/*=========================================================================
 *
 *  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.
 *
 *=========================================================================*/
// Disable warning for long symbol names in this file only

#include "itkDiscreteGaussianDerivativeImageFilter.h"

namespace
{

bool
NormalizeSineWave(double frequencyPerImage, unsigned int order, double pixelSpacing = 1.0)
{
  // For an image f(x) = sin ( w*x ), where w is a measure of
  // frequency, this method verifies that the normalized scale-scale
  // is with in reasonable tolerance of the theoretical value.

  constexpr unsigned int ImageDimension = 1;
  constexpr unsigned int imageSize = 1024;

  const double tolerance1 = std::pow(.001, 1.0 / order); // still larger than it should be!

  double frequency = frequencyPerImage * 2.0 * itk::Math::pi / (imageSize * pixelSpacing);

  // The theoretical maximal value should occur at this sigma
  double sigmaMax = std::sqrt(static_cast<double>(order)) / frequency;

  // The theoreical maximal value of the derivative, obtained at sigmaMax
  double expectedMax = std::pow(static_cast<double>(order), order * 0.5) * std::exp(-0.5 * order);

  using ImageType = itk::Image<double, ImageDimension>;
  auto image = ImageType::New();

  ImageType::SizeType size;
  size.Fill(imageSize);

  image->SetRegions(ImageType::RegionType(size));
  image->Allocate();

  ImageType::SpacingType spacing;
  spacing.Fill(pixelSpacing);

  image->SetSpacing(spacing);

  itk::ImageRegionIterator<ImageType> iter(image, image->GetBufferedRegion());

  // Create a sine wave image
  while (!iter.IsAtEnd())
  {
    ImageType::PointType p;
    image->TransformIndexToPhysicalPoint(iter.GetIndex(), p);
    const double x = p[0];
    double       value = std::sin(x * frequency);

    iter.Set(value);
    ++iter;
  }

  using GaussianFilterType = itk::DiscreteGaussianDerivativeImageFilter<ImageType, ImageType>;
  auto filter = GaussianFilterType::New();

  filter->SetInput(image);
  filter->SetVariance(itk::Math::sqr(sigmaMax));
  filter->SetOrder(order);
  filter->SetUseImageSpacing(true);
  filter->SetMaximumError(.0005);
  filter->SetMaximumKernelWidth(500);
  filter->SetNormalizeAcrossScale(true);

  ImageType::Pointer outputImage = filter->GetOutput();
  outputImage->Update();

  // Maximal value of the first derivative
  double maxLx = itk::NumericTraits<double>::NonpositiveMin();

  itk::ImageRegionConstIterator<ImageType> oiter(outputImage, outputImage->GetBufferedRegion());

  while (!oiter.IsAtEnd())
  {
    maxLx = std::max(maxLx, oiter.Get());
    ++oiter;
  }

  // Check if the maximal is obtained with a little bit smaller Gaussian
  filter->SetVariance(itk::Math::sqr(sigmaMax * 0.95));
  outputImage->Update();
  oiter.GoToBegin();

  while (!oiter.IsAtEnd())
  {
    if (maxLx < oiter.Get() && !itk::Math::FloatAlmostEqual(maxLx, oiter.Get(), 10, tolerance1))
    {
      std::cout.precision(static_cast<int>(itk::Math::abs(std::log10(tolerance1))));
      std::cout << "Error at period: " << 1.0 / frequency << std::endl;
      std::cout << "Expected maximal value: " << maxLx << ", differs from: " << oiter.Get() << " by more than "
                << tolerance1 << std::endl;
      return false;
    }
    ++oiter;
  }

  // Check if the maximumal value is obtained with a little bit bigger Gaussian
  filter->SetVariance(itk::Math::sqr(sigmaMax * 1.05));
  outputImage->Update();
  oiter.GoToBegin();

  while (!oiter.IsAtEnd())
  {
    if (maxLx < oiter.Get() && !itk::Math::FloatAlmostEqual(maxLx, oiter.Get(), 10, tolerance1))
    {
      std::cout.precision(static_cast<int>(itk::Math::abs(std::log10(tolerance1))));
      std::cout << "Error at period: " << 1.0 / frequency << std::endl;
      std::cout << "Expected maximal value: " << maxLx << ", differs from: " << oiter.Get() << " by more than "
                << tolerance1 << std::endl;
      return false;
    }
    ++oiter;
  }

  constexpr double tolerance2 = 0.01;
  if (!itk::Math::FloatAlmostEqual(maxLx, expectedMax, 10, tolerance2))
  {
    std::cout << "Error at frequency: " << frequencyPerImage << std::endl;
    std::cout << "Expected maximal value: " << expectedMax << ", differs from: " << maxLx << " by more than "
              << tolerance2 << std::endl;
    return false;
  }

  return true;
}

} // namespace

int
itkDiscreteGaussianDerivativeImageFilterScaleSpaceTest(int, char *[])
{
  bool pass = true;

  // Testing first order Gaussian
  pass &= NormalizeSineWave(16, 1);
  pass &= NormalizeSineWave(32, 1);
  pass &= NormalizeSineWave(64, 1);

  // Testing second order Gaussian
  pass &= NormalizeSineWave(16, 2);
  pass &= NormalizeSineWave(32, 2);
  pass &= NormalizeSineWave(64, 2);

  // Testing spacing invariance
  pass &= NormalizeSineWave(16, 2, 0.01);
  pass &= NormalizeSineWave(16, 2, 100);

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

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