tensorflow tfrecoder read write

 #  write in tfrecord

 import tensorflow as tf

 import os

 os.environ['TF_CPP_MIN_LOG_LEVEL'] = ''

 FLAGS = tf.app.flags.FLAGS

 tf.app.flags.DEFINE_string("tfrecords_dir", "./tfrecords/captcha.tfrecords", "验证码tfrecords文件")

 tf.app.flags.DEFINE_string("captcha_dir", "../data/Genpics/", "验证码图片路径")

 tf.app.flags.DEFINE_string("letter", "ABCDEFGHIJKLMNOPQRSTUVWXYZ", "验证码字符的种类")

 def dealwithlabel(label_str):

     # 构建字符索引 {0：'A', 1:'B'......}

     num_letter = dict(enumerate(list(FLAGS.letter)))

     # 键值对反转 {'A':0, 'B':1......}

     letter_num = dict(zip(num_letter.values(), num_letter.keys()))

     print(letter_num)

     # 构建标签的列表

     array = []

     # 给标签数据进行处理[[b"NZPP"]......]

     for string in label_str:

         letter_list = []# [1,2,3,4]

         # 修改编码，bytes --> string

         for letter in string.decode('utf-8'):

             letter_list.append(letter_num[letter])

         array.append(letter_list)

     # [[13, 25, 15, 15], [22, 10, 7, 10], [22, 15, 18, 9], [16, 6, 13, 10], [1, 0, 8, 17], [0, 9, 24, 14].....]

     print(array)

     # 将array转换成tensor类型

     label = tf.constant(array)

     return label

 def get_captcha_image():

     """

     获取验证码图片数据

     :param file_list: 路径+文件名列表

     :return: image

     """

     # 构造文件名

     filename = []

     for i in range(6000):

         string = str(i) + ".jpg"

         filename.append(string)

     # 构造路径+文件

     file_list = [os.path.join(FLAGS.captcha_dir, file) for file in filename]

     # 构造文件队列

     file_queue = tf.train.string_input_producer(file_list, shuffle=False)

     # 构造阅读器

     reader = tf.WholeFileReader()

     # 读取图片数据内容

     key, value = reader.read(file_queue)

     # 解码图片数据

     image = tf.image.decode_jpeg(value)

     image.set_shape([20, 80, 3])

     # 批处理数据 [6000, 20, 80, 3]

     image_batch = tf.train.batch([image], batch_size=6000, num_threads=1, capacity=6000)

     return image_batch

 def get_captcha_label():

     """

     读取验证码图片标签数据

     :return: label

     """

     file_queue = tf.train.string_input_producer(["../data/Genpics/labels.csv"], shuffle=False)

     reader = tf.TextLineReader()

     key, value = reader.read(file_queue)

     records = [[1], ["None"]]

     number, label = tf.decode_csv(value, record_defaults=records)

     # [["NZPP"], ["WKHK"], ["ASDY"]]

     label_batch = tf.train.batch([label], batch_size=6000, num_threads=1, capacity=6000)

     return label_batch

 def write_to_tfrecords(image_batch, label_batch):

     """

     将图片内容和标签写入到tfrecords文件当中

     :param image_batch: 特征值

     :param label_batch: 标签纸

     :return: None

     """

     # 转换类型

     label_batch = tf.cast(label_batch, tf.uint8)

     print(label_batch)

     # 建立TFRecords 存储器

     writer = tf.python_io.TFRecordWriter(FLAGS.tfrecords_dir)

     # 循环将每一个图片上的数据构造example协议块，序列化后写入

     for i in range(6000):

         # 取出第i个图片数据，转换相应类型,图片的特征值要转换成字符串形式

         image_string = image_batch[i].eval().tostring()

         # 标签值，转换成整型

         label_string = label_batch[i].eval().tostring()

         # 构造协议块

         example = tf.train.Example(features=tf.train.Features(feature={

             "image": tf.train.Feature(bytes_list=tf.train.BytesList(value=[image_string])),

             "label": tf.train.Feature(bytes_list=tf.train.BytesList(value=[label_string]))

         }))

         writer.write(example.SerializeToString())

     # 关闭文件

     writer.close()

     return None

 if __name__ == "__main__":

     # 获取验证码文件当中的图片

     image_batch = get_captcha_image()

     # 获取验证码文件当中的标签数据

     label = get_captcha_label()

     print(image_batch, label)

     with tf.Session() as sess:

         coord = tf.train.Coordinator()

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

         # 获取tensor里面的值

         label_str = sess.run(label)

         print(label_str)

         # 处理字符串标签到数字张量

         label_batch = dealwithlabel(label_str)

         print(label_batch)

         # 将图片数据和内容写入到tfrecords文件当中

         write_to_tfrecords(image_batch, label_batch)

         coord.request_stop()

         coord.join(threads)

 # read tfrecords

 def read_and_decode():

     """

     读取验证码数据API

     :return: image_batch, label_batch

     """

     # 1、构建文件队列

     file_queue = tf.train.string_input_producer([FLAGS.captcha_dir])

     # 2、构建阅读器，读取文件内容，默认一个样本

     reader = tf.TFRecordReader()

     # 读取内容

     key, value = reader.read(file_queue)

     # tfrecords格式example,需要解析

     features = tf.parse_single_example(value, features={

         "image": tf.FixedLenFeature([], tf.string),

         "label": tf.FixedLenFeature([], tf.string),

     })

     # 解码内容，字符串内容

     # 1、先解析图片的特征值

     image = tf.decode_raw(features["image"], tf.uint8)

     # 1、先解析图片的目标值

     label = tf.decode_raw(features["label"], tf.uint8)

     # print(image, label)

     # 改变形状

     image_reshape = tf.reshape(image, [20, 80, 3])

     label_reshape = tf.reshape(label, [4])

     print(image_reshape, label_reshape)

     # 进行批处理,每批次读取的样本数 100, 也就是每次训练时候的样本

     image_batch, label_btach = tf.train.batch([image_reshape, label_reshape], batch_size=FLAGS.batch_size, num_threads=1, capacity=FLAGS.batch_size)

     print(image_batch, label_btach)

     return image_batch, label_btach

# write flags

FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_string("tfrecords_dir", "./tfrecords/captcha.tfrecords", "验证码tfrecords文件")

tf.app.flags.DEFINE_string("captcha_dir", "../data/Genpics/", "验证码图片路径")

tf.app.flags.DEFINE_string("letter", "ABCDEFGHIJKLMNOPQRSTUVWXYZ", "验证码字符的种类")

# read flags

tf.app.flags.DEFINE_string("captcha_dir", "./tfrecords/captcha.tfrecords", "验证码数据的路径")

tf.app.flags.DEFINE_integer("batch_size", 100, "每批次训练的样本数")

tf.app.flags.DEFINE_integer("label_num", 4, "每个样本的目标值数量")

tf.app.flags.DEFINE_integer("letter_num", 26, "每个目标值取的字母的可能心个数")

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tensorflow tfrecoder read write

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