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 : BeginCommandLineArgs
  //    INPUTS:  {BrainT1SliceBorder20.png}
  //    INPUTS:  {BrainProtonDensitySliceShifted13x17y.png}
  //    OUTPUTS: {MultiResImageRegistration2Output.png}
  //    ARGUMENTS:    100
  //    OUTPUTS: {MultiResImageRegistration2CheckerboardBefore.png}
  //    OUTPUTS: {MultiResImageRegistration2CheckerboardAfter.png}
  //  Software Guide : EndCommandLineArgs
  
  // Software Guide : BeginLatex
  //
  //  This example illustrates the use of more complex components of the
  //  registration framework. In particular, it introduces the use of the
  //  \doxygen{AffineTransform} and the importance of fine-tuning the scale
  //  parameters of the optimizer.
  //
  // \index{itk::ImageRegistrationMethod!AffineTransform}
  // \index{itk::ImageRegistrationMethod!Scaling parameter space}
  // \index{itk::AffineTransform!Image Registration}
  //
  // The AffineTransform is a linear transformation that maps lines into
  // lines. It can be used to represent translations, rotations, anisotropic
  // scaling, shearing or any combination of them. Details about the affine
  // transform can be seen in Section~\ref{sec:AffineTransform}.
  //
  // In order to use the AffineTransform class, the following header
  // must be included.
  //
  // \index{itk::AffineTransform!Header}
  //
  // Software Guide : EndLatex
  
  
  // Software Guide : BeginCodeSnippet
  #include "itkAffineTransform.h"
  // Software Guide : EndCodeSnippet
  
  
  #include "itkCenteredTransformInitializer.h"
  #include "itkMultiResolutionImageRegistrationMethod.h"
  #include "itkMattesMutualInformationImageToImageMetric.h"
  #include "itkRegularStepGradientDescentOptimizer.h"
  #include "itkRecursiveMultiResolutionPyramidImageFilter.h"
  #include "itkImage.h"
  
  
  #include "itkImageFileReader.h"
  #include "itkImageFileWriter.h"
  
  #include "itkResampleImageFilter.h"
  #include "itkCastImageFilter.h"
  #include "itkCheckerBoardImageFilter.h"
  
  //  The following section of code implements an observer
  //  that will 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_CumulativeIterationIndex(0) {};
  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() << "  " <<
        m_CumulativeIterationIndex++ << std::endl;
      }
  private:
    unsigned int m_CumulativeIterationIndex;
  };
  
  
  //  The following section of code implements a Command observer
  //  that will control the modification of optimizer parameters
  //  at every change of resolution level.
  //
  template <typename TRegistration>
  class RegistrationInterfaceCommand : public itk::Command
  {
  public:
    typedef  RegistrationInterfaceCommand   Self;
    typedef  itk::Command                   Superclass;
    typedef  itk::SmartPointer<Self>        Pointer;
    itkNewMacro( Self );
  protected:
    RegistrationInterfaceCommand() {};
  public:
    typedef   TRegistration                              RegistrationType;
    typedef   RegistrationType *                         RegistrationPointer;
    typedef   itk::RegularStepGradientDescentOptimizer   OptimizerType;
    typedef   OptimizerType *                            OptimizerPointer;
    void Execute(itk::Object * object, const itk::EventObject & event)
    {
      if( !(itk::IterationEvent().CheckEvent( &event )) )
        {
        return;
        }
      RegistrationPointer registration =
                          dynamic_cast<RegistrationPointer>( object );
      OptimizerPointer optimizer = dynamic_cast< OptimizerPointer >(
                         registration->GetOptimizer() );
  
      std::cout << "-------------------------------------" << std::endl;
      std::cout << "MultiResolution Level : "
                << registration->GetCurrentLevel()  << std::endl;
      std::cout << std::endl;
  
      if ( registration->GetCurrentLevel() == 0 )
        {
        optimizer->SetMaximumStepLength( 16.00 );
        optimizer->SetMinimumStepLength(  0.01 );
        }
      else
        {
        optimizer->SetMaximumStepLength( optimizer->GetMaximumStepLength() / 4.0 );
        optimizer->SetMinimumStepLength( optimizer->GetMinimumStepLength() / 10.0 );
        }
    }
    void Execute(const itk::Object * , const itk::EventObject & )
      { return; }
  };
  
  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 [backgroundGrayLevel]";
      std::cerr << " [checkerboardbefore] [CheckerBoardAfter]";
      std::cerr << " [useExplicitPDFderivatives ] " << std::endl;
      std::cerr << " [numberOfBins] [numberOfSamples ] " << std::endl;
      return EXIT_FAILURE;
      }
  
    const    unsigned int    Dimension = 2;
    typedef  unsigned short  PixelType;
  
    typedef itk::Image< PixelType, Dimension >  FixedImageType;
    typedef itk::Image< PixelType, Dimension >  MovingImageType;
  
    typedef   float                                    InternalPixelType;
    typedef itk::Image< InternalPixelType, Dimension > InternalImageType;
  
  
    //  Software Guide : BeginLatex
    //
    //  The configuration of the registration method in this example closely
    //  follows the procedure in the previous section. The main changes involve the
    //  construction and initialization of the transform. The instantiation of
    //  the transform type requires only the dimension of the space and the
    //  type used for representing space coordinates.
    //
    //  \index{itk::AffineTransform!Instantiation}
    //
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    typedef itk::AffineTransform< double, Dimension > TransformType;
    // Software Guide : EndCodeSnippet
  
  
    typedef itk::RegularStepGradientDescentOptimizer       OptimizerType;
    typedef itk::LinearInterpolateImageFunction<
                                      InternalImageType,
                                      double             > InterpolatorType;
    typedef itk::MattesMutualInformationImageToImageMetric<
                                            InternalImageType,
                                            InternalImageType >    MetricType;
  
    typedef OptimizerType::ScalesType       OptimizerScalesType;
  
    typedef itk::MultiResolutionImageRegistrationMethod<
                                      InternalImageType,
                                      InternalImageType    > RegistrationType;
    typedef itk::RecursiveMultiResolutionPyramidImageFilter<
                                      InternalImageType,
                                      InternalImageType  >    FixedImagePyramidType;
    typedef itk::RecursiveMultiResolutionPyramidImageFilter<
                                      InternalImageType,
                                      InternalImageType  >   MovingImagePyramidType;
  
    OptimizerType::Pointer      optimizer     = OptimizerType::New();
    InterpolatorType::Pointer   interpolator  = InterpolatorType::New();
    RegistrationType::Pointer   registration  = RegistrationType::New();
    MetricType::Pointer         metric        = MetricType::New();
  
    registration->SetOptimizer(     optimizer     );
    registration->SetInterpolator(  interpolator  );
    registration->SetMetric( metric  );
  
  
    //  Software Guide : BeginLatex
    //
    //  The transform is constructed using the standard \code{New()} method and
    //  assigning it to a SmartPointer.
    //
    //  \index{itk::AffineTransform!New()}
    //  \index{itk::AffineTransform!Pointer}
    //  \index{itk::Multi\-Resolution\-Image\-Registration\-Method!SetTransform()}
    //
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    TransformType::Pointer   transform  = TransformType::New();
    registration->SetTransform( transform );
    // Software Guide : EndCodeSnippet
  
  
    FixedImagePyramidType::Pointer fixedImagePyramid =
        FixedImagePyramidType::New();
    MovingImagePyramidType::Pointer movingImagePyramid =
        MovingImagePyramidType::New();
  
  
    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] );
  
    typedef itk::CastImageFilter<
                          FixedImageType, InternalImageType > FixedCastFilterType;
    typedef itk::CastImageFilter<
                          MovingImageType, InternalImageType > MovingCastFilterType;
    FixedCastFilterType::Pointer fixedCaster   = FixedCastFilterType::New();
    MovingCastFilterType::Pointer movingCaster = MovingCastFilterType::New();
  
    fixedCaster->SetInput(  fixedImageReader->GetOutput() );
    movingCaster->SetInput( movingImageReader->GetOutput() );
  
    registration->SetFixedImage(    fixedCaster->GetOutput()    );
    registration->SetMovingImage(   movingCaster->GetOutput()   );
  
    fixedCaster->Update();
  
    registration->SetFixedImageRegion(
         fixedCaster->GetOutput()->GetBufferedRegion() );
  
  
    //  Software Guide : BeginLatex
    //
    //  One of the easiest ways of preparing a consistent set of parameters for
    //  the transform is to use the \doxygen{CenteredTransformInitializer}. Once
    //  the transform is initialized, we can invoke its \code{GetParameters()}
    //  method to extract the array of parameters. Finally the array is passed to
    //  the registration method using its \code{SetInitialTransformParameters()}
    //  method.
    //
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    typedef itk::CenteredTransformInitializer<
                                      TransformType,
                                      FixedImageType,
                                      MovingImageType >  TransformInitializerType;
  
    TransformInitializerType::Pointer initializer = TransformInitializerType::New();
  
    initializer->SetTransform(   transform );
    initializer->SetFixedImage(  fixedImageReader->GetOutput() );
    initializer->SetMovingImage( movingImageReader->GetOutput() );
  
    initializer->MomentsOn();
    initializer->InitializeTransform();
  
    registration->SetInitialTransformParameters( transform->GetParameters() );
    // Software Guide : EndCodeSnippet
  
  
    //  Software Guide : BeginLatex
    //
    //  The set of parameters in the AffineTransform have different
    //  dynamic ranges. Typically the parameters associated with the matrix
    //  have values around $[-1:1]$, although they are not restricted to this
    //  interval.  Parameters associated with translations, on the other hand,
    //  tend to have much higher values, typically in the order of $10.0$ to
    //  $100.0$. This difference in dynamic range negatively affects the
    //  performance of gradient descent optimizers. ITK provides a mechanism to
    //  compensate for such differences in values among the parameters when
    //  they are passed to the optimizer. The mechanism consists of providing an
    //  array of scale factors to the optimizer. These factors re-normalize the
    //  gradient components before they are used to compute the step of the
    //  optimizer at the current iteration. In our particular case, a common
    //  choice for the scale parameters is to set to $1.0$ all those associated
    //  with the matrix coefficients, that is, the first $N \times N$
    //  factors. Then, we set the remaining scale factors to a small value. The
    //  following code sets up the scale coefficients.
    //
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    OptimizerScalesType optimizerScales( transform->GetNumberOfParameters() );
  
    optimizerScales[0] = 1.0; // scale for M11
    optimizerScales[1] = 1.0; // scale for M12
    optimizerScales[2] = 1.0; // scale for M21
    optimizerScales[3] = 1.0; // scale for M22
  
    optimizerScales[4] = 1.0 / 1e7; // scale for translation on X
    optimizerScales[5] = 1.0 / 1e7; // scale for translation on Y
    // Software Guide : EndCodeSnippet
  
  
    //  Software Guide : BeginLatex
    //
    //  Here the affine transform is represented by the matrix $\bf{M}$ and the
    //  vector $\bf{T}$. The transformation of a point $\bf{P}$ into $\bf{P'}$
    //  is expressed as
    //
    //  \begin{equation}
    //  \left[
    //  \begin{array}{c}
    //  {P'}_x  \\  {P'}_y  \\  \end{array}
    //  \right]
    //  =
    //  \left[
    //  \begin{array}{cc}
    //  M_{11} & M_{12} \\ M_{21} & M_{22} \\  \end{array}
    //  \right]
    //  \cdot
    //  \left[
    //  \begin{array}{c}
    //  P_x  \\ P_y  \\  \end{array}
    //  \right]
    //  +
    //  \left[
    //  \begin{array}{c}
    //  T_x  \\ T_y  \\  \end{array}
    //  \right]
    //  \end{equation}
    //
    //
    //  Software Guide : EndLatex
  
  
    //  Software Guide : BeginLatex
    //
    //  The array of scales is then passed to the optimizer using the
    //  \code{SetScales()} method.
    //
    //  \index{itk::Optimizer!SetScales()}
    //
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    optimizer->SetScales( optimizerScales );
    // Software Guide : EndCodeSnippet
  
    metric->SetNumberOfHistogramBins( 128 );
    metric->SetNumberOfSpatialSamples( 50000 );
  
  
    if( argc > 8 )
      {
      // optionally, override the values with numbers taken from the command line arguments.
      metric->SetNumberOfHistogramBins( atoi( argv[8] ) );
      }
  
    if( argc > 9 )
      {
      // optionally, override the values with numbers taken from the command line arguments.
      metric->SetNumberOfSpatialSamples( atoi( argv[9] ) );
      }
  
  
   //  Software Guide : BeginLatex
    //
    //  Given that the Mattes Mutual Information metric uses a random iterator in
    //  order to collect the samples from the images, it is usually convenient to
    //  initialize the seed of the random number generator.
    //
    //  \index{itk::Mattes\-Mutual\-Information\-Image\-To\-Image\-Metric!ReinitializeSeed()}
    //
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    metric->ReinitializeSeed( 76926294 );
    // Software Guide : EndCodeSnippet
  
    if( argc > 7 )
      {
      // 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[7] ) );
      }
  
  
    //  Software Guide : BeginLatex
    //
    //  The step length has to be proportional to the expected values of the
    //  parameters in the search space. Since the expected values of the matrix
    //  coefficients are around $1.0$, the initial step of the optimization
    //  should be a small number compared to $1.0$. As a guideline, it is
    //  useful to think of the matrix coefficients as combinations of
    //  $cos(\theta)$ and $sin(\theta)$.  This leads to use values close to the
    //  expected rotation measured in radians. For example, a rotation of $1.0$
    //  degree is about $0.017$ radians. As in the previous example, the
    //  maximum and minimum step length of the optimizer are set by the
    //  \code{RegistrationInterfaceCommand} when it is called at the beginning
    //  of registration at each multi-resolution level.
    //
    //  Software Guide : EndLatex
  
    optimizer->SetNumberOfIterations(  200  );
    optimizer->SetRelaxationFactor( 0.8 );
  
  
    // Create the Command observer and register it with the optimizer.
    //
    CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
    optimizer->AddObserver( itk::IterationEvent(), observer );
  
  
    // Create the Command interface observer and register it with the optimizer.
    //
    typedef RegistrationInterfaceCommand<RegistrationType> CommandType;
    CommandType::Pointer command = CommandType::New();
    registration->AddObserver( itk::IterationEvent(), command );
    registration->SetNumberOfLevels( 3 );
  
    try
      {
      registration->StartRegistration();
      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;
      }
  
    std::cout << "Optimizer Stopping Condition = "
              << optimizer->GetStopCondition() << std::endl;
  
    typedef RegistrationType::ParametersType ParametersType;
    ParametersType finalParameters = registration->GetLastTransformParameters();
  
    double TranslationAlongX = finalParameters[4];
    double TranslationAlongY = finalParameters[5];
  
    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
    //
    //  Let's execute this example using the same multi-modality images as
    //  before.  The registration converges after $5$ iterations in the first
    //  level, $7$ in the second level and $4$ in the third level. The final
    //  results when printed as an array of parameters are
    //
    //  \begin{verbatim}
    // [1.00164, 0.00147688, 0.00168372, 1.0027, 12.6296, 16.4768]
    //  \end{verbatim}
    //
    //  By reordering them as coefficient of matrix $\bf{M}$ and vector $\bf{T}$
    //  they can now be seen as
    //
    //  \begin{equation}
    //  M =
    //  \left[
    //  \begin{array}{cc}
    //  1.00164 & 0.0014 \\ 0.00168 & 1.0027 \\  \end{array}
    //  \right]
    //  \mbox{ and }
    //  T =
    //  \left[
    //  \begin{array}{c}
    //  12.6296  \\  16.4768  \\  \end{array}
    //  \right]
    //  \end{equation}
    //
    //  In this form, it is easier to interpret the effect of the
    //  transform. The matrix $\bf{M}$ is responsible for scaling, rotation and
    //  shearing while $\bf{T}$ is responsible for translations.  It can be seen
    //  that the translation values in this case closely match the true
    //  misalignment introduced in the moving image.
    //
    //  It is important to note that once the images are registered at a
    //  sub-pixel level, any further improvement of the registration relies
    //  heavily on the quality of the interpolator. It may then be reasonable to
    //  use a coarse and fast interpolator in the lower resolution levels and
    //  switch to a high-quality but slow interpolator in the final resolution
    //  level.
    //
    //  Software Guide : EndLatex
  
  
    typedef itk::ResampleImageFilter<
                              MovingImageType,
                              FixedImageType >    ResampleFilterType;
  
    TransformType::Pointer finalTransform = TransformType::New();
  
    finalTransform->SetParameters( finalParameters );
    finalTransform->SetFixedParameters( transform->GetFixedParameters() );
  
    ResampleFilterType::Pointer resample = ResampleFilterType::New();
  
    resample->SetTransform( finalTransform );
    resample->SetInput( movingImageReader->GetOutput() );
  
    FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
  
    PixelType backgroundGrayLevel = 100;
    if( argc > 4 )
      {
      backgroundGrayLevel = atoi( argv[4] );
      }
  
    resample->SetSize(    fixedImage->GetLargestPossibleRegion().GetSize() );
    resample->SetOutputOrigin(  fixedImage->GetOrigin() );
    resample->SetOutputSpacing( fixedImage->GetSpacing() );
    resample->SetOutputDirection( fixedImage->GetDirection() );
    resample->SetDefaultPixelValue( backgroundGrayLevel );
  
  
    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();
  
  
    //  Software Guide : BeginLatex
    //
    // \begin{figure}
    // \center
    // \includegraphics[width=0.32\textwidth]{MultiResImageRegistration2Output.eps}
    // \includegraphics[width=0.32\textwidth]{MultiResImageRegistration2CheckerboardBefore.eps}
    // \includegraphics[width=0.32\textwidth]{MultiResImageRegistration2CheckerboardAfter.eps}
    // \itkcaption[Multi-Resolution Registration Input Images]{Mapped moving image
    // (left) and composition of fixed and moving images before (center) and
    // after (right) multi-resolution registration with the AffineTransform class.}
    // \label{fig:MultiResImageRegistration2Output}
    // \end{figure}
    //
    //  The result of resampling the moving image is shown in the left image
    //  of Figure \ref{fig:MultiResImageRegistration2Output}. The center and
    //  right images of the figure present a checkerboard composite of the fixed
    //  and moving images before and after registration.
    //
    //  Software Guide : EndLatex
  
    //  Software Guide : BeginLatex
    //
    // \begin{figure}
    // \center
    // \includegraphics[height=0.44\textwidth]{MultiResImageRegistration2TraceTranslations.eps}
    // \includegraphics[height=0.44\textwidth]{MultiResImageRegistration2TraceMetric.eps}
    // \itkcaption[Multi-Resolution Registration output plots]{Sequence of
    // translations and metric values at each iteration of the optimizer for
    // multi-resolution with the AffineTransform class.}
    // \label{fig:MultiResImageRegistration2Trace}
    // \end{figure}
    //
    //  Figure \ref{fig:MultiResImageRegistration2Trace} (left) presents the
    //  sequence of translations followed by the optimizer as it searched the
    //  parameter space. The right side of the same figure shows the sequence of
    //  metric values computed as the optimizer explored the parameter space.
    //
    //  Software Guide : EndLatex
  
    //
    // Generate checkerboards before and after registration
    //
    typedef itk::CheckerBoardImageFilter< FixedImageType > CheckerBoardFilterType;
  
    CheckerBoardFilterType::Pointer checker = CheckerBoardFilterType::New();
  
    checker->SetInput1( fixedImage );
    checker->SetInput2( resample->GetOutput() );
  
    caster->SetInput( checker->GetOutput() );
    writer->SetInput( caster->GetOutput()   );
  
    resample->SetDefaultPixelValue( 0 );
  
    // Write out checkerboard outputs
    // Before registration
    TransformType::Pointer identityTransform = TransformType::New();
    identityTransform->SetIdentity();
    resample->SetTransform( identityTransform );
  
    if( argc > 5 )
      {
      writer->SetFileName( argv[5] );
      writer->Update();
      }
  
  
    // After registration
    resample->SetTransform( finalTransform );
    if( argc > 6 )
      {
      writer->SetFileName( argv[6] );
      writer->Update();
      }
  
  
    return EXIT_SUCCESS;
  }
  