text: /*
   * Copyright (C) 2005-2019 Centre National d'Etudes Spatiales (CNES)
   *
   * 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.
   */
  
  
  // 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.
  
  #include "otbMachineLearningModelFactory.h"
  #include "otbImageClassificationFilter.h"
  
  #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];
  
    // We will assume double precision input images and will also define
    // the type for the labeled pixels.
  
    const unsigned int     Dimension = 2;
    typedef double         PixelType;
    typedef unsigned short LabeledPixelType;
    // 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.
    typedef otb::VectorImage<PixelType, Dimension>  ImageType;
    typedef otb::Image<LabeledPixelType, Dimension> LabeledImageType;
  
    // We can now define the type for the classifier filter, which is
    // templated over its input and output image types.
    typedef otb::ImageClassificationFilter<ImageType, LabeledImageType> ClassificationFilterType;
    typedef ClassificationFilterType::ModelType                         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.
    typedef otb::MachineLearningModelFactory<PixelType, LabeledPixelType> MachineLearningModelFactoryType;
  
    // 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.
    typedef otb::ImageFileReader<ImageType>        ReaderType;
    typedef otb::ImageFileWriter<LabeledImageType> WriterType;
  
    // 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();
  
    ReaderType::Pointer reader = ReaderType::New();
    reader->SetFileName(infname);
  
    // 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;
    model = MachineLearningModelFactoryType::CreateMachineLearningModel(modelfname, MachineLearningModelFactoryType::ReadMode);
    model->Load(modelfname);
  
    filter->SetModel(model);
  
    // We plug the pipeline and
    // trigger its execution by updating the output of the writer.
  
    filter->SetInput(reader->GetOutput());
  
    WriterType::Pointer writer = WriterType::New();
    writer->SetInput(filter->GetOutput());
    writer->SetFileName(outfname);
    writer->Update();
    return EXIT_SUCCESS;
  }
  