Examples/RegistrationITKv4/ImageRegistration15.cxx

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 *  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.
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// Software Guide : BeginLatex
//
//  This example illustrates how to do registration with a 2D Translation Transform,
//  the Normalized Mutual Information metric and the One+One evolutionary optimizer.
//
// Software Guide : EndLatex


// Software Guide : BeginCodeSnippet
#include "itkImageRegistrationMethod.h"

#include "itkTranslationTransform.h"
#include "itkNormalizedMutualInformationHistogramImageToImageMetric.h"
#include "itkOnePlusOneEvolutionaryOptimizer.h"
#include "itkNormalVariateGenerator.h"

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

#include "itkResampleImageFilter.h"
#include "itkCastImageFilter.h"


//  The following section of code implements a Command observer
//  used to monitor the evolution of the registration process.
//
#include "itkCommand.h"
class CommandIterationUpdate : public itk::Command
{
public:
  using Self = CommandIterationUpdate;
  using Superclass = itk::Command;
  using Pointer = itk::SmartPointer<Self>;
  itkNewMacro( Self );

protected:
  CommandIterationUpdate() {m_LastMetricValue = 0;}

public:
  using OptimizerType = itk::OnePlusOneEvolutionaryOptimizer;
  using OptimizerPointer = const OptimizerType *;

  void Execute(itk::Object *caller,
               const itk::EventObject & event) override
    {
    Execute( (const itk::Object *)caller, event);
    }

  void Execute(const itk::Object * object,
               const itk::EventObject & event) override
    {
      auto optimizer = static_cast< OptimizerPointer >( object );
      if( ! itk::IterationEvent().CheckEvent( &event ) )
        {
        return;
        }
      double currentValue = optimizer->GetValue();
      // Only print out when the Metric value changes
      if( std::fabs( m_LastMetricValue - currentValue ) > 1e-7 )
        {
        std::cout << optimizer->GetCurrentIteration() << "   ";
        std::cout << currentValue << "   ";
        std::cout << optimizer->GetFrobeniusNorm() << "   ";
        std::cout << optimizer->GetCurrentPosition() << std::endl;
        m_LastMetricValue = currentValue;
        }
    }

private:
  double m_LastMetricValue;
};


int main( int argc, char *argv[] )
{
  if( argc < 4 )
    {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " fixedImageFile  movingImageFile ";
    std::cerr << "outputImagefile [numberOfHistogramBins] ";
    std::cerr << "[initialRadius] [epsilon] [initialTx] [initialTy]"
              << std::endl;
    return EXIT_FAILURE;
    }

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

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

  using TransformType = itk::TranslationTransform< double, Dimension >;

  using OptimizerType = itk::OnePlusOneEvolutionaryOptimizer;
  using InterpolatorType = itk::LinearInterpolateImageFunction<
                                    MovingImageType,
                                    double             >;
  using RegistrationType = itk::ImageRegistrationMethod<
                                    FixedImageType,
                                    MovingImageType    >;


  using MetricType =
    itk::NormalizedMutualInformationHistogramImageToImageMetric<
                                          FixedImageType,
                                          MovingImageType >;

  TransformType::Pointer      transform     = TransformType::New();
  OptimizerType::Pointer      optimizer     = OptimizerType::New();
  InterpolatorType::Pointer   interpolator  = InterpolatorType::New();
  RegistrationType::Pointer   registration  = RegistrationType::New();

  registration->SetOptimizer(     optimizer     );
  registration->SetTransform(     transform     );
  registration->SetInterpolator(  interpolator  );


  MetricType::Pointer metric = MetricType::New();
  registration->SetMetric( metric  );


  unsigned int numberOfHistogramBins = 32;
  if( argc > 4 )
    {
    numberOfHistogramBins = std::stoi( argv[4] );
    std::cout << "Using " << numberOfHistogramBins << " Histogram bins"
              << std::endl;
    }
  MetricType::HistogramType::SizeType histogramSize;
  histogramSize.SetSize(2);
  histogramSize[0] = numberOfHistogramBins;
  histogramSize[1] = numberOfHistogramBins;
  metric->SetHistogramSize( histogramSize );


  const unsigned int numberOfParameters = transform->GetNumberOfParameters();

  using ScalesType = MetricType::ScalesType;
  ScalesType scales( numberOfParameters );

  scales.Fill( 1.0 );

  metric->SetDerivativeStepLengthScales(scales);

  using FixedImageReaderType = itk::ImageFileReader< FixedImageType  >;
  using MovingImageReaderType = itk::ImageFileReader< MovingImageType >;

  FixedImageReaderType::Pointer fixedImageReader = FixedImageReaderType::New();
  MovingImageReaderType::Pointer movingImageReader
                                                = MovingImageReaderType::New();

  fixedImageReader->SetFileName(  argv[1] );
  movingImageReader->SetFileName( argv[2] );

  registration->SetFixedImage(    fixedImageReader->GetOutput()    );
  registration->SetMovingImage(   movingImageReader->GetOutput()   );

  fixedImageReader->Update();
  movingImageReader->Update();

  FixedImageType::ConstPointer fixedImage = fixedImageReader->GetOutput();
  registration->SetFixedImageRegion( fixedImage->GetBufferedRegion() );

  transform->SetIdentity();
  using ParametersType = RegistrationType::ParametersType;
  ParametersType initialParameters =  transform->GetParameters();
  initialParameters[0] = 0.0;
  initialParameters[1] = 0.0;

  if( argc > 8 )
    {
    initialParameters[0] = std::stod( argv[7] );
    initialParameters[1] = std::stod( argv[8] );
    }
  registration->SetInitialTransformParameters( initialParameters  );

  std::cout << "Initial transform parameters = ";
  std::cout << initialParameters << std::endl;

  using OptimizerScalesType = OptimizerType::ScalesType;
  OptimizerScalesType optimizerScales( transform->GetNumberOfParameters() );

  FixedImageType::RegionType region = fixedImage->GetLargestPossibleRegion();
  FixedImageType::SizeType size = region.GetSize();
  FixedImageType::SpacingType spacing = fixedImage->GetSpacing();

  optimizerScales[0] = 1.0 / ( 0.1 * size[0] * spacing[0] );
  optimizerScales[1] = 1.0 / ( 0.1 * size[1] * spacing[1] );
  optimizer->SetScales( optimizerScales );

  using GeneratorType = itk::Statistics::NormalVariateGenerator;
  GeneratorType::Pointer generator = GeneratorType::New();
  generator->Initialize(12345);

  optimizer->MaximizeOn();
  optimizer->SetNormalVariateGenerator( generator );
  double initialRadius = 0.01;
  if( argc > 5 )
    {
    initialRadius = std::stod( argv[5] );
    std::cout << "Using initial radius = " << initialRadius << std::endl;
    }
  optimizer->Initialize( initialRadius );
  double epsilon = 0.001;
  if( argc > 6 )
    {
    epsilon = std::stod( argv[6] );
    std::cout << "Using epsilon = " << epsilon << std::endl;
    }
  optimizer->SetEpsilon( epsilon );
  optimizer->SetMaximumIteration( 2000 );

  // Create the Command observer and register it with the optimizer.
  //
  CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
  optimizer->AddObserver( itk::IterationEvent(), observer );
  try
    {
    registration->Update();
    std::cout << "Optimizer stop condition: "
              << registration->GetOptimizer()->GetStopConditionDescription()
              << std::endl;
    }
  catch( itk::ExceptionObject & err )
    {
    std::cout << "ExceptionObject caught !" << std::endl;
    std::cout << err << std::endl;
    return EXIT_FAILURE;
    }

  ParametersType finalParameters = registration->GetLastTransformParameters();
  const double finalTranslationX    = finalParameters[0];
  const double finalTranslationY    = finalParameters[1];
  unsigned int numberOfIterations = optimizer->GetCurrentIteration();
  const double bestValue = optimizer->GetValue();

  // Print out results
  std::cout << "Result = " << std::endl;
  std::cout << " Translation X = " << finalTranslationX  << std::endl;
  std::cout << " Translation Y = " << finalTranslationY  << std::endl;
  std::cout << " Iterations    = " << numberOfIterations << std::endl;
  std::cout << " Metric value  = " << bestValue          << std::endl;

  using ResampleFilterType = itk::ResampleImageFilter<
            MovingImageType, FixedImageType >;

  TransformType::Pointer finalTransform = TransformType::New();
  finalTransform->SetParameters( finalParameters );
  finalTransform->SetFixedParameters( transform->GetFixedParameters() );

  ResampleFilterType::Pointer resample = ResampleFilterType::New();

  resample->SetTransform( finalTransform );
  resample->SetInput( movingImageReader->GetOutput() );
  resample->SetSize(    fixedImage->GetLargestPossibleRegion().GetSize() );
  resample->SetOutputOrigin(  fixedImage->GetOrigin() );
  resample->SetOutputSpacing( fixedImage->GetSpacing() );
  resample->SetOutputDirection( fixedImage->GetDirection() );
  resample->SetDefaultPixelValue( 100 );

  using OutputImageType = itk::Image< PixelType, Dimension >;
  using WriterType = itk::ImageFileWriter< OutputImageType >;

  WriterType::Pointer      writer =  WriterType::New();
  writer->SetFileName( argv[3] );
  writer->SetInput( resample->GetOutput() );
  writer->Update();
  // Software Guide : EndCodeSnippet
  return EXIT_SUCCESS;
}