/*=========================================================================
 *
 *  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 itkDiscreteHessianGaussianImageFunction_hxx
#define itkDiscreteHessianGaussianImageFunction_hxx

#include "itkNeighborhoodOperatorImageFilter.h"

namespace itk
{
/** Set the Input Image */
template <typename TInputImage, typename TOutput>
DiscreteHessianGaussianImageFunction<TInputImage, TOutput>::DiscreteHessianGaussianImageFunction()
{
  m_Variance.Fill(1.0);
  m_OperatorImageFunction = OperatorImageFunctionType::New();
}

/** Print self method */
template <typename TInputImage, typename TOutput>
void
DiscreteHessianGaussianImageFunction<TInputImage, TOutput>::PrintSelf(std::ostream & os, Indent indent) const
{
  this->Superclass::PrintSelf(os, indent);
  os << indent << "UseImageSpacing: " << (m_UseImageSpacing ? "On" : "Off") << std::endl;
  os << indent << "NormalizeAcrossScale: " << m_NormalizeAcrossScale << std::endl;
  os << indent << "Variance: " << m_Variance << std::endl;
  os << indent << "MaximumError: " << m_MaximumError << std::endl;
  os << indent << "MaximumKernelWidth: " << m_MaximumKernelWidth << std::endl;
  os << indent << "OperatorArray: " << m_OperatorArray << std::endl;
  os << indent << "KernelArray: " << m_KernelArray << std::endl;
  os << indent << "OperatorImageFunction: " << m_OperatorImageFunction << std::endl;
  os << indent << "InterpolationMode: " << m_InterpolationMode << std::endl;
}

/** Set the input image */
template <typename TInputImage, typename TOutput>
void
DiscreteHessianGaussianImageFunction<TInputImage, TOutput>::SetInputImage(const InputImageType * ptr)
{
  Superclass::SetInputImage(ptr);
  m_OperatorImageFunction->SetInputImage(ptr);
}

/** Recompute the gaussian kernel used to evaluate indexes
 *  This should use a fastest Derivative Gaussian operator */
template <typename TInputImage, typename TOutput>
void
DiscreteHessianGaussianImageFunction<TInputImage, TOutput>::RecomputeGaussianKernel()
{
  /* Create 3*N operators (N=ImageDimension) where the
   * first N are zero-order, the second N are first-order
   * and the third N are second order */
  unsigned int idx;
  unsigned int maxRadius = 0;

  for (unsigned int direction = 0; direction < Self::ImageDimension2; ++direction)
  {
    for (unsigned int order = 0; order <= 2; ++order)
    {
      idx = Self::ImageDimension2 * order + direction;
      m_OperatorArray[idx].SetDirection(direction);
      m_OperatorArray[idx].SetMaximumKernelWidth(m_MaximumKernelWidth);
      m_OperatorArray[idx].SetMaximumError(m_MaximumError);

      if (m_UseImageSpacing && (this->GetInputImage()))
      {
        m_OperatorArray[idx].SetSpacing(this->GetInputImage()->GetSpacing()[direction]);
      }

      // NOTE: GaussianDerivativeOperator modifies the variance when
      // setting image spacing
      m_OperatorArray[idx].SetVariance(m_Variance[direction]);
      m_OperatorArray[idx].SetOrder(order);
      m_OperatorArray[idx].SetNormalizeAcrossScale(m_NormalizeAcrossScale);
      m_OperatorArray[idx].CreateDirectional();

      // Check for maximum radius
      for (unsigned int i = 0; i < Self::ImageDimension2; ++i)
      {
        if (m_OperatorArray[idx].GetRadius()[i] > maxRadius)
        {
          maxRadius = m_OperatorArray[idx].GetRadius()[i];
        }
      }
    }
  }

  // Now precompute the n-dimensional kernel. This fastest as we don't
  // have to perform N convolutions for each point we calculate but
  // only one.

  using KernelImageType = itk::Image<TOutput, Self::ImageDimension2>;
  auto kernelImage = KernelImageType::New();

  using RegionType = typename KernelImageType::RegionType;
  RegionType region;

  typename RegionType::SizeType size;
  size.Fill(4 * maxRadius + 1);
  region.SetSize(size);

  kernelImage->SetRegions(region);
  kernelImage->AllocateInitialized();

  // Initially the kernel image will be an impulse at the center
  typename KernelImageType::IndexType centerIndex;
  centerIndex.Fill(2 * maxRadius); // include also boundaries

  // Create an image region to be used later that does not include boundaries
  RegionType kernelRegion;
  size.Fill(2 * maxRadius + 1);
  typename RegionType::IndexType origin;
  origin.Fill(maxRadius);
  kernelRegion.SetSize(size);
  kernelRegion.SetIndex(origin);

  // Now create an image filter to perform successive convolutions
  using NeighborhoodFilterType = itk::NeighborhoodOperatorImageFilter<KernelImageType, KernelImageType>;
  auto convolutionFilter = NeighborhoodFilterType::New();

  // Array that stores the current order for each direction
  using OrderArrayType = FixedArray<unsigned int, Self::ImageDimension2>;
  OrderArrayType orderArray;

  // Precalculate compound derivative kernels (n-dimensional)
  // The order of calculation in the 3D case is: dxx, dxy, dxz, dyy,
  // dyz, dzz

  unsigned int opidx; // current operator index in m_OperatorArray
  unsigned int kernelidx = 0;

  for (unsigned int i = 0; i < Self::ImageDimension2; ++i)
  {
    for (unsigned int j = i; j < Self::ImageDimension2; ++j)
    {
      orderArray.Fill(0);
      ++orderArray[i];
      ++orderArray[j];

      // Reset kernel image
      kernelImage->FillBuffer(TOutput{});
      kernelImage->SetPixel(centerIndex, itk::NumericTraits<TOutput>::OneValue());

      for (unsigned int direction = 0; direction < Self::ImageDimension2; ++direction)
      {
        opidx = Self::ImageDimension2 * orderArray[direction] + direction;
        convolutionFilter->SetInput(kernelImage);
        convolutionFilter->SetOperator(m_OperatorArray[opidx]);
        convolutionFilter->Update();
        kernelImage = convolutionFilter->GetOutput();
        kernelImage->DisconnectPipeline();
      }

      // Set the size of the current kernel
      m_KernelArray[kernelidx].SetRadius(maxRadius);

      // Copy kernel image to neighborhood. Do not copy boundaries.
      ImageRegionConstIterator<KernelImageType> it(kernelImage, kernelRegion);
      it.GoToBegin();
      idx = 0;

      while (!it.IsAtEnd())
      {
        m_KernelArray[kernelidx][idx] = it.Get();
        ++idx;
        ++it;
      }
      ++kernelidx;
    }
  }
}

/** Evaluate the function at the specified index */
template <typename TInputImage, typename TOutput>
auto
DiscreteHessianGaussianImageFunction<TInputImage, TOutput>::EvaluateAtIndex(const IndexType & index) const -> OutputType
{
  OutputType hessian;

  for (unsigned int i = 0; i < m_KernelArray.Size(); ++i)
  {
    m_OperatorImageFunction->SetOperator(m_KernelArray[i]);
    hessian[i] = m_OperatorImageFunction->EvaluateAtIndex(index);
  }
  return hessian;
}

/** Evaluate the function at the specified point */
template <typename TInputImage, typename TOutput>
auto
DiscreteHessianGaussianImageFunction<TInputImage, TOutput>::Evaluate(const PointType & point) const -> OutputType
{
  if (m_InterpolationMode == InterpolationModeEnum::NearestNeighbourInterpolation)
  {
    IndexType index;
    this->ConvertPointToNearestIndex(point, index);
    return this->EvaluateAtIndex(index);
  }
  else
  {
    ContinuousIndexType cindex;
    this->ConvertPointToContinuousIndex(point, cindex);
    return this->EvaluateAtContinuousIndex(cindex);
  }
}

/** Evaluate the function at specified ContinuousIndex position.*/
template <typename TInputImage, typename TOutput>
auto
DiscreteHessianGaussianImageFunction<TInputImage, TOutput>::EvaluateAtContinuousIndex(
  const ContinuousIndexType & cindex) const -> OutputType
{
  if (m_InterpolationMode == InterpolationModeEnum::NearestNeighbourInterpolation)
  {
    IndexType index;
    this->ConvertContinuousIndexToNearestIndex(cindex, index);
    return this->EvaluateAtIndex(index);
  }
  else
  {
    using NumberOfNeighborsType = unsigned int;

    unsigned int          dim; // index over dimension
    NumberOfNeighborsType neighbors = 1 << ImageDimension2;

    // Compute base index = closet index below point
    // Compute distance from point to base index
    IndexType baseIndex;
    double    distance[ImageDimension2];

    for (dim = 0; dim < ImageDimension2; ++dim)
    {
      baseIndex[dim] = Math::Floor<IndexValueType>(cindex[dim]);
      distance[dim] = cindex[dim] - static_cast<double>(baseIndex[dim]);
    }

    // Interpolated value is the weighted sum of each of the surrounding
    // neighbors. The weight for each neighbor is the fraction overlap
    // of the neighbor pixel with respect to a pixel centered on point.
    OutputType hessian, currentHessian;
    TOutput    totalOverlap{};

    for (NumberOfNeighborsType counter = 0; counter < neighbors; ++counter)
    {
      double                overlap = 1.0;   // fraction overlap
      NumberOfNeighborsType upper = counter; // each bit indicates upper/lower neighbour
      IndexType             neighIndex;

      // get neighbor index and overlap fraction
      for (dim = 0; dim < ImageDimension2; ++dim)
      {
        if (upper & 1)
        {
          neighIndex[dim] = baseIndex[dim] + 1;
          overlap *= distance[dim];
        }
        else
        {
          neighIndex[dim] = baseIndex[dim];
          overlap *= 1.0 - distance[dim];
        }
        upper >>= 1;
      }

      // get neighbor value only if overlap is not zero
      if (overlap)
      {
        currentHessian = this->EvaluateAtIndex(neighIndex);
        for (unsigned int i = 0; i < hessian.Size(); ++i)
        {
          hessian[i] += overlap * currentHessian[i];
        }
        totalOverlap += overlap;
      }

      if (totalOverlap == 1.0)
      {
        // finished
        break;
      }
    }

    return hessian;
  }
}
} // end namespace itk

#endif