An efficient computer vision module for detecting AprilTag markers used in camera calibration and robotic pose estimation.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install apriltag-detector
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 apriltag-detector 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 AprilTag Detector?
The Pywayne AprilTag Detector is a specialized computer vision utility designed to identify and track AprilTag fiducial markers with high precision. As a robust entry in the Openclaw Skills ecosystem, it provides developers with a streamlined interface to extract spatial data from images, including tag IDs, center points, and corner coordinates.
This skill is particularly valuable for researchers and engineers working on camera calibration, augmented reality, or robotics. By automating the detection process and handling dependency management via integrated tools, it allows users to focus on high-level logic rather than low-level image processing implementation.
Pywayne AprilTag Detector Use Cases
- High-precision camera calibration using AprilTag grids.
- Real-time pose estimation and localization for autonomous robots.
- Object tracking and identification in industrial automation environments.
- Automated image annotation and debugging with visual tag overlays.
How Pywayne AprilTag Detector Works
- The skill initializes the ApriltagCornerDetector and checks for the necessary binary libraries.
- It consumes input images provided as either a local file path or a pre-loaded numpy array.
- The detector performs internal grayscale conversion to optimize the marker scanning process.
- It identifies AprilTag 36h11 markers and calculates metadata including the Hamming distance and precise pixel corners.
- The results are returned as a structured list of detection objects or rendered directly onto the source image for visualization.
Pywayne AprilTag Detector Setup
To use this skill, ensure you have the core Python dependencies installed. The module will automatically attempt to fetch the underlying detection library using gettool.
pip install opencv-python numpy gettool
Once installed, you can initialize the detector in your script:
from pywayne.cv.apriltag_detector import ApriltagCornerDetector
detector = ApriltagCornerDetector()
Pywayne AprilTag Detector Data Schema & Taxonomy
The skill returns a list of detection results. Each entry in the list follows this schema:
| Field | Type | Description |
|---|---|---|
| id | int | The unique identifier of the detected AprilTag. |
| hamming_distance | int | The error correction measure (lower is more accurate). |
| center | tuple | The (x, y) coordinates of the tag's center point. |
| corners | list | A list of four (x, y) tuples representing the tag corners. |
Pywayne AprilTag Detector Advanced Features
- Integrated library management that automatically installs the apriltag_detection library via gettool if it is missing.
- Dual-mode input support for both standard file paths and real-time OpenCV/NumPy image buffers.
- Advanced visualization through the detect_and_draw method, which handles dynamic scaling of labels and polygons based on image resolution.
- Specialized support for the AprilTag 36h11 family, ensuring compatibility with standard calibration patterns used in the Openclaw Skills community.
SKILL.md
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