def __init__(self):
print("Started Gesture Recognition")
self._setConfig()
self.soundPlayer = Sound(self.audioConfig, self.osConfig)
self.gestureFileIO = GestureFileIO(self.name, self.path)
self.recorder = SwhRecorder(self.gestureFileIO, self.audioConfig, self.recordConfig)
self.view = Console(self.recorder, self.soundPlayer, self.applicationClose, self.gestureFileIO.setFileName, self.gestureFileIO.getFileName)
self.t1 = None
self.t2 = None
self.t3 = None
def getAverage(cut, fold):
global avg
# Use this if reaults are too bad by creating avg on demand with recorded gestures
# the avg from all gestures isn't correct anymore -> hardcoded avg
# avg = [ 0.01405917, 0.01451394, 0.01501277, 0.01552741, 0.01608232, 0.0166838,
# 0.017318, 0.01801043, 0.01876477, 0.01958107, 0.02046903, 0.02144034,
# 0.02251206, 0.02370547, 0.02503494, 0.02651884, 0.02819135, 0.03008983,
# 0.03227148, 0.03479038, 0.03773177, 0.04122532, 0.04543961, 0.05061085,
# 0.05710018, 0.06553213, 0.07690142, 0.09310018, 0.11811639, 0.16215195,
# 0.26233294, 0.83321468, 0.99991455, 0.4672319, 0.2171238, 0.1442126,
# 0.10842227, 0.08702495, 0.07270665, 0.06246511, 0.0547552, 0.04876873,
# 0.04395861, 0.03999377, 0.03669765, 0.03390901, 0.03149998, 0.02941618,
# 0.02760306, 0.02599722, 0.02456548, 0.02327461, 0.02212122, 0.02108119,
# 0.02013257, 0.01926706, 0.01846762, 0.01773872, 0.01707368, 0.01645111,
# 0.01586561, 0.0153235, 0.01481276, 0.01433922]
if avg == None:
gesturepath = c.getInstance().getPathsConfig()['gesturepath']
g = GestureFileIO(gesturePath=gesturepath)
avg = g.getAvgFrequency()
avg = preprocessFrame(avg, cut, fold)
return avg
def getAverage(cut, fold):
global avg
# Use this if reaults are too bad by creating avg on demand with recorded gestures
# the avg from all gestures isn't correct anymore -> hardcoded avg
# avg = [ 0.01405917, 0.01451394, 0.01501277, 0.01552741, 0.01608232, 0.0166838,
# 0.017318, 0.01801043, 0.01876477, 0.01958107, 0.02046903, 0.02144034,
# 0.02251206, 0.02370547, 0.02503494, 0.02651884, 0.02819135, 0.03008983,
# 0.03227148, 0.03479038, 0.03773177, 0.04122532, 0.04543961, 0.05061085,
# 0.05710018, 0.06553213, 0.07690142, 0.09310018, 0.11811639, 0.16215195,
# 0.26233294, 0.83321468, 0.99991455, 0.4672319, 0.2171238, 0.1442126,
# 0.10842227, 0.08702495, 0.07270665, 0.06246511, 0.0547552, 0.04876873,
# 0.04395861, 0.03999377, 0.03669765, 0.03390901, 0.03149998, 0.02941618,
# 0.02760306, 0.02599722, 0.02456548, 0.02327461, 0.02212122, 0.02108119,
# 0.02013257, 0.01926706, 0.01846762, 0.01773872, 0.01707368, 0.01645111,
# 0.01586561, 0.0153235, 0.01481276, 0.01433922]
if avg == None:
gesturepath = c.getInstance().getPathsConfig()['gesturepath']
g = GestureFileIO(gesturePath=gesturepath)
avg = g.getAvgFrequency()
avg = preprocessFrame(avg, cut, fold)
return avg
def __loadDataFromFile(merge67="false"):
gesturepath = c.getInstance().getPathsConfig()['gesturepath']
g = GestureFileIO(gesturePath=gesturepath, relative=relative)
data = [0] * nClasses
getData = None
if (average == "false"):
getData = g.getGesture3DNormalized
else:
getData = g.getGesture3DDiffAvg
if (merge67 == "true"):
merge67 = True
else:
merge67 = False
for i in classes:
data[i] = getData(i, [], merge67)
if (i == 6 and merge67):
data7 = getData(7, [], merge67)
data[i] = np.append(data[i], data7, axis=0)
print("data " + str(i) + " loaded shape: " +
str(np.shape(data[i])))
print("data loaded, now creating dataset")
return data
def __init__(self):
self.fileIO = GestureFileIO(relative="")
class DataUtil:
''' Class for loading and preprocessing gesture-data '''
def __init__(self):
self.fileIO = GestureFileIO(relative="")
def loadRaw3dGesture(self, recordClass, recordNames=None):
''' loads raw data from the gesture dir '''
if recordNames is None:
return self.fileIO.getGesture3D(recordClass, c.names)
else:
return self.fileIO.getGesture3D(recordClass, recordNames)
def _reduceBins(self, data, leftBorder=c.leftBorder, rightBorder=c.rightBorder+1):
''' reduces array from 64 to 16 bins '''
return data[:, :, leftBorder:rightBorder]
def _amplifyFunction(self, x):
return x * (2.8 * ( x - 1.15 )**2 + 0.75)
def _normalize(self, data):
''' normalizes gesture between 0 and 1 '''
result = []
for gesture in data:
aMax = np.amax(gesture)
result.append(gesture/aMax)
return np.array(result)
def splitData(self, data):
''' splits data in 3/4 training, 1/4 test '''
train, test = list(data[::4]) + list(data[1::4]) + list(data[2::4]), data[3::4]
return np.array(train), test
def _cutThresholdAndAmplify(self, data, lowerBound=0.15):
'''
Sets values under lowerBound to 0
and amplifiy / reduce values
(uses normalized data)
'''
result = np.where(data[:,:,:]<lowerBound, 0.0, self._amplifyFunction(data[:,:,:]))
return result
def preprocessData(self, data):
''' public method to send data through the preproc pipeline'''
data = self._reduceBins(data)
data = self._normalize(data)
data = self._cutThresholdAndAmplify(data)
data = np.round(data, 1)
return data
def loadData(self, gesture):
''' public method to load preproc data '''
data= self.loadRaw3dGesture(gesture)
data = self.preprocessData(data)
return data
def loadSplitData(self, gesture):
''' public method to load splitted preproc data '''
data = self.loadData(gesture)
return self.splitData(data)
def _getHighestSum(self, gesture):
highestValue = 0
position = 0
for i in range(len(gesture)):
highTmp = np.sum(gesture[i])
if highTmp > highestValue:
highestValue = highTmp
position = i
return position
'''
def __init__(self):
self.fileIO = GestureFileIO(relative="")
class DataUtil:
''' Class for loading and preprocessing gesture-data '''
def __init__(self):
self.fileIO = GestureFileIO(relative="")
def loadRaw3dGesture(self, recordClass, recordNames=None):
''' loads raw data from the gesture dir '''
if recordNames is None:
return self.fileIO.getGesture3D(recordClass, c.names)
else:
return self.fileIO.getGesture3D(recordClass, recordNames)
def _reduceBins(self,
data,
leftBorder=c.leftBorder,
rightBorder=c.rightBorder + 1):
''' reduces array from 64 to 16 bins '''
return data[:, :, leftBorder:rightBorder]
def _amplifyFunction(self, x):
return x * (2.8 * (x - 1.15)**2 + 0.75)
def _normalize(self, data):
''' normalizes gesture between 0 and 1 '''
result = []
for gesture in data:
aMax = np.amax(gesture)
result.append(gesture / aMax)
return np.array(result)
def splitData(self, data):
''' splits data in 3/4 training, 1/4 test '''
train, test = list(data[::4]) + list(data[1::4]) + list(
data[2::4]), data[3::4]
return np.array(train), test
def _cutThresholdAndAmplify(self, data, lowerBound=0.15):
'''
Sets values under lowerBound to 0
and amplifiy / reduce values
(uses normalized data)
'''
result = np.where(data[:, :, :] < lowerBound, 0.0,
self._amplifyFunction(data[:, :, :]))
return result
def preprocessData(self, data):
''' public method to send data through the preproc pipeline'''
data = self._reduceBins(data)
data = self._normalize(data)
data = self._cutThresholdAndAmplify(data)
data = np.round(data, 1)
return data
def loadData(self, gesture):
''' public method to load preproc data '''
data = self.loadRaw3dGesture(gesture)
data = self.preprocessData(data)
return data
def loadSplitData(self, gesture):
''' public method to load splitted preproc data '''
data = self.loadData(gesture)
return self.splitData(data)
def _getHighestSum(self, gesture):
highestValue = 0
position = 0
for i in range(len(gesture)):
highTmp = np.sum(gesture[i])
if highTmp > highestValue:
highestValue = highTmp
position = i
return position
'''
