Hi Michael,

Thanks for sharing your code and example. The imfilter command is equivalent to scipy.signal.correlate and scipy.ndimage.correlate (the one in scipy.ndimage is faster I believe). These implement simple correlation-based filtering given a finite kernel.

To get the same output you would need to generate the same kind of kernel in Python as the Matlab fspecial command is producing. one approach to get you started and to help separate the problem into 2 stages (reproducing the imfilter and reproducing the fspecial filter) is to export the results of the Matlab fspecial command and use that kernel in Python code (save it as a .mat file and read that .mat file with Python).

SciPy does not have the equivalent to the fspecial command but you can generate all kinds of 1-d special filters with scipy.signal.get_window. You can also "generate" the filters you need directly from code.

Here is some untested code for generating something close to what fspecial('gaussian", [10,10], 2.5) would be producing

import numpy as np

def fgaussian(size, sigma):

m,n = size

h, k = m//2, n//2

x, y = np.mgrid[-h:h, -k:k]

On Wed, May 15, 2013 at 9:19 PM, Brickle Macho <bricklemacho@gmail.com> wrote:

I am porting some Matlab code to python. When I run the ported version the output differs. It appears the difference may be due to how I have translated Matlab's imfilter() and/or fspecial(). Below are my translation, are there any Scipy/Matlab gurus that can let me know if there are correct or if I have made some errors, or could suggest better translations.

Matlab: imfilter(saliencyMap, fspecial('gaussian', [10, 10], 2.5))
Scipy: ndimage.gaussian_filter(saliencyMap, sigma=2.5)

Also the following. At the rsik of highlighting my lack of understanding of the temrinology, Is uniform_filter the same as 'average'?

Matlab: imfilter(myLogAmplitude, fspecial('average', 3), 'replicate');
Scipy: scipy.ndimage.uniform_filter(mylogAmplitude, size=3, mode="nearest")

Thanks,

Michael.
--

If curious here are the 8 lines of matlab code I am trying to port:

%% Read image from file
inImg = im2double(rgb2gray(imread('yourImage.jpg')));
inImg = imresize(inImg, 64/size(inImg, 2));

%% Spectral Residual
myFFT = fft2(inImg);
myLogAmplitude = log(abs(myFFT));
myPhase = angle(myFFT);
mySpectralResidual = myLogAmplitude - imfilter(myLogAmplitude, fspecial('average', 3), 'replicate');
saliencyMap = abs(ifft2(exp(mySpectralResidual + i*myPhase))).^2;

%% After Effect
saliencyMap = mat2gray(imfilter(saliencyMap, fspecial('gaussian', [10, 10], 2.5)));
imshow(saliencyMap);

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Continuum Analytics, Inc.

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