test.py

# # import os
# # import glob
# # import numpy as np
# # from PIL import Image

# # # VOC Colormap
# # VOC_COLORMAP = [
# #     (0, 0, 0), (128, 0, 0), (0, 128, 0), (128, 128, 0),
# #     (0, 0, 128), (128, 0, 128), (0, 128, 128), (128, 128, 128),
# #     (64, 0, 0), (192, 0, 0), (64, 128, 0), (192, 128, 0),
# #     (64, 0, 128), (192, 0, 128), (64, 128, 128), (192, 128, 128),
# #     (0, 64, 0), (128, 64, 0), (0, 192, 0), (128, 192, 0),
# #     (0, 64, 128)
# # ]

# # def mask_to_class(mask):
# #     mask = np.array(mask)
# #     label = np.zeros(mask.shape[:2], dtype=np.int64)
# #     for idx, color in enumerate(VOC_COLORMAP):
# #         matches = np.all(mask == color, axis=-1)
# #         label[matches] = idx
# #     return label

# # # Path to extracted dataset
# # dataset_path = "./data/pascal-voc-2012-dataset/VOC2012_train_val/VOC2012_train_val"
# # mask_dir = os.path.join(dataset_path, "SegmentationClass")

# # mask_paths = glob.glob(os.path.join(mask_dir, "*.png"))
# # print(f"Found {len(mask_paths)} mask files.")

# # # Check only first 50 masks to avoid freezing
# # check_count = min(50, len(mask_paths))
# # invalid_masks = []
# # max_label = -1

# # for i, p in enumerate(mask_paths[:check_count]):
# #     mask_class = mask_to_class(Image.open(p).convert("RGB"))
# #     max_val = mask_class.max()
# #     if max_val >= len(VOC_COLORMAP):
# #         invalid_masks.append(p)
# #     if max_val > max_label:
# #         max_label = max_val
# #     if (i + 1) % 10 == 0:
# #         print(f"Checked {i + 1}/{check_count} masks...")

# # print("Max class label in first", check_count, "masks:", max_label)
# # if invalid_masks:
# #     print("Masks with invalid labels:", invalid_masks)
# # else:
# #     print("All checked masks are valid.")



# from pathlib import Path
# from PIL import Image
# import math

# # -------------------------------------------------
# # Config
# # -------------------------------------------------
# RESULT_DIR = Path(__file__).resolve().parent / "test/test_results"
# OUTPUT_PATH = RESULT_DIR / "all_results.png"
# IMAGE_EXTS = (".png", ".jpg", ".jpeg")

# # Grid layout (auto if None)
# N_COLS = None   # e.g. 3, 4, 5 or None for auto
# RESIZE_TO = None  # (width, height) or None to use first image size

# # -------------------------------------------------
# # Load images
# # -------------------------------------------------
# image_paths = sorted(
#     [p for p in RESULT_DIR.iterdir() if p.suffix.lower() in IMAGE_EXTS]
# )

# if not image_paths:
#     raise RuntimeError(f"No images found in {RESULT_DIR}")

# images = [Image.open(p).convert("RGB") for p in image_paths]

# # -------------------------------------------------
# # Resize images (optional)
# # -------------------------------------------------
# if RESIZE_TO is None:
#     w, h = images[0].size
# else:
#     w, h = RESIZE_TO
#     images = [img.resize((w, h), Image.BILINEAR) for img in images]

# # -------------------------------------------------
# # Grid size
# # -------------------------------------------------
# n_images = len(images)

# if N_COLS is None:
#     N_COLS = int(math.sqrt(n_images))

# N_ROWS = math.ceil(n_images / N_COLS)

# # -------------------------------------------------
# # Create canvas
# # -------------------------------------------------
# collage = Image.new(
#     "RGB",
#     (N_COLS * w, N_ROWS * h),
#     color=(0, 0, 0)
# )

# # -------------------------------------------------
# # Paste images
# # -------------------------------------------------
# for idx, img in enumerate(images):
#     row = idx // N_COLS
#     col = idx % N_COLS
#     collage.paste(img, (col * w, row * h))

# # -------------------------------------------------
# # Save
# # -------------------------------------------------
# collage.save(OUTPUT_PATH)
# print(f"Saved collage to: {OUTPUT_PATH}")