/*
 * 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"
#include "itkNeighborhoodAlgorithm.h"
#include "itkGaussianOperator.h"
#include "itkNeighborhoodInnerProduct.h"

// This example introduces slice-based neighborhood processing.  A slice, in
// this context, is a 1D path through an ND neighborhood. Slices are defined
// for generic arrays by the \code{std::slice} class as a start index, a step
// size, and an end index.  Slices simplify the implementation of certain
// neighborhood calculations.  They also provide a mechanism for taking inner
// products with subregions of neighborhoods.
//
// Suppose, for example, that we want to take partial derivatives in the $y$
// direction of a neighborhood, but offset those derivatives by one pixel
// position along the positive $x$ direction.  For a $3\times3$, 2D
// neighborhood iterator, we can construct an \code{std::slice}, \code{(start =
// 2, stride = 3, end = 8)}, that represents the neighborhood offsets $(1,
// -1)$, $(1, 0)$, $(1, 1)$ (see Figure~\ref{fig:NeighborhoodIteratorFig2}). If we
// pass this slice as an extra argument to the
// \doxygen{itk}{NeighborhoodInnerProduct} function, then the inner product is taken
// only along that slice.  This ``sliced'' inner product with a 1D
// \doxygen{itk}{DerivativeOperator} gives the desired derivative.
//
// The previous separable Gaussian filtering example can be rewritten using
// slices and slice-based inner products.  In general, slice-based processing
// is most useful when doing many different calculations on the same
// neighborhood, where defining multiple iterators as in
// Section~\ref{sec:NeighborhoodExample4} becomes impractical or inefficient.
// Good examples of slice-based neighborhood processing can be found in any of
// the ND anisotropic diffusion function objects, such as
// \doxygen{itk}{CurvatureNDAnisotropicDiffusionFunction}.

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 sigma" << 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::NeighborhoodInnerProduct<ImageType> innerProduct;

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

  FaceCalculatorType                         faceCalculator;
  FaceCalculatorType::FaceListType           faceList;
  FaceCalculatorType::FaceListType::iterator fit;

  IteratorType             out;
  NeighborhoodIteratorType it;

  // The first difference between this example and the previous example is that
  // the Gaussian operator is only initialized once.  Its direction is not
  // important because it is only a 1D array of coefficients.

  itk::GaussianOperator<PixelType, 2> gaussianOperator;
  gaussianOperator.SetDirection(0);
  gaussianOperator.SetVariance(::atof(argv[3]) * ::atof(argv[3]));
  gaussianOperator.CreateDirectional();

  // Next we need to define a radius for the iterator.  The radius in all
  // directions matches that of the single extent of the Gaussian operator,
  // defining a square neighborhood.

  NeighborhoodIteratorType::RadiusType radius;
  radius.Fill(gaussianOperator.GetRadius()[0]);

  // The inner product and face calculator are defined for the main processing
  // loop as before, but now the iterator is reinitialized each iteration with
  // the square \code{radius} instead of the radius of the operator.  The
  // inner product is taken using a slice along the axial direction corresponding
  // to the current iteration.  Note the use of \code{GetSlice()} to return the
  // proper slice from the iterator itself.  \code{GetSlice()} can only be used
  // to return the slice along the complete extent of the axial direction of a
  // neighborhood.

  ImageType::Pointer input = reader->GetOutput();
  faceList                 = faceCalculator(input, output->GetRequestedRegion(), radius);

  for (unsigned int i = 0; i < ImageType::ImageDimension; ++i)
  {
    for (fit = faceList.begin(); fit != faceList.end(); ++fit)
    {
      it  = NeighborhoodIteratorType(radius, input, *fit);
      out = IteratorType(output, *fit);
      for (it.GoToBegin(), out.GoToBegin(); !it.IsAtEnd(); ++it, ++out)
      {
        out.Set(innerProduct(it.GetSlice(i), it, gaussianOperator));
      }
    }

    // Swap the input and output buffers
    if (i != ImageType::ImageDimension - 1)
    {
      ImageType::Pointer tmp = input;
      input                  = output;
      output                 = tmp;
    }
  }

  // This technique produces exactly the same results as the previous example.  A
  // little experimentation, however, will reveal that it is less efficient since
  // the neighborhood iterator is keeping track of extra, unused pixel locations
  // for each iteration, while the previous example only references those pixels
  // that it needs.  In cases, however, where an algorithm takes multiple
  // derivatives or convolution products over the same neighborhood, slice-based
  // processing can increase efficiency and simplify the implementation.

  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;
}
