Create quick_analysis.py

scripts/quick_analysis.py

@@ -0,0 +1,98 @@
+import os
+from PIL import Image
+from collections import Counter
+
+def analyze_images(directory):
+    analysis_results = {}
+    
+    for root, dirs, files in os.walk(directory):
+        if files:
+            model_folder_name = os.path.basename(root)
+            if model_folder_name not in analysis_results:
+                analysis_results[model_folder_name] = {
+                    'image_count': 0,
+                    'total_size': 0,
+                    'resolutions': Counter()
+                }
+            
+            for file in files:
+                file_path = os.path.join(root, file)
+                
+                # Count the image
+                analysis_results[model_folder_name]['image_count'] += 1
+                
+                # Calculate the size of the image
+                try:
+                    with Image.open(file_path) as img:
+                        # Get the size of the image in bytes
+                        file_size = os.path.getsize(file_path)
+                        analysis_results[model_folder_name]['total_size'] += file_size
+                        
+                        # Get image dimensions
+                        width, height = img.size
+                        analysis_results[model_folder_name]['resolutions'][(width, height)] += 1
+                except Exception as e:
+                    print(f"Error reading file {file_path}: {e}")
+    
+    return analysis_results
+
+def print_and_log_analysis_results(analysis_results, dataset_name, log_file):
+    # Determine the maximum length of model names
+    max_model_length = max(len(model) for model in analysis_results.keys())
+    model_column_width = max(max_model_length, 20)  # Ensure at least 20 characters
+    
+    # Define column widths
+    image_count_width = 12
+    total_size_width = 14
+    resolution_width = 25
+    
+    # Create header
+    header = f"{'Model':<{model_column_width}} | {'Image Count':>{image_count_width}} | {'Total Size (MB)':>{total_size_width}} | {'Most Common Resolution':<{resolution_width}}"
+    separator = "-" * (model_column_width + image_count_width + total_size_width + resolution_width + 7)  # 7 for separators
+    
+    result_lines = []
+    result_lines.append(f"Analysis for {dataset_name}:\n")
+    result_lines.append(header + "\n")
+    result_lines.append(separator + "\n")
+    
+    for model, data in analysis_results.items():
+        total_size_mb = data['total_size'] / (1024 * 1024)
+        most_common_resolution = data['resolutions'].most_common(1)
+        
+        if most_common_resolution:
+            common_res = f"{most_common_resolution[0][0][0]}x{most_common_resolution[0][0][1]} ({most_common_resolution[0][1]} images)"
+        else:
+            common_res = "None"
+        
+        result_lines.append(f"{model:<{model_column_width}} | {data['image_count']:>{image_count_width}} | {total_size_mb:>{total_size_width}.2f} | {common_res:<{resolution_width}}\n")
+    
+    result_lines.append("\n")
+    
+    # Print to console
+    for line in result_lines:
+        print(line, end='')
+    
+    # Write to log file
+    with open(log_file, 'a') as f:
+        f.writelines(result_lines)
+
+def main():
+    # Define directories
+    generated_dir = 'resampledEvalSet'
+    real_dir = 'real'
+    log_file = 'analysis_results.txt'
+    
+    # Clear the log file (optional, comment out if you want to append)
+    with open(log_file, 'w') as f:
+        pass
+    
+    # Analyze generated images
+    generated_analysis_results = analyze_images(generated_dir)
+    print_and_log_analysis_results(generated_analysis_results, "Generated Images", log_file)
+    
+    # Analyze real images
+    real_analysis_results = analyze_images(real_dir)
+    print_and_log_analysis_results(real_analysis_results, "Real Images", log_file)
+
+if __name__ == "__main__":
+    main()