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Commit 7d04da10 by vincent
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check in latest build

parent 7152e12d
Showing with 159 additions and 143 deletions
build/allFacesFactory.d.ts
......@@ -3,4 +3,4 @@ import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { FullFaceDescription } from './FullFaceDescription';
import { TNetInput } from './types';
export declare function allFacesFactory(detectionNet: FaceDetectionNet, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence: number) => Promise<FullFaceDescription[]>;
export declare function allFacesFactory(detectionNet: FaceDetectionNet, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
build/allFacesFactory.js
......@@ -4,34 +4,45 @@ var tslib_1 = require("tslib");
var extractFaceTensors_1 = require("./extractFaceTensors");
var FullFaceDescription_1 = require("./FullFaceDescription");
function allFacesFactory(detectionNet, landmarkNet, recognitionNet) {
return function (input, minConfidence) {
return function (input, minConfidence, useBatchProcessing) {
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var detections, faceTensors, faceLandmarksByFace, alignedFaceBoxes, alignedFaceTensors, descriptors;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
var detections, faceTensors, faceLandmarksByFace, _a, alignedFaceBoxes, alignedFaceTensors, descriptors, _b;
return tslib_1.__generator(this, function (_c) {
switch (_c.label) {
case 0: return [4 /*yield*/, detectionNet.locateFaces(input, minConfidence)];
case 1:
detections = _a.sent();
return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, detections)
/**
const faceLandmarksByFace = await Promise.all(faceTensors.map(
faceTensor => landmarkNet.detectLandmarks(faceTensor)
)) as FaceLandmarks[]
*/
];
detections = _c.sent();
return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, detections)];
case 2:
faceTensors = _a.sent();
faceTensors = _c.sent();
if (!useBatchProcessing) return [3 /*break*/, 4];
return [4 /*yield*/, landmarkNet.detectLandmarks(faceTensors)];
case 3:
faceLandmarksByFace = _a.sent();
_a = _c.sent();
return [3 /*break*/, 6];
case 4: return [4 /*yield*/, Promise.all(faceTensors.map(function (faceTensor) { return landmarkNet.detectLandmarks(faceTensor); }))];
case 5:
_a = _c.sent();
_c.label = 6;
case 6:
faceLandmarksByFace = _a;
faceTensors.forEach(function (t) { return t.dispose(); });
alignedFaceBoxes = faceLandmarksByFace.map(function (landmarks, i) { return landmarks.align(detections[i].getBox()); });
return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, alignedFaceBoxes)];
case 4:
alignedFaceTensors = _a.sent();
return [4 /*yield*/, Promise.all(alignedFaceTensors.map(function (faceTensor) { return recognitionNet.computeFaceDescriptor(faceTensor); }))];
case 5:
descriptors = _a.sent();
case 7:
alignedFaceTensors = _c.sent();
if (!useBatchProcessing) return [3 /*break*/, 9];
return [4 /*yield*/, recognitionNet.computeFaceDescriptor(alignedFaceTensors)];
case 8:
_b = _c.sent();
return [3 /*break*/, 11];
case 9: return [4 /*yield*/, Promise.all(alignedFaceTensors.map(function (faceTensor) { return recognitionNet.computeFaceDescriptor(faceTensor); }))];
case 10:
_b = _c.sent();
_c.label = 11;
case 11:
descriptors = _b;
alignedFaceTensors.forEach(function (t) { return t.dispose(); });
return [2 /*return*/, detections.map(function (detection, i) {
return new FullFaceDescription_1.FullFaceDescription(detection, faceLandmarksByFace[i].shiftByPoint(detection.getBox()), descriptors[i]);
......
build/allFacesFactory.js.map
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\ No newline at end of file
build/faceRecognitionNet/FaceRecognitionNet.d.ts
......@@ -5,7 +5,7 @@ export declare class FaceRecognitionNet {
private _params;
load(weightsOrUrl: Float32Array | string | undefined): Promise<void>;
extractWeights(weights: Float32Array): void;
forwardInput(input: NetInput): Promise<tf.Tensor2D>;
forwardInput(input: NetInput): tf.Tensor2D;
forward(input: TNetInput): Promise<tf.Tensor2D>;
computeFaceDescriptor(input: TNetInput): Promise<Float32Array>;
computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
}
build/faceRecognitionNet/FaceRecognitionNet.js
......@@ -37,36 +37,32 @@ var FaceRecognitionNet = /** @class */ (function () {
this._params = extractParams_1.extractParams(weights);
};
FaceRecognitionNet.prototype.forwardInput = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _this = this;
return tslib_1.__generator(this, function (_a) {
if (!this._params) {
throw new Error('FaceRecognitionNet - load model before inference');
}
return [2 /*return*/, tf.tidy(function () {
var batchTensor = input.toBatchTensor(150, true);
var normalized = normalize_1.normalize(batchTensor);
var out = convLayer_1.convDown(normalized, _this._params.conv32_down);
out = tf.maxPool(out, 3, 2, 'valid');
out = residualLayer_1.residual(out, _this._params.conv32_1);
out = residualLayer_1.residual(out, _this._params.conv32_2);
out = residualLayer_1.residual(out, _this._params.conv32_3);
out = residualLayer_1.residualDown(out, _this._params.conv64_down);
out = residualLayer_1.residual(out, _this._params.conv64_1);
out = residualLayer_1.residual(out, _this._params.conv64_2);
out = residualLayer_1.residual(out, _this._params.conv64_3);
out = residualLayer_1.residualDown(out, _this._params.conv128_down);
out = residualLayer_1.residual(out, _this._params.conv128_1);
out = residualLayer_1.residual(out, _this._params.conv128_2);
out = residualLayer_1.residualDown(out, _this._params.conv256_down);
out = residualLayer_1.residual(out, _this._params.conv256_1);
out = residualLayer_1.residual(out, _this._params.conv256_2);
out = residualLayer_1.residualDown(out, _this._params.conv256_down_out);
var globalAvg = out.mean([1, 2]);
var fullyConnected = tf.matMul(globalAvg, _this._params.fc);
return fullyConnected;
})];
});
var _this = this;
if (!this._params) {
throw new Error('FaceRecognitionNet - load model before inference');
}
return tf.tidy(function () {
var batchTensor = input.toBatchTensor(150, true);
var normalized = normalize_1.normalize(batchTensor);
var out = convLayer_1.convDown(normalized, _this._params.conv32_down);
out = tf.maxPool(out, 3, 2, 'valid');
out = residualLayer_1.residual(out, _this._params.conv32_1);
out = residualLayer_1.residual(out, _this._params.conv32_2);
out = residualLayer_1.residual(out, _this._params.conv32_3);
out = residualLayer_1.residualDown(out, _this._params.conv64_down);
out = residualLayer_1.residual(out, _this._params.conv64_1);
out = residualLayer_1.residual(out, _this._params.conv64_2);
out = residualLayer_1.residual(out, _this._params.conv64_3);
out = residualLayer_1.residualDown(out, _this._params.conv128_down);
out = residualLayer_1.residual(out, _this._params.conv128_1);
out = residualLayer_1.residual(out, _this._params.conv128_2);
out = residualLayer_1.residualDown(out, _this._params.conv256_down);
out = residualLayer_1.residual(out, _this._params.conv256_1);
out = residualLayer_1.residual(out, _this._params.conv256_2);
out = residualLayer_1.residualDown(out, _this._params.conv256_down_out);
var globalAvg = out.mean([1, 2]);
var fullyConnected = tf.matMul(globalAvg, _this._params.fc);
return fullyConnected;
});
};
FaceRecognitionNet.prototype.forward = function (input) {
......@@ -84,20 +80,21 @@ var FaceRecognitionNet = /** @class */ (function () {
};
FaceRecognitionNet.prototype.computeFaceDescriptor = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var result, _a, data;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = this.forward;
return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
case 1: return [4 /*yield*/, _a.apply(this, [_b.sent()])];
var _this = this;
var netInput, faceDescriptorTensors, faceDescriptorsForBatch;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
case 1:
netInput = _a.sent();
faceDescriptorTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });
return [4 /*yield*/, Promise.all(faceDescriptorTensors.map(function (t) { return t.data(); }))];
case 2:
result = _b.sent();
return [4 /*yield*/, result.data()];
case 3:
data = _b.sent();
result.dispose();
return [2 /*return*/, data];
faceDescriptorsForBatch = _a.sent();
faceDescriptorTensors.forEach(function (t) { return t.dispose(); });
return [2 /*return*/, netInput.isBatchInput
? faceDescriptorsForBatch
: faceDescriptorsForBatch[0]];
}
});
});
......
build/faceRecognitionNet/FaceRecognitionNet.js.map
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\ No newline at end of file
build/faceRecognitionNet/normalize.js
......@@ -3,9 +3,9 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function normalize(x) {
return tf.tidy(function () {
var avg_r = tf.fill([1, 150, 150, 1], 122.782);
var avg_g = tf.fill([1, 150, 150, 1], 117.001);
var avg_b = tf.fill([1, 150, 150, 1], 104.298);
var avg_r = tf.fill(x.shape.slice(0, 3).concat([1]), 122.782);
var avg_g = tf.fill(x.shape.slice(0, 3).concat([1]), 117.001);
var avg_b = tf.fill(x.shape.slice(0, 3).concat([1]), 104.298);
var avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);
return tf.div(tf.sub(x, avg_rgb), tf.scalar(256));
});
......
build/faceRecognitionNet/normalize.js.map
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\ No newline at end of file
build/globalApi.d.ts
......@@ -16,5 +16,5 @@ export declare function loadFaceRecognitionModel(url: string): Promise<void>;
export declare function loadModels(url: string): Promise<[void, void, void]>;
export declare function locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
export declare function detectLandmarks(input: TNetInput): Promise<FaceLandmarks | FaceLandmarks[]>;
export declare function computeFaceDescriptor(input: TNetInput): Promise<Float32Array>;
export declare const allFaces: (input: tf.Tensor | NetInput | TNetInput, minConfidence: number) => Promise<FullFaceDescription[]>;
export declare function computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
export declare const allFaces: (input: tf.Tensor | NetInput | TNetInput, minConfidence: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
build/globalApi.js.map
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\ No newline at end of file
dist/face-api.js.map
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dist/face-api.min.js
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