39 lines
1.7 KiB
JavaScript
39 lines
1.7 KiB
JavaScript
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import { FaceDetection } from '../classes/FaceDetection';
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import { env } from '../env';
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import { createCanvas } from './createCanvas';
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import { getContext2dOrThrow } from './getContext2dOrThrow';
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import { imageTensorToCanvas } from './imageTensorToCanvas';
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import { toNetInput } from './toNetInput';
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/**
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* Extracts the image regions containing the detected faces.
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*
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* @param input The image 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 The Canvases of the corresponding image region for each detected face.
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*/
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export async function extractFaces(input, detections) {
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const { Canvas } = env.getEnv();
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let canvas = input;
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if (!(input instanceof Canvas)) {
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const netInput = await toNetInput(input);
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if (netInput.batchSize > 1) {
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throw new Error('extractFaces - batchSize > 1 not supported');
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}
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const tensorOrCanvas = netInput.getInput(0);
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canvas = tensorOrCanvas instanceof Canvas
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? tensorOrCanvas
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: await imageTensorToCanvas(tensorOrCanvas);
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}
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const ctx = getContext2dOrThrow(canvas);
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const boxes = detections.map(det => det instanceof FaceDetection
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? det.forSize(canvas.width, canvas.height).box.floor()
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: det)
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.map(box => box.clipAtImageBorders(canvas.width, canvas.height));
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return boxes.map(({ x, y, width, height }) => {
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const faceImg = createCanvas({ width, height });
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getContext2dOrThrow(faceImg)
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.putImageData(ctx.getImageData(x, y, width, height), 0, 0);
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return faceImg;
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});
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}
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//# sourceMappingURL=extractFaces.js.map
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