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CNN破解简单验证码(Tensorflow实现)

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使用CNN破解一下自己生成的图片验证码,因为电脑性能不行,只破解四位的数字验证码,代码实现中可以对符号、字符和数字混合的验证码进行破解,原理相同,有高性能GPU的童鞋可以试试玩玩。CNN使用简单的三层卷积,人懒结构手绘如下图:


生成验证码的代码,使用了第三方库:

#coding=utf-8
import as tf
import numpy as np
import matplotlib.pyplot as plt
#conda install Pillow
from PIL import Image
import random
#pip install captcha 安装验证码库
from captcha.image import ImageCaptcha

#本代码生成验证码图片
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']

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

def gen_captcha_text_and_image():
#构造captcha对象
image = ImageCaptcha()

captcha_text = random_captcha_text()
#list->string
captcha_text = ''.join(captcha_text)
#生成图像验证码
captcha = image.generate(captcha_text)
#image.write(captcha_text, captcha_text + '.jpg')

captcha_image = Image.open(captcha)
#转换为numpu array格式
captcha_image = np.array(captcha_image)
#返回Label和验证码
return captcha_text, captcha_image
if __name__ == '__main__':
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()
生成的效果如图:

验证码识别代码:

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

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']

#def random_captcha_text(char_set=number+alphabet+ALPHABET, captcha_size=4):
#生成验证码文本
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

#生成验证码图片(H*W*Chanel)和标签
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')

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

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

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):
idx = i * CHAR_SET_LEN + int(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))
"""
text=[]
char_pos = vec.nonzero()[0]
for i, c in enumerate(char_pos):
number = i % 10
text.append(str(number))

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

# 定义CNN
# w_alpha, b_alpha传入一个很小的值作为初始化值
def crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1):
#传入的X为[batch_size,H,W],需要转换为Tensorflow格式[batch_size,H,W,Chanel]
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
#filter:3*3,输入通道1(灰度图),输出(特征图):32
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*20*64, 1024]))
b_d = tf.Variable(b_alpha*tf.random_normal([1024]))
#卷积结果扁平化
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)
return out

# 训练
def train_crack_captcha_cnn():
#三层CNN预测输出
output = crack_captcha_cnn()
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=Y))
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 % 10 == 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 > 0.50:
saver.save(sess, "./model/crack_capcha.model", global_step=step)
break

step += 1
def crack_captcha(captcha_image):
output = crack_captcha_cnn()

saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, "./model/crack_capcha.model-810")

predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
text_list = sess.run(predict, feed_dict={X: [captcha_image], keep_prob: 1})
text = text_list[0].tolist()
return text
if __name__ == '__main__':
#定义train变量,train=0:网络测试,train=1:网络训练
train = 0
if train == 0:
number = ['0','1','2','3','4','5','6','7','8','9']
#没有GPU,为加快训练速度,暂时只训练仅含数字的验证码
#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']

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)
# 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 = number
CHAR_SET_LEN = len(char_set)
#验证码识别中,颜色用处不大,因此将彩色图转换为灰度图,加快训练速度
X = tf.placeholder(tf.float32, [None, IMAGE_HEIGHT*IMAGE_WIDTH]) #60*160
#四位验证码:采用四组10位的one-hot
Y = tf.placeholder(tf.float32, [None, MAX_CAPTCHA*CHAR_SET_LEN]) #4*10
# dropout
keep_prob = tf.placeholder(tf.float32)

train_crack_captcha_cnn()
if train == 1:
number = ['0','1','2','3','4','5','6','7','8','9']
IMAGE_HEIGHT = 60
IMAGE_WIDTH = 160
char_set = number
CHAR_SET_LEN = len(char_set)

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()

MAX_CAPTCHA = len(text)
image = convert2gray(image)
image = image.flatten() / 255

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

predict_text = crack_captcha(image)
print("正确: {} 预测: {}".format(text, predict_text))

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