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import os
from argparse import ArgumentParser
import torch
import torchvision.io as io
from PIL import Image
from tqdm import tqdm
def parse_args():
parser = ArgumentParser()
parser.add_argument("--base_path", type=str)
parser.add_argument("--video_process", action="store_true")
return parser.parse_args()
def main():
torch.manual_seed(42)
args = parse_args()
predictor = torch.hub.load(
"Stable-X/StableNormal",
"StableNormal",
trust_repo=True,
local_cache_dir="/home/lff/bigdata1/cjw/model_cache"
)
if not args.video_process:
base_path = args.base_path
img_names = os.listdir(os.path.join(base_path, "rgb"))
for img_name in img_names:
img = Image.open(os.path.join(base_path, "rgb", img_name))
normal_img = predictor(img)
normal_path = os.path.join(base_path, "normal")
os.makedirs(normal_path, exist_ok=True)
normal_img.save(os.path.join(normal_path, img_name))
else:
video_tensor, _, _ = io.read_video(args.base_path, pts_unit="sec")
for frame_ind, frame in enumerate(tqdm(video_tensor)):
normal_frame = predictor(Image.fromarray(frame.numpy()))
normal_frame.save(os.path.join(args.normal_save_path, f"{frame_ind:04d}.png"))
if __name__ == "__main__":
main()
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