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face
Commit f0ae9c70 by vincent
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check in latest build

parent 1c56acb1
Showing with 922 additions and 570 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)];
case 1:
netInput = _a.sent();
if (netInput.batchSize > 1) {
if (netInput.isManaged) {
netInput.dispose();
}
throw new Error('extractFaceTensors - batchSize > 1 not supported'); throw new Error('extractFaceTensors - batchSize > 1 not supported');
} }
return [2 /*return*/, tf.tidy(function () { return [2 /*return*/, tf.tidy(function () {
var imgTensor = netInput.inputs[0].expandDims().toFloat(); var _a = imageTensor.shape.slice(tfjs_image_recognition_base_1.isTensor4D(imageTensor) ? 1 : 0), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];
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 var boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
? det.forSize(imgWidth, imgHeight).getBox() ? det.forSize(imgWidth, imgHeight).getBox()
: det; }) : det; })
.map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); }); .map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); });
var faceTensors = boxes.map(function (_a) { var faceTensors = boxes.map(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height; 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]); return tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]);
}); });
if (netInput.isManaged) {
netInput.dispose();
}
return faceTensors; 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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\ No newline at end of file \ No newline at end of file
dist/face-api.js
...@@ -307,137 +307,164 @@ ...@@ -307,137 +307,164 @@
return pts.reduce(function (sum$$1, pt) { return sum$$1.add(pt); }, new Point(0, 0)) return pts.reduce(function (sum$$1, pt) { return sum$$1.add(pt); }, new Point(0, 0))
.div(new Point(pts.length, pts.length)); .div(new Point(pts.length, pts.length));
} }
function range$1(num, start, step$$1) {
var Rect = /** @class */ (function () { return Array(num).fill(0).map(function (_, i) { return start + (i * step$$1); });
function Rect(x, y, width, height) {
this.x = x;
this.y = y;
this.width = width;
this.height = height;
} }
Object.defineProperty(Rect.prototype, "right", { function isValidNumber(num) {
return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0;
}
function isValidProbablitiy(num) {
return isValidNumber(num) && 0 <= num && num <= 1.0;
}
var Box = /** @class */ (function () {
// TODO: MTCNN boxes sometimes have negative widths or heights, figure out why and remove
// allowNegativeDimensions flag again
function Box(_box, allowNegativeDimensions) {
if (allowNegativeDimensions === void 0) { allowNegativeDimensions = false; }
var box = _box || {};
var isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);
var isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);
if (!isRect && !isBbox) {
throw new Error("Box.constructor - expected box to be IBoundingBox | IRect, instead have " + JSON.stringify(box));
}
var _a = isRect
? [box.x, box.y, box.width, box.height]
: [box.left, box.top, box.right - box.left, box.bottom - box.top], x = _a[0], y = _a[1], width = _a[2], height = _a[3];
Box.assertIsValidBox({ x: x, y: y, width: width, height: height }, 'Box.constructor', allowNegativeDimensions);
this._x = x;
this._y = y;
this._width = width;
this._height = height;
}
Box.isRect = function (rect) {
return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);
};
Box.assertIsValidBox = function (box, callee, allowNegativeDimensions) {
if (allowNegativeDimensions === void 0) { allowNegativeDimensions = false; }
if (!Box.isRect(box)) {
throw new Error(callee + " - invalid box: " + JSON.stringify(box) + ", expected object with properties x, y, width, height");
}
if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {
throw new Error(callee + " - width (" + box.width + ") and height (" + box.height + ") must be positive numbers");
}
};
Object.defineProperty(Box.prototype, "x", {
get: function () { get: function () {
return this.x + this.width; return this._x;
}, },
enumerable: true, enumerable: true,
configurable: true configurable: true
}); });
Object.defineProperty(Rect.prototype, "bottom", { Object.defineProperty(Box.prototype, "y", {
get: function () { get: function () {
return this.y + this.height; return this._y;
}, },
enumerable: true, enumerable: true,
configurable: true configurable: true
}); });
Rect.prototype.toSquare = function () { Object.defineProperty(Box.prototype, "width", {
var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var diff = Math.abs(width - height);
if (width < height) {
x -= (diff / 2);
width += diff;
}
if (height < width) {
y -= (diff / 2);
height += diff;
}
return new Rect(x, y, width, height);
};
Rect.prototype.pad = function (padX, padY) {
var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
return new Rect(x - (padX / 2), y - (padY / 2), width + padX, height + padY);
};
Rect.prototype.floor = function () {
return new Rect(Math.floor(this.x), Math.floor(this.y), Math.floor(this.width), Math.floor(this.height));
};
Rect.prototype.toBoundingBox = function () {
return new BoundingBox(this.x, this.y, this.x + this.width, this.y + this.height);
};
Rect.prototype.clipAtImageBorders = function (imgWidth, imgHeight) {
var _a = this, x = _a.x, y = _a.y, right = _a.right, bottom = _a.bottom;
var clippedX = Math.max(x, 0);
var clippedY = Math.max(y, 0);
var newWidth = right - clippedX;
var newHeight = bottom - clippedY;
var clippedWidth = Math.min(newWidth, imgWidth - clippedX);
var clippedHeight = Math.min(newHeight, imgHeight - clippedY);
return (new Rect(clippedX, clippedY, clippedWidth, clippedHeight)).floor();
};
return Rect;
}());
var BoundingBox = /** @class */ (function () {
function BoundingBox(_left, _top, _right, _bottom) {
this._left = _left;
this._top = _top;
this._right = _right;
this._bottom = _bottom;
}
Object.defineProperty(BoundingBox.prototype, "left", {
get: function () { get: function () {
return this._left; return this._width;
}, },
enumerable: true, enumerable: true,
configurable: true configurable: true
}); });
Object.defineProperty(BoundingBox.prototype, "top", { Object.defineProperty(Box.prototype, "height", {
get: function () { get: function () {
return this._top; return this._height;
}, },
enumerable: true, enumerable: true,
configurable: true configurable: true
}); });
Object.defineProperty(BoundingBox.prototype, "right", { Object.defineProperty(Box.prototype, "left", {
get: function () { get: function () {
return this._right; return this.x;
}, },
enumerable: true, enumerable: true,
configurable: true configurable: true
}); });
Object.defineProperty(BoundingBox.prototype, "bottom", { Object.defineProperty(Box.prototype, "top", {
get: function () { get: function () {
return this._bottom; return this.y;
}, },
enumerable: true, enumerable: true,
configurable: true configurable: true
}); });
Object.defineProperty(BoundingBox.prototype, "width", { Object.defineProperty(Box.prototype, "right", {
get: function () { get: function () {
return this.right - this.left; return this.x + this.width;
}, },
enumerable: true, enumerable: true,
configurable: true configurable: true
}); });
Object.defineProperty(BoundingBox.prototype, "height", { Object.defineProperty(Box.prototype, "bottom", {
get: function () { get: function () {
return this.bottom - this.top; return this.y + this.height;
}, },
enumerable: true, enumerable: true,
configurable: true configurable: true
}); });
Object.defineProperty(BoundingBox.prototype, "area", { Object.defineProperty(Box.prototype, "area", {
get: function () { get: function () {
return this.width * this.height; return this.width * this.height;
}, },
enumerable: true, enumerable: true,
configurable: true configurable: true
}); });
BoundingBox.prototype.toSquare = function () { Box.prototype.round = function () {
var _a = this, left = _a.left, top = _a.top, right = _a.right, bottom = _a.bottom; var _a = [this.x, this.y, this.width, this.height]
var off = (Math.abs(this.width - this.height) / 2); .map(function (val) { return Math.round(val); }), x = _a[0], y = _a[1], width = _a[2], height = _a[3];
if (this.width < this.height) { return new Box({ x: x, y: y, width: width, height: height });
left -= off; };
right += off; Box.prototype.floor = function () {
var _a = [this.x, this.y, this.width, this.height]
.map(function (val) { return Math.floor(val); }), x = _a[0], y = _a[1], width = _a[2], height = _a[3];
return new Box({ x: x, y: y, width: width, height: height });
};
Box.prototype.toSquare = function () {
var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var diff = Math.abs(width - height);
if (width < height) {
x -= (diff / 2);
width += diff;
} }
if (this.height < this.width) { if (height < width) {
top -= off; y -= (diff / 2);
bottom += off; height += diff;
} }
return new BoundingBox(left, top, right, bottom); return new Box({ x: x, y: y, width: width, height: height });
}; };
BoundingBox.prototype.round = function () { Box.prototype.rescale = function (s) {
return new BoundingBox(Math.round(this.left), Math.round(this.top), Math.round(this.right), Math.round(this.bottom)); var scaleX = isDimensions(s) ? s.width : s;
var scaleY = isDimensions(s) ? s.height : s;
return new Box({
x: this.x * scaleX,
y: this.y * scaleY,
width: this.width * scaleX,
height: this.height * scaleY
});
}; };
BoundingBox.prototype.padAtBorders = function (imageHeight, imageWidth) { Box.prototype.pad = function (padX, padY) {
var _a = [
this.x - (padX / 2),
this.y - (padY / 2),
this.width + padX,
this.height + padY
], x = _a[0], y = _a[1], width = _a[2], height = _a[3];
return new Box({ x: x, y: y, width: width, height: height });
};
Box.prototype.clipAtImageBorders = function (imgWidth, imgHeight) {
var _a = this, x = _a.x, y = _a.y, right = _a.right, bottom = _a.bottom;
var clippedX = Math.max(x, 0);
var clippedY = Math.max(y, 0);
var newWidth = right - clippedX;
var newHeight = bottom - clippedY;
var clippedWidth = Math.min(newWidth, imgWidth - clippedX);
var clippedHeight = Math.min(newHeight, imgHeight - clippedY);
return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight })).floor();
};
Box.prototype.padAtBorders = function (imageHeight, imageWidth) {
var w = this.width + 1; var w = this.width + 1;
var h = this.height + 1; var h = this.height + 1;
var dx = 1; var dx = 1;
...@@ -466,19 +493,132 @@ ...@@ -466,19 +493,132 @@
} }
return { dy: dy, edy: edy, dx: dx, edx: edx, y: y, ey: ey, x: x, ex: ex, w: w, h: h }; return { dy: dy, edy: edy, dx: dx, edx: edx, y: y, ey: ey, x: x, ex: ex, w: w, h: h };
}; };
BoundingBox.prototype.calibrate = function (region) { Box.prototype.calibrate = function (region) {
return new BoundingBox(this.left + (region.left * this.width), this.top + (region.top * this.height), this.right + (region.right * this.width), this.bottom + (region.bottom * this.height)).toSquare().round(); return new Box({
left: this.left + (region.left * this.width),
top: this.top + (region.top * this.height),
right: this.right + (region.right * this.width),
bottom: this.bottom + (region.bottom * this.height)
}).toSquare().round();
}; };
BoundingBox.prototype.rescale = function (s) { return Box;
var scaleX = isDimensions(s) ? s.width : s; }());
var scaleY = isDimensions(s) ? s.height : s;
return new BoundingBox(this.left * scaleX, this.top * scaleY, this.right * scaleX, this.bottom * scaleY); /*! *****************************************************************************
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use
this file except in compliance with the License. You may obtain a copy of the
License at http://www.apache.org/licenses/LICENSE-2.0
THIS CODE IS PROVIDED ON AN *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
MERCHANTABLITY OR NON-INFRINGEMENT.
See the Apache Version 2.0 License for specific language governing permissions
and limitations under the License.
***************************************************************************** */
/* global Reflect, Promise */
var extendStatics$1 = function(d, b) {
extendStatics$1 = Object.setPrototypeOf ||
({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
return extendStatics$1(d, b);
};
function __extends$1(d, b) {
extendStatics$1(d, b);
function __() { this.constructor = d; }
d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
}
var __assign$1 = function() {
__assign$1 = Object.assign || function __assign(t) {
for (var s, i = 1, n = arguments.length; i < n; i++) {
s = arguments[i];
for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];
}
return t;
}; };
BoundingBox.prototype.toRect = function () { return __assign$1.apply(this, arguments);
return new Rect(this.left, this.top, this.width, this.height);
}; };
function __awaiter$1(thisArg, _arguments, P, generator) {
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
}
function __generator$1(thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
}
var BoundingBox = /** @class */ (function (_super) {
__extends$1(BoundingBox, _super);
function BoundingBox(left, top, right, bottom) {
return _super.call(this, { left: left, top: top, right: right, bottom: bottom }) || this;
}
return BoundingBox; return BoundingBox;
}()); }(Box));
var LabeledBox = /** @class */ (function (_super) {
__extends$1(LabeledBox, _super);
function LabeledBox(box, label) {
var _this = _super.call(this, box) || this;
_this._label = label;
return _this;
}
LabeledBox.assertIsValidLabeledBox = function (box, callee) {
Box.assertIsValidBox(box, callee);
if (!isValidNumber(box.label)) {
throw new Error(callee + " - expected property label (" + box.label + ") to be a number");
}
};
Object.defineProperty(LabeledBox.prototype, "label", {
get: function () {
return this._label;
},
enumerable: true,
configurable: true
});
return LabeledBox;
}(Box));
var Rect = /** @class */ (function (_super) {
__extends$1(Rect, _super);
function Rect(x, y, width, height) {
return _super.call(this, { x: x, y: y, width: width, height: height }) || this;
}
return Rect;
}(Box));
var ObjectDetection = /** @class */ (function () { var ObjectDetection = /** @class */ (function () {
function ObjectDetection(score, classScore, className, relativeBox, imageDims) { function ObjectDetection(score, classScore, className, relativeBox, imageDims) {
...@@ -560,6 +700,38 @@ ...@@ -560,6 +700,38 @@
return ObjectDetection; return ObjectDetection;
}()); }());
var PredictedBox = /** @class */ (function (_super) {
__extends$1(PredictedBox, _super);
function PredictedBox(box, label, score, classScore) {
var _this = _super.call(this, box, label) || this;
_this._score = score;
_this._classScore = classScore;
return _this;
}
PredictedBox.assertIsValidPredictedBox = function (box, callee) {
LabeledBox.assertIsValidLabeledBox(box, callee);
if (!isValidProbablitiy(box.score)
|| !isValidProbablitiy(box.classScore)) {
throw new Error(callee + " - expected properties score (" + box.score + ") and (" + box.classScore + ") to be a number between [0, 1]");
}
};
Object.defineProperty(PredictedBox.prototype, "score", {
get: function () {
return this._score;
},
enumerable: true,
configurable: true
});
Object.defineProperty(PredictedBox.prototype, "classScore", {
get: function () {
return this._classScore;
},
enumerable: true,
configurable: true
});
return PredictedBox;
}(LabeledBox));
function disposeUnusedWeightTensors(weightMap, paramMappings) { function disposeUnusedWeightTensors(weightMap, paramMappings) {
Object.keys(weightMap).forEach(function (path) { Object.keys(weightMap).forEach(function (path) {
if (!paramMappings.some(function (pm) { return pm.originalPath === path; })) { if (!paramMappings.some(function (pm) { return pm.originalPath === path; })) {
...@@ -761,83 +933,6 @@ ...@@ -761,83 +933,6 @@
}); });
} }
/*! *****************************************************************************
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use
this file except in compliance with the License. You may obtain a copy of the
License at http://www.apache.org/licenses/LICENSE-2.0
THIS CODE IS PROVIDED ON AN *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
MERCHANTABLITY OR NON-INFRINGEMENT.
See the Apache Version 2.0 License for specific language governing permissions
and limitations under the License.
***************************************************************************** */
/* global Reflect, Promise */
var extendStatics$1 = function(d, b) {
extendStatics$1 = Object.setPrototypeOf ||
({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
return extendStatics$1(d, b);
};
function __extends$1(d, b) {
extendStatics$1(d, b);
function __() { this.constructor = d; }
d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
}
var __assign$1 = function() {
__assign$1 = Object.assign || function __assign(t) {
for (var s, i = 1, n = arguments.length; i < n; i++) {
s = arguments[i];
for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];
}
return t;
};
return __assign$1.apply(this, arguments);
};
function __awaiter$1(thisArg, _arguments, P, generator) {
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
}
function __generator$1(thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
}
function imageTensorToCanvas(imgTensor, canvas) { function imageTensorToCanvas(imgTensor, canvas) {
return __awaiter$1(this, void 0, void 0, function () { return __awaiter$1(this, void 0, void 0, function () {
var targetCanvas, _a, height, width, numChannels, imgTensor3D; var targetCanvas, _a, height, width, numChannels, imgTensor3D;
...@@ -857,7 +952,8 @@ ...@@ -857,7 +952,8 @@
}); });
} }
function imageToSquare(input, inputSize) { function imageToSquare(input, inputSize, centerImage) {
if (centerImage === void 0) { centerImage = false; }
if (!(input instanceof HTMLImageElement || input instanceof HTMLCanvasElement)) { if (!(input instanceof HTMLImageElement || input instanceof HTMLCanvasElement)) {
throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement'); throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');
} }
...@@ -867,7 +963,10 @@ ...@@ -867,7 +963,10 @@
var height = scale * dims.height; var height = scale * dims.height;
var targetCanvas = createCanvas({ width: inputSize, height: inputSize }); var targetCanvas = createCanvas({ width: inputSize, height: inputSize });
var inputCanvas = input instanceof HTMLCanvasElement ? input : createCanvasFromMedia(input); var inputCanvas = input instanceof HTMLCanvasElement ? input : createCanvasFromMedia(input);
getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, 0, 0, width, height); var offset = Math.abs(width - height) / 2;
var dx = centerImage && width < height ? offset : 0;
var dy = centerImage && height < width ? offset : 0;
getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);
return targetCanvas; return targetCanvas;
} }
...@@ -898,7 +997,8 @@ ...@@ -898,7 +997,8 @@
* Pads the smaller dimension of an image tensor with zeros, such that width === height. * Pads the smaller dimension of an image tensor with zeros, such that width === height.
* *
* @param imgTensor The image tensor. * @param imgTensor The image tensor.
* @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image. * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on
* both sides of the minor dimension oof the image.
* @returns The padded tensor with width === height. * @returns The padded tensor with width === height.
*/ */
function padToSquare(imgTensor, isCenterImage) { function padToSquare(imgTensor, isCenterImage) {
...@@ -932,46 +1032,41 @@ ...@@ -932,46 +1032,41 @@
} }
var NetInput = /** @class */ (function () { var NetInput = /** @class */ (function () {
function NetInput(inputs, isBatchInput, keepCanvases) { function NetInput(inputs, treatAsBatchInput) {
if (isBatchInput === void 0) { isBatchInput = false; } if (treatAsBatchInput === void 0) { treatAsBatchInput = false; }
if (keepCanvases === void 0) { keepCanvases = false; }
var _this = this; var _this = this;
this._inputs = []; this._imageTensors = [];
this._canvases = []; this._canvases = [];
this._isManaged = false; this._treatAsBatchInput = false;
this._isBatchInput = false;
this._inputDimensions = []; this._inputDimensions = [];
this._paddings = []; if (!Array.isArray(inputs)) {
if (isTensor4D(inputs)) { throw new Error("NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have " + inputs);
this._inputs = unstack(inputs);
} }
if (Array.isArray(inputs)) { this._treatAsBatchInput = treatAsBatchInput;
this._inputs = inputs.map(function (input, idx) { this._batchSize = inputs.length;
inputs.forEach(function (input, idx) {
if (isTensor3D(input)) { if (isTensor3D(input)) {
// TODO: make sure not to dispose original tensors passed in by the user _this._imageTensors[idx] = input;
return clone(input); _this._inputDimensions[idx] = input.shape;
return;
} }
if (isTensor4D(input)) { if (isTensor4D(input)) {
var shape = input.shape; var batchSize = input.shape[0];
var batchSize = shape[0];
if (batchSize !== 1) { if (batchSize !== 1) {
throw new Error("NetInput - tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array"); throw new Error("NetInput - tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array");
} }
return input.reshape(shape.slice(1)); _this._imageTensors[idx] = input;
_this._inputDimensions[idx] = input.shape.slice(1);
return;
} }
var canvas = input instanceof HTMLCanvasElement ? input : createCanvasFromMedia(input); var canvas = input instanceof HTMLCanvasElement ? input : createCanvasFromMedia(input);
if (keepCanvases) {
_this._canvases[idx] = canvas; _this._canvases[idx] = canvas;
} _this._inputDimensions[idx] = [canvas.height, canvas.width, 3];
return fromPixels(canvas);
}); });
} }
this._isBatchInput = this.batchSize > 1 || isBatchInput; Object.defineProperty(NetInput.prototype, "imageTensors", {
this._inputDimensions = this._inputs.map(function (t) { return t.shape; });
}
Object.defineProperty(NetInput.prototype, "inputs", {
get: function () { get: function () {
return this._inputs; return this._imageTensors;
}, },
enumerable: true, enumerable: true,
configurable: true configurable: true
...@@ -983,23 +1078,16 @@ ...@@ -983,23 +1078,16 @@
enumerable: true, enumerable: true,
configurable: true configurable: true
}); });
Object.defineProperty(NetInput.prototype, "isManaged", {
get: function () {
return this._isManaged;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "isBatchInput", { Object.defineProperty(NetInput.prototype, "isBatchInput", {
get: function () { get: function () {
return this._isBatchInput; return this.batchSize > 1 || this._treatAsBatchInput;
}, },
enumerable: true, enumerable: true,
configurable: true configurable: true
}); });
Object.defineProperty(NetInput.prototype, "batchSize", { Object.defineProperty(NetInput.prototype, "batchSize", {
get: function () { get: function () {
return this._inputs.length; return this._batchSize;
}, },
enumerable: true, enumerable: true,
configurable: true configurable: true
...@@ -1011,13 +1099,6 @@ ...@@ -1011,13 +1099,6 @@
enumerable: true, enumerable: true,
configurable: true configurable: true
}); });
Object.defineProperty(NetInput.prototype, "paddings", {
get: function () {
return this._paddings;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "inputSize", { Object.defineProperty(NetInput.prototype, "inputSize", {
get: function () { get: function () {
return this._inputSize; return this._inputSize;
...@@ -1025,22 +1106,17 @@ ...@@ -1025,22 +1106,17 @@
enumerable: true, enumerable: true,
configurable: true configurable: true
}); });
Object.defineProperty(NetInput.prototype, "relativePaddings", {
get: function () {
var _this = this;
return Array(this.inputs.length).fill(0).map(function (_, batchIdx) { return _this.getRelativePaddings(batchIdx); });
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "reshapedInputDimensions", { Object.defineProperty(NetInput.prototype, "reshapedInputDimensions", {
get: function () { get: function () {
var _this = this; var _this = this;
return Array(this.inputs.length).fill(0).map(function (_, batchIdx) { return _this.getReshapedInputDimensions(batchIdx); }); return range$1(this.batchSize, 0, 1).map(function (_, batchIdx) { return _this.getReshapedInputDimensions(batchIdx); });
}, },
enumerable: true, enumerable: true,
configurable: true configurable: true
}); });
NetInput.prototype.getInput = function (batchIdx) {
return this.canvases[batchIdx] || this.imageTensors[batchIdx];
};
NetInput.prototype.getInputDimensions = function (batchIdx) { NetInput.prototype.getInputDimensions = function (batchIdx) {
return this._inputDimensions[batchIdx]; return this._inputDimensions[batchIdx];
}; };
...@@ -1050,12 +1126,6 @@ ...@@ -1050,12 +1126,6 @@
NetInput.prototype.getInputWidth = function (batchIdx) { NetInput.prototype.getInputWidth = function (batchIdx) {
return this._inputDimensions[batchIdx][1]; return this._inputDimensions[batchIdx][1];
}; };
NetInput.prototype.getPaddings = function (batchIdx) {
return this._paddings[batchIdx];
};
NetInput.prototype.getRelativePaddings = function (batchIdx) {
return new Point((this.getPaddings(batchIdx).x + this.getInputWidth(batchIdx)) / this.getInputWidth(batchIdx), (this.getPaddings(batchIdx).y + this.getInputHeight(batchIdx)) / this.getInputHeight(batchIdx));
};
NetInput.prototype.getReshapedInputDimensions = function (batchIdx) { NetInput.prototype.getReshapedInputDimensions = function (batchIdx) {
if (typeof this.inputSize !== 'number') { if (typeof this.inputSize !== 'number') {
throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet'); throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');
...@@ -1064,40 +1134,39 @@ ...@@ -1064,40 +1134,39 @@
var height = this.getInputHeight(batchIdx); var height = this.getInputHeight(batchIdx);
return computeReshapedDimensions({ width: width, height: height }, this.inputSize); return computeReshapedDimensions({ width: width, height: height }, this.inputSize);
}; };
/**
* Create a batch tensor from all input canvases and tensors
* with size [batchSize, inputSize, inputSize, 3].
*
* @param inputSize Height and width of the tensor.
* @param isCenterImage (optional, default: false) If true, add an equal amount of padding on
* both sides of the minor dimension oof the image.
* @returns The batch tensor.
*/
NetInput.prototype.toBatchTensor = function (inputSize, isCenterInputs) { NetInput.prototype.toBatchTensor = function (inputSize, isCenterInputs) {
var _this = this; var _this = this;
if (isCenterInputs === void 0) { isCenterInputs = true; } if (isCenterInputs === void 0) { isCenterInputs = true; }
this._inputSize = inputSize; this._inputSize = inputSize;
return tidy(function () { return tidy(function () {
var inputTensors = _this._inputs.map(function (inputTensor) { var inputTensors = range$1(_this.batchSize, 0, 1).map(function (batchIdx) {
var _a = inputTensor.shape, originalHeight = _a[0], originalWidth = _a[1]; var input = _this.getInput(batchIdx);
var imgTensor = inputTensor.expandDims().toFloat(); if (input instanceof Tensor) {
var imgTensor = isTensor4D(input) ? input : input.expandDims();
imgTensor = padToSquare(imgTensor, isCenterInputs); imgTensor = padToSquare(imgTensor, isCenterInputs);
var _b = imgTensor.shape.slice(1), heightAfterPadding = _b[0], widthAfterPadding = _b[1]; if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {
if (heightAfterPadding !== inputSize || widthAfterPadding !== inputSize) {
imgTensor = image_ops.resizeBilinear(imgTensor, [inputSize, inputSize]); imgTensor = image_ops.resizeBilinear(imgTensor, [inputSize, inputSize]);
} }
_this._paddings.push(new Point(widthAfterPadding - originalWidth, heightAfterPadding - originalHeight)); return imgTensor.as3D(inputSize, inputSize, 3);
return imgTensor; }
}); if (input instanceof HTMLCanvasElement) {
var batchTensor = stack(inputTensors).as4D(_this.batchSize, inputSize, inputSize, 3); return fromPixels(imageToSquare(input, inputSize, isCenterInputs));
if (_this.isManaged) {
_this.dispose();
} }
throw new Error("toBatchTensor - at batchIdx " + batchIdx + ", expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have " + input);
});
var batchTensor = stack(inputTensors.map(function (t) { return t.toFloat(); })).as4D(_this.batchSize, inputSize, inputSize, 3);
return batchTensor; return batchTensor;
}); });
}; };
/**
* By setting the isManaged flag, all newly created tensors will be
* automatically disposed after the batch tensor has been created
*/
NetInput.prototype.managed = function () {
this._isManaged = true;
return this;
};
NetInput.prototype.dispose = function () {
this._inputs.forEach(function (t) { return t.dispose(); });
};
return NetInput; return NetInput;
}()); }());
...@@ -1106,27 +1175,17 @@ ...@@ -1106,27 +1175,17 @@
* to be finished loading. * to be finished loading.
* *
* @param input The input, which can be a media element or an array of different media elements. * @param input The input, which can be a media element or an array of different media elements.
* @param manageCreatedInput If a new NetInput instance is created from the inputs, this flag
* determines, whether to set the NetInput as managed or not.
* @returns A NetInput instance, which can be passed into one of the neural networks. * @returns A NetInput instance, which can be passed into one of the neural networks.
*/ */
function toNetInput(inputs, manageCreatedInput, keepCanvases) { function toNetInput(inputs) {
if (manageCreatedInput === void 0) { manageCreatedInput = false; }
if (keepCanvases === void 0) { keepCanvases = false; }
return __awaiter$1(this, void 0, void 0, function () { return __awaiter$1(this, void 0, void 0, function () {
var afterCreate, inputArgArray, getIdxHint, inputArray; var inputArgArray, getIdxHint, inputArray;
return __generator$1(this, function (_a) { return __generator$1(this, function (_a) {
switch (_a.label) { switch (_a.label) {
case 0: case 0:
if (inputs instanceof NetInput) { if (inputs instanceof NetInput) {
return [2 /*return*/, inputs]; return [2 /*return*/, inputs];
} }
afterCreate = function (netInput) { return manageCreatedInput
? netInput.managed()
: netInput; };
if (isTensor4D(inputs)) {
return [2 /*return*/, afterCreate(new NetInput(inputs))];
}
inputArgArray = Array.isArray(inputs) inputArgArray = Array.isArray(inputs)
? inputs ? inputs
: [inputs]; : [inputs];
...@@ -1155,7 +1214,7 @@ ...@@ -1155,7 +1214,7 @@
case 1: case 1:
// wait for all media elements being loaded // wait for all media elements being loaded
_a.sent(); _a.sent();
return [2 /*return*/, afterCreate(new NetInput(inputArray, Array.isArray(inputs), keepCanvases))]; return [2 /*return*/, new NetInput(inputArray, Array.isArray(inputs))];
} }
}); });
}); });
...@@ -1584,29 +1643,30 @@ ...@@ -1584,29 +1643,30 @@
*/ */
function extractFaces(input, detections) { function extractFaces(input, detections) {
return __awaiter$1(this, void 0, void 0, function () { return __awaiter$1(this, void 0, void 0, function () {
var canvas, netInput, ctx, boxes; var canvas, netInput, tensorOrCanvas, _a, ctx, boxes;
return __generator$1(this, function (_a) { return __generator$1(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*/, toNetInput(input, true)]; return [4 /*yield*/, 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*/, 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*/, 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 = getContext2dOrThrow(canvas); ctx = getContext2dOrThrow(canvas);
boxes = detections.map(function (det) { return det instanceof FaceDetection boxes = detections.map(function (det) { return det instanceof FaceDetection
? det.forSize(canvas.width, canvas.height).getBox().floor() ? det.forSize(canvas.width, canvas.height).getBox().floor()
...@@ -1630,41 +1690,28 @@ ...@@ -1630,41 +1690,28 @@
* 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 __awaiter$1(this, void 0, void 0, function () { return __awaiter$1(this, void 0, void 0, function () {
var netInput;
return __generator$1(this, function (_a) { return __generator$1(this, function (_a) {
switch (_a.label) { if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
case 0: return [4 /*yield*/, toNetInput(input, true)];
case 1:
netInput = _a.sent();
if (netInput.batchSize > 1) {
if (netInput.isManaged) {
netInput.dispose();
}
throw new Error('extractFaceTensors - batchSize > 1 not supported'); throw new Error('extractFaceTensors - batchSize > 1 not supported');
} }
return [2 /*return*/, tidy(function () { return [2 /*return*/, tidy(function () {
var imgTensor = netInput.inputs[0].expandDims().toFloat(); var _a = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];
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 var boxes = detections.map(function (det) { return det instanceof FaceDetection
? det.forSize(imgWidth, imgHeight).getBox() ? det.forSize(imgWidth, imgHeight).getBox()
: det; }) : det; })
.map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); }); .map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); });
var faceTensors = boxes.map(function (_a) { var faceTensors = boxes.map(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height; var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
return slice4d(imgTensor, [0, y, x, 0], [1, height, width, numChannels]); return slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]);
}); });
if (netInput.isManaged) {
netInput.dispose();
}
return faceTensors; return faceTensors;
})]; })];
}
}); });
}); });
} }
...@@ -2147,6 +2194,24 @@ ...@@ -2147,6 +2194,24 @@
SizeType["LG"] = "lg"; SizeType["LG"] = "lg";
})(SizeType || (SizeType = {})); })(SizeType || (SizeType = {}));
function extractSeparableConvParamsFactory(extractWeights, paramMappings) {
return function (channelsIn, channelsOut, mappedPrefix) {
var depthwise_filter = tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
var pointwise_filter = tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
var bias = tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/depthwise_filter" }, { paramPath: mappedPrefix + "/pointwise_filter" }, { paramPath: mappedPrefix + "/bias" });
return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
};
}
function loadSeparableConvParamsFactory(extractWeightEntry) {
return function (prefix) {
var depthwise_filter = extractWeightEntry(prefix + "/depthwise_filter", 4);
var pointwise_filter = extractWeightEntry(prefix + "/pointwise_filter", 4);
var bias = extractWeightEntry(prefix + "/bias", 1);
return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
};
}
var isNumber = function (arg) { return typeof arg === 'number'; }; var isNumber = function (arg) { return typeof arg === 'number'; };
function validateConfig(config) { function validateConfig(config) {
if (!config) { if (!config) {
...@@ -2183,6 +2248,9 @@ ...@@ -2183,6 +2248,9 @@
var INPUT_SIZES = { xs: 224, sm: 320, md: 416, lg: 608 }; var INPUT_SIZES = { xs: 224, sm: 320, md: 416, lg: 608 };
var CELL_SIZE = 32; var CELL_SIZE = 32;
var DEFAULT_FILTER_SIZES = [
3, 16, 32, 64, 128, 256, 512, 1024, 1024
];
function leaky(x) { function leaky(x) {
return tidy(function () { return tidy(function () {
...@@ -2222,33 +2290,28 @@ ...@@ -2222,33 +2290,28 @@
var bn = extractBatchNormParams(channelsOut, mappedPrefix + "/bn"); var bn = extractBatchNormParams(channelsOut, mappedPrefix + "/bn");
return { conv: conv, bn: bn }; return { conv: conv, bn: bn };
} }
function extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix) { var extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
var depthwise_filter = tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
var pointwise_filter = tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
var bias = tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/depthwise_filter" }, { paramPath: mappedPrefix + "/pointwise_filter" }, { paramPath: mappedPrefix + "/bias" });
return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
}
return { return {
extractConvParams: extractConvParams, extractConvParams: extractConvParams,
extractConvWithBatchNormParams: extractConvWithBatchNormParams, extractConvWithBatchNormParams: extractConvWithBatchNormParams,
extractSeparableConvParams: extractSeparableConvParams extractSeparableConvParams: extractSeparableConvParams
}; };
} }
function extractParams$1(weights, withSeparableConvs, boxEncodingSize) { function extractParams$1(weights, withSeparableConvs, boxEncodingSize, filterSizes) {
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights; var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var paramMappings = []; var paramMappings = [];
var _b = extractorsFactory$2(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractConvWithBatchNormParams = _b.extractConvWithBatchNormParams, extractSeparableConvParams = _b.extractSeparableConvParams; var _b = extractorsFactory$2(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractConvWithBatchNormParams = _b.extractConvWithBatchNormParams, extractSeparableConvParams = _b.extractSeparableConvParams;
var extractConvFn = withSeparableConvs ? extractSeparableConvParams : extractConvWithBatchNormParams; var extractConvFn = withSeparableConvs ? extractSeparableConvParams : extractConvWithBatchNormParams;
var conv0 = extractConvFn(3, 16, 'conv0'); var s0 = filterSizes[0], s1 = filterSizes[1], s2 = filterSizes[2], s3 = filterSizes[3], s4 = filterSizes[4], s5 = filterSizes[5], s6 = filterSizes[6], s7 = filterSizes[7], s8 = filterSizes[8];
var conv1 = extractConvFn(16, 32, 'conv1'); var conv0 = extractConvFn(s0, s1, 'conv0');
var conv2 = extractConvFn(32, 64, 'conv2'); var conv1 = extractConvFn(s1, s2, 'conv1');
var conv3 = extractConvFn(64, 128, 'conv3'); var conv2 = extractConvFn(s2, s3, 'conv2');
var conv4 = extractConvFn(128, 256, 'conv4'); var conv3 = extractConvFn(s3, s4, 'conv3');
var conv5 = extractConvFn(256, 512, 'conv5'); var conv4 = extractConvFn(s4, s5, 'conv4');
var conv6 = extractConvFn(512, 1024, 'conv6'); var conv5 = extractConvFn(s5, s6, 'conv5');
var conv7 = extractConvFn(1024, 1024, 'conv7'); var conv6 = extractConvFn(s6, s7, 'conv6');
var conv8 = extractConvParams(1024, 5 * boxEncodingSize, 1, 'conv8'); var conv7 = extractConvFn(s7, s8, 'conv7');
var conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');
if (getRemainingWeights().length !== 0) { if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length); throw new Error("weights remaing after extract: " + getRemainingWeights().length);
} }
...@@ -2285,12 +2348,7 @@ ...@@ -2285,12 +2348,7 @@
var bn = extractBatchNormParams(prefix + "/bn"); var bn = extractBatchNormParams(prefix + "/bn");
return { conv: conv, bn: bn }; return { conv: conv, bn: bn };
} }
function extractSeparableConvParams(prefix) { var extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
var depthwise_filter = extractWeightEntry(prefix + "/depthwise_filter", 4);
var pointwise_filter = extractWeightEntry(prefix + "/pointwise_filter", 4);
var bias = extractWeightEntry(prefix + "/bias", 1);
return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
}
return { return {
extractConvParams: extractConvParams, extractConvParams: extractConvParams,
extractConvWithBatchNormParams: extractConvWithBatchNormParams, extractConvWithBatchNormParams: extractConvWithBatchNormParams,
...@@ -2363,7 +2421,7 @@ ...@@ -2363,7 +2421,7 @@
throw new Error('TinyYolov2 - load model before inference'); throw new Error('TinyYolov2 - load model before inference');
} }
var out = tidy(function () { var out = tidy(function () {
var batchTensor = input.toBatchTensor(inputSize, false); var batchTensor = input.toBatchTensor(inputSize, false).toFloat();
batchTensor = _this.config.meanRgb batchTensor = _this.config.meanRgb
? normalize(batchTensor, _this.config.meanRgb) ? normalize(batchTensor, _this.config.meanRgb)
: batchTensor; : batchTensor;
...@@ -2394,7 +2452,7 @@ ...@@ -2394,7 +2452,7 @@
switch (_b.label) { switch (_b.label) {
case 0: case 0:
_a = this.forwardInput; _a = this.forwardInput;
return [4 /*yield*/, toNetInput(input, true, true)]; return [4 /*yield*/, toNetInput(input)];
case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(), inputSize])]; case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(), inputSize])];
case 2: return [2 /*return*/, _b.sent()]; case 2: return [2 /*return*/, _b.sent()];
} }
...@@ -2416,7 +2474,7 @@ ...@@ -2416,7 +2474,7 @@
if (typeof inputSize !== 'number') { if (typeof inputSize !== 'number') {
throw new Error("TinyYolov2 - unknown inputSize: " + inputSize + ", expected number or one of xs | sm | md | lg"); throw new Error("TinyYolov2 - unknown inputSize: " + inputSize + ", expected number or one of xs | sm | md | lg");
} }
return [4 /*yield*/, toNetInput(input, true)]; return [4 /*yield*/, toNetInput(input)];
case 1: case 1:
netInput = _b.sent(); netInput = _b.sent();
return [4 /*yield*/, this.forwardInput(netInput, inputSize)]; return [4 /*yield*/, this.forwardInput(netInput, inputSize)];
...@@ -2433,10 +2491,10 @@ ...@@ -2433,10 +2491,10 @@
boxes = results.map(function (res) { return res.box; }); boxes = results.map(function (res) { return res.box; });
scores = results.map(function (res) { return res.score; }); scores = results.map(function (res) { return res.score; });
classScores = results.map(function (res) { return res.classScore; }); classScores = results.map(function (res) { return res.classScore; });
classNames = results.map(function (res) { return _this.config.classes[res.classLabel]; }); classNames = results.map(function (res) { return _this.config.classes[res.label]; });
indices = nonMaxSuppression$1(boxes.map(function (box) { return box.rescale(inputSize); }), scores, this.config.iouThreshold, true); indices = nonMaxSuppression$1(boxes.map(function (box) { return box.rescale(inputSize); }), scores, this.config.iouThreshold, true);
detections = indices.map(function (idx) { detections = indices.map(function (idx) {
return new ObjectDetection(scores[idx], classScores[idx], classNames[idx], boxes[idx].toRect(), inputDimensions); return new ObjectDetection(scores[idx], classScores[idx], classNames[idx], boxes[idx], inputDimensions);
}); });
return [2 /*return*/, detections]; return [2 /*return*/, detections];
} }
...@@ -2451,7 +2509,12 @@ ...@@ -2451,7 +2509,12 @@
return loadQuantizedParams$1(modelUri, this.config.withSeparableConvs, defaultModelName); return loadQuantizedParams$1(modelUri, this.config.withSeparableConvs, defaultModelName);
}; };
TinyYolov2.prototype.extractParams = function (weights) { TinyYolov2.prototype.extractParams = function (weights) {
return extractParams$1(weights, this.config.withSeparableConvs, this.boxEncodingSize); var filterSizes = this.config.filterSizes || DEFAULT_FILTER_SIZES;
var numFilters = filterSizes ? filterSizes.length : undefined;
if (numFilters !== 9) {
throw new Error("TinyYolov2 - expected 9 convolutional filters, but found " + numFilters + " filterSizes in config");
}
return extractParams$1(weights, this.config.withSeparableConvs, this.boxEncodingSize, filterSizes);
}; };
TinyYolov2.prototype.extractBoxes = function (outputTensor, inputBlobDimensions, scoreThreshold) { TinyYolov2.prototype.extractBoxes = function (outputTensor, inputBlobDimensions, scoreThreshold) {
var _this = this; var _this = this;
...@@ -2485,8 +2548,8 @@ ...@@ -2485,8 +2548,8 @@
var pos = { row: row, col: col, anchor: anchor }; var pos = { row: row, col: col, anchor: anchor };
var _b = this.withClassScores var _b = this.withClassScores
? this.extractPredictedClass(classScoresTensor, pos) ? this.extractPredictedClass(classScoresTensor, pos)
: { classScore: 1, classLabel: 0 }, classScore = _b.classScore, classLabel = _b.classLabel; : { classScore: 1, label: 0 }, classScore = _b.classScore, label = _b.label;
results.push(__assign$1({ box: new BoundingBox(x, y, x + width_1, y + height_1), score: score, classScore: score * classScore, classLabel: classLabel }, pos)); results.push(__assign$1({ box: new BoundingBox(x, y, x + width_1, y + height_1), score: score, classScore: score * classScore, label: label }, pos));
} }
} }
} }
...@@ -2500,9 +2563,9 @@ ...@@ -2500,9 +2563,9 @@
var row = pos.row, col = pos.col, anchor = pos.anchor; var row = pos.row, col = pos.col, anchor = pos.anchor;
return Array(this.config.classes.length).fill(0) return Array(this.config.classes.length).fill(0)
.map(function (_, i) { return classesTensor.get(row, col, anchor, i); }) .map(function (_, i) { return classesTensor.get(row, col, anchor, i); })
.map(function (classScore, classLabel) { return ({ .map(function (classScore, label) { return ({
classScore: classScore, classScore: classScore,
classLabel: classLabel label: label
}); }) }); })
.reduce(function (max$$1, curr) { return max$$1.classScore > curr.classScore ? max$$1 : curr; }); .reduce(function (max$$1, curr) { return max$$1.classScore > curr.classScore ? max$$1 : curr; });
}; };
...@@ -2690,9 +2753,9 @@ ...@@ -2690,9 +2753,9 @@
TinyYolov2LossFunction.prototype.validateGroundTruthBoxes = function (groundTruth) { TinyYolov2LossFunction.prototype.validateGroundTruthBoxes = function (groundTruth) {
var _this = this; var _this = this;
groundTruth.forEach(function (_a) { groundTruth.forEach(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height, classLabel = _a.classLabel; var x = _a.x, y = _a.y, width = _a.width, height = _a.height, label = _a.label;
if (typeof classLabel !== 'number' || classLabel < 0 || classLabel > (_this.config.classes.length - 1)) { if (typeof label !== 'number' || label < 0 || label > (_this.config.classes.length - 1)) {
throw new Error("invalid ground truth data, expected classLabel to be a number in [0, " + (_this.config.classes.length - 1) + "]"); throw new Error("invalid ground truth data, expected label to be a number in [0, " + (_this.config.classes.length - 1) + "]");
} }
if (x < 0 || x > 1 || y < 0 || y > 1 || width < 0 || (x + width) > 1 || height < 0 || (y + height) > 1) { if (x < 0 || x > 1 || y < 0 || y > 1 || width < 0 || (x + width) > 1 || height < 0 || (y + height) > 1) {
throw new Error("invalid ground truth data, box is out of image boundaries " + JSON.stringify({ x: x, y: y, width: width, height: height })); throw new Error("invalid ground truth data, box is out of image boundaries " + JSON.stringify({ x: x, y: y, width: width, height: height }));
...@@ -2703,15 +2766,15 @@ ...@@ -2703,15 +2766,15 @@
var _this = this; var _this = this;
var groundTruthBoxes = groundTruth var groundTruthBoxes = groundTruth
.map(function (_a) { .map(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height, classLabel = _a.classLabel; var x = _a.x, y = _a.y, width = _a.width, height = _a.height, label = _a.label;
return ({ return ({
box: (new Rect(x, y, width, height)).toBoundingBox(), box: new Rect(x, y, width, height),
classLabel: classLabel label: label
}); });
}); });
return groundTruthBoxes.map(function (_a) { return groundTruthBoxes.map(function (_a) {
var box = _a.box, classLabel = _a.classLabel; var box = _a.box, label = _a.label;
var _b = box.rescale(_this.reshapedImgDims), left = _b.left, top = _b.top, width = _b.width, height = _b.height; var _b = box.rescale(_this.reshapedImgDims), left = _b.left, top = _b.top, bottom = _b.bottom, right = _b.right, x = _b.x, y = _b.y, width = _b.width, height = _b.height;
var ctX = left + (width / 2); var ctX = left + (width / 2);
var ctY = top + (height / 2); var ctY = top + (height / 2);
var col = Math.floor((ctX / _this.inputSize) * _this.numCells); var col = Math.floor((ctX / _this.inputSize) * _this.numCells);
...@@ -2721,7 +2784,7 @@ ...@@ -2721,7 +2784,7 @@
iou: iou(new BoundingBox(0, 0, anchor.x * CELL_SIZE, anchor.y * CELL_SIZE), new BoundingBox(0, 0, width, height)) iou: iou(new BoundingBox(0, 0, anchor.x * CELL_SIZE, anchor.y * CELL_SIZE), new BoundingBox(0, 0, width, height))
}); }).sort(function (a1, a2) { return a2.iou - a1.iou; }); }); }).sort(function (a1, a2) { return a2.iou - a1.iou; });
var anchor = anchorsByIou[0].idx; var anchor = anchorsByIou[0].idx;
return { row: row, col: col, anchor: anchor, box: box, classLabel: classLabel }; return { row: row, col: col, anchor: anchor, box: box, label: label };
}); });
}; };
TinyYolov2LossFunction.prototype.createGroundTruthMask = function () { TinyYolov2LossFunction.prototype.createGroundTruthMask = function () {
...@@ -2764,8 +2827,8 @@ ...@@ -2764,8 +2827,8 @@
var buf = mask.buffer(); var buf = mask.buffer();
var classValuesOffset = 5; var classValuesOffset = 5;
this.groundTruth.forEach(function (_a) { this.groundTruth.forEach(function (_a) {
var row = _a.row, col = _a.col, anchor = _a.anchor, classLabel = _a.classLabel; var row = _a.row, col = _a.col, anchor = _a.anchor, label = _a.label;
buf.set(1, row, col, anchor, classValuesOffset + classLabel); buf.set(1, row, col, anchor, classValuesOffset + label);
}); });
return mask; return mask;
}; };
...@@ -2878,11 +2941,15 @@ ...@@ -2878,11 +2941,15 @@
coordLoss: coordLoss.dataSync()[0], coordLoss: coordLoss.dataSync()[0],
classLoss: classLoss.dataSync()[0] classLoss: classLoss.dataSync()[0]
}; };
reportLosses(losses, filteredGroundTruthBoxes.length); var report = {
losses: losses,
numBoxes: filteredGroundTruthBoxes.length,
inputSize: inputSize
};
reportLosses(report);
} }
return totalLoss; return totalLoss;
}, true); }, true);
netInput.dispose();
return [2 /*return*/, loss]; return [2 /*return*/, loss];
} }
}); });
...@@ -2905,7 +2972,6 @@ ...@@ -2905,7 +2972,6 @@
return groundTruth.filter(function (_a) { return groundTruth.filter(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height; var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var box = (new Rect(x, y, width, height)) var box = (new Rect(x, y, width, height))
.toBoundingBox()
.rescale({ height: imgHeight, width: imgWidth }); .rescale({ height: imgHeight, width: imgWidth });
var isTooTiny = box.width < minBoxSize || box.height < minBoxSize; var isTooTiny = box.width < minBoxSize || box.height < minBoxSize;
return !isTooTiny; return !isTooTiny;
...@@ -2994,7 +3060,7 @@ ...@@ -2994,7 +3060,7 @@
throw new Error('FaceDetectionNet - load model before inference'); throw new Error('FaceDetectionNet - load model before inference');
} }
return tidy(function () { return tidy(function () {
var batchTensor = input.toBatchTensor(512, false); var batchTensor = input.toBatchTensor(512, false).toFloat();
var x = sub(mul(batchTensor, scalar(0.007843137718737125)), scalar(1)); var x = sub(mul(batchTensor, scalar(0.007843137718737125)), scalar(1));
var features = mobileNetV1(x, params.mobilenetv1); var features = mobileNetV1(x, params.mobilenetv1);
var _a = predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions; var _a = predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
...@@ -3008,7 +3074,7 @@ ...@@ -3008,7 +3074,7 @@
switch (_b.label) { switch (_b.label) {
case 0: case 0:
_a = this.forwardInput; _a = this.forwardInput;
return [4 /*yield*/, toNetInput(input, true)]; return [4 /*yield*/, toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])]; case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
} }
}); });
...@@ -3018,10 +3084,10 @@ ...@@ -3018,10 +3084,10 @@
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 __awaiter$1(this, void 0, void 0, function () { return __awaiter$1(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 __generator$1(this, function (_d) { return __generator$1(this, function (_d) {
switch (_d.label) { switch (_d.label) {
case 0: return [4 /*yield*/, toNetInput(input, true)]; case 0: return [4 /*yield*/, 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;
...@@ -3037,17 +3103,20 @@ ...@@ -3037,17 +3103,20 @@
scoresData = _c.apply(_b, [_d.sent()]); scoresData = _c.apply(_b, [_d.sent()]);
iouThreshold = 0.5; iouThreshold = 0.5;
indices = nonMaxSuppression$2(boxes, scoresData, maxResults, iouThreshold, minConfidence); indices = nonMaxSuppression$2(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(scoresData[idx], new Rect(left, top, right - left, bottom - top), { return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), {
height: netInput.getInputHeight(0), height: netInput.getInputHeight(0),
width: netInput.getInputWidth(0) width: netInput.getInputWidth(0)
...@@ -3114,6 +3183,114 @@ ...@@ -3114,6 +3183,114 @@
}; };
} }
var FaceLandmark68NetBase = /** @class */ (function (_super) {
__extends$1(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 tidy(function () {
var createInterleavedTensor = function (fillX, fillY) {
return stack([
fill([68], fillX),
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(fill([batchSize, 136], inputSize))
.sub(stack(Array.from(Array(batchSize), function (_, batchIdx) {
return createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx));
})))
.div(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 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 __awaiter$1(this, void 0, void 0, function () {
var _a;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
});
};
FaceLandmark68NetBase.prototype.detectLandmarks = function (input) {
return __awaiter$1(this, void 0, void 0, function () {
var _this = this;
var netInput, landmarkTensors, landmarksForBatch;
return __generator$1(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, toNetInput(input)];
case 1:
netInput = _a.sent();
landmarkTensors = tidy(function () { return unstack(_this.forwardInput(netInput)); });
return [4 /*yield*/, Promise.all(landmarkTensors.map(function (landmarkTensor, batchIdx) { return __awaiter$1(_this, void 0, void 0, function () {
var landmarksArray, _a, _b, xCoords, yCoords;
return __generator$1(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 isEven(i); });
yCoords = landmarksArray.filter(function (_, i) { return !isEven(i); });
return [2 /*return*/, new FaceLandmarks68(Array(68).fill(0).map(function (_, i) { return new 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;
}(NeuralNetwork));
function fullyConnectedLayer(x, params) { function fullyConnectedLayer(x, params) {
return tidy(function () { return tidy(function () {
return add(matMul(x, params.weights), params.bias); return add(matMul(x, params.weights), params.bias);
...@@ -3174,18 +3351,18 @@ ...@@ -3174,18 +3351,18 @@
if (strides === void 0) { strides = [2, 2]; } if (strides === void 0) { strides = [2, 2]; }
return maxPool(x, [2, 2], strides, 'valid'); return maxPool(x, [2, 2], strides, 'valid');
} }
var FaceLandmarkNet = /** @class */ (function (_super) { var FaceLandmark68Net = /** @class */ (function (_super) {
__extends$1(FaceLandmarkNet, _super); __extends$1(FaceLandmark68Net, _super);
function FaceLandmarkNet() { function FaceLandmark68Net() {
return _super.call(this, 'FaceLandmarkNet') || this; return _super.call(this, 'FaceLandmark68Net') || this;
} }
FaceLandmarkNet.prototype.forwardInput = function (input) { FaceLandmark68Net.prototype.runNet = function (input) {
var params = this.params; var params = this.params;
if (!params) { if (!params) {
throw new Error('FaceLandmarkNet - load model before inference'); throw new Error('FaceLandmark68Net - load model before inference');
} }
return tidy(function () { return tidy(function () {
var batchTensor = input.toBatchTensor(128, true); var batchTensor = input.toBatchTensor(128, true).toFloat();
var out = conv(batchTensor, params.conv0); var out = conv(batchTensor, params.conv0);
out = maxPool$1(out); out = maxPool$1(out);
out = conv(out, params.conv1); out = conv(out, params.conv1);
...@@ -3199,90 +3376,25 @@ ...@@ -3199,90 +3376,25 @@
out = maxPool$1(out, [1, 1]); out = maxPool$1(out, [1, 1]);
out = conv(out, params.conv7); out = conv(out, params.conv7);
var fc0 = relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0)); var fc0 = relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0));
var fc1 = fullyConnectedLayer(fc0, params.fc1); return fullyConnectedLayer(fc0, params.fc1);
var createInterleavedTensor = function (fillX, fillY) {
return stack([
fill([68], fillX),
fill([68], fillY)
], 1).as2D(1, 136).as1D();
};
/* shift coordinates back, to undo centered padding
x = ((x * widthAfterPadding) - shiftX) / width
y = ((y * heightAfterPadding) - shiftY) / height
*/
var landmarkTensors = fc1
.mul(stack(Array.from(Array(input.batchSize), function (_, batchIdx) {
return createInterleavedTensor(input.getPaddings(batchIdx).x + input.getInputWidth(batchIdx), input.getPaddings(batchIdx).y + input.getInputHeight(batchIdx));
})))
.sub(stack(Array.from(Array(input.batchSize), function (_, batchIdx) {
return createInterleavedTensor(Math.floor(input.getPaddings(batchIdx).x / 2), Math.floor(input.getPaddings(batchIdx).y / 2));
})))
.div(stack(Array.from(Array(input.batchSize), function (_, batchIdx) {
return createInterleavedTensor(input.getInputWidth(batchIdx), input.getInputHeight(batchIdx));
})));
return landmarkTensors;
});
};
FaceLandmarkNet.prototype.forward = function (input) {
return __awaiter$1(this, void 0, void 0, function () {
var _a;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, toNetInput(input, true)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
});
};
FaceLandmarkNet.prototype.detectLandmarks = function (input) {
return __awaiter$1(this, void 0, void 0, function () {
var _this = this;
var netInput, landmarkTensors, landmarksForBatch;
return __generator$1(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, toNetInput(input, true)];
case 1:
netInput = _a.sent();
landmarkTensors = tidy(function () { return unstack(_this.forwardInput(netInput)); });
return [4 /*yield*/, Promise.all(landmarkTensors.map(function (landmarkTensor, batchIdx) { return __awaiter$1(_this, void 0, void 0, function () {
var landmarksArray, _a, _b, xCoords, yCoords;
return __generator$1(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 isEven(i); });
yCoords = landmarksArray.filter(function (_, i) { return !isEven(i); });
return [2 /*return*/, new FaceLandmarks68(Array(68).fill(0).map(function (_, i) { return new 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]];
}
});
}); });
}; };
FaceLandmarkNet.prototype.loadQuantizedParams = function (uri) { FaceLandmark68Net.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams$2(uri); return loadQuantizedParams$2(uri);
}; };
FaceLandmarkNet.prototype.extractParams = function (weights) { FaceLandmark68Net.prototype.extractParams = function (weights) {
return extractParams$2(weights); return extractParams$2(weights);
}; };
return FaceLandmarkNet; return FaceLandmark68Net;
}(NeuralNetwork)); }(FaceLandmark68NetBase));
var FaceLandmarkNet = /** @class */ (function (_super) {
__extends$1(FaceLandmarkNet, _super);
function FaceLandmarkNet() {
return _super !== null && _super.apply(this, arguments) || this;
}
return FaceLandmarkNet;
}(FaceLandmark68Net));
function createFaceLandmarkNet(weights) { function createFaceLandmarkNet(weights) {
var net = new FaceLandmarkNet(); var net = new FaceLandmarkNet();
net.extractWeights(weights); net.extractWeights(weights);
...@@ -3521,7 +3633,7 @@ ...@@ -3521,7 +3633,7 @@
throw new Error('FaceRecognitionNet - load model before inference'); throw new Error('FaceRecognitionNet - load model before inference');
} }
return tidy(function () { return 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 = normalize(batchTensor, meanRgb).div(scalar(256)); var normalized = normalize(batchTensor, meanRgb).div(scalar(256));
var out = convDown(normalized, params.conv32_down); var out = convDown(normalized, params.conv32_down);
...@@ -3552,7 +3664,7 @@ ...@@ -3552,7 +3664,7 @@
switch (_b.label) { switch (_b.label) {
case 0: case 0:
_a = this.forwardInput; _a = this.forwardInput;
return [4 /*yield*/, toNetInput(input, true)]; return [4 /*yield*/, toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])]; case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
} }
}); });
...@@ -3564,7 +3676,7 @@ ...@@ -3564,7 +3676,7 @@
var netInput, faceDescriptorTensors, faceDescriptorsForBatch; var netInput, faceDescriptorTensors, faceDescriptorsForBatch;
return __generator$1(this, function (_a) { return __generator$1(this, function (_a) {
switch (_a.label) { switch (_a.label) {
case 0: return [4 /*yield*/, toNetInput(input, true)]; case 0: return [4 /*yield*/, toNetInput(input)];
case 1: case 1:
netInput = _a.sent(); netInput = _a.sent();
faceDescriptorTensors = tidy(function () { return unstack(_this.forwardInput(netInput)); }); faceDescriptorTensors = tidy(function () { return unstack(_this.forwardInput(netInput)); });
...@@ -3601,24 +3713,23 @@ ...@@ -3601,24 +3713,23 @@
function computeDescriptorsFactory(recognitionNet) { function computeDescriptorsFactory(recognitionNet) {
return function (input, alignedFaceBoxes, useBatchProcessing) { return function (input, alignedFaceBoxes, useBatchProcessing) {
return __awaiter$1(this, void 0, void 0, function () { return __awaiter$1(this, void 0, void 0, function () {
var alignedFaceTensors, descriptors, _a; var alignedFaceCanvases, descriptors, _a;
return __generator$1(this, function (_b) { return __generator$1(this, function (_b) {
switch (_b.label) { switch (_b.label) {
case 0: return [4 /*yield*/, extractFaceTensors(input, alignedFaceBoxes)]; case 0: return [4 /*yield*/, 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];
} }
}); });
...@@ -3630,27 +3741,26 @@ ...@@ -3630,27 +3741,26 @@
return function (input, useBatchProcessing) { return function (input, useBatchProcessing) {
if (useBatchProcessing === void 0) { useBatchProcessing = false; } if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return __awaiter$1(this, void 0, void 0, function () { return __awaiter$1(this, void 0, void 0, function () {
var detections, faceTensors, faceLandmarksByFace, _a, alignedFaceBoxes, descriptors; var detections, faceCanvases, faceLandmarksByFace, _a, alignedFaceBoxes, descriptors;
return __generator$1(this, function (_b) { return __generator$1(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*/, extractFaceTensors(input, detections)]; return [4 /*yield*/, 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:
...@@ -3896,6 +4006,14 @@ ...@@ -3896,6 +4006,14 @@
return scales; return scales;
} }
var MtcnnBox = /** @class */ (function (_super) {
__extends$1(MtcnnBox, _super);
function MtcnnBox(left, top, right, bottom) {
return _super.call(this, { left: left, top: top, right: right, bottom: bottom }, true) || this;
}
return MtcnnBox;
}(Box));
function normalize$1(x) { function normalize$1(x) {
return tidy(function () { return mul(sub(x, scalar(127.5)), scalar(0.0078125)); }); return tidy(function () { return mul(sub(x, scalar(127.5)), scalar(0.0078125)); });
} }
...@@ -3953,7 +4071,7 @@ ...@@ -3953,7 +4071,7 @@
var boundingBoxes = indices.map(function (idx) { var boundingBoxes = indices.map(function (idx) {
var cell = new BoundingBox(Math.round((idx.y * CELL_STRIDE + 1) / scale), Math.round((idx.x * CELL_STRIDE + 1) / scale), Math.round((idx.y * CELL_STRIDE + CELL_SIZE$1) / scale), Math.round((idx.x * CELL_STRIDE + CELL_SIZE$1) / scale)); var cell = new BoundingBox(Math.round((idx.y * CELL_STRIDE + 1) / scale), Math.round((idx.x * CELL_STRIDE + 1) / scale), Math.round((idx.y * CELL_STRIDE + CELL_SIZE$1) / scale), Math.round((idx.x * CELL_STRIDE + CELL_SIZE$1) / scale));
var score = scoresTensor.get(idx.y, idx.x); var score = scoresTensor.get(idx.y, idx.x);
var region = new 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(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,
...@@ -4121,7 +4239,7 @@ ...@@ -4121,7 +4239,7 @@
indicesNms = nonMaxSuppression$1(filteredBoxes, filteredScores, 0.7); indicesNms = nonMaxSuppression$1(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 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(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]); });
...@@ -4191,7 +4309,7 @@ ...@@ -4191,7 +4309,7 @@
var idx = _a.idx; var idx = _a.idx;
return idx; return idx;
}); });
filteredRegions = indices.map(function (idx) { return new 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(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]; });
...@@ -4233,7 +4351,7 @@ ...@@ -4233,7 +4351,7 @@
Mtcnn.prototype.forwardInput = function (input, forwardParams) { Mtcnn.prototype.forwardInput = function (input, forwardParams) {
if (forwardParams === void 0) { forwardParams = {}; } if (forwardParams === void 0) { forwardParams = {}; }
return __awaiter$1(this, void 0, void 0, function () { 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; var params, 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) { return __generator$1(this, function (_c) {
switch (_c.label) { switch (_c.label) {
case 0: case 0:
...@@ -4241,7 +4359,6 @@ ...@@ -4241,7 +4359,6 @@
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.');
...@@ -4249,12 +4366,11 @@ ...@@ -4249,12 +4366,11 @@
stats = {}; stats = {};
tsTotal = Date.now(); tsTotal = Date.now();
imgTensor = tidy(function () { imgTensor = tidy(function () {
return bgrToRgbTensor(expandDims(inputTensor).toFloat()); return bgrToRgbTensor(expandDims(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;
}; };
...@@ -4313,7 +4429,7 @@ ...@@ -4313,7 +4429,7 @@
switch (_b.label) { switch (_b.label) {
case 0: case 0:
_a = this.forwardInput; _a = this.forwardInput;
return [4 /*yield*/, toNetInput(input, true, true)]; return [4 /*yield*/, 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];
...@@ -4329,7 +4445,7 @@ ...@@ -4329,7 +4445,7 @@
switch (_b.label) { switch (_b.label) {
case 0: case 0:
_a = this.forwardInput; _a = this.forwardInput;
return [4 /*yield*/, toNetInput(input, true, true)]; return [4 /*yield*/, toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent(), case 1: return [2 /*return*/, _a.apply(this, [_b.sent(),
forwardParams])]; forwardParams])];
} }
...@@ -4421,7 +4537,7 @@ ...@@ -4421,7 +4537,7 @@
}(TinyYolov2)); }(TinyYolov2));
var detectionNet = new FaceDetectionNet(); var detectionNet = new FaceDetectionNet();
var landmarkNet = new FaceLandmarkNet(); var landmarkNet = new FaceLandmark68Net();
var recognitionNet = new FaceRecognitionNet(); var recognitionNet = new 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
...@@ -4494,9 +4610,12 @@ ...@@ -4494,9 +4610,12 @@
} }
exports.tf = tfCore_esm; exports.tf = tfCore_esm;
exports.Box = Box;
exports.BoundingBox = BoundingBox; exports.BoundingBox = BoundingBox;
exports.LabeledBox = LabeledBox;
exports.ObjectDetection = ObjectDetection; exports.ObjectDetection = ObjectDetection;
exports.Point = Point; exports.Point = Point;
exports.PredictedBox = PredictedBox;
exports.Rect = Rect; exports.Rect = Rect;
exports.disposeUnusedWeightTensors = disposeUnusedWeightTensors; exports.disposeUnusedWeightTensors = disposeUnusedWeightTensors;
exports.extractWeightEntryFactory = extractWeightEntryFactory; exports.extractWeightEntryFactory = extractWeightEntryFactory;
...@@ -4538,6 +4657,9 @@ ...@@ -4538,6 +4657,9 @@
exports.isDimensions = isDimensions; exports.isDimensions = isDimensions;
exports.computeReshapedDimensions = computeReshapedDimensions; exports.computeReshapedDimensions = computeReshapedDimensions;
exports.getCenterPoint = getCenterPoint; exports.getCenterPoint = getCenterPoint;
exports.range = range$1;
exports.isValidNumber = isValidNumber;
exports.isValidProbablitiy = isValidProbablitiy;
exports.NeuralNetwork = NeuralNetwork; exports.NeuralNetwork = NeuralNetwork;
exports.FaceDetection = FaceDetection; exports.FaceDetection = FaceDetection;
exports.FaceLandmarks = FaceLandmarks; exports.FaceLandmarks = FaceLandmarks;
...@@ -4552,9 +4674,10 @@ ...@@ -4552,9 +4674,10 @@
exports.createFaceDetectionNet = createFaceDetectionNet; exports.createFaceDetectionNet = createFaceDetectionNet;
exports.faceDetectionNet = faceDetectionNet; exports.faceDetectionNet = faceDetectionNet;
exports.FaceDetectionNet = FaceDetectionNet; exports.FaceDetectionNet = FaceDetectionNet;
exports.FaceLandmarkNet = FaceLandmarkNet;
exports.createFaceLandmarkNet = createFaceLandmarkNet; exports.createFaceLandmarkNet = createFaceLandmarkNet;
exports.faceLandmarkNet = faceLandmarkNet; exports.faceLandmarkNet = faceLandmarkNet;
exports.FaceLandmarkNet = FaceLandmarkNet; exports.FaceLandmark68Net = FaceLandmark68Net;
exports.createFaceRecognitionNet = createFaceRecognitionNet; exports.createFaceRecognitionNet = createFaceRecognitionNet;
exports.faceRecognitionNet = faceRecognitionNet; exports.faceRecognitionNet = faceRecognitionNet;
exports.FaceRecognitionNet = FaceRecognitionNet; exports.FaceRecognitionNet = FaceRecognitionNet;
......
dist/face-api.js.map
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dist/face-api.min.js
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