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from __future__ import print_function
import vigra
from vigra import graphs
from vigra import numpy
from vigra import Timer
from vigra import blockwise as bw
numpy.random.seed(42)
# input
shape = (500, 500, 500)
data = numpy.random.rand(*shape).astype('float32')
print("make options object")
options = bw.BlockwiseConvolutionOptions3D()
print(type(options))
sigma = 1.0
options.stdDev = (sigma, )*3
options.blockShape = (128, )*3
print("stddev",options.stdDev)
print("call blockwise filter")
with vigra.Timer("AllThread"):
res = bw.gaussianSmooth(data, options)
with vigra.Timer("1thread"):
resRef = vigra.gaussianSmoothing(data, sigma)
print(numpy.sum(numpy.abs(res-resRef)))
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