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face
Commit f0ae9c70 by vincent
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check in latest build

parent 1c56acb1
Showing with 353 additions and 124 deletions
build/allFacesFactory.d.ts
...@@ -3,10 +3,10 @@ import { TinyYolov2Types } from 'tfjs-tiny-yolov2'; ...@@ -3,10 +3,10 @@ import { TinyYolov2Types } from 'tfjs-tiny-yolov2';
import { TinyYolov2 } from '.'; import { TinyYolov2 } from '.';
import { FullFaceDescription } from './classes/FullFaceDescription'; import { FullFaceDescription } from './classes/FullFaceDescription';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet'; import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet'; import { FaceLandmark68Net } from './faceLandmarkNet/FaceLandmark68Net';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet'; import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { Mtcnn } from './mtcnn/Mtcnn'; import { Mtcnn } from './mtcnn/Mtcnn';
import { MtcnnForwardParams } from './mtcnn/types'; import { MtcnnForwardParams } from './mtcnn/types';
export declare function allFacesSsdMobilenetv1Factory(ssdMobilenetv1: FaceDetectionNet, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>; export declare function allFacesSsdMobilenetv1Factory(ssdMobilenetv1: FaceDetectionNet, landmarkNet: FaceLandmark68Net, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare function allFacesTinyYolov2Factory(tinyYolov2: TinyYolov2, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, forwardParams?: TinyYolov2Types.TinyYolov2ForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>; export declare function allFacesTinyYolov2Factory(tinyYolov2: TinyYolov2, landmarkNet: FaceLandmark68Net, recognitionNet: FaceRecognitionNet): (input: TNetInput, forwardParams?: TinyYolov2Types.TinyYolov2ForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare function allFacesMtcnnFactory(mtcnn: Mtcnn, recognitionNet: FaceRecognitionNet): (input: TNetInput, mtcnnForwardParams?: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>; export declare function allFacesMtcnnFactory(mtcnn: Mtcnn, recognitionNet: FaceRecognitionNet): (input: TNetInput, mtcnnForwardParams?: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
build/allFacesFactory.js
...@@ -7,24 +7,23 @@ var dom_1 = require("./dom"); ...@@ -7,24 +7,23 @@ var dom_1 = require("./dom");
function computeDescriptorsFactory(recognitionNet) { function computeDescriptorsFactory(recognitionNet) {
return function (input, alignedFaceBoxes, useBatchProcessing) { return function (input, alignedFaceBoxes, useBatchProcessing) {
return tslib_1.__awaiter(this, void 0, void 0, function () { return tslib_1.__awaiter(this, void 0, void 0, function () {
var alignedFaceTensors, descriptors, _a; var alignedFaceCanvases, descriptors, _a;
return tslib_1.__generator(this, function (_b) { return tslib_1.__generator(this, function (_b) {
switch (_b.label) { switch (_b.label) {
case 0: return [4 /*yield*/, dom_1.extractFaceTensors(input, alignedFaceBoxes)]; case 0: return [4 /*yield*/, dom_1.extractFaces(input, alignedFaceBoxes)];
case 1: case 1:
alignedFaceTensors = _b.sent(); alignedFaceCanvases = _b.sent();
if (!useBatchProcessing) return [3 /*break*/, 3]; if (!useBatchProcessing) return [3 /*break*/, 3];
return [4 /*yield*/, recognitionNet.computeFaceDescriptor(alignedFaceTensors)]; return [4 /*yield*/, recognitionNet.computeFaceDescriptor(alignedFaceCanvases)];
case 2: case 2:
_a = _b.sent(); _a = _b.sent();
return [3 /*break*/, 5]; return [3 /*break*/, 5];
case 3: return [4 /*yield*/, Promise.all(alignedFaceTensors.map(function (faceTensor) { return recognitionNet.computeFaceDescriptor(faceTensor); }))]; case 3: return [4 /*yield*/, Promise.all(alignedFaceCanvases.map(function (canvas) { return recognitionNet.computeFaceDescriptor(canvas); }))];
case 4: case 4:
_a = _b.sent(); _a = _b.sent();
_b.label = 5; _b.label = 5;
case 5: case 5:
descriptors = _a; descriptors = _a;
alignedFaceTensors.forEach(function (t) { return t.dispose(); });
return [2 /*return*/, descriptors]; return [2 /*return*/, descriptors];
} }
}); });
...@@ -36,27 +35,26 @@ function allFacesFactory(detectFaces, landmarkNet, recognitionNet) { ...@@ -36,27 +35,26 @@ function allFacesFactory(detectFaces, landmarkNet, recognitionNet) {
return function (input, useBatchProcessing) { return function (input, useBatchProcessing) {
if (useBatchProcessing === void 0) { useBatchProcessing = false; } if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return tslib_1.__awaiter(this, void 0, void 0, function () { return tslib_1.__awaiter(this, void 0, void 0, function () {
var detections, faceTensors, faceLandmarksByFace, _a, alignedFaceBoxes, descriptors; var detections, faceCanvases, faceLandmarksByFace, _a, alignedFaceBoxes, descriptors;
return tslib_1.__generator(this, function (_b) { return tslib_1.__generator(this, function (_b) {
switch (_b.label) { switch (_b.label) {
case 0: return [4 /*yield*/, detectFaces(input)]; case 0: return [4 /*yield*/, detectFaces(input)];
case 1: case 1:
detections = _b.sent(); detections = _b.sent();
return [4 /*yield*/, dom_1.extractFaceTensors(input, detections)]; return [4 /*yield*/, dom_1.extractFaces(input, detections)];
case 2: case 2:
faceTensors = _b.sent(); faceCanvases = _b.sent();
if (!useBatchProcessing) return [3 /*break*/, 4]; if (!useBatchProcessing) return [3 /*break*/, 4];
return [4 /*yield*/, landmarkNet.detectLandmarks(faceTensors)]; return [4 /*yield*/, landmarkNet.detectLandmarks(faceCanvases)];
case 3: case 3:
_a = _b.sent(); _a = _b.sent();
return [3 /*break*/, 6]; return [3 /*break*/, 6];
case 4: return [4 /*yield*/, Promise.all(faceTensors.map(function (faceTensor) { return landmarkNet.detectLandmarks(faceTensor); }))]; case 4: return [4 /*yield*/, Promise.all(faceCanvases.map(function (canvas) { return landmarkNet.detectLandmarks(canvas); }))];
case 5: case 5:
_a = _b.sent(); _a = _b.sent();
_b.label = 6; _b.label = 6;
case 6: case 6:
faceLandmarksByFace = _a; faceLandmarksByFace = _a;
faceTensors.forEach(function (t) { return t.dispose(); });
alignedFaceBoxes = faceLandmarksByFace.map(function (landmarks, i) { return landmarks.align(detections[i].getBox()); }); alignedFaceBoxes = faceLandmarksByFace.map(function (landmarks, i) { return landmarks.align(detections[i].getBox()); });
return [4 /*yield*/, computeDescriptors(input, alignedFaceBoxes, useBatchProcessing)]; return [4 /*yield*/, computeDescriptors(input, alignedFaceBoxes, useBatchProcessing)];
case 7: case 7:
......
build/allFacesFactory.js.map
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\ No newline at end of file \ No newline at end of file
build/dom/extractFaceTensors.d.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { Rect, TNetInput } from 'tfjs-image-recognition-base'; import { Rect } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection'; import { FaceDetection } from '../classes/FaceDetection';
/** /**
* Extracts the tensors of the image regions containing the detected faces. * Extracts the tensors of the image regions containing the detected faces.
...@@ -7,8 +7,8 @@ import { FaceDetection } from '../classes/FaceDetection'; ...@@ -7,8 +7,8 @@ import { FaceDetection } from '../classes/FaceDetection';
* Using this method is faster then extracting a canvas for each face and * Using this method is faster then extracting a canvas for each face and
* converting them to tensors individually. * converting them to tensors individually.
* *
* @param input The image that face detection has been performed on. * @param imageTensor The image tensor that face detection has been performed on.
* @param detections The face detection results or face bounding boxes for that image. * @param detections The face detection results or face bounding boxes for that image.
* @returns Tensors of the corresponding image region for each detected face. * @returns Tensors of the corresponding image region for each detected face.
*/ */
export declare function extractFaceTensors(input: TNetInput, detections: Array<FaceDetection | Rect>): Promise<tf.Tensor4D[]>; export declare function extractFaceTensors(imageTensor: tf.Tensor3D | tf.Tensor4D, detections: Array<FaceDetection | Rect>): Promise<tf.Tensor3D[]>;
build/dom/extractFaceTensors.js
...@@ -10,41 +10,28 @@ var FaceDetection_1 = require("../classes/FaceDetection"); ...@@ -10,41 +10,28 @@ var FaceDetection_1 = require("../classes/FaceDetection");
* Using this method is faster then extracting a canvas for each face and * Using this method is faster then extracting a canvas for each face and
* converting them to tensors individually. * converting them to tensors individually.
* *
* @param input The image that face detection has been performed on. * @param imageTensor The image tensor that face detection has been performed on.
* @param detections The face detection results or face bounding boxes for that image. * @param detections The face detection results or face bounding boxes for that image.
* @returns Tensors of the corresponding image region for each detected face. * @returns Tensors of the corresponding image region for each detected face.
*/ */
function extractFaceTensors(input, detections) { function extractFaceTensors(imageTensor, detections) {
return tslib_1.__awaiter(this, void 0, void 0, function () { return tslib_1.__awaiter(this, void 0, void 0, function () {
var netInput;
return tslib_1.__generator(this, function (_a) { return tslib_1.__generator(this, function (_a) {
switch (_a.label) { if (tfjs_image_recognition_base_1.isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)]; throw new Error('extractFaceTensors - batchSize > 1 not supported');
case 1:
netInput = _a.sent();
if (netInput.batchSize > 1) {
if (netInput.isManaged) {
netInput.dispose();
}
throw new Error('extractFaceTensors - batchSize > 1 not supported');
}
return [2 /*return*/, tf.tidy(function () {
var imgTensor = netInput.inputs[0].expandDims().toFloat();
var _a = imgTensor.shape.slice(1), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];
var boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
? det.forSize(imgWidth, imgHeight).getBox()
: det; })
.map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); });
var faceTensors = boxes.map(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
return tf.slice4d(imgTensor, [0, y, x, 0], [1, height, width, numChannels]);
});
if (netInput.isManaged) {
netInput.dispose();
}
return faceTensors;
})];
} }
return [2 /*return*/, tf.tidy(function () {
var _a = imageTensor.shape.slice(tfjs_image_recognition_base_1.isTensor4D(imageTensor) ? 1 : 0), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];
var boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
? det.forSize(imgWidth, imgHeight).getBox()
: det; })
.map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); });
var faceTensors = boxes.map(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
return tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]);
});
return faceTensors;
})];
}); });
}); });
} }
......
build/dom/extractFaceTensors.js.map
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\ No newline at end of file \ No newline at end of file
build/dom/extractFaces.js
...@@ -12,29 +12,30 @@ var FaceDetection_1 = require("../classes/FaceDetection"); ...@@ -12,29 +12,30 @@ var FaceDetection_1 = require("../classes/FaceDetection");
*/ */
function extractFaces(input, detections) { function extractFaces(input, detections) {
return tslib_1.__awaiter(this, void 0, void 0, function () { return tslib_1.__awaiter(this, void 0, void 0, function () {
var canvas, netInput, ctx, boxes; var canvas, netInput, tensorOrCanvas, _a, ctx, boxes;
return tslib_1.__generator(this, function (_a) { return tslib_1.__generator(this, function (_b) {
switch (_a.label) { switch (_b.label) {
case 0: case 0:
canvas = input; canvas = input;
if (!!(input instanceof HTMLCanvasElement)) return [3 /*break*/, 3]; if (!!(input instanceof HTMLCanvasElement)) return [3 /*break*/, 5];
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)]; return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1: case 1:
netInput = _a.sent(); netInput = _b.sent();
if (netInput.batchSize > 1) { if (netInput.batchSize > 1) {
if (netInput.isManaged) {
netInput.dispose();
}
throw new Error('extractFaces - batchSize > 1 not supported'); throw new Error('extractFaces - batchSize > 1 not supported');
} }
return [4 /*yield*/, tfjs_image_recognition_base_1.imageTensorToCanvas(netInput.inputs[0])]; tensorOrCanvas = netInput.getInput(0);
case 2: if (!(tensorOrCanvas instanceof HTMLCanvasElement)) return [3 /*break*/, 2];
canvas = _a.sent(); _a = tensorOrCanvas;
if (netInput.isManaged) { return [3 /*break*/, 4];
netInput.dispose(); case 2: return [4 /*yield*/, tfjs_image_recognition_base_1.imageTensorToCanvas(tensorOrCanvas)];
}
_a.label = 3;
case 3: case 3:
_a = _b.sent();
_b.label = 4;
case 4:
canvas = _a;
_b.label = 5;
case 5:
ctx = tfjs_image_recognition_base_1.getContext2dOrThrow(canvas); ctx = tfjs_image_recognition_base_1.getContext2dOrThrow(canvas);
boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
? det.forSize(canvas.width, canvas.height).getBox().floor() ? det.forSize(canvas.width, canvas.height).getBox().floor()
......
build/dom/extractFaces.js.map
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\ No newline at end of file \ No newline at end of file
build/faceDetectionNet/FaceDetectionNet.js
...@@ -21,7 +21,7 @@ var FaceDetectionNet = /** @class */ (function (_super) { ...@@ -21,7 +21,7 @@ var FaceDetectionNet = /** @class */ (function (_super) {
throw new Error('FaceDetectionNet - load model before inference'); throw new Error('FaceDetectionNet - load model before inference');
} }
return tf.tidy(function () { return tf.tidy(function () {
var batchTensor = input.toBatchTensor(512, false); var batchTensor = input.toBatchTensor(512, false).toFloat();
var x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)); var x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1));
var features = mobileNetV1_1.mobileNetV1(x, params.mobilenetv1); var features = mobileNetV1_1.mobileNetV1(x, params.mobilenetv1);
var _a = predictionLayer_1.predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions; var _a = predictionLayer_1.predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
...@@ -35,7 +35,7 @@ var FaceDetectionNet = /** @class */ (function (_super) { ...@@ -35,7 +35,7 @@ var FaceDetectionNet = /** @class */ (function (_super) {
switch (_b.label) { switch (_b.label) {
case 0: case 0:
_a = this.forwardInput; _a = this.forwardInput;
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)]; return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])]; case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
} }
}); });
...@@ -45,10 +45,10 @@ var FaceDetectionNet = /** @class */ (function (_super) { ...@@ -45,10 +45,10 @@ var FaceDetectionNet = /** @class */ (function (_super) {
if (minConfidence === void 0) { minConfidence = 0.8; } if (minConfidence === void 0) { minConfidence = 0.8; }
if (maxResults === void 0) { maxResults = 100; } if (maxResults === void 0) { maxResults = 100; }
return tslib_1.__awaiter(this, void 0, void 0, function () { return tslib_1.__awaiter(this, void 0, void 0, function () {
var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, paddings, results; var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, reshapedDims, inputSize, padX, padY, results;
return tslib_1.__generator(this, function (_d) { return tslib_1.__generator(this, function (_d) {
switch (_d.label) { switch (_d.label) {
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)]; case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1: case 1:
netInput = _d.sent(); netInput = _d.sent();
_a = this.forwardInput(netInput), _boxes = _a.boxes, _scores = _a.scores; _a = this.forwardInput(netInput), _boxes = _a.boxes, _scores = _a.scores;
...@@ -64,17 +64,20 @@ var FaceDetectionNet = /** @class */ (function (_super) { ...@@ -64,17 +64,20 @@ var FaceDetectionNet = /** @class */ (function (_super) {
scoresData = _c.apply(_b, [_d.sent()]); scoresData = _c.apply(_b, [_d.sent()]);
iouThreshold = 0.5; iouThreshold = 0.5;
indices = nonMaxSuppression_1.nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence); indices = nonMaxSuppression_1.nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
paddings = netInput.getRelativePaddings(0); reshapedDims = netInput.getReshapedInputDimensions(0);
inputSize = netInput.inputSize;
padX = inputSize / reshapedDims.width;
padY = inputSize / reshapedDims.height;
results = indices results = indices
.map(function (idx) { .map(function (idx) {
var _a = [ var _a = [
Math.max(0, boxes.get(idx, 0)), Math.max(0, boxes.get(idx, 0)),
Math.min(1.0, boxes.get(idx, 2)) Math.min(1.0, boxes.get(idx, 2))
].map(function (val) { return val * paddings.y; }), top = _a[0], bottom = _a[1]; ].map(function (val) { return val * padY; }), top = _a[0], bottom = _a[1];
var _b = [ var _b = [
Math.max(0, boxes.get(idx, 1)), Math.max(0, boxes.get(idx, 1)),
Math.min(1.0, boxes.get(idx, 3)) Math.min(1.0, boxes.get(idx, 3))
].map(function (val) { return val * paddings.x; }), left = _b[0], right = _b[1]; ].map(function (val) { return val * padX; }), left = _b[0], right = _b[1];
return new FaceDetection_1.FaceDetection(scoresData[idx], new tfjs_image_recognition_base_1.Rect(left, top, right - left, bottom - top), { return new FaceDetection_1.FaceDetection(scoresData[idx], new tfjs_image_recognition_base_1.Rect(left, top, right - left, bottom - top), {
height: netInput.getInputHeight(0), height: netInput.getInputHeight(0),
width: netInput.getInputWidth(0) width: netInput.getInputWidth(0)
......
build/faceDetectionNet/FaceDetectionNet.js.map
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\ No newline at end of file \ No newline at end of file
build/faceLandmarkNet/FaceLandmark68Net.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from 'tfjs-image-recognition-base';
import { FaceLandmark68NetBase } from './FaceLandmark68NetBase';
import { NetParams } from './types';
export declare class FaceLandmark68Net extends FaceLandmark68NetBase<NetParams> {
constructor();
runNet(input: NetInput): tf.Tensor2D;
protected loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
protected extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
};
}
build/faceLandmarkNet/FaceLandmark68Net.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
var extractParams_1 = require("./extractParams");
var FaceLandmark68NetBase_1 = require("./FaceLandmark68NetBase");
var fullyConnectedLayer_1 = require("./fullyConnectedLayer");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
function conv(x, params) {
return tfjs_tiny_yolov2_1.convLayer(x, params, 'valid', true);
}
function maxPool(x, strides) {
if (strides === void 0) { strides = [2, 2]; }
return tf.maxPool(x, [2, 2], strides, 'valid');
}
var FaceLandmark68Net = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmark68Net, _super);
function FaceLandmark68Net() {
return _super.call(this, 'FaceLandmark68Net') || this;
}
FaceLandmark68Net.prototype.runNet = function (input) {
var params = this.params;
if (!params) {
throw new Error('FaceLandmark68Net - load model before inference');
}
return tf.tidy(function () {
var batchTensor = input.toBatchTensor(128, true).toFloat();
var out = conv(batchTensor, params.conv0);
out = maxPool(out);
out = conv(out, params.conv1);
out = conv(out, params.conv2);
out = maxPool(out);
out = conv(out, params.conv3);
out = conv(out, params.conv4);
out = maxPool(out);
out = conv(out, params.conv5);
out = conv(out, params.conv6);
out = maxPool(out, [1, 1]);
out = conv(out, params.conv7);
var fc0 = tf.relu(fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0));
return fullyConnectedLayer_1.fullyConnectedLayer(fc0, params.fc1);
});
};
FaceLandmark68Net.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams_1.loadQuantizedParams(uri);
};
FaceLandmark68Net.prototype.extractParams = function (weights) {
return extractParams_1.extractParams(weights);
};
return FaceLandmark68Net;
}(FaceLandmark68NetBase_1.FaceLandmark68NetBase));
exports.FaceLandmark68Net = FaceLandmark68Net;
//# sourceMappingURL=FaceLandmark68Net.js.map
\ No newline at end of file
build/faceLandmarkNet/FaceLandmark68Net.js.map 0 → 100644
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\ No newline at end of file
build/faceLandmarkNet/FaceLandmark68NetBase.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { NetInput, NeuralNetwork, TNetInput, Dimensions } from 'tfjs-image-recognition-base';
import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
export declare class FaceLandmark68NetBase<NetParams> extends NeuralNetwork<NetParams> {
private __name;
constructor(_name: string);
runNet(_: NetInput): tf.Tensor2D;
postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: Dimensions[]): tf.Tensor2D;
forwardInput(input: NetInput): tf.Tensor2D;
forward(input: TNetInput): Promise<tf.Tensor2D>;
detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;
}
build/faceLandmarkNet/FaceLandmark68NetBase.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceLandmarks68_1 = require("../classes/FaceLandmarks68");
var FaceLandmark68NetBase = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmark68NetBase, _super);
function FaceLandmark68NetBase(_name) {
var _this = _super.call(this, _name) || this;
_this.__name = _name;
return _this;
}
FaceLandmark68NetBase.prototype.runNet = function (_) {
throw new Error(this.__name + " - runNet not implemented");
};
FaceLandmark68NetBase.prototype.postProcess = function (output, inputSize, originalDimensions) {
var inputDimensions = originalDimensions.map(function (_a) {
var width = _a.width, height = _a.height;
var scale = inputSize / Math.max(height, width);
return {
width: width * scale,
height: height * scale
};
});
var batchSize = inputDimensions.length;
return tf.tidy(function () {
var createInterleavedTensor = function (fillX, fillY) {
return tf.stack([
tf.fill([68], fillX),
tf.fill([68], fillY)
], 1).as2D(1, 136).as1D();
};
var getPadding = function (batchIdx, cond) {
var _a = inputDimensions[batchIdx], width = _a.width, height = _a.height;
return cond(width, height) ? Math.abs(width - height) / 2 : 0;
};
var getPaddingX = function (batchIdx) { return getPadding(batchIdx, function (w, h) { return w < h; }); };
var getPaddingY = function (batchIdx) { return getPadding(batchIdx, function (w, h) { return h < w; }); };
var landmarkTensors = output
.mul(tf.fill([batchSize, 136], inputSize))
.sub(tf.stack(Array.from(Array(batchSize), function (_, batchIdx) {
return createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx));
})))
.div(tf.stack(Array.from(Array(batchSize), function (_, batchIdx) {
return createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height);
})));
return landmarkTensors;
});
};
FaceLandmark68NetBase.prototype.forwardInput = function (input) {
var _this = this;
return tf.tidy(function () {
var out = _this.runNet(input);
return _this.postProcess(out, input.inputSize, input.inputDimensions.map(function (_a) {
var height = _a[0], width = _a[1];
return ({ height: height, width: width });
}));
});
};
FaceLandmark68NetBase.prototype.forward = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
});
};
FaceLandmark68NetBase.prototype.detectLandmarks = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _this = this;
var netInput, landmarkTensors, landmarksForBatch;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1:
netInput = _a.sent();
landmarkTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });
return [4 /*yield*/, Promise.all(landmarkTensors.map(function (landmarkTensor, batchIdx) { return tslib_1.__awaiter(_this, void 0, void 0, function () {
var landmarksArray, _a, _b, xCoords, yCoords;
return tslib_1.__generator(this, function (_c) {
switch (_c.label) {
case 0:
_b = (_a = Array).from;
return [4 /*yield*/, landmarkTensor.data()];
case 1:
landmarksArray = _b.apply(_a, [_c.sent()]);
xCoords = landmarksArray.filter(function (_, i) { return tfjs_image_recognition_base_1.isEven(i); });
yCoords = landmarksArray.filter(function (_, i) { return !tfjs_image_recognition_base_1.isEven(i); });
return [2 /*return*/, new FaceLandmarks68_1.FaceLandmarks68(Array(68).fill(0).map(function (_, i) { return new tfjs_image_recognition_base_1.Point(xCoords[i], yCoords[i]); }), {
height: netInput.getInputHeight(batchIdx),
width: netInput.getInputWidth(batchIdx),
})];
}
});
}); }))];
case 2:
landmarksForBatch = _a.sent();
landmarkTensors.forEach(function (t) { return t.dispose(); });
return [2 /*return*/, netInput.isBatchInput
? landmarksForBatch
: landmarksForBatch[0]];
}
});
});
};
return FaceLandmark68NetBase;
}(tfjs_image_recognition_base_1.NeuralNetwork));
exports.FaceLandmark68NetBase = FaceLandmark68NetBase;
//# sourceMappingURL=FaceLandmark68NetBase.js.map
\ No newline at end of file
build/faceLandmarkNet/FaceLandmark68NetBase.js.map 0 → 100644
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build/faceLandmarkNet/index.d.ts
import { FaceLandmarkNet } from './FaceLandmarkNet'; import { FaceLandmark68Net } from './FaceLandmark68Net';
export * from './FaceLandmarkNet'; export * from './FaceLandmark68Net';
export declare class FaceLandmarkNet extends FaceLandmark68Net {
}
export declare function createFaceLandmarkNet(weights: Float32Array): FaceLandmarkNet; export declare function createFaceLandmarkNet(weights: Float32Array): FaceLandmarkNet;
export declare function faceLandmarkNet(weights: Float32Array): FaceLandmarkNet; export declare function faceLandmarkNet(weights: Float32Array): FaceLandmarkNet;
build/faceLandmarkNet/index.js
"use strict"; "use strict";
Object.defineProperty(exports, "__esModule", { value: true }); Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib"); var tslib_1 = require("tslib");
var FaceLandmarkNet_1 = require("./FaceLandmarkNet"); var FaceLandmark68Net_1 = require("./FaceLandmark68Net");
tslib_1.__exportStar(require("./FaceLandmarkNet"), exports); tslib_1.__exportStar(require("./FaceLandmark68Net"), exports);
var FaceLandmarkNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmarkNet, _super);
function FaceLandmarkNet() {
return _super !== null && _super.apply(this, arguments) || this;
}
return FaceLandmarkNet;
}(FaceLandmark68Net_1.FaceLandmark68Net));
exports.FaceLandmarkNet = FaceLandmarkNet;
function createFaceLandmarkNet(weights) { function createFaceLandmarkNet(weights) {
var net = new FaceLandmarkNet_1.FaceLandmarkNet(); var net = new FaceLandmarkNet();
net.extractWeights(weights); net.extractWeights(weights);
return net; return net;
} }
......
build/faceLandmarkNet/index.js.map
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build/faceRecognitionNet/FaceRecognitionNet.js
...@@ -18,7 +18,7 @@ var FaceRecognitionNet = /** @class */ (function (_super) { ...@@ -18,7 +18,7 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
throw new Error('FaceRecognitionNet - load model before inference'); throw new Error('FaceRecognitionNet - load model before inference');
} }
return tf.tidy(function () { return tf.tidy(function () {
var batchTensor = input.toBatchTensor(150, true); var batchTensor = input.toBatchTensor(150, true).toFloat();
var meanRgb = [122.782, 117.001, 104.298]; var meanRgb = [122.782, 117.001, 104.298];
var normalized = tfjs_image_recognition_base_1.normalize(batchTensor, meanRgb).div(tf.scalar(256)); var normalized = tfjs_image_recognition_base_1.normalize(batchTensor, meanRgb).div(tf.scalar(256));
var out = convLayer_1.convDown(normalized, params.conv32_down); var out = convLayer_1.convDown(normalized, params.conv32_down);
...@@ -49,7 +49,7 @@ var FaceRecognitionNet = /** @class */ (function (_super) { ...@@ -49,7 +49,7 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
switch (_b.label) { switch (_b.label) {
case 0: case 0:
_a = this.forwardInput; _a = this.forwardInput;
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)]; return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])]; case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
} }
}); });
...@@ -61,7 +61,7 @@ var FaceRecognitionNet = /** @class */ (function (_super) { ...@@ -61,7 +61,7 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
var netInput, faceDescriptorTensors, faceDescriptorsForBatch; var netInput, faceDescriptorTensors, faceDescriptorsForBatch;
return tslib_1.__generator(this, function (_a) { return tslib_1.__generator(this, function (_a) {
switch (_a.label) { switch (_a.label) {
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)]; case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1: case 1:
netInput = _a.sent(); netInput = _a.sent();
faceDescriptorTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); }); faceDescriptorTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });
......
build/faceRecognitionNet/FaceRecognitionNet.js.map
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build/globalApi.d.ts
...@@ -5,17 +5,17 @@ import { FaceDetection } from './classes/FaceDetection'; ...@@ -5,17 +5,17 @@ import { FaceDetection } from './classes/FaceDetection';
import { FaceLandmarks68 } from './classes/FaceLandmarks68'; import { FaceLandmarks68 } from './classes/FaceLandmarks68';
import { FullFaceDescription } from './classes/FullFaceDescription'; import { FullFaceDescription } from './classes/FullFaceDescription';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet'; import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet'; import { FaceLandmark68Net } from './faceLandmarkNet/FaceLandmark68Net';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet'; import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { Mtcnn } from './mtcnn/Mtcnn'; import { Mtcnn } from './mtcnn/Mtcnn';
import { MtcnnForwardParams, MtcnnResult } from './mtcnn/types'; import { MtcnnForwardParams, MtcnnResult } from './mtcnn/types';
import { TinyYolov2 } from './tinyYolov2/TinyYolov2'; import { TinyYolov2 } from './tinyYolov2/TinyYolov2';
export declare const detectionNet: FaceDetectionNet; export declare const detectionNet: FaceDetectionNet;
export declare const landmarkNet: FaceLandmarkNet; export declare const landmarkNet: FaceLandmark68Net;
export declare const recognitionNet: FaceRecognitionNet; export declare const recognitionNet: FaceRecognitionNet;
export declare const nets: { export declare const nets: {
ssdMobilenetv1: FaceDetectionNet; ssdMobilenetv1: FaceDetectionNet;
faceLandmark68Net: FaceLandmarkNet; faceLandmark68Net: FaceLandmark68Net;
faceRecognitionNet: FaceRecognitionNet; faceRecognitionNet: FaceRecognitionNet;
mtcnn: Mtcnn; mtcnn: Mtcnn;
tinyYolov2: TinyYolov2; tinyYolov2: TinyYolov2;
......
build/globalApi.js
...@@ -2,12 +2,12 @@ ...@@ -2,12 +2,12 @@
Object.defineProperty(exports, "__esModule", { value: true }); Object.defineProperty(exports, "__esModule", { value: true });
var allFacesFactory_1 = require("./allFacesFactory"); var allFacesFactory_1 = require("./allFacesFactory");
var FaceDetectionNet_1 = require("./faceDetectionNet/FaceDetectionNet"); var FaceDetectionNet_1 = require("./faceDetectionNet/FaceDetectionNet");
var FaceLandmarkNet_1 = require("./faceLandmarkNet/FaceLandmarkNet"); var FaceLandmark68Net_1 = require("./faceLandmarkNet/FaceLandmark68Net");
var FaceRecognitionNet_1 = require("./faceRecognitionNet/FaceRecognitionNet"); var FaceRecognitionNet_1 = require("./faceRecognitionNet/FaceRecognitionNet");
var Mtcnn_1 = require("./mtcnn/Mtcnn"); var Mtcnn_1 = require("./mtcnn/Mtcnn");
var TinyYolov2_1 = require("./tinyYolov2/TinyYolov2"); var TinyYolov2_1 = require("./tinyYolov2/TinyYolov2");
exports.detectionNet = new FaceDetectionNet_1.FaceDetectionNet(); exports.detectionNet = new FaceDetectionNet_1.FaceDetectionNet();
exports.landmarkNet = new FaceLandmarkNet_1.FaceLandmarkNet(); exports.landmarkNet = new FaceLandmark68Net_1.FaceLandmark68Net();
exports.recognitionNet = new FaceRecognitionNet_1.FaceRecognitionNet(); exports.recognitionNet = new FaceRecognitionNet_1.FaceRecognitionNet();
// nets need more specific names, to avoid ambiguity in future // nets need more specific names, to avoid ambiguity in future
// when alternative net implementations are provided // when alternative net implementations are provided
......
build/globalApi.js.map
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\ No newline at end of file \ No newline at end of file
build/mtcnn/Mtcnn.js
...@@ -23,7 +23,7 @@ var Mtcnn = /** @class */ (function (_super) { ...@@ -23,7 +23,7 @@ var Mtcnn = /** @class */ (function (_super) {
Mtcnn.prototype.forwardInput = function (input, forwardParams) { Mtcnn.prototype.forwardInput = function (input, forwardParams) {
if (forwardParams === void 0) { forwardParams = {}; } if (forwardParams === void 0) { forwardParams = {}; }
return tslib_1.__awaiter(this, void 0, void 0, function () { 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; var params, 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) { return tslib_1.__generator(this, function (_c) {
switch (_c.label) { switch (_c.label) {
case 0: case 0:
...@@ -31,7 +31,6 @@ var Mtcnn = /** @class */ (function (_super) { ...@@ -31,7 +31,6 @@ var Mtcnn = /** @class */ (function (_super) {
if (!params) { if (!params) {
throw new Error('Mtcnn - load model before inference'); throw new Error('Mtcnn - load model before inference');
} }
inputTensor = input.inputs[0];
inputCanvas = input.canvases[0]; inputCanvas = input.canvases[0];
if (!inputCanvas) { if (!inputCanvas) {
throw new Error('Mtcnn - inputCanvas is not defined, note that passing tensors into Mtcnn.forwardInput is not supported yet.'); throw new Error('Mtcnn - inputCanvas is not defined, note that passing tensors into Mtcnn.forwardInput is not supported yet.');
...@@ -39,12 +38,11 @@ var Mtcnn = /** @class */ (function (_super) { ...@@ -39,12 +38,11 @@ var Mtcnn = /** @class */ (function (_super) {
stats = {}; stats = {};
tsTotal = Date.now(); tsTotal = Date.now();
imgTensor = tf.tidy(function () { imgTensor = tf.tidy(function () {
return bgrToRgbTensor_1.bgrToRgbTensor(tf.expandDims(inputTensor).toFloat()); return bgrToRgbTensor_1.bgrToRgbTensor(tf.expandDims(tf.fromPixels(inputCanvas)).toFloat());
}); });
onReturn = function (results) { onReturn = function (results) {
// dispose tensors on return // dispose tensors on return
imgTensor.dispose(); imgTensor.dispose();
input.dispose();
stats.total = Date.now() - tsTotal; stats.total = Date.now() - tsTotal;
return results; return results;
}; };
...@@ -103,7 +101,7 @@ var Mtcnn = /** @class */ (function (_super) { ...@@ -103,7 +101,7 @@ var Mtcnn = /** @class */ (function (_super) {
switch (_b.label) { switch (_b.label) {
case 0: case 0:
_a = this.forwardInput; _a = this.forwardInput;
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true, true)]; return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(), case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(),
forwardParams])]; forwardParams])];
case 2: return [2 /*return*/, (_b.sent()).results]; case 2: return [2 /*return*/, (_b.sent()).results];
...@@ -119,7 +117,7 @@ var Mtcnn = /** @class */ (function (_super) { ...@@ -119,7 +117,7 @@ var Mtcnn = /** @class */ (function (_super) {
switch (_b.label) { switch (_b.label) {
case 0: case 0:
_a = this.forwardInput; _a = this.forwardInput;
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true, true)]; return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent(), case 1: return [2 /*return*/, _a.apply(this, [_b.sent(),
forwardParams])]; forwardParams])];
} }
......
build/mtcnn/Mtcnn.js.map
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\ No newline at end of file \ No newline at end of file
build/mtcnn/MtcnnBox.d.ts 0 → 100644
import { Box } from 'tfjs-image-recognition-base';
export declare class MtcnnBox extends Box<MtcnnBox> {
constructor(left: number, top: number, right: number, bottom: number);
}
build/mtcnn/MtcnnBox.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var MtcnnBox = /** @class */ (function (_super) {
tslib_1.__extends(MtcnnBox, _super);
function MtcnnBox(left, top, right, bottom) {
return _super.call(this, { left: left, top: top, right: right, bottom: bottom }, true) || this;
}
return MtcnnBox;
}(tfjs_image_recognition_base_1.Box));
exports.MtcnnBox = MtcnnBox;
//# sourceMappingURL=MtcnnBox.js.map
\ No newline at end of file
build/mtcnn/MtcnnBox.js.map 0 → 100644
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\ No newline at end of file
build/mtcnn/extractImagePatches.d.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { BoundingBox, Dimensions } from 'tfjs-image-recognition-base'; import { Box, Dimensions } from 'tfjs-image-recognition-base';
export declare function extractImagePatches(img: HTMLCanvasElement, boxes: BoundingBox[], {width, height}: Dimensions): Promise<tf.Tensor4D[]>; export declare function extractImagePatches(img: HTMLCanvasElement, boxes: Box[], {width, height}: Dimensions): Promise<tf.Tensor4D[]>;
build/mtcnn/extractImagePatches.js
...@@ -2,8 +2,8 @@ ...@@ -2,8 +2,8 @@
Object.defineProperty(exports, "__esModule", { value: true }); Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib"); var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core"); var tf = require("@tensorflow/tfjs-core");
var normalize_1 = require("./normalize");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base"); var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var normalize_1 = require("./normalize");
function extractImagePatches(img, boxes, _a) { function extractImagePatches(img, boxes, _a) {
var width = _a.width, height = _a.height; var width = _a.width, height = _a.height;
return tslib_1.__awaiter(this, void 0, void 0, function () { return tslib_1.__awaiter(this, void 0, void 0, function () {
......
build/mtcnn/extractImagePatches.js.map
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\ No newline at end of file \ No newline at end of file
build/mtcnn/stage1.js
...@@ -4,6 +4,7 @@ var tf = require("@tensorflow/tfjs-core"); ...@@ -4,6 +4,7 @@ var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base"); var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var config_1 = require("./config"); var config_1 = require("./config");
var getSizesForScale_1 = require("./getSizesForScale"); var getSizesForScale_1 = require("./getSizesForScale");
var MtcnnBox_1 = require("./MtcnnBox");
var normalize_1 = require("./normalize"); var normalize_1 = require("./normalize");
var PNet_1 = require("./PNet"); var PNet_1 = require("./PNet");
function rescaleAndNormalize(x, scale) { function rescaleAndNormalize(x, scale) {
...@@ -27,7 +28,7 @@ function extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold ...@@ -27,7 +28,7 @@ function extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold
var boundingBoxes = indices.map(function (idx) { var boundingBoxes = indices.map(function (idx) {
var cell = new tfjs_image_recognition_base_1.BoundingBox(Math.round((idx.y * config_1.CELL_STRIDE + 1) / scale), Math.round((idx.x * config_1.CELL_STRIDE + 1) / scale), Math.round((idx.y * config_1.CELL_STRIDE + config_1.CELL_SIZE) / scale), Math.round((idx.x * config_1.CELL_STRIDE + config_1.CELL_SIZE) / scale)); var cell = new tfjs_image_recognition_base_1.BoundingBox(Math.round((idx.y * config_1.CELL_STRIDE + 1) / scale), Math.round((idx.x * config_1.CELL_STRIDE + 1) / scale), Math.round((idx.y * config_1.CELL_STRIDE + config_1.CELL_SIZE) / scale), Math.round((idx.x * config_1.CELL_STRIDE + config_1.CELL_SIZE) / scale));
var score = scoresTensor.get(idx.y, idx.x); var score = scoresTensor.get(idx.y, idx.x);
var region = new tfjs_image_recognition_base_1.BoundingBox(regionsTensor.get(idx.y, idx.x, 0), regionsTensor.get(idx.y, idx.x, 1), regionsTensor.get(idx.y, idx.x, 2), regionsTensor.get(idx.y, idx.x, 3)); var region = new MtcnnBox_1.MtcnnBox(regionsTensor.get(idx.y, idx.x, 0), regionsTensor.get(idx.y, idx.x, 1), regionsTensor.get(idx.y, idx.x, 2), regionsTensor.get(idx.y, idx.x, 3));
return { return {
cell: cell, cell: cell,
score: score, score: score,
......
build/mtcnn/stage1.js.map
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\ No newline at end of file \ No newline at end of file
build/mtcnn/stage2.d.ts
import { BoundingBox } from 'tfjs-image-recognition-base'; import { Box } from 'tfjs-image-recognition-base';
import { RNetParams } from './types'; import { RNetParams } from './types';
export declare function stage2(img: HTMLCanvasElement, inputBoxes: BoundingBox[], scoreThreshold: number, params: RNetParams, stats: any): Promise<{ export declare function stage2(img: HTMLCanvasElement, inputBoxes: Box[], scoreThreshold: number, params: RNetParams, stats: any): Promise<{
boxes: BoundingBox[]; boxes: Box<any>[];
scores: number[]; scores: number[];
}>; }>;
build/mtcnn/stage2.js
...@@ -4,6 +4,7 @@ var tslib_1 = require("tslib"); ...@@ -4,6 +4,7 @@ var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core"); var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base"); var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var extractImagePatches_1 = require("./extractImagePatches"); var extractImagePatches_1 = require("./extractImagePatches");
var MtcnnBox_1 = require("./MtcnnBox");
var RNet_1 = require("./RNet"); var RNet_1 = require("./RNet");
function stage2(img, inputBoxes, scoreThreshold, params, stats) { function stage2(img, inputBoxes, scoreThreshold, params, stats) {
return tslib_1.__awaiter(this, void 0, void 0, function () { return tslib_1.__awaiter(this, void 0, void 0, function () {
...@@ -47,7 +48,7 @@ function stage2(img, inputBoxes, scoreThreshold, params, stats) { ...@@ -47,7 +48,7 @@ function stage2(img, inputBoxes, scoreThreshold, params, stats) {
indicesNms = tfjs_image_recognition_base_1.nonMaxSuppression(filteredBoxes, filteredScores, 0.7); indicesNms = tfjs_image_recognition_base_1.nonMaxSuppression(filteredBoxes, filteredScores, 0.7);
stats.stage2_nms = Date.now() - ts; stats.stage2_nms = Date.now() - ts;
regions_1 = indicesNms.map(function (idx) { regions_1 = indicesNms.map(function (idx) {
return new tfjs_image_recognition_base_1.BoundingBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3)); return new MtcnnBox_1.MtcnnBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3));
}); });
finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; }); finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
finalBoxes = indicesNms.map(function (idx, i) { return filteredBoxes[idx].calibrate(regions_1[i]); }); finalBoxes = indicesNms.map(function (idx, i) { return filteredBoxes[idx].calibrate(regions_1[i]); });
......
build/mtcnn/stage2.js.map
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\ No newline at end of file \ No newline at end of file
build/mtcnn/stage3.d.ts
import { BoundingBox, Point } from 'tfjs-image-recognition-base'; import { BoundingBox, Box, Point } from 'tfjs-image-recognition-base';
import { ONetParams } from './types'; import { ONetParams } from './types';
export declare function stage3(img: HTMLCanvasElement, inputBoxes: BoundingBox[], scoreThreshold: number, params: ONetParams, stats: any): Promise<{ export declare function stage3(img: HTMLCanvasElement, inputBoxes: BoundingBox[], scoreThreshold: number, params: ONetParams, stats: any): Promise<{
boxes: BoundingBox[]; boxes: Box<any>[];
scores: number[]; scores: number[];
points: Point[][]; points: Point[][];
}>; }>;
build/mtcnn/stage3.js
...@@ -4,6 +4,7 @@ var tslib_1 = require("tslib"); ...@@ -4,6 +4,7 @@ var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core"); var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base"); var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var extractImagePatches_1 = require("./extractImagePatches"); var extractImagePatches_1 = require("./extractImagePatches");
var MtcnnBox_1 = require("./MtcnnBox");
var ONet_1 = require("./ONet"); var ONet_1 = require("./ONet");
function stage3(img, inputBoxes, scoreThreshold, params, stats) { function stage3(img, inputBoxes, scoreThreshold, params, stats) {
return tslib_1.__awaiter(this, void 0, void 0, function () { return tslib_1.__awaiter(this, void 0, void 0, function () {
...@@ -38,7 +39,7 @@ function stage3(img, inputBoxes, scoreThreshold, params, stats) { ...@@ -38,7 +39,7 @@ function stage3(img, inputBoxes, scoreThreshold, params, stats) {
var idx = _a.idx; var idx = _a.idx;
return idx; return idx;
}); });
filteredRegions = indices.map(function (idx) { return new tfjs_image_recognition_base_1.BoundingBox(onetOuts[idx].regions.get(0, 0), onetOuts[idx].regions.get(0, 1), onetOuts[idx].regions.get(0, 2), onetOuts[idx].regions.get(0, 3)); }); filteredRegions = indices.map(function (idx) { return new MtcnnBox_1.MtcnnBox(onetOuts[idx].regions.get(0, 0), onetOuts[idx].regions.get(0, 1), onetOuts[idx].regions.get(0, 2), onetOuts[idx].regions.get(0, 3)); });
filteredBoxes = indices filteredBoxes = indices
.map(function (idx, i) { return inputBoxes[idx].calibrate(filteredRegions[i]); }); .map(function (idx, i) { return inputBoxes[idx].calibrate(filteredRegions[i]); });
filteredScores = indices.map(function (idx) { return scores[idx]; }); filteredScores = indices.map(function (idx) { return scores[idx]; });
......
build/mtcnn/stage3.js.map
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dist/face-api.js.map
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dist/face-api.min.js
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