course-image-parser

To extract course geometry from a robotrace diagram image, the skill automates board normalization, centerline tracing, and helper-circle candidate generation using computer vision pipelines. This converts visual course data directly into structured JSON design artifacts for CAD modeling.

The best way to convert printed robotrace course images into CAD-ready files is through automated image analysis that detects board contours, start/goal positions, and centerline paths. It eliminates manual geometry estimation by integrating OCR of design annotations with scripted support-circle detection.

Yes, you can automate the extraction of start and goal positions from a course diagram. The workflow applies image processing and computer vision techniques to detect board contours and centerline paths, integrating AI-assisted OCR for red design annotations to consolidate the extracted data.

Yes, you need uv to run the course image parser. It is a required dependency for executing the scripts that handle the end-to-end workflow of board normalization, centerline tracing, and helper-circle candidate generation from your robotrace course diagram images.

Yes, the course geometry extraction handles hand-drawn design annotations through AI-assisted OCR. It specifically reads red design annotations on the diagram and integrates them with scripted support-circle and slalom template detections to generate a validated JSON model.

The limitations of automated centerline tracing from course images include the need for final review and refinement in the Robotrace Course CAD environment. The generated helper-circle JSON candidates serve as an initial model, requiring validation against the original visual course data.