rexis_rag/multimodal/

image_processor.rs

//! # Image Processing
//!
//! Advanced image processing with CLIP embeddings, feature extraction, and captioning.

use super::{
    Color, CompositionType, DetectedObject, ImageMetadata, ImageProcessingConfig, ImageProcessor,
    ImageQuality, ProcessedImage, SpatialLayout, VisualFeatures,
};
use crate::{RragError, RragResult};
use std::path::Path;

/// Default image processor implementation
pub struct DefaultImageProcessor {
    /// Configuration
    config: ImageProcessingConfig,

    /// CLIP model for embeddings
    clip_model: Option<CLIPModel>,

    /// Captioning model
    captioning_model: Option<CaptioningModel>,

    /// Feature extractor
    feature_extractor: FeatureExtractor,
}

/// CLIP model for image-text embeddings
pub struct CLIPModel {
    /// Model name/path
    model_path: String,

    /// Model configuration
    config: CLIPConfig,
}

/// CLIP configuration
#[derive(Debug, Clone)]
pub struct CLIPConfig {
    /// Model variant
    pub variant: CLIPVariant,

    /// Input image size
    pub image_size: (u32, u32),

    /// Embedding dimension
    pub embedding_dim: usize,

    /// Normalization parameters
    pub normalization: ImageNormalization,
}

/// CLIP model variants
#[derive(Debug, Clone, Copy)]
pub enum CLIPVariant {
    ViTB32,
    ViTB16,
    ViTL14,
    ResNet50,
}

/// Image captioning model
pub struct CaptioningModel {
    /// Model path
    model_path: String,

    /// Generation config
    generation_config: GenerationConfig,
}

/// Caption generation configuration
#[derive(Debug, Clone)]
pub struct GenerationConfig {
    /// Maximum sequence length
    pub max_length: usize,

    /// Beam search width
    pub num_beams: usize,

    /// Temperature for sampling
    pub temperature: f32,

    /// Top-p sampling
    pub top_p: f32,
}

/// Feature extraction utilities
pub struct FeatureExtractor {
    /// Color analysis
    color_analyzer: ColorAnalyzer,

    /// Object detection
    object_detector: Option<ObjectDetector>,

    /// Quality assessment
    quality_analyzer: QualityAnalyzer,

    /// Layout analysis
    layout_analyzer: SpatialAnalyzer,
}

/// Color analysis component
pub struct ColorAnalyzer;

/// Object detection component
pub struct ObjectDetector {
    /// Model type
    model_type: ObjectDetectionModel,

    /// Confidence threshold
    confidence_threshold: f32,
}

/// Object detection models
#[derive(Debug, Clone, Copy)]
pub enum ObjectDetectionModel {
    YOLO,
    SSD,
    FasterRCNN,
    RetinaNet,
}

/// Image quality analyzer
pub struct QualityAnalyzer;

/// Spatial layout analyzer
pub struct SpatialAnalyzer;

/// Image normalization parameters
#[derive(Debug, Clone)]
pub struct ImageNormalization {
    pub mean: [f32; 3],
    pub std: [f32; 3],
}

impl DefaultImageProcessor {
    /// Create new image processor
    pub fn new(config: ImageProcessingConfig) -> RragResult<Self> {
        let clip_model = if config.use_clip {
            Some(CLIPModel::new("openai/clip-vit-base-patch32")?)
        } else {
            None
        };

        let captioning_model = if config.generate_captions {
            Some(CaptioningModel::new(
                "nlpconnect/vit-gpt2-image-captioning",
            )?)
        } else {
            None
        };

        let feature_extractor = FeatureExtractor::new();

        Ok(Self {
            config,
            clip_model,
            captioning_model,
            feature_extractor,
        })
    }

    /// Preprocess image for models
    fn preprocess_image(&self, image_path: &Path) -> RragResult<PreprocessedImage> {
        // Load image
        let image = self.load_image(image_path)?;

        // Resize if needed
        let resized = self.resize_image(image, self.config.max_width, self.config.max_height)?;

        // Normalize for models
        let normalized = self.normalize_image(resized)?;

        Ok(normalized)
    }

    /// Load image from path
    fn load_image(&self, _path: &Path) -> RragResult<RawImage> {
        // Simulate image loading
        Ok(RawImage {
            data: vec![],
            width: 224,
            height: 224,
            channels: 3,
        })
    }

    /// Resize image maintaining aspect ratio
    fn resize_image(
        &self,
        image: RawImage,
        max_width: u32,
        max_height: u32,
    ) -> RragResult<RawImage> {
        // Calculate new dimensions
        let aspect_ratio = image.width as f32 / image.height as f32;

        let (new_width, new_height) = if aspect_ratio > (max_width as f32 / max_height as f32) {
            // Width is limiting factor
            let new_width = max_width;
            let new_height = (max_width as f32 / aspect_ratio) as u32;
            (new_width, new_height)
        } else {
            // Height is limiting factor
            let new_height = max_height;
            let new_width = (max_height as f32 * aspect_ratio) as u32;
            (new_width, new_height)
        };

        // Simulate resizing
        Ok(RawImage {
            data: vec![],
            width: new_width,
            height: new_height,
            channels: image.channels,
        })
    }

    /// Normalize image for model input
    fn normalize_image(&self, image: RawImage) -> RragResult<PreprocessedImage> {
        // Apply normalization (ImageNet stats typically)
        let _normalization = ImageNormalization {
            mean: [0.485, 0.456, 0.406],
            std: [0.229, 0.224, 0.225],
        };

        // Simulate normalization
        Ok(PreprocessedImage {
            tensor: vec![
                vec![vec![0.0; image.width as usize]; image.height as usize];
                image.channels
            ],
            original_size: (image.width, image.height),
        })
    }
}

impl ImageProcessor for DefaultImageProcessor {
    fn process_image(&self, image_path: &Path) -> RragResult<ProcessedImage> {
        let id = format!(
            "img_{}",
            uuid::Uuid::new_v4().to_string().split('-').next().unwrap()
        );

        // Basic metadata
        let metadata = self.extract_metadata(image_path)?;

        // Generate caption if enabled
        let caption = if self.config.generate_captions {
            Some(self.generate_caption(image_path)?)
        } else {
            None
        };

        // Extract visual features if enabled
        let features = if self.config.extract_features {
            Some(self.extract_features(image_path)?)
        } else {
            None
        };

        // Generate CLIP embedding if enabled
        let clip_embedding = if self.config.use_clip {
            Some(self.generate_clip_embedding(image_path)?)
        } else {
            None
        };

        // OCR text would be handled by OCR engine
        let ocr_text = None;

        Ok(ProcessedImage {
            id,
            source: image_path.to_string_lossy().to_string(),
            caption,
            ocr_text,
            features,
            clip_embedding,
            metadata,
        })
    }

    fn extract_features(&self, image_path: &Path) -> RragResult<VisualFeatures> {
        let preprocessed = self.preprocess_image(image_path)?;

        // Extract colors
        let colors = self
            .feature_extractor
            .color_analyzer
            .extract_colors(&preprocessed)?;

        // Detect objects if available
        let objects = if let Some(ref detector) = self.feature_extractor.object_detector {
            detector.detect_objects(&preprocessed)?
        } else {
            vec![]
        };

        // Classify scene (simplified)
        let scene = Some("indoor".to_string());

        // Assess quality
        let quality = self
            .feature_extractor
            .quality_analyzer
            .assess_quality(&preprocessed)?;

        // Analyze layout
        let layout = self
            .feature_extractor
            .layout_analyzer
            .analyze_layout(&preprocessed)?;

        Ok(VisualFeatures {
            colors,
            objects,
            scene,
            quality,
            layout,
        })
    }

    fn generate_caption(&self, image_path: &Path) -> RragResult<String> {
        if let Some(ref model) = self.captioning_model {
            let preprocessed = self.preprocess_image(image_path)?;
            model.generate_caption(&preprocessed)
        } else {
            Ok("Image captioning not available".to_string())
        }
    }

    fn generate_clip_embedding(&self, image_path: &Path) -> RragResult<Vec<f32>> {
        if let Some(ref model) = self.clip_model {
            let preprocessed = self.preprocess_image(image_path)?;
            model.generate_embedding(&preprocessed)
        } else {
            Err(RragError::configuration("CLIP model not available"))
        }
    }
}

impl DefaultImageProcessor {
    /// Extract image metadata
    fn extract_metadata(&self, _image_path: &Path) -> RragResult<ImageMetadata> {
        // In real implementation, would use image crate or similar
        Ok(ImageMetadata {
            width: 1920,
            height: 1080,
            format: "JPEG".to_string(),
            size_bytes: 1024000,
            dpi: Some(72),
            color_space: Some("RGB".to_string()),
        })
    }
}

impl CLIPModel {
    /// Create new CLIP model
    pub fn new(model_path: &str) -> RragResult<Self> {
        let config = CLIPConfig {
            variant: CLIPVariant::ViTB32,
            image_size: (224, 224),
            embedding_dim: 512,
            normalization: ImageNormalization {
                mean: [0.48145466, 0.4578275, 0.40821073],
                std: [0.26862954, 0.26130258, 0.27577711],
            },
        };

        Ok(Self {
            model_path: model_path.to_string(),
            config,
        })
    }

    /// Generate CLIP embedding for image
    pub fn generate_embedding(&self, _image: &PreprocessedImage) -> RragResult<Vec<f32>> {
        // Simulate CLIP embedding generation
        let embedding = vec![0.1; self.config.embedding_dim];
        Ok(embedding)
    }

    /// Generate text embedding for comparison
    pub fn generate_text_embedding(&self, _text: &str) -> RragResult<Vec<f32>> {
        // Simulate text embedding generation
        let embedding = vec![0.1; self.config.embedding_dim];
        Ok(embedding)
    }

    /// Calculate similarity between image and text
    pub fn calculate_similarity(&self, image: &PreprocessedImage, text: &str) -> RragResult<f32> {
        let img_emb = self.generate_embedding(image)?;
        let text_emb = self.generate_text_embedding(text)?;

        // Cosine similarity
        let dot_product: f32 = img_emb
            .iter()
            .zip(text_emb.iter())
            .map(|(a, b)| a * b)
            .sum();
        let norm_img: f32 = img_emb.iter().map(|x| x * x).sum::<f32>().sqrt();
        let norm_text: f32 = text_emb.iter().map(|x| x * x).sum::<f32>().sqrt();

        Ok(dot_product / (norm_img * norm_text))
    }
}

impl CaptioningModel {
    /// Create new captioning model
    pub fn new(model_path: &str) -> RragResult<Self> {
        let generation_config = GenerationConfig {
            max_length: 50,
            num_beams: 4,
            temperature: 1.0,
            top_p: 0.9,
        };

        Ok(Self {
            model_path: model_path.to_string(),
            generation_config,
        })
    }

    /// Generate caption for image
    pub fn generate_caption(&self, image: &PreprocessedImage) -> RragResult<String> {
        // Simulate caption generation
        let captions = vec![
            "A person sitting at a desk with a computer",
            "A scenic view of mountains and trees",
            "A group of people having a meeting",
            "A chart showing data trends",
            "A building with modern architecture",
        ];

        // Return random caption for simulation
        let idx =
            (image.original_size.0 as usize + image.original_size.1 as usize) % captions.len();
        Ok(captions[idx].to_string())
    }

    /// Generate multiple captions with scores
    pub fn generate_captions_with_scores(
        &self,
        image: &PreprocessedImage,
    ) -> RragResult<Vec<(String, f32)>> {
        let caption = self.generate_caption(image)?;
        Ok(vec![(caption, 0.85)])
    }
}

impl FeatureExtractor {
    /// Create new feature extractor
    pub fn new() -> Self {
        Self {
            color_analyzer: ColorAnalyzer,
            object_detector: Some(ObjectDetector::new()),
            quality_analyzer: QualityAnalyzer,
            layout_analyzer: SpatialAnalyzer,
        }
    }
}

impl ColorAnalyzer {
    /// Extract dominant colors from image
    pub fn extract_colors(&self, _image: &PreprocessedImage) -> RragResult<Vec<Color>> {
        // Simulate color extraction
        Ok(vec![
            Color {
                rgb: (255, 255, 255),
                percentage: 0.4,
                name: Some("White".to_string()),
            },
            Color {
                rgb: (0, 0, 0),
                percentage: 0.3,
                name: Some("Black".to_string()),
            },
            Color {
                rgb: (128, 128, 128),
                percentage: 0.2,
                name: Some("Gray".to_string()),
            },
        ])
    }

    /// Analyze color harmony
    pub fn analyze_harmony(&self, _colors: &[Color]) -> RragResult<ColorHarmony> {
        Ok(ColorHarmony {
            harmony_type: HarmonyType::Complementary,
            harmony_score: 0.75,
        })
    }
}

impl ObjectDetector {
    /// Create new object detector
    pub fn new() -> Self {
        Self {
            model_type: ObjectDetectionModel::YOLO,
            confidence_threshold: 0.5,
        }
    }

    /// Detect objects in image
    pub fn detect_objects(&self, _image: &PreprocessedImage) -> RragResult<Vec<DetectedObject>> {
        // Simulate object detection
        Ok(vec![
            DetectedObject {
                class: "person".to_string(),
                confidence: 0.95,
                bounding_box: (0.1, 0.2, 0.3, 0.6),
            },
            DetectedObject {
                class: "laptop".to_string(),
                confidence: 0.87,
                bounding_box: (0.4, 0.5, 0.2, 0.2),
            },
        ])
    }

    /// Filter objects by confidence
    pub fn filter_by_confidence(&self, objects: Vec<DetectedObject>) -> Vec<DetectedObject> {
        objects
            .into_iter()
            .filter(|obj| obj.confidence >= self.confidence_threshold)
            .collect()
    }
}

impl QualityAnalyzer {
    /// Assess image quality
    pub fn assess_quality(&self, _image: &PreprocessedImage) -> RragResult<ImageQuality> {
        // Simulate quality assessment
        Ok(ImageQuality {
            sharpness: 0.8,
            contrast: 0.7,
            brightness: 0.6,
            noise_level: 0.2,
        })
    }

    /// Calculate overall quality score
    pub fn calculate_score(&self, quality: &ImageQuality) -> f32 {
        (quality.sharpness + quality.contrast + quality.brightness + (1.0 - quality.noise_level))
            / 4.0
    }
}

impl SpatialAnalyzer {
    /// Analyze spatial layout
    pub fn analyze_layout(&self, _image: &PreprocessedImage) -> RragResult<SpatialLayout> {
        // Simulate layout analysis
        Ok(SpatialLayout {
            composition_type: CompositionType::RuleOfThirds,
            focal_points: vec![(0.33, 0.33), (0.67, 0.67)],
            balance: 0.75,
        })
    }

    /// Detect rule of thirds alignment
    pub fn detect_rule_of_thirds(&self, focal_points: &[(f32, f32)]) -> bool {
        // Check if focal points align with rule of thirds grid
        for &(x, y) in focal_points {
            if (x - 0.33).abs() < 0.1
                || (x - 0.67).abs() < 0.1
                || (y - 0.33).abs() < 0.1
                || (y - 0.67).abs() < 0.1
            {
                return true;
            }
        }
        false
    }
}

// Supporting types

/// Raw image data
#[derive(Debug, Clone)]
pub struct RawImage {
    pub data: Vec<u8>,
    pub width: u32,
    pub height: u32,
    pub channels: usize,
}

/// Preprocessed image tensor
#[derive(Debug, Clone)]
pub struct PreprocessedImage {
    pub tensor: Vec<Vec<Vec<f32>>>,
    pub original_size: (u32, u32),
}

/// Color harmony analysis
#[derive(Debug, Clone)]
pub struct ColorHarmony {
    pub harmony_type: HarmonyType,
    pub harmony_score: f32,
}

/// Harmony types
#[derive(Debug, Clone, Copy)]
pub enum HarmonyType {
    Monochromatic,
    Analogous,
    Complementary,
    Triadic,
    Tetradic,
}

#[cfg(test)]
mod tests {
    use super::*;
    use tempfile::NamedTempFile;

    #[test]
    fn test_image_processor_creation() {
        let config = ImageProcessingConfig::default();
        let processor = DefaultImageProcessor::new(config).unwrap();

        assert!(processor.clip_model.is_some());
        assert!(processor.captioning_model.is_some());
    }

    #[test]
    fn test_clip_config() {
        let config = CLIPConfig {
            variant: CLIPVariant::ViTB32,
            image_size: (224, 224),
            embedding_dim: 512,
            normalization: ImageNormalization {
                mean: [0.5, 0.5, 0.5],
                std: [0.5, 0.5, 0.5],
            },
        };

        assert_eq!(config.embedding_dim, 512);
        assert_eq!(config.image_size, (224, 224));
    }

    #[test]
    fn test_color_analyzer() {
        let analyzer = ColorAnalyzer;
        let image = PreprocessedImage {
            tensor: vec![],
            original_size: (100, 100),
        };

        let colors = analyzer.extract_colors(&image).unwrap();
        assert!(!colors.is_empty());
    }

    #[test]
    fn test_quality_analyzer() {
        let analyzer = QualityAnalyzer;
        let image = PreprocessedImage {
            tensor: vec![],
            original_size: (100, 100),
        };

        let quality = analyzer.assess_quality(&image).unwrap();
        let score = analyzer.calculate_score(&quality);

        assert!(score >= 0.0 && score <= 1.0);
    }
}