def classify(classiFile):
global previousFile
if (previousFile == "none"):
previousFile = classiFile
FolderWatch.FolderWatch(CLASSIFY_FOLDER, classify)
else:
#print("classifying: " + previousFile)
dataFile = pd.read_csv(previousFile, header=0)
try:
print("trying...")
dataFile = analyzer.normalize(dataFile)
dataFile = analyzer.autoCorrelate(dataFile)
#is already being autocorrelated by getBPM
autoanalyzer = DataAnalyzer.AutoAnalyzer(dataFile)
output = autoanalyzer.getLastPeakTime()
detectedBPM = output['bpm']
time = output['time']
row = []
for secondData in training_data:
row.append(analyzer.DTWSimilarity(dataFile, secondData))
print("Classify: \t", str(model.predict([row])), ", BPM: ",
str(detectedBPM), ", time: ", str(time))
except:
print('raising exception')
pass
os.remove(previousFile)
previousFile = classiFile
FolderWatch.FolderWatch(CLASSIFY_FOLDER, classify)
def classify(classiFile):
global previousFile
if (previousFile == "none"):
previousFile = classiFile
FolderWatch.FolderWatch(CLASSIFY_FOLDER, classify)
else:
#print("classifying: " + previousFile)
try:
dataFile = pd.read_csv(previousFile, header=0)
except:
print("[EXCEPTION] cant read previous file")
pass
try:
print("Classificating...")
dataFile = analyzer.normalize(dataFile)
dataFile = analyzer.autoCorrelate(dataFile)
#is already being autocorrelated by getBPM
autoanalyzer = DataAnalyzer.AutoAnalyzer(dataFile)
output = autoanalyzer.getLastPeakTime() #calculates where the first occuring peak is in the file
detectedBPM = output['bpm']
time = output['time']
peakIndex = output['index'] #index number of the peak in the file
endPeakIndex = output['endPeriodIndex']
periodData = autoanalyzer.getPeriodsFromDataIndex(1, peakIndex)['data'] #get one period of data starting from peak index.
row = []
for secondData in training_data: # calculate the DTW Similarity between the sample and all items in the training dataset.
row.append(analyzer.DTWSimilarity(periodData, secondData))
gesture = model.predict([row])[0] #predict the gesture with KNN using the calculated distance matrix
# if BPM is larger than 200, you know for sure it isn't a movement. Temporary 'knutseloplossing'
if detectedBPM > 200:
gesture = "rest"
print("Classify: \t", str(gesture), ", BPM: ", str(detectedBPM), ", time: ", str(time))
socket.sendClassify(str(gesture), detectedBPM, time) #send back the classified gesture through the socket.
dataFile[peakIndex:endPeakIndex].to_csv(CLASSIFY_SAVE_FOLDER + str(gesture) + "-" + str(detectedBPM) + "-" + str(time) + ".csv")
except:
print('[EXCEPTION] during classification')
pass
try:
os.remove(previousFile)
except:
print("[EXCEPTION] cant delete previous file")
pass
previousFile = classiFile
FolderWatch.FolderWatch(CLASSIFY_FOLDER, classify)
def classify(classiFile):
global previousFile
if (previousFile == "none"):
previousFile = classiFile
FolderWatch.FolderWatch(CLASSIFY_FOLDER, classify)
else:
#print("classifying: " + previousFile)
try:
dataFile = pd.read_csv(previousFile, header=0)
except:
print("[EXCEPTION] cant read previous file")
pass
try:
print("Classificating...")
dataFile = analyzer.normalize(dataFile)
dataFile = analyzer.autoCorrelate(dataFile)
#is already being autocorrelated by getBPM
autoanalyzer = DataAnalyzer.AutoAnalyzer(dataFile)
output = autoanalyzer.getLastPeakTime()
detectedBPM = output['bpm']
time = output['time']
periodData = autoanalyzer.getPeriods(1, startIndexPeriod=1)['data']
row = []
for secondData in training_data:
row.append(analyzer.DTWSimilarity(periodData, secondData))
gesture = model.predict([row])[0]
#knutseloplossing
if detectedBPM > 200:
gesture = "rest"
print("Classify: \t", str(gesture), ", BPM: ", str(detectedBPM),
", time: ", str(time))
socket.sendClassify(str(gesture), detectedBPM, time)
print("tried to send...")
except:
print('[EXCEPTION] during classification')
pass
try:
os.remove(previousFile)
except:
print("[EXCEPTION] cant delete previous file")
pass
previousFile = classiFile
FolderWatch.FolderWatch(CLASSIFY_FOLDER, classify)
print("[TESTING]")
test_data = []
test_labels = []
test_data_length = []
files = getDataFileNames("test")
for trainingFile in files:
dataObject = pd.read_csv(DATA_FOLDER + trainingFile, header = 0)
#data = [dataFile['accX'], dataFile['accY'], dataFile['accZ']]
#data = [dataFile['alpha'], dataFile['beta'], dataFile['gamma'], dataFile['accX'], dataFile['accY'], dataFile['accZ']]
dataObject = analyzer.normalize(dataObject)
dataObject = analyzer.autoCorrelate(dataObject)
autoAnalyzer = DataAnalyzer.AutoAnalyzer(dataObject)
output = autoAnalyzer.getLastPeakTime(periods=2, startingPeriod=1)
peakIndex = output['index']
periodData = autoAnalyzer.getPeriodsFromDataIndex(1, peakIndex)['data']
#periodData = autoAnalyzer.getPeriods(1, startIndexPeriod=1)['data']
test_data.append(periodData)
if "updown" in trainingFile:
test_labels.append("updown")
elif "leftright" in trainingFile:
test_labels.append("leftright")
elif "rotateclock" in trainingFile:
test_labels.append("rotateclockwise")
elif "square" in trainingFile:
"../data/FLAWED/lessthan1period/updown-35-49-R53P95G3cV93UMlKAAB0-3_1.csv",
header=0)
da = DataAnalyzer.DataAnalyzer()
#Normalize each stream individually:
'''
dataNorm = dataFile.copy()
dataNorm['accX'] = skNorm(dataNorm['accX'])[0]
dataNorm['accY'] = skNorm(dataNorm['accY'])[0]
dataNorm['accZ'] = skNorm(dataNorm['accZ'])[0]
'''
#dataFile = da.normalize(dataFile)
#dataFile = da.autoCorrelate(dataFile)
daa = DataAnalyzer.AutoAnalyzer(dataFile)
visualizer = Visualizer.Visualizer(dataFile)
#visualizer = Visualizer.Visualizer(dataFile[90:120])
'''
time = np.linspace(0,dataFile[10:110].shape[0], dataFile[10:110].shape[0])
fig = plt.figure(figsize=(12,4))
_ = plt.plot(time, dataFile[10:110]['accZ'], label='Normalized')
_ = plt.plot(time, dataFileCorrelated[10:110]['accZ'], label='Autocorrelated')
#_ = plt.title('DTW distance betw')
_ = plt.ylabel('Amplitude')
_ = plt.xlabel('Time')
_ = plt.legend()
plt.show()
'''