/*=========================================================================
 *
 *  Copyright UMC Utrecht and contributors
 *
 *  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.
 *
 *=========================================================================*/
/*=========================================================================

  Program:   Insight Segmentation & Registration Toolkit
  Module:    $RCSfile: itkMultiOrderBSplineDecompositionImageFilter.txx,v $
  Date:      $Date: 2010-03-19 07:06:01 $
  Version:   $Revision: 1.14 $

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

  Portions of this code are covered under the VTK copyright.
  See VTKCopyright.txt or http://www.kitware.com/VTKCopyright.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.

=========================================================================*/
#ifndef itkMultiOrderBSplineDecompositionImageFilter_hxx
#define itkMultiOrderBSplineDecompositionImageFilter_hxx

#include "itkMultiOrderBSplineDecompositionImageFilter.h"
#include "itkImageRegionConstIteratorWithIndex.h"
#include "itkImageRegionIterator.h"
#include "itkProgressReporter.h"
#include "itkVector.h"

namespace itk
{

/**
 * Constructor
 */
template <typename TInputImage, typename TOutputImage>
MultiOrderBSplineDecompositionImageFilter<TInputImage, TOutputImage>::MultiOrderBSplineDecompositionImageFilter()
{
  int splineOrder = 3;
  m_Tolerance = 1e-10; // Need some guidance on this one...what is reasonable?
  m_IteratorDirection = 0;
  this->SetSplineOrder(splineOrder);
}


/**
 * Standard "PrintSelf" method
 */
template <typename TInputImage, typename TOutputImage>
void
MultiOrderBSplineDecompositionImageFilter<TInputImage, TOutputImage>::PrintSelf(std::ostream & os, Indent indent) const
{
  Superclass::PrintSelf(os, indent);
  os << indent << "Spline Order: ";
  for (unsigned int d = 0; d < ImageDimension; ++d)
  {
    os << m_SplineOrder[d];
    if (d != ImageDimension - 1)
    {
      os << ", ";
    }
  }
  os << std::endl;
}


template <typename TInputImage, typename TOutputImage>
bool
MultiOrderBSplineDecompositionImageFilter<TInputImage, TOutputImage>::DataToCoefficients1D()
{

  // See Unser, 1993, Part II, Equation 2.5,
  //   or Unser, 1999, Box 2. for an explaination.

  double c0 = 1.0;

  if (m_DataLength[m_IteratorDirection] == 1) // Required by mirror boundaries
  {
    return false;
  }

  // Compute overall gain
  for (int k = 0; k < m_NumberOfPoles; ++k)
  {
    // Note for cubic splines lambda = 6
    c0 = c0 * (1.0 - m_SplinePoles[k]) * (1.0 - 1.0 / m_SplinePoles[k]);
  }

  // apply the gain
  for (SizeValueType n = 0; n < m_DataLength[m_IteratorDirection]; ++n)
  {
    m_Scratch[n] *= c0;
  }

  // loop over all poles
  for (int k = 0; k < m_NumberOfPoles; ++k)
  {
    // causal initialization
    this->SetInitialCausalCoefficient(m_SplinePoles[k]);
    // causal recursion
    for (SizeValueType n = 1; n < m_DataLength[m_IteratorDirection]; ++n)
    {
      m_Scratch[n] += m_SplinePoles[k] * m_Scratch[n - 1];
    }

    // anticausal initialization
    this->SetInitialAntiCausalCoefficient(m_SplinePoles[k]);
    // anticausal recursion
    for (int n = m_DataLength[m_IteratorDirection] - 2; 0 <= n; n--)
    {
      m_Scratch[n] = m_SplinePoles[k] * (m_Scratch[n + 1] - m_Scratch[n]);
    }
  }
  return true;
}


template <typename TInputImage, typename TOutputImage>
void
MultiOrderBSplineDecompositionImageFilter<TInputImage, TOutputImage>::SetSplineOrder(unsigned int order)
{
  bool notchanged = true;
  for (unsigned int d = 0; d < ImageDimension; ++d)
  {
    notchanged = notchanged && m_SplineOrder[d] == order;
  }
  if (notchanged)
  {
    return;
  }
  for (unsigned int d = 0; d < ImageDimension; ++d)
  {
    m_SplineOrder[d] = order;
  }
  this->SetPoles(0);
  this->Modified();
}


template <typename TInputImage, typename TOutputImage>
void
MultiOrderBSplineDecompositionImageFilter<TInputImage, TOutputImage>::SetSplineOrder(unsigned int dimension,
                                                                                     unsigned int order)
{
  if (m_SplineOrder[dimension] == order)
  {
    return;
  }
  m_SplineOrder[dimension] = order;
  this->SetPoles(dimension);
  this->Modified();
}


template <typename TInputImage, typename TOutputImage>
void
MultiOrderBSplineDecompositionImageFilter<TInputImage, TOutputImage>::SetPoles(unsigned int dimension)
{
  /* See Unser, 1997. Part II, Table I for Pole values */
  // See also, Handbook of Medical Imaging, Processing and Analysis, Ed. Isaac N. Bankman,
  //  2000, pg. 416.
  switch (m_SplineOrder[dimension])
  {
    case 3:
      m_NumberOfPoles = 1;
      m_SplinePoles[0] = std::sqrt(3.0) - 2.0;
      break;
    case 0:
      m_NumberOfPoles = 0;
      break;
    case 1:
      m_NumberOfPoles = 0;
      break;
    case 2:
      m_NumberOfPoles = 1;
      m_SplinePoles[0] = std::sqrt(8.0) - 3.0;
      break;
    case 4:
      m_NumberOfPoles = 2;
      m_SplinePoles[0] = std::sqrt(664.0 - std::sqrt(438976.0)) + std::sqrt(304.0) - 19.0;
      m_SplinePoles[1] = std::sqrt(664.0 + std::sqrt(438976.0)) - std::sqrt(304.0) - 19.0;
      break;
    case 5:
      m_NumberOfPoles = 2;
      m_SplinePoles[0] = std::sqrt(135.0 / 2.0 - std::sqrt(17745.0 / 4.0)) + std::sqrt(105.0 / 4.0) - 13.0 / 2.0;
      m_SplinePoles[1] = std::sqrt(135.0 / 2.0 + std::sqrt(17745.0 / 4.0)) - std::sqrt(105.0 / 4.0) - 13.0 / 2.0;
      break;
    default:
      // SplineOrder not implemented yet.
      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 <typename TInputImage, typename TOutputImage>
void
MultiOrderBSplineDecompositionImageFilter<TInputImage, TOutputImage>::SetInitialCausalCoefficient(double z)
{
  // See Unser, 1999, Box 2 for explanation

  double zn = z;

  if (m_Tolerance > 0.0)
  {
    if (const auto horizon = static_cast<SizeValueType>(std::ceil(std::log(m_Tolerance) / std::log(std::abs(z))));
        horizon < m_DataLength[m_IteratorDirection])
    {
      // Accelerated loop
      CoeffType sum = m_Scratch[0]; // verify this
      for (SizeValueType n = 1; n < horizon; ++n)
      {
        sum += zn * m_Scratch[n];
        zn *= z;
      }
      m_Scratch[0] = sum;

      // Return early.
      return;
    }
  }

  // Full loop
  const double iz = 1.0 / z;
  double       z2n = std::pow(z, static_cast<double>(m_DataLength[m_IteratorDirection] - 1));
  CoeffType    sum = m_Scratch[0] + z2n * m_Scratch[m_DataLength[m_IteratorDirection] - 1];
  z2n *= z2n * iz;
  for (SizeValueType n = 1; n <= (m_DataLength[m_IteratorDirection] - 2); ++n)
  {
    sum += (zn + z2n) * m_Scratch[n];
    zn *= z;
    z2n *= iz;
  }
  m_Scratch[0] = sum / (1.0 - zn * zn);
}


template <typename TInputImage, typename TOutputImage>
void
MultiOrderBSplineDecompositionImageFilter<TInputImage, TOutputImage>::SetInitialAntiCausalCoefficient(double z)
{
  // this initialization corresponds to mirror boundaries
  /* See Unser, 1999, Box 2 for explaination */
  //  Also see erratum at http://bigwww.epfl.ch/publications/unser9902.html
  m_Scratch[m_DataLength[m_IteratorDirection] - 1] =
    (z / (z * z - 1.0)) *
    (z * m_Scratch[m_DataLength[m_IteratorDirection] - 2] + m_Scratch[m_DataLength[m_IteratorDirection] - 1]);
}


template <typename TInputImage, typename TOutputImage>
void
MultiOrderBSplineDecompositionImageFilter<TInputImage, TOutputImage>::DataToCoefficientsND()
{
  OutputImagePointer output = this->GetOutput();

  Size<ImageDimension> size = output->GetBufferedRegion().GetSize();

  unsigned int count = output->GetBufferedRegion().GetNumberOfPixels() / size[0] * ImageDimension;

  ProgressReporter progress(this, 0, count, 10);

  // Initialize coeffient array
  this->CopyImageToImage(); // Coefficients are initialized to the input data

  for (unsigned int n = 0; n < ImageDimension; ++n)
  {
    m_IteratorDirection = n;
    // Loop through each dimension

    // Compute poles for this dimension
    this->SetPoles(n);

    // Initialize iterators
    OutputLinearIterator CIterator(output, output->GetBufferedRegion());
    CIterator.SetDirection(m_IteratorDirection);
    // For each data vector
    while (!CIterator.IsAtEnd())
    {
      // Copy coefficients to scratch
      this->CopyCoefficientsToScratch(CIterator);

      // Perform 1D BSpline calculations
      this->DataToCoefficients1D();

      // Copy scratch back to coefficients.
      // Brings us back to the end of the line we were working on.
      CIterator.GoToBeginOfLine();
      this->CopyScratchToCoefficients(CIterator); // m_Scratch = m_Image;
      CIterator.NextLine();
      progress.CompletedPixel();
    }
  }
}


/**
 * Copy the input image into the output image
 */
template <typename TInputImage, typename TOutputImage>
void
MultiOrderBSplineDecompositionImageFilter<TInputImage, TOutputImage>::CopyImageToImage()
{
  using InputIterator = ImageRegionConstIteratorWithIndex<TInputImage>;
  using OutputIterator = ImageRegionIterator<TOutputImage>;
  using OutputPixelType = typename TOutputImage::PixelType;

  InputIterator  inIt(this->GetInput(), this->GetInput()->GetBufferedRegion());
  OutputIterator outIt(this->GetOutput(), this->GetOutput()->GetBufferedRegion());

  while (!outIt.IsAtEnd())
  {
    outIt.Set(static_cast<OutputPixelType>(inIt.Get()));
    ++inIt;
    ++outIt;
  }
}


/**
 * Copy the scratch to one line of the output image
 */
template <typename TInputImage, typename TOutputImage>
void
MultiOrderBSplineDecompositionImageFilter<TInputImage, TOutputImage>::CopyScratchToCoefficients(
  OutputLinearIterator & Iter)
{
  using OutputPixelType = typename TOutputImage::PixelType;
  unsigned long j = 0;
  while (!Iter.IsAtEndOfLine())
  {
    Iter.Set(static_cast<OutputPixelType>(m_Scratch[j]));
    ++Iter;
    ++j;
  }
}


/**
 * Copy one line of the output image to the scratch
 */
template <typename TInputImage, typename TOutputImage>
void
MultiOrderBSplineDecompositionImageFilter<TInputImage, TOutputImage>::CopyCoefficientsToScratch(
  OutputLinearIterator & Iter)
{
  unsigned long j = 0;
  while (!Iter.IsAtEndOfLine())
  {
    m_Scratch[j] = static_cast<CoeffType>(Iter.Get());
    ++Iter;
    ++j;
  }
}


/**
 * GenerateInputRequestedRegion method.
 */
template <typename TInputImage, typename TOutputImage>
void
MultiOrderBSplineDecompositionImageFilter<TInputImage, TOutputImage>::GenerateInputRequestedRegion()
{
  // this filter requires the all of the input image to be in
  // the buffer
  InputImagePointer inputPtr = const_cast<TInputImage *>(this->GetInput());
  if (inputPtr)
  {
    inputPtr->SetRequestedRegionToLargestPossibleRegion();
  }
}


/**
 * EnlargeOutputRequestedRegion method.
 */
template <typename TInputImage, typename TOutputImage>
void
MultiOrderBSplineDecompositionImageFilter<TInputImage, TOutputImage>::EnlargeOutputRequestedRegion(DataObject * output)
{

  // this filter requires the all of the output image to be in
  // the buffer
  TOutputImage * imgData = dynamic_cast<TOutputImage *>(output);
  if (imgData)
  {
    imgData->SetRequestedRegionToLargestPossibleRegion();
  }
}


/**
 * Generate data
 */
template <typename TInputImage, typename TOutputImage>
void
MultiOrderBSplineDecompositionImageFilter<TInputImage, TOutputImage>::GenerateData()
{

  // Allocate scratch memory
  InputImageConstPointer inputPtr = this->GetInput();
  m_DataLength = inputPtr->GetBufferedRegion().GetSize();

  unsigned long maxLength = 0;
  for (unsigned int n = 0; n < ImageDimension; ++n)
  {
    if (m_DataLength[n] > maxLength)
    {
      maxLength = m_DataLength[n];
    }
  }
  m_Scratch.resize(maxLength);

  // Allocate memory for output image
  OutputImagePointer outputPtr = this->GetOutput();
  outputPtr->SetBufferedRegion(outputPtr->GetRequestedRegion());
  outputPtr->Allocate();

  // Calculate actual output
  this->DataToCoefficientsND();

  // Clean up
  m_Scratch.clear();
}


} // namespace itk

#endif