import os import cv2 import time import glob import argparse import face_alignment import numpy as np from PIL import Image from tqdm import tqdm from itertools import cycle from torch.multiprocessing import Pool, Process, set_start_method class KeypointExtractor(): def __init__(self): self.detector = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D) def extract_keypoint(self, images, name=None, info=True): if isinstance(images, list): keypoints = [] if info: i_range = tqdm(images,desc='landmark Det:') else: i_range = images for image in i_range: current_kp = self.extract_keypoint(image) if np.mean(current_kp) == -1 and keypoints: keypoints.append(keypoints[-1]) else: keypoints.append(current_kp[None]) keypoints = np.concatenate(keypoints, 0) np.savetxt(os.path.splitext(name)[0]+'.txt', keypoints.reshape(-1)) return keypoints else: while True: try: keypoints = self.detector.get_landmarks_from_image(np.array(images))[0] break except RuntimeError as e: if str(e).startswith('CUDA'): print("Warning: out of memory, sleep for 1s") time.sleep(1) else: print(e) break except TypeError: print('No face detected in this image') shape = [68, 2] keypoints = -1. * np.ones(shape) break if name is not None: np.savetxt(os.path.splitext(name)[0]+'.txt', keypoints.reshape(-1)) return keypoints def read_video(filename): frames = [] cap = cv2.VideoCapture(filename) while cap.isOpened(): ret, frame = cap.read() if ret: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frame = Image.fromarray(frame) frames.append(frame) else: break cap.release() return frames def run(data): filename, opt, device = data os.environ['CUDA_VISIBLE_DEVICES'] = device kp_extractor = KeypointExtractor() images = read_video(filename) name = filename.split('/')[-2:] os.makedirs(os.path.join(opt.output_dir, name[-2]), exist_ok=True) kp_extractor.extract_keypoint( images, name=os.path.join(opt.output_dir, name[-2], name[-1]) ) if __name__ == '__main__': set_start_method('spawn') parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--input_dir', type=str, help='the folder of the input files') parser.add_argument('--output_dir', type=str, help='the folder of the output files') parser.add_argument('--device_ids', type=str, default='0,1') parser.add_argument('--workers', type=int, default=4) opt = parser.parse_args() filenames = list() VIDEO_EXTENSIONS_LOWERCASE = {'mp4'} VIDEO_EXTENSIONS = VIDEO_EXTENSIONS_LOWERCASE.union({f.upper() for f in VIDEO_EXTENSIONS_LOWERCASE}) extensions = VIDEO_EXTENSIONS for ext in extensions: os.listdir(f'{opt.input_dir}') print(f'{opt.input_dir}/*.{ext}') filenames = sorted(glob.glob(f'{opt.input_dir}/*.{ext}')) print('Total number of videos:', len(filenames)) pool = Pool(opt.workers) args_list = cycle([opt]) device_ids = opt.device_ids.split(",") device_ids = cycle(device_ids) for data in tqdm(pool.imap_unordered(run, zip(filenames, args_list, device_ids))): None