diff --git a/Modules/Applications/AppClassification/app/otbSampleSelection.cxx b/Modules/Applications/AppClassification/app/otbSampleSelection.cxx
index ef81a64d5fd8564197b0ffe21741f0b3b17ff20c..5ff2926888f5a97b2e735fd8ac3e895dae114829 100644
--- a/Modules/Applications/AppClassification/app/otbSampleSelection.cxx
+++ b/Modules/Applications/AppClassification/app/otbSampleSelection.cxx
@@ -73,8 +73,6 @@ public:
private:
SampleSelection()
{
- m_Periodic = PeriodicSamplerType::New();
- m_Random = RandomSamplerType::New();
m_ReaderStat = XMLReaderType::New();
m_RateCalculator = RateCalculatorType::New();
}
@@ -258,15 +256,9 @@ private:
{
// Clear state
m_RateCalculator->ClearRates();
- m_Periodic->GetFilter()->ClearOutputs();
- m_Random->GetFilter()->ClearOutputs();
otb::Wrapper::ElevationParametersHandler::SetupDEMHandlerFromElevationParameters(this,"elev");
- // Setup ram
- m_Periodic->GetStreamer()->SetAutomaticAdaptativeStreaming(GetParameterInt("ram"));
- m_Random->GetStreamer()->SetAutomaticAdaptativeStreaming(GetParameterInt("ram"));
-
// Get field name
std::vector<int> selectedCFieldIdx = GetSelectedItems("field");
@@ -415,37 +407,41 @@ private:
PeriodicSamplerType::SamplerParameterType param;
param.Offset = 0;
param.MaxJitter = this->GetParameterInt("sampler.periodic.jitter");
-
- m_Periodic->SetInput(this->GetParameterImage("in"));
- m_Periodic->SetOGRData(reprojVector);
- m_Periodic->SetOutputPositionContainerAndRates(outputSamples, rates);
- m_Periodic->SetFieldName(fieldName);
- m_Periodic->SetLayerIndex(this->GetParameterInt("layer"));
- m_Periodic->SetSamplerParameters(param);
+ param.MaxBufferSize = 100000000UL;
+ PeriodicSamplerType::Pointer periodicFilt = PeriodicSamplerType::New();
+ periodicFilt->SetInput(this->GetParameterImage("in"));
+ periodicFilt->SetOGRData(reprojVector);
+ periodicFilt->SetOutputPositionContainerAndRates(outputSamples, rates);
+ periodicFilt->SetFieldName(fieldName);
+ periodicFilt->SetLayerIndex(this->GetParameterInt("layer"));
+ periodicFilt->SetSamplerParameters(param);
if (IsParameterEnabled("mask") && HasValue("mask"))
{
- m_Periodic->SetMask(this->GetParameterImage<UInt8ImageType>("mask"));
+ periodicFilt->SetMask(this->GetParameterImage<UInt8ImageType>("mask"));
}
- m_Periodic->GetStreamer()->SetAutomaticTiledStreaming(this->GetParameterInt("ram"));
- AddProcess(m_Periodic->GetStreamer(),"Selecting positions with periodic sampler...");
- m_Periodic->Update();
+ periodicFilt->GetStreamer()->SetAutomaticTiledStreaming(this->GetParameterInt("ram"));
+ AddProcess(periodicFilt->GetStreamer(),"Selecting positions with periodic sampler...");
+ periodicFilt->Update();
}
break;
// random
case 1:
{
- m_Random->SetInput(this->GetParameterImage("in"));
- m_Random->SetOGRData(reprojVector);
- m_Random->SetOutputPositionContainerAndRates(outputSamples, rates);
- m_Random->SetFieldName(fieldName);
- m_Random->SetLayerIndex(this->GetParameterInt("layer"));
+ RandomSamplerType::Pointer randomFilt = RandomSamplerType::New();
+ randomFilt->SetInput(this->GetParameterImage("in"));
+ randomFilt->SetOGRData(reprojVector);
+ randomFilt->SetOutputPositionContainerAndRates(outputSamples, rates);
+ randomFilt->SetFieldName(fieldName);
+ randomFilt->SetLayerIndex(this->GetParameterInt("layer"));
if (IsParameterEnabled("mask") && HasValue("mask"))
{
- m_Random->SetMask(this->GetParameterImage<UInt8ImageType>("mask"));
+ randomFilt->SetMask(this->GetParameterImage<UInt8ImageType>("mask"));
}
- m_Random->GetStreamer()->SetAutomaticTiledStreaming(this->GetParameterInt("ram"));
- AddProcess(m_Random->GetStreamer(),"Selecting positions with random sampler...");
- m_Random->Update();
+ randomFilt->GetStreamer()->SetAutomaticTiledStreaming(this->GetParameterInt("ram"));
+ AddProcess(randomFilt->GetStreamer(),"Selecting positions with random sampler...");
+ randomFilt->Update();
+
+ randomFilt = RandomSamplerType::New();
}
break;
default:
@@ -455,10 +451,6 @@ private:
}
RateCalculatorType::Pointer m_RateCalculator;
-
- PeriodicSamplerType::Pointer m_Periodic;
- RandomSamplerType::Pointer m_Random;
-
XMLReaderType::Pointer m_ReaderStat;
};
diff --git a/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx b/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx
index 932454f0b5ebe89bd84c9367b6c582b9cd3c0970..201dbfd705bb51fe152d9118fd9d39d8f6e5aa4a 100644
--- a/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx
+++ b/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx
@@ -14,94 +14,68 @@
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
-#include "otbLearningApplicationBase.h"
+#include "otbWrapperCompositeApplication.h"
#include "otbWrapperApplicationFactory.h"
-#include "otbListSampleGenerator.h"
-
-// Statistic XML Reader
-#include "otbStatisticsXMLFileReader.h"
-
-// Validation
-#include "otbConfusionMatrixCalculator.h"
-
-#include "itkTimeProbe.h"
-#include "otbStandardFilterWatcher.h"
-
-// Normalize the samples
-#include "otbShiftScaleSampleListFilter.h"
-
-// List sample concatenation
-#include "otbConcatenateSampleListFilter.h"
-
-// Balancing ListSample
-#include "otbListSampleToBalancedListSampleFilter.h"
-
-// VectorData projection filter
-
-// Extract a ROI of the vectordata
-#include "otbVectorDataIntoImageProjectionFilter.h"
-
-// Elevation handler
-#include "otbWrapperElevationParametersHandler.h"
+#include "otbOGRDataToSamplePositionFilter.h"
+#include "otbSamplingRateCalculator.h"
namespace otb
{
namespace Wrapper
{
-class TrainImagesClassifier: public LearningApplicationBase<float,int>
+class TrainImagesClassifier: public CompositeApplication
{
public:
/** Standard class typedefs. */
typedef TrainImagesClassifier Self;
- typedef LearningApplicationBase<float,int> Superclass;
+ typedef CompositeApplication Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
/** Standard macro */
itkNewMacro(Self)
- itkTypeMacro(TrainImagesClassifier, otb::Wrapper::LearningApplicationBase)
-
- typedef Superclass::SampleType SampleType;
- typedef Superclass::ListSampleType ListSampleType;
- typedef Superclass::TargetSampleType TargetSampleType;
- typedef Superclass::TargetListSampleType TargetListSampleType;
+ itkTypeMacro(TrainImagesClassifier, otb::Wrapper::CompositeApplication)
- typedef Superclass::SampleImageType SampleImageType;
- typedef SampleImageType::PixelType PixelType;
+ /** filters typedefs*/
+ typedef otb::OGRDataToSamplePositionFilter<
+ FloatVectorImageType,
+ UInt8ImageType,
+ otb::PeriodicSampler> PeriodicSamplerType;
- // SampleList manipulation
- typedef otb::ListSampleGenerator<SampleImageType, VectorDataType> ListSampleGeneratorType;
-
- typedef otb::Statistics::ConcatenateSampleListFilter<ListSampleType> ConcatenateListSampleFilterType;
- typedef otb::Statistics::ConcatenateSampleListFilter<TargetListSampleType> ConcatenateLabelListSampleFilterType;
-
- // Statistic XML file Reader
- typedef otb::StatisticsXMLFileReader<SampleType> StatisticsReader;
-
- // Enhance List Sample typedef otb::Statistics::ListSampleToBalancedListSampleFilter<ListSampleType, LabelListSampleType> BalancingListSampleFilterType;
- typedef otb::Statistics::ShiftScaleSampleListFilter<ListSampleType, ListSampleType> ShiftScaleFilterType;
-
- // Estimate performance on validation sample
- typedef otb::ConfusionMatrixCalculator<TargetListSampleType, TargetListSampleType> ConfusionMatrixCalculatorType;
- typedef ConfusionMatrixCalculatorType::ConfusionMatrixType ConfusionMatrixType;
- typedef ConfusionMatrixCalculatorType::MapOfIndicesType MapOfIndicesType;
- typedef ConfusionMatrixCalculatorType::ClassLabelType ClassLabelType;
-
- // VectorData projection filter
- typedef otb::VectorDataProjectionFilter<VectorDataType, VectorDataType> VectorDataProjectionFilterType;
-
- // Extract ROI
- typedef otb::VectorDataIntoImageProjectionFilter<VectorDataType, SampleImageType> VectorDataReprojectionType;
+ typedef otb::SamplingRateCalculator::MapRateType MapRateType;
protected:
- //using Superclass::AddParameter;
- //friend void InitSVMParams(TrainImagesClassifier & app);
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;
+}
+
void DoInit() ITK_OVERRIDE
{
SetName("TrainImagesClassifier");
@@ -126,62 +100,99 @@ void DoInit() ITK_OVERRIDE
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_InputFilename, "io.imstat", "Input XML image statistics file");
- MandatoryOff("io.imstat");
- SetParameterDescription("io.imstat",
- "Input XML file containing the mean and the standard deviation of the input images.");
- 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).");
-
- // Elevation
- ElevationParametersHandler::AddElevationParameters(this, "elev");
-
- //Group Sample list
+
+ 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.");
-
+ "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");
- //MandatoryOff("mt");
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.");
+ 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");
- // MandatoryOff("mv");
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.");
-
+ 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_Empty, "sample.edg", "On edge pixel inclusion");
- SetParameterDescription("sample.edg",
- "Takes pixels on polygon edge into consideration when building training and validation samples.");
- MandatoryOff("sample.edg");
-
+ 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).");
+ "Ratio between training and validation samples (0.0 = all training, 1.0 = "
+ "all validation) (default = 0.5).");
SetParameterFloat("sample.vtr", 0.5);
+ SetMaximumParameterFloatValue("sample.vtr",1.0);
+ SetMinimumParameterFloatValue("sample.vtr",0.0);
+
+ ShareParameter("sample.vfn","polystat.field");
- AddParameter(ParameterType_String, "sample.vfn", "Name of the discrimination field");
- SetParameterDescription("sample.vfn", "Name of the field used to discriminate class labels in the input vector data files.");
- SetParameterString("sample.vfn", "Class");
+ // hide sampling parameters
+ //ShareParameter("sample.strategy","rates.strategy");
+ //ShareParameter("sample.mim","rates.mim");
- Superclass::DoInit();
+ // 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");
- AddRANDParameter();
// Doc example parameter settings
SetDocExampleParameterValue("io.il", "QB_1_ortho.tif");
SetDocExampleParameterValue("io.vd", "VectorData_QB1.shp");
@@ -201,396 +212,305 @@ void DoInit() ITK_OVERRIDE
void DoUpdateParameters() ITK_OVERRIDE
{
- // Nothing to do here : all parameters are independent
+ if ( HasValue("io.vd") )
+ {
+ std::vector<std::string> vectorFileList = GetParameterStringList("io.vd");
+ GetInternalApplication("polystat")->SetParameterString("vec",vectorFileList[0]);
+ UpdateInternalParameters("polystat");
+ }
}
-void LogConfusionMatrix(ConfusionMatrixCalculatorType* confMatCalc)
+void DoExecute() ITK_OVERRIDE
{
- ConfusionMatrixCalculatorType::ConfusionMatrixType matrix = confMatCalc->GetConfusionMatrix();
-
- // Compute minimal width
- size_t minwidth = 0;
-
- for (unsigned int i = 0; i < matrix.Rows(); i++)
+ FloatVectorImageListType* imageList = GetParameterImageList("io.il");
+ std::vector<std::string> vectorFileList = GetParameterStringList("io.vd");
+ unsigned int nbInputs = imageList->Size();
+ if (nbInputs > vectorFileList.size())
{
- for (unsigned int j = 0; j < matrix.Cols(); j++)
- {
- std::ostringstream os;
- os << matrix(i, j);
- size_t size = os.str().size();
-
- if (size > minwidth)
- {
- minwidth = size;
- }
- }
+ otbAppLogFATAL("Missing input vector data files to match number of images ("<<nbInputs<<").");
}
- MapOfIndicesType mapOfIndices = confMatCalc->GetMapOfIndices();
-
- MapOfIndicesType::const_iterator it = mapOfIndices.begin();
- MapOfIndicesType::const_iterator end = mapOfIndices.end();
-
- for (; it != end; ++it)
+ // check if validation vectors are given
+ std::vector<std::string> validationVectorFileList;
+ bool dedicatedValidation = false;
+ if (IsParameterEnabled("io.valid") && HasValue("io.valid"))
{
- std::ostringstream os;
- os << "[" << it->second << "]";
-
- size_t size = os.str().size();
- if (size > minwidth)
+ dedicatedValidation = true;
+ validationVectorFileList = GetParameterStringList("io.valid");
+ if (nbInputs > validationVectorFileList.size())
{
- minwidth = size;
+ otbAppLogFATAL("Missing validation vector data files to match number of images ("<<nbInputs<<").");
}
}
- // Generate matrix string, with 'minwidth' as size specifier
- std::ostringstream os;
-
- // Header line
- for (size_t i = 0; i < minwidth; ++i)
- os << " ";
- os << " ";
-
- it = mapOfIndices.begin();
- end = mapOfIndices.end();
- for (; it != end; ++it)
+ // 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)
{
- os << "[" << it->second << "]" << " ";
+ rateTrainOut = outModel + "_ratesTrain.csv";
}
-
- os << std::endl;
-
- // Each line of confusion matrix
- for (unsigned int i = 0; i < matrix.Rows(); i++)
+ else
{
- 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;
+ rateTrainOut = outModel + "_rates.csv";
}
-
- otbAppLogINFO("Confusion matrix (rows = reference labels, columns = produced labels):\n" << os.str());
-}
-
-void DoExecute() ITK_OVERRIDE
-{
- //Create training and validation for list samples and label list samples
- ConcatenateLabelListSampleFilterType::Pointer concatenateTrainingLabels =
- ConcatenateLabelListSampleFilterType::New();
- ConcatenateListSampleFilterType::Pointer concatenateTrainingSamples = ConcatenateListSampleFilterType::New();
- ConcatenateLabelListSampleFilterType::Pointer concatenateValidationLabels =
- ConcatenateLabelListSampleFilterType::New();
- ConcatenateListSampleFilterType::Pointer concatenateValidationSamples = ConcatenateListSampleFilterType::New();
-
- SampleType meanMeasurementVector;
- SampleType stddevMeasurementVector;
-
- // Setup the DEM Handler
- otb::Wrapper::ElevationParametersHandler::SetupDEMHandlerFromElevationParameters(this, "elev");
-
- //--------------------------
- // Load measurements from images
- unsigned int nbBands = 0;
- //Iterate over all input images
-
- FloatVectorImageListType* imageList = GetParameterImageList("io.il");
- VectorDataListType* vectorDataList = GetParameterVectorDataList("io.vd");
-
- vdreproj = VectorDataReprojectionType::New();
-
- //Iterate over all input images
- for (unsigned int imgIndex = 0; imgIndex < imageList->Size(); ++imgIndex)
+ std::string rateValidOut(outModel + "_ratesValid.csv");
+ for (unsigned int i=0 ; i<nbInputs ; i++)
{
- std::ostringstream oss1, oss2;
- oss1 << "Reproject polygons for image " << (imgIndex+1) << " ...";
- oss2 << "Extract samples from image " << (imgIndex+1) << " ...";
-
- FloatVectorImageType::Pointer image = imageList->GetNthElement(imgIndex);
- image->UpdateOutputInformation();
-
- if (imgIndex == 0)
+ std::ostringstream oss;
+ oss <<i+1;
+ std::string strIndex(oss.str());
+ if (dedicatedValidation)
{
- nbBands = image->GetNumberOfComponentsPerPixel();
+ 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");
}
-
- // read the Vectordata
- vdreproj->SetInputImage(image);
- vdreproj->SetInput(vectorDataList->GetNthElement(imgIndex));
- vdreproj->SetUseOutputSpacingAndOriginFromImage(false);
-
- AddProcess(vdreproj, oss1.str());
- vdreproj->Update();
-
- //Sample list generator
- ListSampleGeneratorType::Pointer sampleGenerator = ListSampleGeneratorType::New();
-
- sampleGenerator->SetInput(image);
- sampleGenerator->SetInputVectorData(vdreproj->GetOutput());
-
- sampleGenerator->SetClassKey(GetParameterString("sample.vfn"));
- sampleGenerator->SetMaxTrainingSize(GetParameterInt("sample.mt"));
- sampleGenerator->SetMaxValidationSize(GetParameterInt("sample.mv"));
- sampleGenerator->SetValidationTrainingProportion(GetParameterFloat("sample.vtr"));
- sampleGenerator->SetBoundByMin(GetParameterInt("sample.bm")!=0);
-
- // take pixel located on polygon edge into consideration
- if (IsParameterEnabled("sample.edg"))
+ else
{
- sampleGenerator->SetPolygonEdgeInclusion(true);
+ polyStatTrainOutputs.push_back(outModel + "_stats_" + strIndex + ".xml");
+ ratesTrainOutputs.push_back(outModel + "_rates_" + strIndex + ".csv");
+ sampleOutputs.push_back(outModel + "_samples_" + strIndex + ".shp");
}
-
- AddProcess(sampleGenerator, oss2.str());
- sampleGenerator->Update();
-
- TargetListSampleType::Pointer trainLabels = sampleGenerator->GetTrainingListLabel();
- ListSampleType::Pointer trainSamples = sampleGenerator->GetTrainingListSample();
- TargetListSampleType::Pointer validLabels = sampleGenerator->GetValidationListLabel();
- ListSampleType::Pointer validSamples = sampleGenerator->GetValidationListSample();
-
- trainLabels->DisconnectPipeline();
- trainSamples->DisconnectPipeline();
- validLabels->DisconnectPipeline();
- validSamples->DisconnectPipeline();
-
- //Concatenate training and validation samples from the image
- concatenateTrainingLabels->AddInput(trainLabels);
- concatenateTrainingSamples->AddInput(trainSamples);
- concatenateValidationLabels->AddInput(validLabels);
- concatenateValidationSamples->AddInput(validSamples);
+ sampleTrainOutputs.push_back(outModel + "_samplesTrain_" + strIndex + ".shp");
+ sampleValidOutputs.push_back(outModel + "_samplesValid_" + strIndex + ".shp");
}
- // Update
- AddProcess(concatenateValidationLabels, "Concatenate samples ...");
- concatenateTrainingSamples->Update();
- concatenateTrainingLabels->Update();
- concatenateValidationSamples->Update();
- concatenateValidationLabels->Update();
-
- if (concatenateTrainingSamples->GetOutput()->Size() == 0)
+
+ // ---------------------------------------------------------------------------
+ // Polygons stats
+ for (unsigned int i=0 ; i<nbInputs ; i++)
{
- otbAppLogFATAL("No training samples, cannot perform SVM training.");
+ GetInternalApplication("polystat")->SetParameterInputImage("in",imageList->GetNthElement(i));
+ GetInternalApplication("polystat")->SetParameterString("vec",vectorFileList[i]);
+ GetInternalApplication("polystat")->SetParameterString("out",polyStatTrainOutputs[i]);
+ ExecuteInternal("polystat");
+ // analyse polygons given for validation
+ if (dedicatedValidation)
+ {
+ GetInternalApplication("polystat")->SetParameterString("vec",validationVectorFileList[i]);
+ GetInternalApplication("polystat")->SetParameterString("out",polyStatValidOutputs[i]);
+ ExecuteInternal("polystat");
+ }
}
- if (concatenateValidationSamples->GetOutput()->Size() == 0)
+ // ---------------------------------------------------------------------------
+ // Compute sampling rates
+ GetInternalApplication("rates")->SetParameterString("mim","proportional");
+ 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)
{
- otbAppLogWARNING("No validation samples.");
+ 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
+ {
+ // only fmt will be used for both training and validation samples
+ // So we try to compute the total number of samples given input
+ // parameters mt, mv and vtr.
+ if (mt > -1 && mv > -1)
+ {
+ fmt = mt + mv;
+ }
+ if (mt > -1 && mv <= -1 && vtr < 0.99999)
+ {
+ fmt = static_cast<long>((double) mt / (1.0 - vtr));
+ }
+ if (mt <= -1 && mv > -1 && vtr > 0.00001)
+ {
+ fmt = static_cast<long>((double) mv / vtr);
+ }
+ }
}
- if (IsParameterEnabled("io.imstat"))
+ // Sampling rates for training
+ GetInternalApplication("rates")->SetParameterStringList("il",polyStatTrainOutputs);
+ GetInternalApplication("rates")->SetParameterString("out",rateTrainOut);
+ if (GetParameterInt("sample.bm") != 0)
{
- StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
- statisticsReader->SetFileName(GetParameterString("io.imstat"));
- meanMeasurementVector = statisticsReader->GetStatisticVectorByName("mean");
- stddevMeasurementVector = statisticsReader->GetStatisticVectorByName("stddev");
+ GetInternalApplication("rates")->SetParameterString("strategy","smallest");
}
else
{
- meanMeasurementVector.SetSize(nbBands);
- meanMeasurementVector.Fill(0.);
- stddevMeasurementVector.SetSize(nbBands);
- stddevMeasurementVector.Fill(1.);
+ if (fmt > -1)
+ {
+ GetInternalApplication("rates")->SetParameterString("strategy","constant");
+ GetInternalApplication("rates")->SetParameterInt("strategy.constant.nb",fmt);
+ }
+ else
+ {
+ GetInternalApplication("rates")->SetParameterString("strategy","all");
+ }
}
-
- // Shift scale the samples
- ShiftScaleFilterType::Pointer trainingShiftScaleFilter = ShiftScaleFilterType::New();
- trainingShiftScaleFilter->SetInput(concatenateTrainingSamples->GetOutput());
- trainingShiftScaleFilter->SetShifts(meanMeasurementVector);
- trainingShiftScaleFilter->SetScales(stddevMeasurementVector);
- AddProcess(trainingShiftScaleFilter, "Normalize training samples ...");
- trainingShiftScaleFilter->Update();
-
- ListSampleType::Pointer validationListSample=ListSampleType::New();
-
- //Test if the validation test is empty
- if ( concatenateValidationSamples->GetOutput()->Size() != 0 )
+ ExecuteInternal("rates");
+ // Sampling rates for validation
+ if (dedicatedValidation)
{
- ShiftScaleFilterType::Pointer validationShiftScaleFilter = ShiftScaleFilterType::New();
- validationShiftScaleFilter->SetInput(concatenateValidationSamples->GetOutput());
- validationShiftScaleFilter->SetShifts(meanMeasurementVector);
- validationShiftScaleFilter->SetScales(stddevMeasurementVector);
- AddProcess(validationShiftScaleFilter, "Normalize validation samples ...");
- validationShiftScaleFilter->Update();
- validationListSample = validationShiftScaleFilter->GetOutput();
+ GetInternalApplication("rates")->SetParameterStringList("il",polyStatValidOutputs);
+ GetInternalApplication("rates")->SetParameterString("out",rateValidOut);
+ if (GetParameterInt("sample.bm") != 0)
+ {
+ GetInternalApplication("rates")->SetParameterString("strategy","smallest");
+ }
+ else
+ {
+ if (fmv > -1)
+ {
+ GetInternalApplication("rates")->SetParameterString("strategy","constant");
+ GetInternalApplication("rates")->SetParameterInt("strategy.constant.nb",fmv);
+ }
+ else
+ {
+ GetInternalApplication("rates")->SetParameterString("strategy","all");
+ }
+ }
+ ExecuteInternal("rates");
}
- ListSampleType::Pointer listSample;
- TargetListSampleType::Pointer labelListSample;
- //--------------------------
- // Balancing training sample (if needed)
- // if (IsParameterEnabled("sample.b"))
- // {
- // // Balance the list sample.
- // otbAppLogINFO("Number of training samples before balancing: " << concatenateTrainingSamples->GetOutput()->Size())
- // BalancingListSampleFilterType::Pointer balancingFilter = BalancingListSampleFilterType::New();
- // balancingFilter->SetInput(trainingShiftScaleFilter->GetOutput());
- // balancingFilter->SetInputLabel(concatenateTrainingLabels->GetOutput());
- // balancingFilter->SetBalancingFactor(GetParameterInt("sample.b"));
- // balancingFilter->Update();
- // listSample = balancingFilter->GetOutput();
- // labelListSample = balancingFilter->GetOutputLabelSampleList();
- // otbAppLogINFO("Number of samples after balancing: " << balancingFilter->GetOutput()->Size());
-
- // }
- // else
- // {
- listSample = trainingShiftScaleFilter->GetOutput();
- labelListSample = concatenateTrainingLabels->GetOutput();
- otbAppLogINFO("Number of training samples: " << concatenateTrainingSamples->GetOutput()->Size());
- // }
- //--------------------------
- // Split the data set into training/validation set
- ListSampleType::Pointer trainingListSample = listSample;
- TargetListSampleType::Pointer trainingLabeledListSample = labelListSample;
-
- TargetListSampleType::Pointer validationLabeledListSample = concatenateValidationLabels->GetOutput();
- otbAppLogINFO("Size of training set: " << trainingListSample->Size());
- otbAppLogINFO("Size of validation set: " << validationListSample->Size());
- otbAppLogINFO("Size of labeled training set: " << trainingLabeledListSample->Size());
- otbAppLogINFO("Size of labeled validation set: " << validationLabeledListSample->Size());
-
- //--------------------------
- // Estimate model
- //--------------------------
- this->Train(trainingListSample,trainingLabeledListSample,GetParameterString("io.out"));
-
- //--------------------------
- // Performances estimation
- //--------------------------
- TargetListSampleType::Pointer predictedList = TargetListSampleType::New();
- ListSampleType::Pointer performanceListSample=ListSampleType::New();
- TargetListSampleType::Pointer performanceLabeledListSample=TargetListSampleType::New();
-
- //Test the input validation set size
- if(validationLabeledListSample->Size() != 0)
+ // ---------------------------------------------------------------------------
+ // Select & extract samples
+ GetInternalApplication("select")->SetParameterString("sampler", "periodic");
+ GetInternalApplication("select")->SetParameterInt("sampler.periodic.jitter",50);
+ GetInternalApplication("select")->SetParameterString("strategy","byclass");
+ GetInternalApplication("extraction")->SetParameterString("outfield", "prefix");
+ GetInternalApplication("extraction")->SetParameterString("outfield.prefix.name","value_");
+ for (unsigned int i=0 ; i<nbInputs ; i++)
{
- 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++)
+ GetInternalApplication("select")->SetParameterInputImage("in",imageList->GetNthElement(i));
+ GetInternalApplication("select")->SetParameterString("vec",vectorFileList[i]);
+ GetInternalApplication("select")->SetParameterString("out",sampleOutputs[i]);
+ GetInternalApplication("select")->SetParameterString("instats",polyStatTrainOutputs[i]);
+ GetInternalApplication("select")->SetParameterString("strategy.byclass.in",ratesTrainOutputs[i]);
+ // select sample positions
+ ExecuteInternal("select");
+ // extract sample descriptors
+ ExecuteInternal("extraction");
+
+ if (dedicatedValidation)
{
- 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");
+ GetInternalApplication("select")->SetParameterString("vec",validationVectorFileList[i]);
+ GetInternalApplication("select")->SetParameterString("out",sampleValidOutputs[i]);
+ GetInternalApplication("select")->SetParameterString("instats",polyStatValidOutputs[i]);
+ GetInternalApplication("select")->SetParameterString("strategy.byclass.in",ratesValidOutputs[i]);
+ // select sample positions
+ ExecuteInternal("select");
+ // extract sample descriptors
+ ExecuteInternal("extraction");
}
- otbAppLogINFO("Global performance, Kappa index: " << confMatCalc->GetKappaIndex());
-
-
- if (this->HasValue("io.confmatout"))
+ else
{
- // 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())
+ // 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)
{
- // labels labelValid of mapOfIndicesValid are already sorted in otbConfusionMatrixCalculator
- labelValid = itMapOfIndicesValid->second;
-
- otbAppLogINFO("mapOfIndicesValid[" << itMapOfIndicesValid->first << "] = " << labelValid);
-
- ossHeaderValidLabels << labelValid;
- ossHeaderPredLabels << labelValid;
-
- ++itMapOfIndicesValid;
-
- if (itMapOfIndicesValid != mapOfIndicesValid.end())
- {
- ossHeaderValidLabels << separatorChar;
- ossHeaderPredLabels << separatorChar;
- }
- else
- {
- ossHeaderValidLabels << std::endl;
- ossHeaderPredLabels << std::endl;
- }
+ 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;
}
- 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;
-
- for (itMapOfIndicesValid = mapOfIndicesValid.begin(); itMapOfIndicesValid != mapOfIndicesValid.end(); ++itMapOfIndicesValid)
- {
- indexLabelPred = 0;
-
- 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;
- }
-
- ++indexLabelValid;
- }
+ // 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();
+ }
+ }
- outFile.close();
- } // END if (this->HasValue("io.confmatout"))
+ // ---------------------------------------------------------------------------
+ // Train model
+ GetInternalApplication("training")->SetParameterStringList("io.vd",sampleTrainOutputs);
+ GetInternalApplication("training")->SetParameterStringList("valid.vd",sampleValidOutputs);
+ 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);
+ ExecuteInternal("training");
- // TODO: implement hyperplane distance classifier and performance validation (cf. object detection) ?
+ // 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]);
+ }
}
- VectorDataReprojectionType::Pointer vdreproj;
};
} // end namespace Wrapper
diff --git a/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx b/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
index 7f92b72cc42b464394af17c548bc954710e85d83..d9aa85f98d059649753be036a1d039af54f1a5ed 100644
--- a/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
+++ b/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
@@ -410,9 +410,9 @@ void DoExecute()
// Check all needed fields are present :
// - check class field
- cFieldIndex = feature.ogr().GetFieldIndex(GetParameterString("cfield").c_str());
+ cFieldIndex = feature.ogr().GetFieldIndex(selectedCFieldName.c_str());
if (cFieldIndex < 0)
- otbAppLogFATAL("The field name for class label ("<<GetParameterString("cfield")
+ otbAppLogFATAL("The field name for class label ("<<selectedCFieldName
<<") has not been found in the input vector file! Choices are "<< availableFields);
// - check feature fields
for (unsigned int i=0 ; i<nbFeatures ; i++)
diff --git a/Modules/Applications/AppClassification/test/CMakeLists.txt b/Modules/Applications/AppClassification/test/CMakeLists.txt
index 76715f8d20cd4a35dd144fbdd71f471bb149bed4..aaa2f9cfe113a49279b2247b018b651f5ff05780 100644
--- a/Modules/Applications/AppClassification/test/CMakeLists.txt
+++ b/Modules/Applications/AppClassification/test/CMakeLists.txt
@@ -141,6 +141,7 @@ foreach(classifier ${classifierList})
-classifier ${lclassifier}
${${lclassifier}_parameters}
-io.out ${TEMP}/${OUTMODELFILE}
+ -sample.vfn Class
-rand 121212
VALID ${valid}
@@ -162,6 +163,7 @@ foreach(classifier ${classifierList})
${${lclassifier}_parameters}
-io.out ${TEMP}/OutXML1_${OUTMODELFILE}
-rand 121212
+ -sample.vfn Class
-outxml ${TEMP}/cl${classifier}_OutXML1.xml
VALID ${valid}
@@ -181,7 +183,7 @@ foreach(classifier ${classifierList})
-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}
)
@@ -647,6 +649,7 @@ otb_test_application(NAME apTvClTrainSVMImagesClassifierQB1_allOpt_InXML
-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
@@ -659,6 +662,7 @@ otb_test_application(NAME apTvClTrainSVMImagesClassifierQB1_OutXML
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
@@ -677,6 +681,7 @@ otb_test_application(NAME apTvClTrainSVMImagesClassifierQB1_OutXML
${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
@@ -693,6 +698,7 @@ otb_test_application(NAME apTvClTrainSVMImagesClassifierQB1
-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}
@@ -708,6 +714,7 @@ otb_test_application(NAME apTvClTrainSVMImagesClassifierQB1
${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
@@ -727,6 +734,7 @@ otb_test_application(NAME apTvClTrainSVMImagesClassifierQB1_allOpt
-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
diff --git a/Modules/Filtering/Statistics/include/otbPeriodicSampler.h b/Modules/Filtering/Statistics/include/otbPeriodicSampler.h
index 3940bcd796b022d9d9ff7d9dbff42bacd9255fc4..3cc91743e735c0302436c078a842f265d17184f0 100644
--- a/Modules/Filtering/Statistics/include/otbPeriodicSampler.h
+++ b/Modules/Filtering/Statistics/include/otbPeriodicSampler.h
@@ -51,6 +51,9 @@ public:
/** Maximum jitter to introduce (0 means no jitter) */
unsigned long MaxJitter;
+
+ /** Maximum buffer size for internal jitter values */
+ unsigned long MaxBufferSize;
bool operator!=(const struct Parameter & param) const;
} ParameterType;
@@ -109,6 +112,9 @@ private:
/** Internal current offset value
* (either fixed, or reset each time a sample is taken)*/
double m_OffsetValue;
+
+ /** jitter offsets computed up to MaxBufferSize */
+ std::vector<double> m_JitterValues;
};
} // namespace otb
diff --git a/Modules/Filtering/Statistics/src/otbPeriodicSampler.cxx b/Modules/Filtering/Statistics/src/otbPeriodicSampler.cxx
index ad4a6d88df1ed46ace6da5af59ac6f5d4b1894b5..1aba9367ffb3fe2c3e6ef2ab373c631a66fcbb82 100644
--- a/Modules/Filtering/Statistics/src/otbPeriodicSampler.cxx
+++ b/Modules/Filtering/Statistics/src/otbPeriodicSampler.cxx
@@ -27,7 +27,8 @@ bool
PeriodicSampler::ParameterType::operator!=(const PeriodicSampler::ParameterType & param) const
{
return bool((Offset != param.Offset)||
- (MaxJitter != param.MaxJitter));
+ (MaxJitter != param.MaxJitter) ||
+ (MaxBufferSize != param.MaxBufferSize));
}
void
@@ -39,9 +40,20 @@ PeriodicSampler::Reset(void)
if (m_JitterSize > 0.0)
{
// Using jitter : compute random offset value
- m_OffsetValue =
- itk::Statistics::MersenneTwisterRandomVariateGenerator::GetInstance()
+ m_JitterValues.resize(std::min(this->GetNeededElements(), this->m_Parameters.MaxBufferSize));
+ for (unsigned long i=0UL ; i<m_JitterValues.size() ; i++)
+ {
+ m_JitterValues[i] = itk::Statistics::MersenneTwisterRandomVariateGenerator::GetInstance()
->GetUniformVariate(0.0,m_JitterSize);
+ }
+ if (m_JitterValues.empty())
+ {
+ m_OffsetValue = 0.0;
+ }
+ else
+ {
+ m_OffsetValue = m_JitterValues[0];
+ }
}
else
{
@@ -71,9 +83,7 @@ PeriodicSampler::TakeSample(void)
if (m_JitterSize > 0.0)
{
// Using jitter : compute random offset value
- m_OffsetValue =
- itk::Statistics::MersenneTwisterRandomVariateGenerator::GetInstance()
- ->GetUniformVariate(0.0,m_JitterSize);
+ m_OffsetValue = m_JitterValues[this->m_ChosenElements%m_JitterValues.size()];
}
ret = true;
}
@@ -84,6 +94,7 @@ PeriodicSampler::PeriodicSampler()
{
this->m_Parameters.Offset = 0UL;
this->m_Parameters.MaxJitter = 0UL;
+ this->m_Parameters.MaxBufferSize = 100000000UL;
m_JitterSize = 0.0;
m_OffsetValue = 0.0;
}
diff --git a/Modules/Filtering/Statistics/test/otbSamplerTest.cxx b/Modules/Filtering/Statistics/test/otbSamplerTest.cxx
index d50684d5410abb2f28c4269cf66d6c1580a2e1ea..9d29cb5869bd8915e2832c1c060d57879119bcde 100644
--- a/Modules/Filtering/Statistics/test/otbSamplerTest.cxx
+++ b/Modules/Filtering/Statistics/test/otbSamplerTest.cxx
@@ -72,6 +72,7 @@ int otbPeriodicSamplerTest(int, char *[])
param.Offset = 0;
param.MaxJitter = 10;
+ param.MaxBufferSize = 1000000UL;
sampler->SetRate(0.2,50);
sampler->SetParameters(param);
std::string test2 = RunSampler<otb::PeriodicSampler>(sampler,50);
diff --git a/Modules/Learning/Sampling/include/otbOGRDataToSamplePositionFilter.h b/Modules/Learning/Sampling/include/otbOGRDataToSamplePositionFilter.h
index a654b79d75b633632c07d569ff3e8cf75cc0a3ff..c602f1276a7b1131ee07d70af5f192c35bee8073 100644
--- a/Modules/Learning/Sampling/include/otbOGRDataToSamplePositionFilter.h
+++ b/Modules/Learning/Sampling/include/otbOGRDataToSamplePositionFilter.h
@@ -148,8 +148,12 @@ private:
/** Internal samplers*/
std::vector<SamplerMapType> m_Samplers;
- /** Field name to store the FID of the geometry each sample comes from */
+ /** Field name to store the FID of the geometry each sample comes from.
+ * When this name is empty, no FID is stored. */
std::string m_OriginFieldName;
+
+ /** Flag to enable/disable origin FID in outputs */
+ bool m_UseOriginField;
};
/**
diff --git a/Modules/Learning/Sampling/include/otbOGRDataToSamplePositionFilter.txx b/Modules/Learning/Sampling/include/otbOGRDataToSamplePositionFilter.txx
index d2e6847e1f64bf95a19d6ba9c12d1023632c4fa2..d881897996d7f79e4dce1fddd4b2add79c04db6d 100644
--- a/Modules/Learning/Sampling/include/otbOGRDataToSamplePositionFilter.txx
+++ b/Modules/Learning/Sampling/include/otbOGRDataToSamplePositionFilter.txx
@@ -31,6 +31,7 @@ PersistentOGRDataToSamplePositionFilter<TInputImage,TMaskImage,TSampler>
{
this->SetNumberOfRequiredOutputs(2);
m_OriginFieldName = std::string("originfid");
+ m_UseOriginField = true;
}
template<class TInputImage, class TMaskImage, class TSampler>
@@ -51,7 +52,11 @@ PersistentOGRDataToSamplePositionFilter<TInputImage,TMaskImage,TSampler>
// Add an extra field for the original FID
this->ClearAdditionalFields();
- this->CreateAdditionalField(this->GetOriginFieldName(),OFTInteger,12);
+ m_UseOriginField = (this->GetOriginFieldName().size() > 0);
+ if (m_UseOriginField)
+ {
+ this->CreateAdditionalField(this->GetOriginFieldName(),OFTInteger,12);
+ }
// compute label mapping
this->ComputeClassPartition();
@@ -179,7 +184,10 @@ PersistentOGRDataToSamplePositionFilter<TInputImage,TMaskImage,TSampler>
ogr::Layer outputLayer = this->GetInMemoryOutput(threadid,i);
ogr::Feature feat(outputLayer.GetLayerDefn());
feat.SetFrom(feature);
- feat[this->GetOriginFieldName()].SetValue(static_cast<int>(feature.GetFID()));
+ if (m_UseOriginField)
+ {
+ feat[this->GetOriginFieldName()].SetValue(static_cast<int>(feature.GetFID()));
+ }
feat.SetGeometry(&ogrTmpPoint);
outputLayer.CreateFeature(feat);
break;
diff --git a/Modules/Wrappers/ApplicationEngine/include/otbWrapperCompositeApplication.h b/Modules/Wrappers/ApplicationEngine/include/otbWrapperCompositeApplication.h
index 5af08d98241f6795147832f9f566642ee325d448..caa6b6754cfcf4cb9949ef025873ee53be4f39db 100644
--- a/Modules/Wrappers/ApplicationEngine/include/otbWrapperCompositeApplication.h
+++ b/Modules/Wrappers/ApplicationEngine/include/otbWrapperCompositeApplication.h
@@ -84,6 +84,13 @@ protected:
*/
bool AddApplication(std::string appType, std::string key, std::string desc);
+ /**
+ * Method to remove all internal applications. Application deriving from
+ * CompositeApplication should call this method at the begining of their
+ * DoInit().
+ */
+ void ClearApplications();
+
/**
* Connect two existing parameters together. The first parameter will point to
* the second parameter.
diff --git a/Modules/Wrappers/ApplicationEngine/src/otbWrapperCompositeApplication.cxx b/Modules/Wrappers/ApplicationEngine/src/otbWrapperCompositeApplication.cxx
index a3a264a02dcbbe290f2c59b6c8981b2fcf0c06c8..09f9e0fc3034ddcd1d26723f41e1c48dc281d844 100644
--- a/Modules/Wrappers/ApplicationEngine/src/otbWrapperCompositeApplication.cxx
+++ b/Modules/Wrappers/ApplicationEngine/src/otbWrapperCompositeApplication.cxx
@@ -69,6 +69,13 @@ CompositeApplication
return true;
}
+void
+CompositeApplication
+::ClearApplications()
+{
+ m_AppContainer.clear();
+}
+
bool
CompositeApplication
::Connect(std::string fromKey, std::string toKey)
@@ -169,16 +176,40 @@ CompositeApplication
::ExecuteInternal(std::string key)
{
otbAppLogINFO(<< GetInternalAppDescription(key) <<"...");
- GetInternalApplication(key)->Execute();
- otbAppLogINFO(<< "\n" << m_Oss.str());
- m_Oss.str(std::string(""));
+ try
+ {
+ GetInternalApplication(key)->Execute();
+ }
+ catch(...)
+ {
+ this->GetLogger()->Write( itk::LoggerBase::FATAL, std::string("\n") + m_Oss.str() );
+ throw;
+ }
+ if(!m_Oss.str().empty())
+ {
+ otbAppLogINFO(<< "\n" << m_Oss.str());
+ m_Oss.str(std::string(""));
+ }
}
void
CompositeApplication
::UpdateInternalParameters(std::string key)
{
- GetInternalApplication(key)->UpdateParameters();
+ try
+ {
+ GetInternalApplication(key)->UpdateParameters();
+ }
+ catch(...)
+ {
+ this->GetLogger()->Write( itk::LoggerBase::FATAL, std::string("\n") + m_Oss.str() );
+ throw;
+ }
+ if(!m_Oss.str().empty())
+ {
+ otbAppLogINFO(<< "\n" << m_Oss.str());
+ m_Oss.str(std::string(""));
+ }
}
} // end namespace Wrapper