Ajout exemple classif image

Examples/Learning/SVMImageClassificationExample.cxx

+#if defined(_MSC_VER)
+#pragma warning ( disable : 4786 )
+#endif
+
+#include <fstream>
+
+
+
+
+
+#include "otbImageFileReader.h"
+#include "otbImageFileWriter.h"
+#include "itkImageRegionIterator.h"
+#include "itkRescaleIntensityImageFilter.h"
+#include "otbImage.h"
+
+
+//  Software Guide : BeginCommandLineArgs
+//    INPUTS: {ROI_QB_MUL_1.png} 
+//    OUTPUTS: {ROI_QB_MUL_1_SVN_CLASS.png}
+//    ${OTB_SOURCE_DIR}/Examples/Data/svm_image_model.svm
+//  Software Guide : EndCommandLineArgs
+
+//  Software Guide : BeginLatex
+// This example illustrates the use of the
+// \doxygen{otb::SVMClassifier} class for performing SVM
+// classification on images.
+// In this example, we will use an SVM model estimated in the example
+// of section \ref{sec:LearningWithImages}
+// to separate between water and non-water pixels by using the RGB
+// values only. The images used for this example are shown in
+// figure~\ref{fig:SVMROIS}. 
+// The first thing to do is include the header file for the
+// class. Since the \doxygen{otb::SVMClassifier} takes
+// \doxygen{itk::ListSample}s as input, the class
+// \doxygen{itk::PointSetToListAdaptor} is needed.\\
+//
+//
+//  Software Guide : EndLatex 
+
+
+
+#include "itkImageToListAdaptor.h"
+#include "itkListSample.h"
+#include "otbSVMClassifier.h"
+
+
+
+int main(int argc, char* argv[] )
+{
+
+    namespace stat = itk::Statistics ;
+ 
+    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];
+       
+
+
+// Software Guide : BeginLatex
+// 
+// In the framework of supervised learning and classification, we will
+// always use feature vectors for the characterization of the
+// classes. On the other hand, the class labels are scalar
+// values. Here, we start by defining the type of the features as the
+// \code{PixelType}, which will be used to define the feature
+// \code{VectorType}. We also declare the type for the labels.
+//
+// Software Guide : EndLatex 
+
+// Software Guide : BeginCodeSnippet
+    
+
+    typedef double                          PixelType;
+    typedef std::vector<PixelType>          VectorType;
+    typedef int                             LabelPixelType;
+
+// Software Guide : EndCodeSnippet  
+    const   unsigned int        	         Dimension = 2;
+
+    
+// Software Guide : BeginLatex
+//
+// We can now proceed to define the image type used for storing the
+// features. We also define the reader.
+//
+// Software Guide : EndLatex
+  
+// Software Guide : BeginCodeSnippet    
+    typedef otb::Image< itk::FixedArray<PixelType,3>,
+                              Dimension >	        InputImageType;
+
+
+    typedef otb::ImageFileReader< InputImageType  >         ReaderType;
+
+// Software Guide : EndCodeSnippet    
+
+
+// Software Guide : BeginLatex
+//
+// We can now read the image by calling he \code{Update} method of the reader.
+//
+// Software Guide : EndLatex
+
+// Software Guide : BeginCodeSnippet  
+    
+
+    ReaderType::Pointer reader = ReaderType::New();
+
+
+    reader->SetFileName( imageFilename  );
+
+    reader->Update();
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// The image has now to be transformed to a sample which
+// is compatible with the classification framework. We will use a
+// \doxygen{itk::Statistics::ImageToListAdaptor} for this
+// task. This class is templated over the image type used for
+// storing the measures.
+//
+// Software Guide : EndLatex
+    
+// Software Guide : BeginCodeSnippet      
+    
+
+    typedef itk::Statistics::ImageToListAdaptor< InputImageType > SampleType;
+    SampleType::Pointer sample = SampleType::New();
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// After instatiantion, we can set the image as an imput of our
+// sample adaptor.
+//
+// Software Guide : EndLatex
+    
+// Software Guide : BeginCodeSnippet      
+    
+    sample->SetImage(reader->GetOutput());
+
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// Now, 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{sec:LearningWithImages} for an example of model
+// estimation and storage to a file.
+//
+// Software Guide : EndLatex
+    
+// Software Guide : BeginCodeSnippet      
+    
+    model->LoadModel( modelFilename );
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// We have now all the elements to create a classifier. The classifier
+// is templated over the sample type (the type of the data to be
+// classified) and the label type (the type of the ouput of the classifier).
+//
+// Software Guide : EndLatex
+    
+// Software Guide : BeginCodeSnippet      
+    
+    
+    typedef otb::SVMClassifier< SampleType, LabelPixelType > ClassifierType ;
+
+    ClassifierType::Pointer classifier = ClassifierType::New() ;
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// We set the classifier parameters : number of classes, SVM model,
+// the sample data. And we trigger the classification process by
+// calling the \code{Update} method.
+//
+// Software Guide : EndLatex
+    
+// Software Guide : BeginCodeSnippet          
+
+    int numberOfClasses = model->GetNumberOfClasses();    
+    classifier->SetNumberOfClasses(numberOfClasses) ;
+    classifier->SetModel( model );
+    classifier->SetSample(sample.GetPointer()) ;
+    classifier->Update() ;
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// After the classification step, we usually want to get the
+// results. The classifier gives an output under the form of a sample
+// list. This list supports the classical STL iterators. Therefore, we
+// will create an output image and fill it up with the results of the
+// classification. The pixel type of the output image is the same as
+// the one used for the labels.
+//
+// Software Guide : EndLatex
+    
+// Software Guide : BeginCodeSnippet      
+    
+    
+
+    typedef ClassifierType::ClassLabelType	            OutputPixelType;
+    typedef otb::Image< OutputPixelType, Dimension >        OutputImageType;
+
+    OutputImageType::Pointer outputImage = OutputImageType::New();
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// We allocate the memory for the outpu image using the information
+// from the input image.
+//
+// Software Guide : EndLatex
+    
+// Software Guide : BeginCodeSnippet      
+    
+    
+
+    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();
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// We can now declare the interators on the list that we get at the
+// output of the classifier as well as the iterator to fill the output image.
+//
+// Software Guide : EndLatex
+    
+// Software Guide : BeginCodeSnippet      
+    
+    
+    
+    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();
+
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// We will iterate through the list, get the labels and assign pixel
+// values to the output image.
+//
+// Software Guide : EndLatex
+    
+// Software Guide : BeginCodeSnippet      
+    
+
+    
+    while (m_iter != m_last && !outIt.IsAtEnd())
+    {
+    outIt.Set(m_iter.GetClassLabel());
+    ++m_iter ;
+    ++outIt;
+    }
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// Only for visualization purposes, we choose to rescale the image of
+// classes before sving it to a file. We will use the
+// \doxygen{itk::RescaleIntensityImageFilter} for this purpose.
+//
+// Software Guide : EndLatex
+    
+// Software Guide : BeginCodeSnippet      
+    
+
+    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 );
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// We can now create an image file writer and save the image.
+//
+// Software Guide : EndLatex
+    
+// Software Guide : BeginCodeSnippet      
+
+    typedef otb::ImageFileWriter< FileImageType >         WriterType;
+	
+    WriterType::Pointer writer = WriterType::New();
+
+    writer->SetFileName( outputFilename  );
+    writer->SetInput( rescaler->GetOutput() );
+    
+    writer->Update();
+// Software Guide : EndCodeSnippet          
+
+
+
+//  Software Guide : BeginLatex
+// Figure \ref{fig:SVMCLASS} shows the result of the SVM classification.
+// \begin{figure}
+// \center
+// \includegraphics[width=0.35\textwidth]{ROI_QB_MUL_1.eps}
+// \includegraphics[width=0.35\textwidth]{ROI_QB_MUL_1_SVN_CLASS.eps}
+// \itkcaption[SVM Image Classification]{Result of the SVM
+// classification . Left: RGB image. Right: image of classes.} 
+// \label{fig:SVMCLASS}
+// \end{figure}
+//  Software Guide : EndLatex
+ 
+  return EXIT_SUCCESS;
+}
+
+
+
+
+
+
+