# Specification of model type and training parameters
model_type = 'mrd'
model_num_inducing = 30
model_num_iterations = 700
model_init_iterations = 2000
fname = modelPath + '/models/' + 'mActions_' + model_type + '_exp' + str(experiment_number) #+ '.pickle'
# Enable to save the model and visualise GP nearest neighbour matching
save_model=True
economy_save = True # ATTENTION!! This is still BETA!!
visualise_output=False
test_mode = False
# Reading face data, preparation of data and training of the model
mySAMpy.readData(root_data_dir, participant_index, pose_index)
(Yall, Lall, YtestAll, LtestAll) = mySAMpy.prepareData(model_type, Ntr, pose_selection, randSeed=experiment_number)
Lunique = numpy.unique(mySAMpy.L)
L_index = dict()
for i in range(len(Lunique)):
L_index[Lunique[i]] = numpy.where(mySAMpy.L == i)[0]
mm = []
for i in range(len(Lunique)):
print('# Considering label: ' + str(Lunique[i]))
cur = SAMDriver_interaction(True, imgH = 400, imgW = 400, imgHNew = 200, imgWNew = 200,inputImagePort="/CLM/imageSeg/out", openPorts=False)
##############
Y_cur = Yall[L_index[i],:].copy()
Ytest_cur = YtestAll[L_index[i],:].copy()
L_cur = Lall[L_index[i],:].copy()
# # 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]
(Yalli, Lalli, YtestAlli, LtestAlli) = mySAMpy.prepareData(model_type,
Ntr,
pose_selection,
randSeed=2)
if (i == 0):
Yall = Yalli.copy()
Lall = Lalli.copy()
YtestAll = YtestAlli.copy()
LtestAll = LtestAlli.copy()
else:
Yall = np.vstack([Yall, Yalli])
Lall = np.vstack([Lall, Lalli])
YtestAll = np.vstack([YtestAll, YtestAlli])
LtestAll = np.vstack([LtestAll, LtestAlli])
allPersonsY = None
alPersonsL = None
pose_selection = 0
# Specification of model type and training parameters
model_type = 'mrd'
model_num_inducing = 30
model_num_iterations = 150
model_init_iterations = 400
fname = modelPath + '/models/' + 'mActions_' + model_type + '_exp' + str(experiment_number) #+ '.pickle'
# Enable to save the model and visualise GP nearest neighbour matching
save_model=True
visualise_output=True
# Reading face data, preparation of data and training of the model
mySAMpy.readData(root_data_dir, participant_index, pose_index)
mySAMpy.prepareData(model_type, Ntr, pose_selection)
mySAMpy.training(model_num_inducing, model_num_iterations, model_init_iterations, fname, save_model)
while( not(yarp.Network.isConnected("/speechInteraction/behaviour:o","/sam/face/interaction:i")) ):
print "Waiting for connection with behaviour port..."
pass
# This is for visualising the mapping of the test face back to the internal memory
if visualise_output:
ax = mySAMpy.SAMObject.visualise()
visualiseInfo=dict()
visualiseInfo['ax']=ax
ytmp = mySAMpy.SAMObject.recall(0)
ytmp = numpy.reshape(ytmp,(mySAMpy.imgHeightNew,mySAMpy.imgWidthNew))
fig_nn = pb.figure()
# # 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] (Yalli, Lalli, YtestAlli, LtestAlli) = mySAMpy.prepareData(model_type, Ntr, pose_selection, randSeed=2) if(i==0): Yall = Yalli.copy(); Lall = Lalli.copy(); YtestAll = YtestAlli.copy() LtestAll = LtestAlli.copy() else: Yall = np.vstack([Yall,Yalli]) Lall = np.vstack([Lall,Lalli]) YtestAll = np.vstack([YtestAll,YtestAlli]) LtestAll = np.vstack([LtestAll, LtestAlli]) allPersonsY = None alPersonsL = None
# Reading face data, preparation of data and training of the model mySAMpy.readData(root_data_dir, participant_index, 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] (Yalli, Lalli, YtestAlli, LtestAlli) = mySAMpy.prepareData(model_type, Ntr, pose_selection, randSeed=experiment_number) if(i==0): Yall = Yalli.copy(); Lall = Lalli.copy(); YtestAll = YtestAlli.copy() LtestAll = LtestAlli.copy() else: Yall = np.vstack([Yall,Yalli]) Lall = np.vstack([Lall,Lalli]) YtestAll = np.vstack([YtestAll,YtestAlli]) LtestAll = np.vstack([LtestAll, LtestAlli]) allPersonsY = None alPersonsL = None mm = []
model_type = 'mrd'
model_num_inducing = 30
model_num_iterations = 150
model_init_iterations = 400
fname = modelPath + '/models/' + 'mActions_' + model_type + '_exp' + str(
experiment_number) #+ '.pickle'
# Enable to save the model and visualise GP nearest neighbour matching
save_model = True
economy_save = True # ATTENTION!! This is still BETA!!
visualise_output = True
# Reading face data, preparation of data and training of the model
mySAMpy.readData(root_data_dir, participant_index, pose_index)
mySAMpy.prepareData(model_type,
Ntr,
pose_selection,
randSeed=experiment_number)
mySAMpy.training(model_num_inducing, model_num_iterations,
model_init_iterations, fname, save_model, economy_save)
if yarpRunning:
while (not (yarp.Network.isConnected("/speechInteraction/behaviour:o",
"/sam/face/interaction:i"))):
print "Waiting for connection with behaviour port..."
pass
# This is for visualising the mapping of the test face back to the internal memory
if visualise_output:
ax = mySAMpy.SAMObject.visualise()
visualiseInfo = dict()
visualiseInfo['ax'] = ax
# Specification of model type and training parameters
model_type = 'mrd'
model_num_inducing = 30
model_num_iterations = 150
model_init_iterations = 400
fname = modelPath + '/models/' + 'mActions_' + model_type + '_exp' + str(experiment_number) #+ '.pickle'
# Enable to save the model and visualise GP nearest neighbour matching
save_model=True
economy_save = True # ATTENTION!! This is still BETA!!
visualise_output=True
# Reading face data, preparation of data and training of the model
mySAMpy.readData(root_data_dir, participant_index, pose_index)
mySAMpy.prepareData(model_type, Ntr, pose_selection, randSeed=experiment_number)
mySAMpy.training(model_num_inducing, model_num_iterations, model_init_iterations, fname, save_model, economy_save)
if yarpRunning:
while( not(yarp.Network.isConnected("/speechInteraction/behaviour:o","/sam/face/interaction:i")) ):
print "Waiting for connection with behaviour port..."
pass
# This is for visualising the mapping of the test face back to the internal memory
if visualise_output:
ax = mySAMpy.SAMObject.visualise()
visualiseInfo=dict()
visualiseInfo['ax']=ax
ytmp = mySAMpy.SAMObject.recall(0)