/*
 * 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: {ROISpot5.png}
//    OUTPUTS: {LaplacianRecursiveGaussianImageFilteroutput3.hdr}
//    3
//    OUTPUTS: {LaplacianRecursiveGaussianImageFilterOutput3.png}
//  Software Guide : EndCommandLineArgs

//  Software Guide : BeginCommandLineArgs
//    INPUTS: {ROISpot5.png}
//    OUTPUTS: {LaplacianRecursiveGaussianImageFilteroutput5.hdr}
//    5
//    OUTPUTS: {LaplacianRecursiveGaussianImageFilterOutput5.png}
//  Software Guide : EndCommandLineArgs

//  Software Guide : BeginLatex
//
//  This example illustrates how to use the
//  \doxygen{itk}{RecursiveGaussianImageFilter} for computing the
//  Laplacian of an
//  image.
//
//  \index{itk::RecursiveGaussianImageFilter}
//
//  Software Guide : EndLatex

#include "otbImage.h"
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "itkAddImageFilter.h"
#include "itkUnaryFunctorImageFilter.h"

//  Software Guide : BeginLatex
//
//  The first step required to use this filter is to include its header file.
//
//  \index{itk::RecursiveGaussianImageFilter!header}
//
//  Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
#include "itkRecursiveGaussianImageFilter.h"
// Software Guide : EndCodeSnippet
#include "itkRescaleIntensityImageFilter.h"

int main(int argc, char * argv[])
{
  if (argc < 4)
    {
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0] <<
    "  inputImageFile  outputImageFile  sigma [RescaledOutputImageFile] " <<
    std::endl;
    return EXIT_FAILURE;
    }

  //  Software Guide : BeginLatex
  //
  //  Types should be selected on the desired input and output pixel types.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  typedef    float InputPixelType;
  typedef    float OutputPixelType;
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  The input and output image types are instantiated using the pixel types.
  //
  //  Software Guide : EndLatex

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

  typedef otb::ImageFileReader<InputImageType> ReaderType;

  //  Software Guide : BeginLatex
  //
  //  The filter type is now instantiated using both the input image and the
  //  output image types.
  //
  //  \index{itk::RecursiveGaussianImageFilter!Instantiation}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  typedef itk::RecursiveGaussianImageFilter<
      InputImageType, OutputImageType>  FilterType;
  // Software Guide : EndCodeSnippet

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

  //  Software Guide : BeginLatex
  //
  //  This filter applies the approximation of the convolution along a single
  //  dimension. It is therefore necessary to concatenate several of these filters
  //  to produce smoothing in all directions.  In this example, we create a pair
  //  of filters since we are processing a $2D$ image.  The filters are
  //  created by invoking the \code{New()} method and assigning the result to
  //  a \doxygen{itk}{SmartPointer}.
  //
  //  We need two filters for computing the X component of the Laplacian and
  //  two other filters for computing the Y component.
  //
  //  \index{itk::RecursiveGaussianImageFilter!New()}
  //  \index{itk::RecursiveGaussianImageFilter!Pointer}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  FilterType::Pointer filterX1 = FilterType::New();
  FilterType::Pointer filterY1 = FilterType::New();

  FilterType::Pointer filterX2 = FilterType::New();
  FilterType::Pointer filterY2 = FilterType::New();
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  Since each one of the newly created filters has the potential to perform
  //  filtering along any dimension, we have to restrict each one to a
  //  particular direction. This is done with the \code{SetDirection()} method.
  //
  //  \index{RecursiveGaussianImageFilter!SetDirection()}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  filterX1->SetDirection(0);     // 0 --> X direction
  filterY1->SetDirection(1);     // 1 --> Y direction

  filterX2->SetDirection(0);     // 0 --> X direction
  filterY2->SetDirection(1);     // 1 --> Y direction
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  The \doxygen{itk}{RecursiveGaussianImageFilter} can approximate the
  //  convolution with the Gaussian or with its first and second
  //  derivatives. We select one of these options by using the
  //  \code{SetOrder()} method. Note that the argument is an \code{enum} whose
  //  values can be \code{ZeroOrder}, \code{FirstOrder} and
  //  \code{SecondOrder}. For example, to compute the $x$ partial derivative we
  //  should select \code{FirstOrder} for $x$ and \code{ZeroOrder} for
  //  $y$. Here we want only to smooth in $x$ and $y$, so we select
  //  \code{ZeroOrder} in both directions.
  //
  //  \index{RecursiveGaussianImageFilter!SetOrder()}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  filterX1->SetOrder(FilterType::ZeroOrder);
  filterY1->SetOrder(FilterType::SecondOrder);

  filterX2->SetOrder(FilterType::SecondOrder);
  filterY2->SetOrder(FilterType::ZeroOrder);
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  There are two typical ways of normalizing Gaussians depending on their
  //  application. For scale-space analysis it is desirable to use a
  //  normalization that will preserve the maximum value of the input. This
  //  normalization is represented by the following equation.
  //
  //  \begin{equation}
  //          \frac{ 1 }{ \sigma  \sqrt{ 2 \pi } }
  //  \end{equation}
  //
  //  In applications that use the Gaussian as a solution of the diffusion
  //  equation it is desirable to use a normalization that preserve the
  //  integral of the signal. This last approach can be seen as a conservation
  //  of mass principle. This is represented by the following equation.
  //
  //  \begin{equation}
  //          \frac{ 1 }{ \sigma^2  \sqrt{ 2 \pi } }
  //  \end{equation}
  //
  //  The \doxygen{itk}{RecursiveGaussianImageFilter} has a boolean flag that allows
  //  users to select between these two normalization options. Selection is
  //  done with the method \code{SetNormalizeAcrossScale()}. Enable this flag
  //  to analyzing an image across scale-space.  In the current example, this
  //  setting has no impact because we are actually renormalizing the output to
  //  the dynamic range of the reader, so we simply disable the flag.
  //
  //  \index{RecursiveGaussianImageFilter!SetNormalizeAcrossScale()}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  const bool normalizeAcrossScale = false;
  filterX1->SetNormalizeAcrossScale(normalizeAcrossScale);
  filterY1->SetNormalizeAcrossScale(normalizeAcrossScale);
  filterX2->SetNormalizeAcrossScale(normalizeAcrossScale);
  filterY2->SetNormalizeAcrossScale(normalizeAcrossScale);
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  The input image can be obtained from the output of another
  //  filter. Here, an image reader is used as the source. The image is passed to
  //  the $x$ filter and then to the $y$ filter. The reason for keeping these
  //  two filters separate is that it is usual in scale-space applications to
  //  compute not only the smoothing but also combinations of derivatives at
  //  different orders and smoothing. Some factorization is possible when
  //  separate filters are used to generate the intermediate results. Here
  //  this capability is less interesting, though, since we only want to smooth
  //  the image in all directions.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  filterX1->SetInput(reader->GetOutput());
  filterY1->SetInput(filterX1->GetOutput());

  filterY2->SetInput(reader->GetOutput());
  filterX2->SetInput(filterY2->GetOutput());
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  It is now time to select the $\sigma$ of the Gaussian used to
  //  smooth the data.  Note that $\sigma$ must be passed to both
  //  filters and that sigma is considered to be in the units of the
  //  image spacing. That is, at the moment of applying the smoothing
  //  process, the filter will take into account the spacing values
  //  defined in the image.
  //
  //  \index{itk::RecursiveGaussianImageFilter!SetSigma()}
  //  \index{SetSigma()!itk::RecursiveGaussianImageFilter}
  //
  //  Software Guide : EndLatex

  const double sigma = atof(argv[3]);

  // Software Guide : BeginCodeSnippet
  filterX1->SetSigma(sigma);
  filterY1->SetSigma(sigma);
  filterX2->SetSigma(sigma);
  filterY2->SetSigma(sigma);
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  Finally the two components of the Laplacian should be added together. The
  //  \doxygen{itk}{AddImageFilter} is used for this purpose.
  //
  //  \index{itk::AddImageFilter!Instantiation}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  typedef itk::AddImageFilter<
      OutputImageType,
      OutputImageType,
      OutputImageType> AddFilterType;

  AddFilterType::Pointer addFilter = AddFilterType::New();

  addFilter->SetInput1(filterY1->GetOutput());
  addFilter->SetInput2(filterX2->GetOutput());
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  The filters are triggered by invoking \code{Update()} on the Add filter
  //  at the end of the pipeline.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  try
    {
    addFilter->Update();
    }
  catch (itk::ExceptionObject& err)
    {
    std::cout << "ExceptionObject caught !" << std::endl;
    std::cout << err << std::endl;
    return EXIT_FAILURE;
    }
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  The resulting image could be saved to a file using the
  //  \doxygen{otb}{ImageFileWriter} class.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  typedef  float WritePixelType;

  typedef otb::Image<WritePixelType, 2> WriteImageType;

  typedef otb::ImageFileWriter<WriteImageType> WriterType;

  WriterType::Pointer writer = WriterType::New();

  writer->SetInput(addFilter->GetOutput());

  writer->SetFileName(argv[2]);

  writer->Update();
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  // \begin{figure}
  // \center
  // \includegraphics[width=0.44\textwidth]{LaplacianRecursiveGaussianImageFilterOutput3.eps}
  // \includegraphics[width=0.44\textwidth]{LaplacianRecursiveGaussianImageFilterOutput5.eps}
  // \itkcaption[Output of the RecursiveGaussianImageFilter.]{Effect of the
  // RecursiveGaussianImageFilter.}
  // \label{fig:LaplacianRecursiveGaussianImageFilterInputOutput}
  // \end{figure}
  //
  //  Figure~\ref{fig:LaplacianRecursiveGaussianImageFilterInputOutput} illustrates the
  //  effect of this filter using
  //  $\sigma$ values of $3$ (left) and $5$ (right).  The figure shows how the
  //  attenuation of noise can be regulated by selecting the appropriate
  //  standard deviation.  This type of scale-tunable filter is suitable for
  //  performing scale-space analysis.
  //
  //  Software Guide : EndLatex

  // Rescale float outputs to png for inclusion in the Software guide
  //
  if (argc > 4)
    {
    typedef unsigned char                CharPixelType;
    typedef otb::Image<CharPixelType, 2> CharImageType;
    typedef itk::RescaleIntensityImageFilter<OutputImageType, CharImageType>
    RescaleFilterType;
    RescaleFilterType::Pointer rescale = RescaleFilterType::New();
    rescale->SetInput(addFilter->GetOutput());
    rescale->SetOutputMinimum(0);
    rescale->SetOutputMaximum(255);
    typedef otb::ImageFileWriter<CharImageType> CharWriterType;
    CharWriterType::Pointer charWriter = CharWriterType::New();
    charWriter->SetFileName(argv[4]);
    charWriter->SetInput(rescale->GetOutput());
    charWriter->Update();
    }

  return EXIT_SUCCESS;
}
