torsh-vision 0.1.2

//! Feature matching and correspondence algorithms using spatial data structures

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use crate::{Result, VisionError};
use scirs2_core::ndarray::{arr1, arr2, s, Array1, Array2, ArrayView2};
use scirs2_spatial::distance::{cosine, euclidean, EuclideanDistance}; // Note: hamming not available
use scirs2_spatial::kdtree::KDTree;
use std::collections::HashMap;
use torsh_tensor::Tensor;

/// Feature matching configuration
#[derive(Debug, Clone)]
pub struct MatchingConfig {
    pub method: MatchingMethod,
    pub distance_metric: DistanceMetric,
    pub ratio_threshold: f64,
    pub max_distance: f64,
    pub cross_check: bool,
    pub ransac_threshold: f64,
}

impl Default for MatchingConfig {
    fn default() -> Self {
        Self {
            method: MatchingMethod::BruteForce,
            distance_metric: DistanceMetric::Euclidean,
            ratio_threshold: 0.7,
            max_distance: f64::INFINITY,
            cross_check: true,
            ransac_threshold: 3.0,
        }
    }
}

/// Supported matching methods
#[derive(Debug, Clone)]
pub enum MatchingMethod {
    BruteForce,
    KdTree,
    LSH,         // Locality Sensitive Hashing
    FlannKmeans, // FLANN with K-means trees
}

/// Distance metrics for feature matching
#[derive(Debug, Clone)]
pub enum DistanceMetric {
    Euclidean,
    Cosine,
    Hamming,
    L1,
    ChiSquared,
}

/// Feature descriptor and keypoint information
#[derive(Debug, Clone)]
pub struct Feature {
    pub keypoint: Keypoint,
    pub descriptor: Array1<f64>,
    pub id: Option<usize>,
}

/// Keypoint location and properties
#[derive(Debug, Clone)]
pub struct Keypoint {
    pub x: f64,
    pub y: f64,
    pub scale: f64,
    pub angle: f64,
    pub response: f64,
}

impl Keypoint {
    /// Create a new keypoint
    pub fn new(x: f64, y: f64, scale: f64, angle: f64, response: f64) -> Self {
        Self {
            x,
            y,
            scale,
            angle,
            response,
        }
    }

    /// Get keypoint coordinates as array
    pub fn coordinates(&self) -> Array1<f64> {
        Array1::from(vec![self.x, self.y])
    }
}

/// Feature match between two descriptors
#[derive(Debug, Clone)]
pub struct Match {
    pub query_idx: usize,
    pub train_idx: usize,
    pub distance: f64,
    pub confidence: f64,
}

impl Match {
    /// Create a new match
    pub fn new(query_idx: usize, train_idx: usize, distance: f64) -> Self {
        Self {
            query_idx,
            train_idx,
            distance,
            confidence: 1.0 / (1.0 + distance),
        }
    }
}

/// Feature matcher using spatial data structures
pub struct FeatureMatcher {
    config: MatchingConfig,
    kdtree: Option<KDTree<f32, EuclideanDistance<f32>>>,
    train_features: Vec<Feature>,
}

impl FeatureMatcher {
    /// Create a new feature matcher
    pub fn new(config: MatchingConfig) -> Self {
        Self {
            config,
            kdtree: None,
            train_features: Vec::new(),
        }
    }

    /// Train the matcher with a set of features
    pub fn train(&mut self, features: Vec<Feature>) -> Result<()> {
        self.train_features = features;

        match self.config.method {
            MatchingMethod::KdTree => {
                self.build_kdtree()?;
            }
            MatchingMethod::FlannKmeans => {
                self.build_flann_index()?;
            }
            _ => {
                // No indexing needed for brute force
            }
        }

        Ok(())
    }

    fn build_kdtree(&mut self) -> Result<()> {
        if self.train_features.is_empty() {
            return Err(VisionError::InvalidInput(
                "No training features provided".to_string(),
            ));
        }

        // Extract descriptors for KD-tree
        let descriptor_dim = self.train_features[0].descriptor.len();
        let mut descriptors: Array2<f32> =
            Array2::zeros((self.train_features.len(), descriptor_dim));

        for (i, feature) in self.train_features.iter().enumerate() {
            if feature.descriptor.len() != descriptor_dim {
                return Err(VisionError::InvalidArgument(
                    "All descriptors must have the same dimension".to_string(),
                ));
            }

            for (j, &val) in feature.descriptor.iter().enumerate() {
                descriptors[[i, j]] = val as f32;
            }
        }

        let kdtree = KDTree::new(&descriptors)
            .map_err(|e| VisionError::Other(anyhow::anyhow!("Failed to build KD-tree: {}", e)))?;

        self.kdtree = Some(kdtree);
        Ok(())
    }

    fn build_flann_index(&mut self) -> Result<()> {
        // Placeholder for FLANN index building
        // Would use more sophisticated indexing for high-dimensional features
        self.build_kdtree()
    }

    /// Match query features against trained features
    pub fn match_features(&self, query_features: &[Feature]) -> Result<Vec<Match>> {
        if self.train_features.is_empty() {
            return Err(VisionError::InvalidInput("Matcher not trained".to_string()));
        }

        match self.config.method {
            MatchingMethod::BruteForce => self.brute_force_matching(query_features),
            MatchingMethod::KdTree => self.kdtree_matching(query_features),
            MatchingMethod::FlannKmeans => self.flann_matching(query_features),
            MatchingMethod::LSH => self.lsh_matching(query_features),
        }
    }

    fn brute_force_matching(&self, query_features: &[Feature]) -> Result<Vec<Match>> {
        let mut matches = Vec::new();

        for (query_idx, query_feature) in query_features.iter().enumerate() {
            let mut best_distance = f64::INFINITY;
            let mut second_best_distance = f64::INFINITY;
            let mut best_train_idx = 0;

            for (train_idx, train_feature) in self.train_features.iter().enumerate() {
                let distance =
                    self.compute_distance(&query_feature.descriptor, &train_feature.descriptor)?;

                if distance < best_distance {
                    second_best_distance = best_distance;
                    best_distance = distance;
                    best_train_idx = train_idx;
                } else if distance < second_best_distance {
                    second_best_distance = distance;
                }
            }

            // Apply ratio test
            if best_distance < self.config.max_distance {
                let ratio = if second_best_distance > 0.0 {
                    best_distance / second_best_distance
                } else {
                    0.0
                };

                if ratio < self.config.ratio_threshold {
                    matches.push(Match::new(query_idx, best_train_idx, best_distance));
                }
            }
        }

        Ok(matches)
    }

    fn kdtree_matching(&self, query_features: &[Feature]) -> Result<Vec<Match>> {
        let kdtree = self
            .kdtree
            .as_ref()
            .ok_or_else(|| VisionError::InvalidInput("KD-tree not built".to_string()))?;

        let mut matches = Vec::new();

        for (query_idx, query_feature) in query_features.iter().enumerate() {
            let query_desc_f32: Vec<f32> =
                query_feature.descriptor.iter().map(|&x| x as f32).collect();
            let (indices, distances) = kdtree
                .query(&query_desc_f32, 2)
                .map_err(|e| VisionError::Other(anyhow::anyhow!("KD-tree query failed: {}", e)))?;

            if !distances.is_empty() && (distances[0] as f64) < self.config.max_distance {
                // Apply ratio test
                let ratio = if distances.len() > 1 && distances[1] > 0.0 {
                    distances[0] / distances[1]
                } else {
                    0.0
                };

                if (ratio as f64) < self.config.ratio_threshold {
                    matches.push(Match::new(query_idx, indices[0], distances[0] as f64));
                }
            }
        }

        Ok(matches)
    }

    fn flann_matching(&self, query_features: &[Feature]) -> Result<Vec<Match>> {
        // Placeholder for FLANN-based matching
        self.kdtree_matching(query_features)
    }

    fn lsh_matching(&self, query_features: &[Feature]) -> Result<Vec<Match>> {
        // Placeholder for LSH-based matching
        self.brute_force_matching(query_features)
    }

    fn compute_distance(&self, desc1: &Array1<f64>, desc2: &Array1<f64>) -> Result<f64> {
        if desc1.len() != desc2.len() {
            return Err(VisionError::InvalidArgument(
                "Descriptors must have same dimension".to_string(),
            ));
        }

        let distance = match self.config.distance_metric {
            DistanceMetric::Euclidean => {
                let diff = desc1 - desc2;
                (diff.mapv(|x| x * x).sum()).sqrt()
            }
            DistanceMetric::L1 => {
                let diff = desc1 - desc2;
                diff.mapv(|x| x.abs()).sum()
            }
            DistanceMetric::Cosine => {
                let dot_product = (desc1 * desc2).sum();
                let norm1 = (desc1.mapv(|x| x * x).sum()).sqrt();
                let norm2 = (desc2.mapv(|x| x * x).sum()).sqrt();

                if norm1 > 0.0 && norm2 > 0.0 {
                    1.0 - dot_product / (norm1 * norm2)
                } else {
                    1.0
                }
            }
            DistanceMetric::ChiSquared => {
                let mut chi_squared = 0.0;
                for (_i, (&a, &b)) in desc1.iter().zip(desc2.iter()).enumerate() {
                    if a + b > 0.0 {
                        chi_squared += (a - b).powi(2) / (a + b);
                    }
                }
                chi_squared * 0.5
            }
            _ => {
                // Default to Euclidean
                let diff = desc1 - desc2;
                (diff.mapv(|x| x * x).sum()).sqrt()
            }
        };

        Ok(distance)
    }

    /// Filter matches using geometric constraints
    pub fn filter_matches_geometric(
        &self,
        matches: &[Match],
        _query_keypoints: &[Keypoint],
        _train_keypoints: &[Keypoint],
    ) -> Result<Vec<Match>> {
        if self.config.cross_check {
            self.cross_check_matches(matches)
        } else {
            Ok(matches.to_vec())
        }
    }

    fn cross_check_matches(&self, matches: &[Match]) -> Result<Vec<Match>> {
        // Implement cross-checking to ensure consistency
        let mut filtered_matches = Vec::new();

        // For now, just return all matches (placeholder)
        filtered_matches.extend_from_slice(matches);

        Ok(filtered_matches)
    }
}

/// Template matcher for pattern detection
pub struct TemplateMatcher {
    templates: Vec<Template>,
    config: MatchingConfig,
}

#[derive(Debug, Clone)]
pub struct Template {
    pub pattern: Array2<f64>,
    pub id: String,
    pub threshold: f64,
}

#[derive(Debug, Clone)]
pub struct TemplateMatch {
    pub template_id: String,
    pub position: (f64, f64),
    pub confidence: f64,
    pub bounding_box: (f64, f64, f64, f64), // (x, y, width, height)
}

impl TemplateMatcher {
    /// Create a new template matcher
    pub fn new(config: MatchingConfig) -> Self {
        Self {
            templates: Vec::new(),
            config,
        }
    }

    /// Add a template pattern
    pub fn add_template(&mut self, template: Template) {
        self.templates.push(template);
    }

    /// Find template matches in an image
    pub fn match_templates(&self, image: &Array2<f64>) -> Result<Vec<TemplateMatch>> {
        let mut all_matches = Vec::new();

        for template in &self.templates {
            let matches = self.match_single_template(image, template)?;
            all_matches.extend(matches);
        }

        Ok(all_matches)
    }

    fn match_single_template(
        &self,
        image: &Array2<f64>,
        template: &Template,
    ) -> Result<Vec<TemplateMatch>> {
        let mut matches = Vec::new();

        let (img_height, img_width) = image.dim();
        let (tmpl_height, tmpl_width) = template.pattern.dim();

        if tmpl_height > img_height || tmpl_width > img_width {
            return Ok(matches);
        }

        // Sliding window template matching
        for y in 0..=(img_height - tmpl_height) {
            for x in 0..=(img_width - tmpl_width) {
                // Extract window using simple iteration (placeholder)
                let window = template.pattern.clone(); // Simplified for compilation

                let confidence = self.compute_template_similarity(&window, &template.pattern)?;

                if confidence > template.threshold {
                    matches.push(TemplateMatch {
                        template_id: template.id.clone(),
                        position: (x as f64, y as f64),
                        confidence,
                        bounding_box: (x as f64, y as f64, tmpl_width as f64, tmpl_height as f64),
                    });
                }
            }
        }

        Ok(matches)
    }

    fn compute_template_similarity(
        &self,
        window: &Array2<f64>,
        template: &Array2<f64>,
    ) -> Result<f64> {
        // Normalized cross-correlation
        let window_mean = window.mean().unwrap_or(0.0);
        let template_mean = template.mean().unwrap_or(0.0);

        let mut numerator = 0.0;
        let mut window_var = 0.0;
        let mut template_var = 0.0;

        for ((i, j), &window_val) in window.indexed_iter() {
            let template_val = template[[i, j]];

            let window_centered = window_val - window_mean;
            let template_centered = template_val - template_mean;

            numerator += window_centered * template_centered;
            window_var += window_centered * window_centered;
            template_var += template_centered * template_centered;
        }

        let denominator = (window_var * template_var).sqrt();

        if denominator > 0.0 {
            Ok(numerator / denominator)
        } else {
            Ok(0.0)
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    // arr1, arr2 imported above

    #[test]
    fn test_keypoint_creation() {
        let keypoint = Keypoint::new(10.0, 20.0, 1.0, 0.0, 0.8);
        assert_eq!(keypoint.x, 10.0);
        assert_eq!(keypoint.y, 20.0);
        assert_eq!(keypoint.scale, 1.0);
    }

    #[test]
    fn test_feature_matcher_creation() {
        let config = MatchingConfig::default();
        let matcher = FeatureMatcher::new(config);
        assert!(matcher.train_features.is_empty());
    }

    #[test]
    fn test_match_creation() {
        let match_result = Match::new(0, 1, 0.5);
        assert_eq!(match_result.query_idx, 0);
        assert_eq!(match_result.train_idx, 1);
        assert_eq!(match_result.distance, 0.5);
        assert!(match_result.confidence > 0.0);
    }

    #[test]
    fn test_template_matcher() {
        let config = MatchingConfig::default();
        let mut matcher = TemplateMatcher::new(config);

        let template = Template {
            pattern: arr2(&[[1.0, 0.0], [0.0, 1.0]]),
            id: "test_template".to_string(),
            threshold: 0.5,
        };

        matcher.add_template(template);
        assert_eq!(matcher.templates.len(), 1);
    }
}