/*=========================================================================
 *
 *  Copyright NumFOCUS
 *
 *  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
 *
 *         https://www.apache.org/licenses/LICENSE-2.0.txt
 *
 *  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 "itkBinaryMinMaxCurvatureFlowImageFilter.h"
#include "itkCommand.h"
#include "vnl/vnl_sample.h"


// The following class is used to support callbacks
// on the filter in the pipeline that follows later
namespace
{
class ShowProgressObject
{
public:
  ShowProgressObject(itk::ProcessObject * o) { m_Process = o; }
  void
  ShowProgress()
  {
    std::cout << "Progress " << m_Process->GetProgress() << std::endl;
  }
  itk::ProcessObject::Pointer m_Process;
};
} // namespace


#define MAXRUNS 5 // maximum number of runs

template <unsigned int VImageDimension>
int
testBinaryMinMaxCurvatureFlow(itk::Size<VImageDimension> & size,
                              double                       threshold,
                              double                       radius,
                              int                          numberOfRuns,
                              unsigned int                 niter[],
                              unsigned long                radii[]);

/**
 * This file tests the functionality of the BinaryMinMaxCurvatureFlowImageFilter.
 * The test uses a binary image of a circle/sphere with intensity value
 * of 0 (black). The background is white ( intensity = 255 ).
 * X% salt and pepper noise is added to the the input image. Specifically,
 * X% of the pixels is replaced with a value chosen from a uniform
 * distribution between 0 and 255.
 *
 * We then test the ability of BinaryMinMaxCurvatureFlowImageFilter to denoise
 * the binary image.
 */
int
itkBinaryMinMaxCurvatureFlowImageFilterTest(int, char *[])
{

  double        radius;
  int           numberOfRuns;
  unsigned int  niter[MAXRUNS];
  unsigned long radii[MAXRUNS];

  itk::Size<2> size2D;
  size2D[0] = 64;
  size2D[1] = 64;
  radius = 20.0;
  numberOfRuns = 2;
  niter[0] = 100;
  niter[1] = 100;
  radii[0] = 1;
  radii[1] = 3;

  const int err2D = testBinaryMinMaxCurvatureFlow(size2D, 127.5, radius, numberOfRuns, niter, radii);

  if (err2D)
  {
    return EXIT_FAILURE;
  }
  return EXIT_SUCCESS;
}


template <unsigned int VImageDimension>
int
testBinaryMinMaxCurvatureFlow(itk::Size<VImageDimension> & size, // ND image size
                              double                       threshold,
                              double                       radius,       // ND-sphere radius
                              int                          numberOfRuns, // number of times to run the filter
                              unsigned int                 niter[],      // number of iterations
                              unsigned long                radii[]       // stencil radius
)
{

  using PixelType = float;
  enum
  {
    ImageDimension = VImageDimension
  };
  using ImageType = itk::Image<PixelType, ImageDimension>;
  using IteratorType = itk::ImageRegionIterator<ImageType>;
  using DenoiserType = itk::BinaryMinMaxCurvatureFlowImageFilter<ImageType, ImageType>;

  auto denoiser = DenoiserType::New();

  int j;

  /**
   * Create an image containing a circle/sphere with intensity of 0
   * and background of 255 with added salt and pepper noise.
   */
  double    sqrRadius = itk::Math::sqr(radius); // radius of the circle/sphere
  double    fractionNoise = 0.30;               // salt & pepper noise fraction
  PixelType foreground = 0.0;                   // intensity value of the foreground
  PixelType background = 255.0;                 // intensity value of the background

  std::cout << "Create an image of circle/sphere with noise" << std::endl;
  auto circleImage = ImageType::New();


  typename ImageType::RegionType region;
  region.SetSize(size);

  circleImage->SetLargestPossibleRegion(region);
  circleImage->SetBufferedRegion(region);
  circleImage->Allocate();

  for (IteratorType circleIter(circleImage, circleImage->GetBufferedRegion()); !circleIter.IsAtEnd(); ++circleIter)
  {
    typename ImageType::IndexType index = circleIter.GetIndex();
    float                         value;

    double lhs = 0.0;
    for (j = 0; j < ImageDimension; ++j)
    {
      lhs += itk::Math::sqr(static_cast<double>(index[j]) - static_cast<double>(size[j]) * 0.5);
    }
    if (lhs < sqrRadius)
    {
      value = foreground;
    }
    else
    {
      value = background;
    }

    if (vnl_sample_uniform(0.0, 1.0) < fractionNoise)
    {
      value = vnl_sample_uniform(std::min(foreground, background), std::max(foreground, background));
    }
    circleIter.Set(value);
  }

  /**
   * Run MinMaxCurvatureFlowImageFilter several times using the previous
   * output as the input in the next run.
   */

  std::cout << "Run BinaryMinMaxCurvatureFlowImageFiler.." << std::endl;

  // set other denoiser parameters here
  denoiser->SetTimeStep(0.05);
  denoiser->SetThreshold(threshold);

  // attach a progress watcher to the denoiser
  ShowProgressObject                                    progressWatch(denoiser);
  itk::SimpleMemberCommand<ShowProgressObject>::Pointer command;
  command = itk::SimpleMemberCommand<ShowProgressObject>::New();
  command->SetCallbackFunction(&progressWatch, &ShowProgressObject::ShowProgress);
  denoiser->AddObserver(itk::ProgressEvent(), command);


  typename ImageType::Pointer swapPointer = circleImage;

  for (j = 0; j < numberOfRuns; ++j)
  {

    denoiser->SetInput(swapPointer);

    // set the stencil radius and number of iterations
    denoiser->SetStencilRadius(radii[j]);
    denoiser->SetNumberOfIterations(niter[j]);

    std::cout << " Run: " << j;
    std::cout << " Radius: " << denoiser->GetStencilRadius();
    std::cout << " Iter: " << denoiser->GetNumberOfIterations();
    std::cout << std::endl;

    // run the filter
    denoiser->Update();

    swapPointer = denoiser->GetOutput();
    swapPointer->DisconnectPipeline();
  }


  /**
   * Check the quality of the output by comparing it against a
   * clean image of the circle/sphere.
   * An output pixel is okay if it is within
   * 0.1 * |foreground - background| of the true value.
   * This test is considered as passed if the fraction of wrong
   * pixels is less than the original noise fraction.
   */
  std::cout << "Checking the output..." << std::endl;
  const PixelType tolerance = itk::Math::abs(foreground - background) * 0.1;

  unsigned long numPixelsWrong = 0;
  for (IteratorType outIter(swapPointer, swapPointer->GetBufferedRegion()); !outIter.IsAtEnd(); ++outIter)
  {
    typename ImageType::IndexType index = outIter.GetIndex();
    PixelType                     value = outIter.Get();

    double lhs = 0.0;
    for (j = 0; j < ImageDimension; ++j)
    {
      lhs += itk::Math::sqr(static_cast<double>(index[j]) - static_cast<double>(size[j]) * 0.5);
    }
    if (lhs < sqrRadius)
    {
      if (itk::Math::abs(foreground - value) > tolerance)
      {
        numPixelsWrong++;
      }
    }
    else if (itk::Math::abs(background - value) > tolerance)
    {
      numPixelsWrong++;
    }
  }

  const double fractionWrong = static_cast<double>(numPixelsWrong) / static_cast<double>(region.GetNumberOfPixels());

  std::cout << "Noise reduced from " << fractionNoise << " to ";
  std::cout << fractionWrong << std::endl;

  bool passed = true;
  if (fractionWrong > fractionNoise)
  {
    std::cout << "Test failed." << std::endl;
    return EXIT_FAILURE;
  }

  /**
   * Exercise other member functions here
   */
  denoiser->Print(std::cout);
  std::cout << "GetThreshold: " << denoiser->GetThreshold() << std::endl;

  /**
   * Exercise error handling
   */
  using WrongFunctionType = itk::CurvatureFlowFunction<ImageType>;
  auto wrongFunction = WrongFunctionType::New();

  passed = false;
  try
  {
    denoiser->SetDifferenceFunction(wrongFunction);
    denoiser->Update();
  }
  catch (const itk::ExceptionObject & err)
  {
    passed = true;
    std::cout << "Caught expected exception." << std::endl;
    std::cout << err << std::endl;
  }

  if (!passed)
  {
    std::cout << "Test failed." << std::endl;
    return EXIT_FAILURE;
  }

  std::cout << "Test passed." << std::endl;
  return EXIT_SUCCESS;
}
