# NeighborhoodIterators2.cxx¶

Example source code (NeighborhoodIterators2.cxx):

#include "otbImage.h"
#include "otbImageFileWriter.h"
#include "itkUnaryFunctorImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkConstNeighborhoodIterator.h"
#include "itkImageRegionIterator.h"

// In this example, the Sobel edge-detection routine is rewritten using
// convolution filtering.  Convolution filtering is a standard image processing
// technique that can be implemented numerically as the inner product of all
// image neighborhoods with a convolution kernel \cite{Gonzalez1993}
// \cite{Castleman1996}.  In ITK, we use a class of objects called
// \emph{neighborhood operators} as convolution kernels and a special function
// object called \doxygen{itk}{NeighborhoodInnerProduct} to calculate inner
// products.
//
// The basic ITK convolution filtering routine is to step through the image
// with a neighborhood iterator and use NeighborhoodInnerProduct to
// find the inner product of each neighborhood with the desired kernel. The
// resulting values are written to an output image.  This example uses a
// neighborhood operator called the \doxygen{itk}{SobelOperator}, but all
// neighborhood operators can be convolved with images using this basic
// routine.  Other examples of neighborhood operators include derivative
// kernels, Gaussian kernels, and morphological
// operators. \doxygen{itk}{NeighborhoodOperatorImageFilter} is a generalization of
// the code in this section to ND images and arbitrary convolution kernels.
//
// We start writing this example by including the header files for the Sobel
// kernel and the inner product function.

#include "itkSobelOperator.h"
#include "itkNeighborhoodInnerProduct.h"

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 -1;
}

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

using NeighborhoodIteratorType = itk::ConstNeighborhoodIterator<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();

// \index{convolution!kernels}
// \index{convolution!operators}
// \index{iterators!neighborhood!and convolution}
//
// Refer to the previous example for a description of reading the input image and
// setting up the output image and iterator.
//
// The following code creates a Sobel operator.  The Sobel operator requires
// a direction for its partial derivatives.  This direction is read from the command line.
// Changing the direction of the derivatives changes the bias of the edge
// detection, i.e. maximally vertical or maximally horizontal.

itk::SobelOperator<PixelType, 2> sobelOperator;
sobelOperator.SetDirection(::atoi(argv[3]));
sobelOperator.CreateDirectional();

// The neighborhood iterator is initialized as before, except that now it takes
// its radius directly from the radius of the Sobel operator.  The inner
// product function object is templated over image type and requires no
// initialization.

itk::NeighborhoodInnerProduct<ImageType> innerProduct;

// Using the Sobel operator, inner product, and neighborhood iterator objects,
// we can now write a very simple \code{for} loop for performing convolution
// filtering.  As before, out-of-bounds pixel values are supplied automatically
// by the iterator.

for (it.GoToBegin(), out.GoToBegin(); !it.IsAtEnd(); ++it, ++out)
{
out.Set(innerProduct(it, sobelOperator));
}

// The output is rescaled and written as in the previous example.  Applying
// this example in the $x$ and $y$ directions produces the images at the center
// and right of Figure~\ref{fig:NeighborhoodExamples1}. Note that x-direction
// operator produces the same output image as in the previous example.

using WritePixelType = unsigned char;
using WriteImageType = otb::Image<WritePixelType, 2>;
using WriterType     = otb::ImageFileWriter<WriteImageType>;

using RescaleFilterType = itk::RescaleIntensityImageFilter<ImageType, WriteImageType>;

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

return EXIT_SUCCESS;
}