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/*
* Copyright (C) 2005-2022 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 previous examples, we have used the
// \doxygen{otb}{SOMClassifier}, which uses the ITK classification
// framework. This good for compatibility with the ITK framework, but
// introduces the limitations of not being able to use streaming and
// being able to know at compilation time the number of bands of the
// image to be classified. In OTB we have avoided this limitation by
// developing the \doxygen{otb}{SOMImageClassificationFilter}. In
// this example we will illustrate its use. We start by including the
// appropriate header file.
#include "otbSOMImageClassificationFilter.h"
#include "otbImage.h"
#include "otbSOMMap.h"
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
int main(int itkNotUsed(argc), char* argv[])
{
const char* infname = argv[1];
const char* somfname = 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 will be generic enough to be able to process images
// with any number of bands. We read the images as
// \doxygen{otb}{VectorImage}s. 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 and the SOM type.
using SOMMapType = otb::SOMMap<ImageType::PixelType>;
using ClassificationFilterType = otb::SOMImageClassificationFilter<ImageType, LabeledImageType, SOMMapType>;
// And finally, we define the readers (for the input image and theSOM)
// 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 SOMReaderType = otb::ImageFileReader<SOMMapType>;
using WriterType = otb::ImageFileWriter<LabeledImageType>;
// We instantiate the classifier and the reader objects and we set
// the existing SOM obtained in a previous training step.
ClassificationFilterType::Pointer filter = ClassificationFilterType::New();
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName(infname);
SOMReaderType::Pointer somreader = SOMReaderType::New();
somreader->SetFileName(somfname);
somreader->Update();
filter->SetMap(somreader->GetOutput());
// 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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