NeighborhoodIterators6.cxx¶

Example usage:

./NeighborhoodIterators6 Output/NeighborhoodIterators6a.png 100 100


Example usage:

./NeighborhoodIterators6 Output/NeighborhoodIterators6b.png 50 150


Example usage:

./NeighborhoodIterators6 Output/NeighborhoodIterators6c.png 150 50


Example source code (NeighborhoodIterators6.cxx):

#include "otbImage.h"
#include "otbImageFileWriter.h"
#include "itkUnaryFunctorImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkNeighborhoodIterator.h"
#include "itkImageRegionIterator.h"
#include "itkFastMarchingImageFilter.h"

// Some image processing routines do not need to visit every pixel in an
// image. Flood-fill and connected-component algorithms, for example, only
// visit pixels that are locally connected to one another.  Algorithms
// such as these can be efficiently written using the random access
// capabilities of the neighborhood iterator.
//
// The following example finds local minima.  Given a seed point, we can search
// the neighborhood of that point and pick the smallest value $m$.  While $m$
// is not at the center of our current neighborhood, we move in the direction
// of $m$ and repeat the analysis.  Eventually we discover a local minimum and
// stop.  This algorithm is made trivially simple in ND using an ITK
// neighborhood iterator.
//
// To illustrate the process, we create an image that descends everywhere to a
// single minimum:  a positive distance transform to a point.  The details of
// creating the distance transform are not relevant to the discussion of
// neighborhood iterators, but can be found in the source code of this
// example. Some noise has been added to the distance transform image for

int main(int argc, char* argv[])
{
if (argc < 4)
{
std::cerr << "Missing parameters. " << std::endl;
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0] << " outputImageFile startX startY" << std::endl;
return -1;
}

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

using FastMarchingFilterType = itk::FastMarchingImageFilter<ImageType, ImageType>;

FastMarchingFilterType::Pointer fastMarching = FastMarchingFilterType::New();

using NodeContainer = FastMarchingFilterType::NodeContainer;
using NodeType      = FastMarchingFilterType::NodeType;

NodeContainer::Pointer seeds = NodeContainer::New();

ImageType::IndexType seedPosition;

seedPosition[0]              = 128;
seedPosition[1]              = 128;
const double initialDistance = 1.0;

NodeType node;

const double seedValue = -initialDistance;

ImageType::SizeType size = {{256, 256}};

node.SetValue(seedValue);
node.SetIndex(seedPosition);
seeds->Initialize();
seeds->InsertElement(0, node);

fastMarching->SetTrialPoints(seeds);
fastMarching->SetSpeedConstant(1.0);

// Allocate the noise image
ImageType::Pointer    noise = ImageType::New();
ImageType::RegionType noiseRegion;
noiseRegion.SetSize(size);
noise->SetRegions(noiseRegion);
noise->Allocate();

// Fill the noise image
itk::ImageRegionIterator<ImageType> itNoise(noise, noiseRegion);
itNoise.GoToBegin();

// Random number seed
unsigned int sample_seed = 12345;
double       u           = 0.;
double       rnd         = 0.;
double       dMin        = -.7;
double       dMax        = .8;

while (!itNoise.IsAtEnd())
{
sample_seed = (sample_seed * 16807) % 2147483647L;
u           = static_cast<double>(sample_seed) / 2147483711UL;
rnd         = (1.0 - u) * dMin + u * dMax;

itNoise.Set((PixelType)rnd);
++itNoise;
}

try
{
fastMarching->SetOutputSize(size);
fastMarching->Update();

}
catch (itk::ExceptionObject& excep)
{
std::cerr << "Exception caught !" << std::endl;
std::cerr << excep << std::endl;
}

// The variable \code{input} is the pointer to the distance transform image.
// The local minimum algorithm is initialized with a seed point read from the
// command line.

ImageType::IndexType index;
index[0] = ::atoi(argv[2]);
index[1] = ::atoi(argv[3]);

// Next we create the neighborhood iterator and position it at the seed point.

it.SetLocation(index);

// Searching for the local minimum involves finding the minimum in the current
// neighborhood, then shifting the neighborhood in the direction of that
// minimum.  The \code{for} loop below records the \doxygen{itk}{Offset} of the
// minimum neighborhood pixel.  The neighborhood iterator is then moved using
// that offset.  When a local minimum is detected, \code{flag} will remain
// false and the \code{while} loop will exit.  Note that this code is
// valid for an image of any dimensionality.

bool flag = true;
while (flag == true)
{
NeighborhoodIteratorType::OffsetType nextMove;
nextMove.Fill(0);

flag = false;

PixelType min = it.GetCenterPixel();
for (unsigned i = 0; i < it.Size(); ++i)
{
if (it.GetPixel(i) < min)
{
min      = it.GetPixel(i);
nextMove = it.GetOffset(i);
flag     = true;
}
}
it.SetCenterPixel(255.0);
it += nextMove;
}

// Figure~\ref{fig:NeighborhoodExample6} shows the results of the algorithm
// for several seed points.  The white line is the path of the iterator from
// the seed point to the minimum in the center of the image.  The effect of the
// additive noise is visible as the small perturbations in the paths.
//
// \begin{figure} \centering
// \includegraphics[width=0.3\textwidth]{NeighborhoodIterators6a.eps}
// \includegraphics[width=0.3\textwidth]{NeighborhoodIterators6b.eps}
// \includegraphics[width=0.3\textwidth]{NeighborhoodIterators6c.eps}
// \itkcaption[Finding local minima]{Paths traversed by the neighborhood
// iterator from different seed points to the local minimum.
// The true minimum is at the center
// of the image.  The path of the iterator is shown in white. The effect of
// noise in the image is seen as small perturbations in each path. }
// \protect\label{fig:NeighborhoodExample6} \end{figure}

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(input);

WriterType::Pointer writer = WriterType::New();
writer->SetFileName(argv[1]);
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;
}