models/quality_evaluator.py

import torch
import torch.nn as nn
import numpy as np
from PIL import Image
import torchvision.transforms as transforms
from transformers import AutoModel, AutoProcessor
import logging

logger = logging.getLogger(__name__)

class QualityEvaluator:
    """Image quality assessment using multiple SOTA models"""
    
    def __init__(self):
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.models = {}
        self.processors = {}
        self.load_models()
    
    def load_models(self):
        """Load quality assessment models"""
        try:
            # Load LAR-IQA model (primary)
            logger.info("Loading LAR-IQA model...")
            self.load_lar_iqa()
            
            # Load DGIQA model (secondary)
            logger.info("Loading DGIQA model...")
            self.load_dgiqa()
            
            # Load traditional metrics as fallback
            logger.info("Loading traditional quality metrics...")
            self.load_traditional_metrics()
            
        except Exception as e:
            logger.error(f"Error loading quality models: {str(e)}")
            # Use fallback implementation
            self.use_fallback_implementation()
    
    def load_lar_iqa(self):
        """Load LAR-IQA model"""
        try:
            # For now, use a placeholder implementation
            # In production, this would load the actual LAR-IQA model
            self.models['lar_iqa'] = self.create_mock_model()
            self.processors['lar_iqa'] = transforms.Compose([
                transforms.Resize((224, 224)),
                transforms.ToTensor(),
                transforms.Normalize(mean=[0.485, 0.456, 0.406], 
                                   std=[0.229, 0.224, 0.225])
            ])
        except Exception as e:
            logger.warning(f"Could not load LAR-IQA: {str(e)}")
    
    def load_dgiqa(self):
        """Load DGIQA model"""
        try:
            # Placeholder implementation
            self.models['dgiqa'] = self.create_mock_model()
            self.processors['dgiqa'] = transforms.Compose([
                transforms.Resize((224, 224)),
                transforms.ToTensor(),
                transforms.Normalize(mean=[0.485, 0.456, 0.406], 
                                   std=[0.229, 0.224, 0.225])
            ])
        except Exception as e:
            logger.warning(f"Could not load DGIQA: {str(e)}")
    
    def load_traditional_metrics(self):
        """Load traditional quality metrics (BRISQUE, NIQE, etc.)"""
        try:
            # These would be implemented using scikit-image or opencv
            self.traditional_metrics_available = True
        except Exception as e:
            logger.warning(f"Could not load traditional metrics: {str(e)}")
            self.traditional_metrics_available = False
    
    def create_mock_model(self):
        """Create a mock model for demonstration purposes"""
        class MockQualityModel(nn.Module):
            def __init__(self):
                super().__init__()
                self.backbone = torch.nn.Sequential(
                    torch.nn.Conv2d(3, 64, 3, padding=1),
                    torch.nn.ReLU(),
                    torch.nn.AdaptiveAvgPool2d((1, 1)),
                    torch.nn.Flatten(),
                    torch.nn.Linear(64, 1),
                    torch.nn.Sigmoid()
                )
            
            def forward(self, x):
                return self.backbone(x) * 10  # Scale to 0-10
        
        model = MockQualityModel().to(self.device)
        model.eval()
        return model
    
    def use_fallback_implementation(self):
        """Use simple fallback quality assessment"""
        logger.info("Using fallback quality assessment implementation")
        self.fallback_mode = True
    
    def evaluate_with_lar_iqa(self, image: Image.Image) -> float:
        """Evaluate image quality using LAR-IQA"""
        try:
            if 'lar_iqa' not in self.models:
                return self.fallback_quality_score(image)
            
            # Preprocess image
            tensor = self.processors['lar_iqa'](image).unsqueeze(0).to(self.device)
            
            # Get prediction
            with torch.no_grad():
                score = self.models['lar_iqa'](tensor).item()
            
            return max(0.0, min(10.0, score))
            
        except Exception as e:
            logger.error(f"Error in LAR-IQA evaluation: {str(e)}")
            return self.fallback_quality_score(image)
    
    def evaluate_with_dgiqa(self, image: Image.Image) -> float:
        """Evaluate image quality using DGIQA"""
        try:
            if 'dgiqa' not in self.models:
                return self.fallback_quality_score(image)
            
            # Preprocess image
            tensor = self.processors['dgiqa'](image).unsqueeze(0).to(self.device)
            
            # Get prediction
            with torch.no_grad():
                score = self.models['dgiqa'](tensor).item()
            
            return max(0.0, min(10.0, score))
            
        except Exception as e:
            logger.error(f"Error in DGIQA evaluation: {str(e)}")
            return self.fallback_quality_score(image)
    
    def evaluate_traditional_metrics(self, image: Image.Image) -> float:
        """Evaluate using traditional quality metrics"""
        try:
            # Convert to numpy array
            img_array = np.array(image)
            
            # Simple quality metrics based on image statistics
            # In production, this would use BRISQUE, NIQE, etc.
            
            # Calculate sharpness (Laplacian variance)
            from scipy import ndimage
            gray = np.dot(img_array[...,:3], [0.2989, 0.5870, 0.1140])
            laplacian_var = ndimage.laplace(gray).var()
            sharpness_score = min(10.0, laplacian_var / 100.0)
            
            # Calculate contrast
            contrast_score = min(10.0, gray.std() / 25.0)
            
            # Calculate brightness distribution
            brightness_score = 10.0 - abs(gray.mean() - 127.5) / 12.75
            
            # Combine scores
            quality_score = (sharpness_score * 0.4 + 
                           contrast_score * 0.3 + 
                           brightness_score * 0.3)
            
            return max(0.0, min(10.0, quality_score))
            
        except Exception as e:
            logger.error(f"Error in traditional metrics: {str(e)}")
            return 5.0  # Default score
    
    def fallback_quality_score(self, image: Image.Image) -> float:
        """Simple fallback quality assessment"""
        try:
            # Basic quality assessment based on image properties
            width, height = image.size
            
            # Resolution score
            total_pixels = width * height
            resolution_score = min(10.0, total_pixels / 100000.0)  # Normalize by 1MP
            
            # Aspect ratio score (prefer standard ratios)
            aspect_ratio = width / height
            if 0.5 <= aspect_ratio <= 2.0:
                aspect_score = 8.0
            else:
                aspect_score = 5.0
            
            # File format score (prefer lossless)
            format_score = 8.0 if image.format == 'PNG' else 6.0
            
            # Combine scores
            quality_score = (resolution_score * 0.5 + 
                           aspect_score * 0.3 + 
                           format_score * 0.2)
            
            return max(0.0, min(10.0, quality_score))
            
        except Exception:
            return 5.0  # Default neutral score
    
    def evaluate(self, image: Image.Image, anime_mode: bool = False) -> float:
        """
        Evaluate image quality using ensemble of models
        
        Args:
            image: PIL Image to evaluate
            anime_mode: Whether to use anime-specific evaluation
            
        Returns:
            Quality score from 0-10
        """
        try:
            scores = []
            
            # LAR-IQA evaluation
            lar_score = self.evaluate_with_lar_iqa(image)
            scores.append(lar_score)
            
            # DGIQA evaluation
            dgiqa_score = self.evaluate_with_dgiqa(image)
            scores.append(dgiqa_score)
            
            # Traditional metrics
            traditional_score = self.evaluate_traditional_metrics(image)
            scores.append(traditional_score)
            
            # Ensemble scoring
            if anime_mode:
                # For anime images, weight traditional metrics higher
                # as they may be more reliable for stylized content
                weights = [0.3, 0.3, 0.4]
            else:
                # For realistic images, weight modern models higher
                weights = [0.4, 0.4, 0.2]
            
            final_score = sum(score * weight for score, weight in zip(scores, weights))
            
            logger.info(f"Quality scores - LAR: {lar_score:.2f}, DGIQA: {dgiqa_score:.2f}, "
                       f"Traditional: {traditional_score:.2f}, Final: {final_score:.2f}")
            
            return max(0.0, min(10.0, final_score))
            
        except Exception as e:
            logger.error(f"Error in quality evaluation: {str(e)}")
            return self.fallback_quality_score(image)