/*
 * Copyright (C) 2005-2022 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.
 */


/* Example usage:
./NeighborhoodIterators1 Input/ROI_QB_PAN_1.tif Output/NeighborhoodIterators1a.png
*/


// This example uses the \doxygen{itk}{NeighborhoodIterator} to implement a simple
// Sobel edge detection algorithm \cite{Gonzalez1993}.  The algorithm uses the
// neighborhood iterator to iterate through an input image and calculate a
// series of finite difference derivatives.  Since the derivative results
// cannot be written back to the input image without affecting later
// calculations, they are written instead to a second, output image.  Most
// neighborhood processing algorithms follow this read-only model on their
// inputs.
//
// We begin by including the proper header files.  The
// \doxygen{itk}{ImageRegionIterator} will be used to write the results of
// computations to the output image.  A const version of the neighborhood
// iterator is used because the input image is read-only.

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

#include "itkConstNeighborhoodIterator.h"
#include "itkImageRegionIterator.h"

int main(int argc, char* argv[])
{
  if (argc < 3)
  {
    std::cerr << "Missing parameters. " << std::endl;
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0] << " inputImageFile outputImageFile" << std::endl;
    return -1;
  }

  // The finite difference calculations
  // in this algorithm require floating point values.  Hence, we define the image
  // pixel type to be \code{float} and the file reader will
  // automatically cast fixed-point data to \code{float}.
  //
  // We declare the iterator types using the image type as
  // the template parameter. The second template parameter of the
  // neighborhood iterator, which specifies
  // the boundary condition, has been omitted because the default condition is
  // appropriate for this algorithm.

  using PixelType  = float;
  using ImageType  = otb::Image<PixelType, 2>;
  using ReaderType = otb::ImageFileReader<ImageType>;

  using NeighborhoodIteratorType = itk::ConstNeighborhoodIterator<ImageType>;
  using IteratorType             = itk::ImageRegionIterator<ImageType>;

  // The following code creates and executes the OTB image reader.
  // The \code{Update}
  // call on the reader object is surrounded by the standard \code{try/catch}
  // blocks to handle any exceptions that may be thrown by the reader.

  ReaderType::Pointer reader = ReaderType::New();
  reader->SetFileName(argv[1]);
  try
  {
    reader->Update();
  }
  catch (itk::ExceptionObject& err)
  {
    std::cout << "ExceptionObject caught !" << std::endl;
    std::cout << err << std::endl;
    return -1;
  }

  //  We can now create a neighborhood iterator to range over the output of the
  //  reader. For Sobel edge-detection in 2D, we need a square iterator that
  //  extends one pixel away from the neighborhood center in every dimension.

  NeighborhoodIteratorType::RadiusType radius;
  radius.Fill(1);
  NeighborhoodIteratorType it(radius, reader->GetOutput(), reader->GetOutput()->GetRequestedRegion());

  // The following code creates an output image and iterator.

  ImageType::Pointer output = ImageType::New();
  output->SetRegions(reader->GetOutput()->GetRequestedRegion());
  output->Allocate();

  IteratorType out(output, reader->GetOutput()->GetRequestedRegion());

  // Sobel edge detection uses weighted finite difference calculations to
  // construct an edge magnitude image.  Normally the edge magnitude is the
  // root sum of squares of partial derivatives in all directions, but for
  // simplicity this example only calculates the $x$ component. The result is a
  // derivative image biased toward maximally vertical edges.
  //
  // The finite differences are computed from pixels at six locations in the
  // neighborhood.  In this example, we use the iterator \code{GetPixel()}
  // method to query the values from their offsets in the neighborhood.
  // The example in Section~\ref{sec:NeighborhoodExample2} uses convolution
  // with a Sobel kernel instead.
  //
  // Six positions in the neighborhood are necessary for the finite difference
  // calculations. These positions are recorded in \code{offset1} through
  // \code{offset6}.

  NeighborhoodIteratorType::OffsetType offset1 = {{-1, -1}};
  NeighborhoodIteratorType::OffsetType offset2 = {{1, -1}};
  NeighborhoodIteratorType::OffsetType offset3 = {{-1, 0}};
  NeighborhoodIteratorType::OffsetType offset4 = {{1, 0}};
  NeighborhoodIteratorType::OffsetType offset5 = {{-1, 1}};
  NeighborhoodIteratorType::OffsetType offset6 = {{1, 1}};

  // It is equivalent to use the six corresponding integer array indices instead.
  // For example, the offsets \code{(-1, -1)} and \code{(1, -1)} are
  // equivalent to the integer indices \code{0} and \code{2}, respectively.
  //
  // The calculations are done in a \code{for} loop that moves the input and
  // output iterators synchronously across their respective images.  The
  // \code{sum} variable is used to sum the results of the finite differences.

  for (it.GoToBegin(), out.GoToBegin(); !it.IsAtEnd(); ++it, ++out)
  {
    float sum;
    sum = it.GetPixel(offset2) - it.GetPixel(offset1);
    sum += 2.0 * it.GetPixel(offset4) - 2.0 * it.GetPixel(offset3);
    sum += it.GetPixel(offset6) - it.GetPixel(offset5);
    out.Set(sum);
  }

  // The last step is to write the output buffer to an image file.  Writing is
  // done inside a \code{try/catch} block to handle any exceptions.  The output
  // is rescaled to intensity range $[0, 255]$ and cast to unsigned char so that
  // it can be saved and visualized as a PNG image.

  using WritePixelType = unsigned char;
  using WriteImageType = otb::Image<WritePixelType, 2>;
  using WriterType     = otb::ImageFileWriter<WriteImageType>;

  using RescaleFilterType = itk::RescaleIntensityImageFilter<ImageType, WriteImageType>;

  RescaleFilterType::Pointer rescaler = RescaleFilterType::New();

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

  WriterType::Pointer writer = WriterType::New();
  writer->SetFileName(argv[2]);
  writer->SetInput(rescaler->GetOutput());
  try
  {
    writer->Update();
  }
  catch (itk::ExceptionObject& err)
  {
    std::cout << "ExceptionObject caught !" << std::endl;
    std::cout << err << std::endl;
    return -1;
  }

  // The center image of Figure~\ref{fig:NeighborhoodExamples1} shows the
  // output of the Sobel algorithm applied to
  // \code{Examples/Data/ROI\_QB\_PAN\_1.tif}.
  //
  // \begin{figure} \centering
  // \includegraphics[width=0.45\textwidth]{ROI_QB_PAN_1.eps}
  // \includegraphics[width=0.45\textwidth]{NeighborhoodIterators1a.eps}
  // \itkcaption[Sobel edge detection results]{Applying the Sobel operator to an image (left) produces $x$ (right)  derivative image.}
  // \protect\label{fig:NeighborhoodExamples1}
  // \end{figure}

  return EXIT_SUCCESS;
}