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/*=========================================================================
Program: ORFEO Toolbox
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
See OTBCopyright.txt for details.
Copyright (c) Institut Mines-Telecom. All rights reserved.
See IMTCopyright.txt for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#include <fstream>
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "otbImage.h"
#include "otbVectorImage.h"
// Software Guide : BeginLatex
// This example illustrates the modifications to be added to use the
// \doxygen{otb}{SVMClassifier} class for performing SVM
// classification on images with a user-defined kernel.
// In this example, we will use an SVM model estimated in the previous
// section to separate between water and non-water pixels by using the RGB
// values only.
// The first thing to do is include the header file for the
// class as well as the header of the appropriated kernel to be used.
//
//
// Software Guide : EndLatex
#include "itkImageToListSampleAdaptor.h"
// Software Guide : BeginCodeSnippet
#include "otbSVMClassifier.h"
#include "otbMixturePolyRBFKernelFunctor.h"
// Software Guide : EndCodeSnippet
int main(int argc, char* argv[])
{
if (argc != 4)
{
std::cout << "Usage : " << argv[0]
<< " inputImage outputImage modelFile " << std::endl;
return EXIT_FAILURE;
}
const char * imageFilename = argv[1];
const char * modelFilename = argv[3];
const char * outputFilename = argv[2];
typedef double PixelType;
typedef int LabelPixelType;
const unsigned int Dimension = 2;
typedef otb::Image<itk::FixedArray<PixelType, 3>,
Dimension> InputImageType;
typedef otb::ImageFileReader<InputImageType> ReaderType;
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName(imageFilename);
reader->Update();
typedef itk::Statistics::ImageToListSampleAdaptor<InputImageType> SampleType;
SampleType::Pointer sample = SampleType::New();
sample->SetImage(reader->GetOutput());
// Software Guide : BeginLatex
//
// We need to declare the SVM model which is to be used by the
// classifier. The SVM model is templated over the type of value used
// for the measures and the type of pixel used for the labels.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef otb::SVMModel<PixelType, LabelPixelType> ModelType;
ModelType::Pointer model = ModelType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// After instantiation, we can load a model saved to a file (see
// section \ref{ssec:LearningFromImages} for an example of model
// estimation and storage to a file).
//
// When using a user defined kernel, an explicit instantiation has
// to be performed.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
otb::MixturePolyRBFKernelFunctor myKernel;
model->SetKernelFunctor(&myKernel);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Then, the rest of the classification program remains unchanged.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
model->LoadModel(modelFilename);
// Software Guide : EndCodeSnippet
typedef otb::SVMClassifier<SampleType, LabelPixelType> ClassifierType;
ClassifierType::Pointer classifier = ClassifierType::New();
int numberOfClasses = model->GetNumberOfClasses();
classifier->SetNumberOfClasses(numberOfClasses);
classifier->SetModel(model);
classifier->SetInput(sample.GetPointer());
classifier->Update();
typedef ClassifierType::ClassLabelType OutputPixelType;
typedef otb::Image<OutputPixelType, Dimension> OutputImageType;
OutputImageType::Pointer outputImage = OutputImageType::New();
typedef itk::Index<Dimension> myIndexType;
typedef itk::Size<Dimension> mySizeType;
typedef itk::ImageRegion<Dimension> myRegionType;
mySizeType size;
size[0] = reader->GetOutput()->GetRequestedRegion().GetSize()[0];
size[1] = reader->GetOutput()->GetRequestedRegion().GetSize()[1];
myIndexType start;
start[0] = 0;
start[1] = 0;
myRegionType region;
region.SetIndex(start);
region.SetSize(size);
outputImage->SetRegions(region);
outputImage->Allocate();
ClassifierType::OutputType* membershipSample =
classifier->GetOutput();
ClassifierType::OutputType::ConstIterator m_iter =
membershipSample->Begin();
ClassifierType::OutputType::ConstIterator m_last =
membershipSample->End();
typedef itk::ImageRegionIterator<OutputImageType> OutputIteratorType;
OutputIteratorType outIt(outputImage,
outputImage->GetBufferedRegion());
outIt.GoToBegin();
while (m_iter != m_last && !outIt.IsAtEnd())
{
outIt.Set(m_iter.GetClassLabel());
++m_iter;
++outIt;
}
typedef otb::Image<unsigned char, Dimension> FileImageType;
typedef itk::RescaleIntensityImageFilter<OutputImageType,
FileImageType> RescalerType;
RescalerType::Pointer rescaler = RescalerType::New();
rescaler->SetOutputMinimum(itk::NumericTraits<unsigned char>::min());
rescaler->SetOutputMaximum(itk::NumericTraits<unsigned char>::max());
rescaler->SetInput(outputImage);
typedef otb::ImageFileWriter<FileImageType> WriterType;
WriterType::Pointer writer = WriterType::New();
writer->SetFileName(outputFilename);
writer->SetInput(rescaler->GetOutput());
writer->Update();
return EXIT_SUCCESS;
}
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