TouziEdgeDetectorExample.cxxΒΆ

Example usage:

./TouziEdgeDetectorExample Input/amst2.png Output/amstTouziEdges.png Output/amstTouziDirections.png 3


Example source code (TouziEdgeDetectorExample.cxx):

// This example illustrates the use of the \doxygen{otb}{TouziEdgeDetectorImageFilter}.
// This filter belongs to the family of the fixed false alarm rate
// edge detectors but it is appropriate for SAR images, where the
// speckle noise is considered as multiplicative. By analogy with the
// classical gradient-based edge detectors which are suited to the
// additive noise case, this filter computes a ratio of local means in
// both sides of the edge \cite{touzi88}. In order to have a
// normalized response, the following computation is performed :
//
// r = 1 - min\{\frac{\mu_A}{\mu_B},\frac{\mu_B}{\mu_A} \},
//
// where $\mu_A$ and $\mu_B$ are the local means computed at both
// sides of the edge. In order to detect edges with any orientation,
// $r$ is computed for the 4 principal directions and the maximum
// response is kept.
//
// The first step required to use this filter is to include its header file.

#include "otbTouziEdgeDetectorImageFilter.h"

#include "otbImageFileReader.h"
#include "itkUnaryFunctorImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "otbImageFileWriter.h"

int main(int argc, char* argv[])
{

if (argc != 5)
{
std::cerr << "Usage: " << argv[0] << " inputImageFile ";
std::cerr << " outputEdgesImageFile outputDirectionsImageFile radius" << std::endl;
return EXIT_FAILURE;
}

//  Then we must decide what pixel type to use for the image. We
//  choose to make all computations with floating point precision
//  and rescale the results between 0 and 255 in order to export PNG images.

using InternalPixelType = float;
using OutputPixelType   = unsigned char;

//  The images are defined using the pixel type and the dimension.

using InternalImageType = otb::Image<InternalPixelType, 2>;
using OutputImageType   = otb::Image<OutputPixelType, 2>;

//  The filter can be instantiated using the image types defined above.

using FilterType = otb::TouziEdgeDetectorImageFilter<InternalImageType, InternalImageType>;

//  An \doxygen{otb}{ImageFileReader} class is also instantiated in order to read
//  image data from a file.

using ReaderType = otb::ImageFileReader<InternalImageType>;

// An \doxygen{otb}{ImageFileWriter} is instantiated in order to write the
// output image to a file.

using WriterType = otb::ImageFileWriter<OutputImageType>;

// The intensity rescaling of the results will be carried out by the
// \code{itk::RescaleIntensityImageFilter} which is templated by the
// input and output image types.

using RescalerType = itk::RescaleIntensityImageFilter<InternalImageType, OutputImageType>;

//  Both the filter and the reader are created by invoking their \code{New()}
//  methods and assigning the result to SmartPointers.

ReaderType::Pointer reader = ReaderType::New();
FilterType::Pointer filter = FilterType::New();

//  The same is done for the rescaler and the writer.

RescalerType::Pointer rescaler = RescalerType::New();
WriterType::Pointer   writer   = WriterType::New();

reader->SetFileName(argv[1]);

//  The \code{itk::RescaleIntensityImageFilter} needs to know which
//  is the minimu and maximum values of the output generated
//  image. Those can be chosen in a generic way by using the
//  \code{NumericTraits} functions, since they are templated over
//  the pixel type.

rescaler->SetOutputMinimum(itk::NumericTraits<OutputPixelType>::min());
rescaler->SetOutputMaximum(itk::NumericTraits<OutputPixelType>::max());

//  The image obtained with the reader is passed as input to the
//  \doxygen{otb}{TouziEdgeDetectorImageFilter}. The pipeline is built as follows.
//
//  \index{otb::TouziEdgeDetectorImageFilter!SetInput()}

filter->SetInput(reader->GetOutput());
rescaler->SetInput(filter->GetOutput());
writer->SetInput(rescaler->GetOutput());

//  The method \code{SetRadius()} defines the size of the window to
//  be used for the computation of the local means.
//
//  \index{otb::LeeImageFilter!SetRadius()}
//  \index{otb::LeeImageFilter!NbLooks()}
//  \index{SetNbLooks()!otb::LeeImageFilter}

FilterType::SizeType Radius;
Radius[0] = atoi(argv[4]);
Radius[1] = atoi(argv[4]);

filter->SetRadius(Radius);

//  The filter is executed by invoking the \code{Update()} method. If the
//  filter is part of a larger image processing pipeline, calling
//  \code{Update()} on a downstream filter will also trigger update of this
//  filter.

filter->Update();

writer->SetFileName(argv[2]);
writer->Update();

// We can also obtain the direction of the edges by invoking the
// \code{GetOutputDirection()} method.

writer->SetFileName(argv[3]);
rescaler->SetInput(filter->GetOutputDirection());
writer->SetInput(rescaler->GetOutput());
writer->Update();

// Figure~\ref{fig:TOUZI_FILTER} shows the result of applying the Touzi
// edge detector
// filter to a SAR image.
// \begin{figure}
// \center
// \includegraphics[width=0.25\textwidth]{amst.eps}
// \includegraphics[width=0.25\textwidth]{amstTouziEdges.eps}
// \includegraphics[width=0.25\textwidth]{amstTouziDirections.eps}
// \itkcaption[Touzi Edge Detector Application]{Result of applying the
// \doxygen{otb}{TouziEdgeDetectorImageFilter} to a SAR image. From left to right :
// original image, edge intensity and edge orientation.}
// \label{fig:TOUZI_FILTER}
// \end{figure}

return EXIT_SUCCESS;
}