TEST: Add tests of several classifiers for the OpenCV framework in the...

TEST: Add tests of several classifiers for the OpenCV framework in the TrainImagesClassifier application

Applications/Classification/otbTrainDecisionTree.cxx

@@ -29,7 +29,7 @@ void TrainImagesClassifier::InitDecisionTreeParams()
                           "See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/decision_trees.html}.");
   //MaxDepth
   AddParameter(ParameterType_Int, "classifier.dt.max", "Maximum depth of the tree");
-  SetParameterInt("classifier.dt.max", 65000);
+  SetParameterInt("classifier.dt.max", 65535);
   SetParameterDescription(
       "classifier.dt.max", "The training algorithm attempts to split each node while its depth is smaller than the maximum "
       "possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or "

Applications/Classification/otbTrainGradientBoostedTree.cxx

@@ -54,7 +54,7 @@ void TrainImagesClassifier::InitGradientBoostedTreeParams()
 
   //MaxDepth
   AddParameter(ParameterType_Int, "classifier.gbt.max", "Maximum depth of the tree");
-  SetParameterInt("classifier.gbt.max", 65000);
+  SetParameterInt("classifier.gbt.max", 3);
   SetParameterDescription(
         "classifier.gbt.max", "The training algorithm attempts to split each node while its depth is smaller than the maximum "
         "possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or "

Applications/Classification/otbTrainKNN.cxx

@@ -30,7 +30,7 @@ namespace Wrapper
 
     //K parameter
     AddParameter(ParameterType_Int, "classifier.knn.k", "Number of Neighbors");
-    SetParameterInt("classifier.knn.k", 32);
+    SetParameterInt("classifier.knn.k", 10);
     SetParameterDescription("classifier.knn.k","The number of neighbors to use.");
 
   }

Applications/Classification/otbTrainNeuralNetwork.cxx

@@ -141,14 +141,29 @@ void TrainImagesClassifier::TrainNeuralNetwork(ListSampleType::Pointer trainingL
   std::vector<unsigned int> layerSizes;
   std::vector<std::string> sizes = GetParameterStringList("classifier.ann.sizes");
 
-  layerSizes.push_back(trainingListSample->Size());
+
+  unsigned int nbImageBands = trainingListSample->GetMeasurementVectorSize();
+  layerSizes.push_back(nbImageBands);
   for (unsigned int i = 0; i < sizes.size(); i++)
     {
     unsigned int nbNeurons = boost::lexical_cast<unsigned int>(sizes[i]);
     layerSizes.push_back(nbNeurons);
     }
 
-  layerSizes.push_back(trainingLabeledListSample->Size());
+
+  unsigned int nbClasses = 0;
+  LabelType currentLabel = 0, prevLabel = 0;
+  for (unsigned int itLab = 0; itLab < trainingLabeledListSample->Size(); ++itLab)
+    {
+    currentLabel = trainingLabeledListSample->GetMeasurementVector(itLab);
+    if ((currentLabel != prevLabel) || (itLab == 0))
+      {
+      ++nbClasses;
+      }
+    prevLabel = currentLabel;
+    }
+
+  layerSizes.push_back(nbClasses);
   classifier->SetLayerSizes(layerSizes);
 
   switch (GetParameterInt("classifier.ann.f"))

Code/UtilitiesAdapters/OpenCV/otbMachineLearningModelFactory.txx

@@ -18,7 +18,7 @@
 #include "otbMachineLearningModelFactory.h"
 
 #ifdef OTB_USE_OPENCV
-//#include "otbKNearestNeighborsMachineLearningModelFactory.h"
+#include "otbKNearestNeighborsMachineLearningModelFactory.h"
 #include "otbRandomForestsMachineLearningModelFactory.h"
 #include "otbSVMMachineLearningModelFactory.h"
 #include "otbBoostMachineLearningModelFactory.h"
@@ -99,6 +99,7 @@ MachineLearningModelFactory<TInputValue,TOutputValue>
   RegisterFactory(NormalBayesMachineLearningModelFactory<TInputValue,TOutputValue>::New());
   RegisterFactory(DecisionTreeMachineLearningModelFactory<TInputValue,TOutputValue>::New());
   RegisterFactory(GradientBoostedTreeMachineLearningModelFactory<TInputValue,TOutputValue>::New());
+  RegisterFactory(KNearestNeighborsMachineLearningModelFactory<TInputValue,TOutputValue>::New());
 #endif
 
 }

Testing/Applications/Classification/CMakeLists.txt

@@ -12,6 +12,77 @@ OTB_TEST_APPLICATION(NAME apTvClComputeImagesStatisticsQB1
 
 
 IF(OTB_USE_OPENCV)
+
+  # Input file types
+  SET(raster_input_format ".tif")
+  SET(vector_input_format ".shp")
+  SET(stat_input_format ".xml")
+  
+  # Input file depending on classifier
+  SET(libsvm_input "")
+  SET(svm_input "")
+  SET(boost_input "Water_Vegetation")
+  SET(dt_input "")
+  SET(gbt_input "")
+  SET(ann_input "")
+  SET(bayes_input "")
+  SET(rf_input "")
+  SET(knn_input "")
+  
+  # Output model type depending on classifier
+  SET(libsvm_output_format ".libsvm")
+  SET(svm_output_format ".svm")
+  SET(boost_output_format ".boost")
+  SET(dt_output_format ".dt")
+  SET(gbt_output_format ".gbt")
+  SET(ann_output_format ".ann")
+  SET(bayes_output_format ".bayes")
+  SET(rf_output_format ".rf")
+  SET(knn_output_format ".knn")
+  
+  # Training algorithms parameters
+  SET(libsvm_parameters "-classifier.libsvm.k linear -classifier.libsvm.opt true")
+  SET(svm_parameters "-classifier.svm.m csvc -classifier.svm.k linear -classifier.svm.opt true")
+  SET(boost_parameters "-classifier.boost.t real -classifier.boost.w 100 -classifier.boost.r 0.95 -classifier.boost.m 1")
+  SET(dt_parameters "-classifier.dt.max 65535 -classifier.dt.min 10 -classifier.dt.ra 0.01 -classifier.dt.cat 10 -classifier.dt.f 10 -classifier.dt.r false -classifier.dt.t false")
+  SET(gbt_parameters "-classifier.gbt.w 200 -classifier.gbt.s 0.8 -classifier.gbt.p 0.01 -classifier.gbt.max 3")
+  SET(ann_parameters "-classifier.ann.t reg -classifier.ann.sizes 10 15 15 11 -classifier.ann.f sig -classifier.ann.a 0 -classifier.ann.b 0 -classifier.ann.bpdw 0.1 -classifier.ann.bpms 0.1 -classifier.ann.rdw 0.1 -classifier.ann.rdwm 1e-07 -classifier.ann.term all -classifier.ann.eps 0.01 -classifier.ann.iter 1000")
+  SET(bayes_parameters "")
+  SET(rf_parameters "-classifier.rf.max 25 -classifier.rf.min 5 -classifier.rf.ra 0 -classifier.rf.cat 15 -classifier.rf.var 4 -classifier.rf.nbtrees 100 -classifier.rf.acc 0.01")
+  SET(knn_parameters "-classifier.knn.k 10")
+  
+  # Validation depending on mode 
+  SET(ascii_comparison --compare-ascii ${NOTOL})
+  
+  # Reference ffiles depending on modes
+  SET(ascii_ref_path ${OTBAPP_BASELINE_FILES})
+  
+  # Loop on classifiers
+  FOREACH(classifier "LIBSVM" "SVM" "BOOST" "DT" "GBT" "ANN" "BAYES" "RF" "KNN")
+  STRING(TOLOWER ${classifier} lclassifier)
+
+    # Derive output file name
+    SET(OUTFILE cl${classifier}ModelQB1${${lclassifier}_output_format})
+    
+    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 ${OTBAPP_BASELINE_FILES}/clImageStatisticsQB1${stat_input_format}
+                                  -classifier ${lclassifier}
+                                  ${${lclassifier}_parameters}
+                                  -io.out ${TEMP}/${OUTFILE}
+                                  -rand 121212
+
+                         VALID    ${ascii_comparison}
+                                  ${ascii_ref_path}/${OUTFILE}
+                                  ${TEMP}/${OUTFILE}
+                         )
+
+  ENDFOREACH(classifier)
+
+
   OTB_TEST_APPLICATION(NAME apTvClTrainSVMImagesClassifierQB1
                        APP  TrainImagesClassifier
                        OPTIONS -io.il ${INPUTDATA}/Classification/QB_1_ortho.tif