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Unverified Commit a988c98a by justadudewhohacks Committed by GitHub
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Merge pull request #58 from justadudewhohacks/ssd-allfaces-facealignment-fix

Ssd allfaces facealignment fix
parents 5cc2741b 8fda8661
Showing with 370 additions and 185 deletions
build/FaceLandmarks.js
......@@ -69,7 +69,7 @@ var FaceLandmarks = /** @class */ (function () {
// 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));
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');
......
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, 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[]>;
export declare function allFacesFactory(detectionNet: FaceDetectionNet, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare function allFacesMtcnnFactory(mtcnn: Mtcnn, recognitionNet: FaceRecognitionNet): (input: TNetInput, mtcnnForwardParams?: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
build/allFacesFactory.js
......@@ -3,8 +3,37 @@ 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, computeDescriptors) {
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];
}
});
});
};
}
function allFacesFactory(detectionNet, landmarkNet, recognitionNet) {
var computeDescriptors = computeDescriptorsFactory(recognitionNet);
return function (input, minConfidence, useBatchProcessing) {
if (minConfidence === void 0) { minConfidence = 0.8; }
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var detections, faceTensors, faceLandmarksByFace, _a, alignedFaceBoxes, descriptors;
......@@ -41,8 +70,10 @@ function allFacesFactory(detectionNet, landmarkNet, computeDescriptors) {
};
}
exports.allFacesFactory = allFacesFactory;
function allFacesMtcnnFactory(mtcnn, computeDescriptors) {
function allFacesMtcnnFactory(mtcnn, recognitionNet) {
var computeDescriptors = computeDescriptorsFactory(recognitionNet);
return function (input, mtcnnForwardParams, useBatchProcessing) {
if (mtcnnForwardParams === void 0) { mtcnnForwardParams = {}; }
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var results, alignedFaceBoxes, descriptors;
......
build/allFacesFactory.js.map
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\ No newline at end of file
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\ No newline at end of file
build/globalApi.d.ts
......@@ -27,5 +27,7 @@ export declare function locateFaces(input: TNetInput, minConfidence?: number, ma
export declare function detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;
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[]>;
export declare type allFacesFunction = (input: tf.Tensor | NetInput | TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare const allFaces: allFacesFunction;
export declare type allFacesMtcnnFunction = (input: tf.Tensor | NetInput | TNetInput, mtcnnForwardParams?: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare const allFacesMtcnn: allFacesMtcnnFunction;
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");
......@@ -59,33 +57,6 @@ function mtcnn(input, forwardParams) {
return exports.nets.mtcnn.forward(input, forwardParams);
}
exports.mtcnn = mtcnn;
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];
}
});
});
};
}
exports.allFaces = allFacesFactory_1.allFacesFactory(exports.nets.ssdMobilenet, exports.nets.faceLandmark68Net, exports.nets.faceRecognitionNet);
exports.allFacesMtcnn = allFacesFactory_1.allFacesMtcnnFactory(exports.nets.mtcnn, exports.nets.faceRecognitionNet);
//# sourceMappingURL=globalApi.js.map
\ No newline at end of file
build/globalApi.js.map
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build/mtcnn/Mtcnn.d.ts
......@@ -4,12 +4,12 @@ import { TNetInput } from '../types';
import { MtcnnForwardParams, MtcnnResult, NetParams } from './types';
export declare class Mtcnn extends NeuralNetwork<NetParams> {
constructor();
forwardInput(input: NetInput, forwardParams: MtcnnForwardParams): Promise<{
forwardInput(input: NetInput, forwardParams?: MtcnnForwardParams): Promise<{
results: MtcnnResult[];
stats: any;
}>;
forward(input: TNetInput, forwardParams: MtcnnForwardParams): Promise<MtcnnResult[]>;
forwardWithStats(input: TNetInput, forwardParams: MtcnnForwardParams): Promise<{
forward(input: TNetInput, forwardParams?: MtcnnForwardParams): Promise<MtcnnResult[]>;
forwardWithStats(input: TNetInput, forwardParams?: MtcnnForwardParams): Promise<{
results: MtcnnResult[];
stats: any;
}>;
......
build/mtcnn/Mtcnn.js
......@@ -24,6 +24,7 @@ var Mtcnn = /** @class */ (function (_super) {
return _super.call(this, 'Mtcnn') || this;
}
Mtcnn.prototype.forwardInput = function (input, forwardParams) {
if (forwardParams === void 0) { forwardParams = {}; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var params, inputTensor, inputCanvas, stats, tsTotal, imgTensor, onReturn, _a, height, width, _b, minFaceSize, scaleFactor, maxNumScales, scoreThresholds, scaleSteps, scales, ts, out1, out2, out3, results;
return tslib_1.__generator(this, function (_c) {
......@@ -98,6 +99,7 @@ var Mtcnn = /** @class */ (function (_super) {
});
};
Mtcnn.prototype.forward = function (input, forwardParams) {
if (forwardParams === void 0) { forwardParams = {}; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a;
return tslib_1.__generator(this, function (_b) {
......@@ -113,6 +115,7 @@ var Mtcnn = /** @class */ (function (_super) {
});
};
Mtcnn.prototype.forwardWithStats = function (input, forwardParams) {
if (forwardParams === void 0) { forwardParams = {}; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a;
return tslib_1.__generator(this, function (_b) {
......
build/mtcnn/Mtcnn.js.map
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dist/face-api.js
......@@ -1095,7 +1095,7 @@
// 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(x, y, Math.min(size, this._imageWidth - x), Math.min(size, this._imageHeight - y));
return new 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');
......@@ -2443,8 +2443,37 @@
return createFaceRecognitionNet(weights);
}
function allFacesFactory(detectionNet, landmarkNet, computeDescriptors) {
function computeDescriptorsFactory(recognitionNet) {
return function (input, alignedFaceBoxes, useBatchProcessing) {
return __awaiter$1(this, void 0, void 0, function () {
var alignedFaceTensors, descriptors, _a;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, 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];
}
});
});
};
}
function allFacesFactory(detectionNet, landmarkNet, recognitionNet) {
var computeDescriptors = computeDescriptorsFactory(recognitionNet);
return function (input, minConfidence, useBatchProcessing) {
if (minConfidence === void 0) { minConfidence = 0.8; }
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return __awaiter$1(this, void 0, void 0, function () {
var detections, faceTensors, faceLandmarksByFace, _a, alignedFaceBoxes, descriptors;
......@@ -2480,8 +2509,10 @@
});
};
}
function allFacesMtcnnFactory(mtcnn, computeDescriptors) {
function allFacesMtcnnFactory(mtcnn, recognitionNet) {
var computeDescriptors = computeDescriptorsFactory(recognitionNet);
return function (input, mtcnnForwardParams, useBatchProcessing) {
if (mtcnnForwardParams === void 0) { mtcnnForwardParams = {}; }
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return __awaiter$1(this, void 0, void 0, function () {
var results, alignedFaceBoxes, descriptors;
......@@ -3167,6 +3198,7 @@
return _super.call(this, 'Mtcnn') || this;
}
Mtcnn.prototype.forwardInput = function (input, forwardParams) {
if (forwardParams === void 0) { forwardParams = {}; }
return __awaiter$1(this, void 0, void 0, function () {
var params, inputTensor, inputCanvas, stats, tsTotal, imgTensor, onReturn, _a, height, width, _b, minFaceSize, scaleFactor, maxNumScales, scoreThresholds, scaleSteps, scales, ts, out1, out2, out3, results;
return __generator$1(this, function (_c) {
......@@ -3241,6 +3273,7 @@
});
};
Mtcnn.prototype.forward = function (input, forwardParams) {
if (forwardParams === void 0) { forwardParams = {}; }
return __awaiter$1(this, void 0, void 0, function () {
var _a;
return __generator$1(this, function (_b) {
......@@ -3256,6 +3289,7 @@
});
};
Mtcnn.prototype.forwardWithStats = function (input, forwardParams) {
if (forwardParams === void 0) { forwardParams = {}; }
return __awaiter$1(this, void 0, void 0, function () {
var _a;
return __generator$1(this, function (_b) {
......@@ -3322,35 +3356,8 @@
function mtcnn(input, forwardParams) {
return nets.mtcnn.forward(input, forwardParams);
}
var allFaces = allFacesFactory(detectionNet, landmarkNet, computeDescriptorsFactory(nets.faceRecognitionNet));
var allFacesMtcnn = allFacesMtcnnFactory(nets.mtcnn, computeDescriptorsFactory(nets.faceRecognitionNet));
function computeDescriptorsFactory(recognitionNet) {
return function (input, alignedFaceBoxes, useBatchProcessing) {
return __awaiter$1(this, void 0, void 0, function () {
var alignedFaceTensors, descriptors, _a;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, 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];
}
});
});
};
}
var allFaces = allFacesFactory(nets.ssdMobilenet, nets.faceLandmark68Net, nets.faceRecognitionNet);
var allFacesMtcnn = allFacesMtcnnFactory(nets.mtcnn, nets.faceRecognitionNet);
function createMtcnn(weights) {
var net = new Mtcnn();
......
dist/face-api.js.map
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dist/face-api.min.js
This source diff could not be displayed because it is too large. You can view the blob instead.
src/FaceLandmarks.ts
......@@ -105,7 +105,7 @@ export class FaceLandmarks {
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))
return new Rect(x, y, Math.min(size, this._imageWidth + x), Math.min(size, this._imageHeight + y))
}
protected getRefPointsForAlignment(): Point[] {
......
src/allFacesFactory.ts
......@@ -2,25 +2,45 @@ 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';
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
}
}
export function allFacesFactory(
detectionNet: FaceDetectionNet,
landmarkNet: FaceLandmarkNet,
computeDescriptors: (input: TNetInput, alignedFaceBoxes: Rect[], useBatchProcessing: boolean) => Promise<Float32Array[]>
recognitionNet: FaceRecognitionNet
) {
const computeDescriptors = computeDescriptorsFactory(recognitionNet)
return async function(
input: TNetInput,
minConfidence: number,
minConfidence: number = 0.8,
useBatchProcessing: boolean = false
): Promise<FullFaceDescription[]> {
const detections = await detectionNet.locateFaces(input, minConfidence)
const faceTensors = await extractFaceTensors(input, detections)
const faceLandmarksByFace = useBatchProcessing
......@@ -50,11 +70,13 @@ export function allFacesFactory(
export function allFacesMtcnnFactory(
mtcnn: Mtcnn,
computeDescriptors: (input: TNetInput, alignedFaceBoxes: Rect[], useBatchProcessing: boolean) => Promise<Float32Array[]>
recognitionNet: FaceRecognitionNet
) {
const computeDescriptors = computeDescriptorsFactory(recognitionNet)
return async function(
input: TNetInput,
mtcnnForwardParams: MtcnnForwardParams,
mtcnnForwardParams: MtcnnForwardParams = {},
useBatchProcessing: boolean = false
): Promise<FullFaceDescription[]> {
......
src/faceDetectionNet/FaceDetectionNet.ts
......@@ -50,7 +50,7 @@ export class FaceDetectionNet extends NeuralNetwork<NetParams> {
public async locateFaces(
input: TNetInput,
minConfidence: number = 0.8,
maxResults: number = 100,
maxResults: number = 100
): Promise<FaceDetection[]> {
const netInput = await toNetInput(input, true)
......
src/globalApi.ts
import * as tf from '@tensorflow/tfjs-core';
import { allFacesFactory, allFacesMtcnnFactory } from './allFacesFactory';
import { extractFaceTensors } from './extractFaceTensors';
import { FaceDetection } from './FaceDetection';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
......@@ -11,7 +10,6 @@ import { FullFaceDescription } from './FullFaceDescription';
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()
......@@ -79,39 +77,25 @@ export function mtcnn(
return nets.mtcnn.forward(input, forwardParams)
}
export const allFaces: (
export type allFacesFunction = (
input: tf.Tensor | NetInput | TNetInput,
minConfidence: number,
minConfidence?: number,
useBatchProcessing?: boolean
) => Promise<FullFaceDescription[]> = allFacesFactory(
detectionNet,
landmarkNet,
computeDescriptorsFactory(nets.faceRecognitionNet)
) => Promise<FullFaceDescription[]>
export const allFaces: allFacesFunction = allFacesFactory(
nets.ssdMobilenet,
nets.faceLandmark68Net,
nets.faceRecognitionNet
)
export const allFacesMtcnn: (
export type allFacesMtcnnFunction = (
input: tf.Tensor | NetInput | TNetInput,
mtcnnForwardParams: MtcnnForwardParams,
mtcnnForwardParams?: MtcnnForwardParams,
useBatchProcessing?: boolean
) => Promise<FullFaceDescription[]> = allFacesMtcnnFactory(
) => Promise<FullFaceDescription[]>
export const allFacesMtcnn: allFacesMtcnnFunction = allFacesMtcnnFactory(
nets.mtcnn,
computeDescriptorsFactory(nets.faceRecognitionNet)
nets.faceRecognitionNet
)
\ No newline at end of file
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/Mtcnn.ts
......@@ -28,7 +28,7 @@ export class Mtcnn extends NeuralNetwork<NetParams> {
public async forwardInput(
input: NetInput,
forwardParams: MtcnnForwardParams
forwardParams: MtcnnForwardParams = {}
): Promise<{ results: MtcnnResult[], stats: any }> {
const { params } = this
......@@ -132,7 +132,7 @@ export class Mtcnn extends NeuralNetwork<NetParams> {
public async forward(
input: TNetInput,
forwardParams: MtcnnForwardParams
forwardParams: MtcnnForwardParams = {}
): Promise<MtcnnResult[]> {
return (
await this.forwardInput(
......@@ -144,7 +144,7 @@ export class Mtcnn extends NeuralNetwork<NetParams> {
public async forwardWithStats(
input: TNetInput,
forwardParams: MtcnnForwardParams
forwardParams: MtcnnForwardParams = {}
): Promise<{ results: MtcnnResult[], stats: any }> {
return this.forwardInput(
await toNetInput(input, true, true),
......
test/tests/e2e/allFaces.test.ts 0 → 100644
import * as faceapi from '../../../src';
import { describeWithNets, expectAllTensorsReleased, expectRectClose, expectPointClose } from '../../utils';
import { expectedSsdBoxes } from './expectedResults';
import { NetInput } from '../../../src/NetInput';
import { toNetInput } from '../../../src';
import * as tf from '@tensorflow/tfjs-core';
import { Point } from '../../../src/Point';
describe('allFaces', () => {
let imgEl: HTMLImageElement
let facesFaceLandmarkPositions: Point[][]
let facesFaceDescriptors: number[][]
beforeAll(async () => {
const img = await (await fetch('base/test/images/faces.jpg')).blob()
imgEl = await faceapi.bufferToImage(img)
facesFaceLandmarkPositions = await (await fetch('base/test/data/facesFaceLandmarkPositions.json')).json()
facesFaceDescriptors = await (await fetch('base/test/data/facesFaceDescriptorsSsd.json')).json()
})
describeWithNets('computes full face descriptions', { withAllFaces: true }, ({ allFaces }) => {
const expectedScores = [0.97, 0.88, 0.83, 0.82, 0.59, 0.52]
const maxBoxDelta = 5
const maxLandmarkPointsDelta = 1
it('scores > 0.8', async () => {
const results = await allFaces(imgEl, 0.8)
expect(results.length).toEqual(4)
results.forEach(({ detection, landmarks, descriptor }, i) => {
expect(detection.getImageWidth()).toEqual(imgEl.width)
expect(detection.getImageHeight()).toEqual(imgEl.height)
expect(detection.getScore()).toBeCloseTo(expectedScores[i], 2)
expectRectClose(detection.getBox(), expectedSsdBoxes[i], maxBoxDelta)
landmarks.getPositions().forEach((pt, j) => expectPointClose(pt, facesFaceLandmarkPositions[i][j], maxLandmarkPointsDelta))
expect(descriptor).toEqual(new Float32Array(facesFaceDescriptors[i]))
})
})
it('scores > 0.5', async () => {
const results = await allFaces(imgEl, 0.5)
expect(results.length).toEqual(6)
results.forEach(({ detection, landmarks, descriptor }, i) => {
expect(detection.getImageWidth()).toEqual(imgEl.width)
expect(detection.getImageHeight()).toEqual(imgEl.height)
expect(detection.getScore()).toBeCloseTo(expectedScores[i], 2)
expectRectClose(detection.getBox(), expectedSsdBoxes[i], maxBoxDelta)
landmarks.getPositions().forEach((pt, j) => expectPointClose(pt, facesFaceLandmarkPositions[i][j], maxLandmarkPointsDelta))
expect(descriptor).toEqual(new Float32Array(facesFaceDescriptors[i]))
})
})
})
describeWithNets('no memory leaks', { withAllFaces: true }, ({ allFaces }) => {
it('single image element', async () => {
await expectAllTensorsReleased(async () => {
await allFaces(imgEl)
})
})
it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(imgEl)
await expectAllTensorsReleased(async () => {
const netInput = (new NetInput([tensor])).managed()
await allFaces(netInput)
})
tensor.dispose()
})
it('single batch size 1 tf.Tensor4Ds', async () => {
const tensor = tf.tidy(() => tf.fromPixels(imgEl).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => {
await allFaces(await toNetInput(tensor, true))
})
tensor.dispose()
})
})
})
\ No newline at end of file
test/tests/e2e/allFacesMtcnn.test.ts 0 → 100644
import * as faceapi from '../../../src';
import { FaceLandmarks5 } from '../../../src/mtcnn/FaceLandmarks5';
import { NetInput } from '../../../src/NetInput';
import { describeWithNets, expectAllTensorsReleased } from '../../utils';
import { expectMtcnnResults } from './expectedResults';
describe('allFacesMtcnn', () => {
let imgEl: HTMLImageElement
let facesFaceDescriptors: number[][]
beforeAll(async () => {
const img = await (await fetch('base/test/images/faces.jpg')).blob()
imgEl = await faceapi.bufferToImage(img)
facesFaceDescriptors = await (await fetch('base/test/data/facesFaceDescriptorsMtcnn.json')).json()
})
describeWithNets('computes full face descriptions', { withAllFacesMtcnn: true }, ({ allFacesMtcnn }) => {
it('minFaceSize = 20', async () => {
const forwardParams = {
minFaceSize: 20
}
const results = await allFacesMtcnn(imgEl, forwardParams)
expect(results.length).toEqual(6)
const mtcnnResult = results.map(res => ({
faceDetection: res.detection,
faceLandmarks: res.landmarks as FaceLandmarks5
}))
expectMtcnnResults(mtcnnResult, [0, 1, 2, 3, 4, 5], 1, 1)
results.forEach(({ descriptor }, i) => {
expect(descriptor).toEqual(new Float32Array(facesFaceDescriptors[i]))
})
})
})
describeWithNets('no memory leaks', { withAllFacesMtcnn: true }, ({ allFacesMtcnn }) => {
it('single image element', async () => {
await expectAllTensorsReleased(async () => {
await allFacesMtcnn(imgEl)
})
})
})
})
\ No newline at end of file
test/tests/e2e/expectedResults.ts 0 → 100644
import * as faceapi from '../../../src';
import { FaceLandmarks5 } from '../../../src/mtcnn/FaceLandmarks5';
import { Point } from '../../../src/Point';
import { expectMaxDelta, expectPointClose, expectRectClose } from '../../utils';
export const expectedSsdBoxes = [
{ x: 48, y: 253, width: 104, height: 129 },
{ x: 260, y: 227, width: 76, height: 117 },
{ x: 466, y: 165, width: 88, height: 130 },
{ x: 234, y: 36, width: 84, height: 119 },
{ x: 577, y: 65, width: 84, height: 105 },
{ x: 84, y: 14, width: 79, height: 132 }
]
export const expectedMtcnnBoxes = [
{ x: 70, y: 21, width: 112, height: 112 },
{ x: 36, y: 250, width: 133, height: 132 },
{ x: 221, y: 43, width: 112, height: 111 },
{ x: 247, y: 231, width: 106, height: 107 },
{ x: 566, y: 67, width: 104, height: 104 },
{ x: 451, y: 176, width: 122, height: 122 }
]
export const expectedMtcnnFaceLandmarks = [
[new Point(117, 58), new Point(156, 63), new Point(141, 86), new Point(109, 98), new Point(147, 104)],
[new Point(82, 292), new Point(134, 304), new Point(104, 330), new Point(72, 342), new Point(120, 353)],
[new Point(261, 82), new Point(306, 83), new Point(282, 113), new Point(257, 124), new Point(306, 126)],
[new Point(277, 273), new Point(318, 273), new Point(295, 300), new Point(279, 311), new Point(316, 313)],
[new Point(607, 110), new Point(645, 115), new Point(626, 138), new Point(601, 144), new Point(639, 150)],
[new Point(489, 224), new Point(534, 223), new Point(507, 250), new Point(493, 271), new Point(530, 270)]
]
export function expectMtcnnResults(
results: { faceDetection: faceapi.FaceDetection, faceLandmarks: faceapi.FaceLandmarks5 }[],
boxOrder: number[],
maxBoxDelta: number,
maxLandmarkPointsDelta: number
) {
results.forEach((result, i) => {
const { faceDetection, faceLandmarks } = result
expect(faceDetection instanceof faceapi.FaceDetection).toBe(true)
expect(faceLandmarks instanceof faceapi.FaceLandmarks5).toBe(true)
expectRectClose(faceDetection.getBox(), expectedMtcnnBoxes[boxOrder[i]], maxBoxDelta)
faceLandmarks.getPositions().forEach((pt, j) => expectPointClose(pt, expectedMtcnnFaceLandmarks[boxOrder[i]][j], maxLandmarkPointsDelta))
expectMaxDelta(faceDetection.getScore(), 0.99, 0.01)
})
}
\ No newline at end of file
test/tests/e2e/faceDetectionNet.test.ts
import * as faceapi from '../../../src';
import { describeWithNets, expectAllTensorsReleased, expectRectClose } from '../../utils';
const expectedBoxes = [
{ x: 48, y: 253, width: 104, height: 129 },
{ x: 260, y: 227, width: 76, height: 117 },
{ x: 466, y: 165, width: 88, height: 130 },
{ x: 234, y: 36, width: 84, height: 119 },
{ x: 577, y: 65, width: 84, height: 105 },
{ x: 84, y: 14, width: 79, height: 132 }
]
import { expectedSsdBoxes } from './expectedResults';
describe('faceDetectionNet', () => {
......@@ -32,7 +24,7 @@ describe('faceDetectionNet', () => {
expect(det.getImageWidth()).toEqual(imgEl.width)
expect(det.getImageHeight()).toEqual(imgEl.height)
expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
expectRectClose(det.getBox(), expectedBoxes[i], maxBoxDelta)
expectRectClose(det.getBox(), expectedSsdBoxes[i], maxBoxDelta)
})
})
......@@ -44,7 +36,7 @@ describe('faceDetectionNet', () => {
expect(det.getImageWidth()).toEqual(imgEl.width)
expect(det.getImageHeight()).toEqual(imgEl.height)
expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
expectRectClose(det.getBox(), expectedBoxes[i], maxBoxDelta)
expectRectClose(det.getBox(), expectedSsdBoxes[i], maxBoxDelta)
})
})
......@@ -63,7 +55,7 @@ describe('faceDetectionNet', () => {
expect(det.getImageWidth()).toEqual(imgEl.width)
expect(det.getImageHeight()).toEqual(imgEl.height)
expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
expectRectClose(det.getBox(), expectedBoxes[i], maxBoxDelta)
expectRectClose(det.getBox(), expectedSsdBoxes[i], maxBoxDelta)
})
})
......@@ -75,7 +67,7 @@ describe('faceDetectionNet', () => {
expect(det.getImageWidth()).toEqual(imgEl.width)
expect(det.getImageHeight()).toEqual(imgEl.height)
expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
expectRectClose(det.getBox(), expectedBoxes[i], maxBoxDelta)
expectRectClose(det.getBox(), expectedSsdBoxes[i], maxBoxDelta)
})
})
......
test/tests/e2e/faceLandmarkNet.test.ts
......@@ -4,7 +4,7 @@ import * as faceapi from '../../../src';
import { isTensor3D } from '../../../src/commons/isTensor';
import { Point } from '../../../src/Point';
import { Dimensions, TMediaElement } from '../../../src/types';
import { expectMaxDelta, expectAllTensorsReleased, tensor3D, describeWithNets } from '../../utils';
import { expectMaxDelta, expectAllTensorsReleased, describeWithNets } from '../../utils';
import { NetInput } from '../../../src/NetInput';
import { toNetInput } from '../../../src';
......
test/tests/e2e/mtcnn.test.ts
import * as faceapi from '../../../src';
import { MtcnnResult } from '../../../src/mtcnn/types';
import { Point } from '../../../src/Point';
import { describeWithNets, expectAllTensorsReleased, expectPointClose, expectRectClose, expectMaxDelta } from '../../utils';
const expectedBoxes = [
{ x: 70, y: 21, width: 112, height: 112 },
{ x: 36, y: 250, width: 133, height: 132 },
{ x: 221, y: 43, width: 112, height: 111 },
{ x: 247, y: 231, width: 106, height: 107 },
{ x: 566, y: 67, width: 104, height: 104 },
{ x: 451, y: 176, width: 122, height: 122 }
]
const expectedFaceLandmarks = [
[new Point(117, 58), new Point(156, 63), new Point(141, 86), new Point(109, 98), new Point(147, 104)],
[new Point(82, 292), new Point(134, 304), new Point(104, 330), new Point(72, 342), new Point(120, 353)],
[new Point(261, 82), new Point(306, 83), new Point(282, 113), new Point(257, 124), new Point(306, 126)],
[new Point(277, 273), new Point(318, 273), new Point(295, 300), new Point(279, 311), new Point(316, 313)],
[new Point(607, 110), new Point(645, 115), new Point(626, 138), new Point(601, 144), new Point(639, 150)],
[new Point(489, 224), new Point(534, 223), new Point(507, 250), new Point(493, 271), new Point(530, 270)]
]
import { describeWithNets, expectAllTensorsReleased } from '../../utils';
import { expectMtcnnResults } from './expectedResults';
describe('mtcnn', () => {
......@@ -32,21 +14,6 @@ describe('mtcnn', () => {
describeWithNets('uncompressed weights', { withMtcnn: { quantized: false } }, ({ mtcnn }) => {
function expectResults(
results: MtcnnResult[],
boxOrder: number[],
maxBoxDelta: number,
maxLandmarkPointsDelta: number
) {
results.forEach((result, i) => {
const { faceDetection, faceLandmarks } = result
expect(faceDetection instanceof faceapi.FaceDetection).toBe(true)
expect(faceLandmarks instanceof faceapi.FaceLandmarks5).toBe(true)
expectRectClose(faceDetection.getBox(), expectedBoxes[boxOrder[i]], maxBoxDelta)
faceLandmarks.getPositions().forEach((pt, j) => expectPointClose(pt, expectedFaceLandmarks[boxOrder[i]][j], maxLandmarkPointsDelta))
expectMaxDelta(faceDetection.getScore(), 0.99, 0.01)
})
}
it('minFaceSize = 20, finds all faces', async () => {
const forwardParams = {
......@@ -55,7 +22,7 @@ describe('mtcnn', () => {
const results = await mtcnn.forward(imgEl, forwardParams)
expect(results.length).toEqual(6)
expectResults(results, [0, 1, 2, 3, 4, 5], 1, 1)
expectMtcnnResults(results, [0, 1, 2, 3, 4, 5], 1, 1)
})
it('minFaceSize = 80, finds all faces', async () => {
......@@ -66,7 +33,7 @@ describe('mtcnn', () => {
const results = await mtcnn.forward(imgEl, forwardParams)
expect(results.length).toEqual(6)
expectResults(results, [0, 5, 3, 1, 2, 4], 12, 12)
expectMtcnnResults(results, [0, 5, 3, 1, 2, 4], 12, 12)
})
it('all optional params passed, finds all faces', async () => {
......@@ -79,7 +46,7 @@ describe('mtcnn', () => {
const results = await mtcnn.forward(imgEl, forwardParams)
expect(results.length).toEqual(6)
expectResults(results, [5, 1, 4, 3, 2, 0], 6, 10)
expectMtcnnResults(results, [5, 1, 4, 3, 2, 0], 6, 10)
})
it('scale steps passed, finds all faces', async () => {
......@@ -89,7 +56,7 @@ describe('mtcnn', () => {
const results = await mtcnn.forward(imgEl, forwardParams)
expect(results.length).toEqual(6)
expectResults(results, [5, 1, 3, 0, 2, 4], 7, 15)
expectMtcnnResults(results, [5, 1, 3, 0, 2, 4], 7, 15)
})
})
......
test/utils.ts
......@@ -4,6 +4,8 @@ import { IRect } from '../build/Rect';
import * as faceapi from '../src/';
import { NeuralNetwork } from '../src/commons/NeuralNetwork';
import { IPoint } from '../src/';
import { allFacesFactory, allFacesMtcnnFactory } from '../src/allFacesFactory';
import { allFacesMtcnnFunction, allFacesFunction } from '../src/globalApi';
export function zeros(length: number): Float32Array {
return new Float32Array(length)
......@@ -57,9 +59,14 @@ export type InjectNetArgs = {
faceLandmarkNet: faceapi.FaceLandmarkNet
faceRecognitionNet: faceapi.FaceRecognitionNet
mtcnn: faceapi.Mtcnn
allFaces: allFacesFunction
allFacesMtcnn: allFacesMtcnnFunction
}
export type DescribeWithNetsOptions = {
withAllFaces?: boolean
withAllFacesMtcnn?: boolean
withFaceDetectionNet?: WithNetOptions
withFaceLandmarkNet?: WithNetOptions
withFaceRecognitionNet?: WithNetOptions
......@@ -92,37 +99,43 @@ export function describeWithNets(
let faceLandmarkNet: faceapi.FaceLandmarkNet = new faceapi.FaceLandmarkNet()
let faceRecognitionNet: faceapi.FaceRecognitionNet = new faceapi.FaceRecognitionNet()
let mtcnn: faceapi.Mtcnn = new faceapi.Mtcnn()
let allFaces = allFacesFactory(faceDetectionNet, faceLandmarkNet, faceRecognitionNet)
let allFacesMtcnn = allFacesMtcnnFactory(mtcnn, faceRecognitionNet)
beforeAll(async () => {
const {
withFaceDetectionNet,
withFaceLandmarkNet,
withFaceRecognitionNet,
withMtcnn
withMtcnn,
withAllFaces,
withAllFacesMtcnn
} = options
if (withFaceDetectionNet) {
if (withFaceDetectionNet || withAllFaces) {
await initNet<faceapi.FaceDetectionNet>(
faceDetectionNet,
!withFaceDetectionNet.quantized && 'face_detection_model.weights'
!!withFaceDetectionNet && !withFaceDetectionNet.quantized && 'face_detection_model.weights'
)
}
if (withFaceLandmarkNet) {
if (withFaceLandmarkNet || withAllFaces) {
await initNet<faceapi.FaceLandmarkNet>(
faceLandmarkNet,
!withFaceLandmarkNet.quantized && 'face_landmark_68_model.weights'
!!withFaceLandmarkNet && !withFaceLandmarkNet.quantized && 'face_landmark_68_model.weights'
)
}
if (withFaceRecognitionNet) {
if (withFaceRecognitionNet || withAllFaces || withAllFacesMtcnn) {
await initNet<faceapi.FaceRecognitionNet>(
faceRecognitionNet,
!withFaceRecognitionNet.quantized && 'face_recognition_model.weights'
// TODO: figure out why quantized weights results in NaNs in testcases
'face_recognition_model.weights'
)
}
if (withMtcnn) {
if (withMtcnn || withAllFacesMtcnn) {
await initNet<faceapi.Mtcnn>(
mtcnn,
!withMtcnn.quantized && 'mtcnn_model.weights'
!!withMtcnn && !withMtcnn.quantized && 'mtcnn_model.weights'
)
}
})
......@@ -134,7 +147,7 @@ export function describeWithNets(
mtcnn && mtcnn.dispose()
})
specDefinitions({ faceDetectionNet, faceLandmarkNet, faceRecognitionNet, mtcnn })
specDefinitions({ faceDetectionNet, faceLandmarkNet, faceRecognitionNet, mtcnn, allFaces, allFacesMtcnn })
})
}
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