face-api.js
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JavaScript API for face detection and face recognition in the browser with tensorflow.js
39 lines • 2.22 kB
JavaScript
import { __awaiter, __generator } from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { FaceDetection } from '../classes/FaceDetection';
import { isTensor3D, isTensor4D } from '../utils';
/**
* Extracts the tensors of the image regions containing the detected faces.
* Useful if you want to compute the face descriptors for the face images.
* Using this method is faster then extracting a canvas for each face and
* converting them to tensors individually.
*
* @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.
* @returns Tensors of the corresponding image region for each detected face.
*/
export function extractFaceTensors(imageTensor, detections) {
return __awaiter(this, void 0, void 0, function () {
return __generator(this, function (_a) {
if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {
throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');
}
if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
throw new Error('extractFaceTensors - batchSize > 1 not supported');
}
return [2 /*return*/, tf.tidy(function () {
var _a = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];
var boxes = detections.map(function (det) { return det instanceof FaceDetection
? det.forSize(imgWidth, imgHeight).box
: det; })
.map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); });
var faceTensors = boxes.map(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
return tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]);
});
return faceTensors;
})];
});
});
}
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