}(DetectFaceLandmarksTaskBase));
+export { DetectSingleFaceLandmarksTask };
+//# sourceMappingURL=DetectFaceLandmarksTasks.js.map
\ No newline at end of file

build/es6/globalApi/DetectFaceLandmarksTasks.js.map

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\ No newline at end of file

build/es6/globalApi/DetectFacesTasks.d.ts

+import { TNetInput } from 'tfjs-image-recognition-base';
+import { FaceDetection } from '../classes/FaceDetection';
+import { ComposableTask } from './ComposableTask';
+import { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';
+import { FaceDetectionOptions } from './types';
+export declare function detectSingleFace(input: TNetInput, options?: FaceDetectionOptions): DetectSingleFaceTask;
+export declare function detectAllFaces(input: TNetInput, options?: FaceDetectionOptions): DetectAllFacesTask;
+export declare class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {
+    protected input: TNetInput;
+    protected options: FaceDetectionOptions;
+    constructor(input: TNetInput, options?: FaceDetectionOptions);
+}
+export declare class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {
+    run(): Promise<FaceDetection[]>;
+    withFaceLandmarks(useTinyLandmarkNet?: boolean): DetectAllFaceLandmarksTask;
+}
+export declare class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {
+    run(): Promise<FaceDetection | undefined>;
+    withFaceLandmarks(useTinyLandmarkNet?: boolean): DetectSingleFaceLandmarksTask;
+}

build/es6/globalApi/DetectFacesTasks.js

+import * as tslib_1 from "tslib";
+import { TinyYolov2Options } from 'tfjs-tiny-yolov2';
+import { MtcnnOptions } from '../mtcnn/MtcnnOptions';
+import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
+import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
+import { ComposableTask } from './ComposableTask';
+import { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';
+import { nets } from './nets';
+export function detectSingleFace(input, options) {
+    if (options === void 0) { options = new SsdMobilenetv1Options(); }
+    return new DetectSingleFaceTask(input, options);
+}
+export function detectAllFaces(input, options) {
+    if (options === void 0) { options = new SsdMobilenetv1Options(); }
+    return new DetectAllFacesTask(input, options);
+}
+var DetectFacesTaskBase = /** @class */ (function (_super) {
+    tslib_1.__extends(DetectFacesTaskBase, _super);
+    function DetectFacesTaskBase(input, options) {
+        if (options === void 0) { options = new SsdMobilenetv1Options(); }
+        var _this = _super.call(this) || this;
+        _this.input = input;
+        _this.options = options;
+        return _this;
+    }
+    return DetectFacesTaskBase;
+}(ComposableTask));
+export { DetectFacesTaskBase };
+var DetectAllFacesTask = /** @class */ (function (_super) {
+    tslib_1.__extends(DetectAllFacesTask, _super);
+    function DetectAllFacesTask() {
+        return _super !== null && _super.apply(this, arguments) || this;
+    }
+    DetectAllFacesTask.prototype.run = function () {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var _a, input, options, faceDetectionFunction;
+            return tslib_1.__generator(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        _a = this, input = _a.input, options = _a.options;
+                        if (!(options instanceof MtcnnOptions)) return [3 /*break*/, 2];
+                        return [4 /*yield*/, nets.mtcnn.forward(input, options)];
+                    case 1: return [2 /*return*/, (_b.sent())
+                            .map(function (result) { return result.faceDetection; })];
+                    case 2:
+                        faceDetectionFunction = options instanceof TinyFaceDetectorOptions
+                            ? function (input) { return nets.tinyFaceDetector.locateFaces(input, options); }
+                            : (options instanceof SsdMobilenetv1Options
+                                ? function (input) { return nets.ssdMobilenetv1.locateFaces(input, options); }
+                                : (options instanceof TinyYolov2Options
+                                    ? function (input) { return nets.tinyYolov2.locateFaces(input, options); }
+                                    : null));
+                        if (!faceDetectionFunction) {
+                            throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options');
+                        }
+                        return [2 /*return*/, faceDetectionFunction(input)];
+                }
+            });
+        });
+    };
+    DetectAllFacesTask.prototype.withFaceLandmarks = function (useTinyLandmarkNet) {
+        if (useTinyLandmarkNet === void 0) { useTinyLandmarkNet = false; }
+        return new DetectAllFaceLandmarksTask(this, this.input, useTinyLandmarkNet);
+    };
+    return DetectAllFacesTask;
+}(DetectFacesTaskBase));
+export { DetectAllFacesTask };
+var DetectSingleFaceTask = /** @class */ (function (_super) {
+    tslib_1.__extends(DetectSingleFaceTask, _super);
+    function DetectSingleFaceTask() {
+        return _super !== null && _super.apply(this, arguments) || this;
+    }
+    DetectSingleFaceTask.prototype.run = function () {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            return tslib_1.__generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4 /*yield*/, new DetectAllFacesTask(this.input, this.options)];
+                    case 1: return [2 /*return*/, (_a.sent())
+                            .sort(function (f1, f2) { return f1.score - f2.score; })[0]];
+                }
+            });
+        });
+    };
+    DetectSingleFaceTask.prototype.withFaceLandmarks = function (useTinyLandmarkNet) {
+        if (useTinyLandmarkNet === void 0) { useTinyLandmarkNet = false; }
+        return new DetectSingleFaceLandmarksTask(this, this.input, useTinyLandmarkNet);
+    };
+    return DetectSingleFaceTask;
+}(DetectFacesTaskBase));
+export { DetectSingleFaceTask };
+//# sourceMappingURL=DetectFacesTasks.js.map
\ No newline at end of file

build/es6/globalApi/DetectFacesTasks.js.map

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\ No newline at end of file

build/es6/globalApi/FaceMatcher.d.ts

+import { FaceMatch } from '../classes/FaceMatch';
+import { FullFaceDescription } from '../classes/FullFaceDescription';
+import { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';
+export declare class FaceMatcher {
+    private _labeledDescriptors;
+    private _distanceThreshold;
+    constructor(inputs: LabeledFaceDescriptors | FullFaceDescription | Float32Array | Array<LabeledFaceDescriptors | FullFaceDescription | Float32Array>, distanceThreshold?: number);
+    readonly labeledDescriptors: LabeledFaceDescriptors[];
+    readonly distanceThreshold: number;
+    computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number;
+    matchDescriptor(queryDescriptor: Float32Array): FaceMatch;
+    findBestMatch(queryDescriptor: Float32Array): FaceMatch;
+}

build/es6/globalApi/FaceMatcher.js

+import { FaceMatch } from '../classes/FaceMatch';
+import { FullFaceDescription } from '../classes/FullFaceDescription';
+import { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';
+import { euclideanDistance } from '../euclideanDistance';
+var FaceMatcher = /** @class */ (function () {
+    function FaceMatcher(inputs, distanceThreshold) {
+        if (distanceThreshold === void 0) { distanceThreshold = 0.6; }
+        this._distanceThreshold = distanceThreshold;
+        var inputArray = Array.isArray(inputs) ? inputs : [inputs];
+        if (!inputArray.length) {
+            throw new Error("FaceRecognizer.constructor - expected atleast one input");
+        }
+        var count = 1;
+        var createUniqueLabel = function () { return "person " + count++; };
+        this._labeledDescriptors = inputArray.map(function (desc) {
+            if (desc instanceof LabeledFaceDescriptors) {
+                return desc;
+            }
+            if (desc instanceof FullFaceDescription) {
+                return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);
+            }
+            if (desc instanceof Float32Array) {
+                return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);
+            }
+            throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | FullFaceDescription | Float32Array | Array<LabeledFaceDescriptors | FullFaceDescription | Float32Array>");
+        });
+    }
+    Object.defineProperty(FaceMatcher.prototype, "labeledDescriptors", {
+        get: function () { return this._labeledDescriptors; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(FaceMatcher.prototype, "distanceThreshold", {
+        get: function () { return this._distanceThreshold; },
+        enumerable: true,
+        configurable: true
+    });
+    FaceMatcher.prototype.computeMeanDistance = function (queryDescriptor, descriptors) {
+        return descriptors
+            .map(function (d) { return euclideanDistance(d, queryDescriptor); })
+            .reduce(function (d1, d2) { return d1 + d2; }, 0)
+            / (descriptors.length || 1);
+    };
+    FaceMatcher.prototype.matchDescriptor = function (queryDescriptor) {
+        var _this = this;
+        return this.labeledDescriptors
+            .map(function (_a) {
+            var descriptors = _a.descriptors, label = _a.label;
+            return new FaceMatch(label, _this.computeMeanDistance(queryDescriptor, descriptors));
+        })
+            .reduce(function (best, curr) { return best.distance < curr.distance ? best : curr; });
+    };
+    FaceMatcher.prototype.findBestMatch = function (queryDescriptor) {
+        var bestMatch = this.matchDescriptor(queryDescriptor);
+        return bestMatch.distance < this.distanceThreshold
+            ? bestMatch
+            : new FaceMatch('unknown', bestMatch.distance);
+    };
+    return FaceMatcher;
+}());
+export { FaceMatcher };
+//# sourceMappingURL=FaceMatcher.js.map
\ No newline at end of file

build/es6/globalApi/FaceMatcher.js.map

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\ No newline at end of file

build/es6/globalApi/allFaces.d.ts

+import { TNetInput } from 'tfjs-image-recognition-base';
+import { ITinyYolov2Options } from 'tfjs-tiny-yolov2';
+import { FullFaceDescription } from '../classes';
+import { IMtcnnOptions } from '../mtcnn/MtcnnOptions';
+export declare function allFacesSsdMobilenetv1(input: TNetInput, minConfidence?: number): Promise<FullFaceDescription[]>;
+export declare function allFacesTinyYolov2(input: TNetInput, forwardParams?: ITinyYolov2Options): Promise<FullFaceDescription[]>;
+export declare function allFacesMtcnn(input: TNetInput, forwardParams?: IMtcnnOptions): Promise<FullFaceDescription[]>;
+export declare const allFaces: typeof allFacesSsdMobilenetv1;

build/es6/globalApi/allFaces.js

+import * as tslib_1 from "tslib";
+import { TinyYolov2Options } from 'tfjs-tiny-yolov2';
+import { MtcnnOptions } from '../mtcnn/MtcnnOptions';
+import { SsdMobilenetv1Options } from '../ssdMobilenetv1';
+import { detectAllFaces } from './DetectFacesTasks';
+// export allFaces API for backward compatibility
+export function allFacesSsdMobilenetv1(input, minConfidence) {
+    return tslib_1.__awaiter(this, void 0, void 0, function () {
+        return tslib_1.__generator(this, function (_a) {
+            switch (_a.label) {
+                case 0: return [4 /*yield*/, detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence: minConfidence } : {}))
+                        .withFaceLandmarks()
+                        .withFaceDescriptors()];
+                case 1: return [2 /*return*/, _a.sent()];
+            }
+        });
+    });
+}
+export function allFacesTinyYolov2(input, forwardParams) {
+    if (forwardParams === void 0) { forwardParams = {}; }
+    return tslib_1.__awaiter(this, void 0, void 0, function () {
+        return tslib_1.__generator(this, function (_a) {
+            switch (_a.label) {
+                case 0: return [4 /*yield*/, detectAllFaces(input, new TinyYolov2Options(forwardParams))
+                        .withFaceLandmarks()
+                        .withFaceDescriptors()];
+                case 1: return [2 /*return*/, _a.sent()];
+            }
+        });
+    });
+}
+export function allFacesMtcnn(input, forwardParams) {
+    if (forwardParams === void 0) { forwardParams = {}; }
+    return tslib_1.__awaiter(this, void 0, void 0, function () {
+        return tslib_1.__generator(this, function (_a) {
+            switch (_a.label) {
+                case 0: return [4 /*yield*/, detectAllFaces(input, new MtcnnOptions(forwardParams))
+                        .withFaceLandmarks()
+                        .withFaceDescriptors()];
+                case 1: return [2 /*return*/, _a.sent()];
+            }
+        });
+    });
+}
+export var allFaces = allFacesSsdMobilenetv1;
+//# sourceMappingURL=allFaces.js.map
\ No newline at end of file

build/es6/globalApi/allFaces.js.map

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\ No newline at end of file

build/es6/globalApi/index.d.ts

+export * from './allFaces';
+export * from './ComposableTask';
+export * from './ComputeFaceDescriptorsTasks';
+export * from './DetectFacesTasks';
+export * from './DetectFaceLandmarksTasks';
+export * from './FaceMatcher';
+export * from './nets';
+export * from './types';

build/es6/globalApi/index.js

+export * from './allFaces';
+export * from './ComposableTask';
+export * from './ComputeFaceDescriptorsTasks';
+export * from './DetectFacesTasks';
+export * from './DetectFaceLandmarksTasks';
+export * from './FaceMatcher';
+export * from './nets';
+//# sourceMappingURL=index.js.map
\ No newline at end of file

build/es6/globalApi/index.js.map

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\ No newline at end of file

build/es6/globalApi/nets.d.ts

+import { TNetInput } from 'tfjs-image-recognition-base';
+import { ITinyYolov2Options } from 'tfjs-tiny-yolov2';
+import { FaceDetection } from '../classes/FaceDetection';
+import { FaceDetectionWithLandmarks } from '../classes/FaceDetectionWithLandmarks';
+import { FaceLandmarks5 } from '../classes/FaceLandmarks5';
+import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
+import { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';
+import { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';
+import { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';
+import { Mtcnn } from '../mtcnn/Mtcnn';
+import { MtcnnOptions } from '../mtcnn/MtcnnOptions';
+import { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';
+import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
+import { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';
+import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
+import { TinyYolov2 } from '../tinyYolov2/TinyYolov2';
+export declare const nets: {
+    ssdMobilenetv1: SsdMobilenetv1;
+    tinyFaceDetector: TinyFaceDetector;
+    tinyYolov2: TinyYolov2;
+    mtcnn: Mtcnn;
+    faceLandmark68Net: FaceLandmark68Net;
+    faceLandmark68TinyNet: FaceLandmark68TinyNet;
+    faceRecognitionNet: FaceRecognitionNet;
+};
+/**
+ * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.
+ *
+ * @param input The input image.
+ * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).
+ * @returns Bounding box of each face with score.
+ */
+export declare const ssdMobilenetv1: (input: TNetInput, options: SsdMobilenetv1Options) => Promise<FaceDetection[]>;
+/**
+ * Attempts to detect all faces in an image using the Tiny Face Detector.
+ *
+ * @param input The input image.
+ * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).
+ * @returns Bounding box of each face with score.
+ */
+export declare const tinyFaceDetector: (input: TNetInput, options: TinyFaceDetectorOptions) => Promise<FaceDetection[]>;
+/**
+ * Attempts to detect all faces in an image using the Tiny Yolov2 Network.
+ *
+ * @param input The input image.
+ * @param options (optional, default: see TinyYolov2Options constructor for default parameters).
+ * @returns Bounding box of each face with score.
+ */
+export declare const tinyYolov2: (input: TNetInput, options: ITinyYolov2Options) => Promise<FaceDetection[]>;
+/**
+ * Attempts to detect all faces in an image and the 5 point face landmarks
+ * of each detected face using the MTCNN Network.
+ *
+ * @param input The input image.
+ * @param options (optional, default: see MtcnnOptions constructor for default parameters).
+ * @returns Bounding box of each face with score and 5 point face landmarks.
+ */
+export declare const mtcnn: (input: TNetInput, options: MtcnnOptions) => Promise<FaceDetectionWithLandmarks<FaceLandmarks5>[]>;
+/**
+ * Detects the 68 point face landmark positions of the face shown in an image.
+ *
+ * @param inputs The face image extracted from the bounding box of a face. Can
+ * also be an array of input images, which will be batch processed.
+ * @returns 68 point face landmarks or array thereof in case of batch input.
+ */
+export declare const detectFaceLandmarks: (input: TNetInput) => Promise<FaceLandmarks68 | FaceLandmarks68[]>;
+/**
+ * Detects the 68 point face landmark positions of the face shown in an image
+ * using a tinier version of the 68 point face landmark model, which is slightly
+ * faster at inference, but also slightly less accurate.
+ *
+ * @param inputs The face image extracted from the bounding box of a face. Can
+ * also be an array of input images, which will be batch processed.
+ * @returns 68 point face landmarks or array thereof in case of batch input.
+ */
+export declare const detectFaceLandmarksTiny: (input: TNetInput) => Promise<FaceLandmarks68 | FaceLandmarks68[]>;
+/**
+ * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,
+ * which uniquely represents the features of that persons face. The computed face descriptor can
+ * be used to measure the similarity between faces, by computing the euclidean distance of two
+ * face descriptors.
+ *
+ * @param inputs The face image extracted from the aligned bounding box of a face. Can
+ * also be an array of input images, which will be batch processed.
+ * @returns Face descriptor with 128 entries or array thereof in case of batch input.
+ */
+export declare const computeFaceDescriptor: (input: TNetInput) => Promise<Float32Array | Float32Array[]>;
+export declare const loadSsdMobilenetv1Model: (url: string) => Promise<void>;
+export declare const loadTinyFaceDetectorModel: (url: string) => Promise<void>;
+export declare const loadMtcnnModel: (url: string) => Promise<void>;
+export declare const loadTinyYolov2Model: (url: string) => Promise<void>;
+export declare const loadFaceLandmarkModel: (url: string) => Promise<void>;
+export declare const loadFaceLandmarkTinyModel: (url: string) => Promise<void>;
+export declare const loadFaceRecognitionModel: (url: string) => Promise<void>;
+export declare const loadFaceDetectionModel: (url: string) => Promise<void>;
+export declare const locateFaces: (input: TNetInput, options: SsdMobilenetv1Options) => Promise<FaceDetection[]>;
+export declare const detectLandmarks: (input: TNetInput) => Promise<FaceLandmarks68 | FaceLandmarks68[]>;

build/es6/globalApi/nets.js

+import { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';
+import { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';
+import { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';
+import { Mtcnn } from '../mtcnn/Mtcnn';
+import { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';
+import { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';
+import { TinyYolov2 } from '../tinyYolov2/TinyYolov2';
+export var nets = {
+    ssdMobilenetv1: new SsdMobilenetv1(),
+    tinyFaceDetector: new TinyFaceDetector(),
+    tinyYolov2: new TinyYolov2(),
+    mtcnn: new Mtcnn(),
+    faceLandmark68Net: new FaceLandmark68Net(),
+    faceLandmark68TinyNet: new FaceLandmark68TinyNet(),
+    faceRecognitionNet: new FaceRecognitionNet()
+};
+/**
+ * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.
+ *
+ * @param input The input image.
+ * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).
+ * @returns Bounding box of each face with score.
+ */
+export var ssdMobilenetv1 = function (input, options) {
+    return nets.ssdMobilenetv1.locateFaces(input, options);
+};
+/**
+ * Attempts to detect all faces in an image using the Tiny Face Detector.
+ *
+ * @param input The input image.
+ * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).
+ * @returns Bounding box of each face with score.
+ */
+export var tinyFaceDetector = function (input, options) {
+    return nets.tinyFaceDetector.locateFaces(input, options);
+};
+/**
+ * Attempts to detect all faces in an image using the Tiny Yolov2 Network.
+ *
+ * @param input The input image.
+ * @param options (optional, default: see TinyYolov2Options constructor for default parameters).
+ * @returns Bounding box of each face with score.
+ */
+export var tinyYolov2 = function (input, options) {
+    return nets.tinyYolov2.locateFaces(input, options);
+};
+/**
+ * Attempts to detect all faces in an image and the 5 point face landmarks
+ * of each detected face using the MTCNN Network.
+ *
+ * @param input The input image.
+ * @param options (optional, default: see MtcnnOptions constructor for default parameters).
+ * @returns Bounding box of each face with score and 5 point face landmarks.
+ */
+export var mtcnn = function (input, options) {
+    return nets.mtcnn.forward(input, options);
+};
+/**
+ * Detects the 68 point face landmark positions of the face shown in an image.
+ *
+ * @param inputs The face image extracted from the bounding box of a face. Can
+ * also be an array of input images, which will be batch processed.
+ * @returns 68 point face landmarks or array thereof in case of batch input.
+ */
+export var detectFaceLandmarks = function (input) {
+    return nets.faceLandmark68Net.detectLandmarks(input);
+};
+/**
+ * Detects the 68 point face landmark positions of the face shown in an image
+ * using a tinier version of the 68 point face landmark model, which is slightly
+ * faster at inference, but also slightly less accurate.
+ *
+ * @param inputs The face image extracted from the bounding box of a face. Can
+ * also be an array of input images, which will be batch processed.
+ * @returns 68 point face landmarks or array thereof in case of batch input.
+ */
+export var detectFaceLandmarksTiny = function (input) {
+    return nets.faceLandmark68TinyNet.detectLandmarks(input);
+};
+/**
+ * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,
+ * which uniquely represents the features of that persons face. The computed face descriptor can
+ * be used to measure the similarity between faces, by computing the euclidean distance of two
+ * face descriptors.
+ *
+ * @param inputs The face image extracted from the aligned bounding box of a face. Can
+ * also be an array of input images, which will be batch processed.
+ * @returns Face descriptor with 128 entries or array thereof in case of batch input.
+ */
+export var computeFaceDescriptor = function (input) {
+    return nets.faceRecognitionNet.computeFaceDescriptor(input);
+};
+export var loadSsdMobilenetv1Model = function (url) { return nets.ssdMobilenetv1.load(url); };
+export var loadTinyFaceDetectorModel = function (url) { return nets.tinyFaceDetector.load(url); };
+export var loadMtcnnModel = function (url) { return nets.mtcnn.load(url); };
+export var loadTinyYolov2Model = function (url) { return nets.tinyYolov2.load(url); };
+export var loadFaceLandmarkModel = function (url) { return nets.faceLandmark68Net.load(url); };
+export var loadFaceLandmarkTinyModel = function (url) { return nets.faceLandmark68TinyNet.load(url); };
+export var loadFaceRecognitionModel = function (url) { return nets.faceRecognitionNet.load(url); };
+// backward compatibility
+export var loadFaceDetectionModel = loadSsdMobilenetv1Model;
+export var locateFaces = ssdMobilenetv1;
+export var detectLandmarks = detectFaceLandmarks;
+//# sourceMappingURL=nets.js.map
\ No newline at end of file

build/es6/globalApi/nets.js.map

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\ No newline at end of file

build/es6/globalApi/types.d.ts

+import { TNetInput } from 'tfjs-image-recognition-base';
+import { TinyYolov2Options } from 'tfjs-tiny-yolov2';
+import { FaceDetection } from '../classes/FaceDetection';
+import { MtcnnOptions } from '../mtcnn/MtcnnOptions';
+import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
+import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
+export declare type FaceDetectionOptions = TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options;
+export declare type FaceDetectionFunction = (input: TNetInput) => Promise<FaceDetection[]>;

build/es6/globalApi/types.js

+//# sourceMappingURL=types.js.map
\ No newline at end of file

build/es6/globalApi/types.js.map

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

build/es6/index.d.ts

@@ -4,9 +4,10 @@ export * from 'tfjs-image-recognition-base';
 export * from './classes';
 export * from './dom';
 export * from './euclideanDistance';
-export * from './faceDetectionNet';
 export * from './faceLandmarkNet';
 export * from './faceRecognitionNet';
 export * from './globalApi';
 export * from './mtcnn';
+export * from './ssdMobilenetv1';
+export * from './tinyFaceDetector';
 export * from './tinyYolov2';

build/es6/index.js

@@ -4,10 +4,11 @@ export * from 'tfjs-image-recognition-base';
 export * from './classes';
 export * from './dom';
 export * from './euclideanDistance';
-export * from './faceDetectionNet';
 export * from './faceLandmarkNet';
 export * from './faceRecognitionNet';
 export * from './globalApi';
 export * from './mtcnn';
+export * from './ssdMobilenetv1';
+export * from './tinyFaceDetector';
 export * from './tinyYolov2';
 //# sourceMappingURL=index.js.map
\ No newline at end of file

build/es6/index.js.map

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\ No newline at end of file
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\ No newline at end of file

build/es6/mtcnn/Mtcnn.d.ts

 import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
-import { MtcnnForwardParams, MtcnnResult, NetParams } from './types';
+import { FaceDetectionWithLandmarks } from '../classes/FaceDetectionWithLandmarks';
+import { FaceLandmarks5 } from '../classes/FaceLandmarks5';
+import { IMtcnnOptions } from './MtcnnOptions';
+import { NetParams } from './types';
 export declare class Mtcnn extends NeuralNetwork<NetParams> {
     constructor();
-    forwardInput(input: NetInput, forwardParams?: MtcnnForwardParams): Promise<{
-        results: MtcnnResult[];
+    forwardInput(input: NetInput, forwardParams?: IMtcnnOptions): Promise<{
+        results: FaceDetectionWithLandmarks<FaceLandmarks5>[];
         stats: any;
     }>;
-    forward(input: TNetInput, forwardParams?: MtcnnForwardParams): Promise<MtcnnResult[]>;
-    forwardWithStats(input: TNetInput, forwardParams?: MtcnnForwardParams): Promise<{
-        results: MtcnnResult[];
+    forward(input: TNetInput, forwardParams?: IMtcnnOptions): Promise<FaceDetectionWithLandmarks<FaceLandmarks5>[]>;
+    forwardWithStats(input: TNetInput, forwardParams?: IMtcnnOptions): Promise<{
+        results: FaceDetectionWithLandmarks<FaceLandmarks5>[];
         stats: any;
     }>;
     protected loadQuantizedParams(uri: string | undefined): Promise<{

build/es6/mtcnn/Mtcnn.js

@@ -2,13 +2,14 @@ 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 { FaceDetectionWithLandmarks } from '../classes/FaceDetectionWithLandmarks';
 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 { MtcnnOptions } from './MtcnnOptions';
 import { pyramidDown } from './pyramidDown';
 import { stage1 } from './stage1';
 import { stage2 } from './stage2';
@@ -45,7 +46,7 @@ var Mtcnn = /** @class */ (function (_super) {
                             return results;
                         };
                         _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
-                        _b = Object.assign({}, getDefaultMtcnnForwardParams(), forwardParams), minFaceSize = _b.minFaceSize, scaleFactor = _b.scaleFactor, maxNumScales = _b.maxNumScales, scoreThresholds = _b.scoreThresholds, scaleSteps = _b.scaleSteps;
+                        _b = new MtcnnOptions(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(scale, [height, width]);
@@ -79,13 +80,10 @@ var Mtcnn = /** @class */ (function (_super) {
                     case 3:
                         out3 = _c.sent();
                         stats.total_stage3 = Date.now() - ts;
-                        results = out3.boxes.map(function (box, idx) { return ({
-                            faceDetection: new FaceDetection(out3.scores[idx], new Rect(box.left / width, box.top / height, box.width / width, box.height / height), {
+                        results = out3.boxes.map(function (box, idx) { return new FaceDetectionWithLandmarks(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(out3.points[idx].map(function (pt) { return pt.div(new Point(width, height)); }), { width: width, height: height })
-                        }); });
+                        }), new FaceLandmarks5(out3.points[idx].map(function (pt) { return pt.sub(new Point(box.left, box.top)).div(new Point(box.width, box.height)); }), { width: box.width, height: box.height })); });
                         return [2 /*return*/, onReturn({ results: results, stats: stats })];
                 }
             });

build/es6/mtcnn/Mtcnn.js.map

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\ No newline at end of file
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\ No newline at end of file

build/es6/mtcnn/MtcnnOptions.d.ts

+export interface IMtcnnOptions {
+    minFaceSize?: number;
+    scaleFactor?: number;
+    maxNumScales?: number;
+    scoreThresholds?: number[];
+    scaleSteps?: number[];
+}
+export declare class MtcnnOptions {
+    protected _name: string;
+    private _minFaceSize;
+    private _scaleFactor;
+    private _maxNumScales;
+    private _scoreThresholds;
+    private _scaleSteps;
+    constructor({minFaceSize, scaleFactor, maxNumScales, scoreThresholds, scaleSteps}?: IMtcnnOptions);
+    readonly minFaceSize: number;
+    readonly scaleFactor: number;
+    readonly maxNumScales: number;
+    readonly scoreThresholds: number[];
+    readonly scaleSteps: number[] | undefined;
+}

build/es6/mtcnn/MtcnnOptions.js

+var MtcnnOptions = /** @class */ (function () {
+    function MtcnnOptions(_a) {
+        var _b = _a === void 0 ? {} : _a, minFaceSize = _b.minFaceSize, scaleFactor = _b.scaleFactor, maxNumScales = _b.maxNumScales, scoreThresholds = _b.scoreThresholds, scaleSteps = _b.scaleSteps;
+        this._name = 'MtcnnOptions';
+        this._minFaceSize = minFaceSize || 20;
+        this._scaleFactor = scaleFactor || 0.709;
+        this._maxNumScales = maxNumScales || 10;
+        this._scoreThresholds = scoreThresholds || [0.6, 0.7, 0.7];
+        this._scaleSteps = scaleSteps;
+        if (typeof this._minFaceSize !== 'number' || this._minFaceSize < 0) {
+            throw new Error(this._name + " - expected minFaceSize to be a number > 0");
+        }
+        if (typeof this._scaleFactor !== 'number' || this._scaleFactor <= 0 || this._scaleFactor >= 1) {
+            throw new Error(this._name + " - expected scaleFactor to be a number between 0 and 1");
+        }
+        if (typeof this._maxNumScales !== 'number' || this._maxNumScales < 0) {
+            throw new Error(this._name + " - expected maxNumScales to be a number > 0");
+        }
+        if (!Array.isArray(this._scoreThresholds)
+            || this._scoreThresholds.length !== 3
+            || this._scoreThresholds.some(function (th) { return typeof th !== 'number'; })) {
+            throw new Error(this._name + " - expected scoreThresholds to be an array of numbers of length 3");
+        }
+        if (this._scaleSteps
+            && (!Array.isArray(this._scaleSteps) || this._scaleSteps.some(function (th) { return typeof th !== 'number'; }))) {
+            throw new Error(this._name + " - expected scaleSteps to be an array of numbers");
+        }
+    }
+    Object.defineProperty(MtcnnOptions.prototype, "minFaceSize", {
+        get: function () { return this._minFaceSize; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(MtcnnOptions.prototype, "scaleFactor", {
+        get: function () { return this._scaleFactor; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(MtcnnOptions.prototype, "maxNumScales", {
+        get: function () { return this._maxNumScales; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(MtcnnOptions.prototype, "scoreThresholds", {
+        get: function () { return this._scoreThresholds; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(MtcnnOptions.prototype, "scaleSteps", {
+        get: function () { return this._scaleSteps; },
+        enumerable: true,
+        configurable: true
+    });
+    return MtcnnOptions;
+}());
+export { MtcnnOptions };
+//# sourceMappingURL=MtcnnOptions.js.map
\ No newline at end of file

build/es6/mtcnn/MtcnnOptions.js.map

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build/es6/mtcnn/extractImagePatches.d.ts

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

build/es6/mtcnn/extractImagePatches.js.map

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\ No newline at end of file
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\ No newline at end of file

build/es6/mtcnn/index.d.ts

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

build/es6/mtcnn/index.js

 import { Mtcnn } from './Mtcnn';
 export * from './Mtcnn';
+export * from './MtcnnOptions';
 export function createMtcnn(weights) {
     var net = new Mtcnn();
     net.extractWeights(weights);

build/es6/mtcnn/index.js.map

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\ No newline at end of file
+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/mtcnn/index.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,KAAK,EAAE,MAAM,SAAS,CAAC;AAEhC,cAAc,SAAS,CAAC;AACxB,cAAc,gBAAgB,CAAC;AAE/B,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/es6/mtcnn/types.d.ts

 import * as tf from '@tensorflow/tfjs-core';
 import { ConvParams, FCParams } from 'tfjs-tiny-yolov2';
-import { FaceDetection } from '../classes/FaceDetection';
-import { FaceLandmarks5 } from '../classes/FaceLandmarks5';
 export declare type SharedParams = {
     conv1: ConvParams;
     prelu1_alpha: tf.Tensor1D;
@@ -34,14 +32,3 @@ export declare type NetParams = {
     rnet: RNetParams;
     onet: ONetParams;
 };
-export declare type MtcnnResult = {
-    faceDetection: FaceDetection;
-    faceLandmarks: FaceLandmarks5;
-};
-export declare type MtcnnForwardParams = {
-    minFaceSize?: number;
-    scaleFactor?: number;
-    maxNumScales?: number;
-    scoreThresholds?: number[];
-    scaleSteps?: number[];
-};

build/es6/ssdMobilenetv1/SsdMobilenetv1.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
+import { FaceDetection } from '../classes/FaceDetection';
+import { ISsdMobilenetv1Options } from './SsdMobilenetv1Options';
+import { NetParams } from './types';
+export declare class SsdMobilenetv1 extends NeuralNetwork<NetParams> {
+    constructor();
+    forwardInput(input: NetInput): {
+        boxes: tf.Tensor<tf.Rank.R2>[];
+        scores: tf.Tensor<tf.Rank.R1>[];
+    };
+    forward(input: TNetInput): Promise<{
+        boxes: tf.Tensor<tf.Rank.R2>[];
+        scores: tf.Tensor<tf.Rank.R1>[];
+    }>;
+    locateFaces(input: TNetInput, options?: ISsdMobilenetv1Options): Promise<FaceDetection[]>;
+    protected loadQuantizedParams(uri: string | undefined): Promise<{
+        params: NetParams;
+        paramMappings: {
+            originalPath?: string | undefined;
+            paramPath: string;
+        }[];
+    }>;
+    protected extractParams(weights: Float32Array): {
+        params: NetParams;
+        paramMappings: {
+            originalPath?: string | undefined;
+            paramPath: string;
+        }[];
+    };
+}

build/es6/ssdMobilenetv1/SsdMobilenetv1.js

+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';
+import { SsdMobilenetv1Options } from './SsdMobilenetv1Options';
+var SsdMobilenetv1 = /** @class */ (function (_super) {
+    tslib_1.__extends(SsdMobilenetv1, _super);
+    function SsdMobilenetv1() {
+        return _super.call(this, 'SsdMobilenetv1') || this;
+    }
+    SsdMobilenetv1.prototype.forwardInput = function (input) {
+        var params = this.params;
+        if (!params) {
+            throw new Error('SsdMobilenetv1 - load model before inference');
+        }
+        return tf.tidy(function () {
+            var batchTensor = input.toBatchTensor(512, false).toFloat();
+            var x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1));
+            var features = mobileNetV1(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);
+        });
+    };
+    SsdMobilenetv1.prototype.forward = function (input) {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var _a;
+            return tslib_1.__generator(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        _a = this.forwardInput;
+                        return [4 /*yield*/, toNetInput(input)];
+                    case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
+                }
+            });
+        });
+    };
+    SsdMobilenetv1.prototype.locateFaces = function (input, options) {
+        if (options === void 0) { options = {}; }
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var _a, maxResults, minConfidence, netInput, _b, _boxes, _scores, boxes, scores, i, scoresData, _c, _d, iouThreshold, indices, reshapedDims, inputSize, padX, padY, results;
+            return tslib_1.__generator(this, function (_e) {
+                switch (_e.label) {
+                    case 0:
+                        _a = new SsdMobilenetv1Options(options), maxResults = _a.maxResults, minConfidence = _a.minConfidence;
+                        return [4 /*yield*/, toNetInput(input)];
+                    case 1:
+                        netInput = _e.sent();
+                        _b = this.forwardInput(netInput), _boxes = _b.boxes, _scores = _b.scores;
+                        boxes = _boxes[0];
+                        scores = _scores[0];
+                        for (i = 1; i < _boxes.length; i++) {
+                            _boxes[i].dispose();
+                            _scores[i].dispose();
+                        }
+                        _d = (_c = Array).from;
+                        return [4 /*yield*/, scores.data()];
+                    case 2:
+                        scoresData = _d.apply(_c, [_e.sent()]);
+                        iouThreshold = 0.5;
+                        indices = nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
+                        reshapedDims = netInput.getReshapedInputDimensions(0);
+                        inputSize = netInput.inputSize;
+                        padX = inputSize / reshapedDims.width;
+                        padY = inputSize / reshapedDims.height;
+                        results = indices
+                            .map(function (idx) {
+                            var _a = [
+                                Math.max(0, boxes.get(idx, 0)),
+                                Math.min(1.0, boxes.get(idx, 2))
+                            ].map(function (val) { return val * padY; }), top = _a[0], bottom = _a[1];
+                            var _b = [
+                                Math.max(0, boxes.get(idx, 1)),
+                                Math.min(1.0, boxes.get(idx, 3))
+                            ].map(function (val) { return val * padX; }), left = _b[0], right = _b[1];
+                            return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), {
+                                height: netInput.getInputHeight(0),
+                                width: netInput.getInputWidth(0)
+                            });
+                        });
+                        boxes.dispose();
+                        scores.dispose();
+                        return [2 /*return*/, results];
+                }
+            });
+        });
+    };
+    SsdMobilenetv1.prototype.loadQuantizedParams = function (uri) {
+        return loadQuantizedParams(uri);
+    };
+    SsdMobilenetv1.prototype.extractParams = function (weights) {
+        return extractParams(weights);
+    };
+    return SsdMobilenetv1;
+}(NeuralNetwork));
+export { SsdMobilenetv1 };
+//# sourceMappingURL=SsdMobilenetv1.js.map
\ No newline at end of file

build/es6/ssdMobilenetv1/SsdMobilenetv1.js.map

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\ No newline at end of file

build/es6/ssdMobilenetv1/SsdMobilenetv1Options.d.ts

+export interface ISsdMobilenetv1Options {
+    minConfidence?: number;
+    maxResults?: number;
+}
+export declare class SsdMobilenetv1Options {
+    protected _name: string;
+    private _minConfidence;
+    private _maxResults;
+    constructor({minConfidence, maxResults}?: ISsdMobilenetv1Options);
+    readonly minConfidence: number;
+    readonly maxResults: number;
+}

build/es6/ssdMobilenetv1/SsdMobilenetv1Options.js

+var SsdMobilenetv1Options = /** @class */ (function () {
+    function SsdMobilenetv1Options(_a) {
+        var _b = _a === void 0 ? {} : _a, minConfidence = _b.minConfidence, maxResults = _b.maxResults;
+        this._name = 'SsdMobilenetv1Options';
+        this._minConfidence = minConfidence || 0.5;
+        this._maxResults = maxResults || 100;
+        if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {
+            throw new Error(this._name + " - expected minConfidence to be a number between 0 and 1");
+        }
+        if (typeof this._maxResults !== 'number') {
+            throw new Error(this._name + " - expected maxResults to be a number");
+        }
+    }
+    Object.defineProperty(SsdMobilenetv1Options.prototype, "minConfidence", {
+        get: function () { return this._minConfidence; },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(SsdMobilenetv1Options.prototype, "maxResults", {
+        get: function () { return this._maxResults; },
+        enumerable: true,
+        configurable: true
+    });
+    return SsdMobilenetv1Options;
+}());
+export { SsdMobilenetv1Options };
+//# sourceMappingURL=SsdMobilenetv1Options.js.map
\ No newline at end of file

build/es6/ssdMobilenetv1/SsdMobilenetv1Options.js.map

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\ No newline at end of file

build/es6/ssdMobilenetv1/boxPredictionLayer.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { BoxPredictionParams } from './types';
+export declare function boxPredictionLayer(x: tf.Tensor4D, params: BoxPredictionParams): {
+    boxPredictionEncoding: tf.Tensor<tf.Rank>;
+    classPrediction: tf.Tensor<tf.Rank>;
+};

build/es6/ssdMobilenetv1/boxPredictionLayer.js

+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(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
+        };
+    });
+}
+//# sourceMappingURL=boxPredictionLayer.js.map
\ No newline at end of file

build/es6/ssdMobilenetv1/boxPredictionLayer.js.map

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\ No newline at end of file

build/es6/ssdMobilenetv1/extractParams.d.ts

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

build/es6/ssdMobilenetv1/extractParams.js

+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]);
+        var batch_norm_scale = tf.tensor1d(extractWeights(numChannels));
+        var batch_norm_offset = tf.tensor1d(extractWeights(numChannels));
+        var batch_norm_mean = tf.tensor1d(extractWeights(numChannels));
+        var batch_norm_variance = tf.tensor1d(extractWeights(numChannels));
+        paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/batch_norm_scale" }, { paramPath: mappedPrefix + "/batch_norm_offset" }, { paramPath: mappedPrefix + "/batch_norm_mean" }, { paramPath: mappedPrefix + "/batch_norm_variance" });
+        return {
+            filters: filters,
+            batch_norm_scale: batch_norm_scale,
+            batch_norm_offset: batch_norm_offset,
+            batch_norm_mean: batch_norm_mean,
+            batch_norm_variance: batch_norm_variance
+        };
+    }
+    function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
+        var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
+        var bias = tf.tensor1d(extractWeights(channelsOut));
+        paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/" + (isPointwiseConv ? 'batch_norm_offset' : 'bias') });
+        return { filters: filters, bias: bias };
+    }
+    function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
+        var _a = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true), filters = _a.filters, bias = _a.bias;
+        return {
+            filters: filters,
+            batch_norm_offset: bias
+        };
+    }
+    function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) {
+        var depthwise_conv = extractDepthwiseConvParams(channelsIn, mappedPrefix + "/depthwise_conv");
+        var pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, mappedPrefix + "/pointwise_conv");
+        return { depthwise_conv: depthwise_conv, pointwise_conv: pointwise_conv };
+    }
+    function extractMobilenetV1Params() {
+        var conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');
+        var conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');
+        var conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');
+        var conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');
+        var conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');
+        var conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');
+        var conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');
+        var conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');
+        var conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');
+        var conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');
+        var conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');
+        var conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');
+        var conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');
+        var conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');
+        return {
+            conv_0: conv_0,
+            conv_1: conv_1,
+            conv_2: conv_2,
+            conv_3: conv_3,
+            conv_4: conv_4,
+            conv_5: conv_5,
+            conv_6: conv_6,
+            conv_7: conv_7,
+            conv_8: conv_8,
+            conv_9: conv_9,
+            conv_10: conv_10,
+            conv_11: conv_11,
+            conv_12: conv_12,
+            conv_13: conv_13
+        };
+    }
+    function extractPredictionLayerParams() {
+        var conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');
+        var conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');
+        var conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');
+        var conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');
+        var conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');
+        var conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');
+        var conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');
+        var conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');
+        var box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');
+        var class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');
+        var box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');
+        var class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');
+        var box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');
+        var class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');
+        var box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');
+        var class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');
+        var box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');
+        var class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');
+        var box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');
+        var class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');
+        var box_predictor_0 = {
+            box_encoding_predictor: box_encoding_0_predictor,
+            class_predictor: class_predictor_0
+        };
+        var box_predictor_1 = {
+            box_encoding_predictor: box_encoding_1_predictor,
+            class_predictor: class_predictor_1
+        };
+        var box_predictor_2 = {
+            box_encoding_predictor: box_encoding_2_predictor,
+            class_predictor: class_predictor_2
+        };
+        var box_predictor_3 = {
+            box_encoding_predictor: box_encoding_3_predictor,
+            class_predictor: class_predictor_3
+        };
+        var box_predictor_4 = {
+            box_encoding_predictor: box_encoding_4_predictor,
+            class_predictor: class_predictor_4
+        };
+        var box_predictor_5 = {
+            box_encoding_predictor: box_encoding_5_predictor,
+            class_predictor: class_predictor_5
+        };
+        return {
+            conv_0: conv_0,
+            conv_1: conv_1,
+            conv_2: conv_2,
+            conv_3: conv_3,
+            conv_4: conv_4,
+            conv_5: conv_5,
+            conv_6: conv_6,
+            conv_7: conv_7,
+            box_predictor_0: box_predictor_0,
+            box_predictor_1: box_predictor_1,
+            box_predictor_2: box_predictor_2,
+            box_predictor_3: box_predictor_3,
+            box_predictor_4: box_predictor_4,
+            box_predictor_5: box_predictor_5
+        };
+    }
+    return {
+        extractMobilenetV1Params: extractMobilenetV1Params,
+        extractPredictionLayerParams: extractPredictionLayerParams
+    };
+}
+export function extractParams(weights) {
+    var paramMappings = [];
+    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();
+    var extra_dim = tf.tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
+    var output_layer = {
+        extra_dim: extra_dim
+    };
+    paramMappings.push({ paramPath: 'output_layer/extra_dim' });
+    if (getRemainingWeights().length !== 0) {
+        throw new Error("weights remaing after extract: " + getRemainingWeights().length);
+    }
+    return {
+        params: {
+            mobilenetv1: mobilenetv1,
+            prediction_layer: prediction_layer,
+            output_layer: output_layer
+        },
+        paramMappings: paramMappings
+    };
+}
+//# sourceMappingURL=extractParams.js.map
\ No newline at end of file

build/es6/ssdMobilenetv1/extractParams.js.map

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\ No newline at end of file

build/es6/ssdMobilenetv1/index.d.ts

+import { SsdMobilenetv1 } from './SsdMobilenetv1';
+export * from './SsdMobilenetv1';
+export * from './SsdMobilenetv1Options';
+export declare function createSsdMobilenetv1(weights: Float32Array): SsdMobilenetv1;
+export declare function createFaceDetectionNet(weights: Float32Array): SsdMobilenetv1;
+export declare class FaceDetectionNet extends SsdMobilenetv1 {
+}

build/es6/ssdMobilenetv1/index.js

+import * as tslib_1 from "tslib";
+import { SsdMobilenetv1 } from './SsdMobilenetv1';
+export * from './SsdMobilenetv1';
+export * from './SsdMobilenetv1Options';
+export function createSsdMobilenetv1(weights) {
+    var net = new SsdMobilenetv1();
+    net.extractWeights(weights);
+    return net;
+}
+export function createFaceDetectionNet(weights) {
+    return createSsdMobilenetv1(weights);
+}
+// alias for backward compatibily
+var FaceDetectionNet = /** @class */ (function (_super) {
+    tslib_1.__extends(FaceDetectionNet, _super);
+    function FaceDetectionNet() {
+        return _super !== null && _super.apply(this, arguments) || this;
+    }
+    return FaceDetectionNet;
+}(SsdMobilenetv1));
+export { FaceDetectionNet };
+//# sourceMappingURL=index.js.map
\ No newline at end of file

build/es6/ssdMobilenetv1/index.js.map

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\ No newline at end of file

build/es6/ssdMobilenetv1/loadQuantizedParams.d.ts

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

build/es6/ssdMobilenetv1/loadQuantizedParams.js

+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(weightMap, paramMappings);
+    function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
+        var filters = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/weights", 4, mappedPrefix + "/filters");
+        var batch_norm_offset = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset", 1, mappedPrefix + "/batch_norm_offset");
+        return { filters: filters, batch_norm_offset: batch_norm_offset };
+    }
+    function extractConvPairParams(idx) {
+        var mappedPrefix = "mobilenetv1/conv_" + idx;
+        var prefixDepthwiseConv = "MobilenetV1/Conv2d_" + idx + "_depthwise";
+        var mappedPrefixDepthwiseConv = mappedPrefix + "/depthwise_conv";
+        var mappedPrefixPointwiseConv = mappedPrefix + "/pointwise_conv";
+        var filters = extractWeightEntry(prefixDepthwiseConv + "/depthwise_weights", 4, mappedPrefixDepthwiseConv + "/filters");
+        var batch_norm_scale = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/gamma", 1, mappedPrefixDepthwiseConv + "/batch_norm_scale");
+        var batch_norm_offset = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/beta", 1, mappedPrefixDepthwiseConv + "/batch_norm_offset");
+        var batch_norm_mean = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_mean", 1, mappedPrefixDepthwiseConv + "/batch_norm_mean");
+        var batch_norm_variance = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_variance", 1, mappedPrefixDepthwiseConv + "/batch_norm_variance");
+        return {
+            depthwise_conv: {
+                filters: filters,
+                batch_norm_scale: batch_norm_scale,
+                batch_norm_offset: batch_norm_offset,
+                batch_norm_mean: batch_norm_mean,
+                batch_norm_variance: batch_norm_variance
+            },
+            pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)
+        };
+    }
+    function extractMobilenetV1Params() {
+        return {
+            conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),
+            conv_1: extractConvPairParams(1),
+            conv_2: extractConvPairParams(2),
+            conv_3: extractConvPairParams(3),
+            conv_4: extractConvPairParams(4),
+            conv_5: extractConvPairParams(5),
+            conv_6: extractConvPairParams(6),
+            conv_7: extractConvPairParams(7),
+            conv_8: extractConvPairParams(8),
+            conv_9: extractConvPairParams(9),
+            conv_10: extractConvPairParams(10),
+            conv_11: extractConvPairParams(11),
+            conv_12: extractConvPairParams(12),
+            conv_13: extractConvPairParams(13)
+        };
+    }
+    function extractConvParams(prefix, mappedPrefix) {
+        var filters = extractWeightEntry(prefix + "/weights", 4, mappedPrefix + "/filters");
+        var bias = extractWeightEntry(prefix + "/biases", 1, mappedPrefix + "/bias");
+        return { filters: filters, bias: bias };
+    }
+    function extractBoxPredictorParams(idx) {
+        var box_encoding_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor", "prediction_layer/box_predictor_" + idx + "/box_encoding_predictor");
+        var class_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/ClassPredictor", "prediction_layer/box_predictor_" + idx + "/class_predictor");
+        return { box_encoding_predictor: box_encoding_predictor, class_predictor: class_predictor };
+    }
+    function extractPredictionLayerParams() {
+        return {
+            conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),
+            conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),
+            conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),
+            conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),
+            conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),
+            conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),
+            conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),
+            conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),
+            box_predictor_0: extractBoxPredictorParams(0),
+            box_predictor_1: extractBoxPredictorParams(1),
+            box_predictor_2: extractBoxPredictorParams(2),
+            box_predictor_3: extractBoxPredictorParams(3),
+            box_predictor_4: extractBoxPredictorParams(4),
+            box_predictor_5: extractBoxPredictorParams(5)
+        };
+    }
+    return {
+        extractMobilenetV1Params: extractMobilenetV1Params,
+        extractPredictionLayerParams: extractPredictionLayerParams
+    };
+}
+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(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 (!isTensor3D(extra_dim)) {
+                        throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have " + extra_dim);
+                    }
+                    params = {
+                        mobilenetv1: extractMobilenetV1Params(),
+                        prediction_layer: extractPredictionLayerParams(),
+                        output_layer: {
+                            extra_dim: extra_dim
+                        }
+                    };
+                    disposeUnusedWeightTensors(weightMap, paramMappings);
+                    return [2 /*return*/, { params: params, paramMappings: paramMappings }];
+            }
+        });
+    });
+}
+//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file

build/es6/ssdMobilenetv1/loadQuantizedParams.js.map

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build/es6/ssdMobilenetv1/mobileNetV1.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { MobileNetV1 } from './types';
+export declare function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params): {
+    out: tf.Tensor<tf.Rank.R4>;
+    conv11: any;
+};

build/es6/ssdMobilenetv1/mobileNetV1.js

+import * as tf from '@tensorflow/tfjs-core';
+import { pointwiseConvLayer } from './pointwiseConvLayer';
+var epsilon = 0.0010000000474974513;
+function depthwiseConvLayer(x, params, strides) {
+    return tf.tidy(function () {
+        var out = tf.depthwiseConv2d(x, params.filters, strides, 'same');
+        out = tf.batchNormalization(out, params.batch_norm_mean, params.batch_norm_variance, epsilon, params.batch_norm_scale, params.batch_norm_offset);
+        return tf.clipByValue(out, 0, 6);
+    });
+}
+function getStridesForLayerIdx(layerIdx) {
+    return [2, 4, 6, 12].some(function (idx) { return idx === layerIdx; }) ? [2, 2] : [1, 1];
+}
+export function mobileNetV1(x, params) {
+    return tf.tidy(function () {
+        var conv11 = null;
+        var out = pointwiseConvLayer(x, params.conv_0, [2, 2]);
+        var convPairParams = [
+            params.conv_1,
+            params.conv_2,
+            params.conv_3,
+            params.conv_4,
+            params.conv_5,
+            params.conv_6,
+            params.conv_7,
+            params.conv_8,
+            params.conv_9,
+            params.conv_10,
+            params.conv_11,
+            params.conv_12,
+            params.conv_13
+        ];
+        convPairParams.forEach(function (param, i) {
+            var layerIdx = i + 1;
+            var depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
+            out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);
+            out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);
+            if (layerIdx === 11) {
+                conv11 = out;
+            }
+        });
+        if (conv11 === null) {
+            throw new Error('mobileNetV1 - output of conv layer 11 is null');
+        }
+        return {
+            out: out,
+            conv11: conv11
+        };
+    });
+}
+//# sourceMappingURL=mobileNetV1.js.map
\ No newline at end of file

build/es6/ssdMobilenetv1/mobileNetV1.js.map

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\ No newline at end of file

build/es6/ssdMobilenetv1/nonMaxSuppression.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+export declare function nonMaxSuppression(boxes: tf.Tensor2D, scores: number[], maxOutputSize: number, iouThreshold: number, scoreThreshold: number): number[];

build/es6/ssdMobilenetv1/nonMaxSuppression.js

+export function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+    var numBoxes = boxes.shape[0];
+    var outputSize = Math.min(maxOutputSize, numBoxes);
+    var candidates = scores
+        .map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })
+        .filter(function (c) { return c.score > scoreThreshold; })
+        .sort(function (c1, c2) { return c2.score - c1.score; });
+    var suppressFunc = function (x) { return x <= iouThreshold ? 1 : 0; };
+    var selected = [];
+    candidates.forEach(function (c) {
+        if (selected.length >= outputSize) {
+            return;
+        }
+        var originalScore = c.score;
+        for (var j = selected.length - 1; j >= 0; --j) {
+            var iou = IOU(boxes, c.boxIndex, selected[j]);
+            if (iou === 0.0) {
+                continue;
+            }
+            c.score *= suppressFunc(iou);
+            if (c.score <= scoreThreshold) {
+                break;
+            }
+        }
+        if (originalScore === c.score) {
+            selected.push(c.boxIndex);
+        }
+    });
+    return selected;
+}
+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));
+    var ymaxI = Math.max(boxes.get(i, 0), boxes.get(i, 2));
+    var xmaxI = Math.max(boxes.get(i, 1), boxes.get(i, 3));
+    var yminJ = Math.min(boxes.get(j, 0), boxes.get(j, 2));
+    var xminJ = Math.min(boxes.get(j, 1), boxes.get(j, 3));
+    var ymaxJ = Math.max(boxes.get(j, 0), boxes.get(j, 2));
+    var xmaxJ = Math.max(boxes.get(j, 1), boxes.get(j, 3));
+    var areaI = (ymaxI - yminI) * (xmaxI - xminI);
+    var areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
+    if (areaI <= 0 || areaJ <= 0) {
+        return 0.0;
+    }
+    var intersectionYmin = Math.max(yminI, yminJ);
+    var intersectionXmin = Math.max(xminI, xminJ);
+    var intersectionYmax = Math.min(ymaxI, ymaxJ);
+    var intersectionXmax = Math.min(xmaxI, xmaxJ);
+    var intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *
+        Math.max(intersectionXmax - intersectionXmin, 0.0);
+    return intersectionArea / (areaI + areaJ - intersectionArea);
+}
+//# sourceMappingURL=nonMaxSuppression.js.map
\ No newline at end of file

build/es6/ssdMobilenetv1/nonMaxSuppression.js.map

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build/es6/ssdMobilenetv1/outputLayer.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { OutputLayerParams } from './types';
+export declare function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams): {
+    boxes: tf.Tensor<tf.Rank.R2>[];
+    scores: tf.Tensor<tf.Rank.R1>[];
+};

build/es6/ssdMobilenetv1/outputLayer.js

+import * as tf from '@tensorflow/tfjs-core';
+function getCenterCoordinatesAndSizesLayer(x) {
+    var vec = tf.unstack(tf.transpose(x, [1, 0]));
+    var sizes = [
+        tf.sub(vec[2], vec[0]),
+        tf.sub(vec[3], vec[1])
+    ];
+    var centers = [
+        tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),
+        tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))
+    ];
+    return {
+        sizes: sizes,
+        centers: centers
+    };
+}
+function decodeBoxesLayer(x0, x1) {
+    var _a = getCenterCoordinatesAndSizesLayer(x0), sizes = _a.sizes, centers = _a.centers;
+    var vec = tf.unstack(tf.transpose(x1, [1, 0]));
+    var div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));
+    var add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);
+    var div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));
+    var add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);
+    return tf.transpose(tf.stack([
+        tf.sub(add0_out, div0_out),
+        tf.sub(add1_out, div1_out),
+        tf.add(add0_out, div0_out),
+        tf.add(add1_out, div1_out)
+    ]), [1, 0]);
+}
+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]));
+        boxes = tf.reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);
+        var scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
+        var scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]);
+        scores = tf.reshape(scores, [batchSize, scores.shape[1]]);
+        var boxesByBatch = tf.unstack(boxes);
+        var scoresByBatch = tf.unstack(scores);
+        return {
+            boxes: boxesByBatch,
+            scores: scoresByBatch
+        };
+    });
+}
+//# sourceMappingURL=outputLayer.js.map
\ No newline at end of file

build/es6/ssdMobilenetv1/outputLayer.js.map

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\ No newline at end of file

build/es6/ssdMobilenetv1/pointwiseConvLayer.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { PointwiseConvParams } from './types';
+export declare function pointwiseConvLayer(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]): tf.Tensor<tf.Rank.R4>;

build/es6/ssdMobilenetv1/pointwiseConvLayer.js

+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);
+    });
+}
+//# sourceMappingURL=pointwiseConvLayer.js.map
\ No newline at end of file

build/es6/ssdMobilenetv1/pointwiseConvLayer.js.map

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\ No newline at end of file

build/es6/ssdMobilenetv1/predictionLayer.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { PredictionLayerParams } from './types';
+export declare function predictionLayer(x: tf.Tensor4D, conv11: tf.Tensor4D, params: PredictionLayerParams): {
+    boxPredictions: tf.Tensor<tf.Rank.R4>;
+    classPredictions: tf.Tensor<tf.Rank.R4>;
+};

build/es6/ssdMobilenetv1/predictionLayer.js

+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(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,
+            boxPrediction2.boxPredictionEncoding,
+            boxPrediction3.boxPredictionEncoding,
+            boxPrediction4.boxPredictionEncoding,
+            boxPrediction5.boxPredictionEncoding
+        ], 1);
+        var classPredictions = tf.concat([
+            boxPrediction0.classPrediction,
+            boxPrediction1.classPrediction,
+            boxPrediction2.classPrediction,
+            boxPrediction3.classPrediction,
+            boxPrediction4.classPrediction,
+            boxPrediction5.classPrediction
+        ], 1);
+        return {
+            boxPredictions: boxPredictions,
+            classPredictions: classPredictions
+        };
+    });
+}
+//# sourceMappingURL=predictionLayer.js.map
\ No newline at end of file

build/es6/ssdMobilenetv1/predictionLayer.js.map

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\ No newline at end of file

build/es6/ssdMobilenetv1/types.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { ConvParams } from 'tfjs-tiny-yolov2';
+export declare type PointwiseConvParams = {
+    filters: tf.Tensor4D;
+    batch_norm_offset: tf.Tensor1D;
+};
+export declare namespace MobileNetV1 {
+    type DepthwiseConvParams = {
+        filters: tf.Tensor4D;
+        batch_norm_scale: tf.Tensor1D;
+        batch_norm_offset: tf.Tensor1D;
+        batch_norm_mean: tf.Tensor1D;
+        batch_norm_variance: tf.Tensor1D;
+    };
+    type ConvPairParams = {
+        depthwise_conv: DepthwiseConvParams;
+        pointwise_conv: PointwiseConvParams;
+    };
+    type Params = {
+        conv_0: PointwiseConvParams;
+        conv_1: ConvPairParams;
+        conv_2: ConvPairParams;
+        conv_3: ConvPairParams;
+        conv_4: ConvPairParams;
+        conv_5: ConvPairParams;
+        conv_6: ConvPairParams;
+        conv_7: ConvPairParams;
+        conv_8: ConvPairParams;
+        conv_9: ConvPairParams;
+        conv_10: ConvPairParams;
+        conv_11: ConvPairParams;
+        conv_12: ConvPairParams;
+        conv_13: ConvPairParams;
+    };
+}
+export declare type BoxPredictionParams = {
+    box_encoding_predictor: ConvParams;
+    class_predictor: ConvParams;
+};
+export declare type PredictionLayerParams = {
+    conv_0: PointwiseConvParams;
+    conv_1: PointwiseConvParams;
+    conv_2: PointwiseConvParams;
+    conv_3: PointwiseConvParams;
+    conv_4: PointwiseConvParams;
+    conv_5: PointwiseConvParams;
+    conv_6: PointwiseConvParams;
+    conv_7: PointwiseConvParams;
+    box_predictor_0: BoxPredictionParams;
+    box_predictor_1: BoxPredictionParams;
+    box_predictor_2: BoxPredictionParams;
+    box_predictor_3: BoxPredictionParams;
+    box_predictor_4: BoxPredictionParams;
+    box_predictor_5: BoxPredictionParams;
+};
+export declare type OutputLayerParams = {
+    extra_dim: tf.Tensor3D;
+};
+export declare type NetParams = {
+    mobilenetv1: MobileNetV1.Params;
+    prediction_layer: PredictionLayerParams;
+    output_layer: OutputLayerParams;
+};

build/es6/ssdMobilenetv1/types.js

+//# sourceMappingURL=types.js.map
\ No newline at end of file

build/es6/ssdMobilenetv1/types.js.map

+{"version":3,"file":"types.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/types.ts"],"names":[],"mappings":""}
\ No newline at end of file

build/es6/tinyFaceDetector/TinyFaceDetector.d.ts

+import { Point, TNetInput } from 'tfjs-image-recognition-base';
+import { TinyYolov2 as TinyYolov2Base, ITinyYolov2Options } from 'tfjs-tiny-yolov2';
+import { FaceDetection } from '../classes';
+export declare class TinyFaceDetector extends TinyYolov2Base {
+    constructor();
+    readonly anchors: Point[];
+    locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]>;
+    protected loadQuantizedParams(modelUri: string | undefined): any;
+}

build/es6/tinyFaceDetector/TinyFaceDetector.js

+import * as tslib_1 from "tslib";
+import { TinyYolov2 as TinyYolov2Base } from 'tfjs-tiny-yolov2';
+import { FaceDetection } from '../classes';
+import { BOX_ANCHORS, DEFAULT_MODEL_NAME, IOU_THRESHOLD, MEAN_RGB } from './const';
+var TinyFaceDetector = /** @class */ (function (_super) {
+    tslib_1.__extends(TinyFaceDetector, _super);
+    function TinyFaceDetector() {
+        var _this = this;
+        var config = {
+            withSeparableConvs: true,
+            iouThreshold: IOU_THRESHOLD,
+            classes: ['face'],
+            anchors: BOX_ANCHORS,
+            meanRgb: MEAN_RGB,
+            isFirstLayerConv2d: true,
+            filterSizes: [3, 16, 32, 64, 128, 256, 512]
+        };
+        _this = _super.call(this, config) || this;
+        return _this;
+    }
+    Object.defineProperty(TinyFaceDetector.prototype, "anchors", {
+        get: function () {
+            return this.config.anchors;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    TinyFaceDetector.prototype.locateFaces = function (input, forwardParams) {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var objectDetections;
+            return tslib_1.__generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4 /*yield*/, this.detect(input, forwardParams)];
+                    case 1:
+                        objectDetections = _a.sent();
+                        return [2 /*return*/, objectDetections.map(function (det) { return new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }); })];
+                }
+            });
+        });
+    };
+    TinyFaceDetector.prototype.loadQuantizedParams = function (modelUri) {
+        var defaultModelName = DEFAULT_MODEL_NAME;
+        return _super.prototype.loadQuantizedParams.call(this, modelUri, defaultModelName);
+    };
+    return TinyFaceDetector;
+}(TinyYolov2Base));
+export { TinyFaceDetector };
+//# sourceMappingURL=TinyFaceDetector.js.map
\ No newline at end of file

build/es6/tinyFaceDetector/TinyFaceDetector.js.map

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\ No newline at end of file

build/es6/tinyFaceDetector/TinyFaceDetectorOptions.d.ts

+import { ITinyYolov2Options, TinyYolov2Options } from 'tfjs-tiny-yolov2';
+export interface ITinyFaceDetectorOptions extends ITinyYolov2Options {
+}
+export declare class TinyFaceDetectorOptions extends TinyYolov2Options {
+    protected _name: string;
+}

build/es6/tinyFaceDetector/TinyFaceDetectorOptions.js

+import * as tslib_1 from "tslib";
+import { TinyYolov2Options } from 'tfjs-tiny-yolov2';
+var TinyFaceDetectorOptions = /** @class */ (function (_super) {
+    tslib_1.__extends(TinyFaceDetectorOptions, _super);
+    function TinyFaceDetectorOptions() {
+        var _this = _super !== null && _super.apply(this, arguments) || this;
+        _this._name = 'TinyFaceDetectorOptions';
+        return _this;
+    }
+    return TinyFaceDetectorOptions;
+}(TinyYolov2Options));
+export { TinyFaceDetectorOptions };
+//# sourceMappingURL=TinyFaceDetectorOptions.js.map
\ No newline at end of file

build/es6/tinyFaceDetector/TinyFaceDetectorOptions.js.map

+{"version":3,"file":"TinyFaceDetectorOptions.js","sourceRoot":"","sources":["../../../src/tinyFaceDetector/TinyFaceDetectorOptions.ts"],"names":[],"mappings":";AAAA,OAAO,EAAsB,iBAAiB,EAAE,MAAM,kBAAkB,CAAC;AAIzE;IAA6C,mDAAiB;IAA9D;QAAA,qEAEC;QADW,WAAK,GAAW,yBAAyB,CAAA;;IACrD,CAAC;IAAD,8BAAC;AAAD,CAAC,AAFD,CAA6C,iBAAiB,GAE7D"}
\ No newline at end of file

build/es6/tinyFaceDetector/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 MEAN_RGB: [number, number, number];
+export declare const DEFAULT_MODEL_NAME = "tiny_face_detector_model";

build/es6/tinyFaceDetector/const.js

+import { Point } from 'tfjs-image-recognition-base';
+export var IOU_THRESHOLD = 0.4;
+export var BOX_ANCHORS = [
+    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 = [117.001, 114.697, 97.404];
+export var DEFAULT_MODEL_NAME = 'tiny_face_detector_model';
+//# sourceMappingURL=const.js.map
\ No newline at end of file

build/es6/tinyFaceDetector/const.js.map

+{"version":3,"file":"const.js","sourceRoot":"","sources":["../../../src/tinyFaceDetector/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,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,QAAQ,GAA6B,CAAC,OAAO,EAAE,OAAO,EAAE,MAAM,CAAC,CAAA;AAE5E,MAAM,CAAC,IAAM,kBAAkB,GAAG,0BAA0B,CAAA"}
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build/es6/tinyFaceDetector/index.d.ts

+import { TinyFaceDetector } from './TinyFaceDetector';
+export * from './TinyFaceDetector';
+export * from './TinyFaceDetectorOptions';
+export declare function createTinyFaceDetector(weights: Float32Array): TinyFaceDetector;

build/es6/tinyFaceDetector/index.js

+import { TinyFaceDetector } from './TinyFaceDetector';
+export * from './TinyFaceDetector';
+export * from './TinyFaceDetectorOptions';
+export function createTinyFaceDetector(weights) {
+    var net = new TinyFaceDetector();
+    net.extractWeights(weights);
+    return net;
+}
+//# sourceMappingURL=index.js.map
\ No newline at end of file

build/es6/tinyFaceDetector/index.js.map

+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/tinyFaceDetector/index.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,gBAAgB,EAAE,MAAM,oBAAoB,CAAC;AAEtD,cAAc,oBAAoB,CAAC;AACnC,cAAc,2BAA2B,CAAC;AAE1C,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"}
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build/es6/tinyYolov2/TinyYolov2.d.ts

 import { Point, TNetInput } from 'tfjs-image-recognition-base';
-import { TinyYolov2 as TinyYolov2Base, TinyYolov2Types } from 'tfjs-tiny-yolov2';
+import { ITinyYolov2Options, TinyYolov2 as TinyYolov2Base } from 'tfjs-tiny-yolov2';
 import { FaceDetection } from '../classes';
 export declare class TinyYolov2 extends TinyYolov2Base {
     constructor(withSeparableConvs?: boolean);
     readonly withSeparableConvs: boolean;
     readonly anchors: Point[];
-    locateFaces(input: TNetInput, forwardParams: TinyYolov2Types.TinyYolov2ForwardParams): Promise<FaceDetection[]>;
+    locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]>;
     protected loadQuantizedParams(modelUri: string | undefined): any;
 }

build/es6/tinyYolov2/TinyYolov2.js.map

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