//! Autonomous vehicle perception module
//!
//! Provides:
//! - LiDAR point cloud processing and segmentation
//! - Sensor fusion (camera, LiDAR, radar integration)
//! - Path planning integration with perception
//! - Object tracking and trajectory prediction
//! - Safety-critical validation and verification

// Modules to be implemented
// pub mod lidar;
// pub mod fusion;
// pub mod tracking;
// pub mod planning;
// pub mod safety;
// pub mod calibration;

// Placeholder structs for unimplemented modules
/// Path plan result
#[derive(Debug, Clone)]
pub struct PathPlan {
    pub waypoints: Vec<[f32; 3]>,
    pub trajectory_cost: f32,
}

/// Placeholder PathPlanner
#[derive(Debug, Clone)]
pub struct PathPlanner {
    // Placeholder implementation
}

impl PathPlanner {
    /// Plan a path based on tracked objects and sensor frame
    pub fn plan(
        &mut self,
        _objects: &[TrackedObject],
        _frame: &SensorFrame,
    ) -> VisionResult<PathPlan> {
        // Placeholder implementation
        Ok(PathPlan {
            waypoints: vec![],
            trajectory_cost: 0.0,
        })
    }
}

use crate::detection::three_d::{BoundingBox3D, Detection3DResult, PointCloud};
use crate::{DetectionResult, VisionError, VisionResult};
use rtx_tensor::Tensor;
use std::collections::HashMap;
use tracing::debug;

/// Autonomous driving sensor types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum SensorType {
    /// Light Detection and Ranging
    LiDAR,
    /// Camera (RGB)
    Camera,
    /// Radar
    Radar,
    /// Inertial Measurement Unit
    IMU,
    /// Global Navigation Satellite System
    GNSS,
    /// Wheel odometry
    Odometry,
}

/// Sensor data timestamp
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
pub struct Timestamp {
    pub seconds: u64,
    pub nanoseconds: u32,
}

impl Timestamp {
    pub fn now() -> Self {
        let duration = std::time::SystemTime::now()
            .duration_since(std::time::UNIX_EPOCH)
            .unwrap();

        Self {
            seconds: duration.as_secs(),
            nanoseconds: duration.subsec_nanos(),
        }
    }

    pub fn to_seconds(&self) -> f64 {
        self.seconds as f64 + self.nanoseconds as f64 * 1e-9
    }

    pub fn duration_since(&self, other: &Self) -> f64 {
        self.to_seconds() - other.to_seconds()
    }
}

/// Multi-sensor data frame
#[derive(Debug, Clone)]
pub struct SensorFrame {
    /// Frame timestamp
    pub timestamp: Timestamp,
    /// LiDAR point cloud data
    pub lidar_data: Option<LidarData>,
    /// Camera image data
    pub camera_data: Option<CameraData>,
    /// Radar detection data
    pub radar_data: Option<RadarData>,
    /// IMU measurement
    pub imu_data: Option<ImuData>,
    /// GNSS position
    pub gnss_data: Option<GnssData>,
    /// Odometry data
    pub odometry_data: Option<OdometryData>,
}

/// LiDAR sensor data
#[derive(Debug, Clone)]
pub struct LidarData {
    /// Point cloud
    pub point_cloud: PointCloud,
    /// Sensor pose relative to vehicle
    pub sensor_pose: Pose3D,
    /// Sensor configuration
    pub config: LidarConfig,
}

/// LiDAR configuration
#[derive(Debug, Clone)]
pub struct LidarConfig {
    /// Sensor model name
    pub model: String,
    /// Vertical field of view (degrees)
    pub vertical_fov: (f32, f32), // (min, max)
    /// Horizontal field of view (degrees)
    pub horizontal_fov: f32, // typically 360
    /// Vertical resolution (number of beams)
    pub vertical_resolution: usize,
    /// Angular resolution (degrees per step)
    pub angular_resolution: f32,
    /// Maximum range (meters)
    pub max_range: f32,
    /// Minimum range (meters)
    pub min_range: f32,
}

impl Default for LidarConfig {
    fn default() -> Self {
        // Default configuration similar to Velodyne VLP-16
        Self {
            model: "VLP-16".to_string(),
            vertical_fov: (-15.0, 15.0),
            horizontal_fov: 360.0,
            vertical_resolution: 16,
            angular_resolution: 0.2, // 0.2 degrees
            max_range: 100.0,
            min_range: 0.3,
        }
    }
}

/// Camera sensor data
#[derive(Debug, Clone)]
pub struct CameraData {
    /// RGB image tensor (3, H, W)
    pub image: Tensor,
    /// Camera intrinsic parameters
    pub intrinsics: CameraIntrinsics,
    /// Camera pose relative to vehicle
    pub sensor_pose: Pose3D,
    /// Exposure and timing info
    pub metadata: CameraMetadata,
}

/// Camera intrinsic parameters
#[derive(Debug, Clone)]
pub struct CameraIntrinsics {
    /// Focal length (fx, fy)
    pub focal_length: (f32, f32),
    /// Principal point (cx, cy)
    pub principal_point: (f32, f32),
    /// Distortion coefficients [k1, k2, p1, p2, k3]
    pub distortion: [f32; 5],
    /// Image size (width, height)
    pub image_size: (usize, usize),
}

/// Camera metadata
#[derive(Debug, Clone)]
pub struct CameraMetadata {
    /// Exposure time (seconds)
    pub exposure_time: f32,
    /// ISO sensitivity
    pub iso: u32,
    /// Frame sequence number
    pub frame_id: u64,
}

/// Radar sensor data
#[derive(Debug, Clone)]
pub struct RadarData {
    /// Radar detections
    pub detections: Vec<RadarDetection>,
    /// Sensor pose relative to vehicle
    pub sensor_pose: Pose3D,
    /// Radar configuration
    pub config: RadarConfig,
}

/// Individual radar detection
#[derive(Debug, Clone)]
pub struct RadarDetection {
    /// Range (meters)
    pub range: f32,
    /// Azimuth angle (radians)
    pub azimuth: f32,
    /// Elevation angle (radians)
    pub elevation: f32,
    /// Radial velocity (m/s)
    pub velocity: f32,
    /// Radar cross section (dBsm)
    pub rcs: f32,
    /// Signal-to-noise ratio (dB)
    pub snr: f32,
}

/// Radar configuration
#[derive(Debug, Clone)]
pub struct RadarConfig {
    /// Sensor model
    pub model: String,
    /// Frequency (GHz)
    pub frequency: f32,
    /// Maximum range (meters)
    pub max_range: f32,
    /// Range resolution (meters)
    pub range_resolution: f32,
    /// Azimuth field of view (degrees)
    pub azimuth_fov: f32,
    /// Elevation field of view (degrees)
    pub elevation_fov: f32,
}

/// IMU sensor data
#[derive(Debug, Clone)]
pub struct ImuData {
    /// Linear acceleration (m/s²) in body frame
    pub acceleration: [f32; 3],
    /// Angular velocity (rad/s) in body frame
    pub angular_velocity: [f32; 3],
    /// Magnetic field (µT) in body frame
    pub magnetic_field: Option<[f32; 3]>,
    /// Measurement covariance
    pub covariance: Option<[[f32; 6]; 6]>,
}

/// GNSS sensor data
#[derive(Debug, Clone)]
pub struct GnssData {
    /// Latitude (degrees)
    pub latitude: f64,
    /// Longitude (degrees)
    pub longitude: f64,
    /// Altitude (meters above sea level)
    pub altitude: f32,
    /// Position accuracy (meters)
    pub position_accuracy: f32,
    /// Fix quality
    pub fix_quality: GnssFixQuality,
    /// Number of satellites
    pub num_satellites: u8,
}

/// GNSS fix quality
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum GnssFixQuality {
    NoFix,
    GPS,
    DGPS,
    RTK,
    FloatRTK,
}

/// Vehicle odometry data
#[derive(Debug, Clone)]
pub struct OdometryData {
    /// Vehicle velocity (m/s) in body frame
    pub velocity: [f32; 3],
    /// Vehicle pose estimate
    pub pose: Pose3D,
    /// Pose covariance
    pub covariance: [[f32; 6]; 6],
    /// Wheel speeds (rad/s) [front_left, front_right, rear_left, rear_right]
    pub wheel_speeds: Option<[f32; 4]>,
}

/// 3D pose representation
#[derive(Debug, Clone)]
pub struct Pose3D {
    /// Position (x, y, z) in meters
    pub position: [f32; 3],
    /// Orientation quaternion (w, x, y, z)
    pub orientation: [f32; 4],
}

impl Default for Pose3D {
    fn default() -> Self {
        Self {
            position: [0.0, 0.0, 0.0],
            orientation: [1.0, 0.0, 0.0, 0.0], // Identity quaternion
        }
    }
}

impl Pose3D {
    /// Create identity pose
    pub fn identity() -> Self {
        Self::default()
    }

    /// Create pose from position and Euler angles
    pub fn from_position_euler(position: [f32; 3], euler: [f32; 3]) -> Self {
        let (roll, pitch, yaw) = (euler[0], euler[1], euler[2]);

        // Convert Euler angles to quaternion
        let cr = (roll * 0.5).cos();
        let sr = (roll * 0.5).sin();
        let cp = (pitch * 0.5).cos();
        let sp = (pitch * 0.5).sin();
        let cy = (yaw * 0.5).cos();
        let sy = (yaw * 0.5).sin();

        let w = cr * cp * cy + sr * sp * sy;
        let x = sr * cp * cy - cr * sp * sy;
        let y = cr * sp * cy + sr * cp * sy;
        let z = cr * cp * sy - sr * sp * cy;

        Self {
            position,
            orientation: [w, x, y, z],
        }
    }

    /// Get transformation matrix (4x4)
    pub fn to_matrix(&self) -> [[f32; 4]; 4] {
        let [x, y, z] = self.position;
        let [w, qx, qy, qz] = self.orientation;

        // Convert quaternion to rotation matrix
        let xx = qx * qx;
        let yy = qy * qy;
        let zz = qz * qz;
        let xy = qx * qy;
        let xz = qx * qz;
        let yz = qy * qz;
        let wx = w * qx;
        let wy = w * qy;
        let wz = w * qz;

        [
            [1.0 - 2.0 * (yy + zz), 2.0 * (xy - wz), 2.0 * (xz + wy), x],
            [2.0 * (xy + wz), 1.0 - 2.0 * (xx + zz), 2.0 * (yz - wx), y],
            [2.0 * (xz - wy), 2.0 * (yz + wx), 1.0 - 2.0 * (xx + yy), z],
            [0.0, 0.0, 0.0, 1.0],
        ]
    }

    /// Transform point from this coordinate frame to world
    pub fn transform_point(&self, point: [f32; 3]) -> [f32; 3] {
        let matrix = self.to_matrix();
        [
            matrix[0][0] * point[0]
                + matrix[0][1] * point[1]
                + matrix[0][2] * point[2]
                + matrix[0][3],
            matrix[1][0] * point[0]
                + matrix[1][1] * point[1]
                + matrix[1][2] * point[2]
                + matrix[1][3],
            matrix[2][0] * point[0]
                + matrix[2][1] * point[1]
                + matrix[2][2] * point[2]
                + matrix[2][3],
        ]
    }
}

/// Autonomous driving perception pipeline
pub struct AutonomousPipeline {
    /// Object detection models
    detectors: HashMap<SensorType, Box<dyn AutonomousDetector>>,
    /// Sensor fusion module
    // fusion: fusion::SensorFusion,
    /// Object tracker
    // tracker: tracking::MultiObjectTracker,
    /// Path planner integration
    path_planner: Option<PathPlanner>,
    // Safety validator
    // safety_validator: safety::SafetyValidator,
}

impl AutonomousPipeline {
    /// Create new autonomous perception pipeline
    pub fn new() -> VisionResult<Self> {
        let detectors = HashMap::new();

        // Initialize default detectors
        // detectors.insert(SensorType::LiDAR, Box::new(lidar::LidarDetector::new()?));

        Ok(Self {
            detectors,
            // fusion: fusion::SensorFusion::new()?,
            // tracker: tracking::MultiObjectTracker::new()?,
            path_planner: None,
            // safety_validator: safety::SafetyValidator::new(),
        })
    }

    /// Process multi-sensor frame
    pub fn process_frame(
        &mut self,
        frame: &SensorFrame,
    ) -> VisionResult<AutonomousPerceptionResult> {
        let start_time = std::time::Instant::now();

        let mut detections = Vec::new();
        let mut detection_results = HashMap::new();

        // Process LiDAR data
        if let Some(ref lidar_data) = frame.lidar_data
            && let Some(detector) = self.detectors.get_mut(&SensorType::LiDAR)
        {
            let result = detector.detect_lidar(lidar_data)?;
            detections.extend(result.boxes_3d.clone());
            detection_results.insert(
                SensorType::LiDAR,
                DetectionResultVariant::Detection3D(result),
            );
        }

        // Process camera data
        if let Some(ref camera_data) = frame.camera_data
            && let Some(detector) = self.detectors.get_mut(&SensorType::Camera)
        {
            let result = detector.detect_camera(camera_data)?;
            // Convert 2D detections to 3D estimates (simplified)
            detection_results.insert(
                SensorType::Camera,
                DetectionResultVariant::Detection2D(result),
            );
        }

        // Process radar data
        if let Some(ref radar_data) = frame.radar_data {
            let radar_detections = self.process_radar_data(radar_data)?;
            detections.extend(radar_detections);
        }

        // Sensor fusion
        // let fused_detections = self.fusion.fuse_detections(&detections, frame)?;
        let fused_detections = detections.clone(); // Temporary: use raw detections

        // Object tracking
        // let tracked_objects = self.tracker.update(&fused_detections, frame.timestamp)?;
        let tracked_objects = vec![]; // Temporary: empty tracking

        // Safety validation
        // let safety_status = self.safety_validator.validate(&tracked_objects, frame)?;
        let safety_status = SafetyStatus {
            safety_level: SafetyLevel::Safe,
            collision_risks: vec![],
            recommended_actions: vec![],
            time_to_collision: None,
        }; // Temporary default

        // Path planning (if available)
        let path_planning_result = if let Some(ref mut planner) = self.path_planner {
            Some(planner.plan(&tracked_objects, frame)?)
        } else {
            None
        };

        let processing_time = start_time.elapsed().as_millis() as f32;

        Ok(AutonomousPerceptionResult {
            timestamp: frame.timestamp,
            raw_detections: detection_results,
            fused_detections,
            tracked_objects,
            safety_status,
            path_plan: path_planning_result,
            processing_time_ms: processing_time,
        })
    }

    /// Add detector for specific sensor type
    pub fn add_detector(&mut self, sensor_type: SensorType, detector: Box<dyn AutonomousDetector>) {
        self.detectors.insert(sensor_type, detector);
    }

    /// Set path planner
    pub fn set_path_planner(&mut self, planner: PathPlanner) {
        self.path_planner = Some(planner);
    }

    /// Process radar data into 3D detections
    fn process_radar_data(&self, radar_data: &RadarData) -> VisionResult<Vec<BoundingBox3D>> {
        let mut detections = Vec::new();

        for detection in &radar_data.detections {
            if detection.range > 0.5 && detection.snr > 10.0 {
                // Basic filtering
                // Convert spherical to cartesian coordinates
                let x = detection.range * detection.azimuth.cos() * detection.elevation.cos();
                let y = detection.range * detection.azimuth.sin() * detection.elevation.cos();
                let z = detection.range * detection.elevation.sin();

                // Transform to vehicle coordinates
                let point_sensor = [x, y, z];
                let point_vehicle = radar_data.sensor_pose.transform_point(point_sensor);

                // Create 3D bounding box (with estimated size)
                let bbox_3d = BoundingBox3D::new(
                    point_vehicle,
                    [2.0, 1.0, 1.5],      // Estimated vehicle dimensions
                    [0.0, 0.0, 0.0],      // No rotation estimate from radar
                    detection.snr / 50.0, // Convert SNR to confidence
                    0,                    // Unknown class from radar alone
                );

                detections.push(bbox_3d);
            }
        }

        debug!(
            "Processed {} radar detections into {} 3D boxes",
            radar_data.detections.len(),
            detections.len()
        );
        Ok(detections)
    }
}

/// Autonomous detector trait
pub trait AutonomousDetector {
    /// Detect objects from LiDAR data
    fn detect_lidar(&mut self, _lidar_data: &LidarData) -> VisionResult<Detection3DResult> {
        Err(VisionError::invalid_input("LiDAR detection not supported"))
    }

    /// Detect objects from camera data
    fn detect_camera(&mut self, _camera_data: &CameraData) -> VisionResult<DetectionResult> {
        Err(VisionError::invalid_input("Camera detection not supported"))
    }

    /// Get supported sensor types
    fn supported_sensors(&self) -> Vec<SensorType>;
}

/// Detection result variants for different sensors
#[derive(Debug, Clone)]
pub enum DetectionResultVariant {
    Detection2D(DetectionResult),
    Detection3D(Detection3DResult),
}

/// Tracked object in 3D space
#[derive(Debug, Clone)]
pub struct TrackedObject {
    /// Unique track ID
    pub track_id: usize,
    /// Current 3D bounding box
    pub bbox_3d: BoundingBox3D,
    /// Object velocity (m/s) in world coordinates
    pub velocity: [f32; 3],
    /// Track confidence
    pub track_confidence: f32,
    /// Object classification
    pub object_class: ObjectClass,
    /// Track age (number of frames)
    pub age: usize,
    /// Number of consecutive detections
    pub hits: usize,
    /// Time since last detection
    pub time_since_update: f32,
    /// Predicted trajectory
    pub predicted_trajectory: Vec<TrajectoryPoint>,
}

/// Object classification for autonomous driving
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ObjectClass {
    Vehicle,
    Pedestrian,
    Cyclist,
    Motorcycle,
    Truck,
    Bus,
    TrafficSign,
    TrafficLight,
    Barrier,
    Construction,
    Animal,
    Unknown,
}

/// Point in predicted trajectory
#[derive(Debug, Clone)]
pub struct TrajectoryPoint {
    /// Position at time t
    pub position: [f32; 3],
    /// Time from now (seconds)
    pub time_offset: f32,
    /// Uncertainty covariance
    pub covariance: [[f32; 3]; 3],
}

/// Safety assessment result
#[derive(Debug, Clone)]
pub struct SafetyStatus {
    /// Overall safety level
    pub safety_level: SafetyLevel,
    /// Potential collision risks
    pub collision_risks: Vec<CollisionRisk>,
    /// Recommended actions
    pub recommended_actions: Vec<SafetyAction>,
    /// Time to collision estimates
    pub time_to_collision: Option<f32>,
}

/// Safety levels
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum SafetyLevel {
    Safe,
    Caution,
    Warning,
    Critical,
    Emergency,
}

/// Collision risk assessment
#[derive(Debug, Clone)]
pub struct CollisionRisk {
    /// Object involved in potential collision
    pub object_id: usize,
    /// Risk probability [0, 1]
    pub probability: f32,
    /// Time to collision (seconds)
    pub time_to_collision: f32,
    /// Collision severity estimate
    pub severity: CollisionSeverity,
}

/// Collision severity levels
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum CollisionSeverity {
    Minor,
    Moderate,
    Major,
    Severe,
}

/// Safety actions
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum SafetyAction {
    None,
    SlowDown,
    Stop,
    SteerLeft,
    SteerRight,
    EmergencyBrake,
    PullOver,
}

/// Complete perception result
#[derive(Debug, Clone)]
pub struct AutonomousPerceptionResult {
    /// Frame timestamp
    pub timestamp: Timestamp,
    /// Raw detection results per sensor
    pub raw_detections: HashMap<SensorType, DetectionResultVariant>,
    /// Fused 3D detections
    pub fused_detections: Vec<BoundingBox3D>,
    /// Tracked objects with motion
    pub tracked_objects: Vec<TrackedObject>,
    /// Safety assessment
    pub safety_status: SafetyStatus,
    /// Path planning result
    pub path_plan: Option<PathPlan>,
    /// Total processing time
    pub processing_time_ms: f32,
}

/// Vehicle state for motion planning
#[derive(Debug, Clone)]
pub struct VehicleState {
    /// Current pose
    pub pose: Pose3D,
    /// Linear velocity (m/s)
    pub velocity: [f32; 3],
    /// Angular velocity (rad/s)
    pub angular_velocity: [f32; 3],
    /// Acceleration (m/s²)
    pub acceleration: [f32; 3],
    /// Steering angle (radians)
    pub steering_angle: f32,
}

/// Coordinate system transformations
pub struct CoordinateTransforms;

impl CoordinateTransforms {
    /// Convert from LiDAR coordinates to vehicle coordinates
    pub fn lidar_to_vehicle(point: [f32; 3], sensor_pose: &Pose3D) -> [f32; 3] {
        sensor_pose.transform_point(point)
    }

    /// Convert from vehicle coordinates to world coordinates
    pub fn vehicle_to_world(point: [f32; 3], vehicle_pose: &Pose3D) -> [f32; 3] {
        vehicle_pose.transform_point(point)
    }

    /// Project 3D point to camera image
    pub fn world_to_camera_image(
        point: [f32; 3],
        camera_pose: &Pose3D,
        intrinsics: &CameraIntrinsics,
    ) -> Option<(f32, f32)> {
        // Transform to camera coordinates
        let matrix = camera_pose.to_matrix();

        // Inverse transform (world to camera)
        let cam_x = matrix[0][0] * (point[0] - matrix[0][3])
            + matrix[1][0] * (point[1] - matrix[1][3])
            + matrix[2][0] * (point[2] - matrix[2][3]);
        let cam_y = matrix[0][1] * (point[0] - matrix[0][3])
            + matrix[1][1] * (point[1] - matrix[1][3])
            + matrix[2][1] * (point[2] - matrix[2][3]);
        let cam_z = matrix[0][2] * (point[0] - matrix[0][3])
            + matrix[1][2] * (point[1] - matrix[1][3])
            + matrix[2][2] * (point[2] - matrix[2][3]);

        // Check if point is in front of camera
        if cam_z <= 0.0 {
            return None;
        }

        // Project to image plane
        let fx = intrinsics.focal_length.0;
        let fy = intrinsics.focal_length.1;
        let cx = intrinsics.principal_point.0;
        let cy = intrinsics.principal_point.1;

        let u = fx * cam_x / cam_z + cx;
        let v = fy * cam_y / cam_z + cy;

        // Check if projection is within image bounds
        if u >= 0.0
            && u < intrinsics.image_size.0 as f32
            && v >= 0.0
            && v < intrinsics.image_size.1 as f32
        {
            Some((u, v))
        } else {
            None
        }
    }
}

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

    #[test]
    fn test_timestamp() {
        let ts1 = Timestamp {
            seconds: 100,
            nanoseconds: 500_000_000,
        };
        let ts2 = Timestamp {
            seconds: 101,
            nanoseconds: 0,
        };

        assert_eq!(ts1.to_seconds(), 100.5);
        assert_eq!(ts2.duration_since(&ts1), 0.5);
    }

    #[test]
    fn test_pose_3d() {
        let pose =
            Pose3D::from_position_euler([1.0, 2.0, 3.0], [0.0, 0.0, std::f32::consts::PI / 2.0]);
        let point = [1.0, 0.0, 0.0];
        let transformed = pose.transform_point(point);

        // After 90 degree yaw rotation, (1,0,0) should become (0,1,0), then add translation
        assert!((transformed[0] - 1.0).abs() < 0.01); // x = 1 + 0
        assert!((transformed[1] - 3.0).abs() < 0.01); // y = 2 + 1
        assert!((transformed[2] - 3.0).abs() < 0.01); // z = 3 + 0
    }

    #[test]
    fn test_sensor_frame_creation() {
        let frame = SensorFrame {
            timestamp: Timestamp::now(),
            lidar_data: None,
            camera_data: None,
            radar_data: None,
            imu_data: None,
            gnss_data: None,
            odometry_data: None,
        };

        assert!(frame.timestamp.seconds > 0);
    }
}