ENH: Add KNN classification in the TrainMachineLearningImagesClassifier application.

Applications/Classification/CMakeLists.txt

@@ -38,7 +38,7 @@ IF(OTB_USE_OPENCV)
 	OTB_CREATE_APPLICATION(NAME           TrainMachineLearningImagesClassifier
 	                       SOURCES        otbTrainMachineLearningImagesClassifier.cxx otbTrainSVM.cxx otbTrainLibSVM.cxx otbTrainBoost.cxx 
 	                       				  otbTrainDecisionTree.cxx otbTrainGradientBoostedTree.cxx otbTrainNeuralNetwork.cxx otbTrainNormalBayes.cxx
-	                       				  otbTrainRandomForests.cxx
+	                       				  otbTrainRandomForests.cxx otbTrainKNN.cxx
 	                       LINK_LIBRARIES OTBIO;OTBCommon;OTBBasicFilters;OTBFeatureExtraction;OTBLearning;OTBMachineLearning)
 
 	OTB_CREATE_APPLICATION(NAME           ImageClassifier

Applications/Classification/otbTrainKNN.cxx

+/*=========================================================================
+ Program:   ORFEO Toolbox
+ Language:  C++
+ Date:      $Date$
+ Version:   $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE.  See the above copyright notices for more information.
+
+ =========================================================================*/
+
+#include "otbTrainMachineLearningImagesClassifier.h"
+
+
+namespace otb
+{
+namespace Wrapper
+{
+  void TrainMachineLearningImagesClassifier::InitKNNParams()
+  {
+    AddChoice("classifier.knn", "KNN classifier");
+    SetParameterDescription("classifier.knn", "This group of parameters allows to set KNN classifier parameters."
+        "See complete documentation here http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html");
+
+    //K parameter
+    AddParameter(ParameterType_Int, "classifier.knn.k", "Number of Neighbors");
+    SetParameterInt("classifier.knn.k", 32);
+    SetParameterDescription("classifier.knn.k","The number of neighbors to used.");
+
+  }
+
+
+  void TrainMachineLearningImagesClassifier::TrainKNN(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample)
+  {
+    KNNType::Pointer knnClassifier = KNNType::New();
+    knnClassifier->SetInputListSample(trainingListSample);
+    knnClassifier->SetTargetListSample(trainingLabeledListSample);
+    knnClassifier->SetK(GetParameterInt("classifier.knn.k"));
+
+    knnClassifier->Train();
+    knnClassifier->Save(GetParameterString("io.out"));
+  }
+
+} //end namespace wrapper
+} //end namespace otb

Applications/Classification/otbTrainMachineLearningImagesClassifier.cxx

@@ -404,10 +404,10 @@ namespace Wrapper
       {
       TrainRandomForests(trainingListSample, trainingLabeledListSample);
       }
-    /*else if (classifierType == "knn")
+    else if (classifierType == "knn")
       {
       TrainKNN(trainingListSample, trainingLabeledListSample);
-      }  */
+      }
 
     //--------------------------
     // Performances estimation

Applications/Classification/otbTrainMachineLearningImagesClassifier.h

@@ -155,7 +155,7 @@ private:
   void InitNeuralNetworkParams();
   void InitNormalBayesParams();
   void InitRandomForestsParams();
-  void InitKNNParams(){}
+  void InitKNNParams();
 
   void TrainLibSVM(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample);
   void TrainBoost(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample);
@@ -165,7 +165,7 @@ private:
   void TrainNeuralNetwork(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample);
   void TrainNormalBayes(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample);
   void TrainRandomForests(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample);
-  void TrainKNN(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample){}
+  void TrainKNN(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample);
 
   void Classify(ListSampleType::Pointer validationListSample, LabelListSampleType::Pointer predictedList);