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)
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)
data = [dataFile['accX'], dataFile['accY'], dataFile['accZ']]
data = normalize(np.ravel(data))
print("Guess: " + str(clf1.predict(data)))
os.remove(previousFile)
previousFile = classiFile
FolderWatch.FolderWatch(CLASSIFY_FOLDER, classify)
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)
#sort of while loop; when done classifying, watch the folder again!
FolderWatch.FolderWatch(CLASSIFY_FOLDER, classify)