args = parser.parse_args()
cache_dir = Path(args.cache_dir)
cache_dir.mkdir(parents=True, exist_ok=True)
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
print("Downloading Shape Predictor")
f = open_url(
"https://drive.google.com/uc?id=1xtxqSWYHADTEO9ptPG5174DbV3FYJPev",
cache_dir=cache_dir,
return_path=True)
predictor = dlib.shape_predictor(f)
for im in Path(args.input_dir).glob("*.*"):
faces = align_face(str(im), predictor)
for i, face in enumerate(faces):
if (args.output_size):
factor = 1024 // args.output_size
assert args.output_size * factor == 1024
D = BicubicDownSample(factor=factor)
face_tensor = torchvision.transforms.ToTensor()(face).unsqueeze(
0).cuda()
face_tensor_lr = D(face_tensor)[0].cpu().detach().clamp(0, 1)
face = torchvision.transforms.ToPILImage()(face_tensor_lr)
face.save(Path(args.output_dir) / (im.stem + f"_{i}.png"))
cache_dir.mkdir(parents=True, exist_ok=True)
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
print("Downloading Shape Predictor")
f = open_url(
"https://drive.google.com/uc?id=1huhv8PYpNNKbGCLOaYUjOgR1pY5pmbJx",
cache_dir=cache_dir,
return_path=True)
predictor = dlib.shape_predictor(f)
for im in os.listdir(args.input_dir):
if im.endswith('.png'):
original_im = im
im = os.path.join(args.input_dir, im)
faces = align_face(im, predictor)
for i, face in enumerate(faces):
if (args.output_size):
factor = 1024 // args.output_size
assert args.output_size * factor == 1024
D = BicubicDownSample(factor=factor)
face_tensor = torchvision.transforms.ToTensor()(
face).unsqueeze(0).cuda()
face_tensor_lr = D(face_tensor)[0].cpu().detach().clamp(0, 1)
face = torchvision.transforms.ToPILImage()(face_tensor_lr)
face.save(Path(args.output_dir) / original_im)
