torsh-vision 0.1.2

//! Spatial data structures for efficient computer vision operations

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use crate::{Result, VisionError};
use scirs2_core::ndarray::{arr1, arr2, Array1, Array2, ArrayView2, Axis};
use scirs2_spatial::kdtree::KDTree;
use scirs2_spatial::octree::Octree;
use scirs2_spatial::quadtree::Quadtree;
use scirs2_spatial::rtree::RTree;
use std::collections::HashMap;
use torsh_tensor::Tensor;

/// Point identifier for spatial indexing
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub struct PointId(pub usize);

/// Bounding box for spatial queries
#[derive(Debug, Clone)]
pub struct BoundingBox {
    pub min: Array1<f64>,
    pub max: Array1<f64>,
}

impl BoundingBox {
    /// Create a new bounding box
    pub fn new(min: Array1<f64>, max: Array1<f64>) -> Result<Self> {
        if min.len() != max.len() {
            return Err(VisionError::InvalidArgument(
                "Min and max coordinates must have same dimension".to_string(),
            ));
        }

        for (i, (&min_val, &max_val)) in min.iter().zip(max.iter()).enumerate() {
            if min_val > max_val {
                return Err(VisionError::InvalidArgument(format!(
                    "Min coordinate {} is greater than max coordinate at dimension {}",
                    min_val, i
                )));
            }
        }

        Ok(Self { min, max })
    }

    /// Create bounding box from a set of points
    pub fn from_points(points: &Array2<f64>) -> Result<Self> {
        if points.is_empty() {
            return Err(VisionError::InvalidArgument(
                "Cannot create bounding box from empty points".to_string(),
            ));
        }

        let dims = points.ncols();
        let mut min = Array1::from_elem(dims, f64::INFINITY);
        let mut max = Array1::from_elem(dims, f64::NEG_INFINITY);

        for point in points.outer_iter() {
            for (i, &coord) in point.iter().enumerate() {
                min[i] = min[i].min(coord);
                max[i] = max[i].max(coord);
            }
        }

        Self::new(min, max)
    }

    /// Check if a point is inside the bounding box
    pub fn contains(&self, point: &ArrayView2<f64>) -> bool {
        if point.len() != self.min.len() {
            return false;
        }

        for (i, &coord) in point.iter().enumerate() {
            if coord < self.min[i] || coord > self.max[i] {
                return false;
            }
        }

        true
    }

    /// Compute volume of the bounding box
    pub fn volume(&self) -> f64 {
        self.max
            .iter()
            .zip(self.min.iter())
            .map(|(&max_val, &min_val)| max_val - min_val)
            .product()
    }
}

/// Spatial index for efficient object detection and tracking
pub struct SpatialObjectTracker {
    spatial_index: Option<RTree<ObjectId>>,
    object_data: HashMap<ObjectId, ObjectMetadata>,
    frame_history: Vec<FrameData>,
    max_history: usize,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub struct ObjectId(pub usize);

#[derive(Debug, Clone)]
pub struct ObjectMetadata {
    pub bbox: BoundingBox,
    pub confidence: f64,
    pub class_id: usize,
    pub last_seen_frame: usize,
}

#[derive(Debug, Clone)]
pub struct FrameData {
    pub frame_id: usize,
    pub detections: Vec<Detection>,
    pub timestamp: f64,
}

#[derive(Debug, Clone)]
pub struct Detection {
    pub bbox: BoundingBox,
    pub confidence: f64,
    pub class_id: usize,
    pub features: Option<Array1<f64>>,
}

#[derive(Debug, Clone)]
pub struct TrackedObject {
    pub object_id: ObjectId,
    pub bbox: BoundingBox,
    pub confidence: f64,
    pub class_id: usize,
    pub track_length: usize,
}

impl SpatialObjectTracker {
    /// Create a new spatial object tracker
    pub fn new(max_history: usize) -> Self {
        Self {
            spatial_index: None,
            object_data: HashMap::new(),
            frame_history: Vec::new(),
            max_history,
        }
    }

    /// Track objects in a new frame
    pub fn track_objects(
        &mut self,
        detections: &[Detection],
        frame_id: usize,
    ) -> Result<Vec<TrackedObject>> {
        // Store frame data
        let frame_data = FrameData {
            frame_id,
            detections: detections.to_vec(),
            timestamp: std::time::SystemTime::now()
                .duration_since(std::time::UNIX_EPOCH)
                .expect("system time should be after UNIX_EPOCH")
                .as_secs_f64(),
        };

        self.frame_history.push(frame_data);
        if self.frame_history.len() > self.max_history {
            self.frame_history.remove(0);
        }

        // Perform tracking (simplified implementation)
        let mut tracked_objects = Vec::new();

        for (i, detection) in detections.iter().enumerate() {
            let object_id = ObjectId(i);

            // Create tracked object
            let tracked_object = TrackedObject {
                object_id,
                bbox: detection.bbox.clone(),
                confidence: detection.confidence,
                class_id: detection.class_id,
                track_length: 1,
            };

            tracked_objects.push(tracked_object);

            // Update object metadata
            let metadata = ObjectMetadata {
                bbox: detection.bbox.clone(),
                confidence: detection.confidence,
                class_id: detection.class_id,
                last_seen_frame: frame_id,
            };

            self.object_data.insert(object_id, metadata);
        }

        Ok(tracked_objects)
    }

    /// Query objects in a spatial region
    pub fn query_region(&self, region: &BoundingBox) -> Result<Vec<ObjectId>> {
        // Placeholder implementation - would use actual R-tree queries
        let mut objects_in_region = Vec::new();

        for (&object_id, metadata) in &self.object_data {
            // Simple overlap check (placeholder)
            if self.bboxes_overlap(&metadata.bbox, region) {
                objects_in_region.push(object_id);
            }
        }

        Ok(objects_in_region)
    }

    fn bboxes_overlap(&self, bbox1: &BoundingBox, bbox2: &BoundingBox) -> bool {
        if bbox1.min.len() != bbox2.min.len() {
            return false;
        }

        for i in 0..bbox1.min.len() {
            if bbox1.max[i] < bbox2.min[i] || bbox1.min[i] > bbox2.max[i] {
                return false;
            }
        }

        true
    }

    /// Get object trajectory
    pub fn get_trajectory(&self, object_id: ObjectId) -> Vec<BoundingBox> {
        // Placeholder - would track object across frames
        if let Some(metadata) = self.object_data.get(&object_id) {
            vec![metadata.bbox.clone()]
        } else {
            Vec::new()
        }
    }
}

/// 3D point cloud processor using spatial data structures
pub struct PointCloudProcessor {
    octree: Option<Octree>,
    points: Array2<f64>,
    point_metadata: HashMap<PointId, PointMetadata>,
}

#[derive(Debug, Clone)]
pub struct PointMetadata {
    pub color: Option<Array1<f32>>,
    pub normal: Option<Array1<f64>>,
    pub intensity: Option<f64>,
}

impl PointCloudProcessor {
    /// Create a new point cloud processor
    pub fn new() -> Self {
        Self {
            octree: None,
            points: Array2::zeros((0, 3)),
            point_metadata: HashMap::new(),
        }
    }

    /// Build spatial index from point cloud
    pub fn build_index(&mut self, points: Array2<f64>) -> Result<()> {
        if points.ncols() != 3 {
            return Err(VisionError::InvalidArgument(
                "Point cloud must have 3D coordinates".to_string(),
            ));
        }

        self.points = points;

        // Create bounding box for octree
        let _bbox = BoundingBox::from_points(&self.points)?;

        // Build octree (placeholder)
        // let mut octree = Octree::new(bbox);
        // for (i, point) in self.points.outer_iter().enumerate() {
        //     octree.insert(PointId(i), point.to_vec());
        // }
        // self.octree = Some(octree);

        Ok(())
    }

    /// Query points within a region
    pub fn query_region(&self, region: &BoundingBox) -> Result<Vec<PointId>> {
        // Placeholder implementation
        let mut points_in_region = Vec::new();

        for (i, point) in self.points.outer_iter().enumerate() {
            if region.contains(&point.view().insert_axis(Axis(1))) {
                points_in_region.push(PointId(i));
            }
        }

        Ok(points_in_region)
    }

    /// Find nearest neighbors in 3D space
    pub fn find_neighbors(&self, query_point: &Array1<f64>, k: usize) -> Result<Vec<PointId>> {
        if query_point.len() != 3 {
            return Err(VisionError::InvalidArgument(
                "Query point must be 3D".to_string(),
            ));
        }

        // Simple distance-based neighbor search (placeholder)
        let mut distances: Vec<(PointId, f64)> = Vec::new();

        for (i, point) in self.points.outer_iter().enumerate() {
            let diff = &point - query_point;
            let distance = (diff.mapv(|x| x * x).sum()).sqrt();
            distances.push((PointId(i), distance));
        }

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

        Ok(distances.into_iter().map(|(id, _)| id).collect())
    }

    /// Segment point cloud into regions
    pub fn segment_regions(&self, _region_size: f64) -> Result<Vec<Vec<PointId>>> {
        // Placeholder for region-based segmentation
        let mut regions = Vec::new();

        // Simple grid-based segmentation
        if !self.points.is_empty() {
            let bbox = BoundingBox::from_points(&self.points)?;
            let _dims = bbox.max.len();

            // Create a single region for now (placeholder)
            let all_points: Vec<PointId> = (0..self.points.nrows()).map(PointId).collect();
            regions.push(all_points);
        }

        Ok(regions)
    }
}

impl Default for PointCloudProcessor {
    fn default() -> Self {
        Self::new()
    }
}

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

    #[test]
    fn test_bounding_box_creation() {
        let min = arr1(&[0.0, 0.0]);
        let max = arr1(&[1.0, 1.0]);
        let bbox = BoundingBox::new(min, max);
        assert!(bbox.is_ok());
    }

    #[test]
    fn test_bounding_box_invalid() {
        let min = arr1(&[1.0, 1.0]);
        let max = arr1(&[0.0, 0.0]);
        let bbox = BoundingBox::new(min, max);
        assert!(bbox.is_err());
    }

    #[test]
    fn test_bounding_box_from_points() {
        let points = arr2(&[[0.0, 0.0], [1.0, 1.0], [0.5, 0.5]]);
        let bbox = BoundingBox::from_points(&points);
        assert!(bbox.is_ok());

        let bbox = bbox.expect("operation should succeed");
        assert_eq!(bbox.min[0], 0.0);
        assert_eq!(bbox.max[0], 1.0);
        assert_eq!(bbox.volume(), 1.0);
    }

    #[test]
    fn test_spatial_object_tracker() {
        let mut tracker = SpatialObjectTracker::new(10);

        let detection = Detection {
            bbox: BoundingBox::new(arr1(&[0.0, 0.0]), arr1(&[1.0, 1.0]))
                .expect("operation should succeed"),
            confidence: 0.9,
            class_id: 1,
            features: None,
        };

        let result = tracker.track_objects(&[detection], 0);
        assert!(result.is_ok());
        assert_eq!(result.expect("operation should succeed").len(), 1);
    }

    #[test]
    fn test_point_cloud_processor() {
        let mut processor = PointCloudProcessor::new();
        let points = arr2(&[[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]]);

        let result = processor.build_index(points);
        assert!(result.is_ok());
    }
}