""" Test wrist camera projection with simple fixed points at known locations. This verifies the projection math is working, ignoring gripper semantics. """ import sys from pathlib import Path sys.path.append(str(Path(__file__).parent.parent)) import numpy as np 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 from utils.load_camera_calibration import CameraCalibrationLoader from utils.franka_mesh_projection import FrankaMeshProjector def find_closest_calibration(episode, uuid_list): 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 main(): output_dir = Path('/tmp/droid_wrist_fixed_test') output_dir.mkdir(parents=True, exist_ok=True) print("=" * 80) print("Testing Wrist Projection with Fixed Points") print("=" * 80) print("\nStrategy: Project fixed points slightly in front of first frame action") print("This bypasses gripper rotation issues to test projection math.") 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"))] droid_path = '/mnt/kevin/data/droid/droid/1.0.0' builder = tfds.builder_from_directory(droid_path) dataset = builder.as_dataset(split='train') for episode_idx, episode in enumerate(dataset): if episode_idx > 3: # Just test first few break uuid = find_closest_calibration(episode, uuid_list) if uuid is None or not calib_loader.has_refined_extrinsics(uuid): continue print(f"\n{'='*80}") print(f"Episode {episode_idx}") print(f"{'='*80}") # Get first action position as reference step0 = next(iter(episode['steps'])) action0 = step0['action'].numpy() pos0 = action0[:3] print(f"First frame gripper position: {pos0}") # Create simple test points: grid in front of gripper test_points = [] for dx in [-0.05, 0, 0.05]: for dy in [-0.05, 0, 0.05]: test_points.append(pos0 + np.array([dx, dy, 0.1])) # 10cm in front test_points = np.array(test_points) print(f"Created {len(test_points)} test points in 3x3 grid") # Get wrist camera params dual_params = calib_loader.get_dual_view_params(uuid, param_type='refined', require_refined=True) K_wrist, E_wrist = dual_params['wrist'] # Get wrist frames frames_wrist = [] for step_idx, step in enumerate(episode['steps']): if step_idx >= 16: break img = step['observation']['wrist_image_left'].numpy() if img is not None: frames_wrist.append(img) if len(frames_wrist) < 10: continue img_h, img_w = frames_wrist[0].shape[:2] # Test both approaches E_wrist_inv = np.linalg.inv(E_wrist) # Project with both pts_2d_no_inv, vis_no_inv = projector._project_3d_to_2d( test_points, K_wrist, E_wrist, img_h=img_h, img_w=img_w ) pts_2d_inv, vis_inv = projector._project_3d_to_2d( test_points, K_wrist, E_wrist_inv, img_h=img_h, img_w=img_w ) print(f"\nProjection results on first frame:") print(f" No inversion: {vis_no_inv.sum()}/{len(test_points)} visible") print(f" With inversion: {vis_inv.sum()}/{len(test_points)} visible") # Create video video_frames = [] for frame in frames_wrist: viz_no_inv = frame.copy() viz_inv = frame.copy() # Draw points for i in range(len(test_points)): if vis_no_inv[i]: pt = tuple(pts_2d_no_inv[i].astype(int)) cv2.circle(viz_no_inv, pt, 3, (0, 255, 0), -1) cv2.putText(viz_no_inv, str(i), (pt[0]+5, pt[1]-5), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 255, 0), 1) if vis_inv[i]: pt = tuple(pts_2d_inv[i].astype(int)) cv2.circle(viz_inv, pt, 3, (0, 255, 0), -1) cv2.putText(viz_inv, str(i), (pt[0]+5, pt[1]-5), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 255, 0), 1) cv2.putText(viz_no_inv, "NO Inversion", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2) cv2.putText(viz_inv, "WITH Inversion", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2) combined = np.concatenate([viz_no_inv, viz_inv], axis=1) video_frames.append(combined) output_path = output_dir / f"fixed_points_episode_{episode_idx:04d}.mp4" media.write_video(str(output_path), video_frames, fps=10) print(f" ✓ Saved: {output_path}") print("\n" + "=" * 80) print("NOTE: Points should stay roughly fixed in wrist view since") print(" they're defined relative to first gripper position.") print("=" * 80) if __name__ == "__main__": main()