text: /*=========================================================================
   *
   *  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 do registration with a 2D Translation Transform,
  //  the Normalized Mutual Information metric and the Amoeba optimizer.
  //
  // Software Guide : EndLatex
  
  
  // Software Guide : BeginCodeSnippet
  #include "itkImageRegistrationMethod.h"
  
  #include "itkTranslationTransform.h"
  #include "itkMattesMutualInformationImageToImageMetric.h"
  #include "itkAmoebaOptimizer.h"
  #include "itkMersenneTwisterRandomVariateGenerator.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_IterationNumber=0;
      }
  
  public:
    using OptimizerType = itk::AmoebaOptimizer;
    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
      {
      OptimizerPointer optimizer = static_cast< OptimizerPointer >( object );
      if( ! itk::IterationEvent().CheckEvent( &event ) )
        {
        return;
        }
      std::cout << m_IterationNumber++ << "   ";
      std::cout << optimizer->GetCachedValue() << "   ";
      std::cout << optimizer->GetCachedCurrentPosition() << std::endl;
      }
  
  private:
    unsigned long m_IterationNumber;
  };
  
  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 ";
      std::cerr << " [initialTx] [initialTy]";
      std::cerr << "[useExplicitPDFderivatives ] " << 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::AmoebaOptimizer;
    using InterpolatorType = itk::LinearInterpolateImageFunction<
                                      MovingImageType,
                                      double             >;
    using RegistrationType = itk::ImageRegistrationMethod<
                                      FixedImageType,
                                      MovingImageType    >;
  
  
    using MetricType = itk::MattesMutualInformationImageToImageMetric<
                                            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  );
  
  
    //  Software Guide : BeginLatex
    //
    //  The metric requires two parameters to be selected: the number
    //  of bins used to compute the entropy and the number of spatial samples
    //  used to compute the density estimates. In typical application, 50
    //  histogram bins are sufficient and the metric is relatively insensitive
    //  to changes in the number of bins. The number of spatial samples
    //  to be used depends on the content of the image. If the images are
    //  smooth and do not contain much detail, then using approximately
    //  $1$ percent of the pixels will do. On the other hand, if the images
    //  are detailed, it may be necessary to use a much higher proportion,
    //  such as $20$ percent.
    //
    //  \index{itk::Mattes\-Mutual\-Information\-Image\-To\-Image\-Metric!SetNumberOfHistogramBins()}
    //  \index{itk::Mattes\-Mutual\-Information\-Image\-To\-Image\-Metric!SetNumberOfSpatialSamples()}
    //
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    metric->SetNumberOfHistogramBins( 20 );
    metric->SetNumberOfSpatialSamples( 10000 );
    // Software Guide : EndCodeSnippet
  
    // For consistent results when regression testing.
    metric->ReinitializeSeed( 121213 );
  
    if( argc > 6 )
      {
      // Define whether to calculate the metric derivative by explicitly
      // computing the derivatives of the joint PDF with respect to the Transform
      // parameters, or doing it by progressively accumulating contributions from
      // each bin in the joint PDF.
      metric->SetUseExplicitPDFDerivatives( atoi( argv[6] ) );
      }
  
  
    const unsigned int numberOfParameters = transform->GetNumberOfParameters();
  
  
    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 > 5 )
      {
      initialParameters[0] = atof( argv[4] );
      initialParameters[1] = atof( argv[5] );
      }
  
    registration->SetInitialTransformParameters( initialParameters  );
  
    std::cout << "Initial transform parameters = ";
    std::cout << initialParameters << std::endl;
  
  
    //  Software Guide : BeginLatex
    //
    //  The AmoebaOptimizer moves a simplex around the cost surface.  Here we set
    //  the initial size of the simplex (5 units in each of the parameters)
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    OptimizerType::ParametersType simplexDelta( numberOfParameters );
    simplexDelta.Fill( 5.0 );
  
    optimizer->AutomaticInitialSimplexOff();
    optimizer->SetInitialSimplexDelta( simplexDelta );
    // Software Guide : EndCodeSnippet
  
  
    //  Software Guide : BeginLatex
    //
    //  We also adjust the tolerances on the optimizer to define convergence.
    //  Here, we used a tolerance on the parameters of 0.1 (which will be one
    //  tenth of image unit, in this case pixels). We also set the tolerance on
    //  the cost function value to define convergence.  The metric we are using
    //  returns the value of Mutual Information.  So we set the function
    //  convergence to be 0.001 bits (bits are the appropriate units for
    //  measuring Information).
    //
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    optimizer->SetParametersConvergenceTolerance( 0.1 );  // 1/10th pixel
    optimizer->SetFunctionConvergenceTolerance(0.001);    // 0.001 bits
    // Software Guide : EndCodeSnippet
  
  
    //  Software Guide : BeginLatex
    //  In the case where the optimizer never succeeds in reaching the desired
    //  precision tolerance, it is prudent to establish a limit on the number of
    //  iterations to be performed. This maximum number is defined with the
    //  method \code{SetMaximumNumberOfIterations()}.
    //
    //  \index{itk::Amoeba\-Optimizer!SetMaximumNumberOfIterations()}
    //
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    optimizer->SetMaximumNumberOfIterations( 200 );
    // Software Guide : EndCodeSnippet
  
  
    // 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];
  
    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 << " 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;
  }
  