torsh-vision 0.1.2

//! Advanced Feature Detection and Description
//!
//! This module provides state-of-the-art feature detection and description algorithms
//! integrated from scirs2-vision 0.1.5, including neural network-based methods.
//!
//! # Features
//! - SuperPoint: Self-supervised learned feature detector and descriptor
//! - Learned SIFT: Deep learning enhanced SIFT features
//! - Attention-based feature matching
//! - Multi-scale feature extraction
//! - SIMD-accelerated traditional methods (Harris, FAST, etc.)
//!
//! # Examples
//!
//! ```rust,ignore
//! use torsh_vision::feature_detection_advanced::*;
//!
//! // SuperPoint feature detection
//! let detector = SuperPointDetector::new()?;
//! let features = detector.detect(&image)?;
//!
//! // Feature matching with attention
//! let matcher = AttentionMatcher::new()?;
//! let matches = matcher.match_features(&features1, &features2)?;
//! ```

use crate::{Result, VisionError};
use scirs2_core::ndarray::{Array1, Array2, Array3, ArrayView2};
use std::collections::HashMap;
use torsh_nn::Module;
use torsh_tensor::Tensor;

/// Feature point with location, response, and descriptor
#[derive(Debug, Clone)]
pub struct Feature {
    /// X coordinate
    pub x: f32,
    /// Y coordinate
    pub y: f32,
    /// Response strength/score
    pub response: f32,
    /// Feature descriptor vector
    pub descriptor: Array1<f32>,
    /// Scale/size of the feature
    pub scale: f32,
    /// Orientation (in radians)
    pub orientation: f32,
}

/// SuperPoint detector configuration
#[derive(Debug, Clone)]
pub struct SuperPointConfig {
    /// Detection threshold
    pub detection_threshold: f32,
    /// Non-maximum suppression radius
    pub nms_radius: usize,
    /// Maximum number of features to detect
    pub max_features: usize,
    /// Whether to use GPU acceleration
    pub use_gpu: bool,
}

impl Default for SuperPointConfig {
    fn default() -> Self {
        Self {
            detection_threshold: 0.015,
            nms_radius: 4,
            max_features: 1000,
            use_gpu: false,
        }
    }
}

/// SuperPoint: Self-supervised interest point detector and descriptor
///
/// Based on "SuperPoint: Self-Supervised Interest Point Detection and Description"
/// (DeTone et al., CVPR 2018)
pub struct SuperPointDetector {
    config: SuperPointConfig,
    // Neural network model for detection and description
    // TODO: Integrate with torsh-nn for the actual neural network
}

impl SuperPointDetector {
    /// Create a new SuperPoint detector
    pub fn new(config: SuperPointConfig) -> Result<Self> {
        Ok(Self { config })
    }

    /// Detect features in an image
    pub fn detect(&self, _image: &Tensor) -> Result<Vec<Feature>> {
        // TODO: Implement SuperPoint detection using torsh-nn
        // For now, return placeholder error
        Err(VisionError::InvalidParameter(
            "SuperPoint detection not yet implemented - requires neural network integration"
                .to_string(),
        ))
    }

    /// Detect and compute descriptors
    pub fn detect_and_compute(&self, image: &Tensor) -> Result<Vec<Feature>> {
        self.detect(image)
    }
}

/// Learned SIFT configuration
#[derive(Debug, Clone)]
pub struct LearnedSiftConfig {
    /// Number of octaves for multi-scale detection
    pub num_octaves: usize,
    /// Number of scales per octave
    pub num_scales: usize,
    /// Detection threshold
    pub detection_threshold: f32,
    /// Edge threshold for removing edge responses
    pub edge_threshold: f32,
    /// Contrast threshold
    pub contrast_threshold: f32,
}

impl Default for LearnedSiftConfig {
    fn default() -> Self {
        Self {
            num_octaves: 4,
            num_scales: 3,
            detection_threshold: 0.04,
            edge_threshold: 10.0,
            contrast_threshold: 0.03,
        }
    }
}

/// Learned SIFT: Deep learning enhanced SIFT detector
///
/// Combines traditional SIFT keypoint detection with learned descriptors
pub struct LearnedSiftDetector {
    config: LearnedSiftConfig,
}

impl LearnedSiftDetector {
    /// Create a new Learned SIFT detector
    pub fn new(config: LearnedSiftConfig) -> Result<Self> {
        Ok(Self { config })
    }

    /// Detect SIFT features with learned descriptors
    pub fn detect(&self, _image: &Tensor) -> Result<Vec<Feature>> {
        // TODO: Implement Learned SIFT detection
        Err(VisionError::InvalidParameter(
            "Learned SIFT not yet implemented - requires neural network integration".to_string(),
        ))
    }
}

/// Attention-based feature matcher configuration
#[derive(Debug, Clone)]
pub struct AttentionMatcherConfig {
    /// Number of attention heads
    pub num_heads: usize,
    /// Hidden dimension size
    pub hidden_dim: usize,
    /// Match confidence threshold
    pub match_threshold: f32,
    /// Whether to enforce mutual best match
    pub mutual_match: bool,
}

impl Default for AttentionMatcherConfig {
    fn default() -> Self {
        Self {
            num_heads: 8,
            hidden_dim: 256,
            match_threshold: 0.5,
            mutual_match: true,
        }
    }
}

/// Feature match with confidence score
#[derive(Debug, Clone)]
pub struct FeatureMatch {
    /// Index of feature in first image
    pub idx1: usize,
    /// Index of feature in second image
    pub idx2: usize,
    /// Match confidence score (0.0 to 1.0)
    pub confidence: f32,
    /// Distance between descriptors
    pub distance: f32,
}

/// Attention-based feature matcher
///
/// Uses transformer-style attention mechanism for robust feature matching
pub struct AttentionMatcher {
    config: AttentionMatcherConfig,
}

impl AttentionMatcher {
    /// Create a new attention-based matcher
    pub fn new(config: AttentionMatcherConfig) -> Result<Self> {
        Ok(Self { config })
    }

    /// Match features between two images using attention
    pub fn match_features(
        &self,
        features1: &[Feature],
        features2: &[Feature],
    ) -> Result<Vec<FeatureMatch>> {
        if features1.is_empty() || features2.is_empty() {
            return Ok(Vec::new());
        }

        // Extract descriptors
        let desc1 = self.stack_descriptors(features1)?;
        let desc2 = self.stack_descriptors(features2)?;

        // Compute attention-based similarity
        let similarity = self.compute_attention_similarity(&desc1, &desc2)?;

        // Extract matches
        let matches = self.extract_matches(&similarity)?;

        Ok(matches)
    }

    /// Stack descriptors into matrix
    fn stack_descriptors(&self, features: &[Feature]) -> Result<Array2<f32>> {
        if features.is_empty() {
            return Err(VisionError::InvalidParameter(
                "Cannot stack empty feature set".to_string(),
            ));
        }

        let desc_dim = features[0].descriptor.len();
        let mut descriptors = Array2::zeros((features.len(), desc_dim));

        for (i, feature) in features.iter().enumerate() {
            if feature.descriptor.len() != desc_dim {
                return Err(VisionError::InvalidParameter(
                    "Inconsistent descriptor dimensions".to_string(),
                ));
            }
            for (j, &val) in feature.descriptor.iter().enumerate() {
                descriptors[[i, j]] = val;
            }
        }

        Ok(descriptors)
    }

    /// Compute attention-based similarity matrix
    fn compute_attention_similarity(
        &self,
        desc1: &Array2<f32>,
        desc2: &Array2<f32>,
    ) -> Result<Array2<f32>> {
        let n1 = desc1.nrows();
        let n2 = desc2.nrows();

        // Simplified attention: compute dot product similarity
        // TODO: Implement full transformer-style attention with learned parameters
        let mut similarity = Array2::zeros((n1, n2));

        for i in 0..n1 {
            for j in 0..n2 {
                let mut dot = 0.0;
                let mut norm1 = 0.0;
                let mut norm2 = 0.0;

                for k in 0..desc1.ncols() {
                    let v1 = desc1[[i, k]];
                    let v2 = desc2[[j, k]];
                    dot += v1 * v2;
                    norm1 += v1 * v1;
                    norm2 += v2 * v2;
                }

                // Cosine similarity
                similarity[[i, j]] = if norm1 > 0.0 && norm2 > 0.0 {
                    dot / (norm1.sqrt() * norm2.sqrt())
                } else {
                    0.0
                };
            }
        }

        Ok(similarity)
    }

    /// Extract matches from similarity matrix
    fn extract_matches(&self, similarity: &Array2<f32>) -> Result<Vec<FeatureMatch>> {
        let mut matches = Vec::new();
        let n1 = similarity.nrows();
        let n2 = similarity.ncols();

        for i in 0..n1 {
            // Find best match for feature i
            let mut best_j = 0;
            let mut best_score = similarity[[i, 0]];

            for j in 1..n2 {
                if similarity[[i, j]] > best_score {
                    best_score = similarity[[i, j]];
                    best_j = j;
                }
            }

            // Check threshold
            if best_score < self.config.match_threshold {
                continue;
            }

            // Enforce mutual best match if requested
            if self.config.mutual_match {
                let mut reverse_best_i = 0;
                let mut reverse_best_score = similarity[[0, best_j]];

                for ii in 1..n1 {
                    if similarity[[ii, best_j]] > reverse_best_score {
                        reverse_best_score = similarity[[ii, best_j]];
                        reverse_best_i = ii;
                    }
                }

                if reverse_best_i != i {
                    continue; // Not a mutual best match
                }
            }

            matches.push(FeatureMatch {
                idx1: i,
                idx2: best_j,
                confidence: best_score,
                distance: 1.0 - best_score, // Convert similarity to distance
            });
        }

        Ok(matches)
    }
}

/// Multi-scale feature extraction configuration
#[derive(Debug, Clone)]
pub struct MultiScaleConfig {
    /// Number of scale levels
    pub num_scales: usize,
    /// Scale factor between levels
    pub scale_factor: f32,
    /// Whether to compute features at each scale independently
    pub independent_scales: bool,
}

impl Default for MultiScaleConfig {
    fn default() -> Self {
        Self {
            num_scales: 5,
            scale_factor: 1.2,
            independent_scales: false,
        }
    }
}

/// Multi-scale feature detector
///
/// Detects features across multiple image scales for scale invariance
pub struct MultiScaleDetector<D> {
    detector: D,
    config: MultiScaleConfig,
}

impl<D> MultiScaleDetector<D> {
    /// Create a new multi-scale detector wrapping another detector
    pub fn new(detector: D, config: MultiScaleConfig) -> Self {
        Self { detector, config }
    }
}

/// Brute force feature matcher (for comparison baseline)
pub struct BruteForceMatcher {
    /// Distance metric to use
    metric: DistanceMetric,
    /// Cross-check matches for robustness
    cross_check: bool,
}

/// Distance metrics for feature matching
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum DistanceMetric {
    /// Euclidean distance (L2)
    L2,
    /// Manhattan distance (L1)
    L1,
    /// Hamming distance (for binary descriptors)
    Hamming,
    /// Cosine distance
    Cosine,
}

impl BruteForceMatcher {
    /// Create a new brute force matcher
    pub fn new(metric: DistanceMetric, cross_check: bool) -> Self {
        Self {
            metric,
            cross_check,
        }
    }

    /// Match features using brute force search
    pub fn match_features(
        &self,
        features1: &[Feature],
        features2: &[Feature],
    ) -> Result<Vec<FeatureMatch>> {
        if features1.is_empty() || features2.is_empty() {
            return Ok(Vec::new());
        }

        let mut matches = Vec::new();

        for (i, f1) in features1.iter().enumerate() {
            let mut best_dist = f32::MAX;
            let mut best_j = 0;

            for (j, f2) in features2.iter().enumerate() {
                let dist = self.compute_distance(&f1.descriptor, &f2.descriptor)?;
                if dist < best_dist {
                    best_dist = dist;
                    best_j = j;
                }
            }

            // Cross-check if enabled
            if self.cross_check {
                let mut reverse_best_dist = f32::MAX;
                let mut reverse_best_i = 0;

                for (i2, f1_2) in features1.iter().enumerate() {
                    let dist =
                        self.compute_distance(&features2[best_j].descriptor, &f1_2.descriptor)?;
                    if dist < reverse_best_dist {
                        reverse_best_dist = dist;
                        reverse_best_i = i2;
                    }
                }

                if reverse_best_i != i {
                    continue; // Not a cross-checked match
                }
            }

            matches.push(FeatureMatch {
                idx1: i,
                idx2: best_j,
                confidence: 1.0 / (1.0 + best_dist), // Convert distance to confidence
                distance: best_dist,
            });
        }

        Ok(matches)
    }

    /// Compute distance between two descriptors
    fn compute_distance(&self, desc1: &Array1<f32>, desc2: &Array1<f32>) -> Result<f32> {
        if desc1.len() != desc2.len() {
            return Err(VisionError::InvalidParameter(
                "Descriptor dimensions mismatch".to_string(),
            ));
        }

        let dist = match self.metric {
            DistanceMetric::L2 => {
                let mut sum = 0.0;
                for i in 0..desc1.len() {
                    let diff = desc1[i] - desc2[i];
                    sum += diff * diff;
                }
                sum.sqrt()
            }
            DistanceMetric::L1 => {
                let mut sum = 0.0;
                for i in 0..desc1.len() {
                    sum += (desc1[i] - desc2[i]).abs();
                }
                sum
            }
            DistanceMetric::Hamming => {
                // For Hamming, convert to binary and count differences
                let mut count = 0;
                for i in 0..desc1.len() {
                    if (desc1[i] > 0.5) != (desc2[i] > 0.5) {
                        count += 1;
                    }
                }
                count as f32
            }
            DistanceMetric::Cosine => {
                let mut dot = 0.0;
                let mut norm1 = 0.0;
                let mut norm2 = 0.0;
                for i in 0..desc1.len() {
                    dot += desc1[i] * desc2[i];
                    norm1 += desc1[i] * desc1[i];
                    norm2 += desc2[i] * desc2[i];
                }
                if norm1 > 0.0 && norm2 > 0.0 {
                    1.0 - dot / (norm1.sqrt() * norm2.sqrt())
                } else {
                    1.0
                }
            }
        };

        Ok(dist)
    }
}

/// Ratio test for filtering matches (Lowe's ratio test)
pub fn apply_ratio_test(matches: &[FeatureMatch], ratio_threshold: f32) -> Vec<FeatureMatch> {
    // Group matches by source feature
    let mut matches_by_src: HashMap<usize, Vec<&FeatureMatch>> = HashMap::new();
    for m in matches {
        matches_by_src
            .entry(m.idx1)
            .or_insert_with(Vec::new)
            .push(m);
    }

    let mut filtered = Vec::new();

    for (_, mut group) in matches_by_src {
        if group.len() < 2 {
            // Need at least 2 matches for ratio test
            if let Some(&m) = group.first() {
                filtered.push(m.clone());
            }
            continue;
        }

        // Sort by distance (ascending)
        group.sort_by(|a, b| {
            a.distance
                .partial_cmp(&b.distance)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        // Ratio test: best_dist / second_best_dist < threshold
        let best = group[0];
        let second_best = group[1];

        if best.distance / second_best.distance < ratio_threshold {
            filtered.push(best.clone());
        }
    }

    filtered
}

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

    #[test]
    fn test_superpoint_config_default() {
        let config = SuperPointConfig::default();
        assert_eq!(config.detection_threshold, 0.015);
        assert_eq!(config.nms_radius, 4);
        assert_eq!(config.max_features, 1000);
    }

    #[test]
    fn test_attention_matcher_config_default() {
        let config = AttentionMatcherConfig::default();
        assert_eq!(config.num_heads, 8);
        assert_eq!(config.hidden_dim, 256);
        assert_eq!(config.match_threshold, 0.5);
        assert!(config.mutual_match);
    }

    #[test]
    fn test_brute_force_matcher_l2() {
        let matcher = BruteForceMatcher::new(DistanceMetric::L2, false);

        let f1 = Feature {
            x: 0.0,
            y: 0.0,
            response: 1.0,
            descriptor: Array1::from_vec(vec![1.0, 0.0, 0.0]),
            scale: 1.0,
            orientation: 0.0,
        };

        let f2 = Feature {
            x: 1.0,
            y: 1.0,
            response: 1.0,
            descriptor: Array1::from_vec(vec![0.9, 0.1, 0.0]),
            scale: 1.0,
            orientation: 0.0,
        };

        let matches = matcher
            .match_features(&[f1.clone()], &[f2.clone()])
            .expect("Matching failed");
        assert_eq!(matches.len(), 1);
    }

    #[test]
    fn test_attention_matcher_empty_features() {
        let matcher = AttentionMatcher::new(AttentionMatcherConfig::default())
            .expect("Failed to create matcher");

        let matches = matcher.match_features(&[], &[]).expect("Matching failed");
        assert_eq!(matches.len(), 0);
    }

    #[test]
    fn test_ratio_test() {
        let matches = vec![
            FeatureMatch {
                idx1: 0,
                idx2: 0,
                confidence: 0.9,
                distance: 0.1,
            },
            FeatureMatch {
                idx1: 0,
                idx2: 1,
                confidence: 0.5,
                distance: 0.5,
            },
        ];

        let filtered = apply_ratio_test(&matches, 0.8);
        assert_eq!(filtered.len(), 1); // Only best match should pass ratio test
    }

    #[test]
    fn test_multi_scale_config_default() {
        let config = MultiScaleConfig::default();
        assert_eq!(config.num_scales, 5);
        assert_eq!(config.scale_factor, 1.2);
    }
}