For context, you can install opencv, tensorflow, ROS, matplotlib, and the entire scipy stack in a virtualenv, with no external dependencies, using wheels.
This means that you can generate images, train a machine learning algorithm on them, compare the results to conventional CV algorithms, and display them in an ipython notebook all from a venv. There's a huge amount of C++ and even qt integration in that pipeline, all isolated.
It won't be maximally performant (ie for massive training), but for that you'd want distributed docker deploys or similar anyway.