libotb-dev 5.8.0+dfsg-3 / usr / include / OTB-5.8 / otbBSplineInterpolateImageFunction.txx

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

  Program:   ORFEO Toolbox
  Language:  C++
  Date:      $Date$
  Version:   $Revision$


  Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
  See OTBCopyright.txt 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.

=========================================================================*/
#ifndef otbBSplineInterpolateImageFunction_txx
#define otbBSplineInterpolateImageFunction_txx
#include "otbBSplineInterpolateImageFunction.h"
#include "itkImageLinearIteratorWithIndex.h"
#include "itkImageRegionConstIteratorWithIndex.h"
#include "itkImageRegionIterator.h"

#include "itkVector.h"

#include "itkMatrix.h"

namespace otb
{

/**
 * Constructor
 */
template <class TImageType, class TCoordRep, class TCoefficientType>
BSplineInterpolateImageFunction<TImageType, TCoordRep, TCoefficientType>
::BSplineInterpolateImageFunction()
{
  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);
}

/**
 * Standard "PrintSelf" method
 */
template <class TImageType, class TCoordRep, class TCoefficientType>
void
BSplineInterpolateImageFunction<TImageType, TCoordRep, TCoefficientType>
::PrintSelf(
  std::ostream& os,
  itk::Indent indent) const
{
  Superclass::PrintSelf(os, indent);
  os << indent << "Spline Order: " << m_SplineOrder << std::endl;

}

template <class TImageType, class TCoordRep, class TCoefficientType>
void
BSplineInterpolateImageFunction<TImageType, TCoordRep, TCoefficientType>
::UpdateCoefficientsFilter(void)
{
  m_CoefficientFilter->GetOutput()->UpdateOutputInformation();
  m_CoefficientFilter->GetOutput()->SetRequestedRegion(m_CoefficientFilter->GetInput()->GetBufferedRegion());
  m_CoefficientFilter->GetOutput()->PropagateRequestedRegion();
  m_CoefficientFilter->GetOutput()->UpdateOutputData();
  m_Coefficients = m_CoefficientFilter->GetOutput();
  m_CurrentBufferedRegion = m_CoefficientFilter->GetInput()->GetBufferedRegion();
}
template <class TImageType, class TCoordRep, class TCoefficientType>
void
BSplineInterpolateImageFunction<TImageType, TCoordRep, TCoefficientType>
::SetInputImage(const TImageType * inputData)
{
  if (inputData)
    {
    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?

    UpdateCoefficientsFilter();

    // 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 = ITK_NULLPTR;
    }
}

template <class TImageType, class TCoordRep, class TCoefficientType>
void
BSplineInterpolateImageFunction<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
BSplineInterpolateImageFunction<TImageType, TCoordRep, TCoefficientType>
::OutputType
BSplineInterpolateImageFunction<TImageType, TCoordRep, TCoefficientType>
::EvaluateAtContinuousIndex(const ContinuousIndexType& x) const
{
  //UpdateCoefficientsFilter();
  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);

  //std::cout<<"EvaluateIndex: "<<std::endl;
  //std::cout<<EvaluateIndex[0][0]<<"\t"<<EvaluateIndex[0][1]<<"\t"
  //     <<EvaluateIndex[0][2]<<"\t"<<EvaluateIndex[0][3]<<std::endl;
  //std::cout<<EvaluateIndex[1][0]<<"\t"<<EvaluateIndex[1][1]<<"\t"
  //     <<EvaluateIndex[1][2]<<"\t"<<EvaluateIndex[1][3]<<std::endl;

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

  // perform interpolation
  double    interpolated = 0.0;
  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.
      //std::cout<<"From inside: "<<n<<" "<<p<<" "<<m_PointsToIndex[p][n]<<" "<< EvaluateIndex[n][m_PointsToIndex[p][n]]<<std::endl;
      }
    //std::cout<<"CoefficientIndex: "<<coefficientIndex<<std::endl;
    // Convert our step p to the appropriate point in ND space in the
    // m_Coefficients cube.
    interpolated += w * m_Coefficients->GetPixel(coefficientIndex);
    }

  /*  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
BSplineInterpolateImageFunction<TImageType, TCoordRep, TCoefficientType>
::CovariantVectorType
BSplineInterpolateImageFunction<TImageType, TCoordRep, TCoefficientType>
::EvaluateDerivativeAtContinuousIndex(const ContinuousIndexType& x) const
{
  UpdateCoefficientsFilter();
  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);

  // 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] /= this->GetInputImage()->GetSpacing()[n];   // take spacing into account
    }

  return (derivativeValue);

}

template <class TImageType, class TCoordRep, class TCoefficientType>
void
BSplineInterpolateImageFunction<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
BSplineInterpolateImageFunction<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 possibility 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
BSplineInterpolateImageFunction<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
BSplineInterpolateImageFunction<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) vcl_floor((float) x[n]) - splineOrder / 2;
      //std::cout<<"x: "<<x<<std::endl;
      //std::cout<<"splineOrder: "<<splineOrder<<std::endl;
      //std::cout<<"indx: "<<indx<<std::endl;
      for (unsigned int k = 0; k <= splineOrder; ++k)
        {
        evaluateIndex[n][k] = indx++;
        }
      }
    else                       // Use this index calculation for even splineOrder
      {

      indx = (long) vcl_floor((float) (x[n] + 0.5)) - splineOrder / 2;
      //std::cout<<"x: "<<x<<std::endl;
      //std::cout<<"splineOrder: "<<splineOrder<<std::endl;
      //std::cout<<"indx: "<<indx<<std::endl;
      for (unsigned int k = 0; k <= splineOrder; ++k)
        {
        evaluateIndex[n][k] = indx++;
        }
      }
    }
}

template <class TImageType, class TCoordRep, class TCoefficientType>
void
BSplineInterpolateImageFunction<TImageType, TCoordRep, TCoefficientType>
::ApplyMirrorBoundaryConditions(vnl_matrix<long>& evaluateIndex,
                                unsigned int splineOrder) const
{

  for (unsigned int n = 0; n < ImageDimension; ++n)
    {
    long dataLength =  m_DataLength[n];
    long dataOffset = m_CurrentBufferedRegion.GetIndex()[n];

    // 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] < dataOffset) ? (dataOffset + (dataOffset - evaluateIndex[n][k]) % dataLength)
          : (evaluateIndex[n][k]);
        if ((long) dataLength + dataOffset <= evaluateIndex[n][k])
          {
          evaluateIndex[n][k] = dataOffset + dataLength - (evaluateIndex[n][k] - dataOffset - dataLength) % dataLength;
          }
        }

      }

    }
}

} // namespace otb

#endif