import os import json import shutil from pathlib import Path import re def extract_info_from_filename(filename, attack_type): """Extract relevant information from filename.""" # Extract label information (if exists) label = 'True' if 'labelTrue' in filename else 'False' if 'labelFalse' in filename else None # Extract prediction if it exists pred_match = re.search(r'pred(\d+)', filename) prediction = int(pred_match.group(1)) if pred_match else float('nan') # Extract base number using different patterns base_num_match = re.search(r'[_](\d+)(?:_|\.)', filename) base_num = base_num_match.group(1) if base_num_match else None # Extract image type based on prefix and attack type if filename.startswith('adv_'): img_type = 'adversarial' elif filename.startswith('orig_'): img_type = 'original' elif filename.startswith(('perturbation_', 'transformation_')): img_type = 'perturbation' else: img_type = None return label, prediction, img_type, base_num def create_new_filename(filename, attack_name, base_num): """Create new filename with attack name and PCam-style numbering.""" # Split filename into parts name_parts = filename.rsplit('.', 1) extension = name_parts[1] if len(name_parts) > 1 else 'png' if filename.startswith(('perturbation_', 'transformation_')): prefix = 'perturbation_' if filename.startswith('perturbation_') else 'transformation_' return f"{prefix}{base_num}_{attack_name}.{extension}" elif filename.startswith('adv_'): return f"adv_{base_num}_{attack_name}.{extension}" elif filename.startswith('orig_'): return f"orig_{base_num}_{attack_name}.{extension}" return filename def determine_attack_category(path): """Determine if the attack is black box or non-black box based on path.""" path_str = str(path).lower() if "black_box_attacks" in path_str: return "black_box_attacks" elif "non_black_box_attacks" in path_str: return "non_black_box_attacks" return None def organize_dataset(base_path, cleanup_original=False): """ Organize dataset into PCam-style structure with only train split. """ base_path = Path(base_path) # Create output directories in PCam style output_base = base_path / "organized_dataset" labels = ['0', '1'] # PCam uses 0/1 instead of False/True # Create directory structure for label in labels: (output_base / 'train' / label).mkdir(parents=True, exist_ok=True) # Create perturbations and originals directories (output_base / 'perturbations').mkdir(parents=True, exist_ok=True) (output_base / 'originals').mkdir(parents=True, exist_ok=True) # Dictionary to store dataset information dataset_entries = [] file_groups = {} # Walk through the directory for root, _, files in os.walk(base_path): for file in files: if file.endswith(('.png', '.jpg', '.jpeg')) and file != '.DS_Store': full_path = Path(root) / file # Determine attack category attack_category = determine_attack_category(full_path) if not attack_category: continue # Extract attack type from path attack_type = full_path.parent.name if attack_type in ['black_box_attacks', 'non_black_box_attacks', '.DS_Store']: continue # Extract file information label, prediction, img_type, base_num = extract_info_from_filename(file, attack_type) if base_num: key = (base_num, attack_type, attack_category) if key not in file_groups: file_groups[key] = [] file_groups[key].append((full_path, label, prediction, img_type)) # Process each group of files for key, files in file_groups.items(): base_num, attack_type, attack_category = key entry = { "attack": attack_type, "type": attack_category, "perturbation": None, "adversarial": None, "original": [], "label": None, "prediction": None } # First pass to find label from adversarial examples for file_path, label, prediction, img_type in files: if img_type == 'adversarial' and label: entry["label"] = 1 if label == "True" else 0 entry["prediction"] = prediction break if entry["label"] is None: continue # Second pass to organize files label_str = str(entry["label"]) dest_folder = output_base / 'train' / label_str for file_path, _, _, img_type in files: old_filename = file_path.name new_filename = create_new_filename(old_filename, attack_type, base_num) # Determine destination folder and path based on image type if img_type == 'perturbation': dest = output_base / 'perturbations' rel_path = f"perturbations/{new_filename}" elif img_type == 'original': dest = output_base / 'originals' rel_path = f"originals/{new_filename}" else: # adversarial images go to train folders dest = dest_folder rel_path = f"train/{label_str}/{new_filename}" # Copy file to the appropriate folder shutil.copy2(file_path, dest / new_filename) if img_type == 'perturbation': entry["perturbation"] = rel_path elif img_type == 'adversarial': entry["adversarial"] = rel_path elif img_type == 'original': entry["original"].append(rel_path) # Only add entries that have at least one image path if entry["perturbation"] or entry["adversarial"] or entry["original"]: dataset_entries.append(entry) # Create Hugging Face compatible dataset.json hf_dataset = { "train": { "features": { "image_path": {"dtype": "string", "_type": "Value"}, "label": {"dtype": "int64", "_type": "Value"}, "prediction": {"dtype": "int64", "_type": "Value"}, "attack": {"dtype": "string", "_type": "Value"}, "attack_type": {"dtype": "string", "_type": "Value"}, "perturbation_path": {"dtype": "string", "_type": "Value"}, "original_path": {"dtype": "string", "_type": "Value"} }, "rows": [] } } # Convert entries to Hugging Face format for entry in dataset_entries: if entry["adversarial"]: # Only include entries that have adversarial images hf_entry = { "image_path": entry["adversarial"], "label": entry["label"], "prediction": entry["prediction"] if entry["prediction"] is not None else -1, "attack": entry["attack"], "attack_type": entry["type"], "perturbation_path": entry["perturbation"] if entry["perturbation"] else "", "original_path": entry["original"][0] if entry["original"] else "" } hf_dataset["train"]["rows"].append(hf_entry) # Save Hugging Face compatible dataset.json with open(output_base / "dataset.json", 'w') as f: json.dump(hf_dataset, f, indent=4) # If cleanup is requested and everything was successful if cleanup_original: print("Cleaning up original files...") for folder in ['black_box_attacks', 'non_black_box_attacks']: folder_path = base_path / folder if folder_path.exists(): shutil.rmtree(folder_path) print(f"Deleted {folder}") return output_base if __name__ == "__main__": # Ask user about cleanup cleanup = input("Do you want to delete original files after organization? (yes/no): ").lower() == 'yes' # Script will work relative to its location script_dir = Path(__file__).parent output_path = organize_dataset(script_dir, cleanup) print(f"Dataset organized and saved to: {output_path}")