| import os | |
| import argparse | |
| import cv2 | |
| import numpy as np | |
| import json | |
| from datetime import datetime | |
| from skimage.metrics import peak_signal_noise_ratio as psnr | |
| from skimage.metrics import structural_similarity as ssim | |
| import glob | |
| def find_single_image(directory, pattern): | |
| """Find single image file in specified directory using glob pattern.""" | |
| files = glob.glob(os.path.join(directory, pattern)) | |
| if len(files) == 1: | |
| return files[0] | |
| elif len(files) == 0: | |
| print(f"⚠️ No matching {pattern} image found in {directory}") | |
| else: | |
| print(f"⚠️ Multiple matching {pattern} images found in {directory}") | |
| return None | |
| def evaluate_quality(pred_dir, gt_dir, threshold_ssim=0.65, threshold_psnr=15, result_file=None): | |
| result = { | |
| "Process": True, | |
| "Result": False, | |
| "TimePoint": datetime.now().strftime("%Y-%m-%dT%H:%M:%S"), | |
| "comments": "" | |
| } | |
| print(f"\nStarting evaluation task:") | |
| print(f"Predicted images path: {pred_dir}") | |
| print(f"Ground truth images path: {gt_dir}\n") | |
| if not os.path.exists(pred_dir) or not os.path.exists(gt_dir): | |
| result["Process"] = False | |
| result["comments"] = "Path does not exist" | |
| print("❌ Path does not exist") | |
| save_result(result_file, result) | |
| return | |
| pred_path = find_single_image(pred_dir, "output.*") | |
| gt_path = find_single_image(gt_dir, "gt.*") | |
| if not pred_path or not gt_path: | |
| result["Process"] = False | |
| result["comments"] = "Predicted or GT image missing or multiple matches" | |
| save_result(result_file, result) | |
| return | |
| pred_img = cv2.imread(pred_path) | |
| gt_img = cv2.imread(gt_path) | |
| if pred_img is None or gt_img is None: | |
| result["Process"] = False | |
| result["comments"] = "Failed to read images" | |
| print("⚠️ Failed to read images") | |
| save_result(result_file, result) | |
| return | |
| pred_img = cv2.resize(pred_img, (gt_img.shape[1], gt_img.shape[0])) | |
| pred_gray = cv2.cvtColor(pred_img, cv2.COLOR_BGR2GRAY) | |
| gt_gray = cv2.cvtColor(gt_img, cv2.COLOR_BGR2GRAY) | |
| ssim_val = ssim(gt_gray, pred_gray) | |
| psnr_val = psnr(gt_gray, pred_gray) | |
| print(f"Structural Similarity (SSIM): {ssim_val:.4f}") | |
| print(f"Peak Signal-to-Noise Ratio (PSNR): {psnr_val:.2f}") | |
| if ssim_val >= threshold_ssim and psnr_val >= threshold_psnr: | |
| result["Result"] = True | |
| result["comments"] = f"Test passed, SSIM={ssim_val:.4f}, PSNR={psnr_val:.2f}" | |
| print("✅ Restoration quality meets requirements") | |
| else: | |
| result["Result"] = False | |
| result["comments"] = f"Test failed, SSIM={ssim_val:.4f}, PSNR={psnr_val:.2f}" | |
| print("❌ Restoration quality does not meet requirements") | |
| save_result(result_file, result) | |
| def save_result(result_file, result): | |
| if result_file: | |
| try: | |
| with open(result_file, "a", encoding="utf-8") as f: | |
| f.write(json.dumps(result, ensure_ascii=False) + "\n") | |
| print(f"[Success] Output file: {result_file}") | |
| except Exception as e: | |
| print(f"⚠️ Failed to write result file: {e}") | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--output', type=str, required=True, help='Predicted results folder') | |
| parser.add_argument('--groundtruth', type=str, required=True, help='Original GT folder') | |
| parser.add_argument('--result', type=str, required=True, help='Output JSONL file for results') | |
| args = parser.parse_args() | |
| evaluate_quality(args.output, args.groundtruth, result_file=args.result) |