# ShapedNeighborhoodIterators1.cxx¶

Example source code (ShapedNeighborhoodIterators1.cxx):

#include "otbImage.h"
#include "otbImageFileWriter.h"
#include "itkNeighborhoodAlgorithm.h"
#include <math.h>

// This example uses \doxygen{itk}{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.

#include "itkConstShapedNeighborhoodIterator.h"
#include "itkImageRegionIterator.h"

int main(int argc, char* argv[])
{
if (argc < 4)
{
std::cerr << "Missing parameters. " << std::endl;
std::cerr << "Usage: " << std::endl;
std::cerr << argv << " inputImageFile outputImageFile element_radius" << std::endl;
return -1;
}

// 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.

using PixelType = unsigned char;
using ImageType = otb::Image<PixelType, 2>;

using ShapedNeighborhoodIteratorType = itk::ConstShapedNeighborhoodIterator<ImageType>;

using IteratorType = itk::ImageRegionIterator<ImageType>;

try
{
}
catch (itk::ExceptionObject& err)
{
std::cout << "ExceptionObject caught !" << std::endl;
std::cout << err << std::endl;
return -1;
}

ImageType::Pointer output = ImageType::New();
output->Allocate();

// 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.

// The face calculator object introduced in
// Section~\ref{sec:NeighborhoodExample3} is created and used as before.

using FaceCalculatorType = itk::NeighborhoodAlgorithm::ImageBoundaryFacesCalculator<ImageType>;

FaceCalculatorType                         faceCalculator;
FaceCalculatorType::FaceListType           faceList;
FaceCalculatorType::FaceListType::iterator fit;

// Now we initialize some variables and constants.

IteratorType out;

const PixelType background_value = 0;
const PixelType foreground_value = 255;

// 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.

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 = ::sqrt(x * x + y * y);
{
off = static_cast<int>(x);
off = static_cast<int>(y);
it.ActivateOffset(off);
}
}
}

// 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.

// 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);
}
}
}

using WriterType = otb::ImageFileWriter<ImageType>;

WriterType::Pointer writer = WriterType::New();
writer->SetFileName(argv);
writer->SetInput(output);
try
{
writer->Update();
}
catch (itk::ExceptionObject& err)
{
std::cout << "ExceptionObject caught !" << std::endl;
std::cout << err << std::endl;
return -1;
}

return EXIT_SUCCESS;
}