Comparing computer vision libraries in python
I have worked mainly with OpenCV and also with scikit-image. I would say that while OpenCV is more focus on computer vision (classification, feature detection and extraction,...). However lately scikit-image is improving rapidly. 
I faced that some algorithms perform faster under OpenCV, however in most cases I find much more easier working with scikit-image, OpenCV documentations is quite cryptic.
As long as OpenCV 2.x bindings works with numpy as well as scikit-image I would take into account using both libraries, trying to take the better of each of them. At least is what I have done in my last project.
I want to decide about a Python computer vision library. I had used OpenCV in C++, and like it very much. However this time I need to develop my algorithm in Python. My short list has three libraries:
1- OpenCV (Python wrapper)
2- PIL (Python Image Processing Library)
3- scikit-image
Would you please help me to compare these libraries?
I use numpy, scipy, scikit-learn in the rest of my code. The performance and ease of use is an important factor, also, portability is an important factor for me.
Thanks for your help