| import argparse |
| import math |
| import threading |
| from pathlib import Path |
|
|
| import numpy as np |
| import torch |
| import torchvision.transforms.functional as TF |
| from PIL import Image |
| from tqdm import tqdm |
|
|
|
|
| def rotate_normal_map(normal_map, angle_deg): |
| angle_rad = angle_deg * (torch.pi / 180.0) |
|
|
| normal_map = normal_map * 2.0 - 1.0 |
| normal_map = normal_map.unsqueeze(0) |
|
|
| |
| rotation_matrix = torch.tensor([[math.cos(angle_rad), -math.sin(angle_rad), 0], |
| [math.sin(angle_rad), math.cos(angle_rad), 0], |
| [0, 0, 1]], device=normal_map.device) |
|
|
| |
| reshaped_normal_map = normal_map.view(1, 3, -1) |
| rotation_matrix = rotation_matrix.view(1, 3, 3) |
|
|
| |
| rotated_vectors = torch.bmm(rotation_matrix, reshaped_normal_map) |
| rotated_vectors = rotated_vectors.view(1, 3, normal_map.size(2), normal_map.size(3)) |
|
|
| rotated_vectors = rotated_vectors / 2.0 + 0.5 |
|
|
| return rotated_vectors[0] |
|
|
| def process_map(map, mat_dest): |
| map_name = map.stem |
| img = Image.open(map) |
| img = TF.to_tensor(img).cuda() |
| img = TF.resize(img, (4096, 4096), antialias=True) |
|
|
| img = img.repeat(1, 3, 3) |
| img = TF.center_crop(img, (5793, 5793)) |
|
|
| for rot_angle in range(0, 360, 45): |
| crop_i = 0 |
|
|
| if "normal" in map_name: |
| rot_img = rotate_normal_map(img, axis='z', angle_deg=rot_angle) |
| rot_img = TF.rotate(rot_img, rot_angle) |
| else: |
| rot_img = TF.rotate(img, rot_angle) |
|
|
| rot_img = TF.center_crop(rot_img, (4096, 4096)) |
| |
| for crop_res in [4096, 2048, 1024]: |
| |
| crops = rot_img.unfold(1, crop_res, crop_res).unfold(2, crop_res, crop_res) |
| crops = crops.permute(1, 2, 0, 3, 4) |
| crops = crops.reshape(-1, crops.size(2), crop_res, crop_res) |
|
|
| for crop in crops: |
| crop_dir = mat_dest / f"rot_{rot_angle:03d}_crop_{crop_i:03d}" |
| crop_dir.mkdir(parents=True, exist_ok=True) |
|
|
| crop = TF.resize(crop, (1024, 1024), antialias=True) |
| |
| if map_name in ["height", "displacement"]: |
| crop = crop.permute(1, 2, 0).cpu().numpy() |
| crop = crop.astype(np.uint16) |
| crop = Image.fromarray(crop[..., 0]) |
| crop.save(crop_dir / f"{map_name}.png") |
| else: |
| TF.to_pil_image(crop).save(crop_dir / f"{map_name}.png") |
|
|
| crop_i += 1 |
|
|
| if __name__ == "__main__": |
| |
| parser = argparse.ArgumentParser(description="Make dataset crops.") |
| parser.add_argument("--source_dir", required=True, help="Directory where the original 4K maps are stored.") |
| parser.add_argument("--dest_dir", required=True , help="Destination directory to store the 1K crops.") |
| args = parser.parse_args() |
|
|
| source_dir = Path(args.source_dir) |
| dest_dir = Path(args.dest_dir) |
|
|
| |
| for file in tqdm([x for x in source_dir.glob("**/basecolor.png")]): |
| mat_dir = file.parent |
|
|
| name = mat_dir.stem |
| category = mat_dir.parent.stem |
| split = mat_dir.parent.parent.stem |
|
|
| mat_dest = dest_dir / split / category / name |
| mat_dest.mkdir(parents=True, exist_ok=True) |
|
|
| thread = [] |
| for map in mat_dir.glob("*.png"): |
| t = threading.Thread(target=process_map, args=(map, mat_dest)) |
| t.start() |
|
|
| thread.append(t) |
|
|
| for t in thread: |
| t.join() |
