# SupervisedImageClassificationExample.cxx¶

Example source code (SupervisedImageClassificationExample.cxx):

// 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

#include "otbMachineLearningModelFactory.h"
#include "otbImageClassificationFilter.h"

#include "otbVectorImage.h"
#include "otbImage.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];

// We will assume double precision input images and will also define
// the type for the labeled pixels.

const unsigned int Dimension = 2;
using PixelType              = double;
using LabeledPixelType       = unsigned short;
// 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.
using ImageType        = otb::VectorImage<PixelType, Dimension>;
using LabeledImageType = otb::Image<LabeledPixelType, Dimension>;

// We can now define the type for the classifier filter, which is
// templated over its input and output image types.
using ClassificationFilterType = otb::ImageClassificationFilter<ImageType, LabeledImageType>;
using ModelType                = ClassificationFilterType::ModelType;

// 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.
using MachineLearningModelFactoryType = otb::MachineLearningModelFactory<PixelType, LabeledPixelType>;

// 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.
using WriterType = otb::ImageFileWriter<LabeledImageType>;

// We instantiate the classifier and the reader objects and we set
// the existing model obtained in a previous training step.
ClassificationFilterType::Pointer filter = ClassificationFilterType::New();

// The input model file is parsed according to its content and the generated model
// is then loaded within the \doxygen{otb}{ImageClassificationFilter}.
ModelType::Pointer model;

filter->SetModel(model);

// We plug the pipeline and
// trigger its execution by updating the output of the writer.