The best would be for us to take a look at your code, maybe if you have it on github or such? Generally, python is slower than C++. All the opencv functions in python are just wrappers around the core opencv which is written in C++. Also, with C++ you can very easily create parallel processing threads.
Here are a few things to consider:
- you should not be loading your XML files on every frame, you can either create a postInit() override in your module and load the files there, or just load them once at the first frame and store the trained classifiers in some data member of your module class. See http://jevois.org/doc/classjevois_1_1Component.html
for info about postInit() (Module inherits from Component).
- try the JeVois profiler to see which step is slow: http://jevois.org/doc/classjevois_1_1Profiler.html
For an example of use, search for "itsProfiler" several times in https://github.com/jevois/jevoisbase/blob/master/src/Components/OpticalFlow/FastOpticalFlow.C
The profiler will send messages to serlog. So make sure you have "setpar serlog USB" to see those messages in a serial terminal connected to JeVois.
- try parallel threads. See http://jevois.org/doc/ModuleTutorial.html
about running 4 edge detectors in parallel. Using std::async() and lambdas makes it super easy to spawn threads. Just remember to catch the return value of async (a std::future) and to store it into some variable, and then when you need the results of your thread, call get() on that variable. If you do not grab the future returned by async(), its destructor will call get() and will just wait for the thread to complete before proceeding any further. Most of the modules in jevoisbase use threads, so you may want to look at their source for inspiration: http://jevois.org/basedoc/dir_eeb7fcc90d516a232deaaf4de23f9c95.html