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- from scipy.ndimage import convolve1d
- from pylab import ceil, zeros, pi, e, exp, sqrt, array
- def f(x, s):
- """Return the value of a 1D Gaussian function for a given x and scale."""
- return exp(-(x ** 2 / (2 * s ** 2))) / (sqrt(2 * pi) * s)
-
- def gauss1(s, order=0):
- """Sample a one-dimensional Gaussian function of scale s."""
- s = float(s)
- r = int(ceil(3 * s))
- size = 2 * r + 1
- W = zeros(size)
- # Sample the Gaussian function
- W = array([f(x - r, s) for x in xrange(size)])
- if not order:
- # Make sure that the sum of all kernel values is equal to one
- W /= W.sum()
- return W
-
- def filterNoise(image, s):
- '''Apply a gaussian blur to an image, to suppress noise.'''
- filt = gauss1(s)
- image = convolve1d(image.data, filt, axis=0, mode='nearest')
- return convolve1d(image, filt, axis=1, mode='nearest')
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