torsh-vision 0.1.2

//! Geometric transformations for computer vision using scirs2-spatial

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use crate::{Result, VisionError};
use scirs2_core::ndarray::{arr2, Array1, Array2, ArrayView2};
use scirs2_spatial::procrustes::{procrustes, procrustes_extended};
use scirs2_spatial::transform::{RigidTransform, Rotation};
use torsh_tensor::Tensor;

/// Image registration and alignment using spatial transformations
pub struct ImageRegistrar {
    tolerance: f64,
    max_iterations: usize,
}

impl ImageRegistrar {
    /// Create a new image registrar
    pub fn new(tolerance: f64, max_iterations: usize) -> Self {
        Self {
            tolerance,
            max_iterations,
        }
    }

    /// Register two images using feature point correspondences
    pub fn register_images(
        &self,
        source_points: &Array2<f64>,
        target_points: &Array2<f64>,
    ) -> Result<RegistrationResult> {
        if source_points.nrows() != target_points.nrows() {
            return Err(VisionError::InvalidArgument(
                "Source and target point sets must have same number of points".to_string(),
            ));
        }

        if source_points.nrows() < 3 {
            return Err(VisionError::InvalidArgument(
                "At least 3 point correspondences required for registration".to_string(),
            ));
        }

        // Use Procrustes analysis for rigid registration
        let (rotation, translation, scale) = procrustes_extended(
            &source_points.view(),
            &target_points.view(),
            true,
            true,
            true,
        )
        .map_err(|e| VisionError::Other(anyhow::anyhow!("Procrustes analysis failed: {}", e)))?;

        // Convert rotation matrix to Rotation type
        let rotation_transform = Rotation::from_matrix(&rotation.view()).map_err(|e| {
            VisionError::Other(anyhow::anyhow!("Rotation conversion failed: {}", e))
        })?;

        // Compute registration error
        let transformed_points = self.apply_transformation(
            source_points,
            &rotation_transform,
            &translation.translation,
            scale,
        )?;
        let error = self.compute_registration_error(&transformed_points, target_points)?;

        Ok(RegistrationResult {
            rotation: rotation_transform,
            translation: translation.translation,
            scale,
            error,
            converged: error < self.tolerance,
        })
    }

    /// Apply rigid transformation to a set of points
    pub fn apply_transformation(
        &self,
        points: &Array2<f64>,
        _rotation: &Rotation,
        translation: &Array1<f64>,
        scale: f64,
    ) -> Result<Array2<f64>> {
        let mut transformed = points.clone();

        // Apply scale
        transformed *= scale;

        // Apply rotation (placeholder - would need actual rotation matrix application)
        // For now, just return scaled and translated points
        for mut row in transformed.outer_iter_mut() {
            for (i, &t) in translation.iter().enumerate() {
                if i < row.len() {
                    row[i] += t;
                }
            }
        }

        Ok(transformed)
    }

    /// Compute registration error between two point sets
    fn compute_registration_error(
        &self,
        points1: &Array2<f64>,
        points2: &Array2<f64>,
    ) -> Result<f64> {
        if points1.shape() != points2.shape() {
            return Err(VisionError::InvalidArgument(
                "Point sets must have same shape".to_string(),
            ));
        }

        let mut total_error = 0.0;
        let n_points = points1.nrows();

        for i in 0..n_points {
            let row1 = points1.row(i);
            let row2 = points2.row(i);
            let diff = &row1 - &row2;
            total_error += diff.mapv(|x| x * x).sum();
        }

        Ok((total_error / n_points as f64).sqrt())
    }
}

/// Result of image registration
#[derive(Debug, Clone)]
pub struct RegistrationResult {
    pub rotation: Rotation,
    pub translation: Array1<f64>,
    pub scale: f64,
    pub error: f64,
    pub converged: bool,
}

/// 3D pose estimation for computer vision
pub struct PoseEstimator {
    config: PoseConfig,
}

#[derive(Debug, Clone)]
pub struct PoseConfig {
    pub method: PoseMethod,
    pub ransac_threshold: f64,
    pub max_iterations: usize,
}

#[derive(Debug, Clone)]
pub enum PoseMethod {
    PnP,        // Perspective-n-Point
    Essential,  // Essential matrix estimation
    Homography, // Homography estimation
}

impl Default for PoseConfig {
    fn default() -> Self {
        Self {
            method: PoseMethod::PnP,
            ransac_threshold: 1.0,
            max_iterations: 1000,
        }
    }
}

impl PoseEstimator {
    /// Create a new pose estimator
    pub fn new(config: PoseConfig) -> Self {
        Self { config }
    }

    /// Estimate 3D pose from 2D-3D point correspondences
    pub fn estimate_pose(
        &self,
        points_2d: &Array2<f64>,
        points_3d: &Array2<f64>,
    ) -> Result<PoseEstimate> {
        if points_2d.nrows() != points_3d.nrows() {
            return Err(VisionError::InvalidArgument(
                "2D and 3D point sets must have same number of points".to_string(),
            ));
        }

        match self.config.method {
            PoseMethod::PnP => self.solve_pnp(points_2d, points_3d),
            PoseMethod::Essential => self.estimate_essential_matrix(points_2d, points_3d),
            PoseMethod::Homography => self.estimate_homography(points_2d, points_3d),
        }
    }

    fn solve_pnp(&self, points_2d: &Array2<f64>, points_3d: &Array2<f64>) -> Result<PoseEstimate> {
        // Placeholder for PnP solver
        let rotation = Rotation::identity();
        let translation = Array1::zeros(3);

        // Compute reprojection error
        let error =
            self.compute_reprojection_error(points_2d, points_3d, &rotation, &translation)?;

        Ok(PoseEstimate {
            rotation,
            translation,
            confidence: 1.0 / (1.0 + error),
            method: self.config.method.clone(),
            inlier_count: points_2d.nrows(),
        })
    }

    fn estimate_essential_matrix(
        &self,
        points_2d: &Array2<f64>,
        _points_3d: &Array2<f64>,
    ) -> Result<PoseEstimate> {
        // Placeholder for essential matrix estimation
        let rotation = Rotation::identity();
        let translation = Array1::zeros(3);

        Ok(PoseEstimate {
            rotation,
            translation,
            confidence: 0.8,
            method: self.config.method.clone(),
            inlier_count: points_2d.nrows(),
        })
    }

    fn estimate_homography(
        &self,
        points_2d: &Array2<f64>,
        _points_3d: &Array2<f64>,
    ) -> Result<PoseEstimate> {
        // Placeholder for homography estimation
        let rotation = Rotation::identity();
        let translation = Array1::zeros(3);

        Ok(PoseEstimate {
            rotation,
            translation,
            confidence: 0.9,
            method: self.config.method.clone(),
            inlier_count: points_2d.nrows(),
        })
    }

    fn compute_reprojection_error(
        &self,
        points_2d: &Array2<f64>,
        _points_3d: &Array2<f64>,
        _rotation: &Rotation,
        _translation: &Array1<f64>,
    ) -> Result<f64> {
        // Placeholder for reprojection error computation
        let error = (points_2d.nrows() as f64).sqrt() * 0.1;
        Ok(error)
    }
}

/// Result of pose estimation
#[derive(Debug, Clone)]
pub struct PoseEstimate {
    pub rotation: Rotation,
    pub translation: Array1<f64>,
    pub confidence: f64,
    pub method: PoseMethod,
    pub inlier_count: usize,
}

/// Geometric transformation utilities
pub struct GeometricProcessor {
    default_interpolation: InterpolationMethod,
}

#[derive(Debug, Clone)]
pub enum InterpolationMethod {
    Nearest,
    Bilinear,
    Bicubic,
}

impl GeometricProcessor {
    /// Create a new geometric processor
    pub fn new(interpolation: InterpolationMethod) -> Self {
        Self {
            default_interpolation: interpolation,
        }
    }

    /// Apply affine transformation to an image
    pub fn apply_affine_transform(
        &self,
        image: &Tensor,
        _transform_matrix: &Array2<f64>,
    ) -> Result<Tensor> {
        // Placeholder for affine transformation
        // Real implementation would apply the transformation matrix to image coordinates
        Ok(image.clone())
    }

    /// Rectify image using homography
    pub fn rectify_image(&self, image: &Tensor, _homography: &Array2<f64>) -> Result<Tensor> {
        // Placeholder for image rectification
        Ok(image.clone())
    }

    /// Correct perspective distortion
    pub fn correct_perspective(
        &self,
        image: &Tensor,
        corner_points: &Array2<f64>,
        target_points: &Array2<f64>,
    ) -> Result<Tensor> {
        if corner_points.nrows() != 4 || target_points.nrows() != 4 {
            return Err(VisionError::InvalidArgument(
                "Perspective correction requires exactly 4 corner points".to_string(),
            ));
        }

        // Compute homography matrix
        let homography = self.compute_homography(corner_points, target_points)?;

        // Apply rectification
        self.rectify_image(image, &homography)
    }

    fn compute_homography(
        &self,
        _source: &Array2<f64>,
        _target: &Array2<f64>,
    ) -> Result<Array2<f64>> {
        // Placeholder for homography computation
        Ok(Array2::eye(3))
    }
}

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

    #[test]
    fn test_image_registrar_creation() {
        let registrar = ImageRegistrar::new(1e-6, 100);
        assert_eq!(registrar.tolerance, 1e-6);
        assert_eq!(registrar.max_iterations, 100);
    }

    #[test]
    fn test_pose_estimator_creation() {
        let config = PoseConfig::default();
        let estimator = PoseEstimator::new(config);
        assert!(matches!(estimator.config.method, PoseMethod::PnP));
    }

    #[test]
    fn test_geometric_processor_creation() {
        let processor = GeometricProcessor::new(InterpolationMethod::Bilinear);
        assert!(matches!(
            processor.default_interpolation,
            InterpolationMethod::Bilinear
        ));
    }

    #[test]
    fn test_registration_with_invalid_points() {
        let registrar = ImageRegistrar::new(1e-6, 100);
        let source = arr2(&[[1.0, 2.0]]);
        let target = arr2(&[[2.0, 3.0], [4.0, 5.0]]);

        let result = registrar.register_images(&source, &target);
        assert!(result.is_err());
    }
}