//--------------------------------------------------------------
void testApp::setup(){
ofSetFrameRate(30);
//Initialize the training and info variables
infoText = "";
trainingClassLabel = 1;
record = false;
//Open the connection with Synapse
synapseStreamer.openSynapseConnection();
//Set which joints we want to track : we track only the hand joints
synapseStreamer.trackAllJoints(false);
synapseStreamer.trackLeftHand(true);
synapseStreamer.trackRightHand(true);
synapseStreamer.computeHandDistFeature(true);
//The input to the training data will be the [x y z] of the two hands
//so we set the number of dimensions to 6
trainingData.setNumDimensions( 6 );
trainingClassLabel = 1; // on ne s'occupe pas du trainingClassLabel pour la reconnaissance
//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( PRECISION_RECO );
//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 );
//Load the data from TrainingData.txt, and train the pipeline
if( trainingData.loadDatasetFromFile("TrainingData.txt") )
{
infoText = "Training data saved to file";
if( pipeline.train( trainingData ) )
{
infoText = "Pipeline Trained";
}
else infoText = "WARNING: Failed to train pipeline";
}
else infoText = "WARNING: Failed to load training data from file";
}
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;
}
//--------------------------------------------------------------
void testApp::setup() {
ofSetFrameRate(30);
//Initialize the training and info variables
infoText = "";
trainingClassLabel = 1;
record = false;
noOfHands = 1;
noOfTrackedHands = 0;
//The input to the training data will be the [x y] from the mouse, so we set the number of dimensions to 2
trainingData.setNumDimensions( noOfHands*3 );
//trainingData.setNumDimensions( 3 );
//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( 3 );
//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.inputVectorDimensions(3*noOfHands);
///setup nite
niteRc = nite::NiTE::initialize();
if (niteRc != nite::STATUS_OK)
{
printf("NiTE initialization failed\n");
return;
}
niteRc = handTracker.create();
if (niteRc != nite::STATUS_OK)
{
printf("Couldn't create user tracker\n");
return;
}
handTracker.startGestureDetection(nite::GESTURE_CLICK);
printf("\nPoint with your hand to start tracking it...\n");
//put cursor in the middle of the screen: SPI_GETWORKAREA
int screenX = GetSystemMetrics(SM_CXSCREEN);
int screenY = GetSystemMetrics(SM_CYSCREEN);
SetCursorPos(screenX / 2, screenY / 2);
xcursorpos = 500;
ycursorpos = 500;
}
