def loadData(self, dataFolder, augment):
def shuffle_unison(a, b):
assert len(a) == len(b)
p = numpy.random.permutation(len(a))
return a[p], b[p]
assert (not dataFolder == None
or not dataFolder == ""), "Empty Data Folder!"
dataX = []
dataLabels = []
classesDictionary = []
gestures = self.orderClassesFolder(dataFolder + "/")
self.logManager.write("Gestures reading order: " + str(gestures))
lastImage = None
numberOfVideos = 0
gestureNumber = 0
for e in gestures:
gestureNumber = gestureNumber + 1
loadedDataPoints = 0
classesDictionary.append("'" + str(gestureNumber) + "':'" +
str(e) + "',")
numberOfVideos = numberOfVideos + 1
time = datetime.datetime.now()
#print dataFolder + "/" + v+"/"+dataPointLocation
dataFrames = self.orderDataFolder(dataFolder + "/" + e)
for dataFrame in dataFrames:
dataPoint = self.preProcess(
dataFolder + "/" + e + "/" + dataFrame, augment)
dataX.append(dataPoint)
dataLabels.append(gestureNumber - 1)
loadedDataPoints = loadedDataPoints + 1
self.logManager.write("--- Gestures: " + str(e) + "(" +
str(loadedDataPoints) + " Data points - " +
str((datetime.datetime.now() -
time).total_seconds()) + " seconds" +
")")
dataLabels = np_utils.to_categorical(dataLabels, gestureNumber)
dataX = numpy.array(dataX)
print "Shape Labels:", dataLabels.shape
print "Shape DataX:", dataX.shape
dataX, dataLabels = shuffle_unison(dataX, dataLabels)
dataX = numpy.array(dataX)
dataLabels = numpy.array(dataLabels)
return Data.Data(dataX, dataLabels, classesDictionary)
def loadData(self, dataFolder):
def shuffle_unison(a, b):
assert len(a) == len(b)
p = numpy.random.permutation(len(a))
return a[p], b[p]
assert (not dataFolder == None
or not dataFolder == ""), "Empty Data Folder!"
dataX = []
dataLabels = []
classesDictionary = []
emotions = self.orderClassesFolder(dataFolder + "/")
self.logManager.write("Emotions reading order: " + str(emotions))
emotionNumber = 0
for e in emotions:
if not e == "Surprised":
emotionNumber = emotionNumber + 1
loadedDataPoints = 0
classesDictionary.append("'" + str(emotionNumber) + "':'" +
str(e) + "',")
time = datetime.datetime.now()
audios = os.listdir(dataFolder + "/" + e + "/")
for audio in audios:
dataPoint = self.preProcess(dataFolder + "/" + e + "/" +
audio)
for audio in dataPoint:
dataX.append(audio)
dataLabels.append(emotionNumber - 1)
loadedDataPoints = loadedDataPoints + 1
self.logManager.write("--- Emotion: " + str(e) + "(" +
str(loadedDataPoints) +
" Data points - " +
str((datetime.datetime.now() -
time).total_seconds()) +
" seconds" + ")")
dataLabels = np_utils.to_categorical(dataLabels, emotionNumber)
dataX = numpy.array(dataX)
dataX, dataLabels = shuffle_unison(dataX, dataLabels)
dataX = numpy.array(dataX)
dataLabels = numpy.array(dataLabels)
return Data.Data(dataX, dataLabels, classesDictionary)
def loadData(self, dataFolder, labelDirectory, augmentData):
assert (not dataFolder == None
or not dataFolder == ""), "Empty Data Folder!"
dataX = []
dataLabels = []
classesDictionary = []
labelsFile = open(labelDirectory, "r")
classesDictionary.append("Neutral:0")
classesDictionary.append("Happiness:1")
classesDictionary.append("Surprise:2")
classesDictionary.append("Sadness:3")
classesDictionary.append("Anger:4")
classesDictionary.append("Disgust:5")
classesDictionary.append("Fear:6")
classesDictionary.append("Contempt:7")
time = datetime.datetime.now()
for line in labelsFile:
lineSplitted = line.split(",")
fileName = lineSplitted[0]
labelDistribution = [float(i) for i in lineSplitted[5:13]]
dataPoint = self.preProcess(dataFolder + "/" + fileName,
augmentData)
if augmentData:
for dp in dataPoint:
dataX.append(dp)
dataLabels.append(labelDistribution)
else:
dataX.append(dataPoint)
dataLabels.append(labelDistribution)
dataLabels = numpy.array(dataLabels)
dataX = numpy.array(dataX)
dataX = dataX.astype('float32')
dataX, dataLabels = self.shuffleData(dataX, dataLabels)
self.logManager.write("--- Folder: " + dataFolder + "(" +
str((datetime.datetime.now() -
time).total_seconds()) + " seconds" + ")")
dataX = numpy.array(dataX)
dataLabels = numpy.array(dataLabels)
return Data.Data(dataX, dataLabels, classesDictionary)
def loadData(self, dataFolder, dataFile):
def shuffle_unison(a, b):
assert len(a) == len(b)
p = numpy.random.permutation(len(a))
return a[p], b[p]
dataX = []
dataLabels = []
classesDictionary = []
time = datetime.datetime.now()
with open(dataFile, 'rb') as csvfile:
#print "Reading From:", dataFile
# raw_input(("here"))
reader = csv.reader(csvfile)
rownum = 0
for row in reader:
# print "Row:", row
if not len(row) == 0:
if rownum >= 1:
video = row[3]
utterance = row[4]
arousal = row[5]
valence = row[6]
readFrom = dataFolder + "/" + video + "/video/" + utterance
imageFaces = os.listdir(readFrom)
shuffle(imageFaces)
imageFaces = imageFaces[0:10]
for imageName in imageFaces:
try:
dataPoint = self.preProcess(
readFrom + "/" + imageName, False)
dataX.append(dataPoint)
dataLabels.append(
[float(arousal),
float(valence)])
except:
print "problem:", readFrom + "/" + imageName
rownum = rownum + 1
dataX = numpy.array(dataX)
dataLabels = numpy.array(dataLabels)
self.logManager.write("(" + str(len(dataX)) + " Data points - " +
str((datetime.datetime.now() -
time).total_seconds()) + " seconds" + ")")
dataX, dataLabels = shuffle_unison(dataX, dataLabels)
return Data.Data(dataX, dataLabels, classesDictionary)
def loadData(self, dataFolder, dataFile):
def shuffle_unison(a, b):
assert len(a) == len(b)
p = numpy.random.permutation(len(a))
return a[p], b[p]
dataX = []
dataLabels = []
classesDictionary = []
time = datetime.datetime.now()
with open(dataFile, 'rb') as csvfile:
reader = csv.reader(csvfile)
rownum = 0
for row in reader:
if not len(row) == 0:
if rownum >= 1:
video = row[3]
utterance = row[4]
arousal = row[5]
valence = row[6]
readFrom = dataFolder + "/" + video + "/audio/" + utterance + "/audio.wav"
dataPoint = self.preProcess(readFrom)
for audio in dataPoint:
dataX.append(audio)
dataLabels.append([float(arousal), float(valence)])
rownum = rownum + 1
dataX = numpy.array(dataX)
dataLabels = numpy.array(dataLabels)
self.logManager.write("(" + str(len(dataX)) + " Data points - " +
str((datetime.datetime.now() -
time).total_seconds()) + " seconds" + ")")
dataX, dataLabels = shuffle_unison(dataX, dataLabels)
return Data.Data(dataX, dataLabels, classesDictionary)
def loadData(self, dataFolder, augment):
def shuffle_unison(a, b):
assert len(a) == len(b)
p = numpy.random.permutation(len(a))
return a[p], b[p]
assert (not dataFolder == None or not dataFolder == ""), "Empty Data Folder!"
dataX = []
dataLabels = []
classesDictionary = []
objects = self.orderClassesFolder(dataFolder + "/")
self.logManager.write("Objects reading order: " + str(objects))
lastImage = None
numberOfVideos = 0
emotionNumber = 0
for e in objects:
# if numberOfVideos < 5:
emotionNumber = emotionNumber + 1
loadedDataPoints = 0
classesDictionary.append("'" + str(emotionNumber) + "':'" + str(e) + "',")
numberOfVideos = numberOfVideos + 1
time = datetime.datetime.now()
# print dataFolder + "/" + v+"/"+dataPointLocation
dataFrames = self.orderDataFolder(dataFolder + "/" + e)
for dataFrame in dataFrames:
try:
dataPoint = self.preProcess(dataFolder + "/" + e + "/" + dataFrame,
augment)
if augment:
lastImage = dataPoint[0]
else:
lastImage = dataPoint
except:
print "Error:", dataFolder + "/" + e + "/" + dataFrame
if augment:
dataPoint = [lastImage]
else:
dataPoint = lastImage
dataX.append(dataPoint)
dataLabels.append(emotionNumber - 1)
loadedDataPoints = loadedDataPoints + 1
self.logManager.write(
"--- Position: " + str(e) + "(" + str(loadedDataPoints) + " Data points - " + str(
(datetime.datetime.now() - time).total_seconds()) + " seconds" + ")")
# print "Labels before:", dataLabels
dataLabels = np_utils.to_categorical(dataLabels, emotionNumber)
# print "Labels After:", dataLabels
dataX = numpy.array(dataX)
# dataX = numpy.swapaxes(dataX, 1, 2)
# print "Shape Labels:", dataLabels.shape
# print "Shape DataX:", dataX.shape
# dataLabels = numpy.array(dataLabels).reshape((len(dataLabels), 2))
# dataX = dataX.astype('float32')
dataX, dataLabels = shuffle_unison(dataX, dataLabels)
# print "dataX shape:", numpy.array(dataX).shape
# raw_input("here")
dataX = numpy.array(dataX)
dataLabels = numpy.array(dataLabels)
return Data.Data(dataX, dataLabels, classesDictionary)
def loadData(self, dataLipsFolder, dataArmsFolder, dataSoundFolder, dataParticipantsFolder,participantNumbers, augmentData):
def shuffle_unison(a, b):
assert len(a) == len(b)
p = numpy.random.permutation(len(a))
return a[p], b[p]
assert (not dataParticipantsFolder == None or not dataParticipantsFolder == ""), "Empty Participants Folder!"
dataX = []
dataLabels = []
readingFrom = ""
participantsCSVs = os.listdir(dataParticipantsFolder)
participantNumber = 0
for participantCSV in participantsCSVs:
loadedDataPoints = 0
time = datetime.datetime.now()
if participantNumber in participantNumbers:
with open(dataParticipantsFolder+"/"+ participantCSV, 'rb') as csvfile:
readingFrom = dataParticipantsFolder+"/"+ participantCSV
#print "Reading From:", readingFrom
#raw_input(("here"))
reader = csv.reader(csvfile)
rownum = 0
for row in reader:
#print "Row:", row
if rownum >= 1:
#print "Row:", row
audioLabel = row[6]
lipsLabel = row[7]
armsLabel = row[8]
congruencyLabel = row[9]
conditionLabel = row[10]
phonemeLabel = row[-2]
decisionLabel = row[-1]
if not decisionLabel == "Na":
# partialLabel = str(audioLabel) + "_" + str(lipsLabel) + "_" + str(
# armsLabel) + "_" + str(congruencyLabel) + "_" + str(conditionLabel) + "_" + str(
# phonemeLabel)
partialLabel = str(audioLabel) + "_" + str(lipsLabel) + "_" + str(
armsLabel)
#Load Arms
armsFolderList = os.listdir(dataArmsFolder +"/"+str(armsLabel)+"/")
matchingarms = [s for s in armsFolderList if partialLabel in s]
shuffle(matchingarms)
armDataPoint = self.preProcess(dataArmsFolder +"/"+str(armsLabel)+"/"+matchingarms[0],imageSize=(80,60), grayScale=True, augment=False)
#Load Lips
lipsFolderList = os.listdir(dataLipsFolder+"/"+str(lipsLabel)+"/")
matchingLips = [s for s in lipsFolderList if partialLabel in s]
shuffle(matchingLips)
lipsImagesList = self.orderDataFolder(dataLipsFolder+"/"+str(lipsLabel)+"/"+matchingLips[0])
lipsDataPoint = []
for lipsImage in lipsImagesList:
#print "Opening:", dataLipsFolder + "/" + str(lipsLabel) + "/" + matchingLips[0] + "/" + lipsImage
lipDataPoint = self.preProcess(
dataLipsFolder + "/" + str(lipsLabel) + "/" + matchingLips[0] + "/" + lipsImage,
imageSize=(120, 120), grayScale=True, augment=False)
lipsDataPoint.append(lipDataPoint)
lipsDataPoint = numpy.array(lipsDataPoint)
lipsDataPoint = lipsDataPoint.squeeze(axis=1)
#Load Sound
soundFolderList = os.listdir(dataSoundFolder + "/" + str(audioLabel) + "/")
matchingAudio = [s for s in soundFolderList if partialLabel in s]
shuffle(matchingAudio)
# print "Sounds:", matchingAudio
# print "List:", dataSoundFolder + "/" + str(audioLabel) + "/"
# print "Label:", partialLabel
# print "Opening:", dataSoundFolder + "/" + str(audioLabel) + "/"+matchingAudio[0]
audioDataPoint = self.preProcessAudio(dataSoundFolder + "/" + str(audioLabel) + "/"+matchingAudio[0])[0]
# print "Shape Arms:", armDataPoint.shape
# print "Shape Lips:", lipsDataPoint.shape
# print "Shape Audio:", audioDataPoint.shape
# print "Label:", decisionLabel
dataX.append([numpy.array(armDataPoint).astype('float32'), numpy.array(lipsDataPoint).astype('float32'),numpy.array(audioDataPoint).astype('float32')])
dataLabels.append(int(decisionLabel)-1)
loadedDataPoints = loadedDataPoints+1
rownum += 1
# if rownum == 30:
# break
self.logManager.write(
"--- Participant: " + str(participantNumber) + "(" + str(
loadedDataPoints) + " Data points - " + str(
(datetime.datetime.now() - time).total_seconds()) + " seconds" + ")")
participantNumber = participantNumber + 1
dataLabels = np_utils.to_categorical(dataLabels, 4)
dataX = numpy.array(dataX)
#print "dataX shape:", numpy.array(dataX).shape
# dataX = numpy.swapaxes(dataX, 1, 2)
#dataX = numpy.squeeze(dataX, axis=2)
# dataLabels = numpy.array(dataLabels).reshape((len(dataLabels), 2))
# print "Shape DataX:", dataX.shape
# dataX = dataX.astype('float32')
#dataX, dataLabels = shuffle_unison(dataX, dataLabels)
print "dataX shape:", numpy.array(dataX).shape
print "dataY shape:", numpy.array(dataLabels).shape
# raw_input("here")
dataX = numpy.array(dataX)
dataLabels = numpy.array(dataLabels)
return Data.Data(dataX, dataLabels, []), readingFrom
def loadData(self, dataFolder, augment):
def shuffle_unison(a, b):
assert len(a) == len(b)
p = numpy.random.permutation(len(a))
return a[p], b[p]
assert (not dataFolder == None
or not dataFolder == ""), "Empty Data Folder!"
dataX = []
dataLabels = []
classesDictionary = []
classes = self.orderClassesFolder(dataFolder + "/")
self.logManager.write("Videos reading order: " + str(classes))
lastImage = None
numberOfVideos = 0
classNumber = 0
for c in classes:
videos = self.orderClassesFolder(dataFolder + "/" + str(c) + "/")
loadedDataPoints = 0
time = datetime.datetime.now()
for v in videos:
# if numberOfVideos < 5:
numberOfVideos = numberOfVideos + 1
# print dataFolder + "/" + v+"/"+dataPointLocation
dataFrames = self.orderDataFolder(dataFolder + "/" + str(c) +
"/" + v)
sequenceFrames = []
for i in range(len(dataFrames)):
dataFrame = dataFrames[i]
try:
dataPoint = self.preProcess(
dataFolder + "/" + str(c) + "/" + v + "/" +
dataFrame, augment)
if augment:
lastImage = dataPoint[0]
else:
lastImage = dataPoint
except:
print "Error:", dataFolder + "/" + str(
c) + "/" + v + "/" + dataFrame
if augment:
dataPoint = [lastImage]
else:
dataPoint = lastImage
#print "DataPoint Shape:", dataPoint.shape
sequenceFrames.append(dataPoint)
sequenceFrames = numpy.array(sequenceFrames)
# print "Shape:", sequenceFrames.shape
dataX.append(sequenceFrames)
dataLabels.append(classNumber)
loadedDataPoints = loadedDataPoints + 1
classNumber = classNumber + 1
self.logManager.write("--- Arm Position: " + str(c) + "(" +
str(loadedDataPoints) + " Data points - " +
str((datetime.datetime.now() -
time).total_seconds()) + " seconds" +
")")
dataLabels = np_utils.to_categorical(dataLabels, classNumber)
dataX = numpy.array(dataX)
print "dataX shape:", numpy.array(dataX).shape
# dataX = numpy.swapaxes(dataX, 1, 2)
dataX = numpy.squeeze(dataX, axis=2)
# dataLabels = numpy.array(dataLabels).reshape((len(dataLabels), 2))
dataX = dataX.astype('float32')
dataX, dataLabels = shuffle_unison(dataX, dataLabels)
print "dataX shape:", numpy.array(dataX).shape
print "dataY shape:", numpy.array(dataLabels).shape
# raw_input("here")
dataX = numpy.array(dataX)
dataLabels = numpy.array(dataLabels)
return Data.Data(dataX, dataLabels, classesDictionary)