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