awoo

Signed-off-by: Balazs Horvath <[email protected]>

scripts/check_img_resolutions.py

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+"""
+This script checks the resolution of all images in a specified directory and its subdirectories.
+If the resolution of an image exceeds a certain limit, the path of the image is written to an output file.
+The script uses multiprocessing to speed up the process.
+"""
+
+from pathlib import Path
+import multiprocessing
+import os
+from PIL import Image
+
+
+def check_image_resolution(filepath, output_file):
+    """
+    Checks the resolution of an image and writes the path of the image to a file if its resolution exceeds a certain limit.
+
+    Parameters:
+    filepath (Path): The path of the image file.
+    output_file (str): The path of the output file where the paths of oversized images will be written.
+
+    Returns:
+    None
+    """
+    if filepath.suffix in [".jpg", ".jpeg", ".png"]:
+        img = Image.open(filepath)
+        width, height = img.size
+        resolution = width * height
+        if resolution > 16777216:
+            normalized_path = os.path.normpath(str(filepath))
+            print(
+                f"The image {normalized_path} has a resolution of {resolution} pixels which is more than 16777216 pixels."
+            )
+            with open(output_file, "a", encoding="utf-8") as f:
+                f.write(f"{normalized_path}\n")
+
+
+def process_directory(directory, output_file):
+    """
+    Processes all files in a directory and its subdirectories.
+
+    Parameters:
+    directory (str): The path of the directory to be processed.
+    output_file (str): The path of the output file where the paths of oversized images will be written.
+
+    Returns:
+    None
+    """
+    for filepath in Path(directory).rglob("*"):
+        check_image_resolution(filepath, output_file)
+
+
+def main(output_file):
+    """
+    Main function that creates a pool of worker processes and applies the process_directory function asynchronously.
+
+    Parameters:
+    output_file (str): The path of the output file where the paths of oversized images will be written.
+
+    Returns:
+    None
+    """
+    # Get the number of available CPU cores
+    num_cores = multiprocessing.cpu_count()
+
+    # Create a pool of worker processes
+    pool = multiprocessing.Pool(num_cores)
+
+    # Call the function with the path to your directory
+    pool.apply_async(process_directory, args=(r"E:\training_dir", output_file))
+
+    # Close the pool and wait for all tasks to complete
+    pool.close()
+    pool.join()
+
+
+if __name__ == "__main__":
+    OUTPUT_FILE = "oversized.txt"
+    main(OUTPUT_FILE)