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Unverified Commit 64cf04a4 by justadudewhohacks Committed by GitHub
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Merge pull request #8 from justadudewhohacks/face-recognition-center-face

Face recognition center face
parents cc99df49 fea53c37
Showing with 180 additions and 69 deletions
dist/extractFaceTensors.js
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { getImageTensor } from './transformInputs'; import { getImageTensor } from './getImageTensor';
/** /**
* Extracts the tensors of the image regions containing the detected faces. * Extracts the tensors of the image regions containing the detected faces.
* Returned tensors have to be disposed manually once you don't need them anymore! * Returned tensors have to be disposed manually once you don't need them anymore!
......
dist/extractFaceTensors.js.map
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\ No newline at end of file \ No newline at end of file
dist/face-recognition.js
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dist/face-recognition.min.js
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dist/faceDetectionNet/index.js
import * as tslib_1 from "tslib"; import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { getImageTensor, padToSquare } from '../transformInputs'; import { getImageTensor } from '../getImageTensor';
import { padToSquare } from '../padToSquare';
import { extractParams } from './extractParams'; import { extractParams } from './extractParams';
import { FaceDetectionResult } from './FaceDetectionResult'; import { FaceDetectionResult } from './FaceDetectionResult';
import { mobileNetV1 } from './mobileNetV1'; import { mobileNetV1 } from './mobileNetV1';
...@@ -32,7 +33,7 @@ export function faceDetectionNet(weights) { ...@@ -32,7 +33,7 @@ export function faceDetectionNet(weights) {
paddedHeightRelative = 1, paddedWidthRelative = 1; paddedHeightRelative = 1, paddedWidthRelative = 1;
_a = tf.tidy(function () { _a = tf.tidy(function () {
var imgTensor = getImageTensor(input); var imgTensor = getImageTensor(input);
var _a = imgTensor.shape, _ = _a[0], height = _a[1], width = _a[2]; var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
imgTensor = padToSquare(imgTensor); imgTensor = padToSquare(imgTensor);
paddedHeightRelative = imgTensor.shape[1] / height; paddedHeightRelative = imgTensor.shape[1] / height;
paddedWidthRelative = imgTensor.shape[2] / width; paddedWidthRelative = imgTensor.shape[2] / width;
......
dist/faceDetectionNet/index.js.map
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dist/faceRecognitionNet/index.js
import * as tslib_1 from "tslib"; import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { getImageTensor, padToSquare } from '../transformInputs'; import { getImageTensor } from '../getImageTensor';
import { padToSquare } from '../padToSquare';
import { convDown } from './convLayer'; import { convDown } from './convLayer';
import { extractParams } from './extractParams'; import { extractParams } from './extractParams';
import { normalize } from './normalize'; import { normalize } from './normalize';
...@@ -10,8 +11,7 @@ export function faceRecognitionNet(weights) { ...@@ -10,8 +11,7 @@ export function faceRecognitionNet(weights) {
var params = extractParams(weights); var params = extractParams(weights);
function forward(input) { function forward(input) {
return tf.tidy(function () { return tf.tidy(function () {
// TODO pad on both sides, to keep face centered var x = padToSquare(getImageTensor(input), true);
var x = padToSquare(getImageTensor(input));
// work with 150 x 150 sized face images // work with 150 x 150 sized face images
if (x.shape[1] !== 150 || x.shape[2] !== 150) { if (x.shape[1] !== 150 || x.shape[2] !== 150) {
x = tf.image.resizeBilinear(x, [150, 150]); x = tf.image.resizeBilinear(x, [150, 150]);
......
dist/faceRecognitionNet/index.js.map
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\ No newline at end of file \ No newline at end of file
dist/getImageTensor.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from './NetInput';
import { TNetInput } from './types';
export declare function getImageTensor(input: tf.Tensor | NetInput | TNetInput): tf.Tensor4D;
dist/getImageTensor.js 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from './NetInput';
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=getImageTensor.js.map
\ No newline at end of file
dist/getImageTensor.js.map 0 → 100644
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\ No newline at end of file
dist/index.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { euclideanDistance } from './euclideanDistance'; import { euclideanDistance } from './euclideanDistance';
import { faceDetectionNet } from './faceDetectionNet'; import { faceDetectionNet } from './faceDetectionNet';
import { faceRecognitionNet } from './faceRecognitionNet'; import { faceRecognitionNet } from './faceRecognitionNet';
import { NetInput } from './NetInput'; import { NetInput } from './NetInput';
import * as tf from '@tensorflow/tfjs-core'; import { padToSquare } from './padToSquare';
export { euclideanDistance, faceDetectionNet, faceRecognitionNet, NetInput, tf }; export { euclideanDistance, faceDetectionNet, faceRecognitionNet, NetInput, tf, padToSquare };
export * from './extractFaces'; export * from './extractFaces';
export * from './extractFaceTensors'; export * from './extractFaceTensors';
export * from './utils'; export * from './utils';
dist/index.js
import * as tf from '@tensorflow/tfjs-core';
import { euclideanDistance } from './euclideanDistance'; import { euclideanDistance } from './euclideanDistance';
import { faceDetectionNet } from './faceDetectionNet'; import { faceDetectionNet } from './faceDetectionNet';
import { faceRecognitionNet } from './faceRecognitionNet'; import { faceRecognitionNet } from './faceRecognitionNet';
import { NetInput } from './NetInput'; import { NetInput } from './NetInput';
import * as tf from '@tensorflow/tfjs-core'; import { padToSquare } from './padToSquare';
export { euclideanDistance, faceDetectionNet, faceRecognitionNet, NetInput, tf }; export { euclideanDistance, faceDetectionNet, faceRecognitionNet, NetInput, tf, padToSquare };
export * from './extractFaces'; export * from './extractFaces';
export * from './extractFaceTensors'; export * from './extractFaceTensors';
export * from './utils'; export * from './utils';
......
dist/index.js.map
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\ No newline at end of file \ No newline at end of file
dist/padToSquare.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
/**
* Pads the smaller dimension of an image tensor with zeros, such that width === height.
*
* @param imgTensor The image tensor.
* @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image
* @returns The padded tensor with width === height.
*/
export declare function padToSquare(imgTensor: tf.Tensor4D, isCenterImage?: boolean): tf.Tensor4D;
dist/padToSquare.js 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
/**
* Pads the smaller dimension of an image tensor with zeros, such that width === height.
*
* @param imgTensor The image tensor.
* @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image
* @returns The padded tensor with width === height.
*/
export function padToSquare(imgTensor, isCenterImage) {
if (isCenterImage === void 0) { isCenterImage = false; }
return tf.tidy(function () {
var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
if (height === width) {
return imgTensor;
}
var paddingAmount = Math.floor(Math.abs(height - width) * (isCenterImage ? 0.5 : 1));
var paddingAxis = height > width ? 2 : 1;
var paddingTensorShape = imgTensor.shape.slice();
paddingTensorShape[paddingAxis] = paddingAmount;
var tensorsToStack = (isCenterImage ? [tf.fill(paddingTensorShape, 0)] : [])
.concat([imgTensor, tf.fill(paddingTensorShape, 0)]);
return tf.concat(tensorsToStack, paddingAxis);
});
}
//# sourceMappingURL=padToSquare.js.map
\ No newline at end of file
dist/padToSquare.js.map 0 → 100644
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\ No newline at end of file
dist/transformInputs.d.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from './NetInput'; import { NetInput } from './NetInput';
import { TNetInput } from './types'; import { TNetInput } from './types';
export declare function padToSquare(imgTensor: tf.Tensor4D): tf.Tensor4D; /**
* Pads the smaller dimension of an image tensor with zeros, such that width === height.
*
* @param imgTensor The image tensor.
* @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image
* @returns The padded tensor with width === height.
*/
export declare function padToSquare(imgTensor: tf.Tensor4D, isCenterImage?: boolean): tf.Tensor4D;
export declare function getImageTensor(input: tf.Tensor | NetInput | TNetInput): tf.Tensor4D; export declare function getImageTensor(input: tf.Tensor | NetInput | TNetInput): tf.Tensor4D;
dist/transformInputs.js
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from './NetInput'; import { NetInput } from './NetInput';
export function padToSquare(imgTensor) { /**
* Pads the smaller dimension of an image tensor with zeros, such that width === height.
*
* @param imgTensor The image tensor.
* @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image
* @returns The padded tensor with width === height.
*/
export function padToSquare(imgTensor, isCenterImage) {
if (isCenterImage === void 0) { isCenterImage = false; }
return tf.tidy(function () { return tf.tidy(function () {
var _a = imgTensor.shape, _ = _a[0], height = _a[1], width = _a[2]; var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
if (height === width) { if (height === width) {
return imgTensor; return imgTensor;
} }
if (height > width) { var paddingAmount = Math.floor(Math.abs(height - width) * (isCenterImage ? 0.5 : 1));
var pad_1 = tf.fill([1, height, height - width, 3], 0); var paddingAxis = height > width ? 2 : 1;
return tf.concat([imgTensor, pad_1], 2); var paddingTensorShape = imgTensor.shape.slice();
} paddingTensorShape[paddingAxis] = paddingAmount;
var pad = tf.fill([1, width - height, width, 3], 0); var tensorsToStack = (isCenterImage ? [tf.fill(paddingTensorShape, 0)] : [])
return tf.concat([imgTensor, pad], 1); .concat([imgTensor, tf.fill(paddingTensorShape, 0)]);
console.log(tensorsToStack);
return tf.concat(tensorsToStack, paddingAxis);
}); });
} }
export function getImageTensor(input) { export function getImageTensor(input) {
......
dist/transformInputs.js.map
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\ No newline at end of file \ No newline at end of file
examples/public/commons.js
...@@ -25,28 +25,39 @@ async function initFaceRecognitionNet() { ...@@ -25,28 +25,39 @@ async function initFaceRecognitionNet() {
} }
// fetch first image of each class and compute their descriptors // fetch first image of each class and compute their descriptors
async function initTrainDescriptorsByClass(net) { async function initTrainDescriptorsByClass(net, numImagesForTraining = 1) {
const maxAvailableImagesPerClass = 5
numImagesForTraining = Math.min(numImagesForTraining, maxAvailableImagesPerClass)
return Promise.all(classes.map( return Promise.all(classes.map(
async className => { async className => {
const img = await facerecognition.bufferToImage( const descriptors = []
await fetchImage(getFaceImageUri(className, 1)) for (let i = 1; i < (numImagesForTraining + 1); i++) {
) const img = await facerecognition.bufferToImage(
const descriptor = await net.computeFaceDescriptor(img) await fetchImage(getFaceImageUri(className, i))
)
descriptors.push(await net.computeFaceDescriptor(img))
}
return { return {
descriptor, descriptors,
className className
} }
} }
)) ))
} }
function getBestMatch(allDescriptors, queryDescriptor) { function getBestMatch(descriptorsByClass, queryDescriptor) {
return allDescriptors function computeMeanDistance(descriptorsOfClass) {
return facerecognition.round(
descriptorsOfClass
.map(d => facerecognition.euclideanDistance(d, queryDescriptor))
.reduce((d1, d2) => d1 + d2, 0)
/ (descriptorsOfClass.length || 1)
)
}
return descriptorsByClass
.map( .map(
({ descriptor, className }) => ({ ({ descriptors, className }) => ({
distance: facerecognition.round( distance: computeMeanDistance(descriptors),
facerecognition.euclideanDistance(descriptor, queryDescriptor)
),
className className
}) })
) )
......
examples/views/detectAndRecognizeFaces.html
...@@ -41,7 +41,7 @@ ...@@ -41,7 +41,7 @@
</div> </div>
<script> <script>
const threshold = 0.6 const threshold = 0.8
let minConfidence = 0.7 let minConfidence = 0.7
let detectionNet, recognitionNet let detectionNet, recognitionNet
let trainDescriptorsByClass = [] let trainDescriptorsByClass = []
...@@ -71,7 +71,7 @@ ...@@ -71,7 +71,7 @@
const detectionsForSize = detections.map(det => det.forSize(width, height)) const detectionsForSize = detections.map(det => det.forSize(width, height))
facerecognition.drawDetection('overlay', detectionsForSize, { withScore: false }) facerecognition.drawDetection('overlay', detectionsForSize, { withScore: false })
const faceTensors = await facerecognition.extractFaceTensors(input, detections) const faceTensors = (await facerecognition.extractFaceTensors(input, detections))
const descriptors = await Promise.all(faceTensors.map(t => recognitionNet.computeFaceDescriptor(t))) const descriptors = await Promise.all(faceTensors.map(t => recognitionNet.computeFaceDescriptor(t)))
// free memory for face image tensors after we computed their descriptors // free memory for face image tensors after we computed their descriptors
...@@ -94,7 +94,7 @@ ...@@ -94,7 +94,7 @@
async function run() { async function run() {
detectionNet = await initFaceDetectionNet() detectionNet = await initFaceDetectionNet()
recognitionNet = await initFaceRecognitionNet() recognitionNet = await initFaceRecognitionNet()
trainDescriptorsByClass = await initTrainDescriptorsByClass(recognitionNet) trainDescriptorsByClass = await initTrainDescriptorsByClass(recognitionNet, 1)
$('#loader').hide() $('#loader').hide()
onSelectionChanged($('#selectList select').val()) onSelectionChanged($('#selectList select').val())
} }
......
src/extractFaceTensors.ts
...@@ -2,7 +2,7 @@ import * as tf from '@tensorflow/tfjs-core'; ...@@ -2,7 +2,7 @@ import * as tf from '@tensorflow/tfjs-core';
import { FaceDetectionResult } from './faceDetectionNet/FaceDetectionResult'; import { FaceDetectionResult } from './faceDetectionNet/FaceDetectionResult';
import { NetInput } from './NetInput'; import { NetInput } from './NetInput';
import { getImageTensor } from './transformInputs'; import { getImageTensor } from './getImageTensor';
import { TNetInput } from './types'; import { TNetInput } from './types';
/** /**
......
src/faceDetectionNet/index.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { getImageTensor } from '../getImageTensor';
import { NetInput } from '../NetInput'; import { NetInput } from '../NetInput';
import { getImageTensor, padToSquare } from '../transformInputs'; import { padToSquare } from '../padToSquare';
import { TNetInput } from '../types'; import { TNetInput } from '../types';
import { extractParams } from './extractParams'; import { extractParams } from './extractParams';
import { FaceDetectionResult } from './FaceDetectionResult'; import { FaceDetectionResult } from './FaceDetectionResult';
...@@ -49,7 +50,7 @@ export function faceDetectionNet(weights: Float32Array) { ...@@ -49,7 +50,7 @@ export function faceDetectionNet(weights: Float32Array) {
} = tf.tidy(() => { } = tf.tidy(() => {
let imgTensor = getImageTensor(input) let imgTensor = getImageTensor(input)
const [_, height, width] = imgTensor.shape const [height, width] = imgTensor.shape.slice(1)
imgTensor = padToSquare(imgTensor) imgTensor = padToSquare(imgTensor)
paddedHeightRelative = imgTensor.shape[1] / height paddedHeightRelative = imgTensor.shape[1] / height
......
src/faceRecognitionNet/index.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { getImageTensor } from '../getImageTensor';
import { NetInput } from '../NetInput'; import { NetInput } from '../NetInput';
import { getImageTensor, padToSquare } from '../transformInputs'; import { padToSquare } from '../padToSquare';
import { TNetInput } from '../types'; import { TNetInput } from '../types';
import { convDown } from './convLayer'; import { convDown } from './convLayer';
import { extractParams } from './extractParams'; import { extractParams } from './extractParams';
...@@ -14,8 +15,7 @@ export function faceRecognitionNet(weights: Float32Array) { ...@@ -14,8 +15,7 @@ export function faceRecognitionNet(weights: Float32Array) {
function forward(input: tf.Tensor | NetInput | TNetInput) { function forward(input: tf.Tensor | NetInput | TNetInput) {
return tf.tidy(() => { return tf.tidy(() => {
// TODO pad on both sides, to keep face centered let x = padToSquare(getImageTensor(input), true)
let x = padToSquare(getImageTensor(input))
// work with 150 x 150 sized face images // work with 150 x 150 sized face images
if (x.shape[1] !== 150 || x.shape[2] !== 150) { if (x.shape[1] !== 150 || x.shape[2] !== 150) {
x = tf.image.resizeBilinear(x, [150, 150]) x = tf.image.resizeBilinear(x, [150, 150])
......
src/transformInputs.ts → src/getImageTensor.ts
...@@ -3,23 +3,6 @@ import * as tf from '@tensorflow/tfjs-core'; ...@@ -3,23 +3,6 @@ import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from './NetInput'; import { NetInput } from './NetInput';
import { TNetInput } from './types'; import { TNetInput } from './types';
export function padToSquare(imgTensor: tf.Tensor4D): tf.Tensor4D {
return tf.tidy(() => {
const [_, height, width] = imgTensor.shape
if (height === width) {
return imgTensor
}
if (height > width) {
const pad = tf.fill([1, height, height - width, 3], 0) as tf.Tensor4D
return tf.concat([imgTensor, pad], 2)
}
const pad = tf.fill([1, width - height, width, 3], 0) as tf.Tensor4D
return tf.concat([imgTensor, pad], 1)
})
}
export function getImageTensor(input: tf.Tensor | NetInput | TNetInput): tf.Tensor4D { export function getImageTensor(input: tf.Tensor | NetInput | TNetInput): tf.Tensor4D {
return tf.tidy(() => { return tf.tidy(() => {
if (input instanceof tf.Tensor) { if (input instanceof tf.Tensor) {
......
src/index.ts
import * as tf from '@tensorflow/tfjs-core';
import { euclideanDistance } from './euclideanDistance'; import { euclideanDistance } from './euclideanDistance';
import { faceDetectionNet } from './faceDetectionNet'; import { faceDetectionNet } from './faceDetectionNet';
import { faceRecognitionNet } from './faceRecognitionNet'; import { faceRecognitionNet } from './faceRecognitionNet';
import { NetInput } from './NetInput'; import { NetInput } from './NetInput';
import * as tf from '@tensorflow/tfjs-core'; import { padToSquare } from './padToSquare';
export { export {
euclideanDistance, euclideanDistance,
faceDetectionNet, faceDetectionNet,
faceRecognitionNet, faceRecognitionNet,
NetInput, NetInput,
tf tf,
padToSquare
} }
export * from './extractFaces' export * from './extractFaces'
......
src/padToSquare.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
/**
* Pads the smaller dimension of an image tensor with zeros, such that width === height.
*
* @param imgTensor The image tensor.
* @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image
* @returns The padded tensor with width === height.
*/
export function padToSquare(
imgTensor: tf.Tensor4D,
isCenterImage: boolean = false
): tf.Tensor4D {
return tf.tidy(() => {
const [height, width] = imgTensor.shape.slice(1)
if (height === width) {
return imgTensor
}
const paddingAmount = Math.floor(Math.abs(height - width) * (isCenterImage ? 0.5 : 1))
const paddingAxis = height > width ? 2 : 1
const paddingTensorShape = imgTensor.shape.slice() as [number, number, number, number]
paddingTensorShape[paddingAxis] = paddingAmount
const tensorsToStack = (isCenterImage ? [tf.fill(paddingTensorShape, 0)] : [])
.concat([imgTensor, tf.fill(paddingTensorShape, 0)]) as tf.Tensor4D[]
return tf.concat(tensorsToStack, paddingAxis)
})
}
\ No newline at end of file
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