# 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)