Create give_colors_to_mask.py

give_colors_to_mask.py

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+import os
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+from concurrent.futures import ThreadPoolExecutor
+
+# Define the function to retrieve the color palette for a given dataset
+def get_palette(dataset_name: str):
+    if dataset_name in ["cloudsen12_high_l1c", "cloudsen12_high_l2a"]:
+        return [79, 253, 199, 77, 2, 115, 251, 255, 41, 221, 53, 223]
+    if dataset_name == "l8_biome":
+        return [79, 253, 199, 221, 53, 223, 251, 255, 41, 77, 2, 115]
+    if dataset_name in ["gf12ms_whu_gf1", "gf12ms_whu_gf2", "hrc_whu"]:
+        return [79, 253, 199, 77, 2, 115]
+    raise Exception("dataset_name not supported")
+
+# Function to apply the color palette to a mask
+def give_colors_to_mask(mask: np.ndarray, colors=None) -> np.ndarray:
+    """Convert a mask to a colorized version using the specified palette."""
+    im = Image.fromarray(mask.astype(np.uint8)).convert("P")
+    im.putpalette(colors)
+    return im
+
+# Function to process a single file
+def process_file(file_path, palette):
+    try:
+        # Load the mask
+        mask = np.array(Image.open(file_path))
+
+        # Apply the color palette
+        colored_mask = give_colors_to_mask(mask, palette)
+
+        # Save the colored mask, overwriting the original file
+        colored_mask.save(file_path)
+        return True
+    except Exception as e:
+        print(f"Error processing {file_path}: {e}")
+        return False
+
+# Main processing function for a dataset
+def process_dataset(dataset_name, base_root, progress_bar):
+    ann_dir = os.path.join(base_root, dataset_name, "ann_dir")
+    if not os.path.exists(ann_dir):
+        print(f"Annotation directory does not exist for {dataset_name}: {ann_dir}")
+        return
+
+    # Get the color palette for this dataset
+    palette = get_palette(dataset_name)
+
+    # Gather all files to process
+    files_to_process = []
+    for split in ["train", "val", "test"]:
+        split_dir = os.path.join(ann_dir, split)
+        if not os.path.exists(split_dir):
+            print(f"Split directory does not exist for {dataset_name}: {split_dir}")
+            continue
+
+        # Add all png files in the directory to the list
+        for file_name in os.listdir(split_dir):
+            if file_name.endswith(".png"):
+                files_to_process.append(os.path.join(split_dir, file_name))
+
+    # Multi-threaded processing
+    with ThreadPoolExecutor() as executor:
+        results = list(tqdm(
+            executor.map(lambda f: process_file(f, palette), files_to_process),
+            total=len(files_to_process),
+            desc=f"Processing {dataset_name}",
+            leave=False
+        ))
+
+    # Update the progress bar
+    progress_bar.update(len(files_to_process))
+
+    print(f"{dataset_name}: Processed {sum(results)} files out of {len(files_to_process)}.")
+
+# Define the root directory and datasets
+base_root = "data"  # Replace with your datasets' root directory
+dataset_names = [
+    "cloudsen12_high_l1c",
+    "cloudsen12_high_l2a",
+    "gf12ms_whu_gf1",
+    "gf12ms_whu_gf2",
+    "hrc_whu",
+    "l8_biome"
+]
+
+# Main script
+if __name__ == "__main__":
+    # Calculate total number of files for all datasets
+    total_files = 0
+    for dataset_name in dataset_names:
+        ann_dir = os.path.join(base_root, dataset_name, "ann_dir")
+        for split in ["train", "val", "test"]:
+            split_dir = os.path.join(ann_dir, split)
+            if os.path.exists(split_dir):
+                total_files += len([f for f in os.listdir(split_dir) if f.endswith(".png")])
+
+    # Create a progress bar
+    with tqdm(total=total_files, desc="Overall Progress") as progress_bar:
+        for dataset_name in dataset_names:
+            process_dataset(dataset_name, base_root, progress_bar)