#if defined(_MSC_VER)
# pragma warning(disable : 4786)
#endif
// SimpleITK includes
#include "SimpleITK.h"
// ITK includes
#include "itkImage.h"
#include "itkCurvatureFlowImageFilter.h"
// create convenient namespace alias
namespace sitk = itk::simple;
/**
* This example shows how ITK and SimpleITK can be used together to work
* on the same data. We use the same example application as the one presented
* in the Segmentation/ConnectedThresholdImageFilter.cxx example, but we
* replace the SimpleITK version of CurvatureFlowImageFilter with the
* corresponding ITK version. While not terribly useful in this situation since
* CurvatureFlowImageFilter is already available in SimpleITK this demonstrates
* how ITK filters that have not been converted for SimpleITK can still be used
* in a SimpleITK context
*/
int
main(int argc, char * argv[])
{
//
// Check command line parameters
//
if (argc < 7)
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " inputImage outputImage lowerThreshold upperThreshold "
"seedX seedY [seed2X seed2Y ... ]"
<< std::endl;
return 1;
}
//
// Read the image
//
sitk::ImageFileReader reader;
reader.SetFileName(std::string(argv[1]));
sitk::Image image = reader.Execute();
//
// Set up writer
//
sitk::ImageFileWriter writer;
writer.SetFileName(std::string(argv[2]));
//////
// Blur using CurvatureFlowImageFilter
//
// Here we demonstrate the use of the ITK version of CurvatureFlowImageFilter
// instead of the SimpleITK version.
//////
//
// First, define the type alias that correspond to the types of the input
// image. This requires foreknowledge of the data type of the input image.
//
const unsigned int Dimension = 2;
using InternalPixelType = float;
using InternalImageType = itk::Image<InternalPixelType, Dimension>;
//
// We must check the image dimension and the pixel type of the
// SimpleITK image match the ITK image we will cast to.s
//
if (image.GetDimension() != Dimension)
{
std::cerr << "Input image is not a " << Dimension << " dimensional image as expected!" << std::endl;
return 1;
}
//
// The read sitk::Image could be any pixel type. Cast the image, to
// float so we know what type we have.
//
sitk::CastImageFilter caster;
caster.SetOutputPixelType(sitk::sitkFloat32);
image = caster.Execute(image);
//
// Extract the itk image from the SimpleITK image
//
InternalImageType::Pointer itkImage = dynamic_cast<InternalImageType *>(image.GetITKBase());
//
// Always check the results of dynamic_casts
//
if (itkImage.IsNull())
{
std::cerr << "Unexpected error converting SimpleITK image to ITK image!" << std::endl;
return 1;
}
//
// Set up the blur filter and attach it to the pipeline.
//
using BlurFilterType = itk::CurvatureFlowImageFilter<InternalImageType, InternalImageType>;
BlurFilterType::Pointer blurFilter = BlurFilterType::New();
blurFilter->SetInput(itkImage);
blurFilter->SetNumberOfIterations(5);
blurFilter->SetTimeStep(0.125);
//
// Execute the Blur pipeline by calling Update() on the blur filter.
//
blurFilter->Update();
//
// Return to the simpleITK setting by making a SimpleITK image using the
// output of the blur filter.
//
sitk::Image blurredImage = sitk::Image(blurFilter->GetOutput());
//////
// Now that we have finished the ITK section, we return to the SimpleITK API
//////
//
// Set up ConnectedThresholdImageFilter for segmentation
//
sitk::ConnectedThresholdImageFilter segmentationFilter;
segmentationFilter.SetLower(atof(argv[3]));
segmentationFilter.SetUpper(atof(argv[4]));
segmentationFilter.SetReplaceValue(255);
for (int i = 5; i + 1 < argc; i += 2)
{
std::vector<unsigned int> seed = { (unsigned int)atoi(argv[i]), (unsigned int)atoi(argv[i + 1]) };
segmentationFilter.AddSeed(seed);
std::cout << "Adding a seed at ";
for (unsigned int j = 0; j < seed.size(); ++i)
{
std::cout << seed[j] << " ";
}
std::cout << std::endl;
}
sitk::Image outImage = segmentationFilter.Execute(blurredImage);
//
// Write out the resulting file
//
writer.Execute(outImage);
return 0;
}