import os
import tensorflow as tf 
from PIL import Image

cwd = os.getcwd()

'''
此处我加载的数据目录如下：
0 -- img1.jpg
   img2.jpg
   img3.jpg
   ...
1 -- img1.jpg
   img2.jpg
   ...
2 -- ...
 这里的0， 1， 2...就是类别，也就是下文中的classes
 classes是我根据自己数据类型定义的一个列表，大家可以根据自己的数据情况灵活运用
...
'''
writer = tf.python_io.TFRecordWriter("train.tfrecords")
for index, name in enumerate(classes):
  class_path = cwd + name + "/"
  for img_name in os.listdir(class_path):
    img_path = class_path + img_name
      img = Image.open(img_path)
      img = img.resize((224, 224))
    img_raw = img.tobytes()       #将图片转化为原生bytes
    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]))
    }))
    writer.write(example.SerializeToString()) #序列化为字符串
writer.close()

for serialized_example in tf.python_io.tf_record_iterator("train.tfrecords"):
  example = tf.train.Example()
  example.ParseFromString(serialized_example)

  image = example.features.feature['image'].bytes_list.value
  label = example.features.feature['label'].int64_list.value
  # 可以做一些预处理之类的
  print image, label

def read_and_decode(filename):
  #根据文件名生成一个队列
  filename_queue = tf.train.string_input_producer([filename])

  reader = tf.TFRecordReader()
  _, serialized_example = reader.read(filename_queue)  #返回文件名和文件
  features = tf.parse_single_example(serialized_example,
                    features={
                      'label': tf.FixedLenFeature([], tf.int64),
                      'img_raw' : tf.FixedLenFeature([], tf.string),
                    })

  img = tf.decode_raw(features['img_raw'], tf.uint8)
  img = tf.reshape(img, [224, 224, 3])
  img = tf.cast(img, tf.float32) * (1. / 255) - 0.5
  label = tf.cast(features['label'], tf.int32)

  return img, label

img, label = read_and_decode("train.tfrecords")

#使用shuffle_batch可以随机打乱输入
img_batch, label_batch = tf.train.shuffle_batch([img, label],
                        batch_size=30, capacity=2000,
                        min_after_dequeue=1000)
init = tf.initialize_all_variables()

with tf.Session() as sess:
  sess.run(init)
  threads = tf.train.start_queue_runners(sess=sess)
  for i in range(3):
    val, l= sess.run([img_batch, label_batch])
    #我们也可以根据需要对val， l进行处理
    #l = to_categorical(l, 12) 
    print(val.shape, l)