Examples/Statistics/ScalarImageKmeansClassifier.cxx

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//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {BrainT1Slice.png}
//    OUTPUTS: {BrainT1Slice_labelled.png}
//    ARGUMENTS:    1 3 14.8 91.6 134.9
//  Software Guide : EndCommandLineArgs
 
// Software Guide : BeginLatex
//
// This example shows how to use the KMeans model for classifying the pixel of
// a scalar image.
//
// The  \subdoxygen{Statistics}{ScalarImageKmeansImageFilter} is used for
// taking a scalar image and applying the K-Means algorithm in order to define
// classes that represents statistical distributions of intensity values in
// the pixels. The classes are then used in this filter for generating a
// labeled image where every pixel is assigned to one of the classes.
//
// Software Guide : EndLatex
 
 
// Software Guide : BeginCodeSnippet
#include "itkImage.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkScalarImageKmeansImageFilter.h"
// Software Guide : EndCodeSnippet
 
int
main(int argc, char * argv[])
{
  if (argc < 5)
  {
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0];
    std::cerr << " inputScalarImage outputLabeledImage nonContiguousLabels";
    std::cerr << " numberOfClasses mean1 mean2... meanN " << std::endl;
    return EXIT_FAILURE;
  }
 
  const char * inputImageFileName = argv[1];
 
 
  // Software Guide : BeginLatex
  //
  // First we define the pixel type and dimension of the image that we intend
  // to classify. With this image type we can also declare the
  // \doxygen{ImageFileReader} needed for reading the input image, create one
  // and set its input filename.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using PixelType = short;
  constexpr unsigned int Dimension = 2;
 
  using ImageType = itk::Image<PixelType, Dimension>;
 
  using ReaderType = itk::ImageFileReader<ImageType>;
  auto reader = ReaderType::New();
  reader->SetFileName(inputImageFileName);
  // Software Guide : EndCodeSnippet
 
 
  // Software Guide : BeginLatex
  //
  // With the \code{ImageType} we instantiate the type of the
  // \doxygen{ScalarImageKmeansImageFilter} that will compute the K-Means
  // model and then classify the image pixels.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using KMeansFilterType = itk::ScalarImageKmeansImageFilter<ImageType>;
 
  auto kmeansFilter = KMeansFilterType::New();
 
  kmeansFilter->SetInput(reader->GetOutput());
 
  const unsigned int numberOfInitialClasses = std::stoi(argv[4]);
  // Software Guide : EndCodeSnippet
 
 
  // Software Guide : BeginLatex
  //
  // In general the classification will produce as output an image whose pixel
  // values are integers associated to the labels of the classes. Since
  // typically these integers will be generated in order (0,1,2,...N), the
  // output image will tend to look very dark when displayed with naive
  // viewers. It is therefore convenient to have the option of spreading the
  // label values over the dynamic range of the output image pixel type. When
  // this is done, the dynamic range of the pixels is divided by the number of
  // classes in order to define the increment between labels. For example, an
  // output image of 8 bits will have a dynamic range of [0:256], and when it
  // is used for holding four classes, the non-contiguous labels will be
  // (0,64,128,192). The selection of the mode to use is done with the method
  // \code{SetUseNonContiguousLabels()}.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  const unsigned int useNonContiguousLabels = std::stoi(argv[3]);
 
  kmeansFilter->SetUseNonContiguousLabels(useNonContiguousLabels);
  // Software Guide : EndCodeSnippet
 
 
  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;
  }
 
 
  // Software Guide : BeginLatex
  //
  // For each one of the classes we must provide a tentative initial value for
  // the mean of the class. Given that this is a scalar image, each one of the
  // means is simply a scalar value. Note however that in a general case of
  // K-Means, the input image would be a vector image and therefore the means
  // will be vectors of the same dimension as the image pixels.
  //
  // Software Guide : EndLatex
 
 
  // Software Guide : BeginCodeSnippet
  for (unsigned int k = 0; k < numberOfInitialClasses; ++k)
  {
    const double userProvidedInitialMean = std::stod(argv[k + argoffset]);
    kmeansFilter->AddClassWithInitialMean(userProvidedInitialMean);
  }
  // Software Guide : EndCodeSnippet
 
 
  const char * outputImageFileName = argv[2];
 
 
  // Software Guide : BeginLatex
  //
  // The \doxygen{ScalarImageKmeansImageFilter} is predefined for producing an
  // 8 bits scalar image as output. This output image contains labels
  // associated to each one of the classes in the K-Means algorithm. In the
  // following lines we use the \code{OutputImageType} in order to instantiate
  // the type of a \doxygen{ImageFileWriter}. Then create one, and connect it
  // to the output of the classification filter.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using OutputImageType = KMeansFilterType::OutputImageType;
 
  using WriterType = itk::ImageFileWriter<OutputImageType>;
 
  auto writer = WriterType::New();
 
  writer->SetInput(kmeansFilter->GetOutput());
 
  writer->SetFileName(outputImageFileName);
  // Software Guide : EndCodeSnippet
 
 
  // Software Guide : BeginLatex
  //
  // We are now ready for triggering the execution of the pipeline. This is
  // done by simply invoking the \code{Update()} method in the writer. This
  // call will propagate the update request to the reader and then to the
  // classifier.
  //
  // Software Guide : EndLatex
 
 
  // Software Guide : BeginCodeSnippet
  try
  {
    writer->Update();
  }
  catch (const itk::ExceptionObject & excp)
  {
    std::cerr << "Problem encountered while writing ";
    std::cerr << " image file : " << argv[2] << std::endl;
    std::cerr << excp << std::endl;
    return EXIT_FAILURE;
  }
  // Software Guide : EndCodeSnippet
 
 
  // Software Guide : BeginLatex
  //
  // At this point the classification is done, the labeled image is saved in a
  // file, and we can take a look at the means that were found as a result of
  // the model estimation performed inside the classifier filter.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  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;
  }
 
  // Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  // \begin{figure} \center
  // \includegraphics[width=0.44\textwidth]{BrainT1Slice_labelled}
  // \itkcaption[Output of the KMeans classifier]{Effect of the
  // KMeans classifier on a T1 slice of the brain.}
  // \label{fig:ScalarImageKMeansClassifierOutput}
  // \end{figure}
  //
  //  Figure \ref{fig:ScalarImageKMeansClassifierOutput}
  //  illustrates the effect of this filter with three classes.
  //  The means were estimated by ScalarImageKmeansModelEstimator.cxx.
  //
  //  Software Guide : EndLatex
 
  return EXIT_SUCCESS;
}