/*=========================================================================

  Program:   Insight Segmentation & Registration Toolkit
  Module:    $RCSfile: ImageRegistration3-mod.cxx,v $
  Language:  C++
  Date:      $Date: 2005/06/10 18:31:25 $
  Version:   $Revision: 1.33 $

  Copyright (c) Insight Software Consortium. All rights reserved.
  See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.

     This software is distributed WITHOUT ANY WARRANTY; without even 
     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR 
     PURPOSE.  See the above copyright notices for more information.

=========================================================================*/
#if defined(_MSC_VER)
#pragma warning ( disable : 4786 )
#endif


// Software Guide : BeginLatex
//
// This example illustrates the use of the 
// \doxygen{CommbinedImageToImageMetricAdaptor} class for performing 
// registration of two $2D$ images. The example code is for
// the most part identical to code presented in ImageRegistration3.cxx.
// The major difference is that in the example we use a combined
// mean squares and normalized correlation metric.
//
// Software Guide : EndLatex 

// Software Guide : BeginCodeSnippet
#include "itkMeanSquaresImageToImageMetric.h"
#include "itkNormalizedCorrelationImageToImageMetric.h"
#include "itkCombinedImageToImageMetricAdaptor.h"
// Software Guide : EndCodeSnippet

#include "itkImageRegistrationMethod.h"
#include "itkTranslationTransform.h"
#include "itkLinearInterpolateImageFunction.h"
#include "itkRegularStepGradientDescentOptimizer.h"
#include "itkImage.h"

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

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


#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() {};
public:
  typedef itk::RegularStepGradientDescentOptimizer     OptimizerType;
  typedef const OptimizerType                         *OptimizerPointer;
  void Execute(itk::Object *caller, const itk::EventObject & event)
  {
    Execute( (const itk::Object *)caller, event);
  }
  void Execute(const itk::Object * object, const itk::EventObject & event)
  {
    OptimizerPointer optimizer = 
                         dynamic_cast< OptimizerPointer >( object );
    if( ! itk::IterationEvent().CheckEvent( &event ) )
      {
      return;
      }
      std::cout << optimizer->GetCurrentIteration() << " = ";
      std::cout << optimizer->GetValue() << " : ";
      std::cout << optimizer->GetCurrentPosition() << std::endl;
  }
   
};


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 [differenceImage]" << std::endl;
    return 1;
    }
  
  const    unsigned int    Dimension = 2;
  typedef  unsigned short  PixelType;
  
  typedef itk::Image< PixelType, Dimension >  FixedImageType;
  typedef itk::Image< PixelType, Dimension >  MovingImageType;

  typedef itk::TranslationTransform< double, Dimension > TransformType;

  typedef itk::RegularStepGradientDescentOptimizer       OptimizerType;

  typedef itk::LinearInterpolateImageFunction< 
                                    MovingImageType,
                                    double             > InterpolatorType;

  typedef itk::ImageRegistrationMethod< 
                                    FixedImageType, 
                                    MovingImageType   >  RegistrationType;

// Software Guide : BeginCodeSnippet
  typedef itk::MeanSquaresImageToImageMetric< 
                                      FixedImageType, 
                                      MovingImageType >  Metric1Type;
  typedef itk::NormalizedCorrelationImageToImageMetric<
                                      FixedImageType,
                                      MovingImageType >  Metric2Type;
  typedef itk::CombinedImageToImageMetricAdaptor<
                                      FixedImageType,
                                      MovingImageType >  MetricType;
// Software Guide : EndCodeSnippet


  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  );
  
  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(); // This is needed to make the BufferedRegion below valid.

  registration->SetFixedImageRegion( 
       fixedImageReader->GetOutput()->GetBufferedRegion() );

  typedef RegistrationType::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
  
  registration->SetInitialTransformParameters( initialParameters );

  optimizer->SetMaximumStepLength( 4.00 );  
  optimizer->SetMinimumStepLength( 0.01 );
  optimizer->SetNumberOfIterations( 200 );

  optimizer->MaximizeOff();

  //----------------------------------------------------
  // Setting up the combined weighted metric
  //----------------------------------------------------

  // Instantiate the each of the underlying metrics
  Metric1Type::Pointer       metric1      = Metric1Type::New();
  Metric2Type::Pointer       metric2      = Metric2Type::New();

  // Instantiate the combined metric
  MetricType::Pointer        metric       = MetricType::New();

  // We have to manually hook things up to the metric
  // since we can potentially have metric which is based on
  // different images and/or masks etc

  // Setup for metric1: mean squares
  InterpolatorType::Pointer interpolator1 = InterpolatorType::New();
  metric1->SetFixedImage(    fixedImageReader->GetOutput()    );
  metric1->SetMovingImage(   movingImageReader->GetOutput()   );
  metric1->SetFixedImageRegion( 
       fixedImageReader->GetOutput()->GetBufferedRegion() );
  metric1->SetInterpolator( interpolator1 );

  // Setup for metric2: normalized correlation
  InterpolatorType::Pointer interpolator2 = InterpolatorType::New();
  metric2->SetFixedImage(    fixedImageReader->GetOutput()    );
  metric2->SetMovingImage(   movingImageReader->GetOutput()   );
  metric2->SetFixedImageRegion( 
       fixedImageReader->GetOutput()->GetBufferedRegion() );
  metric2->SetInterpolator( interpolator2 );

  // Plug the underlying metrics into the adaptor
  metric->SetMetric( 0, metric1, 0.001 );
  metric->SetMetric( 1, metric2, 1.0 );

  // Plug adaptor into the registration class
  registration->SetMetric( metric );

  CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
  optimizer->AddObserver( itk::IterationEvent(), observer );
 
  try 
    { 
    registration->StartRegistration(); 
    } 
  catch( itk::ExceptionObject & err ) 
    { 
    std::cout << "ExceptionObject caught !" << std::endl; 
    std::cout << err << std::endl; 
    return -1;
    } 

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

  std::cout << "Registration done !" << std::endl;
  std::cout << "Number of iterations = " << numberOfIterations << std::endl;
  std::cout << "Translation along X  = " << TranslationAlongX << std::endl;
  std::cout << "Translation along Y  = " << TranslationAlongY << std::endl;
  std::cout << "Optimal metric value = " << bestValue << std::endl;


  // Prepare the resampling filter in order to map the moving image.
  //
  typedef itk::ResampleImageFilter< 
                            MovingImageType, 
                            FixedImageType >    ResampleFilterType;

  TransformType::Pointer finalTransform = TransformType::New();

  finalTransform->SetParameters( finalParameters );

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

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

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

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


  // Prepare a writer and caster filters to send the resampled moving image to
  // a file
  //
  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 0;
}