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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.
 *  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.
 *
 *=========================================================================*/

// Software Guide : BeginLatex
//
// This example illustrates how to combine the MutualInformation metric with an
// Evolutionary algorithm for optimization.  Evolutionary algorithms are
// naturally well-suited for optimizing the Mutual Information metric given its
// random and noisy behavior.
//
// The structure of the example is almost identical to the one illustrated in
// ImageRegistration4. Therefore we focus here on the setup that is
// specifically required for the evolutionary optimizer.
//
//
// \index{itk::ImageRegistrationMethodv4!Multi-Modality}
// \index{itk::OnePlusOneEvolutionaryOptimizerv4!Multi-Modality}
//
// Software Guide : EndLatex


// Software Guide : BeginCodeSnippet
#include "itkImageRegistrationMethodv4.h"
#include "itkTranslationTransform.h"
#include "itkMattesMutualInformationImageToImageMetricv4.h"
#include "itkOnePlusOneEvolutionaryOptimizerv4.h"
#include "itkNormalVariateGenerator.h"
// Software Guide : EndCodeSnippet


#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:
  typedef  CommandIterationUpdate   Self;
  typedef  itk::Command             Superclass;
  typedef itk::SmartPointer<Self>   Pointer;
  itkNewMacro( Self );

protected:
  CommandIterationUpdate() { m_LastMetricValue = 0.0; };

public:
  typedef itk::OnePlusOneEvolutionaryOptimizerv4<double>   OptimizerType;
  typedef   const OptimizerType *                          OptimizerPointer;

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

  void Execute(const itk::Object * object, const itk::EventObject & event) ITK_OVERRIDE
    {
      OptimizerPointer 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->GetCurrentPosition() << std::endl;
        m_LastMetricValue = currentValue;
        }
    }

private:
  double m_LastMetricValue;
};


int main( int argc, char *argv[] )
{
  if( argc < 3 )
    {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " fixedImageFile  movingImageFile ";
    std::cerr << "outputImagefile ";
    std::cerr << "[samplingPercentage ] " << std::endl;
    return EXIT_FAILURE;
    }

  const    unsigned int    Dimension = 2;
  typedef  float           PixelType;

  typedef itk::Image< PixelType, Dimension >  FixedImageType;
  typedef itk::Image< PixelType, Dimension >  MovingImageType;

  typedef itk::TranslationTransform< double, Dimension >    TransformType;
  typedef itk::OnePlusOneEvolutionaryOptimizerv4< double >  OptimizerType;
  typedef itk::ImageRegistrationMethodv4<
                                    FixedImageType,
                                    MovingImageType >       RegistrationType;

  //  Software Guide : BeginLatex
  //
  //  In this example the image types and all registration components,
  //  except the metric, are declared as in Section
  //  \ref{sec:IntroductionImageRegistration}.
  //  The Mattes mutual information metric type is
  //  instantiated using the image types.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  typedef itk::MattesMutualInformationImageToImageMetricv4<
                                          FixedImageType,
                                          MovingImageType >    MetricType;
  // Software Guide : EndCodeSnippet

  TransformType::Pointer      transform     = TransformType::New();
  OptimizerType::Pointer      optimizer     = OptimizerType::New();
  MetricType::Pointer         metric        = MetricType::New();
  RegistrationType::Pointer   registration  = RegistrationType::New();

  registration->SetOptimizer(     optimizer     );
  registration->SetMetric( metric  );

  //  Software Guide : BeginLatex
  //
  // The histogram bins metric parameter is set as follows.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  metric->SetNumberOfHistogramBins( 20 );
  // Software Guide : EndCodeSnippet

  double samplingPercentage = 0.20;
  if( argc > 4 )
    {
    samplingPercentage = atof( argv[4] );
    }

  //  Software Guide : BeginLatex
  //
  //  As our previous discussion in section ~\ref{sec:MultiModalityRegistrationMattes},
  //  only a subsample of the virtual domain is needed to evaluate the metric.
  //  The number of spatial samples to be used depends on the content of the image, and
  //  the user can define the sampling percentage and the way that sampling operation
  //  is managed by the registration framework as follows. Sampling startegy can
  //  can be defined as \code{REGULAR} or \code{RANDOM}, while the default value
  //  is \code{NONE}.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  registration->SetMetricSamplingPercentage( samplingPercentage );

  RegistrationType::MetricSamplingStrategyType  samplingStrategy  =
                                                      RegistrationType::RANDOM;
  registration->SetMetricSamplingStrategy( samplingStrategy );
  // Software Guide : EndCodeSnippet

  typedef itk::ImageFileReader< FixedImageType  > FixedImageReaderType;
  typedef itk::ImageFileReader< MovingImageType > MovingImageReaderType;

  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();


  typedef TransformType::ParametersType ParametersType;
  ParametersType initialParameters( transform->GetNumberOfParameters() );

  initialParameters[0] = 0.0;  // Initial offset in mm along X
  initialParameters[1] = 0.0;  // Initial offset in mm along Y

  transform->SetParameters( initialParameters );

  registration->SetInitialTransform( transform );
  registration->InPlaceOn();

  //  Software Guide : BeginLatex
  //
  //  Evolutionary algorithms are based on testing random variations
  //  of parameters. In order to support the computation of random values,
  //  ITK provides a family of random number generators. In this example, we
  //  use the \doxygen{NormalVariateGenerator} which generates values with a
  //  normal distribution.
  //
  //  \index{itk::NormalVariateGenerator!New()}
  //  \index{itk::NormalVariateGenerator!Pointer}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  typedef itk::Statistics::NormalVariateGenerator  GeneratorType;

  GeneratorType::Pointer generator = GeneratorType::New();
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  The random number generator must be initialized with a seed.
  //
  //  \index{itk::NormalVariateGenerator!Initialize()}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  generator->Initialize(12345);
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  Now we set the optimizer parameters.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  optimizer->SetNormalVariateGenerator( generator );
  optimizer->Initialize( 10 );
  optimizer->SetEpsilon( 1.0 );
  optimizer->SetMaximumIteration( 4000 );
  // Software Guide : EndCodeSnippet

  // One level registration process without shrinking and smoothing.
  //
  const unsigned int numberOfLevels = 1;

  RegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel;
  shrinkFactorsPerLevel.SetSize( 1 );
  shrinkFactorsPerLevel[0] = 1;

  RegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;
  smoothingSigmasPerLevel.SetSize( 1 );
  smoothingSigmasPerLevel[0] = 0;

  registration->SetNumberOfLevels ( numberOfLevels );
  registration->SetSmoothingSigmasPerLevel( smoothingSigmasPerLevel );
  registration->SetShrinkFactorsPerLevel( shrinkFactorsPerLevel );

  // 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 << "Registration completed!" << std::endl;
    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 = transform->GetParameters();

  double TranslationAlongX = finalParameters[0];
  double TranslationAlongY = finalParameters[1];

  unsigned int numberOfIterations = optimizer->GetCurrentIteration();

  double bestValue = optimizer->GetValue();


  // Print out results
  //
  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;


  //  Software Guide : BeginLatex
  //
  //  This example is executed using the same multi-modality images as
  //  in the previous one.  The registration converges after $24$ iterations and produces
  //  the following results:
  //
  //  \begin{verbatim}
  //  Translation X = 13.1719
  //  Translation Y = 16.9006
  //  \end{verbatim}
  //  These values are a very close match to
  //  the true misalignment introduced in the moving image.
  //
  //  Software Guide : EndLatex


  typedef itk::ResampleImageFilter<
                            MovingImageType,
                            FixedImageType >    ResampleFilterType;

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

  resample->SetTransform( transform );
  resample->SetInput( movingImageReader->GetOutput() );

  FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();

  resample->SetSize(    fixedImage->GetLargestPossibleRegion().GetSize() );
  resample->SetOutputOrigin(  fixedImage->GetOrigin() );
  resample->SetOutputSpacing( fixedImage->GetSpacing() );
  resample->SetOutputDirection( fixedImage->GetDirection() );
  resample->SetDefaultPixelValue( 100 );


  typedef  unsigned char                           OutputPixelType;
  typedef itk::Image< OutputPixelType, Dimension > OutputImageType;
  typedef itk::CastImageFilter<
                        FixedImageType,
                        OutputImageType > CastFilterType;
  typedef itk::ImageFileWriter< OutputImageType >  WriterType;

  WriterType::Pointer      writer =  WriterType::New();
  CastFilterType::Pointer  caster =  CastFilterType::New();

  writer->SetFileName( argv[3] );

  caster->SetInput( resample->GetOutput() );
  writer->SetInput( caster->GetOutput()   );
  writer->Update();

  return EXIT_SUCCESS;
}