text: /*=========================================================================
   *
   *  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 "itkGradientRecursiveGaussianImageFilter.h"
  #include "itkGradientImageFilter.h"
  #include "itkVectorMagnitudeImageFilter.h"
  #include "itkGradientVectorFlowImageFilter.h"
  #include "itkTestingMacros.h"
  
  int
  itkGradientVectorFlowImageFilterTest(int, char *[])
  {
    // Define the dimension of the images
    constexpr unsigned int myDimension = 2;
  
    // Declare gradient type
    using myGradientType = itk::CovariantVector<double, myDimension>;
  
    // Declare the types of the images
    using myImageType = itk::Image<double, myDimension>;
    using myGradientImageType = itk::Image<myGradientType, myDimension>;
  
    // Declare the type of the index to access images
    using myIndexType = itk::Index<myDimension>;
  
    // Declare the type of the size
    using mySizeType = itk::Size<myDimension>;
  
    // Declare the type of the Region
    using myRegionType = itk::ImageRegion<myDimension>;
  
    using myLaplacianFilterType = itk::LaplacianImageFilter<myImageType, myImageType>;
    using myGVFFilterType = itk::GradientVectorFlowImageFilter<myGradientImageType, myGradientImageType>;
  
    using myGFilterType = itk::GradientImageFilter<myImageType, double, double>;
  
    using myVectorMagnitudeFilterType = itk::VectorMagnitudeImageFilter<myGradientImageType, myImageType>;
    // Create the image
    auto inputImage = myImageType::New();
    auto interImage = myImageType::New();
    auto inter1Image = myImageType::New();
  
  
    // Define their size, and start index
    mySizeType size;
    size[0] = 128;
    size[1] = 128;
  
    myIndexType start;
    start.Fill(0);
  
    myRegionType region;
    region.SetIndex(start);
    region.SetSize(size);
  
    // Initialize Image A
    inputImage->SetRegions(region);
    inputImage->Allocate();
  
    interImage->SetRegions(region);
    interImage->Allocate();
  
    inter1Image->SetRegions(region);
    inter1Image->Allocate();
  
    // Declare Iterator types apropriated for each image
    using myIteratorType = itk::ImageRegionIteratorWithIndex<myImageType>;
  
    using myOutputIteratorType = itk::ImageRegionIteratorWithIndex<myGradientImageType>;
  
    // Create one iterator for the Input Image A (this is a light object)
    myIteratorType it(inputImage, inputImage->GetRequestedRegion());
  
    // Initialize the content of Image A
    std::cout << "Input Image initialization " << std::endl;
  
    while (!it.IsAtEnd())
    {
      it.Set(0.0);
      ++it;
    }
  
    size[0] = 100;
    size[1] = 100;
  
    start[0] = 14;
    start[1] = 14;
  
    // Create one iterator for an internal region
    region.SetSize(size);
    region.SetIndex(start);
    myIteratorType itb(inputImage, region);
  
    // Initialize the content the internal region
    while (!itb.IsAtEnd())
    {
      itb.Set(100.0);
      ++itb;
    }
  
    // Declare the type for the
    using myFilterType = itk::GradientRecursiveGaussianImageFilter<myImageType, myGradientImageType>;
  
    // Create a  Filter
    auto filter = myFilterType::New();
  
    auto gfilter = myGFilterType::New();
    auto gtomfilter = myVectorMagnitudeFilterType::New();
  
    // Connect the input images
    filter->SetInput(inputImage);
  
    // Set sigma
    filter->SetSigma(2.0);
  
    // Execute the filter
    filter->Update();
  
    std::cout << "Filter: " << filter;
  
    auto m_LFilter = myLaplacianFilterType::New();
    auto m_GVFFilter = myGVFFilterType::New();
  
    ITK_EXERCISE_BASIC_OBJECT_METHODS(m_GVFFilter, GradientVectorFlowImageFilter, ImageToImageFilter);
  
  
    m_GVFFilter->SetInput(gfilter->GetOutput());
  
    m_GVFFilter->SetLaplacianFilter(m_LFilter);
    ITK_TEST_SET_GET_VALUE(m_LFilter, m_GVFFilter->GetLaplacianFilter());
  
    double noiseLevel = 500;
    m_GVFFilter->SetNoiseLevel(noiseLevel);
    ITK_TEST_SET_GET_VALUE(noiseLevel, m_GVFFilter->GetNoiseLevel());
  
    double timeStep = 0.001;
    m_GVFFilter->SetTimeStep(timeStep);
    ITK_TEST_SET_GET_VALUE(timeStep, m_GVFFilter->GetTimeStep());
  
    int iterationNum = 2;
    m_GVFFilter->SetIterationNum(iterationNum);
    ITK_TEST_SET_GET_VALUE(iterationNum, m_GVFFilter->GetIterationNum());
  
  
    // Get the Smart Pointer to the Filter Output
    // It is important to do it AFTER the filter is Updated
    // Because the object connected to the output may be changed
    // by another during GenerateData() call
    myGradientImageType::Pointer outputImage = filter->GetOutput();
  
    // Create an iterator for going through the output image
    myOutputIteratorType itg(outputImage, outputImage->GetRequestedRegion());
  
    //  Print the content of the result image
    std::cout << " Result " << std::endl;
  
    gtomfilter->SetInput(filter->GetOutput());
    gtomfilter->Update();
  
    gfilter->SetInput(gtomfilter->GetOutput());
    gfilter->Update();
  
    m_GVFFilter->Update();
  
    std::cout << m_GVFFilter->GetTimeStep() << std::endl;
  
    std::cout << m_GVFFilter->GetNoiseLevel() << std::endl;
  
    std::cout << m_GVFFilter->GetIterationNum() << std::endl;
  
    myOutputIteratorType itgvf(m_GVFFilter->GetOutput(), m_GVFFilter->GetOutput()->GetRequestedRegion());
  
    std::cout << "Completed" << std::endl;
    // All objects should be automatically destroyed at this point
    return EXIT_SUCCESS;
  }
  