[SciPy-User] Matlab imfilter/fspecial equivalent

[Travis Oliphant]

I accidentally hit send before I finished the original email (Gmail
interface quirk strikes again...)  I'll including the entire original
email....

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]
     return np.exp(-(x**2 + y**2)/(2*sigma**2))

Up to a scaling constant (and possibly shifted a bit) this should return a
similar filter to fspecial('gaussian', size, sigma).

I'm not completely sure if the ndimage.gaussian_filter does a finite-window
convolution like imfilter (and correlate) or if it does something else.

The uniform filter is the same as an averaging filter (up to a scaling
constant).  To other approach is to just use scipy.ndimage.correlate with
np.ones(size) / np.product(size)  where size is the size of the kernel.

Perhaps you would be willing to post your code when you get it to work.

Best,

-Travis




On Wed, May 15, 2013 at 9:19 PM, Brickle Macho <bricklemacho at 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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>


-- 
---
Travis Oliphant
Continuum Analytics, Inc.
http://www.continuum.io
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