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@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+img_file/abstract.jpg filter=lfs diff=lfs merge=lfs -text
+img_file/OneRestore_poster.png filter=lfs diff=lfs merge=lfs -text
+img_file/pipeline.jpg filter=lfs diff=lfs merge=lfs -text
+img_file/real.jpg filter=lfs diff=lfs merge=lfs -text

img_file/cal_psnr_ssim.py

@@ -0,0 +1,96 @@
+import os
+import cv2
+import numpy as np
+import random
+from skimage.metrics import peak_signal_noise_ratio as compare_psnr
+from skimage.metrics import mean_squared_error as compare_mse
+from skimage.metrics import structural_similarity as compare_ssim
+# Modified function to add progress display using tqdm for better progress tracking
+from tqdm import tqdm
+import pandas as pd
+# Updated function with progress display for PSNR and SSIM calculation
+def calculate_psnr_ssim_with_progress(clear_folder, methods, degradation_types, win_size=7):
+    # Get list of all clear images
+    img_list = [img for img in os.listdir(clear_folder) if img.endswith('.png')]
+    
+    # Initialize matrices to store mean PSNR and SSIM values
+    psnr_matrix = np.zeros((len(methods), len(degradation_types)))
+    ssim_matrix = np.zeros((len(methods), len(degradation_types)))
+
+    # Total number of tasks for progress tracking
+    total_tasks = len(methods) * len(degradation_types) * len(img_list)
+    print(len(methods), len(degradation_types), len(img_list))
+
+    # Create a progress bar
+    with tqdm(total=total_tasks, desc="Processing Images", unit="task") as pbar:
+        # Loop over methods
+        for k, method in enumerate(methods):
+            print(f"Processing method: {method}")
+            
+            # Loop over degradation types
+            for j, degradation_type in enumerate(degradation_types):
+                psnr_values = []
+                ssim_values = []
+                
+                # Loop over each image in the clear folder
+                for img_name in img_list:
+                    clear_img_path = os.path.join(clear_folder, img_name)
+                    degraded_img_path = f'./{method}/{degradation_type}/{img_name}'
+                    
+                    # Read the clear and degraded images
+                    clear_img = cv2.imread(clear_img_path) / 255.0
+                    degraded_img = cv2.imread(degraded_img_path) / 255.0
+                    
+                    # Ensure the images are read correctly
+                    if clear_img is not None and degraded_img is not None:
+                        # Compute PSNR and SSIM between clear and degraded image
+                        psnr_value = compare_psnr(clear_img, degraded_img, data_range=1.0)
+                        
+                        # Compute SSIM with specified window size and for multichannel images
+                        ssim_value = compare_ssim(clear_img, degraded_img, multichannel=True, 
+                                                  win_size=min(win_size, clear_img.shape[0], clear_img.shape[1]), 
+                                                  channel_axis=-1, data_range=1.0)
+                        
+                        # Store values
+                        psnr_values.append(psnr_value)
+                        ssim_values.append(ssim_value)
+
+                    # Update progress bar after processing each image
+                    pbar.update(1)
+                
+                # Calculate mean PSNR and SSIM for the current method and degradation type
+                if psnr_values:
+                    psnr_matrix[k, j] = np.mean(psnr_values)
+                if ssim_values:
+                    ssim_matrix[k, j] = np.mean(ssim_values)
+
+    return psnr_matrix, ssim_matrix
+
+def save_matrices_to_excel(psnr_matrix, ssim_matrix, methods, degradation_types, output_file='metrics.xlsx'):
+    # Create DataFrames for PSNR and SSIM matrices
+    psnr_df = pd.DataFrame(psnr_matrix, index=methods, columns=degradation_types)
+    ssim_df = pd.DataFrame(ssim_matrix, index=methods, columns=degradation_types)
+    
+    # Create a writer to write both DataFrames to the same Excel file
+    with pd.ExcelWriter(output_file) as writer:
+        psnr_df.to_excel(writer, sheet_name='PSNR')
+        ssim_df.to_excel(writer, sheet_name='SSIM')
+    
+    print(f'Matrices saved to {output_file}')
+
+# Define the parameters
+clear_folder = './00_gt'
+methods = ['01_input', '02_MIRNet', '03_MPRNet', '04_MIRNetv2', '05_Restormer', 
+ '06_DGUNet', '07_NAFNet', '08_SRUDC', '09_Fourmer', '10_OKNet', '11_AirNet', 
+ '12_TransWeather', '13_WeatherDiff', '14_PromptIR', '15_WGWSNet', '16_OneRestore_visual', '17_OneRestore']
+degradation_types = ['low', 'haze', 'rain', 'snow', 'low_haze', 'low_rain', 'low_snow', 'haze_rain', 'haze_snow', 'low_haze_rain', 'low_haze_snow']
+
+# This is the function that will be used to calculate the PSNR and SSIM values across methods and degradation types
+# To use the function, uncomment the line below and ensure the file paths are set correctly in your environment
+
+
+psnr_matrix, ssim_matrix = calculate_psnr_ssim_with_progress(clear_folder, methods, degradation_types)
+save_matrices_to_excel(psnr_matrix, ssim_matrix, methods, degradation_types)
+
+
+