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Unverified Commit fb378dd3 by justadudewhohacks Committed by GitHub
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Merge pull request #10 from justadudewhohacks/face-landmarks

68 point face landmark detection
parents c378f933 b9588f96
Showing with 1308 additions and 304 deletions
dist/NetInput.js
import { createCanvas, getContext2dOrThrow, getElement, getMediaDimensions } from './utils';
import { createCanvasFromMedia, getElement } from './utils';
var NetInput = /** @class */ (function () {
function NetInput(mediaArg, dims) {
var _this = this;
......@@ -27,10 +27,8 @@ var NetInput = /** @class */ (function () {
return;
}
// if input is batch type, make sure every canvas has the same dimensions
var _a = this.dims || dims || getMediaDimensions(media), width = _a.width, height = _a.height;
var canvas = createCanvas({ width: width, height: height });
getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
this._canvases.push(canvas);
var canvasDims = this.dims || dims;
this._canvases.push(createCanvasFromMedia(media, canvasDims));
};
Object.defineProperty(NetInput.prototype, "canvases", {
get: function () {
......
dist/NetInput.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/Point.d.ts 0 → 100644
export interface IPoint {
x: number;
y: number;
}
export declare class Point implements IPoint {
x: number;
y: number;
constructor(x: number, y: number);
add(pt: IPoint): Point;
sub(pt: IPoint): Point;
mul(pt: IPoint): Point;
div(pt: IPoint): Point;
}
dist/Point.js 0 → 100644
var Point = /** @class */ (function () {
function Point(x, y) {
this.x = x;
this.y = y;
}
Point.prototype.add = function (pt) {
return new Point(this.x + pt.x, this.y + pt.y);
};
Point.prototype.sub = function (pt) {
return new Point(this.x - pt.x, this.y - pt.y);
};
Point.prototype.mul = function (pt) {
return new Point(this.x * pt.x, this.y * pt.y);
};
Point.prototype.div = function (pt) {
return new Point(this.x / pt.x, this.y / pt.y);
};
return Point;
}());
export { Point };
//# sourceMappingURL=Point.js.map
\ No newline at end of file
dist/Point.js.map 0 → 100644
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\ No newline at end of file
dist/Rect.d.ts 0 → 100644
export declare class Rect {
x: number;
y: number;
width: number;
height: number;
constructor(x: number, y: number, width: number, height: number);
floor(): Rect;
}
dist/Rect.js 0 → 100644
var Rect = /** @class */ (function () {
function Rect(x, y, width, height) {
this.x = x;
this.y = y;
this.width = width;
this.height = height;
}
Rect.prototype.floor = function () {
return new Rect(Math.floor(this.x), Math.floor(this.y), Math.floor(this.width), Math.floor(this.height));
};
return Rect;
}());
export { Rect };
//# sourceMappingURL=Rect.js.map
\ No newline at end of file
dist/Rect.js.map 0 → 100644
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\ No newline at end of file
dist/commons/convLayer.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from './types';
export declare function convLayer(x: tf.Tensor4D, params: ConvParams, padding?: 'valid' | 'same', withRelu?: boolean): tf.Tensor4D;
dist/commons/convLayer.js 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
export function convLayer(x, params, padding, withRelu) {
if (padding === void 0) { padding = 'same'; }
if (withRelu === void 0) { withRelu = false; }
return tf.tidy(function () {
var out = tf.add(tf.conv2d(x, params.filters, [1, 1], padding), params.bias);
return withRelu ? tf.relu(out) : out;
});
}
//# sourceMappingURL=convLayer.js.map
\ No newline at end of file
dist/commons/convLayer.js.map 0 → 100644
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\ No newline at end of file
dist/commons/extractConvParamsFactory.d.ts 0 → 100644
import { ConvParams, ExtractWeightsFunction } from './types';
export declare function extractConvParamsFactory(extractWeights: ExtractWeightsFunction): (channelsIn: number, channelsOut: number, filterSize: number) => ConvParams;
dist/commons/extractConvParamsFactory.js 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
export function extractConvParamsFactory(extractWeights) {
return function (channelsIn, channelsOut, filterSize) {
var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
var bias = tf.tensor1d(extractWeights(channelsOut));
return {
filters: filters,
bias: bias
};
};
}
//# sourceMappingURL=extractConvParamsFactory.js.map
\ No newline at end of file
dist/commons/extractConvParamsFactory.js.map 0 → 100644
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\ No newline at end of file
dist/commons/extractWeightsFactory.d.ts 0 → 100644
export declare function extractWeightsFactory(weights: Float32Array): {
extractWeights: (numWeights: number) => Float32Array;
getRemainingWeights: () => Float32Array;
};
dist/commons/extractWeightsFactory.js 0 → 100644
export function extractWeightsFactory(weights) {
var remainingWeights = weights;
function extractWeights(numWeights) {
var ret = remainingWeights.slice(0, numWeights);
remainingWeights = remainingWeights.slice(numWeights);
return ret;
}
function getRemainingWeights() {
return remainingWeights;
}
return {
extractWeights: extractWeights,
getRemainingWeights: getRemainingWeights
};
}
//# sourceMappingURL=extractWeightsFactory.js.map
\ No newline at end of file
dist/commons/extractWeightsFactory.js.map 0 → 100644
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\ No newline at end of file
dist/commons/types.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
export declare type ConvParams = {
filters: tf.Tensor4D;
bias: tf.Tensor1D;
};
export declare type ExtractWeightsFunction = (numWeights: number) => Float32Array;
dist/commons/types.js 0 → 100644
//# sourceMappingURL=types.js.map
\ No newline at end of file
dist/commons/types.js.map 0 → 100644
{"version":3,"file":"types.js","sourceRoot":"","sources":["../../src/commons/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
dist/extractFaceTensors.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { FaceDetectionResult } from './faceDetectionNet/FaceDetectionResult';
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { NetInput } from './NetInput';
import { TNetInput } from './types';
/**
......@@ -13,4 +13,4 @@ import { TNetInput } from './types';
* @param detections The face detection results for that image.
* @returns Tensors of the corresponding image region for each detected face.
*/
export declare function extractFaceTensors(image: tf.Tensor | NetInput | TNetInput, detections: FaceDetectionResult[]): tf.Tensor4D[];
export declare function extractFaceTensors(image: tf.Tensor | NetInput | TNetInput, detections: FaceDetection[]): tf.Tensor4D[];
dist/extractFaceTensors.js
......@@ -17,7 +17,7 @@ export function extractFaceTensors(image, detections) {
// TODO handle batches
var _a = imgTensor.shape, batchSize = _a[0], imgHeight = _a[1], imgWidth = _a[2], numChannels = _a[3];
var faceTensors = detections.map(function (det) {
var _a = det.forSize(imgWidth, imgHeight).box, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var _a = det.forSize(imgWidth, imgHeight).getBox().floor(), x = _a.x, y = _a.y, width = _a.width, height = _a.height;
return tf.slice(imgTensor, [0, y, x, 0], [1, height, width, numChannels]);
});
return faceTensors;
......
dist/extractFaceTensors.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/extractFaces.d.ts
import { FaceDetectionResult } from './faceDetectionNet/FaceDetectionResult';
import { FaceDetection } from './faceDetectionNet/FaceDetection';
/**
* Extracts the image regions containing the detected faces.
*
......@@ -6,4 +6,4 @@ import { FaceDetectionResult } from './faceDetectionNet/FaceDetectionResult';
* @param detections The face detection results for that image.
* @returns The Canvases of the corresponding image region for each detected face.
*/
export declare function extractFaces(image: HTMLCanvasElement, detections: FaceDetectionResult[]): HTMLCanvasElement[];
export declare function extractFaces(image: HTMLCanvasElement, detections: FaceDetection[]): HTMLCanvasElement[];
dist/extractFaces.js
......@@ -9,7 +9,7 @@ import { createCanvas, getContext2dOrThrow } from './utils';
export function extractFaces(image, detections) {
var ctx = getContext2dOrThrow(image);
return detections.map(function (det) {
var _a = det.forSize(image.width, image.height).box, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var _a = det.forSize(image.width, image.height).getBox().floor(), x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var faceImg = createCanvas({ width: width, height: height });
getContext2dOrThrow(faceImg)
.putImageData(ctx.getImageData(x, y, width, height), 0, 0);
......
dist/extractFaces.js.map
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\ No newline at end of file
dist/face-api.js
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dist/face-api.min.js
This source diff could not be displayed because it is too large. You can view the blob instead.
dist/faceDetectionNet/FaceDetection.d.ts 0 → 100644
import { Rect } from '../Rect';
import { Dimensions } from '../types';
export declare class FaceDetection {
private _score;
private _box;
private _imageWidth;
private _imageHeight;
constructor(score: number, relativeBox: Rect, imageDims: Dimensions);
getScore(): number;
getBox(): Rect;
getImageWidth(): number;
getImageHeight(): number;
getRelativeBox(): Rect;
forSize(width: number, height: number): FaceDetection;
}
dist/faceDetectionNet/FaceDetection.js 0 → 100644
import { Rect } from '../Rect';
var FaceDetection = /** @class */ (function () {
function FaceDetection(score, relativeBox, imageDims) {
var width = imageDims.width, height = imageDims.height;
this._imageWidth = width;
this._imageHeight = height;
this._score = score;
this._box = new Rect(relativeBox.x * width, relativeBox.y * height, relativeBox.width * width, relativeBox.height * height);
}
FaceDetection.prototype.getScore = function () {
return this._score;
};
FaceDetection.prototype.getBox = function () {
return this._box;
};
FaceDetection.prototype.getImageWidth = function () {
return this._imageWidth;
};
FaceDetection.prototype.getImageHeight = function () {
return this._imageHeight;
};
FaceDetection.prototype.getRelativeBox = function () {
return new Rect(this._box.x / this._imageWidth, this._box.y / this._imageHeight, this._box.width / this._imageWidth, this._box.height / this._imageHeight);
};
FaceDetection.prototype.forSize = function (width, height) {
return new FaceDetection(this._score, this.getRelativeBox(), { width: width, height: height });
};
return FaceDetection;
}());
export { FaceDetection };
//# sourceMappingURL=FaceDetection.js.map
\ No newline at end of file
dist/faceDetectionNet/FaceDetection.js.map 0 → 100644
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\ No newline at end of file
dist/faceDetectionNet/FaceDetectionResult.d.ts deleted 100644 → 0
import { FaceDetectionNet } from './types';
export declare class FaceDetectionResult {
private _score;
private _topRelative;
private _leftRelative;
private _bottomRelative;
private _rightRelative;
constructor(score: number, topRelative: number, leftRelative: number, bottomRelative: number, rightRelative: number);
forSize(width: number, height: number): FaceDetectionNet.Detection;
}
dist/faceDetectionNet/FaceDetectionResult.js deleted 100644 → 0
var FaceDetectionResult = /** @class */ (function () {
function FaceDetectionResult(score, topRelative, leftRelative, bottomRelative, rightRelative) {
this._score = score;
this._topRelative = Math.max(0, topRelative),
this._leftRelative = Math.max(0, leftRelative),
this._bottomRelative = Math.min(1.0, bottomRelative),
this._rightRelative = Math.min(1.0, rightRelative);
}
FaceDetectionResult.prototype.forSize = function (width, height) {
var x = Math.floor(this._leftRelative * width);
var y = Math.floor(this._topRelative * height);
return {
score: this._score,
box: {
x: x,
y: y,
width: Math.floor(this._rightRelative * width) - x,
height: Math.floor(this._bottomRelative * height) - y
}
};
};
return FaceDetectionResult;
}());
export { FaceDetectionResult };
//# sourceMappingURL=FaceDetectionResult.js.map
\ No newline at end of file
dist/faceDetectionNet/FaceDetectionResult.js.map deleted 100644 → 0
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\ No newline at end of file
dist/faceDetectionNet/boxPredictionLayer.js
import * as tf from '@tensorflow/tfjs-core';
function convWithBias(x, params) {
return tf.tidy(function () {
return tf.add(tf.conv2d(x, params.filters, [1, 1], 'same'), params.bias);
});
}
import { convLayer } from '../commons/convLayer';
export function boxPredictionLayer(x, params) {
return tf.tidy(function () {
var batchSize = x.shape[0];
var boxPredictionEncoding = tf.reshape(convWithBias(x, params.box_encoding_predictor_params), [batchSize, -1, 1, 4]);
var classPrediction = tf.reshape(convWithBias(x, params.class_predictor_params), [batchSize, -1, 3]);
var boxPredictionEncoding = tf.reshape(convLayer(x, params.box_encoding_predictor_params), [batchSize, -1, 1, 4]);
var classPrediction = tf.reshape(convLayer(x, params.class_predictor_params), [batchSize, -1, 3]);
return {
boxPredictionEncoding: boxPredictionEncoding,
classPrediction: classPrediction
......
dist/faceDetectionNet/boxPredictionLayer.js.map
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\ No newline at end of file
dist/faceDetectionNet/extractParams.js
import * as tf from '@tensorflow/tfjs-core';
import { extractWeightsFactory } from '../commons/extractWeightsFactory';
function extractorsFactory(extractWeights) {
function extractDepthwiseConvParams(numChannels) {
var filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
......@@ -14,7 +15,7 @@ function extractorsFactory(extractWeights) {
batch_norm_variance: batch_norm_variance
};
}
function extractConvWithBiasParams(channelsIn, channelsOut, filterSize) {
function extractConvParams(channelsIn, channelsOut, filterSize) {
var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
var bias = tf.tensor1d(extractWeights(channelsOut));
return {
......@@ -23,7 +24,7 @@ function extractorsFactory(extractWeights) {
};
}
function extractPointwiseConvParams(channelsIn, channelsOut, filterSize) {
var _a = extractConvWithBiasParams(channelsIn, channelsOut, filterSize), filters = _a.filters, bias = _a.bias;
var _a = extractConvParams(channelsIn, channelsOut, filterSize), filters = _a.filters, bias = _a.bias;
return {
filters: filters,
batch_norm_offset: bias
......@@ -72,18 +73,18 @@ function extractorsFactory(extractWeights) {
var conv_5_params = extractPointwiseConvParams(128, 256, 3);
var conv_6_params = extractPointwiseConvParams(256, 64, 1);
var conv_7_params = extractPointwiseConvParams(64, 128, 3);
var box_encoding_0_predictor_params = extractConvWithBiasParams(512, 12, 1);
var class_predictor_0_params = extractConvWithBiasParams(512, 9, 1);
var box_encoding_1_predictor_params = extractConvWithBiasParams(1024, 24, 1);
var class_predictor_1_params = extractConvWithBiasParams(1024, 18, 1);
var box_encoding_2_predictor_params = extractConvWithBiasParams(512, 24, 1);
var class_predictor_2_params = extractConvWithBiasParams(512, 18, 1);
var box_encoding_3_predictor_params = extractConvWithBiasParams(256, 24, 1);
var class_predictor_3_params = extractConvWithBiasParams(256, 18, 1);
var box_encoding_4_predictor_params = extractConvWithBiasParams(256, 24, 1);
var class_predictor_4_params = extractConvWithBiasParams(256, 18, 1);
var box_encoding_5_predictor_params = extractConvWithBiasParams(128, 24, 1);
var class_predictor_5_params = extractConvWithBiasParams(128, 18, 1);
var box_encoding_0_predictor_params = extractConvParams(512, 12, 1);
var class_predictor_0_params = extractConvParams(512, 9, 1);
var box_encoding_1_predictor_params = extractConvParams(1024, 24, 1);
var class_predictor_1_params = extractConvParams(1024, 18, 1);
var box_encoding_2_predictor_params = extractConvParams(512, 24, 1);
var class_predictor_2_params = extractConvParams(512, 18, 1);
var box_encoding_3_predictor_params = extractConvParams(256, 24, 1);
var class_predictor_3_params = extractConvParams(256, 18, 1);
var box_encoding_4_predictor_params = extractConvParams(256, 24, 1);
var class_predictor_4_params = extractConvParams(256, 18, 1);
var box_encoding_5_predictor_params = extractConvParams(128, 24, 1);
var class_predictor_5_params = extractConvParams(128, 18, 1);
var box_predictor_0_params = {
box_encoding_predictor_params: box_encoding_0_predictor_params,
class_predictor_params: class_predictor_0_params
......@@ -131,20 +132,16 @@ function extractorsFactory(extractWeights) {
};
}
export function extractParams(weights) {
var extractWeights = function (numWeights) {
var ret = weights.slice(0, numWeights);
weights = weights.slice(numWeights);
return ret;
};
var _a = extractorsFactory(extractWeights), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var _b = extractorsFactory(extractWeights), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;
var mobilenetv1_params = extractMobilenetV1Params();
var prediction_layer_params = extractPredictionLayerParams();
var extra_dim = tf.tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
var output_layer_params = {
extra_dim: extra_dim
};
if (weights.length !== 0) {
throw new Error("weights remaing after extract: " + weights.length);
if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
}
return {
mobilenetv1_params: mobilenetv1_params,
......
dist/faceDetectionNet/index.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from '../NetInput';
import { FaceDetectionResult } from './FaceDetectionResult';
import { FaceDetection } from './FaceDetection';
export declare function faceDetectionNet(weights: Float32Array): {
forward: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => {
forward: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | NetInput | tf.Tensor<tf.Rank>) => {
boxes: tf.Tensor<tf.Rank.R2>[];
scores: tf.Tensor<tf.Rank.R1>[];
};
locateFaces: (input: tf.Tensor<tf.Rank> | NetInput, minConfidence?: number, maxResults?: number) => Promise<FaceDetectionResult[]>;
locateFaces: (input: NetInput | tf.Tensor<tf.Rank>, minConfidence?: number, maxResults?: number) => Promise<FaceDetection[]>;
};
dist/faceDetectionNet/index.js
......@@ -3,12 +3,13 @@ import * as tf from '@tensorflow/tfjs-core';
import { getImageTensor } from '../getImageTensor';
import { padToSquare } from '../padToSquare';
import { extractParams } from './extractParams';
import { FaceDetectionResult } from './FaceDetectionResult';
import { FaceDetection } from './FaceDetection';
import { mobileNetV1 } from './mobileNetV1';
import { nonMaxSuppression } from './nonMaxSuppression';
import { outputLayer } from './outputLayer';
import { predictionLayer } from './predictionLayer';
import { resizeLayer } from './resizeLayer';
import { Rect } from '../Rect';
export function faceDetectionNet(weights) {
var params = extractParams(weights);
function forwardTensor(imgTensor) {
......@@ -26,7 +27,7 @@ export function faceDetectionNet(weights) {
if (minConfidence === void 0) { minConfidence = 0.8; }
if (maxResults === void 0) { maxResults = 100; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var paddedHeightRelative, paddedWidthRelative, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, results;
var paddedHeightRelative, paddedWidthRelative, imageDimensions, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, results;
return tslib_1.__generator(this, function (_d) {
switch (_d.label) {
case 0:
......@@ -34,6 +35,7 @@ export function faceDetectionNet(weights) {
_a = tf.tidy(function () {
var imgTensor = getImageTensor(input);
var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
imageDimensions = { width: width, height: height };
imgTensor = padToSquare(imgTensor);
paddedHeightRelative = imgTensor.shape[1] / height;
paddedWidthRelative = imgTensor.shape[2] / width;
......@@ -52,7 +54,17 @@ export function faceDetectionNet(weights) {
iouThreshold = 0.5;
indices = nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
results = indices
.map(function (idx) { return new FaceDetectionResult(scoresData[idx], boxes.get(idx, 0) * paddedHeightRelative, boxes.get(idx, 1) * paddedWidthRelative, boxes.get(idx, 2) * paddedHeightRelative, boxes.get(idx, 3) * paddedWidthRelative); });
.map(function (idx) {
var _a = [
Math.max(0, boxes.get(idx, 0)),
Math.min(1.0, boxes.get(idx, 2))
].map(function (val) { return val * paddedHeightRelative; }), top = _a[0], bottom = _a[1];
var _b = [
Math.max(0, boxes.get(idx, 1)),
Math.min(1.0, boxes.get(idx, 3))
].map(function (val) { return val * paddedWidthRelative; }), left = _b[0], right = _b[1];
return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), imageDimensions);
});
boxes.dispose();
scores.dispose();
return [2 /*return*/, results];
......
dist/faceDetectionNet/index.js.map
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\ No newline at end of file
dist/faceDetectionNet/types.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from '../commons/types';
export declare namespace FaceDetectionNet {
type PointwiseConvParams = {
filters: tf.Tensor4D;
......@@ -21,13 +22,9 @@ export declare namespace FaceDetectionNet {
conv_pair_params: ConvPairParams[];
};
}
type ConvWithBiasParams = {
filters: tf.Tensor4D;
bias: tf.Tensor1D;
};
type BoxPredictionParams = {
box_encoding_predictor_params: ConvWithBiasParams;
class_predictor_params: ConvWithBiasParams;
box_encoding_predictor_params: ConvParams;
class_predictor_params: ConvParams;
};
type PredictionLayerParams = {
conv_0_params: PointwiseConvParams;
......@@ -53,13 +50,4 @@ export declare namespace FaceDetectionNet {
prediction_layer_params: PredictionLayerParams;
output_layer_params: OutputLayerParams;
};
type Detection = {
score: number;
box: {
x: number;
y: number;
width: number;
height: number;
};
};
}
dist/faceLandmarkNet/FaceLandmarks.d.ts 0 → 100644
import { Point } from '../Point';
import { Dimensions } from '../types';
export declare class FaceLandmarks {
private _faceLandmarks;
private _imageWidth;
private _imageHeight;
private _shift;
constructor(relativeFaceLandmarkPositions: Point[], imageDims: Dimensions, shift?: Point);
getPositions(): Point[];
getRelativePositions(): Point[];
getJawOutline(): Point[];
getLeftEyeBrow(): Point[];
getRightEyeBrow(): Point[];
getNose(): Point[];
getLeftEye(): Point[];
getRightEye(): Point[];
getMouth(): Point[];
forSize(width: number, height: number): FaceLandmarks;
shift(x: number, y: number): FaceLandmarks;
}
dist/faceLandmarkNet/FaceLandmarks.js 0 → 100644
import { Point } from '../Point';
var FaceLandmarks = /** @class */ (function () {
function FaceLandmarks(relativeFaceLandmarkPositions, imageDims, shift) {
if (shift === void 0) { shift = new Point(0, 0); }
var width = imageDims.width, height = imageDims.height;
this._imageWidth = width;
this._imageHeight = height;
this._shift = shift;
this._faceLandmarks = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new Point(width, height)).add(shift); });
}
FaceLandmarks.prototype.getPositions = function () {
return this._faceLandmarks;
};
FaceLandmarks.prototype.getRelativePositions = function () {
var _this = this;
return this._faceLandmarks.map(function (pt) { return pt.sub(_this._shift).div(new Point(_this._imageWidth, _this._imageHeight)); });
};
FaceLandmarks.prototype.getJawOutline = function () {
return this._faceLandmarks.slice(0, 17);
};
FaceLandmarks.prototype.getLeftEyeBrow = function () {
return this._faceLandmarks.slice(17, 22);
};
FaceLandmarks.prototype.getRightEyeBrow = function () {
return this._faceLandmarks.slice(22, 27);
};
FaceLandmarks.prototype.getNose = function () {
return this._faceLandmarks.slice(27, 36);
};
FaceLandmarks.prototype.getLeftEye = function () {
return this._faceLandmarks.slice(36, 42);
};
FaceLandmarks.prototype.getRightEye = function () {
return this._faceLandmarks.slice(42, 48);
};
FaceLandmarks.prototype.getMouth = function () {
return this._faceLandmarks.slice(48, 68);
};
FaceLandmarks.prototype.forSize = function (width, height) {
return new FaceLandmarks(this.getRelativePositions(), { width: width, height: height });
};
FaceLandmarks.prototype.shift = function (x, y) {
return new FaceLandmarks(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new Point(x, y));
};
return FaceLandmarks;
}());
export { FaceLandmarks };
//# sourceMappingURL=FaceLandmarks.js.map
\ No newline at end of file
dist/faceLandmarkNet/FaceLandmarks.js.map 0 → 100644
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\ No newline at end of file
dist/faceLandmarkNet/extractParams.d.ts 0 → 100644
import { FaceLandmarkNet } from './types';
export declare function extractParams(weights: Float32Array): FaceLandmarkNet.NetParams;
dist/faceLandmarkNet/extractParams.js 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { extractConvParamsFactory } from '../commons/extractConvParamsFactory';
import { extractWeightsFactory } from '../commons/extractWeightsFactory';
export function extractParams(weights) {
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var extractConvParams = extractConvParamsFactory(extractWeights);
function extractFcParams(channelsIn, channelsOut) {
var fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
var fc_bias = tf.tensor1d(extractWeights(channelsOut));
return {
weights: fc_weights,
bias: fc_bias
};
}
var conv0_params = extractConvParams(3, 32, 3);
var conv1_params = extractConvParams(32, 64, 3);
var conv2_params = extractConvParams(64, 64, 3);
var conv3_params = extractConvParams(64, 64, 3);
var conv4_params = extractConvParams(64, 64, 3);
var conv5_params = extractConvParams(64, 128, 3);
var conv6_params = extractConvParams(128, 128, 3);
var conv7_params = extractConvParams(128, 256, 3);
var fc0_params = extractFcParams(6400, 1024);
var fc1_params = extractFcParams(1024, 136);
if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
}
return {
conv0_params: conv0_params,
conv1_params: conv1_params,
conv2_params: conv2_params,
conv3_params: conv3_params,
conv4_params: conv4_params,
conv5_params: conv5_params,
conv6_params: conv6_params,
conv7_params: conv7_params,
fc0_params: fc0_params,
fc1_params: fc1_params
};
}
//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
dist/faceLandmarkNet/extractParams.js.map 0 → 100644
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\ No newline at end of file
dist/faceLandmarkNet/fullyConnectedLayer.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { FaceLandmarkNet } from './types';
export declare function fullyConnectedLayer(x: tf.Tensor2D, params: FaceLandmarkNet.FCParams): tf.Tensor2D;
dist/faceLandmarkNet/fullyConnectedLayer.js 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
export function fullyConnectedLayer(x, params) {
return tf.tidy(function () {
return tf.add(tf.matMul(x, params.weights), params.bias);
});
}
//# sourceMappingURL=fullyConnectedLayer.js.map
\ No newline at end of file
dist/faceLandmarkNet/fullyConnectedLayer.js.map 0 → 100644
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\ No newline at end of file
dist/faceLandmarkNet/index.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from '../NetInput';
import { FaceLandmarks } from './FaceLandmarks';
export declare function faceLandmarkNet(weights: Float32Array): {
detectLandmarks: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | NetInput | tf.Tensor<tf.Rank>) => Promise<FaceLandmarks>;
};
dist/faceLandmarkNet/index.js 0 → 100644
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { convLayer } from '../commons/convLayer';
import { getImageTensor } from '../getImageTensor';
import { Point } from '../Point';
import { extractParams } from './extractParams';
import { FaceLandmarks } from './FaceLandmarks';
import { fullyConnectedLayer } from './fullyConnectedLayer';
function conv(x, params) {
return convLayer(x, params, 'valid', true);
}
function maxPool(x, strides) {
if (strides === void 0) { strides = [2, 2]; }
return tf.maxPool(x, [2, 2], strides, 'valid');
}
export function faceLandmarkNet(weights) {
var params = extractParams(weights);
function detectLandmarks(input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var imageDimensions, outTensor, faceLandmarksArray, _a, _b, xCoords, yCoords;
return tslib_1.__generator(this, function (_c) {
switch (_c.label) {
case 0:
outTensor = tf.tidy(function () {
var imgTensor = getImageTensor(input);
var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
imageDimensions = { width: width, height: height };
// work with 128 x 128 sized face images
if (imgTensor.shape[1] !== 128 || imgTensor.shape[2] !== 128) {
imgTensor = tf.image.resizeBilinear(imgTensor, [128, 128]);
}
var out = conv(imgTensor, params.conv0_params);
out = maxPool(out);
out = conv(out, params.conv1_params);
out = conv(out, params.conv2_params);
out = maxPool(out);
out = conv(out, params.conv3_params);
out = conv(out, params.conv4_params);
out = maxPool(out);
out = conv(out, params.conv5_params);
out = conv(out, params.conv6_params);
out = maxPool(out, [1, 1]);
out = conv(out, params.conv7_params);
var fc0 = tf.relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0_params));
var fc1 = fullyConnectedLayer(fc0, params.fc1_params);
return fc1;
});
_b = (_a = Array).from;
return [4 /*yield*/, outTensor.data()];
case 1:
faceLandmarksArray = _b.apply(_a, [_c.sent()]);
outTensor.dispose();
xCoords = faceLandmarksArray.filter(function (c, i) { return (i - 1) % 2; });
yCoords = faceLandmarksArray.filter(function (c, i) { return i % 2; });
return [2 /*return*/, new FaceLandmarks(Array(68).fill(0).map(function (_, i) { return new Point(xCoords[i], yCoords[i]); }), imageDimensions)];
}
});
});
}
return {
detectLandmarks: detectLandmarks
};
}
//# sourceMappingURL=index.js.map
\ No newline at end of file
dist/faceLandmarkNet/index.js.map 0 → 100644
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\ No newline at end of file
dist/faceLandmarkNet/types.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from '../commons/types';
export declare namespace FaceLandmarkNet {
type FCParams = {
weights: tf.Tensor2D;
bias: tf.Tensor1D;
};
type NetParams = {
conv0_params: ConvParams;
conv1_params: ConvParams;
conv2_params: ConvParams;
conv3_params: ConvParams;
conv4_params: ConvParams;
conv5_params: ConvParams;
conv6_params: ConvParams;
conv7_params: ConvParams;
fc0_params: FCParams;
fc1_params: FCParams;
};
}
dist/faceLandmarkNet/types.js 0 → 100644
//# sourceMappingURL=types.js.map
\ No newline at end of file
dist/faceLandmarkNet/types.js.map 0 → 100644
{"version":3,"file":"types.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
dist/faceRecognitionNet/convLayer.js
import * as tf from '@tensorflow/tfjs-core';
import { scale } from './scaleLayer';
function convLayer(x, params, stride, withRelu, padding) {
function convLayer(x, params, strides, withRelu, padding) {
if (padding === void 0) { padding = 'same'; }
var _a = params.conv, filters = _a.filters, biases = _a.biases;
var out = tf.conv2d(x, filters, [stride, stride], padding);
out = tf.add(out, biases);
var _a = params.conv, filters = _a.filters, bias = _a.bias;
var out = tf.conv2d(x, filters, strides, padding);
out = tf.add(out, bias);
out = scale(out, params.scale);
return withRelu ? tf.relu(out) : out;
}
export function conv(x, params) {
return convLayer(x, params, 1, true);
return convLayer(x, params, [1, 1], true);
}
export function convNoRelu(x, params) {
return convLayer(x, params, 1, false);
return convLayer(x, params, [1, 1], false);
}
export function convDown(x, params) {
return convLayer(x, params, 2, true, 'valid');
return convLayer(x, params, [2, 2], true, 'valid');
}
//# sourceMappingURL=convLayer.js.map
\ No newline at end of file
dist/faceRecognitionNet/convLayer.js.map
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\ No newline at end of file
dist/faceRecognitionNet/extractParams.js
import * as tf from '@tensorflow/tfjs-core';
import { extractWeightsFactory } from '../commons/extractWeightsFactory';
import { isFloat } from '../utils';
function extractorsFactory(extractWeights) {
function extractFilterValues(numFilterValues, numFilters, filterSize) {
......@@ -19,12 +20,12 @@ function extractorsFactory(extractWeights) {
}
function extractConvLayerParams(numFilterValues, numFilters, filterSize) {
var conv_filters = extractFilterValues(numFilterValues, numFilters, filterSize);
var conv_biases = tf.tensor1d(extractWeights(numFilters));
var conv_bias = tf.tensor1d(extractWeights(numFilters));
var scale = extractScaleLayerParams(numFilters);
return {
conv: {
filters: conv_filters,
biases: conv_biases
bias: conv_bias
},
scale: scale
};
......@@ -44,12 +45,8 @@ function extractorsFactory(extractWeights) {
};
}
export function extractParams(weights) {
var extractWeights = function (numWeights) {
var ret = weights.slice(0, numWeights);
weights = weights.slice(numWeights);
return ret;
};
var _a = extractorsFactory(extractWeights), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var _b = extractorsFactory(extractWeights), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams;
var conv32_down = extractConvLayerParams(4704, 32, 7);
var conv32_1 = extractResidualLayerParams(9216, 32, 3);
var conv32_2 = extractResidualLayerParams(9216, 32, 3);
......@@ -66,8 +63,8 @@ export function extractParams(weights) {
var conv256_2 = extractResidualLayerParams(589824, 256, 3);
var conv256_down_out = extractResidualLayerParams(589824, 256, 3);
var fc = tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]);
if (weights.length !== 0) {
throw new Error("weights remaing after extract: " + weights.length);
if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
}
return {
conv32_down: conv32_down,
......
dist/faceRecognitionNet/extractParams.js.map
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dist/faceRecognitionNet/index.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from '../NetInput';
export declare function faceRecognitionNet(weights: Float32Array): {
computeFaceDescriptor: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => Promise<Int32Array | Uint8Array | Float32Array>;
computeFaceDescriptorSync: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => Int32Array | Uint8Array | Float32Array;
forward: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => tf.Tensor<tf.Rank.R2>;
computeFaceDescriptor: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | NetInput | tf.Tensor<tf.Rank>) => Promise<Int32Array | Uint8Array | Float32Array>;
computeFaceDescriptorSync: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | NetInput | tf.Tensor<tf.Rank>) => Int32Array | Uint8Array | Float32Array;
forward: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | NetInput | tf.Tensor<tf.Rank>) => tf.Tensor<tf.Rank.R2>;
};
dist/faceRecognitionNet/types.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from '../commons/types';
export declare namespace FaceRecognitionNet {
type ScaleLayerParams = {
weights: tf.Tensor1D;
......@@ -8,10 +9,6 @@ export declare namespace FaceRecognitionNet {
conv1: ConvLayerParams;
conv2: ConvLayerParams;
};
type ConvParams = {
filters: tf.Tensor4D;
biases: tf.Tensor1D;
};
type ConvLayerParams = {
conv: ConvParams;
scale: ScaleLayerParams;
......
dist/index.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { euclideanDistance } from './euclideanDistance';
import { faceDetectionNet } from './faceDetectionNet';
import { faceLandmarkNet } from './faceLandmarkNet';
import { faceRecognitionNet } from './faceRecognitionNet';
import { NetInput } from './NetInput';
import { padToSquare } from './padToSquare';
export { euclideanDistance, faceDetectionNet, faceRecognitionNet, NetInput, tf, padToSquare };
export { euclideanDistance, faceDetectionNet, faceLandmarkNet, faceRecognitionNet, NetInput, tf, padToSquare };
export * from './extractFaces';
export * from './extractFaceTensors';
export * from './utils';
dist/index.js
import * as tf from '@tensorflow/tfjs-core';
import { euclideanDistance } from './euclideanDistance';
import { faceDetectionNet } from './faceDetectionNet';
import { faceLandmarkNet } from './faceLandmarkNet';
import { faceRecognitionNet } from './faceRecognitionNet';
import { NetInput } from './NetInput';
import { padToSquare } from './padToSquare';
export { euclideanDistance, faceDetectionNet, faceRecognitionNet, NetInput, tf, padToSquare };
export { euclideanDistance, faceDetectionNet, faceLandmarkNet, faceRecognitionNet, NetInput, tf, padToSquare };
export * from './extractFaces';
export * from './extractFaceTensors';
export * from './utils';
......
dist/index.js.map
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dist/types.d.ts
......@@ -6,13 +6,22 @@ export declare type Dimensions = {
height: number;
};
export declare type DrawBoxOptions = {
lineWidth: number;
color: string;
lineWidth?: number;
color?: string;
};
export declare type DrawTextOptions = {
lineWidth?: number;
fontSize?: number;
fontStyle?: string;
color?: string;
};
export declare type DrawLandmarksOptions = {
lineWidth?: number;
color?: string;
};
export declare type DrawOptions = {
lineWidth: number;
fontSize: number;
fontStyle: string;
color: string;
};
export declare type DrawOptions = DrawBoxOptions & DrawTextOptions;
dist/utils.d.ts
import { FaceDetectionNet } from './faceDetectionNet/types';
import { Dimensions, DrawBoxOptions, DrawOptions, DrawTextOptions } from './types';
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { FaceLandmarks } from './faceLandmarkNet/FaceLandmarks';
import { Dimensions, DrawBoxOptions, DrawLandmarksOptions, DrawOptions, DrawTextOptions } from './types';
export declare function isFloat(num: number): boolean;
export declare function round(num: number): number;
export declare function getElement(arg: string | any): any;
export declare function getContext2dOrThrow(canvas: HTMLCanvasElement): CanvasRenderingContext2D;
export declare function createCanvas({width, height}: Dimensions): HTMLCanvasElement;
export declare function createCanvasWithImageData({width, height}: Dimensions, buf: Uint8ClampedArray): HTMLCanvasElement;
export declare function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement, dims?: Dimensions): HTMLCanvasElement;
export declare function getMediaDimensions(media: HTMLImageElement | HTMLVideoElement): {
width: number;
height: number;
......@@ -14,6 +15,9 @@ export declare function bufferToImage(buf: Blob): Promise<HTMLImageElement>;
export declare function getDefaultDrawOptions(): DrawOptions;
export declare function drawBox(ctx: CanvasRenderingContext2D, x: number, y: number, w: number, h: number, options: DrawBoxOptions): void;
export declare function drawText(ctx: CanvasRenderingContext2D, x: number, y: number, text: string, options: DrawTextOptions): void;
export declare function drawDetection(canvasArg: string | HTMLCanvasElement, detection: FaceDetectionNet.Detection | FaceDetectionNet.Detection[], options?: DrawBoxOptions & DrawTextOptions & {
export declare function drawDetection(canvasArg: string | HTMLCanvasElement, detection: FaceDetection | FaceDetection[], options?: DrawBoxOptions & DrawTextOptions & {
withScore: boolean;
}): void;
export declare function drawLandmarks(canvasArg: string | HTMLCanvasElement, faceLandmarks: FaceLandmarks, options?: DrawLandmarksOptions & {
drawLines: boolean;
}): void;
dist/utils.js
......@@ -24,10 +24,10 @@ export function createCanvas(_a) {
canvas.height = height;
return canvas;
}
export function createCanvasWithImageData(_a, buf) {
var width = _a.width, height = _a.height;
export function createCanvasFromMedia(media, dims) {
var _a = dims || getMediaDimensions(media), width = _a.width, height = _a.height;
var canvas = createCanvas({ width: width, height: height });
getContext2dOrThrow(canvas).putImageData(new ImageData(buf, width, height), 0, 0);
getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
return canvas;
}
export function getMediaDimensions(media) {
......@@ -64,15 +64,17 @@ export function getDefaultDrawOptions() {
};
}
export function drawBox(ctx, x, y, w, h, options) {
ctx.strokeStyle = options.color;
ctx.lineWidth = options.lineWidth;
var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
ctx.strokeStyle = drawOptions.color;
ctx.lineWidth = drawOptions.lineWidth;
ctx.strokeRect(x, y, w, h);
}
export function drawText(ctx, x, y, text, options) {
var padText = 2 + options.lineWidth;
ctx.fillStyle = options.color;
ctx.font = options.fontSize + "px " + options.fontStyle;
ctx.fillText(text, x + padText, y + padText + (options.fontSize * 0.6));
var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
var padText = 2 + drawOptions.lineWidth;
ctx.fillStyle = drawOptions.color;
ctx.font = drawOptions.fontSize + "px " + drawOptions.fontStyle;
ctx.fillText(text, x + padText, y + padText + (drawOptions.fontSize * 0.6));
}
export function drawDetection(canvasArg, detection, options) {
var canvas = getElement(canvasArg);
......@@ -83,15 +85,60 @@ export function drawDetection(canvasArg, detection, options) {
? detection
: [detection];
detectionArray.forEach(function (det) {
var score = det.score, box = det.box;
var x = box.x, y = box.y, width = box.width, height = box.height;
var _a = det.getBox(), x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
var withScore = Object.assign({ withScore: true }, (options || {})).withScore;
var ctx = getContext2dOrThrow(canvas);
drawBox(ctx, x, y, width, height, drawOptions);
if (withScore) {
drawText(ctx, x, y, "" + round(score), drawOptions);
drawText(ctx, x, y, "" + round(det.getScore()), drawOptions);
}
});
}
function drawContour(ctx, points, isClosed) {
if (isClosed === void 0) { isClosed = false; }
ctx.beginPath();
points.slice(1).forEach(function (_a, prevIdx) {
var x = _a.x, y = _a.y;
var from = points[prevIdx];
ctx.moveTo(from.x, from.y);
ctx.lineTo(x, y);
});
if (isClosed) {
var from = points[points.length - 1];
var to = points[0];
if (!from || !to) {
return;
}
ctx.moveTo(from.x, from.y);
ctx.lineTo(to.x, to.y);
}
ctx.stroke();
}
export function drawLandmarks(canvasArg, faceLandmarks, options) {
var canvas = getElement(canvasArg);
if (!(canvas instanceof HTMLCanvasElement)) {
throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement');
}
var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
var drawLines = Object.assign({ drawLines: false }, (options || {})).drawLines;
var ctx = getContext2dOrThrow(canvas);
var lineWidth = drawOptions.lineWidth, color = drawOptions.color;
if (drawLines) {
ctx.strokeStyle = color;
ctx.lineWidth = lineWidth;
drawContour(ctx, faceLandmarks.getJawOutline());
drawContour(ctx, faceLandmarks.getLeftEyeBrow());
drawContour(ctx, faceLandmarks.getRightEyeBrow());
drawContour(ctx, faceLandmarks.getNose());
drawContour(ctx, faceLandmarks.getLeftEye(), true);
drawContour(ctx, faceLandmarks.getRightEye(), true);
drawContour(ctx, faceLandmarks.getMouth(), true);
return;
}
// else draw points
var ptOffset = lineWidth / 2;
ctx.fillStyle = color;
faceLandmarks.getPositions().forEach(function (pt) { return ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth); });
}
//# sourceMappingURL=utils.js.map
\ No newline at end of file
dist/utils.js.map
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\ No newline at end of file
examples/public/commons.js
......@@ -24,6 +24,12 @@ async function initFaceRecognitionNet() {
return faceapi.faceRecognitionNet(weights)
}
async function initFaceLandmarkNet() {
const res = await axios.get('face_landmark_68_model.weights', { responseType: 'arraybuffer' })
const weights = new Float32Array(res.data)
return faceapi.faceLandmarkNet(weights)
}
// fetch first image of each class and compute their descriptors
async function initTrainDescriptorsByClass(net, numImagesForTraining = 1) {
const maxAvailableImagesPerClass = 5
......@@ -83,6 +89,14 @@ function renderNavBar(navbarId, exampleUri) {
name: 'Face Similarity'
},
{
uri: 'face_landmarks',
name: 'Face Landmarks'
},
{
uri: 'detect_and_draw_landmarks',
name: 'Detect and Draw Landmarks'
},
{
uri: 'detect_and_draw_faces',
name: 'Detect and Draw Faces'
},
......
examples/server.js
......@@ -15,7 +15,9 @@ app.get('/face_detection', (req, res) => res.sendFile(path.join(viewsDir, 'faceD
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.get('/face_landmarks', (req, res) => res.sendFile(path.join(viewsDir, 'faceLandmarks.html')))
app.get('/detect_and_draw_faces', (req, res) => res.sendFile(path.join(viewsDir, 'detectAndDrawFaces.html')))
app.get('/detect_and_draw_landmarks', (req, res) => res.sendFile(path.join(viewsDir, 'detectAndDrawLandmarks.html')))
app.get('/detect_and_recognize_faces', (req, res) => res.sendFile(path.join(viewsDir, 'detectAndRecognizeFaces.html')))
app.listen(3000, () => console.log('Listening on port 3000!'))
\ No newline at end of file
examples/views/detectAndDrawLandmarks.html 0 → 100644
<!DOCTYPE html>
<html>
<head>
<script src="face-api.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="onDecreaseMinConfidence()"
>
<i class="material-icons left">-</i>
</button>
<button
class="waves-effect waves-light btn"
onclick="onIncreaseMinConfidence()"
>
<i class="material-icons left">+</i>
</button>
</div>
</div>
<script>
let minConfidence = 0.7
let drawLines = true
let detectionNet, landmarkNet
function onIncreaseMinConfidence() {
minConfidence = Math.min(faceapi.round(minConfidence + 0.1), 1.0)
$('#minConfidence').val(minConfidence)
updateResults()
}
function onDecreaseMinConfidence() {
minConfidence = Math.max(faceapi.round(minConfidence - 0.1), 0.1)
$('#minConfidence').val(minConfidence)
updateResults()
}
async function updateResults() {
const inputImgEl = $('#inputImg').get(0)
const { width, height } = inputImgEl
const canvas = $('#overlay').get(0)
canvas.width = width
canvas.height = height
const input = new faceapi.NetInput(inputImgEl)
const locations = await detectionNet.locateFaces(input, minConfidence)
const faceTensors = (await faceapi.extractFaceTensors(input, locations))
landmarksByFace = await Promise.all(faceTensors.map(t => landmarkNet.detectLandmarks(t)))
// free memory for face image tensors after we computed their descriptors
faceTensors.forEach(t => t.dispose())
landmarksByFace.forEach((landmarks, i) => {
// shift and scale the face landmarks to the face image position in the canvas
const box = locations[i].forSize(width, height).getBox()
faceapi.drawLandmarks(canvas, landmarks.forSize(box.width, box.height).shift(box.x, box.y), { lineWidth: drawLines ? 2 : 4, drawLines, color: 'red' })
})
faceapi.drawDetection('overlay', locations.map(det => det.forSize(width, height)))
}
async function onSelectionChanged(uri) {
const imgBuf = await fetchImage(uri)
$(`#inputImg`).get(0).src = (await faceapi.bufferToImage(imgBuf)).src
updateResults()
}
async function run() {
detectionNet = await initFaceDetectionNet()
landmarkNet = await initFaceLandmarkNet()
$('#loader').hide()
onSelectionChanged($('#selectList select').val())
}
$(document).ready(function() {
renderNavBar('#navbar', 'detect_and_draw_landmarks')
renderImageSelectList(
'#selectList',
async (uri) => {
await onSelectionChanged(uri)
},
'bbt1.jpg'
)
run()
})
</script>
</body>
</html>
\ No newline at end of file
examples/views/detectAndRecognizeFaces.html
......@@ -108,7 +108,7 @@
descriptors.forEach((descriptor, i) => {
const bestMatch = getBestMatch(trainDescriptorsByClass, descriptor)
const text = `${bestMatch.distance < maxDistance ? bestMatch.className : 'unkown'} (${bestMatch.distance})`
const { x, y, height: boxHeight } = detectionsForSize[i].box
const { x, y, height: boxHeight } = detectionsForSize[i].getBox()
faceapi.drawText(
canvas.getContext('2d'),
x,
......
examples/views/faceDetectionVideo.html
......@@ -20,7 +20,6 @@
<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">
......
examples/views/faceLandmarks.html 0 → 100644
<!DOCTYPE html>
<html>
<head>
<script src="face-api.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>
<div class="progress" id="loader">
<div class="indeterminate"></div>
</div>
<div class="row side-by-side">
<div class="center-content">
<div id="faceContainer"></div>
<div id="selectList"></div>
</div>
</div>
<p>
<input type="checkbox" id="drawLinesCheckbox" checked="checked" onchange="onChangeDrawLines(event)" />
<label for="drawLinesCheckbox">Draw Lines</label>
</p>
</div>
</div>
<script>
let net
let drawLines = true
let landmarks
let currentImg
function onChangeDrawLines(e) {
drawLines = $(e.target).prop('checked')
redraw()
}
function redraw() {
const canvas = faceapi.createCanvasFromMedia(currentImg)
$('#faceContainer').empty()
$('#faceContainer').append(canvas)
faceapi.drawLandmarks(canvas, landmarks, { lineWidth: drawLines ? 2 : 4, drawLines })
}
async function onSelectionChanged(uri) {
const imgBuf = await fetchImage(uri)
currentImg = await faceapi.bufferToImage(imgBuf)
landmarks = await net.detectLandmarks(currentImg)
redraw()
}
async function run() {
net = await initFaceLandmarkNet()
$('#loader').hide()
await onSelectionChanged($('#selectList select').val())
}
$(document).ready(function() {
renderNavBar('#navbar', 'face_landmarks')
renderFaceImageSelectList(
'#selectList',
onSelectionChanged,
{ className: 'sheldon', imageIdx: 1 }
)
run()
})
</script>
</body>
</html>
\ No newline at end of file
src/NetInput.ts
import { Dimensions, TMediaElement, TNetInput } from './types';
import { createCanvas, getContext2dOrThrow, getElement, getMediaDimensions } from './utils';
import { createCanvasFromMedia, getContext2dOrThrow, getElement, getMediaDimensions } from './utils';
export class NetInput {
private _canvases: HTMLCanvasElement[]
......@@ -40,11 +40,8 @@ export class NetInput {
}
// if input is batch type, make sure every canvas has the same dimensions
const { width, height } = this.dims || dims || getMediaDimensions(media)
const canvas = createCanvas({ width, height })
getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)
this._canvases.push(canvas)
const canvasDims = this.dims || dims
this._canvases.push(createCanvasFromMedia(media, canvasDims))
}
public get canvases() : HTMLCanvasElement[] {
......
src/Point.ts 0 → 100644
export interface IPoint {
x: number
y: number
}
export class Point implements IPoint {
public x: number
public y: number
constructor(x: number, y: number) {
this.x = x
this.y = y
}
public add(pt: IPoint): Point {
return new Point(this.x + pt.x, this.y + pt.y)
}
public sub(pt: IPoint): Point {
return new Point(this.x - pt.x, this.y - pt.y)
}
public mul(pt: IPoint): Point {
return new Point(this.x * pt.x, this.y * pt.y)
}
public div(pt: IPoint): Point {
return new Point(this.x / pt.x, this.y / pt.y)
}
}
\ No newline at end of file
src/Rect.ts 0 → 100644
export class Rect {
public x: number
public y: number
public width: number
public height: number
constructor(x: number, y: number, width: number, height: number) {
this.x = x
this.y = y
this.width = width
this.height = height
}
public floor(): Rect {
return new Rect(
Math.floor(this.x),
Math.floor(this.y),
Math.floor(this.width),
Math.floor(this.height)
)
}
}
\ No newline at end of file
src/commons/convLayer.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from './types';
export function convLayer(
x: tf.Tensor4D,
params: ConvParams,
padding: 'valid' | 'same' = 'same',
withRelu: boolean = false
): tf.Tensor4D {
return tf.tidy(() => {
const out = tf.add(
tf.conv2d(x, params.filters, [1, 1], padding),
params.bias
) as tf.Tensor4D
return withRelu ? tf.relu(out) : out
})
}
\ No newline at end of file
src/commons/extractConvParamsFactory.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams, ExtractWeightsFunction } from './types';
export function extractConvParamsFactory(extractWeights: ExtractWeightsFunction) {
return function (
channelsIn: number,
channelsOut: number,
filterSize: number
): ConvParams {
const filters = tf.tensor4d(
extractWeights(channelsIn * channelsOut * filterSize * filterSize),
[filterSize, filterSize, channelsIn, channelsOut]
)
const bias = tf.tensor1d(extractWeights(channelsOut))
return {
filters,
bias
}
}
}
\ No newline at end of file
src/commons/extractWeightsFactory.ts 0 → 100644
export function extractWeightsFactory(weights: Float32Array) {
let remainingWeights = weights
function extractWeights(numWeights: number): Float32Array {
const ret = remainingWeights.slice(0, numWeights)
remainingWeights = remainingWeights.slice(numWeights)
return ret
}
function getRemainingWeights(): Float32Array {
return remainingWeights
}
return {
extractWeights,
getRemainingWeights
}
}
\ No newline at end of file
src/commons/types.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
export type ConvParams = {
filters: tf.Tensor4D
bias: tf.Tensor1D
}
export type ExtractWeightsFunction = (numWeights: number) => Float32Array
\ No newline at end of file
src/extractFaceTensors.ts
import * as tf from '@tensorflow/tfjs-core';
import { FaceDetectionResult } from './faceDetectionNet/FaceDetectionResult';
import { NetInput } from './NetInput';
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { getImageTensor } from './getImageTensor';
import { NetInput } from './NetInput';
import { TNetInput } from './types';
/**
......@@ -18,7 +18,7 @@ import { TNetInput } from './types';
*/
export function extractFaceTensors(
image: tf.Tensor | NetInput | TNetInput,
detections: FaceDetectionResult[]
detections: FaceDetection[]
): tf.Tensor4D[] {
return tf.tidy(() => {
const imgTensor = getImageTensor(image)
......@@ -27,7 +27,7 @@ export function extractFaceTensors(
const [batchSize, imgHeight, imgWidth, numChannels] = imgTensor.shape
const faceTensors = detections.map(det => {
const { x, y, width, height } = det.forSize(imgWidth, imgHeight).box
const { x, y, width, height } = det.forSize(imgWidth, imgHeight).getBox().floor()
return tf.slice(imgTensor, [0, y, x, 0], [1, height, width, numChannels])
})
......
src/extractFaces.ts
import { FaceDetectionResult } from './faceDetectionNet/FaceDetectionResult';
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { createCanvas, getContext2dOrThrow } from './utils';
/**
......@@ -10,13 +10,12 @@ import { createCanvas, getContext2dOrThrow } from './utils';
*/
export function extractFaces(
image: HTMLCanvasElement,
detections: FaceDetectionResult[]
detections: FaceDetection[]
): HTMLCanvasElement[] {
const ctx = getContext2dOrThrow(image)
return detections.map(det => {
const { x, y, width, height } = det.forSize(image.width, image.height).box
const { x, y, width, height } = det.forSize(image.width, image.height).getBox().floor()
const faceImg = createCanvas({ width, height })
getContext2dOrThrow(faceImg)
.putImageData(ctx.getImageData(x, y, width, height), 0, 0)
......
src/faceDetectionNet/FaceDetection.ts 0 → 100644
import { Rect } from '../Rect';
import { Dimensions } from '../types';
export class FaceDetection {
private _score: number
private _box: Rect
private _imageWidth: number
private _imageHeight: number
constructor(
score: number,
relativeBox: Rect,
imageDims: Dimensions
) {
const { width, height } = imageDims
this._imageWidth = width
this._imageHeight = height
this._score = score
this._box = new Rect(
relativeBox.x * width,
relativeBox.y * height,
relativeBox.width * width,
relativeBox.height * height
)
}
public getScore() {
return this._score
}
public getBox() {
return this._box
}
public getImageWidth() {
return this._imageWidth
}
public getImageHeight() {
return this._imageHeight
}
public getRelativeBox() {
return new Rect(
this._box.x / this._imageWidth,
this._box.y / this._imageHeight,
this._box.width / this._imageWidth,
this._box.height / this._imageHeight
)
}
public forSize(width: number, height: number): FaceDetection {
return new FaceDetection(
this._score,
this.getRelativeBox(),
{ width, height}
)
}
}
\ No newline at end of file
src/faceDetectionNet/FaceDetectionResult.ts deleted 100644 → 0
import { FaceDetectionNet } from './types';
export class FaceDetectionResult {
private _score: number
private _topRelative: number
private _leftRelative: number
private _bottomRelative: number
private _rightRelative: number
constructor(
score: number,
topRelative: number,
leftRelative: number,
bottomRelative: number,
rightRelative: number
) {
this._score = score
this._topRelative = Math.max(0, topRelative),
this._leftRelative = Math.max(0, leftRelative),
this._bottomRelative = Math.min(1.0, bottomRelative),
this._rightRelative = Math.min(1.0, rightRelative)
}
public forSize(width: number, height: number): FaceDetectionNet.Detection {
const x = Math.floor(this._leftRelative * width)
const y = Math.floor(this._topRelative * height)
return {
score: this._score,
box: {
x,
y,
width: Math.floor(this._rightRelative * width) - x,
height: Math.floor(this._bottomRelative * height) - y
}
}
}
}
\ No newline at end of file
src/faceDetectionNet/boxPredictionLayer.ts
import * as tf from '@tensorflow/tfjs-core';
import { convLayer } from '../commons/convLayer';
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,
......@@ -23,11 +13,11 @@ export function boxPredictionLayer(
const batchSize = x.shape[0]
const boxPredictionEncoding = tf.reshape(
convWithBias(x, params.box_encoding_predictor_params),
convLayer(x, params.box_encoding_predictor_params),
[batchSize, -1, 1, 4]
)
const classPrediction = tf.reshape(
convWithBias(x, params.class_predictor_params),
convLayer(x, params.class_predictor_params),
[batchSize, -1, 3]
)
......
src/faceDetectionNet/extractParams.ts
import * as tf from '@tensorflow/tfjs-core';
import { extractWeightsFactory } from '../commons/extractWeightsFactory';
import { ConvParams } from '../commons/types';
import { FaceDetectionNet } from './types';
function extractorsFactory(extractWeights: (numWeights: number) => Float32Array) {
......@@ -20,11 +22,11 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
}
}
function extractConvWithBiasParams(
function extractConvParams(
channelsIn: number,
channelsOut: number,
filterSize: number
): FaceDetectionNet.ConvWithBiasParams {
): ConvParams {
const filters = tf.tensor4d(
extractWeights(channelsIn * channelsOut * filterSize * filterSize),
[filterSize, filterSize, channelsIn, channelsOut]
......@@ -45,7 +47,7 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
const {
filters,
bias
} = extractConvWithBiasParams(channelsIn, channelsOut, filterSize)
} = extractConvParams(channelsIn, channelsOut, filterSize)
return {
filters,
......@@ -104,18 +106,18 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
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_encoding_0_predictor_params = extractConvParams(512, 12, 1)
const class_predictor_0_params = extractConvParams(512, 9, 1)
const box_encoding_1_predictor_params = extractConvParams(1024, 24, 1)
const class_predictor_1_params = extractConvParams(1024, 18, 1)
const box_encoding_2_predictor_params = extractConvParams(512, 24, 1)
const class_predictor_2_params = extractConvParams(512, 18, 1)
const box_encoding_3_predictor_params = extractConvParams(256, 24, 1)
const class_predictor_3_params = extractConvParams(256, 18, 1)
const box_encoding_4_predictor_params = extractConvParams(256, 24, 1)
const class_predictor_4_params = extractConvParams(256, 18, 1)
const box_encoding_5_predictor_params = extractConvParams(128, 24, 1)
const class_predictor_5_params = extractConvParams(128, 18, 1)
const box_predictor_0_params = {
box_encoding_predictor_params: box_encoding_0_predictor_params,
......@@ -169,11 +171,10 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
}
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 {
extractWeights,
getRemainingWeights
} = extractWeightsFactory(weights)
const {
extractMobilenetV1Params,
......@@ -190,8 +191,8 @@ export function extractParams(weights: Float32Array): FaceDetectionNet.NetParams
extra_dim
}
if (weights.length !== 0) {
throw new Error(`weights remaing after extract: ${weights.length}`)
if (getRemainingWeights().length !== 0) {
throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)
}
return {
......
src/faceDetectionNet/index.ts
......@@ -3,14 +3,15 @@ import * as tf from '@tensorflow/tfjs-core';
import { getImageTensor } from '../getImageTensor';
import { NetInput } from '../NetInput';
import { padToSquare } from '../padToSquare';
import { TNetInput } from '../types';
import { TNetInput, Dimensions } from '../types';
import { extractParams } from './extractParams';
import { FaceDetectionResult } from './FaceDetectionResult';
import { FaceDetection } from './FaceDetection';
import { mobileNetV1 } from './mobileNetV1';
import { nonMaxSuppression } from './nonMaxSuppression';
import { outputLayer } from './outputLayer';
import { predictionLayer } from './predictionLayer';
import { resizeLayer } from './resizeLayer';
import { Rect } from '../Rect';
export function faceDetectionNet(weights: Float32Array) {
const params = extractParams(weights)
......@@ -40,9 +41,10 @@ export function faceDetectionNet(weights: Float32Array) {
input: tf.Tensor | NetInput,
minConfidence: number = 0.8,
maxResults: number = 100,
): Promise<FaceDetectionResult[]> {
): Promise<FaceDetection[]> {
let paddedHeightRelative = 1, paddedWidthRelative = 1
let imageDimensions: Dimensions | undefined
const {
boxes: _boxes,
......@@ -51,6 +53,7 @@ export function faceDetectionNet(weights: Float32Array) {
let imgTensor = getImageTensor(input)
const [height, width] = imgTensor.shape.slice(1)
imageDimensions = { width, height }
imgTensor = padToSquare(imgTensor)
paddedHeightRelative = imgTensor.shape[1] / height
......@@ -80,13 +83,26 @@ export function faceDetectionNet(weights: Float32Array) {
)
const results = indices
.map(idx => new FaceDetectionResult(
.map(idx => {
const [top, bottom] = [
Math.max(0, boxes.get(idx, 0)),
Math.min(1.0, boxes.get(idx, 2))
].map(val => val * paddedHeightRelative)
const [left, right] = [
Math.max(0, boxes.get(idx, 1)),
Math.min(1.0, boxes.get(idx, 3))
].map(val => val * paddedWidthRelative)
return new FaceDetection(
scoresData[idx],
boxes.get(idx, 0) * paddedHeightRelative,
boxes.get(idx, 1) * paddedWidthRelative,
boxes.get(idx, 2) * paddedHeightRelative,
boxes.get(idx, 3) * paddedWidthRelative
))
new Rect(
left,
top,
right - left,
bottom - top
),
imageDimensions as Dimensions
)
})
boxes.dispose()
scores.dispose()
......
src/faceDetectionNet/types.ts
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from '../commons/types';
export namespace FaceDetectionNet {
export type PointwiseConvParams = {
......@@ -29,14 +31,9 @@ export namespace FaceDetectionNet {
}
export type ConvWithBiasParams = {
filters: tf.Tensor4D
bias: tf.Tensor1D
}
export type BoxPredictionParams = {
box_encoding_predictor_params: ConvWithBiasParams
class_predictor_params: ConvWithBiasParams
box_encoding_predictor_params: ConvParams
class_predictor_params: ConvParams
}
export type PredictionLayerParams = {
......@@ -65,15 +62,4 @@ export namespace FaceDetectionNet {
prediction_layer_params: PredictionLayerParams,
output_layer_params: OutputLayerParams
}
export type Detection = {
score: number
box: {
x: number,
y: number,
width: number,
height: number
}
}
}
src/faceLandmarkNet/FaceLandmarks.ts 0 → 100644
import { Point, IPoint } from '../Point';
import { Dimensions } from '../types';
export class FaceLandmarks {
private _faceLandmarks: Point[]
private _imageWidth: number
private _imageHeight: number
private _shift: Point
constructor(
relativeFaceLandmarkPositions: Point[],
imageDims: Dimensions,
shift: Point = new Point(0, 0)
) {
const { width, height } = imageDims
this._imageWidth = width
this._imageHeight = height
this._shift = shift
this._faceLandmarks = relativeFaceLandmarkPositions.map(
pt => pt.mul(new Point(width, height)).add(shift)
)
}
public getPositions() {
return this._faceLandmarks
}
public getRelativePositions() {
return this._faceLandmarks.map(
pt => pt.sub(this._shift).div(new Point(this._imageWidth, this._imageHeight))
)
}
public getJawOutline() {
return this._faceLandmarks.slice(0, 17)
}
public getLeftEyeBrow() {
return this._faceLandmarks.slice(17, 22)
}
public getRightEyeBrow() {
return this._faceLandmarks.slice(22, 27)
}
public getNose() {
return this._faceLandmarks.slice(27, 36)
}
public getLeftEye() {
return this._faceLandmarks.slice(36, 42)
}
public getRightEye() {
return this._faceLandmarks.slice(42, 48)
}
public getMouth() {
return this._faceLandmarks.slice(48, 68)
}
public forSize(width: number, height: number): FaceLandmarks {
return new FaceLandmarks(
this.getRelativePositions(),
{ width, height }
)
}
public shift(x: number, y: number): FaceLandmarks {
return new FaceLandmarks(
this.getRelativePositions(),
{ width: this._imageWidth, height: this._imageHeight },
new Point(x, y)
)
}
}
\ No newline at end of file
src/faceLandmarkNet/extractParams.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { extractConvParamsFactory } from '../commons/extractConvParamsFactory';
import { extractWeightsFactory } from '../commons/extractWeightsFactory';
import { FaceLandmarkNet } from './types';
export function extractParams(weights: Float32Array): FaceLandmarkNet.NetParams {
const {
extractWeights,
getRemainingWeights
} = extractWeightsFactory(weights)
const extractConvParams = extractConvParamsFactory(extractWeights)
function extractFcParams(channelsIn: number, channelsOut: number,): FaceLandmarkNet.FCParams {
const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])
const fc_bias = tf.tensor1d(extractWeights(channelsOut))
return {
weights: fc_weights,
bias: fc_bias
}
}
const conv0_params = extractConvParams(3, 32, 3)
const conv1_params = extractConvParams(32, 64, 3)
const conv2_params = extractConvParams(64, 64, 3)
const conv3_params = extractConvParams(64, 64, 3)
const conv4_params = extractConvParams(64, 64, 3)
const conv5_params = extractConvParams(64, 128, 3)
const conv6_params = extractConvParams(128, 128, 3)
const conv7_params = extractConvParams(128, 256, 3)
const fc0_params = extractFcParams(6400, 1024)
const fc1_params = extractFcParams(1024, 136)
if (getRemainingWeights().length !== 0) {
throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)
}
return {
conv0_params,
conv1_params,
conv2_params,
conv3_params,
conv4_params,
conv5_params,
conv6_params,
conv7_params,
fc0_params,
fc1_params
}
}
\ No newline at end of file
src/faceLandmarkNet/fullyConnectedLayer.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { FaceLandmarkNet } from './types';
export function fullyConnectedLayer(
x: tf.Tensor2D,
params: FaceLandmarkNet.FCParams
): tf.Tensor2D {
return tf.tidy(() =>
tf.add(
tf.matMul(x, params.weights),
params.bias
)
)
}
\ No newline at end of file
src/faceLandmarkNet/index.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { convLayer } from '../commons/convLayer';
import { ConvParams } from '../commons/types';
import { getImageTensor } from '../getImageTensor';
import { NetInput } from '../NetInput';
import { Point } from '../Point';
import { Dimensions, TNetInput } from '../types';
import { extractParams } from './extractParams';
import { FaceLandmarks } from './FaceLandmarks';
import { fullyConnectedLayer } from './fullyConnectedLayer';
function conv(x: tf.Tensor4D, params: ConvParams): tf.Tensor4D {
return convLayer(x, params, 'valid', true)
}
function maxPool(x: tf.Tensor4D, strides: [number, number] = [2, 2]): tf.Tensor4D {
return tf.maxPool(x, [2, 2], strides, 'valid')
}
export function faceLandmarkNet(weights: Float32Array) {
const params = extractParams(weights)
async function detectLandmarks(input: tf.Tensor | NetInput | TNetInput) {
let imageDimensions: Dimensions | undefined
const outTensor = tf.tidy(() => {
let imgTensor = getImageTensor(input)
const [height, width] = imgTensor.shape.slice(1)
imageDimensions = { width, height }
// work with 128 x 128 sized face images
if (imgTensor.shape[1] !== 128 || imgTensor.shape[2] !== 128) {
imgTensor = tf.image.resizeBilinear(imgTensor, [128, 128])
}
let out = conv(imgTensor, params.conv0_params)
out = maxPool(out)
out = conv(out, params.conv1_params)
out = conv(out, params.conv2_params)
out = maxPool(out)
out = conv(out, params.conv3_params)
out = conv(out, params.conv4_params)
out = maxPool(out)
out = conv(out, params.conv5_params)
out = conv(out, params.conv6_params)
out = maxPool(out, [1, 1])
out = conv(out, params.conv7_params)
const fc0 = tf.relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0_params))
const fc1 = fullyConnectedLayer(fc0, params.fc1_params)
return fc1
})
const faceLandmarksArray = Array.from(await outTensor.data())
outTensor.dispose()
const xCoords = faceLandmarksArray.filter((c, i) => (i - 1) % 2)
const yCoords = faceLandmarksArray.filter((c, i) => i % 2)
return new FaceLandmarks(
Array(68).fill(0).map((_, i) => new Point(xCoords[i], yCoords[i])),
imageDimensions as Dimensions
)
}
return {
detectLandmarks
}
}
\ No newline at end of file
src/faceLandmarkNet/types.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from '../commons/types';
export namespace FaceLandmarkNet {
export type FCParams = {
weights: tf.Tensor2D
bias: tf.Tensor1D
}
export type NetParams = {
conv0_params: ConvParams
conv1_params: ConvParams
conv2_params: ConvParams
conv3_params: ConvParams
conv4_params: ConvParams
conv5_params: ConvParams
conv6_params: ConvParams
conv7_params: ConvParams
fc0_params: FCParams
fc1_params: FCParams
}
}
\ No newline at end of file
src/faceRecognitionNet/convLayer.ts
......@@ -7,26 +7,26 @@ import { FaceRecognitionNet } from './types';
function convLayer(
x: tf.Tensor4D,
params: FaceRecognitionNet.ConvLayerParams,
stride: number,
strides: [number, number],
withRelu: boolean,
padding: 'valid' | 'same' = 'same'
): tf.Tensor4D {
const { filters, biases } = params.conv
const { filters, bias } = params.conv
let out = tf.conv2d(x, filters, [stride, stride], padding)
out = tf.add(out, biases)
let out = tf.conv2d(x, filters, strides, padding)
out = tf.add(out, bias)
out = scale(out, params.scale)
return withRelu ? tf.relu(out) : out
}
export function conv(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams) {
return convLayer(x, params, 1, true)
return convLayer(x, params, [1, 1], true)
}
export function convNoRelu(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams) {
return convLayer(x, params, 1, false)
return convLayer(x, params, [1, 1], false)
}
export function convDown(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams) {
return convLayer(x, params, 2, true, 'valid')
return convLayer(x, params, [2, 2], true, 'valid')
}
\ No newline at end of file
src/faceRecognitionNet/extractParams.ts
import * as tf from '@tensorflow/tfjs-core';
import { extractWeightsFactory } from '../commons/extractWeightsFactory';
import { ExtractWeightsFunction } from '../commons/types';
import { isFloat } from '../utils';
import { FaceRecognitionNet } from './types';
function extractorsFactory(extractWeights: (numWeights: number) => Float32Array) {
function extractorsFactory(extractWeights: ExtractWeightsFunction) {
function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {
const weights = extractWeights(numFilterValues)
const depth = weights.length / (numFilters * filterSize * filterSize)
if (isFloat(depth)) {
throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)
}
......@@ -29,15 +30,19 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
}
}
function extractConvLayerParams(numFilterValues: number, numFilters: number, filterSize: number): FaceRecognitionNet.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 conv_bias = tf.tensor1d(extractWeights(numFilters))
const scale = extractScaleLayerParams(numFilters)
return {
conv: {
filters: conv_filters,
biases: conv_biases
bias: conv_bias
},
scale
}
......@@ -61,11 +66,10 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
}
export function extractParams(weights: Float32Array): FaceRecognitionNet.NetParams {
const extractWeights = (numWeights: number): Float32Array => {
const ret = weights.slice(0, numWeights)
weights = weights.slice(numWeights)
return ret
}
const {
extractWeights,
getRemainingWeights
} = extractWeightsFactory(weights)
const {
extractConvLayerParams,
......@@ -93,8 +97,8 @@ export function extractParams(weights: Float32Array): FaceRecognitionNet.NetPara
const fc = tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])
if (weights.length !== 0) {
throw new Error(`weights remaing after extract: ${weights.length}`)
if (getRemainingWeights().length !== 0) {
throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)
}
return {
......
src/faceRecognitionNet/types.ts
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from '../commons/types';
export namespace FaceRecognitionNet {
export type ScaleLayerParams = {
......@@ -11,11 +13,6 @@ export namespace FaceRecognitionNet {
conv2: ConvLayerParams
}
export type ConvParams = {
filters: tf.Tensor4D
biases: tf.Tensor1D
}
export type ConvLayerParams = {
conv: ConvParams
scale: ScaleLayerParams
......
src/index.ts
......@@ -2,6 +2,7 @@ import * as tf from '@tensorflow/tfjs-core';
import { euclideanDistance } from './euclideanDistance';
import { faceDetectionNet } from './faceDetectionNet';
import { faceLandmarkNet } from './faceLandmarkNet';
import { faceRecognitionNet } from './faceRecognitionNet';
import { NetInput } from './NetInput';
import { padToSquare } from './padToSquare';
......@@ -9,6 +10,7 @@ import { padToSquare } from './padToSquare';
export {
euclideanDistance,
faceDetectionNet,
faceLandmarkNet,
faceRecognitionNet,
NetInput,
tf,
......
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