python 训练识别验证码_python使用tensorflow深度学习识别验证码

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：

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。

此篇代码大部分是转载的，只改了很少地方。

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

gen_captcha.py代码。

#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()

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。

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