diff --git a/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx b/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
index 61609f080e80677c807a8497d3dc3eb5b6dc6146..9e26e768962bcc06761a0e6a92c49c91353026e0 100644
--- a/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
+++ b/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
@@ -45,6 +45,11 @@ public:
typedef ConfusionMatrixCalculatorType::MapOfIndicesType MapOfIndicesType;
typedef ConfusionMatrixCalculatorType::ClassLabelType ClassLabelType;
+protected :
+ TrainVectorClassifier() : TrainVectorBase()
+ {
+ m_ClassifierCategory = Supervised;
+ }
private:
void DoTrainInit()
@@ -78,7 +83,18 @@ private:
// Nothing to do here
}
- void DoTrainExecute()
+ void DoBeforeTrainExecute()
+ {
+ // Enforce the need of class field name in supervised mode
+ featuresInfo.SetClassFieldNames( GetChoiceNames( "cfield" ), GetSelectedItems( "cfield" ) );
+
+ if( featuresInfo.m_SelectedCFieldIdx.empty() && m_ClassifierCategory == Supervised )
+ {
+ otbAppLogFATAL( << "No field has been selected for data labelling!" );
+ }
+ }
+
+ void DoAfterTrainExecute()
{
ConfusionMatrixCalculatorType::Pointer confMatCalc = ComputeConfusionmatrix( predictedList,
classificationListSamples.labeledListSample );
@@ -86,6 +102,28 @@ private:
}
+ ListSamples ExtractClassificationListSamples(const StatisticsMeasurement &measurement)
+ {
+ ListSamples performanceSample;
+ ListSamples validationListSamples = ExtractListSamples( "valid.vd", "valid.layer", measurement );
+ //Test the input validation set size
+ if( validationListSamples.labeledListSample->Size() != 0 )
+ {
+ performanceSample.listSample = validationListSamples.listSample;
+ performanceSample.labeledListSample = validationListSamples.labeledListSample;
+ }
+ else
+ {
+ otbAppLogWARNING(
+ "The validation set is empty. The performance estimation is done using the input training set in this case." );
+ performanceSample.listSample = trainingListSamples.listSample;
+ performanceSample.labeledListSample = trainingListSamples.labeledListSample;
+ }
+
+ return performanceSample;
+ }
+
+
ConfusionMatrixCalculatorType::Pointer
ComputeConfusionmatrix(const TargetListSampleType::Pointer &predictedListSample,
const TargetListSampleType::Pointer &performanceLabeledListSample)
@@ -285,7 +323,6 @@ private:
otbAppLogINFO( "Confusion matrix (rows = reference labels, columns = produced labels):\n" << os.str() );
}
-
};
}
}
diff --git a/Modules/Applications/AppClassification/app/otbTrainVectorClustering.cxx b/Modules/Applications/AppClassification/app/otbTrainVectorClustering.cxx
index eec6927bd0327e7f3c4bcf1bc68d200397cd5e1f..49acbbc2b3d6e320fcce46145d6a8311bedc2894 100644
--- a/Modules/Applications/AppClassification/app/otbTrainVectorClustering.cxx
+++ b/Modules/Applications/AppClassification/app/otbTrainVectorClustering.cxx
@@ -16,18 +16,15 @@
=========================================================================*/
#include "otbTrainVectorBase.h"
-// Validation
-#include "otbConfusionMatrixCalculator.h"
-
namespace otb
{
namespace Wrapper
{
-class TrainVectorClassifier : public TrainVectorBase
+class TrainVectorClustering : public TrainVectorBase
{
public:
- typedef TrainVectorClassifier Self;
+ typedef TrainVectorClustering Self;
typedef TrainVectorBase Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
@@ -39,10 +36,31 @@ public:
typedef Superclass::ListSampleType ListSampleType;
typedef Superclass::TargetListSampleType TargetListSampleType;
+protected :
+ TrainVectorClustering() : TrainVectorBase()
+ {
+ m_ClassifierCategory = Unsupervised;
+ }
+
private:
void DoTrainInit()
{
- // Nothing to do here
+ SetName( "TrainVectorClustering" );
+ SetDescription( "Train a classifier based on labeled or unlabeled geometries and a list of features to consider." );
+
+ SetDocName( "Train Vector Clustering" );
+ SetDocLongDescription( "This application trains a classifier based on "
+ "labeled or unlabeled geometries and a list of features to consider for classification." );
+ SetDocLimitations( " " );
+ SetDocAuthors( "OTB Team" );
+ SetDocSeeAlso( " " );
+
+ // Doc example parameter settings
+ SetDocExampleParameterValue( "io.vd", "vectorData.shp" );
+ SetDocExampleParameterValue( "io.stats", "meanVar.xml" );
+ SetDocExampleParameterValue( "io.out", "svmModel.svm" );
+ SetDocExampleParameterValue( "feat", "perimeter area width" );
+
}
void DoTrainUpdateParameters()
@@ -50,7 +68,12 @@ private:
// Nothing to do here
}
- void DoTrainExecute()
+ void DoBeforeTrainExecute()
+ {
+ // Nothing to do here
+ }
+
+ void DoAfterTrainExecute()
{
// Nothing to do here
}
diff --git a/Modules/Applications/AppClassification/include/otbTrainVectorBase.h b/Modules/Applications/AppClassification/include/otbTrainVectorBase.h
index 0bff79f53827944fc27e6dc1a91a2814d98b34a2..e9d3be1eb5275af9b1cbab7be204d314fb4f6db7 100644
--- a/Modules/Applications/AppClassification/include/otbTrainVectorBase.h
+++ b/Modules/Applications/AppClassification/include/otbTrainVectorBase.h
@@ -48,11 +48,13 @@ bool IsNotAlphaNum(char c)
class TrainVectorBase : public LearningApplicationBase<float, int>
{
public:
+ /** Standard class typedefs. */
typedef TrainVectorBase Self;
typedef LearningApplicationBase<float, int> Superclass;
typedef itk::SmartPointer <Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
+ /** Standard macro */
itkTypeMacro(Self, Superclass)
typedef Superclass::SampleType SampleType;
@@ -96,29 +98,31 @@ protected:
class FeaturesInfo
{
public:
- /** Index for class field */
- std::vector<int> m_SelectedCFieldIdx;
/** Selected Index */
std::vector<int> m_SelectedIdx;
+ /** Index for class field */
+ std::vector<int> m_SelectedCFieldIdx;
/** Selected class field name */
std::string m_SelectedCFieldName;
/** Selected names */
std::vector <std::string> m_SelectedNames;
unsigned int m_NbFeatures;
- FeaturesInfo(std::vector <std::string> fieldNames, std::vector <std::string> cFieldNames,
- std::vector<int> selectedIdx, std::vector<int> selectedCFieldIdx)
- : m_SelectedIdx( selectedIdx ), m_SelectedCFieldIdx( selectedCFieldIdx )
+ void SetFieldNames(std::vector <std::string> fieldNames, std::vector<int> selectedIdx)
{
+ m_SelectedIdx = selectedIdx;
m_NbFeatures = static_cast<unsigned int>(selectedIdx.size());
m_SelectedNames = std::vector<std::string>( m_NbFeatures );
for( unsigned int i = 0; i < m_NbFeatures; ++i )
{
m_SelectedNames[i] = fieldNames[selectedIdx[i]];
}
-
+ }
+ void SetClassFieldNames(std::vector<std::string> cFieldNames, std::vector<int> selectedCFieldIdx)
+ {
+ m_SelectedCFieldIdx = selectedCFieldIdx;
+ // Handle only one class field name, if several are provided only the first one is used.
m_SelectedCFieldName = cFieldNames[selectedCFieldIdx.front()];
-
}
};
@@ -126,12 +130,11 @@ protected:
protected:
/**
- * Function which extract and store all samples for Training, Classification and Validation.
+ * Function which extract and store all samples for Training and Classification.
* \param measurement statics measurement (mean/stddev)
* \param featuresInfo information about the features
- * \return sample list used for training
*/
- virtual void ExtractSamples(const StatisticsMeasurement &measurement, FeaturesInfo &featuresInfo);
+ virtual void ExtractAllSamples(const StatisticsMeasurement &measurement);
/**
* Extract the training sample list
@@ -139,60 +142,50 @@ protected:
* \param featuresInfo information about the features
* \return sample list used for training
*/
- virtual ListSamples ExtractTrainingListSamples(const StatisticsMeasurement &measurement, FeaturesInfo &featuresInfo);
+ virtual ListSamples ExtractTrainingListSamples(const StatisticsMeasurement &measurement);
/**
- * Extract the validation sample list
- * \param measurement statics measurement (mean/stddev)
- * \param featuresInfo information about the features
- * \return sample list used for validation
- */
- virtual ListSamples ExtractValidationListSamples(const StatisticsMeasurement &measurement, FeaturesInfo &featuresInfo);
-
- /**
- * Extract the sample list classification
+ * Extract classification the sample list
* \param measurement statics measurement (mean/stddev)
* \param featuresInfo information about the features
* \return sample list used for classification
*/
- virtual ListSamples ExtractClassificationListSamples(const StatisticsMeasurement &measurement, FeaturesInfo &featuresInfo);
+ virtual ListSamples ExtractClassificationListSamples(const StatisticsMeasurement &measurement);
+
+
+ /** Extract samples from input file for corresponding field name
+ *
+ * \param parameterName the name of the input file option in the input application parameters
+ * \param parameterLayer the name of the layer option in the input application parameters
+ * \param measurement statics measurement (mean/stddev)
+ * \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);
+
+
+ /**
+ * Retrieve statistics mean and standard deviation if input statistics are provided.
+ * Otherwise mean is set to 0 and standard deviation to 1 for each Features.
+ * \param nbFeatures
+ */
+ StatisticsMeasurement ComputeStatistics(unsigned int nbFeatures);
ListSamples trainingListSamples;
- ListSamples validationListSamples;
ListSamples classificationListSamples;
TargetListSampleType::Pointer predictedList;
+ FeaturesInfo featuresInfo;
private:
virtual void DoTrainInit() = 0;
- virtual void DoTrainExecute() = 0;
+ virtual void DoBeforeTrainExecute() = 0;
+ virtual void DoAfterTrainExecute() = 0;
virtual void DoTrainUpdateParameters() = 0;
- void DoInit();
- void DoUpdateParameters();
- void DoExecute();
-
- /** Extract samples from input file for corresponding field name
- *
- * \param parameterName the name of the input file option in the input application parameters
- * \param parameterLayer the name of the layer option in the input application parameters
- * \param measurement statics measurement (mean/stddev)
- * \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, FeaturesInfo &featuresInfo);
-
-
-
- ListSamples ExtractClassificationListSamples(ListSamples &validationListSamples, ListSamples &trainingListSamples);
-
-
- /**
- * Retrieve statistics mean and standard deviation if input statistics are provided.
- * Otherwise mean is set to 0 and standard deviation to 1 for each Features.
- * \param nbFeatures
- */
- StatisticsMeasurement ComputeStatistics(unsigned int nbFeatures);
+ void DoInit() ITK_OVERRIDE;
+ void DoUpdateParameters() ITK_OVERRIDE;
+ void DoExecute() ITK_OVERRIDE;
};
diff --git a/Modules/Applications/AppClassification/include/otbTrainVectorBase.txx b/Modules/Applications/AppClassification/include/otbTrainVectorBase.txx
index c45d51ad78666bc6e5f48909646ddf5795bc4211..97706bf56c20f4971670ad80b7d0537869f8cd3c 100644
--- a/Modules/Applications/AppClassification/include/otbTrainVectorBase.txx
+++ b/Modules/Applications/AppClassification/include/otbTrainVectorBase.txx
@@ -56,7 +56,10 @@ void TrainVectorBase::DoInit()
MandatoryOff( "layer" );
SetDefaultParameterInt( "layer", 0 );
- //Can be in both Supervised and Unsupervised ?
+ AddParameter(ParameterType_ListView, "feat", "Field names for training features.");
+ SetParameterDescription("feat","List of field names in the input vector data to be used as features for training.");
+
+ // Add validation data used to compute confusion matrix or contingence table
AddParameter( ParameterType_Group, "valid", "Validation data" );
SetParameterDescription( "valid", "This group of parameters defines validation data." );
@@ -70,14 +73,13 @@ void TrainVectorBase::DoInit()
MandatoryOff( "valid.layer" );
SetDefaultParameterInt( "valid.layer", 0 );
- AddParameter(ParameterType_ListView, "feat", "Field names for training features.");
- SetParameterDescription("feat","List of field names in the input vector data to be used as features for training.");
-
+ // Add class field if we used validation
AddParameter(ParameterType_ListView,"cfield","Field containing the class id for supervision");
SetParameterDescription("cfield","Field containing the class id for supervision. "
"Only geometries with this field available will be taken into account.");
SetListViewSingleSelectionMode("cfield",true);
+
// Add parameters for the classifier choice
Superclass::DoInit();
@@ -92,7 +94,7 @@ void TrainVectorBase::DoUpdateParameters()
{
std::vector<std::string> vectorFileList = GetParameterStringList( "io.vd" );
ogr::DataSource::Pointer ogrDS = ogr::DataSource::New( vectorFileList[0], ogr::DataSource::Modes::Read );
- ogr::Layer layer = ogrDS->GetLayer( this->GetParameterInt( "layer" ) );
+ ogr::Layer layer = ogrDS->GetLayer( static_cast<size_t>( this->GetParameterInt( "layer" ) ) );
ogr::Feature feature = layer.ogr().GetNextFeature();
ClearChoices( "feat" );
@@ -109,12 +111,12 @@ void TrainVectorBase::DoUpdateParameters()
if( fieldType == OFTInteger || ogr::version_proxy::IsOFTInteger64( fieldType ) || fieldType == OFTReal )
{
- std::string tmpKey = "feat." + key.substr( 0, end - key.begin() );
+ std::string tmpKey = "feat." + key.substr( 0, static_cast<unsigned long>( end - key.begin() ) );
AddChoice( tmpKey, item );
}
if( fieldType == OFTString || fieldType == OFTInteger || ogr::version_proxy::IsOFTInteger64( fieldType ) )
{
- std::string tmpKey = "cfield." + key.substr( 0, end - key.begin() );
+ std::string tmpKey = "cfield." + key.substr( 0, static_cast<unsigned long>( end - key.begin() ) );
AddChoice( tmpKey, item );
}
}
@@ -125,12 +127,9 @@ void TrainVectorBase::DoUpdateParameters()
void TrainVectorBase::DoExecute()
{
- typedef int LabelPixelType;
- typedef itk::FixedArray<LabelPixelType, 1> LabelSampleType;
- typedef itk::Statistics::ListSample<LabelSampleType> LabelListSampleType;
+ DoBeforeTrainExecute();
- FeaturesInfo featuresInfo( GetChoiceNames( "feat" ), GetChoiceNames( "cfield" ), GetSelectedItems( "feat" ),
- GetSelectedItems( "cfield" ) );
+ featuresInfo.SetFieldNames( GetChoiceNames( "feat" ), GetSelectedItems( "feat" ));
// Check input parameters
if( featuresInfo.m_SelectedIdx.empty() )
@@ -138,64 +137,34 @@ void TrainVectorBase::DoExecute()
otbAppLogFATAL( << "No features have been selected to train the classifier on!" );
}
- // Todo only Log warning and set CFieldName to 0, 1, 2, 3... (default behavior)
- if( featuresInfo.m_SelectedCFieldIdx.empty() )
- {
- otbAppLogFATAL( << "No field has been selected for data labelling!" );
- }
-
StatisticsMeasurement measurement = ComputeStatistics( featuresInfo.m_NbFeatures );
- ExtractSamples(measurement, featuresInfo);
+ ExtractAllSamples( measurement );
this->Train( trainingListSamples.listSample, trainingListSamples.labeledListSample, GetParameterString( "io.out" ) );
predictedList = TargetListSampleType::New();
this->Classify( classificationListSamples.listSample, predictedList, GetParameterString( "io.out" ) );
- DoTrainExecute();
+ DoAfterTrainExecute();
}
-void TrainVectorBase::ExtractSamples(const StatisticsMeasurement &measurement, FeaturesInfo &featuresInfo)
+void TrainVectorBase::ExtractAllSamples(const StatisticsMeasurement &measurement)
{
- trainingListSamples = ExtractTrainingListSamples(measurement, featuresInfo);
- validationListSamples = ExtractValidationListSamples(measurement, featuresInfo);
- classificationListSamples = ExtractClassificationListSamples(measurement, featuresInfo);
+ trainingListSamples = ExtractTrainingListSamples(measurement);
+ classificationListSamples = ExtractClassificationListSamples(measurement);
}
TrainVectorBase::ListSamples
-TrainVectorBase::ExtractTrainingListSamples(const StatisticsMeasurement &measurement, FeaturesInfo &featuresInfo)
+TrainVectorBase::ExtractTrainingListSamples(const StatisticsMeasurement &measurement)
{
- return ExtractListSamples( "io.vd", "layer", measurement, featuresInfo );
+ return ExtractListSamples( "io.vd", "layer", measurement);
}
TrainVectorBase::ListSamples
-TrainVectorBase::ExtractValidationListSamples(const StatisticsMeasurement &measurement, FeaturesInfo &featuresInfo)
-{
- return ExtractListSamples( "valid.vd", "valid.layer", measurement, featuresInfo );
-}
-
-
-TrainVectorBase::ListSamples
-TrainVectorBase::ExtractClassificationListSamples(const StatisticsMeasurement &measurement, FeaturesInfo &featuresInfo)
+TrainVectorBase::ExtractClassificationListSamples(const StatisticsMeasurement &itkNotUsed(measurement))
{
- ListSamples performanceSample;
-
- //Test the input validation set size
- if( validationListSamples.labeledListSample->Size() != 0 )
- {
- performanceSample.listSample = validationListSamples.listSample;
- performanceSample.labeledListSample = validationListSamples.labeledListSample;
- }
- else
- {
- otbAppLogWARNING(
- "The validation set is empty. The performance estimation is done using the input training set in this case." );
- performanceSample.listSample = trainingListSamples.listSample;
- performanceSample.labeledListSample = trainingListSamples.labeledListSample;
- }
-
- return performanceSample;
+ return trainingListSamples;
}
@@ -224,7 +193,7 @@ TrainVectorBase::ComputeStatistics(unsigned int nbFeatures)
TrainVectorBase::ListSamples
TrainVectorBase::ExtractListSamples(std::string parameterName, std::string parameterLayer,
- const StatisticsMeasurement &measurement, FeaturesInfo &featuresInfo)
+ const StatisticsMeasurement &measurement)
{
ListSamples listSamples;
if( HasValue( parameterName ) && IsParameterEnabled( parameterName ) )
@@ -249,12 +218,15 @@ TrainVectorBase::ExtractListSamples(std::string parameterName, std::string param
}
// Check all needed fields are present :
- // - check class field
+ // - 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 )
+ if( cFieldIndex < 0 && !featuresInfo.m_SelectedCFieldName.empty())
+ {
otbAppLogFATAL( "The field name for class label (" << featuresInfo.m_SelectedCFieldName
<< ") has not been found in the vector file "
<< validFileList[k] );
+ }
+
// - check feature fields
std::vector<int> featureFieldIndex( featuresInfo.m_NbFeatures, -1 );
for( unsigned int i = 0; i < featuresInfo.m_NbFeatures; i++ )
@@ -266,18 +238,22 @@ TrainVectorBase::ExtractListSamples(std::string parameterName, std::string param
<< validFileList[k] );
}
+
while( goesOn )
{
- if( feature.ogr().IsFieldSet( cFieldIndex ) )
- {
- MeasurementType mv;
- mv.SetSize( featuresInfo.m_NbFeatures );
- for( unsigned int idx = 0; idx < featuresInfo.m_NbFeatures; ++idx )
- mv[idx] = feature.ogr().GetFieldAsDouble( featureFieldIndex[idx] );
+ // 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[idx] = feature.ogr().GetFieldAsDouble( featureFieldIndex[idx] );
- input->PushBack( mv );
+ input->PushBack( mv );
+
+ if( feature.ogr().IsFieldSet( cFieldIndex ) )
target->PushBack( feature.ogr().GetFieldAsInteger( cFieldIndex ) );
- }
+ else
+ target->PushBack( 0 );
+
feature = layer.ogr().GetNextFeature();
goesOn = feature.addr() != 0;
}