python使用tensorflow深度学习识别验证码,Python3实现的简单验证码识别功能示例

By admin in 编程 on 2019年6月4日

本文研究的主要是Python验证码识别的相关代码,具体如下。

本文实例讲述了Python3实现的简单验证码识别功能。分享给大家供大家参考,具体如下:

本文介绍了python使用tensorflow深度学习识别验证码
,分享给大家,具体如下:

Talk is cheap, show you the Code!

这次的需求是自动登录某机构网站, 其验证码很具特色,
很适合做验证码识别入门demo, 先贴主要代码,
其中图片对比使用了编辑距离算法, 脚本使用了pillow库

除了传统的PIL包处理图片,然后用pytessert+OCR识别意外,还可以使用tessorflow训练来识别验证码。

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from PIL import Image

#打开图像
im=np.array(Image.open('yzm.png'))

#得到图像3个维度
h,w,san=im.shape

X=[(h-x,y) for x in range(h) for y in range (w) if im[x][y][2]<200]

#将X转换成numpy的array类型,方便后续运算操作
X=np.array(X)

n_clusters=4
k_means=KMeans(init='k-means++',n_clusters=n_clusters)
k_means.fit(X)

k_means_labels=k_means.labels_
k_means_cluster_centers=k_means.cluster_centers_
k_means_labels_unique=np.unique(k_means_labels)

colors=['#4EACC5','#FF9C34','#4E9A06','#FF3300']
plt.figure()
plt.hold(True)
for k,col in zip(range(n_clusters),colors):
 my_members=k_means_labels==k
 cluster_center=k_means_cluster_centers[k]
 plt.plot(X[my_members,1],X[my_members,0],'w',markerfacecolor=col,marker='.')
 plt.plot(cluster_center[1],cluster_center[0],'o',markerfacecolor=col,markeredgecolor='k',markersize=6)

plt.title('KMeans')
plt.grid(True)
plt.show()
from PIL import Image
import requests
import re
splitter = re.compile(r'\d{30}') # 分割二值化后的图片
# distance('11110000', '00000000')
# 比较两个字符串有多少位不同, 返回不同的位数
def distance(string1, string2):
  d_str1 = len(string1)
  d_str2 = len(string2)
  d_arr = [[0] * d_str2 for i in range(d_str1)]
  for i in range(d_str1):
    for j in range(d_str2):
      if string1[i] == string2[j]:
        if i == 0 and j == 0:
          d_arr[i][j] = 0
        elif i != 0 and j == 0:
          d_arr[i][j] = d_arr[i - 1][j]
        elif i == 0 and j != 0:
          d_arr[i][j] = d_arr[i][j - 1]
        else:
          d_arr[i][j] = d_arr[i - 1][j - 1]
      else:
        if i == 0 and j == 0:
          d_arr[i][j] = 1
        elif i != 0 and j == 0:
          d_arr[i][j] = d_arr[i - 1][j] + 1
        elif i == 0 and j != 0:
          d_arr[i][j] = d_arr[i][j - 1] + 1
        else:
          d_arr[i][j] = min(d_arr[i][j - 1], d_arr[i - 1][j], d_arr[i - 1][j - 1]) + 1
  current = max(d_arr[d_str1 - 1][d_str2 - 1], abs(d_str2 - d_str1))
  # print("Levenshtein Distance is",current)
  # print(current)
  return current
# 去除字符串里面连续的1
def no_one(string):
  n_arr = splitter.findall(string)
  n_arr = filter(lambda each_str: each_str != '111111111111111111111111111111', n_arr)
  n_result = ''
  for n_each in n_arr:
    n_result += str(n_each)
  return n_result
opener = requests.session()
res = opener.get('http://60.211.254.236:8402/Ajax/ValidCodeImg.ashx').content
with open('verify.gif', 'wb') as v:
  v.write(res)
img = Image.open('verify.gif')
img = img.convert('L')
size = img.size
# img = img.point(table, '1')
img_arr = img.load()
# for x in range(size[0]):
#   for y in range(size[1]):
#     if img_arr[x, y] > 210:
#       img_arr[x, y] = 1
#     else:
#       img_arr[x, y] = 0
# img.save('after.gif')
inc = 0
str1 = ''
str2 = ''
str3 = ''
cur_str = ''
for x in range(size[0]):
  for y in range(size[1]):
    if img_arr[x, y] > 210:
      cur_str += '1'
    else:
      cur_str += '0'
    # print(img_arr[i, j], end='')
    # cur_str += str(img_arr[x, y])
  inc += 1
  # if inc % 18 == 0:
  #   print('\n----')
  # else:
  #   print('')
  if inc == 18:
    str1 = cur_str
    cur_str = ''
  elif inc == 36:
    str2 = cur_str
    cur_str = ''
  elif inc == 54:
    str3 = cur_str
    cur_str = ''
str1 = str1[:-60]
str2 = str2[:-60]
str3 = str3[:-60]
str1 = no_one(str1)
str2 = no_one(str2)
str3 = no_one(str3)
str1 = str1.strip('1')
str2 = str2.strip('1')
str3 = str3.strip('1')
# print(str1)
# print(str3)
with open('./dict/plus') as plus:
  with open('./dict/minus') as minus:
    p = plus.read()
    m = minus.read()
    is_add = 1 if distance(p, str2) < distance(m, str2) else 0
arr1 = []
arr3 = []
for each in range(1, 10):
  with open('./dict/{}'.format(each)) as f:
    ff = f.read()
    arr1.append([each, distance(ff, str1)])
    arr3.append([each, distance(ff, str3)])
arr1 = sorted(arr1, key=lambda item: item[1])
arr3 = sorted(arr3, key=lambda item: item[1])
result = arr1[0][0] + arr3[0][0] if is_add else arr1[0][0] - arr3[0][0]
print(result)
# login_url = 'http://60.211.254.236:8402/Ajax/Login.ashx?Method=G3_Login'
# login_data = {
#   'loginname': usn,
#   'password': pwd,
#   'validcode': result,
#
# }
# opener.get(login_url, login_data)

此篇代码大部分是转载的,只改了很少地方。

总结

以上就是本文关于python验证码识别实例代码的全部内容,希望对大家有所帮助。感兴趣的朋友可以继续参阅本站其他相关专题,如有不足之处,欢迎留言指出。感谢朋友们对本站的支持!

字库已经部署到GitHub地址:

代码是运行在linux环境,tessorflow没有支持windows的python 2.7。

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gen_captcha.py代码。

希望本文所述对大家Python程序设计有所帮助。

#coding=utf-8
from captcha.image import ImageCaptcha # pip install captcha
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import random

# 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']
'''
number=['0','1','2','3','4','5','6','7','8','9']
alphabet =[]
ALPHABET =[]
'''

# 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 + alphabet + ALPHABET, 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():
  while(1):
    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.show()
    captcha_image = np.array(captcha_image)
    if captcha_image.shape==(60,160,3):
      break

  return captcha_text, captcha_image






if __name__ == '__main__':
  # 测试
  text, image = gen_captcha_text_and_image()
  print image
  gray = np.mean(image, -1)
  print gray

  print image.shape
  print gray.shape
  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()

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train.py代码。

#coding=utf-8
from gen_captcha import gen_captcha_text_and_image
from gen_captcha import number
from gen_captcha import alphabet
from gen_captcha import ALPHABET

import numpy as np
import tensorflow as tf

"""
text, image = gen_captcha_text_and_image()
print "验证码图像channel:", image.shape # (60, 160, 3)
# 图像大小
IMAGE_HEIGHT = 60
IMAGE_WIDTH = 160
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
"""
IMAGE_HEIGHT = 60
IMAGE_WIDTH = 160
MAX_CAPTCHA = 4

# 把彩色图像转为灰度图像(色彩对识别验证码没有什么用)
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
    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
"""

# 文本转向量
char_set = number + alphabet + ALPHABET + ['_'] # 如果验证码长度小于4, '_'用来补齐
CHAR_SET_LEN = len(char_set)


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):
    if c == '_':
      k = 62
      return k
    k = ord(c) - 48
    if k > 9:
      k = ord(c) - 55
      if k > 35:
        k = ord(c) - 61
        if k > 61:
          raise ValueError('No Map')
    return k

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

#print text2vec('1aZ_')

# 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)


"""
#向量(大小MAX_CAPTCHA*CHAR_SET_LEN)用0,1编码 每63个编码一个字符,这样顺利有,字符也有
vec = text2vec("F5Sd")
text = vec2text(vec)
print(text) # F5Sd
vec = text2vec("SFd5")
text = vec2text(vec)
print(text) # SFd5
"""


# 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()
      if image.shape == (60, 160, 3):
        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)

  # 3 conv layer
  w_c1 = tf.Variable(w_alpha * tf.random_normal([3, 3, 1, 32]))
  b_c1 = tf.Variable(b_alpha * tf.random_normal([32]))
  conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1))
  conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
  conv1 = tf.nn.dropout(conv1, keep_prob)

  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)

  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
  w_d = tf.Variable(w_alpha * tf.random_normal([8 * 32 * 40, 1024]))
  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():
  import time
  start_time=time.time()
  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(logits=output, labels=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 time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time())),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 u'***************************************************************第%s次的准确率为%s'%(step, acc)
        # 如果准确率大于50%,保存模型,完成训练
        if acc > 0.9:         ##我这里设了0.9,设得越大训练要花的时间越长,如果设得过于接近1,很难达到。如果使用cpu,花的时间很长,cpu占用很高电脑发烫。
          saver.save(sess, "crack_capcha.model", global_step=step)
          print time.time()-start_time
          break

      step += 1


train_crack_captcha_cnn()

测试代码:

output = crack_captcha_cnn()
saver = tf.train.Saver()
sess = tf.Session()
saver.restore(sess, tf.train.latest_checkpoint('.'))

while(1):


  text, image = gen_captcha_text_and_image()
  image = convert2gray(image)
  image = image.flatten() / 255

  predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
  text_list = sess.run(predict, feed_dict={X: [image], keep_prob: 1})
  predict_text = text_list[0].tolist()

  vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)
  i = 0
  for t in predict_text:
    vector[i * 63 + t] = 1
    i += 1
    # break



  print("正确: {} 预测: {}".format(text, vec2text(vector)))

如果想要快点测试代码效果,验证码的字符不要设置太多,例如0123这几个数字就可以了。

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脚本之家。

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