torsh-vision 0.1.2

//! Spatial interpolation methods for image processing

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use crate::{Result, VisionError};
use torsh_tensor::Tensor;
// Note: interpolation module not available in scirs2_spatial, will implement basic interpolation
use scirs2_core::ndarray::{arr1, arr2, Array1, Array2, ArrayView2};

/// Spatial interpolation configuration
#[derive(Debug, Clone)]
pub struct InterpolationConfig {
    pub method: InterpolationMethod,
    pub kernel: KernelType,
    pub power: f64,
    pub smoothing: f64,
}

impl Default for InterpolationConfig {
    fn default() -> Self {
        Self {
            method: InterpolationMethod::NaturalNeighbor,
            kernel: KernelType::Gaussian,
            power: 2.0,
            smoothing: 0.0,
        }
    }
}

/// Supported interpolation methods
#[derive(Debug, Clone)]
pub enum InterpolationMethod {
    NaturalNeighbor,
    RadialBasisFunction,
    InverseDistanceWeighting,
    Bilinear,
    Bicubic,
}

/// Kernel types for RBF interpolation
#[derive(Debug, Clone)]
pub enum KernelType {
    Gaussian,
    Multiquadric,
    InverseMultiquadric,
    ThinPlateSpline,
}

/// Spatial interpolator for image and point data
pub struct SpatialInterpolator {
    config: InterpolationConfig,
}

impl SpatialInterpolator {
    /// Create a new spatial interpolator
    pub fn new(config: InterpolationConfig) -> Self {
        Self { config }
    }

    /// Interpolate sparse data points to a regular grid
    pub fn interpolate_to_grid(
        &self,
        points: &Array2<f64>,
        values: &Array1<f64>,
        grid_points: &Array2<f64>,
    ) -> Result<Array1<f64>> {
        if points.nrows() != values.len() {
            return Err(VisionError::InvalidArgument(
                "Number of points must match number of values".to_string(),
            ));
        }

        if points.ncols() != grid_points.ncols() {
            return Err(VisionError::InvalidArgument(
                "Points and grid points must have same dimensionality".to_string(),
            ));
        }

        match self.config.method {
            InterpolationMethod::NaturalNeighbor => {
                self.natural_neighbor_interpolation(points, values, grid_points)
            }
            InterpolationMethod::RadialBasisFunction => {
                self.rbf_interpolation(points, values, grid_points)
            }
            InterpolationMethod::InverseDistanceWeighting => {
                self.idw_interpolation(points, values, grid_points)
            }
            InterpolationMethod::Bilinear => {
                self.bilinear_interpolation(points, values, grid_points)
            }
            InterpolationMethod::Bicubic => self.bicubic_interpolation(points, values, grid_points),
        }
    }

    fn natural_neighbor_interpolation(
        &self,
        _points: &Array2<f64>,
        _values: &Array1<f64>,
        grid_points: &Array2<f64>,
    ) -> Result<Array1<f64>> {
        // Placeholder for natural neighbor interpolation
        // Real implementation would use scirs2_spatial::interpolation::natural_neighbor
        Ok(Array1::zeros(grid_points.nrows()))
    }

    fn rbf_interpolation(
        &self,
        _points: &Array2<f64>,
        _values: &Array1<f64>,
        grid_points: &Array2<f64>,
    ) -> Result<Array1<f64>> {
        // Placeholder for RBF interpolation
        // Real implementation would use scirs2_spatial::interpolation::radial_basis
        Ok(Array1::zeros(grid_points.nrows()))
    }

    fn idw_interpolation(
        &self,
        points: &Array2<f64>,
        values: &Array1<f64>,
        grid_points: &Array2<f64>,
    ) -> Result<Array1<f64>> {
        // Simple inverse distance weighting implementation
        let mut interpolated = Array1::zeros(grid_points.nrows());

        for (i, grid_point) in grid_points.outer_iter().enumerate() {
            let mut weighted_sum = 0.0;
            let mut weight_sum = 0.0;

            for (j, data_point) in points.outer_iter().enumerate() {
                let diff = &grid_point - &data_point;
                let distance = (diff.mapv(|x| x * x).sum()).sqrt();

                if distance < 1e-10 {
                    // Exact match
                    interpolated[i] = values[j];
                    weight_sum = 1.0;
                    weighted_sum = values[j];
                    break;
                } else {
                    let weight = 1.0 / distance.powf(self.config.power);
                    weighted_sum += weight * values[j];
                    weight_sum += weight;
                }
            }

            if weight_sum > 0.0 {
                interpolated[i] = weighted_sum / weight_sum;
            }
        }

        Ok(interpolated)
    }

    fn bilinear_interpolation(
        &self,
        _points: &Array2<f64>,
        _values: &Array1<f64>,
        grid_points: &Array2<f64>,
    ) -> Result<Array1<f64>> {
        // Placeholder for bilinear interpolation
        // Would implement 2D bilinear interpolation for image-like data
        Ok(Array1::zeros(grid_points.nrows()))
    }

    fn bicubic_interpolation(
        &self,
        _points: &Array2<f64>,
        _values: &Array1<f64>,
        grid_points: &Array2<f64>,
    ) -> Result<Array1<f64>> {
        // Placeholder for bicubic interpolation
        Ok(Array1::zeros(grid_points.nrows()))
    }

    /// Interpolate missing pixels in an image
    pub fn interpolate_image_gaps(&self, image: &Tensor, _mask: &Tensor) -> Result<Tensor> {
        // Placeholder for image inpainting using spatial interpolation
        // Would extract known pixels and interpolate missing ones
        Ok(image.clone())
    }

    /// Super-resolution using spatial interpolation
    pub fn super_resolution(&self, low_res_image: &Tensor, scale_factor: f64) -> Result<Tensor> {
        if scale_factor <= 1.0 {
            return Err(VisionError::InvalidArgument(
                "Scale factor must be greater than 1.0".to_string(),
            ));
        }

        // Placeholder for super-resolution
        // Would use spatial interpolation to upscale image
        Ok(low_res_image.clone())
    }
}

/// Image warping using spatial interpolation
pub struct ImageWarper {
    interpolator: SpatialInterpolator,
}

impl ImageWarper {
    /// Create a new image warper
    pub fn new(config: InterpolationConfig) -> Self {
        Self {
            interpolator: SpatialInterpolator::new(config),
        }
    }

    /// Warp image using a displacement field
    pub fn warp_image(&self, image: &Tensor, _displacement_field: &Array2<f64>) -> Result<Tensor> {
        // Placeholder for image warping
        // Would apply displacement field to image coordinates and interpolate
        Ok(image.clone())
    }

    /// Apply barrel distortion correction
    pub fn correct_barrel_distortion(
        &self,
        image: &Tensor,
        _distortion_coeffs: &Array1<f64>,
    ) -> Result<Tensor> {
        // Placeholder for distortion correction
        Ok(image.clone())
    }

    /// Apply pincushion distortion correction
    pub fn correct_pincushion_distortion(
        &self,
        image: &Tensor,
        _distortion_coeffs: &Array1<f64>,
    ) -> Result<Tensor> {
        // Placeholder for distortion correction
        Ok(image.clone())
    }
}

/// Dense optical flow using spatial interpolation
pub struct OpticalFlowInterpolator {
    config: InterpolationConfig,
}

impl OpticalFlowInterpolator {
    /// Create a new optical flow interpolator
    pub fn new(config: InterpolationConfig) -> Self {
        Self { config }
    }

    /// Interpolate sparse optical flow to dense flow field
    pub fn interpolate_flow(
        &self,
        sparse_points: &Array2<f64>,
        flow_vectors: &Array2<f64>,
        image_size: (usize, usize),
    ) -> Result<Array2<f64>> {
        if sparse_points.nrows() != flow_vectors.nrows() {
            return Err(VisionError::InvalidArgument(
                "Number of points must match number of flow vectors".to_string(),
            ));
        }

        // Create dense grid
        let mut grid_points = Vec::new();
        for y in 0..image_size.1 {
            for x in 0..image_size.0 {
                grid_points.push([x as f64, y as f64]);
            }
        }

        let grid_array = Array2::from_shape_vec(
            (image_size.0 * image_size.1, 2),
            grid_points.into_iter().flatten().collect(),
        )
        .map_err(|e| VisionError::Other(anyhow::anyhow!("Grid creation failed: {}", e)))?;

        // Interpolate flow components separately
        let interpolator = SpatialInterpolator::new(self.config.clone());

        let flow_x = flow_vectors.column(0).to_owned();
        let flow_y = flow_vectors.column(1).to_owned();

        let interpolated_x =
            interpolator.interpolate_to_grid(sparse_points, &flow_x, &grid_array)?;
        let interpolated_y =
            interpolator.interpolate_to_grid(sparse_points, &flow_y, &grid_array)?;

        // Combine interpolated components
        let mut dense_flow = Array2::zeros((grid_array.nrows(), 2));
        for i in 0..grid_array.nrows() {
            dense_flow[[i, 0]] = interpolated_x[i];
            dense_flow[[i, 1]] = interpolated_y[i];
        }

        Ok(dense_flow)
    }

    /// Smooth optical flow field
    pub fn smooth_flow_field(&self, flow_field: &Array2<f64>) -> Result<Array2<f64>> {
        // Placeholder for flow field smoothing
        Ok(flow_field.clone())
    }
}

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

    #[test]
    fn test_interpolation_config_default() {
        let config = InterpolationConfig::default();
        assert!(matches!(
            config.method,
            InterpolationMethod::NaturalNeighbor
        ));
        assert!(matches!(config.kernel, KernelType::Gaussian));
    }

    #[test]
    fn test_spatial_interpolator_creation() {
        let config = InterpolationConfig::default();
        let interpolator = SpatialInterpolator::new(config);
        assert!(matches!(
            interpolator.config.method,
            InterpolationMethod::NaturalNeighbor
        ));
    }

    #[test]
    fn test_idw_interpolation() {
        let config = InterpolationConfig {
            method: InterpolationMethod::InverseDistanceWeighting,
            ..Default::default()
        };

        let interpolator = SpatialInterpolator::new(config);

        let points = arr2(&[[0.0, 0.0], [1.0, 1.0]]);
        let values = arr1(&[0.0, 1.0]);
        let grid_points = arr2(&[[0.5, 0.5]]);

        let result = interpolator.interpolate_to_grid(&points, &values, &grid_points);
        assert!(result.is_ok());
    }

    #[test]
    fn test_image_warper_creation() {
        let config = InterpolationConfig::default();
        let warper = ImageWarper::new(config);
        assert!(matches!(
            warper.interpolator.config.method,
            InterpolationMethod::NaturalNeighbor
        ));
    }

    #[test]
    fn test_optical_flow_interpolator() {
        let config = InterpolationConfig::default();
        let flow_interpolator = OpticalFlowInterpolator::new(config);
        assert!(matches!(
            flow_interpolator.config.method,
            InterpolationMethod::NaturalNeighbor
        ));
    }
}