# test index
s = itk.index(reader)
assert s[0] == s[1] == 0
s = itk.index(reader.GetOutput())
assert s[0] == s[1] == 0
# test region
s = itk.region(reader)
assert s.GetIndex()[0] == s.GetIndex()[1] == 0
assert s.GetSize()[0] == s.GetSize()[1] == 256
s = itk.region(reader.GetOutput())
assert s.GetIndex()[0] == s.GetIndex()[1] == 0
assert s.GetSize()[0] == s.GetSize()[1] == 256
# test range
assert itk.range(reader) == (0, 255)
assert itk.range(reader.GetOutput()) == (0, 255)
# test write
itk.imwrite(reader, sys.argv[3])
itk.imwrite(reader, sys.argv[3], True)
# test read
image = itk.imread(filename)
assert type(image) == itk.Image[itk.RGBPixel[itk.UC], 2]
image = itk.imread(filename, itk.F)
assert type(image) == itk.Image[itk.F, 2]
image = itk.imread(filename, itk.F, fallback_only=True)
assert type(image) == itk.Image[itk.RGBPixel[itk.UC], 2]
try:
image = itk.imread(filename, fallback_only=True)
# for all the z values
proj2D = itk.MaximumProjectionImageFilter.IUS3IUS2.New(mask, ProjectionDimension=1)
proj1D = itk.MaximumProjectionImageFilter.IUS2IUS2.New(proj2D, ProjectionDimension=0)
# binarize the image
th = itk.BinaryThresholdImageFilter.IUS2IUS2.New(proj1D, InsideValue=1)
# and count the pixels to get the resolution on the z axis
labelShape = itk.LabelShapeImageFilter.IUS2.New(th)
# count the object to display a warning
label = itk.ConnectedComponentImageFilter.IUS2IUS2.New(th)
relabel = itk.RelabelComponentImageFilter.IUS2IUS2.New(label)
for f in sys.argv[1:]:
reader.SetFileName(f)
projz.UpdateLargestPossibleRegion()
m, M = itk.range(projz)
watershed.SetLevel(m + (M - m) / 2)
upperdim.SetNewDimensionSapcing(itk.spacing(reader)[2])
upperdim.SetNewDimensionSize(itk.size(reader)[2])
upperdim.UpdateLargestPossibleRegion() # to get a valid number of labels
# store the results so we can compute the mean and the meadian for
# all the beads in the image
results = []
for l in range(1, wrelabel.GetNumberOfObjects() + 1):
selectedLabel.SetUpperThreshold(l)
selectedLabel.SetLowerThreshold(l)
proj1D.UpdateLargestPossibleRegion()
m, M = itk.range(proj1D)
th.SetLowerThreshold((M - m) / 2)
s = itk.index(reader)
assert s[0] == s[1] == 0
s = itk.index(reader.GetOutput())
assert s[0] == s[1] == 0
# test region
s = itk.region(reader)
assert s.GetIndex()[0] == s.GetIndex()[1] == 0
assert s.GetSize()[0] == s.GetSize()[1] == 256
s = itk.region(reader.GetOutput())
assert s.GetIndex()[0] == s.GetIndex()[1] == 0
assert s.GetSize()[0] == s.GetSize()[1] == 256
# test range
assert itk.range(reader) == (0, 255)
assert itk.range(reader.GetOutput()) == (0, 255)
# test write
itk.imwrite(reader, sys.argv[2])
itk.imwrite(reader, sys.argv[2], True)
# test read
image=itk.imread(fileName)
assert type(image) == itk.Image[itk.RGBPixel[itk.UC],2]
image=itk.imread(fileName, itk.F)
assert type(image) == itk.Image[itk.F,2]
# test search
res = itk.search("Index")
# let start, really
statisticsLabelMapRobustNuclei.Update()
shapeLabelMapNuclei.Update()
otsuNuclei.Compute()
if opts.visualValidation:
padLabels.SetRegion( readerNuclei.GetOutput().GetLargestPossibleRegion() )
if opts.saveSegmentation:
itk.write( labelRobustNuclei, readerNuclei.GetFileName()+"-nuclei-segmentation.nrrd", True)
# to be reused later
spacing = itk.spacing(readerNuclei)
# find the labels used - we are not sure to have all the labels in the range because of the attribute openongs
ls = [l+1 for l in range(*itk.range(labelRobustNuclei)) if statisticsLabelMapRobustNuclei.GetOutput().HasLabel(l+1)]
for l in ls :
if opts.verbose:
print >> sys.stderr, " nuclei", l
# set the label
singleMaskNuclei.SetUpperThreshold( l )
singleMaskNuclei.SetLowerThreshold( l )
cropSingleMaskNuclei.SetLabel( l )
cropSingleMaskRobustNuclei.SetLabel( l )
cropSingleMaskNuclei.UpdateLargestPossibleRegion()
cropStatisticsLabelMapRobustNuclei.SetRegion( cropSingleMaskNuclei.GetOutput().GetLargestPossibleRegion() )
maskNucleiWap.SetLabel(l)
