tensorflow动态加载文件

import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline
def get_image(image_path):  
    content = tf.read_file(image_path)
    tf_image = tf.image.decode_jpeg(content, channels=3)
    return tf_image
def plot_pic(batch_img_one_val, batch_img_two_val, label):
    fig = plt.figure(figsize=(6,2))
    plt.suptitle(label)
    ax1 = fig.add_subplot(1,2,1)
    #ax1.set_title(label)
    ax1.imshow(batch_img_one_val)
    ax1.axis('off')
    ax2 = fig.add_subplot(1,2,2)
    ax2.imshow(batch_img_two_val)
    ax2.axis('off')
    plt.show()
    

def slice_input_producer_one_sample():
    # 重置graph
    tf.reset_default_graph()
    batch_size = 1
    images_one_path_list = ['lda.png', 'snapshot.png','hua.jpeg']
    images_two_path_list = ['tuzi.jpg', 'test.png', 'hua.jpeg']
    label_list = ['lad_tuzi', 'snap_test', 'hua']
    
    # 构造数据queue
    train_input_queue = tf.train.slice_input_producer(
        [images_one_path_list, images_two_path_list,label_list], 
          capacity= 1*batch_size, shuffle=False)
    
    # queue输出数据
    img_one_queue = get_image(train_input_queue[0])
    img_two_queue = get_image(train_input_queue[1])
    label_queue = train_input_queue[2]

    sess = tf.Session()
    sess.run(tf.global_variables_initializer())
    # 启动queue线程
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    for i in range(10):
        batch_img_one_val, batch_img_two_val, label= sess.run(
            [img_one_queue, img_two_queue,label_queue])

        plot_pic(batch_img_one_val, batch_img_two_val, label)

    coord.request_stop()  
    coord.join(threads)  
    sess.close()
slice_input_producer_one_sample()

import matplotlib.pyplot as plt
def conver_image_size(img,hsize, wsize):
    img = tf.image.convert_image_dtype(img, dtype=tf.float32)  
    img = tf.image.resize_images(img, [hsize, wsize])
    return img


def slice_input_producer_demo():
    # 重置graph
    tf.reset_default_graph()
    # 获取图片系统路径，标签信息
    batch_size = 3
    hsize = 377
    wsize = 500
 
    images_one_path_list = ['lda.png', 'snapshot.png','hua.jpeg']
    images_two_path_list = ['tuzi.jpg', 'test.png', 'hua.jpeg']
    label_list = ['lad_tuzi', 'snap_test', 'hua']
    
    # 构造数据queue
    train_input_queue = tf.train.slice_input_producer(
        [images_one_path_list, images_two_path_list,label_list], 
          capacity= 3, shuffle=False)
    
    # queue输出数据
    img_one_queue = get_image(train_input_queue[0])
    img_two_queue = get_image(train_input_queue[1])
    label_queue = train_input_queue[2]
    # shuffle_batch 批量从queue批量读取数据

    img_one_queue = conver_image_size(img_one_queue, hsize, wsize)
    img_two_queue = conver_image_size(img_two_queue, hsize, wsize)
    
    batch_img_one, batch_img_two, batch_label = tf.train.shuffle_batch(
             [img_one_queue, img_two_queue, label_queue],
              batch_size=batch_size,
              capacity =  10 + 10* batch_size,
              min_after_dequeue = 10,
              num_threads=16)
    sess = tf.Session()
    sess.run(tf.global_variables_initializer())
    
    # 启动queue线程
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    
    for i in range(2):
        batch_img_one_val, batch_img_two_val, label= sess.run(
            [batch_img_one, batch_img_two,batch_label])
        print label
        fig = plt.figure(figsize=(4,6))
        for k in range(batch_size):
            ax1 = fig.add_subplot(batch_size,2,2*k+1)
            ax1.set_title(label[k])
            plt.imshow(batch_img_one_val[k])
            ax2 = fig.add_subplot(batch_size,2,2*k+2)
            ax2.set_title(label[k])
            ax2.imshow(batch_img_two_val[k])
        plt.show()
    coord.request_stop()  
    coord.join(threads)  
    sess.close()