/usr/include/OTB-5.8/otbNormalBayesMachineLearningModel.txx is in libotb-dev 5.8.0+dfsg-3.
This file is owned by root:root, with mode 0o644.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 | /*=========================================================================
Program: ORFEO Toolbox
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
See OTBCopyright.txt for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#ifndef otbNormalBayesMachineLearningModel_txx
#define otbNormalBayesMachineLearningModel_txx
#include <fstream>
#include "itkMacro.h"
#include "otbNormalBayesMachineLearningModel.h"
#include "otbOpenCVUtils.h"
namespace otb
{
template <class TInputValue, class TOutputValue>
NormalBayesMachineLearningModel<TInputValue,TOutputValue>
::NormalBayesMachineLearningModel() :
m_NormalBayesModel (new CvNormalBayesClassifier)
{
}
template <class TInputValue, class TOutputValue>
NormalBayesMachineLearningModel<TInputValue,TOutputValue>
::~NormalBayesMachineLearningModel()
{
delete m_NormalBayesModel;
}
/** Train the machine learning model */
template <class TInputValue, class TOutputValue>
void
NormalBayesMachineLearningModel<TInputValue,TOutputValue>
::Train()
{
//convert listsample to opencv matrix
cv::Mat samples;
otb::ListSampleToMat<InputListSampleType>(this->GetInputListSample(), samples);
cv::Mat labels;
otb::ListSampleToMat<TargetListSampleType>(this->GetTargetListSample(),labels);
m_NormalBayesModel->train(samples,labels,cv::Mat(),cv::Mat(),false);
}
template <class TInputValue, class TOutputValue>
typename NormalBayesMachineLearningModel<TInputValue,TOutputValue>
::TargetSampleType
NormalBayesMachineLearningModel<TInputValue,TOutputValue>
::DoPredict(const InputSampleType & input, ConfidenceValueType *quality) const
{
//convert listsample to Mat
cv::Mat sample;
otb::SampleToMat<InputSampleType>(input,sample);
cv::Mat missing = cv::Mat(1,input.Size(), CV_8U );
missing.setTo(0);
double result = m_NormalBayesModel->predict(sample);
TargetSampleType target;
target[0] = static_cast<TOutputValue>(result);
if (quality != ITK_NULLPTR)
{
if (!this->HasConfidenceIndex())
{
itkExceptionMacro("Confidence index not available for this classifier !");
}
}
return target;
}
template <class TInputValue, class TOutputValue>
void
NormalBayesMachineLearningModel<TInputValue,TOutputValue>
::Save(const std::string & filename, const std::string & name)
{
if (name == "")
m_NormalBayesModel->save(filename.c_str(), ITK_NULLPTR);
else
m_NormalBayesModel->save(filename.c_str(), name.c_str());
}
template <class TInputValue, class TOutputValue>
void
NormalBayesMachineLearningModel<TInputValue,TOutputValue>
::Load(const std::string & filename, const std::string & name)
{
if (name == "")
m_NormalBayesModel->load(filename.c_str(), ITK_NULLPTR);
else
m_NormalBayesModel->load(filename.c_str(), name.c_str());
}
template <class TInputValue, class TOutputValue>
bool
NormalBayesMachineLearningModel<TInputValue,TOutputValue>
::CanReadFile(const std::string & file)
{
std::ifstream ifs;
ifs.open(file.c_str());
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(CV_TYPE_NAME_ML_NBAYES) != std::string::npos)
{
//std::cout<<"Reading a "<<CV_TYPE_NAME_ML_NBAYES<<" model"<<std::endl;
return true;
}
}
ifs.close();
return false;
}
template <class TInputValue, class TOutputValue>
bool
NormalBayesMachineLearningModel<TInputValue,TOutputValue>
::CanWriteFile(const std::string & itkNotUsed(file))
{
return false;
}
template <class TInputValue, class TOutputValue>
void
NormalBayesMachineLearningModel<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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