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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.
*/
/* Example usage:
./TrainMachineLearningModelFromImagesExample Input/QB_1_ortho.tif Input/VectorData_QB1.shp Output/clLIBSVMModelQB1.libsvm
*/
// This example illustrates the use of the
// \doxygen{otb}{MachineLearningModel} class. This class allows the
// estimation of a classification model (supervised learning) from images. In this example, we will train an SVM
// with 4 classes. We start by including the appropriate header files.
// List sample generator
#include "otbListSampleGenerator.h"
// Extract a ROI of the vectordata
#include "otbVectorDataIntoImageProjectionFilter.h"
// SVM model Estimator
#include "otbSVMMachineLearningModel.h"
// Image
#include "otbVectorImage.h"
#include "otbVectorData.h"
// Reader
#include "otbImageFileReader.h"
#include "otbVectorDataFileReader.h"
// Normalize the samples
//#include "otbShiftScaleSampleListFilter.h"
int main(int itkNotUsed(argc), char* argv[])
{
const char* inputImageFileName = argv[1];
const char* trainingShpFileName = argv[2];
const char* outputModelFileName = argv[3];
using InputPixelType = unsigned int;
const unsigned int Dimension = 2;
using InputImageType = otb::VectorImage<InputPixelType, Dimension>;
using VectorDataType = otb::VectorData<double, 2>;
using InputReaderType = otb::ImageFileReader<InputImageType>;
using VectorDataReaderType = otb::VectorDataFileReader<VectorDataType>;
// In this framework, we must transform the input samples store in a vector
// data into a \subdoxygen{itk}{Statistics}{ListSample} which is the structure
// compatible with the machine learning classes. On the one hand, we are using feature vectors
// for the characterization of the classes, and on the other hand, the class labels
// are scalar values. We first re-project the input vector data over the input image, using the
// \doxygen{otb}{VectorDataIntoImageProjectionFilter} class. To convert the
// input samples store in a vector data into a
// \subdoxygen{itk}{Statistics}{ListSample}, we use the
// \doxygen{otb}{ListSampleGenerator} class.
// VectorData projection filter
using VectorDataReprojectionType = otb::VectorDataIntoImageProjectionFilter<VectorDataType, InputImageType>;
InputReaderType::Pointer inputReader = InputReaderType::New();
inputReader->SetFileName(inputImageFileName);
InputImageType::Pointer image = inputReader->GetOutput();
image->UpdateOutputInformation();
// Read the Vectordata
VectorDataReaderType::Pointer vectorReader = VectorDataReaderType::New();
vectorReader->SetFileName(trainingShpFileName);
vectorReader->Update();
VectorDataType::Pointer vectorData = vectorReader->GetOutput();
vectorData->Update();
VectorDataReprojectionType::Pointer vdreproj = VectorDataReprojectionType::New();
vdreproj->SetInputImage(image);
vdreproj->SetInput(vectorData);
vdreproj->SetUseOutputSpacingAndOriginFromImage(false);
vdreproj->Update();
using ListSampleGeneratorType = otb::ListSampleGenerator<InputImageType, VectorDataType>;
ListSampleGeneratorType::Pointer sampleGenerator;
sampleGenerator = ListSampleGeneratorType::New();
sampleGenerator->SetInput(image);
sampleGenerator->SetInputVectorData(vdreproj->GetOutput());
sampleGenerator->SetClassKey("Class");
sampleGenerator->Update();
// std::cout << "Number of classes: " << sampleGenerator->GetNumberOfClasses() << std::endl;
// using ListSampleType = ListSampleGeneratorType::ListSampleType;
// using ShiftScaleFilterType = otb::Statistics::ShiftScaleSampleListFilter<ListSampleType, ListSampleType>;
// // Shift scale the samples
// ShiftScaleFilterType::Pointer trainingShiftScaleFilter = ShiftScaleFilterType::New();
// trainingShiftScaleFilter->SetInput(concatenateTrainingSamples->GetOutput());
// trainingShiftScaleFilter->SetShifts(meanMeasurementVector);
// trainingShiftScaleFilter->SetScales(stddevMeasurementVector);
// trainingShiftScaleFilter->Update();
// Now, we need to declare the machine learning model which will be used by the
// classifier. In this example, we train an SVM model. The
// \doxygen{otb}{SVMMachineLearningModel} class inherits from the pure virtual
// class \doxygen{otb}{MachineLearningModel} which is templated over the type of
// values used for the measures and the type of pixels used for the labels. Most
// of the classification and regression algorithms available through this
// interface in OTB is based on the OpenCV library \cite{opencv_library}. Specific methods
// can be used to set classifier parameters. In the case of SVM, we set here the type
// of the kernel. Other parameters are let with their default values.
using SVMType = otb::SVMMachineLearningModel<InputImageType::InternalPixelType, ListSampleGeneratorType::ClassLabelType>;
SVMType::Pointer SVMClassifier = SVMType::New();
SVMClassifier->SetInputListSample(sampleGenerator->GetTrainingListSample());
SVMClassifier->SetTargetListSample(sampleGenerator->GetTrainingListLabel());
SVMClassifier->SetKernelType(CvSVM::LINEAR);
// The machine learning interface is generic and gives access to other classifiers. We now train the
// SVM model using the \code{Train} and save the model to a text file using the
// \code{Save} method.
SVMClassifier->Train();
SVMClassifier->Save(outputModelFileName);
// You can now use the \code{Predict} method which takes a
// \subdoxygen{itk}{Statistics}{ListSample} as input and estimates the label of each
// input sample using the model. Finally, the
// \doxygen{otb}{ImageClassificationModel} inherits from the
// \doxygen{itk}{ImageToImageFilter} and allows classifying pixels in the
// input image by predicting their labels using a model.
}
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