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Unverified Commit 8d5a777b by justadudewhohacks Committed by GitHub
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Merge pull request #54 from justadudewhohacks/align-from-5-point-landmarks

Align from 5 point landmarks + allFacesMtcnn
parents 049997bc 5f68495a
Showing with 748 additions and 238 deletions
build/FaceLandmarks.d.ts
import { Point } from './Point';
import { FaceDetection } from './FaceDetection';
import { IPoint, Point } from './Point';
import { Rect } from './Rect';
import { Dimensions } from './types';
export declare class FaceLandmarks {
protected _imageWidth: number;
......@@ -11,4 +13,20 @@ export declare class FaceLandmarks {
getImageHeight(): number;
getPositions(): Point[];
getRelativePositions(): Point[];
forSize<T extends FaceLandmarks>(width: number, height: number): T;
shift<T extends FaceLandmarks>(x: number, y: number): T;
shiftByPoint<T extends FaceLandmarks>(pt: IPoint): T;
/**
* Aligns the face landmarks after face detection from the relative positions of the faces
* bounding box, or it's current shift. This function should be used to align the face images
* after face detection has been performed, before they are passed to the face recognition net.
* This will make the computed face descriptor more accurate.
*
* @param detection (optional) The bounding box of the face or the face detection result. If
* no argument was passed the position of the face landmarks are assumed to be relative to
* it's current shift.
* @returns The bounding box of the aligned face.
*/
align(detection?: FaceDetection | Rect): Rect;
protected getRefPointsForAlignment(): Point[];
}
build/FaceLandmarks.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var getCenterPoint_1 = require("./commons/getCenterPoint");
var FaceDetection_1 = require("./FaceDetection");
var Point_1 = require("./Point");
var Rect_1 = require("./Rect");
// face alignment constants
var relX = 0.5;
var relY = 0.43;
var relScale = 0.45;
var FaceLandmarks = /** @class */ (function () {
function FaceLandmarks(relativeFaceLandmarkPositions, imageDims, shift) {
if (shift === void 0) { shift = new Point_1.Point(0, 0); }
......@@ -26,6 +33,47 @@ var FaceLandmarks = /** @class */ (function () {
var _this = this;
return this._faceLandmarks.map(function (pt) { return pt.sub(_this._shift).div(new Point_1.Point(_this._imageWidth, _this._imageHeight)); });
};
FaceLandmarks.prototype.forSize = function (width, height) {
return new this.constructor(this.getRelativePositions(), { width: width, height: height });
};
FaceLandmarks.prototype.shift = function (x, y) {
return new this.constructor(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new Point_1.Point(x, y));
};
FaceLandmarks.prototype.shiftByPoint = function (pt) {
return this.shift(pt.x, pt.y);
};
/**
* Aligns the face landmarks after face detection from the relative positions of the faces
* bounding box, or it's current shift. This function should be used to align the face images
* after face detection has been performed, before they are passed to the face recognition net.
* This will make the computed face descriptor more accurate.
*
* @param detection (optional) The bounding box of the face or the face detection result. If
* no argument was passed the position of the face landmarks are assumed to be relative to
* it's current shift.
* @returns The bounding box of the aligned face.
*/
FaceLandmarks.prototype.align = function (detection) {
if (detection) {
var box = detection instanceof FaceDetection_1.FaceDetection
? detection.getBox().floor()
: detection;
return this.shift(box.x, box.y).align();
}
var centers = this.getRefPointsForAlignment();
var leftEyeCenter = centers[0], rightEyeCenter = centers[1], mouthCenter = centers[2];
var distToMouth = function (pt) { return mouthCenter.sub(pt).magnitude(); };
var eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;
var size = Math.floor(eyeToMouthDist / relScale);
var refPoint = getCenterPoint_1.getCenterPoint(centers);
// TODO: pad in case rectangle is out of image bounds
var x = Math.floor(Math.max(0, refPoint.x - (relX * size)));
var y = Math.floor(Math.max(0, refPoint.y - (relY * size)));
return new Rect_1.Rect(x, y, Math.min(size, this._imageWidth - x), Math.min(size, this._imageHeight - y));
};
FaceLandmarks.prototype.getRefPointsForAlignment = function () {
throw new Error('getRefPointsForAlignment not implemented by base class');
};
return FaceLandmarks;
}());
exports.FaceLandmarks = FaceLandmarks;
......
build/FaceLandmarks.js.map
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\ No newline at end of file
build/FullFaceDescription.d.ts
import { FaceDetection } from './FaceDetection';
import { FaceLandmarks68 } from './faceLandmarkNet/FaceLandmarks68';
import { FaceLandmarks } from './FaceLandmarks';
export declare class FullFaceDescription {
private _detection;
private _landmarks;
private _descriptor;
constructor(_detection: FaceDetection, _landmarks: FaceLandmarks68, _descriptor: Float32Array);
constructor(_detection: FaceDetection, _landmarks: FaceLandmarks, _descriptor: Float32Array);
readonly detection: FaceDetection;
readonly landmarks: FaceLandmarks68;
readonly landmarks: FaceLandmarks;
readonly descriptor: Float32Array;
forSize(width: number, height: number): FullFaceDescription;
}
build/FullFaceDescription.js.map
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\ No newline at end of file
build/allFacesFactory.d.ts
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { FullFaceDescription } from './FullFaceDescription';
import { Mtcnn } from './mtcnn/Mtcnn';
import { MtcnnForwardParams } from './mtcnn/types';
import { Rect } from './Rect';
import { TNetInput } from './types';
export declare function allFacesFactory(detectionNet: FaceDetectionNet, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare function allFacesFactory(detectionNet: FaceDetectionNet, landmarkNet: FaceLandmarkNet, computeDescriptors: (input: TNetInput, alignedFaceBoxes: Rect[], useBatchProcessing: boolean) => Promise<Float32Array[]>): (input: TNetInput, minConfidence: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare function allFacesMtcnnFactory(mtcnn: Mtcnn, computeDescriptors: (input: TNetInput, alignedFaceBoxes: Rect[], useBatchProcessing: boolean) => Promise<Float32Array[]>): (input: TNetInput, mtcnnForwardParams: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
build/allFacesFactory.js
......@@ -3,47 +3,35 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var extractFaceTensors_1 = require("./extractFaceTensors");
var FullFaceDescription_1 = require("./FullFaceDescription");
function allFacesFactory(detectionNet, landmarkNet, recognitionNet) {
function allFacesFactory(detectionNet, landmarkNet, computeDescriptors) {
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, _a, alignedFaceBoxes, alignedFaceTensors, descriptors, _b;
return tslib_1.__generator(this, function (_c) {
switch (_c.label) {
var detections, faceTensors, faceLandmarksByFace, _a, alignedFaceBoxes, descriptors;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, detectionNet.locateFaces(input, minConfidence)];
case 1:
detections = _c.sent();
detections = _b.sent();
return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, detections)];
case 2:
faceTensors = _c.sent();
faceTensors = _b.sent();
if (!useBatchProcessing) return [3 /*break*/, 4];
return [4 /*yield*/, landmarkNet.detectLandmarks(faceTensors)];
case 3:
_a = _c.sent();
_a = _b.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;
_a = _b.sent();
_b.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)];
return [4 /*yield*/, computeDescriptors(input, alignedFaceBoxes, useBatchProcessing)];
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(); });
descriptors = _b.sent();
return [2 /*return*/, detections.map(function (detection, i) {
return new FullFaceDescription_1.FullFaceDescription(detection, faceLandmarksByFace[i].shiftByPoint(detection.getBox()), descriptors[i]);
})];
......@@ -53,4 +41,31 @@ function allFacesFactory(detectionNet, landmarkNet, recognitionNet) {
};
}
exports.allFacesFactory = allFacesFactory;
function allFacesMtcnnFactory(mtcnn, computeDescriptors) {
return function (input, mtcnnForwardParams, useBatchProcessing) {
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var results, alignedFaceBoxes, descriptors;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, mtcnn.forward(input, mtcnnForwardParams)];
case 1:
results = _a.sent();
alignedFaceBoxes = results.map(function (_a) {
var faceLandmarks = _a.faceLandmarks;
return faceLandmarks.align();
});
return [4 /*yield*/, computeDescriptors(input, alignedFaceBoxes, useBatchProcessing)];
case 2:
descriptors = _a.sent();
return [2 /*return*/, results.map(function (_a, i) {
var faceDetection = _a.faceDetection, faceLandmarks = _a.faceLandmarks;
return new FullFaceDescription_1.FullFaceDescription(faceDetection, faceLandmarks, descriptors[i]);
})];
}
});
});
};
}
exports.allFacesMtcnnFactory = allFacesMtcnnFactory;
//# sourceMappingURL=allFacesFactory.js.map
\ No newline at end of file
build/allFacesFactory.js.map
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\ No newline at end of file
build/faceLandmarkNet/FaceLandmarks68.d.ts
import { FaceDetection } from '../FaceDetection';
import { FaceLandmarks } from '../FaceLandmarks';
import { IPoint, Point } from '../Point';
import { Rect } from '../Rect';
import { Point } from '../Point';
export declare class FaceLandmarks68 extends FaceLandmarks {
getJawOutline(): Point[];
getLeftEyeBrow(): Point[];
......@@ -10,19 +8,5 @@ export declare class FaceLandmarks68 extends FaceLandmarks {
getLeftEye(): Point[];
getRightEye(): Point[];
getMouth(): Point[];
forSize(width: number, height: number): FaceLandmarks68;
shift(x: number, y: number): FaceLandmarks68;
shiftByPoint(pt: IPoint): FaceLandmarks68;
/**
* Aligns the face landmarks after face detection from the relative positions of the faces
* bounding box, or it's current shift. This function should be used to align the face images
* after face detection has been performed, before they are passed to the face recognition net.
* This will make the computed face descriptor more accurate.
*
* @param detection (optional) The bounding box of the face or the face detection result. If
* no argument was passed the position of the face landmarks are assumed to be relative to
* it's current shift.
* @returns The bounding box of the aligned face.
*/
align(detection?: FaceDetection | Rect): Rect;
protected getRefPointsForAlignment(): Point[];
}
build/faceLandmarkNet/FaceLandmarks68.js
......@@ -2,14 +2,7 @@
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var getCenterPoint_1 = require("../commons/getCenterPoint");
var FaceDetection_1 = require("../FaceDetection");
var FaceLandmarks_1 = require("../FaceLandmarks");
var Point_1 = require("../Point");
var Rect_1 = require("../Rect");
// face alignment constants
var relX = 0.5;
var relY = 0.43;
var relScale = 0.45;
var FaceLandmarks68 = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmarks68, _super);
function FaceLandmarks68() {
......@@ -36,47 +29,12 @@ var FaceLandmarks68 = /** @class */ (function (_super) {
FaceLandmarks68.prototype.getMouth = function () {
return this._faceLandmarks.slice(48, 68);
};
FaceLandmarks68.prototype.forSize = function (width, height) {
return new FaceLandmarks68(this.getRelativePositions(), { width: width, height: height });
};
FaceLandmarks68.prototype.shift = function (x, y) {
return new FaceLandmarks68(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new Point_1.Point(x, y));
};
FaceLandmarks68.prototype.shiftByPoint = function (pt) {
return this.shift(pt.x, pt.y);
};
/**
* Aligns the face landmarks after face detection from the relative positions of the faces
* bounding box, or it's current shift. This function should be used to align the face images
* after face detection has been performed, before they are passed to the face recognition net.
* This will make the computed face descriptor more accurate.
*
* @param detection (optional) The bounding box of the face or the face detection result. If
* no argument was passed the position of the face landmarks are assumed to be relative to
* it's current shift.
* @returns The bounding box of the aligned face.
*/
FaceLandmarks68.prototype.align = function (detection) {
if (detection) {
var box = detection instanceof FaceDetection_1.FaceDetection
? detection.getBox().floor()
: detection;
return this.shift(box.x, box.y).align();
}
var centers = [
FaceLandmarks68.prototype.getRefPointsForAlignment = function () {
return [
this.getLeftEye(),
this.getRightEye(),
this.getMouth()
].map(getCenterPoint_1.getCenterPoint);
var leftEyeCenter = centers[0], rightEyeCenter = centers[1], mouthCenter = centers[2];
var distToMouth = function (pt) { return mouthCenter.sub(pt).magnitude(); };
var eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;
var size = Math.floor(eyeToMouthDist / relScale);
var refPoint = getCenterPoint_1.getCenterPoint(centers);
// TODO: pad in case rectangle is out of image bounds
var x = Math.floor(Math.max(0, refPoint.x - (relX * size)));
var y = Math.floor(Math.max(0, refPoint.y - (relY * size)));
return new Rect_1.Rect(x, y, size, size);
};
return FaceLandmarks68;
}(FaceLandmarks_1.FaceLandmarks));
......
build/faceLandmarkNet/FaceLandmarks68.js.map
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\ No newline at end of file
build/globalApi.d.ts
......@@ -15,7 +15,7 @@ export declare const recognitionNet: FaceRecognitionNet;
export declare const nets: {
ssdMobilenet: FaceDetectionNet;
faceLandmark68Net: FaceLandmarkNet;
faceNet: FaceRecognitionNet;
faceRecognitionNet: FaceRecognitionNet;
mtcnn: Mtcnn;
};
export declare function loadFaceDetectionModel(url: string): Promise<void>;
......@@ -28,3 +28,4 @@ export declare function detectLandmarks(input: TNetInput): Promise<FaceLandmarks
export declare function computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
export declare function mtcnn(input: TNetInput, forwardParams: MtcnnForwardParams): Promise<MtcnnResult[]>;
export declare const allFaces: (input: tf.Tensor | NetInput | TNetInput, minConfidence: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare const allFacesMtcnn: (input: tf.Tensor | NetInput | TNetInput, mtcnnForwardParams: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
build/globalApi.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var allFacesFactory_1 = require("./allFacesFactory");
var extractFaceTensors_1 = require("./extractFaceTensors");
var FaceDetectionNet_1 = require("./faceDetectionNet/FaceDetectionNet");
var FaceLandmarkNet_1 = require("./faceLandmarkNet/FaceLandmarkNet");
var FaceRecognitionNet_1 = require("./faceRecognitionNet/FaceRecognitionNet");
......@@ -13,7 +15,7 @@ exports.recognitionNet = new FaceRecognitionNet_1.FaceRecognitionNet();
exports.nets = {
ssdMobilenet: exports.detectionNet,
faceLandmark68Net: exports.landmarkNet,
faceNet: exports.recognitionNet,
faceRecognitionNet: exports.recognitionNet,
mtcnn: new Mtcnn_1.Mtcnn()
};
function loadFaceDetectionModel(url) {
......@@ -25,7 +27,7 @@ function loadFaceLandmarkModel(url) {
}
exports.loadFaceLandmarkModel = loadFaceLandmarkModel;
function loadFaceRecognitionModel(url) {
return exports.nets.faceNet.load(url);
return exports.nets.faceRecognitionNet.load(url);
}
exports.loadFaceRecognitionModel = loadFaceRecognitionModel;
function loadMtcnnModel(url) {
......@@ -50,12 +52,40 @@ function detectLandmarks(input) {
}
exports.detectLandmarks = detectLandmarks;
function computeFaceDescriptor(input) {
return exports.nets.faceNet.computeFaceDescriptor(input);
return exports.nets.faceRecognitionNet.computeFaceDescriptor(input);
}
exports.computeFaceDescriptor = computeFaceDescriptor;
function mtcnn(input, forwardParams) {
return exports.nets.mtcnn.forward(input, forwardParams);
}
exports.mtcnn = mtcnn;
exports.allFaces = allFacesFactory_1.allFacesFactory(exports.detectionNet, exports.landmarkNet, exports.recognitionNet);
exports.allFaces = allFacesFactory_1.allFacesFactory(exports.detectionNet, exports.landmarkNet, computeDescriptorsFactory(exports.nets.faceRecognitionNet));
exports.allFacesMtcnn = allFacesFactory_1.allFacesMtcnnFactory(exports.nets.mtcnn, computeDescriptorsFactory(exports.nets.faceRecognitionNet));
function computeDescriptorsFactory(recognitionNet) {
return function (input, alignedFaceBoxes, useBatchProcessing) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var alignedFaceTensors, descriptors, _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, alignedFaceBoxes)];
case 1:
alignedFaceTensors = _b.sent();
if (!useBatchProcessing) return [3 /*break*/, 3];
return [4 /*yield*/, recognitionNet.computeFaceDescriptor(alignedFaceTensors)];
case 2:
_a = _b.sent();
return [3 /*break*/, 5];
case 3: return [4 /*yield*/, Promise.all(alignedFaceTensors.map(function (faceTensor) { return recognitionNet.computeFaceDescriptor(faceTensor); }))];
case 4:
_a = _b.sent();
_b.label = 5;
case 5:
descriptors = _a;
alignedFaceTensors.forEach(function (t) { return t.dispose(); });
return [2 /*return*/, descriptors];
}
});
});
};
}
//# sourceMappingURL=globalApi.js.map
\ No newline at end of file
build/globalApi.js.map
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build/mtcnn/FaceLandmarks5.d.ts
import { FaceLandmarks } from '../FaceLandmarks';
import { IPoint } from '../Point';
import { Point } from '../Point';
export declare class FaceLandmarks5 extends FaceLandmarks {
forSize(width: number, height: number): FaceLandmarks5;
shift(x: number, y: number): FaceLandmarks5;
shiftByPoint(pt: IPoint): FaceLandmarks5;
protected getRefPointsForAlignment(): Point[];
}
build/mtcnn/FaceLandmarks5.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var getCenterPoint_1 = require("../commons/getCenterPoint");
var FaceLandmarks_1 = require("../FaceLandmarks");
var Point_1 = require("../Point");
var FaceLandmarks5 = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmarks5, _super);
function FaceLandmarks5() {
return _super !== null && _super.apply(this, arguments) || this;
}
FaceLandmarks5.prototype.forSize = function (width, height) {
return new FaceLandmarks5(this.getRelativePositions(), { width: width, height: height });
};
FaceLandmarks5.prototype.shift = function (x, y) {
return new FaceLandmarks5(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new Point_1.Point(x, y));
};
FaceLandmarks5.prototype.shiftByPoint = function (pt) {
return this.shift(pt.x, pt.y);
FaceLandmarks5.prototype.getRefPointsForAlignment = function () {
var pts = this.getPositions();
return [
pts[0],
pts[1],
getCenterPoint_1.getCenterPoint([pts[3], pts[4]])
];
};
return FaceLandmarks5;
}(FaceLandmarks_1.FaceLandmarks));
......
build/mtcnn/FaceLandmarks5.js.map
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\ No newline at end of file
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
......@@ -127,6 +127,14 @@ function renderNavBar(navbarId, exampleUri) {
name: 'MTCNN Face Detection Webcam'
},
{
uri: 'mtcnn_face_recognition',
name: 'MTCNN Face Recognition'
},
{
uri: 'mtcnn_face_recognition_webcam',
name: 'MTCNN Face Recognition Webcam'
},
{
uri: 'batch_face_landmarks',
name: 'Batch Face Landmarks'
},
......
examples/server.js
......@@ -27,6 +27,8 @@ app.get('/detect_and_recognize_faces', (req, res) => res.sendFile(path.join(view
app.get('/mtcnn_face_detection', (req, res) => res.sendFile(path.join(viewsDir, 'mtcnnFaceDetection.html')))
app.get('/mtcnn_face_detection_video', (req, res) => res.sendFile(path.join(viewsDir, 'mtcnnFaceDetectionVideo.html')))
app.get('/mtcnn_face_detection_webcam', (req, res) => res.sendFile(path.join(viewsDir, 'mtcnnFaceDetectionWebcam.html')))
app.get('/mtcnn_face_recognition', (req, res) => res.sendFile(path.join(viewsDir, 'mtcnnFaceRecognition.html')))
app.get('/mtcnn_face_recognition_webcam', (req, res) => res.sendFile(path.join(viewsDir, 'mtcnnFaceRecognitionWebcam.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')))
......
examples/views/faceAlignment.html
......@@ -50,11 +50,17 @@
<i class="material-icons left">+</i>
</button>
</div>
<div class="row">
<p>
<input type="checkbox" id="drawLinesCheckbox" onchange="onChangeUseMtcnn(event)" />
<label for="drawLinesCheckbox">Use Mtcnn</label>
</p>
</div>
</div>
<script>
let minConfidence = 0.7
let drawLines = true
let useMtcnn = false
function onIncreaseMinConfidence() {
minConfidence = Math.min(faceapi.round(minConfidence + 0.1), 1.0)
......@@ -68,26 +74,54 @@
updateResults()
}
function onChangeUseMtcnn(e) {
useMtcnn = $(e.target).prop('checked')
updateResults()
}
async function loadImageFromUrl(url) {
const img = await requestExternalImage($('#imgUrlInput').val())
$('#inputImg').get(0).src = img.src
updateResults()
}
async function updateResults() {
const inputImgEl = $('#inputImg').get(0)
const { width, height } = inputImgEl
const canvas = $('#overlay').get(0)
canvas.width = width
canvas.height = height
async function locateAndAlignFacesWithMtcnn(inputImgEl) {
const input = await faceapi.toNetInput(
inputImgEl,
// dispose input manually
false,
// keep canvases (required for mtcnn)
true
)
const results = await faceapi.mtcnn(input, { minFaceSize: 100 })
const unalignedFaceImages = await faceapi.extractFaces(input.inputs[0], results.map(res => res.faceDetection))
const alignedFaceBoxes = results
.filter(res => res.faceDetection.score > minConfidence)
.map(res => res.faceLandmarks.align())
const alignedFaceImages = await faceapi.extractFaces(input.inputs[0], alignedFaceBoxes)
// free memory for input tensors
input.dispose()
return {
unalignedFaceImages,
alignedFaceImages
}
}
async function locateAndAlignFacesWithSSD(inputImgEl) {
const input = await faceapi.toNetInput(inputImgEl)
const locations = await faceapi.locateFaces(input, minConfidence)
const faceImages = await faceapi.extractFaces(input.inputs[0], locations)
const unalignedFaceImages = await faceapi.extractFaces(input.inputs[0], locations)
// detect landmarks and get the aligned face image bounding boxes
const alignedFaceBoxes = await Promise.all(faceImages.map(
const alignedFaceBoxes = await Promise.all(unalignedFaceImages.map(
async (faceCanvas, i) => {
const faceLandmarks = await faceapi.detectLandmarks(faceCanvas)
return faceLandmarks.align(locations[i])
......@@ -98,8 +132,28 @@
// free memory for input tensors
input.dispose()
return {
unalignedFaceImages,
alignedFaceImages
}
}
async function updateResults() {
const inputImgEl = $('#inputImg').get(0)
const { width, height } = inputImgEl
const canvas = $('#overlay').get(0)
canvas.width = width
canvas.height = height
const {
unalignedFaceImages,
alignedFaceImages
} = useMtcnn
? await locateAndAlignFacesWithMtcnn(inputImgEl)
: await locateAndAlignFacesWithSSD(inputImgEl)
$('#facesContainer').empty()
faceImages.forEach(async (faceCanvas, i) => {
unalignedFaceImages.forEach(async (faceCanvas, i) => {
$('#facesContainer').append(faceCanvas)
$('#facesContainer').append(alignedFaceImages[i])
})
......@@ -114,6 +168,7 @@
async function run() {
await faceapi.loadFaceDetectionModel('/')
await faceapi.loadFaceLandmarkModel('/')
await faceapi.loadMtcnnModel('/')
$('#loader').hide()
onSelectionChanged($('#selectList select').val())
}
......
examples/views/mtcnnFaceDetectionWebcam.html
......@@ -84,10 +84,7 @@
minFaceSize
}
const c = faceapi.createCanvas({ width: width , height: height })
c.getContext('2d').drawImage(videoEl, 0, 0)
const { results, stats } = await faceapi.nets.mtcnn.forwardWithStats(c, mtcnnParams)
const { results, stats } = await faceapi.nets.mtcnn.forwardWithStats(videoEl, mtcnnParams)
updateTimeStats(stats.total)
if (results) {
......
examples/views/mtcnnFaceRecognition.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 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="imgUrlInput">Get image from URL:</label>
<input id="imgUrlInput" type="text" class="bold">
</div>
<button
class="waves-effect waves-light btn"
onclick="loadImageFromUrl()"
>
Ok
</button>
</div>
<div class="row">
<div class="row side-by-side">
<div class="row">
<label for="minFaceSize">Minimum Face Size:</label>
<input disabled value="40" id="minFaceSize" type="text" class="bold">
</div>
<button
class="waves-effect waves-light btn"
onclick="onDecreaseMinFaceSize()"
>
<i class="material-icons left">-</i>
</button>
<button
class="waves-effect waves-light btn"
onclick="onIncreaseMinFaceSize()"
>
<i class="material-icons left">+</i>
</button>
</div>
<div class="row side-by-side">
<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 button-sm"
onclick="onDecreaseMinConfidence()"
>
<i class="material-icons left">-</i>
</button>
<button
class="waves-effect waves-light btn button-sm"
onclick="onIncreaseMinConfidence()"
>
<i class="material-icons left">+</i>
</button>
</div>
<div class="row side-by-side">
<div class="row">
<label for="maxDistance">Max Descriptor Distance:</label>
<input disabled value="0.6" id="maxDistance" type="text" class="bold">
</div>
<button
class="waves-effect waves-light btn button-sm"
onclick="onDecreaseMaxDistance()"
>
<i class="material-icons left">-</i>
</button>
<button
class="waves-effect waves-light btn button-sm"
onclick="onIncreaseMaxDistance()"
>
<i class="material-icons left">+</i>
</button>
</div>
</div>
</div>
<script>
let maxDistance = 0.6
let minConfidence = 0.7
let minFaceSize = 40
let trainDescriptorsByClass = []
function onIncreaseMinFaceSize() {
minFaceSize = Math.min(faceapi.round(minFaceSize + 20), 200)
$('#minFaceSize').val(minFaceSize)
}
function onDecreaseMinFaceSize() {
minFaceSize = Math.max(faceapi.round(minFaceSize - 20), 20)
$('#minFaceSize').val(minFaceSize)
}
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()
}
function onIncreaseMaxDistance() {
maxDistance = Math.min(faceapi.round(maxDistance + 0.1), 1.0)
$('#maxDistance').val(maxDistance)
updateResults()
}
function onDecreaseMaxDistance() {
maxDistance = Math.max(faceapi.round(maxDistance - 0.1), 0.1)
$('#maxDistance').val(maxDistance)
updateResults()
}
async function loadImageFromUrl(url) {
const img = await requestExternalImage($('#imgUrlInput').val())
$('#inputImg').get(0).src = img.src
updateResults()
}
async function updateResults() {
const inputImgEl = $('#inputImg').get(0)
const { width, height } = inputImgEl
const canvas = $('#overlay').get(0)
canvas.width = width
canvas.height = height
const mtcnnParams = {
minFaceSize
}
const fullFaceDescriptions = (await faceapi.allFacesMtcnn(inputImgEl, mtcnnParams))
.map(fd => fd.forSize(width, height))
fullFaceDescriptions.forEach(({ detection, landmarks, descriptor }) => {
faceapi.drawDetection('overlay', [detection], { withScore: false })
faceapi.drawLandmarks('overlay', landmarks, { lineWidth: 4, color: 'red' })
const bestMatch = getBestMatch(trainDescriptorsByClass, descriptor)
const text = `${bestMatch.distance < maxDistance ? bestMatch.className : 'unkown'} (${bestMatch.distance})`
const { x, y, height: boxHeight } = detection.getBox()
faceapi.drawText(
canvas.getContext('2d'),
x,
y + boxHeight,
text,
Object.assign(faceapi.getDefaultDrawOptions(), { color: 'red', fontSize: 16 })
)
})
}
async function onSelectionChanged(uri) {
const imgBuf = await fetchImage(uri)
$(`#inputImg`).get(0).src = (await faceapi.bufferToImage(imgBuf)).src
updateResults()
}
async function run() {
await faceapi.loadMtcnnModel('/')
await faceapi.loadFaceRecognitionModel('/')
trainDescriptorsByClass = await initTrainDescriptorsByClass(faceapi.recognitionNet, 1)
$('#loader').hide()
onSelectionChanged($('#selectList select').val())
}
$(document).ready(function() {
renderNavBar('#navbar', 'mtcnn_face_recognition')
renderImageSelectList(
'#selectList',
async (uri) => {
await onSelectionChanged(uri)
},
'bbt1.jpg'
)
run()
})
</script>
</body>
</html>
\ No newline at end of file
examples/views/mtcnnFaceRecognitionWebcam.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 class="progress" id="loader">
<div class="indeterminate"></div>
</div>
<div style="position: relative" class="margin">
<video onplay="onPlay(this)" id="inputVideo" autoplay muted></video>
<canvas id="overlay" />
</div>
<div class="row side-by-side">
<div class="row">
<label for="minFaceSize">Minimum Face Size:</label>
<input disabled value="200" id="minFaceSize" type="text" class="bold">
</div>
<button
class="waves-effect waves-light btn"
onclick="onDecreaseMinFaceSize()"
>
<i class="material-icons left">-</i>
</button>
<button
class="waves-effect waves-light btn"
onclick="onIncreaseMinFaceSize()"
>
<i class="material-icons left">+</i>
</button>
</div>
<div class="row side-by-side">
<div class="row">
<label for="time">Time:</label>
<input disabled value="-" id="time" type="text" class="bold">
</div>
<div class="row">
<label for="fps">Estimated Fps:</label>
<input disabled value="-" id="fps" type="text" class="bold">
</div>
</div>
</div>
<script>
let modelLoaded = false
let minFaceSize = 200
let maxDistance = 0.6
let minConfidence = 0.9
let forwardTimes = []
function onIncreaseMinFaceSize() {
minFaceSize = Math.min(faceapi.round(minFaceSize + 50), 300)
$('#minFaceSize').val(minFaceSize)
}
function onDecreaseMinFaceSize() {
minFaceSize = Math.max(faceapi.round(minFaceSize - 50), 50)
$('#minFaceSize').val(minFaceSize)
}
function updateTimeStats(timeInMs) {
forwardTimes = [timeInMs].concat(forwardTimes).slice(0, 30)
const avgTimeInMs = forwardTimes.reduce((total, t) => total + t) / forwardTimes.length
$('#time').val(`${Math.round(avgTimeInMs)} ms`)
$('#fps').val(`${faceapi.round(1000 / avgTimeInMs)}`)
}
async function onPlay(videoEl) {
if(videoEl.paused || videoEl.ended || !modelLoaded)
return false
const { width, height } = faceapi.getMediaDimensions(videoEl)
const canvas = $('#overlay').get(0)
canvas.width = width
canvas.height = height
const mtcnnParams = {
minFaceSize
}
const ts = Date.now()
const fullFaceDescriptions = (await faceapi.allFacesMtcnn(videoEl, mtcnnParams))
.map(fd => fd.forSize(width, height))
updateTimeStats(Date.now() - ts)
fullFaceDescriptions.forEach(({ detection, landmarks, descriptor }) => {
faceapi.drawDetection('overlay', [detection], { withScore: false })
faceapi.drawLandmarks('overlay', landmarks.forSize(width, height), { lineWidth: 4, color: 'red' })
const bestMatch = getBestMatch(trainDescriptorsByClass, descriptor)
const text = `${bestMatch.distance < maxDistance ? bestMatch.className : 'unkown'} (${bestMatch.distance})`
const { x, y, height: boxHeight } = detection.getBox()
faceapi.drawText(
canvas.getContext('2d'),
x,
y + boxHeight,
text,
Object.assign(faceapi.getDefaultDrawOptions(), { color: 'red', fontSize: 16 })
)
})
setTimeout(() => onPlay(videoEl))
}
async function run() {
await faceapi.loadMtcnnModel('/')
await faceapi.loadFaceRecognitionModel('/')
// init reference data, e.g. compute a face descriptor for each class
trainDescriptorsByClass = await initTrainDescriptorsByClass(faceapi.recognitionNet)
modelLoaded = true
// try to access users webcam and stream the images
// to the video element
const videoEl = $('#inputVideo').get(0)
navigator.getUserMedia(
{ video: {} },
stream => videoEl.srcObject = stream,
err => console.error(err)
)
$('#loader').hide()
}
$(document).ready(function() {
renderNavBar('#navbar', 'mtcnn_face_recognition_webcam')
run()
})
</script>
</body>
</html>
\ No newline at end of file
src/FaceLandmarks.ts
import { Point } from './Point';
import { getCenterPoint } from './commons/getCenterPoint';
import { FaceDetection } from './FaceDetection';
import { IPoint, Point } from './Point';
import { Rect } from './Rect';
import { Dimensions } from './types';
// face alignment constants
const relX = 0.5
const relY = 0.43
const relScale = 0.45
export class FaceLandmarks {
protected _imageWidth: number
protected _imageHeight: number
......@@ -42,4 +50,65 @@ export class FaceLandmarks {
pt => pt.sub(this._shift).div(new Point(this._imageWidth, this._imageHeight))
)
}
public forSize<T extends FaceLandmarks>(width: number, height: number): T {
return new (this.constructor as any)(
this.getRelativePositions(),
{ width, height }
)
}
public shift<T extends FaceLandmarks>(x: number, y: number): T {
return new (this.constructor as any)(
this.getRelativePositions(),
{ width: this._imageWidth, height: this._imageHeight },
new Point(x, y)
)
}
public shiftByPoint<T extends FaceLandmarks>(pt: IPoint): T {
return this.shift(pt.x, pt.y)
}
/**
* Aligns the face landmarks after face detection from the relative positions of the faces
* bounding box, or it's current shift. This function should be used to align the face images
* after face detection has been performed, before they are passed to the face recognition net.
* This will make the computed face descriptor more accurate.
*
* @param detection (optional) The bounding box of the face or the face detection result. If
* no argument was passed the position of the face landmarks are assumed to be relative to
* it's current shift.
* @returns The bounding box of the aligned face.
*/
public align(
detection?: FaceDetection | Rect
): Rect {
if (detection) {
const box = detection instanceof FaceDetection
? detection.getBox().floor()
: detection
return this.shift(box.x, box.y).align()
}
const centers = this.getRefPointsForAlignment()
const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers
const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()
const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2
const size = Math.floor(eyeToMouthDist / relScale)
const refPoint = getCenterPoint(centers)
// TODO: pad in case rectangle is out of image bounds
const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))
const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))
return new Rect(x, y, Math.min(size, this._imageWidth - x), Math.min(size, this._imageHeight - y))
}
protected getRefPointsForAlignment(): Point[] {
throw new Error('getRefPointsForAlignment not implemented by base class')
}
}
\ No newline at end of file
src/FullFaceDescription.ts
import { FaceDetection } from './FaceDetection';
import { FaceLandmarks68 } from './faceLandmarkNet/FaceLandmarks68';
import { FaceLandmarks } from './FaceLandmarks';
export class FullFaceDescription {
constructor(
private _detection: FaceDetection,
private _landmarks: FaceLandmarks68,
private _landmarks: FaceLandmarks,
private _descriptor: Float32Array
) {}
......@@ -12,7 +12,7 @@ export class FullFaceDescription {
return this._detection
}
public get landmarks(): FaceLandmarks68 {
public get landmarks(): FaceLandmarks {
return this._landmarks
}
......
src/allFacesFactory.ts
......@@ -2,14 +2,16 @@ import { extractFaceTensors } from './extractFaceTensors';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
import { FaceLandmarks68 } from './faceLandmarkNet/FaceLandmarks68';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { FullFaceDescription } from './FullFaceDescription';
import { Mtcnn } from './mtcnn/Mtcnn';
import { MtcnnForwardParams } from './mtcnn/types';
import { Rect } from './Rect';
import { TNetInput } from './types';
export function allFacesFactory(
detectionNet: FaceDetectionNet,
landmarkNet: FaceLandmarkNet,
recognitionNet: FaceRecognitionNet
computeDescriptors: (input: TNetInput, alignedFaceBoxes: Rect[], useBatchProcessing: boolean) => Promise<Float32Array[]>
) {
return async function(
input: TNetInput,
......@@ -32,20 +34,42 @@ export function allFacesFactory(
const alignedFaceBoxes = faceLandmarksByFace.map(
(landmarks, i) => landmarks.align(detections[i].getBox())
)
const alignedFaceTensors = await extractFaceTensors(input, alignedFaceBoxes)
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())
const descriptors = await computeDescriptors(input, alignedFaceBoxes, useBatchProcessing)
return detections.map((detection, i) =>
new FullFaceDescription(
detection,
faceLandmarksByFace[i].shiftByPoint(detection.getBox()),
faceLandmarksByFace[i].shiftByPoint<FaceLandmarks68>(detection.getBox()),
descriptors[i]
)
)
}
}
export function allFacesMtcnnFactory(
mtcnn: Mtcnn,
computeDescriptors: (input: TNetInput, alignedFaceBoxes: Rect[], useBatchProcessing: boolean) => Promise<Float32Array[]>
) {
return async function(
input: TNetInput,
mtcnnForwardParams: MtcnnForwardParams,
useBatchProcessing: boolean = false
): Promise<FullFaceDescription[]> {
const results = await mtcnn.forward(input, mtcnnForwardParams)
const alignedFaceBoxes = results.map(
({ faceLandmarks }) => faceLandmarks.align()
)
const descriptors = await computeDescriptors(input, alignedFaceBoxes, useBatchProcessing)
return results.map(({ faceDetection, faceLandmarks }, i) =>
new FullFaceDescription(
faceDetection,
faceLandmarks,
descriptors[i]
)
)
......
src/faceLandmarkNet/FaceLandmarks68.ts
import { getCenterPoint } from '../commons/getCenterPoint';
import { FaceDetection } from '../FaceDetection';
import { FaceLandmarks } from '../FaceLandmarks';
import { IPoint, Point } from '../Point';
import { Point } from '../Point';
import { Rect } from '../Rect';
// face alignment constants
const relX = 0.5
const relY = 0.43
const relScale = 0.45
export class FaceLandmarks68 extends FaceLandmarks {
public getJawOutline(): Point[] {
return this._faceLandmarks.slice(0, 17)
......@@ -38,64 +33,11 @@ export class FaceLandmarks68 extends FaceLandmarks {
return this._faceLandmarks.slice(48, 68)
}
public forSize(width: number, height: number): FaceLandmarks68 {
return new FaceLandmarks68(
this.getRelativePositions(),
{ width, height }
)
}
public shift(x: number, y: number): FaceLandmarks68 {
return new FaceLandmarks68(
this.getRelativePositions(),
{ width: this._imageWidth, height: this._imageHeight },
new Point(x, y)
)
}
public shiftByPoint(pt: IPoint): FaceLandmarks68 {
return this.shift(pt.x, pt.y)
}
/**
* Aligns the face landmarks after face detection from the relative positions of the faces
* bounding box, or it's current shift. This function should be used to align the face images
* after face detection has been performed, before they are passed to the face recognition net.
* This will make the computed face descriptor more accurate.
*
* @param detection (optional) The bounding box of the face or the face detection result. If
* no argument was passed the position of the face landmarks are assumed to be relative to
* it's current shift.
* @returns The bounding box of the aligned face.
*/
public align(
detection?: FaceDetection | Rect
): Rect {
if (detection) {
const box = detection instanceof FaceDetection
? detection.getBox().floor()
: detection
return this.shift(box.x, box.y).align()
}
const centers = [
protected getRefPointsForAlignment(): Point[] {
return [
this.getLeftEye(),
this.getRightEye(),
this.getMouth()
].map(getCenterPoint)
const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers
const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()
const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2
const size = Math.floor(eyeToMouthDist / relScale)
const refPoint = getCenterPoint(centers)
// TODO: pad in case rectangle is out of image bounds
const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))
const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))
return new Rect(x, y, size, size)
}
}
\ No newline at end of file
src/globalApi.ts
import * as tf from '@tensorflow/tfjs-core';
import { allFacesFactory } from './allFacesFactory';
import { allFacesFactory, allFacesMtcnnFactory } from './allFacesFactory';
import { extractFaceTensors } from './extractFaceTensors';
import { FaceDetection } from './FaceDetection';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
import { FaceLandmarks68 } from './faceLandmarkNet/FaceLandmarks68';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { FullFaceDescription } from './FullFaceDescription';
import { getDefaultMtcnnForwardParams } from './mtcnn/getDefaultMtcnnForwardParams';
import { Mtcnn } from './mtcnn/Mtcnn';
import { MtcnnForwardParams, MtcnnResult } from './mtcnn/types';
import { NetInput } from './NetInput';
import { Rect } from './Rect';
import { TNetInput } from './types';
export const detectionNet = new FaceDetectionNet()
......@@ -22,7 +23,7 @@ export const recognitionNet = new FaceRecognitionNet()
export const nets = {
ssdMobilenet: detectionNet,
faceLandmark68Net: landmarkNet,
faceNet: recognitionNet,
faceRecognitionNet: recognitionNet,
mtcnn: new Mtcnn()
}
......@@ -35,7 +36,7 @@ export function loadFaceLandmarkModel(url: string) {
}
export function loadFaceRecognitionModel(url: string) {
return nets.faceNet.load(url)
return nets.faceRecognitionNet.load(url)
}
export function loadMtcnnModel(url: string) {
......@@ -68,7 +69,7 @@ export function detectLandmarks(
export function computeFaceDescriptor(
input: TNetInput
): Promise<Float32Array | Float32Array[]> {
return nets.faceNet.computeFaceDescriptor(input)
return nets.faceRecognitionNet.computeFaceDescriptor(input)
}
export function mtcnn(
......@@ -85,5 +86,32 @@ export const allFaces: (
) => Promise<FullFaceDescription[]> = allFacesFactory(
detectionNet,
landmarkNet,
recognitionNet
)
\ No newline at end of file
computeDescriptorsFactory(nets.faceRecognitionNet)
)
export const allFacesMtcnn: (
input: tf.Tensor | NetInput | TNetInput,
mtcnnForwardParams: MtcnnForwardParams,
useBatchProcessing?: boolean
) => Promise<FullFaceDescription[]> = allFacesMtcnnFactory(
nets.mtcnn,
computeDescriptorsFactory(nets.faceRecognitionNet)
)
function computeDescriptorsFactory(
recognitionNet: FaceRecognitionNet
) {
return async function(input: TNetInput, alignedFaceBoxes: Rect[], useBatchProcessing: boolean) {
const alignedFaceTensors = await extractFaceTensors(input, alignedFaceBoxes)
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 descriptors
}
}
\ No newline at end of file
src/mtcnn/FaceLandmarks5.ts
import { getCenterPoint } from '../commons/getCenterPoint';
import { FaceLandmarks } from '../FaceLandmarks';
import { IPoint, Point } from '../Point';
import { Point } from '../Point';
export class FaceLandmarks5 extends FaceLandmarks {
public forSize(width: number, height: number): FaceLandmarks5 {
return new FaceLandmarks5(
this.getRelativePositions(),
{ width, height }
)
}
public shift(x: number, y: number): FaceLandmarks5 {
return new FaceLandmarks5(
this.getRelativePositions(),
{ width: this._imageWidth, height: this._imageHeight },
new Point(x, y)
)
}
public shiftByPoint(pt: IPoint): FaceLandmarks5 {
return this.shift(pt.x, pt.y)
protected getRefPointsForAlignment(): Point[] {
const pts = this.getPositions()
return [
pts[0],
pts[1],
getCenterPoint([pts[3], pts[4]])
]
}
}
\ No newline at end of file
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