Commit 5aa70b94 authored by Victor Poughon's avatar Victor Poughon

DOC: review example KullbackLeiblerProfileChDet

parent e3f0c001
......@@ -24,135 +24,85 @@
*/
// This example illustrates the class
// \doxygen{otb}{KullbackLeiblerProfileImageFilter} for detecting changes
// between pairs of images, according to a range of window size.
// This example is very similar, in its principle, to all of the change
// detection examples, especially the distance between distributions one
// (section~\ref{sec:KullbackLeiblerDistance}) which uses a fixed window size.
//
// The main differences are:
// \begin{enumerate}
// \item a set of window range instead of a fixed size of window;
// \item an output of type \doxygen{otb}{VectorImage}.
// \end{enumerate}
// Then, the program begins with the \doxygen{otb}{VectorImage} and the
// \doxygen{otb}{KullbackLeiblerProfileImageFilter} header files in addition
// to those already details in the \doxygen{otb}{MeanRatioImageFilter} example.
#include "otbImage.h"
#include "otbMultiChannelExtractROI.h"
#include "otbVectorRescaleIntensityImageFilter.h"
#include "otbKullbackLeiblerProfileImageFilter.h"
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
int main(int argc, char* argv[])
{
try
{
if (argc != 9)
{
std::cerr << "Change detection based on Kullback-Leibler distance betweenlocal pdf through an Edgeworth approximation\n";
std::cerr << argv[0] << " imgAv imgAp imgResu winSizeMin winSizeMax outRedIndex outGreenIndex outBlueIndex\n";
return 1;
}
char* fileName1 = argv[1];
char* fileName2 = argv[2];
char* fileNameOut = argv[3];
int winSizeMin = atoi(argv[4]);
int winSizeMax = atoi(argv[5]);
unsigned int ri = atoi(argv[6]);
unsigned int gi = atoi(argv[7]);
unsigned int bi = atoi(argv[8]);
const unsigned int Dimension = 2;
using PixelType = double;
using OutPixelType = unsigned char;
// The \doxygen{otb}{KullbackLeiblerProfileImageFilter} is templated over
// the types of the two input images and the type of the generated change
// image (which is now of multi-components), in a similar way as the
// \doxygen{otb}{KullbackLeiblerDistanceImageFilter}.
using ImageType = otb::Image<PixelType, Dimension>;
using VectorImageType = otb::VectorImage<PixelType, Dimension>;
using FilterType = otb::KullbackLeiblerProfileImageFilter<ImageType, ImageType, VectorImageType>;
using OutVectorImageType = otb::VectorImage<OutPixelType, Dimension>;
using ReaderType = otb::ImageFileReader<ImageType>;
using WriterType = otb::ImageFileWriter<OutVectorImageType>;
using ChannelSelecterType = otb::MultiChannelExtractROI<PixelType, PixelType>;
using RescalerType = otb::VectorRescaleIntensityImageFilter<VectorImageType, OutVectorImageType>;
ReaderType::Pointer reader1 = ReaderType::New();
reader1->SetFileName(fileName1);
ReaderType::Pointer reader2 = ReaderType::New();
reader2->SetFileName(fileName2);
// The different elements of the pipeline can now be instantiated in the
// same way as the ratio of means change detector example.
//
// Two parameters are now required to give the minimum and the maximum size
// of the analysis window. The program will begin by performing change
// detection through the smaller window size and then applying moments update
// of eq.~\eqref{eqMomentN} by incrementing the radius of the analysis window
// (i.e. add a ring of width 1 pixel around the current neightborhood shape).
// The process is applied until the larger window size is reached.
//
FilterType::Pointer filter = FilterType::New();
filter->SetRadius((winSizeMin - 1) / 2, (winSizeMax - 1) / 2);
filter->SetInput1(reader1->GetOutput());
filter->SetInput2(reader2->GetOutput());
ChannelSelecterType::Pointer channelSelecter = ChannelSelecterType::New();
channelSelecter->SetInput(filter->GetOutput());
channelSelecter->SetChannel(ri);
channelSelecter->SetChannel(gi);
channelSelecter->SetChannel(bi);
RescalerType::Pointer rescaler = RescalerType::New();
rescaler->SetInput(channelSelecter->GetOutput());
OutVectorImageType::PixelType min, max;
min.SetSize(3);
max.SetSize(3);
min.Fill(0);
max.Fill(255);
rescaler->SetOutputMinimum(min);
rescaler->SetOutputMaximum(max);
WriterType::Pointer writer = WriterType::New();
writer->SetFileName(fileNameOut);
writer->SetInput(rescaler->GetOutput());
writer->Update();
// Figure \ref{fig:RESKLPCHDET} shows the result of the change
// detection by computing the Kullback-Leibler distance between
// local pdf through an Edgeworth approximation.
// \begin{figure}
// \center
// \includegraphics[width=0.35\textwidth]{KLProfileChDet.eps}
// \itkcaption[Kullback-Leibler profile Change Detection Results]{Result of the
// Kullback-Leibler profile change detector, colored composition including the first, 12th and 24th
// channel of the generated output.}
// \label{fig:RESKLPCHDET}
// \end{figure}
}
catch (itk::ExceptionObject& err)
if (argc != 9)
{
std::cout << "Exception itk::ExceptionObject thrown !" << std::endl;
std::cout << err << std::endl;
std::cerr << "Change detection based on Kullback-Leibler distance between local pdf through an Edgeworth approximation\n";
std::cerr << argv[0] << " imgAv imgAp imgResu winSizeMin winSizeMax outRedIndex outGreenIndex outBlueIndex\n";
return EXIT_FAILURE;
}
catch (...)
{
std::cout << "Unknown exception thrown !" << std::endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
char* fileName1 = argv[1];
char* fileName2 = argv[2];
char* fileNameOut = argv[3];
int winSizeMin = atoi(argv[4]);
int winSizeMax = atoi(argv[5]);
unsigned int ri = atoi(argv[6]);
unsigned int gi = atoi(argv[7]);
unsigned int bi = atoi(argv[8]);
const unsigned int Dimension = 2;
using PixelType = double;
using OutPixelType = unsigned char;
// The KullbackLeiblerProfileImageFilter is templated over
// the types of the two input images and the type of the generated change
// image (which is now of multi-components), in a similar way as the
// KullbackLeiblerDistanceImageFilter.
using ImageType = otb::Image<PixelType, Dimension>;
using VectorImageType = otb::VectorImage<PixelType, Dimension>;
using FilterType = otb::KullbackLeiblerProfileImageFilter<ImageType, ImageType, VectorImageType>;
using OutVectorImageType = otb::VectorImage<OutPixelType, Dimension>;
using ReaderType = otb::ImageFileReader<ImageType>;
using WriterType = otb::ImageFileWriter<OutVectorImageType>;
using ChannelSelecterType = otb::MultiChannelExtractROI<PixelType, PixelType>;
using RescalerType = otb::VectorRescaleIntensityImageFilter<VectorImageType, OutVectorImageType>;
// The different elements of the pipeline can now be instantiated in the
// same way as the ratio of means change detector example.
ReaderType::Pointer reader1 = ReaderType::New();
reader1->SetFileName(fileName1);
ReaderType::Pointer reader2 = ReaderType::New();
reader2->SetFileName(fileName2);
// Two parameters are now required to give the minimum and the maximum size
// of the analysis window. The program will begin by performing change
// detection through the smaller window size and then applying moments update
// by incrementing the radius of the analysis window (i.e. add a ring of
// width 1 pixel around the current neightborhood shape). The process is
// applied until the larger window size is reached.
FilterType::Pointer filter = FilterType::New();
filter->SetRadius((winSizeMin - 1) / 2, (winSizeMax - 1) / 2);
filter->SetInput1(reader1->GetOutput());
filter->SetInput2(reader2->GetOutput());
ChannelSelecterType::Pointer channelSelecter = ChannelSelecterType::New();
channelSelecter->SetInput(filter->GetOutput());
channelSelecter->SetChannel(ri);
channelSelecter->SetChannel(gi);
channelSelecter->SetChannel(bi);
RescalerType::Pointer rescaler = RescalerType::New();
rescaler->SetInput(channelSelecter->GetOutput());
OutVectorImageType::PixelType min, max;
min.SetSize(3);
max.SetSize(3);
min.Fill(0);
max.Fill(255);
rescaler->SetOutputMinimum(min);
rescaler->SetOutputMaximum(max);
WriterType::Pointer writer = WriterType::New();
writer->SetFileName(fileNameOut);
writer->SetInput(rescaler->GetOutput());
writer->Update();
}
This example illustrates the class
:doxygen:`KullbackLeiblerProfileImageFilter` for detecting changes
between pairs of images, according to a range of window size.
This example is very similar, in its principle, to all of the change
detection examples, especially the distance between distributions one
which uses a fixed window size.
The main differences are:
* a set of window range instead of a fixed size of window
* an output of type :doxygen:`VectorImage`
Then, the program begins with the :doxygen:`VectorImage` and the
:doxygen:`KullbackLeiblerProfileImageFilter` header files in addition
to those already detailed in the :doxygen:`MeanRatioImageFilter` example.
.. figure:: /Output/KLProfileChDet.png
:align: center
Result of the Kullback-Leibler profile change detector, colored composition including the first, 12th and 24th channel of the generated output.
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