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/*
* Copyright (C) 2005-2020 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;
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 ReaderType = otb::ImageFileReader<ImageType>;
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();
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;
}
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