def main():
args = parse_args()
ids = get_original_video_paths(args.root_dir, basename=True)
os.makedirs(os.path.join(args.root_dir, "landmarks"), exist_ok=True)
with Pool(processes=os.cpu_count()) as p:
with tqdm(total=len(ids)) as pbar:
func = partial(save_landmarks, root_dir=args.root_dir)
for v in p.imap_unordered(func, ids):
pbar.update()
def main():
args = parse_args()
originals = get_original_video_paths(args.root_dir, basename=True)
with Pool(processes=os.cpu_count() - 4) as p:
with tqdm(total=len(originals)) as pbar:
for v in p.imap_unordered(
partial(write_face_encodings, root_dir=args.root_dir),
originals):
pbar.update()
def main():
"""
This script creates for a given video, a random sample of cropped images.
It then uses the python library face_recognition to extract face encodings from each clip and saves these encodings in a file.
"""
args = parse_args()
originals = get_original_video_paths(args.root_dir, basename=True)
with Pool(processes=os.cpu_count() - 4) as p:
with tqdm(total=len(originals)) as pbar:
# imap_unordered: this method chops the iterable into a number of chunks which it submits to the process pool as separate tasks.
# the ordering of the results from the returned iterator are considered arbitrary
for v in p.imap_unordered(partial(write_face_encodings, root_dir=args.root_dir), originals):
pbar.update()
def main():
args = parse_args()
originals = get_original_video_paths(args.root_dir)
process_videos(originals, args.root_dir, args.detector_type)