bool GRT_Recognizer::initPipeline(string trainingdatafile, int dimension)
{
//Initialize the training and info variables
// infoText = "";
// trainingClassLabel = 1;
// noOfHands = 2;
//noOfTrackedHands = 0;
//The input to the training data will be the R[x y z]L[x y z] from the left end right hand
// so we set the number of dimensions to 6
LabelledTimeSeriesClassificationData trainingData;
//trainingData.setNumDimensions(6);
trainingData.loadDatasetFromFile(trainingdatafile);
//Initialize the DTW classifier
DTW dtw;
//Turn on null rejection, this lets the classifier output the predicted class label of 0 when the likelihood of a gesture is low
dtw.enableNullRejection( true);
//Set the null rejection coefficient to 3, this controls the thresholds for the automatic null rejection
//You can increase this value if you find that your real-time gestures are not being recognized
//If you are getting too many false positives then you should decrease this value
dtw.setNullRejectionCoeff(2);
//Turn on the automatic data triming, this will remove any sections of none movement from the start and end of the training samples
dtw.enableTrimTrainingData(true, 0.1, 90);
//Offset the timeseries data by the first sample, this makes your gestures (more) invariant to the location the gesture is performed
dtw.setOffsetTimeseriesUsingFirstSample(true);
//Add the classifier to the pipeline (after we do this, we don't need the DTW classifier anymore)
pipeline.setClassifier( dtw );
//pipeline.addPreProcessingModule(MovingAverageFilter(5,dimension));
//pipeline.addFeatureExtractionModule(FFT(16,1, dimension));
/*ClassLabelFilter myFilter = ClassLabelFilter();
myFilter.setBufferSize(6);
myFilter.setBufferSize(2);*/
pipeline.addPostProcessingModule(ClassLabelChangeFilter());
pipeline.train(trainingData);
return true;
}
/**
*
* Constructor
*
*/
brain::brain(){
trainingModeActive = false;
predictionModeActive = false;
trainingClassLabel = 1;
anbc.setNullRejectionCoeff(1);
anbc.enableScaling(true);
anbc.enableNullRejection(true);
pipeline.setClassifier(anbc);
pipeline.addPostProcessingModule(ClassLabelFilter(30,60));
pipeline.addPostProcessingModule(ClassLabelChangeFilter());
trainingData.setNumDimensions(SAMPLE_DIMENSION);
trainingData.setDatasetName("both-hand-static-gesture");
trainingData.setInfoText("Gesture Recognition For Human-Robot Interaction by Aravinth Panchadcharam <*****@*****.**>");
}
