A sophisticated pybind11 wrapper for the camera_models C++ library, enabling precise 2D-3D camera projection and YAML-based configuration.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install camera-model
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install camera-model using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Pywayne Camera Model?
The pywayne-cv-camera-model is a professional-grade bridge between high-performance C++ camera modeling and Python's flexible development environment. As a specialized component within the Openclaw Skills ecosystem, it provides developers with the tools necessary to handle complex optical geometries, including standard pinhole, catadioptric, and omnidirectional models.
By leveraging this skill, developers can integrate rigorous camera calibration and projection logic into their AI agents. Whether you are working on autonomous navigation or augmented reality, this addition to your Openclaw Skills toolkit ensures that spatial transformations are handled with mathematical precision and optimized performance through pybind11.
Pywayne Camera Model Use Cases
- Lifting 2D pixel coordinates into 3D projective rays for spatial mapping.
- Projecting 3D world points onto 2D image planes for object tracking and visualization.
- Standardizing camera intrinsic and extrinsic parameters across diverse hardware using YAML configurations.
- Building vision-aware agents within Openclaw Skills that require accurate environmental perception.
How Pywayne Camera Model Works
- The skill initializes the CameraModel interface, automatically resolving C++ dependencies via the gettool utility if they are missing.
- A YAML configuration file is loaded, which defines the specific camera model type (e.g., PINHOLE, OCAM) and its distortion parameters.
- The lift_projective function is invoked to transform image points into normalized 3D vectors.
- For reverse mapping, the space_to_plane function calculates the pixel coordinates of a 3D point based on the loaded model.
- All results are returned as optimized numpy arrays, ready for integration into broader Openclaw Skills data pipelines.
Pywayne Camera Model Setup
To utilize this skill, ensure you have the necessary Python environment. The underlying C++ library is managed automatically.
pip install numpy pywayne
Basic initialization in Python:
from pywayne.cv.camera_model import CameraModel
camera = CameraModel()
camera.load_from_yaml('config.yaml')
Pywayne Camera Model Data Schema & Taxonomy
The skill organizes camera data using a standardized schema for different lens types. The following table outlines the core metadata structure:
| Attribute | Description | Typical Values |
|---|---|---|
| model_type | The geometric model used | PINHOLE, CATA, OCAM, EQUIDISTANT |
| image_size | Dimensions of the sensor | width (px), height (px) |
| projection_params | Core focal and principal points | fx, fy, cx, cy |
| distortion_params | Lens correction coefficients | k1, k2, p1, p2, etc. |
Pywayne Camera Model Advanced Features
- Support for Unified Camera Models (OCAM) and Catadioptric systems for wide-angle or fisheye lenses.
- Automated C++ library fetching and environment validation for seamless deployment in Openclaw Skills projects.
- Full numpy compatibility allowing for vectorized inputs and high-speed projective math.
- Comprehensive parameter export functionality to sync camera states across multi-agent systems.
SKILL.md
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