python实现高斯滤波_如何在python中获取高斯滤波器

I am using python to create a gaussian filter of size 5x5.

I saw this post here where they talk about a similar thing but I didn't find the exact way to get equivalent python code to matlab function fspecial('gaussian', f_wid, sigma)

Is there any other way to do it?

I tried using the following code :

size = 2

sizey = None

size = int(size)

if not sizey:

sizey = size

else:

sizey = int(sizey)

x, y = scipy.mgrid[-size: size + 1, -sizey: sizey + 1]

g = scipy.exp(- (x ** 2/float(size) + y ** 2 / float(sizey)))

print g / np.sqrt(2 * np.pi)

The output obtained is

[[ 0.00730688 0.03274718 0.05399097 0.03274718 0.00730688]

[ 0.03274718 0.14676266 0.24197072 0.14676266 0.03274718]

[ 0.05399097 0.24197072 0.39894228 0.24197072 0.05399097]

[ 0.03274718 0.14676266 0.24197072 0.14676266 0.03274718]

[ 0.00730688 0.03274718 0.05399097 0.03274718 0.00730688]]

What I want is something like this:

0.0029690 0.0133062 0.0219382 0.0133062 0.0029690

0.0133062 0.0596343 0.0983203 0.0596343 0.0133062

0.0219382 0.0983203 0.1621028 0.0983203 0.0219382

0.0133062 0.0596343 0.0983203 0.0596343 0.0133062

0.0029690 0.0133062 0.0219382 0.0133062 0.0029690

解决方案

In general terms if you really care about getting the the exact same result as MATLAB, the easiest way to achieve this is often by looking directly at the source of the MATLAB function.

In this case, edit fspecial:

...

case 'gaussian' % Gaussian filter

siz = (p2-1)/2;

std = p3;

[x,y] = meshgrid(-siz(2):siz(2),-siz(1):siz(1));

arg = -(x.*x + y.*y)/(2*std*std);

h = exp(arg);

h(h

sumh = sum(h(:));

if sumh ~= 0,

h = h/sumh;

end;

...

Pretty simple, eh? It's <10mins work to port this to Python:

import numpy as np

def matlab_style_gauss2D(shape=(3,3),sigma=0.5):

"""

2D gaussian mask - should give the same result as MATLAB's

fspecial('gaussian',[shape],[sigma])

"""

m,n = [(ss-1.)/2. for ss in shape]

y,x = np.ogrid[-m:m+1,-n:n+1]

h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )

h[ h < np.finfo(h.dtype).eps*h.max() ] = 0

sumh = h.sum()

if sumh != 0:

h /= sumh

return h

This gives me the same answer as fspecial to within rounding error:

>> fspecial('gaussian',5,1)

0.002969 0.013306 0.021938 0.013306 0.002969

0.013306 0.059634 0.09832 0.059634 0.013306

0.021938 0.09832 0.1621 0.09832 0.021938

0.013306 0.059634 0.09832 0.059634 0.013306

0.002969 0.013306 0.021938 0.013306 0.002969

: matlab_style_gauss2D((5,5),1)

array([[ 0.002969, 0.013306, 0.021938, 0.013306, 0.002969],

[ 0.013306, 0.059634, 0.09832 , 0.059634, 0.013306],

[ 0.021938, 0.09832 , 0.162103, 0.09832 , 0.021938],

[ 0.013306, 0.059634, 0.09832 , 0.059634, 0.013306],

[ 0.002969, 0.013306, 0.021938, 0.013306, 0.002969]])