Merge pull request #2 from justadudewhohacks/ssd_mobilenet_facedetector

dist/NetInput.d.ts

+import { Dimensions, TNetInput } from './types';
+export declare class NetInput {
+    private _canvases;
+    constructor(mediaArg: TNetInput, dims?: Dimensions);
+    private initCanvas(media, dims?);
+    readonly canvases: HTMLCanvasElement[];
+    readonly width: number;
+    readonly height: number;
+    readonly dims: Dimensions | null;
+}

dist/NetInput.js

+import { getContext2dOrThrow, getElement, getMediaDimensions } from './utils';
+var NetInput = /** @class */ (function () {
+    function NetInput(mediaArg, dims) {
+        var _this = this;
+        var mediaArgArray = Array.isArray(mediaArg)
+            ? mediaArg
+            : [mediaArg];
+        if (!mediaArgArray.length) {
+            throw new Error('NetInput - empty array passed as input');
+        }
+        var medias = mediaArgArray.map(getElement);
+        medias.forEach(function (media, i) {
+            if (!(media instanceof HTMLImageElement || media instanceof HTMLVideoElement || media instanceof HTMLCanvasElement)) {
+                var idxHint = Array.isArray(mediaArg) ? " at input index " + i + ":" : '';
+                if (typeof mediaArgArray[i] === 'string') {
+                    throw new Error("NetInput -" + idxHint + " string passed, but could not resolve HTMLElement for element id");
+                }
+                throw new Error("NetInput -" + idxHint + " expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement, or to be an element id");
+            }
+        });
+        this._canvases = [];
+        medias.forEach(function (m) { return _this.initCanvas(m, dims); });
+    }
+    NetInput.prototype.initCanvas = function (media, dims) {
+        if (media instanceof HTMLCanvasElement) {
+            this._canvases.push(media);
+            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 = document.createElement('canvas');
+        canvas.width = width;
+        canvas.height = height;
+        getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
+        this._canvases.push(canvas);
+    };
+    Object.defineProperty(NetInput.prototype, "canvases", {
+        get: function () {
+            return this._canvases;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(NetInput.prototype, "width", {
+        get: function () {
+            return (this._canvases[0] || {}).width;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(NetInput.prototype, "height", {
+        get: function () {
+            return (this._canvases[0] || {}).height;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(NetInput.prototype, "dims", {
+        get: function () {
+            var _a = this, width = _a.width, height = _a.height;
+            return (width > 0 && height > 0) ? { width: width, height: height } : null;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    return NetInput;
+}());
+export { NetInput };
+//# sourceMappingURL=NetInput.js.map
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dist/NetInput.js.map

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dist/faceDetectionNet/FaceDetectionResult.d.ts

+import { FaceDetectionNet } from './types';
+export declare class FaceDetectionResult {
+    private score;
+    private top;
+    private left;
+    private bottom;
+    private right;
+    constructor(score: number, top: number, left: number, bottom: number, right: number);
+    forSize(width: number, height: number): FaceDetectionNet.Detection;
+}

dist/faceDetectionNet/FaceDetectionResult.js

+var FaceDetectionResult = /** @class */ (function () {
+    function FaceDetectionResult(score, top, left, bottom, right) {
+        this.score = score;
+        this.top = Math.max(0, top),
+            this.left = Math.max(0, left),
+            this.bottom = Math.min(1.0, bottom),
+            this.right = Math.min(1.0, right);
+    }
+    FaceDetectionResult.prototype.forSize = function (width, height) {
+        return {
+            score: this.score,
+            box: {
+                top: this.top * height,
+                left: this.left * width,
+                bottom: this.bottom * height,
+                right: this.right * width
+            }
+        };
+    };
+    return FaceDetectionResult;
+}());
+export { FaceDetectionResult };
+//# sourceMappingURL=FaceDetectionResult.js.map
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dist/faceDetectionNet/FaceDetectionResult.js.map

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\ No newline at end of file

dist/faceDetectionNet/boxPredictionLayer.d.ts

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

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);
+    });
+}
+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]);
+        return {
+            boxPredictionEncoding: boxPredictionEncoding,
+            classPrediction: classPrediction
+        };
+    });
+}
+//# sourceMappingURL=boxPredictionLayer.js.map
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dist/faceDetectionNet/boxPredictionLayer.js.map

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dist/faceDetectionNet/extractParams.d.ts

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

dist/faceDetectionNet/extractParams.js

+import * as tf from '@tensorflow/tfjs-core';
+function extractorsFactory(extractWeights) {
+    function extractDepthwiseConvParams(numChannels) {
+        var filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
+        var batch_norm_scale = tf.tensor1d(extractWeights(numChannels));
+        var batch_norm_offset = tf.tensor1d(extractWeights(numChannels));
+        var batch_norm_mean = tf.tensor1d(extractWeights(numChannels));
+        var batch_norm_variance = tf.tensor1d(extractWeights(numChannels));
+        return {
+            filters: filters,
+            batch_norm_scale: batch_norm_scale,
+            batch_norm_offset: batch_norm_offset,
+            batch_norm_mean: batch_norm_mean,
+            batch_norm_variance: batch_norm_variance
+        };
+    }
+    function extractConvWithBiasParams(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
+        };
+    }
+    function extractPointwiseConvParams(channelsIn, channelsOut, filterSize) {
+        var _a = extractConvWithBiasParams(channelsIn, channelsOut, filterSize), filters = _a.filters, bias = _a.bias;
+        return {
+            filters: filters,
+            batch_norm_offset: bias
+        };
+    }
+    function extractConvPairParams(channelsIn, channelsOut) {
+        var depthwise_conv_params = extractDepthwiseConvParams(channelsIn);
+        var pointwise_conv_params = extractPointwiseConvParams(channelsIn, channelsOut, 1);
+        return {
+            depthwise_conv_params: depthwise_conv_params,
+            pointwise_conv_params: pointwise_conv_params
+        };
+    }
+    function extractMobilenetV1Params() {
+        var conv_0_params = extractPointwiseConvParams(3, 32, 3);
+        var channelNumPairs = [
+            [32, 64],
+            [64, 128],
+            [128, 128],
+            [128, 256],
+            [256, 256],
+            [256, 512],
+            [512, 512],
+            [512, 512],
+            [512, 512],
+            [512, 512],
+            [512, 512],
+            [512, 1024],
+            [1024, 1024]
+        ];
+        var conv_pair_params = channelNumPairs.map(function (_a) {
+            var channelsIn = _a[0], channelsOut = _a[1];
+            return extractConvPairParams(channelsIn, channelsOut);
+        });
+        return {
+            conv_0_params: conv_0_params,
+            conv_pair_params: conv_pair_params
+        };
+    }
+    function extractPredictionLayerParams() {
+        var conv_0_params = extractPointwiseConvParams(1024, 256, 1);
+        var conv_1_params = extractPointwiseConvParams(256, 512, 3);
+        var conv_2_params = extractPointwiseConvParams(512, 128, 1);
+        var conv_3_params = extractPointwiseConvParams(128, 256, 3);
+        var conv_4_params = extractPointwiseConvParams(256, 128, 1);
+        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_predictor_0_params = {
+            box_encoding_predictor_params: box_encoding_0_predictor_params,
+            class_predictor_params: class_predictor_0_params
+        };
+        var box_predictor_1_params = {
+            box_encoding_predictor_params: box_encoding_1_predictor_params,
+            class_predictor_params: class_predictor_1_params
+        };
+        var box_predictor_2_params = {
+            box_encoding_predictor_params: box_encoding_2_predictor_params,
+            class_predictor_params: class_predictor_2_params
+        };
+        var box_predictor_3_params = {
+            box_encoding_predictor_params: box_encoding_3_predictor_params,
+            class_predictor_params: class_predictor_3_params
+        };
+        var box_predictor_4_params = {
+            box_encoding_predictor_params: box_encoding_4_predictor_params,
+            class_predictor_params: class_predictor_4_params
+        };
+        var box_predictor_5_params = {
+            box_encoding_predictor_params: box_encoding_5_predictor_params,
+            class_predictor_params: class_predictor_5_params
+        };
+        return {
+            conv_0_params: conv_0_params,
+            conv_1_params: conv_1_params,
+            conv_2_params: conv_2_params,
+            conv_3_params: conv_3_params,
+            conv_4_params: conv_4_params,
+            conv_5_params: conv_5_params,
+            conv_6_params: conv_6_params,
+            conv_7_params: conv_7_params,
+            box_predictor_0_params: box_predictor_0_params,
+            box_predictor_1_params: box_predictor_1_params,
+            box_predictor_2_params: box_predictor_2_params,
+            box_predictor_3_params: box_predictor_3_params,
+            box_predictor_4_params: box_predictor_4_params,
+            box_predictor_5_params: box_predictor_5_params
+        };
+    }
+    return {
+        extractMobilenetV1Params: extractMobilenetV1Params,
+        extractPredictionLayerParams: extractPredictionLayerParams
+    };
+}
+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 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);
+    }
+    return {
+        mobilenetv1_params: mobilenetv1_params,
+        prediction_layer_params: prediction_layer_params,
+        output_layer_params: output_layer_params
+    };
+}
+//# sourceMappingURL=extractParams.js.map
\ No newline at end of file

dist/faceDetectionNet/extractParams.js.map

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\ No newline at end of file

dist/faceDetectionNet/index.d.ts

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

dist/faceDetectionNet/index.js

+import * as tslib_1 from "tslib";
+import * as tf from '@tensorflow/tfjs-core';
+import { getImageTensor, padToSquare } from '../transformInputs';
+import { extractParams } from './extractParams';
+import { FaceDetectionResult } from './FaceDetectionResult';
+import { mobileNetV1 } from './mobileNetV1';
+import { nonMaxSuppression } from './nonMaxSuppression';
+import { outputLayer } from './outputLayer';
+import { predictionLayer } from './predictionLayer';
+import { resizeLayer } from './resizeLayer';
+export function faceDetectionNet(weights) {
+    var params = extractParams(weights);
+    function forwardTensor(imgTensor) {
+        return tf.tidy(function () {
+            var resized = resizeLayer(imgTensor);
+            var features = mobileNetV1(resized, params.mobilenetv1_params);
+            var _a = predictionLayer(features.out, features.conv11, params.prediction_layer_params), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
+            return outputLayer(boxPredictions, classPredictions, params.output_layer_params);
+        });
+    }
+    function forward(input) {
+        return tf.tidy(function () { return forwardTensor(padToSquare(getImageTensor(input))); });
+    }
+    function locateFaces(input, minConfidence, maxResults) {
+        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;
+            return tslib_1.__generator(this, function (_d) {
+                switch (_d.label) {
+                    case 0:
+                        paddedHeightRelative = 1, paddedWidthRelative = 1;
+                        _a = tf.tidy(function () {
+                            var imgTensor = getImageTensor(input);
+                            var _a = imgTensor.shape, _ = _a[0], height = _a[1], width = _a[2];
+                            imgTensor = padToSquare(imgTensor);
+                            paddedHeightRelative = imgTensor.shape[1] / height;
+                            paddedWidthRelative = imgTensor.shape[2] / width;
+                            return forwardTensor(imgTensor);
+                        }), _boxes = _a.boxes, _scores = _a.scores;
+                        boxes = _boxes[0];
+                        scores = _scores[0];
+                        for (i = 1; i < _boxes.length; i++) {
+                            _boxes[i].dispose();
+                            _scores[i].dispose();
+                        }
+                        _c = (_b = Array).from;
+                        return [4 /*yield*/, scores.data()];
+                    case 1:
+                        scoresData = _c.apply(_b, [_d.sent()]);
+                        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); });
+                        boxes.dispose();
+                        scores.dispose();
+                        return [2 /*return*/, results];
+                }
+            });
+        });
+    }
+    return {
+        forward: forward,
+        locateFaces: locateFaces
+    };
+}
+//# sourceMappingURL=index.js.map
\ No newline at end of file

dist/faceDetectionNet/index.js.map

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\ No newline at end of file

dist/faceDetectionNet/mobileNetV1.d.ts

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

dist/faceDetectionNet/mobileNetV1.js

+import * as tf from '@tensorflow/tfjs-core';
+import { pointwiseConvLayer } from './pointwiseConvLayer';
+var epsilon = 0.0010000000474974513;
+function depthwiseConvLayer(x, params, strides) {
+    return tf.tidy(function () {
+        var out = tf.depthwiseConv2d(x, params.filters, strides, 'same');
+        out = tf.batchNormalization(out, params.batch_norm_mean, params.batch_norm_variance, epsilon, params.batch_norm_scale, params.batch_norm_offset);
+        return tf.clipByValue(out, 0, 6);
+    });
+}
+function getStridesForLayerIdx(layerIdx) {
+    return [2, 4, 6, 12].some(function (idx) { return idx === layerIdx; }) ? [2, 2] : [1, 1];
+}
+export function mobileNetV1(x, params) {
+    return tf.tidy(function () {
+        var conv11 = null;
+        var out = pointwiseConvLayer(x, params.conv_0_params, [2, 2]);
+        params.conv_pair_params.forEach(function (param, i) {
+            var layerIdx = i + 1;
+            var depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
+            out = depthwiseConvLayer(out, param.depthwise_conv_params, depthwiseConvStrides);
+            out = pointwiseConvLayer(out, param.pointwise_conv_params, [1, 1]);
+            if (layerIdx === 11) {
+                conv11 = out;
+            }
+        });
+        if (conv11 === null) {
+            throw new Error('mobileNetV1 - output of conv layer 11 is null');
+        }
+        return {
+            out: out,
+            conv11: conv11
+        };
+    });
+}
+//# sourceMappingURL=mobileNetV1.js.map
\ No newline at end of file

dist/faceDetectionNet/mobileNetV1.js.map

+{"version":3,"file":"mobileNetV1.js","sourceRoot":"","sources":["../../src/faceDetectionNet/mobileNetV1.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,kBAAkB,EAAE,MAAM,sBAAsB,CAAC;AAG1D,IAAM,OAAO,GAAG,qBAAqB,CAAA;AAErC,4BACE,CAAc,EACd,MAAwD,EACxD,OAAyB;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAI,GAAG,GAAG,EAAE,CAAC,eAAe,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,OAAO,EAAE,MAAM,CAAC,CAAA;QAChE,GAAG,GAAG,EAAE,CAAC,kBAAkB,CACzB,GAAG,EACH,MAAM,CAAC,eAAe,EACtB,MAAM,CAAC,mBAAmB,EAC1B,OAAO,EACP,MAAM,CAAC,gBAAgB,EACvB,MAAM,CAAC,iBAAiB,CACzB,CAAA;QACD,OAAO,EAAE,CAAC,WAAW,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;IAElC,CAAC,CAAC,CAAA;AACJ,CAAC;AAED,+BAA+B,QAAgB;IAC7C,OAAO,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,IAAI,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,KAAK,QAAQ,EAAhB,CAAgB,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;AACtE,CAAC;AAED,MAAM,sBAAsB,CAAc,EAAE,MAA2C;IACrF,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAI,MAAM,GAAG,IAAI,CAAA;QACjB,IAAI,GAAG,GAAG,kBAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,aAAa,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAE7D,MAAM,CAAC,gBAAgB,CAAC,OAAO,CAAC,UAAC,KAAK,EAAE,CAAC;YACvC,IAAM,QAAQ,GAAG,CAAC,GAAG,CAAC,CAAA;YACtB,IAAM,oBAAoB,GAAG,qBAAqB,CAAC,QAAQ,CAAC,CAAA;YAC5D,GAAG,GAAG,kBAAkB,CAAC,GAAG,EAAE,KAAK,CAAC,qBAAqB,EAAE,oBAAoB,CAAC,CAAA;YAChF,GAAG,GAAG,kBAAkB,CAAC,GAAG,EAAE,KAAK,CAAC,qBAAqB,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;YAClE,IAAI,QAAQ,KAAK,EAAE,EAAE;gBACnB,MAAM,GAAG,GAAG,CAAA;aACb;QACH,CAAC,CAAC,CAAA;QAEF,IAAI,MAAM,KAAK,IAAI,EAAE;YACnB,MAAM,IAAI,KAAK,CAAC,+CAA+C,CAAC,CAAA;SACjE;QAED,OAAO;YACL,GAAG,KAAA;YACH,MAAM,EAAE,MAAa;SACtB,CAAA;IAEH,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file

dist/faceDetectionNet/nonMaxSuppression.d.ts

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

dist/faceDetectionNet/nonMaxSuppression.js

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

dist/faceDetectionNet/nonMaxSuppression.js.map

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\ No newline at end of file

dist/faceDetectionNet/outputLayer.d.ts

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

dist/faceDetectionNet/outputLayer.js

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

dist/faceDetectionNet/outputLayer.js.map

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\ No newline at end of file

dist/faceDetectionNet/pointwiseConvLayer.d.ts

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

dist/faceDetectionNet/pointwiseConvLayer.js

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

dist/faceDetectionNet/pointwiseConvLayer.js.map

+{"version":3,"file":"pointwiseConvLayer.js","sourceRoot":"","sources":["../../src/faceDetectionNet/pointwiseConvLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAI5C,MAAM,6BACJ,CAAc,EACd,MAA4C,EAC5C,OAAyB;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAI,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,OAAO,EAAE,MAAM,CAAC,CAAA;QACvD,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,iBAAiB,CAAC,CAAA;QAC3C,OAAO,EAAE,CAAC,WAAW,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;IAElC,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file

dist/faceDetectionNet/predictionLayer.d.ts

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

dist/faceDetectionNet/predictionLayer.js

+import * as tf from '@tensorflow/tfjs-core';
+import { boxPredictionLayer } from './boxPredictionLayer';
+import { pointwiseConvLayer } from './pointwiseConvLayer';
+export function predictionLayer(x, conv11, params) {
+    return tf.tidy(function () {
+        var conv0 = pointwiseConvLayer(x, params.conv_0_params, [1, 1]);
+        var conv1 = pointwiseConvLayer(conv0, params.conv_1_params, [2, 2]);
+        var conv2 = pointwiseConvLayer(conv1, params.conv_2_params, [1, 1]);
+        var conv3 = pointwiseConvLayer(conv2, params.conv_3_params, [2, 2]);
+        var conv4 = pointwiseConvLayer(conv3, params.conv_4_params, [1, 1]);
+        var conv5 = pointwiseConvLayer(conv4, params.conv_5_params, [2, 2]);
+        var conv6 = pointwiseConvLayer(conv5, params.conv_6_params, [1, 1]);
+        var conv7 = pointwiseConvLayer(conv6, params.conv_7_params, [2, 2]);
+        var boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0_params);
+        var boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1_params);
+        var boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2_params);
+        var boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3_params);
+        var boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4_params);
+        var boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5_params);
+        var boxPredictions = tf.concat([
+            boxPrediction0.boxPredictionEncoding,
+            boxPrediction1.boxPredictionEncoding,
+            boxPrediction2.boxPredictionEncoding,
+            boxPrediction3.boxPredictionEncoding,
+            boxPrediction4.boxPredictionEncoding,
+            boxPrediction5.boxPredictionEncoding
+        ], 1);
+        var classPredictions = tf.concat([
+            boxPrediction0.classPrediction,
+            boxPrediction1.classPrediction,
+            boxPrediction2.classPrediction,
+            boxPrediction3.classPrediction,
+            boxPrediction4.classPrediction,
+            boxPrediction5.classPrediction
+        ], 1);
+        return {
+            boxPredictions: boxPredictions,
+            classPredictions: classPredictions
+        };
+    });
+}
+//# sourceMappingURL=predictionLayer.js.map
\ No newline at end of file

dist/faceDetectionNet/predictionLayer.js.map

+{"version":3,"file":"predictionLayer.js","sourceRoot":"","sources":["../../src/faceDetectionNet/predictionLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,kBAAkB,EAAE,MAAM,sBAAsB,CAAC;AAC1D,OAAO,EAAE,kBAAkB,EAAE,MAAM,sBAAsB,CAAC;AAG1D,MAAM,0BACJ,CAAc,EACd,MAAmB,EACnB,MAA8C;IAE9C,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAM,KAAK,GAAG,kBAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,aAAa,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QACjE,IAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,aAAa,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QACrE,IAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,aAAa,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QACrE,IAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,aAAa,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QACrE,IAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,aAAa,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QACrE,IAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,aAAa,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QACrE,IAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,aAAa,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QACrE,IAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,aAAa,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAErE,IAAM,cAAc,GAAG,kBAAkB,CAAC,MAAM,EAAE,MAAM,CAAC,sBAAsB,CAAC,CAAA;QAChF,IAAM,cAAc,GAAG,kBAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,sBAAsB,CAAC,CAAA;QAC3E,IAAM,cAAc,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,sBAAsB,CAAC,CAAA;QAC/E,IAAM,cAAc,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,sBAAsB,CAAC,CAAA;QAC/E,IAAM,cAAc,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,sBAAsB,CAAC,CAAA;QAC/E,IAAM,cAAc,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,sBAAsB,CAAC,CAAA;QAE/E,IAAM,cAAc,GAAG,EAAE,CAAC,MAAM,CAAC;YAC/B,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;SACrC,EAAE,CAAC,CAAgB,CAAA;QAEpB,IAAM,gBAAgB,GAAG,EAAE,CAAC,MAAM,CAAC;YACjC,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;SAC/B,EAAE,CAAC,CAAgB,CAAA;QAEpB,OAAO;YACL,cAAc,gBAAA;YACd,gBAAgB,kBAAA;SACjB,CAAA;IACH,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file

dist/normalize.d.ts → dist/faceDetectionNet/resizeLayer.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-export declare function normalize(input: number[] | ImageData): tf.Tensor4D;
+export declare function resizeLayer(x: tf.Tensor4D): tf.Tensor<tf.Rank>;

dist/faceDetectionNet/resizeLayer.js

+import * as tf from '@tensorflow/tfjs-core';
+var resizedImageSize = [512, 512];
+var weight = tf.scalar(0.007843137718737125);
+var bias = tf.scalar(1);
+export function resizeLayer(x) {
+    return tf.tidy(function () {
+        var resized = tf.image.resizeBilinear(x, resizedImageSize, false);
+        return tf.sub(tf.mul(resized, weight), bias);
+    });
+}
+//# sourceMappingURL=resizeLayer.js.map
\ No newline at end of file

dist/faceDetectionNet/resizeLayer.js.map

+{"version":3,"file":"resizeLayer.js","sourceRoot":"","sources":["../../src/faceDetectionNet/resizeLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,IAAM,gBAAgB,GAAG,CAAC,GAAG,EAAE,GAAG,CAAqB,CAAA;AACvD,IAAM,MAAM,GAAG,EAAE,CAAC,MAAM,CAAC,oBAAoB,CAAC,CAAA;AAC9C,IAAM,IAAI,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAA;AAEzB,MAAM,sBAAsB,CAAc;IACxC,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAM,OAAO,GAAG,EAAE,CAAC,KAAK,CAAC,cAAc,CAAC,CAAC,EAAE,gBAAgB,EAAE,KAAK,CAAC,CAAA;QACnE,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,OAAO,EAAE,MAAM,CAAC,EAAE,IAAI,CAAC,CAAA;IAE9C,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file

dist/faceDetectionNet/types.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+export declare namespace FaceDetectionNet {
+    type PointwiseConvParams = {
+        filters: tf.Tensor4D;
+        batch_norm_offset: tf.Tensor1D;
+    };
+    namespace MobileNetV1 {
+        type DepthwiseConvParams = {
+            filters: tf.Tensor4D;
+            batch_norm_scale: tf.Tensor1D;
+            batch_norm_offset: tf.Tensor1D;
+            batch_norm_mean: tf.Tensor1D;
+            batch_norm_variance: tf.Tensor1D;
+        };
+        type ConvPairParams = {
+            depthwise_conv_params: DepthwiseConvParams;
+            pointwise_conv_params: PointwiseConvParams;
+        };
+        type Params = {
+            conv_0_params: PointwiseConvParams;
+            conv_pair_params: ConvPairParams[];
+        };
+    }
+    type ConvWithBiasParams = {
+        filters: tf.Tensor4D;
+        bias: tf.Tensor1D;
+    };
+    type BoxPredictionParams = {
+        box_encoding_predictor_params: ConvWithBiasParams;
+        class_predictor_params: ConvWithBiasParams;
+    };
+    type PredictionLayerParams = {
+        conv_0_params: PointwiseConvParams;
+        conv_1_params: PointwiseConvParams;
+        conv_2_params: PointwiseConvParams;
+        conv_3_params: PointwiseConvParams;
+        conv_4_params: PointwiseConvParams;
+        conv_5_params: PointwiseConvParams;
+        conv_6_params: PointwiseConvParams;
+        conv_7_params: PointwiseConvParams;
+        box_predictor_0_params: BoxPredictionParams;
+        box_predictor_1_params: BoxPredictionParams;
+        box_predictor_2_params: BoxPredictionParams;
+        box_predictor_3_params: BoxPredictionParams;
+        box_predictor_4_params: BoxPredictionParams;
+        box_predictor_5_params: BoxPredictionParams;
+    };
+    type OutputLayerParams = {
+        extra_dim: tf.Tensor3D;
+    };
+    type NetParams = {
+        mobilenetv1_params: MobileNetV1.Params;
+        prediction_layer_params: PredictionLayerParams;
+        output_layer_params: OutputLayerParams;
+    };
+    type Detection = {
+        score: number;
+        box: {
+            top: number;
+            left: number;
+            right: number;
+            bottom: number;
+        };
+    };
+}

dist/faceDetectionNet/types.js

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

dist/faceDetectionNet/types.js.map

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

dist/faceRecognitionNet/FaceRecognitionNetParams.js

-//# sourceMappingURL=FaceRecognitionNetParams.js.map
\ No newline at end of file

dist/faceRecognitionNet/FaceRecognitionNetParams.js.map

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

dist/faceRecognitionNet/convLayer.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { ScaleLayerParams } from './scaleLayer';
-export declare type ConvParams = {
-    filters: tf.Tensor4D;
-    biases: tf.Tensor1D;
-};
-export declare type ConvLayerParams = {
-    conv: ConvParams;
-    scale: ScaleLayerParams;
-};
-export declare function conv(x: tf.Tensor4D, params: ConvLayerParams): tf.Tensor<tf.Rank.R4>;
-export declare function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams): tf.Tensor<tf.Rank.R4>;
-export declare function convDown(x: tf.Tensor4D, params: ConvLayerParams): tf.Tensor<tf.Rank.R4>;
+import { FaceRecognitionNet } from './types';
+export declare function conv(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams): tf.Tensor<tf.Rank.R4>;
+export declare function convNoRelu(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams): tf.Tensor<tf.Rank.R4>;
+export declare function convDown(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams): tf.Tensor<tf.Rank.R4>;

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,EAAoB,MAAM,cAAc,CAAC;AAYvD,mBACE,CAAc,EACd,MAAuB,EACvB,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,MAAuB;IAC1D,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,EAAE,IAAI,CAAC,CAAA;AACtC,CAAC;AAED,MAAM,qBAAqB,CAAc,EAAE,MAAuB;IAChE,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,EAAE,KAAK,CAAC,CAAA;AACvC,CAAC;AAED,MAAM,mBAAmB,CAAc,EAAE,MAAuB;IAC9D,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,EAAE,IAAI,EAAE,OAAO,CAAC,CAAA;AAC/C,CAAC"}
\ No newline at end of file
+{"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"}
\ No newline at end of file

dist/faceRecognitionNet/extractParams.d.ts

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

dist/faceRecognitionNet/extractParams.js

 import * as tf from '@tensorflow/tfjs-core';
-function isFloat(num) {
-    return num % 1 !== 0;
-}
+import { isFloat } from '../utils';
 function extractorsFactory(extractWeights) {
     function extractFilterValues(numFilterValues, numFilters, filterSize) {
         var weights = extractWeights(numFilterValues);

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: number[] | ImageData) => Promise<Int32Array | Uint8Array | Float32Array>;
-    computeFaceDescriptorSync: (input: number[] | ImageData) => Int32Array | Uint8Array | Float32Array;
-    forward: (input: number[] | ImageData) => tf.Tensor<tf.Rank.R2>;
+    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>;
 };

dist/faceRecognitionNet/index.js

+import * as tslib_1 from "tslib";
 import * as tf from '@tensorflow/tfjs-core';
-import { normalize } from '../normalize';
+import { getImageTensor, padToSquare } from '../transformInputs';
 import { convDown } from './convLayer';
 import { extractParams } from './extractParams';
+import { normalize } from './normalize';
 import { residual, residualDown } from './residualLayer';
 export function faceRecognitionNet(weights) {
+    var _this = this;
     var params = extractParams(weights);
     function forward(input) {
         return tf.tidy(function () {
-            var x = normalize(input);
+            var x = normalize(padToSquare(getImageTensor(input)));
             var out = convDown(x, params.conv32_down);
             out = tf.maxPool(out, 3, 2, 'valid');
             out = residual(out, params.conv32_1);
@@ -29,8 +32,26 @@ export function faceRecognitionNet(weights) {
             return fullyConnected;
         });
     }
-    var computeFaceDescriptor = function (input) { return forward(input).data(); };
-    var computeFaceDescriptorSync = function (input) { return forward(input).dataSync(); };
+    var computeFaceDescriptor = function (input) { return tslib_1.__awaiter(_this, void 0, void 0, function () {
+        var result, data;
+        return tslib_1.__generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    result = forward(input);
+                    return [4 /*yield*/, result.data()];
+                case 1:
+                    data = _a.sent();
+                    result.dispose();
+                    return [2 /*return*/, data];
+            }
+        });
+    }); };
+    var computeFaceDescriptorSync = function (input) {
+        var result = forward(input);
+        var data = result.dataSync();
+        result.dispose();
+        return data;
+    };
     return {
         computeFaceDescriptor: computeFaceDescriptor,
         computeFaceDescriptorSync: computeFaceDescriptorSync,

dist/faceRecognitionNet/index.js.map

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\ No newline at end of file

dist/faceRecognitionNet/normalize.d.ts

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

dist/normalize.js → dist/faceRecognitionNet/normalize.js

 import * as tf from '@tensorflow/tfjs-core';
-export function normalize(input) {
+export function normalize(x) {
     return tf.tidy(function () {
         var avg_r = tf.fill([1, 150, 150, 1], 122.782);
         var avg_g = tf.fill([1, 150, 150, 1], 117.001);
         var avg_b = tf.fill([1, 150, 150, 1], 104.298);
         var avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);
-        var x = input instanceof ImageData
-            ? tf.cast(tf.reshape(tf.fromPixels(input), [1, 150, 150, 3]), 'float32')
-            : tf.tensor4d(input, [1, 150, 150, 3]);
-        return tf.div(tf.sub(x, avg_rgb), tf.fill(x.shape, 256));
+        return tf.div(tf.sub(x, avg_rgb), tf.scalar(256));
     });
 }
 //# sourceMappingURL=normalize.js.map
\ No newline at end of file

dist/faceRecognitionNet/normalize.js.map

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\ No newline at end of file

dist/faceRecognitionNet/residualLayer.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { ConvLayerParams } from './convLayer';
-export declare type ResidualLayerParams = {
-    conv1: ConvLayerParams;
-    conv2: ConvLayerParams;
-};
-export declare function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D;
-export declare function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D;
+import { FaceRecognitionNet } from './types';
+export declare function residual(x: tf.Tensor4D, params: FaceRecognitionNet.ResidualLayerParams): tf.Tensor4D;
+export declare function residualDown(x: tf.Tensor4D, params: FaceRecognitionNet.ResidualLayerParams): tf.Tensor4D;

dist/faceRecognitionNet/residualLayer.js

 import * as tf from '@tensorflow/tfjs-core';
-import { conv, convNoRelu, convDown } from './convLayer';
+import { conv, convDown, convNoRelu } from './convLayer';
 export function residual(x, params) {
     var out = conv(x, params.conv1);
     out = convNoRelu(out, params.conv2);

dist/faceRecognitionNet/residualLayer.js.map

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

dist/faceRecognitionNet/scaleLayer.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-export declare type ScaleLayerParams = {
-    weights: tf.Tensor1D;
-    biases: tf.Tensor1D;
-};
-export declare function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D;
+import { FaceRecognitionNet } from './types';
+export declare function scale(x: tf.Tensor4D, params: FaceRecognitionNet.ScaleLayerParams): tf.Tensor4D;

dist/faceRecognitionNet/scaleLayer.js.map

-{"version":3,"file":"scaleLayer.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/scaleLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAO5C,MAAM,gBAAgB,CAAc,EAAE,MAAwB;IAC5D,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,CAAC,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;AACzD,CAAC"}
\ No newline at end of file
+{"version":3,"file":"scaleLayer.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/scaleLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAG5C,MAAM,gBAAgB,CAAc,EAAE,MAA2C;IAC/E,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,CAAC,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;AACzD,CAAC"}
\ No newline at end of file

dist/faceRecognitionNet/FaceRecognitionNetParams.d.ts → dist/faceRecognitionNet/types.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { ConvLayerParams } from './convLayer';
-import { ResidualLayerParams } from './residualLayer';
-export declare type FaceRecognitionNetParams = {
-    conv32_down: ConvLayerParams;
-    conv32_1: ResidualLayerParams;
-    conv32_2: ResidualLayerParams;
-    conv32_3: ResidualLayerParams;
-    conv64_down: ResidualLayerParams;
-    conv64_1: ResidualLayerParams;
-    conv64_2: ResidualLayerParams;
-    conv64_3: ResidualLayerParams;
-    conv128_down: ResidualLayerParams;
-    conv128_1: ResidualLayerParams;
-    conv128_2: ResidualLayerParams;
-    conv256_down: ResidualLayerParams;
-    conv256_1: ResidualLayerParams;
-    conv256_2: ResidualLayerParams;
-    conv256_down_out: ResidualLayerParams;
-    fc: tf.Tensor2D;
-};
+export declare namespace FaceRecognitionNet {
+    type ScaleLayerParams = {
+        weights: tf.Tensor1D;
+        biases: tf.Tensor1D;
+    };
+    type ResidualLayerParams = {
+        conv1: ConvLayerParams;
+        conv2: ConvLayerParams;
+    };
+    type ConvParams = {
+        filters: tf.Tensor4D;
+        biases: tf.Tensor1D;
+    };
+    type ConvLayerParams = {
+        conv: ConvParams;
+        scale: ScaleLayerParams;
+    };
+    type NetParams = {
+        conv32_down: ConvLayerParams;
+        conv32_1: ResidualLayerParams;
+        conv32_2: ResidualLayerParams;
+        conv32_3: ResidualLayerParams;
+        conv64_down: ResidualLayerParams;
+        conv64_1: ResidualLayerParams;
+        conv64_2: ResidualLayerParams;
+        conv64_3: ResidualLayerParams;
+        conv128_down: ResidualLayerParams;
+        conv128_1: ResidualLayerParams;
+        conv128_2: ResidualLayerParams;
+        conv256_down: ResidualLayerParams;
+        conv256_1: ResidualLayerParams;
+        conv256_2: ResidualLayerParams;
+        conv256_down_out: ResidualLayerParams;
+        fc: tf.Tensor2D;
+    };
+}

dist/faceRecognitionNet/types.js

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

dist/faceRecognitionNet/types.js.map

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

dist/index.d.ts

 import { euclideanDistance } from './euclideanDistance';
+import { faceDetectionNet } from './faceDetectionNet';
 import { faceRecognitionNet } from './faceRecognitionNet';
-import { normalize } from './normalize';
-export { euclideanDistance, faceRecognitionNet, normalize };
+import { NetInput } from './NetInput';
+export { euclideanDistance, faceDetectionNet, faceRecognitionNet, NetInput };
+export * from './utils';

dist/index.js

 import { euclideanDistance } from './euclideanDistance';
+import { faceDetectionNet } from './faceDetectionNet';
 import { faceRecognitionNet } from './faceRecognitionNet';
-import { normalize } from './normalize';
-export { euclideanDistance, faceRecognitionNet, normalize };
+import { NetInput } from './NetInput';
+export { euclideanDistance, faceDetectionNet, faceRecognitionNet, NetInput };
+export * from './utils';
 //# sourceMappingURL=index.js.map
\ No newline at end of file

dist/index.js.map

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dist/normalize.js.map

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\ No newline at end of file

dist/transformInputs.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { NetInput } from './NetInput';
+import { TNetInput } from './types';
+export declare function padToSquare(imgTensor: tf.Tensor4D): tf.Tensor4D;
+export declare function getImageTensor(input: tf.Tensor | NetInput | TNetInput): tf.Tensor4D;

dist/transformInputs.js

+import * as tf from '@tensorflow/tfjs-core';
+import { NetInput } from './NetInput';
+export function padToSquare(imgTensor) {
+    return tf.tidy(function () {
+        var _a = imgTensor.shape, _ = _a[0], height = _a[1], width = _a[2];
+        if (height === width) {
+            return imgTensor;
+        }
+        if (height > width) {
+            var pad_1 = tf.fill([1, height, height - width, 3], 0);
+            return tf.concat([imgTensor, pad_1], 2);
+        }
+        var pad = tf.fill([1, width - height, width, 3], 0);
+        return tf.concat([imgTensor, pad], 1);
+    });
+}
+export function getImageTensor(input) {
+    return tf.tidy(function () {
+        if (input instanceof tf.Tensor) {
+            var rank = input.shape.length;
+            if (rank !== 3 && rank !== 4) {
+                throw new Error('input tensor must be of rank 3 or 4');
+            }
+            return (rank === 3 ? input.expandDims(0) : input).toFloat();
+        }
+        var netInput = input instanceof NetInput ? input : new NetInput(input);
+        return tf.concat(netInput.canvases.map(function (canvas) {
+            return tf.fromPixels(canvas).expandDims(0).toFloat();
+        }));
+    });
+}
+//# sourceMappingURL=transformInputs.js.map
\ No newline at end of file

dist/transformInputs.js.map

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\ No newline at end of file

dist/types.d.ts

+export declare type TMediaElement = HTMLImageElement | HTMLVideoElement | HTMLCanvasElement;
+export declare type TNetInputArg = string | TMediaElement;
+export declare type TNetInput = TNetInputArg | Array<TNetInputArg>;
+export declare type Dimensions = {
+    width: number;
+    height: number;
+};
+export declare type DrawBoxOptions = {
+    lineWidth: number;
+    color: string;
+};
+export declare type DrawTextOptions = {
+    fontSize: number;
+    fontStyle: string;
+    color: string;
+};

dist/types.js

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

dist/types.js.map

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

dist/utils.d.ts

+import { FaceDetectionNet } from './faceDetectionNet/types';
+import { DrawBoxOptions, 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 getMediaDimensions(media: HTMLImageElement | HTMLVideoElement): HTMLImageElement | {
+    width: number;
+    height: number;
+};
+export declare function bufferToImage(buf: Blob): Promise<HTMLImageElement>;
+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 & {
+    withScore: boolean;
+}): void;

dist/utils.js

+export function isFloat(num) {
+    return num % 1 !== 0;
+}
+export function round(num) {
+    return Math.floor(num * 100) / 100;
+}
+export function getElement(arg) {
+    if (typeof arg === 'string') {
+        return document.getElementById(arg);
+    }
+    return arg;
+}
+export function getContext2dOrThrow(canvas) {
+    var ctx = canvas.getContext('2d');
+    if (!ctx) {
+        throw new Error('canvas 2d context is null');
+    }
+    return ctx;
+}
+export function getMediaDimensions(media) {
+    if (media instanceof HTMLVideoElement) {
+        return { width: media.videoWidth, height: media.videoHeight };
+    }
+    return media;
+}
+export function bufferToImage(buf) {
+    return new Promise(function (resolve, reject) {
+        if (!(buf instanceof Blob)) {
+            return reject('bufferToImage - expected buf to be of type: Blob');
+        }
+        var reader = new FileReader();
+        reader.onload = function () {
+            var img = new Image();
+            img.onload = function () { return resolve(img); };
+            img.onerror = reject;
+            img.src = reader.result;
+        };
+        reader.onerror = reject;
+        reader.readAsDataURL(buf);
+    });
+}
+export function drawBox(ctx, x, y, w, h, options) {
+    ctx.strokeStyle = options.color;
+    ctx.lineWidth = options.lineWidth;
+    ctx.strokeRect(x, y, w, h);
+}
+export function drawText(ctx, x, y, text, options) {
+    ctx.fillStyle = options.color;
+    ctx.font = options.fontSize + "px " + options.fontStyle;
+    ctx.fillText(text, x, y);
+}
+export function drawDetection(canvasArg, detection, options) {
+    var canvas = getElement(canvasArg);
+    if (!(canvas instanceof HTMLCanvasElement)) {
+        throw new Error('drawBox - expected canvas to be of type: HTMLCanvasElement');
+    }
+    var detectionArray = Array.isArray(detection)
+        ? detection
+        : [detection];
+    detectionArray.forEach(function (det) {
+        var score = det.score, box = det.box;
+        var left = box.left, right = box.right, top = box.top, bottom = box.bottom;
+        var _a = (options || {}), _b = _a.color, color = _b === void 0 ? 'blue' : _b, _c = _a.lineWidth, lineWidth = _c === void 0 ? 2 : _c, _d = _a.fontSize, fontSize = _d === void 0 ? 20 : _d, _e = _a.fontStyle, fontStyle = _e === void 0 ? 'Georgia' : _e, _f = _a.withScore, withScore = _f === void 0 ? true : _f;
+        var padText = 2 + lineWidth;
+        var ctx = getContext2dOrThrow(canvas);
+        drawBox(ctx, left, top, right - left, bottom - top, { lineWidth: lineWidth, color: color });
+        if (withScore) {
+            drawText(ctx, left + padText, top + (fontSize * 0.6) + padText, "" + round(score), { fontSize: fontSize, fontStyle: fontStyle, color: color });
+        }
+    });
+}
+//# sourceMappingURL=utils.js.map
\ No newline at end of file

dist/utils.js.map

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\ No newline at end of file

examples/faceRecognition/public/commons.js

-const classes = ['amy', 'bernadette', 'howard', 'leonard', 'penny', 'raj', 'sheldon', 'stuart']
-
-function getImageUri(className, idx) {
-  return `/${className}/${className}${idx}.png`
-}
-
-async function initNet() {
-  const res = await axios.get('face_recognition_model.weights', { responseType: 'arraybuffer' })
-  const weights = new Float32Array(res.data)
-  return facerecognition.faceRecognitionNet(weights)
-}
-
-function bufferToImgSrc(buf) {
-  return new Promise((resolve, reject) => {
-    const reader = new window.FileReader()
-    reader.onload = () => resolve(reader.result)
-    reader.onerror = reject
-    reader.readAsDataURL(buf)
-  })
-}
-
-function imgSrcToData(src) {
-  return new Promise((resolve, reject) => {
-    const canvas = document.createElement('canvas')
-    canvas.width = 150
-    canvas.height = 150
-    const ctx = canvas.getContext('2d')
-    const img = new Image()
-    img.onload = function() {
-      ctx.drawImage(img, 0, 0)
-      resolve(ctx.getImageData(0, 0, 150, 150))
-    }
-    img.onerror = reject
-    img.src = src
-  })
-}
-
-async function bufferToImageData(buf) {
-  return imgSrcToData(await bufferToImgSrc(buf))
-}
-
-async function fetchImage(uri) {
-  return (await axios.get(uri, { responseType: 'blob' })).data
-}
-
-function round(num) {
-  return Math.floor(num * 100) / 100
-}
\ No newline at end of file

examples/faceRecognition/server.js

-const express = require('express')
-const path = require('path')
-
-const app = express()
-
-const viewsDir = path.join(__dirname, 'views')
-app.use(express.static(path.join(__dirname, '../images')))
-app.use(express.static(path.join(__dirname, '../../weights')))
-app.use(express.static(path.join(__dirname, '../../dist')))
-app.use(express.static(path.join(__dirname, './node_modules/axios/dist')))
-app.use(express.static(viewsDir))
-app.use(express.static(path.join(__dirname, './public')))
-
-app.get('/', (req, res) => res.redirect('/faceRecognition'))
-app.get('/faceRecognition', (req, res) => res.sendFile(path.join(viewsDir, 'faceRecognition.html')))
-app.get('/faceSimilarity', (req, res) => res.sendFile(path.join(viewsDir, 'faceSimilarity.html')))
-
-app.listen(3000, () => console.log('Listening on port 3000!'))
\ No newline at end of file

examples/public/commons.js

+const classes = ['amy', 'bernadette', 'howard', 'leonard', 'penny', 'raj', 'sheldon', 'stuart']
+
+function getImageUri(imageName) {
+  return `images/${imageName}`
+}
+
+function getFaceImageUri(className, idx) {
+  return `images/${className}/${className}${idx}.png`
+}
+
+async function fetchImage(uri) {
+  return (await axios.get(uri, { responseType: 'blob' })).data
+}
+
+async function initFaceDetectionNet() {
+  const res = await axios.get('face_detection_model.weights', { responseType: 'arraybuffer' })
+  const weights = new Float32Array(res.data)
+  return facerecognition.faceDetectionNet(weights)
+}
+
+async function initFaceRecognitionNet() {
+  const res = await axios.get('face_recognition_model.weights', { responseType: 'arraybuffer' })
+  const weights = new Float32Array(res.data)
+  return facerecognition.faceRecognitionNet(weights)
+}
+
+function renderNavBar(navbarId, exampleUri) {
+  const examples = [
+    {
+      uri: 'face_detection',
+      name: 'Face Detection'
+    },
+    {
+      uri: 'face_detection_video',
+      name: 'Face Detection Video'
+    },
+    {
+      uri: 'face_recognition',
+      name: 'Face Recognition'
+    },
+    {
+      uri: 'face_similarity',
+      name: 'Face Similarity'
+    }
+  ]
+
+  const navbar = $(navbarId).get(0)
+  navbar.classList.add('row')
+  examples
+    .filter(ex => ex.uri !== exampleUri)
+    .forEach(ex => {
+      const a = document.createElement('a')
+      navbar.appendChild(a)
+
+      a.classList.add('waves-effect', 'waves-light', 'btn', 'margin-sm')
+      a.href = ex.uri
+      a.innerHTML = ex.name
+    })
+}
+
+function renderSelectList(selectListId, onChange, initialValue, renderChildren) {
+  const select = document.createElement('select')
+  $(selectListId).get(0).appendChild(select)
+  renderChildren(select)
+  $(select).val(initialValue)
+  $(select).on('change', (e) => onChange(e.target.value))
+  $(select).material_select()
+}
+
+function renderOption(parent, text, value) {
+  const option = document.createElement('option')
+  option.innerHTML = text
+  option.value = value
+  parent.appendChild(option)
+}
+
+function renderFaceImageSelectList(selectListId, onChange, initialValue) {
+  const indices = [1, 2, 3, 4, 5]
+  function renderChildren(select) {
+    classes.forEach(className => {
+      const optgroup = document.createElement('optgroup')
+      optgroup.label = className
+      select.appendChild(optgroup)
+      indices.forEach(imageIdx =>
+        renderOption(
+          optgroup,
+          `${className} ${imageIdx}`,
+          getFaceImageUri(className, imageIdx)
+        )
+      )
+    })
+  }
+
+  renderSelectList(
+    selectListId,
+    onChange,
+    getFaceImageUri(initialValue.className, initialValue.imageIdx),
+    renderChildren
+  )
+}
+
+function renderImageSelectList(selectListId, onChange, initialValue) {
+  const images = [1, 2, 3, 4, 5].map(idx => `bbt${idx}.jpg`)
+  function renderChildren(select) {
+    images.forEach(imageName =>
+      renderOption(
+        select,
+        imageName,
+        getImageUri(imageName)
+      )
+    )
+  }
+
+  renderSelectList(
+    selectListId,
+    onChange,
+    getImageUri(initialValue),
+    renderChildren
+  )
+}
\ No newline at end of file

examples/faceRecognition/public/styles.css → examples/public/styles.css

 .page-container {
-  position: fixed;
   left: 0;
   right: 0;
   margin: auto;
@@ -18,16 +17,24 @@
   justify-content: center;
   align-items: center;
 }
+.side-by-side >* {
+  margin: 0 5px;
+}
 
 .bold {
   font-weight: bold;
 }
 
+.margin-sm {
+  margin: 5px;
+}
+
 .margin {
   margin: 20px;
 }
 
-img {
-  width: 150px;
-  height: 150px;
+#overlay {
+  position: absolute;
+  top: 0;
+  left: 0;
 }
\ No newline at end of file

examples/server.js

+const express = require('express')
+const path = require('path')
+
+const app = express()
+
+const viewsDir = path.join(__dirname, 'views')
+app.use(express.static(viewsDir))
+app.use(express.static(path.join(__dirname, './public')))
+app.use(express.static(path.join(__dirname, '../weights')))
+app.use(express.static(path.join(__dirname, '../dist')))
+app.use(express.static(path.join(__dirname, './node_modules/axios/dist')))
+
+app.get('/', (req, res) => res.redirect('/face_detection'))
+app.get('/face_detection', (req, res) => res.sendFile(path.join(viewsDir, 'faceDetection.html')))
+app.get('/face_detection_video', (req, res) => res.sendFile(path.join(viewsDir, 'faceDetectionVideo.html')))
+app.get('/face_recognition', (req, res) => res.sendFile(path.join(viewsDir, 'faceRecognition.html')))
+app.get('/face_similarity', (req, res) => res.sendFile(path.join(viewsDir, 'faceSimilarity.html')))
+
+app.listen(3000, () => console.log('Listening on port 3000!'))
\ No newline at end of file

examples/views/faceDetection.html

+<!DOCTYPE html>
+<html>
+<head>
+  <script src="face-recognition.js"></script>
+  <script src="axios.min.js"></script>
+  <script src="commons.js"></script>
+  <link rel="stylesheet" href="styles.css">
+  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.css">
+  <script type="text/javascript" src="https://code.jquery.com/jquery-2.1.1.min.js"></script>
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js"></script>
+</head>
+<body>
+  <div class="center-content page-container">
+    <div id="navbar"></div>
+    <div class="progress" id="loader">
+      <div class="indeterminate"></div>
+    </div>
+    <div style="position: relative" class="margin">
+      <img id="inputImg" src="" style="max-width: 800px;" />
+      <canvas id="overlay" />
+    </div>
+    <div class="row side-by-side">
+      <div id="selectList"></div>
+      <div class="row">
+        <label for="minConfidence">Min Confidence:</label>
+        <input disabled value="0.7" id="minConfidence" type="text" class="bold">
+      </div>
+      <button
+        class="waves-effect waves-light btn"
+        onclick="onDecreaseThreshold()"
+      >
+        <i class="material-icons left">-</i>
+      </button>
+      <button
+        class="waves-effect waves-light btn"
+        onclick="onIncreaseThreshold()"
+      >
+        <i class="material-icons left">+</i>
+      </button>
+    </div>
+  </div>
+
+  <script>
+    let minConfidence = 0.7
+    let net, result
+
+    function onIncreaseThreshold() {
+      minConfidence = Math.min(facerecognition.round(minConfidence + 0.1), 1.0)
+      $('#minConfidence').val(minConfidence)
+      updateResults()
+    }
+
+    function onDecreaseThreshold() {
+      minConfidence = Math.max(facerecognition.round(minConfidence - 0.1), 0.1)
+      $('#minConfidence').val(minConfidence)
+      updateResults()
+    }
+
+    async function updateResults() {
+      const input = new facerecognition.NetInput('inputImg')
+      const { width, height } = input
+      const canvas = $('#overlay').get(0)
+      canvas.width = width
+      canvas.height = height
+
+      result = await net.locateFaces(input, minConfidence)
+      facerecognition.drawDetection('overlay', result.map(det => det.forSize(width, height)))
+    }
+
+    async function onSelectionChanged(uri) {
+      const imgBuf = await fetchImage(uri)
+      $(`#inputImg`).get(0).src = (await facerecognition.bufferToImage(imgBuf)).src
+      updateResults()
+    }
+
+    async function run() {
+      net = await initFaceDetectionNet()
+      $('#loader').hide()
+      onSelectionChanged($('#selectList select').val())
+    }
+
+    $(document).ready(function() {
+      renderNavBar('#navbar', 'face_detection')
+      renderImageSelectList(
+        '#selectList',
+        async (uri) => {
+          await onSelectionChanged(uri)
+        },
+        'bbt1.jpg'
+      )
+      run()
+    })
+  </script>
+</body>
+</html>
\ No newline at end of file

examples/views/faceDetectionVideo.html

+<!DOCTYPE html>
+<html>
+<head>
+  <script src="face-recognition.js"></script>
+  <script src="axios.min.js"></script>
+  <script src="commons.js"></script>
+  <link rel="stylesheet" href="styles.css">
+  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.css">
+  <script type="text/javascript" src="https://code.jquery.com/jquery-2.1.1.min.js"></script>
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js"></script>
+</head>
+<body>
+  <div class="center-content page-container">
+    <div id="navbar"></div>
+    <div class="progress" id="loader">
+      <div class="indeterminate"></div>
+    </div>
+    <div style="position: relative" class="margin">
+      <video src="media/bbt.mp4" onplay="onPlay(this)" autoplay muted></video>
+      <canvas id="overlay" />
+    </div>
+    <div class="row side-by-side">
+      <div id="selectList"></div>
+      <div class="row">
+        <label for="minConfidence">Min Confidence:</label>
+        <input disabled value="0.7" id="minConfidence" type="text" class="bold">
+      </div>
+      <button
+        class="waves-effect waves-light btn"
+        onclick="onDecreaseThreshold()"
+      >
+        <i class="material-icons left">-</i>
+      </button>
+      <button
+        class="waves-effect waves-light btn"
+        onclick="onIncreaseThreshold()"
+      >
+        <i class="material-icons left">+</i>
+      </button>
+    </div>
+    <div class="row side-by-side">
+      <div class="row">
+        <label for="time">Time:</label>
+        <input disabled value="-" id="time" type="text" class="bold">
+      </div>
+      <div class="row">
+        <label for="fps">Estimated Fps:</label>
+        <input disabled value="-" id="fps" type="text" class="bold">
+      </div>
+    </div>
+  </div>
+
+  <script>
+    let minConfidence = 0.7
+    let net, result
+
+    function onIncreaseThreshold() {
+      minConfidence = Math.min(facerecognition.round(minConfidence + 0.1), 1.0)
+      $('#minConfidence').val(minConfidence)
+    }
+
+    function onDecreaseThreshold() {
+      minConfidence = Math.max(facerecognition.round(minConfidence - 0.1), 0.1)
+      $('#minConfidence').val(minConfidence)
+    }
+
+    function displayTimeStats(timeInMs) {
+      $('#time').val(`${timeInMs} ms`)
+      $('#fps').val(`${facerecognition.round(1000 / timeInMs)}`)
+    }
+
+    async function onPlay(videoEl) {
+      if(videoEl.paused || videoEl.ended)
+        return false
+
+      const input = new facerecognition.NetInput(videoEl)
+      const { width, height } = input
+      const canvas = $('#overlay').get(0)
+      canvas.width = width
+      canvas.height = height
+
+      const ts = Date.now()
+      result = await net.locateFaces(input, minConfidence)
+      displayTimeStats(Date.now() - ts)
+
+      facerecognition.drawDetection('overlay', result.map(det => det.forSize(width, height)))
+      setTimeout(() => onPlay(videoEl))
+    }
+
+    async function run() {
+      net = await initFaceDetectionNet()
+      $('#loader').hide()
+    }
+
+    $(document).ready(function() {
+      renderNavBar('#navbar', 'face_detection_video')
+      run()
+    })
+  </script>
+</body>
+</html>
\ No newline at end of file

examples/faceRecognition/views/faceRecognition.html → examples/views/faceRecognition.html

 <!DOCTYPE html>
 <html>
 <head>
-  <script src="face-recognition.min.js"></script>
+  <script src="face-recognition.js"></script>
   <script src="axios.min.js"></script>
   <script src="commons.js"></script>
   <link rel="stylesheet" href="styles.css">
@@ -11,14 +11,8 @@
 </head>
 <body>
   <div class="center-content page-container">
-    <div class="row">
-      <a
-        class="waves-effect waves-light btn"
-        href="faceSimilarity.html"
-      >
-        Go To Face Similarity Example
-      </a>
-    </div>
+    <div id="navbar"></div>
+
     <div>
       <div class="row center-content" id="loader">
         <input disabled value="" id="status" type="text" class="bold">
@@ -53,7 +47,7 @@
           class="waves-effect waves-light btn"
           onclick="onSlower()"
         >
-        <i class="material-icons left">-</i> Slower
+          <i class="material-icons left">-</i> Slower
         </button>
         <button
           class="waves-effect waves-light btn"
@@ -77,17 +71,17 @@
 
     let isStop = false
     let trainDescriptorsByClass = []
-    let net = {}
+    let net
     let currImageIdx = 2, currClassIdx = 0
     let to = null
 
     function onSlower() {
-      interval = Math.max(interval + 100, 0)
+      interval = Math.min(interval + 100, 2000)
       $('#interval').val(interval)
     }
 
     function onFaster() {
-      interval = Math.min(interval - 100, 2000)
+      interval = Math.max(interval - 100, 0)
       $('#interval').val(interval)
     }
 
@@ -107,18 +101,18 @@
 
     function displayTimeStats(timeInMs) {
       $('#time').val(`${timeInMs} ms`)
-      $('#fps').val(`${round(1000 / timeInMs)}`)
+      $('#fps').val(`${facerecognition.round(1000 / timeInMs)}`)
     }
 
     function displayImage(src) {
       getImg().src = src
     }
 
-    async function loadTrainingData(cb) {
+    async function loadTrainingImages() {
       return await Promise.all(classes.map(
         async className => ({
-          imgData: await bufferToImageData(
-            await fetchImage(getImageUri(className, 1))
+          img: await facerecognition.bufferToImage(
+            await fetchImage(getFaceImageUri(className, 1))
           ),
           className
         })
@@ -129,7 +123,9 @@
       return trainDescriptorsByClass
         .map(
           ({ descriptor, className }) => ({
-            distance: round(facerecognition.euclideanDistance(descriptor, queryDescriptor)),
+            distance: facerecognition.round(
+              facerecognition.euclideanDistance(descriptor, queryDescriptor)
+            ),
             className
           })
         )
@@ -138,17 +134,15 @@
 
     async function runFaceRecognition() {
       async function next() {
-        const imgBuf = await fetchImage(getImageUri(classes[currClassIdx], currImageIdx))
+        const imgBuf = await fetchImage(getFaceImageUri(classes[currClassIdx], currImageIdx))
+        const input = await facerecognition.bufferToImage(imgBuf)
         const imgEl = $('#face').get(0)
-        imgEl.src = await bufferToImgSrc(imgBuf)
-
-        const imageData = await imgSrcToData(imgEl.src)
+        imgEl.src = input.src
 
         const ts = Date.now()
-        const result = await net.forward(imageData)
+        const descriptor = await net.computeFaceDescriptor(input)
         displayTimeStats(Date.now() - ts)
 
-        const descriptor = await result.data()
         const bestMatch = getBestMatch(descriptor)
         $('#prediction').val(`${bestMatch.distance < threshold ? bestMatch.className : 'unkown'} (${bestMatch.distance})`)
 
@@ -167,13 +161,13 @@
       try {
         setStatusText('loading model file...')
 
-        net = await initNet()
+        net = await initFaceRecognitionNet()
         setStatusText('computing initial descriptors...')
 
-        const trainImgDatas = await loadTrainingData()
-        trainDescriptorsByClass = await Promise.all(trainImgDatas.map(
-          async ({ className, imgData }) => ({
-            descriptor: await net.computeFaceDescriptor(imgData),
+        const trainImgs = await loadTrainingImages()
+        trainDescriptorsByClass = await Promise.all(trainImgs.map(
+          async ({ className, img }) => ({
+            descriptor: await net.computeFaceDescriptor(img),
             className
           })
         ))
@@ -185,7 +179,11 @@
       }
     }
 
-    run()
+    $(document).ready(function() {
+      renderNavBar('#navbar', 'face_recognition')
+      run()
+    })
+
   </script>
 
 </body>

examples/faceRecognition/views/faceSimilarity.html → examples/views/faceSimilarity.html

 <!DOCTYPE html>
 <html>
 <head>
-  <script src="face-recognition.min.js"></script>
+  <script src="face-recognition.js"></script>
   <script src="axios.min.js"></script>
   <script src="commons.js"></script>
   <link rel="stylesheet" href="styles.css">
@@ -11,14 +11,8 @@
 </head>
 <body>
   <div class="center-content page-container">
-    <div class="row">
-      <a
-        class="waves-effect waves-light btn"
-        href="faceRecognition"
-      >
-        Go To Face Recognition Example
-      </a>
-    </div>
+    <div id="navbar"></div>
+
     <div>
       <div class="progress" id="loader">
         <div class="indeterminate"></div>
@@ -26,11 +20,11 @@
       <div class="row side-by-side">
         <div class="center-content">
           <img id="face1" src="" class="margin"/>
-          <div id="selectList1" class="input-field"></div>
+          <div id="selectList1"></div>
         </div>
         <div class="center-content">
           <img id="face2" src="" class="margin"/>
-          <div id="selectList2" class="input-field"></div>
+          <div id="selectList2"></div>
         </div>
       </div>
       <div class="row">
@@ -45,27 +39,10 @@
     const threshold = 0.6
     let net, descriptors = { desc1: null, desc2: null }
 
-    function renderSelectList(parent) {
-      const select = document.createElement('select')
-      parent.appendChild(select)
-
-      classes.forEach((className) => {
-        const optgroup = document.createElement('optgroup')
-        optgroup.label = className
-        select.appendChild(optgroup)
-
-        const indices = [1, 2, 3, 4, 5]
-        indices.forEach((imageIdx) => {
-          const option = document.createElement('option')
-          option.innerHTML = `${className} ${imageIdx}`
-          option.value = getImageUri(className, imageIdx)
-          optgroup.appendChild(option)
-        })
-      })
-    }
-
     function updateResult() {
-      const distance = round(facerecognition.euclideanDistance(descriptors.desc1, descriptors.desc2))
+      const distance = facerecognition.round(
+        facerecognition.euclideanDistance(descriptors.desc1, descriptors.desc2)
+      )
       let text = distance
       let bgColor = '#ffffff'
       if (distance > threshold) {
@@ -76,48 +53,41 @@
       $('#distance').css('background-color', bgColor)
     }
 
-    async function computeDescriptorFromSrc(imgEl) {
-      return net.computeFaceDescriptor(await imgSrcToData(imgEl.src))
-    }
-
-    async function onSelectionChanged(which) {
-      const selectElSelector = `#selectList${which} select`
-      const imgElSelector = `#face${which}`
-      const whichDesc = `desc${which}`
-
-      const uri = $(selectElSelector).val()
-      const imgEl = $(imgElSelector).get(0)
-
+    async function onSelectionChanged(which, uri) {
       const imgBuf = await fetchImage(uri)
-      imgEl.src = await bufferToImgSrc(imgBuf)
-      descriptors[whichDesc] = await computeDescriptorFromSrc($(imgElSelector).get(0))
+      const input = await facerecognition.bufferToImage(imgBuf)
+      const imgEl = $(`#face${which}`).get(0)
+      imgEl.src = input.src
+      descriptors[`desc${which}`] = await net.computeFaceDescriptor(input)
     }
 
     async function run() {
-      $('#selectList1 select').val(getImageUri('sheldon', 1))
-      $('#selectList1 select').material_select()
-      $('#selectList2 select').val(getImageUri('howard', 1))
-      $('#selectList2 select').material_select()
-      net = await initNet()
+      net = await initFaceRecognitionNet()
       $('#loader').hide()
-      await onSelectionChanged(1)
-      await onSelectionChanged(2)
+      await onSelectionChanged(1, $('#selectList1 select').val())
+      await onSelectionChanged(2, $('#selectList2 select').val())
       updateResult()
     }
 
     $(document).ready(function() {
-      renderSelectList($('#selectList1').get(0))
-      renderSelectList($('#selectList2').get(0))
-      $('select').material_select()
-      $('#selectList1 select').on('change', async () => {
-        await onSelectionChanged(1)
-        updateResult()
-      })
-      $('#selectList2 select').on('change', async () => {
-        await onSelectionChanged(2)
-        updateResult()
-      })
+      renderNavBar('#navbar', 'face_similarity')
+      renderFaceImageSelectList(
+        '#selectList1',
+        async (uri) => {
+          await onSelectionChanged(1, uri)
+          updateResult()
+        },
+        { className: 'sheldon', imageIdx: 1 }
+      )
 
+      renderFaceImageSelectList(
+        '#selectList2',
+        async (uri) => {
+          await onSelectionChanged(2, uri)
+          updateResult()
+        },
+        { className: 'howard', imageIdx: 1 }
+      )
       run()
     })
 

package.json

@@ -5,7 +5,9 @@
   "main": "./dist/index.js",
   "typings": "./dist/index.d.ts",
   "scripts": {
-    "build": "rollup -c rollup.config.js && tsc"
+    "rollup-min": "rollup -c rollup.config.js --environment minify:true",
+    "rollup": "rollup -c rollup.config.js",
+    "build": "npm run rollup && npm run rollup-min && tsc"
   },
   "keywords": [
     "face",

rollup.config.js

@@ -4,17 +4,18 @@ import typescript from 'rollup-plugin-typescript2';
 import uglify from 'rollup-plugin-uglify';
 import path from 'path';
 
+const { minify } = process.env
+
 export default {
   input: 'src/index.ts',
   plugins: [
     typescript(),
     node(),
-    uglify(),
-    commonjs(),
-  ],
+    commonjs()
+  ].concat(minify ? uglify() : []),
   output: {
     extend: true,
-    file: 'dist/face-recognition.min.js',
+    file: `dist/face-recognition${minify ? '.min' : ''}.js`,
     format: 'umd',
     name: 'facerecognition',
     globals: {

src/NetInput.ts

+import { Dimensions, TMediaElement, TNetInput } from './types';
+import { getContext2dOrThrow, getElement, getMediaDimensions } from './utils';
+
+export class NetInput {
+  private _canvases: HTMLCanvasElement[]
+
+  constructor(
+    mediaArg: TNetInput,
+    dims?: Dimensions
+  ) {
+    const mediaArgArray = Array.isArray(mediaArg)
+      ? mediaArg
+      : [mediaArg]
+
+    if (!mediaArgArray.length) {
+      throw new Error('NetInput - empty array passed as input')
+    }
+
+    const medias = mediaArgArray.map(getElement)
+
+
+    medias.forEach((media, i) => {
+      if (!(media instanceof HTMLImageElement || media instanceof HTMLVideoElement || media instanceof HTMLCanvasElement)) {
+        const idxHint = Array.isArray(mediaArg) ? ` at input index ${i}:` : ''
+        if (typeof mediaArgArray[i] === 'string') {
+          throw new Error(`NetInput -${idxHint} string passed, but could not resolve HTMLElement for element id`)
+        }
+        throw new Error(`NetInput -${idxHint} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement, or to be an element id`)
+      }
+    })
+
+    this._canvases = []
+    medias.forEach(m => this.initCanvas(m, dims))
+  }
+
+  private initCanvas(media: TMediaElement, dims?: Dimensions) {
+    if (media instanceof HTMLCanvasElement) {
+      this._canvases.push(media)
+      return
+    }
+
+    // if input is batch type, make sure every canvas has the same dimensions
+    const { width, height } = this.dims || dims || getMediaDimensions(media)
+
+    const canvas = document.createElement('canvas')
+    canvas.width = width
+    canvas.height = height
+
+    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)
+    this._canvases.push(canvas)
+  }
+
+  public get canvases() : HTMLCanvasElement[] {
+    return this._canvases
+  }
+
+  public get width() : number {
+    return (this._canvases[0] || {}).width
+  }
+
+  public get height() : number {
+    return (this._canvases[0] || {}).height
+  }
+
+  public get dims() : Dimensions | null {
+    const { width, height } = this
+    return (width > 0 && height > 0) ? { width, height } : null
+  }
+}
\ No newline at end of file

src/faceDetectionNet/FaceDetectionResult.ts

+import { FaceDetectionNet } from './types';
+
+export class FaceDetectionResult {
+  private score: number
+  private top: number
+  private left: number
+  private bottom: number
+  private right: number
+
+  constructor(
+    score: number,
+    top: number,
+    left: number,
+    bottom: number,
+    right: number
+  ) {
+    this.score = score
+    this.top = Math.max(0, top),
+    this.left = Math.max(0, left),
+    this.bottom  = Math.min(1.0, bottom),
+    this.right = Math.min(1.0, right)
+  }
+
+  public forSize(width: number, height: number): FaceDetectionNet.Detection {
+    return {
+      score: this.score,
+      box: {
+        top: this.top * height,
+        left: this.left * width,
+        bottom: this.bottom * height,
+        right: this.right * width
+      }
+    }
+  }
+}
\ No newline at end of file

src/faceDetectionNet/boxPredictionLayer.ts

+import * as tf from '@tensorflow/tfjs-core';
+
+import { FaceDetectionNet } from './types';
+
+function convWithBias(
+  x: tf.Tensor4D,
+  params: FaceDetectionNet.ConvWithBiasParams
+) {
+  return tf.tidy(() =>
+    tf.add(
+      tf.conv2d(x, params.filters, [1, 1], 'same'),
+      params.bias
+    )
+  )
+}
+
+export function boxPredictionLayer(
+  x: tf.Tensor4D,
+  params: FaceDetectionNet.BoxPredictionParams
+) {
+  return tf.tidy(() => {
+
+    const batchSize = x.shape[0]
+
+    const boxPredictionEncoding = tf.reshape(
+      convWithBias(x, params.box_encoding_predictor_params),
+      [batchSize, -1, 1, 4]
+    )
+    const classPrediction = tf.reshape(
+      convWithBias(x, params.class_predictor_params),
+      [batchSize, -1, 3]
+    )
+
+    return {
+      boxPredictionEncoding,
+      classPrediction
+    }
+  })
+}
\ No newline at end of file

src/faceDetectionNet/extractParams.ts

+import * as tf from '@tensorflow/tfjs-core';
+
+import { FaceDetectionNet } from './types';
+
+function extractorsFactory(extractWeights: (numWeights: number) => Float32Array) {
+
+  function extractDepthwiseConvParams(numChannels: number): FaceDetectionNet.MobileNetV1.DepthwiseConvParams {
+    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])
+    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))
+    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))
+    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))
+    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))
+
+    return {
+      filters,
+      batch_norm_scale,
+      batch_norm_offset,
+      batch_norm_mean,
+      batch_norm_variance
+    }
+  }
+
+  function extractConvWithBiasParams(
+    channelsIn: number,
+    channelsOut: number,
+    filterSize: number
+  ): FaceDetectionNet.ConvWithBiasParams {
+    const filters = tf.tensor4d(
+      extractWeights(channelsIn * channelsOut * filterSize * filterSize),
+      [filterSize, filterSize, channelsIn, channelsOut]
+    )
+    const bias = tf.tensor1d(extractWeights(channelsOut))
+
+    return {
+      filters,
+      bias
+    }
+  }
+
+  function extractPointwiseConvParams(
+    channelsIn: number,
+    channelsOut: number,
+    filterSize: number
+  ): FaceDetectionNet.PointwiseConvParams {
+    const {
+      filters,
+      bias
+    } = extractConvWithBiasParams(channelsIn, channelsOut, filterSize)
+
+    return {
+      filters,
+      batch_norm_offset: bias
+    }
+  }
+
+  function extractConvPairParams(channelsIn: number, channelsOut: number): FaceDetectionNet.MobileNetV1.ConvPairParams {
+    const depthwise_conv_params = extractDepthwiseConvParams(channelsIn)
+    const pointwise_conv_params = extractPointwiseConvParams(channelsIn, channelsOut, 1)
+
+    return {
+      depthwise_conv_params,
+      pointwise_conv_params
+    }
+  }
+
+  function extractMobilenetV1Params(): FaceDetectionNet.MobileNetV1.Params {
+
+    const conv_0_params = extractPointwiseConvParams(3, 32, 3)
+
+    const channelNumPairs = [
+      [32, 64],
+      [64, 128],
+      [128, 128],
+      [128, 256],
+      [256, 256],
+      [256, 512],
+      [512, 512],
+      [512, 512],
+      [512, 512],
+      [512, 512],
+      [512, 512],
+      [512, 1024],
+      [1024, 1024]
+    ]
+
+    const conv_pair_params = channelNumPairs.map(
+      ([channelsIn, channelsOut]) => extractConvPairParams(channelsIn, channelsOut)
+    )
+
+    return {
+      conv_0_params,
+      conv_pair_params
+    }
+
+  }
+
+  function extractPredictionLayerParams(): FaceDetectionNet.PredictionLayerParams {
+    const conv_0_params = extractPointwiseConvParams(1024, 256, 1)
+    const conv_1_params = extractPointwiseConvParams(256, 512, 3)
+    const conv_2_params = extractPointwiseConvParams(512, 128, 1)
+    const conv_3_params = extractPointwiseConvParams(128, 256, 3)
+    const conv_4_params = extractPointwiseConvParams(256, 128, 1)
+    const conv_5_params = extractPointwiseConvParams(128, 256, 3)
+    const conv_6_params = extractPointwiseConvParams(256, 64, 1)
+    const conv_7_params = extractPointwiseConvParams(64, 128, 3)
+
+    const box_encoding_0_predictor_params = extractConvWithBiasParams(512, 12, 1)
+    const class_predictor_0_params = extractConvWithBiasParams(512, 9, 1)
+    const box_encoding_1_predictor_params = extractConvWithBiasParams(1024, 24, 1)
+    const class_predictor_1_params = extractConvWithBiasParams(1024, 18, 1)
+    const box_encoding_2_predictor_params = extractConvWithBiasParams(512, 24, 1)
+    const class_predictor_2_params = extractConvWithBiasParams(512, 18, 1)
+    const box_encoding_3_predictor_params = extractConvWithBiasParams(256, 24, 1)
+    const class_predictor_3_params = extractConvWithBiasParams(256, 18, 1)
+    const box_encoding_4_predictor_params = extractConvWithBiasParams(256, 24, 1)
+    const class_predictor_4_params = extractConvWithBiasParams(256, 18, 1)
+    const box_encoding_5_predictor_params = extractConvWithBiasParams(128, 24, 1)
+    const class_predictor_5_params = extractConvWithBiasParams(128, 18, 1)
+
+    const box_predictor_0_params = {
+      box_encoding_predictor_params: box_encoding_0_predictor_params,
+      class_predictor_params: class_predictor_0_params
+    }
+    const box_predictor_1_params = {
+      box_encoding_predictor_params: box_encoding_1_predictor_params,
+      class_predictor_params: class_predictor_1_params
+    }
+    const box_predictor_2_params = {
+      box_encoding_predictor_params: box_encoding_2_predictor_params,
+      class_predictor_params: class_predictor_2_params
+    }
+    const box_predictor_3_params = {
+      box_encoding_predictor_params: box_encoding_3_predictor_params,
+      class_predictor_params: class_predictor_3_params
+    }
+    const box_predictor_4_params = {
+      box_encoding_predictor_params: box_encoding_4_predictor_params,
+      class_predictor_params: class_predictor_4_params
+    }
+    const box_predictor_5_params = {
+      box_encoding_predictor_params: box_encoding_5_predictor_params,
+      class_predictor_params: class_predictor_5_params
+    }
+
+    return {
+      conv_0_params,
+      conv_1_params,
+      conv_2_params,
+      conv_3_params,
+      conv_4_params,
+      conv_5_params,
+      conv_6_params,
+      conv_7_params,
+      box_predictor_0_params,
+      box_predictor_1_params,
+      box_predictor_2_params,
+      box_predictor_3_params,
+      box_predictor_4_params,
+      box_predictor_5_params
+    }
+  }
+
+
+  return {
+    extractMobilenetV1Params,
+    extractPredictionLayerParams
+  }
+
+}
+
+export function extractParams(weights: Float32Array): FaceDetectionNet.NetParams {
+  const extractWeights = (numWeights: number): Float32Array => {
+    const ret = weights.slice(0, numWeights)
+    weights = weights.slice(numWeights)
+    return ret
+  }
+
+  const {
+    extractMobilenetV1Params,
+    extractPredictionLayerParams
+  } = extractorsFactory(extractWeights)
+
+  const mobilenetv1_params = extractMobilenetV1Params()
+  const prediction_layer_params = extractPredictionLayerParams()
+  const extra_dim = tf.tensor3d(
+    extractWeights(5118 * 4),
+    [1, 5118, 4]
+  )
+  const output_layer_params = {
+    extra_dim
+  }
+
+  if (weights.length !== 0) {
+    throw new Error(`weights remaing after extract: ${weights.length}`)
+  }
+
+  return {
+    mobilenetv1_params,
+    prediction_layer_params,
+    output_layer_params
+  }
+}
\ No newline at end of file

src/faceDetectionNet/index.ts

+import * as tf from '@tensorflow/tfjs-core';
+
+import { NetInput } from '../NetInput';
+import { getImageTensor, padToSquare } from '../transformInputs';
+import { TNetInput } from '../types';
+import { extractParams } from './extractParams';
+import { FaceDetectionResult } from './FaceDetectionResult';
+import { mobileNetV1 } from './mobileNetV1';
+import { nonMaxSuppression } from './nonMaxSuppression';
+import { outputLayer } from './outputLayer';
+import { predictionLayer } from './predictionLayer';
+import { resizeLayer } from './resizeLayer';
+
+export function faceDetectionNet(weights: Float32Array) {
+  const params = extractParams(weights)
+
+  function forwardTensor(imgTensor: tf.Tensor4D) {
+    return tf.tidy(() => {
+
+      const resized = resizeLayer(imgTensor) as tf.Tensor4D
+      const features = mobileNetV1(resized, params.mobilenetv1_params)
+
+      const {
+        boxPredictions,
+        classPredictions
+      } = predictionLayer(features.out, features.conv11, params.prediction_layer_params)
+
+      return outputLayer(boxPredictions, classPredictions, params.output_layer_params)
+    })
+  }
+
+  function forward(input: tf.Tensor | NetInput | TNetInput) {
+    return tf.tidy(
+      () => forwardTensor(padToSquare(getImageTensor(input)))
+    )
+  }
+
+  async function locateFaces(
+    input: tf.Tensor | NetInput,
+    minConfidence: number = 0.8,
+    maxResults: number = 100,
+  ): Promise<FaceDetectionResult[]> {
+
+    let paddedHeightRelative = 1, paddedWidthRelative = 1
+
+    const {
+      boxes: _boxes,
+      scores: _scores
+    } = tf.tidy(() => {
+
+      let imgTensor = getImageTensor(input)
+      const [_, height, width] = imgTensor.shape
+
+      imgTensor = padToSquare(imgTensor)
+      paddedHeightRelative = imgTensor.shape[1] / height
+      paddedWidthRelative = imgTensor.shape[2] / width
+
+      return forwardTensor(imgTensor)
+    })
+
+    // TODO batches
+    const boxes = _boxes[0]
+    const scores = _scores[0]
+    for (let i = 1; i < _boxes.length; i++) {
+      _boxes[i].dispose()
+      _scores[i].dispose()
+    }
+
+    // TODO find a better way to filter by minConfidence
+    const scoresData = Array.from(await scores.data())
+
+    const iouThreshold = 0.5
+    const indices = nonMaxSuppression(
+      boxes,
+      scoresData,
+      maxResults,
+      iouThreshold,
+      minConfidence
+    )
+
+    const results = indices
+      .map(idx => new FaceDetectionResult(
+        scoresData[idx],
+        boxes.get(idx, 0) * paddedHeightRelative,
+        boxes.get(idx, 1) * paddedWidthRelative,
+        boxes.get(idx, 2) * paddedHeightRelative,
+        boxes.get(idx, 3) * paddedWidthRelative
+      ))
+
+    boxes.dispose()
+    scores.dispose()
+
+    return results
+  }
+
+  return {
+    forward,
+    locateFaces
+  }
+}
\ No newline at end of file

src/faceDetectionNet/mobileNetV1.ts

+import * as tf from '@tensorflow/tfjs-core';
+
+import { pointwiseConvLayer } from './pointwiseConvLayer';
+import { FaceDetectionNet } from './types';
+
+const epsilon = 0.0010000000474974513
+
+function depthwiseConvLayer(
+  x: tf.Tensor4D,
+  params: FaceDetectionNet.MobileNetV1.DepthwiseConvParams,
+  strides: [number, number]
+) {
+  return tf.tidy(() => {
+
+    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')
+    out = tf.batchNormalization<tf.Rank.R4>(
+      out,
+      params.batch_norm_mean,
+      params.batch_norm_variance,
+      epsilon,
+      params.batch_norm_scale,
+      params.batch_norm_offset
+    )
+    return tf.clipByValue(out, 0, 6)
+
+  })
+}
+
+function getStridesForLayerIdx(layerIdx: number): [number, number] {
+  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]
+}
+
+export function mobileNetV1(x: tf.Tensor4D, params: FaceDetectionNet.MobileNetV1.Params) {
+  return tf.tidy(() => {
+
+    let conv11 = null
+    let out = pointwiseConvLayer(x, params.conv_0_params, [2, 2])
+
+    params.conv_pair_params.forEach((param, i) => {
+      const layerIdx = i + 1
+      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)
+      out = depthwiseConvLayer(out, param.depthwise_conv_params, depthwiseConvStrides)
+      out = pointwiseConvLayer(out, param.pointwise_conv_params, [1, 1])
+      if (layerIdx === 11) {
+        conv11 = out
+      }
+    })
+
+    if (conv11 === null) {
+      throw new Error('mobileNetV1 - output of conv layer 11 is null')
+    }
+
+    return {
+      out,
+      conv11: conv11 as any
+    }
+
+  })
+}
\ No newline at end of file

src/faceDetectionNet/nonMaxSuppression.ts

+import * as tf from '@tensorflow/tfjs-core'
+
+export function nonMaxSuppression(
+  boxes: tf.Tensor2D,
+  scores: number[],
+  maxOutputSize: number,
+  iouThreshold: number,
+  scoreThreshold: number
+): number[] {
+
+  const numBoxes = boxes.shape[0]
+  const outputSize = Math.min(
+    maxOutputSize,
+    numBoxes
+  )
+
+  const candidates = scores
+    .map((score, boxIndex) => ({ score, boxIndex }))
+    .filter(c => c.score > scoreThreshold)
+    .sort((c1, c2) => c2.score - c1.score)
+
+  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0
+
+  const selected: number[] = []
+
+  candidates.forEach(c => {
+    if (selected.length >= outputSize) {
+      return
+    }
+    const originalScore = c.score
+
+    for (let j = selected.length - 1; j >= 0; --j) {
+      const iou = IOU(boxes, c.boxIndex, selected[j])
+      if (iou === 0.0) {
+        continue
+      }
+      c.score *= suppressFunc(iou)
+      if (c.score <= scoreThreshold) {
+        break
+      }
+    }
+
+    if (originalScore === c.score) {
+      selected.push(c.boxIndex)
+    }
+  })
+
+  return selected
+}
+
+function IOU(boxes: tf.Tensor2D, i: number, j: number) {
+  const yminI = Math.min(boxes.get(i, 0), boxes.get(i, 2))
+  const xminI = Math.min(boxes.get(i, 1), boxes.get(i, 3))
+  const ymaxI = Math.max(boxes.get(i, 0), boxes.get(i, 2))
+  const xmaxI = Math.max(boxes.get(i, 1), boxes.get(i, 3))
+  const yminJ = Math.min(boxes.get(j, 0), boxes.get(j, 2))
+  const xminJ = Math.min(boxes.get(j, 1), boxes.get(j, 3))
+  const ymaxJ = Math.max(boxes.get(j, 0), boxes.get(j, 2))
+  const xmaxJ = Math.max(boxes.get(j, 1), boxes.get(j, 3))
+  const areaI = (ymaxI - yminI) * (xmaxI - xminI)
+  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)
+  if (areaI <= 0 || areaJ <= 0) {
+    return 0.0
+  }
+  const intersectionYmin = Math.max(yminI, yminJ)
+  const intersectionXmin = Math.max(xminI, xminJ)
+  const intersectionYmax = Math.min(ymaxI, ymaxJ)
+  const intersectionXmax = Math.min(xmaxI, xmaxJ)
+  const intersectionArea =
+      Math.max(intersectionYmax - intersectionYmin, 0.0) *
+      Math.max(intersectionXmax - intersectionXmin, 0.0)
+  return intersectionArea / (areaI + areaJ - intersectionArea)
+}
\ No newline at end of file

src/faceDetectionNet/outputLayer.ts

+import * as tf from '@tensorflow/tfjs-core';
+
+import { FaceDetectionNet } from './types';
+
+function getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {
+  const vec = tf.unstack(tf.transpose(x, [1, 0]))
+
+  const sizes = [
+    tf.sub(vec[2], vec[0]),
+    tf.sub(vec[3], vec[1])
+  ]
+
+  const centers = [
+    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),
+    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))
+  ]
+
+  return {
+    sizes,
+    centers
+  }
+}
+
+function decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {
+  const {
+    sizes,
+    centers
+  } = getCenterCoordinatesAndSizesLayer(x0)
+
+  const vec = tf.unstack(tf.transpose(x1, [1, 0]))
+
+  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))
+  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])
+
+  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))
+  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])
+
+  return tf.transpose(
+    tf.stack([
+      tf.sub(add0_out, div0_out),
+      tf.sub(add1_out, div1_out),
+      tf.add(add0_out, div0_out),
+      tf.add(add1_out, div1_out)
+    ]),
+    [1, 0]
+  )
+}
+
+export function outputLayer(
+  boxPredictions: tf.Tensor4D,
+  classPredictions: tf.Tensor4D,
+  params: FaceDetectionNet.OutputLayerParams
+) {
+  return tf.tidy(() => {
+
+    const batchSize = boxPredictions.shape[0]
+
+    let boxes = decodeBoxesLayer(
+      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,
+      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D
+    )
+    boxes = tf.reshape(
+      boxes,
+      [batchSize, (boxes.shape[0] / batchSize), 4]
+    )
+
+    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))
+    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor
+
+    scores = tf.reshape(
+      scores,
+      [batchSize, scores.shape[1]]
+    )
+
+    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]
+    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]
+
+    return {
+      boxes: boxesByBatch,
+      scores: scoresByBatch
+    }
+
+  })
+}
\ No newline at end of file

src/faceDetectionNet/pointwiseConvLayer.ts

+import * as tf from '@tensorflow/tfjs-core';
+
+import { FaceDetectionNet } from './types';
+
+export function pointwiseConvLayer(
+  x: tf.Tensor4D,
+  params: FaceDetectionNet.PointwiseConvParams,
+  strides: [number, number]
+) {
+  return tf.tidy(() => {
+
+    let out = tf.conv2d(x, params.filters, strides, 'same')
+    out = tf.add(out, params.batch_norm_offset)
+    return tf.clipByValue(out, 0, 6)
+
+  })
+}
\ No newline at end of file

src/faceDetectionNet/predictionLayer.ts

+import * as tf from '@tensorflow/tfjs-core';
+
+import { boxPredictionLayer } from './boxPredictionLayer';
+import { pointwiseConvLayer } from './pointwiseConvLayer';
+import { FaceDetectionNet } from './types';
+
+export function predictionLayer(
+  x: tf.Tensor4D,
+  conv11: tf.Tensor4D,
+  params: FaceDetectionNet.PredictionLayerParams
+) {
+  return tf.tidy(() => {
+
+    const conv0 = pointwiseConvLayer(x, params.conv_0_params, [1, 1])
+    const conv1 = pointwiseConvLayer(conv0, params.conv_1_params, [2, 2])
+    const conv2 = pointwiseConvLayer(conv1, params.conv_2_params, [1, 1])
+    const conv3 = pointwiseConvLayer(conv2, params.conv_3_params, [2, 2])
+    const conv4 = pointwiseConvLayer(conv3, params.conv_4_params, [1, 1])
+    const conv5 = pointwiseConvLayer(conv4, params.conv_5_params, [2, 2])
+    const conv6 = pointwiseConvLayer(conv5, params.conv_6_params, [1, 1])
+    const conv7 = pointwiseConvLayer(conv6, params.conv_7_params, [2, 2])
+
+    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0_params)
+    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1_params)
+    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2_params)
+    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3_params)
+    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4_params)
+    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5_params)
+
+    const boxPredictions = tf.concat([
+      boxPrediction0.boxPredictionEncoding,
+      boxPrediction1.boxPredictionEncoding,
+      boxPrediction2.boxPredictionEncoding,
+      boxPrediction3.boxPredictionEncoding,
+      boxPrediction4.boxPredictionEncoding,
+      boxPrediction5.boxPredictionEncoding
+    ], 1) as tf.Tensor4D
+
+    const classPredictions = tf.concat([
+      boxPrediction0.classPrediction,
+      boxPrediction1.classPrediction,
+      boxPrediction2.classPrediction,
+      boxPrediction3.classPrediction,
+      boxPrediction4.classPrediction,
+      boxPrediction5.classPrediction
+    ], 1) as tf.Tensor4D
+
+    return {
+      boxPredictions,
+      classPredictions
+    }
+  })
+}
\ No newline at end of file

src/faceDetectionNet/resizeLayer.ts

+import * as tf from '@tensorflow/tfjs-core';
+
+const resizedImageSize = [512, 512] as [number, number]
+const weight = tf.scalar(0.007843137718737125)
+const bias = tf.scalar(1)
+
+export function resizeLayer(x: tf.Tensor4D) {
+  return tf.tidy(() => {
+
+    const resized = tf.image.resizeBilinear(x, resizedImageSize, false)
+    return tf.sub(tf.mul(resized, weight), bias)
+
+  })
+}
\ No newline at end of file

src/faceDetectionNet/types.ts

+import * as tf from '@tensorflow/tfjs-core';
+
+export namespace FaceDetectionNet {
+
+  export type PointwiseConvParams = {
+    filters: tf.Tensor4D
+    batch_norm_offset: tf.Tensor1D
+  }
+
+  export namespace MobileNetV1 {
+
+    export type DepthwiseConvParams = {
+      filters: tf.Tensor4D
+      batch_norm_scale: tf.Tensor1D
+      batch_norm_offset: tf.Tensor1D
+      batch_norm_mean: tf.Tensor1D
+      batch_norm_variance: tf.Tensor1D
+    }
+
+    export type ConvPairParams = {
+      depthwise_conv_params: DepthwiseConvParams
+      pointwise_conv_params: PointwiseConvParams
+    }
+
+    export type Params = {
+      conv_0_params: PointwiseConvParams
+      conv_pair_params: ConvPairParams[]
+    }
+
+  }
+
+  export type ConvWithBiasParams = {
+    filters: tf.Tensor4D
+    bias: tf.Tensor1D
+  }
+
+  export type BoxPredictionParams = {
+    box_encoding_predictor_params: ConvWithBiasParams
+    class_predictor_params: ConvWithBiasParams
+  }
+
+  export type PredictionLayerParams = {
+    conv_0_params: PointwiseConvParams
+    conv_1_params: PointwiseConvParams
+    conv_2_params: PointwiseConvParams
+    conv_3_params: PointwiseConvParams
+    conv_4_params: PointwiseConvParams
+    conv_5_params: PointwiseConvParams
+    conv_6_params: PointwiseConvParams
+    conv_7_params: PointwiseConvParams
+    box_predictor_0_params: BoxPredictionParams
+    box_predictor_1_params: BoxPredictionParams
+    box_predictor_2_params: BoxPredictionParams
+    box_predictor_3_params: BoxPredictionParams
+    box_predictor_4_params: BoxPredictionParams
+    box_predictor_5_params: BoxPredictionParams
+  }
+
+  export type OutputLayerParams = {
+    extra_dim: tf.Tensor3D
+  }
+
+  export type NetParams = {
+    mobilenetv1_params: MobileNetV1.Params,
+    prediction_layer_params: PredictionLayerParams,
+    output_layer_params: OutputLayerParams
+  }
+
+  export type Detection = {
+    score: number
+    box: {
+      top: number,
+      left: number,
+      right: number,
+      bottom: number
+    }
+  }
+
+}

src/faceRecognitionNet/convLayer.ts

 import * as tf from '@tensorflow/tfjs-core';
 
-import { scale, ScaleLayerParams } from './scaleLayer';
+import { scale } from './scaleLayer';
+import { FaceRecognitionNet } from './types';
 
-export type ConvParams = {
-  filters: tf.Tensor4D
-  biases: tf.Tensor1D
-}
-
-export type ConvLayerParams = {
-  conv: ConvParams
-  scale: ScaleLayerParams
-}
 
 function convLayer(
   x: tf.Tensor4D,
-  params: ConvLayerParams,
+  params: FaceRecognitionNet.ConvLayerParams,
   stride: number,
   withRelu: boolean,
   padding: 'valid' | 'same' = 'same'
@@ -27,14 +19,14 @@ function convLayer(
   return withRelu ? tf.relu(out) : out
 }
 
-export function conv(x: tf.Tensor4D, params: ConvLayerParams) {
+export function conv(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams) {
   return convLayer(x, params, 1, true)
 }
 
-export function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {
+export function convNoRelu(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams) {
   return convLayer(x, params, 1, false)
 }
 
-export function convDown(x: tf.Tensor4D, params: ConvLayerParams) {
+export function convDown(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams) {
   return convLayer(x, params, 2, true, 'valid')
 }
\ No newline at end of file

src/faceRecognitionNet/extractParams.ts

 import * as tf from '@tensorflow/tfjs-core';
 
-import { ConvLayerParams } from './convLayer';
-import { FaceRecognitionNetParams } from './FaceRecognitionNetParams';
-import { ResidualLayerParams } from './residualLayer';
-import { ScaleLayerParams } from './scaleLayer';
-
-function isFloat(num: number) {
-  return num % 1 !== 0
-}
+import { isFloat } from '../utils';
+import { FaceRecognitionNet } from './types';
 
 function extractorsFactory(extractWeights: (numWeights: number) => Float32Array) {
 
@@ -26,7 +20,7 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
     )
   }
 
-  function extractScaleLayerParams(numWeights: number): ScaleLayerParams {
+  function extractScaleLayerParams(numWeights: number): FaceRecognitionNet.ScaleLayerParams {
     const weights = tf.tensor1d(extractWeights(numWeights))
     const biases = tf.tensor1d(extractWeights(numWeights))
     return {
@@ -35,7 +29,7 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
     }
   }
 
-  function extractConvLayerParams(numFilterValues: number, numFilters: number, filterSize: number): ConvLayerParams {
+  function extractConvLayerParams(numFilterValues: number, numFilters: number, filterSize: number): FaceRecognitionNet.ConvLayerParams {
     const conv_filters = extractFilterValues(numFilterValues, numFilters, filterSize)
     const conv_biases = tf.tensor1d(extractWeights(numFilters))
     const scale = extractScaleLayerParams(numFilters)
@@ -49,9 +43,9 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
     }
   }
 
-  function extractResidualLayerParams(numFilterValues: number, numFilters: number, filterSize: number, isDown: boolean = false): ResidualLayerParams {
-    const conv1: ConvLayerParams = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize)
-    const conv2: ConvLayerParams = extractConvLayerParams(numFilterValues, numFilters, filterSize)
+  function extractResidualLayerParams(numFilterValues: number, numFilters: number, filterSize: number, isDown: boolean = false): FaceRecognitionNet.ResidualLayerParams {
+    const conv1: FaceRecognitionNet.ConvLayerParams = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize)
+    const conv2: FaceRecognitionNet.ConvLayerParams = extractConvLayerParams(numFilterValues, numFilters, filterSize)
 
     return {
       conv1,
@@ -66,7 +60,7 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
 
 }
 
-export function extractParams(weights: Float32Array): FaceRecognitionNetParams {
+export function extractParams(weights: Float32Array): FaceRecognitionNet.NetParams {
   const extractWeights = (numWeights: number): Float32Array => {
     const ret = weights.slice(0, numWeights)
     weights = weights.slice(numWeights)

src/faceRecognitionNet/index.ts

 import * as tf from '@tensorflow/tfjs-core';
 
-import { normalize } from '../normalize';
+import { NetInput } from '../NetInput';
+import { getImageTensor, padToSquare } from '../transformInputs';
+import { TNetInput } from '../types';
 import { convDown } from './convLayer';
 import { extractParams } from './extractParams';
+import { normalize } from './normalize';
 import { residual, residualDown } from './residualLayer';
 
 export function faceRecognitionNet(weights: Float32Array) {
   const params = extractParams(weights)
 
-  function forward(input: number[] | ImageData) {
-
+  function forward(input: tf.Tensor | NetInput | TNetInput) {
     return tf.tidy(() => {
 
-      const x = normalize(input)
+      const x = normalize(padToSquare(getImageTensor(input)))
 
       let out = convDown(x, params.conv32_down)
       out = tf.maxPool(out, 3, 2, 'valid')
@@ -42,8 +44,19 @@ export function faceRecognitionNet(weights: Float32Array) {
     })
   }
 
-  const computeFaceDescriptor = (input: number[] | ImageData) => forward(input).data()
-  const computeFaceDescriptorSync = (input: number[] | ImageData) => forward(input).dataSync()
+  const computeFaceDescriptor = async (input: tf.Tensor | NetInput | TNetInput) => {
+    const result = forward(input)
+    const data = await result.data()
+    result.dispose()
+    return data
+  }
+
+  const computeFaceDescriptorSync = (input: tf.Tensor | NetInput | TNetInput) => {
+    const result = forward(input)
+    const data = result.dataSync()
+    result.dispose()
+    return data
+  }
 
   return {
     computeFaceDescriptor,

src/normalize.ts → src/faceRecognitionNet/normalize.ts

 import * as tf from '@tensorflow/tfjs-core';
 
-export function normalize(input: number[] | ImageData): tf.Tensor4D {
+export function normalize(x: tf.Tensor4D): tf.Tensor4D {
   return tf.tidy(() => {
     const avg_r = tf.fill([1, 150, 150, 1], 122.782);
     const avg_g = tf.fill([1, 150, 150, 1], 117.001);
     const avg_b = tf.fill([1, 150, 150, 1], 104.298);
     const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)
 
-    const x = input instanceof ImageData
-      ? tf.cast(tf.reshape(tf.fromPixels(input), [1, 150, 150, 3]), 'float32')
-      : tf.tensor4d(input, [1, 150, 150, 3])
-    return tf.div(tf.sub(x, avg_rgb), tf.fill(x.shape, 256))
+    return tf.div(tf.sub(x, avg_rgb), tf.scalar(256))
   })
 }
\ No newline at end of file

src/faceRecognitionNet/residualLayer.ts

 import * as tf from '@tensorflow/tfjs-core';
 
-import { conv, convNoRelu, ConvLayerParams, convDown } from './convLayer';
+import { conv, convDown, convNoRelu } from './convLayer';
+import { FaceRecognitionNet } from './types';
 
-export type ResidualLayerParams = {
-  conv1: ConvLayerParams
-  conv2: ConvLayerParams
-}
-
-export function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {
+export function residual(x: tf.Tensor4D, params: FaceRecognitionNet.ResidualLayerParams): tf.Tensor4D {
   let out = conv(x, params.conv1)
   out = convNoRelu(out, params.conv2)
   out = tf.add(out, x)
@@ -15,7 +11,7 @@ export function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor
   return out
 }
 
-export function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {
+export function residualDown(x: tf.Tensor4D, params: FaceRecognitionNet.ResidualLayerParams): tf.Tensor4D {
   let out = convDown(x, params.conv1)
   out = convNoRelu(out, params.conv2)
 

src/faceRecognitionNet/scaleLayer.ts

 import * as tf from '@tensorflow/tfjs-core';
+import { FaceRecognitionNet } from './types';
 
-export type ScaleLayerParams = {
-  weights: tf.Tensor1D
-  biases: tf.Tensor1D
-}
-
-export function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {
+export function scale(x: tf.Tensor4D, params: FaceRecognitionNet.ScaleLayerParams): tf.Tensor4D {
   return tf.add(tf.mul(x, params.weights), params.biases)
-}
\ No newline at end of file
+}

src/faceRecognitionNet/FaceRecognitionNetParams.ts → src/faceRecognitionNet/types.ts

 import * as tf from '@tensorflow/tfjs-core';
 
-import { ConvLayerParams } from './convLayer';
-import { ResidualLayerParams } from './residualLayer';
+export namespace FaceRecognitionNet {
+
+  export type ScaleLayerParams = {
+    weights: tf.Tensor1D
+    biases: tf.Tensor1D
+  }
+  export type ResidualLayerParams = {
+    conv1: ConvLayerParams
+    conv2: ConvLayerParams
+  }
+
+  export type ConvParams = {
+    filters: tf.Tensor4D
+    biases: tf.Tensor1D
+  }
+
+  export type ConvLayerParams = {
+    conv: ConvParams
+    scale: ScaleLayerParams
+  }
+
+  export type NetParams = {
+    conv32_down: ConvLayerParams
+    conv32_1: ResidualLayerParams
+    conv32_2: ResidualLayerParams
+    conv32_3: ResidualLayerParams
+    conv64_down: ResidualLayerParams
+    conv64_1: ResidualLayerParams
+    conv64_2: ResidualLayerParams
+    conv64_3: ResidualLayerParams
+    conv128_down: ResidualLayerParams
+    conv128_1: ResidualLayerParams
+    conv128_2: ResidualLayerParams
+    conv256_down: ResidualLayerParams
+    conv256_1: ResidualLayerParams
+    conv256_2: ResidualLayerParams
+    conv256_down_out: ResidualLayerParams
+    fc: tf.Tensor2D
+  }
 
-export type FaceRecognitionNetParams = {
-  conv32_down: ConvLayerParams
-  conv32_1: ResidualLayerParams
-  conv32_2: ResidualLayerParams
-  conv32_3: ResidualLayerParams
-  conv64_down: ResidualLayerParams
-  conv64_1: ResidualLayerParams
-  conv64_2: ResidualLayerParams
-  conv64_3: ResidualLayerParams
-  conv128_down: ResidualLayerParams
-  conv128_1: ResidualLayerParams
-  conv128_2: ResidualLayerParams
-  conv256_down: ResidualLayerParams
-  conv256_1: ResidualLayerParams
-  conv256_2: ResidualLayerParams
-  conv256_down_out: ResidualLayerParams
-  fc: tf.Tensor2D
 }
\ No newline at end of file

src/index.ts

 import { euclideanDistance } from './euclideanDistance';
+import { faceDetectionNet } from './faceDetectionNet';
 import { faceRecognitionNet } from './faceRecognitionNet';
-import { normalize } from './normalize';
+import { NetInput } from './NetInput';
 
 export {
   euclideanDistance,
+  faceDetectionNet,
   faceRecognitionNet,
-  normalize
-}
\ No newline at end of file
+  NetInput
+}
+
+export * from './utils'
\ No newline at end of file

src/transformInputs.ts

+import * as tf from '@tensorflow/tfjs-core';
+
+import { NetInput } from './NetInput';
+import { TNetInput } from './types';
+
+export function padToSquare(imgTensor: tf.Tensor4D): tf.Tensor4D {
+  return tf.tidy(() => {
+
+    const [_, height, width] = imgTensor.shape
+    if (height === width) {
+      return imgTensor
+    }
+
+    if (height > width) {
+      const pad = tf.fill([1, height, height - width, 3], 0) as tf.Tensor4D
+      return tf.concat([imgTensor, pad], 2)
+    }
+    const pad = tf.fill([1, width - height, width, 3], 0) as tf.Tensor4D
+    return tf.concat([imgTensor, pad], 1)
+  })
+}
+
+export function getImageTensor(input: tf.Tensor | NetInput | TNetInput): tf.Tensor4D {
+  return tf.tidy(() => {
+    if (input instanceof tf.Tensor) {
+      const rank = input.shape.length
+      if (rank !== 3 && rank !== 4) {
+        throw new Error('input tensor must be of rank 3 or 4')
+      }
+
+      return (rank === 3 ? input.expandDims(0) : input).toFloat() as tf.Tensor4D
+    }
+
+    const netInput = input instanceof NetInput ? input : new NetInput(input)
+    return tf.concat(
+      netInput.canvases.map(canvas =>
+        tf.fromPixels(canvas).expandDims(0).toFloat()
+      )
+    ) as tf.Tensor4D
+  })
+}
\ No newline at end of file

src/types.ts

+export type TMediaElement = HTMLImageElement | HTMLVideoElement | HTMLCanvasElement
+
+export type TNetInputArg = string | TMediaElement
+
+export type TNetInput = TNetInputArg | Array<TNetInputArg>
+
+export type Dimensions = {
+  width: number
+  height: number
+}
+
+export type DrawBoxOptions = {
+  lineWidth: number
+  color: string
+}
+
+export type DrawTextOptions = {
+  fontSize: number
+  fontStyle: string
+  color: string
+}
\ No newline at end of file

src/utils.ts

+import { FaceDetectionNet } from './faceDetectionNet/types';
+import { DrawBoxOptions, DrawTextOptions } from './types';
+
+export function isFloat(num: number) {
+  return num % 1 !== 0
+}
+
+export function round(num: number) {
+  return Math.floor(num * 100) / 100
+}
+
+export function getElement(arg: string | any) {
+  if (typeof arg === 'string') {
+    return document.getElementById(arg)
+  }
+  return arg
+}
+
+export function getContext2dOrThrow(canvas: HTMLCanvasElement): CanvasRenderingContext2D {
+  const ctx = canvas.getContext('2d')
+  if (!ctx) {
+    throw new Error('canvas 2d context is null')
+  }
+  return ctx
+}
+
+export function getMediaDimensions(media: HTMLImageElement | HTMLVideoElement) {
+  if (media instanceof HTMLVideoElement) {
+    return { width: media.videoWidth, height: media.videoHeight }
+  }
+  return media
+}
+
+export function bufferToImage(buf: Blob): Promise<HTMLImageElement> {
+  return new Promise((resolve, reject) => {
+    if (!(buf instanceof Blob)) {
+      return reject('bufferToImage - expected buf to be of type: Blob')
+    }
+
+    const reader = new FileReader()
+    reader.onload = () => {
+      const img = new Image()
+      img.onload = () => resolve(img)
+      img.onerror = reject
+      img.src = reader.result
+    }
+    reader.onerror = reject
+    reader.readAsDataURL(buf)
+  })
+}
+
+export function drawBox(
+  ctx: CanvasRenderingContext2D,
+  x: number,
+  y: number,
+  w: number,
+  h: number,
+  options: DrawBoxOptions
+) {
+  ctx.strokeStyle = options.color
+  ctx.lineWidth = options.lineWidth
+  ctx.strokeRect(x, y, w, h)
+}
+
+export function drawText(
+  ctx: CanvasRenderingContext2D,
+  x: number,
+  y: number,
+  text: string,
+  options: DrawTextOptions
+) {
+  ctx.fillStyle = options.color
+  ctx.font = `${options.fontSize}px ${options.fontStyle}`
+  ctx.fillText(text, x, y)
+}
+
+export function drawDetection(
+  canvasArg: string | HTMLCanvasElement,
+  detection: FaceDetectionNet.Detection | FaceDetectionNet.Detection[],
+  options?: DrawBoxOptions & DrawTextOptions & { withScore: boolean }
+) {
+  const canvas = getElement(canvasArg)
+  if (!(canvas instanceof HTMLCanvasElement)) {
+    throw new Error('drawBox - expected canvas to be of type: HTMLCanvasElement')
+  }
+
+  const detectionArray = Array.isArray(detection)
+    ? detection
+    : [detection]
+
+  detectionArray.forEach((det) => {
+    const {
+      score,
+      box
+    } = det
+
+    const {
+      left,
+      right,
+      top,
+      bottom
+    } = box
+
+    const {
+      color = 'blue',
+      lineWidth = 2,
+      fontSize = 20,
+      fontStyle = 'Georgia',
+      withScore = true
+    } = (options || {})
+
+    const padText = 2 + lineWidth
+
+    const ctx = getContext2dOrThrow(canvas)
+    drawBox(
+      ctx,
+      left,
+      top,
+      right - left,
+      bottom - top,
+      { lineWidth, color }
+    )
+    if (withScore) {
+      drawText(
+        ctx,
+        left + padText,
+        top + (fontSize * 0.6) + padText,
+        `${round(score)}`,
+        { fontSize, fontStyle, color }
+      )
+    }
+  })
+}
\ No newline at end of file

tsconfig.json

 {
   "compilerOptions": {
+    //"allowUnreachableCode": true,
+    //"noUnusedLocals": false,
+    //"noUnusedParameters": false,
+
+
     "removeComments": false,
     "preserveConstEnums": true,
     "emitDecoratorMetadata": true,
@@ -7,8 +12,6 @@
     "sourceMap": true,
     "declaration": true,
     "noImplicitAny": true,
-    "noUnusedLocals": true,
-    "noUnusedParameters": true,
     "noImplicitReturns": true,
     "noImplicitThis": true,
     "noFallthroughCasesInSwitch": true,
@@ -30,6 +33,10 @@
     "indentSize": 2,
     "tabSize": 2
   },
+  "exclude": [
+    "node_modules",
+    "dist"
+  ],
   "include": [
     "src"
   ]