Datasets:

zhicao
/

openpi

"""
Multi-GPU DROID Preprocessing Script
Processes full DROID dataset in parallel across multiple GPUs.
Usage:
    # Machine 1 (GPUs 0-7):
    bash scripts/run_multigpu_machine1.sh
    # Machine 2 (GPUs 0-7):
    bash scripts/run_multigpu_machine2.sh
Each GPU processes a subset of episodes based on GPU_ID % NUM_GPUS.
"""

import sys
from pathlib import Path
sys.path.append(str(Path(__file__).parent.parent))

import os
import numpy as np
import torch
import mediapy as media
import tensorflow as tf
tf.config.set_visible_devices([], 'GPU')
import tensorflow_datasets as tfds
import cv2
import datetime
import re
import json
from tqdm import tqdm
from scipy.spatial.transform import Rotation as R

from utils.load_camera_calibration import CameraCalibrationLoader
from utils.franka_mesh_projection import FrankaMeshProjector


# 7 gripper offsets in gripper frame
GRIPPER_OFFSETS = np.array([
    [0.0, 0.0, 0.0],      # 0: gripper base
    [0.0, 0.045, 0.161],  # 1: finger 1 tip
    [0.0, -0.045, 0.161], # 2: finger 2 tip
    [0.0, 0.045, 0.13],   # 3: finger 1 end
    [0.0, -0.045, 0.13],  # 4: finger 2 end
    [0.0, 0.0, 0.13],     # 5: gripper center front
    [0.0, 0.0, 0.065],    # 6: gripper center middle
])


def euler_xyz_to_rotation_matrix(euler_xyz):
    """Convert Euler XYZ angles to rotation matrix."""
    return R.from_euler('xyz', euler_xyz).as_matrix()


def transform_gripper_offsets(action):
    """Transform gripper offsets using action position and rotation."""
    pos = action[:3]
    rot_euler = action[3:6]
    rot_matrix = euler_xyz_to_rotation_matrix(rot_euler)
    gripper_points_3d = (rot_matrix @ GRIPPER_OFFSETS.T).T + pos
    return gripper_points_3d


def sample_arm_shaped_points(mesh_2d_visible, img_h, img_w, num_points=993, seed=None):
    """Sample points in arm shape around visible mesh vertices."""
    if seed is not None:
        np.random.seed(seed)

    points = []
    num_visible = len(mesh_2d_visible)

    if num_visible == 0:
        return np.random.rand(num_points, 2) * [img_w, img_h]

    # Gaussian around each visible mesh vertex
    points_per_mesh = min(50, num_points // max(num_visible, 1))
    gaussian_sigma = 15.0

    for mesh_pt in mesh_2d_visible:
        gaussian_pts = np.random.randn(points_per_mesh, 2) * gaussian_sigma + mesh_pt
        gaussian_pts[:, 0] = np.clip(gaussian_pts[:, 0], 0, img_w - 1)
        gaussian_pts[:, 1] = np.clip(gaussian_pts[:, 1], 0, img_h - 1)
        points.append(gaussian_pts)

    # Lines between visible meshes
    if num_visible >= 2:
        points_per_line = 10
        for i in range(num_visible - 1):
            line_pts = np.linspace(mesh_2d_visible[i], mesh_2d_visible[i+1], points_per_line + 2)
            points.append(line_pts[1:-1])

    # Fill remaining with uniform random
    current_count = sum(len(p) for p in points)
    remaining = num_points - current_count

    if remaining > 0:
        uniform_pts = np.random.rand(remaining, 2) * [img_w, img_h]
        points.append(uniform_pts)

    all_points = np.vstack(points) if points else np.empty((0, 2))

    if len(all_points) < num_points:
        extra = np.random.rand(num_points - len(all_points), 2) * [img_w, img_h]
        all_points = np.vstack([all_points, extra])
    elif len(all_points) > num_points:
        all_points = all_points[:num_points]

    return all_points


def sample_wrist_points(img_h, img_w, num_sparse=300, num_dense=700, seed=None):
    """Sample wrist points: sparse uniform + dense in bottom 60%-100%."""
    if seed is not None:
        np.random.seed(seed)

    sparse = np.random.rand(num_sparse, 2) * [img_w, img_h]

    # Dense in bottom 60%-100% region
    y_min = int(img_h * 0.60)
    y_max = img_h
    y_range = y_max - y_min

    dense_x = np.random.rand(num_dense) * img_w
    dense_y = np.random.rand(num_dense) * y_range + y_min
    dense = np.column_stack([dense_x, dense_y])

    return np.vstack([sparse, dense])


def find_closest_calibration(episode, uuid_list):
    """Find closest calibration UUID for episode."""
    try:
        recording_path = episode['episode_metadata']['recording_folderpath'].numpy().decode('utf-8')
        match = re.search(r'/([A-Z]+)/success/(\d{4}-\d{2}-\d{2})/\w+_\w+_+\d+_(\d{2}):(\d{2}):(\d{2})_\d{4}/', recording_path)
        if not match:
            return None
        lab, date, hour, minute, second = match.groups()
        episode_time = datetime.datetime.strptime(f"{date} {hour}:{minute}:{second}", "%Y-%m-%d %H:%M:%S")
        matching_calibs = [uuid for uuid in uuid_list if uuid.startswith(f"{lab}+") and f"+{date}-" in uuid]
        if len(matching_calibs) == 0:
            return None
        best_uuid = None
        min_time_diff = float('inf')
        for calib_uuid in matching_calibs:
            parts = calib_uuid.split('+')
            if len(parts) >= 3:
                time_str = parts[2].replace('_cameras', '')
                match_time = re.search(r'(\d{2})h-(\d{2})m-(\d{2})s', time_str)
                if match_time:
                    calib_hour = int(match_time.group(1))
                    calib_min = int(match_time.group(2))
                    calib_sec = int(match_time.group(3))
                    calib_time = datetime.datetime.strptime(
                        f"{date} {calib_hour}:{calib_min}:{calib_sec}",
                        "%Y-%m-%d %H:%M:%S"
                    )
                    time_diff = abs((episode_time - calib_time).total_seconds())
                    if time_diff < min_time_diff:
                        min_time_diff = time_diff
                        best_uuid = calib_uuid
        return best_uuid
    except:
        return None


def process_episode(episode, episode_idx, projector, calib_loader, uuid, cotracker, device,
                    max_frames=400, save_video=False, output_dir=None):
    """Process a single episode."""

    # Get dual view params
    dual_params = calib_loader.get_dual_view_params(uuid, param_type='refined', require_refined=True)
    K_ext, E_ext = dual_params['exterior_1']
    K_wrist, E_wrist = dual_params['wrist']

    # First pass: count valid frames to check if episode is too long
    valid_frame_count = 0
    for step in episode['steps']:
        img_ext = step['observation']['exterior_image_1_left'].numpy()
        img_wrist = step['observation']['wrist_image_left'].numpy()
        if img_ext is not None and img_wrist is not None:
            valid_frame_count += 1
            if valid_frame_count > max_frames:
                # Episode is too long, skip it
                return None

    # Check minimum length
    if valid_frame_count < 10:
        return None

    # Collect frames and actions
    frames_ext = []
    frames_wrist = []
    actions = []

    for step_idx, step in enumerate(episode['steps']):
        img_ext = step['observation']['exterior_image_1_left'].numpy()
        img_wrist = step['observation']['wrist_image_left'].numpy()
        action = step['action'].numpy()

        if img_ext is not None and img_wrist is not None:
            frames_ext.append(img_ext)
            frames_wrist.append(img_wrist)
            actions.append(action)

    T = len(frames_ext)
    img_h, img_w = frames_ext[0].shape[:2]

    # Step 1: Project mesh vertices for exterior view
    all_mesh_2d_ext = []
    all_mesh_vis_ext = []

    for t in range(T):
        gripper_3d = transform_gripper_offsets(actions[t])
        mesh_2d, mesh_vis = projector._project_3d_to_2d(
            gripper_3d, K_ext, E_ext, img_h=img_h, img_w=img_w
        )
        all_mesh_2d_ext.append(mesh_2d)
        all_mesh_vis_ext.append(mesh_vis)

    all_mesh_2d_ext = np.array(all_mesh_2d_ext)  # [T, 7, 2]
    all_mesh_vis_ext = np.array(all_mesh_vis_ext)  # [T, 7]

    # Check if at least some mesh points visible in first frame
    if np.sum(all_mesh_vis_ext[0]) < 2:
        return None

    # Step 2: Sample CoTracker query points at frame 0
    mesh_2d_0 = all_mesh_2d_ext[0]
    mesh_2d_visible_0 = mesh_2d_0[all_mesh_vis_ext[0]]

    additional_points_ext = sample_arm_shaped_points(
        mesh_2d_visible_0, img_h, img_w, num_points=993, seed=episode_idx
    )
    query_points_ext = np.vstack([mesh_2d_0, additional_points_ext])  # [1000, 2]

    query_points_wrist = sample_wrist_points(
        img_h, img_w, num_sparse=300, num_dense=700, seed=episode_idx
    )

    # Step 3: Run CoTracker
    video_ext_np = np.array(frames_ext).transpose(0, 3, 1, 2)
    video_ext_tensor = torch.from_numpy(video_ext_np).float() / 255.0
    video_ext_tensor = video_ext_tensor.unsqueeze(0).to(device)

    queries_ext = np.zeros((len(query_points_ext), 3))
    queries_ext[:, 0] = 0
    queries_ext[:, 1:] = query_points_ext
    queries_ext_tensor = torch.from_numpy(queries_ext).float().unsqueeze(0).to(device)

    with torch.no_grad():
        tracks_ext, vis_ext = cotracker(
            video_ext_tensor,
            queries=queries_ext_tensor,
            backward_tracking=False
        )

    tracks_ext = tracks_ext[0].cpu().numpy()  # [T, 1000, 2]
    vis_ext = vis_ext[0].cpu().numpy()  # [T, 1000]

    # Wrist view
    video_wrist_np = np.array(frames_wrist).transpose(0, 3, 1, 2)
    video_wrist_tensor = torch.from_numpy(video_wrist_np).float() / 255.0
    video_wrist_tensor = video_wrist_tensor.unsqueeze(0).to(device)

    queries_wrist = np.zeros((len(query_points_wrist), 3))
    queries_wrist[:, 0] = 0
    queries_wrist[:, 1:] = query_points_wrist
    queries_wrist_tensor = torch.from_numpy(queries_wrist).float().unsqueeze(0).to(device)

    with torch.no_grad():
        tracks_wrist, vis_wrist = cotracker(
            video_wrist_tensor,
            queries=queries_wrist_tensor,
            backward_tracking=False
        )

    tracks_wrist = tracks_wrist[0].cpu().numpy()
    vis_wrist = vis_wrist[0].cpu().numpy()

    # Step 4: Save preview video if requested
    if save_video and output_dir is not None:
        video_frames = []
        for t in range(T):
            # Exterior view
            viz_ext = frames_ext[t].copy()

            # Draw ground truth mesh vertices (blue hollow)
            for i in range(7):
                if all_mesh_vis_ext[t, i]:
                    pt = tuple(all_mesh_2d_ext[t, i].astype(int))
                    cv2.circle(viz_ext, pt, 5, (255, 0, 0), 2)
                    cv2.putText(viz_ext, str(i), (pt[0]+6, pt[1]-6),
                               cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255, 0, 0), 1)

            # Draw tracked mesh vertices (red filled) - first 7 of CoTracker
            for i in range(7):
                if vis_ext[t, i]:
                    pt = tuple(tracks_ext[t, i].astype(int))
                    cv2.circle(viz_ext, pt, 3, (0, 0, 255), -1)

            # Draw other tracked points (green)
            for i in range(7, len(tracks_ext[t])):
                if vis_ext[t, i]:
                    pt = tuple(tracks_ext[t, i].astype(int))
                    cv2.circle(viz_ext, pt, 1, (0, 255, 0), -1)

            cv2.putText(viz_ext, f"Ext: GT mesh (blue) | Tracked mesh (red) | Others (green)",
                       (5, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (255, 255, 255), 1)

            # Wrist view
            viz_wrist = frames_wrist[t].copy()

            for i in range(len(tracks_wrist[t])):
                if vis_wrist[t, i]:
                    pt = tuple(tracks_wrist[t, i].astype(int))
                    color = (255, 255, 0) if i < 300 else (0, 255, 255)
                    cv2.circle(viz_wrist, pt, 1, color, -1)

            cv2.putText(viz_wrist, f"Wrist: 300 sparse (cyan) + 700 dense bottom (yellow)",
                       (5, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (255, 255, 255), 1)

            combined = np.concatenate([viz_ext, viz_wrist], axis=1)
            cv2.putText(combined, f"Episode {episode_idx} | Frame {t}/{T}",
                       (5, img_h - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255), 1)
            video_frames.append(combined)

        video_path = output_dir / f"preview_episode_{episode_idx:06d}.mp4"
        media.write_video(str(video_path), video_frames, fps=10)

    return {
        'episode_idx': episode_idx,
        'uuid': uuid,
        'frames_exterior': np.array(frames_ext),
        'frames_wrist': np.array(frames_wrist),
        'actions': np.array(actions),
        'mesh_vertices_2d_exterior': all_mesh_2d_ext,
        'mesh_vertices_vis_exterior': all_mesh_vis_ext,
        'tracks_exterior': tracks_ext,
        'tracks_vis_exterior': vis_ext,
        'tracks_wrist': tracks_wrist,
        'tracks_vis_wrist': vis_wrist,
    }


def main():
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument('--gpu-id', type=int, required=True, help='GPU ID (0-15 for 2 machines)')
    parser.add_argument('--num-gpus', type=int, default=16, help='Total number of GPUs')
    parser.add_argument('--machine-id', type=int, required=True, help='Machine ID (0 or 1)')
    parser.add_argument('--output-dir', type=str, required=True, help='Output directory')
    parser.add_argument('--max-frames', type=int, default=400, help='Max frames per episode')
    parser.add_argument('--preview-total', type=int, default=20, help='Total preview videos across all GPUs')
    args = parser.parse_args()

    # Set CUDA device
    os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_id)
    device = torch.device('cuda:0')

    output_dir = Path(args.output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)
    data_dir = output_dir / 'data'
    data_dir.mkdir(exist_ok=True)
    preview_dir = output_dir / 'preview_videos'
    preview_dir.mkdir(exist_ok=True)

    global_gpu_id = args.machine_id * 8 + args.gpu_id  # Global GPU ID across machines

    print("=" * 80)
    print(f"Multi-GPU DROID Preprocessing")
    print("=" * 80)
    print(f"  Machine ID: {args.machine_id}")
    print(f"  Local GPU ID: {args.gpu_id}")
    print(f"  Global GPU ID: {global_gpu_id}/{args.num_gpus}")
    print(f"  Output: {output_dir}")
    print(f"  Max frames: {args.max_frames}")
    print(f"  Preview videos: {args.preview_total} total (distributed)")
    print("=" * 80)

    # Load calibration
    calib_dir = '/root/workspace/code/wmrl/Dual-Dynamics-Models/DROID-main/vision/u/wenlongh/datasets/droid_v4/cameras'
    calib_loader = CameraCalibrationLoader(calib_dir)
    projector = FrankaMeshProjector(use_gui=False)

    calib_path = Path(calib_dir)
    uuid_list = [f.stem.replace('_cameras', '') for f in sorted(calib_path.glob("*_cameras.json"))]
    print(f"Loaded {len(uuid_list)} camera calibrations")

    # Load CoTracker
    print("Loading CoTracker...")
    from cotracker.predictor import CoTrackerOnlinePredictor, CoTrackerPredictor

    # Try multiple checkpoint locations
    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()

    # Load DROID dataset
    print("Loading DROID dataset...")
    droid_path = '/mnt/kevin/data/droid/droid/1.0.0'
    builder = tfds.builder_from_directory(droid_path)
    dataset = builder.as_dataset(split='train')

    # Count total episodes
    print("Counting total episodes...")
    total_episodes = sum(1 for _ in dataset)
    print(f"Total episodes in dataset: {total_episodes}")

    # Calculate episodes for this GPU
    episodes_per_gpu = total_episodes // args.num_gpus
    start_idx = global_gpu_id * episodes_per_gpu
    end_idx = start_idx + episodes_per_gpu if global_gpu_id < args.num_gpus - 1 else total_episodes

    print(f"This GPU will process episodes {start_idx} to {end_idx-1} ({end_idx - start_idx} episodes)")

    # Determine preview strategy (distribute 20 videos evenly)
    preview_interval = max(1, (end_idx - start_idx) // max(1, args.preview_total // args.num_gpus))

    processed_count = 0
    skipped_count = 0
    preview_count = 0

    pbar = tqdm(total=end_idx - start_idx, desc=f"GPU {global_gpu_id}")

    for episode_idx, episode in enumerate(dataset):
        # Skip episodes not assigned to this GPU
        if episode_idx < start_idx:
            continue
        if episode_idx >= end_idx:
            break

        local_idx = episode_idx - start_idx

        # Find calibration
        uuid = find_closest_calibration(episode, uuid_list)
        if uuid is None or not calib_loader.has_refined_extrinsics(uuid):
            skipped_count += 1
            pbar.update(1)
            continue

        # Decide if we save video for this episode
        save_video = (local_idx % preview_interval == 0) and (preview_count < args.preview_total // args.num_gpus)

        try:
            result = process_episode(
                episode, episode_idx, projector, calib_loader, uuid,
                cotracker, device, max_frames=args.max_frames,
                save_video=save_video, output_dir=preview_dir
            )

            if result is None:
                skipped_count += 1
                pbar.update(1)
                continue

            # Save NPZ
            npz_path = data_dir / f"episode_{episode_idx:06d}.npz"
            np.savez_compressed(
                npz_path,
                episode_idx=result['episode_idx'],
                uuid=result['uuid'],
                images_exterior=result['frames_exterior'],
                images_wrist=result['frames_wrist'],
                actions=result['actions'],
                mesh_vertices_2d_exterior=result['mesh_vertices_2d_exterior'],
                mesh_vertices_vis_exterior=result['mesh_vertices_vis_exterior'],
                tracks_exterior=result['tracks_exterior'],
                tracks_vis_exterior=result['tracks_vis_exterior'],
                tracks_wrist=result['tracks_wrist'],
                tracks_vis_wrist=result['tracks_vis_wrist'],
                mesh_indices=np.array([0, 1, 2, 3, 4, 5, 6], dtype=np.int32),
            )

            processed_count += 1
            if save_video:
                preview_count += 1
            pbar.update(1)

        except Exception as e:
            print(f"\nError processing episode {episode_idx}: {e}")
            skipped_count += 1
            pbar.update(1)
            continue

    pbar.close()

    # Save GPU-specific metadata
    metadata = {
        'gpu_id': global_gpu_id,
        'machine_id': args.machine_id,
        'local_gpu_id': args.gpu_id,
        'processed_episodes': processed_count,
        'skipped_episodes': skipped_count,
        'preview_videos': preview_count,
        'episode_range': [start_idx, end_idx],
        'split': 'train',
        'camera_params': {
            'exterior': {
                'extrinsics': 'refined',
                'intrinsics': 'measured',
                'inversion': False
            },
            'wrist': {
                'sampling': 'random',
                'dense_region': 'bottom_60_100_pct'
            }
        },
        'point_distribution': {
            'exterior': {
                'total_tracked_points': 1000,
                'mesh_vertices_tracked': 7,
                'additional_points': 993,
                'arm_shaped_strategy': 'gaussian_15px_per_mesh + lines_between',
            },
            'wrist': {
                'total_tracked_points': 1000,
                'sparse_uniform': 300,
                'dense_bottom': 700
            }
        },
        'image_resolution': [180, 320],
        'max_frames_per_episode': args.max_frames,
        'cotracker_model': 'scaled_offline.pth',
        'cotracker_chunking': 'automatic_internal_only'
    }

    metadata_path = output_dir / f'metadata_gpu{global_gpu_id:02d}.json'
    with open(metadata_path, 'w') as f:
        json.dump(metadata, f, indent=2)

    print("\n" + "=" * 80)
    print(f"GPU {global_gpu_id} Complete")
    print("=" * 80)
    print(f"  Processed: {processed_count} episodes")
    print(f"  Skipped: {skipped_count} episodes")
    print(f"  Preview videos: {preview_count}")
    print(f"  Metadata: {metadata_path}")
    print("=" * 80)


if __name__ == "__main__":
    main()

	"""
	Multi-GPU DROID Preprocessing Script

	Processes full DROID dataset in parallel across multiple GPUs.
	Usage:
	# Machine 1 (GPUs 0-7):
	bash scripts/run_multigpu_machine1.sh

	# Machine 2 (GPUs 0-7):
	bash scripts/run_multigpu_machine2.sh

	Each GPU processes a subset of episodes based on GPU_ID % NUM_GPUS.
	"""

	import sys
	from pathlib import Path
	sys.path.append(str(Path(__file__).parent.parent))

	import os
	import numpy as np
	import torch
	import mediapy as media
	import tensorflow as tf
	tf.config.set_visible_devices([], 'GPU')
	import tensorflow_datasets as tfds
	import cv2
	import datetime
	import re
	import json
	from tqdm import tqdm
	from scipy.spatial.transform import Rotation as R

	from utils.load_camera_calibration import CameraCalibrationLoader
	from utils.franka_mesh_projection import FrankaMeshProjector


	# 7 gripper offsets in gripper frame
	GRIPPER_OFFSETS = np.array([
	[0.0, 0.0, 0.0], # 0: gripper base
	[0.0, 0.045, 0.161], # 1: finger 1 tip
	[0.0, -0.045, 0.161], # 2: finger 2 tip
	[0.0, 0.045, 0.13], # 3: finger 1 end
	[0.0, -0.045, 0.13], # 4: finger 2 end
	[0.0, 0.0, 0.13], # 5: gripper center front
	[0.0, 0.0, 0.065], # 6: gripper center middle
	])


	def euler_xyz_to_rotation_matrix(euler_xyz):
	"""Convert Euler XYZ angles to rotation matrix."""
	return R.from_euler('xyz', euler_xyz).as_matrix()


	def transform_gripper_offsets(action):
	"""Transform gripper offsets using action position and rotation."""
	pos = action[:3]
	rot_euler = action[3:6]
	rot_matrix = euler_xyz_to_rotation_matrix(rot_euler)
	gripper_points_3d = (rot_matrix @ GRIPPER_OFFSETS.T).T + pos
	return gripper_points_3d


	def sample_arm_shaped_points(mesh_2d_visible, img_h, img_w, num_points=993, seed=None):
	"""Sample points in arm shape around visible mesh vertices."""
	if seed is not None:
	np.random.seed(seed)

	points = []
	num_visible = len(mesh_2d_visible)

	if num_visible == 0:
	return np.random.rand(num_points, 2) * [img_w, img_h]

	# Gaussian around each visible mesh vertex
	points_per_mesh = min(50, num_points // max(num_visible, 1))
	gaussian_sigma = 15.0

	for mesh_pt in mesh_2d_visible:
	gaussian_pts = np.random.randn(points_per_mesh, 2) * gaussian_sigma + mesh_pt
	gaussian_pts[:, 0] = np.clip(gaussian_pts[:, 0], 0, img_w - 1)
	gaussian_pts[:, 1] = np.clip(gaussian_pts[:, 1], 0, img_h - 1)
	points.append(gaussian_pts)

	# Lines between visible meshes
	if num_visible >= 2:
	points_per_line = 10
	for i in range(num_visible - 1):
	line_pts = np.linspace(mesh_2d_visible[i], mesh_2d_visible[i+1], points_per_line + 2)
	points.append(line_pts[1:-1])

	# Fill remaining with uniform random
	current_count = sum(len(p) for p in points)
	remaining = num_points - current_count

	if remaining > 0:
	uniform_pts = np.random.rand(remaining, 2) * [img_w, img_h]
	points.append(uniform_pts)

	all_points = np.vstack(points) if points else np.empty((0, 2))

	if len(all_points) < num_points:
	extra = np.random.rand(num_points - len(all_points), 2) * [img_w, img_h]
	all_points = np.vstack([all_points, extra])
	elif len(all_points) > num_points:
	all_points = all_points[:num_points]

	return all_points


	def sample_wrist_points(img_h, img_w, num_sparse=300, num_dense=700, seed=None):
	"""Sample wrist points: sparse uniform + dense in bottom 60%-100%."""
	if seed is not None:
	np.random.seed(seed)

	sparse = np.random.rand(num_sparse, 2) * [img_w, img_h]

	# Dense in bottom 60%-100% region
	y_min = int(img_h * 0.60)
	y_max = img_h
	y_range = y_max - y_min

	dense_x = np.random.rand(num_dense) * img_w
	dense_y = np.random.rand(num_dense) * y_range + y_min
	dense = np.column_stack([dense_x, dense_y])

	return np.vstack([sparse, dense])


	def find_closest_calibration(episode, uuid_list):
	"""Find closest calibration UUID for episode."""
	try:
	recording_path = episode['episode_metadata']['recording_folderpath'].numpy().decode('utf-8')
	match = re.search(r'/([A-Z]+)/success/(\d{4}-\d{2}-\d{2})/\w+_\w+_+\d+_(\d{2}):(\d{2}):(\d{2})_\d{4}/', recording_path)
	if not match:
	return None
	lab, date, hour, minute, second = match.groups()
	episode_time = datetime.datetime.strptime(f"{date} {hour}:{minute}:{second}", "%Y-%m-%d %H:%M:%S")
	matching_calibs = [uuid for uuid in uuid_list if uuid.startswith(f"{lab}+") and f"+{date}-" in uuid]
	if len(matching_calibs) == 0:
	return None
	best_uuid = None
	min_time_diff = float('inf')
	for calib_uuid in matching_calibs:
	parts = calib_uuid.split('+')
	if len(parts) >= 3:
	time_str = parts[2].replace('_cameras', '')
	match_time = re.search(r'(\d{2})h-(\d{2})m-(\d{2})s', time_str)
	if match_time:
	calib_hour = int(match_time.group(1))
	calib_min = int(match_time.group(2))
	calib_sec = int(match_time.group(3))
	calib_time = datetime.datetime.strptime(
	f"{date} {calib_hour}:{calib_min}:{calib_sec}",
	"%Y-%m-%d %H:%M:%S"
	)
	time_diff = abs((episode_time - calib_time).total_seconds())
	if time_diff < min_time_diff:
	min_time_diff = time_diff
	best_uuid = calib_uuid
	return best_uuid
	except:
	return None


	def process_episode(episode, episode_idx, projector, calib_loader, uuid, cotracker, device,
	max_frames=400, save_video=False, output_dir=None):
	"""Process a single episode."""

	# Get dual view params
	dual_params = calib_loader.get_dual_view_params(uuid, param_type='refined', require_refined=True)
	K_ext, E_ext = dual_params['exterior_1']
	K_wrist, E_wrist = dual_params['wrist']

	# First pass: count valid frames to check if episode is too long
	valid_frame_count = 0
	for step in episode['steps']:
	img_ext = step['observation']['exterior_image_1_left'].numpy()
	img_wrist = step['observation']['wrist_image_left'].numpy()
	if img_ext is not None and img_wrist is not None:
	valid_frame_count += 1
	if valid_frame_count > max_frames:
	# Episode is too long, skip it
	return None

	# Check minimum length
	if valid_frame_count < 10:
	return None

	# Collect frames and actions
	frames_ext = []
	frames_wrist = []
	actions = []

	for step_idx, step in enumerate(episode['steps']):
	img_ext = step['observation']['exterior_image_1_left'].numpy()
	img_wrist = step['observation']['wrist_image_left'].numpy()
	action = step['action'].numpy()

	if img_ext is not None and img_wrist is not None:
	frames_ext.append(img_ext)
	frames_wrist.append(img_wrist)
	actions.append(action)

	T = len(frames_ext)
	img_h, img_w = frames_ext[0].shape[:2]

	# Step 1: Project mesh vertices for exterior view
	all_mesh_2d_ext = []
	all_mesh_vis_ext = []

	for t in range(T):
	gripper_3d = transform_gripper_offsets(actions[t])
	mesh_2d, mesh_vis = projector._project_3d_to_2d(
	gripper_3d, K_ext, E_ext, img_h=img_h, img_w=img_w
	)
	all_mesh_2d_ext.append(mesh_2d)
	all_mesh_vis_ext.append(mesh_vis)

	all_mesh_2d_ext = np.array(all_mesh_2d_ext) # [T, 7, 2]
	all_mesh_vis_ext = np.array(all_mesh_vis_ext) # [T, 7]

	# Check if at least some mesh points visible in first frame
	if np.sum(all_mesh_vis_ext[0]) < 2:
	return None

	# Step 2: Sample CoTracker query points at frame 0
	mesh_2d_0 = all_mesh_2d_ext[0]
	mesh_2d_visible_0 = mesh_2d_0[all_mesh_vis_ext[0]]

	additional_points_ext = sample_arm_shaped_points(
	mesh_2d_visible_0, img_h, img_w, num_points=993, seed=episode_idx
	)
	query_points_ext = np.vstack([mesh_2d_0, additional_points_ext]) # [1000, 2]

	query_points_wrist = sample_wrist_points(
	img_h, img_w, num_sparse=300, num_dense=700, seed=episode_idx
	)

	# Step 3: Run CoTracker
	video_ext_np = np.array(frames_ext).transpose(0, 3, 1, 2)
	video_ext_tensor = torch.from_numpy(video_ext_np).float() / 255.0
	video_ext_tensor = video_ext_tensor.unsqueeze(0).to(device)

	queries_ext = np.zeros((len(query_points_ext), 3))
	queries_ext[:, 0] = 0
	queries_ext[:, 1:] = query_points_ext
	queries_ext_tensor = torch.from_numpy(queries_ext).float().unsqueeze(0).to(device)

	with torch.no_grad():
	tracks_ext, vis_ext = cotracker(
	video_ext_tensor,
	queries=queries_ext_tensor,
	backward_tracking=False
	)

	tracks_ext = tracks_ext[0].cpu().numpy() # [T, 1000, 2]
	vis_ext = vis_ext[0].cpu().numpy() # [T, 1000]

	# Wrist view
	video_wrist_np = np.array(frames_wrist).transpose(0, 3, 1, 2)
	video_wrist_tensor = torch.from_numpy(video_wrist_np).float() / 255.0
	video_wrist_tensor = video_wrist_tensor.unsqueeze(0).to(device)

	queries_wrist = np.zeros((len(query_points_wrist), 3))
	queries_wrist[:, 0] = 0
	queries_wrist[:, 1:] = query_points_wrist
	queries_wrist_tensor = torch.from_numpy(queries_wrist).float().unsqueeze(0).to(device)

	with torch.no_grad():
	tracks_wrist, vis_wrist = cotracker(
	video_wrist_tensor,
	queries=queries_wrist_tensor,
	backward_tracking=False
	)

	tracks_wrist = tracks_wrist[0].cpu().numpy()
	vis_wrist = vis_wrist[0].cpu().numpy()

	# Step 4: Save preview video if requested
	if save_video and output_dir is not None:
	video_frames = []
	for t in range(T):
	# Exterior view
	viz_ext = frames_ext[t].copy()

	# Draw ground truth mesh vertices (blue hollow)
	for i in range(7):
	if all_mesh_vis_ext[t, i]:
	pt = tuple(all_mesh_2d_ext[t, i].astype(int))
	cv2.circle(viz_ext, pt, 5, (255, 0, 0), 2)
	cv2.putText(viz_ext, str(i), (pt[0]+6, pt[1]-6),
	cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255, 0, 0), 1)

	# Draw tracked mesh vertices (red filled) - first 7 of CoTracker
	for i in range(7):
	if vis_ext[t, i]:
	pt = tuple(tracks_ext[t, i].astype(int))
	cv2.circle(viz_ext, pt, 3, (0, 0, 255), -1)

	# Draw other tracked points (green)
	for i in range(7, len(tracks_ext[t])):
	if vis_ext[t, i]:
	pt = tuple(tracks_ext[t, i].astype(int))
	cv2.circle(viz_ext, pt, 1, (0, 255, 0), -1)

	cv2.putText(viz_ext, f"Ext: GT mesh (blue) \| Tracked mesh (red) \| Others (green)",
	(5, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (255, 255, 255), 1)

	# Wrist view
	viz_wrist = frames_wrist[t].copy()

	for i in range(len(tracks_wrist[t])):
	if vis_wrist[t, i]:
	pt = tuple(tracks_wrist[t, i].astype(int))
	color = (255, 255, 0) if i < 300 else (0, 255, 255)
	cv2.circle(viz_wrist, pt, 1, color, -1)

	cv2.putText(viz_wrist, f"Wrist: 300 sparse (cyan) + 700 dense bottom (yellow)",
	(5, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (255, 255, 255), 1)

	combined = np.concatenate([viz_ext, viz_wrist], axis=1)
	cv2.putText(combined, f"Episode {episode_idx} \| Frame {t}/{T}",
	(5, img_h - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255), 1)
	video_frames.append(combined)

	video_path = output_dir / f"preview_episode_{episode_idx:06d}.mp4"
	media.write_video(str(video_path), video_frames, fps=10)

	return {
	'episode_idx': episode_idx,
	'uuid': uuid,
	'frames_exterior': np.array(frames_ext),
	'frames_wrist': np.array(frames_wrist),
	'actions': np.array(actions),
	'mesh_vertices_2d_exterior': all_mesh_2d_ext,
	'mesh_vertices_vis_exterior': all_mesh_vis_ext,
	'tracks_exterior': tracks_ext,
	'tracks_vis_exterior': vis_ext,
	'tracks_wrist': tracks_wrist,
	'tracks_vis_wrist': vis_wrist,
	}


	def main():
	import argparse
	parser = argparse.ArgumentParser()
	parser.add_argument('--gpu-id', type=int, required=True, help='GPU ID (0-15 for 2 machines)')
	parser.add_argument('--num-gpus', type=int, default=16, help='Total number of GPUs')
	parser.add_argument('--machine-id', type=int, required=True, help='Machine ID (0 or 1)')
	parser.add_argument('--output-dir', type=str, required=True, help='Output directory')
	parser.add_argument('--max-frames', type=int, default=400, help='Max frames per episode')
	parser.add_argument('--preview-total', type=int, default=20, help='Total preview videos across all GPUs')
	args = parser.parse_args()

	# Set CUDA device
	os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_id)
	device = torch.device('cuda:0')

	output_dir = Path(args.output_dir)
	output_dir.mkdir(parents=True, exist_ok=True)
	data_dir = output_dir / 'data'
	data_dir.mkdir(exist_ok=True)
	preview_dir = output_dir / 'preview_videos'
	preview_dir.mkdir(exist_ok=True)

	global_gpu_id = args.machine_id * 8 + args.gpu_id # Global GPU ID across machines

	print("=" * 80)
	print(f"Multi-GPU DROID Preprocessing")
	print("=" * 80)
	print(f" Machine ID: {args.machine_id}")
	print(f" Local GPU ID: {args.gpu_id}")
	print(f" Global GPU ID: {global_gpu_id}/{args.num_gpus}")
	print(f" Output: {output_dir}")
	print(f" Max frames: {args.max_frames}")
	print(f" Preview videos: {args.preview_total} total (distributed)")
	print("=" * 80)

	# Load calibration
	calib_dir = '/root/workspace/code/wmrl/Dual-Dynamics-Models/DROID-main/vision/u/wenlongh/datasets/droid_v4/cameras'
	calib_loader = CameraCalibrationLoader(calib_dir)
	projector = FrankaMeshProjector(use_gui=False)

	calib_path = Path(calib_dir)
	uuid_list = [f.stem.replace('_cameras', '') for f in sorted(calib_path.glob("*_cameras.json"))]
	print(f"Loaded {len(uuid_list)} camera calibrations")

	# Load CoTracker
	print("Loading CoTracker...")
	from cotracker.predictor import CoTrackerOnlinePredictor, CoTrackerPredictor

	# Try multiple checkpoint locations
	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()

	# Load DROID dataset
	print("Loading DROID dataset...")
	droid_path = '/mnt/kevin/data/droid/droid/1.0.0'
	builder = tfds.builder_from_directory(droid_path)
	dataset = builder.as_dataset(split='train')

	# Count total episodes
	print("Counting total episodes...")
	total_episodes = sum(1 for _ in dataset)
	print(f"Total episodes in dataset: {total_episodes}")

	# Calculate episodes for this GPU
	episodes_per_gpu = total_episodes // args.num_gpus
	start_idx = global_gpu_id * episodes_per_gpu
	end_idx = start_idx + episodes_per_gpu if global_gpu_id < args.num_gpus - 1 else total_episodes

	print(f"This GPU will process episodes {start_idx} to {end_idx-1} ({end_idx - start_idx} episodes)")

	# Determine preview strategy (distribute 20 videos evenly)
	preview_interval = max(1, (end_idx - start_idx) // max(1, args.preview_total // args.num_gpus))

	processed_count = 0
	skipped_count = 0
	preview_count = 0

	pbar = tqdm(total=end_idx - start_idx, desc=f"GPU {global_gpu_id}")

	for episode_idx, episode in enumerate(dataset):
	# Skip episodes not assigned to this GPU
	if episode_idx < start_idx:
	continue
	if episode_idx >= end_idx:
	break

	local_idx = episode_idx - start_idx

	# Find calibration
	uuid = find_closest_calibration(episode, uuid_list)
	if uuid is None or not calib_loader.has_refined_extrinsics(uuid):
	skipped_count += 1
	pbar.update(1)
	continue

	# Decide if we save video for this episode
	save_video = (local_idx % preview_interval == 0) and (preview_count < args.preview_total // args.num_gpus)

	try:
	result = process_episode(
	episode, episode_idx, projector, calib_loader, uuid,
	cotracker, device, max_frames=args.max_frames,
	save_video=save_video, output_dir=preview_dir
	)

	if result is None:
	skipped_count += 1
	pbar.update(1)
	continue

	# Save NPZ
	npz_path = data_dir / f"episode_{episode_idx:06d}.npz"
	np.savez_compressed(
	npz_path,
	episode_idx=result['episode_idx'],
	uuid=result['uuid'],
	images_exterior=result['frames_exterior'],
	images_wrist=result['frames_wrist'],
	actions=result['actions'],
	mesh_vertices_2d_exterior=result['mesh_vertices_2d_exterior'],
	mesh_vertices_vis_exterior=result['mesh_vertices_vis_exterior'],
	tracks_exterior=result['tracks_exterior'],
	tracks_vis_exterior=result['tracks_vis_exterior'],
	tracks_wrist=result['tracks_wrist'],
	tracks_vis_wrist=result['tracks_vis_wrist'],
	mesh_indices=np.array([0, 1, 2, 3, 4, 5, 6], dtype=np.int32),
	)

	processed_count += 1
	if save_video:
	preview_count += 1
	pbar.update(1)

	except Exception as e:
	print(f"\nError processing episode {episode_idx}: {e}")
	skipped_count += 1
	pbar.update(1)
	continue

	pbar.close()

	# Save GPU-specific metadata
	metadata = {
	'gpu_id': global_gpu_id,
	'machine_id': args.machine_id,
	'local_gpu_id': args.gpu_id,
	'processed_episodes': processed_count,
	'skipped_episodes': skipped_count,
	'preview_videos': preview_count,
	'episode_range': [start_idx, end_idx],
	'split': 'train',
	'camera_params': {
	'exterior': {
	'extrinsics': 'refined',
	'intrinsics': 'measured',
	'inversion': False
	},
	'wrist': {
	'sampling': 'random',
	'dense_region': 'bottom_60_100_pct'
	}
	},
	'point_distribution': {
	'exterior': {
	'total_tracked_points': 1000,
	'mesh_vertices_tracked': 7,
	'additional_points': 993,
	'arm_shaped_strategy': 'gaussian_15px_per_mesh + lines_between',
	},
	'wrist': {
	'total_tracked_points': 1000,
	'sparse_uniform': 300,
	'dense_bottom': 700
	}
	},
	'image_resolution': [180, 320],
	'max_frames_per_episode': args.max_frames,
	'cotracker_model': 'scaled_offline.pth',
	'cotracker_chunking': 'automatic_internal_only'
	}

	metadata_path = output_dir / f'metadata_gpu{global_gpu_id:02d}.json'
	with open(metadata_path, 'w') as f:
	json.dump(metadata, f, indent=2)

	print("\n" + "=" * 80)
	print(f"GPU {global_gpu_id} Complete")
	print("=" * 80)
	print(f" Processed: {processed_count} episodes")
	print(f" Skipped: {skipped_count} episodes")
	print(f" Preview videos: {preview_count}")
	print(f" Metadata: {metadata_path}")
	print("=" * 80)


	if __name__ == "__main__":
	main()