""" Test simple fixed offset to gripper tip. Based on earlier FK analysis, fingers are ~0.058m from flange. Let's test if adding a fixed offset along the gripper z-axis works. """ 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 pybullet as p from scipy.spatial.transform import Rotation as R from utils.load_camera_calibration import CameraCalibrationLoader from utils.franka_mesh_projection import FrankaMeshProjector def load_cotracker(): from cotracker.predictor import CoTrackerPredictor device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = CoTrackerPredictor(checkpoint='/mnt/kevin/vlm_models/cotracker/scaled_offline.pth') model = model.to(device) model.eval() return model, device 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_time = datetime.datetime.strptime( f"{date} {match_time.group(1)}:{match_time.group(2)}:{match_time.group(3)}", "%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 compute_gripper_tip(cartesian_position, joint_position, projector, offset_distance=0.058): """ Compute gripper tip by adding offset along gripper z-axis. Args: cartesian_position: 6D [x, y, z, rx, ry, rz] flange pose joint_position: 7D joint angles projector: FrankaMeshProjector for FK offset_distance: Distance from flange to gripper tip (default 5.8cm) """ # Set joints for FK for i in range(min(7, projector.num_joints)): p.resetJointState(projector.robot_id, i, joint_position[i]) # Get flange orientation flange_state = p.getLinkState(projector.robot_id, 7) flange_orn = np.array(flange_state[5]) # Convert to rotation matrix rot = R.from_quat(flange_orn) rot_matrix = rot.as_matrix() # Offset along local z-axis local_offset = np.array([0.0, 0.0, offset_distance]) world_offset = rot_matrix @ local_offset gripper_tip = cartesian_position[:3] + world_offset return gripper_tip def main(): output_dir = Path('/tmp/droid_test_gripper_tip') output_dir.mkdir(parents=True, exist_ok=True) print("Testing gripper tip with fixed 5.8cm offset") print("=" * 80) # Initialize 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) cotracker, device = load_cotracker() # Load data 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') # Find episode for episode_idx, episode in enumerate(dataset): uuid = find_closest_calibration(episode, uuid_list) if uuid and calib_loader.has_refined_extrinsics(uuid): break print(f"Using episode {episode_idx}, UUID: {uuid}\n") # Get calibration calib = calib_loader.load_calibration(uuid) serials = [k for k in calib.keys() if k not in ['uuid', 'scene_path', 'optimization_summary']] K = np.array(calib[serials[0]]['measured_intrinsics']) E = np.array(calib[serials[0]]['refined_extrinsics']) # Collect data frames, cart_positions, joint_positions = [], [], [] for step_idx, step in enumerate(episode['steps']): if step_idx >= 16: break frames.append(step['observation']['exterior_image_1_left'].numpy()) cart_positions.append(step['observation']['cartesian_position'].numpy()) joint_positions.append(step['observation']['joint_position'].numpy()) img_h, img_w = frames[0].shape[:2] # Compute gripper tips and project all_projections = [] for cart_pos, joint_pos in zip(cart_positions, joint_positions): gripper_tip = compute_gripper_tip(cart_pos, joint_pos, projector, offset_distance=0.058) proj_2d, proj_vis = projector._project_3d_to_2d( gripper_tip.reshape(1, 3), K, E, img_h=img_h, img_w=img_w ) all_projections.append((proj_2d[0], proj_vis[0])) if len(all_projections) == 1: print(f"First frame:") print(f" Flange position: {cart_pos[:3]}") print(f" Gripper tip: {gripper_tip}") print(f" Projected 2D: {proj_2d[0]}") # CoTracker init_2d, init_vis = all_projections[0] if not init_vis: print("Initial point not visible!") return video_np = np.array(frames).transpose(0, 3, 1, 2) video_tensor = torch.from_numpy(video_np).float() / 255.0 video_tensor = video_tensor.unsqueeze(0).to(device) queries = np.array([[[0, init_2d[0], init_2d[1]]]], dtype=np.float32) queries_tensor = torch.from_numpy(queries).to(device) with torch.no_grad(): pred_tracks, pred_visibility = cotracker(video_tensor, queries=queries_tensor, backward_tracking=False) tracks = pred_tracks[0].cpu().numpy() visibility = pred_visibility[0].cpu().numpy() # Visualize video_frames = [] for frame_idx, frame in enumerate(frames): viz = frame.copy() # GT projection (blue) gt_2d, gt_vis = all_projections[frame_idx] if gt_vis: cv2.circle(viz, tuple(gt_2d.astype(int)), 5, (255, 0, 0), 2) # Tracked (green) if visibility[frame_idx, 0]: cv2.circle(viz, tuple(tracks[frame_idx, 0].astype(int)), 3, (0, 255, 0), -1) if frame_idx > 0: for t in range(max(0, frame_idx-10), frame_idx): if visibility[t, 0] and visibility[t+1, 0]: cv2.line(viz, tuple(tracks[t, 0].astype(int)), tuple(tracks[t+1, 0].astype(int)), (0, 255, 0), 1) cv2.putText(viz, "Gripper tip (5.8cm offset)", (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2) cv2.putText(viz, f"Frame {frame_idx}/{len(frames)}", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1) cv2.putText(viz, "Blue=GT, Green=Tracked", (10, 70), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 0), 1) video_frames.append(viz) video_path = output_dir / "gripper_tip_5_8cm.mp4" media.write_video(str(video_path), video_frames, fps=10) print(f"\nVideo saved: {video_path}") if __name__ == "__main__": main()