ITK  5.3.0
Insight Toolkit
SphinxExamples/src/Segmentation/Classifiers/KMeansClustering/Code.cxx
/*=========================================================================
*
* 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
*
* http://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.
*
*=========================================================================*/
#include <itkImage.h>
int
main(int argc, char * argv[])
{
// sample usage
//./kMeansClustering input.jpg output.jpg 1 3 0 100 200
// verify command line arguments
if (argc < 5)
{
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0];
std::cerr << " inputScalarImage outputLabeledImage contiguousLabels";
std::cerr << " numberOfClasses mean1 mean2... meanN " << std::endl;
return EXIT_FAILURE;
}
// parse command line arguments
const char * inputImageFileName = argv[1];
const char * outputImageFileName = argv[2];
const unsigned int useNonContiguousLabels = std::stoi(argv[3]);
const unsigned int numberOfInitialClasses = std::stoi(argv[4]);
constexpr unsigned int argoffset = 5;
if (static_cast<unsigned int>(argc) < numberOfInitialClasses + argoffset)
{
std::cerr << "Error: " << std::endl;
std::cerr << numberOfInitialClasses << " classes has been specified ";
std::cerr << "but no enough means have been provided in the command ";
std::cerr << "line arguments " << std::endl;
return EXIT_FAILURE;
}
std::vector<double> userMeans;
for (unsigned k = 0; k < numberOfInitialClasses; k++)
{
const double userProvidedInitialMean = std::stod(argv[k + argoffset]);
userMeans.push_back(userProvidedInitialMean);
}
// Define the pixel type and dimension of the image that we intend to
// classify.
using PixelType = signed short;
constexpr unsigned int Dimension = 2;
const auto input = itk::ReadImage<ImageType>(inputImageFileName);
// Instantiate the ScalarImageKmeansImageFilter
KMeansFilterType::Pointer kmeansFilter = KMeansFilterType::New();
kmeansFilter->SetInput(input);
// Make the output image intellegable by expanding the range of output image values, if desired
kmeansFilter->SetUseNonContiguousLabels(useNonContiguousLabels);
// initialize using the user input means
for (unsigned k = 0; k < numberOfInitialClasses; k++)
{
kmeansFilter->AddClassWithInitialMean(userMeans[k]);
}
try
{
itk::WriteImage(kmeansFilter->GetOutput(), outputImageFileName);
}
catch (itk::ExceptionObject & excp)
{
std::cerr << "Problem encountered while writing ";
std::cerr << " image file : " << outputImageFileName << std::endl;
std::cerr << excp << std::endl;
return EXIT_FAILURE;
}
// inspect the means
KMeansFilterType::ParametersType estimatedMeans = kmeansFilter->GetFinalMeans();
const unsigned int numberOfClasses = estimatedMeans.Size();
for (unsigned int i = 0; i < numberOfClasses; ++i)
{
std::cout << "cluster[" << i << "] ";
std::cout << " estimated mean : " << estimatedMeans[i] << std::endl;
}
return EXIT_SUCCESS;
}
itk::ScalarImageKmeansImageFilter
Classifies the intensity values of a scalar image using the K-Means algorithm.
Definition: itkScalarImageKmeansImageFilter.h:65
itkImageFileReader.h
itkImage.h
itkImageFileWriter.h
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:88
itkScalarImageKmeansImageFilter.h
itk::GTest::TypedefsAndConstructors::Dimension2::Dimension
constexpr unsigned int Dimension
Definition: itkGTestTypedefsAndConstructors.h:44
itk::WriteImage
ITK_TEMPLATE_EXPORT void WriteImage(TImagePointer &&image, const std::string &filename, bool compress=false)
Definition: itkImageFileWriter.h:254