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Unverified Commit f7c90389 by justadudewhohacks Committed by GitHub
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Merge pull request #2 from justadudewhohacks/ssd_mobilenet_facedetector

Ssd mobilenet facedetector
parents 0387fd20 b5463d66
Showing with 1076 additions and 153 deletions
dist/NetInput.d.ts 0 → 100644
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 0 → 100644
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 0 → 100644
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dist/face-recognition.js 0 → 100644
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dist/face-recognition.min.js
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dist/faceDetectionNet/FaceDetectionResult.d.ts 0 → 100644
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 0 → 100644
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 0 → 100644
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dist/faceDetectionNet/boxPredictionLayer.d.ts 0 → 100644
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 0 → 100644
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 0 → 100644
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dist/faceDetectionNet/extractParams.d.ts 0 → 100644
import { FaceDetectionNet } from './types';
export declare function extractParams(weights: Float32Array): FaceDetectionNet.NetParams;
dist/faceDetectionNet/extractParams.js 0 → 100644
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
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dist/faceDetectionNet/extractParams.js.map 0 → 100644
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dist/faceDetectionNet/index.d.ts 0 → 100644
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 0 → 100644
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
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dist/faceDetectionNet/index.js.map 0 → 100644
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dist/faceDetectionNet/mobileNetV1.d.ts 0 → 100644
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 0 → 100644
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 0 → 100644
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\ No newline at end of file
dist/faceDetectionNet/nonMaxSuppression.d.ts 0 → 100644
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 0 → 100644
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 0 → 100644
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\ No newline at end of file
dist/faceDetectionNet/outputLayer.d.ts 0 → 100644
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 0 → 100644
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 0 → 100644
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\ No newline at end of file
dist/faceDetectionNet/pointwiseConvLayer.d.ts 0 → 100644
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 0 → 100644
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 0 → 100644
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\ No newline at end of file
dist/faceDetectionNet/predictionLayer.d.ts 0 → 100644
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 0 → 100644
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 0 → 100644
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\ No newline at end of file
dist/normalize.d.ts → dist/faceDetectionNet/resizeLayer.d.ts
import * as tf from '@tensorflow/tfjs-core'; 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 0 → 100644
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 0 → 100644
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\ No newline at end of file
dist/faceDetectionNet/types.d.ts 0 → 100644
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 0 → 100644
//# sourceMappingURL=types.js.map
\ No newline at end of file
dist/faceDetectionNet/types.js.map 0 → 100644
{"version":3,"file":"types.js","sourceRoot":"","sources":["../../src/faceDetectionNet/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
dist/faceRecognitionNet/FaceRecognitionNetParams.js deleted 100644 → 0
//# sourceMappingURL=FaceRecognitionNetParams.js.map
\ No newline at end of file
dist/faceRecognitionNet/FaceRecognitionNetParams.js.map deleted 100644 → 0
{"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 * as tf from '@tensorflow/tfjs-core';
import { ScaleLayerParams } from './scaleLayer'; import { FaceRecognitionNet } from './types';
export declare type ConvParams = { export declare function conv(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams): tf.Tensor<tf.Rank.R4>;
filters: tf.Tensor4D; export declare function convNoRelu(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams): tf.Tensor<tf.Rank.R4>;
biases: tf.Tensor1D; export declare function convDown(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams): tf.Tensor<tf.Rank.R4>;
};
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>;
dist/faceRecognitionNet/convLayer.js.map
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\ No newline at end of file \ No newline at end of file
dist/faceRecognitionNet/extractParams.d.ts
import { FaceRecognitionNetParams } from './FaceRecognitionNetParams'; import { FaceRecognitionNet } from './types';
export declare function extractParams(weights: Float32Array): FaceRecognitionNetParams; export declare function extractParams(weights: Float32Array): FaceRecognitionNet.NetParams;
dist/faceRecognitionNet/extractParams.js
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
function isFloat(num) { import { isFloat } from '../utils';
return num % 1 !== 0;
}
function extractorsFactory(extractWeights) { function extractorsFactory(extractWeights) {
function extractFilterValues(numFilterValues, numFilters, filterSize) { function extractFilterValues(numFilterValues, numFilters, filterSize) {
var weights = extractWeights(numFilterValues); var weights = extractWeights(numFilterValues);
......
dist/faceRecognitionNet/extractParams.js.map
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\ No newline at end of file \ No newline at end of file
dist/faceRecognitionNet/index.d.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from '../NetInput';
export declare function faceRecognitionNet(weights: Float32Array): { export declare function faceRecognitionNet(weights: Float32Array): {
computeFaceDescriptor: (input: number[] | ImageData) => Promise<Int32Array | Uint8Array | Float32Array>; computeFaceDescriptor: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => Promise<Int32Array | Uint8Array | Float32Array>;
computeFaceDescriptorSync: (input: number[] | ImageData) => Int32Array | Uint8Array | Float32Array; computeFaceDescriptorSync: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => Int32Array | Uint8Array | Float32Array;
forward: (input: number[] | ImageData) => tf.Tensor<tf.Rank.R2>; 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 * as tf from '@tensorflow/tfjs-core';
import { normalize } from '../normalize'; import { getImageTensor, padToSquare } from '../transformInputs';
import { convDown } from './convLayer'; import { convDown } from './convLayer';
import { extractParams } from './extractParams'; import { extractParams } from './extractParams';
import { normalize } from './normalize';
import { residual, residualDown } from './residualLayer'; import { residual, residualDown } from './residualLayer';
export function faceRecognitionNet(weights) { export function faceRecognitionNet(weights) {
var _this = this;
var params = extractParams(weights); var params = extractParams(weights);
function forward(input) { function forward(input) {
return tf.tidy(function () { return tf.tidy(function () {
var x = normalize(input); var x = normalize(padToSquare(getImageTensor(input)));
var out = convDown(x, params.conv32_down); var out = convDown(x, params.conv32_down);
out = tf.maxPool(out, 3, 2, 'valid'); out = tf.maxPool(out, 3, 2, 'valid');
out = residual(out, params.conv32_1); out = residual(out, params.conv32_1);
...@@ -29,8 +32,26 @@ export function faceRecognitionNet(weights) { ...@@ -29,8 +32,26 @@ export function faceRecognitionNet(weights) {
return fullyConnected; return fullyConnected;
}); });
} }
var computeFaceDescriptor = function (input) { return forward(input).data(); }; var computeFaceDescriptor = function (input) { return tslib_1.__awaiter(_this, void 0, void 0, function () {
var computeFaceDescriptorSync = function (input) { return forward(input).dataSync(); }; 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 { return {
computeFaceDescriptor: computeFaceDescriptor, computeFaceDescriptor: computeFaceDescriptor,
computeFaceDescriptorSync: computeFaceDescriptorSync, computeFaceDescriptorSync: computeFaceDescriptorSync,
......
dist/faceRecognitionNet/index.js.map
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\ No newline at end of file \ No newline at end of file
dist/faceRecognitionNet/normalize.d.ts 0 → 100644
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'; import * as tf from '@tensorflow/tfjs-core';
export function normalize(input) { export function normalize(x) {
return tf.tidy(function () { return tf.tidy(function () {
var avg_r = tf.fill([1, 150, 150, 1], 122.782); var avg_r = tf.fill([1, 150, 150, 1], 122.782);
var avg_g = tf.fill([1, 150, 150, 1], 117.001); var avg_g = tf.fill([1, 150, 150, 1], 117.001);
var avg_b = tf.fill([1, 150, 150, 1], 104.298); var avg_b = tf.fill([1, 150, 150, 1], 104.298);
var avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3); var avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);
var x = input instanceof ImageData return tf.div(tf.sub(x, avg_rgb), tf.scalar(256));
? 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));
}); });
} }
//# sourceMappingURL=normalize.js.map //# sourceMappingURL=normalize.js.map
\ No newline at end of file
dist/faceRecognitionNet/normalize.js.map 0 → 100644
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\ No newline at end of file
dist/faceRecognitionNet/residualLayer.d.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { ConvLayerParams } from './convLayer'; import { FaceRecognitionNet } from './types';
export declare type ResidualLayerParams = { export declare function residual(x: tf.Tensor4D, params: FaceRecognitionNet.ResidualLayerParams): tf.Tensor4D;
conv1: ConvLayerParams; export declare function residualDown(x: tf.Tensor4D, params: FaceRecognitionNet.ResidualLayerParams): tf.Tensor4D;
conv2: ConvLayerParams;
};
export declare function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D;
export declare function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D;
dist/faceRecognitionNet/residualLayer.js
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { conv, convNoRelu, convDown } from './convLayer'; import { conv, convDown, convNoRelu } from './convLayer';
export function residual(x, params) { export function residual(x, params) {
var out = conv(x, params.conv1); var out = conv(x, params.conv1);
out = convNoRelu(out, params.conv2); out = convNoRelu(out, params.conv2);
......
dist/faceRecognitionNet/residualLayer.js.map
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dist/faceRecognitionNet/scaleLayer.d.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
export declare type ScaleLayerParams = { import { FaceRecognitionNet } from './types';
weights: tf.Tensor1D; export declare function scale(x: tf.Tensor4D, params: FaceRecognitionNet.ScaleLayerParams): tf.Tensor4D;
biases: tf.Tensor1D;
};
export declare function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D;
dist/faceRecognitionNet/scaleLayer.js.map
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\ No newline at end of file \ No newline at end of file
dist/faceRecognitionNet/FaceRecognitionNetParams.d.ts → dist/faceRecognitionNet/types.d.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { ConvLayerParams } from './convLayer'; export declare namespace FaceRecognitionNet {
import { ResidualLayerParams } from './residualLayer'; type ScaleLayerParams = {
export declare type FaceRecognitionNetParams = { weights: tf.Tensor1D;
conv32_down: ConvLayerParams; biases: tf.Tensor1D;
conv32_1: ResidualLayerParams; };
conv32_2: ResidualLayerParams; type ResidualLayerParams = {
conv32_3: ResidualLayerParams; conv1: ConvLayerParams;
conv64_down: ResidualLayerParams; conv2: ConvLayerParams;
conv64_1: ResidualLayerParams; };
conv64_2: ResidualLayerParams; type ConvParams = {
conv64_3: ResidualLayerParams; filters: tf.Tensor4D;
conv128_down: ResidualLayerParams; biases: tf.Tensor1D;
conv128_1: ResidualLayerParams; };
conv128_2: ResidualLayerParams; type ConvLayerParams = {
conv256_down: ResidualLayerParams; conv: ConvParams;
conv256_1: ResidualLayerParams; scale: ScaleLayerParams;
conv256_2: ResidualLayerParams; };
conv256_down_out: ResidualLayerParams; type NetParams = {
fc: tf.Tensor2D; 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 0 → 100644
//# sourceMappingURL=types.js.map
\ No newline at end of file
dist/faceRecognitionNet/types.js.map 0 → 100644
{"version":3,"file":"types.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/types.ts"],"names":[],"mappings":""}
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dist/index.d.ts
import { euclideanDistance } from './euclideanDistance'; import { euclideanDistance } from './euclideanDistance';
import { faceDetectionNet } from './faceDetectionNet';
import { faceRecognitionNet } from './faceRecognitionNet'; import { faceRecognitionNet } from './faceRecognitionNet';
import { normalize } from './normalize'; import { NetInput } from './NetInput';
export { euclideanDistance, faceRecognitionNet, normalize }; export { euclideanDistance, faceDetectionNet, faceRecognitionNet, NetInput };
export * from './utils';
dist/index.js
import { euclideanDistance } from './euclideanDistance'; import { euclideanDistance } from './euclideanDistance';
import { faceDetectionNet } from './faceDetectionNet';
import { faceRecognitionNet } from './faceRecognitionNet'; import { faceRecognitionNet } from './faceRecognitionNet';
import { normalize } from './normalize'; import { NetInput } from './NetInput';
export { euclideanDistance, faceRecognitionNet, normalize }; export { euclideanDistance, faceDetectionNet, faceRecognitionNet, NetInput };
export * from './utils';
//# sourceMappingURL=index.js.map //# sourceMappingURL=index.js.map
\ No newline at end of file
dist/index.js.map
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\ No newline at end of file \ No newline at end of file
dist/normalize.js.map deleted 100644 → 0
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\ No newline at end of file
dist/transformInputs.d.ts 0 → 100644
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 0 → 100644
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 0 → 100644
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\ No newline at end of file
dist/types.d.ts 0 → 100644
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 0 → 100644
//# sourceMappingURL=types.js.map
\ No newline at end of file
dist/types.js.map 0 → 100644
{"version":3,"file":"types.js","sourceRoot":"","sources":["../src/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
dist/utils.d.ts 0 → 100644
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 0 → 100644
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 0 → 100644
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\ No newline at end of file
examples/faceRecognition/public/commons.js deleted 100644 → 0
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 deleted 100644 → 0
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 0 → 100644
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
proto/ssd_mobilenet_face_optimized_v2.pbtxt 0 → 100644
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src/NetInput.ts 0 → 100644
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src/types.ts 0 → 100644
This diff is collapsed. Click to expand it.
src/utils.ts 0 → 100644
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