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import numpy as np
from numpy import exp
import scipy.signal as signal
def gauss_kern(size, sizey=None):
""" Returns a normalized 2D gauss kernel array for convolutions """
size = int(size)
sizey = size if sizey is None else int(sizey)
x, y = np.mgrid[-size:size+1, -sizey:sizey+1]
g = exp(-(x**2/float(size)+y**2/float(sizey)))
return g / g.sum()
def blur_image(im, n, ny=None) :
""" blurs the image by convolving with a gaussian kernel of typical
size n. The optional keyword argument ny allows for a different
size in the y direction.
g = gauss_kern(n, sizey=ny)
improc = signal.convolve(im,g, mode='valid')
return improc
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