ArUco markers
Detect square coded markers and recover their identities and ordered corners.
The illustrative module grid is a schematic, not a valid encoded marker.
Locate square-like candidates and refine their corners.
Rectify each candidate into its code grid.
A candidate must pass the dictionary or code validity checks before it yields a payload. The schematic grid above is not decoded.
Decode against a dictionary and reject invalid codes.
Illustrative example. The stages explain the method; they are not a live OpenCV execution.
Try it on an image
Experiment at pixel level
Detect ArUco markers in the selected dictionary and draw their corners and IDs. Upload a marker photo to get detections.
The engine loads on your first run. Your images stay in this browser.
Scroll over either image to zoom at the pointer. Use the scrollbars to pan both views over the same relative area. Zoom is relative to the input; pixel coordinates belong to each image. Warps can change scene correspondence.
Pixel inspector RGBA · native values · matched scale · 9 × 9 output pixels
Select a pixel
Select a pixel
When to use it
Build fiducial tracking, calibration boards and known-geometry pose inputs.
How it works
- 01Locate square-like candidates and refine their corners.
- 02Rectify each candidate into its code grid.
- 03Decode against a dictionary and reject invalid codes.
square candidate → rectified bit grid → dictionary ID
What to tune
Use the same dictionary when generating and detecting. Supply the marker’s physical size and camera calibration for pose estimation.
Where it breaks down
Detection returns image geometry, not a fully calibrated world pose. Motion blur, glare and too-small markers reduce reliability.
TypeScript API
Open an entry for its exact overloads, parameter descriptions, result ownership and pinned upstream source.
All of these calls execute on the CPU. Native objects need explicit disposal. See matrices and ownership and build compatibility.