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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.
*/
#include <iostream>
#include <otbConfigure.h>
#include <otbMachineLearningModel.h>
typedef otb::MachineLearningModel<float, short> MachineLearningModelType;
typedef MachineLearningModelType::InputValueType InputValueType;
typedef MachineLearningModelType::InputSampleType InputSampleType;
typedef MachineLearningModelType::InputListSampleType InputListSampleType;
typedef MachineLearningModelType::TargetValueType TargetValueType;
typedef MachineLearningModelType::TargetSampleType TargetSampleType;
typedef MachineLearningModelType::TargetListSampleType TargetListSampleType;
typedef otb::MachineLearningModel<float, float> MachineLearningModelRegressionType;
typedef MachineLearningModelRegressionType::InputValueType InputValueRegressionType;
typedef MachineLearningModelRegressionType::InputSampleType InputSampleRegressionType;
typedef MachineLearningModelRegressionType::InputListSampleType InputListSampleRegressionType;
typedef MachineLearningModelRegressionType::TargetValueType TargetValueRegressionType;
typedef MachineLearningModelRegressionType::TargetSampleType TargetSampleRegressionType;
typedef MachineLearningModelRegressionType::TargetListSampleType TargetListSampleRegressionType;
#ifdef OTB_USE_SHARK
#include "otbSharkKMeansMachineLearningModel.h"
#include "otb_boost_string_header.h"
#include <chrono>
bool SharkReadDataFile(const std::string& infname, InputListSampleType* samples, TargetListSampleType* labels)
{
std::ifstream ifs(infname);
if (!ifs)
{
std::cout << "Could not read file " << infname << std::endl;
return false;
}
unsigned int nbfeatures = 0;
std::string line;
while (std::getline(ifs, line))
{
boost::algorithm::trim(line);
if (nbfeatures == 0)
{
nbfeatures = std::count(line.begin(), line.end(), ' ');
}
if (line.size() > 1)
{
InputSampleType sample(nbfeatures);
sample.Fill(0);
std::string::size_type pos = line.find_first_of(" ", 0);
// Parse label
TargetSampleType label;
label[0] = std::stoi(line.substr(0, pos).c_str());
bool endOfLine = false;
unsigned int id = 0;
while (!endOfLine)
{
std::string::size_type nextpos = line.find_first_of(" ", pos + 1);
if (pos == std::string::npos)
{
endOfLine = true;
nextpos = line.size() - 1;
}
else
{
std::string feature = line.substr(pos, nextpos - pos);
std::string::size_type semicolonpos = feature.find_first_of(":");
id = std::stoi(feature.substr(0, semicolonpos).c_str());
sample[id - 1] = atof(feature.substr(semicolonpos + 1, feature.size() - semicolonpos).c_str());
pos = nextpos;
}
}
samples->SetMeasurementVectorSize(itk::NumericTraits<InputSampleType>::GetLength(sample));
samples->PushBack(sample);
labels->PushBack(label);
}
}
// std::cout<<"Retrieved "<<samples->Size()<<" samples"<<std::endl;
ifs.close();
return true;
}
int otbSharkKMeansMachineLearningModelTrain(int argc, char* argv[])
{
if (argc != 3)
{
std::cout << "Wrong number of arguments " << std::endl;
std::cout << "Usage : sample file, output file " << std::endl;
return EXIT_FAILURE;
}
typedef otb::SharkKMeansMachineLearningModel<InputValueType, TargetValueType> KMeansType;
InputListSampleType::Pointer samples = InputListSampleType::New();
TargetListSampleType::Pointer labels = TargetListSampleType::New();
if (!SharkReadDataFile(argv[1], samples, labels))
{
std::cout << "Failed to read samples file " << argv[1] << std::endl;
return EXIT_FAILURE;
}
KMeansType::Pointer classifier = KMeansType::New();
classifier->SetInputListSample(samples);
classifier->SetTargetListSample(labels);
classifier->SetRegressionMode(false);
classifier->SetK(3);
classifier->SetMaximumNumberOfIterations(0);
std::cout << "Train\n";
classifier->Train();
std::cout << "Save\n";
classifier->Save(argv[2]);
return EXIT_SUCCESS;
}
int otbSharkKMeansMachineLearningModelPredict(int argc, char* argv[])
{
if (argc != 3)
{
std::cout << "Wrong number of arguments " << std::endl;
std::cout << "Usage : sample file, input model file " << std::endl;
return EXIT_FAILURE;
}
typedef otb::SharkKMeansMachineLearningModel<InputValueType, TargetValueType> KMeansType;
InputListSampleType::Pointer samples = InputListSampleType::New();
TargetListSampleType::Pointer labels = TargetListSampleType::New();
if (!SharkReadDataFile(argv[1], samples, labels))
{
std::cout << "Failed to read samples file " << argv[1] << std::endl;
return EXIT_FAILURE;
}
KMeansType::Pointer classifier = KMeansType::New();
std::cout << "Load\n";
if (!classifier->CanReadFile(argv[2]))
{
std::cerr << "Unable to read model file : " << argv[2] << std::endl;
return EXIT_FAILURE;
}
classifier->Load(argv[2]);
auto start = std::chrono::system_clock::now();
classifier->SetInputListSample(samples);
classifier->SetTargetListSample(labels);
std::cout << "Predict loaded\n";
classifier->PredictBatch(samples, NULL);
using TimeT = std::chrono::milliseconds;
auto duration = std::chrono::duration_cast<TimeT>(std::chrono::system_clock::now() - start);
auto elapsed = duration.count();
std::cout << "PredictAll took " << elapsed << " ms\n";
return EXIT_SUCCESS;
}
#endif
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