//! LiDAR point cloud processing

use crate::{VisionResult, VisionError};
use crate::detection::three_d::{PointCloud, Detection3DResult, BoundingBox3D};
use crate::autonomous::{LidarData, AutonomousDetector, SensorType};
use crate::tensor_utils::TensorExt;
use rtx_tensor::{Tensor, Device, DType};

/// LiDAR-based object detector
pub struct LidarDetector {
    point_net: PointNetBackbone,
    detection_head: LidarDetectionHead,
}

impl LidarDetector {
    pub fn new() -> VisionResult<Self> {
        let point_net = PointNetBackbone::new()?;
        let detection_head = LidarDetectionHead::new(1024, 10)?; // 10 classes
        
        Ok(Self {
            point_net,
            detection_head,
        })
    }
    
    /// Process point cloud for object detection
    pub fn process_point_cloud(&mut self, point_cloud: &PointCloud) -> VisionResult<Vec<BoundingBox3D>> {
        // Extract features
        let features = self.point_net.forward(&point_cloud.points)?;
        
        // Detect objects
        let detections = self.detection_head.forward(&features)?;
        
        Ok(detections)
    }
}

impl AutonomousDetector for LidarDetector {
    fn detect_lidar(&mut self, lidar_data: &LidarData) -> VisionResult<Detection3DResult> {
        let start_time = std::time::Instant::now();
        
        let detections = self.process_point_cloud(&lidar_data.point_cloud)?;
        
        let processing_time = start_time.elapsed().as_millis() as f32;
        
        Ok(Detection3DResult {
            boxes_3d: detections,
            processing_time_ms: processing_time,
            model_name: "LiDAR Detector".to_string(),
        })
    }
    
    fn supported_sensors(&self) -> Vec<SensorType> {
        vec![SensorType::LiDAR]
    }
}

/// PointNet backbone for LiDAR processing
struct PointNetBackbone {
    conv_layers: Vec<Tensor>,
}

impl PointNetBackbone {
    fn new() -> VisionResult<Self> {
        let conv_layers = vec![
            Tensor::randn(&[64, 3, 1], &Device::default())?,
            Tensor::randn(&[128, 64, 1], &Device::default())?,
            Tensor::randn(&[1024, 128, 1], &Device::default())?,
        ];
        
        Ok(Self { conv_layers })
    }
    
    fn forward(&self, points: &Tensor) -> VisionResult<Tensor> {
        let mut x = points.transpose(-2, -1)?; // (B, 3, N)
        
        for conv in &self.conv_layers {
            x = x.conv1d(conv, 0, 1, 1)?.relu()?;
        }
        
        // Global max pooling
        let global_features = x.max_dim(2, true)?;
        Ok(global_features.0)
    }
}

/// LiDAR detection head
struct LidarDetectionHead {
    fc_layers: Vec<Tensor>,
    num_classes: usize,
}

impl LidarDetectionHead {
    fn new(in_features: usize, num_classes: usize) -> VisionResult<Self> {
        let fc_layers = vec![
            Tensor::randn(&[512, in_features], &Device::default())?,
            Tensor::randn(&[256, 512], &Device::default())?,
            Tensor::randn(&[(num_classes + 7) * 100, 256], DType::F32, &Device::default())?, // 7 for box params, 100 proposals
        ];
        
        Ok(Self { fc_layers, num_classes })
    }
    
    fn forward(&self, features: &Tensor) -> VisionResult<Vec<BoundingBox3D>> {
        let mut x = features.clone();
        
        for fc in &self.fc_layers[..2] {
            x = x.matmul(&fc.transpose(-2, -1)?)?.relu()?;
        }
        
        let predictions = x.matmul(&self.fc_layers[2].transpose(-2, -1)?)?;
        
        // Decode predictions
        self.decode_predictions(&predictions)
    }
    
    fn decode_predictions(&self, predictions: &Tensor) -> VisionResult<Vec<BoundingBox3D>> {
        let pred_data = predictions.to_vec()?;
        let mut boxes = Vec::new();
        
        let stride = self.num_classes + 7;
        for i in (0..pred_data.len()).step_by(stride) {
            if i + 6 < pred_data.len() {
                let center = [pred_data[i], pred_data[i + 1], pred_data[i + 2]];
                let size = [pred_data[i + 3], pred_data[i + 4], pred_data[i + 5]];
                let rotation = [pred_data[i + 6], 0.0, 0.0];
                
                // Find best class
                let mut best_class = 0;
                let mut best_confidence = 0.0;
                
                for class_id in 0..self.num_classes {
                    let conf_idx = i + 7 + class_id;
                    if conf_idx < pred_data.len() {
                        let confidence = pred_data[conf_idx].sigmoid();
                        if confidence > best_confidence {
                            best_confidence = confidence;
                            best_class = class_id;
                        }
                    }
                }
                
                if best_confidence > 0.3 {
                    boxes.push(BoundingBox3D::new(center, size, rotation, best_confidence, best_class));
                }
            }
        }
        
        Ok(boxes)
    }
}

/// Point cloud preprocessing utilities
pub struct LidarPreprocessor;

impl LidarPreprocessor {
    /// Remove ground points using RANSAC plane fitting
    pub fn remove_ground_plane(point_cloud: &PointCloud) -> VisionResult<PointCloud> {
        // Simplified ground removal - in practice use proper RANSAC
        Ok(point_cloud.clone())
    }
    
    /// Voxel downsampling to reduce point density
    pub fn voxel_downsample(point_cloud: &PointCloud, voxel_size: f32) -> VisionResult<PointCloud> {
        point_cloud.voxelize(voxel_size).map(|voxels| {
            PointCloud::new(voxels.voxel_coords)
        })
    }
    
    /// Statistical outlier removal
    pub fn remove_outliers(point_cloud: &PointCloud, k_neighbors: usize, std_multiplier: f32) -> VisionResult<PointCloud> {
        // Simplified outlier removal
        Ok(point_cloud.clone())
    }
}