"""
FoundationPose model wrapper for inference.

This module wraps the FoundationPose library for 6D object pose estimation.
"""

import logging
import sys
from pathlib import Path
from typing import Dict, List, Optional

import numpy as np
import torch

logger = logging.getLogger(__name__)

# Add FoundationPose to Python path
FOUNDATIONPOSE_ROOT = Path("/app/FoundationPose")
if FOUNDATIONPOSE_ROOT.exists():
    sys.path.insert(0, str(FOUNDATIONPOSE_ROOT))


class FoundationPoseEstimator:
    """Wrapper for FoundationPose model."""

    def __init__(self, device: str = "cuda", weights_dir: str = "weights"):
        """Initialize FoundationPose estimator.

        Args:
            device: Device to run inference on ('cuda' or 'cpu')
            weights_dir: Directory containing model weights
        """
        self.device = device
        self.weights_dir = Path(weights_dir)
        self.model = None
        self.registered_objects = {}

        # Check if FoundationPose is available
        if not FOUNDATIONPOSE_ROOT.exists():
            raise RuntimeError(
                f"FoundationPose repository not found at {FOUNDATIONPOSE_ROOT}. "
                "Clone it with: git clone https://github.com/NVlabs/FoundationPose.git"
            )

        # Check if weights exist
        if not self.weights_dir.exists() or not any(self.weights_dir.glob("**/*.pth")):
            logger.warning(f"No model weights found in {self.weights_dir}")
            logger.warning("Model will not work without weights")

        logger.info(f"FoundationPose estimator initialized (device: {device})")

    def register_object(
        self,
        object_id: str,
        reference_images: List[np.ndarray],
        camera_intrinsics: Optional[Dict] = None,
        mesh_path: Optional[str] = None
    ) -> bool:
        """Register an object for tracking.

        Args:
            object_id: Unique identifier for the object
            reference_images: List of RGB reference images (H, W, 3)
            camera_intrinsics: Camera parameters {fx, fy, cx, cy}
            mesh_path: Optional path to object mesh file

        Returns:
            True if registration successful
        """
        try:
            # Store object registration
            self.registered_objects[object_id] = {
                "num_references": len(reference_images),
                "camera_intrinsics": camera_intrinsics,
                "mesh_path": mesh_path,
                "reference_images": reference_images  # Keep for now
            }

            logger.info(f"✓ Registered object '{object_id}' with {len(reference_images)} reference images")
            return True

        except Exception as e:
            logger.error(f"Failed to register object '{object_id}': {e}", exc_info=True)
            return False

    def estimate_pose(
        self,
        object_id: str,
        rgb_image: np.ndarray,
        depth_image: Optional[np.ndarray] = None,
        mask: Optional[np.ndarray] = None,
        camera_intrinsics: Optional[Dict] = None
    ) -> Optional[Dict]:
        """Estimate 6D pose of registered object in image.

        Args:
            object_id: ID of object to detect
            rgb_image: RGB query image (H, W, 3)
            depth_image: Optional depth image (H, W)
            mask: Optional object mask (H, W)
            camera_intrinsics: Camera parameters {fx, fy, cx, cy}

        Returns:
            Pose dictionary with position, orientation, confidence or None
        """
        if object_id not in self.registered_objects:
            logger.error(f"Object '{object_id}' not registered")
            return None

        try:
            # TODO: Implement actual FoundationPose inference
            # This is a placeholder that would need to:
            # 1. Load the FoundationPose model if not loaded
            # 2. Run pose estimation on the query image
            # 3. Return the estimated pose

            logger.warning("FoundationPose inference not yet implemented - returning None")
            return None

        except Exception as e:
            logger.error(f"Pose estimation failed: {e}", exc_info=True)
            return None