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"""
Client for FoundationPose Hugging Face Space API
This client can be used from the robot-ml training pipeline to call the
FoundationPose inference API hosted on Hugging Face Spaces.
"""
import json
import logging
import tempfile
from pathlib import Path
from typing import Dict, List, Optional
import cv2
import numpy as np
from gradio_client import Client, handle_file
logger = logging.getLogger(__name__)
class FoundationPoseClient:
"""Client for FoundationPose Gradio API."""
def __init__(self, api_url: str = "https://gpue-foundationpose.hf.space"):
"""Initialize client.
Args:
api_url: Base URL of the FoundationPose Space
"""
self.api_url = api_url.rstrip("/")
logger.info(f"Initializing Gradio client for {self.api_url}")
self.client = Client(self.api_url)
logger.info("Gradio client initialized")
def _save_image_temp(self, image: np.ndarray) -> str:
"""Save image to temporary file.
Args:
image: RGB image as numpy array
Returns:
Path to temporary file
"""
# Convert RGB to BGR for OpenCV
image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# Save to temp file
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg")
cv2.imwrite(temp_file.name, image_bgr, [cv2.IMWRITE_JPEG_QUALITY, 95])
return temp_file.name
def initialize(
self,
object_id: str,
reference_images: List[np.ndarray],
camera_intrinsics: Optional[Dict] = None
) -> bool:
"""Initialize object tracking with reference images.
Args:
object_id: Unique ID for the object
reference_images: List of RGB images (numpy arrays)
camera_intrinsics: Optional camera parameters (dict with fx, fy, cx, cy)
Returns:
True if successful
Raises:
RuntimeError: If initialization fails
"""
logger.info(f"Initializing object '{object_id}' with {len(reference_images)} reference images")
# Save images to temporary files
temp_files = []
try:
for img in reference_images:
temp_path = self._save_image_temp(img)
temp_files.append(temp_path)
# Extract camera intrinsics or use defaults
if camera_intrinsics:
fx = camera_intrinsics.get("fx", 600.0)
fy = camera_intrinsics.get("fy", 600.0)
cx = camera_intrinsics.get("cx", 320.0)
cy = camera_intrinsics.get("cy", 240.0)
else:
fx, fy, cx, cy = 600.0, 600.0, 320.0, 240.0
# Call Gradio API
result = self.client.predict(
object_id=object_id,
reference_files=[handle_file(f) for f in temp_files],
fx=fx,
fy=fy,
cx=cx,
cy=cy,
api_name="/gradio_initialize"
)
# Parse result - Gradio returns plain text
logger.info(f"API result: {result}")
if isinstance(result, str):
# Check if result indicates success (contains ✓ or "initialized")
if "✓" in result or "initialized" in result.lower():
logger.info("Initialization successful")
return True
elif "Error" in result or "error" in result:
raise RuntimeError(f"Initialization failed: {result}")
else:
# Assume success if no error indication
return True
else:
raise RuntimeError(f"Unexpected result type: {type(result)}")
except RuntimeError:
raise
except Exception as e:
logger.error(f"API request failed: {e}")
raise RuntimeError(f"Failed to initialize object: {e}")
finally:
# Clean up temp files
for temp_file in temp_files:
try:
Path(temp_file).unlink()
except Exception:
pass
def estimate_pose(
self,
object_id: str,
query_image: np.ndarray,
camera_intrinsics: Optional[Dict] = None
) -> List[Dict]:
"""Estimate 6D pose of object in query image.
Args:
object_id: ID of object to detect
query_image: RGB query image as numpy array
camera_intrinsics: Optional camera parameters (dict with fx, fy, cx, cy)
Returns:
List of detected poses:
[
{
"object_id": str,
"position": {"x": float, "y": float, "z": float},
"orientation": {"w": float, "x": float, "y": float, "z": float},
"confidence": float,
"dimensions": [float, float, float]
}
]
Raises:
RuntimeError: If estimation fails
"""
# Save query image to temp file
temp_file = self._save_image_temp(query_image)
try:
# Extract camera intrinsics or use defaults
if camera_intrinsics:
fx = camera_intrinsics.get("fx", 600.0)
fy = camera_intrinsics.get("fy", 600.0)
cx = camera_intrinsics.get("cx", 320.0)
cy = camera_intrinsics.get("cy", 240.0)
else:
fx, fy, cx, cy = 600.0, 600.0, 320.0, 240.0
# Call Gradio API
result = self.client.predict(
object_id=object_id,
query_image=handle_file(temp_file),
fx=fx,
fy=fy,
cx=cx,
cy=cy,
api_name="/gradio_estimate"
)
# Parse result - Gradio may return tuple (text, image) or just text
logger.info(f"API result type: {type(result)}")
# If tuple, take first element (text output)
if isinstance(result, tuple):
result = result[0]
if isinstance(result, str):
logger.info(f"API result: {result}")
# Check for errors
if "Error" in result or "not initialized" in result:
raise RuntimeError(f"Pose estimation failed: {result}")
# Try to parse as JSON (in case app.py returns JSON string)
try:
result_dict = json.loads(result)
if isinstance(result_dict, dict) and "poses" in result_dict:
return result_dict["poses"]
except (json.JSONDecodeError, ValueError):
pass
# Check if the result indicates no poses detected
if "No poses detected" in result or "⚠" in result:
logger.info("No poses detected in query image")
return []
# For now, return empty list with a warning
logger.warning(f"Could not parse pose from result: {result}")
return []
else:
raise RuntimeError(f"Unexpected result type: {type(result)}")
except RuntimeError:
raise
except Exception as e:
logger.error(f"API request failed: {e}")
raise RuntimeError(f"Failed to estimate pose: {e}")
finally:
# Clean up temp file
try:
Path(temp_file).unlink()
except Exception:
pass
def load_reference_images(directory: Path) -> List[np.ndarray]:
"""Load reference images from directory.
Args:
directory: Path to directory containing images
Returns:
List of RGB images as numpy arrays
"""
images = []
for img_path in sorted(directory.glob("*.jpg")):
img = cv2.imread(str(img_path))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
images.append(img)
logger.info(f"Loaded {len(images)} reference images from {directory}")
return images
# Example usage
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
# Initialize client
client = FoundationPoseClient()
# Load reference images
ref_dir = Path("../training/perception/reference/target_cube")
if ref_dir.exists():
ref_images = load_reference_images(ref_dir)
# Initialize object
client.initialize("target_cube", ref_images)
# Estimate pose on first reference image (for testing)
poses = client.estimate_pose("target_cube", ref_images[0])
print(f"Detected {len(poses)} poses:")
for pose in poses:
print(f" {pose}")
else:
print(f"Reference directory not found: {ref_dir}")
print("Run 'make capture-reference' to collect reference images first")
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