File size: 8,000 Bytes
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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()
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