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

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。

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。