def augmentGaussian(data, lAdd, gAdd, gMult):
"""
lAdd : sigma of local additive gaussian noise
gAdd : sigma of global additive gaussian noise
gMult : sigma of global multiplicative guasian noise
"""
data = vigra.taggedView(data, 'xyz')
shape = data.shape
# local and global additive and multiplicative
# gaussian noise
toAdd = normalVol(shape, 0.0, lAdd) + numpy.random.normal(0.0, gAdd)
augmentedData = data.copy()
augmentedData += toAdd
augmentedData *= numpy.abs(numpy.random.normal(1.0, gMult))
augmentedData = numpy.clip(augmentedData, 0, 255)
return augmentedData
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))
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))
def checkAboutSame(i1,i2):
assert(i1.shape==i2.shape)
difference=np.sum(np.abs(i1-i2))/float(np.size(i1))
assert(difference<5)
def checkAboutSame(i1,i2):
checkShape(i1.shape, i2.shape)
difference=np.sum(np.abs(i1-i2))/float(np.size(i1))
assert(difference<5)
