From bec02c1fa304d05298e3f10442052fcf9f5444a0 Mon Sep 17 00:00:00 2001
From: Ludovic Hussonnois <ludovic.hussonnois@c-s.fr>
Date: Wed, 15 Feb 2017 15:11:51 +0100
Subject: [PATCH] REFAC: Refactoring TrainVectorClassifier.
Inherit TrainVectorClassifier from TrainVectorBase and use Non-Virtual
Function Idiom to provide common behavior for Unsupervised and
Supervised classification.
---
.../app/otbTrainVectorClassifier.cxx | 676 +++++-------------
.../app/otbTrainVectorClustering.cxx | 64 ++
.../include/otbTrainVectorBase.h | 207 ++++++
.../include/otbTrainVectorBase.txx | 305 ++++++++
4 files changed, 772 insertions(+), 480 deletions(-)
create mode 100644 Modules/Applications/AppClassification/app/otbTrainVectorClustering.cxx
create mode 100644 Modules/Applications/AppClassification/include/otbTrainVectorBase.h
create mode 100644 Modules/Applications/AppClassification/include/otbTrainVectorBase.txx
diff --git a/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx b/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
index fa5209552e..61609f080e 100644
--- a/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
+++ b/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
@@ -14,60 +14,30 @@
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
-#include "otbWrapperApplication.h"
-#include "otbWrapperApplicationFactory.h"
-
-#include "otbLearningApplicationBase.h"
-
-#include "otbOGRDataSourceWrapper.h"
-#include "otbOGRFeatureWrapper.h"
-#include "otbStatisticsXMLFileWriter.h"
-
-#include "itkVariableLengthVector.h"
-#include "otbStatisticsXMLFileReader.h"
-
-#include "itkListSample.h"
-#include "otbShiftScaleSampleListFilter.h"
+#include "otbTrainVectorBase.h"
// Validation
#include "otbConfusionMatrixCalculator.h"
-#include <algorithm>
-#include <locale>
-
namespace otb
{
namespace Wrapper
{
-/** Utility function to negate std::isalnum */
-bool IsNotAlphaNum(char c)
- {
- return !std::isalnum(c);
- }
-
-class TrainVectorClassifier : public LearningApplicationBase<float,int>
+class TrainVectorClassifier : public TrainVectorBase
{
public:
typedef TrainVectorClassifier Self;
- typedef LearningApplicationBase<float, int> Superclass;
+ typedef TrainVectorBase Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
- itkNewMacro(Self)
-
- itkTypeMacro(Self, Superclass)
-
- typedef Superclass::SampleType SampleType;
- typedef Superclass::ListSampleType ListSampleType;
- typedef Superclass::TargetListSampleType TargetListSampleType;
- typedef Superclass::SampleImageType SampleImageType;
-
- typedef double ValueType;
- typedef itk::VariableLengthVector<ValueType> MeasurementType;
+ itkNewMacro( Self )
- typedef otb::StatisticsXMLFileReader<SampleType> StatisticsReader;
+ itkTypeMacro( Self, Superclass )
- typedef otb::Statistics::ShiftScaleSampleListFilter<ListSampleType, ListSampleType> ShiftScaleFilterType;
+ typedef Superclass::SampleType SampleType;
+ typedef Superclass::ListSampleType ListSampleType;
+ typedef Superclass::TargetListSampleType TargetListSampleType;
// Estimate performance on validation sample
typedef otb::ConfusionMatrixCalculator<TargetListSampleType, TargetListSampleType> ConfusionMatrixCalculatorType;
@@ -75,503 +45,249 @@ public:
typedef ConfusionMatrixCalculatorType::MapOfIndicesType MapOfIndicesType;
typedef ConfusionMatrixCalculatorType::ClassLabelType ClassLabelType;
+
private:
- void DoInit()
+ void DoTrainInit()
{
- SetName("TrainVectorClassifier");
- SetDescription("Train a classifier based on labeled geometries and a list of features to consider.");
-
- SetDocName("Train Vector Classifier");
- SetDocLongDescription("This application trains a classifier based on "
- "labeled geometries and a list of features to consider for classification.");
- SetDocLimitations(" ");
- SetDocAuthors("OTB Team");
- SetDocSeeAlso(" ");
-
- //Group IO
- AddParameter(ParameterType_Group, "io", "Input and output data");
- SetParameterDescription("io", "This group of parameters allows setting input and output data.");
-
- AddParameter(ParameterType_InputVectorDataList, "io.vd", "Input Vector Data");
- SetParameterDescription("io.vd", "Input geometries used for training (note : all geometries from the layer will be used)");
-
- AddParameter(ParameterType_InputFilename, "io.stats", "Input XML image statistics file");
- MandatoryOff("io.stats");
- SetParameterDescription("io.stats", "XML file containing mean and variance of each feature.");
-
- AddParameter(ParameterType_OutputFilename, "io.confmatout", "Output confusion matrix");
- SetParameterDescription("io.confmatout", "Output file containing the confusion matrix (.csv format).");
- MandatoryOff("io.confmatout");
-
- AddParameter(ParameterType_OutputFilename, "io.out", "Output model");
- SetParameterDescription("io.out", "Output file containing the model estimated (.txt format).");
-
- 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.");
-
- 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);
-
- AddParameter(ParameterType_Int, "layer", "Layer Index");
- SetParameterDescription("layer", "Index of the layer to use in the input vector file.");
- MandatoryOff("layer");
- SetDefaultParameterInt("layer",0);
-
- AddParameter(ParameterType_Group, "valid", "Validation data");
- SetParameterDescription("valid", "This group of parameters defines validation data.");
-
- AddParameter(ParameterType_InputVectorDataList, "valid.vd", "Validation Vector Data");
- SetParameterDescription("valid.vd", "Geometries used for validation "
- "(must contain the same fields used for training, all geometries from the layer will be used)");
- MandatoryOff("valid.vd");
-
- AddParameter(ParameterType_Int, "valid.layer", "Layer Index");
- SetParameterDescription("valid.layer", "Index of the layer to use in the validation vector file.");
- MandatoryOff("valid.layer");
- SetDefaultParameterInt("valid.layer",0);
-
- // Add parameters for the classifier choice
- Superclass::DoInit();
-
- AddRANDParameter();
+ SetName( "TrainVectorClassifier" );
+ SetDescription( "Train a classifier based on labeled geometries and a list of features to consider." );
+
+ SetDocName( "Train Vector Classifier" );
+ SetDocLongDescription( "This application trains a classifier based on "
+ "labeled geometries and a list of features to consider for classification." );
+ SetDocLimitations( " " );
+ SetDocAuthors( "OTB Team" );
+ SetDocSeeAlso( " " );
+
+ // Add a new parameter to compute confusion matrix
+ AddParameter( ParameterType_OutputFilename, "io.confmatout", "Output confusion matrix" );
+ SetParameterDescription( "io.confmatout", "Output file containing the confusion matrix (.csv format)." );
+ MandatoryOff( "io.confmatout" );
+
// Doc example parameter settings
- SetDocExampleParameterValue("io.vd", "vectorData.shp");
- SetDocExampleParameterValue("io.stats", "meanVar.xml");
- SetDocExampleParameterValue("io.out", "svmModel.svm");
- SetDocExampleParameterValue("feat", "perimeter area width");
- SetDocExampleParameterValue("cfield", "predicted");
+ SetDocExampleParameterValue( "io.vd", "vectorData.shp" );
+ SetDocExampleParameterValue( "io.stats", "meanVar.xml" );
+ SetDocExampleParameterValue( "io.out", "svmModel.svm" );
+ SetDocExampleParameterValue( "feat", "perimeter area width" );
+ SetDocExampleParameterValue( "cfield", "predicted" );
+
}
- void DoUpdateParameters()
+ void DoTrainUpdateParameters()
{
- if ( HasValue("io.vd") )
- {
- 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::Feature feature = layer.ogr().GetNextFeature();
-
- ClearChoices("feat");
- ClearChoices("cfield");
-
- for(int iField=0; iField<feature.ogr().GetFieldCount(); iField++)
- {
- std::string key, item = feature.ogr().GetFieldDefnRef(iField)->GetNameRef();
- key = item;
- std::string::iterator end = std::remove_if(key.begin(),key.end(),IsNotAlphaNum);
- std::transform(key.begin(), end, key.begin(), tolower);
-
- OGRFieldType fieldType = feature.ogr().GetFieldDefnRef(iField)->GetType();
-
- if(fieldType == OFTInteger || ogr::version_proxy::IsOFTInteger64(fieldType) || fieldType == OFTReal)
- {
- std::string tmpKey="feat."+key.substr(0, 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());
- AddChoice(tmpKey,item);
- }
- }
- }
+ // Nothing to do here
+ }
+
+ void DoTrainExecute()
+ {
+ ConfusionMatrixCalculatorType::Pointer confMatCalc = ComputeConfusionmatrix( predictedList,
+ classificationListSamples.labeledListSample );
+ WriteConfusionMatrix( confMatCalc );
}
-void LogConfusionMatrix(ConfusionMatrixCalculatorType* confMatCalc)
-{
- ConfusionMatrixCalculatorType::ConfusionMatrixType matrix = confMatCalc->GetConfusionMatrix();
+ ConfusionMatrixCalculatorType::Pointer
+ ComputeConfusionmatrix(const TargetListSampleType::Pointer &predictedListSample,
+ const TargetListSampleType::Pointer &performanceLabeledListSample)
+ {
+ ConfusionMatrixCalculatorType::Pointer confMatCalc = ConfusionMatrixCalculatorType::New();
+
+ otbAppLogINFO( "Predicted list size : " << predictedListSample->Size() );
+ otbAppLogINFO( "ValidationLabeledListSample size : " << performanceLabeledListSample->Size() );
+ confMatCalc->SetReferenceLabels( performanceLabeledListSample );
+ confMatCalc->SetProducedLabels( predictedListSample );
+ confMatCalc->Compute();
- // Compute minimal width
- size_t minwidth = 0;
+ otbAppLogINFO( "training performances" );
+ LogConfusionMatrix( confMatCalc );
- for (unsigned int i = 0; i < matrix.Rows(); i++)
- {
- for (unsigned int j = 0; j < matrix.Cols(); j++)
+ for( unsigned int itClasses = 0; itClasses < confMatCalc->GetNumberOfClasses(); itClasses++ )
{
- std::ostringstream os;
- os << matrix(i, j);
- size_t size = os.str().size();
+ ConfusionMatrixCalculatorType::ClassLabelType classLabel = confMatCalc->GetMapOfIndices()[itClasses];
- if (size > minwidth)
- {
- minwidth = size;
- }
+ otbAppLogINFO( "Precision of class [" << classLabel << "] vs all: " << confMatCalc->GetPrecisions()[itClasses] );
+ otbAppLogINFO( "Recall of class [" << classLabel << "] vs all: " << confMatCalc->GetRecalls()[itClasses] );
+ otbAppLogINFO(
+ "F-score of class [" << classLabel << "] vs all: " << confMatCalc->GetFScores()[itClasses] << "\n" );
}
- }
+ otbAppLogINFO( "Global performance, Kappa index: " << confMatCalc->GetKappaIndex() );
+ return confMatCalc;
+ }
- MapOfIndicesType mapOfIndices = confMatCalc->GetMapOfIndices();
+ /**
+ * Write the confidence matrix into a file if output is provided.
+ * \param confMatCalc the input matrix to write.
+ */
+ void WriteConfusionMatrix(const ConfusionMatrixCalculatorType::Pointer &confMatCalc)
+ {
+ if( this->HasValue( "io.confmatout" ) )
+ {
+ // Writing the confusion matrix in the output .CSV file
- MapOfIndicesType::const_iterator it = mapOfIndices.begin();
- MapOfIndicesType::const_iterator end = mapOfIndices.end();
+ MapOfIndicesType::iterator itMapOfIndicesValid, itMapOfIndicesPred;
+ ClassLabelType labelValid = 0;
- for (; it != end; ++it)
- {
- std::ostringstream os;
- os << "[" << it->second << "]";
+ ConfusionMatrixType confusionMatrix = confMatCalc->GetConfusionMatrix();
+ MapOfIndicesType mapOfIndicesValid = confMatCalc->GetMapOfIndices();
- size_t size = os.str().size();
- if (size > minwidth)
- {
- minwidth = size;
- }
- }
+ unsigned long nbClassesPred = mapOfIndicesValid.size();
- // Generate matrix string, with 'minwidth' as size specifier
- std::ostringstream os;
+ /////////////////////////////////////////////
+ // Filling the 2 headers for the output file
+ const std::string commentValidStr = "#Reference labels (rows):";
+ const std::string commentPredStr = "#Produced labels (columns):";
+ const char separatorChar = ',';
+ std::ostringstream ossHeaderValidLabels, ossHeaderPredLabels;
- // Header line
- for (size_t i = 0; i < minwidth; ++i)
- os << " ";
- os << " ";
-
- it = mapOfIndices.begin();
- end = mapOfIndices.end();
- for (; it != end; ++it)
- {
- os << "[" << it->second << "]" << " ";
- }
-
- os << std::endl;
-
- // Each line of confusion matrix
- for (unsigned int i = 0; i < matrix.Rows(); i++)
- {
- ConfusionMatrixCalculatorType::ClassLabelType label = mapOfIndices[i];
- os << "[" << std::setw(minwidth - 2) << label << "]" << " ";
- for (unsigned int j = 0; j < matrix.Cols(); j++)
- {
- os << std::setw(minwidth) << matrix(i, j) << " ";
- }
- os << std::endl;
- }
+ // Filling ossHeaderValidLabels and ossHeaderPredLabels for the output file
+ ossHeaderValidLabels << commentValidStr;
+ ossHeaderPredLabels << commentPredStr;
- otbAppLogINFO("Confusion matrix (rows = reference labels, columns = produced labels):\n" << os.str());
-}
+ itMapOfIndicesValid = mapOfIndicesValid.begin();
+ while( itMapOfIndicesValid != mapOfIndicesValid.end() )
+ {
+ // labels labelValid of mapOfIndicesValid are already sorted in otbConfusionMatrixCalculator
+ labelValid = itMapOfIndicesValid->second;
-void DoExecute()
- {
- typedef int LabelPixelType;
- typedef itk::FixedArray<LabelPixelType,1> LabelSampleType;
- typedef itk::Statistics::ListSample <LabelSampleType> LabelListSampleType;
-
- // Prepare selected field names (their position may change between two inputs)
- std::vector<int> selectedIdx = GetSelectedItems("feat");
- std::vector<int> selectedCFieldIdx = GetSelectedItems("cfield");
-
- if(selectedIdx.empty())
- {
- otbAppLogFATAL(<<"No features have been selected to train the classifier on!");
- }
-
- if(selectedCFieldIdx.empty())
- {
- otbAppLogFATAL(<<"No field has been selected for data labelling!");
- }
-
- const unsigned int nbFeatures = selectedIdx.size();
- std::vector<std::string> fieldNames = GetChoiceNames("feat");
- std::vector<std::string> cFieldNames = GetChoiceNames("cfield");
- std::vector<std::string> selectedNames(nbFeatures);
- for (unsigned int i=0 ; i<nbFeatures ; i++)
- {
- selectedNames[i] = fieldNames[selectedIdx[i]];
- }
-
- std::string selectedCFieldName = cFieldNames[selectedCFieldIdx.front()];
-
- std::vector<int> featureFieldIndex(nbFeatures, -1);
- int cFieldIndex = -1;
-
- // Statistics for shift/scale
- MeasurementType meanMeasurementVector;
- MeasurementType stddevMeasurementVector;
- if (HasValue("io.stats") && IsParameterEnabled("io.stats"))
- {
- StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
- std::string XMLfile = GetParameterString("io.stats");
- statisticsReader->SetFileName(XMLfile);
- meanMeasurementVector = statisticsReader->GetStatisticVectorByName("mean");
- stddevMeasurementVector = statisticsReader->GetStatisticVectorByName("stddev");
- }
- else
- {
- meanMeasurementVector.SetSize(nbFeatures);
- meanMeasurementVector.Fill(0.);
- stddevMeasurementVector.SetSize(nbFeatures);
- stddevMeasurementVector.Fill(1.);
- }
-
- ListSampleType::Pointer input = ListSampleType::New();
- LabelListSampleType::Pointer target = LabelListSampleType::New();
- input->SetMeasurementVectorSize(nbFeatures);
-
- std::vector<std::string> vectorFileList = GetParameterStringList("io.vd");
- for (unsigned int k=0 ; k<vectorFileList.size() ; k++)
- {
- otbAppLogINFO("Reading input vector file "<<k+1<<"/"<<vectorFileList.size());
- ogr::DataSource::Pointer source = ogr::DataSource::New(vectorFileList[k], ogr::DataSource::Modes::Read);
- ogr::Layer layer = source->GetLayer(this->GetParameterInt("layer"));
- ogr::Feature feature = layer.ogr().GetNextFeature();
- bool goesOn = feature.addr() != 0;
- if (!goesOn)
- {
- otbAppLogWARNING("The layer "<<GetParameterInt("layer")<<" of "
- <<vectorFileList[k]<<" is empty, input is skipped.");
- continue;
- }
+ otbAppLogINFO( "mapOfIndicesValid[" << itMapOfIndicesValid->first << "] = " << labelValid );
- // Check all needed fields are present :
- // - check class field
- cFieldIndex = feature.ogr().GetFieldIndex(selectedCFieldName.c_str());
- if (cFieldIndex < 0)
- otbAppLogFATAL("The field name for class label ("<<selectedCFieldName
- <<") has not been found in the input vector file "<<vectorFileList[k]);
- // - check feature fields
- for (unsigned int i=0 ; i<nbFeatures ; i++)
- {
- featureFieldIndex[i] = feature.ogr().GetFieldIndex(selectedNames[i].c_str());
- if (featureFieldIndex[i] < 0)
- otbAppLogFATAL("The field name for feature "<<selectedNames[i]
- <<" has not been found in the input vector file "<<vectorFileList[k]);
- }
+ ossHeaderValidLabels << labelValid;
+ ossHeaderPredLabels << labelValid;
- while(goesOn)
- {
- if(feature.ogr().IsFieldSet(cFieldIndex))
- {
- MeasurementType mv;
- mv.SetSize(nbFeatures);
- for(unsigned int idx=0; idx < nbFeatures; ++idx)
- mv[idx] = feature.ogr().GetFieldAsDouble(featureFieldIndex[idx]);
+ ++itMapOfIndicesValid;
- input->PushBack(mv);
- target->PushBack(feature.ogr().GetFieldAsInteger(cFieldIndex));
- }
- feature = layer.ogr().GetNextFeature();
- goesOn = feature.addr() != 0;
- }
- }
-
- ShiftScaleFilterType::Pointer trainingShiftScaleFilter = ShiftScaleFilterType::New();
- trainingShiftScaleFilter->SetInput(input);
- trainingShiftScaleFilter->SetShifts(meanMeasurementVector);
- trainingShiftScaleFilter->SetScales(stddevMeasurementVector);
- trainingShiftScaleFilter->Update();
-
- ListSampleType::Pointer trainingListSample= trainingShiftScaleFilter->GetOutput();
- TargetListSampleType::Pointer trainingLabeledListSample = target;
-
- //--------------------------
- // Estimate model
- //--------------------------
- this->Train(trainingListSample,trainingLabeledListSample,GetParameterString("io.out"));
-
- //--------------------------
- // Performances estimation
- //--------------------------
- ListSampleType::Pointer validationListSample=ListSampleType::New();
- TargetListSampleType::Pointer validationLabeledListSample = TargetListSampleType::New();
-
- // Import validation data
- if (HasValue("valid.vd") && IsParameterEnabled("valid.vd"))
- {
- input = ListSampleType::New();
- target = LabelListSampleType::New();
- input->SetMeasurementVectorSize(nbFeatures);
-
- std::vector<std::string> validFileList = this->GetParameterStringList("valid.vd");
- for (unsigned int k=0 ; k<validFileList.size() ; k++)
- {
- otbAppLogINFO("Reading validation vector file "<<k+1<<"/"<<validFileList.size());
- ogr::DataSource::Pointer source = ogr::DataSource::New(validFileList[k], ogr::DataSource::Modes::Read);
- ogr::Layer layer = source->GetLayer(this->GetParameterInt("valid.layer"));
- ogr::Feature feature = layer.ogr().GetNextFeature();
- bool goesOn = feature.addr() != 0;
- if (!goesOn)
- {
- otbAppLogWARNING("The layer "<<GetParameterInt("valid.layer")<<" of "
- <<validFileList[k]<<" is empty, input is skipped.");
- continue;
+ if( itMapOfIndicesValid != mapOfIndicesValid.end() )
+ {
+ ossHeaderValidLabels << separatorChar;
+ ossHeaderPredLabels << separatorChar;
+ }
+ else
+ {
+ ossHeaderValidLabels << std::endl;
+ ossHeaderPredLabels << std::endl;
+ }
}
- // Check all needed fields are present :
- // - check class field
- cFieldIndex = feature.ogr().GetFieldIndex(selectedCFieldName.c_str());
- if (cFieldIndex < 0)
- otbAppLogFATAL("The field name for class label ("<<selectedCFieldName
- <<") has not been found in the validation vector file "<<validFileList[k]);
- // - check feature fields
- for (unsigned int i=0 ; i<nbFeatures ; i++)
- {
- featureFieldIndex[i] = feature.ogr().GetFieldIndex(selectedNames[i].c_str());
- if (featureFieldIndex[i] < 0)
- otbAppLogFATAL("The field name for feature "<<selectedNames[i]
- <<" has not been found in the validation vector file "<<validFileList[k]);
- }
+ std::ofstream outFile;
+ outFile.open( this->GetParameterString( "io.confmatout" ).c_str() );
+ outFile << std::fixed;
+ outFile.precision( 10 );
+
+ /////////////////////////////////////
+ // Writing the 2 headers
+ outFile << ossHeaderValidLabels.str();
+ outFile << ossHeaderPredLabels.str();
+ /////////////////////////////////////
+
+ unsigned int indexLabelValid = 0, indexLabelPred = 0;
- while(goesOn)
+ for( itMapOfIndicesValid = mapOfIndicesValid.begin();
+ itMapOfIndicesValid != mapOfIndicesValid.end(); ++itMapOfIndicesValid )
{
- if(feature.ogr().IsFieldSet(cFieldIndex))
- {
- MeasurementType mv;
- mv.SetSize(nbFeatures);
- for(unsigned int idx=0; idx < nbFeatures; ++idx)
- mv[idx] = feature.ogr().GetFieldAsDouble(featureFieldIndex[idx]);
+ indexLabelPred = 0;
- input->PushBack(mv);
- target->PushBack(feature.ogr().GetFieldAsInteger(cFieldIndex));
+ for( itMapOfIndicesPred = mapOfIndicesValid.begin();
+ itMapOfIndicesPred != mapOfIndicesValid.end(); ++itMapOfIndicesPred )
+ {
+ // Writing the confusion matrix (sorted in otbConfusionMatrixCalculator) in the output file
+ outFile << confusionMatrix( indexLabelValid, indexLabelPred );
+ if( indexLabelPred < ( nbClassesPred - 1 ) )
+ {
+ outFile << separatorChar;
+ }
+ else
+ {
+ outFile << std::endl;
+ }
+ ++indexLabelPred;
}
- feature = layer.ogr().GetNextFeature();
- goesOn = feature.addr() != 0;
+
+ ++indexLabelValid;
}
+
+ outFile.close();
}
+ }
+
+ /**
+ * Display the log of the confusion matrix computed with
+ * \param confMatCalc the input confusion matrix to display
+ */
+ void LogConfusionMatrix(ConfusionMatrixCalculatorType *confMatCalc)
+ {
+ ConfusionMatrixCalculatorType::ConfusionMatrixType matrix = confMatCalc->GetConfusionMatrix();
+
+ // Compute minimal width
+ size_t minwidth = 0;
- ShiftScaleFilterType::Pointer validShiftScaleFilter = ShiftScaleFilterType::New();
- validShiftScaleFilter->SetInput(input);
- validShiftScaleFilter->SetShifts(meanMeasurementVector);
- validShiftScaleFilter->SetScales(stddevMeasurementVector);
- validShiftScaleFilter->Update();
-
- validationListSample = validShiftScaleFilter->GetOutput();
- validationLabeledListSample = target;
- }
-
- //Test the input validation set size
- TargetListSampleType::Pointer predictedList = TargetListSampleType::New();
- ListSampleType::Pointer performanceListSample;
- TargetListSampleType::Pointer performanceLabeledListSample;
- if(validationLabeledListSample->Size() != 0)
- {
- performanceListSample = validationListSample;
- performanceLabeledListSample = validationLabeledListSample;
- }
- else
- {
- otbAppLogWARNING("The validation set is empty. The performance estimation is done using the input training set in this case.");
- performanceListSample = trainingListSample;
- performanceLabeledListSample = trainingLabeledListSample;
- }
-
- this->Classify(performanceListSample, predictedList, GetParameterString("io.out"));
-
- ConfusionMatrixCalculatorType::Pointer confMatCalc = ConfusionMatrixCalculatorType::New();
-
- otbAppLogINFO("Predicted list size : " << predictedList->Size());
- otbAppLogINFO("ValidationLabeledListSample size : " << performanceLabeledListSample->Size());
- confMatCalc->SetReferenceLabels(performanceLabeledListSample);
- confMatCalc->SetProducedLabels(predictedList);
- confMatCalc->Compute();
-
- otbAppLogINFO("training performances");
- LogConfusionMatrix(confMatCalc);
-
- for (unsigned int itClasses = 0; itClasses < confMatCalc->GetNumberOfClasses(); itClasses++)
- {
- ConfusionMatrixCalculatorType::ClassLabelType classLabel = confMatCalc->GetMapOfIndices()[itClasses];
-
- otbAppLogINFO("Precision of class [" << classLabel << "] vs all: " << confMatCalc->GetPrecisions()[itClasses]);
- otbAppLogINFO("Recall of class [" << classLabel << "] vs all: " << confMatCalc->GetRecalls()[itClasses]);
- otbAppLogINFO(
- "F-score of class [" << classLabel << "] vs all: " << confMatCalc->GetFScores()[itClasses] << "\n");
- }
- otbAppLogINFO("Global performance, Kappa index: " << confMatCalc->GetKappaIndex());
-
-
- if (this->HasValue("io.confmatout"))
- {
- // Writing the confusion matrix in the output .CSV file
-
- MapOfIndicesType::iterator itMapOfIndicesValid, itMapOfIndicesPred;
- ClassLabelType labelValid = 0;
-
- ConfusionMatrixType confusionMatrix = confMatCalc->GetConfusionMatrix();
- MapOfIndicesType mapOfIndicesValid = confMatCalc->GetMapOfIndices();
-
- unsigned int nbClassesPred = mapOfIndicesValid.size();
-
- /////////////////////////////////////////////
- // Filling the 2 headers for the output file
- const std::string commentValidStr = "#Reference labels (rows):";
- const std::string commentPredStr = "#Produced labels (columns):";
- const char separatorChar = ',';
- std::ostringstream ossHeaderValidLabels, ossHeaderPredLabels;
-
- // Filling ossHeaderValidLabels and ossHeaderPredLabels for the output file
- ossHeaderValidLabels << commentValidStr;
- ossHeaderPredLabels << commentPredStr;
-
- itMapOfIndicesValid = mapOfIndicesValid.begin();
-
- while (itMapOfIndicesValid != mapOfIndicesValid.end())
+ for( unsigned int i = 0; i < matrix.Rows(); i++ )
{
- // labels labelValid of mapOfIndicesValid are already sorted in otbConfusionMatrixCalculator
- labelValid = itMapOfIndicesValid->second;
+ for( unsigned int j = 0; j < matrix.Cols(); j++ )
+ {
+ std::ostringstream os;
+ os << matrix( i, j );
+ size_t size = os.str().size();
- otbAppLogINFO("mapOfIndicesValid[" << itMapOfIndicesValid->first << "] = " << labelValid);
+ if( size > minwidth )
+ {
+ minwidth = size;
+ }
+ }
+ }
- ossHeaderValidLabels << labelValid;
- ossHeaderPredLabels << labelValid;
+ MapOfIndicesType mapOfIndices = confMatCalc->GetMapOfIndices();
- ++itMapOfIndicesValid;
+ MapOfIndicesType::const_iterator it = mapOfIndices.begin();
+ MapOfIndicesType::const_iterator end = mapOfIndices.end();
- if (itMapOfIndicesValid != mapOfIndicesValid.end())
- {
- ossHeaderValidLabels << separatorChar;
- ossHeaderPredLabels << separatorChar;
- }
- else
+ for( ; it != end; ++it )
+ {
+ std::ostringstream os;
+ os << "[" << it->second << "]";
+
+ size_t size = os.str().size();
+ if( size > minwidth )
{
- ossHeaderValidLabels << std::endl;
- ossHeaderPredLabels << std::endl;
+ minwidth = size;
}
}
- std::ofstream outFile;
- outFile.open(this->GetParameterString("io.confmatout").c_str());
- outFile << std::fixed;
- outFile.precision(10);
-
- /////////////////////////////////////
- // Writing the 2 headers
- outFile << ossHeaderValidLabels.str();
- outFile << ossHeaderPredLabels.str();
- /////////////////////////////////////
+ // Generate matrix string, with 'minwidth' as size specifier
+ std::ostringstream os;
- unsigned int indexLabelValid = 0, indexLabelPred = 0;
+ // Header line
+ for( size_t i = 0; i < minwidth; ++i )
+ os << " ";
+ os << " ";
- for (itMapOfIndicesValid = mapOfIndicesValid.begin(); itMapOfIndicesValid != mapOfIndicesValid.end(); ++itMapOfIndicesValid)
+ it = mapOfIndices.begin();
+ end = mapOfIndices.end();
+ for( ; it != end; ++it )
{
- indexLabelPred = 0;
+ os << "[" << it->second << "]" << " ";
+ }
+
+ os << std::endl;
- for (itMapOfIndicesPred = mapOfIndicesValid.begin(); itMapOfIndicesPred != mapOfIndicesValid.end(); ++itMapOfIndicesPred)
+ // Each line of confusion matrix
+ for( unsigned int i = 0; i < matrix.Rows(); i++ )
+ {
+ ConfusionMatrixCalculatorType::ClassLabelType label = mapOfIndices[i];
+ os << "[" << std::setw( minwidth - 2 ) << label << "]" << " ";
+ for( unsigned int j = 0; j < matrix.Cols(); j++ )
{
- // Writing the confusion matrix (sorted in otbConfusionMatrixCalculator) in the output file
- outFile << confusionMatrix(indexLabelValid, indexLabelPred);
- if (indexLabelPred < (nbClassesPred - 1))
- {
- outFile << separatorChar;
- }
- else
- {
- outFile << std::endl;
- }
- ++indexLabelPred;
+ os << std::setw( minwidth ) << matrix( i, j ) << " ";
}
-
- ++indexLabelValid;
+ os << std::endl;
}
- outFile.close();
- } // END if (this->HasValue("io.confmatout"))
+ otbAppLogINFO( "Confusion matrix (rows = reference labels, columns = produced labels):\n" << os.str() );
}
+
};
}
}
-OTB_APPLICATION_EXPORT(otb::Wrapper::TrainVectorClassifier)
+OTB_APPLICATION_EXPORT( otb::Wrapper::TrainVectorClassifier )
diff --git a/Modules/Applications/AppClassification/app/otbTrainVectorClustering.cxx b/Modules/Applications/AppClassification/app/otbTrainVectorClustering.cxx
new file mode 100644
index 0000000000..eec6927bd0
--- /dev/null
+++ b/Modules/Applications/AppClassification/app/otbTrainVectorClustering.cxx
@@ -0,0 +1,64 @@
+/*=========================================================================
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+ =========================================================================*/
+#include "otbTrainVectorBase.h"
+
+// Validation
+#include "otbConfusionMatrixCalculator.h"
+
+namespace otb
+{
+namespace Wrapper
+{
+
+class TrainVectorClassifier : public TrainVectorBase
+{
+public:
+ typedef TrainVectorClassifier Self;
+ typedef TrainVectorBase Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+ itkNewMacro( Self )
+
+ itkTypeMacro( Self, Superclass )
+
+ typedef Superclass::SampleType SampleType;
+ typedef Superclass::ListSampleType ListSampleType;
+ typedef Superclass::TargetListSampleType TargetListSampleType;
+
+private:
+ void DoTrainInit()
+ {
+ // Nothing to do here
+ }
+
+ void DoTrainUpdateParameters()
+ {
+ // Nothing to do here
+ }
+
+ void DoTrainExecute()
+ {
+ // Nothing to do here
+ }
+
+
+
+};
+}
+}
+
+OTB_APPLICATION_EXPORT( otb::Wrapper::TrainVectorClustering )
diff --git a/Modules/Applications/AppClassification/include/otbTrainVectorBase.h b/Modules/Applications/AppClassification/include/otbTrainVectorBase.h
new file mode 100644
index 0000000000..0bff79f538
--- /dev/null
+++ b/Modules/Applications/AppClassification/include/otbTrainVectorBase.h
@@ -0,0 +1,207 @@
+/*=========================================================================
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+ =========================================================================*/
+#ifndef otbTrainVectorBase_h
+#define otbTrainVectorBase_h
+
+#include "otbLearningApplicationBase.h"
+#include "otbWrapperApplication.h"
+#include "otbWrapperApplicationFactory.h"
+
+#include "otbOGRDataSourceWrapper.h"
+#include "otbOGRFeatureWrapper.h"
+#include "otbStatisticsXMLFileWriter.h"
+
+#include "itkVariableLengthVector.h"
+#include "otbStatisticsXMLFileReader.h"
+
+#include "itkListSample.h"
+#include "otbShiftScaleSampleListFilter.h"
+
+#include <algorithm>
+#include <locale>
+
+namespace otb
+{
+namespace Wrapper
+{
+
+/** Utility function to negate std::isalnum */
+bool IsNotAlphaNum(char c)
+{
+ return !std::isalnum( c );
+}
+
+class TrainVectorBase : public LearningApplicationBase<float, int>
+{
+public:
+ typedef TrainVectorBase Self;
+ typedef LearningApplicationBase<float, int> Superclass;
+ typedef itk::SmartPointer <Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+ itkTypeMacro(Self, Superclass)
+
+ typedef Superclass::SampleType SampleType;
+ typedef Superclass::ListSampleType ListSampleType;
+ typedef Superclass::TargetListSampleType TargetListSampleType;
+
+ typedef double ValueType;
+ typedef itk::VariableLengthVector <ValueType> MeasurementType;
+
+ typedef otb::StatisticsXMLFileReader<SampleType> StatisticsReader;
+
+ typedef otb::Statistics::ShiftScaleSampleListFilter<ListSampleType, ListSampleType> ShiftScaleFilterType;
+
+protected:
+
+ /** Class used to store statistics Measurment (mean/stddev) */
+ class StatisticsMeasurement
+ {
+ public:
+ MeasurementType meanMeasurementVector;
+ MeasurementType stddevMeasurementVector;
+ };
+
+ /** Class used to store a list of sample and the corresponding label */
+ class ListSamples
+ {
+ public:
+ ListSampleType::Pointer listSample;
+ TargetListSampleType::Pointer labeledListSample;
+ ListSamples()
+ {
+ listSample = ListSampleType::New();
+ labeledListSample = TargetListSampleType::New();
+ }
+ };
+
+ /**
+ * Features information class used to store informations
+ * about the field and class name/id of an input vector
+ */
+ class FeaturesInfo
+ {
+ public:
+ /** Index for class field */
+ std::vector<int> m_SelectedCFieldIdx;
+ /** Selected Index */
+ std::vector<int> m_SelectedIdx;
+ /** 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 )
+ {
+ 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]];
+ }
+
+ m_SelectedCFieldName = cFieldNames[selectedCFieldIdx.front()];
+
+ }
+ };
+
+
+protected:
+
+ /**
+ * Function which extract and store all samples for Training, Classification and Validation.
+ * \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);
+
+ /**
+ * Extract the training sample list
+ * \param measurement statics measurement (mean/stddev)
+ * \param featuresInfo information about the features
+ * \return sample list used for training
+ */
+ virtual ListSamples ExtractTrainingListSamples(const StatisticsMeasurement &measurement, FeaturesInfo &featuresInfo);
+
+ /**
+ * 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
+ * \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);
+
+ ListSamples trainingListSamples;
+ ListSamples validationListSamples;
+ ListSamples classificationListSamples;
+ TargetListSampleType::Pointer predictedList;
+
+private:
+ virtual void DoTrainInit() = 0;
+ virtual void DoTrainExecute() = 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);
+
+};
+
+}
+}
+
+#ifndef OTB_MANUAL_INSTANTIATION
+#include "otbTrainVectorBase.txx"
+#endif
+
+#endif
+
diff --git a/Modules/Applications/AppClassification/include/otbTrainVectorBase.txx b/Modules/Applications/AppClassification/include/otbTrainVectorBase.txx
new file mode 100644
index 0000000000..c45d51ad78
--- /dev/null
+++ b/Modules/Applications/AppClassification/include/otbTrainVectorBase.txx
@@ -0,0 +1,305 @@
+/*=========================================================================
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+ =========================================================================*/
+#ifndef otbTrainVectorBase_txx
+#define otbTrainVectorBase_txx
+
+#include "otbTrainVectorBase.h"
+
+namespace otb
+{
+namespace Wrapper
+{
+
+void TrainVectorBase::DoInit()
+{
+ SetName( "TrainVectorClassifier" );
+ SetDescription( "Train a classifier based on labeled geometries and a list of features to consider." );
+
+ SetDocName( "Train Vector Classifier" );
+ SetDocLongDescription( "This application trains a classifier based on "
+ "labeled geometries and a list of features to consider for classification." );
+ SetDocLimitations( " " );
+ SetDocAuthors( "OTB Team" );
+ SetDocSeeAlso( " " );
+
+ // Common Parameters for all Learning Application
+ AddParameter( ParameterType_Group, "io", "Input and output data" );
+ SetParameterDescription( "io", "This group of parameters allows setting input and output data." );
+
+ AddParameter( ParameterType_InputVectorDataList, "io.vd", "Input Vector Data" );
+ SetParameterDescription( "io.vd",
+ "Input geometries used for training (note : all geometries from the layer will be used)" );
+
+ AddParameter( ParameterType_InputFilename, "io.stats", "Input XML image statistics file" );
+ MandatoryOff( "io.stats" );
+ SetParameterDescription( "io.stats", "XML file containing mean and variance of each feature." );
+
+ AddParameter( ParameterType_OutputFilename, "io.out", "Output model" );
+ SetParameterDescription( "io.out", "Output file containing the model estimated (.txt format)." );
+
+ AddParameter( ParameterType_Int, "layer", "Layer Index" );
+ SetParameterDescription( "layer", "Index of the layer to use in the input vector file." );
+ MandatoryOff( "layer" );
+ SetDefaultParameterInt( "layer", 0 );
+
+ //Can be in both Supervised and Unsupervised ?
+ AddParameter( ParameterType_Group, "valid", "Validation data" );
+ SetParameterDescription( "valid", "This group of parameters defines validation data." );
+
+ AddParameter( ParameterType_InputVectorDataList, "valid.vd", "Validation Vector Data" );
+ SetParameterDescription( "valid.vd", "Geometries used for validation "
+ "(must contain the same fields used for training, all geometries from the layer will be used)" );
+ MandatoryOff( "valid.vd" );
+
+ AddParameter( ParameterType_Int, "valid.layer", "Layer Index" );
+ SetParameterDescription( "valid.layer", "Index of the layer to use in the validation vector file." );
+ 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.");
+
+ 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();
+
+ AddRANDParameter();
+
+ DoTrainInit();
+}
+
+void TrainVectorBase::DoUpdateParameters()
+{
+ if( HasValue( "io.vd" ) )
+ {
+ 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::Feature feature = layer.ogr().GetNextFeature();
+
+ ClearChoices( "feat" );
+ ClearChoices( "cfield" );
+
+ for( int iField = 0; iField < feature.ogr().GetFieldCount(); iField++ )
+ {
+ std::string key, item = feature.ogr().GetFieldDefnRef( iField )->GetNameRef();
+ key = item;
+ std::string::iterator end = std::remove_if( key.begin(), key.end(), IsNotAlphaNum );
+ std::transform( key.begin(), end, key.begin(), tolower );
+
+ OGRFieldType fieldType = feature.ogr().GetFieldDefnRef( iField )->GetType();
+
+ if( fieldType == OFTInteger || ogr::version_proxy::IsOFTInteger64( fieldType ) || fieldType == OFTReal )
+ {
+ std::string tmpKey = "feat." + key.substr( 0, 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() );
+ AddChoice( tmpKey, item );
+ }
+ }
+ }
+
+ DoTrainUpdateParameters();
+}
+
+void TrainVectorBase::DoExecute()
+{
+ typedef int LabelPixelType;
+ typedef itk::FixedArray<LabelPixelType, 1> LabelSampleType;
+ typedef itk::Statistics::ListSample<LabelSampleType> LabelListSampleType;
+
+ FeaturesInfo featuresInfo( GetChoiceNames( "feat" ), GetChoiceNames( "cfield" ), GetSelectedItems( "feat" ),
+ GetSelectedItems( "cfield" ) );
+
+ // Check input parameters
+ if( featuresInfo.m_SelectedIdx.empty() )
+ {
+ 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);
+
+ this->Train( trainingListSamples.listSample, trainingListSamples.labeledListSample, GetParameterString( "io.out" ) );
+
+ predictedList = TargetListSampleType::New();
+ this->Classify( classificationListSamples.listSample, predictedList, GetParameterString( "io.out" ) );
+
+ DoTrainExecute();
+}
+
+
+void TrainVectorBase::ExtractSamples(const StatisticsMeasurement &measurement, FeaturesInfo &featuresInfo)
+{
+ trainingListSamples = ExtractTrainingListSamples(measurement, featuresInfo);
+ validationListSamples = ExtractValidationListSamples(measurement, featuresInfo);
+ classificationListSamples = ExtractClassificationListSamples(measurement, featuresInfo);
+}
+
+TrainVectorBase::ListSamples
+TrainVectorBase::ExtractTrainingListSamples(const StatisticsMeasurement &measurement, FeaturesInfo &featuresInfo)
+{
+ return ExtractListSamples( "io.vd", "layer", measurement, featuresInfo );
+}
+
+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)
+{
+ 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;
+}
+
+
+TrainVectorBase::StatisticsMeasurement
+TrainVectorBase::ComputeStatistics(unsigned int nbFeatures)
+{
+ StatisticsMeasurement measurement = StatisticsMeasurement();
+ if( HasValue( "io.stats" ) && IsParameterEnabled( "io.stats" ) )
+ {
+ StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
+ std::string XMLfile = GetParameterString( "io.stats" );
+ statisticsReader->SetFileName( XMLfile.c_str() );
+ measurement.meanMeasurementVector = statisticsReader->GetStatisticVectorByName( "mean" );
+ measurement.stddevMeasurementVector = statisticsReader->GetStatisticVectorByName( "stddev" );
+ }
+ else
+ {
+ measurement.meanMeasurementVector.SetSize( nbFeatures );
+ measurement.meanMeasurementVector.Fill( 0. );
+ measurement.stddevMeasurementVector.SetSize( nbFeatures );
+ measurement.stddevMeasurementVector.Fill( 1. );
+ }
+ return measurement;
+}
+
+
+TrainVectorBase::ListSamples
+TrainVectorBase::ExtractListSamples(std::string parameterName, std::string parameterLayer,
+ const StatisticsMeasurement &measurement, FeaturesInfo &featuresInfo)
+{
+ ListSamples listSamples;
+ if( HasValue( parameterName ) && IsParameterEnabled( parameterName ) )
+ {
+ ListSampleType::Pointer input = ListSampleType::New();
+ TargetListSampleType::Pointer target = TargetListSampleType::New();
+ input->SetMeasurementVectorSize( featuresInfo.m_NbFeatures );
+
+ std::vector<std::string> validFileList = this->GetParameterStringList( parameterName );
+ for( unsigned int k = 0; k < validFileList.size(); k++ )
+ {
+ otbAppLogINFO( "Reading validation vector file " << k + 1 << "/" << validFileList.size() );
+ ogr::DataSource::Pointer source = ogr::DataSource::New( validFileList[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]
+ << " is empty, input is skipped." );
+ continue;
+ }
+
+ // Check all needed fields are present :
+ // - check class field
+ int cFieldIndex = feature.ogr().GetFieldIndex( featuresInfo.m_SelectedCFieldName.c_str() );
+ if( cFieldIndex < 0 )
+ 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++ )
+ {
+ featureFieldIndex[i] = feature.ogr().GetFieldIndex( featuresInfo.m_SelectedNames[i].c_str() );
+ if( featureFieldIndex[i] < 0 )
+ otbAppLogFATAL( "The field name for feature " << featuresInfo.m_SelectedNames[i]
+ << " has not been found in the vector file "
+ << 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] );
+
+ input->PushBack( mv );
+ target->PushBack( feature.ogr().GetFieldAsInteger( cFieldIndex ) );
+ }
+ feature = layer.ogr().GetNextFeature();
+ goesOn = feature.addr() != 0;
+ }
+ }
+
+ ShiftScaleFilterType::Pointer shiftScaleFilter = ShiftScaleFilterType::New();
+ shiftScaleFilter->SetInput( input );
+ shiftScaleFilter->SetShifts( measurement.meanMeasurementVector );
+ shiftScaleFilter->SetScales( measurement.stddevMeasurementVector );
+ shiftScaleFilter->Update();
+
+ listSamples.listSample = shiftScaleFilter->GetOutput();
+ listSamples.labeledListSample = target;
+ }
+
+ return listSamples;
+}
+
+
+}
+}
+
+#endif
+
+
--
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