Spaces:
Paused
Paused
File size: 4,199 Bytes
24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 2df2c23 24857f8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | """
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
|