Hi Travis, Thanks for the response, it has given a better understanding of the functions. Once working, I will post the new version. The code comes from [1], but [2] questions the use/need of log-amplitude and experimentally show that just using the phase produces the same result. This new approach reduces the computation by about 1/3. I have been able to confirm that using just the phase component produces the same result within either environment, Matlab or Scipy. Obviously I still have the problem that the output from Matlab differs from Scipy. I also discovered that if I don't resize the image (it is currently being reduced to approx 64x64) the output from Matlab is almost the same as Python. This suggests to me that, since keeping the kernels the same and using a smaller/larger images implies the differences in output is due to the kernel being used (I think). So for now I will focus my effort on implementing the the fspecial('gaussian', [10,10], 2.5) using the one provided below as a starting point. Thanks again for the pointers. Michael. -- [1] Xiaodi Hou and Liqing Zhang , Saliency Detection: A Spectral Residual Approach, Journal Computer Vision and Pattern Recognition, IEEE Computer Society Conference 2007 [2] Guo, Chenlei and Ma, Qi and Zhang, Liming, Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform, Computer Vision and Pattern Recognition, 2008. CVPR 2008. On 16/05/13 1:25 PM, Travis Oliphant wrote:
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@gmail.com <mailto: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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