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"""
Process a specific chunk of DROID episodes.
Reads chunk file with episode indices and processes only those episodes.
"""
import json
from pathlib import Path
import tensorflow_datasets as tfds
from tqdm import tqdm
import torch
import numpy as np
import cv2
from droid.calibration.calibration_loader import CalibrationLoader
from droid.misc.projector import Projector
# Import preprocessing functions from the main script
import sys
sys.path.insert(0, str(Path(__file__).parent))
# Copy necessary functions
def find_closest_calibration(episode, uuid_list):
"""Find closest calibration UUID by timestamp."""
ts = float(episode['episode_metadata']['recording_timestamp'].numpy())
closest_uuid = None
min_diff = float('inf')
for uuid, calib_ts in uuid_list:
diff = abs(ts - calib_ts)
if diff < min_diff:
min_diff = diff
closest_uuid = uuid
return closest_uuid
def sample_arm_shaped_points(mesh_2d_visible, img_h, img_w, num_points=993, seed=0):
"""Sample points in arm-shaped region excluding mesh vertices."""
np.random.seed(seed)
u_coords = []
v_coords = []
mesh_u = mesh_2d_visible[:, 0]
mesh_v = mesh_2d_visible[:, 1]
u_min = max(0, int(mesh_u.min()) - 50)
u_max = min(img_w, int(mesh_u.max()) + 50)
v_min = max(0, int(mesh_v.min()) - 50)
v_max = min(img_h, int(mesh_v.max()) + 50)
attempts = 0
max_attempts = num_points * 100
while len(u_coords) < num_points and attempts < max_attempts:
u = np.random.randint(u_min, u_max)
v = np.random.randint(v_min, v_max)
# Check distance from mesh vertices
dists = np.sqrt((mesh_u - u)**2 + (mesh_v - v)**2)
if dists.min() > 10:
u_coords.append(u)
v_coords.append(v)
attempts += 1
if len(u_coords) < num_points:
remaining = num_points - len(u_coords)
u_coords.extend([img_w // 2] * remaining)
v_coords.extend([img_h // 2] * remaining)
return np.array(u_coords), np.array(v_coords)
def sample_wrist_points(img_h, img_w, num_sparse=300, num_dense=700, seed=0):
"""Sample wrist camera points with dense sampling in bottom region."""
np.random.seed(seed)
# Sparse uniform sampling
u_sparse = np.random.randint(0, img_w, num_sparse)
v_sparse = np.random.randint(0, img_h, num_sparse)
# Dense sampling in bottom 60%-100%
v_min_dense = int(img_h * 0.6)
u_dense = np.random.randint(0, img_w, num_dense)
v_dense = np.random.randint(v_min_dense, img_h, num_dense)
u_all = np.concatenate([u_sparse, u_dense])
v_all = np.concatenate([v_sparse, v_dense])
return u_all, v_all
def load_cotracker():
"""Load CoTracker v3 offline model."""
from cotracker.predictor import CoTrackerPredictor
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
cotracker_paths = [
'/mnt/kevin/vlm_models/cotracker/scaled_offline.pth',
'/mnt/kevin/vlm_models/hub/checkpoints/scaled_offline.pth',
'/root/workspace/code/wmrl/Dual-Dynamics-Models/DROID-main/co-tracker/checkpoints/scaled_offline.pth',
]
cotracker_checkpoint = None
for path in cotracker_paths:
if Path(path).exists():
cotracker_checkpoint = path
print(f"Found CoTracker checkpoint: {cotracker_checkpoint}")
break
if cotracker_checkpoint is None:
raise FileNotFoundError(f"CoTracker checkpoint not found. Tried:\n" + "\n".join(cotracker_paths))
cotracker = CoTrackerPredictor(checkpoint=cotracker_checkpoint)
cotracker = cotracker.to(device)
cotracker.eval()
return cotracker, device
def process_episode(episode, episode_idx, uuid, calib_loader, projector, cotracker, device,
max_frames=400, save_preview=False, output_dir=None):
"""Process a single episode."""
# ... [Copy full function from preprocess_droid_rlds_final.py]
# I'll use the existing implementation
from preprocess_droid_rlds_final import process_episode as original_process_episode
return original_process_episode(
episode, episode_idx, uuid, calib_loader, projector, cotracker, device,
max_frames, save_preview, output_dir
)
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--chunk-file', type=str, required=True,
help='Chunk JSON file with episode indices to process')
parser.add_argument('--output-dir', type=str, default='/mnt/kevin/data/droid_processed_1000pts',
help='Output directory')
parser.add_argument('--max-frames', type=int, default=400,
help='Max frames per episode')
parser.add_argument('--save-previews', type=int, default=3,
help='Number of preview videos to save')
parser.add_argument('--chunk-id', type=int, default=None,
help='Chunk ID for logging')
args = parser.parse_args()
# Load chunk data
with open(args.chunk_file) as f:
chunk_data = json.load(f)
chunk_id = chunk_data['chunk_id'] if args.chunk_id is None else args.chunk_id
episode_indices_set = set(chunk_data['episode_indices'])
label = f"Chunk {chunk_id}"
print("="*80)
print(f"DROID Preprocessing: {label}")
print("="*80)
print(f" Chunk file: {args.chunk_file}")
print(f" Episodes to process: {len(episode_indices_set)}")
print(f" Output: {args.output_dir}")
print("="*80)
# Setup output directories
output_dir = Path(args.output_dir)
data_dir = output_dir / 'data'
preview_dir = output_dir / 'preview_videos'
data_dir.mkdir(parents=True, exist_ok=True)
preview_dir.mkdir(parents=True, exist_ok=True)
# Load CoTracker
cotracker, device = load_cotracker()
# Load camera calibrations
calib_loader = CalibrationLoader()
uuid_list = [(uuid, calib_loader.get_timestamp(uuid))
for uuid in calib_loader.list_calibrations()]
print(f"Loaded {len(uuid_list)} camera calibrations")
# Setup projector
projector = Projector()
# Load dataset
droid_path = '/mnt/kevin/data/droid/droid/1.0.0'
print("Loading DROID dataset...")
builder = tfds.builder_from_directory(droid_path)
dataset = builder.as_dataset(split='train')
# Process only episodes in chunk
processed_count = 0
skipped_count = 0
preview_count = 0
pbar = tqdm(total=len(episode_indices_set), desc=label)
for episode_idx, episode in enumerate(dataset):
# Skip episodes not in this chunk
if episode_idx not in episode_indices_set:
continue
# Check if already processed
npz_path = data_dir / f"episode_{episode_idx:06d}.npz"
if npz_path.exists():
processed_count += 1
pbar.update(1)
continue
# Find calibration
uuid = find_closest_calibration(episode, uuid_list)
if uuid is None or not calib_loader.has_refined_extrinsics(uuid):
skipped_count += 1
episode_indices_set.remove(episode_idx)
pbar.update(1)
continue
# Process episode
try:
save_preview = (preview_count < args.save_previews)
result = process_episode(
episode, episode_idx, uuid, calib_loader, projector,
cotracker, device, args.max_frames, save_preview, output_dir
)
if result is not None:
# Save as NPZ
np.savez_compressed(
npz_path,
episode_idx=result['episode_idx'],
tracked_points_exterior=result['tracks_exterior'],
tracked_points_wrist=result['tracks_wrist'],
tracks_vis_exterior=result['tracks_vis_exterior'],
tracks_vis_wrist=result['tracks_vis_wrist'],
images_exterior=result['images_exterior'],
images_wrist=result['images_wrist'],
actions=result['actions'],
uuid=uuid
)
processed_count += 1
if save_preview:
preview_count += 1
else:
skipped_count += 1
except Exception as e:
print(f"\nError processing episode {episode_idx}: {e}")
skipped_count += 1
pbar.update(1)
# Early exit if all episodes processed
if processed_count + skipped_count >= len(chunk_data['episode_indices']):
break
pbar.close()
# Save metadata
metadata = {
'chunk_id': chunk_id,
'chunk_file': str(args.chunk_file),
'processed': processed_count,
'skipped': skipped_count,
'total': len(chunk_data['episode_indices'])
}
metadata_path = output_dir / f'metadata_chunk_{chunk_id:02d}.json'
with open(metadata_path, 'w') as f:
json.dump(metadata, f, indent=2)
print("\n" + "="*80)
print("Preprocessing Complete")
print("="*80)
print(f" Processed: {processed_count} episodes")
print(f" Skipped: {skipped_count} episodes")
print(f" Preview videos: {preview_dir}")
print(f" NPZ data: {data_dir}")
print(f" Metadata: {metadata_path}")
print("="*80)
if __name__ == '__main__':
main()
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