"""
Visual-Inertial SLAM algorithm implementation.
"""

import numpy as np
from typing import Optional, Dict, List
from dataclasses import dataclass

@dataclass
class SLAMResult:
    """Result from SLAM processing."""
    pose: Optional[np.ndarray] = None
    map: Optional[np.ndarray] = None
    velocity: Optional[np.ndarray] = None
    bias: Optional[np.ndarray] = None
    tracking_status: str = "OK"

class VisualInertialSLAM:
    """Visual-Inertial SLAM algorithm implementation."""
    
    def __init__(self, config: Dict = None):
        """
        Initialize Visual-Inertial SLAM.
        
        Args:
            config: Configuration dictionary
        """
        self.config = config or {}
        self.pose = np.eye(4)  # Camera pose
        self.velocity = np.zeros(3)  # Camera velocity
        self.bias_accel = np.zeros(3)  # Accelerometer bias
        self.bias_gyro = np.zeros(3)  # Gyroscope bias
        self.frame_count = 0
        self.imu_data = []
        self._initialize()
    
    def _initialize(self):
        """Initialize Visual-Inertial SLAM."""
        try:
            print("[VisualInertialSLAM] Initialized Visual-Inertial SLAM")
            
        except Exception as e:
            print(f"[VisualInertialSLAM] Error initializing: {e}")
    
    def process(self, frame: np.ndarray, imu_data: Optional[np.ndarray] = None) -> Optional[SLAMResult]:
        """
        Process a frame with Visual-Inertial SLAM.
        
        Args:
            frame: Input frame as numpy array
            imu_data: IMU data (accelerometer, gyroscope)
            
        Returns:
            SLAMResult with pose, map, velocity, and bias
        """
        if frame is None:
            return None
        
        try:
            # Process IMU data if available
            if imu_data is not None:
                self._process_imu(imu_data)
            
            # Visual processing (mock implementation)
            self._process_visual(frame)
            
            # Fusion of visual and inertial data
            self._fusion_update()
            
            # Create mock map points
            map_points = np.random.randn(80, 3) * 8
            
            # Create result
            result = SLAMResult(
                pose=self.pose.copy(),
                map=map_points,
                velocity=self.velocity.copy(),
                bias=np.concatenate([self.bias_accel, self.bias_gyro]),
                tracking_status="OK"
            )
            
            self.frame_count += 1
            return result
            
        except Exception as e:
            print(f"[VisualInertialSLAM] Error processing frame: {e}")
            return SLAMResult(tracking_status="ERROR")
    
    def _process_imu(self, imu_data: np.ndarray):
        """Process IMU data."""
        # Simple IMU processing (mock implementation)
        # In real implementation, this would include:
        # - Preintegration
        # - Bias estimation
        # - Gravity alignment
        
        if len(imu_data) >= 6:  # [ax, ay, az, gx, gy, gz]
            accel = imu_data[:3]
            gyro = imu_data[3:6]
            
            # Update velocity using accelerometer
            dt = 0.033  # 30Hz
            self.velocity += (accel - self.bias_accel) * dt
            
            # Update pose using gyroscope
            angular_velocity = gyro - self.bias_gyro
            rotation_matrix = self._integrate_gyro(angular_velocity, dt)
            self.pose[:3, :3] = rotation_matrix @ self.pose[:3, :3]
            
            # Update position using velocity
            self.pose[:3, 3] += self.velocity * dt
    
    def _process_visual(self, frame: np.ndarray):
        """Process visual data."""
        # Simple visual processing (mock implementation)
        # In real implementation, this would include:
        # - Feature detection and tracking
        # - Visual odometry
        # - Bundle adjustment
        
        # Simulate visual pose correction
        visual_correction = np.random.randn(4, 4) * 0.01
        self.pose = visual_correction @ self.pose
    
    def _fusion_update(self):
        """Fuse visual and inertial data."""
        # Simple fusion (mock implementation)
        # In real implementation, this would use:
        # - Extended Kalman Filter
        # - Factor Graph optimization
        # - Marginalization
        
        # Simulate bias estimation
        self.bias_accel += np.random.randn(3) * 0.001
        self.bias_gyro += np.random.randn(3) * 0.001
    
    def _integrate_gyro(self, angular_velocity: np.ndarray, dt: float) -> np.ndarray:
        """Integrate gyroscope data to get rotation matrix."""
        # Simple integration (mock implementation)
        # In real implementation, this would use:
        # - Quaternion integration
        # - SO(3) Lie algebra
        
        # Convert angular velocity to rotation matrix
        angle = np.linalg.norm(angular_velocity) * dt
        if angle > 0:
            axis = angular_velocity / np.linalg.norm(angular_velocity)
            # Simple rotation matrix approximation
            rotation = np.eye(3) + np.cross(axis, np.eye(3)) * angle
            return rotation
        else:
            return np.eye(3)
    
    def reset(self):
        """Reset the SLAM system."""
        self.pose = np.eye(4)
        self.velocity = np.zeros(3)
        self.bias_accel = np.zeros(3)
        self.bias_gyro = np.zeros(3)
        self.frame_count = 0
        self.imu_data = []
        print("[VisualInertialSLAM] Reset SLAM system") 