SOMExample.cxxΒΆ
Example usage:
./SOMExample Input/ROI_QB_MUL_1.png Output/ROI_QB_MUL_SOM.png Output/ROI_QB_MUL_SOMACT.png 4 4 4 4 20 1.0 0.1 128
Example source code (SOMExample.cxx):
/*
* Copyright (C) 2005-2024 Centre National d'Etudes Spatiales (CNES)
* Copyright (C) 2007-2012 Institut Mines Telecom / Telecom Bretagne
*
* This file is part of Orfeo Toolbox
*
* https://www.orfeo-toolbox.org/
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
// This example illustrates the use of the
// \doxygen{otb}{SOM} class for building Kohonen's Self Organizing
// Maps.
//
// We will use the SOM in order to build a color table from an input
// image. Our input image is coded with $3\times 8$ bits and we would
// like to code it with only 16 levels. We will use the SOM in order
// to learn which are the 16 most representative RGB values of the
// input image and we will assume that this is the optimal color table
// for the image.
//
// The first thing to do is include the header file for the
// class. We will also need the header files for the map itself and
// the activation map builder whose utility will be explained at the
// end of the example.
//
#include "otbSOMMap.h"
#include "otbSOM.h"
#include "otbSOMActivationBuilder.h"
#include "itkMacro.h"
#include "itkVectorExpandImageFilter.h"
#include "itkVectorNearestNeighborInterpolateImageFunction.h"
#include "itkExpandImageFilter.h"
#include "itkNearestNeighborInterpolateImageFunction.h"
#include "otbPerBandVectorImageFilter.h"
#include "otbPrintableImageFilter.h"
// Since the \doxygen{otb}{SOM} class uses a distance, we will need to
// include the header file for the one we want to use
//
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "itkListSample.h"
int main(int itkNotUsed(argc), char* argv[])
{
const char* inputFileName = argv[1];
const char* outputFileName = argv[2];
const char* actMapFileName = argv[3];
unsigned int sizeX = atoi(argv[4]);
unsigned int sizeY = atoi(argv[5]);
unsigned int neighInitX = atoi(argv[6]);
unsigned int neighInitY = atoi(argv[7]);
unsigned int nbIterations = atoi(argv[8]);
double betaInit = atof(argv[9]);
double betaEnd = atof(argv[10]);
double initValue = atof(argv[11]);
// The Self Organizing Map itself is actually an N-dimensional image
// where each pixel contains a neuron. In our case, we decide to build
// a 2-dimensional SOM, where the neurons store RGB values with
// floating point precision.
const unsigned int Dimension = 2;
using PixelType = double;
using ImageType = otb::VectorImage<PixelType, Dimension>;
using VectorType = ImageType::PixelType;
// The distance that we want to apply between the RGB values is the
// Euclidean one. Of course we could choose to use other type of
// distance, as for instance, a distance defined in any other color space.
using DistanceType = itk::Statistics::EuclideanDistanceMetric<VectorType>;
//
// We can now define the type for the map. The \doxygen{otb}{SOMMap}
// class is templated over the neuron type -- \code{PixelType} here
// --, the distance type and the number of dimensions. Note that the
// number of dimensions of the map could be different from the one of
// the images to be processed.
using MapType = otb::SOMMap<VectorType, DistanceType, Dimension>;
//
// We are going to perform the learning directly on the pixels of the
// input image. Therefore, the image type is defined using the same
// pixel type as we used for the map. We also define the type for the
// imge file reader.
using ReaderType = otb::ImageFileReader<ImageType>;
//
// Since the \doxygen{otb}{SOM} class works on lists of samples, it
// will need to access the input image through an adaptor. Its type is
// defined as follows:
using SampleListType = itk::Statistics::ListSample<VectorType>;
//
// We can now define the type for the SOM, which is templated over the
// input sample list and the type of the map to be produced and the two
// functors that hold the training behavior.
using LearningBehaviorFunctorType = otb::Functor::CzihoSOMLearningBehaviorFunctor;
using NeighborhoodBehaviorFunctorType = otb::Functor::CzihoSOMNeighborhoodBehaviorFunctor;
using SOMType = otb::SOM<SampleListType, MapType, LearningBehaviorFunctorType, NeighborhoodBehaviorFunctorType>;
//
// As an alternative to standard \code{SOMType}, one can decide to use
// an \doxygen{otb}{PeriodicSOM}, which behaves like \doxygen{otb}{SOM} but
// is to be considered to as a torus instead of a simple map. Hence, the
// neighborhood behavior of the winning neuron does not depend on its location
// on the map...
// \doxygen{otb}{PeriodicSOM} is defined in otbPeriodicSOM.h.
//
// We can now start building the pipeline. The first step is to
// instantiate the reader and pass its output to the adaptor.
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName(inputFileName);
reader->Update();
SampleListType::Pointer sampleList = SampleListType::New();
sampleList->SetMeasurementVectorSize(reader->GetOutput()->GetVectorLength());
itk::ImageRegionIterator<ImageType> imgIter(reader->GetOutput(), reader->GetOutput()->GetBufferedRegion());
imgIter.GoToBegin();
itk::ImageRegionIterator<ImageType> imgIterEnd(reader->GetOutput(), reader->GetOutput()->GetBufferedRegion());
imgIterEnd.GoToEnd();
do
{
sampleList->PushBack(imgIter.Get());
++imgIter;
} while (imgIter != imgIterEnd);
//
// We can now instantiate the SOM algorithm and set the sample list as input.
SOMType::Pointer som = SOMType::New();
som->SetListSample(sampleList);
//
// We use a \code{SOMType::SizeType} array in order to set the sizes
// of the map.
SOMType::SizeType size;
size[0] = sizeX;
size[1] = sizeY;
som->SetMapSize(size);
//
// The initial size of the neighborhood of each neuron is set in the
// same way.
SOMType::SizeType radius;
radius[0] = neighInitX;
radius[1] = neighInitY;
som->SetNeighborhoodSizeInit(radius);
//
// The other parameters are the number of iterations, the initial and
// the final values for the learning rate -- $\beta$ -- and the
// maximum initial value for the neurons (the map will be randomly
// initialized).
som->SetNumberOfIterations(nbIterations);
som->SetBetaInit(betaInit);
som->SetBetaEnd(betaEnd);
som->SetMaxWeight(static_cast<PixelType>(initValue));
//
// Now comes the initialization of the functors.
LearningBehaviorFunctorType learningFunctor;
learningFunctor.SetIterationThreshold(radius, nbIterations);
som->SetBetaFunctor(learningFunctor);
NeighborhoodBehaviorFunctorType neighborFunctor;
som->SetNeighborhoodSizeFunctor(neighborFunctor);
som->Update();
//
// Finally, we set up the las part of the pipeline where the plug the
// output of the SOM into the writer. The learning procedure is
// triggered by calling the \code{Update()} method on the writer.
// Since the map is itself an image, we can write it to disk with an
// \doxygen{otb}{ImageFileWriter}.
// Just for visualization purposes, we zoom the image, and pass it to the printable image filter
using SingleImageType = otb::Image<PixelType, 2>;
using ExpandType = itk::ExpandImageFilter<SingleImageType, SingleImageType>;
using VectorExpandType = otb::PerBandVectorImageFilter<MapType, MapType, ExpandType>;
using InterpolatorType = itk::NearestNeighborInterpolateImageFunction<SingleImageType, double>;
using PrintableFilterType = otb::PrintableImageFilter<MapType>;
using PrintableWriterType = otb::ImageFileWriter<PrintableFilterType::OutputImageType>;
InterpolatorType::Pointer interpolator = InterpolatorType::New();
VectorExpandType::Pointer expand = VectorExpandType::New();
ExpandType::Pointer scalarExpand = ExpandType::New();
scalarExpand->SetExpandFactors(40);
scalarExpand->SetInterpolator(interpolator);
// scalarExpand->SetEdgePaddingValue(255);
expand->SetFilter(scalarExpand);
expand->SetInput(som->GetOutput());
expand->UpdateOutputInformation();
PrintableFilterType::Pointer printFilter = PrintableFilterType::New();
printFilter->SetInput(expand->GetOutput());
printFilter->SetChannel(1);
printFilter->SetChannel(2);
printFilter->SetChannel(3);
PrintableWriterType::Pointer printWriter = PrintableWriterType::New();
printWriter->SetInput(printFilter->GetOutput());
printWriter->SetFileName(outputFileName);
printWriter->Update();
// Figure \ref{fig:SOMMAP} shows the result of the SOM learning. Since
// we have performed a learning on RGB pixel values, the produced SOM
// can be interpreted as an optimal color table for the input
// image. It can be observed that the obtained colors are
// topologically organised, so similar colors are also close in the
// map. This topological organisation can be exploited to further
// reduce the number of coding levels of the pixels without
// performing a new learning: we can subsample the map to get a new
// color table. Also, a bilinear interpolation between the neurons can
// be used to increase the number of coding levels.
// \begin{figure}
// \center
// \includegraphics[width=0.45\textwidth]{ROI_QB_MUL_1.eps}
// \includegraphics[width=0.2\textwidth]{ROI_QB_MUL_SOM.eps}
// \includegraphics[width=0.2\textwidth]{ROI_QB_MUL_SOMACT.eps}
// \itkcaption[SOM Image Classification]{Result of the SOM
// learning. Left: RGB image. Center: SOM. Right: Activation map}
// \label{fig:SOMMAP}
// \end{figure}
// We can now compute the activation map for the input image. The
// activation map tells us how many times a given neuron is activated
// for the set of examples given to the map. The activation map is
// stored as a scalar image and an integer pixel type is usually enough.
using OutputPixelType = unsigned char;
using OutputImageType = otb::Image<OutputPixelType, Dimension>;
using ActivationWriterType = otb::ImageFileWriter<OutputImageType>;
// In a similar way to the \doxygen{otb}{SOM} class the
// \doxygen{otb}{SOMActivationBuilder} is templated over the sample
// list given as input, the SOM map type and the activation map to be
// built as output.
using SOMActivationBuilderType = otb::SOMActivationBuilder<SampleListType, MapType, OutputImageType>;
// We instantiate the activation map builder and set as input the SOM
// map build before and the image (using the adaptor).
SOMActivationBuilderType::Pointer somAct = SOMActivationBuilderType::New();
somAct->SetInput(som->GetOutput());
somAct->SetListSample(sampleList);
somAct->Update();
// The final step is to write the activation map to a file.
if (actMapFileName != nullptr)
{
ActivationWriterType::Pointer actWriter = ActivationWriterType::New();
actWriter->SetFileName(actMapFileName);
// The righthand side of figure \ref{fig:SOMMAP} shows the activation
// map obtained.
// Just for visualization purposes, we zoom the image.
using ExpandType2 = itk::ExpandImageFilter<OutputImageType, OutputImageType>;
using InterpolatorType2 = itk::NearestNeighborInterpolateImageFunction<OutputImageType, double>;
InterpolatorType2::Pointer interpolator2 = InterpolatorType2::New();
ExpandType2::Pointer expand2 = ExpandType2::New();
expand2->SetInput(somAct->GetOutput());
expand2->SetExpandFactors(20);
expand2->SetInterpolator(interpolator2);
// expand2->SetEdgePaddingValue(255);
expand2->UpdateOutputInformation();
actWriter->SetInput(expand2->GetOutput());
actWriter->Update();
}
else
{
std::cerr << "The activation map file name is null" << std::endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}