A lot depends on how you are more comfortable developing your detection method. I’d suggest:
- If you prefer ImageJ macros, use this on one or more cropped regions - once happy, we can figure out how to get the macro working with QuPath (there are already some options and posts)
- If you prefer Java/Groovy, try writing a Groovy script for the detection, using either ImageJ or OpenCV as required - see here for some starting hints
- If you prefer Python, try writing a Python script that uses OpenCV for processing - by restricting yourself to OpenCV as much as possible, it will be easier to translate the algorithm into a QuPath script later
- If you prefer using ImageJ2 / ImgLib2 / SciJava scripting, develop your algorithm with Fiji & we can then try to figure out how to get it to work with QuPath
- If you prefer something else entirely, we can still explore getting it to work with QuPath - possibly using binary/labelled images output from your code
Personally, I’d use Groovy + ImageJ. But I think it’s best to consider the task of getting the working algorithm and getting it into QuPath separately.