ITK  5.4.0 Insight Toolkit
Examples/Iterators/NeighborhoodIterators3.cxx
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// Software Guide : BeginLatex
//
// This example illustrates a technique for improving the efficiency of
// neighborhood calculations by eliminating unnecessary bounds checking. As
// described in Section~\ref{sec:NeighborhoodIterators}, the neighborhood
// iterator automatically enables or disables bounds checking based on the
// iteration region in which it is initialized. By splitting our image into
// boundary and non-boundary regions, and then processing each region using a
// different neighborhood iterator, the algorithm will only perform
// bounds-checking on those pixels for which it is actually required. This
// trick can provide a significant speedup for simple algorithms such as our
// Sobel edge detection, where iteration speed is a critical.
//
// Splitting the image into the necessary regions is an easy task when you use
// the \doxygen{NeighborhoodAlgorithm::ImageBoundaryFacesCalculator}. The
// face calculator is so named because it returns a list of the faces'' of
// the ND dataset. Faces are those regions whose pixels all lie within a
// distance $d$ from the boundary, where $d$ is the radius of the neighborhood
// stencil used for the numerical calculations. In other words, faces are
// those regions where a neighborhood iterator of radius $d$ will always
// overlap the boundary of the image. The face calculator also returns the
// single \emph{inner} region, in which out-of-bounds values are never
// required and bounds checking is not necessary.
//
// The face calculator object is defined in \code{itkNeighborhoodAlgorithm.h}.
// We include this file in addition to those from the previous two examples.
//
// 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 direction"
<< std::endl;
return EXIT_FAILURE;
}
using PixelType = float;
using ImageType = itk::Image<PixelType, 2>;
using NeighborhoodIteratorType = itk::ConstNeighborhoodIterator<ImageType>;
try
{
}
catch (const itk::ExceptionObject & err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
auto output = ImageType::New();
output->Allocate();
sobelOperator.SetDirection(std::stoi(argv[3]));
sobelOperator.CreateDirectional();
// Software Guide : BeginLatex
//
// First we load the input image and create the output image and inner
// product function as in the previous examples. The image iterators will
// be created in a later step. Next we create a face calculator object. An
// empty list is created to hold the regions that will later on be returned
// by the face calculator.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using FaceCalculatorType =
FaceCalculatorType faceCalculator;
FaceCalculatorType::FaceListType faceList;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The face calculator function is invoked by passing it an image pointer,
// an image region, and a neighborhood radius. The image pointer is the
// same image used to initialize the neighborhood iterator, and the image
// region is the region that the algorithm is going to process. The radius
// is the radius of the iterator.
//
// Notice that in this case the image region is given as the region of the
// \emph{output} image and the image pointer is given as that of the
// \emph{input} image. This is important if the input and output images
// differ in size, i.e. the input image is larger than the output image. ITK
// image filters, for example, operate on data from the input image but only
// generate results in the \code{RequestedRegion} of the output image, which
// may be smaller than the full extent of the input.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
output->GetRequestedRegion(),
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The face calculator has returned a list of $2N+1$ regions. The first
// element in the list is always the inner region, which may or may not be
// important depending on the application. For our purposes it does not
// matter because all regions are processed the same way. We use an
// iterator to traverse the list of faces.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
FaceCalculatorType::FaceListType::iterator fit;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We now rewrite the main loop of the previous example so that each region
// in the list is processed by a separate iterator. The iterators \code{it}
// and \code{out} are reinitialized over each region in turn. Bounds
// checking is automatically enabled for those regions that require it, and
// disabled for the region that does not.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
IteratorType out;
NeighborhoodIteratorType it;
for (fit = faceList.begin(); fit != faceList.end(); ++fit)
{
it = NeighborhoodIteratorType(
out = IteratorType(output, *fit);
for (it.GoToBegin(), out.GoToBegin(); !it.IsAtEnd(); ++it, ++out)
{
out.Set(innerProduct(it, sobelOperator));
}
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The output is written as before. Results for this example are the same
// as the previous example. You may not notice the speedup except on larger
// images. When moving to 3D and higher dimensions, the effects are greater
// because the volume to surface area ratio is usually larger. In other
// words, as the number of interior pixels increases relative to the number
// of face pixels, there is a corresponding increase in efficiency from
// disabling bounds checking on interior pixels.
//
// Software Guide : EndLatex
using WritePixelType = unsigned char;
using WriteImageType = itk::Image<WritePixelType, 2>;
using RescaleFilterType =
auto rescaler = RescaleFilterType::New();
rescaler->SetOutputMinimum(0);
rescaler->SetOutputMaximum(255);
rescaler->SetInput(output);
auto 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
itk::NeighborhoodOperator::SetDirection
void SetDirection(const unsigned long direction)
Definition: itkNeighborhoodOperator.h:94
itk::SobelOperator
A NeighborhoodOperator for performing a directional Sobel edge-detection operation at a pixel locatio...
Definition: itkSobelOperator.h:98
itkConstNeighborhoodIterator.h
itkNeighborhoodAlgorithm.h
itk::SobelOperator::CreateDirectional
void CreateDirectional() override
Definition: itkSobelOperator.h:113
itkImageRegionIterator.h
Data source that reads image data from a single file.
itk::ImageRegionIterator
A multi-dimensional iterator templated over image type that walks a region of pixels.
Definition: itkImageRegionIterator.h:80
Definition: itkNeighborhood.h:129
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:88
itkSobelOperator.h
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:133
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:88
itk::NeighborhoodInnerProduct< ImageType >
New
static Pointer New()