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]
(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])
# # 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] (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
# Normalise test data similarly to training data
Ytestn_cur = Ytest_cur - Ymean_cur
Ytestn_cur /= Ystd_cur
cur.Ymean = Ymean_cur
cur.Ystd = Ystd_cur
# As above but for the labels
#Lmean_cur = L_cur.mean()
#Ln_cur = L_cur - Lmean_cur
#Lstd_cur = Ln_cur.std()
#Ln_cur /= Lstd_cur
#Ltestn_cur = Ltest_cur - Lmean_cur
#Ltestn_cur /= Lstd_cur
cur.X=None
cur.Y = {'Y':Yn_cur}
cur.Ytestn = {'Ytest':Ytestn_cur}
cur.Ltest = {'Ltest':Ltest_cur}
fname_cur = fname + '_L' + str(i)
cur.training(model_num_inducing, model_num_iterations, model_init_iterations, fname_cur, save_model, economy_save)
mm.append(cur)
ss = [];
sstest = [];
for i in range(len(Lunique)):
for j in range(len(Lunique)):
ss = mm[i].SAMObject.familiarity(mm[j].Y['Y'])
print('Familiarity of model ' + participantList[i] + ' given label: ' + participantList[j] + ' using training data is: ' + str(ss))
print("")
print("")
# Normalise test data similarly to training data
Ytestn_cur = Ytest_cur - Ymean_cur
Ytestn_cur /= Ystd_cur
cur.Ymean = Ymean_cur
cur.Ystd = Ystd_cur
# As above but for the labels
#Lmean_cur = L_cur.mean()
#Ln_cur = L_cur - Lmean_cur
#Lstd_cur = Ln_cur.std()
#Ln_cur /= Lstd_cur
#Ltestn_cur = Ltest_cur - Lmean_cur
#Ltestn_cur /= Lstd_cur
cur.X = None
cur.Y = {'Y': Yn_cur}
cur.Ytestn = {'Ytest': Ytestn_cur}
cur.Ltest = {'Ltest': Ltest_cur}
fname_cur = fname + '_L' + str(i)
cur.training(model_num_inducing, model_num_iterations,
model_init_iterations, fname_cur, save_model, economy_save)
mm.append(cur)
ss = []
sstest = []
for i in range(len(Lunique)):
for j in range(len(Lunique)):
ss = mm[i].SAMObject.familiarity(mm[j].Y['Y'])
print('Familiarity of model ' + participantList[i] + ' given label: ' +
participantList[j] + ' using training data is: ' + str(ss))
print("")
