46 lines
1.7 KiB
TypeScript
46 lines
1.7 KiB
TypeScript
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import * as tf from '@tensorflow/tfjs-core';
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import { Rect } from '../classes';
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import { FaceDetection } from '../classes/FaceDetection';
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import { isTensor3D, isTensor4D } from '../utils';
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/**
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* Extracts the tensors of the image regions containing the detected faces.
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* Useful if you want to compute the face descriptors for the face images.
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* Using this method is faster then extracting a canvas for each face and
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* converting them to tensors individually.
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*
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* @param imageTensor The image tensor that face detection has been performed on.
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* @param detections The face detection results or face bounding boxes for that image.
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* @returns Tensors of the corresponding image region for each detected face.
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*/
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export async function extractFaceTensors(
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imageTensor: tf.Tensor3D | tf.Tensor4D,
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detections: Array<FaceDetection | Rect>
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): Promise<tf.Tensor3D[]> {
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if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {
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throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')
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}
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if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
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throw new Error('extractFaceTensors - batchSize > 1 not supported')
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}
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return tf.tidy(() => {
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const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)
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const boxes = detections.map(
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det => det instanceof FaceDetection
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? det.forSize(imgWidth, imgHeight).box
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: det
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)
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.map(box => box.clipAtImageBorders(imgWidth, imgHeight))
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const faceTensors = boxes.map(({ x, y, width, height }) =>
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tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])
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)
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return faceTensors
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})
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}
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