//-------------------------------------------------------------- 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; }
int main (int argc, const char * argv[]) { TimeSeriesClassificationData trainingData; //This will store our training data GestureRecognitionPipeline pipeline; //This is a wrapper for our classifier and any pre/post processing modules string dirPath = "/home/vlad/AndroidStudioProjects/DataCapture/dataSetGenerator/build"; if (!trainingData.loadDatasetFromFile(dirPath + "/acc-training-set-segmented.data")) { printf("Cannot open training segmented set\n"); return 0; } printf("Successfully opened training data set ...\n"); DTW dtw; // LowPassFilter lpf(0.1, 1, 1); // pipeline.setPreProcessingModule(lpf); // DoubleMovingAverageFilter filter( 1000, 3 ); // pipeline.setPreProcessingModule(filter); //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( 5 ); dtw.enableTrimTrainingData(true, 0.1, 90); // dtw.setOffsetTimeseriesUsingFirstSample(true); pipeline.setClassifier( dtw ); UINT KFolds = 5; /* Separate input dataset using KFold */ KfoldTimeSeriesData* kFoldTS = new KfoldTimeSeriesData(trainingData); if( !kFoldTS->spiltDataIntoKFolds(KFolds) ) { printf("BaseTGTestModel: Failed to spiltDataIntoKFolds!"); return 0; } UINT maxTrainigSetSize = trainingData.getNumSamples() * (KFolds - 1) / (KFolds * trainingData.getNumClasses()); // KFolds ofstream myfile; myfile.open ("example.txt"); Float acc = 0; for (GRT::UINT k = 1 ; k < KFolds; k++) { printf("Running tests for: %d fold", k); // maxTrainigSetSize // for (UINT trainingSetSize = 1; trainingSetSize <= maxTrainigSetSize; trainingSetSize ++) { /* Set up training datasets for current fold */ TimeSeriesClassificationData trainingDataset = kFoldTS->getTrainingFoldData(k, maxTrainigSetSize); /* Set up validation datasets for current fold */ TimeSeriesClassificationDataStream testDataset = kFoldTS->getTestFoldData(k); /* Log test dataset size */ //printf("Data set size: training %d; testing %d", // trainingDataset.getNumSamples(), testDataset.getNumSamples()); /* Run test for current fold */ pipeline.train(trainingDataset); pipeline.test(testDataset); myfile << pipeline.getTestAccuracy() << "\n"; // } } myfile.close(); printf("Accuracy = %f ; %d\n", acc, maxTrainigSetSize); }
//-------------------------------------------------------------- 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; }
int main (int argc, const char * argv[]) { //Create a new DTW instance, using the default parameters DTW dtw; //Load some training data to train the classifier - the DTW uses TimeSeriesClassificationData TimeSeriesClassificationData trainingData; if( !trainingData.load("DTWTrainingData.grt") ){ cout << "Failed to load training data!\n"; return EXIT_FAILURE; } //Use 20% of the training dataset to create a test dataset TimeSeriesClassificationData testData = trainingData.partition( 80 ); //Trim the training data for any sections of non-movement at the start or end of the recordings dtw.enableTrimTrainingData(true,0.1,90); //Train the classifier if( !dtw.train( trainingData ) ){ cout << "Failed to train classifier!\n"; return EXIT_FAILURE; } //Save the DTW model to a file if( !dtw.save("DTWModel.grt") ){ cout << "Failed to save the classifier model!\n"; return EXIT_FAILURE; } //Load the DTW model from a file if( !dtw.load("DTWModel.grt") ){ cout << "Failed to load the classifier model!\n"; return EXIT_FAILURE; } //Use the test dataset to test the DTW model double accuracy = 0; for(UINT i=0; i<testData.getNumSamples(); i++){ //Get the i'th test sample - this is a timeseries UINT classLabel = testData[i].getClassLabel(); MatrixDouble timeseries = testData[i].getData(); //Perform a prediction using the classifier if( !dtw.predict( timeseries ) ){ cout << "Failed to perform prediction for test sampel: " << i <<"\n"; return EXIT_FAILURE; } //Get the predicted class label UINT predictedClassLabel = dtw.getPredictedClassLabel(); double maximumLikelihood = dtw.getMaximumLikelihood(); VectorDouble classLikelihoods = dtw.getClassLikelihoods(); VectorDouble classDistances = dtw.getClassDistances(); //Update the accuracy if( classLabel == predictedClassLabel ) accuracy++; cout << "TestSample: " << i << "\tClassLabel: " << classLabel << "\tPredictedClassLabel: " << predictedClassLabel << "\tMaximumLikelihood: " << maximumLikelihood << endl; } cout << "Test Accuracy: " << accuracy/double(testData.getNumSamples())*100.0 << "%" << endl; return EXIT_SUCCESS; }
int main() { vector<string> gestures(0,""); GetFilesInDirectory(gestures, "rawdata"); CreateDirectory("processed", NULL); sort(gestures.begin(), gestures.end()); data = vector<vector<vector<double > > >(gestures.size(), vector<vector<double > >(0,vector<double>(0,0))); for(size_t i = 0; i < gestures.size(); i++) { ifstream fin(gestures[i]); int n; fin >> n; // cerr << gestures[i] << endl; // cerr << n << endl; data[i] = vector<vector<double> >(n, vector<double>(NUMPARAM, 0)); for(int j = 0; j < n; j++) { for(int k = 0; k < NUMPARAM; k++) { fin >> data[i][j][k]; } } fin.close(); } //Create a new instance of the TimeSeriesClassificationDataStream TimeSeriesClassificationData trainingData; // ax, ay, az trainingData.setNumDimensions(3); trainingData.setDatasetName("processed\\GestureTrainingData.txt"); ofstream labelfile("processed\\GestureTrainingDataLabels.txt"); UINT currLabel = 1; Random random; map<string, int> gesturenames; for(size_t overall = 0; overall < gestures.size(); overall++) { string nam = gestures[overall].substr(8,gestures[overall].find_first_of('_')-8); if(gesturenames.count(nam)) currLabel = gesturenames[nam]; else { currLabel = gesturenames.size()+1; gesturenames[nam] = currLabel; labelfile << currLabel << " " << nam << endl; } MatrixDouble trainingSample; VectorDouble currVec( trainingData.getNumDimensions() ); for(size_t k = 1; k < data[overall].size(); k++) { for(UINT j=0; j<currVec.size(); j++){ currVec[j] = data[overall][k][j]; } trainingSample.push_back(currVec); } trainingData.addSample(currLabel, trainingSample); } for(size_t i = 0; i < gestures.size(); i++) { MatrixDouble trainingSample; VectorDouble currVec(trainingData.getNumDimensions()); for(UINT j = 0; j < currVec.size(); j++) { currVec[j] = random.getRandomNumberUniform(-1.0, 1.0); } for(size_t k = 0; k < 100; k++) { trainingSample.push_back(currVec); } trainingData.addSample(0, trainingSample); } //After recording your training data you can then save it to a file if( !trainingData.save( "processed\\TrainingData.grt" ) ){ cout << "ERROR: Failed to save dataset to file!\n"; return EXIT_FAILURE; } //This can then be loaded later if( !trainingData.load( "processed\\TrainingData.grt" ) ){ cout << "ERROR: Failed to load dataset from file!\n"; return EXIT_FAILURE; } //This is how you can get some stats from the training data string datasetName = trainingData.getDatasetName(); string infoText = trainingData.getInfoText(); UINT numSamples = trainingData.getNumSamples(); UINT numDimensions = trainingData.getNumDimensions(); UINT numClasses = trainingData.getNumClasses(); cout << "Dataset Name: " << datasetName << endl; cout << "InfoText: " << infoText << endl; cout << "NumberOfSamples: " << numSamples << endl; cout << "NumberOfDimensions: " << numDimensions << endl; cout << "NumberOfClasses: " << numClasses << endl; //You can also get the minimum and maximum ranges of the data vector< MinMax > ranges = trainingData.getRanges(); cout << "The ranges of the dataset are: \n"; for(UINT j=0; j<ranges.size(); j++){ cout << "Dimension: " << j << " Min: " << ranges[j].minValue << " Max: " << ranges[j].maxValue << endl; } DTW dtw; if( !dtw.train( trainingData ) ){ cerr << "Failed to train classifier!\n"; exit(EXIT_FAILURE); } dtw.enableNullRejection(true); dtw.setNullRejectionCoeff(4); dtw.enableTrimTrainingData(true, 0.1, 90); //Save the DTW model to a file if( !dtw.saveModelToFile("processed\\DTWModel.txt") ){ cerr << "Failed to save the classifier model!\n"; exit(EXIT_FAILURE); } trainingData.clear(); return EXIT_SUCCESS; }