"""
NEUROFLUX ULTIMATE - Medical Image Preprocessing
Advanced preprocessing pipeline for medical imaging
"""

import numpy as np
from typing import Dict, Any, Optional, Tuple
import cv2
from scipy import ndimage
from skimage import filters, exposure
import logging

logger = logging.getLogger(__name__)

class MedicalPreprocessor:
    """Advanced medical image preprocessing"""
    
    def __init__(self):
        self.default_size = (224, 224)
        
    def enhanced_preprocess(
        self,
        medical_data: Dict[str, Any],
        target_size: Optional[Tuple[int, int]] = None,
        quantum_mode: bool = False
    ) -> Dict[str, Any]:
        """
        Enhanced preprocessing with optional quantum enhancement
        
        Args:
            medical_data: Dictionary from MedicalImageIO
            target_size: Target image size
            quantum_mode: Enable quantum-inspired preprocessing
            
        Returns:
            Preprocessed data dictionary
        """
        image = medical_data['image']
        
        # Ensure 2D for processing
        if len(image.shape) == 3:
            # Take middle slice if 3D volume
            image = image[:, :, image.shape[2] // 2]
        
        # Apply preprocessing pipeline
        processed = self._basic_preprocessing(image, target_size)
        
        # Enhanced techniques
        processed = self._denoise(processed)
        processed = self._enhance_contrast(processed)
        processed = self._normalize(processed)
        
        # Quantum-inspired enhancement (if enabled)
        if quantum_mode:
            processed = self._quantum_enhancement(processed)
        
        # Prepare features
        features = self._extract_basic_features(processed)
        
        return {
            'image': processed,
            'features': features,
            'metadata': medical_data.get('metadata', {}),
            'preprocessed': True,
            'quantum_enhanced': quantum_mode
        }
    
    def _basic_preprocessing(
        self,
        image: np.ndarray,
        target_size: Optional[Tuple[int, int]]
    ) -> np.ndarray:
        """Basic preprocessing steps"""
        # Resize if needed
        if target_size is None:
            target_size = self.default_size
        
        if image.shape[:2] != target_size:
            image = cv2.resize(image, target_size, interpolation=cv2.INTER_CUBIC)
        
        return image
    
    def _denoise(self, image: np.ndarray) -> np.ndarray:
        """Advanced denoising"""
        # Use bilateral filter for edge-preserving denoising
        if image.max() > 1.0:
            image_uint8 = image.astype(np.uint8)
            denoised = cv2.bilateralFilter(image_uint8, 9, 75, 75)
            return denoised.astype(np.float32)
        else:
            # For normalized images
            denoised = cv2.bilateralFilter(
                (image * 255).astype(np.uint8), 
                9, 75, 75
            )
            return denoised.astype(np.float32) / 255.0
    
    def _enhance_contrast(self, image: np.ndarray) -> np.ndarray:
        """Advanced contrast enhancement"""
        # Adaptive histogram equalization
        if image.max() <= 1.0:
            image = (image * 255).astype(np.uint8)
        
        clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
        enhanced = clahe.apply(image.astype(np.uint8))
        
        return enhanced.astype(np.float32)
    
    def _normalize(self, image: np.ndarray) -> np.ndarray:
        """Normalize to [0, 1] range"""
        min_val = image.min()
        max_val = image.max()
        
        if max_val - min_val > 0:
            return (image - min_val) / (max_val - min_val)
        return image
    
    def _quantum_enhancement(self, image: np.ndarray) -> np.ndarray:
        """
        Quantum-inspired enhancement using phase coherence
        (Simulation of quantum superposition principles)
        """
        # Apply Fourier transform (quantum-inspired frequency domain)
        f_transform = np.fft.fft2(image)
        f_shift = np.fft.fftshift(f_transform)
        
        # Apply quantum-inspired filter (phase modulation)
        rows, cols = image.shape
        crow, ccol = rows // 2, cols // 2
        
        # Create quantum-inspired mask
        y, x = np.ogrid[:rows, :cols]
        mask = np.exp(-((x - ccol)**2 + (y - crow)**2) / (2 * (min(rows, cols) / 4)**2))
        
        # Apply enhancement
        f_enhanced = f_shift * (1 + 0.3 * mask)
        
        # Inverse transform
        f_ishift = np.fft.ifftshift(f_enhanced)
        image_enhanced = np.fft.ifft2(f_ishift)
        image_enhanced = np.abs(image_enhanced)
        
        # Normalize
        return self._normalize(image_enhanced)
    
    def _extract_basic_features(self, image: np.ndarray) -> Dict[str, float]:
        """Extract basic statistical features"""
        return {
            'mean_intensity': float(np.mean(image)),
            'std_intensity': float(np.std(image)),
            'min_intensity': float(np.min(image)),
            'max_intensity': float(np.max(image)),
            'entropy': float(self._calculate_entropy(image)),
            'contrast': float(np.std(image) / (np.mean(image) + 1e-7))
        }
    
    def _calculate_entropy(self, image: np.ndarray) -> float:
        """Calculate image entropy"""
        histogram, _ = np.histogram(image, bins=256, range=(0, 1))
        histogram = histogram[histogram > 0]
        prob = histogram / histogram.sum()
        entropy = -np.sum(prob * np.log2(prob + 1e-7))
        return entropy
    
    def apply_skull_stripping(self, image: np.ndarray) -> np.ndarray:
        """
        Simplified skull stripping for brain MRI
        (For demonstration purposes)
        """
        # Threshold-based approach
        threshold = filters.threshold_otsu(image)
        binary = image > threshold
        
        # Morphological operations
        binary = ndimage.binary_fill_holes(binary)
        binary = ndimage.binary_erosion(binary, iterations=2)
        binary = ndimage.binary_dilation(binary, iterations=2)
        
        # Apply mask
        stripped = image * binary
        
        return stripped