/*=========================================================================
 *
 *  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 "itkImageFileReader.h"

#include "itkRobustAutomaticThresholdCalculator.h"
#include "itkGradientMagnitudeRecursiveGaussianImageFilter.h"
#include "itkMath.h"
#include "itkTestingMacros.h"


int
itkRobustAutomaticThresholdCalculatorTest(int argc, char * argv[])
{
  if (argc != 4)
  {
    std::cerr << "Missing parameters." << std::endl;
    std::cerr << "Usage: " << itkNameOfTestExecutableMacro(argv);
    std::cerr << " inputImage pow expectedOutput" << std::endl;
    return EXIT_FAILURE;
  }

  constexpr unsigned int Dimension = 2;

  using PixelType = unsigned short;
  using ImageType = itk::Image<PixelType, Dimension>;

  using RealPixelType = float;
  using RealImageType = itk::Image<RealPixelType, Dimension>;

  using ReaderType = itk::ImageFileReader<ImageType>;
  auto reader = ReaderType::New();
  reader->SetFileName(argv[1]);

  using GradientType = itk::GradientMagnitudeRecursiveGaussianImageFilter<ImageType, RealImageType>;
  auto gradient = GradientType::New();
  gradient->SetInput(reader->GetOutput());
  gradient->SetSigma(10);
  gradient->Update();

  using CalculatorType = itk::RobustAutomaticThresholdCalculator<ImageType, RealImageType>;
  auto calculator = CalculatorType::New();

  ITK_EXERCISE_BASIC_OBJECT_METHODS(calculator, RobustAutomaticThresholdCalculator, Object);


  calculator->SetGradient(gradient->GetOutput());

  double pow = std::stod(argv[2]);
  calculator->SetPow(pow);
  ITK_TEST_SET_GET_VALUE(pow, calculator->GetPow());


  // Test input or gradient unset exceptions
  ITK_TRY_EXPECT_EXCEPTION(calculator->Compute());

  ITK_TRY_EXPECT_EXCEPTION(calculator->GetOutput());


  calculator->SetInput(reader->GetOutput());

  ITK_TRY_EXPECT_NO_EXCEPTION(calculator->Compute());


  // Regression test
  auto                           expectedOutput = static_cast<CalculatorType::InputPixelType>(std::stod(argv[3]));
  CalculatorType::InputPixelType computedOutput = calculator->GetOutput();
  if (itk::Math::NotAlmostEquals(expectedOutput, computedOutput))
  {
    std::cerr << "Test failed!" << std::endl;
    std::cerr << "Error in itk::RobustAutomaticThresholdCalculator::GetOutput" << std::endl;
    std::cerr << "Expected: "
              << static_cast<itk::NumericTraits<CalculatorType::InputPixelType>::PrintType>(expectedOutput)
              << std::endl;
    std::cerr << ", but got: "
              << static_cast<itk::NumericTraits<CalculatorType::InputPixelType>::PrintType>(computedOutput)
              << std::endl;
    return EXIT_FAILURE;
  }

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