/*
 * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
 *
 * This file is part of Orfeo Toolbox
 *
 *     https://www.orfeo-toolbox.org/
 *
 * 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
 *
 * 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: {QB_Suburb.png}
//    OUTPUTS: {DiscreteGaussianImageFilterOutput.png}
//    4 9
//  Software Guide : EndCommandLineArgs
//
//  Software Guide : BeginLatex
//
//  \itkpiccaption[DiscreteGaussianImageFilter Gaussian diagram.]{Discretized
//  Gaussian.\label{fig:DiscretizedGaussian}}
//  \parpic(7cm, 4cm)[r]{\includegraphics[width=6cm]{DiscreteGaussian.eps}}
//
//  The \doxygen{itk}{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 "otbImage.h"
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "itkUnaryFunctorImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"

//  Software Guide : BeginLatex
//
//  The first step required to use this filter is to include its header file.
//
//  \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
  typedef    float InputPixelType;
  typedef    float OutputPixelType;

  typedef otb::Image<InputPixelType,  2> InputImageType;
  typedef otb::Image<OutputPixelType, 2> OutputImageType;
  // Software Guide : EndCodeSnippet

  typedef otb::ImageFileReader<InputImageType> ReaderType;

  //  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
  typedef itk::DiscreteGaussianImageFilter<
      InputImageType, OutputImageType>  FilterType;

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

  ReaderType::Pointer 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 = atof(argv[3]);
  const unsigned int maxKernelWidth = atoi(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 would have
  //  triggered the execution of this one. For example, a writer could have
  //  been used after the filter.
  //
  //  Software Guide : EndLatex

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

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

  typedef otb::ImageFileWriter<WriteImageType> WriterType;
  WriterType::Pointer 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]{QB_Suburb.eps}
  // \includegraphics[width=0.44\textwidth]{DiscreteGaussianImageFilterOutput.eps}
  // \itkcaption[DiscreteGaussianImageFilter output]{Effect of the
  // DiscreteGaussianImageFilter.}
  // \label{fig:DiscreteGaussianImageFilterInputOutput}
  // \end{figure}
  //
  //  Figure~\ref{fig:DiscreteGaussianImageFilterInputOutput} illustrates the
  //  effect of this filter.
  //
  //  Note that large Gaussian variances will produce large convolution kernels
  //  and correspondingly slower computation times.  Unless a high degree of
  //  accuracy is required, it may be more desirable to use the approximating
  //  \doxygen{itk}{RecursiveGaussianImageFilter} with large variances.
  //
  //  Software Guide : EndLatex

  return EXIT_SUCCESS;
}
