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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.
*/
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include <fstream>
#include <iostream>
#include "otbVectorImage.h"
#include "otbAttributesMapLabelObjectWithClassLabel.h"
#include "itkLabelImageToLabelMapFilter.h"
#include "otbShapeAttributesLabelMapFilter.h"
#include "otbBandsStatisticsAttributesLabelMapFilter.h"
#include "otbLabelMapWithClassLabelToLabeledSampleListFilter.h"
#include "otbLibSVMMachineLearningModel.h"
#include "otbLabelMapClassifier.h"
#include "otbLabelMapWithClassLabelToClassLabelImageFilter.h"
const unsigned int Dimension = 2;
typedef unsigned short LabelType;
typedef double DoublePixelType;
typedef otb::AttributesMapLabelObjectWithClassLabel<LabelType, Dimension, double, LabelType> LabelObjectType;
typedef itk::LabelMap<LabelObjectType> LabelMapType;
typedef otb::VectorImage<DoublePixelType, Dimension> VectorImageType;
typedef otb::Image<unsigned int, 2> LabeledImageType;
typedef otb::ImageFileReader<VectorImageType> ReaderType;
typedef otb::ImageFileReader<LabeledImageType> LabeledReaderType;
typedef otb::ImageFileWriter<VectorImageType> WriterType;
typedef otb::ImageFileWriter<LabeledImageType> LabeledWriterType;
typedef itk::LabelImageToLabelMapFilter<LabeledImageType, LabelMapType> LabelMapFilterType;
typedef otb::ShapeAttributesLabelMapFilter<LabelMapType> ShapeFilterType;
typedef otb::BandsStatisticsAttributesLabelMapFilter<LabelMapType, VectorImageType> BandsStatisticsFilterType;
// SVM model estimation
typedef itk::VariableLengthVector<double> VectorType;
typedef itk::FixedArray<LabelType, 1> TrainingVectorType;
typedef itk::Statistics::ListSample<VectorType> ListSampleType;
typedef itk::Statistics::ListSample<TrainingVectorType> TrainingListSampleType;
typedef otb::LabelMapWithClassLabelToLabeledSampleListFilter<LabelMapType, ListSampleType, TrainingListSampleType> ListSampleFilterType;
typedef otb::LibSVMMachineLearningModel<double, LabelType> SVMType;
typedef otb::LabelMapClassifier<LabelMapType> ClassifierType;
typedef otb::LabelMapWithClassLabelToClassLabelImageFilter<LabelMapType, LabeledImageType> ClassifImageGeneratorType;
LabelObjectType::Pointer makeTrainingSample(LabelMapType* labelMap, LabelType labelObjectId, LabelType classLabel)
{
LabelObjectType::Pointer newLabelObject = LabelObjectType::New();
newLabelObject->CopyAllFrom(labelMap->GetLabelObject(labelObjectId));
newLabelObject->SetClassLabel(classLabel);
return newLabelObject;
}
int otbLabelMapClassifier(int itkNotUsed(argc), char* argv[])
{
const char* infname = argv[1];
const char* lfname = argv[2];
const char* outfname = argv[3];
// Filters instantiation
ReaderType::Pointer reader = ReaderType::New();
LabeledReaderType::Pointer labeledReader = LabeledReaderType::New();
LabelMapFilterType::Pointer filter = LabelMapFilterType::New();
ShapeFilterType::Pointer shapeFilter = ShapeFilterType::New();
BandsStatisticsFilterType::Pointer radiometricFilter = BandsStatisticsFilterType::New();
LabelMapType::Pointer trainingLabelMap = LabelMapType::New();
ListSampleFilterType::Pointer labelMap2SampleList = ListSampleFilterType::New();
SVMType::Pointer model = SVMType::New();
ClassifierType::Pointer classifier = ClassifierType::New();
ClassifImageGeneratorType::Pointer imGenerator = ClassifImageGeneratorType::New();
LabeledWriterType::Pointer writer = LabeledWriterType::New();
// Read inputs
reader->SetFileName(infname);
labeledReader->SetFileName(lfname);
// Make a LabelMap out of it
filter->SetInput(labeledReader->GetOutput());
filter->SetBackgroundValue(itk::NumericTraits<LabelType>::max());
// Compute shape and radimometric attributes
shapeFilter->SetInput(filter->GetOutput());
radiometricFilter->SetInput(shapeFilter->GetOutput());
radiometricFilter->SetFeatureImage(reader->GetOutput());
radiometricFilter->Update();
// Build a sub-LabelMap with class-labeled LabelObject
LabelMapType::Pointer labelMap = radiometricFilter->GetOutput();
// The following is very specific to the input specified in CMakeLists
// water
trainingLabelMap->PushLabelObject(makeTrainingSample(labelMap, 13, 0));
// road
trainingLabelMap->PushLabelObject(makeTrainingSample(labelMap, 88, 1));
trainingLabelMap->PushLabelObject(makeTrainingSample(labelMap, 114, 1));
trainingLabelMap->PushLabelObject(makeTrainingSample(labelMap, 144, 1));
// boat
trainingLabelMap->PushLabelObject(makeTrainingSample(labelMap, 52, 2));
trainingLabelMap->PushLabelObject(makeTrainingSample(labelMap, 31, 2));
trainingLabelMap->PushLabelObject(makeTrainingSample(labelMap, 11, 2));
// roof
trainingLabelMap->PushLabelObject(makeTrainingSample(labelMap, 58, 3));
trainingLabelMap->PushLabelObject(makeTrainingSample(labelMap, 60, 3));
trainingLabelMap->PushLabelObject(makeTrainingSample(labelMap, 27, 3));
trainingLabelMap->PushLabelObject(makeTrainingSample(labelMap, 81, 3));
// vegetation
trainingLabelMap->PushLabelObject(makeTrainingSample(labelMap, 161, 4));
trainingLabelMap->PushLabelObject(makeTrainingSample(labelMap, 46, 4));
// Make a ListSample out of trainingLabelMap
labelMap2SampleList->SetInputLabelMap(trainingLabelMap);
std::vector<std::string> attributes = labelMap->GetLabelObject(0)->GetAvailableAttributes();
std::vector<std::string>::const_iterator attrIt;
for (attrIt = attributes.begin(); attrIt != attributes.end(); ++attrIt)
{
labelMap2SampleList->GetMeasurementFunctor().AddAttribute((*attrIt).c_str());
}
labelMap2SampleList->Update();
// Estimate SVM model
model->SetInputListSample(const_cast<SVMType::InputListSampleType*>(labelMap2SampleList->GetOutputSampleList()));
model->SetTargetListSample(const_cast<SVMType::TargetListSampleType*>(labelMap2SampleList->GetOutputTrainingSampleList()));
model->Train();
// Classify using the whole LabelMap with estimated model
classifier->SetInput(labelMap);
classifier->SetModel(model);
for (attrIt = attributes.begin(); attrIt != attributes.end(); ++attrIt)
{
classifier->GetMeasurementFunctor().AddAttribute((*attrIt).c_str());
}
classifier->Update();
// Make a labeled image with the classification result
imGenerator->SetInput(classifier->GetOutput());
writer->SetInput(imGenerator->GetOutput());
writer->SetFileName(outfname);
writer->Update();
return EXIT_SUCCESS;
}
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