SphinxExamples/src/Registration/Common/Perform2DTranslationRegistrationWithMeanSquares/Code.cxx

/*=========================================================================
 *
 *  Copyright Insight Software Consortium
 *
 *  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 "itkImageRegistrationMethodv4.h"
#include "itkTranslationTransform.h"
#include "itkMeanSquaresImageToImageMetricv4.h"
#include "itkRegularStepGradientDescentOptimizerv4.h"

#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"

#include "itkResampleImageFilter.h"
#include "itkCastImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkSubtractImageFilter.h"


int main( int argc, char *argv[] )
{
  if( argc != 6 )
    {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " fixedImageFile  movingImageFile ";
    std::cerr << "outputImagefile [differenceImageAfter]";
    std::cerr << "[differenceImageBefore]" << std::endl;
    return EXIT_FAILURE;
    }

  const char * fixedImageFile = argv[1];
  const char * movingImageFile = argv[2];
  const char * outputImageFile = argv[3];
  const char * differenceImageAfterFile = argv[4];
  const char * differenceImageBeforeFile = argv[5];

  constexpr unsigned int Dimension = 2;
  using PixelType = float;

  using FixedImageType = itk::Image< PixelType, Dimension >;
  using MovingImageType = itk::Image< PixelType, Dimension >;


  using FixedImageReaderType = itk::ImageFileReader< FixedImageType  >;
  auto fixedImageReader = FixedImageReaderType::New();
  fixedImageReader->SetFileName( fixedImageFile );

  using MovingImageReaderType = itk::ImageFileReader< MovingImageType >;
  auto movingImageReader = MovingImageReaderType::New();
  movingImageReader->SetFileName( movingImageFile );


  using TransformType = itk::TranslationTransform< double, Dimension >;
  auto initialTransform = TransformType::New();

  using OptimizerType = itk::RegularStepGradientDescentOptimizerv4<double>;
  auto optimizer = OptimizerType::New();
  optimizer->SetLearningRate( 4 );
  optimizer->SetMinimumStepLength( 0.001 );
  optimizer->SetRelaxationFactor( 0.5 );
  optimizer->SetNumberOfIterations( 200 );

  using MetricType = itk::MeanSquaresImageToImageMetricv4<
                                          FixedImageType,
                                          MovingImageType >;
  auto metric = MetricType::New();

  using RegistrationType = itk::ImageRegistrationMethodv4<
                                    FixedImageType,
                                    MovingImageType >;
  auto registration = RegistrationType::New();
  registration->SetInitialTransform( initialTransform );
  registration->SetMetric( metric );
  registration->SetOptimizer( optimizer );
  registration->SetFixedImage( fixedImageReader->GetOutput() );
  registration->SetMovingImage( movingImageReader->GetOutput() );

  auto movingInitialTransform = TransformType::New();
  TransformType::ParametersType initialParameters(
    movingInitialTransform->GetNumberOfParameters() );
  initialParameters[0] = 0.0;
  initialParameters[1] = 0.0;
  movingInitialTransform->SetParameters( initialParameters );
  registration->SetMovingInitialTransform( movingInitialTransform );

  auto identityTransform = TransformType::New();
  identityTransform->SetIdentity();
  registration->SetFixedInitialTransform( identityTransform );

  constexpr unsigned int numberOfLevels = 1;
  registration->SetNumberOfLevels( numberOfLevels );

  RegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel;
  shrinkFactorsPerLevel.SetSize( 1 );
  shrinkFactorsPerLevel[0] = 1;
  registration->SetShrinkFactorsPerLevel( shrinkFactorsPerLevel );

  RegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;
  smoothingSigmasPerLevel.SetSize( 1 );
  smoothingSigmasPerLevel[0] = 0;
  registration->SetSmoothingSigmasPerLevel( smoothingSigmasPerLevel );

  try
    {
    registration->Update();
    std::cout << "Optimizer stop condition: "
      << registration->GetOptimizer()->GetStopConditionDescription()
      << std::endl;
    }
  catch( itk::ExceptionObject & err )
    {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
    }

  auto transform = registration->GetTransform();
  auto finalParameters = transform->GetParameters();
  auto translationAlongX = finalParameters[0];
  auto translationAlongY = finalParameters[1];

  auto numberOfIterations = optimizer->GetCurrentIteration();

  auto bestValue = optimizer->GetValue();

  std::cout << "Result = " << std::endl;
  std::cout << " Translation X = " << translationAlongX  << std::endl;
  std::cout << " Translation Y = " << translationAlongY  << std::endl;
  std::cout << " Iterations    = " << numberOfIterations << std::endl;
  std::cout << " Metric value  = " << bestValue          << std::endl;

  using CompositeTransformType = itk::CompositeTransform<
                                 double,
                                 Dimension >;
  auto outputCompositeTransform = CompositeTransformType::New();
  outputCompositeTransform->AddTransform( movingInitialTransform );
  outputCompositeTransform->AddTransform(
    registration->GetModifiableTransform() );

  using ResampleFilterType = itk::ResampleImageFilter<
                            MovingImageType,
                            FixedImageType >;
  auto resampler = ResampleFilterType::New();
  resampler->SetInput( movingImageReader->GetOutput() );
  resampler->SetTransform( outputCompositeTransform );
  auto fixedImage = fixedImageReader->GetOutput();
  resampler->SetUseReferenceImage( true );
  resampler->SetReferenceImage( fixedImage );
  resampler->SetDefaultPixelValue( 100 );

  using OutputPixelType = unsigned char;
  using OutputImageType = itk::Image< OutputPixelType, Dimension >;

  using CastFilterType = itk::CastImageFilter<
                        FixedImageType,
                        OutputImageType >;
  auto caster = CastFilterType::New();
  caster->SetInput( resampler->GetOutput() );

  using WriterType = itk::ImageFileWriter< OutputImageType >;
  auto writer = WriterType::New();
  writer->SetFileName( outputImageFile );
  writer->SetInput( caster->GetOutput()   );
  writer->Update();

  using DifferenceFilterType = itk::SubtractImageFilter<
                                  FixedImageType,
                                  FixedImageType,
                                  FixedImageType >;
  auto difference = DifferenceFilterType::New();
  difference->SetInput1( fixedImageReader->GetOutput() );
  difference->SetInput2( resampler->GetOutput() );

  using RescalerType = itk::RescaleIntensityImageFilter<
                                  FixedImageType,
                                  OutputImageType >;
  auto intensityRescaler = RescalerType::New();
  intensityRescaler->SetInput( difference->GetOutput() );
  intensityRescaler->SetOutputMinimum( itk::NumericTraits< OutputPixelType >::min() );
  intensityRescaler->SetOutputMaximum( itk::NumericTraits< OutputPixelType >::max() );

  resampler->SetDefaultPixelValue( 1 );

  writer->SetInput( intensityRescaler->GetOutput() );
  writer->SetFileName( differenceImageAfterFile );
  writer->Update();

  resampler->SetTransform( identityTransform );
  writer->SetFileName( differenceImageBeforeFile );
  writer->Update();

  return EXIT_SUCCESS;
}