import matplotlib.pyplot as plt;  
import tensorflow as tf;  
  
% matplotlib inline #将matplotlib绘制的图像直接输出到当前交互式的框架下

image_raw_data_jpg = tf.gfile.FastGFile(‘home/ubuntu-mm/TensorFlow/Learning/D-TestJupyter/image/Train/Pic.jpg‘, ‘r‘).read()  
  
with tf.Session() as sess:  
    img_data_jpg = tf.image.decode_jpeg(image_raw_data_jpg) #图像解码  
    img_data_jpg = tf.image.convert_image_dtype(img_data_jpg, dtype=tf.uint8) #改变图像数据的类型  
  
    plt.figure(1) #图像显示  
    plt.imshow(img_data_jpg.eval())
    print sess.run(img_data_jpg)

import tensorflow as tf;    
import matplotlib.pyplot as plt
  
path = ‘/home/ubuntu-mm/TesorFlow/Learning/D-TestJupyter/images/Train/Pic.jpg‘  
file_queue = tf.train.string_input_producer([path]) #创建输入队列  
image_reader = tf.WholeFileReader()  
_, image = image_reader.read(file_queue)  
image = tf.image.decode_jpeg(image)  
  
with tf.Session() as sess:  
    coord = tf.train.Coordinator() #协同启动的线程  
    threads = tf.train.start_queue_runners(sess=sess, coord=coord) #启动线程运行队列  
    print sess.run(image)  
    coord.request_stop() #停止所有的线程  
    coord.join(threads)  
    image_uint8 = tf.image.convert_image_dtype(image, dtype = tf.uint8)
    plt.imshow(image_uint8.eval())