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face
Commit 8fda8661 by vincent
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check in latest build

parent fdf28275
Showing with 95 additions and 80 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
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