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Unverified Commit 33be8794 by justadudewhohacks Committed by GitHub
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Merge pull request #38 from justadudewhohacks/batch-input-continued

face recognition net now works with batch inputs
parents 8ef9b662 7d04da10
Showing with 309 additions and 120 deletions
build/allFacesFactory.d.ts
......@@ -3,4 +3,4 @@ import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { FullFaceDescription } from './FullFaceDescription';
import { TNetInput } from './types';
export declare function allFacesFactory(detectionNet: FaceDetectionNet, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence: number) => Promise<FullFaceDescription[]>;
export declare function allFacesFactory(detectionNet: FaceDetectionNet, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
build/allFacesFactory.js
......@@ -4,34 +4,45 @@ var tslib_1 = require("tslib");
var extractFaceTensors_1 = require("./extractFaceTensors");
var FullFaceDescription_1 = require("./FullFaceDescription");
function allFacesFactory(detectionNet, landmarkNet, recognitionNet) {
return function (input, minConfidence) {
return function (input, minConfidence, useBatchProcessing) {
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var detections, faceTensors, faceLandmarksByFace, alignedFaceBoxes, alignedFaceTensors, descriptors;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
var detections, faceTensors, faceLandmarksByFace, _a, alignedFaceBoxes, alignedFaceTensors, descriptors, _b;
return tslib_1.__generator(this, function (_c) {
switch (_c.label) {
case 0: return [4 /*yield*/, detectionNet.locateFaces(input, minConfidence)];
case 1:
detections = _a.sent();
return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, detections)
/**
const faceLandmarksByFace = await Promise.all(faceTensors.map(
faceTensor => landmarkNet.detectLandmarks(faceTensor)
)) as FaceLandmarks[]
*/
];
detections = _c.sent();
return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, detections)];
case 2:
faceTensors = _a.sent();
faceTensors = _c.sent();
if (!useBatchProcessing) return [3 /*break*/, 4];
return [4 /*yield*/, landmarkNet.detectLandmarks(faceTensors)];
case 3:
faceLandmarksByFace = _a.sent();
_a = _c.sent();
return [3 /*break*/, 6];
case 4: return [4 /*yield*/, Promise.all(faceTensors.map(function (faceTensor) { return landmarkNet.detectLandmarks(faceTensor); }))];
case 5:
_a = _c.sent();
_c.label = 6;
case 6:
faceLandmarksByFace = _a;
faceTensors.forEach(function (t) { return t.dispose(); });
alignedFaceBoxes = faceLandmarksByFace.map(function (landmarks, i) { return landmarks.align(detections[i].getBox()); });
return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, alignedFaceBoxes)];
case 4:
alignedFaceTensors = _a.sent();
return [4 /*yield*/, Promise.all(alignedFaceTensors.map(function (faceTensor) { return recognitionNet.computeFaceDescriptor(faceTensor); }))];
case 5:
descriptors = _a.sent();
case 7:
alignedFaceTensors = _c.sent();
if (!useBatchProcessing) return [3 /*break*/, 9];
return [4 /*yield*/, recognitionNet.computeFaceDescriptor(alignedFaceTensors)];
case 8:
_b = _c.sent();
return [3 /*break*/, 11];
case 9: return [4 /*yield*/, Promise.all(alignedFaceTensors.map(function (faceTensor) { return recognitionNet.computeFaceDescriptor(faceTensor); }))];
case 10:
_b = _c.sent();
_c.label = 11;
case 11:
descriptors = _b;
alignedFaceTensors.forEach(function (t) { return t.dispose(); });
return [2 /*return*/, detections.map(function (detection, i) {
return new FullFaceDescription_1.FullFaceDescription(detection, faceLandmarksByFace[i].shiftByPoint(detection.getBox()), descriptors[i]);
......
build/allFacesFactory.js.map
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\ No newline at end of file
build/faceRecognitionNet/FaceRecognitionNet.d.ts
......@@ -5,7 +5,7 @@ export declare class FaceRecognitionNet {
private _params;
load(weightsOrUrl: Float32Array | string | undefined): Promise<void>;
extractWeights(weights: Float32Array): void;
forwardInput(input: NetInput): Promise<tf.Tensor2D>;
forwardInput(input: NetInput): tf.Tensor2D;
forward(input: TNetInput): Promise<tf.Tensor2D>;
computeFaceDescriptor(input: TNetInput): Promise<Float32Array>;
computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
}
build/faceRecognitionNet/FaceRecognitionNet.js
......@@ -37,13 +37,11 @@ var FaceRecognitionNet = /** @class */ (function () {
this._params = extractParams_1.extractParams(weights);
};
FaceRecognitionNet.prototype.forwardInput = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _this = this;
return tslib_1.__generator(this, function (_a) {
if (!this._params) {
throw new Error('FaceRecognitionNet - load model before inference');
}
return [2 /*return*/, tf.tidy(function () {
return tf.tidy(function () {
var batchTensor = input.toBatchTensor(150, true);
var normalized = normalize_1.normalize(batchTensor);
var out = convLayer_1.convDown(normalized, _this._params.conv32_down);
......@@ -65,8 +63,6 @@ var FaceRecognitionNet = /** @class */ (function () {
var globalAvg = out.mean([1, 2]);
var fullyConnected = tf.matMul(globalAvg, _this._params.fc);
return fullyConnected;
})];
});
});
};
FaceRecognitionNet.prototype.forward = function (input) {
......@@ -84,20 +80,21 @@ var FaceRecognitionNet = /** @class */ (function () {
};
FaceRecognitionNet.prototype.computeFaceDescriptor = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var result, _a, data;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = this.forward;
return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
case 1: return [4 /*yield*/, _a.apply(this, [_b.sent()])];
var _this = this;
var netInput, faceDescriptorTensors, faceDescriptorsForBatch;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
case 1:
netInput = _a.sent();
faceDescriptorTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });
return [4 /*yield*/, Promise.all(faceDescriptorTensors.map(function (t) { return t.data(); }))];
case 2:
result = _b.sent();
return [4 /*yield*/, result.data()];
case 3:
data = _b.sent();
result.dispose();
return [2 /*return*/, data];
faceDescriptorsForBatch = _a.sent();
faceDescriptorTensors.forEach(function (t) { return t.dispose(); });
return [2 /*return*/, netInput.isBatchInput
? faceDescriptorsForBatch
: faceDescriptorsForBatch[0]];
}
});
});
......
build/faceRecognitionNet/FaceRecognitionNet.js.map
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\ No newline at end of file
build/faceRecognitionNet/normalize.js
......@@ -3,9 +3,9 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function normalize(x) {
return tf.tidy(function () {
var avg_r = tf.fill([1, 150, 150, 1], 122.782);
var avg_g = tf.fill([1, 150, 150, 1], 117.001);
var avg_b = tf.fill([1, 150, 150, 1], 104.298);
var avg_r = tf.fill(x.shape.slice(0, 3).concat([1]), 122.782);
var avg_g = tf.fill(x.shape.slice(0, 3).concat([1]), 117.001);
var avg_b = tf.fill(x.shape.slice(0, 3).concat([1]), 104.298);
var avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);
return tf.div(tf.sub(x, avg_rgb), tf.scalar(256));
});
......
build/faceRecognitionNet/normalize.js.map
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\ No newline at end of file
build/globalApi.d.ts
......@@ -16,5 +16,5 @@ export declare function loadFaceRecognitionModel(url: string): Promise<void>;
export declare function loadModels(url: string): Promise<[void, void, void]>;
export declare function locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
export declare function detectLandmarks(input: TNetInput): Promise<FaceLandmarks | FaceLandmarks[]>;
export declare function computeFaceDescriptor(input: TNetInput): Promise<Float32Array>;
export declare const allFaces: (input: tf.Tensor | NetInput | TNetInput, minConfidence: number) => Promise<FullFaceDescription[]>;
export declare function computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
export declare const allFaces: (input: tf.Tensor | NetInput | TNetInput, minConfidence: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
build/globalApi.js.map
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\ No newline at end of file
dist/face-api.js
......@@ -2128,9 +2128,9 @@
function normalize(x) {
return tidy(function () {
var avg_r = fill([1, 150, 150, 1], 122.782);
var avg_g = fill([1, 150, 150, 1], 117.001);
var avg_b = fill([1, 150, 150, 1], 104.298);
var avg_r = fill(x.shape.slice(0, 3).concat([1]), 122.782);
var avg_g = fill(x.shape.slice(0, 3).concat([1]), 117.001);
var avg_b = fill(x.shape.slice(0, 3).concat([1]), 104.298);
var avg_rgb = concat([avg_r, avg_g, avg_b], 3);
return div(sub(x, avg_rgb), scalar(256));
});
......@@ -2195,13 +2195,11 @@
this._params = extractParams$2(weights);
};
FaceRecognitionNet.prototype.forwardInput = function (input) {
return __awaiter$1(this, void 0, void 0, function () {
var _this = this;
return __generator$1(this, function (_a) {
if (!this._params) {
throw new Error('FaceRecognitionNet - load model before inference');
}
return [2 /*return*/, tidy(function () {
return tidy(function () {
var batchTensor = input.toBatchTensor(150, true);
var normalized = normalize(batchTensor);
var out = convDown(normalized, _this._params.conv32_down);
......@@ -2223,8 +2221,6 @@
var globalAvg = out.mean([1, 2]);
var fullyConnected = matMul(globalAvg, _this._params.fc);
return fullyConnected;
})];
});
});
};
FaceRecognitionNet.prototype.forward = function (input) {
......@@ -2242,20 +2238,21 @@
};
FaceRecognitionNet.prototype.computeFaceDescriptor = function (input) {
return __awaiter$1(this, void 0, void 0, function () {
var result, _a, data;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0:
_a = this.forward;
return [4 /*yield*/, toNetInput(input, true)];
case 1: return [4 /*yield*/, _a.apply(this, [_b.sent()])];
var _this = this;
var netInput, faceDescriptorTensors, faceDescriptorsForBatch;
return __generator$1(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, toNetInput(input, true)];
case 1:
netInput = _a.sent();
faceDescriptorTensors = tidy(function () { return unstack(_this.forwardInput(netInput)); });
return [4 /*yield*/, Promise.all(faceDescriptorTensors.map(function (t) { return t.data(); }))];
case 2:
result = _b.sent();
return [4 /*yield*/, result.data()];
case 3:
data = _b.sent();
result.dispose();
return [2 /*return*/, data];
faceDescriptorsForBatch = _a.sent();
faceDescriptorTensors.forEach(function (t) { return t.dispose(); });
return [2 /*return*/, netInput.isBatchInput
? faceDescriptorsForBatch
: faceDescriptorsForBatch[0]];
}
});
});
......@@ -2270,34 +2267,45 @@
}
function allFacesFactory(detectionNet, landmarkNet, recognitionNet) {
return function (input, minConfidence) {
return function (input, minConfidence, useBatchProcessing) {
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return __awaiter$1(this, void 0, void 0, function () {
var detections, faceTensors, faceLandmarksByFace, alignedFaceBoxes, alignedFaceTensors, descriptors;
return __generator$1(this, function (_a) {
switch (_a.label) {
var detections, faceTensors, faceLandmarksByFace, _a, alignedFaceBoxes, alignedFaceTensors, descriptors, _b;
return __generator$1(this, function (_c) {
switch (_c.label) {
case 0: return [4 /*yield*/, detectionNet.locateFaces(input, minConfidence)];
case 1:
detections = _a.sent();
return [4 /*yield*/, extractFaceTensors(input, detections)
/**
const faceLandmarksByFace = await Promise.all(faceTensors.map(
faceTensor => landmarkNet.detectLandmarks(faceTensor)
)) as FaceLandmarks[]
*/
];
detections = _c.sent();
return [4 /*yield*/, extractFaceTensors(input, detections)];
case 2:
faceTensors = _a.sent();
faceTensors = _c.sent();
if (!useBatchProcessing) return [3 /*break*/, 4];
return [4 /*yield*/, landmarkNet.detectLandmarks(faceTensors)];
case 3:
faceLandmarksByFace = _a.sent();
_a = _c.sent();
return [3 /*break*/, 6];
case 4: return [4 /*yield*/, Promise.all(faceTensors.map(function (faceTensor) { return landmarkNet.detectLandmarks(faceTensor); }))];
case 5:
_a = _c.sent();
_c.label = 6;
case 6:
faceLandmarksByFace = _a;
faceTensors.forEach(function (t) { return t.dispose(); });
alignedFaceBoxes = faceLandmarksByFace.map(function (landmarks, i) { return landmarks.align(detections[i].getBox()); });
return [4 /*yield*/, extractFaceTensors(input, alignedFaceBoxes)];
case 4:
alignedFaceTensors = _a.sent();
return [4 /*yield*/, Promise.all(alignedFaceTensors.map(function (faceTensor) { return recognitionNet.computeFaceDescriptor(faceTensor); }))];
case 5:
descriptors = _a.sent();
case 7:
alignedFaceTensors = _c.sent();
if (!useBatchProcessing) return [3 /*break*/, 9];
return [4 /*yield*/, recognitionNet.computeFaceDescriptor(alignedFaceTensors)];
case 8:
_b = _c.sent();
return [3 /*break*/, 11];
case 9: return [4 /*yield*/, Promise.all(alignedFaceTensors.map(function (faceTensor) { return recognitionNet.computeFaceDescriptor(faceTensor); }))];
case 10:
_b = _c.sent();
_c.label = 11;
case 11:
descriptors = _b;
alignedFaceTensors.forEach(function (t) { return t.dispose(); });
return [2 /*return*/, detections.map(function (detection, i) {
return new FullFaceDescription(detection, faceLandmarksByFace[i].shiftByPoint(detection.getBox()), descriptors[i]);
......
dist/face-api.js.map
This source diff could not be displayed because it is too large. You can view the blob instead.
dist/face-api.min.js
This source diff could not be displayed because it is too large. You can view the blob instead.
examples/public/commons.js
......@@ -117,6 +117,10 @@ function renderNavBar(navbarId, exampleUri) {
{
uri: 'batch_face_landmarks',
name: 'Batch Face Landmarks'
},
{
uri: 'batch_face_recognition',
name: 'Batch Face Recognition'
}
]
......
examples/public/styles.css
......@@ -49,6 +49,10 @@
margin: 20px;
}
.button-sm {
padding: 0 10px !important;
}
#github-link {
display: flex !important;
justify-content: center;
......
examples/server.js
......@@ -25,8 +25,7 @@ app.get('/detect_and_draw_landmarks', (req, res) => res.sendFile(path.join(views
app.get('/face_alignment', (req, res) => res.sendFile(path.join(viewsDir, 'faceAlignment.html')))
app.get('/detect_and_recognize_faces', (req, res) => res.sendFile(path.join(viewsDir, 'detectAndRecognizeFaces.html')))
app.get('/batch_face_landmarks', (req, res) => res.sendFile(path.join(viewsDir, 'batchFaceLandmarks.html')))
app.get('/batch_face_recognition', (req, res) => res.sendFile(path.join(viewsDir, 'batchFaceRecognition.html')))
app.post('/fetch_external_image', async (req, res) => {
const { imageUrl } = req.body
......
examples/views/batchFaceRecognition.html 0 → 100644
<!DOCTYPE html>
<html>
<head>
<script src="face-api.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 id="navbar"></div>
<div class="center-content page-container">
<div>
<div class="progress" id="loader">
<div class="indeterminate"></div>
</div>
<div class="row side-by-side">
<div class="row">
<label for="timeNoBatch">Time for processing each face seperately:</label>
<input disabled value="-" id="timeNoBatch" type="text" class="bold"/>
</div>
<div class="row">
<label for="timeBatch">Time for processing in Batch:</label>
<input disabled value="-" id="timeBatch" type="text" class="bold"/>
</div>
</div>
<div class="row side-by-side">
<div>
<label for="numImages">Num Images:</label>
<input id="numImages" type="text" class="bold" value="32"/>
</div>
<button
class="waves-effect waves-light btn"
onclick="measureTimingsAndDisplay()"
>
Ok
</button>
</div>
<div class="row side-by-side">
<div class="center-content">
<div id="faceContainer"></div>
</div>
</div>
</div>
</div>
<script>
let images = []
let trainDescriptorsByClass = []
let descriptorsByFace = []
let numImages = 32
let maxDistance = 0.6
function onNumImagesChanged(e) {
const val = parseInt(e.target.value) || 32
numImages = Math.min(Math.max(val, 0), 32)
e.target.value = numImages
}
function displayTimeStats(timeNoBatch, timeBatch) {
$('#timeNoBatch').val(`${timeNoBatch} ms`)
$('#timeBatch').val(`${timeBatch} ms`)
}
function drawFaceRecognitionCanvas(img, descriptor) {
const canvas = faceapi.createCanvasFromMedia(img)
$('#faceContainer').append(canvas)
const bestMatch = getBestMatch(trainDescriptorsByClass, descriptor)
const text = `${bestMatch.distance < maxDistance ? bestMatch.className : 'unkown'} (${bestMatch.distance})`
const x = 20, y = canvas.height - 20
faceapi.drawText(
canvas.getContext('2d'),
x,
y,
text,
Object.assign(faceapi.getDefaultDrawOptions(), { color: 'red', fontSize: 16 })
)
}
async function runComputeFaceDescriptors(useBatchInput) {
const ts = Date.now()
descriptorsByFace = useBatchInput
? await faceapi.computeFaceDescriptor(images.slice(0, numImages))
: await Promise.all(images.slice(0, numImages).map(img => faceapi.computeFaceDescriptor(img)))
const time = Date.now() - ts
return time
}
async function measureTimings() {
const timeNoBatch = await runComputeFaceDescriptors(false)
const timeBatch = await runComputeFaceDescriptors(true)
return { timeNoBatch, timeBatch }
}
async function measureTimingsAndDisplay() {
const { timeNoBatch, timeBatch } = await measureTimings()
displayTimeStats(timeNoBatch, timeBatch)
$('#faceContainer').empty()
descriptorsByFace.forEach((descriptor, i) => drawFaceRecognitionCanvas(images[i], descriptor))
}
async function run() {
await faceapi.loadFaceRecognitionModel('/')
trainDescriptorsByClass = await initTrainDescriptorsByClass(faceapi.recognitionNet, 1)
$('#loader').hide()
const imgUris = classes
// skip images with idx 1, as they are used as reference data
.map(clazz => Array.from(Array(4), (_, idx) => getFaceImageUri(clazz, idx + 2)))
.reduce((flat, arr) => flat.concat(arr))
images = await Promise.all(imgUris.map(
async uri => faceapi.bufferToImage(await fetchImage(uri))
))
// warmup
await measureTimings()
// run
measureTimingsAndDisplay()
}
$(document).ready(function() {
$('#numImages').on('change', onNumImagesChanged)
renderNavBar('#navbar', 'batch_face_recognition')
run()
})
</script>
</body>
</html>
\ No newline at end of file
examples/views/detectAndRecognizeFaces.html
......@@ -30,6 +30,10 @@
>
Ok
</button>
<p>
<input type="checkbox" id="useBatchProcessing" onchange="onChangeUseBatchProcessing(event)" />
<label for="useBatchProcessing">Use Batch Processing</label>
</p>
</div>
<div class="row side-by-side">
<div class="row">
......@@ -37,13 +41,13 @@
<input disabled value="0.7" id="minConfidence" type="text" class="bold">
</div>
<button
class="waves-effect waves-light btn"
class="waves-effect waves-light btn button-sm"
onclick="onDecreaseMinConfidence()"
>
<i class="material-icons left">-</i>
</button>
<button
class="waves-effect waves-light btn"
class="waves-effect waves-light btn button-sm"
onclick="onIncreaseMinConfidence()"
>
<i class="material-icons left">+</i>
......@@ -53,13 +57,13 @@
<input disabled value="0.6" id="maxDistance" type="text" class="bold">
</div>
<button
class="waves-effect waves-light btn"
class="waves-effect waves-light btn button-sm"
onclick="onDecreaseMaxDistance()"
>
<i class="material-icons left">-</i>
</button>
<button
class="waves-effect waves-light btn"
class="waves-effect waves-light btn button-sm"
onclick="onIncreaseMaxDistance()"
>
<i class="material-icons left">+</i>
......@@ -70,9 +74,14 @@
<script>
let maxDistance = 0.6
let minConfidence = 0.7
let useBatchProcessing = false
let detectionNet, recognitionNet, landmarkNet
let trainDescriptorsByClass = []
function onChangeUseBatchProcessing(e) {
useBatchProcessing = $(e.target).prop('checked')
}
function onIncreaseMinConfidence() {
minConfidence = Math.min(faceapi.round(minConfidence + 0.1), 1.0)
$('#minConfidence').val(minConfidence)
......@@ -110,7 +119,7 @@
canvas.width = width
canvas.height = height
const fullFaceDescriptions = (await faceapi.allFaces(inputImgEl, minConfidence))
const fullFaceDescriptions = (await faceapi.allFaces(inputImgEl, minConfidence, useBatchProcessing))
.map(fd => fd.forSize(width, height))
fullFaceDescriptions.forEach(({ detection, descriptor }) => {
......
src/allFacesFactory.ts
......@@ -13,18 +13,19 @@ export function allFacesFactory(
) {
return async function(
input: TNetInput,
minConfidence: number
minConfidence: number,
useBatchProcessing: boolean = false
): Promise<FullFaceDescription[]> {
const detections = await detectionNet.locateFaces(input, minConfidence)
const faceTensors = await extractFaceTensors(input, detections)
/**
const faceLandmarksByFace = await Promise.all(faceTensors.map(
const faceLandmarksByFace = useBatchProcessing
? await landmarkNet.detectLandmarks(faceTensors) as FaceLandmarks[]
: await Promise.all(faceTensors.map(
faceTensor => landmarkNet.detectLandmarks(faceTensor)
)) as FaceLandmarks[]
*/
const faceLandmarksByFace = await landmarkNet.detectLandmarks(faceTensors) as FaceLandmarks[]
faceTensors.forEach(t => t.dispose())
......@@ -33,9 +34,12 @@ export function allFacesFactory(
)
const alignedFaceTensors = await extractFaceTensors(input, alignedFaceBoxes)
const descriptors = await Promise.all(alignedFaceTensors.map(
const descriptors = useBatchProcessing
? await recognitionNet.computeFaceDescriptor(alignedFaceTensors) as Float32Array[]
: await Promise.all(alignedFaceTensors.map(
faceTensor => recognitionNet.computeFaceDescriptor(faceTensor)
))
)) as Float32Array[]
alignedFaceTensors.forEach(t => t.dispose())
return detections.map((detection, i) =>
......
src/faceRecognitionNet/FaceRecognitionNet.ts
......@@ -30,12 +30,11 @@ export class FaceRecognitionNet {
this._params = extractParams(weights)
}
public async forwardInput(input: NetInput): Promise<tf.Tensor2D> {
public forwardInput(input: NetInput): tf.Tensor2D {
if (!this._params) {
throw new Error('FaceRecognitionNet - load model before inference')
}
return tf.tidy(() => {
const batchTensor = input.toBatchTensor(150, true)
......@@ -68,14 +67,26 @@ export class FaceRecognitionNet {
return fullyConnected
})
}
public async forward(input: TNetInput): Promise<tf.Tensor2D> {
return this.forwardInput(await toNetInput(input, true))
}
public async computeFaceDescriptor(input: TNetInput) {
const result = await this.forward(await toNetInput(input, true))
const data = await result.data()
result.dispose()
return data as Float32Array
public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {
const netInput = await toNetInput(input, true)
const faceDescriptorTensors = tf.tidy(
() => tf.unstack(this.forwardInput(netInput))
)
const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(
t => t.data()
)) as Float32Array[]
faceDescriptorTensors.forEach(t => t.dispose())
return netInput.isBatchInput
? faceDescriptorsForBatch
: faceDescriptorsForBatch[0]
}
}
\ No newline at end of file
src/faceRecognitionNet/normalize.ts
......@@ -2,9 +2,9 @@ import * as tf from '@tensorflow/tfjs-core';
export function normalize(x: tf.Tensor4D): tf.Tensor4D {
return tf.tidy(() => {
const avg_r = tf.fill([1, 150, 150, 1], 122.782);
const avg_g = tf.fill([1, 150, 150, 1], 117.001);
const avg_b = tf.fill([1, 150, 150, 1], 104.298);
const avg_r = tf.fill([...x.shape.slice(0, 3), 1], 122.782);
const avg_g = tf.fill([...x.shape.slice(0, 3), 1], 117.001);
const avg_b = tf.fill([...x.shape.slice(0, 3), 1], 104.298);
const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)
return tf.div(tf.sub(x, avg_rgb), tf.scalar(256))
......
src/globalApi.ts
......@@ -50,13 +50,14 @@ export function detectLandmarks(
export function computeFaceDescriptor(
input: TNetInput
): Promise<Float32Array> {
): Promise<Float32Array | Float32Array[]> {
return recognitionNet.computeFaceDescriptor(input)
}
export const allFaces: (
input: tf.Tensor | NetInput | TNetInput,
minConfidence: number
minConfidence: number,
useBatchProcessing?: boolean
) => Promise<FullFaceDescription[]> = allFacesFactory(
detectionNet,
landmarkNet,
......
test/data/faceDescriptor2.json 0 → 100644
[-0.08900658041238785, 0.10903996974229813, 0.027176279574632645, 0.04400758072733879, -0.14542895555496216, 0.11051996797323227, -0.04482650384306908, -0.05154910683631897, 0.10313281416893005, -0.09580713510513306, 0.11335672438144684, -0.02723177894949913, -0.2017219066619873, 0.09402787685394287, -0.025814395397901535, 0.07219463586807251, -0.12272300571203232, -0.07349629700183868, -0.1723618507385254, -0.1745331585407257, -0.03420797362923622, 0.10511981695890427, 0.0262751504778862, 0.014430010691285133, -0.2035353034734726, -0.2949812114238739, -0.04833773523569107, -0.10960741341114044, 0.08448510617017746, -0.039910122752189636, -0.03964325413107872, -0.099286288022995, -0.16025686264038086, 0.026379037648439407, 0.09079921245574951, 0.07745557278394699, -0.05415252223610878, -0.017411116510629654, 0.16053830087184906, 0.010681805200874805, -0.11814302206039429, 0.0382964164018631, 0.08098040521144867, 0.29891595244407654, 0.1258186250925064, 0.06479117274284363, 0.02330329827964306, -0.07838230580091476, 0.1363348364830017, -0.21215586364269257, 0.07675530016422272, 0.1447518914937973, 0.14686468243598938, 0.06991209089756012, 0.08843740075826645, -0.11935211718082428, -0.015284902416169643, 0.16930945217609406, -0.044002968817949295, 0.16501764953136444, 0.10481955111026764, -0.013367846608161926, -0.05079612880945206, -0.07971523702144623, 0.2541899085044861, 0.07128541171550751, -0.1458708792924881, -0.15604135394096375, 0.11365226656198502, -0.16018034517765045, -0.034580036997795105, 0.05678928270936012, -0.07191935181617737, -0.15881866216659546, -0.1955043375492096, 0.06456604599952698, 0.5308966040611267, 0.13605228066444397, -0.18340089917182922, -0.054736778140068054, -0.09668046236038208, -0.0006025233305990696, 0.06609033048152924, 0.0835171788930893, -0.13018545508384705, -0.07167276740074158, -0.04313529655337334, 0.08809386193752289, 0.29993879795074463, -0.07008976489305496, 0.005112136714160442, 0.1464609056711197, 0.03064284473657608, 0.005341261625289917, -0.03758316487073898, -0.002741048112511635, -0.19020092487335205, -0.005203879438340664, -0.03693881630897522, 0.017715569585561752, 0.025151528418064117, -0.1393381506204605, 0.04255775362253189, 0.080945685505867, -0.23745450377464294, 0.21049565076828003, -0.01615971140563488, -0.0642223060131073, 0.0915207713842392, 0.10660708695650101, -0.14731745421886444, -0.027426915243268013, 0.2378913164138794, -0.2964036166667938, 0.2034282684326172, 0.2009482979774475, 0.04706001281738281, 0.13964271545410156, 0.05233509838581085, 0.11507777869701385, 0.045886922627687454, 0.12765641510486603, -0.15917260944843292, -0.13223722577095032, -0.023241272196173668, -0.129884734749794, -0.027176398783922195, 0.009421694092452526]
\ No newline at end of file
test/data/faceDescriptorRect.json 0 → 100644
[-0.13293321430683136, 0.09793781489133835, 0.06550372391939163, 0.02364283800125122, -0.043399304151535034, 0.004586201161146164, -0.09000064432621002, -0.05539097636938095, 0.10467389971017838, -0.09715163707733154, 0.18808841705322266, -0.0205547958612442, -0.23795807361602783, -0.026068881154060364, -0.04790578782558441, 0.10736768692731857, -0.1791372150182724, -0.09754926711320877, -0.08212480694055557, -0.07197146117687225, 0.07512062042951584, 0.06562784314155579, -0.06910805404186249, 0.010537944734096527, -0.1353086233139038, -0.29961100220680237, -0.04597249627113342, -0.09019482880830765, 0.04843198508024216, -0.08456507325172424, -0.06385420262813568, 0.09591938555240631, -0.08721363544464111, 0.0029465071856975555, 0.062499962747097015, 0.08367685973644257, 0.004837760701775551, -0.02126195654273033, 0.18138188123703003, -0.0330311618745327, -0.1149168312549591, -0.014434240758419037, 0.04467501491308212, 0.32643717527389526, 0.13417592644691467, 0.049149081110954285, 0.0002636462450027466, -0.030674105510115623, 0.15085124969482422, -0.25617715716362, 0.007638035342097282, 0.20309507846832275, 0.155135378241539, 0.10535001009702682, 0.09949050843715668, -0.19686023890972137, 0.055761925876140594, 0.10784860700368881, -0.16404221951961517, 0.12705324590206146, 0.06780532747507095, -0.12821750342845917, -0.015174079686403275, -0.08541303128004074, 0.23064906895160675, 0.04403648152947426, -0.16575516760349274, -0.10698974132537842, 0.13322079181671143, -0.10516376793384552, -0.03650324046611786, 0.05603502690792084, -0.1468498408794403, -0.21398313343524933, -0.22947216033935547, 0.022328242659568787, 0.4006509780883789, 0.2338075339794159, -0.1980385184288025, 0.05581464245915413, -0.033158354461193085, -0.047999653965234756, 0.10474226623773575, 0.11267579346895218, -0.0938166156411171, -0.005631402134895325, -0.0698985829949379, 0.06661885231733322, 0.18326956033706665, 0.042940653860569, -0.031386956572532654, 0.2056775838136673, 0.011491281911730766, 0.05759737640619278, -0.029466431587934494, -0.04597870260477066, -0.07393362373113632, -0.037820909172296524, -0.07149908691644669, 0.023783499374985695, 0.016364723443984985, -0.09576655924320221, 0.02455282025039196, 0.11984197050333023, -0.11477060616016388, 0.17211446166038513, -0.008100427687168121, 0.09116753190755844, -0.004660069011151791, 0.029939215630292892, -0.10707360506057739, 0.03878428786993027, 0.15494686365127563, -0.2801153063774109, 0.1764734983444214, 0.1614546924829483, 0.09864784777164459, 0.12133727967739105, 0.05214153230190277, 0.04244184494018555, 0.024142231792211533, -0.019513756036758423, -0.22539466619491577, -0.0927465632557869, 0.06196486949920654, -0.09522707760334015, 0.04965142160654068, 0.023237790912389755]
\ No newline at end of file
test/tests/e2e/faceLandmarkNet.test.ts
......@@ -87,7 +87,7 @@ describe('faceLandmarkNet', () => {
await faceLandmarkNet.load('base/weights')
})
it('computes face landmarks', async () => {
it('computes face landmarks for squared input', async () => {
const { width, height } = imgEl1
const result = await faceLandmarkNet.detectLandmarks(imgEl1) as FaceLandmarks
......
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