Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Pywayne Visualization Rerun Utils
Static 3D visualization utilities wrapping Rerun SDK for adding point clouds, trajectories, cameras, planes, and chessboards. Use when visualizing 3D data in...
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Static 3D visualization utilities wrapping Rerun SDK for adding point clouds, trajectories, cameras, planes, and chessboards. Use when visualizing 3D data in...
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Package facts
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- Yavira redirect
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- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- wangyendt
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Documentation
Pywayne Visualization Rerun Utils
pywayne.visualization.rerun_utils.RerunUtils provides static methods for adding 3D elements to a Rerun viewer.
Quick Start
import numpy as np import rerun as rr from pywayne.visualization.rerun_utils import RerunUtils # Initialize Rerun (called once globally) rr.init('my_app', spawn=True) # Use static methods to add elements RerunUtils.add_point_cloud(points, colors=[255, 0, 0]) RerunUtils.add_trajectory(trajectory) RerunUtils.add_camera(pose, image='path/to/image.jpg')
Point Cloud
# Single color (default: red) RerunUtils.add_point_cloud(points) # Multi-color points colors = np.random.randint(0, 255, (100, 3)) RerunUtils.add_point_cloud(points, colors=colors, label='colored') # Data format: points (N, 3)
Trajectory
# Single-color trajectory (default: green) RerunUtils.add_trajectory(trajectory) # Multi-color trajectory colors = np.array([[0, 255, 0], [255, 0, 0]], dtype=np.float32) RerunUtils.add_trajectory(trajectory, colors=colors, label='path') # Data format: traj_endpoints (N, 3)
Camera
# Camera only (no image) RerunUtils.add_camera(pose, label='main_camera') # Camera with image RerunUtils.add_camera(pose, image='path/to/image.jpg', label='rgb_camera') # Data format: camera_pose (4, 4) or (4, 7)
Plane
# Plane by center and normal RerunUtils.add_plane_from_center_and_normal(center, normal, half_size=1.0) # Plane from SE3 transformation matrix RerunUtils.add_plane_from_Twp(Twp, half_size=1.0)
Chessboard
# Standard chessboard RerunUtils.add_chessboard_from_Twp() # Custom chessboard with colors RerunUtils.add_chessboard_from_Twp( rows=9, cols=6, cell_size=0.025, Twp=pose_matrix, color1=np.array([255, 0, 0]), # Red color2=np.array([0, 0, 255]) # Blue label='calib_board' )
Internal Helper
# Get quaternion from v1 to v2 (used internally for plane rotation) quat = RerunUtils._get_quaternion_from_v1_and_v2(v1, v2)
Important Notes
Initialization: Call rr.init('name', spawn=True) once before using methods Static methods: All methods are static class methods, no instance needed Dependencies: Requires Rerun SDK (auto-downloaded via gettool) Data types: All position inputs must be float32 Coordinates: Rerun uses ViewCoordinates.RDF (robot-centric coordinate system) SE3 poses: Support (4, 4) or (4, 7) matrix formats Color format: RGB as numpy arrays with shape (3,)
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md