/*=========================================================================
  Program:   vv                     http://www.creatis.insa-lyon.fr/rio/vv

  Authors belong to:
  - University of LYON              http://www.universite-lyon.fr/
  - Léon Bérard cancer center       http://www.centreleonberard.fr
  - CREATIS CNRS laboratory         http://www.creatis.insa-lyon.fr

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

  It is distributed under dual licence

  - BSD        See included LICENSE.txt file
  - CeCILL-B   http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html
===========================================================================**/
#ifndef _clitkVectorBSplineInterpolateImageFunction_txx
#define _clitkVectorBSplineInterpolateImageFunction_txx
#include "itkConfigure.h"

// Second, redirect to the optimized version if necessary
// #ifdef ITK_USE_OPTIMIZED_REGISTRATION_METHODS
// #include "itkOptVectorBSplineInterpolateImageFunction.txx"
// #else

#include "clitkVectorBSplineInterpolateImageFunction.h"


#include "itkImageLinearIteratorWithIndex.h"
#include "itkImageRegionConstIteratorWithIndex.h"
#include "itkImageRegionIterator.h"

#include "itkVector.h"

#include "itkMatrix.h"

namespace clitk
{

/**
 * Constructor
 */
template <class TImageType, class TCoordRep, class TCoefficientType>
VectorBSplineInterpolateImageFunction<TImageType,TCoordRep,TCoefficientType>
::VectorBSplineInterpolateImageFunction()
{
  m_SplineOrder = 0;
  unsigned int SplineOrder = 3;
  m_CoefficientFilter = CoefficientFilter::New();
  // ***TODO: Should we store coefficients in a variable or retrieve from filter?
  m_Coefficients = CoefficientImageType::New();
  this->SetSplineOrder(SplineOrder);
  this->m_UseImageDirection = false;
}

/**
 * Standard "PrintSelf" method
 */
template <class TImageType, class TCoordRep, class TCoefficientType>
void
VectorBSplineInterpolateImageFunction<TImageType,TCoordRep,TCoefficientType>
::PrintSelf(
  std::ostream& os,
  itk::Indent indent) const
{
  Superclass::PrintSelf( os, indent );
  os << indent << "Spline Order: " << m_SplineOrder << std::endl;
  os << indent << "UseImageDirection = "
     << (this->m_UseImageDirection ? "On" : "Off") << std::endl;

}


template <class TImageType, class TCoordRep, class TCoefficientType>
void
VectorBSplineInterpolateImageFunction<TImageType,TCoordRep,TCoefficientType>
::SetInputImage(const TImageType * inputData)
{
  if ( inputData ) {

    DD("calling decomposition filter");
    m_CoefficientFilter->SetInput(inputData);

    // the Coefficient Filter requires that the spline order and the input data be set.
    // TODO:  We need to ensure that this is only run once and only after both input and
    //        spline order have been set. Should we force an update after the
    //        splineOrder has been set also?

    m_CoefficientFilter->Update();
    m_Coefficients = m_CoefficientFilter->GetOutput();


    // Call the Superclass implementation after, in case the filter
    // pulls in  more of the input image
    Superclass::SetInputImage(inputData);

    m_DataLength = inputData->GetBufferedRegion().GetSize();

  } else {
    m_Coefficients = NULL;
  }
}


template <class TImageType, class TCoordRep, class TCoefficientType>
void
VectorBSplineInterpolateImageFunction<TImageType,TCoordRep,TCoefficientType>
::SetSplineOrder(unsigned int SplineOrder)
{
  if (SplineOrder == m_SplineOrder) {
    return;
  }
  m_SplineOrder = SplineOrder;
  m_CoefficientFilter->SetSplineOrder( SplineOrder );

  //this->SetPoles();/
  m_MaxNumberInterpolationPoints = 1;
  for (unsigned int n=0; n < ImageDimension; n++) {
    m_MaxNumberInterpolationPoints *= ( m_SplineOrder + 1);
  }
  this->GeneratePointsToIndex( );
}


template <class TImageType, class TCoordRep, class TCoefficientType>
typename
VectorBSplineInterpolateImageFunction<TImageType,TCoordRep,TCoefficientType>
::OutputType
VectorBSplineInterpolateImageFunction<TImageType,TCoordRep,TCoefficientType>
::EvaluateAtContinuousIndex( const ContinuousIndexType & x ) const
{
  vnl_matrix<long>        EvaluateIndex(ImageDimension, ( m_SplineOrder + 1 ));

  // compute the interpolation indexes
  this->DetermineRegionOfSupport(EvaluateIndex, x, m_SplineOrder);

  // Determine weights
  vnl_matrix<double>        weights(ImageDimension, ( m_SplineOrder + 1 ));
  SetInterpolationWeights( x, EvaluateIndex, weights, m_SplineOrder );

  // Modify EvaluateIndex at the boundaries using mirror boundary conditions
  this->ApplyMirrorBoundaryConditions(EvaluateIndex, m_SplineOrder);

  // perform interpolation
  //JV
  itk::Vector<double, VectorDimension> interpolated;
  for (unsigned int i=0; i< VectorDimension; i++) interpolated[i]=itk::NumericTraits<double>::Zero;

  IndexType coefficientIndex;
  // Step through eachpoint in the N-dimensional interpolation cube.
  for (unsigned int p = 0; p < m_MaxNumberInterpolationPoints; p++) {
    // translate each step into the N-dimensional index.
    //      IndexType pointIndex = PointToIndex( p );

    double w = 1.0;
    for (unsigned int n = 0; n < ImageDimension; n++ ) {

      w *= weights[n][ m_PointsToIndex[p][n] ];
      coefficientIndex[n] = EvaluateIndex[n][m_PointsToIndex[p][n]];  // Build up ND index for coefficients.
    }
    // Convert our step p to the appropriate point in ND space in the
    // m_Coefficients cube.
    //JV shouldn't be necessary
    for (unsigned int i=0; i<VectorDimension; i++)
      interpolated[i] += w * m_Coefficients->GetPixel(coefficientIndex)[i];
  }

  /*  double interpolated = 0.0;
    IndexType coefficientIndex;
    // Step through eachpoint in the N-dimensional interpolation cube.
    for (unsigned int sp = 0; sp <= m_SplineOrder; sp++)
      {
      for (unsigned int sp1=0; sp1 <= m_SplineOrder; sp1++)
        {

        double w = 1.0;
        for (unsigned int n1 = 0; n1 < ImageDimension; n1++ )
          {
          w *= weights[n1][ sp1 ];
          coefficientIndex[n1] = EvaluateIndex[n1][sp];  // Build up ND index for coefficients.
          }

          interpolated += w * m_Coefficients->GetPixel(coefficientIndex);
        }
      }
  */
  return(interpolated);

}


template <class TImageType, class TCoordRep, class TCoefficientType>
typename
VectorBSplineInterpolateImageFunction<TImageType,TCoordRep,TCoefficientType>
:: CovariantVectorType
VectorBSplineInterpolateImageFunction<TImageType,TCoordRep,TCoefficientType>
::EvaluateDerivativeAtContinuousIndex( const ContinuousIndexType & x ) const
{
  vnl_matrix<long>        EvaluateIndex(ImageDimension, ( m_SplineOrder + 1 ));

  // compute the interpolation indexes
  // TODO: Do we need to revisit region of support for the derivatives?
  this->DetermineRegionOfSupport(EvaluateIndex, x, m_SplineOrder);

  // Determine weights
  vnl_matrix<double>        weights(ImageDimension, ( m_SplineOrder + 1 ));
  SetInterpolationWeights( x, EvaluateIndex, weights, m_SplineOrder );

  vnl_matrix<double>        weightsDerivative(ImageDimension, ( m_SplineOrder + 1));
  SetDerivativeWeights( x, EvaluateIndex, weightsDerivative, ( m_SplineOrder ) );

  // Modify EvaluateIndex at the boundaries using mirror boundary conditions
  this->ApplyMirrorBoundaryConditions(EvaluateIndex, m_SplineOrder);

  const InputImageType * inputImage = this->GetInputImage();
  const typename InputImageType::SpacingType & spacing = inputImage->GetSpacing();

  // Calculate derivative
  CovariantVectorType derivativeValue;
  double tempValue;
  IndexType coefficientIndex;
  for (unsigned int n = 0; n < ImageDimension; n++) {
    derivativeValue[n] = 0.0;
    for (unsigned int p = 0; p < m_MaxNumberInterpolationPoints; p++) {
      tempValue = 1.0 ;
      for (unsigned int n1 = 0; n1 < ImageDimension; n1++) {
        //coefficientIndex[n1] = EvaluateIndex[n1][sp];
        coefficientIndex[n1] = EvaluateIndex[n1][m_PointsToIndex[p][n1]];

        if (n1 == n) {
          //w *= weights[n][ m_PointsToIndex[p][n] ];
          tempValue *= weightsDerivative[n1][ m_PointsToIndex[p][n1] ];
        } else {
          tempValue *= weights[n1][ m_PointsToIndex[p][n1] ];
        }
      }
      derivativeValue[n] += m_Coefficients->GetPixel(coefficientIndex) * tempValue ;
    }
    derivativeValue[n] /= spacing[n];   // take spacing into account
  }

#ifdef ITK_USE_ORIENTED_IMAGE_DIRECTION
  if( this->m_UseImageDirection ) {
    CovariantVectorType orientedDerivative;
    inputImage->TransformLocalVectorToPhysicalVector( derivativeValue, orientedDerivative );
    return orientedDerivative;
  }
#endif

  return(derivativeValue);
}


template <class TImageType, class TCoordRep, class TCoefficientType>
void
VectorBSplineInterpolateImageFunction<TImageType,TCoordRep,TCoefficientType>
::SetInterpolationWeights( const ContinuousIndexType & x, const vnl_matrix<long> & EvaluateIndex,
                           vnl_matrix<double> & weights, unsigned int splineOrder ) const
{
  // For speed improvements we could make each case a separate function and use
  // function pointers to reference the correct weight order.
  // Left as is for now for readability.
  double w, w2, w4, t, t0, t1;

  switch (splineOrder) {
  case 3:
    for (unsigned int n = 0; n < ImageDimension; n++) {
      w = x[n] - (double) EvaluateIndex[n][1];
      weights[n][3] = (1.0 / 6.0) * w * w * w;
      weights[n][0] = (1.0 / 6.0) + 0.5 * w * (w - 1.0) - weights[n][3];
      weights[n][2] = w + weights[n][0] - 2.0 * weights[n][3];
      weights[n][1] = 1.0 - weights[n][0] - weights[n][2] - weights[n][3];
    }
    break;
  case 0:
    for (unsigned int n = 0; n < ImageDimension; n++) {
      weights[n][0] = 1; // implements nearest neighbor
    }
    break;
  case 1:
    for (unsigned int n = 0; n < ImageDimension; n++) {
      w = x[n] - (double) EvaluateIndex[n][0];
      weights[n][1] = w;
      weights[n][0] = 1.0 - w;
    }
    break;
  case 2:
    for (unsigned int n = 0; n < ImageDimension; n++) {
      /* x */
      w = x[n] - (double)EvaluateIndex[n][1];
      weights[n][1] = 0.75 - w * w;
      weights[n][2] = 0.5 * (w - weights[n][1] + 1.0);
      weights[n][0] = 1.0 - weights[n][1] - weights[n][2];
    }
    break;
  case 4:
    for (unsigned int n = 0; n < ImageDimension; n++) {
      /* x */
      w = x[n] - (double)EvaluateIndex[n][2];
      w2 = w * w;
      t = (1.0 / 6.0) * w2;
      weights[n][0] = 0.5 - w;
      weights[n][0] *= weights[n][0];
      weights[n][0] *= (1.0 / 24.0) * weights[n][0];
      t0 = w * (t - 11.0 / 24.0);
      t1 = 19.0 / 96.0 + w2 * (0.25 - t);
      weights[n][1] = t1 + t0;
      weights[n][3] = t1 - t0;
      weights[n][4] = weights[n][0] + t0 + 0.5 * w;
      weights[n][2] = 1.0 - weights[n][0] - weights[n][1] - weights[n][3] - weights[n][4];
    }
    break;
  case 5:
    for (unsigned int n = 0; n < ImageDimension; n++) {
      /* x */
      w = x[n] - (double)EvaluateIndex[n][2];
      w2 = w * w;
      weights[n][5] = (1.0 / 120.0) * w * w2 * w2;
      w2 -= w;
      w4 = w2 * w2;
      w -= 0.5;
      t = w2 * (w2 - 3.0);
      weights[n][0] = (1.0 / 24.0) * (1.0 / 5.0 + w2 + w4) - weights[n][5];
      t0 = (1.0 / 24.0) * (w2 * (w2 - 5.0) + 46.0 / 5.0);
      t1 = (-1.0 / 12.0) * w * (t + 4.0);
      weights[n][2] = t0 + t1;
      weights[n][3] = t0 - t1;
      t0 = (1.0 / 16.0) * (9.0 / 5.0 - t);
      t1 = (1.0 / 24.0) * w * (w4 - w2 - 5.0);
      weights[n][1] = t0 + t1;
      weights[n][4] = t0 - t1;
    }
    break;
  default:
    // SplineOrder not implemented yet.
    itk::ExceptionObject err(__FILE__, __LINE__);
    err.SetLocation( ITK_LOCATION );
    err.SetDescription( "SplineOrder must be between 0 and 5. Requested spline order has not been implemented yet." );
    throw err;
    break;
  }

}

template <class TImageType, class TCoordRep, class TCoefficientType>
void
VectorBSplineInterpolateImageFunction<TImageType,TCoordRep,TCoefficientType>
::SetDerivativeWeights( const ContinuousIndexType & x, const vnl_matrix<long> & EvaluateIndex,
                        vnl_matrix<double> & weights, unsigned int splineOrder ) const
{
  // For speed improvements we could make each case a separate function and use
  // function pointers to reference the correct weight order.
  // Another possiblity would be to loop inside the case statement (reducing the number
  // of switch statement executions to one per routine call.
  // Left as is for now for readability.
  double w, w1, w2, w3, w4, w5, t, t0, t1, t2;
  int derivativeSplineOrder = (int) splineOrder -1;

  switch (derivativeSplineOrder) {

    // Calculates B(splineOrder) ( (x + 1/2) - xi) - B(splineOrder -1) ( (x - 1/2) - xi)
  case -1:
    // Why would we want to do this?
    for (unsigned int n = 0; n < ImageDimension; n++) {
      weights[n][0] = 0.0;
    }
    break;
  case 0:
    for (unsigned int n = 0; n < ImageDimension; n++) {
      weights[n][0] = -1.0;
      weights[n][1] =  1.0;
    }
    break;
  case 1:
    for (unsigned int n = 0; n < ImageDimension; n++) {
      w = x[n] + 0.5 - (double)EvaluateIndex[n][1];
      // w2 = w;
      w1 = 1.0 - w;

      weights[n][0] = 0.0 - w1;
      weights[n][1] = w1 - w;
      weights[n][2] = w;
    }
    break;
  case 2:

    for (unsigned int n = 0; n < ImageDimension; n++) {
      w = x[n] + .5 - (double)EvaluateIndex[n][2];
      w2 = 0.75 - w * w;
      w3 = 0.5 * (w - w2 + 1.0);
      w1 = 1.0 - w2 - w3;

      weights[n][0] = 0.0 - w1;
      weights[n][1] = w1 - w2;
      weights[n][2] = w2 - w3;
      weights[n][3] = w3;
    }
    break;
  case 3:

    for (unsigned int n = 0; n < ImageDimension; n++) {
      w = x[n] + 0.5 - (double)EvaluateIndex[n][2];
      w4 = (1.0 / 6.0) * w * w * w;
      w1 = (1.0 / 6.0) + 0.5 * w * (w - 1.0) - w4;
      w3 = w + w1 - 2.0 * w4;
      w2 = 1.0 - w1 - w3 - w4;

      weights[n][0] = 0.0 - w1;
      weights[n][1] = w1 - w2;
      weights[n][2] = w2 - w3;
      weights[n][3] = w3 - w4;
      weights[n][4] = w4;
    }
    break;
  case 4:
    for (unsigned int n = 0; n < ImageDimension; n++) {
      w = x[n] + .5 - (double)EvaluateIndex[n][3];
      t2 = w * w;
      t = (1.0 / 6.0) * t2;
      w1 = 0.5 - w;
      w1 *= w1;
      w1 *= (1.0 / 24.0) * w1;
      t0 = w * (t - 11.0 / 24.0);
      t1 = 19.0 / 96.0 + t2 * (0.25 - t);
      w2 = t1 + t0;
      w4 = t1 - t0;
      w5 = w1 + t0 + 0.5 * w;
      w3 = 1.0 - w1 - w2 - w4 - w5;

      weights[n][0] = 0.0 - w1;
      weights[n][1] = w1 - w2;
      weights[n][2] = w2 - w3;
      weights[n][3] = w3 - w4;
      weights[n][4] = w4 - w5;
      weights[n][5] = w5;
    }
    break;

  default:
    // SplineOrder not implemented yet.
    itk::ExceptionObject err(__FILE__, __LINE__);
    err.SetLocation( ITK_LOCATION );
    err.SetDescription( "SplineOrder (for derivatives) must be between 1 and 5. Requested spline order has not been implemented yet." );
    throw err;
    break;
  }

}


// Generates m_PointsToIndex;
template <class TImageType, class TCoordRep, class TCoefficientType>
void
VectorBSplineInterpolateImageFunction<TImageType,TCoordRep,TCoefficientType>
::GeneratePointsToIndex( )
{
  // m_PointsToIndex is used to convert a sequential location to an N-dimension
  // index vector.  This is precomputed to save time during the interpolation routine.
  m_PointsToIndex.resize(m_MaxNumberInterpolationPoints);
  for (unsigned int p = 0; p < m_MaxNumberInterpolationPoints; p++) {
    int pp = p;
    unsigned long indexFactor[ImageDimension];
    indexFactor[0] = 1;
    for (int j=1; j< static_cast<int>(ImageDimension); j++) {
      indexFactor[j] = indexFactor[j-1] * ( m_SplineOrder + 1 );
    }
    for (int j = (static_cast<int>(ImageDimension) - 1); j >= 0; j--) {
      m_PointsToIndex[p][j] = pp / indexFactor[j];
      pp = pp % indexFactor[j];
    }
  }
}

template <class TImageType, class TCoordRep, class TCoefficientType>
void
VectorBSplineInterpolateImageFunction<TImageType,TCoordRep,TCoefficientType>
::DetermineRegionOfSupport( vnl_matrix<long> & evaluateIndex,
                            const ContinuousIndexType & x,
                            unsigned int splineOrder ) const
{
  long indx;

// compute the interpolation indexes
  for (unsigned int n = 0; n< ImageDimension; n++) {
    if (splineOrder & 1) {   // Use this index calculation for odd splineOrder
      indx = (long)std::floor((float)x[n]) - splineOrder / 2;
      for (unsigned int k = 0; k <= splineOrder; k++) {
        evaluateIndex[n][k] = indx++;
      }
    } else {                   // Use this index calculation for even splineOrder
      indx = (long)std::floor((float)(x[n] + 0.5)) - splineOrder / 2;
      for (unsigned int k = 0; k <= splineOrder; k++) {
        evaluateIndex[n][k] = indx++;
      }
    }
  }
}

template <class TImageType, class TCoordRep, class TCoefficientType>
void
VectorBSplineInterpolateImageFunction<TImageType,TCoordRep,TCoefficientType>
::ApplyMirrorBoundaryConditions(vnl_matrix<long> & evaluateIndex,
                                unsigned int splineOrder) const
{
  for (unsigned int n = 0; n < ImageDimension; n++) {
    long dataLength2 = 2 * m_DataLength[n] - 2;

    // apply the mirror boundary conditions
    // TODO:  We could implement other boundary options beside mirror
    if (m_DataLength[n] == 1) {
      for (unsigned int k = 0; k <= splineOrder; k++) {
        evaluateIndex[n][k] = 0;
      }
    } else {
      for (unsigned int k = 0; k <= splineOrder; k++) {
        // btw - Think about this couldn't this be replaced with a more elagent modulus method?
        evaluateIndex[n][k] = (evaluateIndex[n][k] < 0L) ? (-evaluateIndex[n][k] - dataLength2 * ((-evaluateIndex[n][k]) / dataLength2))
                              : (evaluateIndex[n][k] - dataLength2 * (evaluateIndex[n][k] / dataLength2));
        if ((long) m_DataLength[n] <= evaluateIndex[n][k]) {
          evaluateIndex[n][k] = dataLength2 - evaluateIndex[n][k];
        }
      }
    }
  }
}

} // namespace itk

#endif

//#endif