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
 *
 *         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.
 *
 *=========================================================================*/
#include <itkImage.h>
#include <itkImageFileReader.h>
#include <itkImageFileWriter.h>
#include <itkScalarImageKmeansImageFilter.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;
  using ImageType = itk::Image<PixelType, Dimension>;
 
  const auto input = itk::ReadImage<ImageType>(inputImageFileName);
 
  // Instantiate the ScalarImageKmeansImageFilter
  using KMeansFilterType = itk::ScalarImageKmeansImageFilter<ImageType>;
 
  auto 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 (const 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;
}