ITKIntegration/ITKIntegration.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.
 *
 *=========================================================================*/
#if defined(_MSC_VER)
#pragma warning ( disable : 4786 )
#endif
 
// SimpleITK includes
#include "SimpleITK.h"
 
// ITK includes
#include "itkImage.h"
#include "itkCurvatureFlowImageFilter.h"
 
// create convenient namespace alias
namespace sitk = itk::simple;
 
int main( int argc, char *argv[])
{
 
  //
  // Check command line parameters
  //
  if( argc < 7 )
    {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " inputImage outputImage lowerThreshold upperThreshold "
      "seedX seedY [seed2X seed2Y ... ]" << std::endl;
    return 1;
    }
 
 
  //
  // Read the image
  //
  sitk::ImageFileReader reader;
  reader.SetFileName( std::string( argv[1] ) );
  sitk::Image image = reader.Execute();
 
 
  //
  // Set up writer
  //
  sitk::ImageFileWriter writer;
  writer.SetFileName( std::string( argv[2] ) );
 
  // Blur using CurvatureFlowImageFilter
  //
  // Here we demonstrate the use of the ITK version of CurvatureFlowImageFilter
  // instead of the SimpleITK version.
 
  //
  // First, define the type alias that correspond to the types of the input
  // image. This requires foreknowlege of the data type of the input image.
  //
  const unsigned int                                 Dimension = 2;
  using InternalPixelType = float;
  using InternalImageType = itk::Image< InternalPixelType, Dimension >;
 
  //
  // We must check the image dimension and the pixel type of the
  // SimpleITK image match the ITK image we will cast to.s
  //
  if ( image.GetDimension() != Dimension )
    {
    std::cerr << "Input image is not a " << Dimension << " dimensional image as expected!" << std::endl;
    return 1;
    }
 
  //
  // The read sitk::Image could be any pixel type. Cast the image, to
  // float so we know what type we have.
  //
  sitk::CastImageFilter caster;
  caster.SetOutputPixelType( sitk::sitkFloat32 );
  image = caster.Execute( image );
 
  //
  // Extract the itk image from the SimpleITK image
  //
  InternalImageType::Pointer itkImage =
    dynamic_cast <InternalImageType*>( image.GetITKBase() );
 
  //
  // Always check the results of dynamic_casts
  //
  if ( itkImage.IsNull() )
    {
    std::cerr << "Unexpected error converting SimpleITK image to ITK image!" << std::endl;
    return 1;
    }
 
  //
  // Set up the blur filter and attach it to the pipeline.
  //
  using BlurFilterType = itk::CurvatureFlowImageFilter< InternalImageType, InternalImageType >;
  BlurFilterType::Pointer blurFilter = BlurFilterType::New();
  blurFilter->SetInput( itkImage );
  blurFilter->SetNumberOfIterations( 5 );
  blurFilter->SetTimeStep( 0.125 );
 
 
 
  //
  // Execute the  Blur pipeline by calling Update() on the blur filter.
  //
  blurFilter->Update();
 
 
  //
  // Return to the simpleITK setting by making a SimpleITK image using the
  // output of the blur filter.
  //
  sitk::Image blurredImage = sitk::Image( blurFilter->GetOutput() );
 
 
  // Now that we have finished the ITK section, we return to the SimpleITK API
 
 
  //
  // Set up ConnectedThresholdImageFilter for segmentation
  //
  sitk::ConnectedThresholdImageFilter segmentationFilter;
  segmentationFilter.SetLower( atof( argv[3] ) );
  segmentationFilter.SetUpper( atof( argv[4] ) );
  segmentationFilter.SetReplaceValue( 255 );
 
  for (int i = 5; i+1 < argc; i+=2)
    {
    std::vector<unsigned int> seed = { (unsigned int) atoi(argv[i]), (unsigned int) atoi(argv[i+1] };
    segmentationFilter.AddSeed(seed);
    std::cout << "Adding a seed at ";
    for( unsigned int j = 0; j < seed.size(); ++i )
      {
      std::cout << seed[j] << " ";
      }
    std::cout << std::endl;
    }
 
  sitk::Image outImage = segmentationFilter.Execute(blurredImage);
 
 
  //
  // Write out the resulting file
  //
  writer.Execute(outImage);
 
  return 0;
}