import tensorflow as tf

from PIL import Image

import os

file_path='C:/Users/1/Desktop/123/'

file_name=os.listdir(file_path)

writer=tf.python_io.TFRecordWriter('C:/Users/1/Desktop/fuck.tfrecords')

for name in file_name:

    img_path=file_path+name

    img=Image.open(img_path)

    img=img.resize((10,10))

    img_raw=img.tobytes()

    print(index)

    example=tf.train.Example(features=tf.train.Features(feature={'label':tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),'img_raw':tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))}))

    imgg=example.SerializeToString()

    writer.write(imgg)

writer.close()

import scipy.misc as misc

import matplotlib.pyplot as plt

filename=tf.train.match_filenames_once('C:/Users/1/Desktop/fuck.*frecords')

files=tf.train.string_input_producer(filename,shuffle=False)

reader=tf.TFRecordReader()

_,seri=reader.read(files)

feature=tf.parse_single_example(seri,features={'label':tf.FixedLenFeature([],tf.int64),'img_raw':tf.FixedLenFeature([],tf.string)})

img=feature['img_raw']

img=tf.decode_raw(img,tf.uint8)

img=tf.reshape(img,[256,256,3])

img=tf.cast(img,tf.float32)

with tf.Session() as sess:

    sess.run(tf.global_variables_initializer())

    sess.run(tf.local_variables_initializer())

    coord=tf.train.Coordinator()

    threads=tf.train.start_queue_runners(sess=sess,coord=coord)

    img=sess.run(img)

    plt.imshow(img)

    plt.show()           #plot.show(）展示的是黑白图

    misc.imsave('C:/Users/1/Desktop/1111.jpg', img)     #使用scipy.misc.imsave来保存np.array数组格式的图片，保存好的图片为彩色图片

    coord.request_stop()

    coord.join(threads)