Commit 9e2ce7b3 authored by Victor Poughon's avatar Victor Poughon

DOC: review MeanShiftSegmentationFilterExample

parent d4cddba4
......@@ -32,29 +32,15 @@
0.1
*/
// This example demonstrates the use of the
// \doxygen{otb}{MeanShiftSegmentationFilter} class which implements
// filtering and clustering using the mean shift algorithm
// \cite{Comaniciu2002}. For a given pixel, the mean shift will
// build a set of neighboring pixels within a given spatial radius
// and a color range. The spatial and color center of this set is
// then computed and the algorithm iterates with this new spatial and
// color center. The Mean Shift can be used for edge-preserving
// smoothing, or for clustering.
#include "otbVectorImage.h"
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "otbImageFileWriter.h"
#include "otbPrintableImageFilter.h"
#include "itkScalarToRGBPixelFunctor.h"
#include "itkUnaryFunctorImageFilter.h"
// We start by including the needed header file.
#include "otbMeanShiftSegmentationFilter.h"
int main(int argc, char* argv[])
{
if (argc != 11)
......@@ -75,9 +61,6 @@ int main(int argc, char* argv[])
const unsigned int maxiter = atoi(argv[9]);
const double thres = atof(argv[10]);
// We start by the classical \code{typedef}s needed for reading and
// writing the images.
const unsigned int Dimension = 2;
typedef float PixelType;
......@@ -94,36 +77,39 @@ int main(int argc, char* argv[])
typedef otb::MeanShiftSegmentationFilter<ImageType, LabelImageType, ImageType> FilterType;
// We instantiate the filter, the reader, and 2 writers (for the
// labeled and clustered images).
// We instantiate the filter, the reader, and 2 writers (for the
// labeled and clustered images).
FilterType::Pointer filter = FilterType::New();
ReaderType::Pointer reader = ReaderType::New();
WriterType::Pointer writer1 = WriterType::New();
LabelWriterType::Pointer writer2 = LabelWriterType::New();
// We set the file names for the reader and the writers:
// We set the file names for the reader and the writers:
reader->SetFileName(infname);
writer1->SetFileName(clusteredfname);
writer2->SetFileName(labeledfname);
// We can now set the parameters for the filter. There are 3 main
// parameters: the spatial radius used for defining the neighborhood,
// the range radius used for defining the interval in the color space
// and the minimum size for the regions to be kept after clustering.
// We can now set the parameters for the filter. There are 3 main
// parameters: the spatial radius used for defining the neighborhood,
// the range radius used for defining the interval in the color space
// and the minimum size for the regions to be kept after clustering.
filter->SetSpatialBandwidth(spatialRadius);
filter->SetRangeBandwidth(rangeRadius);
filter->SetMinRegionSize(minRegionSize);
// Two another parameters can be set : the maximum iteration number, which defines maximum number of iteration until convergence.
// Algorithm iterative scheme will stop if convergence hasn't been reached after the maximum number of iterations.
// Threshold parameter defines mean-shift vector convergence value. Algorithm iterative scheme will stop if mean-shift vector is below this threshold or if
// iteration number reached maximum number of iterations.
// Two another parameters can be set: the maximum iteration number, which
// defines maximum number of iteration until convergence. Algorithm
// iterative scheme will stop if convergence hasn't been reached after the
// maximum number of iterations. Threshold parameter defines mean-shift
// vector convergence value. Algorithm iterative scheme will stop if
// mean-shift vector is below this threshold or if iteration number reached
// maximum number of iterations.
filter->SetMaxIterationNumber(maxiter);
filter->SetThreshold(thres);
// We can now plug the pipeline and run it.
// We can now plug the pipeline and run it.
filter->SetInput(reader->GetOutput());
writer1->SetInput(filter->GetClusteredOutput());
......@@ -132,19 +118,6 @@ int main(int argc, char* argv[])
writer1->Update();
writer2->Update();
// Figure~\ref{fig:MeanShiftSegmentationFilter} shows the result of applying the mean shift
// to a Quickbird image.
// \begin{figure}
// \center
// \includegraphics[width=0.40\textwidth]{ROI_QB_MUL_1.eps}
// \includegraphics[width=0.40\textwidth]{MSClusteredOutput-pretty.eps}
// \includegraphics[width=0.40\textwidth]{MSLabeledOutput-pretty.eps}
// \itkcaption[Mean Shift]{From top to bottom and left to right:
// Original image, image filtered by
// mean shift after clustering , and labeled image.}
// \label{fig:MeanShiftSegmentationFilter}
// \end{figure}
typedef otb::PrintableImageFilter<ImageType> PrintableFilterType;
PrintableFilterType::Pointer printableImageFilter = PrintableFilterType::New();
......@@ -176,6 +149,4 @@ int main(int argc, char* argv[])
labelRGBWriter->SetFileName(labeledpretty);
labelRGBWriter->SetInput(labelToRGB->GetOutput());
labelRGBWriter->Update();
return EXIT_SUCCESS;
}
This example demonstrates the use of the :doxygen:`MeanShiftSegmentationFilter`
class which implements filtering and clustering using the mean shift algorithm.
For a given pixel, the mean shift will build a set of neighboring pixels within
a given spatial radius and a color range. The spatial and color center of this
set is then computed and the algorithm iterates with this new spatial and color
center. The Mean Shift can be used for edge-preserving smoothing, or for
clustering.
.. |image1| image:: /Input/ROI_QB_MUL_1.png
.. |image2| image:: /Output/MSClusteredOutput-pretty.png
.. |image3| image:: /Output/MSLabeledOutput-pretty.png
.. _Figure1:
+--------------------------+-------------------------+-------------------------+
| |image1| | |image2| | |image3| |
+--------------------------+-------------------------+-------------------------+
Original image, image filtered by mean shift after clustering, and labeled image.
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment