"""
GraphSLAM algorithm implementation.
"""

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

@dataclass
class SLAMResult:
    """Result from SLAM processing."""
    pose: Optional[np.ndarray] = None
    map: Optional[np.ndarray] = None
    graph_nodes: Optional[List[np.ndarray]] = None
    graph_edges: Optional[List[Tuple[int, int]]] = None
    tracking_status: str = "OK"

class GraphSLAM:
    """GraphSLAM algorithm implementation using pose graph optimization."""
    
    def __init__(self, config: Dict = None):
        """
        Initialize GraphSLAM.
        
        Args:
            config: Configuration dictionary
        """
        self.config = config or {}
        self.nodes = []  # Pose nodes
        self.edges = []  # Pose constraints
        self.current_pose = np.eye(4)  # Current camera pose
        self.frame_count = 0
        self._initialize()
    
    def _initialize(self):
        """Initialize pose graph."""
        try:
            # Add initial node
            self.nodes.append(self.current_pose.copy())
            
            print("[GraphSLAM] Initialized pose graph")
            
        except Exception as e:
            print(f"[GraphSLAM] Error initializing: {e}")
    
    def process(self, frame: np.ndarray) -> Optional[SLAMResult]:
        """
        Process a frame with GraphSLAM.
        
        Args:
            frame: Input frame as numpy array
            
        Returns:
            SLAMResult with pose, map, and graph information
        """
        if frame is None:
            return None
        
        try:
            # Simulate pose estimation
            self._estimate_pose(frame)
            
            # Add new node to graph
            self.nodes.append(self.current_pose.copy())
            
            # Add edge from previous node
            if len(self.nodes) > 1:
                self.edges.append((len(self.nodes) - 2, len(self.nodes) - 1))
            
            # Simulate loop closure detection
            if self.frame_count % 50 == 0 and self.frame_count > 0:
                self._detect_loop_closure()
            
            # Optimize pose graph
            if len(self.nodes) > 5:
                self._optimize_graph()
            
            # Create mock map points
            map_points = np.random.randn(100, 3) * 10
            
            # Create result
            result = SLAMResult(
                pose=self.current_pose.copy(),
                map=map_points,
                graph_nodes=self.nodes.copy(),
                graph_edges=self.edges.copy(),
                tracking_status="OK"
            )
            
            self.frame_count += 1
            return result
            
        except Exception as e:
            print(f"[GraphSLAM] Error processing frame: {e}")
            return SLAMResult(tracking_status="ERROR")
    
    def _estimate_pose(self, frame: np.ndarray):
        """Estimate camera pose from frame."""
        # Simple pose estimation (mock implementation)
        # In real implementation, this would use feature matching and PnP
        
        # Simulate camera movement
        translation = np.array([0.1, 0, 0])  # Move forward
        rotation = np.eye(3)  # No rotation
        
        # Update current pose
        self.current_pose[:3, 3] += translation
        self.current_pose[:3, :3] = rotation @ self.current_pose[:3, :3]
    
    def _detect_loop_closure(self):
        """Detect loop closures in the pose graph."""
        # Simple loop closure detection (mock implementation)
        # In real implementation, this would use place recognition
        
        if len(self.nodes) > 10:
            # Simulate loop closure between current and earlier node
            loop_node = max(0, len(self.nodes) - 10)
            self.edges.append((loop_node, len(self.nodes) - 1))
            print(f"[GraphSLAM] Detected loop closure: {loop_node} -> {len(self.nodes) - 1}")
    
    def _optimize_graph(self):
        """Optimize the pose graph."""
        # Simple graph optimization (mock implementation)
        # In real implementation, this would use g2o or similar library
        
        # Simulate optimization by slightly adjusting poses
        for i in range(len(self.nodes)):
            noise = np.random.randn(4, 4) * 0.01
            self.nodes[i] += noise
        
        print(f"[GraphSLAM] Optimized pose graph with {len(self.nodes)} nodes")
    
    def reset(self):
        """Reset the SLAM system."""
        self.nodes = []
        self.edges = []
        self.current_pose = np.eye(4)
        self.frame_count = 0
        self._initialize()
        print("[GraphSLAM] Reset SLAM system") 