"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.extractFaceTensors = void 0;
const tf = require("@tensorflow/tfjs-core");
const FaceDetection_1 = require("../classes/FaceDetection");
const utils_1 = require("../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.
 */
async function extractFaceTensors(imageTensor, detections) {
    if (!utils_1.isTensor3D(imageTensor) && !utils_1.isTensor4D(imageTensor)) {
        throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');
    }
    if (utils_1.isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
        throw new Error('extractFaceTensors - batchSize > 1 not supported');
    }
    return tf.tidy(() => {
        const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(utils_1.isTensor4D(imageTensor) ? 1 : 0);
        const boxes = detections.map(det => det instanceof FaceDetection_1.FaceDetection
            ? det.forSize(imgWidth, imgHeight).box
            : det)
            .map(box => box.clipAtImageBorders(imgWidth, imgHeight));
        const faceTensors = boxes.map(({ x, y, width, height }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));
        return faceTensors;
    });
}
exports.extractFaceTensors = extractFaceTensors;
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