Creating HDF5 format for image segmentation task - python-2.7

Question

I started writing a python code for creating HDF5 for image segmentation tasks. I used the code in this link and the link provided by Shai. my images are one channel and in .mat format. I have written the following code, I only want to check with experts whether this code is correct or not. Could experts please have a look? Thanks
import os, h5py
import caffe
import numpy as np
import scipy
import scipy.io as sio
from array import array
import cv2
import matplotlib.pyplot as plt
caffe_root='/home/ss/caffe/'
import sys
sys.path.insert(0,caffe_root+'python')
def img_to_hdf5(paths_src_file,paths_lbl_file,path_dst,msg):
"""
paths_src_file : path to the image paths in a txt file
paths_lbl_file : path to the image paths in a txt file
path_dst = path to the hdf5 file
"""
print(msg)
arrays = {}
SIZE=256 #fixed size of all images
#read the lines of img and lbl path from text file and save into paths_src and paths_lbl
paths_src = []
with open(paths_src_file) as f:
for line in f.readlines():
line = line.strip('\n')
paths_src.append(line)
paths_lbl=[]
with open(paths_lbl_file) as f:
for line in f.readlines():
line=line.strip('\n')
paths_lbl.append(line)
data = np.zeros( (len(paths_src), 1, SIZE, SIZE), dtype='f4' ) #1 channel grayscale image
label = np.zeros( (len(paths_lbl), 1, SIZE, SIZE), dtype='f4' ) #1 channel label image
for in_idx, in_ in enumerate(paths_src):
print in_idx,in_
f=h5py.File(in_,'r')
mat=f['image'].value
im=np.array(mat,dtype=np.float32)
#im = cv2.cvtColor(im,cv2.COLOR_GRAY2RGB)
#im = im[:,:,::-1] #switch from RGB to BGR
im = im.reshape(im.shape[0], im.shape[1], 1)
im = im.transpose((2,0,1)) # convert to CxHxW
data[in_idx]=im
for in_idx, in_ in enumerate(paths_lbl):
print in_idx,in_
f=h5py.File(in_,'r')
mat=f['image'].value
im=np.array(mat,dtype=np.float32)
#im = cv2.cvtColor(im,cv2.COLOR_GRAY2RGB)
#im = im[:,:,::-1] #switch from RGB to BGR
im = im.reshape(im.shape[0], im.shape[1], 1)
im = im.transpose((2,0,1)) # convert to CxHxW
label[in_idx]=im
h5_train = os.path.join(path_dst, 'train_data.h5')
with h5py.File(h5_train,'w') as H:
H.create_dataset( 'data', data=data ) # note the name X given to the dataset!
H.create_dataset( 'label', data=label ) # note the name y given to the dataset!
text_train = os.path.join(path_dst, 'train-path.txt')
with open(text_train,'w') as L:
L.write(h5_train) # list all h5 files you are going to use
train_img_paths = './train_img.txt' #text file of paths to images
train_label_paths = './train_label.txt' #text file of paths to label images (ground truth)
train_img_hdf5 = '/home/ss/workspace/create_hdf5/' # Your path to h5 file
st='Creating Training Data HDF5 File .....'
img_to_hdf5(train_img_paths, train_label_paths,train_img_hdf5,st)
print('DONE...')