build dist

dist/NetInput.js

-import { createCanvas, getContext2dOrThrow, getElement, getMediaDimensions } from './utils';
+import { createCanvasFromMedia, getElement } from './utils';
 var NetInput = /** @class */ (function () {
     function NetInput(mediaArg, dims) {
         var _this = this;
@@ -27,10 +27,8 @@ var NetInput = /** @class */ (function () {
             return;
         }
         // if input is batch type, make sure every canvas has the same dimensions
-        var _a = this.dims || dims || getMediaDimensions(media), width = _a.width, height = _a.height;
-        var canvas = createCanvas({ width: width, height: height });
-        getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
-        this._canvases.push(canvas);
+        var canvasDims = this.dims || dims;
+        this._canvases.push(createCanvasFromMedia(media, canvasDims));
     };
     Object.defineProperty(NetInput.prototype, "canvases", {
         get: function () {

dist/NetInput.js.map

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\ No newline at end of file

dist/Point.d.ts

+export declare class Point {
+    x: number;
+    y: number;
+    constructor(x: number, y: number);
+}

dist/Point.js

+var Point = /** @class */ (function () {
+    function Point(x, y) {
+        this.x = x;
+        this.y = y;
+    }
+    return Point;
+}());
+export { Point };
+//# sourceMappingURL=Point.js.map
\ No newline at end of file

dist/Point.js.map

+{"version":3,"file":"Point.js","sourceRoot":"","sources":["../src/Point.ts"],"names":[],"mappings":"AAAA;IAIE,eAAY,CAAS,EAAE,CAAS;QAC9B,IAAI,CAAC,CAAC,GAAG,CAAC,CAAA;QACV,IAAI,CAAC,CAAC,GAAG,CAAC,CAAA;IACZ,CAAC;IACH,YAAC;AAAD,CAAC,AARD,IAQC"}
\ No newline at end of file

dist/Rect.d.ts

+export declare class Rect {
+    x: number;
+    y: number;
+    width: number;
+    height: number;
+    constructor(x: number, y: number, width: number, height: number);
+}

dist/Rect.js

+var Rect = /** @class */ (function () {
+    function Rect(x, y, width, height) {
+        this.x = x;
+        this.y = y;
+        this.width = width;
+        this.height = height;
+    }
+    return Rect;
+}());
+export { Rect };
+//# sourceMappingURL=Rect.js.map
\ No newline at end of file

dist/Rect.js.map

+{"version":3,"file":"Rect.js","sourceRoot":"","sources":["../src/Rect.ts"],"names":[],"mappings":"AAAA;IAME,cAAY,CAAS,EAAE,CAAS,EAAE,KAAa,EAAE,MAAc;QAC7D,IAAI,CAAC,CAAC,GAAG,CAAC,CAAA;QACV,IAAI,CAAC,CAAC,GAAG,CAAC,CAAA;QACV,IAAI,CAAC,KAAK,GAAG,KAAK,CAAA;QAClB,IAAI,CAAC,MAAM,GAAG,MAAM,CAAA;IACtB,CAAC;IACH,WAAC;AAAD,CAAC,AAZD,IAYC"}
\ No newline at end of file

dist/commons/convLayer.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { ConvParams } from './types';
+export declare function convLayer(x: tf.Tensor4D, params: ConvParams, padding?: 'valid' | 'same', withRelu?: boolean): tf.Tensor4D;

dist/commons/convLayer.js

+import * as tf from '@tensorflow/tfjs-core';
+export function convLayer(x, params, padding, withRelu) {
+    if (padding === void 0) { padding = 'same'; }
+    if (withRelu === void 0) { withRelu = false; }
+    return tf.tidy(function () {
+        var out = tf.add(tf.conv2d(x, params.filters, [1, 1], padding), params.bias);
+        return withRelu ? tf.relu(out) : out;
+    });
+}
+//# sourceMappingURL=convLayer.js.map
\ No newline at end of file

dist/commons/convLayer.js.map

+{"version":3,"file":"convLayer.js","sourceRoot":"","sources":["../../src/commons/convLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAI5C,MAAM,oBACJ,CAAc,EACd,MAAkB,EAClB,OAAkC,EAClC,QAAyB;IADzB,wBAAA,EAAA,gBAAkC;IAClC,yBAAA,EAAA,gBAAyB;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC;QACb,IAAM,GAAG,GAAG,EAAE,CAAC,GAAG,CAChB,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,EAC7C,MAAM,CAAC,IAAI,CACG,CAAA;QAEhB,OAAO,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAA;IACtC,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file

dist/commons/extractConvParamsFactory.d.ts

+import { ConvParams, ExtractWeightsFunction } from './types';
+export declare function extractConvParamsFactory(extractWeights: ExtractWeightsFunction): (channelsIn: number, channelsOut: number, filterSize: number) => ConvParams;

dist/commons/extractConvParamsFactory.js

+import * as tf from '@tensorflow/tfjs-core';
+export function extractConvParamsFactory(extractWeights) {
+    return function (channelsIn, channelsOut, filterSize) {
+        var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
+        var bias = tf.tensor1d(extractWeights(channelsOut));
+        return {
+            filters: filters,
+            bias: bias
+        };
+    };
+}
+//# sourceMappingURL=extractConvParamsFactory.js.map
\ No newline at end of file

dist/commons/extractConvParamsFactory.js.map

+{"version":3,"file":"extractConvParamsFactory.js","sourceRoot":"","sources":["../../src/commons/extractConvParamsFactory.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAI5C,MAAM,mCAAmC,cAAsC;IAC7E,OAAO,UACL,UAAkB,EAClB,WAAmB,EACnB,UAAkB;QAElB,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CACzB,cAAc,CAAC,UAAU,GAAG,WAAW,GAAG,UAAU,GAAG,UAAU,CAAC,EAClE,CAAC,UAAU,EAAE,UAAU,EAAE,UAAU,EAAE,WAAW,CAAC,CAClD,CAAA;QACD,IAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAErD,OAAO;YACL,OAAO,SAAA;YACP,IAAI,MAAA;SACL,CAAA;IACH,CAAC,CAAA;AACH,CAAC"}
\ No newline at end of file

dist/commons/extractWeightsFactory.d.ts

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

dist/commons/extractWeightsFactory.js

+export function extractWeightsFactory(weights) {
+    var remainingWeights = weights;
+    function extractWeights(numWeights) {
+        var ret = remainingWeights.slice(0, numWeights);
+        remainingWeights = remainingWeights.slice(numWeights);
+        return ret;
+    }
+    function getRemainingWeights() {
+        return remainingWeights;
+    }
+    return {
+        extractWeights: extractWeights,
+        getRemainingWeights: getRemainingWeights
+    };
+}
+//# sourceMappingURL=extractWeightsFactory.js.map
\ No newline at end of file

dist/commons/extractWeightsFactory.js.map

+{"version":3,"file":"extractWeightsFactory.js","sourceRoot":"","sources":["../../src/commons/extractWeightsFactory.ts"],"names":[],"mappings":"AAAA,MAAM,gCAAgC,OAAqB;IACzD,IAAI,gBAAgB,GAAG,OAAO,CAAA;IAE9B,wBAAwB,UAAkB;QACxC,IAAM,GAAG,GAAG,gBAAgB,CAAC,KAAK,CAAC,CAAC,EAAE,UAAU,CAAC,CAAA;QACjD,gBAAgB,GAAG,gBAAgB,CAAC,KAAK,CAAC,UAAU,CAAC,CAAA;QACrD,OAAO,GAAG,CAAA;IACZ,CAAC;IAED;QACE,OAAO,gBAAgB,CAAA;IACzB,CAAC;IAED,OAAO;QACL,cAAc,gBAAA;QACd,mBAAmB,qBAAA;KACpB,CAAA;AACH,CAAC"}
\ No newline at end of file

dist/commons/types.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+export declare type ConvParams = {
+    filters: tf.Tensor4D;
+    bias: tf.Tensor1D;
+};
+export declare type ExtractWeightsFunction = (numWeights: number) => Float32Array;

dist/commons/types.js

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

dist/commons/types.js.map

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

dist/extractFaceTensors.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { FaceDetectionResult } from './faceDetectionNet/FaceDetectionResult';
+import { FaceDetection } from './faceDetectionNet/FaceDetection';
 import { NetInput } from './NetInput';
 import { TNetInput } from './types';
 /**
@@ -13,4 +13,4 @@ import { TNetInput } from './types';
  * @param detections The face detection results for that image.
  * @returns Tensors of the corresponding image region for each detected face.
  */
-export declare function extractFaceTensors(image: tf.Tensor | NetInput | TNetInput, detections: FaceDetectionResult[]): tf.Tensor4D[];
+export declare function extractFaceTensors(image: tf.Tensor | NetInput | TNetInput, detections: FaceDetection[]): tf.Tensor4D[];

dist/extractFaceTensors.js

@@ -17,7 +17,7 @@ export function extractFaceTensors(image, detections) {
         // TODO handle batches
         var _a = imgTensor.shape, batchSize = _a[0], imgHeight = _a[1], imgWidth = _a[2], numChannels = _a[3];
         var faceTensors = detections.map(function (det) {
-            var _a = det.forSize(imgWidth, imgHeight).box, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
+            var _a = det.forSize(imgWidth, imgHeight).getBox(), x = _a.x, y = _a.y, width = _a.width, height = _a.height;
             return tf.slice(imgTensor, [0, y, x, 0], [1, height, width, numChannels]);
         });
         return faceTensors;

dist/extractFaceTensors.js.map

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+{"version":3,"file":"extractFaceTensors.js","sourceRoot":"","sources":["../src/extractFaceTensors.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAG5C,OAAO,EAAE,cAAc,EAAE,MAAM,kBAAkB,CAAC;AAIlD;;;;;;;;;;GAUG;AACH,MAAM,6BACJ,KAAuC,EACvC,UAA2B;IAE3B,OAAO,EAAE,CAAC,IAAI,CAAC;QACb,IAAM,SAAS,GAAG,cAAc,CAAC,KAAK,CAAC,CAAA;QAEvC,sBAAsB;QAChB,IAAA,oBAA+D,EAA9D,iBAAS,EAAE,iBAAS,EAAE,gBAAQ,EAAE,mBAAW,CAAmB;QAErE,IAAM,WAAW,GAAG,UAAU,CAAC,GAAG,CAAC,UAAA,GAAG;YAC9B,IAAA,8CAAmE,EAAjE,QAAC,EAAE,QAAC,EAAE,gBAAK,EAAE,kBAAM,CAA8C;YACzE,OAAO,EAAE,CAAC,KAAK,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,MAAM,EAAE,KAAK,EAAE,WAAW,CAAC,CAAC,CAAA;QAC3E,CAAC,CAAC,CAAA;QAEF,OAAO,WAAW,CAAA;IACpB,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file

dist/extractFaces.d.ts

-import { FaceDetectionResult } from './faceDetectionNet/FaceDetectionResult';
+import { FaceDetection } from './faceDetectionNet/FaceDetection';
 /**
  * Extracts the image regions containing the detected faces.
  *
@@ -6,4 +6,4 @@ import { FaceDetectionResult } from './faceDetectionNet/FaceDetectionResult';
  * @param detections The face detection results for that image.
  * @returns The Canvases of the corresponding image region for each detected face.
  */
-export declare function extractFaces(image: HTMLCanvasElement, detections: FaceDetectionResult[]): HTMLCanvasElement[];
+export declare function extractFaces(image: HTMLCanvasElement, detections: FaceDetection[]): HTMLCanvasElement[];

dist/extractFaces.js

@@ -9,7 +9,7 @@ import { createCanvas, getContext2dOrThrow } from './utils';
 export function extractFaces(image, detections) {
     var ctx = getContext2dOrThrow(image);
     return detections.map(function (det) {
-        var _a = det.forSize(image.width, image.height).box, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
+        var _a = det.forSize(image.width, image.height).getBox(), x = _a.x, y = _a.y, width = _a.width, height = _a.height;
         var faceImg = createCanvas({ width: width, height: height });
         getContext2dOrThrow(faceImg)
             .putImageData(ctx.getImageData(x, y, width, height), 0, 0);

dist/extractFaces.js.map

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\ No newline at end of file

dist/faceDetectionNet/FaceDetection.d.ts

+import { Rect } from '../Rect';
+import { Dimensions } from '../types';
+export declare class FaceDetection {
+    private _score;
+    private _box;
+    private _imageWidth;
+    private _imageHeight;
+    constructor(score: number, relativeBox: Rect, imageDims: Dimensions);
+    getScore(): number;
+    getBox(): Rect;
+    getRelativeBox(): Rect;
+    forSize(width: number, height: number): FaceDetection;
+}

dist/faceDetectionNet/FaceDetection.js

+import { Rect } from '../Rect';
+var FaceDetection = /** @class */ (function () {
+    function FaceDetection(score, relativeBox, imageDims) {
+        var width = imageDims.width, height = imageDims.height;
+        this._imageWidth = width;
+        this._imageHeight = height;
+        this._score = score;
+        this._box = new Rect(Math.floor(relativeBox.x * width), Math.floor(relativeBox.y * height), Math.floor(relativeBox.width * width), Math.floor(relativeBox.height * height));
+    }
+    FaceDetection.prototype.getScore = function () {
+        return this._score;
+    };
+    FaceDetection.prototype.getBox = function () {
+        return this._box;
+    };
+    FaceDetection.prototype.getRelativeBox = function () {
+        return new Rect(this._box.x / this._imageWidth, this._box.y / this._imageHeight, this._box.width / this._imageWidth, this._box.height / this._imageHeight);
+    };
+    FaceDetection.prototype.forSize = function (width, height) {
+        return new FaceDetection(this._score, this.getRelativeBox(), { width: width, height: height });
+    };
+    return FaceDetection;
+}());
+export { FaceDetection };
+//# sourceMappingURL=FaceDetection.js.map
\ No newline at end of file

dist/faceDetectionNet/FaceDetection.js.map

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\ No newline at end of file

dist/faceDetectionNet/FaceDetectionResult.d.ts

-import { FaceDetectionNet } from './types';
-export declare class FaceDetectionResult {
-    private _score;
-    private _topRelative;
-    private _leftRelative;
-    private _bottomRelative;
-    private _rightRelative;
-    constructor(score: number, topRelative: number, leftRelative: number, bottomRelative: number, rightRelative: number);
-    forSize(width: number, height: number): FaceDetectionNet.Detection;
-}

dist/faceDetectionNet/FaceDetectionResult.js

-var FaceDetectionResult = /** @class */ (function () {
-    function FaceDetectionResult(score, topRelative, leftRelative, bottomRelative, rightRelative) {
-        this._score = score;
-        this._topRelative = Math.max(0, topRelative),
-            this._leftRelative = Math.max(0, leftRelative),
-            this._bottomRelative = Math.min(1.0, bottomRelative),
-            this._rightRelative = Math.min(1.0, rightRelative);
-    }
-    FaceDetectionResult.prototype.forSize = function (width, height) {
-        var x = Math.floor(this._leftRelative * width);
-        var y = Math.floor(this._topRelative * height);
-        return {
-            score: this._score,
-            box: {
-                x: x,
-                y: y,
-                width: Math.floor(this._rightRelative * width) - x,
-                height: Math.floor(this._bottomRelative * height) - y
-            }
-        };
-    };
-    return FaceDetectionResult;
-}());
-export { FaceDetectionResult };
-//# sourceMappingURL=FaceDetectionResult.js.map
\ No newline at end of file

dist/faceDetectionNet/FaceDetectionResult.js.map

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\ No newline at end of file

dist/faceDetectionNet/boxPredictionLayer.js

 import * as tf from '@tensorflow/tfjs-core';
-function convWithBias(x, params) {
-    return tf.tidy(function () {
-        return tf.add(tf.conv2d(x, params.filters, [1, 1], 'same'), params.bias);
-    });
-}
+import { convLayer } from '../commons/convLayer';
 export function boxPredictionLayer(x, params) {
     return tf.tidy(function () {
         var batchSize = x.shape[0];
-        var boxPredictionEncoding = tf.reshape(convWithBias(x, params.box_encoding_predictor_params), [batchSize, -1, 1, 4]);
-        var classPrediction = tf.reshape(convWithBias(x, params.class_predictor_params), [batchSize, -1, 3]);
+        var boxPredictionEncoding = tf.reshape(convLayer(x, params.box_encoding_predictor_params), [batchSize, -1, 1, 4]);
+        var classPrediction = tf.reshape(convLayer(x, params.class_predictor_params), [batchSize, -1, 3]);
         return {
             boxPredictionEncoding: boxPredictionEncoding,
             classPrediction: classPrediction

dist/faceDetectionNet/boxPredictionLayer.js.map

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+{"version":3,"file":"boxPredictionLayer.js","sourceRoot":"","sources":["../../src/faceDetectionNet/boxPredictionLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,SAAS,EAAE,MAAM,sBAAsB,CAAC;AAIjD,MAAM,6BACJ,CAAc,EACd,MAA4C;IAE5C,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAM,SAAS,GAAG,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;QAE5B,IAAM,qBAAqB,GAAG,EAAE,CAAC,OAAO,CACtC,SAAS,CAAC,CAAC,EAAE,MAAM,CAAC,6BAA6B,CAAC,EAClD,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CACtB,CAAA;QACD,IAAM,eAAe,GAAG,EAAE,CAAC,OAAO,CAChC,SAAS,CAAC,CAAC,EAAE,MAAM,CAAC,sBAAsB,CAAC,EAC3C,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CACnB,CAAA;QAED,OAAO;YACL,qBAAqB,uBAAA;YACrB,eAAe,iBAAA;SAChB,CAAA;IACH,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file

dist/faceDetectionNet/extractParams.js

 import * as tf from '@tensorflow/tfjs-core';
+import { extractWeightsFactory } from '../commons/extractWeightsFactory';
 function extractorsFactory(extractWeights) {
     function extractDepthwiseConvParams(numChannels) {
         var filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
@@ -14,7 +15,7 @@ function extractorsFactory(extractWeights) {
             batch_norm_variance: batch_norm_variance
         };
     }
-    function extractConvWithBiasParams(channelsIn, channelsOut, filterSize) {
+    function extractConvParams(channelsIn, channelsOut, filterSize) {
         var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
         var bias = tf.tensor1d(extractWeights(channelsOut));
         return {
@@ -23,7 +24,7 @@ function extractorsFactory(extractWeights) {
         };
     }
     function extractPointwiseConvParams(channelsIn, channelsOut, filterSize) {
-        var _a = extractConvWithBiasParams(channelsIn, channelsOut, filterSize), filters = _a.filters, bias = _a.bias;
+        var _a = extractConvParams(channelsIn, channelsOut, filterSize), filters = _a.filters, bias = _a.bias;
         return {
             filters: filters,
             batch_norm_offset: bias
@@ -72,18 +73,18 @@ function extractorsFactory(extractWeights) {
         var conv_5_params = extractPointwiseConvParams(128, 256, 3);
         var conv_6_params = extractPointwiseConvParams(256, 64, 1);
         var conv_7_params = extractPointwiseConvParams(64, 128, 3);
-        var box_encoding_0_predictor_params = extractConvWithBiasParams(512, 12, 1);
-        var class_predictor_0_params = extractConvWithBiasParams(512, 9, 1);
-        var box_encoding_1_predictor_params = extractConvWithBiasParams(1024, 24, 1);
-        var class_predictor_1_params = extractConvWithBiasParams(1024, 18, 1);
-        var box_encoding_2_predictor_params = extractConvWithBiasParams(512, 24, 1);
-        var class_predictor_2_params = extractConvWithBiasParams(512, 18, 1);
-        var box_encoding_3_predictor_params = extractConvWithBiasParams(256, 24, 1);
-        var class_predictor_3_params = extractConvWithBiasParams(256, 18, 1);
-        var box_encoding_4_predictor_params = extractConvWithBiasParams(256, 24, 1);
-        var class_predictor_4_params = extractConvWithBiasParams(256, 18, 1);
-        var box_encoding_5_predictor_params = extractConvWithBiasParams(128, 24, 1);
-        var class_predictor_5_params = extractConvWithBiasParams(128, 18, 1);
+        var box_encoding_0_predictor_params = extractConvParams(512, 12, 1);
+        var class_predictor_0_params = extractConvParams(512, 9, 1);
+        var box_encoding_1_predictor_params = extractConvParams(1024, 24, 1);
+        var class_predictor_1_params = extractConvParams(1024, 18, 1);
+        var box_encoding_2_predictor_params = extractConvParams(512, 24, 1);
+        var class_predictor_2_params = extractConvParams(512, 18, 1);
+        var box_encoding_3_predictor_params = extractConvParams(256, 24, 1);
+        var class_predictor_3_params = extractConvParams(256, 18, 1);
+        var box_encoding_4_predictor_params = extractConvParams(256, 24, 1);
+        var class_predictor_4_params = extractConvParams(256, 18, 1);
+        var box_encoding_5_predictor_params = extractConvParams(128, 24, 1);
+        var class_predictor_5_params = extractConvParams(128, 18, 1);
         var box_predictor_0_params = {
             box_encoding_predictor_params: box_encoding_0_predictor_params,
             class_predictor_params: class_predictor_0_params
@@ -131,20 +132,16 @@ function extractorsFactory(extractWeights) {
     };
 }
 export function extractParams(weights) {
-    var extractWeights = function (numWeights) {
-        var ret = weights.slice(0, numWeights);
-        weights = weights.slice(numWeights);
-        return ret;
-    };
-    var _a = extractorsFactory(extractWeights), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
+    var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
+    var _b = extractorsFactory(extractWeights), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;
     var mobilenetv1_params = extractMobilenetV1Params();
     var prediction_layer_params = extractPredictionLayerParams();
     var extra_dim = tf.tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
     var output_layer_params = {
         extra_dim: extra_dim
     };
-    if (weights.length !== 0) {
-        throw new Error("weights remaing after extract: " + weights.length);
+    if (getRemainingWeights().length !== 0) {
+        throw new Error("weights remaing after extract: " + getRemainingWeights().length);
     }
     return {
         mobilenetv1_params: mobilenetv1_params,

dist/faceDetectionNet/index.d.ts

 import * as tf from '@tensorflow/tfjs-core';
 import { NetInput } from '../NetInput';
-import { FaceDetectionResult } from './FaceDetectionResult';
+import { FaceDetection } from './FaceDetection';
 export declare function faceDetectionNet(weights: Float32Array): {
-    forward: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => {
+    forward: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | NetInput | tf.Tensor<tf.Rank>) => {
         boxes: tf.Tensor<tf.Rank.R2>[];
         scores: tf.Tensor<tf.Rank.R1>[];
     };
-    locateFaces: (input: tf.Tensor<tf.Rank> | NetInput, minConfidence?: number, maxResults?: number) => Promise<FaceDetectionResult[]>;
+    locateFaces: (input: NetInput | tf.Tensor<tf.Rank>, minConfidence?: number, maxResults?: number) => Promise<FaceDetection[]>;
 };

dist/faceDetectionNet/index.js

@@ -3,12 +3,13 @@ import * as tf from '@tensorflow/tfjs-core';
 import { getImageTensor } from '../getImageTensor';
 import { padToSquare } from '../padToSquare';
 import { extractParams } from './extractParams';
-import { FaceDetectionResult } from './FaceDetectionResult';
+import { FaceDetection } from './FaceDetection';
 import { mobileNetV1 } from './mobileNetV1';
 import { nonMaxSuppression } from './nonMaxSuppression';
 import { outputLayer } from './outputLayer';
 import { predictionLayer } from './predictionLayer';
 import { resizeLayer } from './resizeLayer';
+import { Rect } from '../Rect';
 export function faceDetectionNet(weights) {
     var params = extractParams(weights);
     function forwardTensor(imgTensor) {
@@ -26,7 +27,7 @@ export function faceDetectionNet(weights) {
         if (minConfidence === void 0) { minConfidence = 0.8; }
         if (maxResults === void 0) { maxResults = 100; }
         return tslib_1.__awaiter(this, void 0, void 0, function () {
-            var paddedHeightRelative, paddedWidthRelative, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, results;
+            var paddedHeightRelative, paddedWidthRelative, imageDimensions, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, results;
             return tslib_1.__generator(this, function (_d) {
                 switch (_d.label) {
                     case 0:
@@ -34,6 +35,7 @@ export function faceDetectionNet(weights) {
                         _a = tf.tidy(function () {
                             var imgTensor = getImageTensor(input);
                             var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
+                            imageDimensions = { width: width, height: height };
                             imgTensor = padToSquare(imgTensor);
                             paddedHeightRelative = imgTensor.shape[1] / height;
                             paddedWidthRelative = imgTensor.shape[2] / width;
@@ -52,7 +54,17 @@ export function faceDetectionNet(weights) {
                         iouThreshold = 0.5;
                         indices = nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
                         results = indices
-                            .map(function (idx) { return new FaceDetectionResult(scoresData[idx], boxes.get(idx, 0) * paddedHeightRelative, boxes.get(idx, 1) * paddedWidthRelative, boxes.get(idx, 2) * paddedHeightRelative, boxes.get(idx, 3) * paddedWidthRelative); });
+                            .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 * paddedHeightRelative; }), 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 * paddedWidthRelative; }), left = _b[0], right = _b[1];
+                            return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), imageDimensions);
+                        });
                         boxes.dispose();
                         scores.dispose();
                         return [2 /*return*/, results];

dist/faceDetectionNet/index.js.map

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\ No newline at end of file

dist/faceDetectionNet/types.d.ts

 import * as tf from '@tensorflow/tfjs-core';
+import { ConvParams } from '../commons/types';
 export declare namespace FaceDetectionNet {
     type PointwiseConvParams = {
         filters: tf.Tensor4D;
@@ -21,13 +22,9 @@ export declare namespace FaceDetectionNet {
             conv_pair_params: ConvPairParams[];
         };
     }
-    type ConvWithBiasParams = {
-        filters: tf.Tensor4D;
-        bias: tf.Tensor1D;
-    };
     type BoxPredictionParams = {
-        box_encoding_predictor_params: ConvWithBiasParams;
-        class_predictor_params: ConvWithBiasParams;
+        box_encoding_predictor_params: ConvParams;
+        class_predictor_params: ConvParams;
     };
     type PredictionLayerParams = {
         conv_0_params: PointwiseConvParams;
@@ -53,13 +50,4 @@ export declare namespace FaceDetectionNet {
         prediction_layer_params: PredictionLayerParams;
         output_layer_params: OutputLayerParams;
     };
-    type Detection = {
-        score: number;
-        box: {
-            x: number;
-            y: number;
-            width: number;
-            height: number;
-        };
-    };
 }

dist/faceLandmarkNet/FaceLandmarks.d.ts

+import { Point } from '../Point';
+import { Dimensions } from '../types';
+export declare class FaceLandmarks {
+    private _faceLandmarks;
+    private _imageWidth;
+    private _imageHeight;
+    constructor(relativeFaceLandmarkPositions: Point[], imageDims: Dimensions);
+    getPositions(): Point[];
+    getRelativePositions(): Point[];
+    getJawOutline(): Point[];
+    getLeftEyeBrow(): Point[];
+    getRightEyeBrow(): Point[];
+    getNose(): Point[];
+    getLeftEye(): Point[];
+    getRightEye(): Point[];
+    getMouth(): Point[];
+    forSize(width: number, height: number): FaceLandmarks;
+}

dist/faceLandmarkNet/FaceLandmarks.js

+import { Point } from '../Point';
+var FaceLandmarks = /** @class */ (function () {
+    function FaceLandmarks(relativeFaceLandmarkPositions, imageDims) {
+        var width = imageDims.width, height = imageDims.height;
+        this._imageWidth = width;
+        this._imageHeight = height;
+        this._faceLandmarks = relativeFaceLandmarkPositions.map(function (pt) { return new Point(pt.x * width, pt.y * height); });
+    }
+    FaceLandmarks.prototype.getPositions = function () {
+        return this._faceLandmarks;
+    };
+    FaceLandmarks.prototype.getRelativePositions = function () {
+        var _this = this;
+        return this._faceLandmarks.map(function (pt) { return new Point(pt.x / _this._imageWidth, pt.y / _this._imageHeight); });
+    };
+    FaceLandmarks.prototype.getJawOutline = function () {
+        return this._faceLandmarks.slice(0, 17);
+    };
+    FaceLandmarks.prototype.getLeftEyeBrow = function () {
+        return this._faceLandmarks.slice(17, 22);
+    };
+    FaceLandmarks.prototype.getRightEyeBrow = function () {
+        return this._faceLandmarks.slice(22, 27);
+    };
+    FaceLandmarks.prototype.getNose = function () {
+        return this._faceLandmarks.slice(27, 36);
+    };
+    FaceLandmarks.prototype.getLeftEye = function () {
+        return this._faceLandmarks.slice(36, 42);
+    };
+    FaceLandmarks.prototype.getRightEye = function () {
+        return this._faceLandmarks.slice(42, 48);
+    };
+    FaceLandmarks.prototype.getMouth = function () {
+        return this._faceLandmarks.slice(48, 68);
+    };
+    FaceLandmarks.prototype.forSize = function (width, height) {
+        return new FaceLandmarks(this.getRelativePositions(), { width: width, height: height });
+    };
+    return FaceLandmarks;
+}());
+export { FaceLandmarks };
+//# sourceMappingURL=FaceLandmarks.js.map
\ No newline at end of file

dist/faceLandmarkNet/FaceLandmarks.js.map

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\ No newline at end of file

dist/faceLandmarkNet/extractParams.d.ts

+import { FaceLandmarkNet } from './types';
+export declare function extractParams(weights: Float32Array): FaceLandmarkNet.NetParams;

dist/faceLandmarkNet/extractParams.js

+import * as tf from '@tensorflow/tfjs-core';
+import { extractConvParamsFactory } from '../commons/extractConvParamsFactory';
+import { extractWeightsFactory } from '../commons/extractWeightsFactory';
+export function extractParams(weights) {
+    var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
+    var extractConvParams = extractConvParamsFactory(extractWeights);
+    function extractFcParams(channelsIn, channelsOut) {
+        var fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
+        var fc_bias = tf.tensor1d(extractWeights(channelsOut));
+        return {
+            weights: fc_weights,
+            bias: fc_bias
+        };
+    }
+    var conv0_params = extractConvParams(3, 32, 3);
+    var conv1_params = extractConvParams(32, 64, 3);
+    var conv2_params = extractConvParams(64, 64, 3);
+    var conv3_params = extractConvParams(64, 64, 3);
+    var conv4_params = extractConvParams(64, 64, 3);
+    var conv5_params = extractConvParams(64, 128, 3);
+    var conv6_params = extractConvParams(128, 128, 3);
+    var conv7_params = extractConvParams(128, 256, 3);
+    var fc0_params = extractFcParams(6400, 1024);
+    var fc1_params = extractFcParams(1024, 136);
+    if (getRemainingWeights().length !== 0) {
+        throw new Error("weights remaing after extract: " + getRemainingWeights().length);
+    }
+    return {
+        conv0_params: conv0_params,
+        conv1_params: conv1_params,
+        conv2_params: conv2_params,
+        conv3_params: conv3_params,
+        conv4_params: conv4_params,
+        conv5_params: conv5_params,
+        conv6_params: conv6_params,
+        conv7_params: conv7_params,
+        fc0_params: fc0_params,
+        fc1_params: fc1_params
+    };
+}
+//# sourceMappingURL=extractParams.js.map
\ No newline at end of file

dist/faceLandmarkNet/extractParams.js.map

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\ No newline at end of file

dist/faceLandmarkNet/fullyConnectedLayer.d.ts

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

dist/faceLandmarkNet/fullyConnectedLayer.js

+import * as tf from '@tensorflow/tfjs-core';
+export function fullyConnectedLayer(x, params) {
+    return tf.tidy(function () {
+        return tf.add(tf.matMul(x, params.weights), params.bias);
+    });
+}
+//# sourceMappingURL=fullyConnectedLayer.js.map
\ No newline at end of file

dist/faceLandmarkNet/fullyConnectedLayer.js.map

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\ No newline at end of file

dist/faceLandmarkNet/index.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { NetInput } from '../NetInput';
+import { FaceLandmarks } from './FaceLandmarks';
+export declare function faceLandmarkNet(weights: Float32Array): {
+    detectLandmarks: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | NetInput | tf.Tensor<tf.Rank>) => Promise<FaceLandmarks>;
+};

dist/faceLandmarkNet/index.js

+import * as tslib_1 from "tslib";
+import * as tf from '@tensorflow/tfjs-core';
+import { convLayer } from '../commons/convLayer';
+import { getImageTensor } from '../getImageTensor';
+import { padToSquare } from '../padToSquare';
+import { extractParams } from './extractParams';
+import { FaceLandmarks } from './FaceLandmarks';
+import { fullyConnectedLayer } from './fullyConnectedLayer';
+function conv(x, params) {
+    return convLayer(x, params, 'valid', true);
+}
+function maxPool(x, strides) {
+    if (strides === void 0) { strides = [2, 2]; }
+    return tf.maxPool(x, [2, 2], strides, 'valid');
+}
+export function faceLandmarkNet(weights) {
+    var params = extractParams(weights);
+    function detectLandmarks(input) {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var adjustRelativeX, adjustRelativeY, imageDimensions, outTensor, faceLandmarksArray, _a, _b, xCoords, yCoords;
+            return tslib_1.__generator(this, function (_c) {
+                switch (_c.label) {
+                    case 0:
+                        adjustRelativeX = 0;
+                        adjustRelativeY = 0;
+                        outTensor = tf.tidy(function () {
+                            var imgTensor = getImageTensor(input);
+                            var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
+                            imageDimensions = { width: width, height: height };
+                            imgTensor = padToSquare(imgTensor, true);
+                            adjustRelativeX = (height > width) ? imgTensor.shape[2] / (2 * width) : 0;
+                            adjustRelativeY = (width > height) ? imgTensor.shape[1] / (2 * height) : 0;
+                            // work with 128 x 128 sized face images
+                            if (imgTensor.shape[1] !== 128 || imgTensor.shape[2] !== 128) {
+                                imgTensor = tf.image.resizeBilinear(imgTensor, [128, 128]);
+                            }
+                            var out = conv(imgTensor, params.conv0_params);
+                            out = maxPool(out);
+                            out = conv(out, params.conv1_params);
+                            out = conv(out, params.conv2_params);
+                            out = maxPool(out);
+                            out = conv(out, params.conv3_params);
+                            out = conv(out, params.conv4_params);
+                            out = maxPool(out);
+                            out = conv(out, params.conv5_params);
+                            out = conv(out, params.conv6_params);
+                            out = maxPool(out, [1, 1]);
+                            out = conv(out, params.conv7_params);
+                            var fc0 = tf.relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0_params));
+                            var fc1 = fullyConnectedLayer(fc0, params.fc1_params);
+                            return fc1;
+                        });
+                        _b = (_a = Array).from;
+                        return [4 /*yield*/, outTensor.data()];
+                    case 1:
+                        faceLandmarksArray = _b.apply(_a, [_c.sent()]);
+                        xCoords = faceLandmarksArray.filter(function (c, i) { return (i - 1) % 2; }).map(function (x) { return x + adjustRelativeX; });
+                        yCoords = faceLandmarksArray.filter(function (c, i) { return i % 2; }).map(function (y) { return y + adjustRelativeY; });
+                        outTensor.dispose();
+                        return [2 /*return*/, new FaceLandmarks(Array(68).fill(0).map(function (_, i) { return ({ x: xCoords[i], y: yCoords[i] }); }), imageDimensions)];
+                }
+            });
+        });
+    }
+    return {
+        detectLandmarks: detectLandmarks
+    };
+}
+//# sourceMappingURL=index.js.map
\ No newline at end of file

dist/faceLandmarkNet/index.js.map

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\ No newline at end of file

dist/faceLandmarkNet/types.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { ConvParams } from '../commons/types';
+export declare namespace FaceLandmarkNet {
+    type FCParams = {
+        weights: tf.Tensor2D;
+        bias: tf.Tensor1D;
+    };
+    type NetParams = {
+        conv0_params: ConvParams;
+        conv1_params: ConvParams;
+        conv2_params: ConvParams;
+        conv3_params: ConvParams;
+        conv4_params: ConvParams;
+        conv5_params: ConvParams;
+        conv6_params: ConvParams;
+        conv7_params: ConvParams;
+        fc0_params: FCParams;
+        fc1_params: FCParams;
+    };
+}

dist/faceLandmarkNet/types.js

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

dist/faceLandmarkNet/types.js.map

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

dist/faceRecognitionNet/convLayer.js

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

dist/faceRecognitionNet/convLayer.js.map

-{"version":3,"file":"convLayer.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/convLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,KAAK,EAAE,MAAM,cAAc,CAAC;AAIrC,mBACE,CAAc,EACd,MAA0C,EAC1C,MAAc,EACd,QAAiB,EACjB,OAAkC;IAAlC,wBAAA,EAAA,gBAAkC;IAE5B,IAAA,gBAAiC,EAA/B,oBAAO,EAAE,kBAAM,CAAgB;IAEvC,IAAI,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,OAAO,EAAE,CAAC,MAAM,EAAE,MAAM,CAAC,EAAE,OAAO,CAAC,CAAA;IAC1D,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,CAAA;IACzB,GAAG,GAAG,KAAK,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IAC9B,OAAO,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAA;AACtC,CAAC;AAED,MAAM,eAAe,CAAc,EAAE,MAA0C;IAC7E,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,EAAE,IAAI,CAAC,CAAA;AACtC,CAAC;AAED,MAAM,qBAAqB,CAAc,EAAE,MAA0C;IACnF,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,EAAE,KAAK,CAAC,CAAA;AACvC,CAAC;AAED,MAAM,mBAAmB,CAAc,EAAE,MAA0C;IACjF,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,EAAE,IAAI,EAAE,OAAO,CAAC,CAAA;AAC/C,CAAC"}
+{"version":3,"file":"convLayer.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/convLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,KAAK,EAAE,MAAM,cAAc,CAAC;AAIrC,mBACE,CAAc,EACd,MAA0C,EAC1C,OAAyB,EACzB,QAAiB,EACjB,OAAkC;IAAlC,wBAAA,EAAA,gBAAkC;IAE5B,IAAA,gBAA+B,EAA7B,oBAAO,EAAE,cAAI,CAAgB;IAErC,IAAI,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;IACjD,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;IACvB,GAAG,GAAG,KAAK,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IAC9B,OAAO,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAA;AACtC,CAAC;AAED,MAAM,eAAe,CAAc,EAAE,MAA0C;IAC7E,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,IAAI,CAAC,CAAA;AAC3C,CAAC;AAED,MAAM,qBAAqB,CAAc,EAAE,MAA0C;IACnF,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,CAAA;AAC5C,CAAC;AAED,MAAM,mBAAmB,CAAc,EAAE,MAA0C;IACjF,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,IAAI,EAAE,OAAO,CAAC,CAAA;AACpD,CAAC"}
\ No newline at end of file

dist/faceRecognitionNet/extractParams.js

 import * as tf from '@tensorflow/tfjs-core';
+import { extractWeightsFactory } from '../commons/extractWeightsFactory';
 import { isFloat } from '../utils';
 function extractorsFactory(extractWeights) {
     function extractFilterValues(numFilterValues, numFilters, filterSize) {
@@ -19,12 +20,12 @@ function extractorsFactory(extractWeights) {
     }
     function extractConvLayerParams(numFilterValues, numFilters, filterSize) {
         var conv_filters = extractFilterValues(numFilterValues, numFilters, filterSize);
-        var conv_biases = tf.tensor1d(extractWeights(numFilters));
+        var conv_bias = tf.tensor1d(extractWeights(numFilters));
         var scale = extractScaleLayerParams(numFilters);
         return {
             conv: {
                 filters: conv_filters,
-                biases: conv_biases
+                bias: conv_bias
             },
             scale: scale
         };
@@ -44,12 +45,8 @@ function extractorsFactory(extractWeights) {
     };
 }
 export function extractParams(weights) {
-    var extractWeights = function (numWeights) {
-        var ret = weights.slice(0, numWeights);
-        weights = weights.slice(numWeights);
-        return ret;
-    };
-    var _a = extractorsFactory(extractWeights), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;
+    var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
+    var _b = extractorsFactory(extractWeights), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams;
     var conv32_down = extractConvLayerParams(4704, 32, 7);
     var conv32_1 = extractResidualLayerParams(9216, 32, 3);
     var conv32_2 = extractResidualLayerParams(9216, 32, 3);
@@ -66,8 +63,8 @@ export function extractParams(weights) {
     var conv256_2 = extractResidualLayerParams(589824, 256, 3);
     var conv256_down_out = extractResidualLayerParams(589824, 256, 3);
     var fc = tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]);
-    if (weights.length !== 0) {
-        throw new Error("weights remaing after extract: " + weights.length);
+    if (getRemainingWeights().length !== 0) {
+        throw new Error("weights remaing after extract: " + getRemainingWeights().length);
     }
     return {
         conv32_down: conv32_down,

dist/faceRecognitionNet/extractParams.js.map

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\ No newline at end of file

dist/faceRecognitionNet/index.d.ts

 import * as tf from '@tensorflow/tfjs-core';
 import { NetInput } from '../NetInput';
 export declare function faceRecognitionNet(weights: Float32Array): {
-    computeFaceDescriptor: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => Promise<Int32Array | Uint8Array | Float32Array>;
-    computeFaceDescriptorSync: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => Int32Array | Uint8Array | Float32Array;
-    forward: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => tf.Tensor<tf.Rank.R2>;
+    computeFaceDescriptor: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | NetInput | tf.Tensor<tf.Rank>) => Promise<Int32Array | Uint8Array | Float32Array>;
+    computeFaceDescriptorSync: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | NetInput | tf.Tensor<tf.Rank>) => Int32Array | Uint8Array | Float32Array;
+    forward: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | NetInput | tf.Tensor<tf.Rank>) => tf.Tensor<tf.Rank.R2>;
 };

dist/faceRecognitionNet/types.d.ts

 import * as tf from '@tensorflow/tfjs-core';
+import { ConvParams } from '../commons/types';
 export declare namespace FaceRecognitionNet {
     type ScaleLayerParams = {
         weights: tf.Tensor1D;
@@ -8,10 +9,6 @@ export declare namespace FaceRecognitionNet {
         conv1: ConvLayerParams;
         conv2: ConvLayerParams;
     };
-    type ConvParams = {
-        filters: tf.Tensor4D;
-        biases: tf.Tensor1D;
-    };
     type ConvLayerParams = {
         conv: ConvParams;
         scale: ScaleLayerParams;

dist/index.d.ts

 import * as tf from '@tensorflow/tfjs-core';
 import { euclideanDistance } from './euclideanDistance';
 import { faceDetectionNet } from './faceDetectionNet';
+import { faceLandmarkNet } from './faceLandmarkNet';
 import { faceRecognitionNet } from './faceRecognitionNet';
 import { NetInput } from './NetInput';
 import { padToSquare } from './padToSquare';
-export { euclideanDistance, faceDetectionNet, faceRecognitionNet, NetInput, tf, padToSquare };
+export { euclideanDistance, faceDetectionNet, faceLandmarkNet, faceRecognitionNet, NetInput, tf, padToSquare };
 export * from './extractFaces';
 export * from './extractFaceTensors';
 export * from './utils';

dist/index.js

 import * as tf from '@tensorflow/tfjs-core';
 import { euclideanDistance } from './euclideanDistance';
 import { faceDetectionNet } from './faceDetectionNet';
+import { faceLandmarkNet } from './faceLandmarkNet';
 import { faceRecognitionNet } from './faceRecognitionNet';
 import { NetInput } from './NetInput';
 import { padToSquare } from './padToSquare';
-export { euclideanDistance, faceDetectionNet, faceRecognitionNet, NetInput, tf, padToSquare };
+export { euclideanDistance, faceDetectionNet, faceLandmarkNet, faceRecognitionNet, NetInput, tf, padToSquare };
 export * from './extractFaces';
 export * from './extractFaceTensors';
 export * from './utils';

dist/index.js.map

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+{"version":3,"file":"index.js","sourceRoot":"","sources":["../src/index.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,iBAAiB,EAAE,MAAM,qBAAqB,CAAC;AACxD,OAAO,EAAE,gBAAgB,EAAE,MAAM,oBAAoB,CAAC;AACtD,OAAO,EAAE,eAAe,EAAE,MAAM,mBAAmB,CAAC;AACpD,OAAO,EAAE,kBAAkB,EAAE,MAAM,sBAAsB,CAAC;AAC1D,OAAO,EAAE,QAAQ,EAAE,MAAM,YAAY,CAAC;AACtC,OAAO,EAAE,WAAW,EAAE,MAAM,eAAe,CAAC;AAE5C,OAAO,EACL,iBAAiB,EACjB,gBAAgB,EAChB,eAAe,EACf,kBAAkB,EAClB,QAAQ,EACR,EAAE,EACF,WAAW,EACZ,CAAA;AAED,cAAc,gBAAgB,CAAA;AAC9B,cAAc,sBAAsB,CAAA;AACpC,cAAc,SAAS,CAAA"}
\ No newline at end of file

dist/types.d.ts

@@ -6,13 +6,22 @@ export declare type Dimensions = {
     height: number;
 };
 export declare type DrawBoxOptions = {
-    lineWidth: number;
-    color: string;
+    lineWidth?: number;
+    color?: string;
 };
 export declare type DrawTextOptions = {
+    lineWidth?: number;
+    fontSize?: number;
+    fontStyle?: string;
+    color?: string;
+};
+export declare type DrawLandmarksOptions = {
+    lineWidth?: number;
+    color?: string;
+};
+export declare type DrawOptions = {
     lineWidth: number;
     fontSize: number;
     fontStyle: string;
     color: string;
 };
-export declare type DrawOptions = DrawBoxOptions & DrawTextOptions;

dist/utils.d.ts

-import { FaceDetectionNet } from './faceDetectionNet/types';
-import { Dimensions, DrawBoxOptions, DrawOptions, DrawTextOptions } from './types';
+import { FaceDetection } from './faceDetectionNet/FaceDetection';
+import { FaceLandmarks } from './faceLandmarkNet/FaceLandmarks';
+import { Dimensions, DrawBoxOptions, DrawLandmarksOptions, DrawOptions, DrawTextOptions } from './types';
 export declare function isFloat(num: number): boolean;
 export declare function round(num: number): number;
 export declare function getElement(arg: string | any): any;
 export declare function getContext2dOrThrow(canvas: HTMLCanvasElement): CanvasRenderingContext2D;
 export declare function createCanvas({width, height}: Dimensions): HTMLCanvasElement;
-export declare function createCanvasWithImageData({width, height}: Dimensions, buf: Uint8ClampedArray): HTMLCanvasElement;
+export declare function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement, dims?: Dimensions): HTMLCanvasElement;
 export declare function getMediaDimensions(media: HTMLImageElement | HTMLVideoElement): {
     width: number;
     height: number;
@@ -14,6 +15,9 @@ export declare function bufferToImage(buf: Blob): Promise<HTMLImageElement>;
 export declare function getDefaultDrawOptions(): DrawOptions;
 export declare function drawBox(ctx: CanvasRenderingContext2D, x: number, y: number, w: number, h: number, options: DrawBoxOptions): void;
 export declare function drawText(ctx: CanvasRenderingContext2D, x: number, y: number, text: string, options: DrawTextOptions): void;
-export declare function drawDetection(canvasArg: string | HTMLCanvasElement, detection: FaceDetectionNet.Detection | FaceDetectionNet.Detection[], options?: DrawBoxOptions & DrawTextOptions & {
+export declare function drawDetection(canvasArg: string | HTMLCanvasElement, detection: FaceDetection | FaceDetection[], options?: DrawBoxOptions & DrawTextOptions & {
     withScore: boolean;
 }): void;
+export declare function drawLandmarks(canvasArg: string | HTMLCanvasElement, faceLandmarks: FaceLandmarks, options?: DrawLandmarksOptions & {
+    drawLines: boolean;
+}): void;

dist/utils.js

@@ -24,10 +24,10 @@ export function createCanvas(_a) {
     canvas.height = height;
     return canvas;
 }
-export function createCanvasWithImageData(_a, buf) {
-    var width = _a.width, height = _a.height;
+export function createCanvasFromMedia(media, dims) {
+    var _a = dims || getMediaDimensions(media), width = _a.width, height = _a.height;
     var canvas = createCanvas({ width: width, height: height });
-    getContext2dOrThrow(canvas).putImageData(new ImageData(buf, width, height), 0, 0);
+    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
     return canvas;
 }
 export function getMediaDimensions(media) {
@@ -64,15 +64,17 @@ export function getDefaultDrawOptions() {
     };
 }
 export function drawBox(ctx, x, y, w, h, options) {
-    ctx.strokeStyle = options.color;
-    ctx.lineWidth = options.lineWidth;
+    var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
+    ctx.strokeStyle = drawOptions.color;
+    ctx.lineWidth = drawOptions.lineWidth;
     ctx.strokeRect(x, y, w, h);
 }
 export function drawText(ctx, x, y, text, options) {
-    var padText = 2 + options.lineWidth;
-    ctx.fillStyle = options.color;
-    ctx.font = options.fontSize + "px " + options.fontStyle;
-    ctx.fillText(text, x + padText, y + padText + (options.fontSize * 0.6));
+    var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
+    var padText = 2 + drawOptions.lineWidth;
+    ctx.fillStyle = drawOptions.color;
+    ctx.font = drawOptions.fontSize + "px " + drawOptions.fontStyle;
+    ctx.fillText(text, x + padText, y + padText + (drawOptions.fontSize * 0.6));
 }
 export function drawDetection(canvasArg, detection, options) {
     var canvas = getElement(canvasArg);
@@ -83,15 +85,60 @@ export function drawDetection(canvasArg, detection, options) {
         ? detection
         : [detection];
     detectionArray.forEach(function (det) {
-        var score = det.score, box = det.box;
-        var x = box.x, y = box.y, width = box.width, height = box.height;
+        var _a = det.getBox(), x = _a.x, y = _a.y, width = _a.width, height = _a.height;
         var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
         var withScore = Object.assign({ withScore: true }, (options || {})).withScore;
         var ctx = getContext2dOrThrow(canvas);
         drawBox(ctx, x, y, width, height, drawOptions);
         if (withScore) {
-            drawText(ctx, x, y, "" + round(score), drawOptions);
+            drawText(ctx, x, y, "" + round(det.getScore()), drawOptions);
         }
     });
 }
+function drawContour(ctx, points, isClosed) {
+    if (isClosed === void 0) { isClosed = false; }
+    ctx.beginPath();
+    points.slice(1).forEach(function (_a, prevIdx) {
+        var x = _a.x, y = _a.y;
+        var from = points[prevIdx];
+        ctx.moveTo(from.x, from.y);
+        ctx.lineTo(x, y);
+    });
+    if (isClosed) {
+        var from = points[points.length - 1];
+        var to = points[0];
+        if (!from || !to) {
+            return;
+        }
+        ctx.moveTo(from.x, from.y);
+        ctx.lineTo(to.x, to.y);
+    }
+    ctx.stroke();
+}
+export function drawLandmarks(canvasArg, faceLandmarks, options) {
+    var canvas = getElement(canvasArg);
+    if (!(canvas instanceof HTMLCanvasElement)) {
+        throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement');
+    }
+    var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
+    var drawLines = Object.assign({ drawLines: false }, (options || {})).drawLines;
+    var ctx = getContext2dOrThrow(canvas);
+    var lineWidth = drawOptions.lineWidth, color = drawOptions.color;
+    if (drawLines) {
+        ctx.strokeStyle = color;
+        ctx.lineWidth = lineWidth;
+        drawContour(ctx, faceLandmarks.getJawOutline());
+        drawContour(ctx, faceLandmarks.getLeftEyeBrow());
+        drawContour(ctx, faceLandmarks.getRightEyeBrow());
+        drawContour(ctx, faceLandmarks.getNose());
+        drawContour(ctx, faceLandmarks.getLeftEye(), true);
+        drawContour(ctx, faceLandmarks.getRightEye(), true);
+        drawContour(ctx, faceLandmarks.getMouth(), true);
+        return;
+    }
+    // else draw points
+    var ptOffset = lineWidth / 2;
+    ctx.fillStyle = color;
+    faceLandmarks.getPositions().forEach(function (pt) { return ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth); });
+}
 //# sourceMappingURL=utils.js.map
\ No newline at end of file

dist/utils.js.map

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