def generateTwoTestAngleData():
'''
This creates a list of data samples to be tested.
Theses samples are from participants 2 and 3 and
the calculated angles are used as features.
'''
testSamples = []
index = 0
for filename in sub2:
#Open the file
fIn = open(filename, 'r')
#For each line of the file calculate the
# angles inbetween joints and use the resulting
# array as the feature vector. Add that to the list.
for line in fIn:
features = generateAngles(line)
testSamples.append(sample(features, labels[index]))
fIn.close()
index += 1
index = 0
for filename in sub3:
#Open the file
fIn = open(filename, 'r')
#For each line of the file calculate the
# angles inbetween joints and use the resulting
# array as the feature vector. Add that to the list.
for line in fIn:
features = generateAngles(line)
testSamples.append(sample(features, labels[index]))
fIn.close()
index += 1
return testSamples
def generateTwoAngleTrainingData():
'''
This function creates training data using the first
two participants' data and has joint angles as features
for the samples.
'''
trainingData = TrainingData("Testing Data")
index = 0
for i in xrange(len(labels)):
#Open the file
fIn = open(sub1[i], 'r')
f2In = open(sub2[i], 'r')
#For each line of the files calculate the
# angles inbetween joints and use the resulting
# array as the feature vector. Add that to the trainingData.
for line in fIn:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
fIn.close()
for line in f2In:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
f2In.close()
index += 1
return trainingData
def generateTwoTestAngleData():
'''
This creates a list of data samples to be tested.
Theses samples are from participants 2 and 3 and
the calculated angles are used as features.
'''
testSamples = []
index = 0
for filename in sub2:
#Open the file
fIn = open(filename, 'r')
#For each line of the file calculate the
# angles inbetween joints and use the resulting
# array as the feature vector. Add that to the list.
for line in fIn:
features = generateAngles(line)
testSamples.append(sample(features, labels[index]))
fIn.close()
index += 1
index = 0
for filename in sub3:
#Open the file
fIn = open(filename, 'r')
#For each line of the file calculate the
# angles inbetween joints and use the resulting
# array as the feature vector. Add that to the list.
for line in fIn:
features = generateAngles(line)
testSamples.append(sample(features, labels[index]))
fIn.close()
index += 1
return testSamples
def generateTwoAngleTrainingData():
'''
This function creates training data using the first
two participants' data and has joint angles as features
for the samples.
'''
trainingData = TrainingData("Testing Data")
index = 0
for i in xrange(len(labels)):
#Open the file
fIn = open(sub1[i], 'r')
f2In = open(sub2[i], 'r')
#For each line of the files calculate the
# angles inbetween joints and use the resulting
# array as the feature vector. Add that to the trainingData.
for line in fIn:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
fIn.close()
for line in f2In:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
f2In.close()
index += 1
return trainingData
def publishJointAngles(self):
#Get a list of frames and turn it into a list of angles
values = self.kinect.get_posture()
angleString = jsonMaker((values,))
angles = generateAngles(angleString)
#Offset the angles appropriately
index = 0
stringNums = ""
print len(angles)
for val in angles:
newValue = float(val) - self.offsets[index]
#Adjust to keep values in safe joint angles
if newValue > self.upper[index]:
newValue = self.upper[index]
if newValue < self.lower[index]:
newValue = self.lower[index]
#Filter the value
newValue = (self.alpha * newValue) + ((1-self.alpha) * self.oldFilter[index])
self.oldFilter[index] = newValue
stringNums += str(newValue) + " "
index += 1
stringNums = stringNums[:-1]
#Publish the angles
service = rospy.ServiceProxy("setProperties", setProperties)
service("REB LEB RSY LSY RSR LSR", "position position position position position position", stringNums)
print stringNums
def generateAllAngleTrainingData():
'''
This function creates one large TrainingData object
containing data from all three participants but uses
calculated angles as the features for each data sample.
'''
#Maybe TODO: Make this so it's easier to add lists of data.
trainingData = []
trainingLabels = []
index = 0
for i in xrange(len(labels)):
#Open the file
fIn = open(sub1[i], 'r')
f2In = open(sub2[i], 'r')
f3In = open(sub3[i], 'r')
#For each line of the files calculate the
# angles inbetween joints and use the resulting
# array as the feature vector. Add that to the trainingData.
for line in fIn:
features = generateAngles(line)
trainingData.append(features)
trainingLabels.append(labels[index])
fIn.close()
for line in f2In:
features = generateAngles(line)
trainingData.append(features)
trainingLabels.append(labels[index])
f2In.close()
for line in f3In:
features = generateAngles(line)
trainingData.append(features)
trainingLabels.append(labels[index])
f3In.close()
index += 1
#Return the data object
return trainingData, trainingLabels
def generateAllAngleTrainingData():
'''
This function creates one large TrainingData object
containing data from all three participants but uses
calculated angles as the features for each data sample.
'''
trainingData = TrainingData("Testing Data")
index = 0
for i in xrange(len(labels)):
#Open the file
fIn = open(sub1[i], 'r')
f2In = open(sub2[i], 'r')
f3In = open(sub3[i], 'r')
#For each line of the files calculate the
# angles inbetween joints and use the resulting
# array as the feature vector. Add that to the trainingData.
for line in fIn:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
fIn.close()
for line in f2In:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
f2In.close()
for line in f3In:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
f3In.close()
index += 1
#Return the data object
return trainingData
def generateAllAngleTrainingData():
'''
This function creates one large TrainingData object
containing data from all three participants but uses
calculated angles as the features for each data sample.
'''
trainingData = TrainingData("Testing Data")
index = 0
for i in xrange(len(labels)):
#Open the file
fIn = open(sub1[i], 'r')
f2In = open(sub2[i], 'r')
f3In = open(sub3[i], 'r')
#For each line of the files calculate the
# angles inbetween joints and use the resulting
# array as the feature vector. Add that to the trainingData.
for line in fIn:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
fIn.close()
for line in f2In:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
f2In.close()
for line in f3In:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
f3In.close()
index += 1
#Return the data object
return trainingData
def MainAngle():
#run the simulation showing the angles that change
global continuing
signal.signal(signal.SIGINT, endDemo)
kinect = Kinect()
plotter = SkeletonPlotter()
continuing = True
while continuing:
time.sleep(.05)
value = kinect.get_posture()
if value is None:
continue
jointString = jsonMaker((value, ))
anglesList = generateAngles(jointString)
plotter.showSkeletonAngles(jointString, anglesList, 'b')
def checkSlidingWindow(self, f):
#Get a list of frames and turn it into a list of angles
values = self.kinect.get_posture()
if values is None:
return None
angleString = jsonMaker((values, ))
angles = generateAngles(angleString)
f.write(str(angles) + "\n")
#Add the angle list to the list of angles in the sliding
# window list. If the window if above capacity pop the oldest
# set of angles.
self.slidingWindow.append(angles)
if len(self.slidingWindow) > self.capacity:
self.slidingWindow.pop(0)
#Let the window fill up before we start trying to classify
if len(self.slidingWindow) != self.capacity:
return angles
#Get the average of the sliding window angles and run them
# through the classifier
avgAngles = self.getAvgAngles()
#Classify the average angles
result = classifySample(avgAngles)
print "Result: " + str(result)
if result is None:
return angles
result = result[0]
if result == "Disco":
stayAliveRobot(self.robot)
elif result == "ChickenDance":
chickenDanceRobot(self.robot)
elif result == "WalkLikeAnEgyptian":
walkLikeAnEgyptianRobot(self.robot)
elif result == "YMCA":
doTheYMCARobot(self.robot)
#Reset the sliding window after a dance was recognized
self.slidingWindow = []
return angles
def checkSlidingWindow(self, f):
#Get a list of frames and turn it into a list of angles
values = self.kinect.get_posture()
if values is None:
return None
angleString = jsonMaker((values,))
angles = generateAngles(angleString)
f.write(str(angles) + "\n")
#Add the angle list to the list of angles in the sliding
# window list. If the window if above capacity pop the oldest
# set of angles.
self.slidingWindow.append(angles)
if len(self.slidingWindow) > self.capacity:
self.slidingWindow.pop(0)
#Let the window fill up before we start trying to classify
if len(self.slidingWindow) != self.capacity:
return angles
#Get the average of the sliding window angles and run them
# through the classifier
avgAngles = self.getAvgAngles()
#Classify the average angles
result = classifySample(avgAngles)
print "Result: " + str(result)
if result is None:
return angles
result = result[0]
if result == "Disco":
stayAliveRobot(self.robot)
elif result == "ChickenDance":
chickenDanceRobot(self.robot)
elif result == "WalkLikeAnEgyptian":
walkLikeAnEgyptianRobot(self.robot)
elif result == "YMCA":
doTheYMCARobot(self.robot)
#Reset the sliding window after a dance was recognized
self.slidingWindow = []
return angles
def generateOneTrainAngleData():
'''
This creates a data set to be used to train a classifier.
The data is from participant 1 and uses the calculated
joint angles as features.
'''
trainingData = TrainingData("Angle Data")
index = 0
for filename in sub1:
#Open the file
fIn = open(filename, 'r')
#For each line of the file calculate the
# angles inbetween joints and use the resulting
# array as the feature vector. Add that to the trainingData.
for line in fIn:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
fIn.close()
index += 1
return trainingData
def recordJointAngles(self):
#Get the values from the kinect
values = self.kinect.get_posture()
if values is None:
#recursivly try again
self.recordJointAngles()
return None
angleString = jsonMaker((values,))
#Make them into angles
angles = generateAngles(angleString)
#Parse angles
stringNums = ""
for angle in angles:
stringNums += str(angle) + " "
stringNums = stringNums[:-1]
#Write the angles to a file
self.output.write(" robot.setProperties(\"REP LEP RSY LSY RSR LSR RSP LSP\", \"position position position position position position position position\", " + stringNums + ")")
def generateOneTrainAngleData():
'''
This creates a data set to be used to train a classifier.
The data is from participant 1 and uses the calculated
joint angles as features.
'''
trainingData = TrainingData("Angle Data")
index = 0
for filename in sub1:
#Open the file
fIn = open(filename, 'r')
#For each line of the file calculate the
# angles inbetween joints and use the resulting
# array as the feature vector. Add that to the trainingData.
for line in fIn:
features = generateAngles(line)
trainingData.addSampleFromFeatures(features, labels[index])
fIn.close()
index += 1
return trainingData
def generateOneTestAngleData():
'''
This creates a list of sample objects that can
be used for testing. This list is built from the third
participant's data and uses angles as features.
'''
testSamples = []
index = 0
for filename in sub3:
#Open the file
fIn = open(filename, 'r')
#For each line of the file calculate the
# angles inbetween joints and use the resulting
# array as the feature vector. Add that to the list.
for line in fIn:
features = generateAngles(line)
testSamples.append(sample(features, labels[index]))
fIn.close()
index += 1
return testSamples
def generateOneTestAngleData():
'''
This creates a list of sample objects that can
be used for testing. This list is built from the third
participant's data and uses angles as features.
'''
testSamples = []
index = 0
for filename in sub3:
#Open the file
fIn = open(filename, 'r')
#For each line of the file calculate the
# angles inbetween joints and use the resulting
# array as the feature vector. Add that to the list.
for line in fIn:
features = generateAngles(line)
testSamples.append(sample(features, labels[index]))
fIn.close()
index += 1
return testSamples
def recordJointAngles(self):
#Get the values from the kinect
values = self.kinect.get_posture()
if values is None:
#recursivly try again
self.recordJointAngles()
return None
angleString = jsonMaker((values, ))
#Make them into angles
angles = generateAngles(angleString)
#Parse angles
stringNums = ""
for angle in angles:
stringNums += str(angle) + " "
stringNums = stringNums[:-1]
#Write the angles to a file
self.output.write(
" robot.setProperties(\"REP LEP RSY LSY RSR LSR RSP LSP\", \"position position position position position position position position\", "
+ stringNums + ")")