学习TensorFlow，生成tensorflow输入输出的图像格式

TensorFLow能够识别的图像文件，可以通过numpy，使用tf.Variable或者tf.placeholder加载进tensorflow；也可以通过自带函数（tf.read）读取，当图像文件过多时，一般使用pipeline通过队列的方法进行读取。下面我们介绍两种生成tensorflow的图像格式的方法，供给tensorflow的graph的输入与输出。

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import cv2
import numpy as np
import h5py

height = 460
width = 345

with h5py.File('make3d_dataset_f460.mat','r') as f:
	images = f['images'][:]
 	
image_num = len(images)

data = np.zeros((image_num, height, width, 3), np.uint8)
data = images.transpose((0,3,2,1))

先生成图像文件的路径：ls *.jpg> list.txt

import cv2
import numpy as np

image_path = './'
list_file  = 'list.txt'
height = 48
width = 48

image_name_list = [] # read image
with open(image_path + list_file) as fid:
	image_name_list = [x.