Numba Filter 2D test

numba_filter2d.py

import numpy

def filter2d(image, filt):
    M, N = image.shape
    Mf, Nf = filt.shape
    Mf2 = Mf // 2
    Nf2 = Nf // 2
    result = numpy.zeros_like(image)
    for i in range(Mf2, M - Mf2):
        for j in range(Nf2, N - Nf2):
            num = 0.0
            for ii in range(Mf):
                for jj in range(Nf):
                    num += (filt[Mf-1-ii, Nf-1-jj] * image[i-Mf2+ii, 
j-Nf2+jj])
            result[i, j] = num
    return result

# This kind of quadruply-nested for-loop is going to be quite slow.
# Using Numba we can compile this code to LLVM which then gets
# compiled to machine code:

from numba import double, jit

fastfilter_2d = jit(double[:,:](double[:,:], double[:,:]))(filter2d)

# Now fastfilter_2d runs at speeds as if you had first translated
# it to C, compiled the code and wrapped it with Python
image = numpy.random.random((100, 100))
filt = numpy.random.random((10, 10))
res = fastfilter_2d(image, filt)