OpenCV is great for all kinds of computer vision tasks. Many of these can run in a fully automated fashion, where parameters for the CV algorithms are provided by the user before the program begins or can be determined algorithmically at run time. Some, however, cannot.
For example, for my current project I am trying to find the optimal settings for OpenCV’s implementation of the stereo block matching algorithm. This requires computing disparity pictures, examining them visually, and deciding whether the parameters let the block matcher perform well or not. This is fairly subjective work, and it’s really annoying if you have to restart your program in order to see results with other settings or, if you’re using e.g. the Python interpreter, retype your arguments and display the window again. Of course, you could run a loop over all possible parameter combinations, but that makes it hard to experiment.
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