@vladmandic/face-api/src/dom/extractFaces.ts

FaceAPI: AI-powered Face Detection & Rotation Tracking, Face Description & Recognition, Age & Gender & Emotion Prediction for Browser and NodeJS using TensorFlow/JS

import { FaceDetection } from '../classes/FaceDetection';
import { Rect } from '../classes/Rect';
import { env } from '../env/index';
import { createCanvas } from './createCanvas';
import { getContext2dOrThrow } from './getContext2dOrThrow';
import { imageTensorToCanvas } from './imageTensorToCanvas';
import { toNetInput } from './toNetInput';
import { TNetInput } from './types';

/**
 * Extracts the image regions containing the detected faces.
 *
 * @param input The image that face detection has been performed on.
 * @param detections The face detection results or face bounding boxes for that image.
 * @returns The Canvases of the corresponding image region for each detected face.
 */
export async function extractFaces(input: TNetInput, detections: Array<FaceDetection | Rect>): Promise<HTMLCanvasElement[]> {
  const { Canvas } = env.getEnv();
  let canvas = input as HTMLCanvasElement;
  if (!(input instanceof Canvas)) {
    const netInput = await toNetInput(input);
    if (netInput.batchSize > 1) throw new Error('extractFaces - batchSize > 1 not supported');
    const tensorOrCanvas = netInput.getInput(0);
    canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas);
  }
  const ctx = getContext2dOrThrow(canvas);
  const boxes = detections
    .map((det) => (det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det))
    .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));
  return boxes.map(({ x, y, width, height }) => {
    const faceImg = createCanvas({ width, height });
    if (width > 0 && height > 0) getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0);
    return faceImg;
  });
}