Face recognition train and test data preparation

face_recognition_train_test_data.py

# Prepare Train Data
x_train=[]
y_train=[]
person_rep=dict()
person_folders=os.listdir(path+'/Images_crop/')
for i,person in enumerate(person_folders):
  person_rep[i]=person
  image_names=os.listdir('Images_crop/'+person+'/')
  for image_name in image_names:
    img=load_img(path+'/Images_crop/'+person+'/'+image_name,target_size=(224,224))
    img=img_to_array(img)
    img=np.expand_dims(img,axis=0)
    img=preprocess_input(img)
    img_encode=vgg_face(img)
    x_train.append(np.squeeze(K.eval(img_encode)).tolist())
    y_train.append(i)
    
# Prepare Test Data
    x_test=[]
    y_test=[]
    person_folders=os.listdir(path+'/Test_Images_crop/')
    test_image_names=os.listdir('Test_Images_crop/'+person+'/')
    for image_name in test_image_names:
	img=load_img(path+'/Test_Images_crop/'+person+'/'+image_name,target_size=(224,224))
    	img=img_to_array(img)
    	img=np.expand_dims(img,axis=0)
    	img=preprocess_input(img)
   	img_encode=vgg_face(img)
	x_test.append(np.squeeze(K.eval(img_encode)).tolist())
    	y_test.append(i)