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| """ | |
| 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 | |