【验证码生成及破解】第一部分：验证码生成及验证码图片生成TFRecord

本部分是生成简单的4位数字验证码，及将验证码生成TFRecord.

1.生成数字验证码

#验证码生成
from captcha.image import ImageCaptcha   #pip install captcha
import numpy as np
from PIL import Image
import random
import sys

number = ['0','1','2','3','4','5','6','7','8','9']
#alphabet = ['a','b','c','d','e','f','g','h','i','j','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']
#ALPHABET = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']
def random_captcha_text(char_set=number,captcha_size=4):
    #验证码列表
    captcha_text=[]
    for i in range(captcha_size):
        #随机选择
        c = random.choice(char_set)
        #加入验证码列表
        captcha_text.append(c)
    return captcha_text

#生成字符对应的验证码
def gen_captcha_text_and_image():
    image = ImageCaptcha()  #默认高度60，宽度160
    captcha_text = random_captcha_text()
    captcha_string = ''.join(captcha_text)
    #生成验证码
    captcha = image.generate(captcha_string)
    image.write(captcha_string,'captcha/images/'+ captcha_string + '.jpg') #写到文件

#数量少于10000，因为重名
num = 10000
if __name__=='__main__':
    for i in range(num):
        gen_captcha_text_and_image()
        sys.stdout.write('\r>> Creating image %d/%d ' %(i+1,num))
        sys.stdout.flush()
    sys.stdout.write('\n')
    sys.stdout.flush()
    print('生成完毕')

实际生成了6386张不重复的验证码：

2.将图片转化为TFRecord

import tensorflow as tf
import os
import random
import math
import sys
from PIL import Image
import numpy as np

#  验证集数量
_NUM_TEST = 500
#s随机种子
_RANDOM_SEED = 0
#数据集路径
DATASET_DIR = "D:/Jupyter_path/TFstudy2019/captcha/images/"

#tfrecord文件存放路径 
TFRECORD_DIR = "D:/Jupyter_path/TFstudy2019/captcha"


#判断tfrecord文件是否存在
def _dataset_exists(dataset_dir):
    for split_name in ["train","test"]:
        output_filename = os.path.join(dataset_dir,split_name+'.tfrecords')
        if not tf.gfile.Exists(output_filename):
            return False
    return True

#获取所有文件及分类
def _get_filenames_and_classes(dataset_dir):  
    photo_filenames = []
    #循环每个分类的文件夹
    for filename in os.listdir(dataset_dir):
        #
        path = os.path.join(dataset_dir,filename) #此filename是图片名字
        #把图片加入图片列表
        photo_filenames.append(path)         
    return photo_filenames           #photo_filenames中保存的是图片的绝对路径

def int64_feature(values):
    if not isinstance(values,(tuple,list)):
        values = [values]
    return tf.train.Feature(int64_list=tf.train.Int64List(value=values))

def bytes_feature(values):
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[values]))
tf.train.Feature

def image_to_tfexample(image_data,label0,label1,label2,label3):
    #abatract base class for protocol messages
    return tf.train.Example(features=tf.train.Features(feature={
        'image':bytes_feature(image_data),
        'label0':int64_feature(label0),
        'label1':int64_feature(label1),
        'label2':int64_feature(label2),
        'label3':int64_feature(label3),
    }))


#把数据转换为TFRecord格式
def _convert_dataset(split_name,filenames,dataset_dir):
    assert split_name in ['train','test']
    with tf.Session() as sess:
        #定义tfrecords文件的路径+名字
        output_filename =  os.path.join(TFRECORD_DIR,split_name+'.tfrecords')
        with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
            for i,filename in enumerate(filenames):
                try:   #将损坏的图片跳过
                    sys.stdout.write('\r>> Converting image %d%d' % (i+1,len(filenames)))
                    sys.stdout.flush()
                    #读取图片
                    image_data = Image.open(filename)
                    #根据模型的结构resize
                    image_data = image_data.resize((224,224))
                    #灰度化
                    image_data = np.array(image_data.convert('L'))
                    #将图片转化为bytes
                    image_data = image_data.tobytes()
                    #获取label
                    #print('\n filename:'+filename+'\n')
                    labels = filename.split('/')[-1][0:4]
                    #print('\n labels:'+labels+'\n')
                    num_labels = []
                    for j in range(4):
                        num_labels.append(int(labels[j]))
                    #生成protocol数据模型
                    example = image_to_tfexample(image_data,num_labels[0],num_labels[1],num_labels[2],num_labels[3])
                    tfrecord_writer.write(example.SerializeToString())
                except IOError as e:
                    print("could not read:",filenames[i])
                    print("Error:",e)
                    print("skip it\n")
    sys.stdout.write('\n')
    sys.stdout.flush()

if __name__ == '__main__':
    #判断tfrecord文件是否存在
    if _dataset_exists(DATASET_DIR):
        print('tfrecord文件已存在')
    else:
        #获得所有图片及分类
        photo_filenames = _get_filenames_and_classes(DATASET_DIR)
        
        #把数据切分成训练集和数据集
        random.seed(_RANDOM_SEED)
        random.shuffle(photo_filenames)
        training_filenames = photo_filenames[_NUM_TEST:]
        testing_filenames = photo_filenames[:_NUM_TEST]
        
        #数据转化
        _convert_dataset('train',training_filenames,DATASET_DIR)
        _convert_dataset('test',testing_filenames,DATASET_DIR)
        
        print('生成tfrecord文件')

程序运行完毕后生成了训练集和测试集的tfrecord: