diff --git a/Applications/Classification/otbTrainSVMImagesClassifier.cxx b/Applications/Classification/otbTrainSVMImagesClassifier.cxx
index b4deedff4fda70b6275733eb6da775e30ae9294f..ef2d5ae41ade02e058c4a1c62f909cd1d0f951d9 100644
--- a/Applications/Classification/otbTrainSVMImagesClassifier.cxx
+++ b/Applications/Classification/otbTrainSVMImagesClassifier.cxx
@@ -1,5 +1,4 @@
/*=========================================================================
-
Program: ORFEO Toolbox
Language: C++
Date: $Date$
@@ -170,29 +169,9 @@ private:
SetParameterDescription("io.out", "Output SVM model");
SetParameterRole("io.out", Role_Output);
- //Group SVM
- AddParameter(ParameterType_Group,"svm","SVM classifier parameters");
- SetParameterDescription("svm","This group of parameters allows to set SVM classifier parameters.");
- AddParameter(ParameterType_Choice, "svm.k", "SVM Kernel Type");
- AddChoice("svm.k.linear", "Linear");
- AddChoice("svm.k.rbf", "Neareast Neighbor");
- AddChoice("svm.k.poly", "Polynomial");
- AddChoice("svm.k.sigmoid", "Sigmoid");
- SetParameterString("svm.k", "linear");
- SetParameterDescription("svm.k", "SVM Kernel Type.");
- AddParameter(ParameterType_Float, "svm.m", "Margin for SVM learning");
- SetParameterFloat("svm.m", 1.0);
- SetParameterDescription("svm.m", "Margin for SVM learning.(1 by default).");
- AddParameter(ParameterType_Empty, "svm.opt", "parameters optimization");
- MandatoryOff("svm.opt");
- SetParameterDescription("svm.opt", "SVM parameters optimization");
-
//Group Sample list
AddParameter(ParameterType_Group,"sample","Training and validation samples parameters");
- SetParameterDescription("svm","This group of parameters allows to set training and validation sample lists parameters.");
- AddParameter(ParameterType_Int, "sample.b", "Balance and grow the training set");
- SetParameterDescription("sample.b", "Balance and grow the training set.");
- MandatoryOff("sample.b");
+ SetParameterDescription("sample","This group of parameters allows to set training and validation sample lists parameters.");
AddParameter(ParameterType_Int, "sample.mt", "Maximum training sample size");
//MandatoryOff("mt");
@@ -202,6 +181,11 @@ private:
// MandatoryOff("mv");
SetDefaultParameterInt("sample.mv", -1);
SetParameterDescription("sample.mv", "Maximum size of the validation sample (default = -1)");
+
+ // AddParameter(ParameterType_Int, "sample.b", "Balance and grow the training set");
+ // SetParameterDescription("sample.b", "Balance and grow the training set.");
+ // MandatoryOff("sample.b");
+
AddParameter(ParameterType_Float, "sample.vtr", "training and validation sample ratio");
SetParameterDescription("sample.vtr",
"Ratio between training and validation sample (0.0 = all training, 1.0 = all validation) default = 0.5.");
@@ -210,6 +194,23 @@ private:
AddParameter(ParameterType_Filename, "sample.vfn", "Name of the discrimination field");
SetParameterDescription("sample.vfn", "Name of the field using to discriminate class in the vector data files.");
SetParameterString("sample.vfn", "Class");
+
+ //Group SVM
+ AddParameter(ParameterType_Group,"svm","SVM classifier parameters");
+ SetParameterDescription("svm","This group of parameters allows to set SVM classifier parameters.");
+ AddParameter(ParameterType_Choice, "svm.k", "SVM Kernel Type");
+ AddChoice("svm.k.linear", "Linear");
+ AddChoice("svm.k.rbf", "Neareast Neighbor");
+ AddChoice("svm.k.poly", "Polynomial");
+ AddChoice("svm.k.sigmoid", "Sigmoid");
+ SetParameterString("svm.k", "linear");
+ SetParameterDescription("svm.k", "SVM Kernel Type.");
+ AddParameter(ParameterType_Float, "svm.m", "Margin for SVM learning");
+ SetParameterFloat("svm.m", 1.0);
+ SetParameterDescription("svm.m", "Margin for SVM learning.(1 by default).");
+ AddParameter(ParameterType_Empty, "svm.opt", "parameters optimization");
+ MandatoryOff("svm.opt");
+ SetParameterDescription("svm.opt", "SVM parameters optimization");
}
void DoUpdateParameters()
@@ -333,26 +334,26 @@ private:
LabelListSampleType::Pointer labelListSample;
//--------------------------
// Balancing training sample (if needed)
- if (IsParameterEnabled("sample.b"))
- {
- // Balance the list sample.
- otbAppLogINFO("Number of training samples before balancing: " << concatenateTrainingSamples->GetOutputSampleList()->Size())
- BalancingListSampleFilterType::Pointer balancingFilter = BalancingListSampleFilterType::New();
- balancingFilter->SetInput(trainingShiftScaleFilter->GetOutput()/*GetOutputSampleList()*/);
- balancingFilter->SetInputLabel(concatenateTrainingLabels->GetOutput()/*GetOutputSampleList()*/);
- balancingFilter->SetBalancingFactor(GetParameterInt("sample.b"));
- balancingFilter->Update();
- listSample = balancingFilter->GetOutputSampleList();
- labelListSample = balancingFilter->GetOutputLabelSampleList();
- otbAppLogINFO("Number of samples after balancing: " << balancingFilter->GetOutputSampleList()->Size());
-
- }
- else
- {
+ // if (IsParameterEnabled("sample.b"))
+ // {
+ // // Balance the list sample.
+ // otbAppLogINFO("Number of training samples before balancing: " << concatenateTrainingSamples->GetOutputSampleList()->Size())
+ // BalancingListSampleFilterType::Pointer balancingFilter = BalancingListSampleFilterType::New();
+ // balancingFilter->SetInput(trainingShiftScaleFilter->GetOutput()/*GetOutputSampleList()*/);
+ // balancingFilter->SetInputLabel(concatenateTrainingLabels->GetOutput()/*GetOutputSampleList()*/);
+ // balancingFilter->SetBalancingFactor(GetParameterInt("sample.b"));
+ // balancingFilter->Update();
+ // listSample = balancingFilter->GetOutputSampleList();
+ // labelListSample = balancingFilter->GetOutputLabelSampleList();
+ // otbAppLogINFO("Number of samples after balancing: " << balancingFilter->GetOutputSampleList()->Size());
+
+ // }
+ // else
+ // {
listSample = trainingShiftScaleFilter->GetOutputSampleList();
labelListSample = concatenateTrainingLabels->GetOutputSampleList();
otbAppLogINFO("Number of training samples: " << concatenateTrainingSamples->GetOutputSampleList()->Size());
- }
+ // }
//--------------------------
// Split the data set into training/validation set
ListSampleType::Pointer trainingListSample = listSample;
diff --git a/Testing/Applications/Classification/CMakeLists.txt b/Testing/Applications/Classification/CMakeLists.txt
index 702338eccbbc41e7e3b5a55335b043a4b7a2ee39..a51f1e9e7b7d4a5fcf1a81205289df69512f36e7 100644
--- a/Testing/Applications/Classification/CMakeLists.txt
+++ b/Testing/Applications/Classification/CMakeLists.txt
@@ -16,7 +16,7 @@ OTB_TEST_APPLICATION(NAME apTvClTrainSVMImagesClassifierQB1
OPTIONS --io.il ${INPUTDATA}/Classification/QB_1_ortho.tif
--io.vd ${INPUTDATA}/Classification/VectorData_QB1.shp
--io.imstat ${TEMP}/apTvClEstimateImageStatisticsQB1.xml
- --sample.b 2
+ ##--sample.b 2
--svm.opt true
--io.out ${TEMP}/clsvmModelQB1.svm
VALID --compare-ascii ${NOTOL}
@@ -30,7 +30,7 @@ OTB_TEST_APPLICATION(NAME apTvClTrainSVMImagesClassifierQB1_allOpt
OPTIONS --io.il ${INPUTDATA}/Classification/QB_1_ortho.tif
--io.vd ${INPUTDATA}/Classification/VectorData_QB1.shp
--io.imstat ${TEMP}/apTvClEstimateImageStatisticsQB1.xml
- --sample.b 2
+ ##--sample.b 2
--sample.mv 100
--sample.mt 100
--sample.vtr 0.5
@@ -116,7 +116,7 @@ OTB_TEST_APPLICATION(NAME apTvClTrainSVMImagesClassifierQB123
${INPUTDATA}/Classification/VectorData_QB2.shp
${INPUTDATA}/Classification/VectorData_QB3.shp
--io.imstat ${TEMP}/apTvClEstimateImageStatisticsQB123.xml
- --sample.b 2
+ #--sample.b 2
--svm.opt true
--io.out ${TEMP}/clsvmModelQB123.svm
VALID --compare-ascii ${NOTOL}
@@ -221,7 +221,7 @@ OTB_TEST_APPLICATION(NAME apTvClTrainSVMImagesClassifierQB456
${INPUTDATA}/Classification/VectorData_QB5.shp
${INPUTDATA}/Classification/VectorData_QB6.shp
--io.imstat ${TEMP}/apTvClEstimateImageStatisticsQB456.xml
- --sample.b 2
+ #--sample.b 2
--svm.opt true
--io.out ${TEMP}/clsvmModelQB456.svm
VALID --compare-ascii ${NOTOL}