/*=========================================================================
 *
 *  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 itkGradientMagnitudeRecursiveGaussianImageFilter_hxx
#define itkGradientMagnitudeRecursiveGaussianImageFilter_hxx

#include "itkImageRegionIterator.h"
#include "itkProgressAccumulator.h"
#include "itkMath.h"
#include <algorithm> // For generate.

namespace itk
{

template <typename TInputImage, typename TOutputImage>
GradientMagnitudeRecursiveGaussianImageFilter<TInputImage,
                                              TOutputImage>::GradientMagnitudeRecursiveGaussianImageFilter()
  : m_DerivativeFilter(DerivativeFilterType::New())

{
  m_DerivativeFilter->SetOrder(GaussianOrderEnum::FirstOrder);
  m_DerivativeFilter->SetNormalizeAcrossScale(m_NormalizeAcrossScale);
  m_DerivativeFilter->InPlaceOff();
  m_DerivativeFilter->ReleaseDataFlagOn();

  std::generate(m_SmoothingFilters.begin(), m_SmoothingFilters.end(), [this] {
    const auto filter = GaussianFilterType::New();
    filter->SetOrder(GaussianOrderEnum::ZeroOrder);
    filter->SetNormalizeAcrossScale(m_NormalizeAcrossScale);
    filter->InPlaceOn();
    return filter;
  });

  m_SmoothingFilters[0]->SetInput(m_DerivativeFilter->GetOutput());
  for (unsigned int i = 1; i < ImageDimension - 1; ++i)
  {
    m_SmoothingFilters[i]->SetInput(m_SmoothingFilters[i - 1]->GetOutput());
  }

  m_SqrSpacingFilter = SqrSpacingFilterType::New();
  m_SqrSpacingFilter->SetInput(1, m_SmoothingFilters.back()->GetOutput());
  // run that filter in place for much efficiency
  m_SqrSpacingFilter->InPlaceOn();

  // input of SqrtFilter is the cumulative image - we can't set it now
  m_SqrtFilter = SqrtFilterType::New();
  m_SqrtFilter->InPlaceOn();

  this->SetSigma(1.0);
  this->InPlaceOff();
}

template <typename TInputImage, typename TOutputImage>
void
GradientMagnitudeRecursiveGaussianImageFilter<TInputImage, TOutputImage>::PrintSelf(std::ostream & os,
                                                                                    Indent         indent) const
{
  Superclass::PrintSelf(os, indent);

  os << indent << "SmoothingFilters: ";
  for (const auto & elem : m_SmoothingFilters)
  {
    os << indent.GetNextIndent() << elem << std::endl;
  }

  itkPrintSelfObjectMacro(DerivativeFilter);
  itkPrintSelfObjectMacro(SqrSpacingFilter);
  itkPrintSelfObjectMacro(SqrtFilter);

  itkPrintSelfBooleanMacro(NormalizeAcrossScale);
}

template <typename TInputImage, typename TOutputImage>
void
GradientMagnitudeRecursiveGaussianImageFilter<TInputImage, TOutputImage>::SetSigma(RealType sigma)
{
  if (Math::NotExactlyEquals(sigma, this->GetSigma()))
  {
    for (const auto & filter : m_SmoothingFilters)
    {
      filter->SetSigma(sigma);
    }
    m_DerivativeFilter->SetSigma(sigma);

    this->Modified();
  }
}

template <typename TInputImage, typename TOutputImage>
auto
GradientMagnitudeRecursiveGaussianImageFilter<TInputImage, TOutputImage>::GetSigma() -> RealType
{
  // just return the sigma value of one filter
  return m_DerivativeFilter->GetSigma();
}

template <typename TInputImage, typename TOutputImage>
void
GradientMagnitudeRecursiveGaussianImageFilter<TInputImage, TOutputImage>::SetNumberOfWorkUnits(ThreadIdType nb)
{
  Superclass::SetNumberOfWorkUnits(nb);
  for (const auto & filter : m_SmoothingFilters)
  {
    filter->SetNumberOfWorkUnits(nb);
  }
  m_DerivativeFilter->SetNumberOfWorkUnits(nb);
  m_SqrSpacingFilter->SetNumberOfWorkUnits(nb);
  m_SqrtFilter->SetNumberOfWorkUnits(nb);
}

template <typename TInputImage, typename TOutputImage>
void
GradientMagnitudeRecursiveGaussianImageFilter<TInputImage, TOutputImage>::SetNormalizeAcrossScale(bool normalize)
{
  if (m_NormalizeAcrossScale != normalize)
  {
    m_NormalizeAcrossScale = normalize;

    for (const auto & filter : m_SmoothingFilters)
    {
      filter->SetNormalizeAcrossScale(normalize);
    }
    m_DerivativeFilter->SetNormalizeAcrossScale(normalize);

    this->Modified();
  }
}

template <typename TInputImage, typename TOutputImage>
void
GradientMagnitudeRecursiveGaussianImageFilter<TInputImage, TOutputImage>::GenerateInputRequestedRegion()
{
  // call the superclass' implementation of this method. this should
  // copy the output requested region to the input requested region
  Superclass::GenerateInputRequestedRegion();

  // This filter needs all of the input
  const typename GradientMagnitudeRecursiveGaussianImageFilter<TInputImage, TOutputImage>::InputImagePointer image =
    const_cast<InputImageType *>(this->GetInput());
  if (image)
  {
    image->SetRequestedRegion(this->GetInput()->GetLargestPossibleRegion());
  }
}

template <typename TInputImage, typename TOutputImage>
void
GradientMagnitudeRecursiveGaussianImageFilter<TInputImage, TOutputImage>::EnlargeOutputRequestedRegion(
  DataObject * output)
{
  auto * out = dynamic_cast<TOutputImage *>(output);

  if (out)
  {
    out->SetRequestedRegion(out->GetLargestPossibleRegion());
  }
}

template <typename TInputImage, typename TOutputImage>
void
GradientMagnitudeRecursiveGaussianImageFilter<TInputImage, TOutputImage>::GenerateData()
{
  itkDebugMacro("GradientMagnitudeRecursiveGaussianImageFilter generating data ");

  const typename TInputImage::ConstPointer inputImage(this->GetInput());

  const typename TOutputImage::Pointer outputImage(this->GetOutput());

  // Reset progress of internal filters to zero,
  // otherwise progress starts from non-zero value the second time the filter is invoked.
  m_DerivativeFilter->UpdateProgress(0.0);
  for (unsigned int k = 0; k < ImageDimension - 1; ++k)
  {
    m_SmoothingFilters[k]->UpdateProgress(0.0);
  }
  m_SqrSpacingFilter->UpdateProgress(0.0);
  m_SqrtFilter->UpdateProgress(0.0);

  // Create a process accumulator for tracking the progress of this minipipeline
  auto progress = ProgressAccumulator::New();
  progress->SetMiniPipelineFilter(this);

  // If the last filter is running in-place then this bulk data is not
  // needed, release it to save memory
  if (m_SqrtFilter->CanRunInPlace())
  {
    outputImage->ReleaseData();
  }


  auto cumulativeImage = CumulativeImageType::New();
  cumulativeImage->SetRegions(inputImage->GetBufferedRegion());
  cumulativeImage->AllocateInitialized();
  // The output's information must match the input's information
  cumulativeImage->CopyInformation(this->GetInput());

  m_DerivativeFilter->SetInput(inputImage);

  const unsigned int numberOfFilterRuns = 1 + ImageDimension * (ImageDimension + 1);
  progress->RegisterInternalFilter(m_DerivativeFilter, 1.0f / numberOfFilterRuns);
  for (const auto & filter : m_SmoothingFilters)
  {
    progress->RegisterInternalFilter(filter, 1.0f / numberOfFilterRuns);
  }
  progress->RegisterInternalFilter(m_SqrSpacingFilter, 1.0f / numberOfFilterRuns);
  progress->RegisterInternalFilter(m_SqrtFilter, 1.0f / numberOfFilterRuns);

  for (unsigned int dim = 0; dim < ImageDimension; ++dim)
  {
    unsigned int i = 0;
    unsigned int j = 0;
    while (i < ImageDimension - 1)
    {
      if (i == dim)
      {
        ++j;
      }
      m_SmoothingFilters[i]->SetDirection(j);
      ++i;
      ++j;
    }
    m_DerivativeFilter->SetDirection(dim);

    const double lambdaSpacing = inputImage->GetSpacing()[dim];
    m_SqrSpacingFilter->SetFunctor([lambdaSpacing](const InternalRealType & a, const InternalRealType & b) {
      return a + itk::Math::sqr(b / lambdaSpacing);
    });
    m_SqrSpacingFilter->SetInput(cumulativeImage);

    // run the mini pipeline for that dimension
    // Note: we must take care to update the requested region. Without that, a
    // second run of the
    // filter with a smaller input than in the first run trigger an exception,
    // because the filter
    // ask for a larger region than available. TODO: there should be a way to do
    // that only once
    // per GenerateData() call.
    m_SqrSpacingFilter->UpdateLargestPossibleRegion();

    // and user the result as the cumulative image
    cumulativeImage = m_SqrSpacingFilter->GetOutput();
    cumulativeImage->DisconnectPipeline();
  }
  m_SqrtFilter->SetInput(cumulativeImage);
  m_SqrtFilter->GraftOutput(this->GetOutput());
  m_SqrtFilter->UpdateLargestPossibleRegion();
  this->GraftOutput(m_SqrtFilter->GetOutput());
}
} // end namespace itk

#endif
