print '-------------------'
# Creates and opens ports for interaction with speech module
if yarpRunning:
yarp.Network.init()
inputInteractionPort = yarp.Port()
inputInteractionPort.open("/sam/face/rpc:i");
inputBottle = yarp.Bottle();
outputBottle = yarp.Bottle();
imgHNew = 200
imgWNew = 200
# Creates a SAMpy object
mySAMpy = SAMDriver_interaction(True, imgH = 400, imgW = 400, imgHNew = imgHNew, imgWNew = imgWNew,inputImagePort="/CLM/imageSeg/out")
# Specification of the experiment number
experiment_number = 1007#42
# Location of face data
root_data_dir=dataPath
# Image format
image_suffix=".ppm"
# Array of participants to be recognised
participant_index=participantList
# Poses used during the data collection
pose_index=['Seg']
# Use a subset of the data for training
ratioData = modelPickle['percentTestData']
image_suffix = modelPickle['image_suffix']
model_type = modelPickle['model_type']
model_num_inducing = modelPickle['num_inducing']
model_init_iterations = modelPickle['model_init_iterations']
model_num_iterations = modelPickle['model_num_iterations']
kernelString = modelPickle['kernelString']
Q = modelPickle['Q']
economy_save = True
pose_index = ['']
pose_selection = 0
# # Creates a SAMpy object
mySAMpy = SAMDriver_interaction(False,
imgH=imgH,
imgW=imgW,
imgHNew=imgHNew,
imgWNew=imgWNew)
# # Reading face data, preparation of data and training of the model
mySAMpy.readData(dataPath, participantList, pose_index)
minImages = mySAMpy.Y.shape[1]
Ntr = int(minImages * ratioData / 100)
Ntest = minImages - Ntr
allPersonsY = mySAMpy.Y
allPersonsL = mySAMpy.L
for i in range(len(participantList)):
#print participantList[i]
print '-------------------'
print 'Config file found: ' + pathFound[0]
print dataPath
print modelPath
print participantList
print '-------------------'
# Creates and opens ports for interaction with speech module
yarp.Network.init()
inputInteractionPort = yarp.BufferedPortBottle()
inputInteractionPort.open("/sam/face/interaction:i");
choice = yarp.Bottle();
# Creates a SAMpy object
mySAMpy = SAMDriver_interaction(True, imgH = 400, imgW = 400, imgHNew = 200, imgWNew = 200,inputImagePort="/visionDriver/image:o")
# Specification of the experiment number
experiment_number = 1007#42
# Location of face data
root_data_dir=dataPath
# Image format
image_suffix=".ppm"
# Array of participants to be recognised
participant_index=participantList
# Poses used during the data collection
pose_index=['Seg']
# Use a subset of the data for training
imgHNew = modelPickle['imgHNew'] imgWNew = modelPickle['imgWNew'] ratioData = modelPickle['percentTestData'] image_suffix = modelPickle['image_suffix'] model_type = modelPickle['model_type'] model_num_inducing = modelPickle['num_inducing'] model_init_iterations = modelPickle['model_init_iterations'] model_num_iterations = modelPickle['model_num_iterations'] kernelString = modelPickle['kernelString'] Q = modelPickle['Q'] economy_save = True pose_index=[''] pose_selection = 0 # # Creates a SAMpy object mySAMpy = SAMDriver_interaction(False, imgH = imgH, imgW = imgW, imgHNew = imgHNew, imgWNew = imgWNew) # # Reading face data, preparation of data and training of the model mySAMpy.readData(dataPath, participantList, pose_index) minImages = mySAMpy.Y.shape[1] Ntr = int(minImages*ratioData/100) Ntest = minImages - Ntr allPersonsY = mySAMpy.Y; allPersonsL = mySAMpy.L; for i in range(len(participantList)): #print participantList[i] mySAMpy.Y = allPersonsY[:,:,i,None] mySAMpy.L = allPersonsL[:,:,i,None]
modelPickle = pickle.load(open(modelPath ,'rb')) imgH = modelPickle['imgH'] imgW = modelPickle['imgW'] imgHNew = modelPickle['imgHNew'] imgWNew = modelPickle['imgWNew'] ratioData = modelPickle['percentTestData'] image_suffix = modelPickle['image_suffix'] model_type = modelPickle['model_type'] model_num_inducing = modelPickle['num_inducing'] model_init_iterations = modelPickle['model_init_iterations'] model_num_iterations = modelPickle['model_num_iterations'] kernelString = modelPickle['kernelString'] # Creates a SAMpy object mySAMpy = SAMDriver_interaction(yarpRunning, imgH = imgH, imgW = imgW, imgHNew = imgHNew, imgWNew = imgWNew) # Location of face data root_data_dir=dataPath # Array of participants to be recognised participant_index=participantList # Poses used during the data collection pose_index=[''] # Pose selected for training pose_selection = 0 print 'modelPath: ' + modelPath
print dataPath
print modelPath
print participantList
print '-------------------'
# Creates and opens ports for interaction with speech module
if yarpRunning:
yarp.Network.init()
inputInteractionPort = yarp.BufferedPortBottle()
inputInteractionPort.open("/sam/face/interaction:i")
choice = yarp.Bottle()
# Creates a SAMpy object
mySAMpy = SAMDriver_interaction(True,
imgH=400,
imgW=400,
imgHNew=200,
imgWNew=200,
inputImagePort="/visionDriver/image:o")
# Specification of the experiment number
experiment_number = 1007 #42
# Location of face data
root_data_dir = dataPath
# Image format
image_suffix = ".ppm"
# Array of participants to be recognised
participant_index = participantList
# Poses used during the data collection
# Creates and opens ports for interaction with speech module
if yarpRunning:
yarp.Network.init()
inputInteractionPort = yarp.Port()
inputInteractionPort.open("/sam/face/rpc:i")
inputBottle = yarp.Bottle()
outputBottle = yarp.Bottle()
imgHNew = 200
imgWNew = 200
# Creates a SAMpy object
mySAMpy = SAMDriver_interaction(True,
imgH=400,
imgW=400,
imgHNew=imgHNew,
imgWNew=imgWNew,
inputImagePort="/CLM/imageSeg/out")
# Specification of the experiment number
experiment_number = 1007 #42
# Location of face data
root_data_dir = dataPath
# Image format
image_suffix = ".ppm"
# Array of participants to be recognised
participant_index = participantList
# Poses used during the data collection
