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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.
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.
=========================================================================*/
// Software Guide : BeginLatex
//
// In OTB, a generic streamed filter called \doxygen{otb}{ImageClassificationFilter}
// is available to classify any input multi-channel image according to an input
// classification model file. This filter is generic because it works with any
// classification model type (SVM, KNN, Artificial Neural Network,...) generated
// within the OTB generic Machine Learning framework based on OpenCV (\cite{opencv_library}).
// The input model file is smartly parsed according to its content in order to
// identify which learning method was used to generate it. Once the classification
// method and model are known, the input image can be classified. More details are
// given in subsections \ref{ssec:LearningFromSamples} and \ref{ssec:LearningFromImages}
// to generate a classification model either from samples or from images.
// In this example we will illustrate its use. We start by including the
// appropriate header files.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "otbMachineLearningModelFactory.h"
#include "otbImageClassificationFilter.h"
// Software Guide : EndCodeSnippet
#include "otbVectorImage.h"
#include "otbImage.h"
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
int main(int itkNotUsed(argc), char * argv[])
{
const char * infname = argv[1];
const char * modelfname = argv[2];
const char * outfname = argv[3];
// Software Guide : BeginLatex
//
// We will assume double precision input images and will also define
// the type for the labeled pixels.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const unsigned int Dimension = 2;
typedef double PixelType;
typedef unsigned short LabeledPixelType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Our classifier is generic enough to be able to process images
// with any number of bands. We read the input image as a
// \doxygen{otb}{VectorImage}. The labeled image will be a scalar image.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef otb::VectorImage<PixelType, Dimension> ImageType;
typedef otb::Image<LabeledPixelType, Dimension> LabeledImageType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We can now define the type for the classifier filter, which is
// templated over its input and output image types.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef otb::ImageClassificationFilter<ImageType, LabeledImageType>
ClassificationFilterType;
typedef ClassificationFilterType::ModelType ModelType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Moreover, it is necessary to define a \doxygen{otb}{MachineLearningModelFactory}
// which is templated over its input and output pixel types. This factory is used
// to parse the input model file and to define which classification method to use.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef otb::MachineLearningModelFactory<PixelType, LabeledPixelType>
MachineLearningModelFactoryType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// And finally, we define the reader and the writer. Since the images
// to classify can be very big, we will use a streamed writer which
// will trigger the streaming ability of the classifier.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef otb::ImageFileReader<ImageType> ReaderType;
typedef otb::ImageFileWriter<LabeledImageType> WriterType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We instantiate the classifier and the reader objects and we set
// the existing model obtained in a previous training step.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
ClassificationFilterType::Pointer filter = ClassificationFilterType::New();
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName(infname);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The input model file is parsed according to its content and the generated model
// is then loaded within the \doxygen{otb}{ImageClassificationFilter}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
ModelType::Pointer model;
model = MachineLearningModelFactoryType::CreateMachineLearningModel(
modelfname,
MachineLearningModelFactoryType::ReadMode);
model->Load(modelfname);
filter->SetModel(model);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We plug the pipeline and
// trigger its execution by updating the output of the writer.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetInput(reader->GetOutput());
WriterType::Pointer writer = WriterType::New();
writer->SetInput(filter->GetOutput());
writer->SetFileName(outfname);
writer->Update();
// Software Guide : EndCodeSnippet
return EXIT_SUCCESS;
}
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