From 9e2ce7b38c856c4c25043ea530eb0915afcf0c35 Mon Sep 17 00:00:00 2001
From: Victor Poughon <victor.poughon@cnes.fr>
Date: Wed, 24 Apr 2019 17:02:37 +0200
Subject: [PATCH] DOC: review MeanShiftSegmentationFilterExample
---
Data/Output/MSClusteredOutput-pretty.png | 3 +
Data/Output/MSLabeledOutput-pretty.png | 3 +
.../MeanShiftSegmentationFilterExample.cxx | 63 +++++--------------
.../MeanShiftSegmentationFilterExample.rst | 21 +++++++
4 files changed, 44 insertions(+), 46 deletions(-)
create mode 100644 Data/Output/MSClusteredOutput-pretty.png
create mode 100644 Data/Output/MSLabeledOutput-pretty.png
create mode 100644 Examples/BasicFilters/MeanShiftSegmentationFilterExample.rst
diff --git a/Data/Output/MSClusteredOutput-pretty.png b/Data/Output/MSClusteredOutput-pretty.png
new file mode 100644
index 0000000000..caa286e698
--- /dev/null
+++ b/Data/Output/MSClusteredOutput-pretty.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf676b3188f175f21ea758ccfb7f551eb9468398d67f926ef677a89ca68dc622
+size 105266
diff --git a/Data/Output/MSLabeledOutput-pretty.png b/Data/Output/MSLabeledOutput-pretty.png
new file mode 100644
index 0000000000..ec6a18fbe4
--- /dev/null
+++ b/Data/Output/MSLabeledOutput-pretty.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e2abea57f9bd5d62e708f6971178d2a3dd94997db37ab0846a66d6d99d37152a
+size 105590
diff --git a/Examples/BasicFilters/MeanShiftSegmentationFilterExample.cxx b/Examples/BasicFilters/MeanShiftSegmentationFilterExample.cxx
index f3992ead40..9e665a35f2 100644
--- a/Examples/BasicFilters/MeanShiftSegmentationFilterExample.cxx
+++ b/Examples/BasicFilters/MeanShiftSegmentationFilterExample.cxx
@@ -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;
}
diff --git a/Examples/BasicFilters/MeanShiftSegmentationFilterExample.rst b/Examples/BasicFilters/MeanShiftSegmentationFilterExample.rst
new file mode 100644
index 0000000000..48e8fc0940
--- /dev/null
+++ b/Examples/BasicFilters/MeanShiftSegmentationFilterExample.rst
@@ -0,0 +1,21 @@
+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.
--
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