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image cleaner
This commit is contained in:
Simon Martens
291
scripts/ex/config_tuner.py
Executable file
291
scripts/ex/config_tuner.py
Executable file
@@ -0,0 +1,291 @@
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#!/usr/bin/env python3
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"""
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Interactive Parameter Tuning Tool for Newspaper Image Cleaning
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This tool helps you find optimal parameters for your specific images
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by providing an interactive tuning interface.
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"""
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import cv2
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import json
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import numpy as np
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from pathlib import Path
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from image_cleaner import NewspaperImageCleaner
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class ParameterTuner:
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"""Interactive parameter tuning for image cleaning pipeline."""
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def __init__(self, sample_image_path):
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"""Initialize with a sample image for tuning."""
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self.original = cv2.imread(str(sample_image_path))
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if self.original is None:
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raise ValueError(f"Could not load image: {sample_image_path}")
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# Resize large images for faster processing during tuning
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height, width = self.original.shape[:2]
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if height > 1500 or width > 1500:
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scale = min(1500/height, 1500/width)
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new_width = int(width * scale)
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new_height = int(height * scale)
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self.original = cv2.resize(self.original, (new_width, new_height))
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print(f"Resized image to {new_width}x{new_height} for faster tuning")
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self.current_params = self._get_default_params()
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self.cleaner = NewspaperImageCleaner(self.current_params)
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def _get_default_params(self):
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"""Get default parameters as starting point."""
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return {
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'bilateral_d': 9,
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'bilateral_sigma_color': 75,
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'bilateral_sigma_space': 75,
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'clahe_clip_limit': 2.0,
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'clahe_grid_size': (8, 8),
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'gamma': 1.2,
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'denoise_h': 10,
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'morph_kernel_size': 2,
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'unsharp_amount': 1.5,
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'unsharp_radius': 1.0,
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'unsharp_threshold': 0,
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}
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def update_parameter(self, param_name, value):
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"""Update a single parameter and refresh the cleaner."""
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if param_name in self.current_params:
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# Handle special cases
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if param_name == 'clahe_grid_size':
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self.current_params[param_name] = (int(value), int(value))
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else:
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self.current_params[param_name] = value
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# Update cleaner with new parameters
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self.cleaner = NewspaperImageCleaner(self.current_params)
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print(f"Updated {param_name} = {value}")
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def process_with_current_params(self, steps=None):
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"""Process the sample image with current parameters."""
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if steps is None:
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steps = ['denoise', 'contrast', 'background', 'sharpen']
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image = self.original.copy()
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# Apply processing steps
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if 'denoise' in steps:
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image = self.cleaner.reduce_noise(image)
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if 'contrast' in steps:
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image = self.cleaner.enhance_contrast(image)
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if 'background' in steps:
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image = self.cleaner.clean_background(image)
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if 'sharpen' in steps:
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image = self.cleaner.sharpen_image(image)
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return image
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def create_comparison(self, steps=None):
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"""Create side-by-side comparison with current parameters."""
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processed = self.process_with_current_params(steps)
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# Create side-by-side comparison
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height = max(self.original.shape[0], processed.shape[0])
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comparison = np.hstack([
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cv2.resize(self.original, (self.original.shape[1], height)),
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cv2.resize(processed, (processed.shape[1], height))
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])
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return comparison
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def save_comparison(self, output_path, steps=None):
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"""Save comparison image to file."""
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comparison = self.create_comparison(steps)
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cv2.imwrite(str(output_path), comparison)
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print(f"Comparison saved to: {output_path}")
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def save_config(self, config_path):
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"""Save current parameters to JSON config file."""
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# Convert tuple to list for JSON serialization
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config_to_save = self.current_params.copy()
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if 'clahe_grid_size' in config_to_save:
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config_to_save['clahe_grid_size'] = list(config_to_save['clahe_grid_size'])
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with open(config_path, 'w') as f:
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json.dump(config_to_save, f, indent=4)
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print(f"Configuration saved to: {config_path}")
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def load_config(self, config_path):
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"""Load parameters from JSON config file."""
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with open(config_path, 'r') as f:
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loaded_params = json.load(f)
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# Convert list back to tuple if needed
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if 'clahe_grid_size' in loaded_params:
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loaded_params['clahe_grid_size'] = tuple(loaded_params['clahe_grid_size'])
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self.current_params.update(loaded_params)
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self.cleaner = NewspaperImageCleaner(self.current_params)
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print(f"Configuration loaded from: {config_path}")
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def interactive_tune(self):
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"""Start interactive tuning session."""
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print("\n" + "="*60)
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print("INTERACTIVE PARAMETER TUNING")
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print("="*60)
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print("Commands:")
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print(" set <param> <value> - Set parameter value")
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print(" show - Show current parameters")
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print(" test [steps] - Test current parameters")
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print(" save <file> - Save configuration to file")
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print(" load <file> - Load configuration from file")
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print(" compare [file] - Save comparison image")
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print(" presets - Show parameter presets")
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print(" help - Show this help")
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print(" quit - Exit tuning")
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print("\nParameters you can adjust:")
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for param in self.current_params:
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print(f" {param}")
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while True:
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try:
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command = input("\ntuner> ").strip().split()
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if not command:
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continue
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cmd = command[0].lower()
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if cmd == 'quit' or cmd == 'exit':
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break
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elif cmd == 'show':
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self._show_parameters()
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elif cmd == 'set' and len(command) >= 3:
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param = command[1]
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try:
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value = float(command[2]) if '.' in command[2] else int(command[2])
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except ValueError:
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value = command[2]
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self.update_parameter(param, value)
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elif cmd == 'test':
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steps = command[1:] if len(command) > 1 else None
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print("Processing with current parameters...")
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processed = self.process_with_current_params(steps)
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print(f"Processed image shape: {processed.shape}")
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elif cmd == 'save' and len(command) > 1:
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self.save_config(command[1])
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elif cmd == 'load' and len(command) > 1:
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self.load_config(command[1])
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elif cmd == 'compare':
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output = command[1] if len(command) > 1 else "tuning_comparison.jpg"
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self.save_comparison(output)
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elif cmd == 'presets':
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self._show_presets()
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elif cmd == 'help':
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self._show_help()
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else:
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print("Unknown command. Type 'help' for available commands.")
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except KeyboardInterrupt:
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print("\nExiting tuner...")
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break
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except Exception as e:
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print(f"Error: {str(e)}")
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def _show_parameters(self):
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"""Display current parameter values."""
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print("\nCurrent Parameters:")
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print("-" * 30)
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for param, value in self.current_params.items():
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print(f" {param:<20} = {value}")
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def _show_presets(self):
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"""Show preset configurations for different image types."""
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presets = {
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"light_cleaning": {
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"bilateral_d": 5,
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"denoise_h": 5,
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"clahe_clip_limit": 1.5,
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"gamma": 1.1,
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"unsharp_amount": 1.2
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},
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"heavy_cleaning": {
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"bilateral_d": 15,
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"denoise_h": 15,
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"clahe_clip_limit": 3.0,
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"gamma": 1.3,
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"unsharp_amount": 2.0
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},
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"high_contrast": {
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"clahe_clip_limit": 4.0,
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"gamma": 1.4,
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"unsharp_amount": 2.5
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}
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}
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print("\nAvailable Presets:")
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print("-" * 30)
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for name, params in presets.items():
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print(f"{name}:")
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for param, value in params.items():
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print(f" {param} = {value}")
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print()
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def _show_help(self):
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"""Show detailed help information."""
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help_text = """
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Parameter Descriptions:
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-----------------------
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bilateral_d : Neighborhood diameter for bilateral filtering (5-15)
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bilateral_sigma_color: Filter sigma in color space (50-150)
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bilateral_sigma_space: Filter sigma in coordinate space (50-150)
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clahe_clip_limit : Contrast limit for CLAHE (1.0-4.0)
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clahe_grid_size : CLAHE tile grid size (4-16)
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gamma : Gamma correction value (0.8-2.0)
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denoise_h : Denoising filter strength (5-20)
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morph_kernel_size : Morphological operation kernel size (1-5)
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unsharp_amount : Unsharp masking amount (0.5-3.0)
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unsharp_radius : Unsharp masking radius (0.5-2.0)
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unsharp_threshold : Unsharp masking threshold (0-10)
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Tips:
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- Start with small adjustments (±20% of current value)
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- Test frequently with 'compare' command
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- Save working configurations before major changes
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- Use 'test denoise' to test individual steps
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"""
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print(help_text)
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def main():
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"""Main function for command line usage."""
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import argparse
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parser = argparse.ArgumentParser(description="Interactive parameter tuning for newspaper image cleaning")
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parser.add_argument("image", help="Sample image path for tuning")
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parser.add_argument("-c", "--config", help="Load initial config from file")
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args = parser.parse_args()
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try:
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tuner = ParameterTuner(args.image)
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if args.config:
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tuner.load_config(args.config)
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tuner.interactive_tune()
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except Exception as e:
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print(f"Error: {str(e)}")
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if __name__ == "__main__":
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main()
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