convolve
Simple demonstration of separable convolutions. I've written this in python 3 using cython for a significant speed boost. This is a demo project only, it could contain errors! In most cases if you want to use convolutions you're safer using a library like numpy or opencv. These tend to use a fourier transform for large convolutions, so I avoided them here just to show the performance benefits.
This is written in python, but I may well add a C# implementation soon, since getting cython compiled in windows is a little more work.
Python
To run this code you'll need python 3, and cython installed. I've used this in linux, it should port fairly easily to other operating systems. Cython in windows is a little more difficult, but doable!
Compiling with Cython
Cython compiles your (almost) python into C, which you then compile into a python module. You can compile using cython like this:
cython -a compute.pyx
The -a flag outputs a nice HTML file showing where C and Python interact. I've optimised this code for C, so there aren't many yellow lines. It was a lot worse when I started!
This will produce a c file, which you compile using something like this:
gcc -shared -pthread -fPIC -fwrapv -O2 -Wall -fno-strict-aliasing -I/usr/include/python3.6m -o compute.so compute.c
Notice you need to point the compiler ar your installed python headers - which may not be in the same place as mine.
If compilation is successful, then you'll obtain a compute.so file, which can be imported from python as normal. Windows compilation will be a little more complex, as gcc isn't available to you. Here might help!
Running the code
You can run the code like this:
python run.py --sigma 3.0 ./christmas.jpg ./christmas.out.jpg
My included christmas tree picture is fairly large, so even a small convolution will take a while. You'll see quite a difference if you run this with the --no_separable_filters option though.