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| 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, device): | |
| self.detector = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, | |
| device=device) | |
| 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 | |