SimpleITK  1.3.0.dev466
ITKIntegration/ITKIntegration.cxx
/*=========================================================================
*
* Copyright Insight Software Consortium
*
* 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
*
* http://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.
*
*=========================================================================*/
#if defined(_MSC_VER)
#pragma warning ( disable : 4786 )
#endif
// SimpleITK includes
#include "SimpleITK.h"
// ITK includes
#include "itkImage.h"
// create convenient namespace alias
namespace sitk = itk::simple;
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
//
reader.SetFileName( std::string( argv[1] ) );
sitk::Image image = reader.Execute();
//
// Set up 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 typedefs that correspond to the types of the input
// image. This requires foreknowlege of the data type of the input image.
//
const unsigned int Dimension = 2;
typedef float InternalPixelType;
typedef itk::Image< InternalPixelType, Dimension > InternalImageType;
//
// 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.
//
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.
//
BlurFilterType;
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
//
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;
seed.push_back(atoi(argv[i]));
seed.push_back(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;
}