import tensorflow as tf
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import os
i = 0
j = 0
num_shards = 100#总共写入的文件个数
instances_per_shard = 2#每个文件中的数据个数
sess=tf.InteractiveSession()
cwd = "F:/寒假/google--data/新建文件夹/" #图片数据所在目录位置(读者自己去改就好了)
classes = {'daisy','rose'} #预先自己定义的类别，根据自己的需要修改


def _int64_feature(value):#生成整数型的属性
   return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))

def _bytes_feature(value):#生成字符串型的属性
   return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
   
for index, name in enumerate(classes):#枚举函数
  class_path = cwd + name + "/"#选取具体数据目录
  for img_name in os.listdir(class_path):#遍历文件列表
    img_path = class_path + img_name#图片路径
    img = Image.open(img_path)
    img = img.resize((299, 299)) #图像reshape大小设置，根据自己的需要修改
    img_raw = img.tobytes()
      
    example = tf.train.Example(features=tf.train.Features(feature={
          'label': _int64_feature(index),
          'img_raw': _bytes_feature(img_raw),
          'i': _int64_feature(i),
          'j': _int64_feature(j)
        }))
    filename = ("F:/寒假/google--data/data.tfrecords-%.5d-of-%.5d"%(i,num_shards))
    if j == instances_per_shard-1:
      i+=1
    j+=1
    if j == instances_per_shard:
      j=0
    writer = tf.python_io.TFRecordWriter(filename)
      
    writer.write(example.SerializeToString())#将一个example写入tfrecord文件
writer.close()