def load_and_f(path,files):
#Mapping for lbp
mapping = IP.getmapping(8)
for k in range(len(files)):
#Load file
file = os.path.join(path,files[k])
try:
file = sio.loadmat(file)
Mz = file['Mz']
sz = file['sz']
except NotImplementedError:
file = h5py.File(file)
Mz = file['Mz'][()]
sz = file['sz'][()]
#images
#Combine mean and sd images
image = Mz+sz
#Grayscale normalization
image = IP.localstandard(image,23,5,5,1)
#image = image[20:-20,20:-20]
#Feature extraction
dict = {'R':9,'r':3,'wc':5,'wr':(5,5)}
f1,f2,f3,f4 = IP.MRELBP(image,8,dict['R'],dict['r'],dict['wc'],dict['wr'])
#Normalization and mapping of the features f2(large neighbourhood lbp) and f4(radial lbp)
#f1 = 1/np.linalg.norm(f1)*f1
f2 = IP.maplbp(f2,mapping)
#f2 = 1/np.linalg.norm(f2)*f2
f3 = IP.maplbp(f3,mapping)
#f3 = 1/np.linalg.norm(f3)*f3
f4 = IP.maplbp(f4,mapping)
#f4 = 1/np.linalg.norm(f4)*f4
#Concatenate features
f = np.concatenate((f1.T,f2.T,f3.T,f4.T),axis=0)
try:
features = np.concatenate((features,f),axis=1)
except NameError:
features = f
return features
