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 *
 *  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.
 *
 *=========================================================================*/

//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {BrainProtonDensitySlice.png}
//    OUTPUTS: {DiscreteGaussianImageFilterOutput.png}
//    ARGUMENTS:    4 9
//  Software Guide : EndCommandLineArgs
//
//  Software Guide : BeginLatex
//
//  \begin{floatingfigure}[rlp]{6cm}
//    \centering
//    \includegraphics[width=6cm]{DiscreteGaussian}
//    \caption[DiscreteGaussianImageFilter Gaussian diagram.]
//            {Discretized Gaussian.\label{fig:DiscretizedGaussian}}
//  \end{floatingfigure}
//
//  The \doxygen{DiscreteGaussianImageFilter} computes the convolution of the
//  input image with a Gaussian kernel.  This is done in $ND$ by taking
//  advantage of the separability of the Gaussian kernel.  A one-dimensional
//  Gaussian function is discretized on a convolution kernel.  The size of the
//  kernel is extended until there are enough discrete points in the Gaussian
//  to ensure that a user-provided maximum error is not exceeded.  Since the
//  size of the kernel is unknown a priori, it is necessary to impose a limit
//  to its growth. The user can thus provide a value to be the maximum
//  admissible size of the kernel. Discretization error is defined as the
//  difference between the area under the discrete Gaussian curve (which has
//  finite support) and the area under the continuous Gaussian.
//
//  Gaussian kernels in ITK are constructed according to the theory of Tony
//  Lindeberg \cite{Lindeberg1994} so that smoothing and derivative operations
//  commute before and after discretization.  In other words, finite
//  difference derivatives on an image $I$ that has been smoothed by
//  convolution with the Gaussian are equivalent to finite differences
//  computed on $I$ by convolving with a derivative of the Gaussian.
//
//  \index{itk::DiscreteGaussianImageFilter}
//
//  Software Guide : EndLatex


#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkRescaleIntensityImageFilter.h"

//  Software Guide : BeginLatex
//
//  The first step required to use this filter is to include its header file.
//  As with other examples, the includes here are truncated to those specific
//  for this example.\newline
//
//  \index{itk::DiscreteGaussianImageFilter!header}
//
//  Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
#include "itkDiscreteGaussianImageFilter.h"
// Software Guide : EndCodeSnippet


int
main(int argc, char * argv[])
{
  if (argc < 5)
  {
    std::cerr << "Usage: " << std::endl;
    std::cerr
      << argv[0]
      << "  inputImageFile  outputImageFile  variance  maxKernelWidth "
      << std::endl;
    return EXIT_FAILURE;
  }


  //  Software Guide : BeginLatex
  //
  //  Types should be chosen for the pixels of the input and output images.
  //  Image types can be instantiated using the pixel type and dimension.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using InputPixelType = float;
  using OutputPixelType = float;

  using InputImageType = itk::Image<InputPixelType, 2>;
  using OutputImageType = itk::Image<OutputPixelType, 2>;
  // Software Guide : EndCodeSnippet


  using ReaderType = itk::ImageFileReader<InputImageType>;


  //  Software Guide : BeginLatex
  //
  //  The discrete Gaussian filter type is instantiated using the
  //  input and output image types.  A corresponding filter object is created.
  //
  //  \index{itk::DiscreteGaussianImageFilter!instantiation}
  //  \index{itk::DiscreteGaussianImageFilter!New()}
  //  \index{itk::DiscreteGaussianImageFilter!Pointer}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using FilterType =
    itk::DiscreteGaussianImageFilter<InputImageType, OutputImageType>;

  auto filter = FilterType::New();
  // Software Guide : EndCodeSnippet


  auto reader = ReaderType::New();
  reader->SetFileName(argv[1]);


  //  Software Guide : BeginLatex
  //
  //  The input image can be obtained from the output of another
  //  filter. Here, an image reader is used as its input.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  filter->SetInput(reader->GetOutput());
  // Software Guide : EndCodeSnippet


  const double       gaussianVariance = std::stod(argv[3]);
  const unsigned int maxKernelWidth = std::stoi(argv[4]);


  //  Software Guide : BeginLatex
  //
  //  The filter requires the user to provide a value for the variance
  //  associated with the Gaussian kernel. The method \code{SetVariance()} is
  //  used for this purpose. The discrete Gaussian is constructed as a
  //  convolution kernel.  The maximum kernel size can be set by the user.
  //  Note that the combination of variance and kernel-size values may result
  //  in a truncated Gaussian kernel.
  //
  //  \index{itk::DiscreteGaussianImageFilter!SetVariance()}
  //  \index{itk::DiscreteGaussianImageFilter!SetMaximumKernelWidth()}
  //
  //  Software Guide : EndLatex


  // Software Guide : BeginCodeSnippet
  filter->SetVariance(gaussianVariance);
  filter->SetMaximumKernelWidth(maxKernelWidth);
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  Finally, the filter is executed by invoking the \code{Update()} method.
  //
  //  \index{itk::DiscreteGaussianImageFilter!Update()}
  //
  //  Software Guide : EndLatex


  // Software Guide : BeginCodeSnippet
  filter->Update();
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  If the output of this filter has been connected to other filters down
  //  the pipeline, updating any of the downstream filters will
  //  trigger the execution of this one. For example, a writer could
  //  be used after the filter.
  //
  //  Software Guide : EndLatex

  using WritePixelType = unsigned char;
  using WriteImageType = itk::Image<WritePixelType, 2>;
  using RescaleFilterType =
    itk::RescaleIntensityImageFilter<OutputImageType, WriteImageType>;
  auto rescaler = RescaleFilterType::New();

  rescaler->SetOutputMinimum(0);
  rescaler->SetOutputMaximum(255);

  using WriterType = itk::ImageFileWriter<WriteImageType>;
  auto writer = WriterType::New();
  writer->SetFileName(argv[2]);

  // Software Guide : BeginCodeSnippet
  rescaler->SetInput(filter->GetOutput());
  writer->SetInput(rescaler->GetOutput());
  writer->Update();
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  // \begin{figure}
  // \center
  // \includegraphics[width=0.44\textwidth]{BrainProtonDensitySlice}
  // \includegraphics[width=0.44\textwidth]{DiscreteGaussianImageFilterOutput}
  // \itkcaption[DiscreteGaussianImageFilter output]{Effect of the
  // DiscreteGaussianImageFilter on a slice from a MRI proton density image of
  // the brain.}
  // \label{fig:DiscreteGaussianImageFilterInputOutput}
  // \end{figure}
  //
  //  Figure~\ref{fig:DiscreteGaussianImageFilterInputOutput} illustrates the
  //  effect of this filter on a MRI proton density image of the brain.
  //
  //  Note that large Gaussian variances will produce large convolution
  //  kernels and correspondingly longer computation times.  Unless a high
  //  degree of accuracy is required, it may be more desirable to use the
  //  approximating \doxygen{RecursiveGaussianImageFilter} with large
  //  variances.
  //
  //  Software Guide : EndLatex

  return EXIT_SUCCESS;
}
