From 9b617cfa5426d6543b31033c1967847ab7480906 Mon Sep 17 00:00:00 2001
From: Ludovic Hussonnois <ludovic.hussonnois@c-s.fr>
Date: Wed, 5 Apr 2017 17:01:05 +0200
Subject: [PATCH] REFAC: Apply RFC 85 review
---
.../app/otbTrainVectorClassifier.cxx | 40 +++----
.../include/otbTrainImagesBase.h | 2 +-
.../include/otbTrainSharkKMeans.txx | 10 +-
.../include/otbTrainVectorBase.h | 33 +++---
.../include/otbTrainVectorBase.txx | 108 +++++++++---------
5 files changed, 89 insertions(+), 104 deletions(-)
diff --git a/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx b/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
index c9926bd37f..935de9401c 100644
--- a/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
+++ b/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
@@ -49,44 +49,42 @@ public:
typedef ConfusionMatrixCalculatorType::MapOfIndicesType MapOfIndicesType;
typedef ConfusionMatrixCalculatorType::ClassLabelType ClassLabelType;
-private:
- void DoTrainInit()
+protected:
+ void DoInit()
{
- // Nothing to do here
+ TrainVectorBase::DoInit();
}
- void DoTrainUpdateParameters()
+ void DoUpdateParameters()
{
- // Nothing to do here
+ TrainVectorBase::DoUpdateParameters();
}
- void DoBeforeTrainExecute()
+ void DoExecute()
{
// Enforce the need of class field name in supervised mode
if (GetClassifierCategory() == Supervised)
{
- featuresInfo.SetClassFieldNames( GetChoiceNames( "cfield" ), GetSelectedItems( "cfield" ) );
+ m_featuresInfo.SetClassFieldNames( GetChoiceNames( "cfield" ), GetSelectedItems( "cfield" ) );
- if( featuresInfo.m_SelectedCFieldIdx.empty() )
+ if( m_featuresInfo.m_SelectedCFieldIdx.empty() )
{
otbAppLogFATAL( << "No field has been selected for data labelling!" );
}
}
- }
- void DoAfterTrainExecute()
- {
+ TrainVectorBase::DoExecute();
- if (GetClassifierCategory() == Supervised)
- {
- ConfusionMatrixCalculatorType::Pointer confMatCalc = ComputeConfusionMatrix( predictedList,
- classificationListSamples.labeledListSample );
- WriteConfusionMatrix( confMatCalc );
- }
- else
- {
- // TODO Compute Contingency Table
- }
+ if (GetClassifierCategory() == Supervised)
+ {
+ ConfusionMatrixCalculatorType::Pointer confMatCalc = ComputeConfusionMatrix( m_predictedList,
+ m_classificationSamplesWithLabel.labeledListSample );
+ WriteConfusionMatrix( confMatCalc );
+ }
+ else
+ {
+ // TODO Compute Contingency Table
+ }
}
diff --git a/Modules/Applications/AppClassification/include/otbTrainImagesBase.h b/Modules/Applications/AppClassification/include/otbTrainImagesBase.h
index af17e02e5d..a01eb6c430 100644
--- a/Modules/Applications/AppClassification/include/otbTrainImagesBase.h
+++ b/Modules/Applications/AppClassification/include/otbTrainImagesBase.h
@@ -35,7 +35,7 @@ namespace Wrapper
{
/** \class TrainImagesBase
- * \brief Base class for the TrainImagesBaseClassifier and Clustering
+ * \brief Base class for the TrainImagesClassifier
*
* This class intends to hold common input/output parameters and
* composite application connection for both supervised and unsupervised
diff --git a/Modules/Applications/AppClassification/include/otbTrainSharkKMeans.txx b/Modules/Applications/AppClassification/include/otbTrainSharkKMeans.txx
index 0dba5c6ab7..e3cf63af98 100644
--- a/Modules/Applications/AppClassification/include/otbTrainSharkKMeans.txx
+++ b/Modules/Applications/AppClassification/include/otbTrainSharkKMeans.txx
@@ -35,11 +35,11 @@ void LearningApplicationBase<TInputValue, TOutputValue>::InitSharkKMeansParams()
"See complete documentation here "
"\\url{http://image.diku.dk/shark/sphinx_pages/build/html/rest_sources/tutorials/algorithms/kmeans.html}.\n " );
//MaxNumberOfIterations
- AddParameter( ParameterType_Int, "classifier.sharkkm.nbmaxiter",
+ AddParameter( ParameterType_Int, "classifier.sharkkm.maxiter",
"Maximum number of iteration for the kmeans algorithm." );
- SetParameterInt( "classifier.sharkkm.nbmaxiter", 10 );
- SetMinimumParameterIntValue( "classifier.sharkkm.nbmaxiter", 0 );
- SetParameterDescription( "classifier.sharkkm.nbmaxiter",
+ SetParameterInt( "classifier.sharkkm.maxiter", 10 );
+ SetMinimumParameterIntValue( "classifier.sharkkm.maxiter", 0 );
+ SetParameterDescription( "classifier.sharkkm.maxiter",
"The maximum number of iteration for the kmeans algorithm. 0=unlimited" );
//MaxNumberOfIterations
@@ -55,7 +55,7 @@ void LearningApplicationBase<TInputValue, TOutputValue>::TrainSharkKMeans(
typename ListSampleType::Pointer trainingListSample,
typename TargetListSampleType::Pointer trainingLabeledListSample, std::string modelPath)
{
- unsigned int nbMaxIter = static_cast<unsigned int>(abs( GetParameterInt( "classifier.sharkkm.nbmaxiter" ) ));
+ unsigned int nbMaxIter = static_cast<unsigned int>(abs( GetParameterInt( "classifier.sharkkm.maxiter" ) ));
unsigned int k = static_cast<unsigned int>(abs( GetParameterInt( "classifier.sharkkm.k" ) ));
typename SharkKMeansType::Pointer classifier = SharkKMeansType::New();
diff --git a/Modules/Applications/AppClassification/include/otbTrainVectorBase.h b/Modules/Applications/AppClassification/include/otbTrainVectorBase.h
index bdd844ca1d..f01a99737b 100644
--- a/Modules/Applications/AppClassification/include/otbTrainVectorBase.h
+++ b/Modules/Applications/AppClassification/include/otbTrainVectorBase.h
@@ -74,7 +74,7 @@ public:
protected:
/** Class used to store statistics Measurment (mean/stddev) */
- class StatisticsMeasurement
+ class ShiftScaleParameters
{
public:
MeasurementType meanMeasurementVector;
@@ -82,12 +82,12 @@ protected:
};
/** Class used to store a list of sample and the corresponding label */
- class ListSamples
+ class SamplesWithLabel
{
public:
ListSampleType::Pointer listSample;
TargetListSampleType::Pointer labeledListSample;
- ListSamples()
+ SamplesWithLabel()
{
listSample = ListSampleType::New();
labeledListSample = TargetListSampleType::New();
@@ -137,7 +137,7 @@ protected:
* \param measurement statics measurement (mean/stddev)
* \param featuresInfo information about the features
*/
- virtual void ExtractAllSamples(const StatisticsMeasurement &measurement);
+ virtual void ExtractAllSamples(const ShiftScaleParameters &measurement);
/**
* Extract the training sample list
@@ -145,7 +145,7 @@ protected:
* \param featuresInfo information about the features
* \return sample list used for training
*/
- virtual ListSamples ExtractTrainingListSamples(const StatisticsMeasurement &measurement);
+ virtual SamplesWithLabel ExtractTrainingSamplesWithLabel(const ShiftScaleParameters &measurement);
/**
* Extract classification the sample list
@@ -153,7 +153,7 @@ protected:
* \param featuresInfo information about the features
* \return sample list used for classification
*/
- virtual ListSamples ExtractClassificationListSamples(const StatisticsMeasurement &measurement);
+ virtual SamplesWithLabel ExtractClassificationSamplesWithLabel(const ShiftScaleParameters &measurement);
/** Extract samples from input file for corresponding field name
@@ -164,8 +164,8 @@ protected:
* \param nbFeatures the number of features.
* \return the list of samples and their corresponding labels.
*/
- ListSamples
- ExtractListSamples(std::string parameterName, std::string parameterLayer, const StatisticsMeasurement &measurement);
+ SamplesWithLabel
+ ExtractSamplesWithLabel(std::string parameterName, std::string parameterLayer, const ShiftScaleParameters &measurement);
/**
@@ -173,18 +173,12 @@ protected:
* Otherwise mean is set to 0 and standard deviation to 1 for each Features.
* \param nbFeatures
*/
- StatisticsMeasurement ComputeStatistics(unsigned int nbFeatures);
+ ShiftScaleParameters ComputeStatistics(unsigned int nbFeatures);
- ListSamples trainingListSamples;
- ListSamples classificationListSamples;
- TargetListSampleType::Pointer predictedList;
- FeaturesInfo featuresInfo;
-
-private:
- virtual void DoTrainInit() = 0;
- virtual void DoBeforeTrainExecute() = 0;
- virtual void DoAfterTrainExecute() = 0;
- virtual void DoTrainUpdateParameters() = 0;
+ SamplesWithLabel m_trainingSamplesWithLabel;
+ SamplesWithLabel m_classificationSamplesWithLabel;
+ TargetListSampleType::Pointer m_predictedList;
+ FeaturesInfo m_featuresInfo;
void DoInit() ITK_OVERRIDE;
void DoUpdateParameters() ITK_OVERRIDE;
@@ -200,4 +194,3 @@ private:
#endif
#endif
-
diff --git a/Modules/Applications/AppClassification/include/otbTrainVectorBase.txx b/Modules/Applications/AppClassification/include/otbTrainVectorBase.txx
index 82d4f7028a..52263be4aa 100644
--- a/Modules/Applications/AppClassification/include/otbTrainVectorBase.txx
+++ b/Modules/Applications/AppClassification/include/otbTrainVectorBase.txx
@@ -102,7 +102,7 @@ void TrainVectorBase::DoInit()
AddRANDParameter();
- DoTrainInit();
+ DoInit();
}
void TrainVectorBase::DoUpdateParameters()
@@ -142,79 +142,75 @@ void TrainVectorBase::DoUpdateParameters()
}
}
- DoTrainUpdateParameters();
+ DoUpdateParameters();
}
void TrainVectorBase::DoExecute()
{
- DoBeforeTrainExecute();
-
- featuresInfo.SetFieldNames( GetChoiceNames( "feat" ), GetSelectedItems( "feat" ));
+ m_featuresInfo.SetFieldNames( GetChoiceNames( "feat" ), GetSelectedItems( "feat" ));
// Check input parameters
- if( featuresInfo.m_SelectedIdx.empty() )
+ if( m_featuresInfo.m_SelectedIdx.empty() )
{
otbAppLogFATAL( << "No features have been selected to train the classifier on!" );
}
- StatisticsMeasurement measurement = ComputeStatistics( featuresInfo.m_NbFeatures );
+ ShiftScaleParameters measurement = ComputeStatistics( m_featuresInfo.m_NbFeatures );
ExtractAllSamples( measurement );
- this->Train( trainingListSamples.listSample, trainingListSamples.labeledListSample, GetParameterString( "io.out" ) );
-
- predictedList = TargetListSampleType::New();
- this->Classify( classificationListSamples.listSample, predictedList, GetParameterString( "io.out" ) );
+ this->Train( m_trainingSamplesWithLabel.listSample, m_trainingSamplesWithLabel.labeledListSample, GetParameterString( "io.out" ) );
- DoAfterTrainExecute();
+ m_predictedList = TargetListSampleType::New();
+ this->Classify( m_classificationSamplesWithLabel.listSample, m_predictedList, GetParameterString( "io.out" ) );
}
-void TrainVectorBase::ExtractAllSamples(const StatisticsMeasurement &measurement)
+void TrainVectorBase::ExtractAllSamples(const ShiftScaleParameters &measurement)
{
- trainingListSamples = ExtractTrainingListSamples(measurement);
- classificationListSamples = ExtractClassificationListSamples(measurement);
+ m_trainingSamplesWithLabel = ExtractTrainingSamplesWithLabel(measurement);
+ m_classificationSamplesWithLabel = ExtractClassificationSamplesWithLabel(measurement);
}
-TrainVectorBase::ListSamples
-TrainVectorBase::ExtractTrainingListSamples(const StatisticsMeasurement &measurement)
+TrainVectorBase::SamplesWithLabel
+TrainVectorBase::ExtractTrainingSamplesWithLabel(const ShiftScaleParameters &measurement)
{
- return ExtractListSamples( "io.vd", "layer", measurement);
+ return ExtractSamplesWithLabel( "io.vd", "layer", measurement);
}
-TrainVectorBase::ListSamples
-TrainVectorBase::ExtractClassificationListSamples(const StatisticsMeasurement &measurement)
+TrainVectorBase::SamplesWithLabel
+TrainVectorBase::ExtractClassificationSamplesWithLabel(const ShiftScaleParameters &measurement)
{
if(GetClassifierCategory() == Supervised)
{
- ListSamples tmpListSamples;
- ListSamples validationListSamples = ExtractListSamples( "valid.vd", "valid.layer", measurement );
+ SamplesWithLabel tmpSamplesWithLabel;
+ SamplesWithLabel validationSamplesWithLabel = ExtractSamplesWithLabel( "valid.vd", "valid.layer", measurement );
//Test the input validation set size
- if( validationListSamples.labeledListSample->Size() != 0 )
+ if( validationSamplesWithLabel.labeledListSample->Size() != 0 )
{
- tmpListSamples.listSample = validationListSamples.listSample;
- tmpListSamples.labeledListSample = validationListSamples.labeledListSample;
+ tmpSamplesWithLabel.listSample = validationSamplesWithLabel.listSample;
+ tmpSamplesWithLabel.labeledListSample = validationSamplesWithLabel.labeledListSample;
}
else
{
otbAppLogWARNING(
"The validation set is empty. The performance estimation is done using the input training set in this case." );
- tmpListSamples.listSample = trainingListSamples.listSample;
- tmpListSamples.labeledListSample = trainingListSamples.labeledListSample;
+ tmpSamplesWithLabel.listSample = m_trainingSamplesWithLabel.listSample;
+ tmpSamplesWithLabel.labeledListSample = m_trainingSamplesWithLabel.labeledListSample;
}
- return tmpListSamples;
+ return tmpSamplesWithLabel;
}
else
{
- return trainingListSamples;
+ return m_trainingSamplesWithLabel;
}
}
-TrainVectorBase::StatisticsMeasurement
+TrainVectorBase::ShiftScaleParameters
TrainVectorBase::ComputeStatistics(unsigned int nbFeatures)
{
- StatisticsMeasurement measurement = StatisticsMeasurement();
+ ShiftScaleParameters measurement = ShiftScaleParameters();
if( HasValue( "io.stats" ) && IsParameterEnabled( "io.stats" ) )
{
StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
@@ -234,51 +230,51 @@ TrainVectorBase::ComputeStatistics(unsigned int nbFeatures)
}
-TrainVectorBase::ListSamples
-TrainVectorBase::ExtractListSamples(std::string parameterName, std::string parameterLayer,
- const StatisticsMeasurement &measurement)
+TrainVectorBase::SamplesWithLabel
+TrainVectorBase::ExtractSamplesWithLabel(std::string parameterName, std::string parameterLayer,
+ const ShiftScaleParameters &measurement)
{
- ListSamples listSamples;
+ SamplesWithLabel samplesWithLabel;
if( HasValue( parameterName ) && IsParameterEnabled( parameterName ) )
{
ListSampleType::Pointer input = ListSampleType::New();
TargetListSampleType::Pointer target = TargetListSampleType::New();
- input->SetMeasurementVectorSize( featuresInfo.m_NbFeatures );
+ input->SetMeasurementVectorSize( m_featuresInfo.m_NbFeatures );
- std::vector<std::string> validFileList = this->GetParameterStringList( parameterName );
- for( unsigned int k = 0; k < validFileList.size(); k++ )
+ std::vector<std::string> fileList = this->GetParameterStringList( parameterName );
+ for( unsigned int k = 0; k < fileList.size(); k++ )
{
- otbAppLogINFO( "Reading validation vector file " << k + 1 << "/" << validFileList.size() );
- ogr::DataSource::Pointer source = ogr::DataSource::New( validFileList[k], ogr::DataSource::Modes::Read );
+ otbAppLogINFO( "Reading vector file " << k + 1 << "/" << fileList.size() );
+ ogr::DataSource::Pointer source = ogr::DataSource::New( fileList[k], ogr::DataSource::Modes::Read );
ogr::Layer layer = source->GetLayer( static_cast<size_t>(this->GetParameterInt( parameterLayer )) );
ogr::Feature feature = layer.ogr().GetNextFeature();
bool goesOn = feature.addr() != 0;
if( !goesOn )
{
- otbAppLogWARNING( "The layer " << GetParameterInt( parameterLayer ) << " of " << validFileList[k]
+ otbAppLogWARNING( "The layer " << GetParameterInt( parameterLayer ) << " of " << fileList[k]
<< " is empty, input is skipped." );
continue;
}
// Check all needed fields are present :
// - check class field if we use supervised classification or if class field name is not empty
- int cFieldIndex = feature.ogr().GetFieldIndex( featuresInfo.m_SelectedCFieldName.c_str() );
- if( cFieldIndex < 0 && !featuresInfo.m_SelectedCFieldName.empty())
+ int cFieldIndex = feature.ogr().GetFieldIndex( m_featuresInfo.m_SelectedCFieldName.c_str() );
+ if( cFieldIndex < 0 && !m_featuresInfo.m_SelectedCFieldName.empty())
{
- otbAppLogFATAL( "The field name for class label (" << featuresInfo.m_SelectedCFieldName
+ otbAppLogFATAL( "The field name for class label (" << m_featuresInfo.m_SelectedCFieldName
<< ") has not been found in the vector file "
- << validFileList[k] );
+ << fileList[k] );
}
// - check feature fields
- std::vector<int> featureFieldIndex( featuresInfo.m_NbFeatures, -1 );
- for( unsigned int i = 0; i < featuresInfo.m_NbFeatures; i++ )
+ std::vector<int> featureFieldIndex( m_featuresInfo.m_NbFeatures, -1 );
+ for( unsigned int i = 0; i < m_featuresInfo.m_NbFeatures; i++ )
{
- featureFieldIndex[i] = feature.ogr().GetFieldIndex( featuresInfo.m_SelectedNames[i].c_str() );
+ featureFieldIndex[i] = feature.ogr().GetFieldIndex( m_featuresInfo.m_SelectedNames[i].c_str() );
if( featureFieldIndex[i] < 0 )
- otbAppLogFATAL( "The field name for feature " << featuresInfo.m_SelectedNames[i]
+ otbAppLogFATAL( "The field name for feature " << m_featuresInfo.m_SelectedNames[i]
<< " has not been found in the vector file "
- << validFileList[k] );
+ << fileList[k] );
}
@@ -286,8 +282,8 @@ TrainVectorBase::ExtractListSamples(std::string parameterName, std::string param
{
// Retrieve all the features for each field in the ogr layer.
MeasurementType mv;
- mv.SetSize( featuresInfo.m_NbFeatures );
- for( unsigned int idx = 0; idx < featuresInfo.m_NbFeatures; ++idx )
+ mv.SetSize( m_featuresInfo.m_NbFeatures );
+ for( unsigned int idx = 0; idx < m_featuresInfo.m_NbFeatures; ++idx )
mv[idx] = feature.ogr().GetFieldAsDouble( featureFieldIndex[idx] );
input->PushBack( mv );
@@ -310,11 +306,11 @@ TrainVectorBase::ExtractListSamples(std::string parameterName, std::string param
shiftScaleFilter->SetScales( measurement.stddevMeasurementVector );
shiftScaleFilter->Update();
- listSamples.listSample = shiftScaleFilter->GetOutput();
- listSamples.labeledListSample = target;
+ samplesWithLabel.listSample = shiftScaleFilter->GetOutput();
+ samplesWithLabel.labeledListSample = target;
}
- return listSamples;
+ return samplesWithLabel;
}
@@ -322,5 +318,3 @@ TrainVectorBase::ExtractListSamples(std::string parameterName, std::string param
}
#endif
-
-
--
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