| import argparse |
| from pathlib import Path |
|
|
| import torchvision.transforms.functional as TF |
| from PIL import Image |
| from tqdm import tqdm |
|
|
| if __name__ == "__main__": |
| |
| parser = argparse.ArgumentParser(description="Assemble renders.") |
| parser.add_argument("--source_dir", required=True, help="Directory where the dataset is stored.") |
| args = parser.parse_args() |
|
|
| source_dir = Path(args.source_dir) |
|
|
| |
| for render_dir in tqdm([x for x in source_dir.glob("**/renders/")]): |
| passes_dir = render_dir/"passes" |
| num_renders = len(list(passes_dir.glob("*diffuse.png"))) |
| |
| for i in range(num_renders): |
| diff_path = passes_dir/f"render_{i:02d}_diffuse.png" |
| glossy_path = passes_dir/f"render_{i:02d}_glossy.png" |
|
|
| full_path = render_dir/f"render_{i:02d}.png" |
|
|
| diffuse = TF.to_tensor(Image.open(diff_path)) |
| glossy = TF.to_tensor(Image.open(glossy_path)) |
|
|
| diffuse = TF.adjust_gamma(diffuse, 2.2) |
| glossy = TF.adjust_gamma(glossy, 2.2) |
|
|
| render = diffuse + glossy |
|
|
| render = TF.adjust_gamma(render, 1/2.2) |
| render = TF.to_pil_image(render) |
| render.save(full_path) |
