| import os |
| import glob |
| import hashlib |
| from PIL import Image |
|
|
| DATASET_ROOT = os.path.join(os.path.dirname(__file__), '..', 'dataset') |
| REAL_DIR = os.path.join(DATASET_ROOT, 'real') |
| SCREEN_DIR = os.path.join(DATASET_ROOT, 'screen') |
|
|
| def hash_file(filepath): |
| hasher = hashlib.sha256() |
| with open(filepath, 'rb') as f: |
| while chunk := f.read(8192): |
| hasher.update(chunk) |
| return hasher.hexdigest() |
|
|
| def validate_dataset(): |
| print("Validating dataset...") |
| |
| real_images = glob.glob(os.path.join(REAL_DIR, '*.*')) |
| screen_images = glob.glob(os.path.join(SCREEN_DIR, '*.*')) |
| |
| all_images = real_images + screen_images |
| |
| print(f"Real images found: {len(real_images)}") |
| print(f"Screen images found: {len(screen_images)}") |
| print(f"Total images: {len(all_images)}") |
| |
| if len(all_images) == 0: |
| print("Dataset is empty. Exiting.") |
| return |
|
|
| print("Class balance:") |
| print(f" Real: {len(real_images) / len(all_images) * 100:.2f}%") |
| print(f" Screen: {len(screen_images) / len(all_images) * 100:.2f}%") |
|
|
| extensions = {} |
| corrupted = 0 |
| hashes = set() |
| duplicates = 0 |
| widths = [] |
| heights = [] |
|
|
| for img_path in all_images: |
| ext = os.path.splitext(img_path)[1].lower() |
| extensions[ext] = extensions.get(ext, 0) + 1 |
| |
| try: |
| with Image.open(img_path) as img: |
| img.verify() |
| |
| with Image.open(img_path) as img: |
| w, h = img.size |
| widths.append(w) |
| heights.append(h) |
| |
| hsh = hash_file(img_path) |
| if hsh in hashes: |
| duplicates += 1 |
| hashes.add(hsh) |
| |
| except Exception: |
| corrupted += 1 |
|
|
| print("\nExtensions distribution:") |
| for ext, count in extensions.items(): |
| print(f" {ext}: {count}") |
| |
| print(f"\nCorrupted/Unreadable images: {corrupted}") |
| print(f"Duplicate hashes found (across entire dataset): {duplicates}") |
| |
| if len(widths) > 0: |
| print(f"\nResolution stats:") |
| print(f" Width : min={min(widths)}, max={max(widths)}, avg={sum(widths)/len(widths):.1f}") |
| print(f" Height: min={min(heights)}, max={max(heights)}, avg={sum(heights)/len(heights):.1f}") |
| |
| print("\nValidation complete.") |
|
|
| if __name__ == "__main__": |
| validate_dataset() |
|
|
