Hi Nadav, would you need everything to be faster, or do you have some identified bottlenecks that take most of the computing time? In the latter case, you may want to keep using the most standard tools for most image processing steps, and pick a specialized and fast module for the most critical steps, like OpenCV. Also, the algorithms in scikits-image shouldn't do too bad in terms of executing speed, since they rely either on numpy arrays or on Cython code: what speed factor would you expect to gain with another library such as OpenCV? As for alternative librairies, ITK is very powerful and fast and has Python bindings. The learning curve looks a bit steep, though. Cheers, Emmanuelle On Thu, Dec 22, 2011 at 10:12:59PM +0200, Nadav Horesh wrote:

I have several scripts dealing with image procession of digital cameras raw data. Currently in use are filters and array processing from numpy, scipy.signal, scipy.ndimage and numexpr. I am looking for faster tools that cold be used without any extensive learning period. I came up of the following options: 1, Theano: Can be very fast, but missing some important features� like a median filter 2. OpenCV: Fast and features rich, but the interface is not easy to learn.

Any recommendation for alternative libraries or easy interfaces to opencv? (I looked at pyopencv, but the project doen't seems to be alive these days)

�� Nadav