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Test Preprocessing with Visualizations
Quick test of DROID preprocessing pipeline with visual output:
- Processes small number of episodes (5-10)
- Uses cartesian_position + refined_extrinsics filtering
- Generates visualization images showing projected tracks
- Saves NPZ files for inspection
"""
import numpy as np
import tensorflow_datasets as tfds
from pathlib import Path
import argparse
import cv2
import sys
from tqdm import tqdm
import os
# Force TensorFlow to use CPU to avoid GPU memory issues
os.environ['CUDA_VISIBLE_DEVICES'] = ''
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
# Add parent directory to path
sys.path.append(str(Path(__file__).parent.parent))
from utils.load_camera_calibration import CameraCalibrationLoader
from utils.franka_mesh_projection import FrankaMeshProjector
def visualize_tracks(img: np.ndarray,
tracks: np.ndarray,
visibility: np.ndarray,
title: str = "") -> np.ndarray:
"""
Visualize 32 tracks on image.
Args:
img: RGB image (H, W, 3)
tracks: Track points (32, 2)
visibility: Visibility mask (32,)
title: Title for image
Returns:
Annotated image
"""
viz = img.copy()
# Draw grid points (0-24) in blue
for i in range(25):
if visibility[i]:
pt = tuple(tracks[i].astype(int))
cv2.circle(viz, pt, 4, (0, 0, 255), -1)
# Draw mesh points (25-31) in green
for i in range(25, 32):
if visibility[i]:
pt = tuple(tracks[i].astype(int))
cv2.circle(viz, pt, 5, (0, 255, 0), -1)
cv2.putText(viz, str(i-25), (pt[0]+7, pt[1]-7),
cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
# Add title
cv2.putText(viz, title, (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)
# Add statistics
visible_grid = np.sum(visibility[:25])
visible_mesh = np.sum(visibility[25:])
cv2.putText(viz, f"Grid: {visible_grid}/25 Mesh: {visible_mesh}/7", (10, 60),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 0), 1)
return viz
def test_preprocessing(droid_path: str,
calib_dir: str,
output_dir: str,
num_episodes: int = 5,
use_cartesian: bool = True,
require_refined: bool = True):
"""
Test preprocessing with visualizations.
Args:
droid_path: Path to DROID RLDS dataset
calib_dir: Path to camera calibration directory
output_dir: Output directory for test results
num_episodes: Number of episodes to process
use_cartesian: Use cartesian_position (vs FK)
require_refined: Require refined_extrinsics
"""
print("=" * 80)
print("DROID Preprocessing Test with Visualizations")
print("=" * 80)
print(f" Output: {output_dir}")
print(f" Episodes: {num_episodes}")
print(f" Projection: {'cartesian_position' if use_cartesian else 'joint_position (FK)'}")
print(f" Require refined: {require_refined}")
print()
# Create output directories
output_path = Path(output_dir)
npz_path = output_path / "npz_files"
viz_path = output_path / "visualizations"
npz_path.mkdir(parents=True, exist_ok=True)
viz_path.mkdir(parents=True, exist_ok=True)
# Initialize tools
calib_loader = CameraCalibrationLoader(calib_dir)
projector = FrankaMeshProjector(use_gui=False)
# Load RLDS dataset
print("Loading DROID dataset...")
builder = tfds.builder_from_directory(droid_path)
dataset = builder.as_dataset(split='train')
# Get list of all calibration files for UUID matching
calib_path = Path(calib_dir)
calib_files = sorted(calib_path.glob("*_cameras.json"))
uuid_list = [f.stem.replace('_cameras', '') for f in calib_files]
print(f"Found {len(uuid_list)} camera calibration files")
# Filter to refined only
if require_refined:
refined_uuids = [uuid for uuid in uuid_list if calib_loader.has_refined_extrinsics(uuid)]
print(f" {len(refined_uuids)}/{len(uuid_list)} have refined_extrinsics ({100*len(refined_uuids)/len(uuid_list):.1f}%)")
uuid_list = refined_uuids
# Process episodes
processed = 0
skipped = 0
skipped_reasons = {
'no_uuid': 0,
'no_calib': 0,
'decode_error': 0,
'other': 0
}
pbar = tqdm(total=num_episodes, desc="Processing")
for episode_idx, episode in enumerate(dataset):
if processed >= num_episodes:
break
try:
# Try to match UUID first (before loading steps)
uuid = uuid_list[episode_idx % len(uuid_list)]
# Load calibration
try:
dual_params = calib_loader.get_dual_view_params(
uuid,
param_type='refined',
require_refined=require_refined
)
if dual_params is None:
skipped += 1
skipped_reasons['no_calib'] += 1
continue
except Exception as e:
skipped += 1
skipped_reasons['no_calib'] += 1
continue
# IMPORTANT: Don't call list() on episode['steps'] - iterate directly
# This avoids loading entire episode into memory
all_tracks_ext = []
all_tracks_wrist = []
all_vis_ext = []
all_vis_wrist = []
all_images_ext = []
all_images_wrist = []
decode_failed = False
step_count = 0
max_steps_per_episode = 30 # Limit to 30 steps for testing
# Iterate steps incrementally without loading all at once
for step_idx, step in enumerate(episode['steps']):
# Limit steps per episode
if step_idx >= max_steps_per_episode:
break
step_count += 1
# Get robot state
if use_cartesian:
cart_pos = step['observation']['cartesian_position'].numpy()
else:
joint_pos = step['observation']['joint_position'].numpy()
# Load images
img_ext_bytes = step['observation']['exterior_image_1_left'].numpy()
img_ext = cv2.imdecode(np.frombuffer(img_ext_bytes, dtype=np.uint8), cv2.IMREAD_COLOR)
img_wrist_bytes = step['observation']['wrist_image_left'].numpy()
img_wrist = cv2.imdecode(np.frombuffer(img_wrist_bytes, dtype=np.uint8), cv2.IMREAD_COLOR)
if img_ext is None or img_wrist is None:
decode_failed = True
break
# Resize and convert
img_ext = cv2.resize(img_ext, (448, 448))
img_ext = cv2.cvtColor(img_ext, cv2.COLOR_BGR2RGB)
all_images_ext.append(img_ext)
img_wrist = cv2.resize(img_wrist, (448, 448))
img_wrist = cv2.cvtColor(img_wrist, cv2.COLOR_BGR2RGB)
all_images_wrist.append(img_wrist)
# Project tracks
K_ext, E_ext = dual_params['exterior_1']
K_wrist, E_wrist = dual_params['wrist']
if use_cartesian:
tracks_ext, vis_ext = projector.project_32_points_cartesian(
cart_pos, K_ext, E_ext, img_h=448, img_w=448, rotation_format='euler_xyz'
)
tracks_wrist, vis_wrist = projector.project_32_points_cartesian(
cart_pos, K_wrist, E_wrist, img_h=448, img_w=448, rotation_format='euler_xyz'
)
else:
tracks_ext, vis_ext = projector.project_32_points(
joint_pos, K_ext, E_ext, img_h=448, img_w=448
)
tracks_wrist, vis_wrist = projector.project_32_points(
joint_pos, K_wrist, E_wrist, img_h=448, img_w=448
)
all_tracks_ext.append(tracks_ext)
all_tracks_wrist.append(tracks_wrist)
all_vis_ext.append(vis_ext)
all_vis_wrist.append(vis_wrist)
# Create visualizations for first, middle, and last frames
viz_indices = [0, max_steps_per_episode//2, max_steps_per_episode-1]
if step_idx in viz_indices:
viz_ext = visualize_tracks(
img_ext,
tracks_ext,
vis_ext,
title=f"Episode {processed} | Frame {step_idx} | Exterior"
)
viz_wrist = visualize_tracks(
img_wrist,
tracks_wrist,
vis_wrist,
title=f"Episode {processed} | Frame {step_idx} | Wrist"
)
# Save visualizations
cv2.imwrite(
str(viz_path / f"ep{processed:03d}_frame{step_idx:04d}_exterior.jpg"),
cv2.cvtColor(viz_ext, cv2.COLOR_RGB2BGR)
)
cv2.imwrite(
str(viz_path / f"ep{processed:03d}_frame{step_idx:04d}_wrist.jpg"),
cv2.cvtColor(viz_wrist, cv2.COLOR_RGB2BGR)
)
if decode_failed:
skipped += 1
skipped_reasons['decode_error'] += 1
continue
# Skip if too few steps
if step_count < 10:
skipped += 1
skipped_reasons['other'] += 1
continue
# Convert to arrays
tracks_ext = np.array(all_tracks_ext)
tracks_wrist = np.array(all_tracks_wrist)
vis_ext = np.array(all_vis_ext)
vis_wrist = np.array(all_vis_wrist)
images_ext = np.array(all_images_ext)
images_wrist = np.array(all_images_wrist)
# Get language instruction from first stored step
language = "unknown" # Default if not available
# Save NPZ
npz_file = npz_path / f"episode_{processed:03d}.npz"
np.savez_compressed(
npz_file,
tracks_exterior=tracks_ext,
tracks_wrist=tracks_wrist,
vis_exterior=vis_ext,
vis_wrist=vis_wrist,
images_exterior=images_ext,
images_wrist=images_wrist,
language=language,
uuid=uuid,
num_steps=step_count
)
processed += 1
pbar.update(1)
except Exception as e:
print(f"\nError processing episode {episode_idx}: {e}")
skipped += 1
skipped_reasons['other'] += 1
continue
pbar.close()
# Print summary
print("\n" + "=" * 80)
print("Test Preprocessing Complete")
print("=" * 80)
print(f" Processed: {processed} episodes")
print(f" Skipped: {skipped} episodes")
print(f" No calibration: {skipped_reasons['no_calib']}")
print(f" Decode error: {skipped_reasons['decode_error']}")
print(f" Other: {skipped_reasons['other']}")
print()
print(f" NPZ files: {npz_path}")
print(f" Visualizations: {viz_path}")
print(f" ({processed * 6} images generated)")
print("=" * 80)
# Create montage of first episode visualizations
if processed > 0:
print("\nCreating summary montage...")
montage_images = []
# Look for existing visualization files for first episode
viz_files = sorted(viz_path.glob("ep000_frame*_exterior.jpg"))
for ext_file in viz_files[:3]: # Take up to 3 frames
wrist_file = ext_file.parent / ext_file.name.replace('_exterior.jpg', '_wrist.jpg')
if ext_file.exists() and wrist_file.exists():
img_ext = cv2.imread(str(ext_file))
img_wrist = cv2.imread(str(wrist_file))
if img_ext is not None and img_wrist is not None:
montage_images.append(np.hstack([img_ext, img_wrist]))
if montage_images:
montage = np.vstack(montage_images)
montage_file = viz_path / "summary_montage.jpg"
cv2.imwrite(str(montage_file), montage)
print(f" ✓ Saved summary montage: {montage_file}")
def main():
parser = argparse.ArgumentParser(
description="Test DROID preprocessing with visualizations"
)
parser.add_argument(
'--droid-path',
type=str,
default='/mnt/kevin/data/droid/droid/1.0.0',
help='Path to DROID RLDS dataset'
)
parser.add_argument(
'--calib-dir',
type=str,
default='/root/workspace/code/wmrl/Dual-Dynamics-Models/DROID-main/vision/u/wenlongh/datasets/droid_v4/cameras',
help='Path to camera calibration directory'
)
parser.add_argument(
'--output-dir',
type=str,
default='/tmp/droid_preprocessing_test',
help='Output directory for test results'
)
parser.add_argument(
'--num-episodes',
type=int,
default=5,
help='Number of episodes to process'
)
parser.add_argument(
'--use-cartesian',
action='store_true',
default=True,
help='Use cartesian_position (default: True)'
)
parser.add_argument(
'--use-joints',
dest='use_cartesian',
action='store_false',
help='Use joint_position with FK'
)
parser.add_argument(
'--require-refined',
action='store_true',
default=True,
help='Require refined_extrinsics (default: True)'
)
parser.add_argument(
'--no-require-refined',
dest='require_refined',
action='store_false',
help='Allow measured extrinsics'
)
args = parser.parse_args()
test_preprocessing(
args.droid_path,
args.calib_dir,
args.output_dir,
args.num_episodes,
args.use_cartesian,
args.require_refined
)
if __name__ == "__main__":
main()
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