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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.
*/
#ifndef otbSVMMachineLearningModel_hxx
#define otbSVMMachineLearningModel_hxx
#include <fstream>
#include "itkMacro.h"
#include "otbSVMMachineLearningModel.h"
#include "otbOpenCVUtils.h"
namespace otb
{
template <class TInputValue, class TOutputValue>
SVMMachineLearningModel<TInputValue, TOutputValue>::SVMMachineLearningModel()
:
m_SVMModel(cv::ml::SVM::create()),
m_SVMType(CvSVM::C_SVC),
m_KernelType(CvSVM::RBF),
m_Degree(0),
m_Gamma(1),
m_Coef0(0),
m_C(1),
m_Nu(0),
m_P(0),
m_TermCriteriaType(CV_TERMCRIT_ITER),
m_MaxIter(1000),
m_Epsilon(FLT_EPSILON),
m_ParameterOptimization(false),
m_OutputDegree(0),
m_OutputGamma(1),
m_OutputCoef0(0),
m_OutputC(1),
m_OutputNu(0),
m_OutputP(0)
{
this->m_ConfidenceIndex = true;
this->m_IsRegressionSupported = true;
}
/** Train the machine learning model */
template <class TInputValue, class TOutputValue>
void SVMMachineLearningModel<TInputValue, TOutputValue>::Train()
{
// Check that the SVM type is compatible with the chosen mode (classif/regression)
if (bool(m_SVMType == CvSVM::NU_SVR || m_SVMType == CvSVM::EPS_SVR) != this->m_RegressionMode)
{
itkGenericExceptionMacro(
"SVM type incompatible with chosen mode (classification or regression."
"SVM types for classification are C_SVC, NU_SVC, ONE_CLASS. "
"SVM types for regression are NU_SVR, EPS_SVR");
}
// convert listsample to opencv matrix
cv::Mat samples;
otb::ListSampleToMat<InputListSampleType>(this->GetInputListSample(), samples);
cv::Mat labels;
otb::ListSampleToMat<TargetListSampleType>(this->GetTargetListSample(), labels);
cv::Mat var_type = cv::Mat(this->GetInputListSample()->GetMeasurementVectorSize() + 1, 1, CV_8U);
var_type.setTo(cv::Scalar(CV_VAR_NUMERICAL)); // all inputs are numerical
if (!this->m_RegressionMode) // Classification
var_type.at<uchar>(this->GetInputListSample()->GetMeasurementVectorSize(), 0) = CV_VAR_CATEGORICAL;
m_SVMModel->setType(m_SVMType);
m_SVMModel->setKernel(m_KernelType);
m_SVMModel->setDegree(m_Degree);
m_SVMModel->setGamma(m_Gamma);
m_SVMModel->setCoef0(m_Coef0);
m_SVMModel->setC(m_C);
m_SVMModel->setNu(m_Nu);
m_SVMModel->setP(m_P);
m_SVMModel->setTermCriteria(cv::TermCriteria(m_TermCriteriaType, m_MaxIter, m_Epsilon));
if (!m_ParameterOptimization)
{
m_SVMModel->train(cv::ml::TrainData::create(samples, cv::ml::ROW_SAMPLE, labels, cv::noArray(), cv::noArray(), cv::noArray(), var_type));
}
else
{
m_SVMModel->trainAuto(cv::ml::TrainData::create(samples, cv::ml::ROW_SAMPLE, labels, cv::noArray(), cv::noArray(), cv::noArray(), var_type));
}
m_OutputDegree = m_SVMModel->getDegree();
m_OutputGamma = m_SVMModel->getGamma();
m_OutputCoef0 = m_SVMModel->getCoef0();
m_OutputC = m_SVMModel->getC();
m_OutputNu = m_SVMModel->getNu();
m_OutputP = m_SVMModel->getP();
}
template <class TInputValue, class TOutputValue>
typename SVMMachineLearningModel<TInputValue, TOutputValue>::TargetSampleType
SVMMachineLearningModel<TInputValue, TOutputValue>::DoPredict(const InputSampleType& input, ConfidenceValueType* quality, ProbaSampleType* proba) const
{
TargetSampleType target;
// convert listsample to Mat
cv::Mat sample;
otb::SampleToMat<InputSampleType>(input, sample);
double result = m_SVMModel->predict(sample);
target[0] = static_cast<TOutputValue>(result);
if (quality != nullptr)
{
(*quality) = m_SVMModel->predict(sample, cv::noArray(), cv::ml::StatModel::RAW_OUTPUT);
}
if (proba != nullptr && !this->m_ProbaIndex)
itkExceptionMacro("Probability per class not available for this classifier !");
return target;
}
template <class TInputValue, class TOutputValue>
void SVMMachineLearningModel<TInputValue, TOutputValue>::Save(const std::string& filename, const std::string& name)
{
cv::FileStorage fs(filename, cv::FileStorage::WRITE);
fs << (name.empty() ? m_SVMModel->getDefaultName() : cv::String(name)) << "{";
m_SVMModel->write(fs);
fs << "}";
fs.release();
}
template <class TInputValue, class TOutputValue>
void SVMMachineLearningModel<TInputValue, TOutputValue>::Load(const std::string& filename, const std::string& name)
{
cv::FileStorage fs(filename, cv::FileStorage::READ);
m_SVMModel->read(name.empty() ? fs.getFirstTopLevelNode() : fs[name]);
}
template <class TInputValue, class TOutputValue>
bool SVMMachineLearningModel<TInputValue, TOutputValue>::CanReadFile(const std::string& file)
{
std::ifstream ifs;
ifs.open(file);
if (!ifs)
{
std::cerr << "Could not read file " << file << std::endl;
return false;
}
while (!ifs.eof())
{
std::string line;
std::getline(ifs, line);
// if (line.find(m_SVMModel->getName()) != std::string::npos)
if (line.find(CV_TYPE_NAME_ML_SVM) != std::string::npos || line.find(m_SVMModel->getDefaultName()) != std::string::npos)
{
return true;
}
}
ifs.close();
return false;
}
template <class TInputValue, class TOutputValue>
bool SVMMachineLearningModel<TInputValue, TOutputValue>::CanWriteFile(const std::string& itkNotUsed(file))
{
return false;
}
template <class TInputValue, class TOutputValue>
void SVMMachineLearningModel<TInputValue, TOutputValue>::PrintSelf(std::ostream& os, itk::Indent indent) const
{
// Call superclass implementation
Superclass::PrintSelf(os, indent);
}
} // end namespace otb
#endif
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