SphinxExamples/src/Segmentation/Watersheds/SegmentWithWatershedImageFilter/Code.py

#!/usr/bin/env python
 
# Copyright NumFOCUS
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#        https://www.apache.org/licenses/LICENSE-2.0.txt
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
 
# Run with:
# ./WatershedImageFilter.py <InputFileName> <OutputFileName> <Threshold> <Level>
# e.g.
# ./WatershedImageFilter.py BrainProtonDensitySlice.png OutBrainWatershed.png 0.005 .5
# (A rule of thumb is to set the Threshold to be about 1 / 100 of the Level.)
#
#  threshold: absolute minimum height value used during processing.
#        Raising this threshold percentage effectively decreases the number of local minima in the input,
#        resulting in an initial segmentation with fewer regions.
#        The assumption is that the shallow regions that thresholding removes are of of less interest.
#  level: parameter controls the depth of metaphorical flooding of the image.
#        That is, it sets the maximum saliency value of interest in the result.
#        Raising and lowering the Level influences the number of segments
#        in the basic segmentation that are merged to produce the final output.
#        A level of 1.0 is analogous to flooding the image up to a
#        depth that is 100 percent of the maximum value in the image.
#        A level of 0.0 produces the basic segmentation, which will typically be very oversegmented.
#        Level values of interest are typically low (i.e. less than about 0.40 or 40%),
#        since higher values quickly start to undersegment the image.
 
import itk
import argparse
 
parser = argparse.ArgumentParser(description="Segment With Watershed Image Filter.")
parser.add_argument("input_image")
parser.add_argument("output_image")
parser.add_argument("threshold", type=float)
parser.add_argument("level", type=float)
args = parser.parse_args()
 
 
Dimension = 2
 
FloatPixelType = itk.ctype("float")
FloatImageType = itk.Image[FloatPixelType, Dimension]
 
reader = itk.ImageFileReader[FloatImageType].New()
reader.SetFileName(args.input_image)
 
gradientMagnitude = itk.GradientMagnitudeImageFilter.New(Input=reader.GetOutput())
 
watershed = itk.WatershedImageFilter.New(Input=gradientMagnitude.GetOutput())
 
threshold = args.threshold
level = args.level
watershed.SetThreshold(threshold)
watershed.SetLevel(level)
 
LabeledImageType = type(watershed.GetOutput())
 
PixelType = itk.ctype("unsigned char")
RGBPixelType = itk.RGBPixel[PixelType]
RGBImageType = itk.Image[RGBPixelType, Dimension]
 
ScalarToRGBColormapFilterType = itk.ScalarToRGBColormapImageFilter[
    LabeledImageType, RGBImageType
]
colormapImageFilter = ScalarToRGBColormapFilterType.New()
colormapImageFilter.SetColormap(
    itk.ScalarToRGBColormapImageFilterEnums.RGBColormapFilter_Jet
)
colormapImageFilter.SetInput(watershed.GetOutput())
colormapImageFilter.Update()
 
WriterType = itk.ImageFileWriter[RGBImageType]
writer = WriterType.New()
writer.SetFileName(args.output_image)
writer.SetInput(colormapImageFilter.GetOutput())
writer.Update()