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Mild optimization of the wavelet transform #329

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Mild optimization of the wavelet transform #329

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11 changes: 8 additions & 3 deletions nanshe/imp/filters/wavelet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -406,6 +406,7 @@ def transform(im0,
imCur = im0.astype(numpy.float32)


ident_ker = vigra.filters.Kernel1D()
for i in irange(1, scale.max() + 1):
if include_intermediates:
imPrev = imCur
Expand All @@ -414,11 +415,15 @@ def transform(im0,
else:
imPrev[:] = imCur

h_ker = binomial_1D_vigra_kernel(i)

h_ker_i = binomial_1D_vigra_kernel(i)
h_ker = tuple()
for d in irange(len(scale)):
if i <= scale[d]:
vigra.filters.convolveOneDimension(imCur, d, h_ker, out=imCur)
h_ker += (h_ker_i,)
else:
h_ker += (ident_ker,)

vigra.filters.convolve(imCur, h_ker, out=imCur)

if include_intermediates or include_lower_scales:
W[i - 1] = imPrev - imCur
Expand Down