/*=========================================================================
 *
 *  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.
 *
 *=========================================================================*/
/*=========================================================================
 *
 *  Portions of this file are subject to the VTK Toolkit Version 3 copyright.
 *
 *  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen
 *
 *  For complete copyright, license and disclaimer of warranty information
 *  please refer to the NOTICE file at the top of the ITK source tree.
 *
 *=========================================================================*/
#include "itkSimpleFilterWatcher.h"
#include "vnl/vnl_sample.h"
#include "makeRandomImageBsplineInterpolator.h"
#include "itkMath.h"
#include "itkTestingMacros.h"


template <typename TFilter>
typename TFilter::SplinePolesVectorType
ParseSplinePoles(char * splinePolesIn)
{
  typename TFilter::SplinePolesVectorType splinePolesOut;

  // Get the individual components
  char *                                              endPtr;
  typename TFilter::SplinePolesVectorType::value_type value;
  while (*splinePolesIn)
  {
    value = strtod(splinePolesIn, &endPtr);
    if (splinePolesIn == endPtr)
    {
      (*splinePolesIn)++;
    }
    else if (endPtr == nullptr || *endPtr == 0)
    {
      splinePolesOut.push_back(value);
      break;
    }
    else
    {
      splinePolesOut.push_back(value);
      splinePolesIn = endPtr + 1;
    }
  }

  return splinePolesOut;
}


/** Note:  This is the same test used for the itkBSplineResampleImageFunctionTest
 *        It is duplicated here because it exercises the itkBSplineDecompositionFilter
 *        and demonstrates its use.
 */

int
itkBSplineDecompositionImageFilterTest(int argc, char * argv[])
{
  if (argc != 3)
  {
    std::cerr << "Usage: " << itkNameOfTestExecutableMacro(argv);
    std::cerr << " splineOrder";
    std::cerr << " splinePoles(e.g. 0.12753,-0.48673,0.76439, [0.12753,-0.48673,0.76439], \"[0.12753 -0.48673 "
                 "0.76439]\", \"(0.12753 -0.48673 0.76439)\", or \"0.12753 -0.48673 0.76439\")";
    std::cerr << std::endl;
    return EXIT_FAILURE;
  }

  constexpr unsigned int ImageDimension = 2;
  using PixelType = float;
  using ImageType = itk::Image<PixelType, ImageDimension>;
  using BSplineInterpolatorFunctionType = itk::BSplineInterpolateImageFunction<ImageType, double, double>;

  unsigned int                             splineOrder = std::stoi(argv[1]);
  BSplineInterpolatorFunctionType::Pointer interpolator =
    makeRandomImageInterpolator<BSplineInterpolatorFunctionType>(splineOrder);
  ImageType::ConstPointer randImage = interpolator->GetInputImage();

  using FilterType = itk::BSplineDecompositionImageFilter<ImageType, ImageType>;
  auto filter = FilterType::New();

  ITK_EXERCISE_BASIC_OBJECT_METHODS(filter, BSplineDecompositionImageFilter, ImageToImageFilter);

  itk::SimpleFilterWatcher watcher(filter, "BSplineDecompositionImageFilter");

  filter->SetInput(randImage);

  int unsupportedSplineOrder = 6;

  ITK_TRY_EXPECT_EXCEPTION(filter->SetSplineOrder(unsupportedSplineOrder));

  filter->SetSplineOrder(interpolator->GetSplineOrder());
  ITK_TEST_SET_GET_VALUE(interpolator->GetSplineOrder(), filter->GetSplineOrder());

  FilterType::SplinePolesVectorType expectedSplinePoles = ParseSplinePoles<FilterType>(argv[2]);

  int expectedNumberOfPoles = expectedSplinePoles.size();
  int resultNumberOfPoles = filter->GetNumberOfPoles();
  if (!itk::Math::ExactlyEquals(expectedNumberOfPoles, resultNumberOfPoles))
  {
    std::cout << "Test failed!" << std::endl;
    std::cout << "Error in GetNumberOfPoles()" << std::endl;
    std::cout << "Expected: " << expectedNumberOfPoles << std::endl;
    std::cout << " , but got: " << resultNumberOfPoles << std::endl;
    return EXIT_FAILURE;
  }

  FilterType::SplinePolesVectorType resultSplinePoles = filter->GetSplinePoles();
  double                            tolerance1 = 1e-10;
  for (unsigned int i = 0; i < resultSplinePoles.size(); ++i)
  {
    FilterType::SplinePolesVectorType::value_type expectedSplinePole = expectedSplinePoles[i];
    FilterType::SplinePolesVectorType::value_type resultSplinePole = resultSplinePoles[i];
    if (!itk::Math::FloatAlmostEqual(expectedSplinePole, resultSplinePole, 10, tolerance1))
    {
      std::cout.precision(static_cast<int>(itk::Math::abs(std::log10(tolerance1))));
      std::cout << "Test failed!" << std::endl;
      std::cout << "Error in GetSplinePoles() at index: [" << i << ']' << std::endl;
      std::cout << "Expected: " << expectedSplinePole << std::endl;
      std::cout << " , but got: " << resultSplinePole << std::endl;
      std::cout << " Values differ by more than: " << tolerance1 << std::endl;
      return EXIT_FAILURE;
    }
  }

  ITK_TRY_EXPECT_NO_EXCEPTION(filter->Update());


  // Set up a BSplineResampleImageFunction.
  using ResampleFunctionType = itk::BSplineResampleImageFunction<ImageType, double>;
  auto resample = ResampleFunctionType::New();

  resample->SetSplineOrder(interpolator->GetSplineOrder());
  resample->SetInputImage(filter->GetOutput());

  // Compare 10 values at random points.

  ImageType::IndexType last;
  last.Fill(0);
  last[0] = randImage->GetLargestPossibleRegion().GetSize()[0] - 1;
  ImageType::PointType lastPhysicalLocation;
  randImage->TransformIndexToPhysicalPoint(last, lastPhysicalLocation);

  const double minValue = randImage->GetOrigin()[0];
  const double maxValue = lastPhysicalLocation[0];

  double tolerance2 = 1e-5;
  for (unsigned int k = 0; k < 10; ++k)
  {
    ResampleFunctionType::PointType point;
    for (unsigned int j = 0; j < ImageDimension; ++j)
    {
      point[j] = vnl_sample_uniform(minValue, maxValue);
    }

    const double f = resample->Evaluate(point);
    const double g = interpolator->Evaluate(point);

    if (!itk::Math::FloatAlmostEqual(f, g, 10, tolerance2))
    {
      std::cout.precision(static_cast<int>(itk::Math::abs(std::log10(tolerance2))));
      std::cout << " Test failed! " << std::endl;
      std::cout << "Resample and Interpolated points are different." << std::endl;
      std::cout << " Point: " << point << std::endl;
      std::cout << " Resample: " << resample->Evaluate(point) << std::endl;
      std::cout << " Interpolator: " << interpolator->Evaluate(point) << std::endl;
      std::cout << " Values differ by more than: " << tolerance2 << std::endl;
      return EXIT_FAILURE;
    }
  }

  // Instantiation test with a std::complex pixel
  using ComplexPixelType = std::complex<PixelType>;
  using ComplexImageType = itk::Image<ComplexPixelType, ImageDimension>;
  using ComplexFilterType = itk::BSplineDecompositionImageFilter<ComplexImageType, ComplexImageType>;
  auto complexFilter = ComplexFilterType::New();

  ITK_EXERCISE_BASIC_OBJECT_METHODS(filter, BSplineDecompositionImageFilter, ImageToImageFilter);

  return EXIT_SUCCESS;
}
