check in latest build

build/BoundingBox.d.ts

+import { Rect } from './Rect';
+export declare class BoundingBox {
+    private _left;
+    private _top;
+    private _right;
+    private _bottom;
+    constructor(_left: number, _top: number, _right: number, _bottom: number);
+    readonly left: number;
+    readonly top: number;
+    readonly right: number;
+    readonly bottom: number;
+    readonly width: number;
+    readonly height: number;
+    toSquare(): BoundingBox;
+    round(): BoundingBox;
+    padAtBorders(imageHeight: number, imageWidth: number): {
+        dy: number;
+        edy: number;
+        dx: number;
+        edx: number;
+        y: number;
+        ey: number;
+        x: number;
+        ex: number;
+        w: number;
+        h: number;
+    };
+    calibrate(region: BoundingBox): BoundingBox;
+    toRect(): Rect;
+}

build/BoundingBox.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var Rect_1 = require("./Rect");
+var BoundingBox = /** @class */ (function () {
+    function BoundingBox(_left, _top, _right, _bottom) {
+        this._left = _left;
+        this._top = _top;
+        this._right = _right;
+        this._bottom = _bottom;
+    }
+    Object.defineProperty(BoundingBox.prototype, "left", {
+        get: function () {
+            return this._left;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(BoundingBox.prototype, "top", {
+        get: function () {
+            return this._top;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(BoundingBox.prototype, "right", {
+        get: function () {
+            return this._right;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(BoundingBox.prototype, "bottom", {
+        get: function () {
+            return this._bottom;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(BoundingBox.prototype, "width", {
+        get: function () {
+            return this.right - this.left;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(BoundingBox.prototype, "height", {
+        get: function () {
+            return this.bottom - this.top;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    BoundingBox.prototype.toSquare = function () {
+        var _a = this, left = _a.left, top = _a.top, right = _a.right, bottom = _a.bottom;
+        var off = (Math.abs(this.width - this.height) / 2);
+        if (this.width < this.height) {
+            left -= off;
+            right += off;
+        }
+        if (this.height < this.width) {
+            top -= off;
+            bottom += off;
+        }
+        return new BoundingBox(left, top, right, bottom);
+    };
+    BoundingBox.prototype.round = function () {
+        return new BoundingBox(Math.round(this.left), Math.round(this.top), Math.round(this.right), Math.round(this.bottom));
+    };
+    BoundingBox.prototype.padAtBorders = function (imageHeight, imageWidth) {
+        var w = this.width + 1;
+        var h = this.height + 1;
+        var dx = 1;
+        var dy = 1;
+        var edx = w;
+        var edy = h;
+        var x = this.left;
+        var y = this.top;
+        var ex = this.right;
+        var ey = this.bottom;
+        if (ex > imageWidth) {
+            edx = -ex + imageWidth + w;
+            ex = imageWidth;
+        }
+        if (ey > imageHeight) {
+            edy = -ey + imageHeight + h;
+            ey = imageHeight;
+        }
+        if (x < 1) {
+            edy = 2 - x;
+            x = 1;
+        }
+        if (y < 1) {
+            edy = 2 - y;
+            y = 1;
+        }
+        return { dy: dy, edy: edy, dx: dx, edx: edx, y: y, ey: ey, x: x, ex: ex, w: w, h: h };
+    };
+    BoundingBox.prototype.calibrate = function (region) {
+        return new BoundingBox(this.left + (region.left * this.width), this.top + (region.top * this.height), this.right + (region.right * this.width), this.bottom + (region.bottom * this.height)).toSquare().round();
+    };
+    BoundingBox.prototype.toRect = function () {
+        return new Rect_1.Rect(this.left, this.top, this.width, this.height);
+    };
+    return BoundingBox;
+}());
+exports.BoundingBox = BoundingBox;
+//# sourceMappingURL=BoundingBox.js.map
\ No newline at end of file

build/BoundingBox.js.map

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\ No newline at end of file

build/commons/nonMaxSuppression.d.ts

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

build/commons/nonMaxSuppression.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+function nonMaxSuppression(boxes, scores, iouThreshold, isIOU) {
+    if (isIOU === void 0) { isIOU = true; }
+    var areas = boxes.map(function (box) { return (box.width + 1) * (box.height + 1); });
+    var indicesSortedByScore = scores
+        .map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })
+        .sort(function (c1, c2) { return c1.score - c2.score; })
+        .map(function (c) { return c.boxIndex; });
+    var pick = [];
+    var _loop_1 = function () {
+        var curr = indicesSortedByScore.pop();
+        pick.push(curr);
+        var indices = indicesSortedByScore;
+        var outputs = [];
+        for (var i = 0; i < indices.length; i++) {
+            var idx = indices[i];
+            var currBox = boxes[curr];
+            var idxBox = boxes[idx];
+            var width = Math.max(0.0, Math.min(currBox.right, idxBox.right) - Math.max(currBox.left, idxBox.left) + 1);
+            var height = Math.max(0.0, Math.min(currBox.bottom, idxBox.bottom) - Math.max(currBox.top, idxBox.top) + 1);
+            var interSection = width * height;
+            var out = isIOU
+                ? interSection / (areas[curr] + areas[idx] - interSection)
+                : interSection / Math.min(areas[curr], areas[idx]);
+            outputs.push(out);
+        }
+        indicesSortedByScore = indicesSortedByScore.filter(function (_, j) { return outputs[j] <= iouThreshold; });
+    };
+    while (indicesSortedByScore.length > 0) {
+        _loop_1();
+    }
+    return pick;
+}
+exports.nonMaxSuppression = nonMaxSuppression;
+//# sourceMappingURL=nonMaxSuppression.js.map
\ No newline at end of file

build/commons/nonMaxSuppression.js.map

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build/faceDetectionNet/loadQuantizedParams.js

@@ -5,7 +5,7 @@ var disposeUnusedWeightTensors_1 = require("../commons/disposeUnusedWeightTensor
 var extractWeightEntryFactory_1 = require("../commons/extractWeightEntryFactory");
 var isTensor_1 = require("../commons/isTensor");
 var loadWeightMap_1 = require("../commons/loadWeightMap");
-var DEFAULT_MODEL_NAME = 'face_detection_model';
+var DEFAULT_MODEL_NAME = 'ssd_mobilenetv1_model';
 function extractorsFactory(weightMap, paramMappings) {
     var extractWeightEntry = extractWeightEntryFactory_1.extractWeightEntryFactory(weightMap, paramMappings);
     function extractPointwiseConvParams(prefix, idx, mappedPrefix) {

build/faceDetectionNet/loadQuantizedParams.js.map

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\ No newline at end of file

build/globalApi.d.ts

@@ -8,6 +8,8 @@ import { FullFaceDescription } from './FullFaceDescription';
 import { Mtcnn } from './mtcnn/Mtcnn';
 import { MtcnnForwardParams, MtcnnResult } from './mtcnn/types';
 import { NetInput } from './NetInput';
+import { TinyYolov2 } from './tinyYolov2/TinyYolov2';
+import { TinyYolov2ForwardParams } from './tinyYolov2/types';
 import { TNetInput } from './types';
 export declare const detectionNet: FaceDetectionNet;
 export declare const landmarkNet: FaceLandmarkNet;
@@ -17,16 +19,19 @@ export declare const nets: {
     faceLandmark68Net: FaceLandmarkNet;
     faceRecognitionNet: FaceRecognitionNet;
     mtcnn: Mtcnn;
+    tinyYolov2: TinyYolov2;
 };
 export declare function loadFaceDetectionModel(url: string): Promise<void>;
 export declare function loadFaceLandmarkModel(url: string): Promise<void>;
 export declare function loadFaceRecognitionModel(url: string): Promise<void>;
 export declare function loadMtcnnModel(url: string): Promise<void>;
-export declare function loadModels(url: string): Promise<[void, void, void, void]>;
+export declare function loadTinyYolov2Model(url: string): Promise<void>;
+export declare function loadModels(url: string): Promise<[void, void, void, void, void]>;
 export declare function locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
 export declare function detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;
 export declare function computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
 export declare function mtcnn(input: TNetInput, forwardParams: MtcnnForwardParams): Promise<MtcnnResult[]>;
+export declare function tinyYolov2(input: TNetInput, forwardParams: TinyYolov2ForwardParams): Promise<FaceDetection[]>;
 export declare type allFacesFunction = (input: tf.Tensor | NetInput | TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
 export declare const allFaces: allFacesFunction;
 export declare type allFacesMtcnnFunction = (input: tf.Tensor | NetInput | TNetInput, mtcnnForwardParams?: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;

build/globalApi.js

@@ -5,6 +5,7 @@ var FaceDetectionNet_1 = require("./faceDetectionNet/FaceDetectionNet");
 var FaceLandmarkNet_1 = require("./faceLandmarkNet/FaceLandmarkNet");
 var FaceRecognitionNet_1 = require("./faceRecognitionNet/FaceRecognitionNet");
 var Mtcnn_1 = require("./mtcnn/Mtcnn");
+var TinyYolov2_1 = require("./tinyYolov2/TinyYolov2");
 exports.detectionNet = new FaceDetectionNet_1.FaceDetectionNet();
 exports.landmarkNet = new FaceLandmarkNet_1.FaceLandmarkNet();
 exports.recognitionNet = new FaceRecognitionNet_1.FaceRecognitionNet();
@@ -14,7 +15,8 @@ exports.nets = {
     ssdMobilenet: exports.detectionNet,
     faceLandmark68Net: exports.landmarkNet,
     faceRecognitionNet: exports.recognitionNet,
-    mtcnn: new Mtcnn_1.Mtcnn()
+    mtcnn: new Mtcnn_1.Mtcnn(),
+    tinyYolov2: new TinyYolov2_1.TinyYolov2()
 };
 function loadFaceDetectionModel(url) {
     return exports.nets.ssdMobilenet.load(url);
@@ -32,12 +34,17 @@ function loadMtcnnModel(url) {
     return exports.nets.mtcnn.load(url);
 }
 exports.loadMtcnnModel = loadMtcnnModel;
+function loadTinyYolov2Model(url) {
+    return exports.nets.tinyYolov2.load(url);
+}
+exports.loadTinyYolov2Model = loadTinyYolov2Model;
 function loadModels(url) {
     return Promise.all([
         loadFaceDetectionModel(url),
         loadFaceLandmarkModel(url),
         loadFaceRecognitionModel(url),
-        loadMtcnnModel(url)
+        loadMtcnnModel(url),
+        loadTinyYolov2Model(url)
     ]);
 }
 exports.loadModels = loadModels;
@@ -57,6 +64,10 @@ function mtcnn(input, forwardParams) {
     return exports.nets.mtcnn.forward(input, forwardParams);
 }
 exports.mtcnn = mtcnn;
+function tinyYolov2(input, forwardParams) {
+    return exports.nets.tinyYolov2.locateFaces(input, forwardParams);
+}
+exports.tinyYolov2 = tinyYolov2;
 exports.allFaces = allFacesFactory_1.allFacesFactory(exports.nets.ssdMobilenet, exports.nets.faceLandmark68Net, exports.nets.faceRecognitionNet);
 exports.allFacesMtcnn = allFacesFactory_1.allFacesMtcnnFactory(exports.nets.mtcnn, exports.nets.faceRecognitionNet);
 //# sourceMappingURL=globalApi.js.map
\ No newline at end of file

build/globalApi.js.map

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\ No newline at end of file

build/index.d.ts

@@ -15,5 +15,6 @@ export * from './faceRecognitionNet';
 export * from './globalApi';
 export * from './mtcnn';
 export * from './padToSquare';
+export * from './tinyYolov2';
 export * from './toNetInput';
 export * from './utils';

build/index.js

@@ -18,6 +18,7 @@ tslib_1.__exportStar(require("./faceRecognitionNet"), exports);
 tslib_1.__exportStar(require("./globalApi"), exports);
 tslib_1.__exportStar(require("./mtcnn"), exports);
 tslib_1.__exportStar(require("./padToSquare"), exports);
+tslib_1.__exportStar(require("./tinyYolov2"), exports);
 tslib_1.__exportStar(require("./toNetInput"), exports);
 tslib_1.__exportStar(require("./utils"), exports);
 //# sourceMappingURL=index.js.map
\ No newline at end of file

build/index.js.map

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\ No newline at end of file

build/mtcnn/BoundingBox.d.ts

+import { Rect } from '../Rect';
 export declare class BoundingBox {
     private _left;
     private _top;
@@ -25,4 +26,5 @@ export declare class BoundingBox {
         h: number;
     };
     calibrate(region: BoundingBox): BoundingBox;
+    toRect(): Rect;
 }

build/mtcnn/BoundingBox.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
+var Rect_1 = require("../Rect");
 var BoundingBox = /** @class */ (function () {
     function BoundingBox(_left, _top, _right, _bottom) {
         this._left = _left;
@@ -97,6 +98,9 @@ var BoundingBox = /** @class */ (function () {
     BoundingBox.prototype.calibrate = function (region) {
         return new BoundingBox(this.left + (region.left * this.width), this.top + (region.top * this.height), this.right + (region.right * this.width), this.bottom + (region.bottom * this.height)).toSquare().round();
     };
+    BoundingBox.prototype.toRect = function () {
+        return new Rect_1.Rect(this.left, this.top, this.width, this.height);
+    };
     return BoundingBox;
 }());
 exports.BoundingBox = BoundingBox;

build/mtcnn/BoundingBox.js.map

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\ No newline at end of file

build/mtcnn/extractImagePatches.d.ts

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

build/mtcnn/extractImagePatches.js.map

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\ No newline at end of file

build/mtcnn/stage1.d.ts

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

build/mtcnn/stage1.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
 var tf = require("@tensorflow/tfjs-core");
+var BoundingBox_1 = require("../BoundingBox");
+var nonMaxSuppression_1 = require("../commons/nonMaxSuppression");
 var Point_1 = require("../Point");
-var BoundingBox_1 = require("./BoundingBox");
 var config_1 = require("./config");
-var nms_1 = require("./nms");
+var getSizesForScale_1 = require("./getSizesForScale");
 var normalize_1 = require("./normalize");
 var PNet_1 = require("./PNet");
-var getSizesForScale_1 = require("./getSizesForScale");
 function rescaleAndNormalize(x, scale) {
     return tf.tidy(function () {
         var _a = getSizesForScale_1.getSizesForScale(scale, x.shape.slice(1)), height = _a.height, width = _a.width;
@@ -65,7 +65,7 @@ function stage1(imgTensor, scales, scoreThreshold, params, stats) {
             return [];
         }
         var ts = Date.now();
-        var indices = nms_1.nms(boundingBoxes.map(function (bbox) { return bbox.cell; }), boundingBoxes.map(function (bbox) { return bbox.score; }), 0.5);
+        var indices = nonMaxSuppression_1.nonMaxSuppression(boundingBoxes.map(function (bbox) { return bbox.cell; }), boundingBoxes.map(function (bbox) { return bbox.score; }), 0.5);
         statsForScale.nms = Date.now() - ts;
         statsForScale.numBoxes = indices.length;
         stats.stage1.push(statsForScale);
@@ -76,7 +76,7 @@ function stage1(imgTensor, scales, scoreThreshold, params, stats) {
     var finalScores = [];
     if (allBoxes.length > 0) {
         var ts = Date.now();
-        var indices = nms_1.nms(allBoxes.map(function (bbox) { return bbox.cell; }), allBoxes.map(function (bbox) { return bbox.score; }), 0.7);
+        var indices = nonMaxSuppression_1.nonMaxSuppression(allBoxes.map(function (bbox) { return bbox.cell; }), allBoxes.map(function (bbox) { return bbox.score; }), 0.7);
         stats.stage1_nms = Date.now() - ts;
         finalScores = indices.map(function (idx) { return allBoxes[idx].score; });
         finalBoxes = indices

build/mtcnn/stage1.js.map

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\ No newline at end of file

build/mtcnn/stage2.d.ts

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

build/mtcnn/stage2.js

@@ -2,9 +2,9 @@
 Object.defineProperty(exports, "__esModule", { value: true });
 var tslib_1 = require("tslib");
 var tf = require("@tensorflow/tfjs-core");
-var BoundingBox_1 = require("./BoundingBox");
+var BoundingBox_1 = require("../BoundingBox");
+var nonMaxSuppression_1 = require("../commons/nonMaxSuppression");
 var extractImagePatches_1 = require("./extractImagePatches");
-var nms_1 = require("./nms");
 var RNet_1 = require("./RNet");
 function stage2(img, inputBoxes, scoreThreshold, params, stats) {
     return tslib_1.__awaiter(this, void 0, void 0, function () {
@@ -45,7 +45,7 @@ function stage2(img, inputBoxes, scoreThreshold, params, stats) {
                     finalScores = [];
                     if (filteredBoxes.length > 0) {
                         ts = Date.now();
-                        indicesNms = nms_1.nms(filteredBoxes, filteredScores, 0.7);
+                        indicesNms = nonMaxSuppression_1.nonMaxSuppression(filteredBoxes, filteredScores, 0.7);
                         stats.stage2_nms = Date.now() - ts;
                         regions_1 = indicesNms.map(function (idx) {
                             return new BoundingBox_1.BoundingBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3));

build/mtcnn/stage2.js.map

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\ No newline at end of file

build/mtcnn/stage3.d.ts

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

build/mtcnn/stage3.js

@@ -2,10 +2,10 @@
 Object.defineProperty(exports, "__esModule", { value: true });
 var tslib_1 = require("tslib");
 var tf = require("@tensorflow/tfjs-core");
+var BoundingBox_1 = require("../BoundingBox");
+var nonMaxSuppression_1 = require("../commons/nonMaxSuppression");
 var Point_1 = require("../Point");
-var BoundingBox_1 = require("./BoundingBox");
 var extractImagePatches_1 = require("./extractImagePatches");
-var nms_1 = require("./nms");
 var ONet_1 = require("./ONet");
 function stage3(img, inputBoxes, scoreThreshold, params, stats) {
     return tslib_1.__awaiter(this, void 0, void 0, function () {
@@ -49,7 +49,7 @@ function stage3(img, inputBoxes, scoreThreshold, params, stats) {
                     points = [];
                     if (filteredBoxes.length > 0) {
                         ts = Date.now();
-                        indicesNms = nms_1.nms(filteredBoxes, filteredScores, 0.7, false);
+                        indicesNms = nonMaxSuppression_1.nonMaxSuppression(filteredBoxes, filteredScores, 0.7, false);
                         stats.stage3_nms = Date.now() - ts;
                         finalBoxes = indicesNms.map(function (idx) { return filteredBoxes[idx]; });
                         finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });

build/mtcnn/stage3.js.map

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\ No newline at end of file

build/tinyYolov2/TinyYolov2.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { NeuralNetwork } from '../commons/NeuralNetwork';
+import { FaceDetection } from '../FaceDetection';
+import { NetInput } from '../NetInput';
+import { TNetInput } from '../types';
+import { NetParams, TinyYolov2ForwardParams } from './types';
+export declare class TinyYolov2 extends NeuralNetwork<NetParams> {
+    constructor();
+    forwardInput(input: NetInput, inputSize: number): tf.Tensor4D;
+    forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D>;
+    locateFaces(input: TNetInput, forwardParams?: TinyYolov2ForwardParams): 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/tinyYolov2/TinyYolov2.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tslib_1 = require("tslib");
+var tf = require("@tensorflow/tfjs-core");
+var BoundingBox_1 = require("../BoundingBox");
+var convLayer_1 = require("../commons/convLayer");
+var NeuralNetwork_1 = require("../commons/NeuralNetwork");
+var nonMaxSuppression_1 = require("../commons/nonMaxSuppression");
+var FaceDetection_1 = require("../FaceDetection");
+var toNetInput_1 = require("../toNetInput");
+var config_1 = require("./config");
+var convWithBatchNorm_1 = require("./convWithBatchNorm");
+var extractParams_1 = require("./extractParams");
+var getDefaultParams_1 = require("./getDefaultParams");
+var loadQuantizedParams_1 = require("./loadQuantizedParams");
+var TinyYolov2 = /** @class */ (function (_super) {
+    tslib_1.__extends(TinyYolov2, _super);
+    function TinyYolov2() {
+        return _super.call(this, 'TinyYolov2') || this;
+    }
+    TinyYolov2.prototype.forwardInput = function (input, inputSize) {
+        var params = this.params;
+        if (!params) {
+            throw new Error('TinyYolov2 - load model before inference');
+        }
+        var out = tf.tidy(function () {
+            var batchTensor = input.toBatchTensor(inputSize, false).div(tf.scalar(255)).toFloat();
+            var out = convWithBatchNorm_1.convWithBatchNorm(batchTensor, params.conv0);
+            out = tf.maxPool(out, [2, 2], [2, 2], 'same');
+            out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv1);
+            out = tf.maxPool(out, [2, 2], [2, 2], 'same');
+            out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv2);
+            out = tf.maxPool(out, [2, 2], [2, 2], 'same');
+            out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv3);
+            out = tf.maxPool(out, [2, 2], [2, 2], 'same');
+            out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv4);
+            out = tf.maxPool(out, [2, 2], [2, 2], 'same');
+            out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv5);
+            out = tf.maxPool(out, [2, 2], [1, 1], 'same');
+            out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv6);
+            out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv7);
+            out = convLayer_1.convLayer(out, params.conv8, 'valid', false);
+            return out;
+        });
+        return out;
+    };
+    TinyYolov2.prototype.forward = function (input, inputSize) {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var _a;
+            return tslib_1.__generator(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        _a = this.forwardInput;
+                        return [4 /*yield*/, toNetInput_1.toNetInput(input, true, true)];
+                    case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(), inputSize])];
+                    case 2: return [2 /*return*/, _b.sent()];
+                }
+            });
+        });
+    };
+    TinyYolov2.prototype.locateFaces = function (input, forwardParams) {
+        if (forwardParams === void 0) { forwardParams = {}; }
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var _a, _inputSize, scoreThreshold, inputSize, netInput, out, numCells, _b, boxesTensor, scoresTensor, expit, paddedHeightRelative, paddedWidthRelative, boxes, scores, row, col, box, score, ctX, ctY, width, height, x, y, indices, detections;
+            return tslib_1.__generator(this, function (_c) {
+                switch (_c.label) {
+                    case 0:
+                        _a = getDefaultParams_1.getDefaultParams(forwardParams), _inputSize = _a.inputSize, scoreThreshold = _a.scoreThreshold;
+                        inputSize = typeof _inputSize === 'string'
+                            ? config_1.INPUT_SIZES[_inputSize]
+                            : _inputSize;
+                        if (typeof inputSize !== 'number') {
+                            throw new Error("TinyYolov2 - unkown inputSize: " + inputSize + ", expected number or one of xs | sm | md | lg");
+                        }
+                        return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
+                    case 1:
+                        netInput = _c.sent();
+                        return [4 /*yield*/, this.forwardInput(netInput, inputSize)];
+                    case 2:
+                        out = _c.sent();
+                        numCells = out.shape[1];
+                        _b = tf.tidy(function () {
+                            var reshaped = out.reshape([numCells, numCells, config_1.NUM_BOXES, 6]);
+                            out.dispose();
+                            var boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, config_1.NUM_BOXES, 4]);
+                            var scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, config_1.NUM_BOXES, 1]);
+                            return [boxes, scores];
+                        }), boxesTensor = _b[0], scoresTensor = _b[1];
+                        expit = function (x) { return 1 / (1 + Math.exp(-x)); };
+                        paddedHeightRelative = (netInput.getPaddings(0).y + netInput.getInputHeight(0)) / netInput.getInputHeight(0);
+                        paddedWidthRelative = (netInput.getPaddings(0).x + netInput.getInputWidth(0)) / netInput.getInputWidth(0);
+                        boxes = [];
+                        scores = [];
+                        for (row = 0; row < numCells; row++) {
+                            for (col = 0; col < numCells; col++) {
+                                for (box = 0; box < config_1.NUM_BOXES; box++) {
+                                    score = expit(scoresTensor.get(row, col, box, 0));
+                                    if (score > scoreThreshold) {
+                                        ctX = ((col + expit(boxesTensor.get(row, col, box, 0))) / numCells) * paddedWidthRelative;
+                                        ctY = ((row + expit(boxesTensor.get(row, col, box, 1))) / numCells) * paddedHeightRelative;
+                                        width = ((Math.exp(boxesTensor.get(row, col, box, 2)) * config_1.BOX_ANCHORS[box].x) / numCells) * paddedWidthRelative;
+                                        height = ((Math.exp(boxesTensor.get(row, col, box, 3)) * config_1.BOX_ANCHORS[box].y) / numCells) * paddedHeightRelative;
+                                        x = (ctX - (width / 2));
+                                        y = (ctY - (height / 2));
+                                        boxes.push(new BoundingBox_1.BoundingBox(x, y, x + width, y + height));
+                                        scores.push(score);
+                                    }
+                                }
+                            }
+                        }
+                        boxesTensor.dispose();
+                        scoresTensor.dispose();
+                        indices = nonMaxSuppression_1.nonMaxSuppression(boxes.map(function (box) { return new BoundingBox_1.BoundingBox(box.left * inputSize, box.top * inputSize, box.right * inputSize, box.bottom * inputSize); }), scores, config_1.IOU_THRESHOLD, true);
+                        detections = indices.map(function (idx) {
+                            return new FaceDetection_1.FaceDetection(scores[idx], boxes[idx].toRect(), { width: netInput.getInputWidth(0), height: netInput.getInputHeight(0) });
+                        });
+                        return [2 /*return*/, detections];
+                }
+            });
+        });
+    };
+    TinyYolov2.prototype.loadQuantizedParams = function (uri) {
+        return loadQuantizedParams_1.loadQuantizedParams(uri);
+    };
+    TinyYolov2.prototype.extractParams = function (weights) {
+        return extractParams_1.extractParams(weights);
+    };
+    return TinyYolov2;
+}(NeuralNetwork_1.NeuralNetwork));
+exports.TinyYolov2 = TinyYolov2;
+//# sourceMappingURL=TinyYolov2.js.map
\ No newline at end of file

build/tinyYolov2/TinyYolov2.js.map

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\ No newline at end of file

build/tinyYolov2/config.d.ts

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

build/tinyYolov2/config.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var Point_1 = require("../Point");
+exports.INPUT_SIZES = { xs: 224, sm: 320, md: 416, lg: 608 };
+exports.NUM_BOXES = 5;
+exports.IOU_THRESHOLD = 0.4;
+exports.BOX_ANCHORS = [
+    new Point_1.Point(0.738768, 0.874946),
+    new Point_1.Point(2.42204, 2.65704),
+    new Point_1.Point(4.30971, 7.04493),
+    new Point_1.Point(10.246, 4.59428),
+    new Point_1.Point(12.6868, 11.8741)
+];
+//# sourceMappingURL=config.js.map
\ No newline at end of file

build/tinyYolov2/config.js.map

+{"version":3,"file":"config.js","sourceRoot":"","sources":["../../src/tinyYolov2/config.ts"],"names":[],"mappings":";;AAAA,kCAAiC;AAEpB,QAAA,WAAW,GAAG,EAAE,EAAE,EAAE,GAAG,EAAE,EAAE,EAAE,GAAG,EAAE,EAAE,EAAE,GAAG,EAAE,EAAE,EAAE,GAAG,EAAE,CAAA;AACpD,QAAA,SAAS,GAAG,CAAC,CAAA;AACb,QAAA,aAAa,GAAG,GAAG,CAAA;AAEnB,QAAA,WAAW,GAAG;IACzB,IAAI,aAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,aAAK,CAAC,OAAO,EAAE,OAAO,CAAC;IAC3B,IAAI,aAAK,CAAC,OAAO,EAAE,OAAO,CAAC;IAC3B,IAAI,aAAK,CAAC,MAAM,EAAE,OAAO,CAAC;IAC1B,IAAI,aAAK,CAAC,OAAO,EAAE,OAAO,CAAC;CAC5B,CAAA"}
\ No newline at end of file

build/tinyYolov2/convWithBatchNorm.d.ts

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

build/tinyYolov2/convWithBatchNorm.js

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

build/tinyYolov2/convWithBatchNorm.js.map

+{"version":3,"file":"convWithBatchNorm.js","sourceRoot":"","sources":["../../src/tinyYolov2/convWithBatchNorm.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,iCAAgC;AAGhC,2BAAkC,CAAc,EAAE,MAAyB;IACzE,OAAO,EAAE,CAAC,IAAI,CAAC;QACb,IAAI,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAgB,CAAA;QACpE,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,GAAG,EAAE,MAAM,CAAC,IAAI,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;QAC1D,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,EAAE,CAAC,GAAG,CAAC,CAAA;QAChC,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,EAAE,CAAC,OAAO,CAAC,CAAA;QACpC,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,IAAI,CAAC,IAAI,CAAC,CAAA;QACnC,OAAO,aAAK,CAAC,GAAG,CAAC,CAAA;IACnB,CAAC,CAAC,CAAA;AACJ,CAAC;AATD,8CASC"}
\ No newline at end of file

build/tinyYolov2/extractParams.d.ts

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

build/tinyYolov2/extractParams.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("@tensorflow/tfjs-core");
+var extractConvParamsFactory_1 = require("../commons/extractConvParamsFactory");
+var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
+function extractorsFactory(extractWeights, paramMappings) {
+    var extractConvParams = extractConvParamsFactory_1.extractConvParamsFactory(extractWeights, paramMappings);
+    function extractBatchNormParams(size, mappedPrefix) {
+        var sub = tf.tensor1d(extractWeights(size));
+        var truediv = tf.tensor1d(extractWeights(size));
+        paramMappings.push({ paramPath: mappedPrefix + "/sub" }, { paramPath: mappedPrefix + "/truediv" });
+        return { sub: sub, truediv: truediv };
+    }
+    function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) {
+        var conv = extractConvParams(channelsIn, channelsOut, 3, mappedPrefix + "/conv");
+        var bn = extractBatchNormParams(channelsOut, mappedPrefix + "/bn");
+        return { conv: conv, bn: bn };
+    }
+    return {
+        extractConvParams: extractConvParams,
+        extractConvWithBatchNormParams: extractConvWithBatchNormParams
+    };
+}
+function extractParams(weights) {
+    var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
+    var paramMappings = [];
+    var _b = extractorsFactory(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractConvWithBatchNormParams = _b.extractConvWithBatchNormParams;
+    var conv0 = extractConvWithBatchNormParams(3, 16, 'conv0');
+    var conv1 = extractConvWithBatchNormParams(16, 32, 'conv1');
+    var conv2 = extractConvWithBatchNormParams(32, 64, 'conv2');
+    var conv3 = extractConvWithBatchNormParams(64, 128, 'conv3');
+    var conv4 = extractConvWithBatchNormParams(128, 256, 'conv4');
+    var conv5 = extractConvWithBatchNormParams(256, 512, 'conv5');
+    var conv6 = extractConvWithBatchNormParams(512, 1024, 'conv6');
+    var conv7 = extractConvWithBatchNormParams(1024, 1024, 'conv7');
+    var conv8 = extractConvParams(1024, 30, 1, 'conv8');
+    if (getRemainingWeights().length !== 0) {
+        throw new Error("weights remaing after extract: " + getRemainingWeights().length);
+    }
+    var params = { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3, conv4: conv4, conv5: conv5, conv6: conv6, conv7: conv7, conv8: conv8 };
+    return { params: params, paramMappings: paramMappings };
+}
+exports.extractParams = extractParams;
+//# sourceMappingURL=extractParams.js.map
\ No newline at end of file

build/tinyYolov2/extractParams.js.map

+{"version":3,"file":"extractParams.js","sourceRoot":"","sources":["../../src/tinyYolov2/extractParams.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,gFAA+E;AAC/E,0EAAyE;AAIzE,2BAA2B,cAAsC,EAAE,aAA6B;IAE9F,IAAM,iBAAiB,GAAG,mDAAwB,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAEjF,gCAAgC,IAAY,EAAE,YAAoB;QAEhE,IAAM,GAAG,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,IAAI,CAAC,CAAC,CAAA;QAC7C,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,IAAI,CAAC,CAAC,CAAA;QAEjD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAK,YAAY,SAAM,EAAE,EACpC,EAAE,SAAS,EAAK,YAAY,aAAU,EAAE,CACzC,CAAA;QAED,OAAO,EAAE,GAAG,KAAA,EAAE,OAAO,SAAA,EAAE,CAAA;IACzB,CAAC;IAED,wCAAwC,UAAkB,EAAE,WAAmB,EAAE,YAAoB;QAEnG,IAAM,IAAI,GAAG,iBAAiB,CAAC,UAAU,EAAE,WAAW,EAAE,CAAC,EAAK,YAAY,UAAO,CAAC,CAAA;QAClF,IAAM,EAAE,GAAG,sBAAsB,CAAC,WAAW,EAAK,YAAY,QAAK,CAAC,CAAA;QAEpE,OAAO,EAAE,IAAI,MAAA,EAAE,EAAE,IAAA,EAAE,CAAA;IACrB,CAAC;IAED,OAAO;QACL,iBAAiB,mBAAA;QACjB,8BAA8B,gCAAA;KAC/B,CAAA;AAEH,CAAC;AAED,uBAA8B,OAAqB;IAE3C,IAAA,2DAG4B,EAFhC,kCAAc,EACd,4CAAmB,CACa;IAElC,IAAM,aAAa,GAAmB,EAAE,CAAA;IAElC,IAAA,qDAG8C,EAFlD,wCAAiB,EACjB,kEAA8B,CACoB;IAEpD,IAAM,KAAK,GAAG,8BAA8B,CAAC,CAAC,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;IAC5D,IAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;IAC7D,IAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;IAC7D,IAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,GAAG,EAAE,OAAO,CAAC,CAAA;IAC9D,IAAM,KAAK,GAAG,8BAA8B,CAAC,GAAG,EAAE,GAAG,EAAE,OAAO,CAAC,CAAA;IAC/D,IAAM,KAAK,GAAG,8BAA8B,CAAC,GAAG,EAAE,GAAG,EAAE,OAAO,CAAC,CAAA;IAC/D,IAAM,KAAK,GAAG,8BAA8B,CAAC,GAAG,EAAE,IAAI,EAAE,OAAO,CAAC,CAAA;IAChE,IAAM,KAAK,GAAG,8BAA8B,CAAC,IAAI,EAAE,IAAI,EAAE,OAAO,CAAC,CAAA;IACjE,IAAM,KAAK,GAAG,iBAAiB,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;IAErD,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,oCAAkC,mBAAmB,EAAE,CAAC,MAAQ,CAAC,CAAA;KAClF;IAED,IAAM,MAAM,GAAG,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,CAAA;IAEhF,OAAO,EAAE,MAAM,QAAA,EAAE,aAAa,eAAA,EAAE,CAAA;AAClC,CAAC;AA/BD,sCA+BC"}
\ No newline at end of file

build/tinyYolov2/getDefaultParams.d.ts

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

build/tinyYolov2/getDefaultParams.js

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

build/tinyYolov2/getDefaultParams.js.map

+{"version":3,"file":"getDefaultParams.js","sourceRoot":"","sources":["../../src/tinyYolov2/getDefaultParams.ts"],"names":[],"mappings":";;AAAA,iCAA4D;AAE5D,0BAAiC,MAA+B;IAC9D,OAAO,MAAM,CAAC,MAAM,CAClB,EAAE,EACF;QACE,QAAQ,EAAE,gBAAQ,CAAC,EAAE;QACrB,cAAc,EAAE,GAAG;KACpB,EACD,MAAM,CACP,CAAA;AACH,CAAC;AATD,4CASC"}
\ No newline at end of file

build/tinyYolov2/index.d.ts

+import { TinyYolov2 } from './TinyYolov2';
+export * from './TinyYolov2';
+export declare function createTinyYolov2(weights: Float32Array): TinyYolov2;

build/tinyYolov2/index.js

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

build/tinyYolov2/index.js.map

+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/tinyYolov2/index.ts"],"names":[],"mappings":";;;AAAA,2CAA0C;AAE1C,uDAA6B;AAE7B,0BAAiC,OAAqB;IACpD,IAAM,GAAG,GAAG,IAAI,uBAAU,EAAE,CAAA;IAC5B,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAJD,4CAIC"}
\ No newline at end of file

build/tinyYolov2/leaky.d.ts

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

build/tinyYolov2/leaky.js

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

build/tinyYolov2/leaky.js.map

+{"version":3,"file":"leaky.js","sourceRoot":"","sources":["../../src/tinyYolov2/leaky.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,eAAsB,CAAc;IAClC,OAAO,EAAE,CAAC,IAAI,CAAC;QACb,OAAO,EAAE,CAAC,OAAO,CAAC,CAAC,EAAE,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,mBAAmB,CAAC,CAAC,CAAC,CAAA;IACjE,CAAC,CAAC,CAAA;AACJ,CAAC;AAJD,sBAIC"}
\ No newline at end of file

build/tinyYolov2/loadQuantizedParams.d.ts

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

build/tinyYolov2/loadQuantizedParams.js

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

build/tinyYolov2/loadQuantizedParams.js.map

+{"version":3,"file":"loadQuantizedParams.js","sourceRoot":"","sources":["../../src/tinyYolov2/loadQuantizedParams.ts"],"names":[],"mappings":";;;AAEA,oFAAmF;AACnF,kFAAiF;AACjF,0DAAyD;AAIzD,IAAM,kBAAkB,GAAG,mBAAmB,CAAA;AAE9C,2BAA2B,SAAc,EAAE,aAA6B;IAEtE,IAAM,kBAAkB,GAAG,qDAAyB,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAE9E,gCAAgC,MAAc;QAC5C,IAAM,GAAG,GAAG,kBAAkB,CAAiB,MAAM,SAAM,EAAE,CAAC,CAAC,CAAA;QAC/D,IAAM,OAAO,GAAG,kBAAkB,CAAiB,MAAM,aAAU,EAAE,CAAC,CAAC,CAAA;QACvE,OAAO,EAAE,GAAG,KAAA,EAAE,OAAO,SAAA,EAAE,CAAA;IACzB,CAAC;IAED,2BAA2B,MAAc;QACvC,IAAM,OAAO,GAAG,kBAAkB,CAAiB,MAAM,aAAU,EAAE,CAAC,CAAC,CAAA;QACvE,IAAM,IAAI,GAAG,kBAAkB,CAAiB,MAAM,UAAO,EAAE,CAAC,CAAC,CAAA;QACjE,OAAO,EAAE,OAAO,SAAA,EAAE,IAAI,MAAA,EAAE,CAAA;IAC1B,CAAC;IAED,wCAAwC,MAAc;QACpD,IAAM,IAAI,GAAG,iBAAiB,CAAI,MAAM,UAAO,CAAC,CAAA;QAChD,IAAM,EAAE,GAAG,sBAAsB,CAAI,MAAM,QAAK,CAAC,CAAA;QACjD,OAAO,EAAE,IAAI,MAAA,EAAE,EAAE,IAAA,EAAE,CAAA;IACrB,CAAC;IAED,OAAO;QACL,iBAAiB,mBAAA;QACjB,8BAA8B,gCAAA;KAC/B,CAAA;AAEH,CAAC;AAED,6BACE,GAAuB;;;;;wBAGL,qBAAM,6BAAa,CAAC,GAAG,EAAE,kBAAkB,CAAC,EAAA;;oBAAxD,SAAS,GAAG,SAA4C;oBACxD,aAAa,GAAmB,EAAE,CAAA;oBAElC,KAGF,iBAAiB,CAAC,SAAS,EAAE,aAAa,CAAC,EAF7C,iBAAiB,uBAAA,EACjB,8BAA8B,oCAAA,CACe;oBAGzC,MAAM,GAAG;wBACb,KAAK,EAAE,8BAA8B,CAAC,OAAO,CAAC;wBAC9C,KAAK,EAAE,8BAA8B,CAAC,OAAO,CAAC;wBAC9C,KAAK,EAAE,8BAA8B,CAAC,OAAO,CAAC;wBAC9C,KAAK,EAAE,8BAA8B,CAAC,OAAO,CAAC;wBAC9C,KAAK,EAAE,8BAA8B,CAAC,OAAO,CAAC;wBAC9C,KAAK,EAAE,8BAA8B,CAAC,OAAO,CAAC;wBAC9C,KAAK,EAAE,8BAA8B,CAAC,OAAO,CAAC;wBAC9C,KAAK,EAAE,8BAA8B,CAAC,OAAO,CAAC;wBAC9C,KAAK,EAAE,iBAAiB,CAAC,OAAO,CAAC;qBAClC,CAAA;oBAED,uDAA0B,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;oBAEpD,sBAAO,EAAE,MAAM,QAAA,EAAE,aAAa,eAAA,EAAE,EAAA;;;;CACjC;AA5BD,kDA4BC"}
\ No newline at end of file

build/tinyYolov2/types.d.ts

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

build/tinyYolov2/types.js

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

build/tinyYolov2/types.js.map

+{"version":3,"file":"types.js","sourceRoot":"","sources":["../../src/tinyYolov2/types.ts"],"names":[],"mappings":";;AA0BA,IAAY,QAKX;AALD,WAAY,QAAQ;IAClB,qBAAS,CAAA;IACT,qBAAS,CAAA;IACT,qBAAS,CAAA;IACT,qBAAS,CAAA;AACX,CAAC,EALW,QAAQ,GAAR,gBAAQ,KAAR,gBAAQ,QAKnB"}
\ No newline at end of file

dist/face-api.js

@@ -1728,7 +1728,7 @@
       };
   }
 
-  var DEFAULT_MODEL_NAME$1 = 'face_detection_model';
+  var DEFAULT_MODEL_NAME$1 = 'ssd_mobilenetv1_model';
   function extractorsFactory$2(weightMap, paramMappings) {
       var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
       function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
@@ -2839,10 +2839,13 @@
       BoundingBox.prototype.calibrate = function (region) {
           return new BoundingBox(this.left + (region.left * this.width), this.top + (region.top * this.height), this.right + (region.right * this.width), this.bottom + (region.bottom * this.height)).toSquare().round();
       };
+      BoundingBox.prototype.toRect = function () {
+          return new Rect(this.left, this.top, this.width, this.height);
+      };
       return BoundingBox;
   }());
 
-  function nms(boxes, scores, iouThreshold, isIOU) {
+  function nonMaxSuppression$1(boxes, scores, iouThreshold, isIOU) {
       if (isIOU === void 0) { isIOU = true; }
       var areas = boxes.map(function (box) { return (box.width + 1) * (box.height + 1); });
       var indicesSortedByScore = scores
@@ -2968,7 +2971,7 @@
               return [];
           }
           var ts = Date.now();
-          var indices = nms(boundingBoxes.map(function (bbox) { return bbox.cell; }), boundingBoxes.map(function (bbox) { return bbox.score; }), 0.5);
+          var indices = nonMaxSuppression$1(boundingBoxes.map(function (bbox) { return bbox.cell; }), boundingBoxes.map(function (bbox) { return bbox.score; }), 0.5);
           statsForScale.nms = Date.now() - ts;
           statsForScale.numBoxes = indices.length;
           stats.stage1.push(statsForScale);
@@ -2979,7 +2982,7 @@
       var finalScores = [];
       if (allBoxes.length > 0) {
           var ts = Date.now();
-          var indices = nms(allBoxes.map(function (bbox) { return bbox.cell; }), allBoxes.map(function (bbox) { return bbox.score; }), 0.7);
+          var indices = nonMaxSuppression$1(allBoxes.map(function (bbox) { return bbox.cell; }), allBoxes.map(function (bbox) { return bbox.score; }), 0.7);
           stats.stage1_nms = Date.now() - ts;
           finalScores = indices.map(function (idx) { return allBoxes[idx].score; });
           finalBoxes = indices
@@ -3097,7 +3100,7 @@
                       finalScores = [];
                       if (filteredBoxes.length > 0) {
                           ts = Date.now();
-                          indicesNms = nms(filteredBoxes, filteredScores, 0.7);
+                          indicesNms = nonMaxSuppression$1(filteredBoxes, filteredScores, 0.7);
                           stats.stage2_nms = Date.now() - ts;
                           regions_1 = indicesNms.map(function (idx) {
                               return new BoundingBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3));
@@ -3179,7 +3182,7 @@
                       points = [];
                       if (filteredBoxes.length > 0) {
                           ts = Date.now();
-                          indicesNms = nms(filteredBoxes, filteredScores, 0.7, false);
+                          indicesNms = nonMaxSuppression$1(filteredBoxes, filteredScores, 0.7, false);
                           stats.stage3_nms = Date.now() - ts;
                           finalBoxes = indicesNms.map(function (idx) { return filteredBoxes[idx]; });
                           finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
@@ -3325,6 +3328,253 @@
       return Mtcnn;
   }(NeuralNetwork));
 
+  var INPUT_SIZES = { xs: 224, sm: 320, md: 416, lg: 608 };
+  var NUM_BOXES = 5;
+  var IOU_THRESHOLD = 0.4;
+  var BOX_ANCHORS = [
+      new Point(0.738768, 0.874946),
+      new Point(2.42204, 2.65704),
+      new Point(4.30971, 7.04493),
+      new Point(10.246, 4.59428),
+      new Point(12.6868, 11.8741)
+  ];
+
+  function leaky(x) {
+      return tidy(function () {
+          return maximum(x, mul(x, scalar(0.10000000149011612)));
+      });
+  }
+
+  function convWithBatchNorm(x, params) {
+      return tidy(function () {
+          var out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
+          out = conv2d(out, params.conv.filters, [1, 1], 'valid');
+          out = sub(out, params.bn.sub);
+          out = mul(out, params.bn.truediv);
+          out = add(out, params.conv.bias);
+          return leaky(out);
+      });
+  }
+
+  function extractorsFactory$7(extractWeights, paramMappings) {
+      var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
+      function extractBatchNormParams(size, mappedPrefix) {
+          var sub$$1 = tensor1d(extractWeights(size));
+          var truediv = tensor1d(extractWeights(size));
+          paramMappings.push({ paramPath: mappedPrefix + "/sub" }, { paramPath: mappedPrefix + "/truediv" });
+          return { sub: sub$$1, truediv: truediv };
+      }
+      function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) {
+          var conv = extractConvParams(channelsIn, channelsOut, 3, mappedPrefix + "/conv");
+          var bn = extractBatchNormParams(channelsOut, mappedPrefix + "/bn");
+          return { conv: conv, bn: bn };
+      }
+      return {
+          extractConvParams: extractConvParams,
+          extractConvWithBatchNormParams: extractConvWithBatchNormParams
+      };
+  }
+  function extractParams$4(weights) {
+      var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
+      var paramMappings = [];
+      var _b = extractorsFactory$7(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractConvWithBatchNormParams = _b.extractConvWithBatchNormParams;
+      var conv0 = extractConvWithBatchNormParams(3, 16, 'conv0');
+      var conv1 = extractConvWithBatchNormParams(16, 32, 'conv1');
+      var conv2 = extractConvWithBatchNormParams(32, 64, 'conv2');
+      var conv3 = extractConvWithBatchNormParams(64, 128, 'conv3');
+      var conv4 = extractConvWithBatchNormParams(128, 256, 'conv4');
+      var conv5 = extractConvWithBatchNormParams(256, 512, 'conv5');
+      var conv6 = extractConvWithBatchNormParams(512, 1024, 'conv6');
+      var conv7 = extractConvWithBatchNormParams(1024, 1024, 'conv7');
+      var conv8 = extractConvParams(1024, 30, 1, 'conv8');
+      if (getRemainingWeights().length !== 0) {
+          throw new Error("weights remaing after extract: " + getRemainingWeights().length);
+      }
+      var params = { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3, conv4: conv4, conv5: conv5, conv6: conv6, conv7: conv7, conv8: conv8 };
+      return { params: params, paramMappings: paramMappings };
+  }
+
+  var SizeType;
+  (function (SizeType) {
+      SizeType["XS"] = "xs";
+      SizeType["SM"] = "sm";
+      SizeType["MD"] = "md";
+      SizeType["LG"] = "lg";
+  })(SizeType || (SizeType = {}));
+
+  function getDefaultParams(params) {
+      return Object.assign({}, {
+          sizeType: SizeType.MD,
+          scoreThreshold: 0.5
+      }, params);
+  }
+
+  var DEFAULT_MODEL_NAME$4 = 'tiny_yolov2_model';
+  function extractorsFactory$8(weightMap, paramMappings) {
+      var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
+      function extractBatchNormParams(prefix) {
+          var sub = extractWeightEntry(prefix + "/sub", 1);
+          var truediv = extractWeightEntry(prefix + "/truediv", 1);
+          return { sub: sub, truediv: truediv };
+      }
+      function extractConvParams(prefix) {
+          var filters = extractWeightEntry(prefix + "/filters", 4);
+          var bias = extractWeightEntry(prefix + "/bias", 1);
+          return { filters: filters, bias: bias };
+      }
+      function extractConvWithBatchNormParams(prefix) {
+          var conv = extractConvParams(prefix + "/conv");
+          var bn = extractBatchNormParams(prefix + "/bn");
+          return { conv: conv, bn: bn };
+      }
+      return {
+          extractConvParams: extractConvParams,
+          extractConvWithBatchNormParams: extractConvWithBatchNormParams
+      };
+  }
+  function loadQuantizedParams$4(uri) {
+      return __awaiter$1(this, void 0, void 0, function () {
+          var weightMap, paramMappings, _a, extractConvParams, extractConvWithBatchNormParams, params;
+          return __generator$1(this, function (_b) {
+              switch (_b.label) {
+                  case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME$4)];
+                  case 1:
+                      weightMap = _b.sent();
+                      paramMappings = [];
+                      _a = extractorsFactory$8(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractConvWithBatchNormParams = _a.extractConvWithBatchNormParams;
+                      params = {
+                          conv0: extractConvWithBatchNormParams('conv0'),
+                          conv1: extractConvWithBatchNormParams('conv1'),
+                          conv2: extractConvWithBatchNormParams('conv2'),
+                          conv3: extractConvWithBatchNormParams('conv3'),
+                          conv4: extractConvWithBatchNormParams('conv4'),
+                          conv5: extractConvWithBatchNormParams('conv5'),
+                          conv6: extractConvWithBatchNormParams('conv6'),
+                          conv7: extractConvWithBatchNormParams('conv7'),
+                          conv8: extractConvParams('conv8')
+                      };
+                      disposeUnusedWeightTensors(weightMap, paramMappings);
+                      return [2 /*return*/, { params: params, paramMappings: paramMappings }];
+              }
+          });
+      });
+  }
+
+  var TinyYolov2 = /** @class */ (function (_super) {
+      __extends$1(TinyYolov2, _super);
+      function TinyYolov2() {
+          return _super.call(this, 'TinyYolov2') || this;
+      }
+      TinyYolov2.prototype.forwardInput = function (input, inputSize) {
+          var params = this.params;
+          if (!params) {
+              throw new Error('TinyYolov2 - load model before inference');
+          }
+          var out = tidy(function () {
+              var batchTensor = input.toBatchTensor(inputSize, false).div(scalar(255)).toFloat();
+              var out = convWithBatchNorm(batchTensor, params.conv0);
+              out = maxPool(out, [2, 2], [2, 2], 'same');
+              out = convWithBatchNorm(out, params.conv1);
+              out = maxPool(out, [2, 2], [2, 2], 'same');
+              out = convWithBatchNorm(out, params.conv2);
+              out = maxPool(out, [2, 2], [2, 2], 'same');
+              out = convWithBatchNorm(out, params.conv3);
+              out = maxPool(out, [2, 2], [2, 2], 'same');
+              out = convWithBatchNorm(out, params.conv4);
+              out = maxPool(out, [2, 2], [2, 2], 'same');
+              out = convWithBatchNorm(out, params.conv5);
+              out = maxPool(out, [2, 2], [1, 1], 'same');
+              out = convWithBatchNorm(out, params.conv6);
+              out = convWithBatchNorm(out, params.conv7);
+              out = convLayer(out, params.conv8, 'valid', false);
+              return out;
+          });
+          return out;
+      };
+      TinyYolov2.prototype.forward = function (input, inputSize) {
+          return __awaiter$1(this, void 0, void 0, function () {
+              var _a;
+              return __generator$1(this, function (_b) {
+                  switch (_b.label) {
+                      case 0:
+                          _a = this.forwardInput;
+                          return [4 /*yield*/, toNetInput(input, true, true)];
+                      case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(), inputSize])];
+                      case 2: return [2 /*return*/, _b.sent()];
+                  }
+              });
+          });
+      };
+      TinyYolov2.prototype.locateFaces = function (input, forwardParams) {
+          if (forwardParams === void 0) { forwardParams = {}; }
+          return __awaiter$1(this, void 0, void 0, function () {
+              var _a, _inputSize, scoreThreshold, inputSize, netInput, out, numCells, _b, boxesTensor, scoresTensor, expit, paddedHeightRelative, paddedWidthRelative, boxes, scores, row, col, box, score, ctX, ctY, width, height, x, y, indices, detections;
+              return __generator$1(this, function (_c) {
+                  switch (_c.label) {
+                      case 0:
+                          _a = getDefaultParams(forwardParams), _inputSize = _a.inputSize, scoreThreshold = _a.scoreThreshold;
+                          inputSize = typeof _inputSize === 'string'
+                              ? INPUT_SIZES[_inputSize]
+                              : _inputSize;
+                          if (typeof inputSize !== 'number') {
+                              throw new Error("TinyYolov2 - unkown inputSize: " + inputSize + ", expected number or one of xs | sm | md | lg");
+                          }
+                          return [4 /*yield*/, toNetInput(input, true)];
+                      case 1:
+                          netInput = _c.sent();
+                          return [4 /*yield*/, this.forwardInput(netInput, inputSize)];
+                      case 2:
+                          out = _c.sent();
+                          numCells = out.shape[1];
+                          _b = tidy(function () {
+                              var reshaped = out.reshape([numCells, numCells, NUM_BOXES, 6]);
+                              out.dispose();
+                              var boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, NUM_BOXES, 4]);
+                              var scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, NUM_BOXES, 1]);
+                              return [boxes, scores];
+                          }), boxesTensor = _b[0], scoresTensor = _b[1];
+                          expit = function (x) { return 1 / (1 + Math.exp(-x)); };
+                          paddedHeightRelative = (netInput.getPaddings(0).y + netInput.getInputHeight(0)) / netInput.getInputHeight(0);
+                          paddedWidthRelative = (netInput.getPaddings(0).x + netInput.getInputWidth(0)) / netInput.getInputWidth(0);
+                          boxes = [];
+                          scores = [];
+                          for (row = 0; row < numCells; row++) {
+                              for (col = 0; col < numCells; col++) {
+                                  for (box = 0; box < NUM_BOXES; box++) {
+                                      score = expit(scoresTensor.get(row, col, box, 0));
+                                      if (score > scoreThreshold) {
+                                          ctX = ((col + expit(boxesTensor.get(row, col, box, 0))) / numCells) * paddedWidthRelative;
+                                          ctY = ((row + expit(boxesTensor.get(row, col, box, 1))) / numCells) * paddedHeightRelative;
+                                          width = ((Math.exp(boxesTensor.get(row, col, box, 2)) * BOX_ANCHORS[box].x) / numCells) * paddedWidthRelative;
+                                          height = ((Math.exp(boxesTensor.get(row, col, box, 3)) * BOX_ANCHORS[box].y) / numCells) * paddedHeightRelative;
+                                          x = (ctX - (width / 2));
+                                          y = (ctY - (height / 2));
+                                          boxes.push(new BoundingBox(x, y, x + width, y + height));
+                                          scores.push(score);
+                                      }
+                                  }
+                              }
+                          }
+                          boxesTensor.dispose();
+                          scoresTensor.dispose();
+                          indices = nonMaxSuppression$1(boxes.map(function (box) { return new BoundingBox(box.left * inputSize, box.top * inputSize, box.right * inputSize, box.bottom * inputSize); }), scores, IOU_THRESHOLD, true);
+                          detections = indices.map(function (idx) {
+                              return new FaceDetection(scores[idx], boxes[idx].toRect(), { width: netInput.getInputWidth(0), height: netInput.getInputHeight(0) });
+                          });
+                          return [2 /*return*/, detections];
+                  }
+              });
+          });
+      };
+      TinyYolov2.prototype.loadQuantizedParams = function (uri) {
+          return loadQuantizedParams$4(uri);
+      };
+      TinyYolov2.prototype.extractParams = function (weights) {
+          return extractParams$4(weights);
+      };
+      return TinyYolov2;
+  }(NeuralNetwork));
+
   var detectionNet = new FaceDetectionNet();
   var landmarkNet = new FaceLandmarkNet();
   var recognitionNet = new FaceRecognitionNet();
@@ -3334,7 +3584,8 @@
       ssdMobilenet: detectionNet,
       faceLandmark68Net: landmarkNet,
       faceRecognitionNet: recognitionNet,
-      mtcnn: new Mtcnn()
+      mtcnn: new Mtcnn(),
+      tinyYolov2: new TinyYolov2()
   };
   function loadFaceDetectionModel(url) {
       return nets.ssdMobilenet.load(url);
@@ -3348,12 +3599,16 @@
   function loadMtcnnModel(url) {
       return nets.mtcnn.load(url);
   }
+  function loadTinyYolov2Model(url) {
+      return nets.tinyYolov2.load(url);
+  }
   function loadModels(url) {
       return Promise.all([
           loadFaceDetectionModel(url),
           loadFaceLandmarkModel(url),
           loadFaceRecognitionModel(url),
-          loadMtcnnModel(url)
+          loadMtcnnModel(url),
+          loadTinyYolov2Model(url)
       ]);
   }
   function locateFaces(input, minConfidence, maxResults) {
@@ -3368,6 +3623,9 @@
   function mtcnn(input, forwardParams) {
       return nets.mtcnn.forward(input, forwardParams);
   }
+  function tinyYolov2(input, forwardParams) {
+      return nets.tinyYolov2.locateFaces(input, forwardParams);
+  }
   var allFaces = allFacesFactory(nets.ssdMobilenet, nets.faceLandmark68Net, nets.faceRecognitionNet);
   var allFacesMtcnn = allFacesMtcnnFactory(nets.mtcnn, nets.faceRecognitionNet);
 
@@ -3377,6 +3635,12 @@
       return net;
   }
 
+  function createTinyYolov2(weights) {
+      var net = new TinyYolov2();
+      net.extractWeights(weights);
+      return net;
+  }
+
   exports.tf = tfCore_esm;
   exports.FaceDetection = FaceDetection;
   exports.FullFaceDescription = FullFaceDescription;
@@ -3409,17 +3673,21 @@
   exports.loadFaceLandmarkModel = loadFaceLandmarkModel;
   exports.loadFaceRecognitionModel = loadFaceRecognitionModel;
   exports.loadMtcnnModel = loadMtcnnModel;
+  exports.loadTinyYolov2Model = loadTinyYolov2Model;
   exports.loadModels = loadModels;
   exports.locateFaces = locateFaces;
   exports.detectLandmarks = detectLandmarks;
   exports.computeFaceDescriptor = computeFaceDescriptor;
   exports.mtcnn = mtcnn;
+  exports.tinyYolov2 = tinyYolov2;
   exports.allFaces = allFaces;
   exports.allFacesMtcnn = allFacesMtcnn;
   exports.createMtcnn = createMtcnn;
   exports.Mtcnn = Mtcnn;
   exports.FaceLandmarks5 = FaceLandmarks5;
   exports.padToSquare = padToSquare;
+  exports.createTinyYolov2 = createTinyYolov2;
+  exports.TinyYolov2 = TinyYolov2;
   exports.toNetInput = toNetInput;
   exports.isFloat = isFloat;
   exports.isEven = isEven;