Tensorflow简单验证码识别应用


Posted in Python onMay 25, 2017

简单的Tensorflow验证码识别应用,供大家参考,具体内容如下

1.Tensorflow的安装方式简单,在此就不赘述了.

2.训练集训练集以及测试及如下(纯手工打造,所以数量不多):

Tensorflow简单验证码识别应用

Tensorflow简单验证码识别应用

3.实现代码部分(参考了网上的一些实现来完成的)

main.py(主要的神经网络代码)

from gen_check_code import gen_captcha_text_and_image_new,gen_captcha_text_and_image
from gen_check_code import number
from test_check_code import get_test_captcha_text_and_image
import numpy as np
import tensorflow as tf

text, image = gen_captcha_text_and_image_new()
print("验证码图像channel:", image.shape) # (60, 160, 3) 
# 图像大小 
IMAGE_HEIGHT = image.shape[0]
IMAGE_WIDTH = image.shape[1]
image_shape = image.shape
MAX_CAPTCHA = len(text)
print("验证码文本最长字符数", MAX_CAPTCHA) # 验证码最长4字符; 我全部固定为4,可以不固定. 如果验证码长度小于4,用'_'补齐


# 把彩色图像转为灰度图像(色彩对识别验证码没有什么用)
# 度化是将三分量转化成一样数值的过程
def convert2gray(img):
 if len(img.shape) > 2:
  gray = np.mean(img, -1)
  # 上面的转法较快,正规转法如下 
  # r, g, b = img[:,:,0], img[:,:,1], img[:,:,2] 
  # gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
  # int gray = (int) (0.3 * r + 0.59 * g + 0.11 * b);
  return gray
 else:
  return img


""" 
cnn在图像大小是2的倍数时性能最高, 如果你用的图像大小不是2的倍数,可以在图像边缘补无用像素。 
np.pad(image,((2,3),(2,2)), 'constant', constant_values=(255,)) # 在图像上补2行,下补3行,左补2行,右补2行 
"""


char_set = number # 如果验证码长度小于4, '_'用来补齐
CHAR_SET_LEN = len(char_set)

# 文本转向量
def text2vec(text):
 text_len = len(text)
 if text_len > MAX_CAPTCHA:
  raise ValueError('验证码最长4个字符')

 vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)

 def char2pos(c):
  try:
   k = ord(c)-ord('0')
  except:
   raise ValueError('No Map')
  return k

 for i, c in enumerate(text):
  idx = i * CHAR_SET_LEN + char2pos(c)
  vector[idx] = 1
 return vector


# 向量转回文本
def vec2text(vec):
 char_pos = vec.nonzero()[0]
 text = []
 for i, c in enumerate(char_pos):
  char_at_pos = i # c/63
  char_idx = c % CHAR_SET_LEN
  if char_idx < 10:
   char_code = char_idx + ord('0')
  elif char_idx < 36:
   char_code = char_idx - 10 + ord('A')
  elif char_idx < 62:
   char_code = char_idx - 36 + ord('a')
  elif char_idx == 62:
   char_code = ord('_')
  else:
   raise ValueError('error')
  text.append(chr(char_code))
 return "".join(text)


# 生成一个训练batch
def get_next_batch(batch_size=128):
 batch_x = np.zeros([batch_size, IMAGE_HEIGHT * IMAGE_WIDTH])
 batch_y = np.zeros([batch_size, MAX_CAPTCHA * CHAR_SET_LEN])

 # 有时生成图像大小不是(60, 160, 3) 
 def wrap_gen_captcha_text_and_image():
  while True:
   text, image = gen_captcha_text_and_image_new()

   if image.shape == image_shape:
    return text, image

 for i in range(batch_size):
  text, image = wrap_gen_captcha_text_and_image()
  image = convert2gray(image)


  batch_x[i, :] = image.flatten() / 255 # (image.flatten()-128)/128 mean为0
  batch_y[i, :] = text2vec(text)

 return batch_x, batch_y


####################################################################

X = tf.placeholder(tf.float32, [None, IMAGE_HEIGHT * IMAGE_WIDTH])
Y = tf.placeholder(tf.float32, [None, MAX_CAPTCHA * CHAR_SET_LEN])
keep_prob = tf.placeholder(tf.float32) # dropout


# 定义CNN
def crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1):
 x = tf.reshape(X, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1])

 # w_c1_alpha = np.sqrt(2.0/(IMAGE_HEIGHT*IMAGE_WIDTH)) #
 # w_c2_alpha = np.sqrt(2.0/(3*3*32))
 # w_c3_alpha = np.sqrt(2.0/(3*3*64))
 # w_d1_alpha = np.sqrt(2.0/(8*32*64))
 # out_alpha = np.sqrt(2.0/1024)

 # 定义三层的卷积神经网络

 # 定义第一层的卷积神经网络
 # 定义第一层权重
 w_c1 = tf.Variable(w_alpha * tf.random_normal([3, 3, 1, 32]))
 # 定义第一层的偏置
 b_c1 = tf.Variable(b_alpha * tf.random_normal([32]))
 # 定义第一层的激励函数
 conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1))
 # conv1 为输入 ksize 表示使用2*2池化,即将2*2的色块转化成1*1的色块
 conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
 # dropout防止过拟合。
 conv1 = tf.nn.dropout(conv1, keep_prob)

 # 定义第二层的卷积神经网络
 w_c2 = tf.Variable(w_alpha * tf.random_normal([3, 3, 32, 64]))
 b_c2 = tf.Variable(b_alpha * tf.random_normal([64]))
 conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, w_c2, strides=[1, 1, 1, 1], padding='SAME'), b_c2))
 conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
 conv2 = tf.nn.dropout(conv2, keep_prob)

 # 定义第三层的卷积神经网络
 w_c3 = tf.Variable(w_alpha * tf.random_normal([3, 3, 64, 64]))
 b_c3 = tf.Variable(b_alpha * tf.random_normal([64]))
 conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, w_c3, strides=[1, 1, 1, 1], padding='SAME'), b_c3))
 conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
 conv3 = tf.nn.dropout(conv3, keep_prob)

 # Fully connected layer
 # 随机生成权重
 w_d = tf.Variable(w_alpha * tf.random_normal([1536, 1024]))
 # 随机生成偏置
 b_d = tf.Variable(b_alpha * tf.random_normal([1024]))
 dense = tf.reshape(conv3, [-1, w_d.get_shape().as_list()[0]])
 dense = tf.nn.relu(tf.add(tf.matmul(dense, w_d), b_d))
 dense = tf.nn.dropout(dense, keep_prob)

 w_out = tf.Variable(w_alpha * tf.random_normal([1024, MAX_CAPTCHA * CHAR_SET_LEN]))
 b_out = tf.Variable(b_alpha * tf.random_normal([MAX_CAPTCHA * CHAR_SET_LEN]))
 out = tf.add(tf.matmul(dense, w_out), b_out)
 # out = tf.nn.softmax(out)
 return out


# 训练
def train_crack_captcha_cnn():
 # X = tf.placeholder(tf.float32, [None, IMAGE_HEIGHT * IMAGE_WIDTH])
 # Y = tf.placeholder(tf.float32, [None, MAX_CAPTCHA * CHAR_SET_LEN])
 # keep_prob = tf.placeholder(tf.float32) # dropout
 output = crack_captcha_cnn()
 # loss 
 # loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(output, Y))
 loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(output, Y))
 # 最后一层用来分类的softmax和sigmoid有什么不同?
 # optimizer 为了加快训练 learning_rate应该开始大,然后慢慢衰 
 optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)

 predict = tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN])
 max_idx_p = tf.argmax(predict, 2)
 max_idx_l = tf.argmax(tf.reshape(Y, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
 correct_pred = tf.equal(max_idx_p, max_idx_l)
 accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

 saver = tf.train.Saver()
 with tf.Session() as sess:
   sess.run(tf.global_variables_initializer())

   step = 0
   while True:
    batch_x, batch_y = get_next_batch(64)
    _, loss_ = sess.run([optimizer, loss], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75})
    print(step, loss_)

    # 每100 step计算一次准确率
    if step % 100 == 0:
     batch_x_test, batch_y_test = get_next_batch(100)
     acc = sess.run(accuracy, feed_dict={X: batch_x_test, Y: batch_y_test, keep_prob: 1.})
     print(step, acc)
     # 如果准确率大于50%,保存模型,完成训练
     if acc > 0.99:
      saver.save(sess, "./crack_capcha.model", global_step=step)
      break
    step += 1

## 训练(如果要训练则去掉下面一行的注释)
train_crack_captcha_cnn()


def crack_captcha():
 output = crack_captcha_cnn()

 saver = tf.train.Saver()
 with tf.Session() as sess:
  saver.restore(sess, tf.train.latest_checkpoint('.'))

  predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
  count = 0
  # 因为测试集共40个...写的很草率
  for i in range(40):
   text, image = get_test_captcha_text_and_image(i)
   image = convert2gray(image)
   captcha_image = image.flatten() / 255
   text_list = sess.run(predict, feed_dict={X: [captcha_image], keep_prob: 1})
   predict_text = text_list[0].tolist()
   predict_text = str(predict_text)
   predict_text = predict_text.replace("[", "").replace("]", "").replace(",", "").replace(" ","")
   if text == predict_text:
    count += 1
    check_result = ",预测结果正确"
   else:
    check_result = ",预测结果不正确"
    print("正确: {} 预测: {}".format(text, predict_text) + check_result)

  print("正确率:" + str(count) + "/40")
# 测试(如果要测试则去掉下面一行的注释)
# crack_captcha()

gen_check_code.py(得到训练集输入,需要注意修改root_dir为训练集的输入文件夹,下同)

from captcha.image import ImageCaptcha # pip install captcha
import numpy as np
from PIL import Image
import random
# import matplotlib.pyplot as plt
import os
from random import choice

# 验证码中的字符, 就不用汉字了
number = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
# alphabet = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', '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', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U',
#    'V', 'W', 'X', 'Y', 'Z']

root_dir = "d:\\train"

# 验证码一般都无视大小写;验证码长度4个字符
def random_captcha_text(char_set=number, captcha_size=4):
 captcha_text = []
 for i in range(captcha_size):
  c = random.choice(char_set)
  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()

 captcha_text = random_captcha_text()
 captcha_text = ''.join(captcha_text)

 captcha = image.generate(captcha_text)
 # image.write(captcha_text, captcha_text + '.jpg') # 写到文件

 captcha_image = Image.open(captcha)
 captcha_image = np.array(captcha_image)
 return captcha_text, captcha_image


def gen_list():
 img_list = []
 for parent, dirnames, filenames in os.walk(root_dir): # 三个参数:分别返回1.父目录 2.所有文件夹名字(不含路径) 3.所有文件名字
  for filename in filenames: # 输出文件信息
   img_list.append(filename.replace(".gif",""))
   # print("parent is:" + parent)
   # print("filename is:" + filename)
   # print("the full name of the file is:" + os.path.join(parent, filename)) # 输出文件路径信息
 return img_list
img_list = gen_list()
def gen_captcha_text_and_image_new():
 img = choice(img_list)
 captcha_image = Image.open(root_dir + "\\" + img + ".gif")
 captcha_image = np.array(captcha_image)
 return img, captcha_image


# if __name__ == '__main__':
#  # 测试
#  # text, image = gen_captcha_text_and_image()
#  #
#  # f = plt.figure()
#  # ax = f.add_subplot(111)
#  # ax.text(0.1, 0.9, text, ha='center', va='center', transform=ax.transAxes)
#  # plt.imshow(image)
#  # plt.show()
#  #
#
#  text, image = gen_captcha_text_and_image_new()
#
#  f = plt.figure()
#  ax = f.add_subplot(111)
#  ax.text(0.1, 0.9, text, ha='center', va='center', transform=ax.transAxes)
#  plt.imshow(image)
#  plt.show()

test_check_code.py(得到测试集输入)

from captcha.image import ImageCaptcha # pip install captcha
import numpy as np
from PIL import Image
import random
import matplotlib.pyplot as plt
import os
from random import choice


root_dir = "d:\\test"



img_list = []
def gen_list():

 for parent, dirnames, filenames in os.walk(root_dir): # 三个参数:分别返回1.父目录 2.所有文件夹名字(不含路径) 3.所有文件名字
  for filename in filenames: # 输出文件信息
   img_list.append(filename.replace(".gif",""))
   # print("parent is:" + parent)
   # print("filename is:" + filename)
   # print("the full name of the file is:" + os.path.join(parent, filename)) # 输出文件路径信息
 return img_list

img_list = gen_list()
def get_test_captcha_text_and_image(i=None):
 img = img_list[i]
 captcha_image = Image.open(root_dir + "\\" + img + ".gif")
 captcha_image = np.array(captcha_image)
 return img, captcha_image

4.效果

在测试集上的识别率

Tensorflow简单验证码识别应用

5.相关文件下载

训练集以及测试集 下载

以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持三水点靠木。

Python 相关文章推荐
使用Anaconda3建立虚拟独立的python2.7环境方法
Jun 11 Python
Python for循环生成列表的实例
Jun 15 Python
python和pygame实现简单俄罗斯方块游戏
Feb 19 Python
python3 拼接字符串的7种方法
Sep 12 Python
python 划分数据集为训练集和测试集的方法
Dec 11 Python
使用python批量化音乐文件格式转换的实例
Jan 09 Python
python实现文件助手中查看微信撤回消息
Apr 29 Python
Python实现的对一个数进行因式分解操作示例
Jun 27 Python
Python序列类型的打包和解包实例
Dec 21 Python
计算pytorch标准化(Normalize)所需要数据集的均值和方差实例
Jan 15 Python
scrapy爬虫:scrapy.FormRequest中formdata参数详解
Apr 30 Python
90行Python代码开发个人云盘应用
Apr 20 Python
Python 编码Basic Auth使用方法简单实例
May 25 #Python
Python 含参构造函数实例详解
May 25 #Python
Python爬虫之模拟知乎登录的方法教程
May 25 #Python
python爬虫入门教程--优雅的HTTP库requests(二)
May 25 #Python
Python操作使用MySQL数据库的实例代码
May 25 #Python
python爬虫入门教程--快速理解HTTP协议(一)
May 25 #Python
用生成器来改写直接返回列表的函数方法
May 25 #Python
You might like
PHP开发环境配置(MySQL数据库安装图文教程)
2010/04/28 PHP
php 无限级数据JSON格式及JS解析
2010/07/17 PHP
php中转义mysql语句的实现代码
2011/06/24 PHP
SSO单点登录的PHP实现方法(Laravel框架)
2016/03/23 PHP
关于laravel 子查询 &amp; join的使用
2019/10/16 PHP
可缩放Reloaded-一个针对可缩放元素的复用组件
2007/03/10 Javascript
JavaScript表达式:URL 协议介绍
2013/03/10 Javascript
js计算字符串长度包含的中文是utf8格式
2013/10/15 Javascript
把jQuery的类、插件封装成seajs的模块的方法
2014/03/12 Javascript
详解JavaScript逻辑Not运算符
2015/12/04 Javascript
JSON遍历方式实例总结
2015/12/07 Javascript
前端js文件合并的三种方式推荐
2016/05/19 Javascript
JavaScript实现打地鼠小游戏
2020/04/23 Javascript
用node-webkit把web应用打包成桌面应用(windows环境)
2018/02/01 Javascript
es6数值的扩展方法
2019/03/11 Javascript
深入了解query和params的使用区别
2019/06/24 Javascript
前端天气插件tpwidget使用方法详解
2019/06/24 Javascript
python使用sorted函数对列表进行排序的方法
2015/04/04 Python
Python之re操作方法(详解)
2017/06/14 Python
Django ORM框架的定时任务如何使用详解
2017/10/19 Python
pandas 对每一列数据进行标准化的方法
2018/06/09 Python
python实现将一个数组逆序输出的方法
2018/06/25 Python
Python爬虫实现抓取京东店铺信息及下载图片功能示例
2018/08/07 Python
python写日志文件操作类与应用示例
2019/07/01 Python
如何使用python进行pdf文件分割
2019/11/11 Python
Python单链表原理与实现方法详解
2020/02/22 Python
Python Websocket服务端通信的使用示例
2020/02/25 Python
python实现从ftp服务器下载文件
2020/03/03 Python
Python如何将函数值赋给变量
2020/04/28 Python
python 发送邮件的四种方法汇总
2020/12/02 Python
求职简历推荐信范文
2013/12/02 职场文书
学校德育工作总结2015
2015/05/11 职场文书
党课主持词大全
2015/06/30 职场文书
Redis如何一键部署脚本
2021/04/12 Redis
Javascript之datagrid查询详解
2021/09/15 Javascript
一文弄懂MySQL索引创建原则
2022/02/28 MySQL