/*=========================================================================
 *
 *  Copyright NumFOCUS
 *
 *  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
 *
 *         https://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.
 *
 *=========================================================================*/
#ifndef itkVectorGradientNDAnisotropicDiffusionFunction_hxx
#define itkVectorGradientNDAnisotropicDiffusionFunction_hxx


namespace itk
{
template <typename TImage>
double VectorGradientNDAnisotropicDiffusionFunction<TImage>::m_MIN_NORM = 1.0e-10;

template <typename TImage>
VectorGradientNDAnisotropicDiffusionFunction<TImage>::VectorGradientNDAnisotropicDiffusionFunction()
  : m_K(0.0)
{
  unsigned int i, j;
  RadiusType   r;

  for (i = 0; i < ImageDimension; ++i)
  {
    r[i] = 1;
  }
  this->SetRadius(r);

  // Dummy neighborhood used to set up the slices.
  Neighborhood<PixelType, ImageDimension> it;
  it.SetRadius(r);

  // Slice the neighborhood
  m_Center = it.Size() / 2;

  for (i = 0; i < ImageDimension; ++i)
  {
    m_Stride[i] = it.GetStride(i);
  }

  for (i = 0; i < ImageDimension; ++i)
  {
    x_slice[i] = std::slice(m_Center - m_Stride[i], 3, m_Stride[i]);
  }

  for (i = 0; i < ImageDimension; ++i)
  {
    for (j = 0; j < ImageDimension; ++j)
    {
      // For taking derivatives in the i direction that are offset one
      // pixel in the j direction.
      xa_slice[i][j] = std::slice((m_Center + m_Stride[j]) - m_Stride[i], 3, m_Stride[i]);
      xd_slice[i][j] = std::slice((m_Center - m_Stride[j]) - m_Stride[i], 3, m_Stride[i]);
    }
  }

  // Allocate the derivative operator.
  m_DerivativeOperator.SetDirection(0); // Not relevant, we'll apply in a slice-based
                                        // fashion
  m_DerivativeOperator.SetOrder(1);
  m_DerivativeOperator.CreateDirectional();
}

template <typename TImage>
auto
VectorGradientNDAnisotropicDiffusionFunction<TImage>::ComputeUpdate(const NeighborhoodType & it,
                                                                    void *,
                                                                    const FloatOffsetType &) -> PixelType
{
  unsigned int i, j, k;
  PixelType    delta;

  double GradMag;
  double GradMag_d;
  double Cx[ImageDimension];
  double Cxd[ImageDimension];

  // Remember: PixelType is a Vector of length VectorDimension.
  PixelType dx_forward[ImageDimension];
  PixelType dx_backward[ImageDimension];
  PixelType dx[ImageDimension];
  PixelType dx_aug;
  PixelType dx_dim;

  // Calculate the directional and centralized derivatives.
  for (i = 0; i < ImageDimension; ++i)
  {
    // "Half" derivatives
    dx_forward[i] = it.GetPixel(m_Center + m_Stride[i]) - it.GetPixel(m_Center);
    dx_forward[i] = dx_forward[i] * this->m_ScaleCoefficients[i];
    dx_backward[i] = it.GetPixel(m_Center) - it.GetPixel(m_Center - m_Stride[i]);
    dx_backward[i] = dx_backward[i] * this->m_ScaleCoefficients[i];

    // Centralized differences
    dx[i] = m_InnerProduct(x_slice[i], it, m_DerivativeOperator);
    dx[i] = dx[i] * this->m_ScaleCoefficients[i];
  }

  // Calculate the conductance term for each dimension.
  for (i = 0; i < ImageDimension; ++i)
  {
    // Calculate gradient magnitude approximation in this
    // dimension linked (summed) across the vector components.
    GradMag = 0.0;
    GradMag_d = 0.0;
    for (k = 0; k < VectorDimension; ++k)
    {
      GradMag += itk::Math::sqr(dx_forward[i][k]);
      GradMag_d += itk::Math::sqr(dx_backward[i][k]);

      for (j = 0; j < ImageDimension; ++j)
      {
        if (j != i)
        {
          dx_aug = m_InnerProduct(xa_slice[j][i], it, m_DerivativeOperator);
          dx_aug = dx_aug * this->m_ScaleCoefficients[j];
          dx_dim = m_InnerProduct(xd_slice[j][i], it, m_DerivativeOperator);
          dx_dim = dx_dim * this->m_ScaleCoefficients[j];
          GradMag += 0.25f * itk::Math::sqr(dx[j][k] + dx_aug[k]);
          GradMag_d += 0.25f * itk::Math::sqr(dx[j][k] + dx_dim[k]);
        }
      }
    }

    if (m_K == 0.0)
    {
      Cx[i] = 0.0;
      Cxd[i] = 0.0;
    }
    else
    {
      Cx[i] = std::exp(GradMag / m_K);
      Cxd[i] = std::exp(GradMag_d / m_K);
    }
  }

  // Compute update value
  for (k = 0; k < VectorDimension; ++k)
  {
    delta[k] = ScalarValueType{};

    for (i = 0; i < ImageDimension; ++i)
    {
      dx_forward[i][k] *= Cx[i];
      dx_backward[i][k] *= Cxd[i];
      delta[k] += dx_forward[i][k] - dx_backward[i][k];
    }
  }

  return delta;
}
} // end namespace itk

#endif