Datasets:

zhicao
/

openpi

#!/usr/bin/env python3
"""
Inspect preprocessed DROID data to verify structure and visualize tracks.
Checks:
1. Data structure (keys, shapes)
2. Track counts (should be 1105 for both views after reprocessing)
3. Wrist mesh vertices existence
4. Visualize tracked points on sample frames
"""

import numpy as np
import cv2
import sys
from pathlib import Path
import random

# Add parent to path
sys.path.insert(0, str(Path(__file__).parent.parent))


def inspect_npz_structure(npz_path):
    """Print detailed structure of .npz file."""
    data = np.load(npz_path, allow_pickle=True)

    print(f"\n{'='*80}")
    print(f"File: {npz_path.name}")
    print(f"{'='*80}")

    # Print all keys
    print("\nKeys in file:")
    for key in sorted(data.keys()):
        print(f"  {key}")

    # Print shapes
    print("\nData shapes:")
    for key in sorted(data.keys()):
        value = data[key]
        if hasattr(value, 'shape'):
            print(f"  {key:40s}: {value.shape}")
        else:
            print(f"  {key:40s}: {type(value).__name__} = {value}")

    # Check for specific keys
    print("\nStructure checks:")

    # Check exterior tracks
    if 'tracks_exterior' in data:
        tracks_ext = data['tracks_exterior']
        print(f"  ✓ Exterior tracks: {tracks_ext.shape} (expected: [T, 1105, 2])")
        if tracks_ext.shape[1] == 1105:
            print(f"    ✓ Correct point count (1105)")
        else:
            print(f"    ✗ WRONG point count! Expected 1105, got {tracks_ext.shape[1]}")
    else:
        print(f"  ✗ Missing 'tracks_exterior'")

    # Check wrist tracks
    if 'tracks_wrist' in data:
        tracks_wrist = data['tracks_wrist']
        print(f"  ✓ Wrist tracks: {tracks_wrist.shape} (expected: [T, 1105, 2])")
        if tracks_wrist.shape[1] == 1105:
            print(f"    ✓ Correct point count (1105)")
        else:
            print(f"    ✗ WRONG point count! Expected 1105, got {tracks_wrist.shape[1]}")
    else:
        print(f"  ✗ Missing 'tracks_wrist'")

    # Check wrist mesh vertices (fixed)
    if 'mesh_vertices_2d_wrist_fixed' in data:
        mesh_wrist = data['mesh_vertices_2d_wrist_fixed']
        print(f"  ✓ Wrist fixed mesh: {mesh_wrist.shape} (expected: [T, 7, 2])")
    else:
        print(f"  ✗ Missing 'mesh_vertices_2d_wrist_fixed'")

    # Check exterior mesh vertices
    if 'mesh_vertices_2d_exterior' in data:
        mesh_ext = data['mesh_vertices_2d_exterior']
        print(f"  ✓ Exterior mesh: {mesh_ext.shape} (expected: [T, 7, 2])")
    else:
        print(f"  ✗ Missing 'mesh_vertices_2d_exterior'")

    return data


def visualize_tracks(data, npz_path, output_dir, num_frames=5):
    """
    Visualize tracked points on sample frames.
    Shows:
    - Exterior: mesh vertices (blue) + tracked points (green)
    - Wrist: fixed mesh (cyan) + tracked mesh (magenta) + tracked points (green)
    """
    images_ext = data['images_exterior']  # [T, H, W, 3]
    images_wrist = data['images_wrist']
    tracks_ext = data['tracks_exterior']  # [T, N, 2]
    tracks_wrist = data['tracks_wrist']
    tracks_vis_ext = data['tracks_vis_exterior']
    tracks_vis_wrist = data['tracks_vis_wrist']

    # Get mesh data
    mesh_2d_ext = data.get('mesh_vertices_2d_exterior', None)  # [T, 7, 2]
    mesh_vis_ext = data.get('mesh_vertices_vis_exterior', None)  # [T, 7]
    mesh_2d_wrist_fixed = data.get('mesh_vertices_2d_wrist_fixed', None)

    T = images_ext.shape[0]

    # Sample frames evenly
    frame_indices = np.linspace(0, T-1, min(num_frames, T), dtype=int)

    print(f"\nVisualizing {len(frame_indices)} frames...")

    output_dir = Path(output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)

    for idx, t in enumerate(frame_indices):
        # Exterior view
        viz_ext = images_ext[t].copy()

        # Draw ground truth mesh vertices (blue circles)
        if mesh_2d_ext is not None and mesh_vis_ext is not None:
            for i in range(7):
                if mesh_vis_ext[t, i]:
                    pt = tuple(mesh_2d_ext[t, i].astype(int))
                    cv2.circle(viz_ext, pt, 5, (255, 0, 0), 2)  # Blue hollow
                    cv2.putText(viz_ext, str(i), (pt[0]+6, pt[1]-6),
                               cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255, 0, 0), 1)

        # Draw all tracked points (green, smaller)
        for i in range(tracks_ext.shape[1]):
            if tracks_vis_ext[t, i]:
                pt = tuple(tracks_ext[t, i].astype(int))
                # First 7 points are mesh - draw in red
                if i < 7:
                    cv2.circle(viz_ext, pt, 3, (0, 0, 255), -1)  # Red filled
                else:
                    cv2.circle(viz_ext, pt, 1, (0, 255, 0), -1)  # Green

        cv2.putText(viz_ext, f"Ext: GT mesh (blue) | Tracked mesh (red) | Others (green) | {tracks_ext.shape[1]} pts",
                   (5, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (255, 255, 255), 1)

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

        # Draw TRACKED mesh vertices (first 7 points) - magenta filled circles
        for i in range(min(7, tracks_wrist.shape[1])):
            if tracks_vis_wrist[t, i]:
                pt = tuple(tracks_wrist[t, i].astype(int))
                cv2.circle(viz_wrist, pt, 3, (255, 0, 255), -1)  # Magenta filled

        # Draw FIXED mesh vertices (from separate array) - cyan hollow circles
        if mesh_2d_wrist_fixed is not None:
            for i in range(7):
                pt = tuple(mesh_2d_wrist_fixed[t, i].astype(int))
                cv2.circle(viz_wrist, pt, 4, (255, 255, 0), 2)  # Cyan hollow
                # Label finger tips and joints
                if i in [1, 2, 3, 4]:
                    cv2.putText(viz_wrist, str(i), (pt[0]+5, pt[1]-5),
                               cv2.FONT_HERSHEY_SIMPLEX, 0.25, (255, 255, 0), 1)

        # Draw other tracked points (grid + random, green, smaller)
        for i in range(7, tracks_wrist.shape[1]):
            if tracks_vis_wrist[t, i]:
                pt = tuple(tracks_wrist[t, i].astype(int))
                cv2.circle(viz_wrist, pt, 1, (0, 255, 0), -1)  # Green

        cv2.putText(viz_wrist, f"Wrist: Tracked mesh (magenta) | Fixed mesh (cyan) | Others (green) | {tracks_wrist.shape[1]} pts",
                   (5, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255, 255, 255), 1)

        # Concatenate side by side
        combined = np.concatenate([viz_ext, viz_wrist], axis=1)
        cv2.putText(combined, f"Episode {data['episode_idx']} | Frame {t}/{T}",
                   (combined.shape[1]//2 - 60, 15),
                   cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

        # Save frame
        output_path = output_dir / f"{npz_path.stem}_frame_{t:03d}.png"
        cv2.imwrite(str(output_path), combined)
        print(f"  Saved: {output_path.name}")


def main():
    import argparse
    parser = argparse.ArgumentParser(description="Inspect preprocessed DROID data")
    parser.add_argument('--data-dir', type=str, default='/mnt/kevin/data/droid_processed_1000pts',
                       help='Directory with preprocessed .npz files')
    parser.add_argument('--num-samples', type=int, default=5,
                       help='Number of episodes to sample (default: 5)')
    parser.add_argument('--num-frames', type=int, default=5,
                       help='Number of frames to visualize per episode (default: 5)')
    parser.add_argument('--output-dir', type=str, default='/tmp/droid_inspect',
                       help='Output directory for visualizations')
    parser.add_argument('--specific-episodes', type=int, nargs='+', default=None,
                       help='Specific episode indices to inspect (e.g., 100 200 300)')
    args = parser.parse_args()

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

    # Get all .npz files
    npz_files = sorted(data_dir.glob('episode_*.npz'))
    print(f"Found {len(npz_files)} preprocessed episodes in {data_dir}")

    if len(npz_files) == 0:
        print("No .npz files found!")
        return

    # Select episodes to inspect
    if args.specific_episodes:
        # Use specific episode indices
        sample_files = []
        for ep_idx in args.specific_episodes:
            ep_file = data_dir / f"episode_{ep_idx:06d}.npz"
            if ep_file.exists():
                sample_files.append(ep_file)
            else:
                print(f"Warning: Episode {ep_idx} not found")
    else:
        # Random sample
        sample_files = random.sample(npz_files, min(args.num_samples, len(npz_files)))

    print(f"\nInspecting {len(sample_files)} episodes...\n")

    # Inspect each file
    for npz_path in sample_files:
        try:
            # Print structure
            data = inspect_npz_structure(npz_path)

            # Visualize tracks
            visualize_tracks(data, npz_path, output_dir, num_frames=args.num_frames)

        except Exception as e:
            print(f"\nError processing {npz_path.name}: {e}")
            import traceback
            traceback.print_exc()
            continue

    print(f"\n{'='*80}")
    print(f"Inspection complete!")
    print(f"Visualizations saved to: {output_dir}")
    print(f"{'='*80}")


if __name__ == '__main__':
    main()

	#!/usr/bin/env python3
	"""
	Inspect preprocessed DROID data to verify structure and visualize tracks.

	Checks:
	1. Data structure (keys, shapes)
	2. Track counts (should be 1105 for both views after reprocessing)
	3. Wrist mesh vertices existence
	4. Visualize tracked points on sample frames
	"""

	import numpy as np
	import cv2
	import sys
	from pathlib import Path
	import random

	# Add parent to path
	sys.path.insert(0, str(Path(__file__).parent.parent))


	def inspect_npz_structure(npz_path):
	"""Print detailed structure of .npz file."""
	data = np.load(npz_path, allow_pickle=True)

	print(f"\n{'='*80}")
	print(f"File: {npz_path.name}")
	print(f"{'='*80}")

	# Print all keys
	print("\nKeys in file:")
	for key in sorted(data.keys()):
	print(f" {key}")

	# Print shapes
	print("\nData shapes:")
	for key in sorted(data.keys()):
	value = data[key]
	if hasattr(value, 'shape'):
	print(f" {key:40s}: {value.shape}")
	else:
	print(f" {key:40s}: {type(value).__name__} = {value}")

	# Check for specific keys
	print("\nStructure checks:")

	# Check exterior tracks
	if 'tracks_exterior' in data:
	tracks_ext = data['tracks_exterior']
	print(f" ✓ Exterior tracks: {tracks_ext.shape} (expected: [T, 1105, 2])")
	if tracks_ext.shape[1] == 1105:
	print(f" ✓ Correct point count (1105)")
	else:
	print(f" ✗ WRONG point count! Expected 1105, got {tracks_ext.shape[1]}")
	else:
	print(f" ✗ Missing 'tracks_exterior'")

	# Check wrist tracks
	if 'tracks_wrist' in data:
	tracks_wrist = data['tracks_wrist']
	print(f" ✓ Wrist tracks: {tracks_wrist.shape} (expected: [T, 1105, 2])")
	if tracks_wrist.shape[1] == 1105:
	print(f" ✓ Correct point count (1105)")
	else:
	print(f" ✗ WRONG point count! Expected 1105, got {tracks_wrist.shape[1]}")
	else:
	print(f" ✗ Missing 'tracks_wrist'")

	# Check wrist mesh vertices (fixed)
	if 'mesh_vertices_2d_wrist_fixed' in data:
	mesh_wrist = data['mesh_vertices_2d_wrist_fixed']
	print(f" ✓ Wrist fixed mesh: {mesh_wrist.shape} (expected: [T, 7, 2])")
	else:
	print(f" ✗ Missing 'mesh_vertices_2d_wrist_fixed'")

	# Check exterior mesh vertices
	if 'mesh_vertices_2d_exterior' in data:
	mesh_ext = data['mesh_vertices_2d_exterior']
	print(f" ✓ Exterior mesh: {mesh_ext.shape} (expected: [T, 7, 2])")
	else:
	print(f" ✗ Missing 'mesh_vertices_2d_exterior'")

	return data


	def visualize_tracks(data, npz_path, output_dir, num_frames=5):
	"""
	Visualize tracked points on sample frames.

	Shows:
	- Exterior: mesh vertices (blue) + tracked points (green)
	- Wrist: fixed mesh (cyan) + tracked mesh (magenta) + tracked points (green)
	"""
	images_ext = data['images_exterior'] # [T, H, W, 3]
	images_wrist = data['images_wrist']
	tracks_ext = data['tracks_exterior'] # [T, N, 2]
	tracks_wrist = data['tracks_wrist']
	tracks_vis_ext = data['tracks_vis_exterior']
	tracks_vis_wrist = data['tracks_vis_wrist']

	# Get mesh data
	mesh_2d_ext = data.get('mesh_vertices_2d_exterior', None) # [T, 7, 2]
	mesh_vis_ext = data.get('mesh_vertices_vis_exterior', None) # [T, 7]
	mesh_2d_wrist_fixed = data.get('mesh_vertices_2d_wrist_fixed', None)

	T = images_ext.shape[0]

	# Sample frames evenly
	frame_indices = np.linspace(0, T-1, min(num_frames, T), dtype=int)

	print(f"\nVisualizing {len(frame_indices)} frames...")

	output_dir = Path(output_dir)
	output_dir.mkdir(parents=True, exist_ok=True)

	for idx, t in enumerate(frame_indices):
	# Exterior view
	viz_ext = images_ext[t].copy()

	# Draw ground truth mesh vertices (blue circles)
	if mesh_2d_ext is not None and mesh_vis_ext is not None:
	for i in range(7):
	if mesh_vis_ext[t, i]:
	pt = tuple(mesh_2d_ext[t, i].astype(int))
	cv2.circle(viz_ext, pt, 5, (255, 0, 0), 2) # Blue hollow
	cv2.putText(viz_ext, str(i), (pt[0]+6, pt[1]-6),
	cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255, 0, 0), 1)

	# Draw all tracked points (green, smaller)
	for i in range(tracks_ext.shape[1]):
	if tracks_vis_ext[t, i]:
	pt = tuple(tracks_ext[t, i].astype(int))
	# First 7 points are mesh - draw in red
	if i < 7:
	cv2.circle(viz_ext, pt, 3, (0, 0, 255), -1) # Red filled
	else:
	cv2.circle(viz_ext, pt, 1, (0, 255, 0), -1) # Green

	cv2.putText(viz_ext, f"Ext: GT mesh (blue) \| Tracked mesh (red) \| Others (green) \| {tracks_ext.shape[1]} pts",
	(5, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (255, 255, 255), 1)

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

	# Draw TRACKED mesh vertices (first 7 points) - magenta filled circles
	for i in range(min(7, tracks_wrist.shape[1])):
	if tracks_vis_wrist[t, i]:
	pt = tuple(tracks_wrist[t, i].astype(int))
	cv2.circle(viz_wrist, pt, 3, (255, 0, 255), -1) # Magenta filled

	# Draw FIXED mesh vertices (from separate array) - cyan hollow circles
	if mesh_2d_wrist_fixed is not None:
	for i in range(7):
	pt = tuple(mesh_2d_wrist_fixed[t, i].astype(int))
	cv2.circle(viz_wrist, pt, 4, (255, 255, 0), 2) # Cyan hollow
	# Label finger tips and joints
	if i in [1, 2, 3, 4]:
	cv2.putText(viz_wrist, str(i), (pt[0]+5, pt[1]-5),
	cv2.FONT_HERSHEY_SIMPLEX, 0.25, (255, 255, 0), 1)

	# Draw other tracked points (grid + random, green, smaller)
	for i in range(7, tracks_wrist.shape[1]):
	if tracks_vis_wrist[t, i]:
	pt = tuple(tracks_wrist[t, i].astype(int))
	cv2.circle(viz_wrist, pt, 1, (0, 255, 0), -1) # Green

	cv2.putText(viz_wrist, f"Wrist: Tracked mesh (magenta) \| Fixed mesh (cyan) \| Others (green) \| {tracks_wrist.shape[1]} pts",
	(5, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255, 255, 255), 1)

	# Concatenate side by side
	combined = np.concatenate([viz_ext, viz_wrist], axis=1)
	cv2.putText(combined, f"Episode {data['episode_idx']} \| Frame {t}/{T}",
	(combined.shape[1]//2 - 60, 15),
	cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

	# Save frame
	output_path = output_dir / f"{npz_path.stem}_frame_{t:03d}.png"
	cv2.imwrite(str(output_path), combined)
	print(f" Saved: {output_path.name}")


	def main():
	import argparse
	parser = argparse.ArgumentParser(description="Inspect preprocessed DROID data")
	parser.add_argument('--data-dir', type=str, default='/mnt/kevin/data/droid_processed_1000pts',
	help='Directory with preprocessed .npz files')
	parser.add_argument('--num-samples', type=int, default=5,
	help='Number of episodes to sample (default: 5)')
	parser.add_argument('--num-frames', type=int, default=5,
	help='Number of frames to visualize per episode (default: 5)')
	parser.add_argument('--output-dir', type=str, default='/tmp/droid_inspect',
	help='Output directory for visualizations')
	parser.add_argument('--specific-episodes', type=int, nargs='+', default=None,
	help='Specific episode indices to inspect (e.g., 100 200 300)')
	args = parser.parse_args()

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

	# Get all .npz files
	npz_files = sorted(data_dir.glob('episode_*.npz'))
	print(f"Found {len(npz_files)} preprocessed episodes in {data_dir}")

	if len(npz_files) == 0:
	print("No .npz files found!")
	return

	# Select episodes to inspect
	if args.specific_episodes:
	# Use specific episode indices
	sample_files = []
	for ep_idx in args.specific_episodes:
	ep_file = data_dir / f"episode_{ep_idx:06d}.npz"
	if ep_file.exists():
	sample_files.append(ep_file)
	else:
	print(f"Warning: Episode {ep_idx} not found")
	else:
	# Random sample
	sample_files = random.sample(npz_files, min(args.num_samples, len(npz_files)))

	print(f"\nInspecting {len(sample_files)} episodes...\n")

	# Inspect each file
	for npz_path in sample_files:
	try:
	# Print structure
	data = inspect_npz_structure(npz_path)

	# Visualize tracks
	visualize_tracks(data, npz_path, output_dir, num_frames=args.num_frames)

	except Exception as e:
	print(f"\nError processing {npz_path.name}: {e}")
	import traceback
	traceback.print_exc()
	continue

	print(f"\n{'='*80}")
	print(f"Inspection complete!")
	print(f"Visualizations saved to: {output_dir}")
	print(f"{'='*80}")


	if __name__ == '__main__':
	main()