"""
ORB-SLAM algorithm implementation.
"""

import numpy as np
import cv2
from typing import Optional, Dict, Any
from dataclasses import dataclass

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

class ORBSLAM:
    """ORB-SLAM algorithm implementation."""
    
    def __init__(self, config: Dict = None):
        """
        Initialize ORB-SLAM.
        
        Args:
            config: Configuration dictionary
        """
        self.config = config or {}
        self.orb = None
        self.matcher = None
        self.keypoints = []
        self.descriptors = []
        self.pose = np.eye(4)  # Identity matrix as initial pose
        self.frame_count = 0
        self._initialize()
    
    def _initialize(self):
        """Initialize ORB detector and matcher."""
        try:
            # Initialize ORB detector
            max_features = self.config.get('max_features', 2000)
            scale_factor = self.config.get('scale_factor', 1.2)
            levels = self.config.get('levels', 8)
            min_threshold = self.config.get('min_threshold', 7)
            max_threshold = self.config.get('max_threshold', 20)
            
            self.orb = cv2.ORB_create(
                nfeatures=max_features,
                scaleFactor=scale_factor,
                nlevels=levels,
                edgeThreshold=31,
                firstLevel=0,
                WTA_K=2,
                patchSize=31,
                fastThreshold=min_threshold
            )
            
            # Initialize feature matcher
            self.matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
            
            print("[ORBSLAM] Initialized ORB detector and matcher")
            
        except Exception as e:
            print(f"[ORBSLAM] Error initializing: {e}")
    
    def process(self, frame: np.ndarray) -> Optional[SLAMResult]:
        """
        Process a frame with ORB-SLAM.
        
        Args:
            frame: Input frame as numpy array
            
        Returns:
            SLAMResult with pose, map, and features
        """
        if frame is None:
            return None
        
        try:
            # Convert to grayscale if needed
            if len(frame.shape) == 3:
                gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
            else:
                gray = frame
            
            # Detect ORB features
            keypoints, descriptors = self.orb.detectAndCompute(gray, None)
            
            if keypoints is None or len(keypoints) < 10:
                return SLAMResult(tracking_status="INSUFFICIENT_FEATURES")
            
            # Simple pose estimation (mock implementation)
            # In a real implementation, this would include:
            # - Feature matching between frames
            # - Essential matrix estimation
            # - Pose recovery
            # - Bundle adjustment
            # - Loop closure detection
            
            # Update pose (simple mock update)
            if self.frame_count > 0:
                # Simulate camera movement
                translation = np.array([0.1, 0, 0])  # Move forward
                rotation = np.eye(3)  # No rotation
                
                # Update pose matrix
                self.pose[:3, 3] += translation
                self.pose[:3, :3] = rotation @ self.pose[:3, :3]
            
            # Create mock map points (in real implementation, these would be 3D points)
            map_points = np.random.randn(len(keypoints), 3) * 5
            
            # Create result
            result = SLAMResult(
                pose=self.pose.copy(),
                map=map_points,
                features=keypoints,
                tracking_status="OK"
            )
            
            # Store for next frame
            self.keypoints = keypoints
            self.descriptors = descriptors
            self.frame_count += 1
            
            return result
            
        except Exception as e:
            print(f"[ORBSLAM] Error processing frame: {e}")
            return SLAMResult(tracking_status="ERROR")
    
    def reset(self):
        """Reset the SLAM system."""
        self.pose = np.eye(4)
        self.frame_count = 0
        self.keypoints = []
        self.descriptors = []
        print("[ORBSLAM] Reset SLAM system") 