Hi,
Below you can find a small cython function that implements a simple
max-filter. That is, given an image (a 2d numpy ndarray), each pixel
is replaced by the maximum value in a neighborhood [-dx:dx, -dy:dy]
around that pixel. For simplicity it just ignores the edges of the
image.
I implemented the same function using C++ & ctypes, and the latter
function turns out to be about 100 times faster than the cython one,
for a 500x500 image, and dx,dy = 5,5. This surprises me, because of
the simplicity of the function. I checked the C file, and also the
translation of the maxfilter() function in C is quite simple. The bulk
of the time is of course spent in the loops, and the only bottleneck
that I can see is accessing the numpy arrays. Is there anything I can
do in Cython to speed that up?
Cheers,
Joris
import numpy as np
cimport cython
cimport numpy as np
DTYPE = np.int # our image type
ctypedef np.int_t DTYPE_t # our compile time image type
@cython.boundscheck(False)
def maxfilter(np.ndarray[DTYPE_t, ndim=2] image, int dx, int dy):
cdef int nRow = image.shape[0]
cdef int nCol = image.shape[1]
cdef np.ndarray[DTYPE_t, ndim=2] result = np.zeros((nRow,nCol),
dtype=DTYPE)
cdef DTYPE_t maxvalue
cdef int i,j,k,n
for i from dx <= i < nRow-dx:
for j from dy <= j < nCol-dy:
maxvalue = image[i,j]
for k from -dx <= k <= dx:
for n from -dy <= n <= dy:
if maxvalue < image[i+k,j+n]: maxvalue = image[i
+k,j+n]
result[i,j] = maxvalue
return result
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