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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 "otbTrainImagesBase.h"
namespace otb
{
namespace Wrapper
{
class TrainImagesClassifier : public TrainImagesBase
{
public:
typedef TrainImagesClassifier Self;
typedef TrainImagesBase Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
itkNewMacro(Self) itkTypeMacro(Self, Superclass)
void DoInit() override
{
SetName("TrainImagesClassifier");
SetDescription("Train a classifier from multiple pairs of images and training vector data.");
// Documentation
SetDocLongDescription(
"Train a classifier from multiple pairs of images and training vector data. "
"Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by "
"the ComputeImagesStatistics application.\n\n"
"The training vector data must contain polygons with a positive integer field "
"representing the class label. The name of this field can be set using the *Class label field* parameter.\n\n"
"Training and validation sample lists are built such that each class is equally represented in both lists. One parameter controls the ratio "
"between the number of samples in training and validation sets. Two parameters manage the size of the training and "
"validation sets per class and per image.\n\n"
"In the validation process, the confusion matrix is organized the following way:\n\n"
"* Rows: reference labels,\n"
"* Columns: produced labels.\n\n"
"In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels"
" are ordered according to the rows/columns of the confusion matrix.\n\n"
"This application is based on LibSVM, OpenCV Machine Learning, and Shark ML. "
"The output of this application is a text model file, whose format corresponds to the "
"ML model type chosen. There is no image or vector data output.");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso("OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html ");
AddDocTag(Tags::Learning);
// Perform initialization
ClearApplications();
InitIO();
InitSampling();
InitClassification();
AddDocTag(Tags::Learning);
// Doc example parameter settings
SetDocExampleParameterValue("io.il", "QB_1_ortho.tif");
SetDocExampleParameterValue("io.vd", "VectorData_QB1.shp");
SetDocExampleParameterValue("io.imstat", "EstimateImageStatisticsQB1.xml");
SetDocExampleParameterValue("sample.mv", "100");
SetDocExampleParameterValue("sample.mt", "100");
SetDocExampleParameterValue("sample.vtr", "0.5");
SetDocExampleParameterValue("sample.vfn", "Class");
SetDocExampleParameterValue("classifier", "libsvm");
SetDocExampleParameterValue("classifier.libsvm.k", "linear");
SetDocExampleParameterValue("classifier.libsvm.c", "1");
SetDocExampleParameterValue("classifier.libsvm.opt", "false");
SetDocExampleParameterValue("io.out", "svmModelQB1.txt");
SetDocExampleParameterValue("io.confmatout", "svmConfusionMatrixQB1.csv");
SetOfficialDocLink();
}
void DoUpdateParameters() override
{
if (HasValue("io.vd") && IsParameterEnabled("io.vd"))
{
UpdatePolygonClassStatisticsParameters();
}
}
/**
* Select and Extract samples for validation with computed statistics and rates.
* Validation samples could be empty if sample.vrt == 0 and if no dedicated validation are provided.
* If no dedicated validation is provided the training is split corresponding to the sample.vtr parameter,
* in this case if no vector data have been provided, the training rates and statistics are computed
* on the selection and extraction training result.
* fileNames.sampleOutputs contains training data and after an ExtractValidationData training data will
* be split to fileNames.sampleTrainOutputs.
* \param imageList
* \param fileNames
* \param validationVectorFileList
* \param rates
* \param HasInputVector
*/
void ExtractValidationData(FloatVectorImageListType* imageList, TrainFileNamesHandler& fileNames, std::vector<std::string> validationVectorFileList,
const SamplingRates& rates, bool itkNotUsed(HasInputVector))
{
if (!validationVectorFileList.empty()) // Compute class statistics and sampling rate of validation data if provided.
{
ComputePolygonStatistics(imageList, validationVectorFileList, fileNames.polyStatValidOutputs);
ComputeSamplingRate(fileNames.polyStatValidOutputs, fileNames.rateValidOut, rates.fmv);
SelectAndExtractValidationSamples(fileNames, imageList, validationVectorFileList);
fileNames.sampleTrainOutputs = fileNames.sampleOutputs;
}
else if (GetParameterFloat("sample.vtr") != 0.0) // Split training data to validation
{
SplitTrainingToValidationSamples(fileNames, imageList);
}
else // Update sampleTrainOutputs and clear sampleValidOutputs
{
fileNames.sampleTrainOutputs = fileNames.sampleOutputs;
// Corner case where no dedicated validation set is provided and split ratio is set to 0 (all samples for training)
// In this case SampleValidOutputs should be cleared
fileNames.sampleValidOutputs.clear();
}
}
/**
* Extract Training data depending if input vector is provided
* \param imageList list of the image
* \param fileNames handler that contain filenames
* \param vectorFileList input vector file list (if provided
* \param rates
*/
void ExtractTrainData(FloatVectorImageListType* imageList, const TrainFileNamesHandler& fileNames, std::vector<std::string> vectorFileList,
const SamplingRates& rates)
{
// if( !vectorFileList.empty() ) // Select and Extract samples for training with computed statistics and rates
// {
ComputePolygonStatistics(imageList, vectorFileList, fileNames.polyStatTrainOutputs);
ComputeSamplingRate(fileNames.polyStatTrainOutputs, fileNames.rateTrainOut, rates.fmt);
SelectAndExtractTrainSamples(fileNames, imageList, vectorFileList, Superclass::CLASS);
// }
// else // Select training samples base on geometric sampling if no input vector is provided
// {
// SelectAndExtractTrainSamples( fileNames, imageList, vectorFileList, SamplingStrategy::GEOMETRIC, "fid" );
// }
}
void DoExecute() override
{
TrainFileNamesHandler fileNames;
std::vector<std::string> vectorFileList;
FloatVectorImageListType* imageList = GetParameterImageList("io.il");
bool HasInputVector = IsParameterEnabled("io.vd") && HasValue("io.vd");
if (HasInputVector)
vectorFileList = GetParameterStringList("io.vd");
unsigned long nbInputs = imageList->Size();
if (!HasInputVector)
{
otbAppLogFATAL("Missing input vector data files");
}
if (!vectorFileList.empty() && nbInputs > vectorFileList.size())
{
otbAppLogFATAL("Missing input vector data files to match number of images (" << nbInputs << ").");
}
// check if validation vectors are given
std::vector<std::string> validationVectorFileList;
bool dedicatedValidation = false;
if (IsParameterEnabled("io.valid") && HasValue("io.valid"))
{
validationVectorFileList = GetParameterStringList("io.valid");
if (nbInputs > validationVectorFileList.size())
{
otbAppLogFATAL("Missing validation vector data files to match number of images (" << nbInputs << ").");
}
dedicatedValidation = true;
}
fileNames.CreateTemporaryFileNames(GetParameterString("io.out"), nbInputs, dedicatedValidation);
// Compute final maximum sampling rates for both training and validation samples
SamplingRates rates = ComputeFinalMaximumSamplingRates(dedicatedValidation);
ExtractTrainData(imageList, fileNames, vectorFileList, rates);
ExtractValidationData(imageList, fileNames, validationVectorFileList, rates, HasInputVector);
// Then train the model with extracted samples
TrainModel(imageList, fileNames.sampleTrainOutputs, fileNames.sampleValidOutputs);
// cleanup
if (GetParameterInt("cleanup"))
{
otbAppLogINFO(<< "Final clean-up ...");
fileNames.clear();
}
}
private:
void UpdatePolygonClassStatisticsParameters()
{
std::vector<std::string> vectorFileList = GetParameterStringList("io.vd");
GetInternalApplication("polystat")->SetParameterString("vec", vectorFileList[0]);
UpdateInternalParameters("polystat");
}
};
} // end namespace Wrapper
} // end namespace otb
OTB_APPLICATION_EXPORT(otb::Wrapper::TrainImagesClassifier)
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