def median_filter_with_mask(img, mask, radius_dilatation, radius_median):
# debug
debug = False
if debug:
input = img
itk.imwrite(img, 'ctvi_before.mhd')
ctvim = itk.median_image_filter(img, radius=radius_median)
itk.imwrite(ctvim, 'ctvi_median.mhd')
dimg = dilate_at_boundaries(img, radius_dilatation)
if debug:
itk.imwrite(dimg, 'ctvi_before_dilated.mhd')
imgm = itk.median_image_filter(dimg, radius=radius_median)
if debug:
itk.imwrite(imgm, 'ctvi_median_after_dilated.mhd')
# reapply mask after median
imgm = itk.array_from_image(imgm)
imgm = imgm.astype('float')
imgm[mask == 0] = 0
if debug:
imgm = imgm.astype(np.float32)
a = itk.image_from_array(imgm)
a.CopyInformation(input)
itk.imwrite(a, 'ctvi_median_final.mhd')
return imgm
def visualize(argv):
parser = argparse.ArgumentParser(
description=
'Apply median image filter to input image and visualize result')
parser.add_argument('input_image', type=str, help='Input image file name')
parser.add_argument('output_image',
type=str,
help='Output image file name')
parser.add_argument('radius', type=int, help='Median filter radius')
args = parser.parse_args()
# Open image with ITK
image = itk.imread(args.input_image)
median_image = itk.median_image_filter(image, Radius=args.radius)
itk.imwrite(median_image, args.output_image)
itk.ViewImage[median_image].View(median_image)
# See the License for the specific language governing permissions and # limitations under the License. # #==========================================================================*/ # # Example on the use of the MedianImageFilter # import itk from sys import argv input_filename = argv[1] output_filename = argv[2] radius = int(argv[3]) reader = itk.ImageFileReader.IUC2.New(FileName=input_filename) # test the deduction of the template parameter from the input filt = itk.MedianImageFilter.New(reader, Radius=radius) filt.Update() result = filt.GetOutput() watcher = itk.XMLFilterWatcher(filt, "filter") # test the update of the filter with the snake case function # and the setting of parameter inside it result = itk.median_image_filter(reader, radius=radius) # test the write method itk.imwrite(result, output_filename)
#!/usr/bin/env python3 import itk import sys input_filename = sys.argv[1] # An apriori ImageType PixelType = itk.F ImageType = itk.Image[PixelType, 2] image = itk.imread(input_filename, PixelType) # An image type dynamically determined from the type on disk image = itk.imread(input_filename) ImageType = type(image) # Functional interface # The `ttype` keyword argument specifies the filter type. smoothed = itk.median_image_filter(image, ttype=(ImageType, ImageType)) # Object-oriented interface reader = itk.ImageFileReader[ImageType].New(file_name=input_filename) median = itk.MedianImageFilter[ImageType, ImageType].New() median.SetInput(reader.GetOutput()) median.Update() smoothed = median.GetOutput()
input_filename = argv[1]
output_filename = argv[2]
radius = int(argv[3])
reader = itk.ImageFileReader.IUC2.New(FileName=input_filename)
# test the deduction of the template parameter from the input
filt = itk.MedianImageFilter.New(reader, Radius=radius)
filt.Update()
filt_result = filt.GetOutput()
watcher = itk.XMLFilterWatcher(filt, "filter")
# test the update of the filter with the snake case function
# and the setting of parameter inside it
result_snake_case = itk.median_image_filter(reader, radius=radius)
# SetPrimaryInputName("ValidInput");
compare_filter = itk.ComparisonImageFilter.New(filt_result,
TestInput=result_snake_case)
compare_filter.Update()
assert compare_filter.GetMaximumDifference() < 0.000000001
if version_info >= (3, 8):
# Check the type hints
type_hints = get_type_hints(itk.median_image_filter, globalns={"itk": itk})
assert "args" in type_hints
args_hints = type_hints["args"]
assert get_origin(args_hints) is Union
assert itk.ImageBase in get_args(args_hints)
#!/usr/bin/env python3 import itk import sys input_filename = sys.argv[1] image = itk.imread(input_filename) # Use ITK's functional, Pythonic interface. The filter type is implied by the # type of the input image. The filter is eagerly executed, and the output image # is directly returned. smoothed = itk.median_image_filter(image) # Alternatively, create filter objects. These filter objects can be connected in # a pipeline to stream-process large datasets. To generate the output of the # pipeline, .Update() must explicitly be called on the last filter of the # pipeline. # # We can implicitly instantiate the filter object based on the type # of the input image in multiple ways. # Use itk.ImageFileReader instead of the wrapping function, # itk.imread to illustrate this example. ImageType = itk.Image[itk.UC, 2] reader = itk.ImageFileReader[ImageType].New(FileName=input_filename) # Here we specify the filter input explicitly median = itk.MedianImageFilter.New(reader.GetOutput()) # Same as above but shortened. Input does not have to be specified. median = itk.MedianImageFilter.New(reader.GetOutput()) # Same as above. .GetOutput() does not have to be specified.
#!/usr/bin/env python3 import itk import sys input_filename = sys.argv[1] output_filename = sys.argv[2] image = itk.imread(input_filename) median = itk.median_image_filter(image, radius=2) itk.imwrite(median, output_filename)
input_filename = argv[1]
output_filename = argv[2]
radius = int(argv[3])
reader = itk.ImageFileReader.IUC2.New(FileName=input_filename)
# test the deduction of the template parameter from the input
filt = itk.MedianImageFilter.New(reader, Radius=radius)
filt.Update()
filt_result = filt.GetOutput()
watcher = itk.XMLFilterWatcher(filt, "filter")
# test the update of the filter with the snake case function
# and the setting of parameter inside it
result_snake_case = itk.median_image_filter(reader, radius=radius)
# SetPrimaryInputName("ValidInput");
compare_filter = itk.ComparisonImageFilter.New(filt_result, TestInput=result_snake_case)
compare_filter.Update()
assert compare_filter.GetMaximumDifference() < 0.000000001
# Test that `__call__()` inside itkTemplate is deprecated. Replaced
# by snake_case functions
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
# Trigger a warning.
result = itk.MedianImageFilter(reader, radius=radius)
# Verify some things
assert len(w) == 1
