diff --git a/CMake/OTBModuleHeaderTest.cmake b/CMake/OTBModuleHeaderTest.cmake
index 186c56d487f76374968d54fcd6bbfc8cef13e106..ca2f7cb5ef27ff712e52ed3d05d55d2c499d298b 100644
--- a/CMake/OTBModuleHeaderTest.cmake
+++ b/CMake/OTBModuleHeaderTest.cmake
@@ -46,7 +46,7 @@ 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")
@@ -64,7 +64,7 @@ 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 e28ab734b922b98a067cee9aac88ddc4c832a44f..0d1475994c11bb266b43ef15309394172fee38b3 100644
--- a/Modules/Applications/AppClassification/app/CMakeLists.txt
+++ b/Modules/Applications/AppClassification/app/CMakeLists.txt
@@ -70,7 +70,6 @@ otb_create_application(
SOURCES otbTrainVectorClassifier.cxx
LINK_LIBRARIES ${${otb-module}_LIBRARIES})
-
otb_create_application(
NAME ComputeConfusionMatrix
SOURCES otbComputeConfusionMatrix.cxx
diff --git a/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx b/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx
index 9797ec0fadd347f65f39ec561868e59074ac4f6b..2a84ff53e78b81d71c5eddc3f32967f5cd92fb0b 100644
--- a/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx
+++ b/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx
@@ -18,513 +18,210 @@
* limitations under the License.
*/
-#include "otbWrapperCompositeApplication.h"
-#include "otbWrapperApplicationFactory.h"
-
-#include "otbOGRDataToSamplePositionFilter.h"
-#include "otbSamplingRateCalculator.h"
+#include "otbTrainImagesBase.h"
namespace otb
{
namespace Wrapper
{
-class TrainImagesClassifier: public CompositeApplication
+class TrainImagesClassifier : public TrainImagesBase
{
public:
- /** Standard class typedefs. */
- typedef TrainImagesClassifier Self;
- typedef CompositeApplication Superclass;
- typedef itk::SmartPointer<Self> Pointer;
+ typedef TrainImagesClassifier Self;
+ typedef TrainImagesBase Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
-
- /** Standard macro */
- itkNewMacro(Self)
-
- itkTypeMacro(TrainImagesClassifier, otb::Wrapper::CompositeApplication)
-
- /** filters typedefs*/
- typedef otb::OGRDataToSamplePositionFilter<
- FloatVectorImageType,
- UInt8ImageType,
- otb::PeriodicSampler> PeriodicSamplerType;
-
- typedef otb::SamplingRateCalculator::MapRateType MapRateType;
-
-protected:
-
-private:
-
-bool RemoveFile(std::string &filePath)
-{
- bool res = true;
- if(itksys::SystemTools::FileExists(filePath.c_str()))
- {
- size_t posExt = filePath.rfind('.');
- if (posExt != std::string::npos &&
- filePath.compare(posExt,std::string::npos,".shp") == 0)
+ itkNewMacro( Self )
+ itkTypeMacro( Self, Superclass )
+
+ void DoInit() ITK_OVERRIDE
+ {
+ SetName( "TrainImagesClassifier" );
+ SetDescription( "Train a classifier from multiple pairs of images and training vector data." );
+
+ // Documentation
+ SetDocName( "Train a classifier from multiple images" );
+ SetDocLongDescription(
+ "This application performs a classifier training from multiple pairs of input images and training vector data. "
+ "Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by "
+ "the ComputeImagesStatistics application.\n The training vector data must contain polygons with a positive integer field "
+ "representing the class label. The name of this field can be set using the \"Class label field\" parameter. Training and validation "
+ "sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio "
+ "between the number of samples in training and validation sets. Two parameters allow managing the size of the training and "
+ "validation sets per class and per image.\n Several classifier parameters can be set depending on the chosen classifier. In the "
+ "validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. "
+ "In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels"
+ " are ordered according to the rows/columns of the confusion matrix.\n This application is based on LibSVM and OpenCV Machine Learning "
+ "(2.3.1 and later)." );
+ SetDocLimitations( "None" );
+ SetDocAuthors( "OTB-Team" );
+ SetDocSeeAlso( "OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html " );
+
+ AddDocTag( Tags::Learning );
+
+ // Perform initialization
+ ClearApplications();
+ InitIO();
+ InitSampling();
+ 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" );
+ }
+
+ void DoUpdateParameters() ITK_OVERRIDE
+ {
+ if( HasValue( "io.vd" ) && IsParameterEnabled( "io.vd" ))
{
- std::string shxPath = filePath.substr(0,posExt) + std::string(".shx");
- std::string dbfPath = filePath.substr(0,posExt) + std::string(".dbf");
- std::string prjPath = filePath.substr(0,posExt) + std::string(".prj");
- RemoveFile(shxPath);
- RemoveFile(dbfPath);
- RemoveFile(prjPath);
+ UpdatePolygonClassStatisticsParameters();
}
- res = itksys::SystemTools::RemoveFile(filePath.c_str());
- if (!res)
+ }
+
+ /**
+ * 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 itkNotUsed(HasInputVector) )
+ {
+ if( !validationVectorFileList.empty() ) // Compute class statistics and sampling rate of validation data if provided.
{
- otbAppLogINFO(<<"Unable to remove file "<<filePath);
+ 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;
}
- }
- return res;
-}
-
-void DoInit() ITK_OVERRIDE
-{
- SetName("TrainImagesClassifier");
- SetDescription(
- "Train a classifier from multiple pairs of images and training vector data.");
-
- // Documentation
- SetDocName("Train a classifier from multiple images");
- SetDocLongDescription(
- "This application performs a classifier training from multiple pairs of input images and training vector data. "
- "Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by "
- "the ComputeImagesStatistics application.\n The training vector data must contain polygons with a positive integer field "
- "representing the class label. The name of this field can be set using the \"Class label field\" parameter. Training and validation "
- "sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio "
- "between the number of samples in training and validation sets. Two parameters allow managing the size of the training and "
- "validation sets per class and per image.\n Several classifier parameters can be set depending on the chosen classifier. In the "
- "validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. "
- "In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels"
- " are ordered according to the rows/columns of the confusion matrix.\n This application is based on LibSVM and OpenCV Machine Learning "
- "(2.3.1 and later).");
- SetDocLimitations("None");
- SetDocAuthors("OTB-Team");
- SetDocSeeAlso("OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html ");
-
- AddDocTag(Tags::Learning);
-
- ClearApplications();
- AddApplication("PolygonClassStatistics", "polystat","Polygon analysis");
- AddApplication("MultiImageSamplingRate", "rates", "Sampling rates");
- AddApplication("SampleSelection", "select", "Sample selection");
- AddApplication("SampleExtraction","extraction", "Sample extraction");
- AddApplication("TrainVectorClassifier", "training", "Model training");
-
- //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_InputVectorDataList, "io.valid", "Validation Vector Data List");
- SetParameterDescription("io.valid", "A list of vector data to select the training samples.");
- MandatoryOff("io.valid");
-
- ShareParameter("io.imstat","training.io.stats");
- ShareParameter("io.confmatout","training.io.confmatout");
- ShareParameter("io.out","training.io.out");
-
- ShareParameter("elev","polystat.elev");
-
- // 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);
-
- ShareParameter("sample.vfn","polystat.field");
-
- // hide sampling parameters
- //ShareParameter("sample.strategy","rates.strategy");
- //ShareParameter("sample.mim","rates.mim");
-
- // Classifier settings
- ShareParameter("classifier","training.classifier");
-
- ShareParameter("rand","training.rand");
-
- // Synchronization between applications
- 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");
- Connect("extraction.field", "polystat.field");
- Connect("extraction.layer", "polystat.layer");
-
- Connect("training.cfield", "polystat.field");
-
- ShareParameter("ram","polystat.ram");
- Connect("select.ram", "polystat.ram");
- Connect("extraction.ram", "polystat.ram");
-
- Connect("select.rand", "training.rand");
-
- 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");
-
- // 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");
-}
-
-void DoUpdateParameters() ITK_OVERRIDE
-{
- if ( HasValue("io.vd") )
- {
- std::vector<std::string> vectorFileList = GetParameterStringList("io.vd");
- GetInternalApplication("polystat")->SetParameterString("vec",vectorFileList[0], false);
- UpdateInternalParameters("polystat");
- }
-}
-
-void DoExecute() ITK_OVERRIDE
-{
- FloatVectorImageListType* imageList = GetParameterImageList("io.il");
- std::vector<std::string> vectorFileList = GetParameterStringList("io.vd");
- unsigned int nbInputs = imageList->Size();
- if (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"))
- {
- dedicatedValidation = true;
- validationVectorFileList = GetParameterStringList("io.valid");
- if (nbInputs > validationVectorFileList.size())
+ else if(GetParameterFloat("sample.vtr") != 0.0)// Split training data to validation
{
- otbAppLogFATAL("Missing validation vector data files to match number of images ("<<nbInputs<<").");
+// 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 );
}
- }
-
- // Prepare temporary file names
- std::string outModel(GetParameterString("io.out"));
- std::vector<std::string> polyStatTrainOutputs;
- std::vector<std::string> polyStatValidOutputs;
- std::vector<std::string> ratesTrainOutputs;
- std::vector<std::string> ratesValidOutputs;
- std::vector<std::string> sampleOutputs;
- std::vector<std::string> sampleTrainOutputs;
- std::vector<std::string> sampleValidOutputs;
- std::string rateTrainOut;
- if (dedicatedValidation)
- {
- rateTrainOut = outModel + "_ratesTrain.csv";
- }
- else
- {
- rateTrainOut = outModel + "_rates.csv";
- }
- std::string rateValidOut(outModel + "_ratesValid.csv");
- for (unsigned int i=0 ; i<nbInputs ; i++)
- {
- std::ostringstream oss;
- oss <<i+1;
- std::string strIndex(oss.str());
- if (dedicatedValidation)
+ else // nothing to do, except update fileNames
{
- polyStatTrainOutputs.push_back(outModel + "_statsTrain_" + strIndex + ".xml");
- polyStatValidOutputs.push_back(outModel + "_statsValid_" + strIndex + ".xml");
- ratesTrainOutputs.push_back(outModel + "_ratesTrain_" + strIndex + ".csv");
- ratesValidOutputs.push_back(outModel + "_ratesValid_" + strIndex + ".csv");
- sampleOutputs.push_back(outModel + "_samplesTrain_" + strIndex + ".shp");
+ fileNames.sampleTrainOutputs = fileNames.sampleOutputs;
}
- else
+ }
+
+ /**
+ * 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" );
+ bool HasInputVector = IsParameterEnabled( "io.vd" ) && HasValue( "io.vd" );
+ if(HasInputVector)
+ vectorFileList = GetParameterStringList( "io.vd" );
+
+
+ unsigned long nbInputs = imageList->Size();
+
+ if( !HasInputVector )
{
- polyStatTrainOutputs.push_back(outModel + "_stats_" + strIndex + ".xml");
- ratesTrainOutputs.push_back(outModel + "_rates_" + strIndex + ".csv");
- sampleOutputs.push_back(outModel + "_samples_" + strIndex + ".shp");
+ otbAppLogFATAL( "Missing input vector data files" );
}
- sampleTrainOutputs.push_back(outModel + "_samplesTrain_" + strIndex + ".shp");
- sampleValidOutputs.push_back(outModel + "_samplesValid_" + strIndex + ".shp");
- }
- // ---------------------------------------------------------------------------
- // Polygons stats
- for (unsigned int i=0 ; i<nbInputs ; i++)
- {
- GetInternalApplication("polystat")->SetParameterInputImage("in",imageList->GetNthElement(i));
- GetInternalApplication("polystat")->SetParameterString("vec",vectorFileList[i], false);
- GetInternalApplication("polystat")->SetParameterString("out",polyStatTrainOutputs[i], false);
- ExecuteInternal("polystat");
- // analyse polygons given for validation
- if (dedicatedValidation)
+ if( !vectorFileList.empty() && nbInputs > vectorFileList.size() )
{
- GetInternalApplication("polystat")->SetParameterString("vec",validationVectorFileList[i], false);
- GetInternalApplication("polystat")->SetParameterString("out",polyStatValidOutputs[i], false);
- ExecuteInternal("polystat");
+ otbAppLogFATAL( "Missing input vector data files to match number of images (" << nbInputs << ")." );
}
- }
- // ---------------------------------------------------------------------------
- // Compute sampling 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)
- long fmt = -1;
- long fmv = -1;
- if (GetParameterInt("sample.bm") == 0)
- {
- if (dedicatedValidation)
- {
- // fmt and fmv will be used separately
- fmt = mt;
- fmv = mv;
- if (mt > -1 && mv <= -1 && vtr < 0.99999)
- {
- fmv = static_cast<long>((double) mt * vtr / (1.0 - vtr));
- }
- if (mt <= -1 && mv > -1 && vtr > 0.00001)
- {
- fmt = static_cast<long>((double) mv * (1.0 - vtr) / vtr);
- }
- }
- else
+ // check if validation vectors are given
+ std::vector<std::string> validationVectorFileList;
+ bool dedicatedValidation = false;
+ if( IsParameterEnabled( "io.valid" ) && HasValue( "io.valid" ) )
{
- // 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 && vtr < 0.99999)
- {
- fmt = static_cast<long>((double) mt / (1.0 - vtr));
- }
- if (mv > -1 && vtr > 0.00001)
+ validationVectorFileList = GetParameterStringList( "io.valid" );
+ if( nbInputs > validationVectorFileList.size() )
{
- if (fmt > -1 )
- {
- fmt = std::min(fmt, static_cast<long>((double) mv / vtr));
- }
- else
- {
- fmt = static_cast<long>((double) mv / vtr);
- }
+ otbAppLogFATAL( "Missing validation vector data files to match number of images (" << nbInputs << ")." );
}
- }
- }
- // Sampling rates for training
- GetInternalApplication("rates")->SetParameterStringList("il",polyStatTrainOutputs, false);
- GetInternalApplication("rates")->SetParameterString("out",rateTrainOut, false);
- if (GetParameterInt("sample.bm") != 0)
- {
- GetInternalApplication("rates")->SetParameterString("strategy","smallest", false);
- }
- else
- {
- if (fmt > -1)
- {
- std::ostringstream oss;
- oss << fmt;
- GetInternalApplication("rates")->SetParameterString("strategy","constant", false);
- GetInternalApplication("rates")->SetParameterString("strategy.constant.nb",oss.str());
- }
- else
- {
- GetInternalApplication("rates")->SetParameterString("strategy","all", false);
+ dedicatedValidation = true;
}
- }
- ExecuteInternal("rates");
- // Sampling rates for validation
- if (dedicatedValidation)
- {
- GetInternalApplication("rates")->SetParameterStringList("il",polyStatValidOutputs, false);
- GetInternalApplication("rates")->SetParameterString("out",rateValidOut, false);
- if (GetParameterInt("sample.bm") != 0)
- {
- GetInternalApplication("rates")->SetParameterString("strategy","smallest", false);
- }
- else
- {
- if (fmv > -1)
- {
- std::ostringstream oss;
- oss << fmv;
- GetInternalApplication("rates")->SetParameterString("strategy","constant", false);
- GetInternalApplication("rates")->SetParameterString("strategy.constant.nb",oss.str());
- }
- else
- {
- GetInternalApplication("rates")->SetParameterString("strategy","all", false);
- }
- }
- ExecuteInternal("rates");
- }
- // ---------------------------------------------------------------------------
- // Select & extract samples
- GetInternalApplication("select")->SetParameterString("sampler", "periodic", false);
- GetInternalApplication("select")->SetParameterInt("sampler.periodic.jitter",50);
- GetInternalApplication("select")->SetParameterString("strategy","byclass", false);
- GetInternalApplication("extraction")->SetParameterString("outfield", "prefix", false);
- GetInternalApplication("extraction")->SetParameterString("outfield.prefix.name","value_", false);
- for (unsigned int i=0 ; i<nbInputs ; i++)
- {
- GetInternalApplication("select")->SetParameterInputImage("in",imageList->GetNthElement(i));
- GetInternalApplication("select")->SetParameterString("vec",vectorFileList[i], false);
- GetInternalApplication("select")->SetParameterString("out",sampleOutputs[i], false);
- GetInternalApplication("select")->SetParameterString("instats",polyStatTrainOutputs[i], false);
- GetInternalApplication("select")->SetParameterString("strategy.byclass.in",ratesTrainOutputs[i], false);
- // select sample positions
- ExecuteInternal("select");
- // extract sample descriptors
- ExecuteInternal("extraction");
+ fileNames.CreateTemporaryFileNames( GetParameterString( "io.out" ), nbInputs, dedicatedValidation );
- if (dedicatedValidation)
- {
- GetInternalApplication("select")->SetParameterString("vec",validationVectorFileList[i], false);
- GetInternalApplication("select")->SetParameterString("out",sampleValidOutputs[i], false);
- GetInternalApplication("select")->SetParameterString("instats",polyStatValidOutputs[i], false);
- GetInternalApplication("select")->SetParameterString("strategy.byclass.in",ratesValidOutputs[i], false);
- // select sample positions
- ExecuteInternal("select");
- // extract sample descriptors
- ExecuteInternal("extraction");
- }
- else
- {
- // Split between training and validation
- ogr::DataSource::Pointer source = ogr::DataSource::New(sampleOutputs[i], ogr::DataSource::Modes::Read);
- ogr::DataSource::Pointer destTrain = ogr::DataSource::New(sampleTrainOutputs[i], ogr::DataSource::Modes::Overwrite);
- ogr::DataSource::Pointer destValid = ogr::DataSource::New(sampleValidOutputs[i], ogr::DataSource::Modes::Overwrite);
- // read sampling rates from ratesTrainOutputs[i]
- SamplingRateCalculator::Pointer rateCalculator = SamplingRateCalculator::New();
- rateCalculator->Read(ratesTrainOutputs[i]);
- // 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)
- {
- 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;
- }
+ // Compute final maximum sampling rates for both training and validation samples
+ SamplingRates rates = ComputeFinalMaximumSamplingRates( dedicatedValidation );
+
+ ExtractTrainData(imageList, fileNames, vectorFileList, rates);
+ ExtractValidationData(imageList, fileNames, validationVectorFileList, rates, HasInputVector);
- // Use an otb::OGRDataToSamplePositionFilter with 2 outputs
- PeriodicSamplerType::SamplerParameterType param;
- param.Offset = 0;
- param.MaxJitter = 0;
- PeriodicSamplerType::Pointer splitter = PeriodicSamplerType::New();
- splitter->SetInput(imageList->GetNthElement(i));
- 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(this->GetParameterInt("ram"));
- AddProcess(splitter->GetStreamer(),"Split samples between training and validation...");
- splitter->Update();
+ // Then train the model with extracted samples
+ TrainModel( imageList, fileNames.sampleTrainOutputs, fileNames.sampleValidOutputs );
+
+ // cleanup
+ if( IsParameterEnabled( "cleanup" ) )
+ {
+ otbAppLogINFO( <<"Final clean-up ..." );
+ fileNames.clear();
}
- }
+ }
- // ---------------------------------------------------------------------------
- // Train model
- GetInternalApplication("training")->SetParameterStringList("io.vd",sampleTrainOutputs, false);
- if( vtr!=0.0 && !sampleValidOutputs.empty() )
- GetInternalApplication("training")->SetParameterStringList("valid.vd",sampleValidOutputs, 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");
+private :
- // cleanup
- if(IsParameterEnabled("cleanup"))
- {
- otbAppLogINFO(<<"Final clean-up ...");
- for(unsigned int i=0 ; i<polyStatTrainOutputs.size() ; i++)
- RemoveFile(polyStatTrainOutputs[i]);
- for(unsigned int i=0 ; i<polyStatValidOutputs.size() ; i++)
- RemoveFile(polyStatValidOutputs[i]);
- for(unsigned int i=0 ; i<ratesTrainOutputs.size() ; i++)
- RemoveFile(ratesTrainOutputs[i]);
- for(unsigned int i=0 ; i<ratesValidOutputs.size() ; i++)
- RemoveFile(ratesValidOutputs[i]);
- for(unsigned int i=0 ; i<sampleOutputs.size() ; i++)
- RemoveFile(sampleOutputs[i]);
- for(unsigned int i=0 ; i<sampleTrainOutputs.size() ; i++)
- RemoveFile(sampleTrainOutputs[i]);
- for(unsigned int i=0 ; i<sampleValidOutputs.size() ; i++)
- RemoveFile(sampleValidOutputs[i]);
- }
-}
+ void UpdatePolygonClassStatisticsParameters()
+ {
+ std::vector<std::string> vectorFileList = GetParameterStringList( "io.vd" );
+ GetInternalApplication( "polystat" )->SetParameterString( "vec", vectorFileList[0], false );
+ UpdateInternalParameters( "polystat" );
+ }
};
} // end namespace Wrapper
} // end namespace otb
-OTB_APPLICATION_EXPORT(otb::Wrapper::TrainImagesClassifier)
+OTB_APPLICATION_EXPORT( otb::Wrapper::TrainImagesClassifier )
\ No newline at end of file
diff --git a/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx b/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
index f9acb6fbe0cafd499c68c234d3a976fa223cd47a..b39c6fae197b4c1dddd0e08156b221668fb883a2 100644
--- a/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
+++ b/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
@@ -18,60 +18,30 @@
* limitations under the License.
*/
-#include "otbWrapperApplication.h"
-#include "otbWrapperApplicationFactory.h"
-
-#include "otbLearningApplicationBase.h"
-
-#include "otbOGRDataSourceWrapper.h"
-#include "otbOGRFeatureWrapper.h"
-#include "otbStatisticsXMLFileWriter.h"
-
-#include "itkVariableLengthVector.h"
-#include "otbStatisticsXMLFileReader.h"
-
-#include "itkListSample.h"
-#include "otbShiftScaleSampleListFilter.h"
+#include "otbTrainVectorBase.h"
// Validation
#include "otbConfusionMatrixCalculator.h"
-#include <algorithm>
-#include <locale>
-
namespace otb
{
namespace Wrapper
{
-/** Utility function to negate std::isalnum */
-bool IsNotAlphaNum(char c)
- {
- return !std::isalnum(c);
- }
-
-class TrainVectorClassifier : public LearningApplicationBase<float,int>
+class TrainVectorClassifier : public TrainVectorBase
{
public:
typedef TrainVectorClassifier Self;
- typedef LearningApplicationBase<float, int> Superclass;
+ typedef TrainVectorBase Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
- itkNewMacro(Self)
-
- itkTypeMacro(Self, Superclass)
-
- typedef Superclass::SampleType SampleType;
- typedef Superclass::ListSampleType ListSampleType;
- typedef Superclass::TargetListSampleType TargetListSampleType;
- typedef Superclass::SampleImageType SampleImageType;
-
- typedef double ValueType;
- typedef itk::VariableLengthVector<ValueType> MeasurementType;
+ itkNewMacro( Self )
- typedef otb::StatisticsXMLFileReader<SampleType> StatisticsReader;
+ itkTypeMacro( Self, Superclass )
- typedef otb::Statistics::ShiftScaleSampleListFilter<ListSampleType, ListSampleType> ShiftScaleFilterType;
+ typedef Superclass::SampleType SampleType;
+ typedef Superclass::ListSampleType ListSampleType;
+ typedef Superclass::TargetListSampleType TargetListSampleType;
// Estimate performance on validation sample
typedef otb::ConfusionMatrixCalculator<TargetListSampleType, TargetListSampleType> ConfusionMatrixCalculatorType;
@@ -79,503 +49,257 @@ public:
typedef ConfusionMatrixCalculatorType::MapOfIndicesType MapOfIndicesType;
typedef ConfusionMatrixCalculatorType::ClassLabelType ClassLabelType;
-private:
+protected:
void DoInit()
{
- SetName("TrainVectorClassifier");
- SetDescription("Train a classifier based on labeled geometries and a list of features to consider.");
-
- SetDocName("Train Vector Classifier");
- SetDocLongDescription("This application trains a classifier based on "
- "labeled geometries and a list of features to consider for classification.");
- SetDocLimitations(" ");
- SetDocAuthors("OTB Team");
- SetDocSeeAlso(" ");
-
- //Group IO
- AddParameter(ParameterType_Group, "io", "Input and output data");
- SetParameterDescription("io", "This group of parameters allows setting input and output data.");
-
- AddParameter(ParameterType_InputVectorDataList, "io.vd", "Input Vector Data");
- SetParameterDescription("io.vd", "Input geometries used for training (note : all geometries from the layer will be used)");
-
- AddParameter(ParameterType_InputFilename, "io.stats", "Input XML image statistics file");
- MandatoryOff("io.stats");
- SetParameterDescription("io.stats", "XML file containing mean and variance of each feature.");
-
- AddParameter(ParameterType_OutputFilename, "io.confmatout", "Output confusion matrix");
- SetParameterDescription("io.confmatout", "Output file containing the confusion matrix (.csv format).");
- MandatoryOff("io.confmatout");
-
- AddParameter(ParameterType_OutputFilename, "io.out", "Output model");
- SetParameterDescription("io.out", "Output file containing the model estimated (.txt format).");
-
- AddParameter(ParameterType_ListView, "feat", "Field names for training features.");
- SetParameterDescription("feat","List of field names in the input vector data to be used as features for training.");
-
- AddParameter(ParameterType_ListView,"cfield","Field containing the class id for supervision");
- SetParameterDescription("cfield","Field containing the class id for supervision. "
- "Only geometries with this field available will be taken into account.");
- SetListViewSingleSelectionMode("cfield",true);
-
- AddParameter(ParameterType_Int, "layer", "Layer Index");
- SetParameterDescription("layer", "Index of the layer to use in the input vector file.");
- MandatoryOff("layer");
- SetDefaultParameterInt("layer",0);
-
- AddParameter(ParameterType_Group, "valid", "Validation data");
- SetParameterDescription("valid", "This group of parameters defines validation data.");
-
- AddParameter(ParameterType_InputVectorDataList, "valid.vd", "Validation Vector Data");
- SetParameterDescription("valid.vd", "Geometries used for validation "
- "(must contain the same fields used for training, all geometries from the layer will be used)");
- MandatoryOff("valid.vd");
-
- AddParameter(ParameterType_Int, "valid.layer", "Layer Index");
- SetParameterDescription("valid.layer", "Index of the layer to use in the validation vector file.");
- MandatoryOff("valid.layer");
- SetDefaultParameterInt("valid.layer",0);
-
- // Add parameters for the classifier choice
- Superclass::DoInit();
+ 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( " " );
- AddRANDParameter();
- // 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");
+ Superclass::DoInit();
}
void DoUpdateParameters()
{
- if ( HasValue("io.vd") )
+ Superclass::DoUpdateParameters();
+ }
+
+ void DoExecute()
+ {
+ // Enforce the need of class field name in supervised mode
+ if (GetClassifierCategory() == Supervised)
{
- std::vector<std::string> vectorFileList = GetParameterStringList("io.vd");
- ogr::DataSource::Pointer ogrDS =
- ogr::DataSource::New(vectorFileList[0], ogr::DataSource::Modes::Read);
- ogr::Layer layer = ogrDS->GetLayer(this->GetParameterInt("layer"));
- ogr::Feature feature = layer.ogr().GetNextFeature();
-
- ClearChoices("feat");
- ClearChoices("cfield");
-
- for(int iField=0; iField<feature.ogr().GetFieldCount(); iField++)
+ m_FeaturesInfo.SetClassFieldNames( GetChoiceNames( "cfield" ), GetSelectedItems( "cfield" ) );
+
+ if( m_FeaturesInfo.m_SelectedCFieldIdx.empty() )
{
- std::string key, item = feature.ogr().GetFieldDefnRef(iField)->GetNameRef();
- key = item;
- std::string::iterator end = std::remove_if(key.begin(),key.end(),IsNotAlphaNum);
- std::transform(key.begin(), end, key.begin(), tolower);
-
- OGRFieldType fieldType = feature.ogr().GetFieldDefnRef(iField)->GetType();
-
- if(fieldType == OFTInteger || ogr::version_proxy::IsOFTInteger64(fieldType) || fieldType == OFTReal)
- {
- std::string tmpKey="feat."+key.substr(0, end - key.begin());
- AddChoice(tmpKey,item);
- }
- if(fieldType == OFTString || fieldType == OFTInteger || ogr::version_proxy::IsOFTInteger64(fieldType))
- {
- std::string tmpKey="cfield."+key.substr(0, end - key.begin());
- AddChoice(tmpKey,item);
- }
+ otbAppLogFATAL( << "No field has been selected for data labelling!" );
}
}
+
+ Superclass::DoExecute();
+
+ if (GetClassifierCategory() == Supervised)
+ {
+ ConfusionMatrixCalculatorType::Pointer confMatCalc = ComputeConfusionMatrix( m_PredictedList,
+ m_ClassificationSamplesWithLabel.labeledListSample );
+ WriteConfusionMatrix( confMatCalc );
+ }
+ else
+ {
+ // TODO Compute Contingency Table
+ }
}
-void LogConfusionMatrix(ConfusionMatrixCalculatorType* confMatCalc)
-{
- ConfusionMatrixCalculatorType::ConfusionMatrixType matrix = confMatCalc->GetConfusionMatrix();
- // Compute minimal width
- size_t minwidth = 0;
+ ConfusionMatrixCalculatorType::Pointer
+ ComputeConfusionMatrix(const TargetListSampleType::Pointer &predictedListSample,
+ const TargetListSampleType::Pointer &performanceLabeledListSample)
+ {
+ ConfusionMatrixCalculatorType::Pointer confMatCalc = ConfusionMatrixCalculatorType::New();
- for (unsigned int i = 0; i < matrix.Rows(); i++)
- {
- for (unsigned int j = 0; j < matrix.Cols(); j++)
+ otbAppLogINFO( "Predicted list size : " << predictedListSample->Size() );
+ otbAppLogINFO( "ValidationLabeledListSample size : " << performanceLabeledListSample->Size() );
+ confMatCalc->SetReferenceLabels( performanceLabeledListSample );
+ confMatCalc->SetProducedLabels( predictedListSample );
+ confMatCalc->Compute();
+
+ otbAppLogINFO( "training performances" );
+ LogConfusionMatrix( confMatCalc );
+
+ for( unsigned int itClasses = 0; itClasses < confMatCalc->GetNumberOfClasses(); itClasses++ )
{
- std::ostringstream os;
- os << matrix(i, j);
- size_t size = os.str().size();
+ ConfusionMatrixCalculatorType::ClassLabelType classLabel = confMatCalc->GetMapOfIndices()[itClasses];
- if (size > minwidth)
- {
- minwidth = size;
- }
+ otbAppLogINFO( "Precision of class [" << classLabel << "] vs all: " << confMatCalc->GetPrecisions()[itClasses] );
+ otbAppLogINFO( "Recall of class [" << classLabel << "] vs all: " << confMatCalc->GetRecalls()[itClasses] );
+ otbAppLogINFO(
+ "F-score of class [" << classLabel << "] vs all: " << confMatCalc->GetFScores()[itClasses] << "\n" );
}
- }
+ otbAppLogINFO( "Global performance, Kappa index: " << confMatCalc->GetKappaIndex() );
+ return confMatCalc;
+ }
- MapOfIndicesType mapOfIndices = confMatCalc->GetMapOfIndices();
+ /**
+ * Write the confidence matrix into a file if output is provided.
+ * \param confMatCalc the input matrix to write.
+ */
+ void WriteConfusionMatrix(const ConfusionMatrixCalculatorType::Pointer &confMatCalc)
+ {
+ if( this->HasValue( "io.confmatout" ) )
+ {
+ // Writing the confusion matrix in the output .CSV file
- MapOfIndicesType::const_iterator it = mapOfIndices.begin();
- MapOfIndicesType::const_iterator end = mapOfIndices.end();
+ MapOfIndicesType::iterator itMapOfIndicesValid, itMapOfIndicesPred;
+ ClassLabelType labelValid = 0;
- for (; it != end; ++it)
- {
- std::ostringstream os;
- os << "[" << it->second << "]";
+ ConfusionMatrixType confusionMatrix = confMatCalc->GetConfusionMatrix();
+ MapOfIndicesType mapOfIndicesValid = confMatCalc->GetMapOfIndices();
- size_t size = os.str().size();
- if (size > minwidth)
- {
- minwidth = size;
- }
- }
+ unsigned long nbClassesPred = mapOfIndicesValid.size();
- // Generate matrix string, with 'minwidth' as size specifier
- std::ostringstream os;
+ /////////////////////////////////////////////
+ // Filling the 2 headers for the output file
+ const std::string commentValidStr = "#Reference labels (rows):";
+ const std::string commentPredStr = "#Produced labels (columns):";
+ const char separatorChar = ',';
+ std::ostringstream ossHeaderValidLabels, ossHeaderPredLabels;
- // Header line
- for (size_t i = 0; i < minwidth; ++i)
- os << " ";
- os << " ";
-
- it = mapOfIndices.begin();
- end = mapOfIndices.end();
- for (; it != end; ++it)
- {
- os << "[" << it->second << "]" << " ";
- }
-
- os << std::endl;
-
- // Each line of confusion matrix
- for (unsigned int i = 0; i < matrix.Rows(); i++)
- {
- ConfusionMatrixCalculatorType::ClassLabelType label = mapOfIndices[i];
- os << "[" << std::setw(minwidth - 2) << label << "]" << " ";
- for (unsigned int j = 0; j < matrix.Cols(); j++)
- {
- os << std::setw(minwidth) << matrix(i, j) << " ";
- }
- os << std::endl;
- }
+ // Filling ossHeaderValidLabels and ossHeaderPredLabels for the output file
+ ossHeaderValidLabels << commentValidStr;
+ ossHeaderPredLabels << commentPredStr;
- otbAppLogINFO("Confusion matrix (rows = reference labels, columns = produced labels):\n" << os.str());
-}
+ itMapOfIndicesValid = mapOfIndicesValid.begin();
+ while( itMapOfIndicesValid != mapOfIndicesValid.end() )
+ {
+ // labels labelValid of mapOfIndicesValid are already sorted in otbConfusionMatrixCalculator
+ labelValid = itMapOfIndicesValid->second;
-void DoExecute()
- {
- typedef int LabelPixelType;
- typedef itk::FixedArray<LabelPixelType,1> LabelSampleType;
- typedef itk::Statistics::ListSample <LabelSampleType> LabelListSampleType;
-
- // Prepare selected field names (their position may change between two inputs)
- std::vector<int> selectedIdx = GetSelectedItems("feat");
- std::vector<int> selectedCFieldIdx = GetSelectedItems("cfield");
-
- if(selectedIdx.empty())
- {
- otbAppLogFATAL(<<"No features have been selected to train the classifier on!");
- }
-
- if(selectedCFieldIdx.empty())
- {
- otbAppLogFATAL(<<"No field has been selected for data labelling!");
- }
-
- const unsigned int nbFeatures = selectedIdx.size();
- std::vector<std::string> fieldNames = GetChoiceNames("feat");
- std::vector<std::string> cFieldNames = GetChoiceNames("cfield");
- std::vector<std::string> selectedNames(nbFeatures);
- for (unsigned int i=0 ; i<nbFeatures ; i++)
- {
- selectedNames[i] = fieldNames[selectedIdx[i]];
- }
-
- std::string selectedCFieldName = cFieldNames[selectedCFieldIdx.front()];
-
- std::vector<int> featureFieldIndex(nbFeatures, -1);
- int cFieldIndex = -1;
-
- // Statistics for shift/scale
- MeasurementType meanMeasurementVector;
- MeasurementType stddevMeasurementVector;
- if (HasValue("io.stats") && IsParameterEnabled("io.stats"))
- {
- StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
- std::string XMLfile = GetParameterString("io.stats");
- statisticsReader->SetFileName(XMLfile);
- meanMeasurementVector = statisticsReader->GetStatisticVectorByName("mean");
- stddevMeasurementVector = statisticsReader->GetStatisticVectorByName("stddev");
- }
- else
- {
- meanMeasurementVector.SetSize(nbFeatures);
- meanMeasurementVector.Fill(0.);
- stddevMeasurementVector.SetSize(nbFeatures);
- stddevMeasurementVector.Fill(1.);
- }
-
- ListSampleType::Pointer input = ListSampleType::New();
- LabelListSampleType::Pointer target = LabelListSampleType::New();
- input->SetMeasurementVectorSize(nbFeatures);
-
- std::vector<std::string> vectorFileList = GetParameterStringList("io.vd");
- for (unsigned int k=0 ; k<vectorFileList.size() ; k++)
- {
- otbAppLogINFO("Reading input vector file "<<k+1<<"/"<<vectorFileList.size());
- ogr::DataSource::Pointer source = ogr::DataSource::New(vectorFileList[k], ogr::DataSource::Modes::Read);
- ogr::Layer layer = source->GetLayer(this->GetParameterInt("layer"));
- ogr::Feature feature = layer.ogr().GetNextFeature();
- bool goesOn = feature.addr() != 0;
- if (!goesOn)
- {
- otbAppLogWARNING("The layer "<<GetParameterInt("layer")<<" of "
- <<vectorFileList[k]<<" is empty, input is skipped.");
- continue;
- }
+ otbAppLogINFO( "mapOfIndicesValid[" << itMapOfIndicesValid->first << "] = " << labelValid );
- // Check all needed fields are present :
- // - check class field
- cFieldIndex = feature.ogr().GetFieldIndex(selectedCFieldName.c_str());
- if (cFieldIndex < 0)
- otbAppLogFATAL("The field name for class label ("<<selectedCFieldName
- <<") has not been found in the input vector file "<<vectorFileList[k]);
- // - check feature fields
- for (unsigned int i=0 ; i<nbFeatures ; i++)
- {
- featureFieldIndex[i] = feature.ogr().GetFieldIndex(selectedNames[i].c_str());
- if (featureFieldIndex[i] < 0)
- otbAppLogFATAL("The field name for feature "<<selectedNames[i]
- <<" has not been found in the input vector file "<<vectorFileList[k]);
- }
+ ossHeaderValidLabels << labelValid;
+ ossHeaderPredLabels << labelValid;
- while(goesOn)
- {
- if(feature.ogr().IsFieldSet(cFieldIndex))
- {
- MeasurementType mv;
- mv.SetSize(nbFeatures);
- for(unsigned int idx=0; idx < nbFeatures; ++idx)
- mv[idx] = feature.ogr().GetFieldAsDouble(featureFieldIndex[idx]);
+ ++itMapOfIndicesValid;
- input->PushBack(mv);
- target->PushBack(feature.ogr().GetFieldAsInteger(cFieldIndex));
- }
- feature = layer.ogr().GetNextFeature();
- goesOn = feature.addr() != 0;
- }
- }
-
- ShiftScaleFilterType::Pointer trainingShiftScaleFilter = ShiftScaleFilterType::New();
- trainingShiftScaleFilter->SetInput(input);
- trainingShiftScaleFilter->SetShifts(meanMeasurementVector);
- trainingShiftScaleFilter->SetScales(stddevMeasurementVector);
- trainingShiftScaleFilter->Update();
-
- ListSampleType::Pointer trainingListSample= trainingShiftScaleFilter->GetOutput();
- TargetListSampleType::Pointer trainingLabeledListSample = target;
-
- //--------------------------
- // Estimate model
- //--------------------------
- this->Train(trainingListSample,trainingLabeledListSample,GetParameterString("io.out"));
-
- //--------------------------
- // Performances estimation
- //--------------------------
- ListSampleType::Pointer validationListSample=ListSampleType::New();
- TargetListSampleType::Pointer validationLabeledListSample = TargetListSampleType::New();
-
- // Import validation data
- if (HasValue("valid.vd") && IsParameterEnabled("valid.vd"))
- {
- input = ListSampleType::New();
- target = LabelListSampleType::New();
- input->SetMeasurementVectorSize(nbFeatures);
-
- std::vector<std::string> validFileList = this->GetParameterStringList("valid.vd");
- for (unsigned int k=0 ; k<validFileList.size() ; k++)
- {
- otbAppLogINFO("Reading validation vector file "<<k+1<<"/"<<validFileList.size());
- ogr::DataSource::Pointer source = ogr::DataSource::New(validFileList[k], ogr::DataSource::Modes::Read);
- ogr::Layer layer = source->GetLayer(this->GetParameterInt("valid.layer"));
- ogr::Feature feature = layer.ogr().GetNextFeature();
- bool goesOn = feature.addr() != 0;
- if (!goesOn)
- {
- otbAppLogWARNING("The layer "<<GetParameterInt("valid.layer")<<" of "
- <<validFileList[k]<<" is empty, input is skipped.");
- continue;
+ if( itMapOfIndicesValid != mapOfIndicesValid.end() )
+ {
+ ossHeaderValidLabels << separatorChar;
+ ossHeaderPredLabels << separatorChar;
+ }
+ else
+ {
+ ossHeaderValidLabels << std::endl;
+ ossHeaderPredLabels << std::endl;
+ }
}
- // Check all needed fields are present :
- // - check class field
- cFieldIndex = feature.ogr().GetFieldIndex(selectedCFieldName.c_str());
- if (cFieldIndex < 0)
- otbAppLogFATAL("The field name for class label ("<<selectedCFieldName
- <<") has not been found in the validation vector file "<<validFileList[k]);
- // - check feature fields
- for (unsigned int i=0 ; i<nbFeatures ; i++)
- {
- featureFieldIndex[i] = feature.ogr().GetFieldIndex(selectedNames[i].c_str());
- if (featureFieldIndex[i] < 0)
- otbAppLogFATAL("The field name for feature "<<selectedNames[i]
- <<" has not been found in the validation vector file "<<validFileList[k]);
- }
+ std::ofstream outFile;
+ outFile.open( this->GetParameterString( "io.confmatout" ).c_str() );
+ outFile << std::fixed;
+ outFile.precision( 10 );
+
+ /////////////////////////////////////
+ // Writing the 2 headers
+ outFile << ossHeaderValidLabels.str();
+ outFile << ossHeaderPredLabels.str();
+ /////////////////////////////////////
+
+ unsigned int indexLabelValid = 0, indexLabelPred = 0;
- while(goesOn)
+ for( itMapOfIndicesValid = mapOfIndicesValid.begin();
+ itMapOfIndicesValid != mapOfIndicesValid.end(); ++itMapOfIndicesValid )
{
- if(feature.ogr().IsFieldSet(cFieldIndex))
- {
- MeasurementType mv;
- mv.SetSize(nbFeatures);
- for(unsigned int idx=0; idx < nbFeatures; ++idx)
- mv[idx] = feature.ogr().GetFieldAsDouble(featureFieldIndex[idx]);
+ indexLabelPred = 0;
- input->PushBack(mv);
- target->PushBack(feature.ogr().GetFieldAsInteger(cFieldIndex));
+ for( itMapOfIndicesPred = mapOfIndicesValid.begin();
+ itMapOfIndicesPred != mapOfIndicesValid.end(); ++itMapOfIndicesPred )
+ {
+ // Writing the confusion matrix (sorted in otbConfusionMatrixCalculator) in the output file
+ outFile << confusionMatrix( indexLabelValid, indexLabelPred );
+ if( indexLabelPred < ( nbClassesPred - 1 ) )
+ {
+ outFile << separatorChar;
+ }
+ else
+ {
+ outFile << std::endl;
+ }
+ ++indexLabelPred;
}
- feature = layer.ogr().GetNextFeature();
- goesOn = feature.addr() != 0;
+
+ ++indexLabelValid;
}
+
+ outFile.close();
}
+ }
+
+ /**
+ * Display the log of the confusion matrix computed with
+ * \param confMatCalc the input confusion matrix to display
+ */
+ void LogConfusionMatrix(ConfusionMatrixCalculatorType *confMatCalc)
+ {
+ ConfusionMatrixCalculatorType::ConfusionMatrixType matrix = confMatCalc->GetConfusionMatrix();
+
+ // Compute minimal width
+ size_t minwidth = 0;
- ShiftScaleFilterType::Pointer validShiftScaleFilter = ShiftScaleFilterType::New();
- validShiftScaleFilter->SetInput(input);
- validShiftScaleFilter->SetShifts(meanMeasurementVector);
- validShiftScaleFilter->SetScales(stddevMeasurementVector);
- validShiftScaleFilter->Update();
-
- validationListSample = validShiftScaleFilter->GetOutput();
- validationLabeledListSample = target;
- }
-
- //Test the input validation set size
- TargetListSampleType::Pointer predictedList = TargetListSampleType::New();
- ListSampleType::Pointer performanceListSample;
- TargetListSampleType::Pointer performanceLabeledListSample;
- if(validationLabeledListSample->Size() != 0)
- {
- performanceListSample = validationListSample;
- performanceLabeledListSample = validationLabeledListSample;
- }
- else
- {
- otbAppLogWARNING("The validation set is empty. The performance estimation is done using the input training set in this case.");
- performanceListSample = trainingListSample;
- performanceLabeledListSample = trainingLabeledListSample;
- }
-
- this->Classify(performanceListSample, predictedList, GetParameterString("io.out"));
-
- ConfusionMatrixCalculatorType::Pointer confMatCalc = ConfusionMatrixCalculatorType::New();
-
- otbAppLogINFO("Predicted list size : " << predictedList->Size());
- otbAppLogINFO("ValidationLabeledListSample size : " << performanceLabeledListSample->Size());
- confMatCalc->SetReferenceLabels(performanceLabeledListSample);
- confMatCalc->SetProducedLabels(predictedList);
- confMatCalc->Compute();
-
- otbAppLogINFO("training performances");
- LogConfusionMatrix(confMatCalc);
-
- for (unsigned int itClasses = 0; itClasses < confMatCalc->GetNumberOfClasses(); itClasses++)
- {
- ConfusionMatrixCalculatorType::ClassLabelType classLabel = confMatCalc->GetMapOfIndices()[itClasses];
-
- otbAppLogINFO("Precision of class [" << classLabel << "] vs all: " << confMatCalc->GetPrecisions()[itClasses]);
- otbAppLogINFO("Recall of class [" << classLabel << "] vs all: " << confMatCalc->GetRecalls()[itClasses]);
- otbAppLogINFO(
- "F-score of class [" << classLabel << "] vs all: " << confMatCalc->GetFScores()[itClasses] << "\n");
- }
- otbAppLogINFO("Global performance, Kappa index: " << confMatCalc->GetKappaIndex());
-
-
- if (this->HasValue("io.confmatout"))
- {
- // Writing the confusion matrix in the output .CSV file
-
- MapOfIndicesType::iterator itMapOfIndicesValid, itMapOfIndicesPred;
- ClassLabelType labelValid = 0;
-
- ConfusionMatrixType confusionMatrix = confMatCalc->GetConfusionMatrix();
- MapOfIndicesType mapOfIndicesValid = confMatCalc->GetMapOfIndices();
-
- unsigned int nbClassesPred = mapOfIndicesValid.size();
-
- /////////////////////////////////////////////
- // Filling the 2 headers for the output file
- const std::string commentValidStr = "#Reference labels (rows):";
- const std::string commentPredStr = "#Produced labels (columns):";
- const char separatorChar = ',';
- std::ostringstream ossHeaderValidLabels, ossHeaderPredLabels;
-
- // Filling ossHeaderValidLabels and ossHeaderPredLabels for the output file
- ossHeaderValidLabels << commentValidStr;
- ossHeaderPredLabels << commentPredStr;
-
- itMapOfIndicesValid = mapOfIndicesValid.begin();
-
- while (itMapOfIndicesValid != mapOfIndicesValid.end())
+ for( unsigned int i = 0; i < matrix.Rows(); i++ )
{
- // labels labelValid of mapOfIndicesValid are already sorted in otbConfusionMatrixCalculator
- labelValid = itMapOfIndicesValid->second;
+ for( unsigned int j = 0; j < matrix.Cols(); j++ )
+ {
+ std::ostringstream os;
+ os << matrix( i, j );
+ size_t size = os.str().size();
- otbAppLogINFO("mapOfIndicesValid[" << itMapOfIndicesValid->first << "] = " << labelValid);
+ if( size > minwidth )
+ {
+ minwidth = size;
+ }
+ }
+ }
- ossHeaderValidLabels << labelValid;
- ossHeaderPredLabels << labelValid;
+ MapOfIndicesType mapOfIndices = confMatCalc->GetMapOfIndices();
- ++itMapOfIndicesValid;
+ MapOfIndicesType::const_iterator it = mapOfIndices.begin();
+ MapOfIndicesType::const_iterator end = mapOfIndices.end();
- if (itMapOfIndicesValid != mapOfIndicesValid.end())
- {
- ossHeaderValidLabels << separatorChar;
- ossHeaderPredLabels << separatorChar;
- }
- else
+ for( ; it != end; ++it )
+ {
+ std::ostringstream os;
+ os << "[" << it->second << "]";
+
+ size_t size = os.str().size();
+ if( size > minwidth )
{
- ossHeaderValidLabels << std::endl;
- ossHeaderPredLabels << std::endl;
+ minwidth = size;
}
}
- std::ofstream outFile;
- outFile.open(this->GetParameterString("io.confmatout").c_str());
- outFile << std::fixed;
- outFile.precision(10);
-
- /////////////////////////////////////
- // Writing the 2 headers
- outFile << ossHeaderValidLabels.str();
- outFile << ossHeaderPredLabels.str();
- /////////////////////////////////////
+ // Generate matrix string, with 'minwidth' as size specifier
+ std::ostringstream os;
- unsigned int indexLabelValid = 0, indexLabelPred = 0;
+ // Header line
+ for( size_t i = 0; i < minwidth; ++i )
+ os << " ";
+ os << " ";
- for (itMapOfIndicesValid = mapOfIndicesValid.begin(); itMapOfIndicesValid != mapOfIndicesValid.end(); ++itMapOfIndicesValid)
+ it = mapOfIndices.begin();
+ end = mapOfIndices.end();
+ for( ; it != end; ++it )
{
- indexLabelPred = 0;
+ os << "[" << it->second << "]" << " ";
+ }
- for (itMapOfIndicesPred = mapOfIndicesValid.begin(); itMapOfIndicesPred != mapOfIndicesValid.end(); ++itMapOfIndicesPred)
+ os << std::endl;
+
+ // Each line of confusion matrix
+ for( unsigned int i = 0; i < matrix.Rows(); i++ )
+ {
+ ConfusionMatrixCalculatorType::ClassLabelType label = mapOfIndices[i];
+ os << "[" << std::setw( minwidth - 2 ) << label << "]" << " ";
+ for( unsigned int j = 0; j < matrix.Cols(); j++ )
{
- // Writing the confusion matrix (sorted in otbConfusionMatrixCalculator) in the output file
- outFile << confusionMatrix(indexLabelValid, indexLabelPred);
- if (indexLabelPred < (nbClassesPred - 1))
- {
- outFile << separatorChar;
- }
- else
- {
- outFile << std::endl;
- }
- ++indexLabelPred;
+ os << std::setw( minwidth ) << matrix( i, j ) << " ";
}
-
- ++indexLabelValid;
+ os << std::endl;
}
- outFile.close();
- } // END if (this->HasValue("io.confmatout"))
+ otbAppLogINFO( "Confusion matrix (rows = reference labels, columns = produced labels):\n" << os.str() );
}
};
}
}
-OTB_APPLICATION_EXPORT(otb::Wrapper::TrainVectorClassifier)
+OTB_APPLICATION_EXPORT( otb::Wrapper::TrainVectorClassifier )
diff --git a/Modules/Applications/AppClassification/include/otbLearningApplicationBase.h b/Modules/Applications/AppClassification/include/otbLearningApplicationBase.h
index 79767a1c02509c581fe62e8fe5cb25eba89e19ba..991ee9aa892137f12ee07bdb3b89d15bdbb26d4a 100644
--- a/Modules/Applications/AppClassification/include/otbLearningApplicationBase.h
+++ b/Modules/Applications/AppClassification/include/otbLearningApplicationBase.h
@@ -102,7 +102,22 @@ public:
typedef typename ModelType::TargetSampleType TargetSampleType;
typedef typename ModelType::TargetListSampleType TargetListSampleType;
typedef typename ModelType::TargetValueType TargetValueType;
-
+
+ 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();
@@ -120,15 +135,23 @@ protected:
std::string modelPath);
/** Init method that creates all the parameters for machine learning models */
- void DoInit();
+ void DoInit() ITK_OVERRIDE;
/** Flag to switch between classification and regression mode.
* False by default, child classes may change it in their constructor */
bool m_RegressionFlag;
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
void InitLibSVMParams();
@@ -179,6 +202,10 @@ private:
void TrainSharkRandomForests(typename ListSampleType::Pointer trainingListSample,
typename TargetListSampleType::Pointer trainingLabeledListSample,
std::string modelPath);
+ void InitSharkKMeansParams();
+ void TrainSharkKMeans(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath);
#endif
//@}
};
@@ -203,6 +230,7 @@ private:
#endif
#ifdef OTB_USE_SHARK
#include "otbTrainSharkRandomForests.txx"
+#include "otbTrainSharkKMeans.txx"
#endif
#endif
diff --git a/Modules/Applications/AppClassification/include/otbLearningApplicationBase.txx b/Modules/Applications/AppClassification/include/otbLearningApplicationBase.txx
index c0a66443a8066cebc3bb906cdcb1f68d68981b0d..a40d0e505e3af53a3625976691aa402b1d59b827 100644
--- a/Modules/Applications/AppClassification/include/otbLearningApplicationBase.txx
+++ b/Modules/Applications/AppClassification/include/otbLearningApplicationBase.txx
@@ -54,8 +54,33 @@ LearningApplicationBase<TInputValue,TOutputValue>
AddParameter(ParameterType_Choice, "classifier", "Classifier to use for the training");
SetParameterDescription("classifier", "Choice of the classifier to use for the training.");
+ 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>
+::InitSupervisedClassifierParams()
+{
+
//Group LibSVM
-#ifdef OTB_USE_LIBSVM
+#ifdef OTB_USE_LIBSVM
InitLibSVMParams();
#endif
@@ -81,7 +106,16 @@ LearningApplicationBase<TInputValue,TOutputValue>
#ifdef OTB_USE_SHARK
InitSharkRandomForestsParams();
#endif
-
+}
+
+template <class TInputValue, class TOutputValue>
+void
+LearningApplicationBase<TInputValue,TOutputValue>
+::InitUnsupervisedClassifierParams()
+{
+#ifdef OTB_USE_SHARK
+ InitSharkKMeansParams();
+#endif
}
template <class TInputValue, class TOutputValue>
@@ -151,6 +185,14 @@ LearningApplicationBase<TInputValue,TOutputValue>
otbAppLogFATAL("Module SharkLearning is not installed. You should consider turning OTB_USE_SHARK on during cmake configuration.");
#endif
}
+ else if(modelName == "sharkkm")
+ {
+ #ifdef OTB_USE_SHARK
+ TrainSharkKMeans( trainingListSample, trainingLabeledListSample, modelPath );
+ #else
+ otbAppLogFATAL("Module SharkLearning is not installed. You should consider turning OTB_USE_SHARK on during cmake configuration.");
+ #endif
+ }
else if (modelName == "svm")
{
#ifdef OTB_USE_OPENCV
diff --git a/Modules/Applications/AppClassification/include/otbTrainImagesBase.h b/Modules/Applications/AppClassification/include/otbTrainImagesBase.h
new file mode 100644
index 0000000000000000000000000000000000000000..a01eb6c430fffc3d588d109d0f41f1bf677783b5
--- /dev/null
+++ b/Modules/Applications/AppClassification/include/otbTrainImagesBase.h
@@ -0,0 +1,306 @@
+/*
+ * 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 "otbVectorDataFileWriter.h"
+#include "otbWrapperCompositeApplication.h"
+#include "otbWrapperApplicationFactory.h"
+
+#include "otbStatisticsXMLFileWriter.h"
+#include "otbImageToEnvelopeVectorDataFilter.h"
+#include "otbSamplingRateCalculator.h"
+#include "otbOGRDataToSamplePositionFilter.h"
+
+namespace otb
+{
+namespace Wrapper
+{
+
+/** \class TrainImagesBase
+ * \brief Base class for the TrainImagesClassifier
+ *
+ * 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:
+ /** Standard class typedefs. */
+ typedef TrainImagesBase Self;
+ typedef CompositeApplication Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+ /** Standard macro */
+ itkTypeMacro( TrainImagesBase, Superclass )
+
+ /** filters typedefs*/
+ typedef otb::OGRDataToSamplePositionFilter<FloatVectorImageType, UInt8ImageType, otb::PeriodicSampler> PeriodicSamplerType;
+
+ typedef otb::SamplingRateCalculator::MapRateType MapRateType;
+
+protected:
+
+ enum SamplingStrategy
+ {
+ CLASS, GEOMETRIC
+ };
+ struct SamplingRates;
+ class TrainFileNamesHandler;
+
+ /**
+ * Initialize all the input and output parameter used for the train images
+ */
+ void InitIO();
+
+ /**
+ * Initialize sampling related application and parameters
+ */
+ void InitSampling();
+
+ void ShareSamplingParameters();
+ void ConnectSamplingParameters();
+ void InitClassification();
+ void ShareClassificationParams();
+ void ConnectClassificationParams();
+
+ /**
+ * Compute polygon statistics given provided strategy with PolygonClassStatistics class
+ * \param imageList list of input images
+ * \param vectorFileNames list of input vector file names
+ * \param statisticsFileNames list of out
+ */
+ void ComputePolygonStatistics(FloatVectorImageListType *imageList, const std::vector<std::string> &vectorFileNames,
+ 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);
+
+
+ /**
+ * Compute rates using MultiImageSamplingRate application
+ * \param statisticsFileNames
+ * \param ratesFileName
+ * \param maximum final maximum value computed by ComputeFinalMaximumSamplingRates
+ * \sa ComputeFinalMaximumSamplingRates
+ */
+ 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
+ * \param sampleTrainFileNames files names of the training samples
+ * \param sampleValidationFileNames file names of the validation sample
+ */
+ void TrainModel(FloatVectorImageListType *imageList, const std::vector<std::string> &sampleTrainFileNames,
+ const std::vector<std::string> &sampleValidationFileNames);
+
+ /**
+ * Select samples by class or by geographic strategy
+ * \param image
+ * \param vectorFileName
+ * \param sampleFileName
+ * \param statisticsFileName
+ * \param ratesFileName
+ * \param strategy
+ */
+ void SelectAndExtractSamples(FloatVectorImageType *image, std::string vectorFileName, std::string sampleFileName,
+ std::string statisticsFileName, std::string ratesFileName, SamplingStrategy strategy,
+ 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 = "");
+
+
+ /**
+ * 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>());
+
+ /**
+ * 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);
+
+
+protected:
+
+ struct SamplingRates
+ {
+ long int fmt;
+ long int fmv;
+ };
+
+ /**
+ * \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
+ {
+ public :
+ void CreateTemporaryFileNames(std::string outModel, size_t nbInputs, bool dedicatedValidation)
+ {
+
+ if( dedicatedValidation )
+ {
+ rateTrainOut = outModel + "_ratesTrain.csv";
+ }
+ else
+ {
+ rateTrainOut = outModel + "_rates.csv";
+ }
+
+ rateValidOut = outModel + "_ratesValid.csv";
+ for( unsigned int i = 0; i < nbInputs; i++ )
+ {
+ std::ostringstream oss;
+ oss << i + 1;
+ std::string strIndex( oss.str() );
+ if( dedicatedValidation )
+ {
+ polyStatTrainOutputs.push_back( outModel + "_statsTrain_" + strIndex + ".xml" );
+ polyStatValidOutputs.push_back( outModel + "_statsValid_" + strIndex + ".xml" );
+ ratesTrainOutputs.push_back( outModel + "_ratesTrain_" + strIndex + ".csv" );
+ ratesValidOutputs.push_back( outModel + "_ratesValid_" + strIndex + ".csv" );
+ sampleOutputs.push_back( outModel + "_samplesTrain_" + strIndex + ".shp" );
+ }
+ else
+ {
+ polyStatTrainOutputs.push_back( outModel + "_stats_" + strIndex + ".xml" );
+ ratesTrainOutputs.push_back( outModel + "_rates_" + strIndex + ".csv" );
+ sampleOutputs.push_back( outModel + "_samples_" + strIndex + ".shp" );
+ }
+ sampleTrainOutputs.push_back( outModel + "_samplesTrain_" + strIndex + ".shp" );
+ sampleValidOutputs.push_back( outModel + "_samplesValid_" + strIndex + ".shp" );
+ }
+
+ }
+
+ void clear()
+ {
+ for( unsigned int i = 0; i < polyStatTrainOutputs.size(); i++ )
+ RemoveFile( polyStatTrainOutputs[i] );
+ for( unsigned int i = 0; i < polyStatValidOutputs.size(); i++ )
+ RemoveFile( polyStatValidOutputs[i] );
+ for( unsigned int i = 0; i < ratesTrainOutputs.size(); i++ )
+ RemoveFile( ratesTrainOutputs[i] );
+ for( unsigned int i = 0; i < ratesValidOutputs.size(); i++ )
+ RemoveFile( ratesValidOutputs[i] );
+ for( unsigned int i = 0; i < sampleOutputs.size(); i++ )
+ RemoveFile( sampleOutputs[i] );
+ for( unsigned int i = 0; i < sampleTrainOutputs.size(); i++ )
+ RemoveFile( sampleTrainOutputs[i] );
+ for( unsigned int i = 0; i < sampleValidOutputs.size(); i++ )
+ RemoveFile( sampleValidOutputs[i] );
+ for( unsigned int i = 0; i < tmpVectorFileList.size(); i++ )
+ RemoveFile( tmpVectorFileList[i] );
+ }
+
+ public:
+ std::vector<std::string> polyStatTrainOutputs;
+ std::vector<std::string> polyStatValidOutputs;
+ std::vector<std::string> ratesTrainOutputs;
+ std::vector<std::string> ratesValidOutputs;
+ std::vector<std::string> sampleOutputs;
+ std::vector<std::string> sampleTrainOutputs;
+ std::vector<std::string> sampleValidOutputs;
+ std::vector<std::string> tmpVectorFileList;
+ std::string rateValidOut;
+ std::string rateTrainOut;
+
+ private:
+ bool RemoveFile(std::string &filePath)
+ {
+ bool res = true;
+ if( itksys::SystemTools::FileExists( filePath.c_str() ) )
+ {
+ size_t posExt = filePath.rfind( '.' );
+ if( posExt != std::string::npos && filePath.compare( posExt, std::string::npos, ".shp" ) == 0 )
+ {
+ std::string shxPath = filePath.substr( 0, posExt ) + std::string( ".shx" );
+ std::string dbfPath = filePath.substr( 0, posExt ) + std::string( ".dbf" );
+ std::string prjPath = filePath.substr( 0, posExt ) + std::string( ".prj" );
+ RemoveFile( shxPath );
+ RemoveFile( dbfPath );
+ RemoveFile( prjPath );
+ }
+ res = itksys::SystemTools::RemoveFile( filePath.c_str() );
+ if( !res )
+ {
+ //otbAppLogINFO( <<"Unable to remove file "<<filePath );
+ }
+ }
+ return res;
+ }
+ };
+
+};
+
+} // 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..deb34bfc735224c66b64ac8a4d3d15d0b98ed7b3
--- /dev/null
+++ b/Modules/Applications/AppClassification/include/otbTrainImagesBase.txx
@@ -0,0 +1,407 @@
+/*
+ * 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." );
+ MandatoryOn( "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" );
+
+ 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 && vtr < 0.99999 )
+ {
+ rates.fmt = static_cast<long>(( double ) mt / ( 1.0 - vtr ));
+ }
+ if( mv > -1 && vtr > 0.00001 )
+ {
+ if( rates.fmt > -1 )
+ {
+ rates.fmt = std::min( rates.fmt, static_cast<long>(( double ) mv / vtr) );
+ }
+ else
+ {
+ 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 )
+ {
+ std::ostringstream oss;
+ oss << maximum;
+ GetInternalApplication( "rates" )->SetParameterString( "strategy", "constant", false );
+ GetInternalApplication( "rates" )->SetParameterString( "strategy.constant.nb", oss.str(), 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
new file mode 100644
index 0000000000000000000000000000000000000000..ca03ea33b2d8ee3b3085db3d0f112447eebaf77b
--- /dev/null
+++ b/Modules/Applications/AppClassification/include/otbTrainSharkKMeans.txx
@@ -0,0 +1,76 @@
+/*
+ * 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
+
+#include "otbLearningApplicationBase.h"
+#include "otbSharkKMeansMachineLearningModel.h"
+
+namespace otb
+{
+namespace Wrapper
+{
+template<class TInputValue, class TOutputValue>
+void LearningApplicationBase<TInputValue, TOutputValue>::InitSharkKMeansParams()
+{
+ AddChoice( "classifier.sharkkm", "Shark kmeans classifier" );
+ SetParameterDescription( "classifier.sharkkm",
+ "This group of parameters allows setting Shark kMeans classifier parameters. "
+ "See complete documentation here "
+ "\\url{http://image.diku.dk/shark/sphinx_pages/build/html/rest_sources/tutorials/algorithms/kmeans.html}.\n " );
+ //MaxNumberOfIterations
+ AddParameter( ParameterType_Int, "classifier.sharkkm.maxiter",
+ "Maximum number of iteration for the kmeans algorithm." );
+ SetParameterInt( "classifier.sharkkm.maxiter", 10 );
+ SetMinimumParameterIntValue( "classifier.sharkkm.maxiter", 0 );
+ SetParameterDescription( "classifier.sharkkm.maxiter",
+ "The maximum number of iteration for the kmeans algorithm. 0=unlimited" );
+
+ //MaxNumberOfIterations
+ AddParameter( ParameterType_Int, "classifier.sharkkm.k", "The number of class used for the kmeans algorithm." );
+ SetParameterInt( "classifier.sharkkm.k", 2 );
+ SetParameterDescription( "classifier.sharkkm.k",
+ "The number of class used for the kmeans algorithm. Default set to 2 class" );
+ SetMinimumParameterIntValue( "classifier.sharkkm.k", 2 );
+}
+
+template<class TInputValue, class TOutputValue>
+void LearningApplicationBase<TInputValue, TOutputValue>::TrainSharkKMeans(
+ typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample, std::string modelPath)
+{
+ unsigned int nbMaxIter = static_cast<unsigned int>(abs( GetParameterInt( "classifier.sharkkm.maxiter" ) ));
+ unsigned int k = static_cast<unsigned int>(abs( GetParameterInt( "classifier.sharkkm.k" ) ));
+
+ typedef otb::SharkKMeansMachineLearningModel<InputValueType, OutputValueType> SharkKMeansType;
+ typename SharkKMeansType::Pointer classifier = SharkKMeansType::New();
+ classifier->SetRegressionMode( this->m_RegressionFlag );
+ classifier->SetInputListSample( trainingListSample );
+ classifier->SetTargetListSample( trainingLabeledListSample );
+ classifier->SetK( k );
+ classifier->SetMaximumNumberOfIterations( nbMaxIter );
+ classifier->Train();
+ classifier->Save( modelPath );
+}
+
+} //end namespace wrapper
+} //end namespace otb
+
+#endif
diff --git a/Modules/Applications/AppClassification/include/otbTrainVectorBase.h b/Modules/Applications/AppClassification/include/otbTrainVectorBase.h
new file mode 100644
index 0000000000000000000000000000000000000000..3e1d27b94902a067dfc992a4aefd1f854ffa4177
--- /dev/null
+++ b/Modules/Applications/AppClassification/include/otbTrainVectorBase.h
@@ -0,0 +1,196 @@
+/*
+ * 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
+
+#include "otbLearningApplicationBase.h"
+#include "otbWrapperApplication.h"
+#include "otbWrapperApplicationFactory.h"
+
+#include "otbOGRDataSourceWrapper.h"
+#include "otbOGRFeatureWrapper.h"
+#include "otbStatisticsXMLFileWriter.h"
+
+#include "itkVariableLengthVector.h"
+#include "otbStatisticsXMLFileReader.h"
+
+#include "itkListSample.h"
+#include "otbShiftScaleSampleListFilter.h"
+
+#include <algorithm>
+#include <locale>
+
+namespace otb
+{
+namespace Wrapper
+{
+
+/** Utility function to negate std::isalnum */
+bool IsNotAlphaNum(char c)
+{
+ return !std::isalnum( c );
+}
+
+class TrainVectorBase : public LearningApplicationBase<float, int>
+{
+public:
+ /** Standard class typedefs. */
+ typedef TrainVectorBase Self;
+ typedef LearningApplicationBase<float, int> Superclass;
+ typedef itk::SmartPointer <Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+ /** Standard macro */
+ itkTypeMacro(Self, Superclass)
+
+ typedef Superclass::SampleType SampleType;
+ typedef Superclass::ListSampleType ListSampleType;
+ typedef Superclass::TargetListSampleType TargetListSampleType;
+
+ typedef double ValueType;
+ typedef itk::VariableLengthVector <ValueType> MeasurementType;
+
+ typedef otb::StatisticsXMLFileReader<SampleType> StatisticsReader;
+
+ typedef otb::Statistics::ShiftScaleSampleListFilter<ListSampleType, ListSampleType> ShiftScaleFilterType;
+
+protected:
+
+ /** Class used to store statistics Measurment (mean/stddev) */
+ class ShiftScaleParameters
+ {
+ public:
+ MeasurementType meanMeasurementVector;
+ MeasurementType stddevMeasurementVector;
+ };
+
+ /** Class used to store a list of sample and the corresponding label */
+ class SamplesWithLabel
+ {
+ public:
+ ListSampleType::Pointer listSample;
+ TargetListSampleType::Pointer labeledListSample;
+ SamplesWithLabel()
+ {
+ listSample = ListSampleType::New();
+ labeledListSample = TargetListSampleType::New();
+ }
+ };
+
+ /**
+ * Features information class used to store informations
+ * about the field and class name/id of an input vector
+ */
+ class FeaturesInfo
+ {
+ public:
+ /** Selected Index */
+ std::vector<int> m_SelectedIdx;
+ /** Index for class field */
+ std::vector<int> m_SelectedCFieldIdx;
+ /** Selected class field name */
+ std::string m_SelectedCFieldName;
+ /** Selected names */
+ std::vector <std::string> m_SelectedNames;
+ unsigned int m_NbFeatures;
+
+ void SetFieldNames(std::vector <std::string> fieldNames, std::vector<int> selectedIdx)
+ {
+ m_SelectedIdx = selectedIdx;
+ m_NbFeatures = static_cast<unsigned int>(selectedIdx.size());
+ m_SelectedNames = std::vector<std::string>( m_NbFeatures );
+ for( unsigned int i = 0; i < m_NbFeatures; ++i )
+ {
+ m_SelectedNames[i] = fieldNames[selectedIdx[i]];
+ }
+ }
+ void SetClassFieldNames(std::vector<std::string> cFieldNames, std::vector<int> selectedCFieldIdx)
+ {
+ m_SelectedCFieldIdx = selectedCFieldIdx;
+ // Handle only one class field name, if several are provided only the first one is used.
+ m_SelectedCFieldName = cFieldNames[selectedCFieldIdx.front()];
+ }
+ };
+
+
+protected:
+
+ /**
+ * Function which extract and store all samples for Training and Classification.
+ * \param measurement statics measurement (mean/stddev)
+ * \param featuresInfo information about the features
+ */
+ virtual void ExtractAllSamples(const ShiftScaleParameters &measurement);
+
+ /**
+ * Extract the training sample list
+ * \param measurement statics measurement (mean/stddev)
+ * \param featuresInfo information about the features
+ * \return sample list used for training
+ */
+ virtual SamplesWithLabel ExtractTrainingSamplesWithLabel(const ShiftScaleParameters &measurement);
+
+ /**
+ * Extract classification the sample list
+ * \param measurement statics measurement (mean/stddev)
+ * \param featuresInfo information about the features
+ * \return sample list used for classification
+ */
+ virtual SamplesWithLabel ExtractClassificationSamplesWithLabel(const ShiftScaleParameters &measurement);
+
+
+ /** Extract samples from input file for corresponding field name
+ *
+ * \param parameterName the name of the input file option in the input application parameters
+ * \param parameterLayer the name of the layer option in the input application parameters
+ * \param measurement statics measurement (mean/stddev)
+ * \param nbFeatures the number of features.
+ * \return the list of samples and their corresponding labels.
+ */
+ SamplesWithLabel
+ ExtractSamplesWithLabel(std::string parameterName, std::string parameterLayer, const ShiftScaleParameters &measurement);
+
+
+ /**
+ * Retrieve statistics mean and standard deviation if input statistics are provided.
+ * Otherwise mean is set to 0 and standard deviation to 1 for each Features.
+ * \param nbFeatures
+ */
+ ShiftScaleParameters GetStatistics(unsigned int nbFeatures);
+
+ SamplesWithLabel m_TrainingSamplesWithLabel;
+ SamplesWithLabel m_ClassificationSamplesWithLabel;
+ TargetListSampleType::Pointer m_PredictedList;
+ FeaturesInfo m_FeaturesInfo;
+
+ void DoInit() ITK_OVERRIDE;
+ void DoUpdateParameters() ITK_OVERRIDE;
+ void DoExecute() ITK_OVERRIDE;
+
+};
+
+}
+}
+
+#ifndef OTB_MANUAL_INSTANTIATION
+#include "otbTrainVectorBase.txx"
+#endif
+
+#endif
diff --git a/Modules/Applications/AppClassification/include/otbTrainVectorBase.txx b/Modules/Applications/AppClassification/include/otbTrainVectorBase.txx
new file mode 100644
index 0000000000000000000000000000000000000000..57554baccf5174c0ad084a1e80b99a0d93e5f56c
--- /dev/null
+++ b/Modules/Applications/AppClassification/include/otbTrainVectorBase.txx
@@ -0,0 +1,305 @@
+/*
+ * 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
+
+#include "otbTrainVectorBase.h"
+
+namespace otb
+{
+namespace Wrapper
+{
+
+void TrainVectorBase::DoInit()
+{
+ // Common Parameters for all Learning Application
+ AddParameter( ParameterType_Group, "io", "Input and output data" );
+ SetParameterDescription( "io", "This group of parameters allows setting input and output data." );
+
+ AddParameter( ParameterType_InputVectorDataList, "io.vd", "Input Vector Data" );
+ SetParameterDescription( "io.vd",
+ "Input geometries used for training (note : all geometries from the layer will be used)" );
+
+ AddParameter( ParameterType_InputFilename, "io.stats", "Input XML image statistics file" );
+ MandatoryOff( "io.stats" );
+ SetParameterDescription( "io.stats", "XML file containing mean and variance of each feature." );
+
+ AddParameter( ParameterType_OutputFilename, "io.out", "Output model" );
+ SetParameterDescription( "io.out", "Output file containing the model estimated (.txt format)." );
+
+ AddParameter( ParameterType_Int, "layer", "Layer Index" );
+ SetParameterDescription( "layer", "Index of the layer to use in the input vector file." );
+ MandatoryOff( "layer" );
+ SetDefaultParameterInt( "layer", 0 );
+
+ 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 contingency table
+ AddParameter( ParameterType_Group, "valid", "Validation data" );
+ SetParameterDescription( "valid", "This group of parameters defines validation data." );
+
+ AddParameter( ParameterType_InputVectorDataList, "valid.vd", "Validation Vector Data" );
+ SetParameterDescription( "valid.vd", "Geometries used for validation "
+ "(must contain the same fields used for training, all geometries from the layer will be used)" );
+ MandatoryOff( "valid.vd" );
+
+ AddParameter( ParameterType_Int, "valid.layer", "Layer Index" );
+ SetParameterDescription( "valid.layer", "Index of the layer to use in the validation vector file." );
+ MandatoryOff( "valid.layer" );
+ SetDefaultParameterInt( "valid.layer", 0 );
+
+ // Add class field if we used validation
+ AddParameter( ParameterType_ListView, "cfield", "Field containing the class id for supervision" );
+ SetParameterDescription( "cfield", "Field containing the class id for supervision. "
+ "Only geometries with this field available will be taken into account." );
+ SetListViewSingleSelectionMode( "cfield", true );
+
+ // Add 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
+ Superclass::DoInit();
+
+ AddRANDParameter();
+}
+
+void TrainVectorBase::DoUpdateParameters()
+{
+ // if vector data is present and updated then reload fields
+ if( HasValue( "io.vd" ) )
+ {
+ std::vector<std::string> vectorFileList = GetParameterStringList( "io.vd" );
+ ogr::DataSource::Pointer ogrDS = ogr::DataSource::New( vectorFileList[0], ogr::DataSource::Modes::Read );
+ ogr::Layer layer = ogrDS->GetLayer( static_cast<size_t>( this->GetParameterInt( "layer" ) ) );
+ ogr::Feature feature = layer.ogr().GetNextFeature();
+
+ ClearChoices( "feat" );
+ ClearChoices( "cfield" );
+
+ for( int iField = 0; iField < feature.ogr().GetFieldCount(); iField++ )
+ {
+ std::string key, item = feature.ogr().GetFieldDefnRef( iField )->GetNameRef();
+ key = item;
+ std::string::iterator end = std::remove_if( key.begin(), key.end(), IsNotAlphaNum );
+ std::transform( key.begin(), end, key.begin(), tolower );
+
+ OGRFieldType fieldType = feature.ogr().GetFieldDefnRef( iField )->GetType();
+
+ if( fieldType == OFTInteger || ogr::version_proxy::IsOFTInteger64( fieldType ) || fieldType == OFTReal )
+ {
+ std::string tmpKey = "feat." + key.substr( 0, static_cast<unsigned long>( end - key.begin() ) );
+ AddChoice( tmpKey, item );
+ }
+ if( fieldType == OFTString || fieldType == OFTInteger || ogr::version_proxy::IsOFTInteger64( fieldType ) )
+ {
+ std::string tmpKey = "cfield." + key.substr( 0, static_cast<unsigned long>( end - key.begin() ) );
+ AddChoice( tmpKey, item );
+ }
+ }
+ }
+}
+
+void TrainVectorBase::DoExecute()
+{
+ m_FeaturesInfo.SetFieldNames( GetChoiceNames( "feat" ), GetSelectedItems( "feat" ));
+
+ // Check input parameters
+ if( m_FeaturesInfo.m_SelectedIdx.empty() )
+ {
+ otbAppLogFATAL( << "No features have been selected to train the classifier on!" );
+ }
+
+ ShiftScaleParameters measurement = GetStatistics( m_FeaturesInfo.m_NbFeatures );
+ ExtractAllSamples( measurement );
+
+ this->Train( m_TrainingSamplesWithLabel.listSample, m_TrainingSamplesWithLabel.labeledListSample, GetParameterString( "io.out" ) );
+
+ m_PredictedList = TargetListSampleType::New();
+ this->Classify( m_ClassificationSamplesWithLabel.listSample, m_PredictedList, GetParameterString( "io.out" ) );
+}
+
+
+void TrainVectorBase::ExtractAllSamples(const ShiftScaleParameters &measurement)
+{
+ m_TrainingSamplesWithLabel = ExtractTrainingSamplesWithLabel(measurement);
+ m_ClassificationSamplesWithLabel = ExtractClassificationSamplesWithLabel(measurement);
+}
+
+TrainVectorBase::SamplesWithLabel
+TrainVectorBase::ExtractTrainingSamplesWithLabel(const ShiftScaleParameters &measurement)
+{
+ return ExtractSamplesWithLabel( "io.vd", "layer", measurement);
+}
+
+TrainVectorBase::SamplesWithLabel
+TrainVectorBase::ExtractClassificationSamplesWithLabel(const ShiftScaleParameters &measurement)
+{
+ if(GetClassifierCategory() == Supervised)
+ {
+ SamplesWithLabel tmpSamplesWithLabel;
+ SamplesWithLabel validationSamplesWithLabel = ExtractSamplesWithLabel( "valid.vd", "valid.layer", measurement );
+ //Test the input validation set size
+ if( validationSamplesWithLabel.labeledListSample->Size() != 0 )
+ {
+ tmpSamplesWithLabel.listSample = validationSamplesWithLabel.listSample;
+ tmpSamplesWithLabel.labeledListSample = validationSamplesWithLabel.labeledListSample;
+ }
+ else
+ {
+ otbAppLogWARNING(
+ "The validation set is empty. The performance estimation is done using the input training set in this case." );
+ tmpSamplesWithLabel.listSample = m_TrainingSamplesWithLabel.listSample;
+ tmpSamplesWithLabel.labeledListSample = m_TrainingSamplesWithLabel.labeledListSample;
+ }
+
+ return tmpSamplesWithLabel;
+ }
+ else
+ {
+ return m_TrainingSamplesWithLabel;
+ }
+}
+
+
+TrainVectorBase::ShiftScaleParameters
+TrainVectorBase::GetStatistics(unsigned int nbFeatures)
+{
+ ShiftScaleParameters measurement = ShiftScaleParameters();
+ if( HasValue( "io.stats" ) && IsParameterEnabled( "io.stats" ) )
+ {
+ StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
+ std::string XMLfile = GetParameterString( "io.stats" );
+ statisticsReader->SetFileName( XMLfile.c_str() );
+ measurement.meanMeasurementVector = statisticsReader->GetStatisticVectorByName( "mean" );
+ measurement.stddevMeasurementVector = statisticsReader->GetStatisticVectorByName( "stddev" );
+ }
+ else
+ {
+ measurement.meanMeasurementVector.SetSize( nbFeatures );
+ measurement.meanMeasurementVector.Fill( 0. );
+ measurement.stddevMeasurementVector.SetSize( nbFeatures );
+ measurement.stddevMeasurementVector.Fill( 1. );
+ }
+ return measurement;
+}
+
+
+TrainVectorBase::SamplesWithLabel
+TrainVectorBase::ExtractSamplesWithLabel(std::string parameterName, std::string parameterLayer,
+ const ShiftScaleParameters &measurement)
+{
+ SamplesWithLabel samplesWithLabel;
+ if( HasValue( parameterName ) && IsParameterEnabled( parameterName ) )
+ {
+ ListSampleType::Pointer input = ListSampleType::New();
+ TargetListSampleType::Pointer target = TargetListSampleType::New();
+ input->SetMeasurementVectorSize( m_FeaturesInfo.m_NbFeatures );
+
+ std::vector<std::string> fileList = this->GetParameterStringList( parameterName );
+ for( unsigned int k = 0; k < fileList.size(); k++ )
+ {
+ otbAppLogINFO( "Reading vector file " << k + 1 << "/" << fileList.size() );
+ ogr::DataSource::Pointer source = ogr::DataSource::New( fileList[k], ogr::DataSource::Modes::Read );
+ ogr::Layer layer = source->GetLayer( static_cast<size_t>(this->GetParameterInt( parameterLayer )) );
+ ogr::Feature feature = layer.ogr().GetNextFeature();
+ bool goesOn = feature.addr() != 0;
+ if( !goesOn )
+ {
+ otbAppLogWARNING( "The layer " << GetParameterInt( parameterLayer ) << " of " << fileList[k]
+ << " is empty, input is skipped." );
+ continue;
+ }
+
+ // Check all needed fields are present :
+ // - check class field if we use supervised classification or if class field name is not empty
+ int cFieldIndex = feature.ogr().GetFieldIndex( m_FeaturesInfo.m_SelectedCFieldName.c_str() );
+ if( cFieldIndex < 0 && !m_FeaturesInfo.m_SelectedCFieldName.empty())
+ {
+ otbAppLogFATAL( "The field name for class label (" << m_FeaturesInfo.m_SelectedCFieldName
+ << ") has not been found in the vector file "
+ << fileList[k] );
+ }
+
+ // - check feature fields
+ std::vector<int> featureFieldIndex( m_FeaturesInfo.m_NbFeatures, -1 );
+ for( unsigned int i = 0; i < m_FeaturesInfo.m_NbFeatures; i++ )
+ {
+ featureFieldIndex[i] = feature.ogr().GetFieldIndex( m_FeaturesInfo.m_SelectedNames[i].c_str() );
+ if( featureFieldIndex[i] < 0 )
+ otbAppLogFATAL( "The field name for feature " << m_FeaturesInfo.m_SelectedNames[i]
+ << " has not been found in the vector file "
+ << fileList[k] );
+ }
+
+
+ while( goesOn )
+ {
+ // Retrieve all the features for each field in the ogr layer.
+ MeasurementType mv;
+ mv.SetSize( m_FeaturesInfo.m_NbFeatures );
+ for( unsigned int idx = 0; idx < m_FeaturesInfo.m_NbFeatures; ++idx )
+ mv[idx] = feature.ogr().GetFieldAsDouble( featureFieldIndex[idx] );
+
+ input->PushBack( mv );
+
+ if( feature.ogr().IsFieldSet( cFieldIndex ) && cFieldIndex != -1 )
+ target->PushBack( feature.ogr().GetFieldAsInteger( cFieldIndex ) );
+ else
+ target->PushBack( 0 );
+
+ feature = layer.ogr().GetNextFeature();
+ goesOn = feature.addr() != 0;
+ }
+ }
+
+
+
+ ShiftScaleFilterType::Pointer shiftScaleFilter = ShiftScaleFilterType::New();
+ shiftScaleFilter->SetInput( input );
+ shiftScaleFilter->SetShifts( measurement.meanMeasurementVector );
+ shiftScaleFilter->SetScales( measurement.stddevMeasurementVector );
+ shiftScaleFilter->Update();
+
+ samplesWithLabel.listSample = shiftScaleFilter->GetOutput();
+ samplesWithLabel.labeledListSample = target;
+ samplesWithLabel.listSample->DisconnectPipeline();
+ }
+
+ return samplesWithLabel;
+}
+
+
+}
+}
+
+#endif
diff --git a/Modules/Applications/AppClassification/otb-module.cmake b/Modules/Applications/AppClassification/otb-module.cmake
index 07b121f77de3512038be604d8f9e67f9ffa0985e..ca0c9281275d233b7dcb441d667fb295d5580755 100644
--- a/Modules/Applications/AppClassification/otb-module.cmake
+++ b/Modules/Applications/AppClassification/otb-module.cmake
@@ -31,7 +31,9 @@ otb_module(OTBAppClassification
OTBMajorityVoting
OTBVectorDataIO
OTBSOM
+ OTBLearningBase
OTBSupervised
+ OTBUnsupervised
OTBApplicationEngine
OTBIndices
OTBMathParser
diff --git a/Modules/Applications/AppClassification/test/CMakeLists.txt b/Modules/Applications/AppClassification/test/CMakeLists.txt
index 3a340346d8e549420ba3904edbfe9b99377dee12..3b4e213470625f57238c635383b4bacf22e93e7c 100644
--- a/Modules/Applications/AppClassification/test/CMakeLists.txt
+++ b/Modules/Applications/AppClassification/test/CMakeLists.txt
@@ -21,46 +21,46 @@
otb_module_test()
#----------- ComputeOGRLayersFeaturesStatistics TESTS ----------------
otb_test_application(NAME apTvClComputeOGRLayersFeaturesStatistics
- APP ComputeOGRLayersFeaturesStatistics
- OPTIONS -inshp ${INPUTDATA}/Classification/apTvClLabeledVector.shp
- -feat meanB0 meanB1 meanB2 meanB3 varB0 varB1 varB2 varB3
- -outstats ${TEMP}/apTvClComputeOGRLayersFeaturesStatistics.xml
- VALID --compare-ascii ${NOTOL}
- ${OTBAPP_BASELINE_FILES}/apTvClComputeOGRLayersFeaturesStatistics.xml
- ${TEMP}/apTvClComputeOGRLayersFeaturesStatistics.xml)
+ APP ComputeOGRLayersFeaturesStatistics
+ OPTIONS -inshp ${INPUTDATA}/Classification/apTvClLabeledVector.shp
+ -feat meanB0 meanB1 meanB2 meanB3 varB0 varB1 varB2 varB3
+ -outstats ${TEMP}/apTvClComputeOGRLayersFeaturesStatistics.xml
+ VALID --compare-ascii ${NOTOL}
+ ${OTBAPP_BASELINE_FILES}/apTvClComputeOGRLayersFeaturesStatistics.xml
+ ${TEMP}/apTvClComputeOGRLayersFeaturesStatistics.xml)
#----------- SOMClassification TESTS ----------------
otb_test_application(NAME apTvClSOMClassificationSmall
- APP SOMClassification
- OPTIONS -in ${INPUTDATA}/poupees_sub.png
- -rand 121212
- -out ${TEMP}/apTvClSOMClassificationSmall.tif uint16
- VALID --compare-image ${NOTOL}
- ${BASELINE}/apTvClSOMClassificationSmall.tif
- ${TEMP}/apTvClSOMClassificationSmall.tif)
+ APP SOMClassification
+ OPTIONS -in ${INPUTDATA}/poupees_sub.png
+ -rand 121212
+ -out ${TEMP}/apTvClSOMClassificationSmall.tif uint16
+ VALID --compare-image ${NOTOL}
+ ${BASELINE}/apTvClSOMClassificationSmall.tif
+ ${TEMP}/apTvClSOMClassificationSmall.tif)
otb_test_application(NAME apTvClSOMClassificationFull
- APP SOMClassification
- OPTIONS -in ${INPUTDATA}/poupees_sub.png
- -out ${TEMP}/apTvClSOMClassificationFull.tif uint16
- -vm ${INPUTDATA}/poupees_sub_c1.png
- -tp 0.8
- -ts 13000
- -som ${TEMP}/apTvClSOMClassificationMap.hdr
- -sx 30
- -sy 30
- -nx 9
- -ny 9
- -ni 5
- -bi 1.0
- -bf 0.1
- -iv 0
- -rand 121212
- VALID --compare-n-images ${NOTOL} 2
- ${BASELINE}/apTvClSOMClassificationFull.tif
- ${TEMP}/apTvClSOMClassificationFull.tif
- ${BASELINE}/apTvClSOMClassificationMap.hdr
- ${TEMP}/apTvClSOMClassificationMap.hdr)
+ APP SOMClassification
+ OPTIONS -in ${INPUTDATA}/poupees_sub.png
+ -out ${TEMP}/apTvClSOMClassificationFull.tif uint16
+ -vm ${INPUTDATA}/poupees_sub_c1.png
+ -tp 0.8
+ -ts 13000
+ -som ${TEMP}/apTvClSOMClassificationMap.hdr
+ -sx 30
+ -sy 30
+ -nx 9
+ -ny 9
+ -ni 5
+ -bi 1.0
+ -bf 0.1
+ -iv 0
+ -rand 121212
+ VALID --compare-n-images ${NOTOL} 2
+ ${BASELINE}/apTvClSOMClassificationFull.tif
+ ${TEMP}/apTvClSOMClassificationFull.tif
+ ${BASELINE}/apTvClSOMClassificationMap.hdr
+ ${TEMP}/apTvClSOMClassificationMap.hdr)
#----------- ImageClassifier TESTS ----------------
@@ -96,6 +96,7 @@ 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")
@@ -108,6 +109,7 @@ 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})
@@ -120,7 +122,7 @@ set(raster_ref_path ${OTBAPP_BASELINE})
set(classifierList)
#if(OTB_USE_LIBSVM)
- #list(APPEND classifierList "LIBSVM")
+#list(APPEND classifierList "LIBSVM")
#endif()
if(OTB_USE_OPENCV)
#list(APPEND classifierList "SVM" "BOOST" "DT" "GBT" "ANN" "BAYES" "RF" "KNN")
@@ -130,14 +132,14 @@ if(OTB_USE_OPENCV)
endif()
endif()
if(OTB_USE_SHARK)
- list(APPEND classifierList "SHARKRF")
+ list(APPEND classifierList "SHARKRF" "SHARKKM")
endif()
set(classifier_with_confmap "LIBSVM" "BOOST" "KNN" "ANN" "RF")
# This is a black list for classifier that can not have a baseline
# because they are using randomness and seed can not be set
-set(classifier_without_baseline "SHARKRF")
+set(classifier_without_baseline "SHARKRF" "SHARKKM")
# Loop on classifiers
foreach(classifier ${classifierList})
@@ -154,21 +156,21 @@ foreach(classifier ${classifierList})
else()
set(valid "")
endif()
-
+
otb_test_application(
- NAME apTvClTrainMethod${classifier}ImagesClassifierQB1
- APP TrainImagesClassifier
- 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 ${valid}
- )
+ NAME apTvClTrainMethod${classifier}ImagesClassifierQB1
+ APP TrainImagesClassifier
+ 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})
@@ -177,20 +179,20 @@ foreach(classifier ${classifierList})
endif()
otb_test_application(
- NAME apTvClTrainMethod${classifier}ImagesClassifierQB1_OutXML1
- APP TrainImagesClassifier
- 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 ${valid}
- )
+ NAME apTvClTrainMethod${classifier}ImagesClassifierQB1_OutXML1
+ APP TrainImagesClassifier
+ 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})
@@ -199,744 +201,746 @@ foreach(classifier ${classifierList})
endif()
otb_test_application(
- NAME apTvClTrainMethod${classifier}ImagesClassifierQB1_InXML1
- APP TrainImagesClassifier
- 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 ${valid}
- )
-
- #set_tests_properties(apTvClTrainMethod${classifier}ImagesClassifierQB1_InXML1 PROPERTIES DEPENDS apTvClTrainMethod${classifier}ImagesClassifierQB1_OutXML1)
+ NAME apTvClTrainMethod${classifier}ImagesClassifierQB1_InXML1
+ APP TrainImagesClassifier
+ 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 ${valid}
+ )
+
+ #set_tests_properties(apTvClTrainMethod${classifier}ImagesClassifierQB1_InXML1 PROPERTIES DEPENDS apTvClTrainMethod${classifier}ImagesClassifierQB1_OutXML1)
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}
- )
+ 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}
- )
+ 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)
otb_test_application(NAME apTvClImageSVMClassifierQB2
- APP ImageClassifier
- OPTIONS -in ${INPUTDATA}/Classification/QB_2_ortho.tif
- -imstat ${INPUTDATA}/Classification/clImageStatisticsQB1.xml
- -model ${INPUTDATA}/Classification/clsvmModelQB1.svm
- -out ${TEMP}/clLabeledImageQB2.tif
- VALID --compare-image ${NOTOL}
- ${OTBAPP_BASELINE}/clLabeledImageQB2.tif
- ${TEMP}/clLabeledImageQB2.tif)
+ APP ImageClassifier
+ OPTIONS -in ${INPUTDATA}/Classification/QB_2_ortho.tif
+ -imstat ${INPUTDATA}/Classification/clImageStatisticsQB1.xml
+ -model ${INPUTDATA}/Classification/clsvmModelQB1.svm
+ -out ${TEMP}/clLabeledImageQB2.tif
+ VALID --compare-image ${NOTOL}
+ ${OTBAPP_BASELINE}/clLabeledImageQB2.tif
+ ${TEMP}/clLabeledImageQB2.tif)
otb_test_application(NAME apTvClImageSVMClassifierQB3
- APP ImageClassifier
- OPTIONS -in ${INPUTDATA}/Classification/QB_3_ortho.tif
- -imstat ${INPUTDATA}/Classification/clImageStatisticsQB1.xml
- -model ${INPUTDATA}/Classification/clsvmModelQB1.svm
- -out ${TEMP}/clLabeledImageQB3.tif
- VALID --compare-image ${NOTOL}
- ${OTBAPP_BASELINE}/clLabeledImageQB3.tif
- ${TEMP}/clLabeledImageQB3.tif)
+ APP ImageClassifier
+ OPTIONS -in ${INPUTDATA}/Classification/QB_3_ortho.tif
+ -imstat ${INPUTDATA}/Classification/clImageStatisticsQB1.xml
+ -model ${INPUTDATA}/Classification/clsvmModelQB1.svm
+ -out ${TEMP}/clLabeledImageQB3.tif
+ VALID --compare-image ${NOTOL}
+ ${OTBAPP_BASELINE}/clLabeledImageQB3.tif
+ ${TEMP}/clLabeledImageQB3.tif)
otb_test_application(NAME apTvClImageSVMClassifierQB1
- APP ImageClassifier
- OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho.tif
- -imstat ${INPUTDATA}/Classification/clImageStatisticsQB1.xml
- -model ${INPUTDATA}/Classification/clsvmModelQB1.svm
- -out ${TEMP}/clLabeledImageQB1.tif
- VALID --compare-image ${NOTOL}
- ${OTBAPP_BASELINE}/clLabeledImageQB1.tif
- ${TEMP}/clLabeledImageQB1.tif)
+ APP ImageClassifier
+ OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho.tif
+ -imstat ${INPUTDATA}/Classification/clImageStatisticsQB1.xml
+ -model ${INPUTDATA}/Classification/clsvmModelQB1.svm
+ -out ${TEMP}/clLabeledImageQB1.tif
+ VALID --compare-image ${NOTOL}
+ ${OTBAPP_BASELINE}/clLabeledImageQB1.tif
+ ${TEMP}/clLabeledImageQB1.tif)
otb_test_application(NAME apTvClImageSVMClassifierQB456_6
- APP ImageClassifier
- OPTIONS -in ${INPUTDATA}/Classification/QB_6_extract.tif
- -mask ${INPUTDATA}/Classification/QB_6_mask.tif
- -imstat ${INPUTDATA}/Classification/clImageStatisticsQB456.xml
- -model ${INPUTDATA}/Classification/clsvmModelQB456.svm
- -out ${TEMP}/clLabeledImageQB456_6.tif
- VALID --compare-image ${NOTOL}
- ${OTBAPP_BASELINE}/clLabeledImageQB456_6.tif
- ${TEMP}/clLabeledImageQB456_6.tif)
+ APP ImageClassifier
+ OPTIONS -in ${INPUTDATA}/Classification/QB_6_extract.tif
+ -mask ${INPUTDATA}/Classification/QB_6_mask.tif
+ -imstat ${INPUTDATA}/Classification/clImageStatisticsQB456.xml
+ -model ${INPUTDATA}/Classification/clsvmModelQB456.svm
+ -out ${TEMP}/clLabeledImageQB456_6.tif
+ VALID --compare-image ${NOTOL}
+ ${OTBAPP_BASELINE}/clLabeledImageQB456_6.tif
+ ${TEMP}/clLabeledImageQB456_6.tif)
otb_test_application(NAME apTvClImageSVMClassifierQB456_4
- APP ImageClassifier
- OPTIONS -in ${INPUTDATA}/Classification/QB_4_extract.tif
- -imstat ${INPUTDATA}/Classification/clImageStatisticsQB456.xml
- -model ${INPUTDATA}/Classification/clsvmModelQB456.svm
- -out ${TEMP}/clLabeledImageQB456_4.tif
- VALID --compare-image ${NOTOL}
- ${OTBAPP_BASELINE}/clLabeledImageQB456_4.tif
- ${TEMP}/clLabeledImageQB456_4.tif)
+ APP ImageClassifier
+ OPTIONS -in ${INPUTDATA}/Classification/QB_4_extract.tif
+ -imstat ${INPUTDATA}/Classification/clImageStatisticsQB456.xml
+ -model ${INPUTDATA}/Classification/clsvmModelQB456.svm
+ -out ${TEMP}/clLabeledImageQB456_4.tif
+ VALID --compare-image ${NOTOL}
+ ${OTBAPP_BASELINE}/clLabeledImageQB456_4.tif
+ ${TEMP}/clLabeledImageQB456_4.tif)
otb_test_application(NAME apTvClImageSVMClassifierQB456_5
- APP ImageClassifier
- OPTIONS -in ${INPUTDATA}/Classification/QB_5_extract.tif
- -imstat ${INPUTDATA}/Classification/clImageStatisticsQB456.xml
- -model ${INPUTDATA}/Classification/clsvmModelQB456.svm
- -out ${TEMP}/clLabeledImageQB456_5.tif
- VALID --compare-image ${NOTOL}
- ${OTBAPP_BASELINE}/clLabeledImageQB456_5.tif
- ${TEMP}/clLabeledImageQB456_5.tif)
+ APP ImageClassifier
+ OPTIONS -in ${INPUTDATA}/Classification/QB_5_extract.tif
+ -imstat ${INPUTDATA}/Classification/clImageStatisticsQB456.xml
+ -model ${INPUTDATA}/Classification/clsvmModelQB456.svm
+ -out ${TEMP}/clLabeledImageQB456_5.tif
+ VALID --compare-image ${NOTOL}
+ ${OTBAPP_BASELINE}/clLabeledImageQB456_5.tif
+ ${TEMP}/clLabeledImageQB456_5.tif)
otb_test_application(NAME apTvClImageSVMClassifierQB123_6
- APP ImageClassifier
- OPTIONS -in ${INPUTDATA}/Classification/QB_6_extract.tif
- -imstat ${INPUTDATA}/Classification/clImageStatisticsQB123.xml
- -model ${INPUTDATA}/Classification/clsvmModelQB123.svm
- -out ${TEMP}/clLabeledImageQB123_6.tif
- VALID --compare-image ${NOTOL}
- ${OTBAPP_BASELINE}/clLabeledImageQB123_6.tif
- ${TEMP}/clLabeledImageQB123_6.tif)
+ APP ImageClassifier
+ OPTIONS -in ${INPUTDATA}/Classification/QB_6_extract.tif
+ -imstat ${INPUTDATA}/Classification/clImageStatisticsQB123.xml
+ -model ${INPUTDATA}/Classification/clsvmModelQB123.svm
+ -out ${TEMP}/clLabeledImageQB123_6.tif
+ VALID --compare-image ${NOTOL}
+ ${OTBAPP_BASELINE}/clLabeledImageQB123_6.tif
+ ${TEMP}/clLabeledImageQB123_6.tif)
otb_test_application(NAME apTvClImageSVMClassifierQB456_1
- APP ImageClassifier
- OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho.tif
- -imstat ${INPUTDATA}/Classification/clImageStatisticsQB456.xml
- -model ${INPUTDATA}/Classification/clsvmModelQB456.svm
- -out ${TEMP}/clLabeledImageQB456_1.tif
- VALID --compare-image ${NOTOL}
- ${OTBAPP_BASELINE}/clLabeledImageQB456_1.tif
- ${TEMP}/clLabeledImageQB456_1.tif)
+ APP ImageClassifier
+ OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho.tif
+ -imstat ${INPUTDATA}/Classification/clImageStatisticsQB456.xml
+ -model ${INPUTDATA}/Classification/clsvmModelQB456.svm
+ -out ${TEMP}/clLabeledImageQB456_1.tif
+ VALID --compare-image ${NOTOL}
+ ${OTBAPP_BASELINE}/clLabeledImageQB456_1.tif
+ ${TEMP}/clLabeledImageQB456_1.tif)
otb_test_application(NAME apTvClImageSVMClassifierQB123_3
- APP ImageClassifier
- OPTIONS -in ${INPUTDATA}/Classification/QB_3_ortho.tif
- -imstat ${INPUTDATA}/Classification/clImageStatisticsQB123.xml
- -model ${INPUTDATA}/Classification/clsvmModelQB123.svm
- -out ${TEMP}/clLabeledImageQB123_3.tif
- VALID --compare-image ${NOTOL}
- ${OTBAPP_BASELINE}/clLabeledImageQB123_3.tif
- ${TEMP}/clLabeledImageQB123_3.tif)
+ APP ImageClassifier
+ OPTIONS -in ${INPUTDATA}/Classification/QB_3_ortho.tif
+ -imstat ${INPUTDATA}/Classification/clImageStatisticsQB123.xml
+ -model ${INPUTDATA}/Classification/clsvmModelQB123.svm
+ -out ${TEMP}/clLabeledImageQB123_3.tif
+ VALID --compare-image ${NOTOL}
+ ${OTBAPP_BASELINE}/clLabeledImageQB123_3.tif
+ ${TEMP}/clLabeledImageQB123_3.tif)
otb_test_application(NAME apTvClImageSVMClassifierQB123_2
- APP ImageClassifier
- OPTIONS -in ${INPUTDATA}/Classification/QB_2_ortho.tif
- -imstat ${INPUTDATA}/Classification/clImageStatisticsQB123.xml
- -model ${INPUTDATA}/Classification/clsvmModelQB123.svm
- -out ${TEMP}/clLabeledImageQB123_2.tif
- VALID --compare-image ${NOTOL}
- ${OTBAPP_BASELINE}/clLabeledImageQB123_2.tif
- ${TEMP}/clLabeledImageQB123_2.tif)
+ APP ImageClassifier
+ OPTIONS -in ${INPUTDATA}/Classification/QB_2_ortho.tif
+ -imstat ${INPUTDATA}/Classification/clImageStatisticsQB123.xml
+ -model ${INPUTDATA}/Classification/clsvmModelQB123.svm
+ -out ${TEMP}/clLabeledImageQB123_2.tif
+ VALID --compare-image ${NOTOL}
+ ${OTBAPP_BASELINE}/clLabeledImageQB123_2.tif
+ ${TEMP}/clLabeledImageQB123_2.tif)
otb_test_application(NAME apTvClImageSVMClassifierQB123_1
- APP ImageClassifier
- OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho.tif
- -imstat ${INPUTDATA}/Classification/clImageStatisticsQB123.xml
- -model ${INPUTDATA}/Classification/clsvmModelQB123.svm
- -out ${TEMP}/clLabeledImageQB123_1.tif
- VALID --compare-image ${NOTOL}
- ${OTBAPP_BASELINE}/clLabeledImageQB123_1.tif
- ${TEMP}/clLabeledImageQB123_1.tif)
+ APP ImageClassifier
+ OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho.tif
+ -imstat ${INPUTDATA}/Classification/clImageStatisticsQB123.xml
+ -model ${INPUTDATA}/Classification/clsvmModelQB123.svm
+ -out ${TEMP}/clLabeledImageQB123_1.tif
+ VALID --compare-image ${NOTOL}
+ ${OTBAPP_BASELINE}/clLabeledImageQB123_1.tif
+ ${TEMP}/clLabeledImageQB123_1.tif)
endif()
#----------- TrainOGRLayersClassifier TESTS ----------------
otb_test_application(NAME apTvClTrainOGRLayersClassifier
- APP TrainOGRLayersClassifier
- OPTIONS -inshp ${INPUTDATA}/Classification/apTvClLabeledVector.shp
- -feat meanB0 meanB1 meanB2 meanB3 varB0 varB1 varB2 varB3
- -instats ${TEMP}/apTvClComputeOGRLayersFeaturesStatistics.xml
- -outsvm ${TEMP}/apTvClModel.svm
- VALID --compare-ascii ${NOTOL}
- ${OTBAPP_BASELINE_FILES}/apTvClModel.svm
- ${TEMP}/apTvClModel.svm)
+ APP TrainOGRLayersClassifier
+ OPTIONS -inshp ${INPUTDATA}/Classification/apTvClLabeledVector.shp
+ -feat meanB0 meanB1 meanB2 meanB3 varB0 varB1 varB2 varB3
+ -instats ${TEMP}/apTvClComputeOGRLayersFeaturesStatistics.xml
+ -outsvm ${TEMP}/apTvClModel.svm
+ VALID --compare-ascii ${NOTOL}
+ ${OTBAPP_BASELINE_FILES}/apTvClModel.svm
+ ${TEMP}/apTvClModel.svm)
set_tests_properties(apTvClTrainOGRLayersClassifier PROPERTIES DEPENDS apTvClComputeOGRLayersFeaturesStatistics)
#----------- ComputeConfusionMatrix TESTS ----------------
otb_test_application(NAME apTvComputeConfusionMatrixV
- APP ComputeConfusionMatrix
- OPTIONS -in ${OTBAPP_BASELINE}/clLabeledImageQB123_1.tif
- -ref vector
- -ref.vector.in ${INPUTDATA}/Classification/VectorData_QB1_ter.shp
- -ref.vector.field Class
- -out ${TEMP}/apTvComputeConfusionMatrixTconfusionVOut.csv
- VALID --compare-ascii ${NOTOL}
- ${OTBAPP_BASELINE_FILES}/apTvComputeConfusionMatrixTconfusionVOut.csv
- ${TEMP}/apTvComputeConfusionMatrixTconfusionVOut.csv)
+ APP ComputeConfusionMatrix
+ OPTIONS -in ${OTBAPP_BASELINE}/clLabeledImageQB123_1.tif
+ -ref vector
+ -ref.vector.in ${INPUTDATA}/Classification/VectorData_QB1_ter.shp
+ -ref.vector.field Class
+ -out ${TEMP}/apTvComputeConfusionMatrixTconfusionVOut.csv
+ VALID --compare-ascii ${NOTOL}
+ ${OTBAPP_BASELINE_FILES}/apTvComputeConfusionMatrixTconfusionVOut.csv
+ ${TEMP}/apTvComputeConfusionMatrixTconfusionVOut.csv)
otb_test_application(NAME apTvComputeConfusionMatrixExtraReferenceLabelsV
- APP ComputeConfusionMatrix
- OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho_C7.tif
- -ref vector
- -ref.vector.in ${INPUTDATA}/Classification/VectorData_QB1_ter.shp
- -ref.vector.field Class
- -out ${TEMP}/apTvComputeConfusionMatrixExtraRefLabelsVOut.csv
- VALID --compare-ascii ${NOTOL}
- ${OTBAPP_BASELINE_FILES}/apTvComputeConfusionMatrixExtraRefLabelsVOut.csv
- ${TEMP}/apTvComputeConfusionMatrixExtraRefLabelsVOut.csv)
+ APP ComputeConfusionMatrix
+ OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho_C7.tif
+ -ref vector
+ -ref.vector.in ${INPUTDATA}/Classification/VectorData_QB1_ter.shp
+ -ref.vector.field Class
+ -out ${TEMP}/apTvComputeConfusionMatrixExtraRefLabelsVOut.csv
+ VALID --compare-ascii ${NOTOL}
+ ${OTBAPP_BASELINE_FILES}/apTvComputeConfusionMatrixExtraRefLabelsVOut.csv
+ ${TEMP}/apTvComputeConfusionMatrixExtraRefLabelsVOut.csv)
otb_test_application(NAME apTvComputeConfusionMatrixExtraReferenceLabelsR
- APP ComputeConfusionMatrix
- OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho_C7.tif
- -ref raster
- -ref.raster.in ${INPUTDATA}/Classification/clLabeledImageQB456_1_NoData_255.tif
- -nodatalabel 255
- -out ${TEMP}/apTvComputeConfusionMatrixExtraRefLabelsROut.csv
- VALID --compare-ascii ${NOTOL}
- ${OTBAPP_BASELINE_FILES}/apTvComputeConfusionMatrixExtraRefLabelsROut.csv
- ${TEMP}/apTvComputeConfusionMatrixExtraRefLabelsROut.csv)
+ APP ComputeConfusionMatrix
+ OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho_C7.tif
+ -ref raster
+ -ref.raster.in ${INPUTDATA}/Classification/clLabeledImageQB456_1_NoData_255.tif
+ -nodatalabel 255
+ -out ${TEMP}/apTvComputeConfusionMatrixExtraRefLabelsROut.csv
+ VALID --compare-ascii ${NOTOL}
+ ${OTBAPP_BASELINE_FILES}/apTvComputeConfusionMatrixExtraRefLabelsROut.csv
+ ${TEMP}/apTvComputeConfusionMatrixExtraRefLabelsROut.csv)
otb_test_application(NAME apTvComputeConfusionMatrixR
- APP ComputeConfusionMatrix
- OPTIONS -in ${OTBAPP_BASELINE}/clLabeledImageQB123_1.tif
- -ref raster
- -ref.raster.in ${INPUTDATA}/Classification/clLabeledImageQB456_1_NoData_255.tif
- -nodatalabel 255
- -out ${TEMP}/apTvComputeConfusionMatrixTconfusionROut.csv
- VALID --compare-ascii ${NOTOL}
- ${OTBAPP_BASELINE_FILES}/apTvComputeConfusionMatrixTconfusionROut.csv
- ${TEMP}/apTvComputeConfusionMatrixTconfusionROut.csv)
+ APP ComputeConfusionMatrix
+ OPTIONS -in ${OTBAPP_BASELINE}/clLabeledImageQB123_1.tif
+ -ref raster
+ -ref.raster.in ${INPUTDATA}/Classification/clLabeledImageQB456_1_NoData_255.tif
+ -nodatalabel 255
+ -out ${TEMP}/apTvComputeConfusionMatrixTconfusionROut.csv
+ VALID --compare-ascii ${NOTOL}
+ ${OTBAPP_BASELINE_FILES}/apTvComputeConfusionMatrixTconfusionROut.csv
+ ${TEMP}/apTvComputeConfusionMatrixTconfusionROut.csv)
otb_test_application(NAME apTvComputeConfusionMatrixExtraProducedLabelsR
- APP ComputeConfusionMatrix
- OPTIONS -in ${INPUTDATA}/Classification/clLabeledImageQB456_1_NoData_255.tif
- -ref raster
- -ref.raster.in ${INPUTDATA}/Classification/QB_1_ortho_C8.tif
- -nodatalabel 255
- -out ${TEMP}/apTvComputeConfusionMatrixExtraProdLabelsROut.csv
- VALID --compare-ascii ${NOTOL}
- ${OTBAPP_BASELINE_FILES}/apTvComputeConfusionMatrixExtraProdLabelsROut.csv
- ${TEMP}/apTvComputeConfusionMatrixExtraProdLabelsROut.csv)
+ APP ComputeConfusionMatrix
+ OPTIONS -in ${INPUTDATA}/Classification/clLabeledImageQB456_1_NoData_255.tif
+ -ref raster
+ -ref.raster.in ${INPUTDATA}/Classification/QB_1_ortho_C8.tif
+ -nodatalabel 255
+ -out ${TEMP}/apTvComputeConfusionMatrixExtraProdLabelsROut.csv
+ VALID --compare-ascii ${NOTOL}
+ ${OTBAPP_BASELINE_FILES}/apTvComputeConfusionMatrixExtraProdLabelsROut.csv
+ ${TEMP}/apTvComputeConfusionMatrixExtraProdLabelsROut.csv)
#----------- FusionOfClassifications TESTS ----------------
otb_test_application(NAME apTvFusionOfClassificationsDSPrecision6Inputs
- APP FusionOfClassifications
- OPTIONS -il ${INPUTDATA}/Classification/QB_1_ortho_C1.tif
- ${INPUTDATA}/Classification/QB_1_ortho_C2.tif
- ${INPUTDATA}/Classification/QB_1_ortho_C3.tif
- ${INPUTDATA}/Classification/QB_1_ortho_C4.tif
- ${INPUTDATA}/Classification/QB_1_ortho_C5.tif
- ${INPUTDATA}/Classification/QB_1_ortho_C6.tif
- -method dempstershafer
- -method.dempstershafer.cmfl ${INPUTDATA}/Classification/QB_1_ortho_C1.csv
- ${INPUTDATA}/Classification/QB_1_ortho_C2.csv
- ${INPUTDATA}/Classification/QB_1_ortho_C3.csv
- ${INPUTDATA}/Classification/QB_1_ortho_C4.csv
- ${INPUTDATA}/Classification/QB_1_ortho_C5.csv
- ${INPUTDATA}/Classification/QB_1_ortho_C6.csv
- -method.dempstershafer.mob precision
- -nodatalabel 10
- -undecidedlabel 7
- -out ${TEMP}/apTvFusionOfClassificationsDS6InputsPrecisionOutput.tif uint8
- VALID --compare-image ${NOTOL}
- ${BASELINE}/QB_1_ortho_DS_FUSED_PRECISION.tif
- ${TEMP}/apTvFusionOfClassificationsDS6InputsPrecisionOutput.tif)
+ APP FusionOfClassifications
+ OPTIONS -il ${INPUTDATA}/Classification/QB_1_ortho_C1.tif
+ ${INPUTDATA}/Classification/QB_1_ortho_C2.tif
+ ${INPUTDATA}/Classification/QB_1_ortho_C3.tif
+ ${INPUTDATA}/Classification/QB_1_ortho_C4.tif
+ ${INPUTDATA}/Classification/QB_1_ortho_C5.tif
+ ${INPUTDATA}/Classification/QB_1_ortho_C6.tif
+ -method dempstershafer
+ -method.dempstershafer.cmfl ${INPUTDATA}/Classification/QB_1_ortho_C1.csv
+ ${INPUTDATA}/Classification/QB_1_ortho_C2.csv
+ ${INPUTDATA}/Classification/QB_1_ortho_C3.csv
+ ${INPUTDATA}/Classification/QB_1_ortho_C4.csv
+ ${INPUTDATA}/Classification/QB_1_ortho_C5.csv
+ ${INPUTDATA}/Classification/QB_1_ortho_C6.csv
+ -method.dempstershafer.mob precision
+ -nodatalabel 10
+ -undecidedlabel 7
+ -out ${TEMP}/apTvFusionOfClassificationsDS6InputsPrecisionOutput.tif uint8
+ VALID --compare-image ${NOTOL}
+ ${BASELINE}/QB_1_ortho_DS_FUSED_PRECISION.tif
+ ${TEMP}/apTvFusionOfClassificationsDS6InputsPrecisionOutput.tif)
otb_test_application(NAME apTvFusionOfClassificationsMV3Inputs
- APP FusionOfClassifications
- OPTIONS -il ${INPUTDATA}/Classification/clLabeledImageQB123_1.tif
- ${INPUTDATA}/Classification/clLabeledImageQB456_1.tif
- ${INPUTDATA}/Classification/clLabeledImageQB1.tif
- -method majorityvoting
- -undecidedlabel 100
- -out ${TEMP}/apTvFusionOfClassificationsMV3InputsOutput.tif uint16
- VALID --compare-image ${NOTOL}
- ${OTBAPP_BASELINE}/apTvFusionOfClassifications3InputsOutput.tif
- ${TEMP}/apTvFusionOfClassificationsMV3InputsOutput.tif)
+ APP FusionOfClassifications
+ OPTIONS -il ${INPUTDATA}/Classification/clLabeledImageQB123_1.tif
+ ${INPUTDATA}/Classification/clLabeledImageQB456_1.tif
+ ${INPUTDATA}/Classification/clLabeledImageQB1.tif
+ -method majorityvoting
+ -undecidedlabel 100
+ -out ${TEMP}/apTvFusionOfClassificationsMV3InputsOutput.tif uint16
+ VALID --compare-image ${NOTOL}
+ ${OTBAPP_BASELINE}/apTvFusionOfClassifications3InputsOutput.tif
+ ${TEMP}/apTvFusionOfClassificationsMV3InputsOutput.tif)
otb_test_application(NAME apTvFusionOfClassificationsDSKappa6Inputs
- APP FusionOfClassifications
- OPTIONS -il ${INPUTDATA}/Classification/QB_1_ortho_C1.tif
- ${INPUTDATA}/Classification/QB_1_ortho_C2.tif
- ${INPUTDATA}/Classification/QB_1_ortho_C3.tif
- ${INPUTDATA}/Classification/QB_1_ortho_C4.tif
- ${INPUTDATA}/Classification/QB_1_ortho_C5.tif
- ${INPUTDATA}/Classification/QB_1_ortho_C6.tif
- -method dempstershafer
- -method.dempstershafer.cmfl ${INPUTDATA}/Classification/QB_1_ortho_C1.csv
- ${INPUTDATA}/Classification/QB_1_ortho_C2.csv
- ${INPUTDATA}/Classification/QB_1_ortho_C3.csv
- ${INPUTDATA}/Classification/QB_1_ortho_C4.csv
- ${INPUTDATA}/Classification/QB_1_ortho_C5.csv
- ${INPUTDATA}/Classification/QB_1_ortho_C6.csv
- -method.dempstershafer.mob kappa
- -nodatalabel 10
- -undecidedlabel 7
- -out ${TEMP}/apTvFusionOfClassificationsDS6InputsKappaOutput.tif uint8
- VALID --compare-image ${NOTOL}
- ${BASELINE}/QB_1_ortho_DS_FUSED_KAPPA.tif
- ${TEMP}/apTvFusionOfClassificationsDS6InputsKappaOutput.tif)
+ APP FusionOfClassifications
+ OPTIONS -il ${INPUTDATA}/Classification/QB_1_ortho_C1.tif
+ ${INPUTDATA}/Classification/QB_1_ortho_C2.tif
+ ${INPUTDATA}/Classification/QB_1_ortho_C3.tif
+ ${INPUTDATA}/Classification/QB_1_ortho_C4.tif
+ ${INPUTDATA}/Classification/QB_1_ortho_C5.tif
+ ${INPUTDATA}/Classification/QB_1_ortho_C6.tif
+ -method dempstershafer
+ -method.dempstershafer.cmfl ${INPUTDATA}/Classification/QB_1_ortho_C1.csv
+ ${INPUTDATA}/Classification/QB_1_ortho_C2.csv
+ ${INPUTDATA}/Classification/QB_1_ortho_C3.csv
+ ${INPUTDATA}/Classification/QB_1_ortho_C4.csv
+ ${INPUTDATA}/Classification/QB_1_ortho_C5.csv
+ ${INPUTDATA}/Classification/QB_1_ortho_C6.csv
+ -method.dempstershafer.mob kappa
+ -nodatalabel 10
+ -undecidedlabel 7
+ -out ${TEMP}/apTvFusionOfClassificationsDS6InputsKappaOutput.tif uint8
+ VALID --compare-image ${NOTOL}
+ ${BASELINE}/QB_1_ortho_DS_FUSED_KAPPA.tif
+ ${TEMP}/apTvFusionOfClassificationsDS6InputsKappaOutput.tif)
otb_test_application(NAME apTvFusionOfClassificationsDSRecall6Inputs
- APP FusionOfClassifications
- OPTIONS -il ${INPUTDATA}/Classification/QB_1_ortho_C1.tif
- ${INPUTDATA}/Classification/QB_1_ortho_C2.tif
- ${INPUTDATA}/Classification/QB_1_ortho_C3.tif
- ${INPUTDATA}/Classification/QB_1_ortho_C4.tif
- ${INPUTDATA}/Classification/QB_1_ortho_C5.tif
- ${INPUTDATA}/Classification/QB_1_ortho_C6.tif
- -method dempstershafer
- -method.dempstershafer.cmfl ${INPUTDATA}/Classification/QB_1_ortho_C1.csv
- ${INPUTDATA}/Classification/QB_1_ortho_C2.csv
- ${INPUTDATA}/Classification/QB_1_ortho_C3.csv
- ${INPUTDATA}/Classification/QB_1_ortho_C4.csv
- ${INPUTDATA}/Classification/QB_1_ortho_C5.csv
- ${INPUTDATA}/Classification/QB_1_ortho_C6.csv
- -method.dempstershafer.mob recall
- -nodatalabel 10
- -undecidedlabel 7
- -out ${TEMP}/apTvFusionOfClassificationsDS6InputsRecallOutput.tif uint8
- VALID --compare-image ${NOTOL}
- ${BASELINE}/QB_1_ortho_DS_FUSED_RECALL.tif
- ${TEMP}/apTvFusionOfClassificationsDS6InputsRecallOutput.tif)
+ APP FusionOfClassifications
+ OPTIONS -il ${INPUTDATA}/Classification/QB_1_ortho_C1.tif
+ ${INPUTDATA}/Classification/QB_1_ortho_C2.tif
+ ${INPUTDATA}/Classification/QB_1_ortho_C3.tif
+ ${INPUTDATA}/Classification/QB_1_ortho_C4.tif
+ ${INPUTDATA}/Classification/QB_1_ortho_C5.tif
+ ${INPUTDATA}/Classification/QB_1_ortho_C6.tif
+ -method dempstershafer
+ -method.dempstershafer.cmfl ${INPUTDATA}/Classification/QB_1_ortho_C1.csv
+ ${INPUTDATA}/Classification/QB_1_ortho_C2.csv
+ ${INPUTDATA}/Classification/QB_1_ortho_C3.csv
+ ${INPUTDATA}/Classification/QB_1_ortho_C4.csv
+ ${INPUTDATA}/Classification/QB_1_ortho_C5.csv
+ ${INPUTDATA}/Classification/QB_1_ortho_C6.csv
+ -method.dempstershafer.mob recall
+ -nodatalabel 10
+ -undecidedlabel 7
+ -out ${TEMP}/apTvFusionOfClassificationsDS6InputsRecallOutput.tif uint8
+ VALID --compare-image ${NOTOL}
+ ${BASELINE}/QB_1_ortho_DS_FUSED_RECALL.tif
+ ${TEMP}/apTvFusionOfClassificationsDS6InputsRecallOutput.tif)
otb_test_application(NAME apTvFusionOfClassificationsDSAccuracy6Inputs
- APP FusionOfClassifications
- OPTIONS -il ${INPUTDATA}/Classification/QB_1_ortho_C1.tif
- ${INPUTDATA}/Classification/QB_1_ortho_C2.tif
- ${INPUTDATA}/Classification/QB_1_ortho_C3.tif
- ${INPUTDATA}/Classification/QB_1_ortho_C4.tif
- ${INPUTDATA}/Classification/QB_1_ortho_C5.tif
- ${INPUTDATA}/Classification/QB_1_ortho_C6.tif
- -method dempstershafer
- -method.dempstershafer.cmfl ${INPUTDATA}/Classification/QB_1_ortho_C1.csv
- ${INPUTDATA}/Classification/QB_1_ortho_C2.csv
- ${INPUTDATA}/Classification/QB_1_ortho_C3.csv
- ${INPUTDATA}/Classification/QB_1_ortho_C4.csv
- ${INPUTDATA}/Classification/QB_1_ortho_C5.csv
- ${INPUTDATA}/Classification/QB_1_ortho_C6.csv
- -method.dempstershafer.mob accuracy
- -nodatalabel 10
- -undecidedlabel 7
- -out ${TEMP}/apTvFusionOfClassificationsDS6InputsAccuracyOutput.tif uint8
- VALID --compare-image ${NOTOL}
- ${BASELINE}/QB_1_ortho_DS_FUSED_ACCURACY.tif
- ${TEMP}/apTvFusionOfClassificationsDS6InputsAccuracyOutput.tif)
+ APP FusionOfClassifications
+ OPTIONS -il ${INPUTDATA}/Classification/QB_1_ortho_C1.tif
+ ${INPUTDATA}/Classification/QB_1_ortho_C2.tif
+ ${INPUTDATA}/Classification/QB_1_ortho_C3.tif
+ ${INPUTDATA}/Classification/QB_1_ortho_C4.tif
+ ${INPUTDATA}/Classification/QB_1_ortho_C5.tif
+ ${INPUTDATA}/Classification/QB_1_ortho_C6.tif
+ -method dempstershafer
+ -method.dempstershafer.cmfl ${INPUTDATA}/Classification/QB_1_ortho_C1.csv
+ ${INPUTDATA}/Classification/QB_1_ortho_C2.csv
+ ${INPUTDATA}/Classification/QB_1_ortho_C3.csv
+ ${INPUTDATA}/Classification/QB_1_ortho_C4.csv
+ ${INPUTDATA}/Classification/QB_1_ortho_C5.csv
+ ${INPUTDATA}/Classification/QB_1_ortho_C6.csv
+ -method.dempstershafer.mob accuracy
+ -nodatalabel 10
+ -undecidedlabel 7
+ -out ${TEMP}/apTvFusionOfClassificationsDS6InputsAccuracyOutput.tif uint8
+ VALID --compare-image ${NOTOL}
+ ${BASELINE}/QB_1_ortho_DS_FUSED_ACCURACY.tif
+ ${TEMP}/apTvFusionOfClassificationsDS6InputsAccuracyOutput.tif)
otb_test_application(NAME apTvFusionOfClassificationsMV2Inputs
- APP FusionOfClassifications
- OPTIONS -il ${INPUTDATA}/Classification/clLabeledImageQB123_1.tif
- ${INPUTDATA}/Classification/clLabeledImageQB456_1.tif
- -method majorityvoting
- -undecidedlabel 100
- -out ${TEMP}/apTvFusionOfClassificationsMV2InputsOutput.tif uint16
- VALID --compare-image ${NOTOL}
- ${OTBAPP_BASELINE}/apTvFusionOfClassifications2InputsOutput.tif
- ${TEMP}/apTvFusionOfClassificationsMV2InputsOutput.tif)
+ APP FusionOfClassifications
+ OPTIONS -il ${INPUTDATA}/Classification/clLabeledImageQB123_1.tif
+ ${INPUTDATA}/Classification/clLabeledImageQB456_1.tif
+ -method majorityvoting
+ -undecidedlabel 100
+ -out ${TEMP}/apTvFusionOfClassificationsMV2InputsOutput.tif uint16
+ VALID --compare-image ${NOTOL}
+ ${OTBAPP_BASELINE}/apTvFusionOfClassifications2InputsOutput.tif
+ ${TEMP}/apTvFusionOfClassificationsMV2InputsOutput.tif)
#----------- ComputePolylineFeatureFromImage TESTS ----------------
otb_test_application(NAME apTvCdbComputePolylineFeatureFromImage_LI_NONDVI_wr
- APP ComputePolylineFeatureFromImage
- OPTIONS -in LARGEINPUT{DEMPSTER-SHAFER/NDVI.TIF}
- -vd LARGEINPUT{DEMPSTER-SHAFER/wrongroads.shp}
- -expr "(b1 > 0.4)"
- -field "NONDVI"
- -out ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NONDVI_wr.shp)
+ APP ComputePolylineFeatureFromImage
+ OPTIONS -in LARGEINPUT{DEMPSTER-SHAFER/NDVI.TIF}
+ -vd LARGEINPUT{DEMPSTER-SHAFER/wrongroads.shp}
+ -expr "(b1 > 0.4)"
+ -field "NONDVI"
+ -out ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NONDVI_wr.shp)
# FIXME cascade dependencies to largeinput repository
if(OTB_DATA_USE_LARGEINPUT)
-otb_test_application(NAME apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_RoadExtractionApplication
- APP ComputePolylineFeatureFromImage
- OPTIONS -in LARGEINPUT{DEMPSTER-SHAFER/SpectralAngleRoad.TIF}
- -vd ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NONDVI_RoadExtractionApplication.shp
- -expr "(b1 > 0.25)"
- -field "ROADSA"
- -out ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_RoadExtractionApplication.shp)
-
-set_tests_properties(apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_RoadExtractionApplication PROPERTIES DEPENDS apTvCdbComputePolylineFeatureFromImage_LI_NONDVI_RoadExtractionApplication)
-
-otb_test_application(NAME apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_wr
- APP ComputePolylineFeatureFromImage
- OPTIONS -in LARGEINPUT{DEMPSTER-SHAFER/SpectralAngleRoad.TIF}
- -vd ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NONDVI_wr.shp
- -expr "(b1 > 0.25)"
- -field "ROADSA"
- -out ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_wr.shp)
-
-set_tests_properties(apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_wr PROPERTIES DEPENDS apTvCdbComputePolylineFeatureFromImage_LI_NONDVI_wr)
-
-otb_test_application(NAME apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_wr
- APP ComputePolylineFeatureFromImage
- OPTIONS -in LARGEINPUT{DEMPSTER-SHAFER/BUILDINGS.TIF}
- -vd ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_wr.shp
- -expr "(b1 == 0)"
- -field "NOBUIL"
- -out ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_wr.shp
- VALID --compare-ogr 0.0
- ${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_wr.shp
- ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_wr.shp)
-
-set_tests_properties(apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_wr PROPERTIES DEPENDS apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_wr)
-
-otb_test_application(NAME apTvCdbComputePolylineFeatureFromImage_LI_NONDVI_RoadExtractionApplication
- APP ComputePolylineFeatureFromImage
- OPTIONS -in LARGEINPUT{DEMPSTER-SHAFER/NDVI.TIF}
- -vd LARGEINPUT{DEMPSTER-SHAFER/road_extraction.shp}
- -expr "(b1 > 0.4)"
- -field "NONDVI"
- -out ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NONDVI_RoadExtractionApplication.shp)
-
-otb_test_application(NAME apTvCdbComputePolylineFeatureFromImage_LI_NONDVI_gt
- APP ComputePolylineFeatureFromImage
- OPTIONS -in LARGEINPUT{DEMPSTER-SHAFER/NDVI.TIF}
- -vd LARGEINPUT{DEMPSTER-SHAFER/roads_ground_truth.shp}
- -expr "(b1 > 0.4)"
- -field NONDVI
- -out ${TEMP}/apTvCdbComputePolylineFeatureFromImage.shp)
-
-otb_test_application(NAME apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_gt
- APP ComputePolylineFeatureFromImage
- OPTIONS -in LARGEINPUT{DEMPSTER-SHAFER/SpectralAngleRoad.TIF}
- -vd ${TEMP}/apTvCdbComputePolylineFeatureFromImage.shp
- -expr "(b1 > 0.25)"
- -field "ROADSA"
- -out ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_gt.shp)
-
-set_tests_properties(apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_gt PROPERTIES DEPENDS apTvCdbComputePolylineFeatureFromImage_LI_NONDVI_gt)
-
-otb_test_application(NAME apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_gt
- APP ComputePolylineFeatureFromImage
- OPTIONS -in LARGEINPUT{DEMPSTER-SHAFER/BUILDINGS.TIF}
- -vd ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_gt.shp
- -expr "(b1 == 0)"
- -field "NOBUIL"
- -out ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_gt.shp
- VALID --compare-ogr 0.0
- ${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_gt.shp
- ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_gt.shp)
-
-set_tests_properties(apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_gt PROPERTIES DEPENDS apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_gt)
-
-otb_test_application(NAME apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_RoadExtractionApplication
- APP ComputePolylineFeatureFromImage
- OPTIONS -in LARGEINPUT{DEMPSTER-SHAFER/BUILDINGS.TIF}
- -vd ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_RoadExtractionApplication.shp
- -expr "(b1 == 0)"
- -field "NOBUIL"
- -out ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_RoadExtractionApplication.shp
- VALID --compare-ogr 0.0
- ${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_RoadExtractionApplication.shp
- ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_RoadExtractionApplication.shp)
-
-set_tests_properties(apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_RoadExtractionApplication PROPERTIES DEPENDS apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_RoadExtractionApplication)
+ otb_test_application(NAME apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_RoadExtractionApplication
+ APP ComputePolylineFeatureFromImage
+ OPTIONS -in LARGEINPUT{DEMPSTER-SHAFER/SpectralAngleRoad.TIF}
+ -vd ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NONDVI_RoadExtractionApplication.shp
+ -expr "(b1 > 0.25)"
+ -field "ROADSA"
+ -out ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_RoadExtractionApplication.shp)
+
+ set_tests_properties(apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_RoadExtractionApplication PROPERTIES DEPENDS apTvCdbComputePolylineFeatureFromImage_LI_NONDVI_RoadExtractionApplication)
+
+ otb_test_application(NAME apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_wr
+ APP ComputePolylineFeatureFromImage
+ OPTIONS -in LARGEINPUT{DEMPSTER-SHAFER/SpectralAngleRoad.TIF}
+ -vd ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NONDVI_wr.shp
+ -expr "(b1 > 0.25)"
+ -field "ROADSA"
+ -out ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_wr.shp)
+
+ set_tests_properties(apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_wr PROPERTIES DEPENDS apTvCdbComputePolylineFeatureFromImage_LI_NONDVI_wr)
+
+ otb_test_application(NAME apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_wr
+ APP ComputePolylineFeatureFromImage
+ OPTIONS -in LARGEINPUT{DEMPSTER-SHAFER/BUILDINGS.TIF}
+ -vd ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_wr.shp
+ -expr "(b1 == 0)"
+ -field "NOBUIL"
+ -out ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_wr.shp
+ VALID --compare-ogr 0.0
+ ${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_wr.shp
+ ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_wr.shp)
+
+ set_tests_properties(apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_wr PROPERTIES DEPENDS apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_wr)
+
+ otb_test_application(NAME apTvCdbComputePolylineFeatureFromImage_LI_NONDVI_RoadExtractionApplication
+ APP ComputePolylineFeatureFromImage
+ OPTIONS -in LARGEINPUT{DEMPSTER-SHAFER/NDVI.TIF}
+ -vd LARGEINPUT{DEMPSTER-SHAFER/road_extraction.shp}
+ -expr "(b1 > 0.4)"
+ -field "NONDVI"
+ -out ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NONDVI_RoadExtractionApplication.shp)
+
+ otb_test_application(NAME apTvCdbComputePolylineFeatureFromImage_LI_NONDVI_gt
+ APP ComputePolylineFeatureFromImage
+ OPTIONS -in LARGEINPUT{DEMPSTER-SHAFER/NDVI.TIF}
+ -vd LARGEINPUT{DEMPSTER-SHAFER/roads_ground_truth.shp}
+ -expr "(b1 > 0.4)"
+ -field NONDVI
+ -out ${TEMP}/apTvCdbComputePolylineFeatureFromImage.shp)
+
+ otb_test_application(NAME apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_gt
+ APP ComputePolylineFeatureFromImage
+ OPTIONS -in LARGEINPUT{DEMPSTER-SHAFER/SpectralAngleRoad.TIF}
+ -vd ${TEMP}/apTvCdbComputePolylineFeatureFromImage.shp
+ -expr "(b1 > 0.25)"
+ -field "ROADSA"
+ -out ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_gt.shp)
+
+ set_tests_properties(apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_gt PROPERTIES DEPENDS apTvCdbComputePolylineFeatureFromImage_LI_NONDVI_gt)
+
+ otb_test_application(NAME apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_gt
+ APP ComputePolylineFeatureFromImage
+ OPTIONS -in LARGEINPUT{DEMPSTER-SHAFER/BUILDINGS.TIF}
+ -vd ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_gt.shp
+ -expr "(b1 == 0)"
+ -field "NOBUIL"
+ -out ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_gt.shp
+ VALID --compare-ogr 0.0
+ ${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_gt.shp
+ ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_gt.shp)
+
+ set_tests_properties(apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_gt PROPERTIES DEPENDS apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_gt)
+
+ otb_test_application(NAME apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_RoadExtractionApplication
+ APP ComputePolylineFeatureFromImage
+ OPTIONS -in LARGEINPUT{DEMPSTER-SHAFER/BUILDINGS.TIF}
+ -vd ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_RoadExtractionApplication.shp
+ -expr "(b1 == 0)"
+ -field "NOBUIL"
+ -out ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_RoadExtractionApplication.shp
+ VALID --compare-ogr 0.0
+ ${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_RoadExtractionApplication.shp
+ ${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_RoadExtractionApplication.shp)
+
+ set_tests_properties(apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_RoadExtractionApplication PROPERTIES DEPENDS apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_RoadExtractionApplication)
endif()
#----------- KMeansClassification TESTS ----------------
otb_test_application(NAME apTvClKMeansImageClassification
- APP KMeansClassification
- OPTIONS -in ${INPUTDATA}/qb_RoadExtract.img
- -vm ${INPUTDATA}/qb_RoadExtract_mask.png
- -ts 30000
- -nc 5
- -maxit 10000
- -ct 0.0000001
- -rand 121212
- -out ${TEMP}/apTvClKMeansImageClassificationFilterOutput.tif
- VALID --compare-image ${NOTOL}
- ${OTBAPP_BASELINE}/apTvClKMeansImageClassificationFilterOutput.tif
- ${TEMP}/apTvClKMeansImageClassificationFilterOutput.tif )
+ APP KMeansClassification
+ OPTIONS -in ${INPUTDATA}/qb_RoadExtract.img
+ -vm ${INPUTDATA}/qb_RoadExtract_mask.png
+ -ts 30000
+ -nc 5
+ -maxit 10000
+ -ct 0.0000001
+ -rand 121212
+ -out ${TEMP}/apTvClKMeansImageClassificationFilterOutput.tif
+ VALID --compare-image ${NOTOL}
+ ${OTBAPP_BASELINE}/apTvClKMeansImageClassificationFilterOutput.tif
+ ${TEMP}/apTvClKMeansImageClassificationFilterOutput.tif )
#----------- TrainImagesClassifier TESTS ----------------
if(OTB_USE_LIBSVM)
-otb_test_application(NAME apTvClTrainSVMImagesClassifierQB1_allOpt_InXML
- APP TrainImagesClassifier
- OPTIONS
- -inxml ${INPUTDATA}/clsvmModelQB1_OutXML.xml
- -io.il ${INPUTDATA}/Classification/QB_1_ortho.tif
- -io.vd ${INPUTDATA}/Classification/VectorData_QB1.shp
- -io.imstat ${INPUTDATA}/Classification/clImageStatisticsQB1.xml
- -sample.mv 100
- -sample.mt 100
- -sample.vtr 0.5
- -sample.vfn Class
- -classifier.libsvm.opt true
- -rand 121212
- -io.out ${TEMP}/clsvmModelQB1_allOpt_InXML.svm
- VALID ${ascii_comparison}
- ${OTBAPP_BASELINE_FILES}/clsvmModelQB1.svm
- ${TEMP}/clsvmModelQB1_allOpt_InXML.svm)
-
-otb_test_application(NAME apTvClTrainSVMImagesClassifierQB1_OutXML
- APP TrainImagesClassifier
- OPTIONS -io.il ${INPUTDATA}/Classification/QB_1_ortho.tif
- -io.vd ${INPUTDATA}/Classification/VectorData_QB1.shp
- -io.imstat ${INPUTDATA}/Classification/clImageStatisticsQB1.xml
- -sample.vfn Class
- -classifier libsvm
- -classifier.libsvm.opt true
- -io.out ${TEMP}/clsvmModelQB1_OutXML.svm
- -rand 121212
- -outxml ${TEMP}/clsvmModelQB1_OutXML.xml
- VALID ${ascii_comparison}
- ${OTBAPP_BASELINE_FILES}/clsvmModelQB1.svm
- ${TEMP}/clsvmModelQB1_OutXML.svm)
+ otb_test_application(NAME apTvClTrainSVMImagesClassifierQB1_allOpt_InXML
+ APP TrainImagesClassifier
+ OPTIONS
+ -inxml ${INPUTDATA}/clsvmModelQB1_OutXML.xml
+ -io.il ${INPUTDATA}/Classification/QB_1_ortho.tif
+ -io.vd ${INPUTDATA}/Classification/VectorData_QB1.shp
+ -io.imstat ${INPUTDATA}/Classification/clImageStatisticsQB1.xml
+ -sample.mv 100
+ -sample.mt 100
+ -sample.vtr 0.5
+ -sample.vfn Class
+ -classifier.libsvm.opt true
+ -rand 121212
+ -io.out ${TEMP}/clsvmModelQB1_allOpt_InXML.svm
+ VALID ${ascii_comparison}
+ ${OTBAPP_BASELINE_FILES}/clsvmModelQB1.svm
+ ${TEMP}/clsvmModelQB1_allOpt_InXML.svm)
+
+ otb_test_application(NAME apTvClTrainSVMImagesClassifierQB1_OutXML
+ APP TrainImagesClassifier
+ OPTIONS -io.il ${INPUTDATA}/Classification/QB_1_ortho.tif
+ -io.vd ${INPUTDATA}/Classification/VectorData_QB1.shp
+ -io.imstat ${INPUTDATA}/Classification/clImageStatisticsQB1.xml
+ -sample.vfn Class
+ -classifier libsvm
+ -classifier.libsvm.opt true
+ -io.out ${TEMP}/clsvmModelQB1_OutXML.svm
+ -rand 121212
+ -outxml ${TEMP}/clsvmModelQB1_OutXML.xml
+ VALID ${ascii_comparison}
+ ${OTBAPP_BASELINE_FILES}/clsvmModelQB1.svm
+ ${TEMP}/clsvmModelQB1_OutXML.svm)
otb_test_application(NAME apTvClTrainSVMImagesClassifierQB123
- APP TrainImagesClassifier
- OPTIONS -io.il ${INPUTDATA}/Classification/QB_1_ortho.tif
- ${INPUTDATA}/Classification/QB_2_ortho.tif
- ${INPUTDATA}/Classification/QB_3_ortho.tif
- -io.vd ${INPUTDATA}/Classification/VectorData_QB1.shp
- ${INPUTDATA}/Classification/VectorData_QB2.shp
- ${INPUTDATA}/Classification/VectorData_QB3.shp
- -io.imstat ${INPUTDATA}/Classification/clImageStatisticsQB123.xml
- -sample.vfn Class
- -classifier libsvm
- -classifier.libsvm.opt true
- -io.out ${TEMP}/clsvmModelQB123.svm
- -rand 121212)
-# RK: 09/2016. same reason explained for leTvSVMImageModelEstimatorTrainWithOptimization test
-# VALID --compare-ascii ${NOTOL}
-# ${OTBAPP_BASELINE_FILES}/clsvmModelQB123.svm
-# ${TEMP}/clsvmModelQB123.svm
-
-otb_test_application(NAME apTvClTrainSVMImagesClassifierQB1
- APP TrainImagesClassifier
- OPTIONS -io.il ${INPUTDATA}/Classification/QB_1_ortho.tif
- -io.vd ${INPUTDATA}/Classification/VectorData_QB1.shp
- -io.imstat ${INPUTDATA}/Classification/clImageStatisticsQB1.xml
- -classifier libsvm
- -classifier.libsvm.opt true
- -sample.vfn Class
- -io.out ${TEMP}/clsvmModelQB1.svm
- -rand 121212
- VALID ${ascii_comparison}
- ${OTBAPP_BASELINE_FILES}/clsvmModelQB1.svm
- ${TEMP}/clsvmModelQB1.svm)
+ APP TrainImagesClassifier
+ OPTIONS -io.il ${INPUTDATA}/Classification/QB_1_ortho.tif
+ ${INPUTDATA}/Classification/QB_2_ortho.tif
+ ${INPUTDATA}/Classification/QB_3_ortho.tif
+ -io.vd ${INPUTDATA}/Classification/VectorData_QB1.shp
+ ${INPUTDATA}/Classification/VectorData_QB2.shp
+ ${INPUTDATA}/Classification/VectorData_QB3.shp
+ -io.imstat ${INPUTDATA}/Classification/clImageStatisticsQB123.xml
+ -sample.vfn Class
+ -classifier libsvm
+ -classifier.libsvm.opt true
+ -io.out ${TEMP}/clsvmModelQB123.svm
+ -rand 121212)
+ # RK: 09/2016. same reason explained for leTvSVMImageModelEstimatorTrainWithOptimization test
+ # VALID --compare-ascii ${NOTOL}
+ # ${OTBAPP_BASELINE_FILES}/clsvmModelQB123.svm
+ # ${TEMP}/clsvmModelQB123.svm
+
+ otb_test_application(NAME apTvClTrainSVMImagesClassifierQB1
+ APP TrainImagesClassifier
+ OPTIONS -io.il ${INPUTDATA}/Classification/QB_1_ortho.tif
+ -io.vd ${INPUTDATA}/Classification/VectorData_QB1.shp
+ -io.imstat ${INPUTDATA}/Classification/clImageStatisticsQB1.xml
+ -classifier libsvm
+ -classifier.libsvm.opt true
+ -sample.vfn Class
+ -io.out ${TEMP}/clsvmModelQB1.svm
+ -rand 121212
+ VALID ${ascii_comparison}
+ ${OTBAPP_BASELINE_FILES}/clsvmModelQB1.svm
+ ${TEMP}/clsvmModelQB1.svm)
otb_test_application(NAME apTvClTrainSVMImagesClassifierQB456
- APP TrainImagesClassifier
- OPTIONS -io.il ${INPUTDATA}/Classification/QB_4_extract.tif
- ${INPUTDATA}/Classification/QB_5_extract.tif
- ${INPUTDATA}/Classification/QB_6_extract.tif
- -io.vd ${INPUTDATA}/Classification/VectorData_QB4.shp
- ${INPUTDATA}/Classification/VectorData_QB5.shp
- ${INPUTDATA}/Classification/VectorData_QB6.shp
- -io.imstat ${INPUTDATA}/Classification/clImageStatisticsQB456.xml
- -sample.vfn Class
- -classifier libsvm
- -classifier.libsvm.opt true
- -io.out ${TEMP}/clsvmModelQB456.svm
- -rand 121212)
-
-# RK: 09/2016. same reason explained for leTvSVMImageModelEstimatorTrainWithOptimization test
-# VALID --compare-ascii ${NOTOL}
-# ${OTBAPP_BASELINE_FILES}/clsvmModelQB456.svm
-# ${TEMP}/clsvmModelQB456.svm
-
-otb_test_application(NAME apTvClTrainSVMImagesClassifierQB1_allOpt
- APP TrainImagesClassifier
- OPTIONS -io.il ${INPUTDATA}/Classification/QB_1_ortho.tif
- -io.vd ${INPUTDATA}/Classification/VectorData_QB1.shp
- -io.imstat ${INPUTDATA}/Classification/clImageStatisticsQB1.xml
- -classifier libsvm
- -sample.mv 100
- -sample.mt 100
- -sample.vtr 0.5
- -sample.vfn Class
- -classifier.libsvm.opt true
- -rand 121212
- -io.out ${TEMP}/clsvmModelQB1_allOpt.svm
- VALID ${ascii_comparison}
- ${OTBAPP_BASELINE_FILES}/clsvmModelQB1.svm
- ${TEMP}/clsvmModelQB1_allOpt.svm)
+ APP TrainImagesClassifier
+ OPTIONS -io.il ${INPUTDATA}/Classification/QB_4_extract.tif
+ ${INPUTDATA}/Classification/QB_5_extract.tif
+ ${INPUTDATA}/Classification/QB_6_extract.tif
+ -io.vd ${INPUTDATA}/Classification/VectorData_QB4.shp
+ ${INPUTDATA}/Classification/VectorData_QB5.shp
+ ${INPUTDATA}/Classification/VectorData_QB6.shp
+ -io.imstat ${INPUTDATA}/Classification/clImageStatisticsQB456.xml
+ -sample.vfn Class
+ -classifier libsvm
+ -classifier.libsvm.opt true
+ -io.out ${TEMP}/clsvmModelQB456.svm
+ -rand 121212)
+
+ # RK: 09/2016. same reason explained for leTvSVMImageModelEstimatorTrainWithOptimization test
+ # VALID --compare-ascii ${NOTOL}
+ # ${OTBAPP_BASELINE_FILES}/clsvmModelQB456.svm
+ # ${TEMP}/clsvmModelQB456.svm
+
+ otb_test_application(NAME apTvClTrainSVMImagesClassifierQB1_allOpt
+ APP TrainImagesClassifier
+ OPTIONS -io.il ${INPUTDATA}/Classification/QB_1_ortho.tif
+ -io.vd ${INPUTDATA}/Classification/VectorData_QB1.shp
+ -io.imstat ${INPUTDATA}/Classification/clImageStatisticsQB1.xml
+ -classifier libsvm
+ -sample.mv 100
+ -sample.mt 100
+ -sample.vtr 0.5
+ -sample.vfn Class
+ -classifier.libsvm.opt true
+ -rand 121212
+ -io.out ${TEMP}/clsvmModelQB1_allOpt.svm
+ VALID ${ascii_comparison}
+ ${OTBAPP_BASELINE_FILES}/clsvmModelQB1.svm
+ ${TEMP}/clsvmModelQB1_allOpt.svm)
endif()
#----------- DSFuzzyModelEstimation TESTS ----------------
otb_test_application(NAME apTvCdbDSFuzzyModelEstimation_LI_autoInit
- APP DSFuzzyModelEstimation
- OPTIONS -psin ${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_gt.shp
- -nsin ${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_wr.shp
- -belsup "ROADSA"
- -plasup "NONDVI" "ROADSA" "NOBUIL"
- -desclist "NONDVI" "ROADSA" "NOBUIL"
- -maxnbit 4
- -optobs true
- -out ${TEMP}/apTvCdbDSFuzzyModelEstimatorOutput_LI_autoInit.xml)
+ APP DSFuzzyModelEstimation
+ OPTIONS -psin ${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_gt.shp
+ -nsin ${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_wr.shp
+ -belsup "ROADSA"
+ -plasup "NONDVI" "ROADSA" "NOBUIL"
+ -desclist "NONDVI" "ROADSA" "NOBUIL"
+ -maxnbit 4
+ -optobs true
+ -out ${TEMP}/apTvCdbDSFuzzyModelEstimatorOutput_LI_autoInit.xml)
otb_test_application(NAME apTvCdbDSFuzzyModelEstimation_LI
- APP DSFuzzyModelEstimation
- OPTIONS -psin ${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_gt.shp
- -nsin ${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_wr.shp
- -belsup "ROADSA"
- -plasup "NONDVI" "ROADSA" "NOBUIL"
- -initmod ${OTB_DATA_ROOT}/Input/Dempster-Shafer/DSFuzzyModel_Init.xml
- -maxnbit 4
- -optobs true
- -out ${TEMP}/apTvCdbDSFuzzyModelEstimatorOutput_LI.xml)
+ APP DSFuzzyModelEstimation
+ OPTIONS -psin ${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_gt.shp
+ -nsin ${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_wr.shp
+ -belsup "ROADSA"
+ -plasup "NONDVI" "ROADSA" "NOBUIL"
+ -initmod ${OTB_DATA_ROOT}/Input/Dempster-Shafer/DSFuzzyModel_Init.xml
+ -maxnbit 4
+ -optobs true
+ -out ${TEMP}/apTvCdbDSFuzzyModelEstimatorOutput_LI.xml)
#----------- ClassificationMapRegularization TESTS ----------------
otb_test_application(NAME apTvClassificationMapRegularization
- APP ClassificationMapRegularization
- OPTIONS -io.in ${OTBAPP_BASELINE}/clLabeledImageQB123_1.tif
- -io.out ${TEMP}/clLabeledImageQB123_1_CMR_r2_nodl_10_undl_7.tif
- -ip.radius 2
- -ip.suvbool true
- -ip.nodatalabel 10
- -ip.undecidedlabel 7
- VALID --compare-image ${NOTOL}
- ${OTBAPP_BASELINE}/clLabeledImageQB123_1_CMR_r2_nodl_10_undl_7.tif
- ${TEMP}/clLabeledImageQB123_1_CMR_r2_nodl_10_undl_7.tif)
+ APP ClassificationMapRegularization
+ OPTIONS -io.in ${OTBAPP_BASELINE}/clLabeledImageQB123_1.tif
+ -io.out ${TEMP}/clLabeledImageQB123_1_CMR_r2_nodl_10_undl_7.tif
+ -ip.radius 2
+ -ip.suvbool true
+ -ip.nodatalabel 10
+ -ip.undecidedlabel 7
+ VALID --compare-image ${NOTOL}
+ ${OTBAPP_BASELINE}/clLabeledImageQB123_1_CMR_r2_nodl_10_undl_7.tif
+ ${TEMP}/clLabeledImageQB123_1_CMR_r2_nodl_10_undl_7.tif)
#----------- OGRLayerClassifier TESTS ----------------
#----------- ComputeImagesStatistics TESTS ----------------
otb_test_application(NAME apTvClComputeImagesStatisticsQB1
- APP ComputeImagesStatistics
- OPTIONS -il ${INPUTDATA}/Classification/QB_1_ortho.tif
- -out ${TEMP}/apTvClEstimateImageStatisticsQB1.xml
- VALID --compare-ascii ${NOTOL}
- ${OTBAPP_BASELINE_FILES}/clImageStatisticsQB1.xml
- ${TEMP}/apTvClEstimateImageStatisticsQB1.xml)
+ APP ComputeImagesStatistics
+ OPTIONS -il ${INPUTDATA}/Classification/QB_1_ortho.tif
+ -out ${TEMP}/apTvClEstimateImageStatisticsQB1.xml
+ VALID --compare-ascii ${NOTOL}
+ ${OTBAPP_BASELINE_FILES}/clImageStatisticsQB1.xml
+ ${TEMP}/apTvClEstimateImageStatisticsQB1.xml)
otb_test_application(NAME apTvClComputeImagesStatisticsQB456
- APP ComputeImagesStatistics
- OPTIONS -il ${INPUTDATA}/Classification/QB_4_extract.tif
- ${INPUTDATA}/Classification/QB_5_extract.tif
- ${INPUTDATA}/Classification/QB_6_extract.tif
- -out ${TEMP}/apTvClEstimateImageStatisticsQB456.xml
- VALID --compare-ascii ${NOTOL}
- ${OTBAPP_BASELINE_FILES}/clImageStatisticsQB456.xml
- ${TEMP}/apTvClEstimateImageStatisticsQB456.xml)
+ APP ComputeImagesStatistics
+ OPTIONS -il ${INPUTDATA}/Classification/QB_4_extract.tif
+ ${INPUTDATA}/Classification/QB_5_extract.tif
+ ${INPUTDATA}/Classification/QB_6_extract.tif
+ -out ${TEMP}/apTvClEstimateImageStatisticsQB456.xml
+ VALID --compare-ascii ${NOTOL}
+ ${OTBAPP_BASELINE_FILES}/clImageStatisticsQB456.xml
+ ${TEMP}/apTvClEstimateImageStatisticsQB456.xml)
otb_test_application(NAME apTvClComputeImagesStatisticsQB123
- APP ComputeImagesStatistics
- OPTIONS -il ${INPUTDATA}/Classification/QB_1_ortho.tif
- ${INPUTDATA}/Classification/QB_2_ortho.tif
- ${INPUTDATA}/Classification/QB_3_ortho.tif
- -out ${TEMP}/apTvClEstimateImageStatisticsQB123.xml
- VALID --compare-ascii ${NOTOL}
- ${OTBAPP_BASELINE_FILES}/clImageStatisticsQB123.xml
- ${TEMP}/apTvClEstimateImageStatisticsQB123.xml)
+ APP ComputeImagesStatistics
+ OPTIONS -il ${INPUTDATA}/Classification/QB_1_ortho.tif
+ ${INPUTDATA}/Classification/QB_2_ortho.tif
+ ${INPUTDATA}/Classification/QB_3_ortho.tif
+ -out ${TEMP}/apTvClEstimateImageStatisticsQB123.xml
+ VALID --compare-ascii ${NOTOL}
+ ${OTBAPP_BASELINE_FILES}/clImageStatisticsQB123.xml
+ ${TEMP}/apTvClEstimateImageStatisticsQB123.xml)
#----------- VectorDataDSValidation TESTS ----------------
otb_test_application(NAME cdbTvVectorDataDSValidationGroundTruth_LI
- APP VectorDataDSValidation
- OPTIONS -in ${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_gt.shp
- -belsup "ROADSA"
- -plasup "NONDVI" "ROADSA" "NOBUIL"
- -descmod ${OTB_DATA_ROOT}/Input/Dempster-Shafer/DSFuzzyModel.xml
- -out ${TEMP}/cdbTvVectorDataDSValidationOutpout_LI_gt.shp
- VALID --compare-ogr 0.0
- ${OTBAPP_BASELINE_FILES}/cdbTvVectorDataDSValidationOutpout_LI_gt.shp
- ${TEMP}/cdbTvVectorDataDSValidationOutpout_LI_gt.shp)
+ APP VectorDataDSValidation
+ OPTIONS -in ${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_gt.shp
+ -belsup "ROADSA"
+ -plasup "NONDVI" "ROADSA" "NOBUIL"
+ -descmod ${OTB_DATA_ROOT}/Input/Dempster-Shafer/DSFuzzyModel.xml
+ -out ${TEMP}/cdbTvVectorDataDSValidationOutpout_LI_gt.shp
+ VALID --compare-ogr 0.0
+ ${OTBAPP_BASELINE_FILES}/cdbTvVectorDataDSValidationOutpout_LI_gt.shp
+ ${TEMP}/cdbTvVectorDataDSValidationOutpout_LI_gt.shp)
otb_test_application(NAME apTvCdbVectorDataDSValidationRoadExtractionApplication_LI
- APP VectorDataDSValidation
- OPTIONS -in ${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_RoadExtractionApplication.shp
- -belsup "ROADSA"
- -plasup "NONDVI" "ROADSA" "NOBUIL"
- -descmod ${OTB_DATA_ROOT}/Input/Dempster-Shafer/DSFuzzyModel.xml
- -out ${TEMP}/apTvCdbVectorDataDSValidationOutpout_LI_RoadExtractionApplication.shp)
+ APP VectorDataDSValidation
+ OPTIONS -in ${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_RoadExtractionApplication.shp
+ -belsup "ROADSA"
+ -plasup "NONDVI" "ROADSA" "NOBUIL"
+ -descmod ${OTB_DATA_ROOT}/Input/Dempster-Shafer/DSFuzzyModel.xml
+ -out ${TEMP}/apTvCdbVectorDataDSValidationOutpout_LI_RoadExtractionApplication.shp)
otb_test_application(NAME cdbTvVectorDataDSValidationWrongRoads_LI
- APP VectorDataDSValidation
- OPTIONS -in ${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_wr.shp
- -belsup "ROADSA"
- -plasup "NONDVI" "ROADSA" "NOBUIL"
- -descmod ${OTB_DATA_ROOT}/Input/Dempster-Shafer/DSFuzzyModel.xml
- -out ${TEMP}/cdbTvVectorDataDSValidationOutpout_LI_wr.shp
- VALID --compare-ogr 0.0
- ${OTBAPP_BASELINE_FILES}/cdbTvVectorDataDSValidationOutpout_LI_wr.shp
- ${TEMP}/cdbTvVectorDataDSValidationOutpout_LI_wr.shp)
+ APP VectorDataDSValidation
+ OPTIONS -in ${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_wr.shp
+ -belsup "ROADSA"
+ -plasup "NONDVI" "ROADSA" "NOBUIL"
+ -descmod ${OTB_DATA_ROOT}/Input/Dempster-Shafer/DSFuzzyModel.xml
+ -out ${TEMP}/cdbTvVectorDataDSValidationOutpout_LI_wr.shp
+ VALID --compare-ogr 0.0
+ ${OTBAPP_BASELINE_FILES}/cdbTvVectorDataDSValidationOutpout_LI_wr.shp
+ ${TEMP}/cdbTvVectorDataDSValidationOutpout_LI_wr.shp)
if(OTB_USE_OPENCV)
-#----------- TrainRegression TESTS ----------------
-# y = 0.01*x^2 + 1.5*x - 300
-otb_test_application(NAME apTvClTrainRegressionTest_monovar
- APP TrainRegression
- OPTIONS -io.il ${INPUTDATA}/QB_Toulouse_Ortho_regression.tif
- -io.imstat ${INPUTDATA}/QB_Toulouse_Ortho_regression.xml
- -io.out ${TEMP}/apTvClTrainRegressionTest_monovar.rf
- -sample.mt 20000
- -sample.mv 20000
- -sample.vtr 0.5
- -rand 121212
- -classifier rf
- -classifier.rf.ra 0.0001
- -classifier.rf.max 6
- -classifier.rf.acc 0.0005
- VALID ${ascii_comparison}
- ${OTBAPP_BASELINE_FILES}/apTvClTrainRegressionTest_monovar.rf
- ${TEMP}/apTvClTrainRegressionTest_monovar.rf)
-
-#----------- PredictRegression TESTS ----------------
-otb_test_application(NAME apTvClPredictRegressionTest_monovar
- APP PredictRegression
- OPTIONS -in ${INPUTDATA}/QB_Toulouse_Ortho_PAN.tif
- -model ${OTBAPP_BASELINE_FILES}/apTvClTrainRegressionTest_monovar.rf
- -imstat ${INPUTDATA}/QB_Toulouse_Ortho_regression.xml
- -out ${TEMP}/apTvClPredictRegressionTest_monovar.tif
- VALID --compare-image 1
- ${OTBAPP_BASELINE}/apTvClPredictRegressionTest_monovar.tif
- ${TEMP}/apTvClPredictRegressionTest_monovar.tif)
+ #----------- TrainRegression TESTS ----------------
+ # y = 0.01*x^2 + 1.5*x - 300
+ otb_test_application(NAME apTvClTrainRegressionTest_monovar
+ APP TrainRegression
+ OPTIONS -io.il ${INPUTDATA}/QB_Toulouse_Ortho_regression.tif
+ -io.imstat ${INPUTDATA}/QB_Toulouse_Ortho_regression.xml
+ -io.out ${TEMP}/apTvClTrainRegressionTest_monovar.rf
+ -sample.mt 20000
+ -sample.mv 20000
+ -sample.vtr 0.5
+ -rand 121212
+ -classifier rf
+ -classifier.rf.ra 0.0001
+ -classifier.rf.max 6
+ -classifier.rf.acc 0.0005
+ VALID ${ascii_comparison}
+ ${OTBAPP_BASELINE_FILES}/apTvClTrainRegressionTest_monovar.rf
+ ${TEMP}/apTvClTrainRegressionTest_monovar.rf)
+
+ #----------- PredictRegression TESTS ----------------
+ otb_test_application(NAME apTvClPredictRegressionTest_monovar
+ APP PredictRegression
+ OPTIONS -in ${INPUTDATA}/QB_Toulouse_Ortho_PAN.tif
+ -model ${OTBAPP_BASELINE_FILES}/apTvClTrainRegressionTest_monovar.rf
+ -imstat ${INPUTDATA}/QB_Toulouse_Ortho_regression.xml
+ -out ${TEMP}/apTvClPredictRegressionTest_monovar.tif
+ VALID --compare-image 1
+ ${OTBAPP_BASELINE}/apTvClPredictRegressionTest_monovar.tif
+ ${TEMP}/apTvClPredictRegressionTest_monovar.tif)
endif()
#----------- PolygonClassStatistics TESTS ----------------
otb_test_application(NAME apTvClPolygonClassStatisticsTest
- APP PolygonClassStatistics
- OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho.tif
- -vec ${INPUTDATA}/Classification/VectorData_QB1.shp
- -field Class
- -out ${TEMP}/apTvClPolygonClassStatisticsOut.xml
- VALID --compare-ascii ${NOTOL}
- ${OTBAPP_BASELINE_FILES}/apTvClPolygonClassStatisticsOut.xml
- ${TEMP}/apTvClPolygonClassStatisticsOut.xml)
+ APP PolygonClassStatistics
+ OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho.tif
+ -vec ${INPUTDATA}/Classification/VectorData_QB1.shp
+ -field Class
+ -out ${TEMP}/apTvClPolygonClassStatisticsOut.xml
+ VALID --compare-ascii ${NOTOL}
+ ${OTBAPP_BASELINE_FILES}/apTvClPolygonClassStatisticsOut.xml
+ ${TEMP}/apTvClPolygonClassStatisticsOut.xml)
#----------- SampleSelection TESTS -----------------------
otb_test_application(NAME apTvClSampleSelection
- APP SampleSelection
- OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho.tif
- -vec ${INPUTDATA}/Classification/VectorData_QB1.shp
- -field Class
- -out ${TEMP}/apTvClSampleSelectionOut.sqlite
- -instats ${OTBAPP_BASELINE_FILES}/apTvClPolygonClassStatisticsOut.xml
- -outrates ${TEMP}/apTvClSampleSelectionOutRates.txt
- -strategy byclass
- -strategy.byclass.in ${INPUTDATA}/Classification/sampling_required.csv
- -sampler periodic
- VALID --compare-ogr ${NOTOL}
- ${OTBAPP_BASELINE_FILES}/apTvClSampleSelectionOut.sqlite
- ${TEMP}/apTvClSampleSelectionOut.sqlite)
+ APP SampleSelection
+ OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho.tif
+ -vec ${INPUTDATA}/Classification/VectorData_QB1.shp
+ -field Class
+ -out ${TEMP}/apTvClSampleSelectionOut.sqlite
+ -instats ${OTBAPP_BASELINE_FILES}/apTvClPolygonClassStatisticsOut.xml
+ -outrates ${TEMP}/apTvClSampleSelectionOutRates.txt
+ -strategy byclass
+ -strategy.byclass.in ${INPUTDATA}/Classification/sampling_required.csv
+ -sampler periodic
+ VALID --compare-ogr ${NOTOL}
+ ${OTBAPP_BASELINE_FILES}/apTvClSampleSelectionOut.sqlite
+ ${TEMP}/apTvClSampleSelectionOut.sqlite)
#----------- SampleExtraction TESTS -----------------------
otb_test_application(NAME apTvClSampleExtraction
- APP SampleExtraction
- OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho.tif
- -vec ${INPUTDATA}/Classification/apTvClSampleSelectionOut.sqlite
- -field class
- -out ${TEMP}/apTvClSampleExtractionOut.sqlite
- VALID --compare-ogr ${NOTOL}
- ${OTBAPP_BASELINE_FILES}/apTvClSampleExtractionOut.sqlite
- ${TEMP}/apTvClSampleExtractionOut.sqlite)
+ APP SampleExtraction
+ OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho.tif
+ -vec ${INPUTDATA}/Classification/apTvClSampleSelectionOut.sqlite
+ -field class
+ -out ${TEMP}/apTvClSampleExtractionOut.sqlite
+ VALID --compare-ogr ${NOTOL}
+ ${OTBAPP_BASELINE_FILES}/apTvClSampleExtractionOut.sqlite
+ ${TEMP}/apTvClSampleExtractionOut.sqlite)
#----------- TrainVectorClassifier TESTS ----------------
if(OTB_USE_OPENCV)
@@ -953,6 +957,24 @@ if(OTB_USE_OPENCV)
${TEMP}/apTvClTrainVectorClassifierModel.rf)
endif()
+#----------- TrainVectorClassifier unsupervised TESTS ----------------
+if(OTB_USE_SHARK)
+ 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 apTvClTrainVectorUnsupervisedWithClass
+ APP TrainVectorClassifier
+ OPTIONS -io.vd ${INPUTDATA}/Classification/apTvClSampleExtractionOut.sqlite
+ -feat value_0 value_1 value_2 value_3
+ -cfield class
+ -classifier sharkkm
+ -io.out ${TEMP}/apTvClTrainVectorClusteringModelWithClass.txt)
+endif()
+
#------------ MultiImageSamplingRate TESTS ----------------
otb_test_application(
NAME apTvClMultiImageSamplingRate
@@ -971,4 +993,4 @@ otb_test_application(
${TEMP}/apTvClMultiImageSamplingRate_out_2.csv
${OTBAPP_BASELINE_FILES}/apTvClMultiImageSamplingRate_out_3.csv
${TEMP}/apTvClMultiImageSamplingRate_out_3.csv
- )
+)
diff --git a/Modules/Learning/Supervised/include/otbImageClassificationFilter.h b/Modules/Learning/LearningBase/include/otbImageClassificationFilter.h
similarity index 99%
rename from Modules/Learning/Supervised/include/otbImageClassificationFilter.h
rename to Modules/Learning/LearningBase/include/otbImageClassificationFilter.h
index 35804bc535ab0ce0d2607f2cb14ec5a806a8ae42..620177e5af42420b3a58a0bced7db1abfe82b4ac 100644
--- a/Modules/Learning/Supervised/include/otbImageClassificationFilter.h
+++ b/Modules/Learning/LearningBase/include/otbImageClassificationFilter.h
@@ -37,7 +37,7 @@ namespace otb
* \ingroup Streamed
* \ingroup Threaded
*
- * \ingroup OTBSupervised
+ * \ingroup OTBLearningBase
*/
template <class TInputImage, class TOutputImage, class TMaskImage = TOutputImage>
class ITK_EXPORT ImageClassificationFilter
@@ -90,7 +90,7 @@ 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 100%
rename from Modules/Learning/Supervised/include/otbImageClassificationFilter.txx
rename to Modules/Learning/LearningBase/include/otbImageClassificationFilter.txx
diff --git a/Modules/Learning/Supervised/include/otbMachineLearningModel.h b/Modules/Learning/LearningBase/include/otbMachineLearningModel.h
similarity index 98%
rename from Modules/Learning/Supervised/include/otbMachineLearningModel.h
rename to Modules/Learning/LearningBase/include/otbMachineLearningModel.h
index a71d819eff893cee01c60d411b1116c2351aa6df..12e0e4dbf924b15a02c17a302443edb5855d9205 100644
--- a/Modules/Learning/Supervised/include/otbMachineLearningModel.h
+++ b/Modules/Learning/LearningBase/include/otbMachineLearningModel.h
@@ -60,10 +60,11 @@ namespace otb
* \sa NormalBayesMachineLearningModel
* \sa NeuralNetworkMachineLearningModel
* \sa SharkRandomForestsMachineLearningModel
+ * \sa SharkKMeansMachineLearningModel
* \sa ImageClassificationFilter
*
*
- * \ingroup OTBSupervised
+ * \ingroup OTBLearningBase
*/
template <class TInputValue, class TTargetValue, class TConfidenceValue = double >
class ITK_EXPORT MachineLearningModel
@@ -93,7 +94,7 @@ public:
//@}
/**\name Confidence value typedef */
- typedef TConfidenceValue ConfidenceValueType;
+ typedef TConfidenceValue ConfidenceValueType;
typedef itk::FixedArray<ConfidenceValueType,1> ConfidenceSampleType;
typedef itk::Statistics::ListSample<ConfidenceSampleType> ConfidenceListSampleType;
diff --git a/Modules/Learning/Supervised/include/otbMachineLearningModel.txx b/Modules/Learning/LearningBase/include/otbMachineLearningModel.txx
similarity index 100%
rename from Modules/Learning/Supervised/include/otbMachineLearningModel.txx
rename to Modules/Learning/LearningBase/include/otbMachineLearningModel.txx
diff --git a/Modules/Learning/Supervised/include/otbMachineLearningModelFactoryBase.h b/Modules/Learning/LearningBase/include/otbMachineLearningModelFactoryBase.h
similarity index 98%
rename from Modules/Learning/Supervised/include/otbMachineLearningModelFactoryBase.h
rename to Modules/Learning/LearningBase/include/otbMachineLearningModelFactoryBase.h
index 029b73d49787f4f339843c066c64781626ff6f25..0ca61f2c37483d0735c146f9355b4a4a8d4c5d35 100644
--- a/Modules/Learning/Supervised/include/otbMachineLearningModelFactoryBase.h
+++ b/Modules/Learning/LearningBase/include/otbMachineLearningModelFactoryBase.h
@@ -32,8 +32,7 @@ namespace otb
* This class intends to hold the static attributes that can not be
* part of a template class (ld error).
*
- *
- * \ingroup OTBSupervised
+ * \ingroup OTBLearningBase
*/
class OTBSupervised_EXPORT MachineLearningModelFactoryBase : public itk::Object
{
diff --git a/Modules/Learning/Supervised/include/otbSharkUtils.h b/Modules/Learning/LearningBase/include/otbSharkUtils.h
similarity index 96%
rename from Modules/Learning/Supervised/include/otbSharkUtils.h
rename to Modules/Learning/LearningBase/include/otbSharkUtils.h
index 4774965cfa581ec8d733d1e04aa33857e021b159..9efcf948bdbbfd7e9a672068677f029ac10c7d39 100644
--- a/Modules/Learning/Supervised/include/otbSharkUtils.h
+++ b/Modules/Learning/LearningBase/include/otbSharkUtils.h
@@ -21,14 +21,16 @@
#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 6a94a9a00add529b41d2597acf248457356ea233..b4fab23bbaec200852575ff64dbb2424475542c6 100644
--- a/Modules/Learning/LearningBase/otb-module.cmake
+++ b/Modules/Learning/LearningBase/otb-module.cmake
@@ -25,6 +25,11 @@ otb_module(OTBLearningBase
DEPENDS
OTBCommon
OTBITK
+ OTBImageIO
+ OTBImageBase
+
+ OPTIONAL_DEPENDS
+ OTBShark
TEST_DEPENDS
OTBTestKernel
diff --git a/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.txx
index 9ed1a25ddd076191852388ad7624036f74c77991..3b6dc52a25674819aff7ddb715e2be4b964c0c38 100644
--- a/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.txx
@@ -243,11 +243,21 @@ KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
//first line is the K parameter of this algorithm.
std::string line;
std::getline(ifs, line);
+ std::istringstream iss(line);
+ if( line.find( "K" ) == std::string::npos )
+ {
+ itkExceptionMacro( <<"Could not read file "<<filename );
+ }
std::string::size_type pos = line.find_first_of("=", 0);
std::string::size_type nextpos = line.find_first_of(" \n\r", pos+1);
this->SetK(boost::lexical_cast<int>(line.substr(pos+1, nextpos-pos-1)));
+
//second line is the IsRegression parameter
std::getline(ifs, line);
+ if( line.find( "IsRegression" ) == std::string::npos )
+ {
+ itkExceptionMacro( <<"Could not read file "<<filename );
+ }
pos = line.find_first_of("=", 0);
nextpos = line.find_first_of(" \n\r", pos+1);
this->SetRegressionMode(boost::lexical_cast<bool>(line.substr(pos+1, nextpos-pos-1)));
diff --git a/Modules/Learning/Supervised/include/otbMachineLearningModelFactory.txx b/Modules/Learning/Supervised/include/otbMachineLearningModelFactory.txx
index 51d136a1ea3b8ad021c13ad04f7df1fa3cf83da0..760096feafeada28f0328d8a6fced9e3a7cf9397 100644
--- a/Modules/Learning/Supervised/include/otbMachineLearningModelFactory.txx
+++ b/Modules/Learning/Supervised/include/otbMachineLearningModelFactory.txx
@@ -43,6 +43,7 @@
#ifdef OTB_USE_SHARK
#include "otbSharkRandomForestsMachineLearningModelFactory.h"
+#include "otbSharkKMeansMachineLearningModelFactory.h"
#endif
#include "itkMutexLockHolder.h"
@@ -110,6 +111,7 @@ MachineLearningModelFactory<TInputValue,TOutputValue>
#ifdef OTB_USE_SHARK
RegisterFactory(SharkRandomForestsMachineLearningModelFactory<TInputValue,TOutputValue>::New());
+ RegisterFactory(SharkKMeansMachineLearningModelFactory<TInputValue,TOutputValue>::New());
#endif
#ifdef OTB_USE_OPENCV
@@ -168,6 +170,14 @@ MachineLearningModelFactory<TInputValue,TOutputValue>
itk::ObjectFactoryBase::UnRegisterFactory(sharkRFFactory);
continue;
}
+
+ SharkKMeansMachineLearningModelFactory<TInputValue,TOutputValue> *sharkKMeansFactory =
+ dynamic_cast<SharkKMeansMachineLearningModelFactory<TInputValue,TOutputValue> *>(*itFac);
+ if (sharkKMeansFactory)
+ {
+ itk::ObjectFactoryBase::UnRegisterFactory(sharkKMeansFactory);
+ continue;
+ }
#endif
#ifdef OTB_USE_OPENCV
diff --git a/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.h b/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.h
index 6efe3ecdf7d19220966904e7205115f52599382e..8d2c0c81233c5e5486ec8068f86298a2f7053ab8 100644
--- a/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.h
@@ -21,8 +21,6 @@
#ifndef otbSharkRandomForestsMachineLearningModel_h
#define otbSharkRandomForestsMachineLearningModel_h
-#include "otb_shark.h"
-
#include "itkLightObject.h"
#include "otbMachineLearningModel.h"
@@ -36,6 +34,7 @@
#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 6c90b26f3614398d5b567ad9be5df335722afca8..207f1abdd77e4b5cfffd9bc5d104c4b40232f853 100644
--- a/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.txx
@@ -198,6 +198,8 @@ 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);
}
@@ -208,8 +210,25 @@ 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/Supervised/otb-module.cmake b/Modules/Learning/Supervised/otb-module.cmake
index 32afd5841aeeb097608f5fc4ab3b4e772dac7c17..2c7c4ae51189889628694c0269c7a469a6d3205f 100644
--- a/Modules/Learning/Supervised/otb-module.cmake
+++ b/Modules/Learning/Supervised/otb-module.cmake
@@ -29,12 +29,14 @@ ENABLE_SHARED
OTBCommon
OTBITK
OTBImageBase
+ OTBLearningBase
+ OTBUnsupervised
OPTIONAL_DEPENDS
OTBOpenCV
OTBSVMLearning
OTBShark
-
+
TEST_DEPENDS
OTBTestKernel
OTBImageIO
diff --git a/Modules/Learning/Supervised/test/otbMachineLearningModelCanRead.cxx b/Modules/Learning/Supervised/test/otbMachineLearningModelCanRead.cxx
index df1eccdec10194e71256d0b5a3eb843b2f5eb7c8..49faadbebe6a450d48c1bf8c4e958f4b508fa65a 100644
--- a/Modules/Learning/Supervised/test/otbMachineLearningModelCanRead.cxx
+++ b/Modules/Learning/Supervised/test/otbMachineLearningModelCanRead.cxx
@@ -322,4 +322,5 @@ int otbSharkRFMachineLearningModelCanRead(int argc, char* argv[])
return EXIT_SUCCESS;
}
+
#endif
diff --git a/Modules/Learning/Supervised/test/otbSupervisedTestDriver.cxx b/Modules/Learning/Supervised/test/otbSupervisedTestDriver.cxx
index 7ba47e427e27a004f7a94b00fa4ebd311592d087..d96021c156c46b0e7d72ccac06f0d9006c0eb8b5 100644
--- a/Modules/Learning/Supervised/test/otbSupervisedTestDriver.cxx
+++ b/Modules/Learning/Supervised/test/otbSupervisedTestDriver.cxx
@@ -92,7 +92,7 @@ void RegisterTests()
REGISTER_TEST(otbSharkRFMachineLearningModelCanRead);
REGISTER_TEST(otbSharkImageClassificationFilter);
#endif
-
+
REGISTER_TEST(otbImageClassificationFilterNew);
REGISTER_TEST(otbImageClassificationFilter);
}
diff --git a/Modules/Learning/Supervised/test/otbTrainMachineLearningModel.cxx b/Modules/Learning/Supervised/test/otbTrainMachineLearningModel.cxx
index 5fc1f79f1de5c6af752e37edad080555ae7e58e2..590fae174907817e0ed525cda4d56c1d3e78e2d0 100644
--- a/Modules/Learning/Supervised/test/otbTrainMachineLearningModel.cxx
+++ b/Modules/Learning/Supervised/test/otbTrainMachineLearningModel.cxx
@@ -1290,4 +1290,5 @@ int otbSharkRFMachineLearningModel(int argc, char * argv[])
return EXIT_SUCCESS;
}
+
#endif
diff --git a/Modules/Learning/Supervised/test/tests-shark.cmake b/Modules/Learning/Supervised/test/tests-shark.cmake
index 2f7d6906c74b1be51e7f22260ef68bbb6309fd35..eeda8fff3f9f1413dcffe868c3fb5f8cb4f8f6d0 100644
--- a/Modules/Learning/Supervised/test/tests-shark.cmake
+++ b/Modules/Learning/Supervised/test/tests-shark.cmake
@@ -34,17 +34,17 @@ otb_add_test(NAME leTvSharkRFMachineLearningModelCanRead COMMAND otbSupervisedTe
otb_add_test(NAME leTvSharkRFMachineLearningModelCanReadFail COMMAND otbSupervisedTestDriver
otbSharkRFMachineLearningModelCanRead
- ${INPUTDATA}/ROI_QB_MUL_4_svmModel.txt
+ ${INPUTDATA}/Classification/otbSharkImageClassificationFilter_KMeansmodel.txt
)
set_property(TEST leTvSharkRFMachineLearningModelCanReadFail PROPERTY WILL_FAIL true)
otb_add_test(NAME leTvImageClassificationFilterSharkFast COMMAND otbSupervisedTestDriver
- --compare-n-images ${NOTOL} 2
+ --compare-n-images ${NOTOL} 2
${BASELINE}/leSharkImageClassificationFilterOutput.tif
${TEMP}/leSharkImageClassificationFilterOutput.tif
${BASELINE}/leSharkImageClassificationFilterConfidence.tif
- ${TEMP}/leSharkImageClassificationFilterConfidence.tif
+ ${TEMP}/leSharkImageClassificationFilterConfidence.tif
otbSharkImageClassificationFilter
${INPUTDATA}/Classification/QB_1_ortho.tif
${TEMP}/leSharkImageClassificationFilterOutput.tif
@@ -66,11 +66,11 @@ otb_add_test(NAME leTvImageClassificationFilterSharkFast COMMAND otbSupervisedT
# )
otb_add_test(NAME leTvImageClassificationFilterSharkFastMask COMMAND otbSupervisedTestDriver
- --compare-n-images ${NOTOL} 2
+ --compare-n-images ${NOTOL} 2
${BASELINE}/leSharkImageClassificationFilterWithMaskOutput.tif
${TEMP}/leSharkImageClassificationFilterWithMaskOutput.tif
${BASELINE}/leSharkImageClassificationFilterWithMaskConfidence.tif
- ${TEMP}/leSharkImageClassificationFilterWithMaskConfidence.tif
+ ${TEMP}/leSharkImageClassificationFilterWithMaskConfidence.tif
otbSharkImageClassificationFilter
${INPUTDATA}/Classification/QB_1_ortho.tif
${TEMP}/leSharkImageClassificationFilterWithMaskOutput.tif
@@ -79,3 +79,4 @@ otb_add_test(NAME leTvImageClassificationFilterSharkFastMask COMMAND otbSupervi
${INPUTDATA}/Classification/otbSharkImageClassificationFilter_RFmodel.txt
${INPUTDATA}/Classification/QB_1_ortho_mask.tif
)
+
diff --git a/Modules/Learning/Unsupervised/CMakeLists.txt b/Modules/Learning/Unsupervised/CMakeLists.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e83c272cd617b09367dce57c8ed038757904d14c
--- /dev/null
+++ b/Modules/Learning/Unsupervised/CMakeLists.txt
@@ -0,0 +1,4 @@
+project(OTBUnsupervised)
+
+
+otb_module_impl()
diff --git a/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.h b/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.h
new file mode 100644
index 0000000000000000000000000000000000000000..3084b2503e8680fe36b60c0cf70b979bc7656cc9
--- /dev/null
+++ b/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.h
@@ -0,0 +1,171 @@
+/*
+ * 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"
+
+#if defined(__GNUC__) || defined(__clang__)
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wshadow"
+#pragma GCC diagnostic ignored "-Wunused-parameter"
+#pragma GCC diagnostic ignored "-Woverloaded-virtual"
+#pragma GCC diagnostic ignored "-Wignored-qualifiers"
+#pragma GCC diagnostic ignored "-Wsign-compare"
+#pragma GCC diagnostic ignored "-Wcast-align"
+#pragma GCC diagnostic ignored "-Wunknown-pragmas"
+#endif
+
+#include "otb_shark.h"
+#include "shark/Models/Clustering/HardClusteringModel.h"
+#include "shark/Models/Clustering/SoftClusteringModel.h"
+#include "shark/Models/Clustering/Centroids.h"
+#include "shark/Models/Clustering/ClusteringModel.h"
+#include "shark/Algorithms/KMeans.h"
+
+#if defined(__GNUC__) || defined(__clang__)
+#pragma GCC diagnostic pop
+#endif
+
+/** \class SharkKMeansMachineLearningModel
+ * \brief Shark version of Random Forests algorithm
+ *
+ * This is a specialization of MachineLearningModel class allowing to
+ * use Shark implementation of the Random Forests algorithm.
+ *
+ * It is noteworthy that training step is parallel.
+ *
+ * For more information, see
+ * http://image.diku.dk/shark/sphinx_pages/build/html/rest_sources/tutorials/algorithms/kmeans.html
+ *
+ * \ingroup OTBUnsupervised
+ */
+namespace otb
+{
+template<class TInputValue, class TTargetValue>
+class ITK_EXPORT SharkKMeansMachineLearningModel : public MachineLearningModel<TInputValue, TTargetValue>
+{
+public:
+ /** Standard class typedefs. */
+ 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 shark::HardClusteringModel<shark::RealVector> ClusteringModelType;
+ typedef ClusteringModelType::OutputType ClusteringOutputType;
+
+ /** Run-time type information (and related methods). */
+ itkNewMacro( Self );
+ itkTypeMacro( SharkKMeansMachineLearningModel, MachineLearningModel );
+
+ /** Train the machine learning model */
+ virtual void Train() ITK_OVERRIDE;
+
+ /** Save the model to file */
+ virtual void Save(const std::string &filename, const std::string &name = "") ITK_OVERRIDE;
+
+ /** Load the model from file */
+ virtual void Load(const std::string &filename, const std::string &name = "") ITK_OVERRIDE;
+
+ /**\name Classification model file compatibility tests */
+ //@{
+ /** Is the input model file readable and compatible with the corresponding classifier ? */
+ virtual bool CanReadFile(const std::string &) ITK_OVERRIDE;
+
+ /** Is the input model file writable and compatible with the corresponding classifier ? */
+ virtual bool CanWriteFile(const std::string &) ITK_OVERRIDE;
+ //@}
+
+ /** Get the maximum number of iteration for the kMeans algorithm.*/
+ itkGetMacro( MaximumNumberOfIterations, unsigned );
+ /** Set the maximum number of iteration for the kMeans algorithm.*/
+ itkSetMacro( MaximumNumberOfIterations, unsigned );
+
+ /** Get the number of class for the kMeans algorithm.*/
+ itkGetMacro( K, unsigned );
+ /** Set the number of class for the kMeans algorithm.*/
+ itkSetMacro( K, unsigned );
+
+ /** If true, normalized input data sample list */
+ itkGetMacro( Normalized, bool );
+ itkSetMacro( Normalized, bool );
+
+protected:
+ /** Constructor */
+ SharkKMeansMachineLearningModel();
+
+ /** Destructor */
+ virtual ~SharkKMeansMachineLearningModel();
+
+ /** Predict values using the model */
+ virtual TargetSampleType
+ DoPredict(const InputSampleType &input, ConfidenceValueType *quality = ITK_NULLPTR) const ITK_OVERRIDE;
+
+
+ virtual void DoPredictBatch(const InputListSampleType *, const unsigned int &startIndex, const unsigned int &size,
+ TargetListSampleType *, ConfidenceListSampleType * = ITK_NULLPTR) const ITK_OVERRIDE;
+
+ template<typename DataType>
+ DataType NormalizeData(const DataType &data) const;
+
+ /** PrintSelf method */
+ void PrintSelf(std::ostream &os, itk::Indent indent) const;
+
+private:
+ SharkKMeansMachineLearningModel(const Self &); //purposely not implemented
+ void operator=(const Self &); //purposely not implemented
+
+ // Parameters set by the user
+ bool m_Normalized;
+ unsigned int m_K;
+ unsigned int m_MaximumNumberOfIterations;
+ bool m_CanRead;
+
+
+ /** Centroids results form kMeans */
+ shark::Centroids m_Centroids;
+
+
+ /** shark Model could be SoftClusteringModel or HardClusteringModel */
+ boost::shared_ptr<ClusteringModelType> m_ClusteringModel;
+
+};
+} // end namespace otb
+
+#ifndef OTB_MANUAL_INSTANTIATION
+
+#include "otbSharkKMeansMachineLearningModel.txx"
+
+#endif
+
+#endif
diff --git a/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.txx b/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.txx
new file mode 100644
index 0000000000000000000000000000000000000000..267a676ae668a863928be6087146a1ca7dd61d6e
--- /dev/null
+++ b/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.txx
@@ -0,0 +1,257 @@
+/*
+ * 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"
+#pragma GCC diagnostic ignored "-Wunused-parameter"
+#pragma GCC diagnostic ignored "-Woverloaded-virtual"
+#pragma GCC diagnostic ignored "-Wignored-qualifiers"
+#endif
+
+#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
+
+
+
+namespace otb
+{
+template<class TInputValue, class TOutputValue>
+SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
+::SharkKMeansMachineLearningModel() :
+ m_Normalized( false ), m_K(2), m_MaximumNumberOfIterations( 10 )
+{
+ // Default set HardClusteringModel
+ m_ClusteringModel = boost::shared_ptr<ClusteringModelType>(new ClusteringModelType( &m_Centroids ));
+}
+
+
+template<class TInputValue, class TOutputValue>
+SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
+::~SharkKMeansMachineLearningModel()
+{
+}
+
+/** Train the machine learning model */
+template<class TInputValue, class TOutputValue>
+void
+SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
+::Train()
+{
+ // Parse input data and convert to Shark 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
+ shark::kMeans( data, m_K, m_Centroids, m_MaximumNumberOfIterations );
+ m_ClusteringModel = boost::shared_ptr<ClusteringModelType>(new ClusteringModelType( &m_Centroids ));
+}
+
+template<class TInputValue, class TOutputValue>
+template<typename DataType>
+DataType
+SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
+::NormalizeData(const DataType &data) const
+{
+ shark::Normalizer<> normalizer;
+ shark::NormalizeComponentsUnitVariance<> normalizingTrainer( true );//zero mean
+ normalizingTrainer.train( normalizer, data );
+ return normalizer( data );
+}
+
+template<class TInputValue, class TOutputValue>
+typename SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
+::TargetSampleType
+SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
+::DoPredict(const InputSampleType &value, ConfidenceValueType *quality) const
+{
+ shark::RealVector data( value.Size());
+ for( size_t i = 0; i < value.Size(); i++ )
+ {
+ data.push_back( value[i] );
+ }
+
+ // Change quality measurement only if SoftClustering or other clustering method is used.
+ if( quality != ITK_NULLPTR )
+ {
+ //unsigned int probas = (*m_ClusteringModel)( data );
+ ( *quality ) = ConfidenceValueType( 1.);
+ }
+
+ TargetSampleType target;
+ ClusteringOutputType predictedValue = (*m_ClusteringModel)( data );
+ target[0] = static_cast<TOutputValue>(predictedValue);
+ return target;
+}
+
+template<class TInputValue, class TOutputValue>
+void
+SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
+::DoPredictBatch(const InputListSampleType *input,
+ const unsigned int &startIndex,
+ const unsigned int &size,
+ TargetListSampleType *targets,
+ ConfidenceListSampleType *quality) const
+{
+
+ // Perform check on input values
+ assert( input != ITK_NULLPTR );
+ assert( targets != ITK_NULLPTR );
+
+ // 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() )
+ {
+ 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<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.");
+ }
+
+ unsigned int id = startIndex;
+ for( const auto &p : clusters.elements() )
+ {
+ TargetSampleType target;
+ target[0] = static_cast<TOutputValue>(p);
+ targets->SetMeasurementVector( id, target );
+ ++id;
+ }
+
+ // Change quality measurement only if SoftClustering or other clustering method is used.
+ if( quality != ITK_NULLPTR )
+ {
+ for( unsigned int qid = startIndex; qid < size; ++qid )
+ {
+ quality->SetMeasurementVector( qid, static_cast<ConfidenceValueType>(1.) );
+ }
+ }
+}
+
+
+template<class TInputValue, class TOutputValue>
+void
+SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
+::Save(const std::string &filename, const std::string & itkNotUsed( name ))
+{
+ std::ofstream ofs( filename.c_str());
+ if( !ofs )
+ {
+ itkExceptionMacro( << "Error opening " << filename.c_str());
+ }
+ ofs << "#" << m_ClusteringModel->name() << std::endl;
+ shark::TextOutArchive oa( ofs );
+ m_ClusteringModel->save( oa, 1 );
+}
+
+template<class TInputValue, class TOutputValue>
+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 );
+ m_ClusteringModel->load( ia, 0 );
+ ifs.close();
+}
+
+template<class TInputValue, class TOutputValue>
+bool
+SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
+::CanReadFile(const std::string &file)
+{
+ try
+ {
+ m_CanRead = true;
+ this->Load( file );
+ }
+ catch( ... )
+ {
+ return false;
+ }
+ return m_CanRead;
+}
+
+template<class TInputValue, class TOutputValue>
+bool
+SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
+::CanWriteFile(const std::string & itkNotUsed( file ))
+{
+ return true;
+}
+
+template<class TInputValue, class TOutputValue>
+void
+SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
+::PrintSelf(std::ostream &os, itk::Indent indent) const
+{
+ // Call superclass implementation
+ Superclass::PrintSelf( os, indent );
+}
+} //end namespace otb
+
+#endif
diff --git a/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModelFactory.h b/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModelFactory.h
new file mode 100644
index 0000000000000000000000000000000000000000..a072d5d71921f24cc483e0193d3f0d04a4ab41d2
--- /dev/null
+++ b/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModelFactory.h
@@ -0,0 +1,76 @@
+/*
+ * 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
+
+#include "itkObjectFactoryBase.h"
+#include "itkImageIOBase.h"
+
+namespace otb
+{
+/** \class SharkKMeansMachineLearningModelFactory
+ * \brief Creation of an instance of a SharkKMeansMachineLearningModel object using the object factory
+ *
+ * \ingroup OTBUnsupervised
+ */
+template <class TInputValue, class TTargetValue>
+class ITK_EXPORT SharkKMeansMachineLearningModelFactory : public itk::ObjectFactoryBase
+{
+public:
+ /** Standard class typedefs. */
+ typedef SharkKMeansMachineLearningModelFactory Self;
+ typedef itk::ObjectFactoryBase Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+ /** Class methods used to interface with the registered factories. */
+ virtual const char* GetITKSourceVersion(void) const;
+ virtual const char* GetDescription(void) const;
+
+ /** Method for class instantiation. */
+ itkFactorylessNewMacro(Self);
+
+ /** Run-time type information (and related methods). */
+ itkTypeMacro(SharkKMeansMachineLearningModelFactory, itk::ObjectFactoryBase);
+
+ /** Register one factory of this type */
+ static void RegisterOneFactory(void)
+ {
+ Pointer KMeansFactory = SharkKMeansMachineLearningModelFactory::New();
+ itk::ObjectFactoryBase::RegisterFactory(KMeansFactory);
+ }
+
+protected:
+ SharkKMeansMachineLearningModelFactory();
+ virtual ~SharkKMeansMachineLearningModelFactory();
+
+private:
+ SharkKMeansMachineLearningModelFactory(const Self &); //purposely not implemented
+ void operator =(const Self&); //purposely not implemented
+
+};
+
+} // end namespace otb
+
+#ifndef OTB_MANUAL_INSTANTIATION
+#include "otbSharkKMeansMachineLearningModelFactory.txx"
+#endif
+
+#endif
diff --git a/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModelFactory.txx b/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModelFactory.txx
new file mode 100644
index 0000000000000000000000000000000000000000..a4fe5584e04249875de1193e365fd888a43cfa07
--- /dev/null
+++ b/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModelFactory.txx
@@ -0,0 +1,71 @@
+/*
+ * 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
+
+#include "otbSharkKMeansMachineLearningModelFactory.h"
+
+#include "itkCreateObjectFunction.h"
+#include "otbSharkKMeansMachineLearningModel.h"
+#include "itkVersion.h"
+
+namespace otb
+{
+
+template <class TInputValue, class TOutputValue>
+SharkKMeansMachineLearningModelFactory<TInputValue,TOutputValue>
+::SharkKMeansMachineLearningModelFactory()
+{
+
+ std::string classOverride = std::string("otbMachineLearningModel");
+ std::string subclass = std::string("otbSharkKMeansMachineLearningModel");
+
+ this->RegisterOverride(classOverride.c_str(),
+ subclass.c_str(),
+ "Shark KMeans Machine Learning Model",
+ 1,
+ itk::CreateObjectFunction<SharkKMeansMachineLearningModel<TInputValue,TOutputValue> >::New());
+}
+
+template <class TInputValue, class TOutputValue>
+SharkKMeansMachineLearningModelFactory<TInputValue,TOutputValue>
+::~SharkKMeansMachineLearningModelFactory()
+{
+}
+
+template <class TInputValue, class TOutputValue>
+const char*
+SharkKMeansMachineLearningModelFactory<TInputValue,TOutputValue>
+::GetITKSourceVersion(void) const
+{
+ return ITK_SOURCE_VERSION;
+}
+
+template <class TInputValue, class TOutputValue>
+const char*
+SharkKMeansMachineLearningModelFactory<TInputValue,TOutputValue>
+::GetDescription() const
+{
+ return "Shark KMeans unsupervised machine learning model factory";
+}
+
+} // end namespace otb
+
+#endif
diff --git a/Modules/Learning/Unsupervised/otb-module.cmake b/Modules/Learning/Unsupervised/otb-module.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..edaf52212e6bd849bf0b385bd3657bbc27b072c6
--- /dev/null
+++ b/Modules/Learning/Unsupervised/otb-module.cmake
@@ -0,0 +1,21 @@
+set(DOCUMENTATION "This module provides the Orfeo Toolbox unsupervised
+classification and regression framework, currently based on Shark")
+
+otb_module(OTBUnsupervised
+ DEPENDS
+ OTBCommon
+ OTBITK
+ OTBImageBase
+ OTBLearningBase
+
+ OPTIONAL_DEPENDS
+ OTBShark
+
+ TEST_DEPENDS
+ OTBTestKernel
+ OTBImageIO
+ OTBImageBase
+
+ DESCRIPTION
+ "${DOCUMENTATION}"
+ )
diff --git a/Modules/Learning/Unsupervised/test/CMakeLists.txt b/Modules/Learning/Unsupervised/test/CMakeLists.txt
new file mode 100644
index 0000000000000000000000000000000000000000..57cdb01bd6d73755758b1ec3ba1110e93a2adc36
--- /dev/null
+++ b/Modules/Learning/Unsupervised/test/CMakeLists.txt
@@ -0,0 +1,17 @@
+otb_module_test()
+set(OTBUnsupervisedTests
+ otbUnsupervisedTestDriver.cxx
+ otbMachineLearningUnsupervisedModelCanRead.cxx
+ otbTrainMachineLearningUnsupervisedModel.cxx
+ )
+
+# Tests Declaration
+
+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/otbMachineLearningUnsupervisedModelCanRead.cxx b/Modules/Learning/Unsupervised/test/otbMachineLearningUnsupervisedModelCanRead.cxx
new file mode 100644
index 0000000000000000000000000000000000000000..6856f0d0728327c26df9873af3afa016789c6d10
--- /dev/null
+++ b/Modules/Learning/Unsupervised/test/otbMachineLearningUnsupervisedModelCanRead.cxx
@@ -0,0 +1,64 @@
+/*
+ * 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>
+#include <otbMachineLearningModel.h>
+
+typedef otb::MachineLearningModel<float,short> MachineLearningModelType;
+typedef MachineLearningModelType::InputValueType InputValueType;
+typedef MachineLearningModelType::InputSampleType InputSampleType;
+typedef MachineLearningModelType::InputListSampleType InputListSampleType;
+typedef MachineLearningModelType::TargetValueType TargetValueType;
+typedef MachineLearningModelType::TargetSampleType TargetSampleType;
+typedef MachineLearningModelType::TargetListSampleType TargetListSampleType;
+
+#ifdef OTB_USE_SHARK
+
+#include "otbSharkKMeansMachineLearningModel.h"
+
+int otbSharkKMeansMachineLearningModelCanRead(int argc, char *argv[])
+{
+ if( argc != 2 )
+ {
+ std::cerr << "Usage: " << argv[0] << "<model>" << std::endl;
+ std::cerr << "Called here with " << argc << " arguments\n";
+ for( int i = 1; i < argc; ++i )
+ {
+ std::cerr << " - " << argv[i] << "\n";
+ }
+ return EXIT_FAILURE;
+ }
+ std::string filename( argv[1] );
+ typedef otb::SharkKMeansMachineLearningModel<InputValueType, TargetValueType> KMType;
+ KMType::Pointer classifier = KMType::New();
+ bool lCanRead = classifier->CanReadFile( filename );
+ if( !lCanRead )
+ {
+ std::cerr << "Error otb::SharkKMeansMachineLearningModel : impossible to open the file " << filename << "."
+ << std::endl;
+ return EXIT_FAILURE;
+ }
+
+ return EXIT_SUCCESS;
+}
+
+#endif
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/otbTrainMachineLearningUnsupervisedModel.cxx b/Modules/Learning/Unsupervised/test/otbTrainMachineLearningUnsupervisedModel.cxx
new file mode 100644
index 0000000000000000000000000000000000000000..a0805c29fb38defb049a9588a26e10167cb5920b
--- /dev/null
+++ b/Modules/Learning/Unsupervised/test/otbTrainMachineLearningUnsupervisedModel.cxx
@@ -0,0 +1,194 @@
+/*
+ * 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>
+#include <otbMachineLearningModel.h>
+
+typedef otb::MachineLearningModel<float,short> MachineLearningModelType;
+typedef MachineLearningModelType::InputValueType InputValueType;
+typedef MachineLearningModelType::InputSampleType InputSampleType;
+typedef MachineLearningModelType::InputListSampleType InputListSampleType;
+typedef MachineLearningModelType::TargetValueType TargetValueType;
+typedef MachineLearningModelType::TargetSampleType TargetSampleType;
+typedef MachineLearningModelType::TargetListSampleType TargetListSampleType;
+
+typedef otb::MachineLearningModel<float,float> MachineLearningModelRegressionType;
+typedef MachineLearningModelRegressionType::InputValueType InputValueRegressionType;
+typedef MachineLearningModelRegressionType::InputSampleType InputSampleRegressionType;
+typedef MachineLearningModelRegressionType::InputListSampleType InputListSampleRegressionType;
+typedef MachineLearningModelRegressionType::TargetValueType TargetValueRegressionType;
+typedef MachineLearningModelRegressionType::TargetSampleType TargetSampleRegressionType;
+typedef MachineLearningModelRegressionType::TargetListSampleType TargetListSampleRegressionType;
+
+
+#ifdef OTB_USE_SHARK
+#include "otbSharkKMeansMachineLearningModel.h"
+#include "otb_boost_string_header.h"
+#include <chrono>
+
+bool SharkReadDataFile(const std::string & infname, InputListSampleType * samples, TargetListSampleType * labels)
+{
+ std::ifstream ifs(infname.c_str());
+
+ if(!ifs)
+ {
+ std::cout<<"Could not read file "<<infname<<std::endl;
+ return false;
+ }
+
+ unsigned int nbfeatures = 0;
+
+ std::string line;
+ while (std::getline(ifs, line))
+ {
+ boost::algorithm::trim(line);
+
+ if(nbfeatures == 0)
+ {
+ nbfeatures = std::count(line.begin(),line.end(),' ');
+ }
+
+ if(line.size()>1)
+ {
+ InputSampleType sample(nbfeatures);
+ sample.Fill(0);
+
+ std::string::size_type pos = line.find_first_of(" ", 0);
+
+ // Parse label
+ TargetSampleType label;
+ label[0] = std::stoi(line.substr(0, pos).c_str());
+
+ bool endOfLine = false;
+ unsigned int id = 0;
+
+ while(!endOfLine)
+ {
+ std::string::size_type nextpos = line.find_first_of(" ", pos+1);
+
+ if(pos == std::string::npos)
+ {
+ endOfLine = true;
+ nextpos = line.size()-1;
+ }
+ else
+ {
+ std::string feature = line.substr(pos,nextpos-pos);
+ std::string::size_type semicolonpos = feature.find_first_of(":");
+ id = std::stoi(feature.substr(0,semicolonpos).c_str());
+ sample[id - 1] = atof(feature.substr(semicolonpos+1,feature.size()-semicolonpos).c_str());
+ pos = nextpos;
+ }
+
+ }
+ samples->SetMeasurementVectorSize(itk::NumericTraits<InputSampleType>::GetLength(sample));
+ samples->PushBack(sample);
+ labels->PushBack(label);
+ }
+ }
+
+ //std::cout<<"Retrieved "<<samples->Size()<<" samples"<<std::endl;
+ ifs.close();
+ return true;
+}
+
+
+int otbSharkKMeansMachineLearningModelNew(int itkNotUsed( argc ), char *itkNotUsed( argv )[])
+{
+ typedef otb::SharkKMeansMachineLearningModel<InputValueType, TargetValueType> SharkRFType;
+ SharkRFType::Pointer classifier = SharkRFType::New();
+ return EXIT_SUCCESS;
+}
+
+int otbSharkKMeansMachineLearningModelTrain(int argc, char *argv[])
+{
+ if( argc != 3 )
+ {
+ std::cout << "Wrong number of arguments " << std::endl;
+ std::cout << "Usage : sample file, output file " << std::endl;
+ return EXIT_FAILURE;
+ }
+
+ typedef otb::SharkKMeansMachineLearningModel<InputValueType, TargetValueType> KMeansType;
+ InputListSampleType::Pointer samples = InputListSampleType::New();
+ TargetListSampleType::Pointer labels = TargetListSampleType::New();
+
+ if( !SharkReadDataFile( argv[1], samples, labels ) )
+ {
+ std::cout << "Failed to read samples file " << argv[1] << std::endl;
+ return EXIT_FAILURE;
+ }
+
+ KMeansType::Pointer classifier = KMeansType::New();
+ classifier->SetInputListSample( samples );
+ classifier->SetTargetListSample( labels );
+ classifier->SetRegressionMode( false );
+ classifier->SetK( 3 );
+ classifier->SetMaximumNumberOfIterations( 0 );
+ std::cout << "Train\n";
+ classifier->Train();
+ std::cout << "Save\n";
+ classifier->Save( argv[2] );
+
+ return EXIT_SUCCESS;
+}
+
+
+int otbSharkKMeansMachineLearningModelPredict(int argc, char *argv[])
+{
+ if( argc != 3 )
+ {
+ std::cout << "Wrong number of arguments " << std::endl;
+ std::cout << "Usage : sample file, input model file " << std::endl;
+ return EXIT_FAILURE;
+ }
+
+ typedef otb::SharkKMeansMachineLearningModel<InputValueType, TargetValueType> KMeansType;
+ InputListSampleType::Pointer samples = InputListSampleType::New();
+ TargetListSampleType::Pointer labels = TargetListSampleType::New();
+
+ if( !SharkReadDataFile( argv[1], samples, labels ) )
+ {
+ std::cout << "Failed to read samples file " << argv[1] << std::endl;
+ return EXIT_FAILURE;
+ }
+
+ 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 );
+ classifier->SetTargetListSample( labels );
+ std::cout << "Predict loaded\n";
+ classifier->PredictBatch( samples, NULL );
+ using TimeT = std::chrono::milliseconds;
+ auto duration = std::chrono::duration_cast<TimeT>( std::chrono::system_clock::now() - start );
+ auto elapsed = duration.count();
+ std::cout << "PredictAll took " << elapsed << " ms\n";
+
+ return EXIT_SUCCESS;
+}
+#endif
diff --git a/Modules/Learning/Unsupervised/test/otbUnsupervisedTestDriver.cxx b/Modules/Learning/Unsupervised/test/otbUnsupervisedTestDriver.cxx
new file mode 100644
index 0000000000000000000000000000000000000000..30a7194f52fb61c6370693fcd8b455b1f77ac7a2
--- /dev/null
+++ b/Modules/Learning/Unsupervised/test/otbUnsupervisedTestDriver.cxx
@@ -0,0 +1,30 @@
+/*
+ * 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()
+{
+#ifdef OTB_USE_SHARK
+ REGISTER_TEST(otbSharkKMeansMachineLearningModelCanRead);
+ 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
new file mode 100644
index 0000000000000000000000000000000000000000..df61cb46479f9922e3355441458a1e261879c40c
--- /dev/null
+++ b/Modules/Learning/Unsupervised/test/tests-shark.cmake
@@ -0,0 +1,58 @@
+# kMeans Shark related tests
+
+otb_add_test(NAME leTvSharkKMeansMachineLearningModelNew COMMAND otbUnsupervisedTestDriver
+ otbSharkKMeansMachineLearningModelNew
+ )
+
+
+otb_add_test(NAME leTvSharkKMeansMachineLearningModel COMMAND otbUnsupervisedTestDriver
+ otbSharkKMeansMachineLearningModelTrain
+ ${INPUTDATA}/letter.scale
+ ${TEMP}/shark_km_model.txt
+ )
+
+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
+ 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
+ )