1 # write in tfrecord
2 import tensorflow as tf
3 import os
4 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
5
6
7 FLAGS = tf.app.flags.FLAGS
8 tf.app.flags.DEFINE_string("tfrecords_dir", "./tfrecords/captcha.tfrecords", "验证码tfrecords文件")
9 tf.app.flags.DEFINE_string("captcha_dir", "../data/Genpics/", "验证码图片路径")
10 tf.app.flags.DEFINE_string("letter", "ABCDEFGHIJKLMNOPQRSTUVWXYZ", "验证码字符的种类")
11
12
13 def dealwithlabel(label_str):
14
15 # 构建字符索引 {0：'A', 1:'B'......}
16 num_letter = dict(enumerate(list(FLAGS.letter)))
17
18 # 键值对反转 {'A':0, 'B':1......}
19 letter_num = dict(zip(num_letter.values(), num_letter.keys()))
20
21 print(letter_num)
22
23 # 构建标签的列表
24 array = []
25
26 # 给标签数据进行处理[[b"NZPP"]......]
27 for string in label_str:
28
29 letter_list = []# [1,2,3,4]
30
31 # 修改编码，bytes --> string
32 for letter in string.decode('utf-8'):
33 letter_list.append(letter_num[letter])
34
35 array.append(letter_list)
36
37 # [[13, 25, 15, 15], [22, 10, 7, 10], [22, 15, 18, 9], [16, 6, 13, 10], [1, 0, 8, 17], [0, 9, 24, 14].....]
38 print(array)
39
40 # 将array转换成tensor类型
41 label = tf.constant(array)
42
43 return label
44
45
46 def get_captcha_image():
47 """
48 获取验证码图片数据
49 :param file_list: 路径+文件名列表
50 :return: image
51 """
52 # 构造文件名
53 filename = []
54
55 for i in range(6000):
56 string = str(i) + ".jpg"
57 filename.append(string)
58
59 # 构造路径+文件
60 file_list = [os.path.join(FLAGS.captcha_dir, file) for file in filename]
61
62 # 构造文件队列
63 file_queue = tf.train.string_input_producer(file_list, shuffle=False)
64
65 # 构造阅读器
66 reader = tf.WholeFileReader()
67
68 # 读取图片数据内容
69 key, value = reader.read(file_queue)
70
71 # 解码图片数据
72 image = tf.image.decode_jpeg(value)
73
74 image.set_shape([20, 80, 3])
75
76 # 批处理数据 [6000, 20, 80, 3]
77 image_batch = tf.train.batch([image], batch_size=6000, num_threads=1, capacity=6000)
78
79 return image_batch
80
81
82 def get_captcha_label():
83 """
84 读取验证码图片标签数据
85 :return: label
86 """
87 file_queue = tf.train.string_input_producer(["../data/Genpics/labels.csv"], shuffle=False)
88
89 reader = tf.TextLineReader()
90
91 key, value = reader.read(file_queue)
92
93 records = [[1], ["None"]]
94
95 number, label = tf.decode_csv(value, record_defaults=records)
96
97 # [["NZPP"], ["WKHK"], ["ASDY"]]
98 label_batch = tf.train.batch([label], batch_size=6000, num_threads=1, capacity=6000)
99
100 return label_batch
101
102
103 def write_to_tfrecords(image_batch, label_batch):
104 """
105 将图片内容和标签写入到tfrecords文件当中
106 :param image_batch: 特征值
107 :param label_batch: 标签纸
108 :return: None
109 """
110 # 转换类型
111 label_batch = tf.cast(label_batch, tf.uint8)
112
113 print(label_batch)
114
115 # 建立TFRecords 存储器
116 writer = tf.python_io.TFRecordWriter(FLAGS.tfrecords_dir)
117
118 # 循环将每一个图片上的数据构造example协议块，序列化后写入
119 for i in range(6000):
120 # 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
121 image_string = image_batch[i].eval().tostring()
122
123 # 标签值，转换成整型
124 label_string = label_batch[i].eval().tostring()
125
126 # 构造协议块
127 example = tf.train.Example(features=tf.train.Features(feature={
128 "image": tf.train.Feature(bytes_list=tf.train.BytesList(value=[image_string])),
129 "label": tf.train.Feature(bytes_list=tf.train.BytesList(value=[label_string]))
130 }))
131
132 writer.write(example.SerializeToString())
133
134 # 关闭文件
135 writer.close()
136
137 return None
138
139
140 if __name__ == "__main__":
141
142 # 获取验证码文件当中的图片
143 image_batch = get_captcha_image()
144
145 # 获取验证码文件当中的标签数据
146 label = get_captcha_label()
147
148 print(image_batch, label)
149
150 with tf.Session() as sess:
151
152 coord = tf.train.Coordinator()
153
154 threads = tf.train.start_queue_runners(sess=sess, coord=coord)
155
156 # 获取tensor里面的值
157 label_str = sess.run(label)
158
159 print(label_str)
160
161 # 处理字符串标签到数字张量
162 label_batch = dealwithlabel(label_str)
163
164 print(label_batch)
165
166 # 将图片数据和内容写入到tfrecords文件当中
167 write_to_tfrecords(image_batch, label_batch)
168
169 coord.request_stop()
170
171 coord.join(threads)
1 # read tfrecords
2 def read_and_decode():
3 """
4 读取验证码数据API
5 :return: image_batch, label_batch
6 """
7 # 1、构建文件队列
8 file_queue = tf.train.string_input_producer([FLAGS.captcha_dir])
9
10 # 2、构建阅读器，读取文件内容，默认一个样本
11 reader = tf.TFRecordReader()
12
13 # 读取内容
14 key, value = reader.read(file_queue)
15
16 # tfrecords格式example,需要解析
17 features = tf.parse_single_example(value, features={
18 "image": tf.FixedLenFeature([], tf.string),
19 "label": tf.FixedLenFeature([], tf.string),
20 })
21
22 # 解码内容，字符串内容
23 # 1、先解析图片的特征值
24 image = tf.decode_raw(features["image"], tf.uint8)
25 # 1、先解析图片的目标值
26 label = tf.decode_raw(features["label"], tf.uint8)
27
28 # print(image, label)
29
30 # 改变形状
31 image_reshape = tf.reshape(image, [20, 80, 3])
32
33 label_reshape = tf.reshape(label, [4])
34
35 print(image_reshape, label_reshape)
36
37 # 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
38 image_batch, label_btach = tf.train.batch([image_reshape, label_reshape], batch_size=FLAGS.batch_size, num_threads=1, capacity=FLAGS.batch_size)
39
40 print(image_batch, label_btach)
41 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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