ITK  5.2.0
Insight Toolkit
Examples/Iterators/NeighborhoodIterators4.cxx
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*
* Copyright NumFOCUS
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* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
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* http://www.apache.org/licenses/LICENSE-2.0.txt
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
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// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainT1Slice.png}
// OUTPUTS: {NeighborhoodIterators4a.png}
// ARGUMENTS: 0
// Software Guide : EndCommandLineArgs
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainT1Slice.png}
// OUTPUTS: {NeighborhoodIterators4b.png}
// ARGUMENTS: 1
// Software Guide : EndCommandLineArgs
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainT1Slice.png}
// OUTPUTS: {NeighborhoodIterators4c.png}
// ARGUMENTS: 2
// Software Guide : EndCommandLineArgs
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainT1Slice.png}
// OUTPUTS: {NeighborhoodIterators4d.png}
// ARGUMENTS: 5
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// We now introduce a variation on convolution filtering that is useful when a
// convolution kernel is separable. In this example, we create a different
// neighborhood iterator for each axial direction of the image and then take
// separate inner products with a 1D discrete Gaussian kernel.
// The idea of using several neighborhood iterators at once has applications
// beyond convolution filtering and may improve efficiency when the size of
// the whole neighborhood relative to the portion of the neighborhood used
// in calculations becomes large.
//
// The only new class necessary for this example is the Gaussian operator.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
int
main(int argc, char ** argv)
{
if (argc < 4)
{
std::cerr << "Missing parameters. " << std::endl;
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0] << " inputImageFile outputImageFile sigma"
<< std::endl;
return EXIT_FAILURE;
}
using PixelType = float;
using ImageType = itk::Image<PixelType, 2>;
using ReaderType = itk::ImageFileReader<ImageType>;
using NeighborhoodIteratorType = itk::ConstNeighborhoodIterator<ImageType>;
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName(argv[1]);
try
{
reader->Update();
}
catch (const itk::ExceptionObject & err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
ImageType::Pointer output = ImageType::New();
output->SetRegions(reader->GetOutput()->GetRequestedRegion());
output->Allocate();
using FaceCalculatorType =
FaceCalculatorType faceCalculator;
FaceCalculatorType::FaceListType faceList;
FaceCalculatorType::FaceListType::iterator fit;
IteratorType out;
NeighborhoodIteratorType it;
// Software Guide : BeginLatex
//
// The Gaussian operator, like the Sobel operator, is instantiated with a
// pixel type and a dimensionality. Additionally, we set the variance of
// the Gaussian, which has been read from the command line as standard
// deviation.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
gaussianOperator.SetVariance(::std::stod(argv[3]) * ::std::stod(argv[3]));
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The only further changes from the previous example are in the main loop.
// Once again we use the results from face calculator to construct a loop
// that processes boundary and non-boundary image regions separately.
// Separable convolution, however, requires an additional, outer loop over
// all the image dimensions. The direction of the Gaussian operator is
// reset at each iteration of the outer loop using the new dimension. The
// iterators change direction to match because they are initialized with the
// radius of the Gaussian operator.
//
// Input and output buffers are swapped at each iteration so that the output
// of the previous iteration becomes the input for the current iteration.
// The swap is not performed on the last iteration.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
ImageType::Pointer input = reader->GetOutput();
for (unsigned int i = 0; i < ImageType::ImageDimension; ++i)
{
gaussianOperator.SetDirection(i);
gaussianOperator.CreateDirectional();
faceList = faceCalculator(
input, output->GetRequestedRegion(), gaussianOperator.GetRadius());
for (fit = faceList.begin(); fit != faceList.end(); ++fit)
{
it =
NeighborhoodIteratorType(gaussianOperator.GetRadius(), input, *fit);
out = IteratorType(output, *fit);
for (it.GoToBegin(), out.GoToBegin(); !it.IsAtEnd(); ++it, ++out)
{
out.Set(innerProduct(it, gaussianOperator));
}
}
// Swap the input and output buffers
if (i != ImageType::ImageDimension - 1)
{
ImageType::Pointer tmp = input;
input = output;
output = tmp;
}
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The output is rescaled and written as in the previous examples.
// Figure~\ref{fig:NeighborhoodExample4} shows the results of Gaussian
// blurring the image \code{Examples/Data/BrainT1Slice.png} using increasing
// kernel widths.
//
// \begin{figure}
// \centering
// \includegraphics[width=0.23\textwidth]{NeighborhoodIterators4a}
// \includegraphics[width=0.23\textwidth]{NeighborhoodIterators4b}
// \includegraphics[width=0.23\textwidth]{NeighborhoodIterators4c}
// \includegraphics[width=0.23\textwidth]{NeighborhoodIterators4d}
// \itkcaption[Gaussian blurring by convolution filtering]{Results of
// convolution filtering with a Gaussian kernel of increasing standard
// deviation $\sigma$ (from left to right, $\sigma = 0$, $\sigma = 1$,
// $\sigma = 2$, $\sigma = 5$). Increased blurring reduces contrast and
// changes the average intensity value of the image, which causes the image
// to appear brighter when rescaled.}
// \protect\label{fig:NeighborhoodExample4}
// \end{figure}
//
// Software Guide : EndLatex
using WritePixelType = unsigned char;
using WriteImageType = itk::Image<WritePixelType, 2>;
using RescaleFilterType =
RescaleFilterType::Pointer rescaler = RescaleFilterType::New();
rescaler->SetOutputMinimum(0);
rescaler->SetOutputMaximum(255);
rescaler->SetInput(output);
WriterType::Pointer writer = WriterType::New();
writer->SetFileName(argv[2]);
writer->SetInput(rescaler->GetOutput());
try
{
writer->Update();
}
catch (const itk::ExceptionObject & err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
itk::NeighborhoodAlgorithm::ImageBoundaryFacesCalculator
Splits an image into a main region and several "face" regions which are used to handle computations o...
Definition: itkNeighborhoodAlgorithm.h:63
itkConstNeighborhoodIterator.h
itkNeighborhoodAlgorithm.h
itkImageFileReader.h
itkImageRegionIterator.h
itk::NeighborhoodOperator::CreateDirectional
virtual void CreateDirectional()
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itk::ImageRegionIterator
A multi-dimensional iterator templated over image type that walks a region of pixels.
Definition: itkImageRegionIterator.h:80
itk::Neighborhood::GetRadius
const SizeType GetRadius() const
Definition: itkNeighborhood.h:143
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itk::GaussianOperator
A NeighborhoodOperator whose coefficients are a one dimensional, discrete Gaussian kernel.
Definition: itkGaussianOperator.h:69
itkRescaleIntensityImageFilter.h
itkImageFileWriter.h
itkNeighborhoodInnerProduct.h
itk::ConstNeighborhoodIterator
Const version of NeighborhoodIterator, defining iteration of a local N-dimensional neighborhood of pi...
Definition: itkConstNeighborhoodIterator.h:51
itk::RescaleIntensityImageFilter
Applies a linear transformation to the intensity levels of the input Image.
Definition: itkRescaleIntensityImageFilter.h:154
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:86
itk::NeighborhoodInnerProduct< ImageType >
itkGaussianOperator.h
itk::NeighborhoodOperator::SetDirection
void SetDirection(const unsigned long &direction)
Definition: itkNeighborhoodOperator.h:92
itk::GaussianOperator::SetVariance
void SetVariance(const double &variance)
Definition: itkGaussianOperator.h:81