/*
 * Copyright (C) 2005-2020 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.
 */


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

// This example illustrates a technique for improving the efficiency of
// neighborhood calculations by eliminating unnecessary bounds checking. As
// described in Section~\ref{sec:NeighborhoodIterators}, the neighborhood
// iterator automatically enables or disables bounds checking based on the
// iteration region in which it is initialized. By splitting our image into
// boundary and non-boundary regions, and then processing each region using a
// different neighborhood iterator, the algorithm will only perform
// bounds-checking on those pixels for which it is actually required.  This
// trick can provide a significant speedup for simple algorithms such as our
// Sobel edge detection, where iteration speed is a critical.
//
// Splitting the image into the necessary regions is an easy task when you use
// the \doxygen{itk}{ImageBoundaryFacesCalculator}.  The face
// calculator is so named because it returns a list of the ``faces'' of the ND
// dataset.  Faces are those regions whose pixels all lie within a distance $d$
// from the boundary, where $d$ is the radius of the neighborhood stencil used
// for the numerical calculations. In other words, faces are those regions
// where a neighborhood iterator of radius $d$ will always overlap the boundary
// of the image. The face calculator also returns the single \emph{inner}
// region, in which out-of-bounds values are never required and bounds checking
// is not necessary.
//
// The face calculator object is defined in \code{itkNeighborhoodAlgorithm.h}.
// We include this file in addition to those from the previous two examples.

#include "itkSobelOperator.h"
#include "itkNeighborhoodInnerProduct.h"

#include "itkNeighborhoodAlgorithm.h"

int main(int argc, char* argv[])
{
  if (argc < 4)
  {
    std::cerr << "Missing parameters. " << std::endl;
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0] << " inputImageFile outputImageFile direction" << std::endl;
    return -1;
  }

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

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

  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;
  }

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

  itk::SobelOperator<PixelType, 2> sobelOperator;
  sobelOperator.SetDirection(::atoi(argv[3]));
  sobelOperator.CreateDirectional();

  itk::NeighborhoodInnerProduct<ImageType> innerProduct;

  // First we load the input image and create the output image and inner product
  // function as in the previous examples.  The image iterators will be created
  // in a later step.  Next we create a face calculator object.  An empty list is
  // created to hold the regions that will later on be returned by the face
  // calculator.

  using FaceCalculatorType = itk::NeighborhoodAlgorithm::ImageBoundaryFacesCalculator<ImageType>;

  FaceCalculatorType               faceCalculator;
  FaceCalculatorType::FaceListType faceList;

  // The face calculator function is invoked by passing it an image pointer, an
  // image region, and a neighborhood radius.  The image pointer is the same
  // image used to initialize the neighborhood iterator, and the image region is
  // the region that the algorithm is going to process.  The radius is the radius
  // of the iterator.
  //
  // Notice that in this case the image region is given as the region of the
  // \emph{output} image and the image pointer is given as that of the
  // \emph{input} image.  This is important if the input and output images differ
  // in size, i.e. the input image is larger than the output image.  ITK
  // and OTB image
  // filters, for example, operate on data from the input image but only generate
  // results in the \code{RequestedRegion} of the output image, which may be
  // smaller than the full extent of the input.

  faceList = faceCalculator(reader->GetOutput(), output->GetRequestedRegion(), sobelOperator.GetRadius());

  // The face calculator has returned a list of $2N+1$ regions. The first element
  // in the list is always the inner region, which may or may not be important
  // depending on the application.  For our purposes it does not matter because
  // all regions are processed the same way.  We use an iterator to traverse the
  // list of faces.

  FaceCalculatorType::FaceListType::iterator fit;

  // We now rewrite the main loop of the previous example so that each region in the
  // list is processed by a separate iterator.  The iterators \code{it} and
  // \code{out} are reinitialized over each region in turn.  Bounds checking is
  // automatically enabled for those regions that require it, and disabled for
  // the region that does not.

  IteratorType             out;
  NeighborhoodIteratorType it;

  for (fit = faceList.begin(); fit != faceList.end(); ++fit)
  {
    it  = NeighborhoodIteratorType(sobelOperator.GetRadius(), reader->GetOutput(), *fit);
    out = IteratorType(output, *fit);

    for (it.GoToBegin(), out.GoToBegin(); !it.IsAtEnd(); ++it, ++out)
    {
      out.Set(innerProduct(it, sobelOperator));
    }
  }

  // The output is written as before.  Results for this example are the same as
  // the previous example.  You may not notice the speedup except on larger
  // images.  When moving to 3D and higher dimensions, the effects are greater
  // because the volume to surface area ratio is usually larger.  In other
  // words, as the number of interior pixels increases relative to the number of
  // face pixels, there is a corresponding increase in efficiency from disabling
  // bounds checking on interior pixels.

  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;
  }

  return EXIT_SUCCESS;
}
