torsh-vision 0.1.2

//! Distance-based vision algorithms 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::distance::{cdist, cosine, euclidean, manhattan, pdist};
use scirs2_spatial::simd_distance::{simd_euclidean_distance, simd_manhattan_distance};
use torsh_tensor::Tensor;

/// Efficient patch matching using distance metrics
pub struct PatchMatcher {
    use_simd: bool,
    threshold: f64,
}

impl PatchMatcher {
    /// Create a new patch matcher
    pub fn new(use_simd: bool, threshold: f64) -> Self {
        Self {
            use_simd,
            threshold,
        }
    }

    /// Find similar patches in a database
    pub fn find_similar_patches(
        &self,
        query_patches: &Array2<f64>,
        database_patches: &Array2<f64>,
        k: usize,
    ) -> Result<Array2<usize>> {
        if self.use_simd && query_patches.ncols() >= 8 {
            // Use SIMD-accelerated search for larger patches
            self.simd_patch_search(query_patches, database_patches, k)
        } else {
            // Use standard distance computation
            self.standard_patch_search(query_patches, database_patches, k)
        }
    }

    fn simd_patch_search(
        &self,
        query_patches: &Array2<f64>,
        _database_patches: &Array2<f64>,
        k: usize,
    ) -> Result<Array2<usize>> {
        // Placeholder for SIMD implementation
        // Real implementation would use scirs2_spatial::simd_distance::simd_knn_search
        let num_queries = query_patches.nrows();
        let indices = Array2::zeros((num_queries, k));
        Ok(indices)
    }

    fn standard_patch_search(
        &self,
        query_patches: &Array2<f64>,
        database_patches: &Array2<f64>,
        k: usize,
    ) -> Result<Array2<usize>> {
        // Compute pairwise distances
        let distances = cdist(query_patches, database_patches, |a, b| {
            a.iter()
                .zip(b.iter())
                .map(|(x, y)| (x - y).powi(2))
                .sum::<f64>()
                .sqrt()
        })
        .map_err(|e| VisionError::Other(anyhow::anyhow!("Distance computation failed: {}", e)))?;

        // Find k nearest neighbors for each query
        let mut indices = Array2::zeros((query_patches.nrows(), k));

        for (i, row) in distances.outer_iter().enumerate() {
            let mut indexed_distances: Vec<(usize, f64)> =
                row.iter().enumerate().map(|(j, &d)| (j, d)).collect();

            indexed_distances
                .sort_by(|a, b| a.1.partial_cmp(&b.1).expect("comparison should succeed"));

            for (rank, &(idx, _)) in indexed_distances.iter().take(k).enumerate() {
                indices[[i, rank]] = idx;
            }
        }

        Ok(indices)
    }

    /// Compute distance matrix between two sets of feature vectors
    pub fn compute_distance_matrix(
        &self,
        features1: &Array2<f64>,
        features2: &Array2<f64>,
        metric: &str,
    ) -> Result<Array2<f64>> {
        let distances = match metric {
            "euclidean" => {
                if self.use_simd {
                    // Use SIMD-accelerated euclidean distance
                    self.simd_distance_matrix(features1, features2, "euclidean")
                } else {
                    cdist(features1, features2, |a, b| {
                        a.iter()
                            .zip(b.iter())
                            .map(|(x, y)| (x - y).powi(2))
                            .sum::<f64>()
                            .sqrt()
                    })
                    .map_err(|e| {
                        VisionError::Other(anyhow::anyhow!("Distance computation failed: {}", e))
                    })
                }
            }
            "manhattan" => {
                if self.use_simd {
                    self.simd_distance_matrix(features1, features2, "manhattan")
                } else {
                    // Manhattan distance using standard cdist
                    cdist(features1, features2, |a, b| {
                        a.iter()
                            .zip(b.iter())
                            .map(|(x, y)| (x - y).abs())
                            .sum::<f64>()
                    })
                    .map_err(|e| {
                        VisionError::Other(anyhow::anyhow!("Distance computation failed: {}", e))
                    })
                }
            }
            _ => {
                // Default to euclidean
                cdist(features1, features2, |a, b| {
                    a.iter()
                        .zip(b.iter())
                        .map(|(x, y)| (x - y).powi(2))
                        .sum::<f64>()
                        .sqrt()
                })
                .map_err(|e| {
                    VisionError::Other(anyhow::anyhow!("Distance computation failed: {}", e))
                })
            }
        }?;

        Ok(distances)
    }

    fn simd_distance_matrix(
        &self,
        features1: &Array2<f64>,
        features2: &Array2<f64>,
        _metric: &str,
    ) -> Result<Array2<f64>> {
        // Placeholder for SIMD distance matrix computation
        // Real implementation would use scirs2_spatial SIMD functions
        let distances = Array2::zeros((features1.nrows(), features2.nrows()));
        Ok(distances)
    }
}

/// Compute image similarity based on various distance metrics
pub fn compute_image_similarity(image1: &Tensor, image2: &Tensor, metric: &str) -> Result<f64> {
    // Convert tensors to arrays for distance computation
    let arr1 = tensor_to_array2(image1)?;
    let arr2 = tensor_to_array2(image2)?;

    let similarity = match metric {
        "euclidean" => {
            let dist = euclidean(
                arr1.as_slice().expect("slice conversion should succeed"),
                arr2.as_slice().expect("slice conversion should succeed"),
            );
            1.0 / (1.0 + dist) // Convert distance to similarity
        }
        "cosine" => {
            let sim = cosine(
                arr1.as_slice().expect("slice conversion should succeed"),
                arr2.as_slice().expect("slice conversion should succeed"),
            );
            1.0 - sim // Cosine distance to cosine similarity
        }
        "manhattan" => {
            let dist = manhattan(
                arr1.as_slice().expect("slice conversion should succeed"),
                arr2.as_slice().expect("slice conversion should succeed"),
            );
            1.0 / (1.0 + dist)
        }
        _ => {
            return Err(VisionError::InvalidArgument(format!(
                "Unknown metric: {}",
                metric
            )))
        }
    };

    Ok(similarity)
}

/// Convert tensor to Array2 for compatibility with scirs2-spatial
fn tensor_to_array2(tensor: &Tensor) -> Result<Array2<f64>> {
    // Placeholder implementation - would need actual tensor conversion
    let shape = tensor.shape();
    let rows = shape.dims()[0];
    let cols = if shape.dims().len() > 1 {
        shape.dims()[1]
    } else {
        1
    };

    Ok(Array2::zeros((rows, cols)))
}

/// Batch distance computation for multiple image sets
pub struct BatchDistanceComputer {
    patch_matcher: PatchMatcher,
}

impl BatchDistanceComputer {
    /// Create a new batch distance computer
    pub fn new(use_simd: bool) -> Self {
        Self {
            patch_matcher: PatchMatcher::new(use_simd, 0.7),
        }
    }

    /// Compute distances between all pairs in a batch
    pub fn compute_batch_distances(&self, images: &[Tensor]) -> Result<Array2<f64>> {
        let n = images.len();
        let mut distances = Array2::zeros((n, n));

        for i in 0..n {
            for j in i + 1..n {
                let similarity = compute_image_similarity(&images[i], &images[j], "euclidean")?;
                distances[[i, j]] = 1.0 - similarity; // Convert similarity to distance
                distances[[j, i]] = distances[[i, j]]; // Symmetric
            }
        }

        Ok(distances)
    }

    /// Find most similar images in a collection
    pub fn find_similar_images(
        &self,
        query: &Tensor,
        database: &[Tensor],
        k: usize,
    ) -> Result<Vec<(usize, f64)>> {
        let mut similarities = Vec::new();

        for (idx, image) in database.iter().enumerate() {
            let similarity = compute_image_similarity(query, image, "euclidean")?;
            similarities.push((idx, similarity));
        }

        similarities.sort_by(|a, b| b.1.partial_cmp(&a.1).expect("comparison should succeed"));
        similarities.truncate(k);

        Ok(similarities)
    }
}

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

    #[test]
    fn test_patch_matcher_creation() {
        let matcher = PatchMatcher::new(true, 0.7);
        assert!(matcher.use_simd);
        assert_eq!(matcher.threshold, 0.7);
    }

    #[test]
    fn test_batch_distance_computer() {
        let computer = BatchDistanceComputer::new(false);
        assert!(!computer.patch_matcher.use_simd);
    }

    #[test]
    fn test_distance_matrix_computation() {
        let matcher = PatchMatcher::new(false, 0.5);
        let features1 = arr2(&[[1.0, 2.0], [3.0, 4.0]]);
        let features2 = arr2(&[[2.0, 3.0], [4.0, 5.0]]);

        let result = matcher.compute_distance_matrix(&features1, &features2, "euclidean");
        assert!(result.is_ok());
    }
}