| | import argparse
|
| | import cv2
|
| | import json
|
| | import os
|
| | from datetime import datetime
|
| | from imwatermark import WatermarkDecoder
|
| | from skimage.metrics import peak_signal_noise_ratio as compare_psnr
|
| |
|
| | def evaluate_watermark(original_path, watermark_text, watermarked_path):
|
| | process_status = True
|
| | final_result_status = False
|
| | comments = []
|
| |
|
| |
|
| | time_point = datetime.now().isoformat()
|
| |
|
| |
|
| | if not os.path.exists(original_path) or os.path.getsize(original_path) == 0:
|
| | comments.append(f"Error: Original image file '{original_path}' does not exist or is empty.")
|
| | process_status = False
|
| | if not os.path.exists(watermarked_path) or os.path.getsize(watermarked_path) == 0:
|
| | comments.append(f"Error: Watermarked image file '{watermarked_path}' does not exist or is empty.")
|
| | process_status = False
|
| |
|
| | if process_status:
|
| | bgr_original = cv2.imread(original_path)
|
| | bgr_encoded = cv2.imread(watermarked_path)
|
| | if bgr_original is None or bgr_encoded is None:
|
| | comments.append("Error: Failed to read images, please check if files are corrupted or in correct format.")
|
| | process_status = False
|
| |
|
| | if process_status:
|
| | try:
|
| |
|
| |
|
| |
|
| |
|
| |
|
| | max_bits = 256
|
| | decoder = WatermarkDecoder('bytes', max_bits)
|
| | decoded_bytes = decoder.decode(bgr_encoded, 'dwtDct')
|
| | extracted_text = decoded_bytes.decode('utf-8', errors='ignore')
|
| |
|
| | is_match = (watermark_text in extracted_text)
|
| |
|
| | comments.append(f"{'✅' if is_match else '❌'} Extraction result: '{extracted_text}' | GT: '{watermark_text}'")
|
| | psnr_value = compare_psnr(bgr_original, bgr_encoded)
|
| | comments.append(f"📐 PSNR: {psnr_value:.2f} dB")
|
| |
|
| |
|
| | match_rate = '100%' if is_match else '0%'
|
| | psnr_satisfied = psnr_value >= 30.0
|
| | comments.append(f"🎯 Watermark detection_match: {match_rate}")
|
| | comments.append(f"🎯 PSNR ≥ 30.0: {'✅ Satisfied' if psnr_satisfied else '❌ Not satisfied'}")
|
| |
|
| | final_result_status = is_match and psnr_satisfied
|
| | comments.append(f"Final evaluation result: Watermark match={is_match}, PSNR satisfied={psnr_satisfied}")
|
| |
|
| | except Exception as e:
|
| | comments.append(f"Exception occurred during watermark processing or evaluation: {e}")
|
| | final_result_status = False
|
| |
|
| | output_data = {
|
| | "Process": process_status,
|
| | "Result": final_result_status,
|
| | "TimePoint": time_point,
|
| | "Comments": "\n".join(comments)
|
| | }
|
| | print(output_data["Comments"])
|
| | return output_data
|
| |
|
| | def write_to_jsonl(file_path, data):
|
| | """
|
| | Append single result to JSONL file:
|
| | Each run appends one JSON line.
|
| | """
|
| | try:
|
| | os.makedirs(os.path.dirname(file_path), exist_ok=True)
|
| | with open(file_path, 'a', encoding='utf-8') as f:
|
| |
|
| | f.write(json.dumps(data, ensure_ascii=False, default=str) + '\n')
|
| | print(f"✅ Result appended to JSONL file: {file_path}")
|
| | except Exception as e:
|
| | print(f"❌ Error occurred while writing to JSONL file: {e}")
|
| |
|
| | if __name__ == "__main__":
|
| | parser = argparse.ArgumentParser(
|
| | description="Extract and verify blind watermark, calculate image quality, and store results as JSONL")
|
| | parser.add_argument("--groundtruth", required=True, help="Path to original image")
|
| | parser.add_argument("--output", required=True, help="Path to watermarked image")
|
| | parser.add_argument("--watermark", required=True, help="Expected watermark content to extract")
|
| | parser.add_argument("--result", help="File path to store JSONL results")
|
| |
|
| | args = parser.parse_args()
|
| |
|
| | evaluation_result = evaluate_watermark(
|
| | args.groundtruth, args.watermark, args.output)
|
| |
|
| | if args.result:
|
| | write_to_jsonl(args.result, evaluation_result) |
