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Commit 50bca421 authored by Cédric Traizet's avatar Cédric Traizet
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STY: apply clang format

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Modules/Applications/AppClassification/app/otbVectorClassifier.cxx
+ 52
− 50
View file @ 50bca421
......@@ -27,67 +27,73 @@ namespace Wrapper
using VectorClassifier = VectorPrediction<false>;
template<>
void
VectorClassifier
::DoInitSpecialization()
template <>
void VectorClassifier::DoInitSpecialization()
{
SetName("VectorClassifier");
SetDescription("Performs a classification of the input vector data according to a model file.");
SetDocAuthors("OTB-Team");
SetDocLongDescription("This application performs a vector data classification "
"based on a model file produced by the TrainVectorClassifier application."
"Features of the vector data output will contain the class labels decided by the classifier "
"(maximal class label = 65535). \n"
"There are two modes: \n"
SetDocLongDescription(
"This application performs a vector data classification "
"based on a model file produced by the TrainVectorClassifier application."
"Features of the vector data output will contain the class labels decided by the classifier "
"(maximal class label = 65535). \n"
"There are two modes: \n"
"1) Update mode: add of the 'cfield' field containing the predicted class in the input file. \n"
"2) Write mode: copies the existing fields of the input file to the output file "
" and add the 'cfield' field containing the predicted class. \n"
"If you have declared the output file, the write mode applies. "
"Otherwise, the input file update mode will be applied.");
" and add the 'cfield' field containing the predicted class. \n"
"If you have declared the output file, the write mode applies. "
"Otherwise, the input file update mode will be applied.");
SetDocLimitations("Shapefiles are supported, but the SQLite format is only supported in update mode.");
SetDocSeeAlso("TrainVectorClassifier");
AddDocTag(Tags::Learning);
AddParameter(ParameterType_InputFilename, "in", "Name of the input vector data");
SetParameterDescription("in","The input vector data file to classify.");
SetParameterDescription("in", "The input vector data file to classify.");
AddParameter(ParameterType_InputFilename, "instat", "Statistics file");
SetParameterDescription("instat", "A XML file containing mean and standard deviation to center"
"and reduce samples before classification, produced by ComputeImagesStatistics application.");
SetParameterDescription("instat",
"A XML file containing mean and standard deviation to center"
"and reduce samples before classification, produced by ComputeImagesStatistics application.");
MandatoryOff("instat");
AddParameter(ParameterType_InputFilename, "model", "Model file");
SetParameterDescription("model", "Model file produced by TrainVectorClassifier application.");
AddParameter(ParameterType_String,"cfield","Output field");
SetParameterDescription("cfield","Field containing the predicted class."
"Only geometries with this field available will be taken into account.\n"
"The field is added either in the input file (if 'out' off) or in the output file.\n"
"Caution, the 'cfield' must not exist in the input file if you are updating the file.");
SetParameterString("cfield","predicted");
AddParameter(ParameterType_String, "cfield", "Output field");
SetParameterDescription("cfield",
"Field containing the predicted class."
"Only geometries with this field available will be taken into account.\n"
"The field is added either in the input file (if 'out' off) or in the output file.\n"
"Caution, the 'cfield' must not exist in the input file if you are updating the file.");
SetParameterString("cfield", "predicted");
AddParameter(ParameterType_ListView, "feat", "Field names to be calculated");
SetParameterDescription("feat","List of field names in the input vector data used as features for training. "
"Put the same field names as the TrainVectorClassifier application.");
AddParameter(ParameterType_Bool, "confmap", "Confidence map");
SetParameterDescription( "confmap", "Confidence map of the produced classification. The confidence index depends on the model: \n\n"
"* LibSVM: difference between the two highest probabilities (needs a model with probability estimates, so that classes probabilities can be computed for each sample)\n"
"* Boost: sum of votes\n"
"* DecisionTree: (not supported)\n"
"* KNearestNeighbors: number of neighbors with the same label\n"
"* NeuralNetwork: difference between the two highest responses\n"
"* NormalBayes: (not supported)\n"
"* RandomForest: Confidence (proportion of votes for the majority class). Margin (normalized difference of the votes of the 2 majority classes) is not available for now.\n"
"* SVM: distance to margin (only works for 2-class models)\n");
SetParameterDescription("feat",
"List of field names in the input vector data used as features for training. "
"Put the same field names as the TrainVectorClassifier application.");
AddParameter(ParameterType_Bool, "confmap", "Confidence map");
SetParameterDescription("confmap",
"Confidence map of the produced classification. The confidence index depends on the model: \n\n"
"* LibSVM: difference between the two highest probabilities (needs a model with probability estimates, so that classes probabilities "
"can be computed for each sample)\n"
"* Boost: sum of votes\n"
"* DecisionTree: (not supported)\n"
"* KNearestNeighbors: number of neighbors with the same label\n"
"* NeuralNetwork: difference between the two highest responses\n"
"* NormalBayes: (not supported)\n"
"* RandomForest: Confidence (proportion of votes for the majority class). Margin (normalized difference of the votes of the 2 "
"majority classes) is not available for now.\n"
"* SVM: distance to margin (only works for 2-class models)\n");
AddParameter(ParameterType_OutputFilename, "out", "Output vector data file");
MandatoryOff("out");
SetParameterDescription("out","Output vector data file storing sample values (OGR format)."
"If not given, the input vector data file is updated.");
SetParameterDescription("out",
"Output vector data file storing sample values (OGR format)."
"If not given, the input vector data file is updated.");
// Doc example parameter settings
SetDocExampleParameterValue("in", "vectorData.shp");
......@@ -96,26 +102,22 @@ VectorClassifier
SetDocExampleParameterValue("out", "vectorDataLabeledVector.shp");
SetDocExampleParameterValue("feat", "perimeter area width");
SetDocExampleParameterValue("cfield", "predicted");
SetOfficialDocLink();
}
template<>
bool
VectorClassifier
::shouldComputeConfidenceMap() const
template <>
bool VectorClassifier::shouldComputeConfidenceMap() const
{
bool computeConfidenceMap(GetParameterInt("confmap") && m_Model->HasConfidenceIndex() );
bool computeConfidenceMap(GetParameterInt("confmap") && m_Model->HasConfidenceIndex());
if (!m_Model->HasConfidenceIndex() && GetParameterInt("confmap"))
{
{
otbAppLogWARNING("Confidence map requested but the classifier doesn't support it!");
}
}
return computeConfidenceMap;
}
}
}
......
Modules/Applications/AppClassification/app/otbVectorRegression.cxx
+ 30
− 30
View file @ 50bca421
......@@ -27,56 +27,59 @@ namespace Wrapper
using VectorRegression = VectorPrediction<true>;
template<>
void
VectorRegression
::DoInitSpecialization()
template <>
void VectorRegression::DoInitSpecialization()
{
SetName("VectorRegression");
SetDescription("Performs regression on the input vector data according to a model file.");
SetDocAuthors("OTB-Team");
SetDocLongDescription("This application performs a vector data regression "
"based on a model file produced by the TrainVectorRegression application."
"Features of the vector data output will contain the values predicted by the classifier. \n"
"There are two modes: \n"
SetDocLongDescription(
"This application performs a vector data regression "
"based on a model file produced by the TrainVectorRegression application."
"Features of the vector data output will contain the values predicted by the classifier. \n"
"There are two modes: \n"
"1) Update mode: add of the 'cfield' field containing the predicted value in the input file. \n"
"2) Write mode: copies the existing fields of the input file to the output file "
" and add the 'cfield' field containing the predicted value. \n"
"If you have declared the output file, the write mode applies. "
"Otherwise, the input file update mode will be applied.");
" and add the 'cfield' field containing the predicted value. \n"
"If you have declared the output file, the write mode applies. "
"Otherwise, the input file update mode will be applied.");
SetDocLimitations("Shapefiles are supported, but the SQLite format is only supported in update mode.");
SetDocSeeAlso("TrainVectorRegression");
AddDocTag(Tags::Learning);
AddParameter(ParameterType_InputFilename, "in", "Name of the input vector data");
SetParameterDescription("in","The input vector data file to classify.");
SetParameterDescription("in", "The input vector data file to classify.");
AddParameter(ParameterType_InputFilename, "instat", "Statistics file");
SetParameterDescription("instat", "A XML file containing mean and standard deviation to center"
"and reduce samples before classification, produced by ComputeImagesStatistics application.");
SetParameterDescription("instat",
"A XML file containing mean and standard deviation to center"
"and reduce samples before classification, produced by ComputeImagesStatistics application.");
MandatoryOff("instat");
AddParameter(ParameterType_InputFilename, "model", "Model file");
SetParameterDescription("model", "Model file produced by TrainVectorRegression application.");
AddParameter(ParameterType_String,"cfield","Output field");
SetParameterDescription("cfield","Field containing the predicted value."
"Only geometries with this field available will be taken into account.\n"
"The field is added either in the input file (if 'out' off) or in the output file.\n"
"Caution, the 'cfield' must not exist in the input file if you are updating the file.");
SetParameterString("cfield","predicted");
AddParameter(ParameterType_String, "cfield", "Output field");
SetParameterDescription("cfield",
"Field containing the predicted value."
"Only geometries with this field available will be taken into account.\n"
"The field is added either in the input file (if 'out' off) or in the output file.\n"
"Caution, the 'cfield' must not exist in the input file if you are updating the file.");
SetParameterString("cfield", "predicted");
AddParameter(ParameterType_ListView, "feat", "Field names to be calculated");
SetParameterDescription("feat","List of field names in the input vector data used as features for training. "
"Put the same field names as the TrainVectorRegression application.");
SetParameterDescription("feat",
"List of field names in the input vector data used as features for training. "
"Put the same field names as the TrainVectorRegression application.");
AddParameter(ParameterType_OutputFilename, "out", "Output vector data file");
MandatoryOff("out");
SetParameterDescription("out","Output vector data file storing sample values (OGR format)."
"If not given, the input vector data file is updated.");
SetParameterDescription("out",
"Output vector data file storing sample values (OGR format)."
"If not given, the input vector data file is updated.");
MandatoryOff("out");
// Doc example parameter settings
......@@ -91,14 +94,11 @@ VectorRegression
}
// Confidence map computation is not support for regression.
template<>
bool
VectorRegression
::shouldComputeConfidenceMap() const
template <>
bool VectorRegression::shouldComputeConfidenceMap() const
{
return false;
}
}
}
......
Modules/Applications/AppClassification/include/otbVectorPrediction.h
+ 22
− 21
View file @ 50bca421
......@@ -59,52 +59,53 @@ public:
itkTypeMacro(Self, Application)
/** Filters typedef */
typedef float ValueType;
// Label type is float for regression and unsigned int for classification
typedef typename std::conditional<RegressionMode, float, unsigned int>::type LabelType;
typedef itk::FixedArray<LabelType,1> LabelSampleType;
typedef itk::Statistics::ListSample<LabelSampleType> LabelListSampleType;
typedef otb::MachineLearningModel<ValueType,LabelType> MachineLearningModelType;
typedef otb::MachineLearningModelFactory<ValueType, LabelType> MachineLearningModelFactoryType;
typedef typename MachineLearningModelType::Pointer ModelPointerType;
typedef typename MachineLearningModelType::ConfidenceListSampleType ConfidenceListSampleType;
/** Filters typedef */
typedef float ValueType;
// Label type is float for regression and unsigned int for classification
typedef typename std::conditional<RegressionMode, float, unsigned int>::type LabelType;
typedef itk::FixedArray<LabelType, 1> LabelSampleType;
typedef itk::Statistics::ListSample<LabelSampleType> LabelListSampleType;
typedef otb::MachineLearningModel<ValueType, LabelType> MachineLearningModelType;
typedef otb::MachineLearningModelFactory<ValueType, LabelType> MachineLearningModelFactoryType;
typedef typename MachineLearningModelType::Pointer ModelPointerType;
typedef typename MachineLearningModelType::ConfidenceListSampleType ConfidenceListSampleType;
/** Statistics Filters typedef */
typedef itk::VariableLengthVector<ValueType> MeasurementType;
typedef otb::StatisticsXMLFileReader<MeasurementType> StatisticsReader;
typedef itk::VariableLengthVector<ValueType> MeasurementType;
typedef otb::StatisticsXMLFileReader<MeasurementType> StatisticsReader;
typedef itk::VariableLengthVector<ValueType> InputSampleType;
typedef itk::Statistics::ListSample<InputSampleType> ListSampleType;
typedef itk::VariableLengthVector<ValueType> InputSampleType;
typedef itk::Statistics::ListSample<InputSampleType> ListSampleType;
typedef otb::Statistics::ShiftScaleSampleListFilter<ListSampleType, ListSampleType> ShiftScaleFilterType;
~VectorPrediction() override
{
{
MachineLearningModelFactoryType::CleanFactories();
}
}
private:
/** Utility function to negate std::isalnum */
static bool IsNotAlphaNum(char c)
{
return !std::isalnum(c);
return !std::isalnum(c);
}
void DoInit() override;
/** Method defining the parameters used in the application and their documentation, specialized for RegressionMode=1 and RegrssionMode=0 */
void DoInitSpecialization();
void DoUpdateParameters() override;
void DoExecute() override;
/** Method returning whether the confidence map should be computed, depending on the regression mode and input parameters */
bool shouldComputeConfidenceMap() const;
ModelPointerType m_Model;
};
}
}
......
Modules/Applications/AppClassification/include/otbVectorPrediction.hxx
+ 100
− 112
View file @ 50bca421
......@@ -29,12 +29,10 @@ namespace Wrapper
{
template <bool RegressionMode>
void
VectorPrediction <RegressionMode>
::DoInit()
void VectorPrediction<RegressionMode>::DoInit()
{
DoInitSpecialization();
// Assert that the all needed parameters have ben definied in DoInitSpecialization
assert(GetParameterByKey("in") != nullptr);
assert(GetParameterByKey("instat") != nullptr);
......@@ -45,67 +43,63 @@ VectorPrediction <RegressionMode>
}
template <bool RegressionMode>
void
VectorPrediction <RegressionMode>
::DoUpdateParameters()
void VectorPrediction<RegressionMode>::DoUpdateParameters()
{
if ( HasValue("in") )
if (HasValue("in"))
{
std::string shapefile = GetParameterString("in");
otb::ogr::DataSource::Pointer ogrDS;
ogrDS = otb::ogr::DataSource::New(shapefile, otb::ogr::DataSource::Modes::Read);
otb::ogr::Layer layer = ogrDS->GetLayer(0);
OGRFeatureDefn &layerDefn = layer.GetLayerDefn();
ogrDS = otb::ogr::DataSource::New(shapefile, otb::ogr::DataSource::Modes::Read);
otb::ogr::Layer layer = ogrDS->GetLayer(0);
OGRFeatureDefn& layerDefn = layer.GetLayerDefn();
ClearChoices("feat");
for(int iField=0; iField< layerDefn.GetFieldCount(); iField++)
for (int iField = 0; iField < layerDefn.GetFieldCount(); iField++)
{
std::string item = layerDefn.GetFieldDefn(iField)->GetNameRef();
std::string key(item);
key.erase( std::remove_if(key.begin(),key.end(),IsNotAlphaNum), key.end());
key.erase(std::remove_if(key.begin(), key.end(), IsNotAlphaNum), key.end());
std::transform(key.begin(), key.end(), key.begin(), tolower);
OGRFieldType fieldType = layerDefn.GetFieldDefn(iField)->GetType();
if(fieldType == OFTInteger || fieldType == OFTInteger64 || fieldType == OFTReal)
{
std::string tmpKey="feat."+key;
AddChoice(tmpKey,item);
}
if (fieldType == OFTInteger || fieldType == OFTInteger64 || fieldType == OFTReal)
{
std::string tmpKey = "feat." + key;
AddChoice(tmpKey, item);
}
}
}
}
template <bool RegressionMode>
void
VectorPrediction <RegressionMode>
::DoExecute()
void VectorPrediction<RegressionMode>::DoExecute()
{
clock_t tic = clock();
std::string shapefile = GetParameterString("in");
otb::ogr::DataSource::Pointer source = otb::ogr::DataSource::New(shapefile, otb::ogr::DataSource::Modes::Read);
otb::ogr::Layer layer = source->GetLayer(0);
otb::ogr::Layer layer = source->GetLayer(0);
typename ListSampleType::Pointer input = ListSampleType::New();
const int nbFeatures = GetSelectedItems("feat").size();
input->SetMeasurementVectorSize(nbFeatures);
otb::ogr::Layer::const_iterator it = layer.cbegin();
otb::ogr::Layer::const_iterator it = layer.cbegin();
otb::ogr::Layer::const_iterator itEnd = layer.cend();
for( ; it!=itEnd ; ++it)
{
for (; it != itEnd; ++it)
{
MeasurementType mv;
mv.SetSize(nbFeatures);
for(int idx=0; idx < nbFeatures; ++idx)
{
for (int idx = 0; idx < nbFeatures; ++idx)
{
// Beware that itemIndex differs from ogr layer field index
unsigned int itemIndex = GetSelectedItems("feat")[idx];
std::string fieldName = GetChoiceNames( "feat" )[itemIndex];
std::string fieldName = GetChoiceNames("feat")[itemIndex];
switch ((*it)[fieldName].GetType())
{
case OFTInteger:
......@@ -120,30 +114,28 @@ VectorPrediction <RegressionMode>
default:
itkExceptionMacro(<< "incorrect field type: " << (*it)[fieldName].GetType() << ".");
}
}
input->PushBack(mv);
}
input->PushBack(mv);
}
// Statistics for shift/scale
MeasurementType meanMeasurementVector;
MeasurementType stddevMeasurementVector;
if (HasValue("instat") && IsParameterEnabled("instat"))
{
{
typename StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
std::string XMLfile = GetParameterString("instat");
std::string XMLfile = GetParameterString("instat");
statisticsReader->SetFileName(XMLfile);
meanMeasurementVector = statisticsReader->GetStatisticVectorByName("mean");
meanMeasurementVector = statisticsReader->GetStatisticVectorByName("mean");
stddevMeasurementVector = statisticsReader->GetStatisticVectorByName("stddev");
}
}
else
{
{
meanMeasurementVector.SetSize(nbFeatures);
meanMeasurementVector.Fill(0.);
stddevMeasurementVector.SetSize(nbFeatures);
stddevMeasurementVector.Fill(1.);
}
}
typename ShiftScaleFilterType::Pointer trainingShiftScaleFilter = ShiftScaleFilterType::New();
trainingShiftScaleFilter->SetInput(input);
......@@ -154,13 +146,12 @@ VectorPrediction <RegressionMode>
otbAppLogINFO("standard deviation used: " << stddevMeasurementVector);
otbAppLogINFO("Loading model");
m_Model = MachineLearningModelFactoryType::CreateMachineLearningModel(GetParameterString("model"),
MachineLearningModelFactoryType::ReadMode);
m_Model = MachineLearningModelFactoryType::CreateMachineLearningModel(GetParameterString("model"), MachineLearningModelFactoryType::ReadMode);
if (m_Model.IsNull())
{
{
otbAppLogFATAL(<< "Error when loading model " << GetParameterString("model") << " : unsupported model type");
}
}
m_Model->SetRegressionMode(RegressionMode);
......@@ -175,154 +166,151 @@ VectorPrediction <RegressionMode>
typename LabelListSampleType::Pointer target;
if (computeConfidenceMap)
{
{
quality = ConfidenceListSampleType::New();
target = m_Model->PredictBatch(listSample, quality);
}
else
{
target = m_Model->PredictBatch(listSample, quality);
}
else
{
target = m_Model->PredictBatch(listSample);
}
}
ogr::DataSource::Pointer output;
ogr::DataSource::Pointer buffer = ogr::DataSource::New();
bool updateMode = false;
ogr::DataSource::Pointer buffer = ogr::DataSource::New();
bool updateMode = false;
if (IsParameterEnabled("out") && HasValue("out"))
{
{
// Create new OGRDataSource
output = ogr::DataSource::New(GetParameterString("out"), ogr::DataSource::Modes::Overwrite);
otb::ogr::Layer newLayer = output->CreateLayer(
GetParameterString("out"),
const_cast<OGRSpatialReference*>(layer.GetSpatialRef()),
layer.GetGeomType());
output = ogr::DataSource::New(GetParameterString("out"), ogr::DataSource::Modes::Overwrite);
otb::ogr::Layer newLayer = output->CreateLayer(GetParameterString("out"), const_cast<OGRSpatialReference*>(layer.GetSpatialRef()), layer.GetGeomType());
// Copy existing fields
OGRFeatureDefn &inLayerDefn = layer.GetLayerDefn();
for (int k=0 ; k<inLayerDefn.GetFieldCount() ; k++)
{
OGRFeatureDefn& inLayerDefn = layer.GetLayerDefn();
for (int k = 0; k < inLayerDefn.GetFieldCount(); k++)
{
OGRFieldDefn fieldDefn(inLayerDefn.GetFieldDefn(k));
newLayer.CreateField(fieldDefn);
}
}
}
else
{
{
// Update mode
updateMode = true;
otbAppLogINFO("Update input vector data.");
// fill temporary buffer for the transfer
otb::ogr::Layer inputLayer = layer;
layer = buffer->CopyLayer(inputLayer, std::string("Buffer"));
layer = buffer->CopyLayer(inputLayer, std::string("Buffer"));
// close input data source
source->Clear();
// Re-open input data source in update mode
output = otb::ogr::DataSource::New(shapefile, otb::ogr::DataSource::Modes::Update_LayerUpdate);
}
}
otb::ogr::Layer outLayer = output->GetLayer(0);
OGRErr errStart = outLayer.ogr().StartTransaction();
if (errStart != OGRERR_NONE)
{
{
itkExceptionMacro(<< "Unable to start transaction for OGR layer " << outLayer.ogr().GetName() << ".");
}
}
OGRFeatureDefn &layerDefn = layer.GetLayerDefn();
OGRFeatureDefn& layerDefn = layer.GetLayerDefn();
// Add the field of prediction in the output layer if field not exist
OGRFieldType labelType;
if (RegressionMode==true)
if (RegressionMode == true)
labelType = OFTReal;
else
labelType = OFTInteger;
int idx = layerDefn.GetFieldIndex(GetParameterString("cfield").c_str());
if (idx >= 0)
{
if (layerDefn.GetFieldDefn(idx)->GetType() != labelType)
itkExceptionMacro("Field name "<< GetParameterString("cfield") << " already exists with a different type!");
itkExceptionMacro("Field name " << GetParameterString("cfield") << " already exists with a different type!");
}
else
{
OGRFieldDefn predictedField(GetParameterString("cfield").c_str(), labelType);
OGRFieldDefn predictedField(GetParameterString("cfield").c_str(), labelType);
ogr::FieldDefn predictedFieldDef(predictedField);
outLayer.CreateField(predictedFieldDef);
}
// Add confidence field in the output layer
std::string confFieldName("confidence");
if (computeConfidenceMap)
{
{
idx = layerDefn.GetFieldIndex(confFieldName.c_str());
if (idx >= 0)
{
{
if (layerDefn.GetFieldDefn(idx)->GetType() != OFTReal)
itkExceptionMacro("Field name "<< confFieldName << " already exists with a different type!");
}
itkExceptionMacro("Field name " << confFieldName << " already exists with a different type!");
}
else
{
{
OGRFieldDefn confidenceField(confFieldName.c_str(), OFTReal);
confidenceField.SetWidth(confidenceField.GetWidth());
confidenceField.SetPrecision(confidenceField.GetPrecision());
ogr::FieldDefn confFieldDefn(confidenceField);
outLayer.CreateField(confFieldDefn);
}
}
}
// Fill output layer
unsigned int count=0;
std::string classfieldname = GetParameterString("cfield");
it = layer.cbegin();
itEnd = layer.cend();
for( ; it!=itEnd ; ++it, ++count)
{
unsigned int count = 0;
std::string classfieldname = GetParameterString("cfield");
it = layer.cbegin();
itEnd = layer.cend();
for (; it != itEnd; ++it, ++count)
{
ogr::Feature dstFeature(outLayer.GetLayerDefn());
dstFeature.SetFrom( *it , TRUE);
dstFeature.SetFrom(*it, TRUE);
dstFeature.SetFID(it->GetFID());
switch (dstFeature[classfieldname].GetType())
{
case OFTInteger:
dstFeature[classfieldname].SetValue<int>(target->GetMeasurementVector(count)[0]);
break;
case OFTInteger64:
dstFeature[classfieldname].SetValue<int>(target->GetMeasurementVector(count)[0]);
break;
case OFTReal:
dstFeature[classfieldname].SetValue<double>(target->GetMeasurementVector(count)[0]);
break;
case OFTString:
dstFeature[classfieldname].SetValue<std::string>(std::to_string(target->GetMeasurementVector(count)[0]));
break;
default:
itkExceptionMacro(<< "incorrect field type: " << dstFeature[classfieldname].GetType() << ".");
}
{
case OFTInteger:
dstFeature[classfieldname].SetValue<int>(target->GetMeasurementVector(count)[0]);
break;
case OFTInteger64:
dstFeature[classfieldname].SetValue<int>(target->GetMeasurementVector(count)[0]);
break;
case OFTReal:
dstFeature[classfieldname].SetValue<double>(target->GetMeasurementVector(count)[0]);
break;
case OFTString:
dstFeature[classfieldname].SetValue<std::string>(std::to_string(target->GetMeasurementVector(count)[0]));
break;
default:
itkExceptionMacro(<< "incorrect field type: " << dstFeature[classfieldname].GetType() << ".");
}
if (computeConfidenceMap)
dstFeature[confFieldName].SetValue<double>(quality->GetMeasurementVector(count)[0]);
if (updateMode)
{
{
outLayer.SetFeature(dstFeature);
}
}
else
{
{
outLayer.CreateFeature(dstFeature);
}
}
}
if(outLayer.ogr().TestCapability("Transactions"))
{
if (outLayer.ogr().TestCapability("Transactions"))
{
const OGRErr errCommitX = outLayer.ogr().CommitTransaction();
if (errCommitX != OGRERR_NONE)
{
{
itkExceptionMacro(<< "Unable to commit transaction for OGR layer " << outLayer.ogr().GetName() << ".");
}
}
}
output->SyncToDisk();
clock_t toc = clock();
otbAppLogINFO( "Elapsed: "<< ((double)(toc - tic) / CLOCKS_PER_SEC)<<" seconds.");
otbAppLogINFO("Elapsed: " << ((double)(toc - tic) / CLOCKS_PER_SEC) << " seconds.");
}
} //end namespace wrapper
} //end namespace otb
} // end namespace wrapper
} // end namespace otb
#endif
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