Assemble the main bard app to run live overlay
This will require a bit of coordination amoungst all SNAPPY libraries
- Load config from .json file using scikit-surgerycore ConfigurationManager, like SmartLiver does.
- Load VTK models using scikit-surgeryvtk SurfaceModel, like SmartLiver
- Load camera calibration data using standard numpy functions, (use command line parameter, load text file).
- Read Video from webcam using standard opencv-python (from Tom's base class)
- Use overlay widget from scikit-surgeryvtk (from Tom's base class)
- You'll need a GUI program. Look at scikit-surgeryutils for examples.
Then ASK about:
- Save standard models of Aruco tags, and just pre-load them at startup
- Update transformations using scikit-surgerycore TransformationManager
- Load any necessary fiducial points, using standard numpy functions, at startup
- In the main program loop: extract aruco points, compute registrations, update position of tools, compute position of overlay, render.
About 3 to 4 days work I guess, if all the other bits are done in other libraries.