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

/**
 * Test program for ANTSNeighborhoodCorrelationImageToImageMetricv4 and
 * GradientDescentOptimizerv4 classes.
 *
 * Perform a registration using user-supplied images.
 * No numerical verification is performed. Test passes as long
 * as no exception occurs.
 */
#include "itkANTSNeighborhoodCorrelationImageToImageMetricv4.h"
#include "itkGradientDescentOptimizerv4.h"
#include "itkRegistrationParameterScalesFromPhysicalShift.h"

#include "itkGaussianSmoothingOnUpdateDisplacementFieldTransform.h"

#include "itkCastImageFilter.h"

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

namespace{
// The following class is used to support callbacks
// on the filter in the pipeline that follows later
template<typename TRegistration>
class ShowProgressObject
{
public:
  ShowProgressObject(TRegistration* o)
    {m_Process = o;}
  void ShowProgress()
    {
    std::cout << "Progress: " << m_Process->GetProgress() << "  ";
    std::cout << "Iter: " << m_Process->GetElapsedIterations() << "  ";
    std::cout << "Metric: "   << m_Process->GetMetric()   << "  ";
    std::cout << "RMSChange: " << m_Process->GetRMSChange() << "  ";
    std::cout << std::endl;
    if ( m_Process->GetElapsedIterations() == 10 )
      { m_Process->StopRegistration(); }
    }
  typename TRegistration::Pointer m_Process;
};
}

int itkANTSNeighborhoodCorrelationImageToImageRegistrationTest(int argc, char *argv[])
{

  if( argc < 4 || argc > 8)
    {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " fixedImageFile movingImageFile ";
    std::cerr << " outputImageFile ";
    std::cerr << " [numberOfIterations] ";
    std::cerr << " [learningRate] [deformationLearningRate] " << std::endl;
    return EXIT_FAILURE;
    }

  std::cout << argc << std::endl;
  unsigned int numberOfIterations = 10;
  double learningRate = 0.1;
  double deformationLearningRate = 1;
  if( argc >= 5 )
    {
    numberOfIterations = atoi( argv[4] );
    }
  if( argc >= 6 )
    {
    learningRate = atof( argv[5] );
    }
  if( argc == 7 )
    {
    deformationLearningRate = atof( argv[6] );
    }
  std::cout << " iterations "<< numberOfIterations << " learningRate "<<learningRate << std::endl;

  const unsigned int Dimension = 2;
  typedef double PixelType; //I assume png is unsigned short

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

  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] );

  //get the images
  fixedImageReader->Update();
  FixedImageType::Pointer  fixedImage = fixedImageReader->GetOutput();
  movingImageReader->Update();
  MovingImageType::Pointer movingImage = movingImageReader->GetOutput();

  /** define a resample filter that will ultimately be used to deform the image */
  typedef itk::ResampleImageFilter<
                            MovingImageType,
                            FixedImageType >    ResampleFilterType;
  ResampleFilterType::Pointer resample = ResampleFilterType::New();


  /** create a composite transform holder for other transforms  */
  typedef itk::CompositeTransform<double, Dimension>    CompositeType;

  CompositeType::Pointer compositeTransform = CompositeType::New();

  //create an affine transform
  typedef itk::AffineTransform<double, Dimension>
                                                    AffineTransformType;
  AffineTransformType::Pointer affineTransform = AffineTransformType::New();
  affineTransform->SetIdentity();
  std::cout <<" affineTransform params " << affineTransform->GetParameters() << std::endl;
  typedef itk::GaussianSmoothingOnUpdateDisplacementFieldTransform<
                                                    double, Dimension>
                                                     DisplacementTransformType;
  DisplacementTransformType::Pointer displacementTransform =
                                              DisplacementTransformType::New();
  typedef DisplacementTransformType::DisplacementFieldType
                                                         DisplacementFieldType;
  DisplacementFieldType::Pointer field = DisplacementFieldType::New();

  //set the field to be the same as the fixed image region, which will
  // act by default as the virtual domain in this example.
  field->SetRegions( fixedImage->GetLargestPossibleRegion() );
  //make sure the field has the same spatial information as the image
  field->CopyInformation( fixedImage );
  std::cout << "fixedImage->GetLargestPossibleRegion(): "
            << fixedImage->GetLargestPossibleRegion() << std::endl
            << "fixedImage->GetBufferedRegion(): "
            << fixedImage->GetBufferedRegion() << std::endl;
  field->Allocate();
  // Fill it with 0's
  DisplacementTransformType::OutputVectorType zeroVector;
  zeroVector.Fill( 0 );
  field->FillBuffer( zeroVector );
  // Assign to transform
  displacementTransform->SetDisplacementField( field );
  displacementTransform->SetGaussianSmoothingVarianceForTheUpdateField( 5 );
  displacementTransform->SetGaussianSmoothingVarianceForTheTotalField( 6 );

  //identity transform for fixed image
  typedef itk::IdentityTransform<double, Dimension> IdentityTransformType;
  IdentityTransformType::Pointer identityTransform = IdentityTransformType::New();
  identityTransform->SetIdentity();

  // The metric
  typedef itk::ANTSNeighborhoodCorrelationImageToImageMetricv4
    < FixedImageType, MovingImageType > MetricType;
  MetricType::Pointer metric = MetricType::New();
  itk::Size<Dimension> radSize;
  radSize.Fill(2);
  metric->SetRadius(radSize);

  // Assign images and transforms.
  // By not setting a virtual domain image or virtual domain settings,
  // the metric will use the fixed image for the virtual domain.
  metric->SetVirtualDomainFromImage( fixedImage );
  metric->SetFixedImage( fixedImage );
  metric->SetMovingImage( movingImage );
  metric->SetFixedTransform( identityTransform );
  metric->SetMovingTransform( affineTransform );
  bool gaussian = false;
  metric->SetUseMovingImageGradientFilter( gaussian );
  metric->SetUseFixedImageGradientFilter( gaussian );
  metric->Initialize();

  typedef itk::RegistrationParameterScalesFromPhysicalShift< MetricType > RegistrationParameterScalesFromShiftType;
  RegistrationParameterScalesFromShiftType::Pointer shiftScaleEstimator = RegistrationParameterScalesFromShiftType::New();
  shiftScaleEstimator->SetMetric(metric);
  shiftScaleEstimator->SetTransformForward(true); //by default, scales for the moving transform
  RegistrationParameterScalesFromShiftType::ScalesType movingScales( affineTransform->GetNumberOfLocalParameters() );
  shiftScaleEstimator->EstimateScales(movingScales);
  std::cout << "Shift scales for the affine transform = " << movingScales << std::endl;

  std::cout << "First do an affine registration " << std::endl;
  typedef itk::GradientDescentOptimizerv4  OptimizerType;
  OptimizerType::Pointer  optimizer = OptimizerType::New();
  optimizer->SetMetric( metric );
  optimizer->SetLearningRate( learningRate );
  optimizer->SetNumberOfIterations( numberOfIterations );
  optimizer->SetScales( movingScales );
  optimizer->StartOptimization();

  std::cout << "Follow affine with deformable registration " << std::endl;
  // now add the displacement field to the composite transform
  compositeTransform->AddTransform( affineTransform );
  compositeTransform->AddTransform( displacementTransform );
  compositeTransform->SetAllTransformsToOptimizeOn(); //Set back to optimize all.
  compositeTransform->SetOnlyMostRecentTransformToOptimizeOn(); //set to optimize the displacement field
  metric->SetMovingTransform( compositeTransform );
  metric->Initialize();

  // Optimizer
  RegistrationParameterScalesFromShiftType::ScalesType displacementScales(
    displacementTransform->GetNumberOfLocalParameters());
  displacementScales.Fill(1);
  optimizer->SetMetric( metric );
  optimizer->SetLearningRate( deformationLearningRate );
  optimizer->SetScales( displacementScales );
  optimizer->SetNumberOfIterations( numberOfIterations );
  try
    {
    optimizer->StartOptimization();
    }
  catch( itk::ExceptionObject & e )
    {
    std::cout << "Exception thrown ! " << std::endl;
    std::cout << "An error occurred during deformation Optimization:" << std::endl;
    std::cout << e.GetLocation() << std::endl;
    std::cout << e.GetDescription() << std::endl;
    std::cout << e.what()    << std::endl;
    std::cout << "Test FAILED." << std::endl;
    return EXIT_FAILURE;
    }
  std::cout << "...finished. " << std::endl;

  std::cout << "threads: metric: " << metric->GetNumberOfThreadsUsed() << "  optimizer: " << optimizer->GetNumberOfThreads() << std::endl;

  // try use the sparse sample point set in CC metric
  typedef MetricType::FixedSampledPointSetType  PointSetType;
  typedef PointSetType::PointType               PointType;
  std::cout << "Using sparse point set..." << std::endl;
  PointSetType::Pointer               pset(PointSetType::New());
  unsigned int ind=0,ct=0;
  itk::ImageRegionIteratorWithIndex<FixedImageType> It(fixedImage, fixedImage->GetLargestPossibleRegion() );
  for( It.GoToBegin(); !It.IsAtEnd(); ++It )
    {
    // take every point
      PointType pt;
      fixedImage->TransformIndexToPhysicalPoint( It.GetIndex(), pt);
      pset->SetPoint(ind, pt);
      ind++;
      ct++;
    }
  metric->SetFixedSampledPointSet( pset );
  metric->SetUseFixedSampledPointSet( true );
  metric->Initialize();
  try
    {
    optimizer->StartOptimization();
    }
  catch( itk::ExceptionObject & e )
    {
    std::cout << "Exception thrown using sparse metric ! " << std::endl;
    std::cout << "An error occurred during deformation Optimization using sparse metric:" << std::endl;
    std::cout << e.GetLocation() << std::endl;
    std::cout << e.GetDescription() << std::endl;
    std::cout << e.what()    << std::endl;
    std::cout << "Test FAILED." << std::endl;
    return EXIT_FAILURE;
    }
  std::cout << "...finished. " << std::endl;
  std::cout << "threads: metric: " << metric->GetNumberOfThreadsUsed() << "  optimizer: " << optimizer->GetNumberOfThreads() << std::endl;

  //warp the image with the displacement field
  resample->SetTransform( compositeTransform );
  resample->SetInput( movingImageReader->GetOutput() );
  resample->SetSize(    fixedImage->GetLargestPossibleRegion().GetSize() );
  resample->SetOutputOrigin(  fixedImage->GetOrigin() );
  resample->SetOutputSpacing( fixedImage->GetSpacing() );
  resample->SetOutputDirection( fixedImage->GetDirection() );
  resample->SetDefaultPixelValue( 0 );
  resample->Update();

  //write the warped image into a file
  typedef double                                    OutputPixelType;
  typedef itk::Image< OutputPixelType, Dimension >  OutputImageType;
  typedef itk::CastImageFilter<
                        MovingImageType,
                        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();

  std::cout << "Test PASSED." << affineTransform->GetParameters() << std::endl;
  return EXIT_SUCCESS;
}