ITK  5.0.0 Insight Segmentation and Registration Toolkit
Examples/Iterators/ShapedNeighborhoodIterators1.cxx
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#include <cmath>
// Software Guide : BeginLatex
//
// This example uses \doxygen{ShapedNeighborhoodIterator} to implement a binary
// erosion algorithm. If we think of an image $I$ as a set of pixel indices,
// then erosion of $I$ by a smaller set $E$, called the \emph{structuring
// element}, is the set of all indices at locations $x$ in $I$ such that when
// $E$ is positioned at $x$, every element in $E$ is also contained in $I$.
//
// This type of algorithm is easy to implement with shaped neighborhood
// iterators because we can use the iterator itself as the structuring element
// $E$ and move it sequentially through all positions $x$. The result at $x$
// is obtained by checking values in a simple iteration loop through the
// neighborhood stencil.
//
// We need two iterators, a shaped iterator for the input image and a regular
// image iterator for writing results to the output image.
//
// 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]
<< std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Since we are working with binary images in this example, an \code{unsigned
// char} pixel type will do. The image and iterator types are defined using
// the pixel type.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using PixelType = unsigned char;
using ImageType = itk::Image< PixelType, 2 >;
using ShapedNeighborhoodIteratorType =
// Software Guide : EndCodeSnippet
try
{
}
catch ( itk::ExceptionObject &err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
ImageType::Pointer output = ImageType::New();
output->Allocate();
// Software Guide : BeginLatex
//
// Refer to the examples in Section~\ref{sec:itkNeighborhoodIterator} or the
// source code of this example for a description of how to read the input image
// and allocate a matching output image.
//
// The size of the structuring element is read from the command line and used
// to define a radius for the shaped neighborhood iterator. Using the method
// developed in section~\ref{sec:itkNeighborhoodIterator} to minimize bounds
// checking, the iterator itself is not initialized until entering the
// main processing loop.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
unsigned int element_radius = ::std::stoi( argv[3] );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The face calculator object introduced in
// Section~\ref{sec:NeighborhoodExample3} is created and used as before.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using FaceCalculatorType =
FaceCalculatorType faceCalculator;
FaceCalculatorType::FaceListType faceList;
FaceCalculatorType::FaceListType::iterator fit;
output->GetRequestedRegion(),
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Now we initialize some variables and constants.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
IteratorType out;
constexpr PixelType background_value = 0;
constexpr PixelType foreground_value = 255;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The outer loop of the algorithm is structured as in previous neighborhood
// iterator examples. Each region in the face list is processed in turn. As each new
// region is processed, the input and output iterators are initialized on that
// region.
//
// The shaped iterator that ranges over the input is our structuring element
// and its active stencil must be created accordingly. For this example, the
// structuring element is shaped like a circle of radius
// \code{element\_radius}. Each of the appropriate neighborhood offsets is
// activated in the double \code{for} loop.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
for ( fit=faceList.begin(); fit != faceList.end(); ++fit)
{
out = IteratorType( output, *fit );
// Creates a circular structuring element by activating all the pixels less
// than radius distance from the center of the neighborhood.
{
{
ShapedNeighborhoodIteratorType::OffsetType off;
float dis = std::sqrt( x*x + y*y );
{
off[0] = static_cast<int>(x);
off[1] = static_cast<int>(y);
it.ActivateOffset(off);
}
}
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The inner loop, which implements the erosion algorithm, is fairly simple.
// The \code{for} loop steps the input and output iterators through their
// respective images. At each step, the active stencil of the shaped iterator
// is traversed to determine whether all pixels underneath the stencil contain
// the foreground value, i.e. are contained within the set $I$. Note the use
// of the stencil iterator, \code{ci}, in performing this check.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Implements erosion
for (it.GoToBegin(), out.GoToBegin(); !it.IsAtEnd(); ++it, ++out)
{
ShapedNeighborhoodIteratorType::ConstIterator ci;
bool flag = true;
for (ci = it.Begin(); ci != it.End(); ci++)
{
if (ci.Get() == background_value)
{
flag = false;
break;
}
}
if (flag == true)
{
out.Set(foreground_value);
}
else
{
out.Set(background_value);
}
}
}
// Software Guide : EndCodeSnippet
WriterType::Pointer writer = WriterType::New();
writer->SetFileName( argv[2] );
writer->SetInput( output );
try
{
writer->Update();
}
catch ( itk::ExceptionObject &err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}