diff --git a/Modules/Applications/AppClassification/app/otbTrainImagesRegression.cxx b/Modules/Applications/AppClassification/app/otbTrainImagesRegression.cxx
index 199043becc66d2952c0203e019d6c5e28320595f..268cc23eca51a8a681abdfa8ff071c2f74ea9725 100644
--- a/Modules/Applications/AppClassification/app/otbTrainImagesRegression.cxx
+++ b/Modules/Applications/AppClassification/app/otbTrainImagesRegression.cxx
@@ -32,143 +32,140 @@ class TrainImagesRegression : public CompositeApplication
{
public:
/** Standard class typedefs. */
- typedef TrainImagesRegression Self;
- typedef CompositeApplication Superclass;
- typedef itk::SmartPointer<Self> Pointer;
+ typedef TrainImagesRegression Self;
+ typedef CompositeApplication Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
/** Standard macro */
itkNewMacro(Self);
/** Standard macro */
- itkTypeMacro( TrainImagesRegression, Superclass );
-
-private:
+ itkTypeMacro(TrainImagesRegression, Superclass);
+private:
void DoInit() override
{
SetName("TrainImagesRegression");
- SetDescription("Train a regression model from multiple triplets of feature images, predictor images "
- "and training vector data.");
+ SetDescription(
+ "Train a regression model from multiple triplets of feature images, predictor images "
+ "and training vector data.");
SetDocLongDescription(
"Train a classifier from multiple triplets of feature images, predictor images and training vector data. \n\n"
-
+
"The training vector data must contain polygons corresponding to the input sampling positions. This data "
"is used to extract samples using pixel values in each band of the feature image and the corresponding "
"ground truth extracted from the predictor image.\n\n"
-
+
"At the end of the application, the mean square error between groundtruth and predicted values is computed using "
"the output model and the validation vector data. Note that if no validation data is given, the training data "
"will be used for validation.\n\n"
-
+
"The number of training and validation samples can be specified with parameters. If no size is given, all samples will "
"be used. \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 nor vector data output.");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
- SetDocSeeAlso("TrainVectorRegression \n"
- "TrainImagesClassifier");
-
+ SetDocSeeAlso(
+ "TrainVectorRegression \n"
+ "TrainImagesClassifier");
+
AddDocTag(Tags::Learning);
-
+
ClearApplications();
-
+
InitIO();
InitSampling();
InitLearning();
-
- AddParameter( ParameterType_Bool, "cleanup", "Temporary files cleaning" );
- SetParameterDescription( "cleanup",
- "If activated, the application will try to clean all temporary files it created" );
- SetParameterInt( "cleanup", 1);
-
+
+ AddParameter(ParameterType_Bool, "cleanup", "Temporary files cleaning");
+ SetParameterDescription("cleanup", "If activated, the application will try to clean all temporary files it created");
+ SetParameterInt("cleanup", 1);
+
// Doc example parameter settings
- SetDocExampleParameterValue( "io.il", "inputFeatureImage.tif" );
- SetDocExampleParameterValue( "io.ip", "inputPredictorImage.tif" );
- SetDocExampleParameterValue( "io.vd", "trainingData.shp" );
- SetDocExampleParameterValue( "io.valid", "validationData.shp" );
- SetDocExampleParameterValue( "sample.nt", "500");
- SetDocExampleParameterValue( "sample.nv", "100");
- SetDocExampleParameterValue( "io.imstat", "imageStats.xml" );
- SetDocExampleParameterValue( "classifier", "rf" );
- SetDocExampleParameterValue( "io.out", "model.txt" );
-
+ SetDocExampleParameterValue("io.il", "inputFeatureImage.tif");
+ SetDocExampleParameterValue("io.ip", "inputPredictorImage.tif");
+ SetDocExampleParameterValue("io.vd", "trainingData.shp");
+ SetDocExampleParameterValue("io.valid", "validationData.shp");
+ SetDocExampleParameterValue("sample.nt", "500");
+ SetDocExampleParameterValue("sample.nv", "100");
+ SetDocExampleParameterValue("io.imstat", "imageStats.xml");
+ SetDocExampleParameterValue("classifier", "rf");
+ SetDocExampleParameterValue("io.out", "model.txt");
+
SetOfficialDocLink();
}
-
-protected:
+protected:
/** Holds sampling parameters specific to training or validation */
struct SamplingParameters
{
std::vector<std::string> inputVectorList;
- unsigned int numberOfSamples = 0;
- std::string filePrefix = "";
+ unsigned int numberOfSamples = 0;
+ std::string filePrefix = "";
};
/** Adds a class field to the input vectors, this is needed to perform sampling operations */
- void AddRegressionField(const std::vector<std::string> & inputFileNames,
- const std::string & filePrefix)
+ void AddRegressionField(const std::vector<std::string>& inputFileNames, const std::string& filePrefix)
{
- auto setFieldAppli = GetInternalApplication("setfield");
- auto& outputFileNames = m_FileHandler[ filePrefix+"inputWithClassField" ];
-
+ auto setFieldAppli = GetInternalApplication("setfield");
+ auto& outputFileNames = m_FileHandler[filePrefix + "inputWithClassField"];
+
setFieldAppli->SetParameterString("fn", m_ClassFieldName);
setFieldAppli->SetParameterString("fv", "0");
-
- for (unsigned int i =0; i < inputFileNames.size(); i++)
+
+ for (unsigned int i = 0; i < inputFileNames.size(); i++)
{
- outputFileNames.push_back(GetParameterString("io.out")+"_"+filePrefix+"Withfield"+ std::to_string(i) +".shp");
+ outputFileNames.push_back(GetParameterString("io.out") + "_" + filePrefix + "Withfield" + std::to_string(i) + ".shp");
setFieldAppli->SetParameterString("in", inputFileNames[i]);
setFieldAppli->SetParameterString("out", outputFileNames[i]);
-
+
// Call ExecuteAndWriteOutput because VectorDataSetField's ExecuteInternal() does not write vector data.
setFieldAppli->ExecuteAndWriteOutput();
}
}
/** Prepare and execute polygonClassStatistics on each input vector data file */
- void ComputePolygonStatistics(const std::string & filePrefix)
+ void ComputePolygonStatistics(const std::string& filePrefix)
{
- auto polygonClassAppli = GetInternalApplication("polystat");
- auto& input = m_FileHandler[ filePrefix+"inputWithClassField" ];
- auto& output = m_FileHandler[ filePrefix+"statsFiles" ];
- FloatVectorImageListType* inputImageList = GetParameterImageList( "io.il" );
-
- for (unsigned int i =0; i < input.size(); i++)
+ auto polygonClassAppli = GetInternalApplication("polystat");
+ auto& input = m_FileHandler[filePrefix + "inputWithClassField"];
+ auto& output = m_FileHandler[filePrefix + "statsFiles"];
+ FloatVectorImageListType* inputImageList = GetParameterImageList("io.il");
+
+ for (unsigned int i = 0; i < input.size(); i++)
{
- output.push_back(GetParameterString("io.out")+"_"+filePrefix+"PolygonStats"+ std::to_string(i) +".xml");
-
- polygonClassAppli->SetParameterInputImage( "in", inputImageList->GetNthElement(i) );
- polygonClassAppli->SetParameterString( "vec", input[i]);
- polygonClassAppli->SetParameterString( "out", output[i]);
-
+ output.push_back(GetParameterString("io.out") + "_" + filePrefix + "PolygonStats" + std::to_string(i) + ".xml");
+
+ polygonClassAppli->SetParameterInputImage("in", inputImageList->GetNthElement(i));
+ polygonClassAppli->SetParameterString("vec", input[i]);
+ polygonClassAppli->SetParameterString("out", output[i]);
+
polygonClassAppli->UpdateParameters();
- polygonClassAppli->SetParameterString( "field", m_ClassFieldName);
-
- ExecuteInternal( "polystat" );
+ polygonClassAppli->SetParameterString("field", m_ClassFieldName);
+
+ ExecuteInternal("polystat");
}
-
}
/** Compute the sampling rates using the computed statistics */
- void ComputeSamplingRate(const std::string & filePrefix, unsigned int numberOfSamples)
+ void ComputeSamplingRate(const std::string& filePrefix, unsigned int numberOfSamples)
{
auto samplingRateAppli = GetInternalApplication("rates");
-
- samplingRateAppli->SetParameterStringList( "il", m_FileHandler[ filePrefix+"statsFiles" ]);
-
- std::string outputFileName = GetParameterString("io.out")+"_"+filePrefix+"rates.csv";
+
+ samplingRateAppli->SetParameterStringList("il", m_FileHandler[filePrefix + "statsFiles"]);
+
+ std::string outputFileName = GetParameterString("io.out") + "_" + filePrefix + "rates.csv";
samplingRateAppli->SetParameterString("out", outputFileName);
-
+
if (numberOfSamples)
{
samplingRateAppli->SetParameterString("strategy", "constant");
-
+
// Convert the integer number to string for the MultiImageSamplingRate application
samplingRateAppli->SetParameterString("strategy.constant.nb", std::to_string(GetParameterInt("sample.nt")));
}
@@ -176,259 +173,255 @@ protected:
{
samplingRateAppli->SetParameterString("strategy", "all");
}
-
- ExecuteInternal( "rates");
-
- auto& rateFiles = m_FileHandler[filePrefix+"rateFiles"];
- for (unsigned int i = 0; i< m_FileHandler[filePrefix+"statsFiles"].size(); i++)
+
+ ExecuteInternal("rates");
+
+ auto& rateFiles = m_FileHandler[filePrefix + "rateFiles"];
+ for (unsigned int i = 0; i < m_FileHandler[filePrefix + "statsFiles"].size(); i++)
{
- rateFiles.push_back(GetParameterString("io.out")+"_"+filePrefix+"rates_"+std::to_string(i+1)+".csv");
+ rateFiles.push_back(GetParameterString("io.out") + "_" + filePrefix + "rates_" + std::to_string(i + 1) + ".csv");
}
-
}
/** Configure and execute Sample Selection on each vector using the computed rates and statistic files. */
- void SelectSamples(const std::string & filePrefix)
+ void SelectSamples(const std::string& filePrefix)
{
auto sampleSelection = GetInternalApplication("select");
-
- FloatVectorImageListType* inputImageList = GetParameterImageList( "io.il" );
- auto& inputVectorFiles = m_FileHandler[ filePrefix+"inputWithClassField" ];
- auto& outputVectorFiles = m_FileHandler[ filePrefix+"samples" ];
- auto& rateFiles = m_FileHandler [filePrefix+"rateFiles"];
- auto& statFiles = m_FileHandler [filePrefix+"statsFiles"];
-
- for (unsigned int i =0; i < inputVectorFiles.size(); i++)
+
+ FloatVectorImageListType* inputImageList = GetParameterImageList("io.il");
+ auto& inputVectorFiles = m_FileHandler[filePrefix + "inputWithClassField"];
+ auto& outputVectorFiles = m_FileHandler[filePrefix + "samples"];
+ auto& rateFiles = m_FileHandler[filePrefix + "rateFiles"];
+ auto& statFiles = m_FileHandler[filePrefix + "statsFiles"];
+
+ for (unsigned int i = 0; i < inputVectorFiles.size(); i++)
{
- outputVectorFiles.push_back(GetParameterString("io.out")+"_"+filePrefix+"samples"+std::to_string(i)+".shp");
+ outputVectorFiles.push_back(GetParameterString("io.out") + "_" + filePrefix + "samples" + std::to_string(i) + ".shp");
sampleSelection->SetParameterInputImage("in", inputImageList->GetNthElement(i));
sampleSelection->SetParameterString("vec", inputVectorFiles[i]);
sampleSelection->SetParameterString("instats", statFiles[i]);
sampleSelection->SetParameterString("strategy", "byclass");
sampleSelection->SetParameterString("strategy.byclass.in", rateFiles[i]);
sampleSelection->SetParameterString("out", outputVectorFiles[i]);
-
+
sampleSelection->UpdateParameters();
sampleSelection->SetParameterString("field", m_ClassFieldName);
-
+
ExecuteInternal("select");
}
}
-
- /** Configure and execute sampleExtraction. The application is called twice by input vector
- * first values are extracted from the feature image and then the groundtruth is extracted
+
+ /** Configure and execute sampleExtraction. The application is called twice by input vector
+ * first values are extracted from the feature image and then the groundtruth is extracted
* from the predictor image.*/
- void ExtractSamples(const std::string & filePrefix)
+ void ExtractSamples(const std::string& filePrefix)
{
auto sampleExtraction = GetInternalApplication("extraction");
-
- FloatVectorImageListType* featureImageList = GetParameterImageList( "io.il" );
+
+ FloatVectorImageListType* featureImageList = GetParameterImageList("io.il");
FloatVectorImageListType* predictorImageList = GetParameterImageList("io.ip");
- auto& vectorFiles = m_FileHandler[ filePrefix+"samples" ];
-
- for (unsigned int i =0; i < vectorFiles.size(); i++)
+ auto& vectorFiles = m_FileHandler[filePrefix + "samples"];
+
+ for (unsigned int i = 0; i < vectorFiles.size(); i++)
{
sampleExtraction->SetParameterString("vec", vectorFiles[i]);
sampleExtraction->UpdateParameters();
sampleExtraction->SetParameterString("field", m_ClassFieldName);
-
- //First Extraction : Values from the feature image.
+
+ // First Extraction : Values from the feature image.
sampleExtraction->SetParameterInputImage("in", featureImageList->GetNthElement(i));
sampleExtraction->SetParameterString("outfield", "prefix");
sampleExtraction->SetParameterString("outfield.prefix.name", m_FeaturePrefix);
ExecuteInternal("extraction");
-
- //Second Extraction : groundtruth from the predictor image.
+
+ // Second Extraction : groundtruth from the predictor image.
sampleExtraction->SetParameterInputImage("in", predictorImageList->GetNthElement(i));
sampleExtraction->SetParameterString("outfield", "list");
sampleExtraction->SetParameterStringList("outfield.list.names", {m_PredictionFieldName});
ExecuteInternal("extraction");
}
}
-
+
/** Configure and execute TrainVectorClassifier. Note that many parameters of TrainVectorClassifier
* are shared with the main application during initialization. */
void TrainModel()
{
auto trainVectorRegression = GetInternalApplication("training");
-
- auto& trainSampleFileNameList = m_FileHandler["trainsamples"];
+
+ auto& trainSampleFileNameList = m_FileHandler["trainsamples"];
std::vector<std::string> featureNames;
- for (unsigned int i = 0; i< GetParameterStringList("io.vd").size(); i++)
+ for (unsigned int i = 0; i < GetParameterStringList("io.vd").size(); i++)
{
- featureNames.push_back(m_FeaturePrefix+std::to_string(i));
+ featureNames.push_back(m_FeaturePrefix + std::to_string(i));
}
-
+
trainVectorRegression->SetParameterStringList("io.vd", trainSampleFileNameList);
trainVectorRegression->UpdateParameters();
trainVectorRegression->SetParameterString("cfield", m_PredictionFieldName);
trainVectorRegression->SetParameterStringList("feat", featureNames);
-
- if( IsParameterEnabled( "io.valid" ) && HasValue( "io.valid" ) )
+
+ if (IsParameterEnabled("io.valid") && HasValue("io.valid"))
{
trainVectorRegression->SetParameterStringList("valid.vd", m_FileHandler["validsamples"]);
}
-
+
ExecuteInternal("training");
}
-
+
/** Init input/output parameters */
void InitIO()
{
- AddParameter( ParameterType_Group, "io", "Input and output data" );
- SetParameterDescription( "io", "This group of parameters allows setting input and output data." );
-
- AddParameter( ParameterType_InputImageList, "io.il", "Input Image List" );
- SetParameterDescription( "io.il", "A list of input images." );
- MandatoryOn( "io.il" );
-
- AddParameter( ParameterType_InputImageList, "io.ip", "Input Predictor Image List" );
- SetParameterDescription( "io.ip", "A list of input images." );
- MandatoryOn( "io.ip" );
-
- AddParameter( ParameterType_InputVectorDataList, "io.vd", "Input Vector Data List" );
- SetParameterDescription( "io.vd", "A list of vector data to select the training samples." );
- MandatoryOn( "io.vd" );
-
- AddParameter( ParameterType_InputVectorDataList, "io.valid", "Validation Vector Data List" );
- SetParameterDescription( "io.valid", "A list of vector data to select the validation samples." );
- MandatoryOff( "io.valid" );
+ AddParameter(ParameterType_Group, "io", "Input and output data");
+ SetParameterDescription("io", "This group of parameters allows setting input and output data.");
+
+ AddParameter(ParameterType_InputImageList, "io.il", "Input Image List");
+ SetParameterDescription("io.il", "A list of input images.");
+ MandatoryOn("io.il");
+
+ AddParameter(ParameterType_InputImageList, "io.ip", "Input Predictor Image List");
+ SetParameterDescription("io.ip", "A list of input images.");
+ MandatoryOn("io.ip");
+
+ AddParameter(ParameterType_InputVectorDataList, "io.vd", "Input Vector Data List");
+ SetParameterDescription("io.vd", "A list of vector data to select the training samples.");
+ MandatoryOn("io.vd");
+
+ AddParameter(ParameterType_InputVectorDataList, "io.valid", "Validation Vector Data List");
+ SetParameterDescription("io.valid", "A list of vector data to select the validation samples.");
+ MandatoryOff("io.valid");
}
-
+
/** Init sampling parameters and applications */
void InitSampling()
- {
- AddApplication( "VectorDataSetField", "setfield", "Set additional vector field");
- AddApplication( "PolygonClassStatistics", "polystat", "Polygon analysis" );
- AddApplication( "MultiImageSamplingRate", "rates", "Sampling rates" );
-
- AddParameter( ParameterType_Group, "sample", "Sampling parameters" );
- SetParameterDescription( "sample", "This group of parameters allows setting sampling parameters" );
-
- AddParameter( ParameterType_Int, "sample.nt", "Number of training samples" );
- SetParameterDescription( "sample.nt", "Number of training samples." );
- MandatoryOff( "sample.nt" );
-
- AddParameter( ParameterType_Int, "sample.nv", "Number of validation samples" );
- SetParameterDescription( "sample.nv", "Number of validation samples." );
- MandatoryOff( "sample.nv" );
-
- AddApplication( "SampleSelection", "select", "Sample selection" );
- AddApplication( "SampleExtraction", "extraction", "Sample extraction" );
-
+ {
+ AddApplication("VectorDataSetField", "setfield", "Set additional vector field");
+ AddApplication("PolygonClassStatistics", "polystat", "Polygon analysis");
+ AddApplication("MultiImageSamplingRate", "rates", "Sampling rates");
+
+ AddParameter(ParameterType_Group, "sample", "Sampling parameters");
+ SetParameterDescription("sample", "This group of parameters allows setting sampling parameters");
+
+ AddParameter(ParameterType_Int, "sample.nt", "Number of training samples");
+ SetParameterDescription("sample.nt", "Number of training samples.");
+ MandatoryOff("sample.nt");
+
+ AddParameter(ParameterType_Int, "sample.nv", "Number of validation samples");
+ SetParameterDescription("sample.nv", "Number of validation samples.");
+ MandatoryOff("sample.nv");
+
+ AddApplication("SampleSelection", "select", "Sample selection");
+ AddApplication("SampleExtraction", "extraction", "Sample extraction");
}
-
+
/** Init Learning applications and share learning parameters */
void InitLearning()
{
- AddApplication( "TrainVectorRegression", "training", "Train vector regression");
-
- ShareParameter( "io.imstat", "training.io.stats" );
- ShareParameter( "io.out", "training.io.out" );
+ AddApplication("TrainVectorRegression", "training", "Train vector regression");
+
+ ShareParameter("io.imstat", "training.io.stats");
+ ShareParameter("io.out", "training.io.out");
- ShareParameter( "classifier", "training.classifier" );
- ShareParameter( "rand", "training.rand" );
+ ShareParameter("classifier", "training.classifier");
+ ShareParameter("rand", "training.rand");
- ShareParameter( "io.mse", "training.io.mse" );
+ ShareParameter("io.mse", "training.io.mse");
}
private:
-
void DoUpdateParameters() override
{
}
- /** remove all file in the fileHandler std::map and clear the fileHandler std::map.
+ /** remove all file in the fileHandler std::map and clear the fileHandler std::map.
* For .shp file, this method also removes .shx, .dbf and .prj associated files.*/
bool ClearFileHandler()
{
bool res = true;
- for (const auto & fileNameList : m_FileHandler)
+ for (const auto& fileNameList : m_FileHandler)
{
- for (const auto & filename : fileNameList.second)
+ for (const auto& filename : fileNameList.second)
{
res = true;
- if( itksys::SystemTools::FileExists( filename ) )
+ if (itksys::SystemTools::FileExists(filename))
{
- size_t posExt = filename.rfind( '.' );
- if( posExt != std::string::npos && filename.compare( posExt, std::string::npos, ".shp" ) == 0 )
+ size_t posExt = filename.rfind('.');
+ if (posExt != std::string::npos && filename.compare(posExt, std::string::npos, ".shp") == 0)
{
- std::string shxPath = filename.substr( 0, posExt ) + std::string( ".shx" );
- std::string dbfPath = filename.substr( 0, posExt ) + std::string( ".dbf" );
- std::string prjPath = filename.substr( 0, posExt ) + std::string( ".prj" );
- itksys::SystemTools::RemoveFile( shxPath );
- itksys::SystemTools::RemoveFile( dbfPath );
- itksys::SystemTools::RemoveFile( prjPath );
+ std::string shxPath = filename.substr(0, posExt) + std::string(".shx");
+ std::string dbfPath = filename.substr(0, posExt) + std::string(".dbf");
+ std::string prjPath = filename.substr(0, posExt) + std::string(".prj");
+ itksys::SystemTools::RemoveFile(shxPath);
+ itksys::SystemTools::RemoveFile(dbfPath);
+ itksys::SystemTools::RemoveFile(prjPath);
}
- res = itksys::SystemTools::RemoveFile( filename );
+ res = itksys::SystemTools::RemoveFile(filename);
}
}
}
m_FileHandler.clear();
return res;
}
-
- void PerformSampling(const SamplingParameters & params)
+
+ void PerformSampling(const SamplingParameters& params)
{
- std::vector<std::string> vectorData = params.inputVectorList;
- std::string filePrefix = params.filePrefix;
- unsigned int numberOfSamples = params.numberOfSamples;
-
+ std::vector<std::string> vectorData = params.inputVectorList;
+ std::string filePrefix = params.filePrefix;
+ unsigned int numberOfSamples = params.numberOfSamples;
+
AddRegressionField(vectorData, filePrefix);
ComputePolygonStatistics(filePrefix);
ComputeSamplingRate(filePrefix, numberOfSamples);
SelectSamples(filePrefix);
ExtractSamples(filePrefix);
}
-
-
+
+
void DoExecute() override
{
SamplingParameters trainParams;
trainParams.inputVectorList = GetParameterStringList("io.vd");
- trainParams.filePrefix="train";
+ trainParams.filePrefix = "train";
if (HasValue("sample.nt"))
trainParams.numberOfSamples = GetParameterInt("sample.nt");
-
+
PerformSampling(trainParams);
-
- if( IsParameterEnabled( "io.valid" ) && HasValue( "io.valid" ) )
+
+ if (IsParameterEnabled("io.valid") && HasValue("io.valid"))
{
SamplingParameters validParams;
validParams.inputVectorList = GetParameterStringList("io.valid");
- validParams.filePrefix="valid";
+ validParams.filePrefix = "valid";
if (HasValue("sample.nv"))
validParams.numberOfSamples = GetParameterInt("sample.nv");
-
+
PerformSampling(validParams);
}
- otbAppLogINFO( "Sampling Done." );
-
+ otbAppLogINFO("Sampling Done.");
+
TrainModel();
-
+
if (GetParameterInt("cleanup"))
{
- otbAppLogINFO( "Cleaning temporary files ..." );
+ otbAppLogINFO("Cleaning temporary files ...");
ClearFileHandler();
}
}
-
+
/** Field used for sampling operations. */
std::string m_ClassFieldName = "regclass";
-
+
/** field containg the extracted regression groundtruth */
std::string m_PredictionFieldName = "prediction";
-
+
/** field containing the extracted pixel values for regression */
std::string m_FeaturePrefix = "value_";
-
+
/** Container containing the list of temporary files created during the execution*/
- std::map< std::string, std::vector<std::string>> m_FileHandler;
-
+ std::map<std::string, std::vector<std::string>> m_FileHandler;
};
-} //end namespace Wrapper
-} //end namespace otb
+} // end namespace Wrapper
+} // end namespace otb
OTB_APPLICATION_EXPORT(otb::Wrapper::TrainImagesRegression)
diff --git a/Modules/Applications/AppClassification/app/otbTrainRegression.cxx b/Modules/Applications/AppClassification/app/otbTrainRegression.cxx
index 4d20e591569b703a80c6e04498560f327ddde4e5..18047e3ff3f254fe26cdded4131ce4b21c23eca0 100644
--- a/Modules/Applications/AppClassification/app/otbTrainRegression.cxx
+++ b/Modules/Applications/AppClassification/app/otbTrainRegression.cxx
@@ -1,22 +1,22 @@
-c/*
- * Copyright (C) 2005-2019 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.
- */
+c /*
+ * Copyright (C) 2005-2019 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 "otbLearningApplicationBase.h"
#include "otbWrapperApplicationFactory.h"
@@ -45,7 +45,7 @@ c/*
// Elevation handler
#include "otbWrapperElevationParametersHandler.h"
-namespace otb
+ namespace otb
{
namespace Wrapper
{
@@ -125,9 +125,9 @@ void DoInit() override
SetDocAuthors("OTB-Team");
SetDocSeeAlso("OpenCV documentation for machine learning "
"http://docs.opencv.org/modules/ml/doc/ml.html ");
-
+
AddDocTag(Tags::Deprecated);
-
+
//Group IO
AddParameter( ParameterType_Group , "io" , "Input and output data" );
SetParameterDescription("io" ,