def normalizedXcorr(a, b):
std = N.std(a) * N.std(b)
a_ = (a - N.mean(a)) / std
b_ = (b - N.mean(b)) / std
c = F.convolve(a_, b_, conj=1) # / a.size
return c
def normalizedXcorr(a, b):
std = N.std(a) * N.std(b)
a_ = (a - N.mean(a)) / std
b_ = (b - N.mean(b)) / std
c = F.convolve(a_, b_, conj=1)# / a.size
return c
def arr_edgeFilter(img, sigma=1.5):
"""
average-deviation with a gaussian prefilter
img must be in an even shape
"""
if sigma:
g = gaussianArrND(img.shape, sigma)
g = F.shift(g)
img = F.convolve(img.astype(N.float32), g)
gr = N.gradient(img.astype(N.float32))
ff = N.sum(N.power(gr, 2), 0)
return ff
def arr_edgeFilter(img, sigma=1.5):
"""
average-deviation with a gaussian prefilter
img must be in an even shape
"""
if sigma:
g = gaussianArrND(img.shape, sigma)
g = F.shift(g)
img = F.convolve(img.astype(N.float32), g)
gr = N.gradient(img.astype(N.float32))
ff = N.sum(N.power(gr, 2), 0)
return ff