diff --git a/CMake/OTBModuleHeaderTest.cmake b/CMake/OTBModuleHeaderTest.cmake
index 6a1c72158a365e323c9b4f84529f03f8ccf38b3e..83d7479c3f92cef4c7a532d10c8e9c5cd6e2277b 100644
--- a/CMake/OTBModuleHeaderTest.cmake
+++ b/CMake/OTBModuleHeaderTest.cmake
@@ -26,7 +26,7 @@ if(NOT OTB_USE_OPENCV)
SET(BANNED_HEADERS "${BANNED_HEADERS} otbDecisionTreeMachineLearningModelFactory.h otbDecisionTreeMachineLearningModel.h otbKNearestNeighborsMachineLearningModelFactory.h otbKNearestNeighborsMachineLearningModel.h otbRandomForestsMachineLearningModelFactory.h otbRandomForestsMachineLearningModel.h otbSVMMachineLearningModelFactory.h otbSVMMachineLearningModel.h otbGradientBoostedTreeMachineLearningModelFactory.h otbGradientBoostedTreeMachineLearningModel.h otbBoostMachineLearningModelFactory.h otbBoostMachineLearningModel.h otbNeuralNetworkMachineLearningModelFactory.h otbNeuralNetworkMachineLearningModel.h otbNormalBayesMachineLearningModelFactory.h otbNormalBayesMachineLearningModel.h otbRequiresOpenCVCheck.h otbOpenCVUtils.h otbCvRTreesWrapper.h")
endif()
if(NOT OTB_USE_SHARK)
- SET(BANNED_HEADERS "${BANNED_HEADERS} otbSharkRandomForestsMachineLearningModel.h otbSharkRandomForestsMachineLearningModel.txx otbSharkUtils.h otbRequiresSharkCheck.h otbSharkRandomForestsMachineLearningModelFactory.h")
+ SET(BANNED_HEADERS "${BANNED_HEADERS} otbSharkRandomForestsMachineLearningModel.h otbSharkRandomForestsMachineLearningModel.txx otbSharkUtils.h otbRequiresSharkCheck.h otbSharkRandomForestsMachineLearningModelFactory.h otbSharkKMeansMachineLearningModel.h otbSharkKMeansMachineLearningModel.txx otbSharkKMeansMachineLearningModelFactory.h otbSharkKMeansMachineLearningModelFactory.txx")
endif()
if(NOT OTB_USE_LIBSVM)
SET(BANNED_HEADERS "${BANNED_HEADERS} otbLibSVMMachineLearningModel.h otbLibSVMMachineLearningModelFactory.h")
@@ -44,7 +44,7 @@ endif()
macro( otb_module_headertest _name )
- if( NOT ${_name}_THIRD_PARTY
+ if( NOT ${_name}_THIRD_PARTY
AND EXISTS ${${_name}_SOURCE_DIR}/include
AND PYTHON_EXECUTABLE
AND NOT (PYTHON_VERSION_STRING VERSION_LESS 2.6)
diff --git a/Modules/Applications/AppClassification/app/CMakeLists.txt b/Modules/Applications/AppClassification/app/CMakeLists.txt
index 7da4e25da879e9f8a8f00b806fbd22f789e6f476..79cc0eb6ec558e8b1e69ba007ee300842f95e5dc 100644
--- a/Modules/Applications/AppClassification/app/CMakeLists.txt
+++ b/Modules/Applications/AppClassification/app/CMakeLists.txt
@@ -50,11 +50,6 @@ otb_create_application(
SOURCES otbTrainVectorClassifier.cxx
LINK_LIBRARIES ${${otb-module}_LIBRARIES})
-otb_create_application(
- NAME TrainVectorClustering
- SOURCES otbTrainVectorClustering.cxx
- LINK_LIBRARIES ${${otb-module}_LIBRARIES})
-
otb_create_application(
NAME ComputeConfusionMatrix
SOURCES otbComputeConfusionMatrix.cxx
@@ -80,11 +75,6 @@ otb_create_application(
SOURCES otbTrainImagesClassifier.cxx
LINK_LIBRARIES ${${otb-module}_LIBRARIES})
-otb_create_application(
- NAME TrainImagesClustering
- SOURCES otbTrainImagesClustering.cxx
- LINK_LIBRARIES ${${otb-module}_LIBRARIES})
-
otb_create_application(
NAME TrainRegression
SOURCES otbTrainRegression.cxx
diff --git a/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx b/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx
index bb0f9176b29789139d9007cd63cd0ad27db8bab5..3c1bada305be2ec720d0ee0f32e49d571213fb18 100644
--- a/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx
+++ b/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx
@@ -44,43 +44,133 @@ public:
ClearApplications();
InitIO();
InitSampling();
- InitClassification( true );
+ InitClassification();
+ AddDocTag( Tags::Learning );
// Doc example parameter settings
- SetDocExampleParameterValue("io.il", "QB_1_ortho.tif");
- SetDocExampleParameterValue("io.vd", "VectorData_QB1.shp");
- SetDocExampleParameterValue("io.imstat", "EstimateImageStatisticsQB1.xml");
- SetDocExampleParameterValue("sample.mv", "100");
- SetDocExampleParameterValue("sample.mt", "100");
- SetDocExampleParameterValue("sample.vtr", "0.5");
- SetDocExampleParameterValue("sample.vfn", "Class");
- SetDocExampleParameterValue("classifier", "libsvm");
- SetDocExampleParameterValue("classifier.libsvm.k", "linear");
- SetDocExampleParameterValue("classifier.libsvm.c", "1");
- SetDocExampleParameterValue("classifier.libsvm.opt", "false");
- SetDocExampleParameterValue("io.out", "svmModelQB1.txt");
- SetDocExampleParameterValue("io.confmatout", "svmConfusionMatrixQB1.csv");
+ SetDocExampleParameterValue( "io.il", "QB_1_ortho.tif" );
+ SetDocExampleParameterValue( "io.vd", "VectorData_QB1.shp" );
+ SetDocExampleParameterValue( "io.imstat", "EstimateImageStatisticsQB1.xml" );
+ SetDocExampleParameterValue( "sample.mv", "100" );
+ SetDocExampleParameterValue( "sample.mt", "100" );
+ SetDocExampleParameterValue( "sample.vtr", "0.5" );
+ SetDocExampleParameterValue( "sample.vfn", "Class" );
+ SetDocExampleParameterValue( "classifier", "libsvm" );
+ SetDocExampleParameterValue( "classifier.libsvm.k", "linear" );
+ SetDocExampleParameterValue( "classifier.libsvm.c", "1" );
+ SetDocExampleParameterValue( "classifier.libsvm.opt", "false" );
+ SetDocExampleParameterValue( "io.out", "svmModelQB1.txt" );
+ SetDocExampleParameterValue( "io.confmatout", "svmConfusionMatrixQB1.csv" );
}
void DoUpdateParameters() ITK_OVERRIDE
{
- if( HasValue( "io.vd" ) )
+ if( HasValue( "io.vd" ) && IsParameterEnabled( "io.vd" ))
+ {
+ UpdatePolygonClassStatisticsParameters();
+ }
+
+
+ // Change mandatory of input vector depending on supervised and unsupervised mode.
+ if( HasValue( "classifier" ) )
+ {
+ UpdateInternalParameters( "training" );
+ switch( trainVectorBase->GetClassifierCategory() )
+ {
+ case TrainVectorBase::Unsupervised:
+ MandatoryOff( "io.vd" );
+ break;
+ default:
+ case TrainVectorBase::Supervised:
+ MandatoryOn( "io.vd" );
+ break;
+ }
+ }
+
+ }
+
+ /**
+ * Select and Extract samples for validation with computed statistics and rates.
+ * Validation samples could be empty if sample.vrt == 0 and if no dedicated validation are provided.
+ * If no dedicated validation is provided the training is split corresponding to the sample.vtr parameter,
+ * in this case if no vector data have been provided, the training rates and statistics are computed
+ * on the selection and extraction training result.
+ * fileNames.sampleOutputs contains training data and after an ExtractValidationData training data will
+ * be split to fileNames.sampleTrainOutputs.
+ * \param imageList
+ * \param fileNames
+ * \param validationVectorFileList
+ * \param rates
+ * \param HasInputVector
+ */
+ void ExtractValidationData(FloatVectorImageListType *imageList, TrainFileNamesHandler& fileNames,
+ std::vector<std::string> validationVectorFileList,
+ const SamplingRates& rates, bool HasInputVector )
+ {
+ if( !validationVectorFileList.empty() ) // Compute class statistics and sampling rate of validation data if provided.
+ {
+ ComputePolygonStatistics( imageList, validationVectorFileList, fileNames.polyStatValidOutputs );
+ ComputeSamplingRate( fileNames.polyStatValidOutputs, fileNames.rateValidOut, rates.fmv );
+ SelectAndExtractValidationSamples( fileNames, imageList, validationVectorFileList );
+ if( HasInputVector ) // if input vector is provided the sampleTrainOutputs is the previously extracted sampleOutputs
+ fileNames.sampleTrainOutputs = fileNames.sampleOutputs;
+ }
+ else if(GetParameterFloat("sample.vtr") != 0.0)// Split training data to validation
{
- std::vector<std::string> vectorFileList = GetParameterStringList( "io.vd" );
- GetInternalApplication( "polystat" )->SetParameterString( "vec", vectorFileList[0], false );
- UpdateInternalParameters( "polystat" );
+ if( !HasInputVector ) // Compute one class statistics and sampling rate for the generated vector.
+ ComputePolygonStatistics( imageList, fileNames.sampleOutputs, fileNames.polyStatTrainOutputs );
+ ComputeSamplingRate( fileNames.polyStatTrainOutputs, fileNames.rateTrainOut, rates.fmt );
+ SplitTrainingToValidationSamples( fileNames, imageList );
+ }
+ else // nothing to do, except update fileNames
+ {
+ fileNames.sampleTrainOutputs = fileNames.sampleOutputs;
}
}
- void DoExecute() ITK_OVERRIDE
+ /**
+ * Extract Training data depending if input vector is provided
+ * \param imageList list of the image
+ * \param fileNames handler that contain filenames
+ * \param vectorFileList input vector file list (if provided
+ * \param rates
+ */
+ void ExtractTrainData(FloatVectorImageListType *imageList, const TrainFileNamesHandler& fileNames,
+ std::vector<std::string> vectorFileList,
+ const SamplingRates& rates)
+ {
+ if( !vectorFileList.empty() ) // Select and Extract samples for training with computed statistics and rates
+ {
+ ComputePolygonStatistics( imageList, vectorFileList, fileNames.polyStatTrainOutputs );
+ ComputeSamplingRate( fileNames.polyStatTrainOutputs, fileNames.rateTrainOut, rates.fmt );
+ SelectAndExtractTrainSamples( fileNames, imageList, vectorFileList, SamplingStrategy::CLASS );
+ }
+ else // Select training samples base on geometric sampling if no input vector is provided
+ {
+ SelectAndExtractTrainSamples( fileNames, imageList, vectorFileList, SamplingStrategy::GEOMETRIC, "fid" );
+ }
+ }
+
+
+ void DoExecute()
{
TrainFileNamesHandler fileNames;
+ std::vector<std::string> vectorFileList;
FloatVectorImageListType *imageList = GetParameterImageList( "io.il" );
- std::vector<std::string> vectorFileList = GetParameterStringList( "io.vd" );
+ bool HasInputVector = IsParameterEnabled( "io.vd" ) && HasValue( "io.vd" );
+ if(HasInputVector)
+ vectorFileList = GetParameterStringList( "io.vd" );
+
+
unsigned long nbInputs = imageList->Size();
- if( nbInputs > vectorFileList.size() )
+ if( !HasInputVector && trainVectorBase->GetClassifierCategory() == TrainVectorBase::Supervised )
+ {
+ otbAppLogFATAL( "Missing input vector data files" );
+ }
+
+ if( !vectorFileList.empty() && nbInputs > vectorFileList.size() )
{
otbAppLogFATAL( "Missing input vector data files to match number of images (" << nbInputs << ")." );
}
@@ -104,22 +194,11 @@ public:
// Compute final maximum sampling rates for both training and validation samples
SamplingRates rates = ComputeFinalMaximumSamplingRates( dedicatedValidation );
- // Select and Extract samples for training with computed statistics and rates
- ComputePolygonStatistics(imageList, vectorFileList, fileNames.polyStatTrainOutputs);
- ComputeSamplingRate(fileNames.polyStatTrainOutputs, fileNames.rateTrainOut, rates.fmt);
- SelectAndExtractTrainSamples(fileNames, imageList, vectorFileList, SamplingStrategy::CLASS);
-
- // Select and Extract samples for validation with computed statistics and rates
- // Validation samples could be empty if sample.vrt == 0 and if no dedicated validation are provided
- if( dedicatedValidation ) {
- ComputePolygonStatistics(imageList, validationVectorFileList, fileNames.polyStatValidOutputs);
- ComputeSamplingRate(fileNames.polyStatValidOutputs, fileNames.rateValidOut, rates.fmv);
- }
- SelectAndExtractValidationSamples(fileNames, imageList, validationVectorFileList);
-
+ ExtractTrainData(imageList, fileNames, vectorFileList, rates);
+ ExtractValidationData(imageList, fileNames, validationVectorFileList, rates, HasInputVector);
// Then train the model with extracted samples
- TrainModel( imageList, fileNames.sampleTrainOutputs, fileNames.sampleValidOutputs);
+ TrainModel( imageList, fileNames.sampleTrainOutputs, fileNames.sampleValidOutputs );
// cleanup
if( IsParameterEnabled( "cleanup" ) )
@@ -129,6 +208,15 @@ public:
}
}
+private :
+
+ void UpdatePolygonClassStatisticsParameters()
+ {
+ std::vector<std::string> vectorFileList = GetParameterStringList( "io.vd" );
+ GetInternalApplication( "polystat" )->SetParameterString( "vec", vectorFileList[0], false );
+ UpdateInternalParameters( "polystat" );
+ }
+
};
}
diff --git a/Modules/Applications/AppClassification/app/otbTrainImagesClustering.cxx b/Modules/Applications/AppClassification/app/otbTrainImagesClustering.cxx
deleted file mode 100644
index 9d819e01ecab06ce4b268110bc6bb53edd974198..0000000000000000000000000000000000000000
--- a/Modules/Applications/AppClassification/app/otbTrainImagesClustering.cxx
+++ /dev/null
@@ -1,162 +0,0 @@
-#include "otbTrainImagesBase.h"
-
-namespace otb
-{
-namespace Wrapper
-{
-
-class TrainImagesClustering : public TrainImagesBase
-{
-public:
- typedef TrainImagesClustering Self;
- typedef TrainImagesBase Superclass;
- typedef itk::SmartPointer<Self> Pointer;
- typedef itk::SmartPointer<const Self> ConstPointer;
- itkNewMacro( Self )
- itkTypeMacro( Self, Superclass )
-
- void DoInit() ITK_OVERRIDE
- {
- SetName( "TrainImagesClustering" );
- SetDescription( "Train a classifier from multiple pairs of images and optional input training vector data." );
-
- // Documentation
- SetDocName( "Train a classifier from multiple images" );
- SetDocLongDescription( "TODO" );
- SetDocLimitations( "None" );
- SetDocAuthors( "OTB-Team" );
- SetDocSeeAlso( "OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html " );
-
- AddDocTag( Tags::Learning );
-
- ClearApplications();
- InitIO();
- InitSampling();
- InitClassification( false );
-
- AddParameter( ParameterType_Float, "sample.percent", "Percentage of samples extract in images for "
- "training and validation when only images are provided." );
- SetParameterDescription( "sample.percent", "Percentage of samples extract in images for "
- "training and validation when only images are provided. This parameter is disable when vector data are provided" );
- SetDefaultParameterFloat( "sample.percent", 1.0 );
- SetMinimumParameterFloatValue( "sample.percent", 0.0 );
- SetMaximumParameterFloatValue( "sample.percent", 1.0 );
-
- // Doc example parameter settings
- SetDocExampleParameterValue( "io.il", "QB_1_ortho.tif" );
- SetDocExampleParameterValue( "io.vd", "VectorData_QB1.shp" );
- SetDocExampleParameterValue( "io.imstat", "EstimateImageStatisticsQB1.xml" );
- SetDocExampleParameterValue( "sample.mv", "100" );
- SetDocExampleParameterValue( "sample.mt", "100" );
- SetDocExampleParameterValue( "sample.vtr", "0.5" );
- SetDocExampleParameterValue( "sample.vfn", "Class" );
- SetDocExampleParameterValue( "classifier", "sharkkm" );
- SetDocExampleParameterValue( "classifier.sharkkm.k", "2" );
- SetDocExampleParameterValue( "io.out", "sharkKMModelQB1.txt" );
- }
-
- void DoUpdateParameters() ITK_OVERRIDE
- {
- if( HasValue( "io.vd" ) )
- {
- MandatoryOff( "sample.percent" );
- UpdatePolygonClassStatisticsParameters();
- }
- else
- {
- MandatoryOn( "sample.percent" );
- }
- }
-
- void DoExecute() ITK_OVERRIDE
- {
- TrainFileNamesHandler fileNames;
- FloatVectorImageListType *imageList = GetParameterImageList( "io.il" );
- bool HasInputVector = IsParameterEnabled( "io.vd" ) && HasValue( "io.vd" );
- std::vector<std::string> vectorFileList = GetParameterStringList( "io.vd" );
-
-
- unsigned long nbInputs = imageList->Size();
-
- if( !vectorFileList.empty() && nbInputs > vectorFileList.size() )
- {
- otbAppLogFATAL( "Missing input vector data files to match number of images (" << nbInputs << ")." );
- }
-
- // check if validation vectors are given
- std::vector<std::string> validationVectorFileList;
- bool dedicatedValidation = false;
- if( IsParameterEnabled( "io.valid" ) && HasValue( "io.valid" ) )
- {
- validationVectorFileList = GetParameterStringList( "io.valid" );
- if( nbInputs > validationVectorFileList.size() )
- {
- otbAppLogFATAL( "Missing validation vector data files to match number of images (" << nbInputs << ")." );
- }
-
- dedicatedValidation = true;
- }
-
- fileNames.CreateTemporaryFileNames( GetParameterString( "io.out" ), nbInputs, dedicatedValidation );
-
- // Compute final maximum sampling rates for both training and validation samples
- SamplingRates rates = ComputeFinalMaximumSamplingRates( dedicatedValidation );
-
- if( HasInputVector )
- {
- // Select and Extract samples for training with computed statistics and rates
- ComputePolygonStatistics( imageList, vectorFileList, fileNames.polyStatTrainOutputs );
- ComputeSamplingRate( fileNames.polyStatTrainOutputs, fileNames.rateTrainOut, rates.fmt );
- SelectAndExtractTrainSamples( fileNames, imageList, vectorFileList, SamplingStrategy::CLASS );
- }
- else
- {
- SelectAndExtractTrainSamples( fileNames, imageList, vectorFileList, SamplingStrategy::GEOMETRIC, "fid" );
- }
-
- // Select and Extract samples for validation with computed statistics and rates.
- // Validation samples could be empty if sample.vrt == 0 and if no dedicated validation are provided
- // If no dedicated validation is provided the training is split corresponding to the sample.vtr parameter
- // In this case if no vector data have been provided, the training rates and statistics are computed
- // on the selection and extraction training result.
- if( dedicatedValidation )
- {
- ComputePolygonStatistics( imageList, validationVectorFileList, fileNames.polyStatValidOutputs );
- ComputeSamplingRate( fileNames.polyStatValidOutputs, fileNames.rateValidOut, rates.fmv );
- }
- else if(!HasInputVector)
- {
- ComputePolygonStatistics( imageList, fileNames.sampleOutputs, fileNames.polyStatTrainOutputs );
- ComputeSamplingRate( fileNames.polyStatTrainOutputs, fileNames.rateTrainOut, rates.fmt );
- }
-
-
- // Extract or split validation vector data.
- SelectAndExtractValidationSamples( fileNames, imageList, validationVectorFileList );
-
- // Then train the model with extracted samples
- TrainModel( imageList, fileNames.sampleTrainOutputs, fileNames.sampleValidOutputs );
-
- // cleanup
- if( IsParameterEnabled( "cleanup" ) )
- {
- otbAppLogINFO( <<"Final clean-up ..." );
- fileNames.clear();
- }
- }
-
-private :
-
- void UpdatePolygonClassStatisticsParameters()
- {
- std::vector<std::string> vectorFileList = GetParameterStringList( "io.vd" );
- GetInternalApplication( "polystat" )->SetParameterString( "vec", vectorFileList[0], false );
- UpdateInternalParameters( "polystat" );
- }
-
-};
-
-}
-}
-
-OTB_APPLICATION_EXPORT( otb::Wrapper::TrainImagesClustering )
\ No newline at end of file
diff --git a/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx b/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
index 9e26e768962bcc06761a0e6a92c49c91353026e0..6be11377009ec2085cf3f211d76696b4a75c578a 100644
--- a/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
+++ b/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
@@ -45,37 +45,10 @@ public:
typedef ConfusionMatrixCalculatorType::MapOfIndicesType MapOfIndicesType;
typedef ConfusionMatrixCalculatorType::ClassLabelType ClassLabelType;
-protected :
- TrainVectorClassifier() : TrainVectorBase()
- {
- m_ClassifierCategory = Supervised;
- }
-
private:
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( " " );
-
- // 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" );
-
+ // Nothing to do here
}
void DoTrainUpdateParameters()
@@ -86,46 +59,36 @@ private:
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 )
+ if (GetClassifierCategory() == Supervised)
{
- otbAppLogFATAL( << "No field has been selected for data labelling!" );
+ featuresInfo.SetClassFieldNames( GetChoiceNames( "cfield" ), GetSelectedItems( "cfield" ) );
+
+ if( featuresInfo.m_SelectedCFieldIdx.empty() )
+ {
+ otbAppLogFATAL( << "No field has been selected for data labelling!" );
+ }
}
}
void DoAfterTrainExecute()
{
- ConfusionMatrixCalculatorType::Pointer confMatCalc = ComputeConfusionmatrix( predictedList,
- classificationListSamples.labeledListSample );
- WriteConfusionMatrix( confMatCalc );
- }
-
- 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 )
+ if (GetClassifierCategory() == Supervised)
{
- performanceSample.listSample = validationListSamples.listSample;
- performanceSample.labeledListSample = validationListSamples.labeledListSample;
+ ConfusionMatrixCalculatorType::Pointer confMatCalc = ComputeConfusionMatrix( predictedList,
+ classificationListSamples.labeledListSample );
+ WriteConfusionMatrix( confMatCalc );
}
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;
+ // TODO Compute Contingency Table
}
-
- return performanceSample;
}
+
ConfusionMatrixCalculatorType::Pointer
- ComputeConfusionmatrix(const TargetListSampleType::Pointer &predictedListSample,
+ ComputeConfusionMatrix(const TargetListSampleType::Pointer &predictedListSample,
const TargetListSampleType::Pointer &performanceLabeledListSample)
{
ConfusionMatrixCalculatorType::Pointer confMatCalc = ConfusionMatrixCalculatorType::New();
diff --git a/Modules/Applications/AppClassification/app/otbTrainVectorClustering.cxx b/Modules/Applications/AppClassification/app/otbTrainVectorClustering.cxx
deleted file mode 100644
index 596dbef867bdc828e8a7023f59dc7573139b8e4d..0000000000000000000000000000000000000000
--- a/Modules/Applications/AppClassification/app/otbTrainVectorClustering.cxx
+++ /dev/null
@@ -1,86 +0,0 @@
-/*=========================================================================
- 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"
-
-namespace otb
-{
-namespace Wrapper
-{
-
-class TrainVectorClustering : public TrainVectorBase
-{
-public:
- typedef TrainVectorClustering 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;
-
-protected :
- TrainVectorClustering() : TrainVectorBase()
- {
- m_ClassifierCategory = Unsupervised;
- }
-
-private:
- void DoTrainInit()
- {
- 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.out", "kmeansModel.txt" );
- SetDocExampleParameterValue( "feat", "perimeter width area" );
-
- }
-
- void DoTrainUpdateParameters()
- {
- // Nothing to do here
- }
-
- void DoBeforeTrainExecute()
- {
- // Nothing to do here
- }
-
- void DoAfterTrainExecute()
- {
- // Nothing to do here
- }
-
-
-
-};
-}
-}
-
-OTB_APPLICATION_EXPORT( otb::Wrapper::TrainVectorClustering )
diff --git a/Modules/Applications/AppClassification/include/otbLearningApplicationBase.h b/Modules/Applications/AppClassification/include/otbLearningApplicationBase.h
index 9f8bbcdf7b005982f1ff754c07c4220d5565a053..f7a7c3e65441eb83ed3d0a9063aea7cc68d5a490 100644
--- a/Modules/Applications/AppClassification/include/otbLearningApplicationBase.h
+++ b/Modules/Applications/AppClassification/include/otbLearningApplicationBase.h
@@ -142,7 +142,23 @@ public:
typedef otb::SharkRandomForestsMachineLearningModel<InputValueType, OutputValueType> SharkRandomForestType;
typedef otb::SharkKMeansMachineLearningModel<InputValueType, OutputValueType> SharkKMeansType;
#endif
-
+
+ itkGetConstReferenceMacro(SupervisedClassifier, std::vector<std::string>);
+ itkGetConstReferenceMacro(UnsupervisedClassifier, std::vector<std::string>);
+
+
+ enum ClassifierCategory{
+ Supervised,
+ Unsupervised
+ };
+
+ /**
+ * Retrieve the classifier category (supervisde or unsupervised)
+ * based on the select algorithm from the classifier choice.
+ * @return ClassifierCategory the classifier category
+ */
+ ClassifierCategory GetClassifierCategory();
+
protected:
LearningApplicationBase();
@@ -162,28 +178,24 @@ protected:
/** Init method that creates all the parameters for machine learning models */
void DoInit() ITK_OVERRIDE;
+ /** Init method that creates all the parameters for machine learning models */
+ void DoUpdateParameters() ITK_OVERRIDE;
+
/** Flag to switch between classification and regression mode.
* False by default, child classes may change it in their constructor */
bool m_RegressionFlag;
- /** enum use to selected classifier category */
- enum ClassifierCategory {
- Supervised,
- Unsupervised
- };
-
- /** Enum to switch between unsupervised or supervised classification.
- * Supervised by default, child classes may change it in their constructor */
- ClassifierCategory m_ClassifierCategory;
private:
/** Specific Init and Train methods for each machine learning model */
/** Init Parameters for Supervised Classifier */
void InitSupervisedClassifierParams();
+ std::vector<std::string> m_SupervisedClassifier;
/** Init Parameters for Unsupervised Classifier */
void InitUnsupervisedClassifierParams();
+ std::vector<std::string> m_UnsupervisedClassifier;
//@{
#ifdef OTB_USE_LIBSVM
diff --git a/Modules/Applications/AppClassification/include/otbLearningApplicationBase.txx b/Modules/Applications/AppClassification/include/otbLearningApplicationBase.txx
index 1b75305a570de4a3efab5666c6131fe7e52563bf..adbe6db9ca2cdbc2a00b6f9a193e0f6f9c892d2f 100644
--- a/Modules/Applications/AppClassification/include/otbLearningApplicationBase.txx
+++ b/Modules/Applications/AppClassification/include/otbLearningApplicationBase.txx
@@ -28,7 +28,8 @@ namespace Wrapper
template <class TInputValue, class TOutputValue>
LearningApplicationBase<TInputValue,TOutputValue>
-::LearningApplicationBase() : m_RegressionFlag(false), m_ClassifierCategory(Supervised)
+::LearningApplicationBase() : m_RegressionFlag(false)
+
{
}
@@ -50,17 +51,36 @@ LearningApplicationBase<TInputValue,TOutputValue>
AddParameter(ParameterType_Choice, "classifier", "Classifier to use for the training");
SetParameterDescription("classifier", "Choice of the classifier to use for the training.");
- switch(m_ClassifierCategory)
- {
- case Unsupervised:
- InitUnsupervisedClassifierParams();
- break;
- case Supervised:
- default :
- InitSupervisedClassifierParams();
- }
+ AddParameter(ParameterType_Choice, "category", "Type of classifier use for the training (supervised or unsupervised");
+ SetParameterDescription("category", "Choice of the classifier type to use for the training, "
+ "choice is supervised or unsupervised.");
+
+ InitSupervisedClassifierParams();
+ m_SupervisedClassifier = GetChoiceKeys("classifier");
+
+ InitUnsupervisedClassifierParams();
+ std::vector<std::string> allClassifier = GetChoiceKeys("classifier");
+ m_UnsupervisedClassifier.assign(allClassifier.begin() + m_SupervisedClassifier.size(), allClassifier.end());
}
+template <class TInputValue, class TOutputValue>
+typename LearningApplicationBase<TInputValue,TOutputValue>::ClassifierCategory
+LearningApplicationBase<TInputValue,TOutputValue>
+::GetClassifierCategory()
+{
+ bool foundUnsupervised =
+ std::find(m_UnsupervisedClassifier.begin(), m_UnsupervisedClassifier.end(),
+ GetParameterString("classifier")) != m_UnsupervisedClassifier.end();
+ return foundUnsupervised ? Unsupervised : Supervised;
+}
+
+template <class TInputValue, class TOutputValue>
+void
+LearningApplicationBase<TInputValue,TOutputValue>
+::DoUpdateParameters()
+{
+};
+
template <class TInputValue, class TOutputValue>
void
LearningApplicationBase<TInputValue,TOutputValue>
diff --git a/Modules/Applications/AppClassification/include/otbTrainImagesBase.h b/Modules/Applications/AppClassification/include/otbTrainImagesBase.h
index 0921696cbb615e6afec3dc01ca6c4b5b46c1a20f..be9ad1425ff83bb2f6c8a7996a95031971212d26 100644
--- a/Modules/Applications/AppClassification/include/otbTrainImagesBase.h
+++ b/Modules/Applications/AppClassification/include/otbTrainImagesBase.h
@@ -1,23 +1,26 @@
-/*=========================================================================
- 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.
-
- =========================================================================*/
+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
#ifndef otbTrainImagesBase_h
#define otbTrainImagesBase_h
-
+#include "otbTrainVectorBase.h"
#include "otbVectorDataFileWriter.h"
#include "otbWrapperCompositeApplication.h"
#include "otbWrapperApplicationFactory.h"
@@ -32,6 +35,15 @@ namespace otb
namespace Wrapper
{
+/** \class TrainImagesBase
+ * \brief Base class for the TrainImagesBaseClassifier and Clustering
+ *
+ * This class intends to hold common input/output parameters and
+ * composite application connection for both supervised and unsupervised
+ * model training.
+ *
+ * \ingroup OTBAppClassification
+ */
class TrainImagesBase : public CompositeApplication
{
public:
@@ -55,131 +67,24 @@ protected:
{
CLASS, GEOMETRIC
};
-
struct SamplingRates;
-
class TrainFileNamesHandler;
- void InitIO()
- {
- //Group IO
- AddParameter( ParameterType_Group, "io", "Input and output data" );
- SetParameterDescription( "io", "This group of parameters allows setting input and output data." );
-
- AddParameter( ParameterType_InputImageList, "io.il", "Input Image List" );
- SetParameterDescription( "io.il", "A list of input images." );
- AddParameter( ParameterType_InputVectorDataList, "io.vd", "Input Vector Data List" );
- SetParameterDescription( "io.vd", "A list of vector data to select the training samples." );
-
- AddParameter( ParameterType_Empty, "cleanup", "Temporary files cleaning" );
- EnableParameter( "cleanup" );
- SetParameterDescription( "cleanup",
- "If activated, the application will try to clean all temporary files it created" );
- MandatoryOff( "cleanup" );
- }
-
- void InitSampling()
- {
- AddApplication( "PolygonClassStatistics", "polystat", "Polygon analysis" );
- AddApplication( "MultiImageSamplingRate", "rates", "Sampling rates" );
- AddApplication( "SampleSelection", "select", "Sample selection" );
- AddApplication( "SampleExtraction", "extraction", "Sample extraction" );
-
- // Sampling settings
- AddParameter( ParameterType_Group, "sample", "Training and validation samples parameters" );
- SetParameterDescription( "sample",
- "This group of parameters allows you to set training and validation sample lists parameters." );
- AddParameter( ParameterType_Int, "sample.mt", "Maximum training sample size per class" );
- SetDefaultParameterInt( "sample.mt", 1000 );
- SetParameterDescription( "sample.mt", "Maximum size per class (in pixels) of "
- "the training sample list (default = 1000) (no limit = -1). If equal to -1,"
- " then the maximal size of the available training sample list per class "
- "will be equal to the surface area of the smallest class multiplied by the"
- " training sample ratio." );
- AddParameter( ParameterType_Int, "sample.mv", "Maximum validation sample size per class" );
- SetDefaultParameterInt( "sample.mv", 1000 );
- SetParameterDescription( "sample.mv", "Maximum size per class (in pixels) of "
- "the validation sample list (default = 1000) (no limit = -1). If equal to -1,"
- " then the maximal size of the available validation sample list per class "
- "will be equal to the surface area of the smallest class multiplied by the "
- "validation sample ratio." );
- AddParameter( ParameterType_Int, "sample.bm", "Bound sample number by minimum" );
- SetDefaultParameterInt( "sample.bm", 1 );
- SetParameterDescription( "sample.bm", "Bound the number of samples for each "
- "class by the number of available samples by the smaller class. Proportions "
- "between training and validation are respected. Default is true (=1)." );
- AddParameter( ParameterType_Float, "sample.vtr", "Training and validation sample ratio" );
- SetParameterDescription( "sample.vtr", "Ratio between training and validation samples (0.0 = all training, 1.0 = "
- "all validation) (default = 0.5)." );
- SetParameterFloat( "sample.vtr", 0.5, false );
- SetMaximumParameterFloatValue( "sample.vtr", 1.0 );
- SetMinimumParameterFloatValue( "sample.vtr", 0.0 );
-
- ShareSamplingParameters();
- ConnectSamplingParameters();
- }
-
- void ShareSamplingParameters()
- {
- // hide sampling parameters
- //ShareParameter("sample.strategy","rates.strategy");
- //ShareParameter("sample.mim","rates.mim");
- ShareParameter( "ram", "polystat.ram" );
- ShareParameter( "elev", "polystat.elev" );
- ShareParameter( "sample.vfn", "polystat.field" );
- }
-
- void ConnectSamplingParameters()
- {
- Connect( "extraction.field", "polystat.field" );
- Connect( "extraction.layer", "polystat.layer" );
-
- Connect( "select.ram", "polystat.ram" );
- Connect( "extraction.ram", "polystat.ram" );
-
- Connect( "select.field", "polystat.field" );
- Connect( "select.layer", "polystat.layer" );
- Connect( "select.elev", "polystat.elev" );
-
- Connect( "extraction.in", "select.in" );
- Connect( "extraction.vec", "select.out" );
- }
-
- void InitClassification(bool supervised)
- {
- if( supervised )
- AddApplication( "TrainVectorClassifier", "training", "Model training" );
- else
- AddApplication( "TrainVectorClustering", "training", "Model training" );
-
- AddParameter( ParameterType_InputVectorDataList, "io.valid", "Validation Vector Data List" );
- SetParameterDescription( "io.valid", "A list of vector data to select the training samples." );
- MandatoryOff( "io.valid" );
-
- if( !supervised )
- MandatoryOff( "io.vd" );
-
- ShareClassificationParams( supervised );
- ConnectClassificationParams();
- };
-
- void ShareClassificationParams(bool supervised)
- {
- ShareParameter( "io.imstat", "training.io.stats" );
- ShareParameter( "io.out", "training.io.out" );
-
- ShareParameter( "classifier", "training.classifier" );
- ShareParameter( "rand", "training.rand" );
+ /**
+ * Initialize all the input and output parameter used for the train images
+ */
+ void InitIO();
- if( supervised )
- ShareParameter( "io.confmatout", "training.io.confmatout" );
- }
+ /**
+ * Initialize sampling related application and parameters
+ */
+ void InitSampling();
- void ConnectClassificationParams()
- {
- Connect( "training.cfield", "polystat.field" );
- Connect( "select.rand", "training.rand" );
- }
+ void ShareSamplingParameters();
+ void ConnectSamplingParameters();
+ void InitClassification();
+ void ShareClassificationParams();
+ void ConnectClassificationParams();
/**
* Compute polygon statistics given provided strategy with PolygonClassStatistics class
@@ -188,71 +93,14 @@ protected:
* \param statisticsFileNames list of out
*/
void ComputePolygonStatistics(FloatVectorImageListType *imageList, const std::vector<std::string> &vectorFileNames,
- const std::vector<std::string> &statisticsFileNames)
- {
- unsigned int nbImages = static_cast<unsigned int>(imageList->Size());
- for( unsigned int i = 0; i < nbImages; i++ )
- {
- GetInternalApplication( "polystat" )->SetParameterInputImage( "in", imageList->GetNthElement( i ) );
- GetInternalApplication( "polystat" )->SetParameterString( "vec", vectorFileNames[i], false );
- GetInternalApplication( "polystat" )->SetParameterString( "out", statisticsFileNames[i], false );
- ExecuteInternal( "polystat" );
- }
- }
+ const std::vector<std::string> &statisticsFileNames);
/**
* Compute final maximum training and validation
* \param dedicatedValidation
* \return SamplingRates final maximum training and final maximum validation
*/
- SamplingRates ComputeFinalMaximumSamplingRates(bool dedicatedValidation)
- {
- SamplingRates rates;
- GetInternalApplication( "rates" )->SetParameterString( "mim", "proportional", false );
- double vtr = GetParameterFloat( "sample.vtr" );
- long mt = GetParameterInt( "sample.mt" );
- long mv = GetParameterInt( "sample.mv" );
- // compute final maximum training and final maximum validation
- // By default take all samples (-1 means all samples)
- rates.fmt = -1;
- rates.fmv = -1;
- if( GetParameterInt( "sample.bm" ) == 0 )
- {
- if( dedicatedValidation )
- {
- // fmt and fmv will be used separately
- rates.fmt = mt;
- rates.fmv = mv;
- if( mt > -1 && mv <= -1 && vtr < 0.99999 )
- {
- rates.fmv = static_cast<long>(( double ) mt * vtr / ( 1.0 - vtr ));
- }
- if( mt <= -1 && mv > -1 && vtr > 0.00001 )
- {
- rates.fmt = static_cast<long>(( double ) mv * ( 1.0 - vtr ) / vtr);
- }
- }
- else
- {
- // only fmt will be used for both training and validation samples
- // So we try to compute the total number of samples given input
- // parameters mt, mv and vtr.
- if( mt > -1 && mv > -1 )
- {
- rates.fmt = mt + mv;
- }
- if( mt > -1 && mv <= -1 && vtr < 0.99999 )
- {
- rates.fmt = static_cast<long>(( double ) mt / ( 1.0 - vtr ));
- }
- if( mt <= -1 && mv > -1 && vtr > 0.00001 )
- {
- rates.fmt = static_cast<long>(( double ) mv / vtr);
- }
- }
- }
- return rates;
- }
+ SamplingRates ComputeFinalMaximumSamplingRates(bool dedicatedValidation);
/**
@@ -262,31 +110,9 @@ protected:
* \param maximum final maximum value computed by ComputeFinalMaximumSamplingRates
* \sa ComputeFinalMaximumSamplingRates
*/
- void ComputeSamplingRate(const std::vector<std::string> &statisticsFileNames, const std::string &ratesFileName,
- long maximum)
- {
- // Sampling rates
- GetInternalApplication( "rates" )->SetParameterStringList( "il", statisticsFileNames, false );
- GetInternalApplication( "rates" )->SetParameterString( "out", ratesFileName, false );
- if( GetParameterInt( "sample.bm" ) != 0 )
- {
- GetInternalApplication( "rates" )->SetParameterString( "strategy", "smallest", false );
- }
- else
- {
- if( maximum > -1 )
- {
- GetInternalApplication( "rates" )->SetParameterString( "strategy", "constant", false );
- GetInternalApplication( "rates" )->SetParameterInt( "strategy.constant.nb", static_cast<int>(maximum), false );
- }
- else
- {
- GetInternalApplication( "rates" )->SetParameterString( "strategy", "all", false );
- }
- }
- ExecuteInternal( "rates" );
- }
-
+ void ComputeSamplingRate(const std::vector<std::string> &statisticsFileNames,
+ const std::string &ratesFileName,
+ long maximum);
/**
* Train the model with training and optional validation data samples
* \param imageList list of input images
@@ -294,26 +120,7 @@ protected:
* \param sampleValidationFileNames file names of the validation sample
*/
void TrainModel(FloatVectorImageListType *imageList, const std::vector<std::string> &sampleTrainFileNames,
- const std::vector<std::string> &sampleValidationFileNames)
- {
- GetInternalApplication( "training" )->SetParameterStringList( "io.vd", sampleTrainFileNames, false );
- if( !sampleValidationFileNames.empty() )
- GetInternalApplication( "training" )->SetParameterStringList( "valid.vd", sampleValidationFileNames, false );
-
- UpdateInternalParameters( "training" );
- // set field names
- FloatVectorImageType::Pointer image = imageList->GetNthElement( 0 );
- unsigned int nbBands = image->GetNumberOfComponentsPerPixel();
- std::vector<std::string> selectedNames;
- for( unsigned int i = 0; i < nbBands; i++ )
- {
- std::ostringstream oss;
- oss << i;
- selectedNames.push_back( "value_" + oss.str() );
- }
- GetInternalApplication( "training" )->SetParameterStringList( "feat", selectedNames, false );
- ExecuteInternal( "training" );
- }
+ const std::vector<std::string> &sampleValidationFileNames);
/**
* Select samples by class or by geographic strategy
@@ -326,143 +133,60 @@ protected:
*/
void SelectAndExtractSamples(FloatVectorImageType *image, std::string vectorFileName, std::string sampleFileName,
std::string statisticsFileName, std::string ratesFileName, SamplingStrategy strategy,
- std::string selectedField = "")
- {
- GetInternalApplication( "select" )->SetParameterInputImage( "in", image );
- GetInternalApplication( "select" )->SetParameterString( "out", sampleFileName, false );
-
- // Change the selection strategy based on selected sampling strategy
- switch( strategy )
- {
- case GEOMETRIC:
- GetInternalApplication( "select" )->SetParameterString( "sampler", "random", false );
- GetInternalApplication( "select" )->SetParameterString( "strategy", "percent", false );
- GetInternalApplication( "select" )->SetParameterFloat( "strategy.percent.p",
- GetParameterFloat( "sample.percent" ), false );
- break;
- case CLASS:
- default:
- GetInternalApplication( "select" )->SetParameterString( "vec", vectorFileName, false );
- GetInternalApplication( "select" )->SetParameterString( "instats", statisticsFileName, false );
- GetInternalApplication( "select" )->SetParameterString( "sampler", "periodic", false );
- GetInternalApplication( "select" )->SetParameterInt( "sampler.periodic.jitter", 50 );
- GetInternalApplication( "select" )->SetParameterString( "strategy", "byclass", false );
- GetInternalApplication( "select" )->SetParameterString( "strategy.byclass.in", ratesFileName, false );
- break;
- }
-
- // select sample positions
- ExecuteInternal( "select" );
-
- GetInternalApplication( "extraction" )->SetParameterString( "vec", sampleFileName, false );
- UpdateInternalParameters( "extraction" );
- if( !selectedField.empty() )
- GetInternalApplication( "extraction" )->SetParameterString( "field", selectedField, false );
-
- GetInternalApplication( "extraction" )->SetParameterString( "outfield", "prefix", false );
- GetInternalApplication( "extraction" )->SetParameterString( "outfield.prefix.name", "value_", false );
-
- // extract sample descriptors
- ExecuteInternal( "extraction" );
- }
-
+ std::string selectedField = "");
/**
* Select and extract samples with the SampleSelection and SampleExtraction application.
+ * \param fileNames
+ * \param imageList
+ * \param vectorFileNames
+ * \param strategy the strategy used for selection (by class or with geometry)
+ * \param selectedFieldName
*/
void SelectAndExtractTrainSamples(const TrainFileNamesHandler &fileNames, FloatVectorImageListType *imageList,
std::vector<std::string> vectorFileNames, SamplingStrategy strategy,
- std::string selectedFieldName = "")
- {
-
- for( unsigned int i = 0; i < imageList->Size(); ++i )
- {
- std::string vectorFileName = vectorFileNames.empty() ? "" : vectorFileNames[i];
- SelectAndExtractSamples( imageList->GetNthElement( i ), vectorFileName, fileNames.sampleOutputs[i],
- fileNames.polyStatTrainOutputs[i], fileNames.ratesTrainOutputs[i], strategy,
- selectedFieldName );
- }
- }
+ std::string selectedFieldName = "");
+ /**
+ * Function used to select validation samples based on a defined strategy (geometric in unsupervised mode)
+ * and extract them. With dedicated validation the 'by class' sampling strategy and statistics are used.
+ * Otherwise this function split training to validation samples corresponding to sample.vtr percentage.
+ * or do nothing if this percentage is == 0
+ * \param fileNames
+ * \param imageList
+ * \param validationVectorFileList optional validation vector file for each images
+ */
void SelectAndExtractValidationSamples(const TrainFileNamesHandler &fileNames, FloatVectorImageListType *imageList,
- const std::vector<std::string> &validationVectorFileList = std::vector<std::string>())
- {
- // In dedicated validation mode the by class sampling strategy and statistics are used.
- // Otherwise simply split training to validation samples corresponding to sample.vtr percentage.
- if( !validationVectorFileList.empty() )
- {
- for( unsigned int i = 0; i < imageList->Size(); ++i )
- {
- SelectAndExtractSamples( imageList->GetNthElement( i ), validationVectorFileList[i],
- fileNames.sampleValidOutputs[i], fileNames.polyStatValidOutputs[i],
- fileNames.ratesValidOutputs[i], SamplingStrategy::CLASS );
- }
- }
- else
- {
- for( unsigned int i = 0; i < imageList->Size(); ++i )
- {
- SplitTrainingAndValidationSamples( imageList->GetNthElement( i ), fileNames.sampleOutputs[i],
- fileNames.sampleTrainOutputs[i], fileNames.sampleValidOutputs[i],
- fileNames.ratesTrainOutputs[i] );
- }
- }
- }
+ const std::vector<std::string> &validationVectorFileList = std::vector<std::string>());
+
+ /**
+ * Function used to split all training samples from all images in a set of training and validation.
+ * \param fileNames
+ * \param imageList
+ * \sa SplitTrainingAndValidationSamples
+ */
+ void SplitTrainingToValidationSamples(const TrainFileNamesHandler &fileNames, FloatVectorImageListType *imageList);
private:
+
+ /**
+ * Function used to split training samples in set of training and validation.
+ * \param image input image
+ * \param sampleFileName the input sample file name
+ * \param sampleTrainFileName the input training file name
+ * \param sampleValidFileName the input validation file name
+ * \param ratesTrainFileName the rates file name
+ */
void SplitTrainingAndValidationSamples(FloatVectorImageType *image, std::string sampleFileName,
std::string sampleTrainFileName, std::string sampleValidFileName,
- std::string ratesTrainFileName)
- {
- // Split between training and validation
- ogr::DataSource::Pointer source = ogr::DataSource::New( sampleFileName, ogr::DataSource::Modes::Read );
- ogr::DataSource::Pointer destTrain = ogr::DataSource::New( sampleTrainFileName, ogr::DataSource::Modes::Overwrite );
- ogr::DataSource::Pointer destValid = ogr::DataSource::New( sampleValidFileName, ogr::DataSource::Modes::Overwrite );
- // read sampling rates from ratesTrainOutputs
- SamplingRateCalculator::Pointer rateCalculator = SamplingRateCalculator::New();
- rateCalculator->Read( ratesTrainFileName );
- // Compute sampling rates for train and valid
- const MapRateType &inputRates = rateCalculator->GetRatesByClass();
- MapRateType trainRates;
- MapRateType validRates;
- otb::SamplingRateCalculator::TripletType tpt;
- for( MapRateType::const_iterator it = inputRates.begin(); it != inputRates.end(); ++it )
- {
- double vtr = GetParameterFloat( "sample.vtr" );
- unsigned long total = std::min( it->second.Required, it->second.Tot );
- unsigned long neededValid = static_cast<unsigned long>(( double ) total * vtr );
- unsigned long neededTrain = total - neededValid;
- tpt.Tot = total;
- tpt.Required = neededTrain;
- tpt.Rate = ( 1.0 - vtr );
- trainRates[it->first] = tpt;
- tpt.Tot = neededValid;
- tpt.Required = neededValid;
- tpt.Rate = 1.0;
- validRates[it->first] = tpt;
- }
-
- // Use an otb::OGRDataToSamplePositionFilter with 2 outputs
- PeriodicSamplerType::SamplerParameterType param;
- param.Offset = 0;
- param.MaxJitter = 0;
- PeriodicSamplerType::Pointer splitter = PeriodicSamplerType::New();
- splitter->SetInput( image );
- splitter->SetOGRData( source );
- splitter->SetOutputPositionContainerAndRates( destTrain, trainRates, 0 );
- splitter->SetOutputPositionContainerAndRates( destValid, validRates, 1 );
- splitter->SetFieldName( this->GetParameterStringList( "sample.vfn" )[0] );
- splitter->SetLayerIndex( 0 );
- splitter->SetOriginFieldName( std::string( "" ) );
- splitter->SetSamplerParameters( param );
- splitter->GetStreamer()->SetAutomaticTiledStreaming( static_cast<unsigned int>(this->GetParameterInt( "ram" )) );
- AddProcess( splitter->GetStreamer(), "Split samples between training and validation..." );
- splitter->Update();
- }
+ std::string ratesTrainFileName);
protected:
+ /** Base use for training, this allow to know if the choosed classifier is supervised or unsupervised */
+ TrainVectorBase* trainVectorBase;
+
struct SamplingRates
{
long int fmt;
@@ -473,6 +197,7 @@ protected:
* \class TrainFileNamesHandler
* This class is used to store file names requires for the application's input and output.
* And to clear temporary files generated by the applications
+ * \ingroup OTBAppClassification
*/
class TrainFileNamesHandler
{
@@ -578,5 +303,8 @@ protected:
} // end namespace Wrapper
} // end namespace otb
+#ifndef OTB_MANUAL_INSTANTIATION
+#include "otbTrainImagesBase.txx"
+#endif
#endif //otbTrainImagesBase_h
diff --git a/Modules/Applications/AppClassification/include/otbTrainImagesBase.txx b/Modules/Applications/AppClassification/include/otbTrainImagesBase.txx
new file mode 100644
index 0000000000000000000000000000000000000000..a0b881ee58c6af146c3bf3b911f0bccfc343e529
--- /dev/null
+++ b/Modules/Applications/AppClassification/include/otbTrainImagesBase.txx
@@ -0,0 +1,403 @@
+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef otbTrainImagesBase_txx
+#define otbTrainImagesBase_txx
+
+#include "otbTrainImagesBase.h"
+
+namespace otb
+{
+namespace Wrapper
+{
+void TrainImagesBase::InitIO()
+{
+ //Group IO
+ AddParameter( ParameterType_Group, "io", "Input and output data" );
+ SetParameterDescription( "io", "This group of parameters allows setting input and output data." );
+
+ AddParameter( ParameterType_InputImageList, "io.il", "Input Image List" );
+ SetParameterDescription( "io.il", "A list of input images." );
+ AddParameter( ParameterType_InputVectorDataList, "io.vd", "Input Vector Data List" );
+ SetParameterDescription( "io.vd", "A list of vector data to select the training samples." );
+ MandatoryOff( "io.vd" );
+
+ AddParameter( ParameterType_Empty, "cleanup", "Temporary files cleaning" );
+ EnableParameter( "cleanup" );
+ SetParameterDescription( "cleanup",
+ "If activated, the application will try to clean all temporary files it created" );
+ MandatoryOff( "cleanup" );
+}
+
+void TrainImagesBase::InitSampling()
+{
+ AddApplication( "PolygonClassStatistics", "polystat", "Polygon analysis" );
+ AddApplication( "MultiImageSamplingRate", "rates", "Sampling rates" );
+ AddApplication( "SampleSelection", "select", "Sample selection" );
+ AddApplication( "SampleExtraction", "extraction", "Sample extraction" );
+
+ // Sampling settings
+ AddParameter( ParameterType_Group, "sample", "Training and validation samples parameters" );
+ SetParameterDescription( "sample",
+ "This group of parameters allows you to set training and validation sample lists parameters." );
+ AddParameter( ParameterType_Int, "sample.mt", "Maximum training sample size per class" );
+ SetDefaultParameterInt( "sample.mt", 1000 );
+ SetParameterDescription( "sample.mt", "Maximum size per class (in pixels) of "
+ "the training sample list (default = 1000) (no limit = -1). If equal to -1,"
+ " then the maximal size of the available training sample list per class "
+ "will be equal to the surface area of the smallest class multiplied by the"
+ " training sample ratio." );
+ AddParameter( ParameterType_Int, "sample.mv", "Maximum validation sample size per class" );
+ SetDefaultParameterInt( "sample.mv", 1000 );
+ SetParameterDescription( "sample.mv", "Maximum size per class (in pixels) of "
+ "the validation sample list (default = 1000) (no limit = -1). If equal to -1,"
+ " then the maximal size of the available validation sample list per class "
+ "will be equal to the surface area of the smallest class multiplied by the "
+ "validation sample ratio." );
+ AddParameter( ParameterType_Int, "sample.bm", "Bound sample number by minimum" );
+ SetDefaultParameterInt( "sample.bm", 1 );
+ SetParameterDescription( "sample.bm", "Bound the number of samples for each "
+ "class by the number of available samples by the smaller class. Proportions "
+ "between training and validation are respected. Default is true (=1)." );
+ AddParameter( ParameterType_Float, "sample.vtr", "Training and validation sample ratio" );
+ SetParameterDescription( "sample.vtr", "Ratio between training and validation samples (0.0 = all training, 1.0 = "
+ "all validation) (default = 0.5)." );
+ SetParameterFloat( "sample.vtr", 0.5, false );
+ SetMaximumParameterFloatValue( "sample.vtr", 1.0 );
+ SetMinimumParameterFloatValue( "sample.vtr", 0.0 );
+
+ AddParameter( ParameterType_Float, "sample.percent", "Percentage of sample extract from images" );
+ SetParameterDescription( "sample.percent", "Percentage of sample extract from images for "
+ "training and validation when only images are provided." );
+ SetDefaultParameterFloat( "sample.percent", 1.0 );
+ SetMinimumParameterFloatValue( "sample.percent", 0.0 );
+ SetMaximumParameterFloatValue( "sample.percent", 1.0 );
+
+ ShareSamplingParameters();
+ ConnectSamplingParameters();
+}
+
+void TrainImagesBase::ShareSamplingParameters()
+{
+ // hide sampling parameters
+ //ShareParameter("sample.strategy","rates.strategy");
+ //ShareParameter("sample.mim","rates.mim");
+ ShareParameter( "ram", "polystat.ram" );
+ ShareParameter( "elev", "polystat.elev" );
+ ShareParameter( "sample.vfn", "polystat.field" );
+}
+
+void TrainImagesBase::ConnectSamplingParameters()
+{
+ Connect( "extraction.field", "polystat.field" );
+ Connect( "extraction.layer", "polystat.layer" );
+
+ Connect( "select.ram", "polystat.ram" );
+ Connect( "extraction.ram", "polystat.ram" );
+
+ Connect( "select.field", "polystat.field" );
+ Connect( "select.layer", "polystat.layer" );
+ Connect( "select.elev", "polystat.elev" );
+
+ Connect( "extraction.in", "select.in" );
+ Connect( "extraction.vec", "select.out" );
+}
+
+void TrainImagesBase::InitClassification()
+{
+ AddApplication( "TrainVectorClassifier", "training", "Model training" );
+ trainVectorBase = dynamic_cast<TrainVectorBase*>(GetInternalApplication("training"));
+
+ AddParameter( ParameterType_InputVectorDataList, "io.valid", "Validation Vector Data List" );
+ SetParameterDescription( "io.valid", "A list of vector data to select the training samples." );
+ MandatoryOff( "io.valid" );
+
+ ShareClassificationParams();
+ ConnectClassificationParams();
+};
+
+void TrainImagesBase::ShareClassificationParams()
+{
+ ShareParameter( "io.imstat", "training.io.stats" );
+ ShareParameter( "io.out", "training.io.out" );
+
+ ShareParameter( "classifier", "training.classifier" );
+ ShareParameter( "rand", "training.rand" );
+
+ ShareParameter( "io.confmatout", "training.io.confmatout" );
+}
+
+void TrainImagesBase::ConnectClassificationParams()
+{
+ Connect( "training.cfield", "polystat.field" );
+ Connect( "select.rand", "training.rand" );
+}
+
+void TrainImagesBase::ComputePolygonStatistics(FloatVectorImageListType *imageList,
+ const std::vector<std::string> &vectorFileNames,
+ const std::vector<std::string> &statisticsFileNames)
+{
+ unsigned int nbImages = static_cast<unsigned int>(imageList->Size());
+ for( unsigned int i = 0; i < nbImages; i++ )
+ {
+ GetInternalApplication( "polystat" )->SetParameterInputImage( "in", imageList->GetNthElement( i ) );
+ GetInternalApplication( "polystat" )->SetParameterString( "vec", vectorFileNames[i], false );
+ GetInternalApplication( "polystat" )->SetParameterString( "out", statisticsFileNames[i], false );
+ ExecuteInternal( "polystat" );
+ }
+}
+
+
+TrainImagesBase::SamplingRates TrainImagesBase::ComputeFinalMaximumSamplingRates(bool dedicatedValidation)
+{
+ SamplingRates rates;
+ GetInternalApplication( "rates" )->SetParameterString( "mim", "proportional", false );
+ double vtr = GetParameterFloat( "sample.vtr" );
+ long mt = GetParameterInt( "sample.mt" );
+ long mv = GetParameterInt( "sample.mv" );
+ // compute final maximum training and final maximum validation
+ // By default take all samples (-1 means all samples)
+ rates.fmt = -1;
+ rates.fmv = -1;
+ if( GetParameterInt( "sample.bm" ) == 0 )
+ {
+ if( dedicatedValidation )
+ {
+ // fmt and fmv will be used separately
+ rates.fmt = mt;
+ rates.fmv = mv;
+ if( mt > -1 && mv <= -1 && vtr < 0.99999 )
+ {
+ rates.fmv = static_cast<long>(( double ) mt * vtr / ( 1.0 - vtr ));
+ }
+ if( mt <= -1 && mv > -1 && vtr > 0.00001 )
+ {
+ rates.fmt = static_cast<long>(( double ) mv * ( 1.0 - vtr ) / vtr);
+ }
+ }
+ else
+ {
+ // only fmt will be used for both training and validation samples
+ // So we try to compute the total number of samples given input
+ // parameters mt, mv and vtr.
+ if( mt > -1 && mv > -1 )
+ {
+ rates.fmt = mt + mv;
+ }
+ if( mt > -1 && mv <= -1 && vtr < 0.99999 )
+ {
+ rates.fmt = static_cast<long>(( double ) mt / ( 1.0 - vtr ));
+ }
+ if( mt <= -1 && mv > -1 && vtr > 0.00001 )
+ {
+ rates.fmt = static_cast<long>(( double ) mv / vtr);
+ }
+ }
+ }
+ return rates;
+}
+
+
+void TrainImagesBase::ComputeSamplingRate(const std::vector<std::string> &statisticsFileNames,
+ const std::string &ratesFileName, long maximum)
+{
+ // Sampling rates
+ GetInternalApplication( "rates" )->SetParameterStringList( "il", statisticsFileNames, false );
+ GetInternalApplication( "rates" )->SetParameterString( "out", ratesFileName, false );
+ if( GetParameterInt( "sample.bm" ) != 0 )
+ {
+ GetInternalApplication( "rates" )->SetParameterString( "strategy", "smallest", false );
+ }
+ else
+ {
+ if( maximum > -1 )
+ {
+ GetInternalApplication( "rates" )->SetParameterString( "strategy", "constant", false );
+ GetInternalApplication( "rates" )->SetParameterInt( "strategy.constant.nb", static_cast<int>(maximum), false );
+ }
+ else
+ {
+ GetInternalApplication( "rates" )->SetParameterString( "strategy", "all", false );
+ }
+ }
+ ExecuteInternal( "rates" );
+}
+
+void
+TrainImagesBase::TrainModel(FloatVectorImageListType *imageList, const std::vector<std::string> &sampleTrainFileNames,
+ const std::vector<std::string> &sampleValidationFileNames)
+{
+ GetInternalApplication( "training" )->SetParameterStringList( "io.vd", sampleTrainFileNames, false );
+ if( !sampleValidationFileNames.empty() )
+ GetInternalApplication( "training" )->SetParameterStringList( "valid.vd", sampleValidationFileNames, false );
+
+ UpdateInternalParameters( "training" );
+ // set field names
+ FloatVectorImageType::Pointer image = imageList->GetNthElement( 0 );
+ unsigned int nbBands = image->GetNumberOfComponentsPerPixel();
+ std::vector<std::string> selectedNames;
+ for( unsigned int i = 0; i < nbBands; i++ )
+ {
+ std::ostringstream oss;
+ oss << i;
+ selectedNames.push_back( "value_" + oss.str() );
+ }
+ GetInternalApplication( "training" )->SetParameterStringList( "feat", selectedNames, false );
+ ExecuteInternal( "training" );
+}
+
+void TrainImagesBase::SelectAndExtractSamples(FloatVectorImageType *image, std::string vectorFileName,
+ std::string sampleFileName, std::string statisticsFileName,
+ std::string ratesFileName, SamplingStrategy strategy,
+ std::string selectedField)
+{
+ GetInternalApplication( "select" )->SetParameterInputImage( "in", image );
+ GetInternalApplication( "select" )->SetParameterString( "out", sampleFileName, false );
+
+ // Change the selection strategy based on selected sampling strategy
+ switch( strategy )
+ {
+ case GEOMETRIC:
+ GetInternalApplication( "select" )->SetParameterString( "sampler", "random", false );
+ GetInternalApplication( "select" )->SetParameterString( "strategy", "percent", false );
+ GetInternalApplication( "select" )->SetParameterFloat( "strategy.percent.p",
+ GetParameterFloat( "sample.percent" ), false );
+ break;
+ case CLASS:
+ default:
+ GetInternalApplication( "select" )->SetParameterString( "vec", vectorFileName, false );
+ GetInternalApplication( "select" )->SetParameterString( "instats", statisticsFileName, false );
+ GetInternalApplication( "select" )->SetParameterString( "sampler", "periodic", false );
+ GetInternalApplication( "select" )->SetParameterInt( "sampler.periodic.jitter", 50 );
+ GetInternalApplication( "select" )->SetParameterString( "strategy", "byclass", false );
+ GetInternalApplication( "select" )->SetParameterString( "strategy.byclass.in", ratesFileName, false );
+ break;
+ }
+
+ // select sample positions
+ ExecuteInternal( "select" );
+
+ GetInternalApplication( "extraction" )->SetParameterString( "vec", sampleFileName, false );
+ UpdateInternalParameters( "extraction" );
+ if( !selectedField.empty() )
+ GetInternalApplication( "extraction" )->SetParameterString( "field", selectedField, false );
+
+ GetInternalApplication( "extraction" )->SetParameterString( "outfield", "prefix", false );
+ GetInternalApplication( "extraction" )->SetParameterString( "outfield.prefix.name", "value_", false );
+
+ // extract sample descriptors
+ ExecuteInternal( "extraction" );
+}
+
+
+void TrainImagesBase::SelectAndExtractTrainSamples(const TrainFileNamesHandler &fileNames,
+ FloatVectorImageListType *imageList,
+ std::vector<std::string> vectorFileNames, SamplingStrategy strategy,
+ std::string selectedFieldName)
+{
+
+ for( unsigned int i = 0; i < imageList->Size(); ++i )
+ {
+ std::string vectorFileName = vectorFileNames.empty() ? "" : vectorFileNames[i];
+ SelectAndExtractSamples( imageList->GetNthElement( i ), vectorFileName, fileNames.sampleOutputs[i],
+ fileNames.polyStatTrainOutputs[i], fileNames.ratesTrainOutputs[i], strategy,
+ selectedFieldName );
+ }
+}
+
+
+void TrainImagesBase::SelectAndExtractValidationSamples(const TrainFileNamesHandler &fileNames,
+ FloatVectorImageListType *imageList,
+ const std::vector<std::string> &validationVectorFileList)
+{
+ for( unsigned int i = 0; i < imageList->Size(); ++i )
+ {
+ SelectAndExtractSamples( imageList->GetNthElement( i ), validationVectorFileList[i],
+ fileNames.sampleValidOutputs[i], fileNames.polyStatValidOutputs[i],
+ fileNames.ratesValidOutputs[i], SamplingStrategy::CLASS );
+ }
+}
+
+void TrainImagesBase::SplitTrainingToValidationSamples(const TrainFileNamesHandler &fileNames,
+ FloatVectorImageListType *imageList)
+{
+ for( unsigned int i = 0; i < imageList->Size(); ++i )
+ {
+ SplitTrainingAndValidationSamples( imageList->GetNthElement( i ), fileNames.sampleOutputs[i],
+ fileNames.sampleTrainOutputs[i], fileNames.sampleValidOutputs[i],
+ fileNames.ratesTrainOutputs[i] );
+ }
+}
+
+void TrainImagesBase::SplitTrainingAndValidationSamples(FloatVectorImageType *image, std::string sampleFileName,
+ std::string sampleTrainFileName,
+ std::string sampleValidFileName,
+ std::string ratesTrainFileName)
+
+{
+ // Split between training and validation
+ ogr::DataSource::Pointer source = ogr::DataSource::New( sampleFileName, ogr::DataSource::Modes::Read );
+ ogr::DataSource::Pointer destTrain = ogr::DataSource::New( sampleTrainFileName, ogr::DataSource::Modes::Overwrite );
+ ogr::DataSource::Pointer destValid = ogr::DataSource::New( sampleValidFileName, ogr::DataSource::Modes::Overwrite );
+ // read sampling rates from ratesTrainOutputs
+ SamplingRateCalculator::Pointer rateCalculator = SamplingRateCalculator::New();
+ rateCalculator->Read( ratesTrainFileName );
+ // Compute sampling rates for train and valid
+ const MapRateType &inputRates = rateCalculator->GetRatesByClass();
+ MapRateType trainRates;
+ MapRateType validRates;
+ otb::SamplingRateCalculator::TripletType tpt;
+ for( MapRateType::const_iterator it = inputRates.begin(); it != inputRates.end(); ++it )
+ {
+ double vtr = GetParameterFloat( "sample.vtr" );
+ unsigned long total = std::min( it->second.Required, it->second.Tot );
+ unsigned long neededValid = static_cast<unsigned long>(( double ) total * vtr );
+ unsigned long neededTrain = total - neededValid;
+ tpt.Tot = total;
+ tpt.Required = neededTrain;
+ tpt.Rate = ( 1.0 - vtr );
+ trainRates[it->first] = tpt;
+ tpt.Tot = neededValid;
+ tpt.Required = neededValid;
+ tpt.Rate = 1.0;
+ validRates[it->first] = tpt;
+ }
+
+ // Use an otb::OGRDataToSamplePositionFilter with 2 outputs
+ PeriodicSamplerType::SamplerParameterType param;
+ param.Offset = 0;
+ param.MaxJitter = 0;
+ PeriodicSamplerType::Pointer splitter = PeriodicSamplerType::New();
+ splitter->SetInput( image );
+ splitter->SetOGRData( source );
+ splitter->SetOutputPositionContainerAndRates( destTrain, trainRates, 0 );
+ splitter->SetOutputPositionContainerAndRates( destValid, validRates, 1 );
+ splitter->SetFieldName( this->GetParameterStringList( "sample.vfn" )[0] );
+ splitter->SetLayerIndex( 0 );
+ splitter->SetOriginFieldName( std::string( "" ) );
+ splitter->SetSamplerParameters( param );
+ splitter->GetStreamer()->SetAutomaticTiledStreaming( static_cast<unsigned int>(this->GetParameterInt( "ram" )) );
+ AddProcess( splitter->GetStreamer(), "Split samples between training and validation..." );
+ splitter->Update();
+}
+}
+}
+
+#endif
diff --git a/Modules/Applications/AppClassification/include/otbTrainSharkKMeans.txx b/Modules/Applications/AppClassification/include/otbTrainSharkKMeans.txx
index bd96fe35684b517a5b8f7318938441c3727f5478..0dba5c6ab724588a4fcdfc808c7c5bc6934bfc3b 100644
--- a/Modules/Applications/AppClassification/include/otbTrainSharkKMeans.txx
+++ b/Modules/Applications/AppClassification/include/otbTrainSharkKMeans.txx
@@ -1,19 +1,22 @@
-/*=========================================================================
- 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.
-
- =========================================================================*/
+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
#ifndef otbTrainSharkKMeans_txx
#define otbTrainSharkKMeans_txx
@@ -34,10 +37,10 @@ void LearningApplicationBase<TInputValue, TOutputValue>::InitSharkKMeansParams()
//MaxNumberOfIterations
AddParameter( ParameterType_Int, "classifier.sharkkm.nbmaxiter",
"Maximum number of iteration for the kmeans algorithm." );
- SetParameterInt( "classifier.sharkkm.nbmaxiter", 0 );
+ SetParameterInt( "classifier.sharkkm.nbmaxiter", 10 );
SetMinimumParameterIntValue( "classifier.sharkkm.nbmaxiter", 0 );
SetParameterDescription( "classifier.sharkkm.nbmaxiter",
- "The maximum number of iteration for the kmeans algorithm. Default set to unlimited." );
+ "The maximum number of iteration for the kmeans algorithm. 0=unlimited" );
//MaxNumberOfIterations
AddParameter( ParameterType_Int, "classifier.sharkkm.k", "The number of class used for the kmeans algorithm." );
diff --git a/Modules/Applications/AppClassification/include/otbTrainVectorBase.h b/Modules/Applications/AppClassification/include/otbTrainVectorBase.h
index e9d3be1eb5275af9b1cbab7be204d314fb4f6db7..bdd844ca1d3ae63d1d87a6f4764b64daf5ce8d3d 100644
--- a/Modules/Applications/AppClassification/include/otbTrainVectorBase.h
+++ b/Modules/Applications/AppClassification/include/otbTrainVectorBase.h
@@ -1,19 +1,22 @@
-/*=========================================================================
- 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.
-
- =========================================================================*/
+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
#ifndef otbTrainVectorBase_h
#define otbTrainVectorBase_h
diff --git a/Modules/Applications/AppClassification/include/otbTrainVectorBase.txx b/Modules/Applications/AppClassification/include/otbTrainVectorBase.txx
index ebb651b324597b6b0bd94b5bcfaeafde7b0b1ddd..82d4f7028ac4fe4e60153cc43eb624c527ee6dc5 100644
--- a/Modules/Applications/AppClassification/include/otbTrainVectorBase.txx
+++ b/Modules/Applications/AppClassification/include/otbTrainVectorBase.txx
@@ -1,19 +1,22 @@
-/*=========================================================================
- 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.
-
- =========================================================================*/
+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
#ifndef otbTrainVectorBase_txx
#define otbTrainVectorBase_txx
@@ -59,7 +62,7 @@ void TrainVectorBase::DoInit()
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
+ // Add validation data used to compute confusion matrix or contingency table
AddParameter( ParameterType_Group, "valid", "Validation data" );
SetParameterDescription( "valid", "This group of parameters defines validation data." );
@@ -74,10 +77,24 @@ void TrainVectorBase::DoInit()
SetDefaultParameterInt( "valid.layer", 0 );
// 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);
+ 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 a new parameter to compute confusion matrix / contingency table
+ AddParameter( ParameterType_OutputFilename, "io.confmatout", "Output confusion matrix or contingency table" );
+ SetParameterDescription( "io.confmatout", "Output file containing the confusion matrix or contingency table (.csv format)."
+ "The contingency table is ouput when we unsupervised algorithms is used otherwise the confusion matrix is output." );
+ 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" );
// Add parameters for the classifier choice
@@ -90,6 +107,9 @@ void TrainVectorBase::DoInit()
void TrainVectorBase::DoUpdateParameters()
{
+ LearningApplicationBase::DoUpdateParameters();
+
+ // if vector data is present and updated then reload fields
if( HasValue( "io.vd" ) )
{
std::vector<std::string> vectorFileList = GetParameterStringList( "io.vd" );
@@ -162,9 +182,32 @@ TrainVectorBase::ExtractTrainingListSamples(const StatisticsMeasurement &measure
}
TrainVectorBase::ListSamples
-TrainVectorBase::ExtractClassificationListSamples(const StatisticsMeasurement &itkNotUsed(measurement))
+TrainVectorBase::ExtractClassificationListSamples(const StatisticsMeasurement &measurement)
{
- return trainingListSamples;
+ if(GetClassifierCategory() == Supervised)
+ {
+ ListSamples tmpListSamples;
+ ListSamples validationListSamples = ExtractListSamples( "valid.vd", "valid.layer", measurement );
+ //Test the input validation set size
+ if( validationListSamples.labeledListSample->Size() != 0 )
+ {
+ tmpListSamples.listSample = validationListSamples.listSample;
+ tmpListSamples.labeledListSample = validationListSamples.labeledListSample;
+ }
+ else
+ {
+ otbAppLogWARNING(
+ "The validation set is empty. The performance estimation is done using the input training set in this case." );
+ tmpListSamples.listSample = trainingListSamples.listSample;
+ tmpListSamples.labeledListSample = trainingListSamples.labeledListSample;
+ }
+
+ return tmpListSamples;
+ }
+ else
+ {
+ return trainingListSamples;
+ }
}
diff --git a/Modules/Applications/AppClassification/otb-module.cmake b/Modules/Applications/AppClassification/otb-module.cmake
index 322ba7f9c47e9600a73eafee0ade934b66fa9608..8447da5583203782e7028780d80f6cb034e4fad3 100644
--- a/Modules/Applications/AppClassification/otb-module.cmake
+++ b/Modules/Applications/AppClassification/otb-module.cmake
@@ -11,6 +11,7 @@ otb_module(OTBAppClassification
OTBMajorityVoting
OTBVectorDataIO
OTBSOM
+ OTBLearningBase
OTBSupervised
OTBUnsupervised
OTBApplicationEngine
diff --git a/Modules/Applications/AppClassification/test/CMakeLists.txt b/Modules/Applications/AppClassification/test/CMakeLists.txt
index 94e558f6e59ac549e7d84ba6614027b607781661..9dbca30853a9a4a53b1f82630bdc29a9a2b7a273 100644
--- a/Modules/Applications/AppClassification/test/CMakeLists.txt
+++ b/Modules/Applications/AppClassification/test/CMakeLists.txt
@@ -76,6 +76,7 @@ set(bayes_output_format ".bayes")
set(rf_output_format ".rf")
set(knn_output_format ".knn")
set(sharkrf_output_format ".txt")
+set(sharkkm_output_format ".txt")
# Training algorithms parameters
set(libsvm_parameters "-classifier.libsvm.opt" "true" "-classifier.libsvm.prob" "true")
@@ -88,7 +89,7 @@ set(bayes_parameters "")
set(rf_parameters "")
set(knn_parameters "")
set(sharkrf_parameters "")
-
+set(sharkkm_parameters "")
# Validation depending on mode
set(ascii_comparison --compare-ascii ${EPSILON_6})
@@ -108,7 +109,7 @@ if(OTB_USE_OPENCV)
list(APPEND classifierList "BOOST" "DT" "GBT" "ANN" "BAYES" "RF" "KNN")
endif()
if(OTB_USE_SHARK)
- list(APPEND classifierList "SHARKRF")
+ list(APPEND classifierList "SHARKRF" "SHARKKM")
endif()
set(classifier_with_confmap "LIBSVM" "BOOST" "KNN" "ANN" "RF")
@@ -224,124 +225,6 @@ foreach(classifier ${classifierList})
endforeach()
-
-#----------- TrainImagesClustering TESTS ----------------
-
-set(sharkkm_output_format ".txt")
-set(sharkkm_parameters "")
-
-if(OTB_USE_SHARK)
- list(APPEND clusteringList "SHARKKM")
-endif()
-
-list(APPEND classifier_without_baseline "SHARKKM")
-
-# Loop on classifiers
-foreach(classifier ${clusteringList})
- string(TOLOWER ${classifier} lclassifier)
-
- # Derive output file name
- set(OUTMODELFILE cl${classifier}_ModelQB1${${lclassifier}_output_format})
- set(OUTRASTER cl${classifier}LabeledImageQB1${raster_output_format})
- set(OUTCONFMAP cl${classifier}ConfidenceMapQB1${raster_output_format})
-
- list(FIND classifier_without_baseline ${classifier} _classifier_has_baseline)
- if(${_classifier_has_baseline} EQUAL -1)
- set(valid ${ascii_comparison} ${ascii_ref_path}/${OUTMODELFILE} ${TEMP}/${OUTMODELFILE})
- else()
- set(valid "")
- endif()
-
- otb_test_application(
- NAME apTvClTrainMethod${classifier}ImagesClassifierQB1
- APP TrainImagesClustering
- OPTIONS -io.il ${INPUTDATA}/Classification/QB_1_ortho${raster_input_format}
- -io.vd ${INPUTDATA}/Classification/VectorData_${${lclassifier}_input}QB1${vector_input_format}
- -io.imstat ${INPUTDATA}/Classification/clImageStatisticsQB1${stat_input_format}
- -classifier ${lclassifier}
- ${${lclassifier}_parameters}
- -io.out ${TEMP}/${OUTMODELFILE}
- -sample.vfn Class
- -rand 121212
-
- VALID ${valid}
- )
-
- if(${_classifier_has_baseline} EQUAL -1)
- set(valid ${ascii_comparison} ${ascii_ref_path}/${OUTMODELFILE} ${TEMP}/OutXML1_${OUTMODELFILE})
- else()
- set(valid "")
- endif()
-
- otb_test_application(
- NAME apTvClTrainMethod${classifier}ImagesClassifierQB1_OutXML1
- APP TrainImagesClustering
- OPTIONS -io.il ${INPUTDATA}/Classification/QB_1_ortho${raster_input_format}
- -io.vd ${INPUTDATA}/Classification/VectorData_${${lclassifier}_input}QB1${vector_input_format}
- -io.imstat ${INPUTDATA}/Classification/clImageStatisticsQB1${stat_input_format}
- -classifier ${lclassifier}
- ${${lclassifier}_parameters}
- -io.out ${TEMP}/OutXML1_${OUTMODELFILE}
- -rand 121212
- -sample.vfn Class
- -outxml ${TEMP}/cl${classifier}_OutXML1.xml
-
- VALID ${valid}
- )
-
- if(${_classifier_has_baseline} EQUAL -1)
- set(valid ${ascii_comparison} ${ascii_ref_path}/${OUTMODELFILE} ${TEMP}/OutXML2_${OUTMODELFILE})
- else()
- set(valid "")
- endif()
-
- otb_test_application(
- NAME apTvClTrainMethod${classifier}ImagesClassifierQB1_InXML1
- APP TrainImagesClustering
- OPTIONS -inxml ${INPUTDATA}/cl${classifier}_OutXML1.xml
- -io.il ${INPUTDATA}/Classification/QB_1_ortho${raster_input_format}
- -io.vd ${INPUTDATA}/Classification/VectorData_${${lclassifier}_input}QB1${vector_input_format}
- -io.imstat ${INPUTDATA}/Classification/clImageStatisticsQB1${stat_input_format}
- -io.out ${TEMP}/OutXML2_${OUTMODELFILE}
- -sample.vfn Class
- VALID ${valid}
- )
-
- list(FIND classifier_with_confmap ${classifier} _classifier_has_confmap)
- if(${_classifier_has_confmap} EQUAL -1)
- otb_test_application(
- NAME apTvClMethod${classifier}ImageClassifierQB1
- APP ImageClassifier
- OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho${raster_input_format}
- -model ${INPUTDATA}/Classification/${OUTMODELFILE}
- -imstat ${INPUTDATA}/Classification/clImageStatisticsQB1${stat_input_format}
- -out ${TEMP}/${OUTRASTER} ${raster_output_option}
-
- VALID ${raster_comparison}
- ${raster_ref_path}/${OUTRASTER}
- ${TEMP}/${OUTRASTER}
- )
- else()
- otb_test_application(
- NAME apTvClMethod${classifier}ImageClassifierQB1
- APP ImageClassifier
- OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho${raster_input_format}
- -model ${INPUTDATA}/Classification/${OUTMODELFILE}
- -imstat ${INPUTDATA}/Classification/clImageStatisticsQB1${stat_input_format}
- -out ${TEMP}/${OUTRASTER} ${raster_output_option}
- -confmap ${TEMP}/${OUTCONFMAP}
-
- VALID ${raster_comparison_two}
- ${raster_ref_path}/${OUTRASTER}
- ${TEMP}/${OUTRASTER}
- ${raster_ref_path}/${OUTCONFMAP}
- ${TEMP}/${OUTCONFMAP}
- )
- endif()
-
-endforeach()
-
-
#----------- LIBSVM Classifier TESTS ----------------
if(OTB_USE_LIBSVM)
@@ -1063,17 +946,17 @@ if(OTB_USE_OPENCV)
${TEMP}/apTvClTrainVectorClassifierModel.rf)
endif()
-#----------- TrainVectorClustering TESTS ----------------
+#----------- TrainVectorClassifier unsupervised TESTS ----------------
if(OTB_USE_SHARK)
- otb_test_application(NAME apTvClTrainVectorClustering
- APP TrainVectorClustering
+ otb_test_application(NAME apTvClTrainVectorUnsupervised
+ APP TrainVectorClassifier
OPTIONS -io.vd ${INPUTDATA}/Classification/apTvClSampleExtractionOut.sqlite
-feat value_0 value_1 value_2 value_3
-classifier sharkkm
-io.out ${TEMP}/apTvClTrainVectorClusteringModel.txt)
- otb_test_application(NAME apTvClTrainVectorClusteringWithClass
- APP TrainVectorClustering
+ otb_test_application(NAME apTvClTrainVectorUnsupervisedWithClass
+ APP TrainVectorClassifier
OPTIONS -io.vd ${INPUTDATA}/Classification/apTvClSampleExtractionOut.sqlite
-feat value_0 value_1 value_2 value_3
-cfield class
diff --git a/Modules/Learning/Supervised/include/otbImageClassificationFilter.h b/Modules/Learning/LearningBase/include/otbImageClassificationFilter.h
similarity index 85%
rename from Modules/Learning/Supervised/include/otbImageClassificationFilter.h
rename to Modules/Learning/LearningBase/include/otbImageClassificationFilter.h
index 7d4cf8a9766bf21e8cc1a7c2e77c16150720b218..3dbcbfa938af52dfe2dc1ae154120c71f548a40b 100644
--- a/Modules/Learning/Supervised/include/otbImageClassificationFilter.h
+++ b/Modules/Learning/LearningBase/include/otbImageClassificationFilter.h
@@ -1,20 +1,22 @@
-/*=========================================================================
-
- 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.
-
-=========================================================================*/
+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
#ifndef otbImageClassificationFilter_h
#define otbImageClassificationFilter_h
@@ -34,7 +36,7 @@ namespace otb
* \ingroup Streamed
* \ingroup Threaded
*
- * \ingroup OTBSupervised
+ * \ingroup OTBLearningBase
*/
template <class TInputImage, class TOutputImage, class TMaskImage = TOutputImage>
class ITK_EXPORT ImageClassificationFilter
@@ -87,7 +89,7 @@ public:
itkSetMacro(BatchMode, bool);
itkGetMacro(BatchMode, bool);
itkBooleanMacro(BatchMode);
-
+
/**
* If set, only pixels within the mask will be classified.
* All pixels with a value greater than 0 in the mask, will be classified.
diff --git a/Modules/Learning/Supervised/include/otbImageClassificationFilter.txx b/Modules/Learning/LearningBase/include/otbImageClassificationFilter.txx
similarity index 93%
rename from Modules/Learning/Supervised/include/otbImageClassificationFilter.txx
rename to Modules/Learning/LearningBase/include/otbImageClassificationFilter.txx
index 54656b1948811c9685eaa01d729bc706ab15e98b..85e531cd5548f5fbf9b49540ff4a91b068faa7f8 100644
--- a/Modules/Learning/Supervised/include/otbImageClassificationFilter.txx
+++ b/Modules/Learning/LearningBase/include/otbImageClassificationFilter.txx
@@ -1,20 +1,22 @@
-/*=========================================================================
-
- 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.
-
-=========================================================================*/
+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
#ifndef otbImageClassificationFilter_txx
#define otbImageClassificationFilter_txx
diff --git a/Modules/Learning/LearningBase/include/otbMachineLearningModel.h b/Modules/Learning/LearningBase/include/otbMachineLearningModel.h
index ee84468521b5fc0db1c9d0ac3dcd0d81404b0367..db57e1fdf35f9fddc023597244f013d139377068 100644
--- a/Modules/Learning/LearningBase/include/otbMachineLearningModel.h
+++ b/Modules/Learning/LearningBase/include/otbMachineLearningModel.h
@@ -1,20 +1,22 @@
-/*=========================================================================
-
- 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.
-
-=========================================================================*/
+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
#ifndef otbMachineLearningModel_h
#define otbMachineLearningModel_h
@@ -61,7 +63,7 @@ namespace otb
* \sa ImageClassificationFilter
*
*
- * \ingroup OTBSupervised
+ * \ingroup OTBLearningBase
*/
template <class TInputValue, class TTargetValue, class TConfidenceValue = double >
class ITK_EXPORT MachineLearningModel
diff --git a/Modules/Learning/LearningBase/include/otbMachineLearningModel.txx b/Modules/Learning/LearningBase/include/otbMachineLearningModel.txx
index 3d67a0a3a2a68000a60225d9b822a25157277f14..8265ef9c58a68c267ec765b0fc7c84640c998217 100644
--- a/Modules/Learning/LearningBase/include/otbMachineLearningModel.txx
+++ b/Modules/Learning/LearningBase/include/otbMachineLearningModel.txx
@@ -1,20 +1,22 @@
-/*=========================================================================
-
- 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.
-
-=========================================================================*/
+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
#ifndef otbMachineLearningModel_txx
#define otbMachineLearningModel_txx
diff --git a/Modules/Learning/LearningBase/include/otbMachineLearningModelFactoryBase.h b/Modules/Learning/LearningBase/include/otbMachineLearningModelFactoryBase.h
index 81f771ac5304e7eb58f8f4cc33c49f40f7a550ed..daed6acb7b02b3f6b4733ffe6e869f434664e21d 100644
--- a/Modules/Learning/LearningBase/include/otbMachineLearningModelFactoryBase.h
+++ b/Modules/Learning/LearningBase/include/otbMachineLearningModelFactoryBase.h
@@ -1,20 +1,22 @@
-/*=========================================================================
-
- 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.
-
-=========================================================================*/
+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
#ifndef otbMachineLearningModelFactoryBase_h
#define otbMachineLearningModelFactoryBase_h
@@ -29,7 +31,7 @@ namespace otb
* This class intends to hold the static attributes that can not be
* part of a template class (ld error).
*
- *
+ * \ingroup OTBLearningBase
*/
class OTBSupervised_EXPORT MachineLearningModelFactoryBase : public itk::Object
{
diff --git a/Modules/Learning/LearningBase/include/otbSharkUtils.h b/Modules/Learning/LearningBase/include/otbSharkUtils.h
index 08165a114fdf73b67e463ec9e658aefe138932ce..c872dc9f0956bb4b6e08945e8c2503211697cb66 100644
--- a/Modules/Learning/LearningBase/include/otbSharkUtils.h
+++ b/Modules/Learning/LearningBase/include/otbSharkUtils.h
@@ -1,31 +1,35 @@
-/*=========================================================================
-
- 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.
-
- =========================================================================*/
+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
#ifndef otbSharkUtils_h
#define otbSharkUtils_h
-#include "otb_shark.h"
#include "itkMacro.h"
#if defined(__GNUC__) || defined(__clang__)
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wshadow"
+#pragma GCC diagnostic ignored "-Wunused-parameter"
+#pragma GCC diagnostic ignored "-Wsign-compare"
#endif
-#include <shark/Data/Dataset.h>
+#include "otb_shark.h"
+#include "shark/Data/Dataset.h"
#if defined(__GNUC__) || defined(__clang__)
#pragma GCC diagnostic pop
#endif
diff --git a/Modules/Learning/LearningBase/otb-module.cmake b/Modules/Learning/LearningBase/otb-module.cmake
index 78f6c010994a94ab2c80baf3c477a10c7cb166b6..b73df789bac68fdeb069b523d57eb1a4f1be91ed 100644
--- a/Modules/Learning/LearningBase/otb-module.cmake
+++ b/Modules/Learning/LearningBase/otb-module.cmake
@@ -5,6 +5,8 @@ otb_module(OTBLearningBase
DEPENDS
OTBCommon
OTBITK
+ OTBImageIO
+ OTBImageBase
OPTIONAL_DEPENDS
OTBShark
diff --git a/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.h b/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.h
index b362373e8eb7cad3aca821cbc219444f79b0b1d2..7452193a952fec42e808af8f9cb6da1aca12ae93 100644
--- a/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.h
@@ -18,8 +18,6 @@
#ifndef otbSharkRandomForestsMachineLearningModel_h
#define otbSharkRandomForestsMachineLearningModel_h
-#include "otb_shark.h"
-
#include "itkLightObject.h"
#include "otbMachineLearningModel.h"
@@ -33,6 +31,7 @@
#pragma GCC diagnostic ignored "-Wcast-align"
#pragma GCC diagnostic ignored "-Wunknown-pragmas"
#endif
+#include "otb_shark.h"
#include "shark/Algorithms/Trainers/RFTrainer.h"
#if defined(__GNUC__) || defined(__clang__)
#pragma GCC diagnostic pop
diff --git a/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.txx
index 0e0b08f3e26dd2d4cb8a0e4bddd7e036d9aae444..b8486999dc6f7d456e5ec6cdade49ba3bc3f91fc 100644
--- a/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.txx
@@ -195,6 +195,8 @@ SharkRandomForestsMachineLearningModel<TInputValue,TOutputValue>
{
itkExceptionMacro(<< "Error opening " << filename.c_str() );
}
+ // Add comment with model file name
+ ofs << "#" << m_RFModel.name() << std::endl;
shark::TextOutArchive oa(ofs);
m_RFModel.save(oa,0);
}
@@ -205,8 +207,25 @@ SharkRandomForestsMachineLearningModel<TInputValue,TOutputValue>
::Load(const std::string & filename, const std::string & itkNotUsed(name))
{
std::ifstream ifs(filename.c_str());
- shark::TextInArchive ia(ifs);
- m_RFModel.load(ia,0);
+ if( ifs.good() )
+ {
+ // Check if the first line is a comment and verify the name of the model in this case.
+ std::string line;
+ getline( ifs, line );
+ if( line.at( 0 ) == '#' )
+ {
+ if( line.find( m_RFModel.name() ) == std::string::npos )
+ itkExceptionMacro( "The model file : " + filename + " cannot be read." );
+ }
+ else
+ {
+ // rewind if first line is not a comment
+ ifs.clear();
+ ifs.seekg( 0, std::ios::beg );
+ }
+ shark::TextInArchive ia( ifs );
+ m_RFModel.load( ia, 0 );
+ }
}
template <class TInputValue, class TOutputValue>
diff --git a/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.h b/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.h
index a31891e5d1d633776c67500ceba2eeb136a9b140..3084b2503e8680fe36b60c0cf70b979bc7656cc9 100644
--- a/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.h
+++ b/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.h
@@ -1,25 +1,25 @@
-/*=========================================================================
-
- 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.
-
-=========================================================================*/
+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
#ifndef otbSharkKMeansMachineLearningModel_h
#define otbSharkKMeansMachineLearningModel_h
-
-
#include "itkLightObject.h"
#include "otbMachineLearningModel.h"
@@ -45,8 +45,6 @@
#pragma GCC diagnostic pop
#endif
-using namespace shark;
-
/** \class SharkKMeansMachineLearningModel
* \brief Shark version of Random Forests algorithm
*
@@ -67,23 +65,24 @@ class ITK_EXPORT SharkKMeansMachineLearningModel : public MachineLearningModel<T
{
public:
/** Standard class typedefs. */
- typedef SharkKMeansMachineLearningModel Self;
+ typedef SharkKMeansMachineLearningModel Self;
typedef MachineLearningModel<TInputValue, TTargetValue> Superclass;
- typedef itk::SmartPointer<Self> Pointer;
- typedef itk::SmartPointer<const Self> ConstPointer;
-
- typedef typename Superclass::InputValueType InputValueType;
- typedef typename Superclass::InputSampleType InputSampleType;
- typedef typename Superclass::InputListSampleType InputListSampleType;
- typedef typename Superclass::TargetValueType TargetValueType;
- typedef typename Superclass::TargetSampleType TargetSampleType;
- typedef typename Superclass::TargetListSampleType TargetListSampleType;
- typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
- typedef typename Superclass::ConfidenceSampleType ConfidenceSampleType;
- typedef typename Superclass::ConfidenceListSampleType ConfidenceListSampleType;
-
- typedef HardClusteringModel<RealVector> ClusteringModelType;
- typedef ClusteringModelType::OutputType ClusteringOutputType;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+ typedef typename Superclass::InputValueType InputValueType;
+ typedef typename Superclass::InputSampleType InputSampleType;
+ typedef typename Superclass::InputListSampleType InputListSampleType;
+ typedef typename Superclass::TargetValueType TargetValueType;
+ typedef typename Superclass::TargetSampleType TargetSampleType;
+ typedef typename Superclass::TargetListSampleType TargetListSampleType;
+ typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
+ typedef typename Superclass::ConfidenceSampleType ConfidenceSampleType;
+ typedef typename Superclass::ConfidenceListSampleType ConfidenceListSampleType;
+
+
+ typedef shark::HardClusteringModel<shark::RealVector> ClusteringModelType;
+ typedef ClusteringModelType::OutputType ClusteringOutputType;
/** Run-time type information (and related methods). */
itkNewMacro( Self );
@@ -108,18 +107,14 @@ public:
//@}
/** Get the maximum number of iteration for the kMeans algorithm.*/
- itkGetMacro( MaximumNumberOfIterations, unsigned
- int );
+ itkGetMacro( MaximumNumberOfIterations, unsigned );
/** Set the maximum number of iteration for the kMeans algorithm.*/
- itkSetMacro( MaximumNumberOfIterations, unsigned
- int );
+ itkSetMacro( MaximumNumberOfIterations, unsigned );
/** Get the number of class for the kMeans algorithm.*/
- itkGetMacro( K, unsigned
- int );
+ itkGetMacro( K, unsigned );
/** Set the number of class for the kMeans algorithm.*/
- itkSetMacro( K, unsigned
- int );
+ itkSetMacro( K, unsigned );
/** If true, normalized input data sample list */
itkGetMacro( Normalized, bool );
@@ -154,10 +149,11 @@ private:
bool m_Normalized;
unsigned int m_K;
unsigned int m_MaximumNumberOfIterations;
+ bool m_CanRead;
/** Centroids results form kMeans */
- Centroids centroids;
+ shark::Centroids m_Centroids;
/** shark Model could be SoftClusteringModel or HardClusteringModel */
diff --git a/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.txx b/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.txx
index e3492437367f81a726c25326134900bf686a150f..267a676ae668a863928be6087146a1ca7dd61d6e 100644
--- a/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.txx
+++ b/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.txx
@@ -1,25 +1,29 @@
-/*=========================================================================
-
- 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.
-
-=========================================================================*/
+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
#ifndef otbSharkKMeansMachineLearningModel_txx
#define otbSharkKMeansMachineLearningModel_txx
+
#include <fstream>
#include "itkMacro.h"
#include "otbSharkKMeansMachineLearningModel.h"
+
#if defined(__GNUC__) || defined(__clang__)
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wshadow"
@@ -27,15 +31,19 @@
#pragma GCC diagnostic ignored "-Woverloaded-virtual"
#pragma GCC diagnostic ignored "-Wignored-qualifiers"
#endif
-#include <shark/Algorithms/Trainers/NormalizeComponentsUnitVariance.h> //normalize
-#include <shark/Algorithms/KMeans.h> //k-means algorithm
-#include <shark/Models/Clustering/HardClusteringModel.h>
-#include <shark/Models/Clustering/SoftClusteringModel.h>
-#include <shark/Algorithms/Trainers/NormalizeComponentsUnitVariance.h>
+
+#include "otb_shark.h"
+#include "otbSharkUtils.h"
+#include "shark/Algorithms/Trainers/NormalizeComponentsUnitVariance.h" //normalize
+#include "shark/Algorithms/KMeans.h" //k-means algorithm
+#include "shark/Models/Clustering/HardClusteringModel.h"
+#include "shark/Models/Clustering/SoftClusteringModel.h"
+#include "shark/Algorithms/Trainers/NormalizeComponentsUnitVariance.h"
+
#if defined(__GNUC__) || defined(__clang__)
#pragma GCC diagnostic pop
#endif
-#include "otbSharkUtils.h"
+
namespace otb
@@ -43,10 +51,10 @@ namespace otb
template<class TInputValue, class TOutputValue>
SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
::SharkKMeansMachineLearningModel() :
- m_Normalized( true ), m_K(2), m_MaximumNumberOfIterations( 0 )
+ m_Normalized( false ), m_K(2), m_MaximumNumberOfIterations( 10 )
{
// Default set HardClusteringModel
- m_ClusteringModel = boost::shared_ptr<ClusteringModelType>(new ClusteringModelType( ¢roids ));
+ m_ClusteringModel = boost::shared_ptr<ClusteringModelType>(new ClusteringModelType( &m_Centroids ));
}
@@ -63,16 +71,17 @@ SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
::Train()
{
// Parse input data and convert to Shark Data
- std::vector<RealVector> vector_data;
- Shark::ListSampleToSharkVector( this->GetInputListSample(), vector_data );
- Data<RealVector> data = createDataFromRange( vector_data );
+ std::vector<shark::RealVector> vector_data;
+ otb::Shark::ListSampleToSharkVector( this->GetInputListSample(), vector_data );
+ shark::Data<shark::RealVector> data = shark::createDataFromRange( vector_data );
// Normalized input value if necessary
if( m_Normalized )
data = NormalizeData( data );
// Use a Hard Clustering Model for classification
- kMeans( data, m_K, centroids, m_MaximumNumberOfIterations );
+ shark::kMeans( data, m_K, m_Centroids, m_MaximumNumberOfIterations );
+ m_ClusteringModel = boost::shared_ptr<ClusteringModelType>(new ClusteringModelType( &m_Centroids ));
}
template<class TInputValue, class TOutputValue>
@@ -93,7 +102,7 @@ typename SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
::DoPredict(const InputSampleType &value, ConfidenceValueType *quality) const
{
- RealVector data( value.Size());
+ shark::RealVector data( value.Size());
for( size_t i = 0; i < value.Size(); i++ )
{
data.push_back( value[i] );
@@ -128,20 +137,30 @@ SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
// input list sample and target list sample should be initialized and without
assert( input->Size() == targets->Size() && "Input sample list and target label list do not have the same size." );
- assert((( quality == ITK_NULLPTR ) || ( quality->Size() == input->Size())) &&
- "Quality samples list is not null and does not have the same size as input samples list" );
- if( startIndex + size > input->Size())
+ assert( ( ( quality == ITK_NULLPTR ) || ( quality->Size() == input->Size() ) ) &&
+ "Quality samples list is not null and does not have the same size as input samples list" );
+ if( startIndex + size > input->Size() )
{
itkExceptionMacro(
<<"requested range ["<<startIndex<<", "<<startIndex+size<<"[ partially outside input sample list range.[0,"<<input->Size()<<"[" );
}
// Convert input list of features to shark data format
- std::vector<RealVector> features;
- Shark::ListSampleRangeToSharkVector( input, features, startIndex, size );
- Data<RealVector> inputSamples = shark::createDataFromRange( features );
+ std::vector<shark::RealVector> features;
+ otb::Shark::ListSampleRangeToSharkVector( input, features, startIndex, size );
+ shark::Data<shark::RealVector> inputSamples = shark::createDataFromRange( features );
+
+ shark::Data<ClusteringOutputType> clusters;
+ try
+ {
+ clusters = ( *m_ClusteringModel )( inputSamples );
+ }
+ catch( ... )
+ {
+ itkExceptionMacro( "Failed to run clustering classification. "
+ "The number of features of input samples and the model could differ.");
+ }
- Data<ClusteringOutputType> clusters = ( *m_ClusteringModel )( inputSamples );
unsigned int id = startIndex;
for( const auto &p : clusters.elements() )
{
@@ -159,7 +178,6 @@ SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
quality->SetMeasurementVector( qid, static_cast<ConfidenceValueType>(1.) );
}
}
-
}
@@ -173,9 +191,8 @@ SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
{
itkExceptionMacro( << "Error opening " << filename.c_str());
}
+ ofs << "#" << m_ClusteringModel->name() << std::endl;
shark::TextOutArchive oa( ofs );
- std::string name = m_ClusteringModel->name();
- oa << name;
m_ClusteringModel->save( oa, 1 );
}
@@ -184,13 +201,22 @@ void
SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
::Load(const std::string &filename, const std::string & itkNotUsed( name ))
{
+ m_CanRead = false;
std::ifstream ifs( filename.c_str());
+ if(ifs.good())
+ {
+ // Check if first line contains model name
+ std::string line;
+ std::getline(ifs, line);
+ m_CanRead = line.find(m_ClusteringModel->name()) != std::string::npos;
+ }
+
+ if(!m_CanRead)
+ return;
+
shark::TextInArchive ia( ifs );
- std::string name;
- ia >> name;
- if(name != m_ClusteringModel->name())
- throw new boost::archive::archive_exception(boost::archive::archive_exception::input_stream_error);
- m_ClusteringModel->load( ia, 1 );
+ m_ClusteringModel->load( ia, 0 );
+ ifs.close();
}
template<class TInputValue, class TOutputValue>
@@ -200,13 +226,14 @@ SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
{
try
{
+ m_CanRead = true;
this->Load( file );
}
catch( ... )
{
return false;
}
- return true;
+ return m_CanRead;
}
template<class TInputValue, class TOutputValue>
diff --git a/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModelFactory.h b/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModelFactory.h
index cf0c033eb5a487fbfcda93a4f2d67677d449fec8..a072d5d71921f24cc483e0193d3f0d04a4ab41d2 100644
--- a/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModelFactory.h
+++ b/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModelFactory.h
@@ -1,20 +1,22 @@
-/*=========================================================================
-
- 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.
-
-=========================================================================*/
+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
#ifndef otbSharkKMeansMachineLearningModelFactory_h
#define otbSharkKMeansMachineLearningModelFactory_h
diff --git a/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModelFactory.txx b/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModelFactory.txx
index a698e7b1564fa81c8c5d590c4134914a8bd7f898..a4fe5584e04249875de1193e365fd888a43cfa07 100644
--- a/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModelFactory.txx
+++ b/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModelFactory.txx
@@ -1,20 +1,22 @@
-/*=========================================================================
-
- 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.
-
-=========================================================================*/
+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
#ifndef otbSharkKMeansMachineLearningModelFactory_txx
#define otbSharkKMeansMachineLearningModelFactory_txx
diff --git a/Modules/Learning/Unsupervised/test/CMakeLists.txt b/Modules/Learning/Unsupervised/test/CMakeLists.txt
index 9411a183d46a984ea3d259fdfb2b1d983c34de3f..8407413cd8020676daea852688a42273077e9f33 100644
--- a/Modules/Learning/Unsupervised/test/CMakeLists.txt
+++ b/Modules/Learning/Unsupervised/test/CMakeLists.txt
@@ -1,16 +1,11 @@
otb_module_test()
set(OTBUnsupervisedTests
otbUnsupervisedTestDriver.cxx
- otbMachineLearningClusteringModelCanRead.cxx
- otbTrainMachineLearningClusteringModel.cxx
+ otbMachineLearningUnsupervisedModelCanRead.cxx
+ otbTrainMachineLearningUnsupervisedModel.cxx
otbContingencyTableCalculatorTest.cxx
)
-
-add_executable(otbUnsupervisedTestDriver ${OTBUnsupervisedTests})
-target_link_libraries(otbUnsupervisedTestDriver ${OTBUnsupervised-Test_LIBRARIES})
-otb_module_target_label(otbUnsupervisedTestDriver)
-
# Tests Declaration
otb_add_test(NAME leTuContingencyTableCalculatorNew COMMAND otbUnsupervisedTestDriver
@@ -27,5 +22,10 @@ otb_add_test(NAME leTvContingencyTableCalculatorUpdateWithBaseline COMMAND otbUn
if(OTB_USE_SHARK)
+ set(OTBUnsupervisedTests ${OTBUnsupervisedTests} otbSharkUnsupervisedImageClassificationFilter.cxx)
include(tests-shark.cmake)
endif()
+
+add_executable(otbUnsupervisedTestDriver ${OTBUnsupervisedTests})
+target_link_libraries(otbUnsupervisedTestDriver ${OTBUnsupervised-Test_LIBRARIES})
+otb_module_target_label(otbUnsupervisedTestDriver)
diff --git a/Modules/Learning/Unsupervised/test/otbMachineLearningClusteringModelCanRead.cxx b/Modules/Learning/Unsupervised/test/otbMachineLearningUnsupervisedModelCanRead.cxx
similarity index 64%
rename from Modules/Learning/Unsupervised/test/otbMachineLearningClusteringModelCanRead.cxx
rename to Modules/Learning/Unsupervised/test/otbMachineLearningUnsupervisedModelCanRead.cxx
index 761c2716f5743ecaafd46e0cfddb0f189075e6f1..6856f0d0728327c26df9873af3afa016789c6d10 100644
--- a/Modules/Learning/Unsupervised/test/otbMachineLearningClusteringModelCanRead.cxx
+++ b/Modules/Learning/Unsupervised/test/otbMachineLearningUnsupervisedModelCanRead.cxx
@@ -1,20 +1,22 @@
-/*=========================================================================
-
- 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.
-
-=========================================================================*/
+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
#include <iostream>
@@ -46,8 +48,8 @@ int otbSharkKMeansMachineLearningModelCanRead(int argc, char *argv[])
return EXIT_FAILURE;
}
std::string filename( argv[1] );
- typedef otb::SharkKMeansMachineLearningModel<InputValueType, TargetValueType> RFType;
- RFType::Pointer classifier = RFType::New();
+ typedef otb::SharkKMeansMachineLearningModel<InputValueType, TargetValueType> KMType;
+ KMType::Pointer classifier = KMType::New();
bool lCanRead = classifier->CanReadFile( filename );
if( !lCanRead )
{
diff --git a/Modules/Learning/Unsupervised/test/otbSharkUnsupervisedImageClassificationFilter.cxx b/Modules/Learning/Unsupervised/test/otbSharkUnsupervisedImageClassificationFilter.cxx
new file mode 100644
index 0000000000000000000000000000000000000000..7c101f7e2eb4ab7b6ec1d083c793b84523d4b25e
--- /dev/null
+++ b/Modules/Learning/Unsupervised/test/otbSharkUnsupervisedImageClassificationFilter.cxx
@@ -0,0 +1,168 @@
+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "otbVectorImage.h"
+#include "otbImageFileReader.h"
+#include "otbImageFileWriter.h"
+#include "otbImageClassificationFilter.h"
+#include "otbSharkKMeansMachineLearningModelFactory.h"
+
+#include <random>
+#include <chrono>
+
+
+const unsigned int Dimension = 2;
+typedef float PixelType;
+typedef unsigned short LabeledPixelType;
+
+typedef otb::VectorImage<PixelType, Dimension> ImageType;
+typedef otb::Image<LabeledPixelType, Dimension> LabeledImageType;
+typedef otb::ImageClassificationFilter<ImageType, LabeledImageType> ClassificationFilterType;
+typedef ClassificationFilterType::ModelType ModelType;
+typedef ClassificationFilterType::ValueType ValueType;
+typedef ClassificationFilterType::LabelType LabelType;
+typedef otb::SharkKMeansMachineLearningModelFactory<ValueType, LabelType> MachineLearningModelFactoryType;
+typedef otb::ImageFileReader<ImageType> ReaderType;
+typedef otb::ImageFileReader<LabeledImageType> MaskReaderType;
+typedef otb::ImageFileWriter<LabeledImageType> WriterType;
+
+typedef otb::SharkKMeansMachineLearningModel<PixelType,short unsigned int> MachineLearningModelType;
+typedef MachineLearningModelType::InputValueType LocalInputValueType;
+typedef MachineLearningModelType::InputSampleType LocalInputSampleType;
+typedef MachineLearningModelType::InputListSampleType LocalInputListSampleType;
+typedef MachineLearningModelType::TargetValueType LocalTargetValueType;
+typedef MachineLearningModelType::TargetSampleType LocalTargetSampleType;
+typedef MachineLearningModelType::TargetListSampleType LocalTargetListSampleType;
+
+void generateSamples(unsigned int num_classes, unsigned int num_samples,
+ unsigned int num_features,
+ LocalInputListSampleType * samples,
+ LocalTargetListSampleType * labels)
+{
+ std::default_random_engine randomEngine;
+ std::uniform_int_distribution<int> label_distribution(1,num_classes);
+ std::uniform_int_distribution<int> feat_distribution(0,256);
+ for(size_t scount=0; scount<num_samples; ++scount)
+ {
+ LabeledPixelType label = label_distribution(randomEngine);
+ LocalInputSampleType sample(num_features);
+ for(unsigned int i=0; i<num_features; ++i)
+ sample[i]= feat_distribution(randomEngine);
+ samples->SetMeasurementVectorSize(num_features);
+ samples->PushBack(sample);
+ labels->PushBack(label);
+ }
+}
+
+void buildModel(unsigned int num_classes, unsigned int num_samples,
+ unsigned int num_features, std::string modelfname)
+{
+ LocalInputListSampleType::Pointer samples = LocalInputListSampleType::New();
+ LocalTargetListSampleType::Pointer labels = LocalTargetListSampleType::New();
+
+ std::cout << "Sample generation\n";
+ generateSamples(num_classes, num_samples, num_features, samples, labels);
+
+ MachineLearningModelType::Pointer classifier = MachineLearningModelType::New();
+ classifier->SetInputListSample(samples);
+ classifier->SetTargetListSample(labels);
+ classifier->SetRegressionMode(false);
+ classifier->SetK(3);
+
+ std::cout << "Training\n";
+ using TimeT = std::chrono::milliseconds;
+ auto start = std::chrono::system_clock::now();
+ classifier->Train();
+ auto duration = std::chrono::duration_cast< TimeT>
+ (std::chrono::system_clock::now() - start);
+ auto elapsed = duration.count();
+ std::cout << "Training took " << elapsed << " ms\n";
+ classifier->Save(modelfname);
+}
+
+int otbSharkUnsupervisedImageClassificationFilter(int argc, char * argv[])
+{
+ if(argc<5 || argc>7)
+ {
+ std::cout << "Usage: input_image output_image output_confidence batchmode [in_model_name] [mask_name]\n";
+ }
+ std::string imfname = argv[1];
+ std::string outfname = argv[2];
+ std::string conffname = argv[3];
+ bool batch = (std::string(argv[4])=="1");
+ std::string modelfname = "/tmp/rf_model.txt";
+ std::string maskfname{};
+
+ MaskReaderType::Pointer mask_reader = MaskReaderType::New();
+ ReaderType::Pointer reader = ReaderType::New();
+ reader->SetFileName(imfname);
+ reader->UpdateOutputInformation();
+
+ auto num_features = reader->GetOutput()->GetNumberOfComponentsPerPixel();
+
+ std::cout << "Image has " << num_features << " bands\n";
+
+ if(argc>5)
+ {
+ modelfname = argv[5];
+ }
+ else
+ {
+ buildModel(3, 1000, num_features, modelfname);
+ }
+
+ ClassificationFilterType::Pointer filter = ClassificationFilterType::New();
+
+ MachineLearningModelType::Pointer model = MachineLearningModelType::New();
+ if(!model->CanReadFile(modelfname))
+ {
+ std::cerr << "Unable to read the model : " << modelfname << "\n";
+ return EXIT_FAILURE;
+ }
+
+ filter->SetModel(model);
+ filter->SetInput(reader->GetOutput());
+ if(argc==7)
+ {
+ maskfname = argv[6];
+ mask_reader->SetFileName(maskfname);
+ filter->SetInputMask(mask_reader->GetOutput());
+ }
+
+ WriterType::Pointer writer = WriterType::New();
+ writer->SetInput(filter->GetOutput());
+ writer->SetFileName(outfname);
+ std::cout << "Classification\n";
+ filter->SetBatchMode(batch);
+ filter->SetUseConfidenceMap(true);
+ using TimeT = std::chrono::milliseconds;
+ auto start = std::chrono::system_clock::now();
+ writer->Update();
+ auto duration = std::chrono::duration_cast< TimeT>
+ (std::chrono::system_clock::now() - start);
+ auto elapsed = duration.count();
+ std::cout << "Classification took " << elapsed << " ms\n";
+
+ auto confWriter = otb::ImageFileWriter<ClassificationFilterType::ConfidenceImageType>::New();
+ confWriter->SetInput(filter->GetOutputConfidence());
+ confWriter->SetFileName(conffname);
+ confWriter->Update();
+
+ return EXIT_SUCCESS;
+}
diff --git a/Modules/Learning/Unsupervised/test/otbTrainMachineLearningClusteringModel.cxx b/Modules/Learning/Unsupervised/test/otbTrainMachineLearningUnsupervisedModel.cxx
similarity index 86%
rename from Modules/Learning/Unsupervised/test/otbTrainMachineLearningClusteringModel.cxx
rename to Modules/Learning/Unsupervised/test/otbTrainMachineLearningUnsupervisedModel.cxx
index 7eae88216b1d3da0c7defe2cb2f2fefb642fb4e4..a0805c29fb38defb049a9588a26e10167cb5920b 100644
--- a/Modules/Learning/Unsupervised/test/otbTrainMachineLearningClusteringModel.cxx
+++ b/Modules/Learning/Unsupervised/test/otbTrainMachineLearningUnsupervisedModel.cxx
@@ -1,3 +1,22 @@
+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
#include <iostream>
#include <otbConfigure.h>
@@ -154,6 +173,11 @@ int otbSharkKMeansMachineLearningModelPredict(int argc, char *argv[])
KMeansType::Pointer classifier = KMeansType::New();
std::cout << "Load\n";
+ if(!classifier->CanReadFile(argv[2]))
+ {
+ std::cerr << "Unable to read model file : " << argv[2] << std::endl;
+ return EXIT_FAILURE;
+ }
classifier->Load( argv[2] );
auto start = std::chrono::system_clock::now();
classifier->SetInputListSample( samples );
diff --git a/Modules/Learning/Unsupervised/test/otbUnsupervisedTestDriver.cxx b/Modules/Learning/Unsupervised/test/otbUnsupervisedTestDriver.cxx
index 30b0b20f1904a20aad0865ceae4e944d9c391dc8..876f613508404c96aaa4027978d1e8a7baabc11c 100644
--- a/Modules/Learning/Unsupervised/test/otbUnsupervisedTestDriver.cxx
+++ b/Modules/Learning/Unsupervised/test/otbUnsupervisedTestDriver.cxx
@@ -1,3 +1,22 @@
+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
#include "otbTestMain.h"
void RegisterTests()
{
@@ -12,5 +31,6 @@ void RegisterTests()
REGISTER_TEST(otbSharkKMeansMachineLearningModelNew);
REGISTER_TEST(otbSharkKMeansMachineLearningModelTrain);
REGISTER_TEST(otbSharkKMeansMachineLearningModelPredict);
+ REGISTER_TEST(otbSharkUnsupervisedImageClassificationFilter);
#endif
}
diff --git a/Modules/Learning/Unsupervised/test/tests-shark.cmake b/Modules/Learning/Unsupervised/test/tests-shark.cmake
index 0635d94ec2a85587e6250958f98c39070b6a7442..df61cb46479f9922e3355441458a1e261879c40c 100644
--- a/Modules/Learning/Unsupervised/test/tests-shark.cmake
+++ b/Modules/Learning/Unsupervised/test/tests-shark.cmake
@@ -4,22 +4,55 @@ otb_add_test(NAME leTvSharkKMeansMachineLearningModelNew COMMAND otbUnsupervised
otbSharkKMeansMachineLearningModelNew
)
+
otb_add_test(NAME leTvSharkKMeansMachineLearningModel COMMAND otbUnsupervisedTestDriver
otbSharkKMeansMachineLearningModelTrain
${INPUTDATA}/letter.scale
${TEMP}/shark_km_model.txt
)
-otb_add_test(NAME leTvSharkKMeansMachineLearningModelCanRead COMMAND otbUnsupervisedTestDriver
+otb_add_test(NAME otbSharkKMeansMachineLearningModelPredict COMMAND otbUnsupervisedTestDriver
otbSharkKMeansMachineLearningModelPredict
${INPUTDATA}/letter.scale
+ ${INPUTDATA}/Classification/shark_km_model.txt
+ )
+
+otb_add_test(NAME leTvSharkKMeansMachineLearningModelCanRead COMMAND otbUnsupervisedTestDriver
+ otbSharkKMeansMachineLearningModelCanRead
${INPUTDATA}/Classification/otbSharkImageClassificationFilter_KMeansmodel.txt
)
otb_add_test(NAME leTvSharkKMeansMachineLearningModelCanReadFail COMMAND otbUnsupervisedTestDriver
- otbSharkKMeansMachineLearningModelPredict
- ${INPUTDATA}/letter.scale
+ otbSharkKMeansMachineLearningModelCanRead
${INPUTDATA}/Classification/otbSharkImageClassificationFilter_RFmodel.txt
)
set_property(TEST leTvSharkKMeansMachineLearningModelCanReadFail PROPERTY WILL_FAIL true)
+
+
+
+otb_add_test(NAME leTvImageClassificationFilterSharkKMeans COMMAND otbUnsupervisedTestDriver
+ --compare-n-images ${NOTOL} 1
+ ${BASELINE}/leSharkUnsupervisedImageClassificationFilterOutput.tif
+ ${TEMP}/leSharkUnsupervisedImageClassificationFilterOutput.tif
+ otbSharkUnsupervisedImageClassificationFilter
+ ${INPUTDATA}/Classification/QB_1_ortho.tif
+ ${TEMP}/leSharkUnsupervisedImageClassificationFilterOutput.tif
+ ${TEMP}/leSharkUnsupervisedImageClassificationFilterConfidence.tif
+ 1
+ ${INPUTDATA}/Classification/otbSharkImageClassificationFilter_KMeansmodel.txt
+ )
+
+
+otb_add_test(NAME leTvImageClassificationFilterSharkKMeansMask COMMAND otbUnsupervisedTestDriver
+ --compare-n-images ${NOTOL} 1
+ ${BASELINE}/leSharkUnsupervisedImageClassificationFilterWithMaskOutput.tif
+ ${TEMP}/leSharkUnsupervisedImageClassificationFilterWithMaskOutput.tif
+ otbSharkUnsupervisedImageClassificationFilter
+ ${INPUTDATA}/Classification/QB_1_ortho.tif
+ ${TEMP}/leSharkUnsupervisedImageClassificationFilterWithMaskOutput.tif
+ ${TEMP}/leSharkUnsupervisedImageClassificationFilterWithMaskConfidence.tif
+ 1
+ ${INPUTDATA}/Classification/otbSharkImageClassificationFilter_KMeansmodel.txt
+ ${INPUTDATA}/Classification/QB_1_ortho_mask.tif
+ )