message Example { 
  Features features = 1; 
}; 
message Features { 
  map<string, Feature> feature = 1; 
}; 
message Feature { 
  oneof kind { 
  BytesList bytes_list = 1; 
  FloatList float_list = 2; 
  Int64List int64_list = 3; 
} 
}; 

from random import shuffle 
import numpy as np 
import glob 
import tensorflow as tf 
import cv2 
import sys 
import os 
 
# 因为我装的是CPU版本的，运行起来会有'warning'，解决方法入下，眼不见为净~ 
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' 
 
shuffle_data = True 
image_path = '/path/to/image/*.jpg' 
 
# 取得该路径下所有图片的路径，type（addrs）= list 
addrs = glob.glob(image_path) 
# 标签数据的获得具体情况具体分析，type（labels）= list 
labels = ... 
 
# 这里是打乱数据的顺序 
if shuffle_data: 
  c = list(zip(addrs, labels)) 
  shuffle(c) 
  addrs, labels = zip(*c) 
 
# 按需分割数据集 
train_addrs = addrs[0:int(0.7*len(addrs))] 
train_labels = labels[0:int(0.7*len(labels))] 
 
val_addrs = addrs[int(0.7*len(addrs)):int(0.9*len(addrs))] 
val_labels = labels[int(0.7*len(labels)):int(0.9*len(labels))] 
 
test_addrs = addrs[int(0.9*len(addrs)):] 
test_labels = labels[int(0.9*len(labels)):] 
 
# 上面不是获得了image的地址么，下面这个函数就是根据地址获取图片 
def load_image(addr): # A function to Load image 
  img = cv2.imread(addr) 
  img = cv2.resize(img, (224, 224), interpolation=cv2.INTER_CUBIC) 
  img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) 
  # 这里/255是为了将像素值归一化到[0，1] 
  img = img / 255. 
  img = img.astype(np.float32) 
  return img 
 
# 将数据转化成对应的属性 
def _int64_feature(value):  
  return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) 
 
 
def _bytes_feature(value): 
  return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) 
 
 
def _float_feature(value): 
  return tf.train.Feature(float_list=tf.train.FloatList(value=[value])) 
 
# 下面这段就开始把数据写入TFRecods文件 
 
train_filename = '/path/to/train.tfrecords' # 输出文件地址 
 
# 创建一个writer来写 TFRecords 文件 
writer = tf.python_io.TFRecordWriter(train_filename) 
 
for i in range(len(train_addrs)): 
  # 这是写入操作可视化处理 
  if not i % 1000: 
    print('Train data: {}/{}'.format(i, len(train_addrs))) 
    sys.stdout.flush() 
  # 加载图片 
  img = load_image(train_addrs[i]) 
 
  label = train_labels[i] 
 
  # 创建一个属性（feature） 
  feature = {'train/label': _int64_feature(label), 
        'train/image': _bytes_feature(tf.compat.as_bytes(img.tostring()))} 
 
  # 创建一个 example protocol buffer 
  example = tf.train.Example(features=tf.train.Features(feature=feature)) 
 
  # 将上面的example protocol buffer写入文件 
  writer.write(example.SerializeToString()) 
 
writer.close() 
sys.stdout.flush() 

import tensorflow as tf 
import numpy as np 
import matplotlib.pyplot as plt 
import os  
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' 
data_path = 'train.tfrecords' # tfrecords 文件的地址 
 
with tf.Session() as sess: 
  # 先定义feature，这里要和之前创建的时候保持一致 
  feature = { 
    'train/image': tf.FixedLenFeature([], tf.string), 
    'train/label': tf.FixedLenFeature([], tf.int64) 
  } 
  # 创建一个队列来维护输入文件列表 
  filename_queue = tf.train.string_input_producer([data_path], num_epochs=1) 
 
  # 定义一个 reader ，读取下一个 record 
  reader = tf.TFRecordReader() 
  _, serialized_example = reader.read(filename_queue) 
 
  # 解析读入的一个record 
  features = tf.parse_single_example(serialized_example, features=feature) 
 
  # 将字符串解析成图像对应的像素组 
  image = tf.decode_raw(features['train/image'], tf.float32) 
 
  # 将标签转化成int32 
  label = tf.cast(features['train/label'], tf.int32) 
 
  # 这里将图片还原成原来的维度 
  image = tf.reshape(image, [224, 224, 3]) 
 
  # 你还可以进行其他一些预处理.... 
 
  # 这里是创建顺序随机 batches(函数不懂的自行百度) 
  images, labels = tf.train.shuffle_batch([image, label], batch_size=10, capacity=30, min_after_dequeue=10) 
 
  # 初始化 
  init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) 
  sess.run(init_op) 
 
  # 启动多线程处理输入数据 
  coord = tf.train.Coordinator() 
  threads = tf.train.start_queue_runners(coord=coord) 
 
  .... 
 
  #关闭线程 
  coord.request_stop() 
  coord.join(threads) 
  sess.close()