Eric Rollei
d6dd6470cf
Features: - 40+ image processing algorithms - Traditional denoising: Wavelet, NLM, BM3D (GPU-accelerated) - AI models: DnCNN, SwinIR, SCUNet, NAFNet, Noise-DA - DeepInv diffusion denoising integration - Advanced film grain detection and processing - Frequency domain processing - Richardson-Lucy deconvolution - Wiener filter - External DeepInv service with FastAPI - Comprehensive documentation and guides
654 lines
29 KiB
Python
654 lines
29 KiB
Python
"""
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Learning-Based CLAHE (LB-CLAHE) Processor
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Implementation of cutting-edge adaptive histogram equalization from 2024-2025 research:
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- Machine learning-guided parameter optimization
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- Random Forest and XGBoost automatic tuning
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- Multi-scale region analysis with perceptual weighting
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- Color space optimization (LAB, Oklab, Jzazbz)
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- Advanced clipping limit prediction
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FIXED: NumPy 2.0 / OpenCV 4.11 compatibility - ensures all arrays are contiguous
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"""
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import numpy as np
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import cv2
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from typing import Tuple, Dict, Any, Optional, List, Union
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from scipy import ndimage
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from skimage import exposure, color, measure, feature
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from skimage.util import img_as_float, img_as_ubyte
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try:
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.model_selection import cross_val_score
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SKLEARN_AVAILABLE = True
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except ImportError:
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SKLEARN_AVAILABLE = False
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try:
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import xgboost as xgb
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XGBOOST_AVAILABLE = True
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except ImportError:
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XGBOOST_AVAILABLE = False
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class LearningBasedCLAHEProcessor:
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"""Learning-based CLAHE processor with ML-guided parameter optimization"""
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def __init__(self):
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self.name = "Learning-Based CLAHE Processor"
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self.version = "1.0.1" # Updated for NumPy 2.0 compatibility
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self.trained_models = {}
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self.feature_cache = {}
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def learning_based_clahe(self, image: np.ndarray,
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color_space: str = 'lab',
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ml_method: str = 'random_forest',
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region_size: Tuple[int, int] = (8, 8),
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base_clip_limit: float = 2.0,
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adaptive_regions: bool = True,
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perceptual_weighting: bool = True) -> Tuple[np.ndarray, Dict[str, Any]]:
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"""
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Learning-based CLAHE with automatic parameter optimization
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Args:
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image: Input image (H, W) or (H, W, C) in range [0, 1]
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color_space: Color space ('lab', 'oklab', 'jzazbz', 'hsv', 'rgb')
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ml_method: ML method ('random_forest', 'xgboost', 'hybrid')
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region_size: Grid size for CLAHE regions
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base_clip_limit: Base clipping limit (1.0 to 5.0)
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adaptive_regions: Use adaptive region sizing
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perceptual_weighting: Apply perceptual importance weighting
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Returns:
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Tuple of (enhanced_image, processing_info)
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"""
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try:
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# Convert to working color space
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work_image, color_info = self._convert_to_colorspace(image, color_space)
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# Extract image features for ML guidance
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features = self._extract_image_features(work_image, color_space)
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# Predict optimal parameters using ML
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optimal_params = self._predict_optimal_parameters(
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features, ml_method, base_clip_limit, region_size
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)
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# Apply adaptive region analysis if enabled
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if adaptive_regions:
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region_map = self._analyze_adaptive_regions(work_image, optimal_params)
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else:
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region_map = None
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# Apply perceptual weighting if enabled
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if perceptual_weighting:
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perceptual_weights = self._calculate_perceptual_weights(work_image, color_space)
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else:
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perceptual_weights = None
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# Process luminance channel with learning-based CLAHE
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if color_space in ['lab', 'oklab', 'jzazbz']:
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# Work on luminance channel only
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luminance = np.ascontiguousarray(work_image[:, :, 0]) if len(work_image.shape) == 3 else work_image
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# FIXED: Ensure contiguous array after channel extraction
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luminance = np.ascontiguousarray(luminance)
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enhanced_luminance = self._apply_adaptive_clahe(
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luminance, optimal_params, region_map, perceptual_weights
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)
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# Reconstruct color image
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if len(work_image.shape) == 3:
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enhanced_work = work_image.copy()
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enhanced_work[:, :, 0] = enhanced_luminance
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else:
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enhanced_work = enhanced_luminance
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else:
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# Process all channels for RGB/HSV
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if len(work_image.shape) == 3:
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enhanced_work = np.zeros_like(work_image)
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for c in range(work_image.shape[2]):
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# CRITICAL: Channel slice creates non-contiguous array
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channel = np.ascontiguousarray(work_image[:, :, c])
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enhanced_work[:, :, c] = self._apply_adaptive_clahe(
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channel, optimal_params, region_map, perceptual_weights
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)
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else:
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enhanced_work = self._apply_adaptive_clahe(
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work_image, optimal_params, region_map, perceptual_weights
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)
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# Convert back to RGB
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result = self._convert_from_colorspace(enhanced_work, color_space, color_info)
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# Calculate enhancement metrics
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enhancement_info = self._calculate_enhancement_metrics(
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image, result, optimal_params, features
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)
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info = {
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'method': 'Learning-Based CLAHE',
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'color_space': color_space,
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'ml_method': ml_method,
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'region_size': region_size,
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'base_clip_limit': base_clip_limit,
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'adaptive_regions': adaptive_regions,
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'perceptual_weighting': perceptual_weighting,
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'optimal_parameters': optimal_params,
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'image_features': features,
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'enhancement_metrics': enhancement_info,
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'ml_available': {'sklearn': SKLEARN_AVAILABLE, 'xgboost': XGBOOST_AVAILABLE}
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}
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return result, info
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except Exception as e:
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import traceback
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print(f"\nLearning-based CLAHE error: {str(e)}")
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traceback.print_exc()
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return None, {'error': f"Learning-based CLAHE failed: {str(e)}"}
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def _convert_to_colorspace(self, image: np.ndarray, color_space: str) -> Tuple[np.ndarray, Dict[str, Any]]:
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"""Convert image to specified color space"""
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if len(image.shape) == 2:
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return image, {'grayscale': True}
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color_info = {'grayscale': False, 'original_space': 'rgb'}
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if color_space == 'lab':
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# Convert to LAB
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work_image = color.rgb2lab(image)
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work_image = np.ascontiguousarray(work_image)
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# Normalize L channel to [0, 1]
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work_image[:, :, 0] = work_image[:, :, 0] / 100.0
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# FIXED: Ensure contiguous array after LAB conversion
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work_image = np.ascontiguousarray(work_image)
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color_info['lab_conversion'] = True
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elif color_space == 'oklab':
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# Oklab conversion - using LAB as approximation (close perceptual similarity)
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# FIXED: Use consistent flag name so conversion back works correctly
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work_image = color.rgb2lab(image)
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work_image = np.ascontiguousarray(work_image)
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work_image[:, :, 0] = work_image[:, :, 0] / 100.0
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# FIXED: Ensure contiguous array after LAB conversion
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work_image = np.ascontiguousarray(work_image)
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color_info['oklab_fallback_to_lab'] = True # Match the back-conversion check
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elif color_space == 'jzazbz':
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# Jzazbz conversion - using LAB as approximation (similar perceptual uniformity)
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# FIXED: Use consistent flag name so conversion back works correctly
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work_image = color.rgb2lab(image)
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work_image = np.ascontiguousarray(work_image)
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work_image[:, :, 0] = work_image[:, :, 0] / 100.0
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# FIXED: Ensure contiguous array after LAB conversion
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work_image = np.ascontiguousarray(work_image)
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color_info['jzazbz_fallback_to_lab'] = True # Match the back-conversion check
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elif color_space == 'hsv':
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work_image = color.rgb2hsv(image)
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work_image = np.ascontiguousarray(work_image)
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color_info['hsv_conversion'] = True
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else: # RGB
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work_image = image.copy()
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color_info['rgb_passthrough'] = True
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return work_image, color_info
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def _convert_from_colorspace(self, image: np.ndarray, color_space: str,
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color_info: Dict[str, Any]) -> np.ndarray:
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"""Convert image back from specified color space"""
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if color_info.get('grayscale', False):
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return image
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if color_space == 'lab' or color_info.get('oklab_fallback_to_lab') or color_info.get('jzazbz_fallback_to_lab'):
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# Convert back from LAB
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lab_image = image.copy()
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lab_image[:, :, 0] = lab_image[:, :, 0] * 100.0 # Denormalize L channel
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# CRITICAL FIX: Slice assignment makes array non-contiguous
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lab_image = np.ascontiguousarray(lab_image)
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lab_image = np.ascontiguousarray(lab_image)
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result = color.lab2rgb(lab_image)
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# CRITICAL FIX: color.lab2rgb may return non-contiguous array
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result = np.ascontiguousarray(result)
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elif color_space == 'hsv':
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# CRITICAL FIX: Ensure contiguous before color.hsv2rgb
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image = np.ascontiguousarray(image)
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result = color.hsv2rgb(image)
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# CRITICAL FIX: color.hsv2rgb may return non-contiguous array
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result = np.ascontiguousarray(result)
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else: # RGB
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result = image.copy()
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# CRITICAL FIX: Ensure contiguous for consistency
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result = np.ascontiguousarray(result)
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return np.clip(result, 0, 1)
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def _extract_image_features(self, image: np.ndarray, color_space: str) -> Dict[str, float]:
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"""Extract comprehensive image features for ML guidance"""
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# Use luminance for feature extraction
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if len(image.shape) == 3:
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if color_space in ['lab', 'oklab', 'jzazbz']:
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luminance = np.ascontiguousarray(image[:, :, 0])
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# FIXED: Ensure contiguous array after channel extraction
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luminance = np.ascontiguousarray(luminance)
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else:
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luminance = color.rgb2gray(image)
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# FIXED: Ensure contiguous array after grayscale conversion
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luminance = np.ascontiguousarray(luminance)
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else:
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luminance = np.ascontiguousarray(image)
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features = {}
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try:
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# Basic statistics
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features['mean_luminance'] = float(np.mean(luminance))
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features['std_luminance'] = float(np.std(luminance))
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features['min_luminance'] = float(np.min(luminance))
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features['max_luminance'] = float(np.max(luminance))
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features['range_luminance'] = features['max_luminance'] - features['min_luminance']
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# Histogram features
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hist, bins = np.histogram(luminance, bins=64, range=(0, 1))
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hist = hist.astype(np.float32) / np.sum(hist)
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features['histogram_entropy'] = float(-np.sum(hist * np.log(hist + 1e-10)))
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features['histogram_skewness'] = float(self._calculate_skewness(hist))
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features['histogram_kurtosis'] = float(self._calculate_kurtosis(hist))
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# Contrast measures
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features['rms_contrast'] = float(np.sqrt(np.mean((luminance - features['mean_luminance'])**2)))
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features['michelson_contrast'] = float((features['max_luminance'] - features['min_luminance']) /
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(features['max_luminance'] + features['min_luminance'] + 1e-10))
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# Texture features
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try:
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glcm = feature.graycomatrix((luminance * 255).astype(np.uint8),
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distances=[1], angles=[0], levels=256, symmetric=True, normed=True)
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features['glcm_contrast'] = float(feature.graycoprops(glcm, 'contrast')[0, 0])
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features['glcm_homogeneity'] = float(feature.graycoprops(glcm, 'homogeneity')[0, 0])
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features['glcm_energy'] = float(feature.graycoprops(glcm, 'energy')[0, 0])
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except:
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features['glcm_contrast'] = 0.0
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features['glcm_homogeneity'] = 0.0
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features['glcm_energy'] = 0.0
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# Edge features
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try:
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edges = feature.canny(luminance, sigma=1.0)
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features['edge_density'] = float(np.mean(edges))
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gradient_x = ndimage.sobel(luminance, axis=0)
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gradient_y = ndimage.sobel(luminance, axis=1)
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gradient_magnitude = np.sqrt(gradient_x**2 + gradient_y**2)
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features['mean_gradient'] = float(np.mean(gradient_magnitude))
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features['max_gradient'] = float(np.max(gradient_magnitude))
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except:
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features['edge_density'] = 0.0
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features['mean_gradient'] = 0.0
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features['max_gradient'] = 0.0
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# Local variation features
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try:
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local_std = ndimage.generic_filter(luminance, np.std, size=5)
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features['local_variation_mean'] = float(np.mean(local_std))
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features['local_variation_std'] = float(np.std(local_std))
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except:
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features['local_variation_mean'] = 0.0
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features['local_variation_std'] = 0.0
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# Image dimensions (normalized)
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features['image_width'] = float(image.shape[1] / 1000.0) # Normalize to typical range
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features['image_height'] = float(image.shape[0] / 1000.0)
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features['aspect_ratio'] = features['image_width'] / features['image_height']
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except Exception as e:
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# Fallback features if extraction fails
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features = {
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'mean_luminance': 0.5,
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'std_luminance': 0.2,
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'min_luminance': 0.0,
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'max_luminance': 1.0,
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'range_luminance': 1.0,
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'histogram_entropy': 5.0,
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'error': str(e)
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}
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return features
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def _predict_optimal_parameters(self, features: Dict[str, float],
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ml_method: str, base_clip_limit: float,
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region_size: Tuple[int, int]) -> Dict[str, Any]:
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"""Predict optimal CLAHE parameters using ML"""
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# Prepare feature vector
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feature_keys = ['mean_luminance', 'std_luminance', 'range_luminance',
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'histogram_entropy', 'rms_contrast', 'michelson_contrast',
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'edge_density', 'mean_gradient', 'local_variation_mean']
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feature_vector = np.array([features.get(key, 0.0) for key in feature_keys]).reshape(1, -1)
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# Rule-based prediction with ML enhancement
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optimal_params = self._rule_based_parameter_prediction(features, base_clip_limit, region_size)
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# Enhance with ML if available
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if SKLEARN_AVAILABLE and ml_method in ['random_forest', 'hybrid']:
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ml_params = self._ml_parameter_prediction(feature_vector, 'random_forest')
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optimal_params.update(ml_params)
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if XGBOOST_AVAILABLE and ml_method in ['xgboost', 'hybrid']:
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ml_params = self._ml_parameter_prediction(feature_vector, 'xgboost')
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optimal_params.update(ml_params)
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return optimal_params
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def _rule_based_parameter_prediction(self, features: Dict[str, float],
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base_clip_limit: float,
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region_size: Tuple[int, int]) -> Dict[str, Any]:
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"""Rule-based parameter prediction based on image characteristics"""
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mean_lum = features.get('mean_luminance', 0.5)
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contrast = features.get('rms_contrast', 0.2)
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edge_density = features.get('edge_density', 0.1)
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local_variation = features.get('local_variation_mean', 0.1)
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# Adaptive clip limit based on image characteristics
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if mean_lum < 0.3: # Dark image
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clip_limit = base_clip_limit * 1.5
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elif mean_lum > 0.7: # Bright image
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clip_limit = base_clip_limit * 0.8
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else: # Normal luminance
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clip_limit = base_clip_limit
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# Adjust for contrast
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# MODIFIED: Don't reduce clip limit for high contrast - user chose it for a reason!
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if contrast < 0.1: # Low contrast
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clip_limit *= 1.5 # Increased from 1.3 - boost more for low contrast
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elif contrast > 0.3: # High contrast
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clip_limit *= 1.0 # Changed from 0.7 - use full requested strength!
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# Adaptive region sizing
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base_region_size = region_size
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if edge_density > 0.2: # High edge density - use smaller regions
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adaptive_region_size = (max(4, base_region_size[0] // 2),
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max(4, base_region_size[1] // 2))
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elif edge_density < 0.05: # Low edge density - use larger regions
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adaptive_region_size = (min(16, base_region_size[0] * 2),
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min(16, base_region_size[1] * 2))
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else:
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adaptive_region_size = base_region_size
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# Adaptive enhancement strength
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# MODIFIED: Removed reduction for textured regions - always enhance!
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if local_variation < 0.05: # Smooth regions
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enhancement_strength = 1.3 # Increased from 1.2
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elif local_variation > 0.2: # Textured regions
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enhancement_strength = 1.0 # Changed from 0.8 - use full strength!
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else:
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enhancement_strength = 1.1 # Increased from 1.0
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return {
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'clip_limit': float(np.clip(clip_limit, 1.0, 5.0)),
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'region_size': adaptive_region_size,
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'enhancement_strength': enhancement_strength,
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'prediction_method': 'rule_based'
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}
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def _ml_parameter_prediction(self, feature_vector: np.ndarray, method: str) -> Dict[str, Any]:
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"""ML-based parameter prediction (simplified implementation)"""
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# This is a simplified implementation
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# In a full implementation, you would train models on a large dataset
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# of images with optimal parameters determined by perceptual studies
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try:
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if method == 'random_forest' and SKLEARN_AVAILABLE:
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# Simulate trained Random Forest model
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# In practice, this would be a pre-trained model
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rf_model = RandomForestRegressor(n_estimators=10, random_state=42)
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# Generate synthetic training data for demonstration
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# This would be replaced with real training data
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X_train = np.random.rand(100, feature_vector.shape[1])
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y_train = np.random.rand(100) * 3 + 1 # Clip limits between 1-4
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rf_model.fit(X_train, y_train)
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predicted_clip = rf_model.predict(feature_vector)[0]
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return {
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'ml_clip_limit': float(np.clip(predicted_clip, 1.0, 5.0)),
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'ml_confidence': 0.8, # Would be calculated from model
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'ml_method': 'random_forest'
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}
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elif method == 'xgboost' and XGBOOST_AVAILABLE:
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# Simulate XGBoost prediction
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# This would use a pre-trained XGBoost model
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return {
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'ml_clip_limit': float(np.clip(np.mean(feature_vector) * 4, 1.0, 5.0)),
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'ml_confidence': 0.75,
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'ml_method': 'xgboost'
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}
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except Exception as e:
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return {'ml_error': str(e)}
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return {}
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def _analyze_adaptive_regions(self, image: np.ndarray, params: Dict[str, Any]) -> np.ndarray:
|
|
"""Analyze image for adaptive region sizing"""
|
|
try:
|
|
# Use luminance for analysis
|
|
if len(image.shape) == 3:
|
|
luminance = np.ascontiguousarray(image[:, :, 0])
|
|
else:
|
|
luminance = image
|
|
|
|
# Calculate local complexity
|
|
local_std = ndimage.generic_filter(luminance, np.std, size=5)
|
|
local_edges = ndimage.generic_filter(luminance, lambda x: np.sum(np.abs(np.diff(x))), size=3)
|
|
|
|
# Combine measures
|
|
complexity = local_std + 0.5 * local_edges
|
|
|
|
# Create region map (simplified)
|
|
# High complexity -> smaller regions, Low complexity -> larger regions
|
|
region_map = np.ones_like(complexity)
|
|
|
|
# Normalize complexity
|
|
complexity_norm = (complexity - complexity.min()) / (complexity.max() - complexity.min() + 1e-10)
|
|
|
|
# Map complexity to region scale factors
|
|
region_map = 1.0 - 0.5 * complexity_norm # Range [0.5, 1.0]
|
|
|
|
return region_map
|
|
|
|
except Exception as e:
|
|
# Return uniform region map if analysis fails
|
|
return np.ones(image.shape[:2])
|
|
|
|
def _calculate_perceptual_weights(self, image: np.ndarray, color_space: str) -> np.ndarray:
|
|
"""Calculate perceptual importance weights for different image regions"""
|
|
try:
|
|
# Use luminance for weight calculation
|
|
if len(image.shape) == 3:
|
|
if color_space in ['lab', 'oklab', 'jzazbz']:
|
|
luminance = np.ascontiguousarray(image[:, :, 0])
|
|
else:
|
|
luminance = color.rgb2gray(image)
|
|
else:
|
|
luminance = image
|
|
|
|
# Calculate saliency-like weights
|
|
# This is a simplified version - full implementation would use advanced saliency models
|
|
|
|
# Edge-based importance
|
|
gradient_x = ndimage.sobel(luminance, axis=0)
|
|
gradient_y = ndimage.sobel(luminance, axis=1)
|
|
edge_strength = np.sqrt(gradient_x**2 + gradient_y**2)
|
|
|
|
# Center bias (images often have important content in center)
|
|
h, w = luminance.shape
|
|
y, x = np.ogrid[:h, :w]
|
|
center_y, center_x = h // 2, w // 2
|
|
center_distance = np.sqrt((x - center_x)**2 + (y - center_y)**2)
|
|
max_distance = np.sqrt(center_x**2 + center_y**2)
|
|
center_bias = 1.0 - (center_distance / max_distance)
|
|
|
|
# Combine weights
|
|
perceptual_weights = 0.6 * edge_strength + 0.4 * center_bias
|
|
|
|
# Normalize
|
|
perceptual_weights = (perceptual_weights - perceptual_weights.min()) / \
|
|
(perceptual_weights.max() - perceptual_weights.min() + 1e-10)
|
|
|
|
return perceptual_weights
|
|
|
|
except Exception as e:
|
|
# Return uniform weights if calculation fails
|
|
return np.ones(image.shape[:2])
|
|
|
|
def _apply_adaptive_clahe(self, image: np.ndarray, params: Dict[str, Any],
|
|
region_map: Optional[np.ndarray] = None,
|
|
perceptual_weights: Optional[np.ndarray] = None) -> np.ndarray:
|
|
"""Apply adaptive CLAHE with learned parameters"""
|
|
try:
|
|
# FIXED: Ensure contiguous array before OpenCV operations
|
|
image = np.ascontiguousarray(image)
|
|
|
|
# Convert to 8-bit for OpenCV CLAHE
|
|
image_8bit = img_as_ubyte(image)
|
|
# FIXED: Ensure 8-bit array is also contiguous for OpenCV
|
|
image_8bit = np.ascontiguousarray(image_8bit)
|
|
|
|
# Get parameters
|
|
clip_limit = params.get('clip_limit', 2.0)
|
|
region_size = params.get('region_size', (8, 8))
|
|
enhancement_strength = params.get('enhancement_strength', 1.0)
|
|
|
|
# Apply CLAHE
|
|
clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=region_size)
|
|
image_8bit = np.ascontiguousarray(image_8bit)
|
|
|
|
enhanced_8bit = clahe.apply(image_8bit)
|
|
enhanced = img_as_float(enhanced_8bit)
|
|
# FIXED: Ensure contiguous after float conversion
|
|
enhanced = np.ascontiguousarray(enhanced)
|
|
|
|
# Apply enhancement strength first
|
|
if enhancement_strength != 1.0:
|
|
enhanced = image + enhancement_strength * (enhanced - image)
|
|
enhanced = np.clip(enhanced, 0, 1)
|
|
# FIXED: Ensure contiguous after arithmetic
|
|
enhanced = np.ascontiguousarray(enhanced)
|
|
|
|
# Then apply perceptual weighting if provided
|
|
# MODIFIED: Use minimum 60% effect everywhere (increased from old formula)
|
|
# This ensures visible enhancement across entire image
|
|
if perceptual_weights is not None:
|
|
weight_blend = 0.6 + 0.4 * perceptual_weights # Range: 0.6 to 1.0 (was 0-1)
|
|
enhanced = image + weight_blend * (enhanced - image)
|
|
enhanced = np.clip(enhanced, 0, 1)
|
|
# FIXED: Ensure contiguous after arithmetic
|
|
enhanced = np.ascontiguousarray(enhanced)
|
|
|
|
return enhanced
|
|
|
|
except Exception as e:
|
|
# Return original image if CLAHE fails
|
|
print(f"CLAHE application error: {e}")
|
|
return image
|
|
|
|
def _calculate_enhancement_metrics(self, original: np.ndarray, enhanced: np.ndarray,
|
|
params: Dict[str, Any], features: Dict[str, float]) -> Dict[str, Any]:
|
|
"""Calculate metrics to evaluate enhancement quality"""
|
|
try:
|
|
# Convert to grayscale for metric calculation
|
|
if len(original.shape) == 3:
|
|
orig_gray = color.rgb2gray(original)
|
|
enh_gray = color.rgb2gray(enhanced)
|
|
else:
|
|
orig_gray = original
|
|
enh_gray = enhanced
|
|
|
|
# Contrast improvement
|
|
orig_contrast = np.std(orig_gray)
|
|
enh_contrast = np.std(enh_gray)
|
|
contrast_improvement = enh_contrast / (orig_contrast + 1e-10)
|
|
|
|
# Histogram spread improvement
|
|
orig_hist, _ = np.histogram(orig_gray, bins=64, range=(0, 1))
|
|
enh_hist, _ = np.histogram(enh_gray, bins=64, range=(0, 1))
|
|
|
|
orig_entropy = -np.sum((orig_hist + 1e-10) * np.log(orig_hist + 1e-10))
|
|
enh_entropy = -np.sum((enh_hist + 1e-10) * np.log(enh_hist + 1e-10))
|
|
entropy_improvement = enh_entropy / (orig_entropy + 1e-10)
|
|
|
|
# Dynamic range utilization
|
|
orig_range = np.max(orig_gray) - np.min(orig_gray)
|
|
enh_range = np.max(enh_gray) - np.min(enh_gray)
|
|
range_improvement = enh_range / (orig_range + 1e-10)
|
|
|
|
return {
|
|
'contrast_improvement': float(contrast_improvement),
|
|
'entropy_improvement': float(entropy_improvement),
|
|
'range_improvement': float(range_improvement),
|
|
'original_contrast': float(orig_contrast),
|
|
'enhanced_contrast': float(enh_contrast),
|
|
'original_entropy': float(orig_entropy),
|
|
'enhanced_entropy': float(enh_entropy)
|
|
}
|
|
|
|
except Exception as e:
|
|
return {'metrics_error': str(e)}
|
|
|
|
# Helper methods
|
|
def _calculate_skewness(self, data: np.ndarray) -> float:
|
|
"""Calculate skewness of distribution"""
|
|
mean = np.mean(data)
|
|
std = np.std(data)
|
|
if std == 0:
|
|
return 0.0
|
|
return np.mean(((data - mean) / std) ** 3)
|
|
|
|
def _calculate_kurtosis(self, data: np.ndarray) -> float:
|
|
"""Calculate kurtosis of distribution"""
|
|
mean = np.mean(data)
|
|
std = np.std(data)
|
|
if std == 0:
|
|
return 0.0
|
|
return np.mean(((data - mean) / std) ** 4) - 3
|
|
|
|
def process_image(self, image: np.ndarray, method: str = 'auto', **kwargs) -> Tuple[Optional[np.ndarray], Dict[str, Any]]:
|
|
"""
|
|
Main processing method
|
|
|
|
Args:
|
|
image: Input image (H, W) or (H, W, C) in range [0, 1]
|
|
method: Processing method (only 'auto' for now, plans for multiple methods)
|
|
**kwargs: Method-specific parameters
|
|
|
|
Returns:
|
|
Tuple of (processed_image, processing_info)
|
|
"""
|
|
try:
|
|
return self.learning_based_clahe(image, **kwargs)
|
|
except Exception as e:
|
|
import traceback
|
|
print(f"\nLearning-based CLAHE processing error: {str(e)}")
|
|
traceback.print_exc()
|
|
return None, {'error': f"Learning-based CLAHE processing failed: {str(e)}"}
|
|
|
|
def get_method_info(self) -> Dict[str, str]:
|
|
"""Get information about available methods"""
|
|
return {
|
|
'learning_based_clahe': 'Learning-Based CLAHE with ML-guided parameter optimization and perceptual weighting'
|
|
}
|