You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
209 lines
7.8 KiB
209 lines
7.8 KiB
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,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_image():
|
|
image=ImageCaptcha()
|
|
captcha_text=random_captcha_text()
|
|
captcha_text=''.join(captcha_text)
|
|
captcha=image.generate(captcha_text)
|
|
captcha_image=Image.open(captcha)
|
|
captcha_image=np.array(captcha_image)
|
|
return captcha_text,captcha_image
|
|
|
|
|
|
def convert2gray(img):
|
|
if len(img.shape)>2:
|
|
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 + char2pos(c)
|
|
vector[idx] = 1
|
|
return vector
|
|
|
|
|
|
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])
|
|
|
|
def wrap_gen_captcha_text_and_image():
|
|
while True:
|
|
text, image = gen_captcha_text_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
|
|
batch_y[i, :] = text2vec(text)
|
|
|
|
return batch_x, batch_y
|
|
|
|
def cnn_structure(w_alpha=0.01, b_alpha=0.1):
|
|
x = tf.reshape(X, shape=[-1, image_height, image_width, 1])
|
|
|
|
|
|
wc1=tf.get_variable(name='wc1',shape=[3,3,1,32],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer())
|
|
#wc1 = tf.Variable(w_alpha * tf.random_normal([3, 3, 1, 32]))
|
|
bc1 = tf.Variable(b_alpha * tf.random_normal([32]))
|
|
conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, wc1, strides=[1, 1, 1, 1], padding='SAME'), bc1))
|
|
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)
|
|
|
|
wc2=tf.get_variable(name='wc2',shape=[3,3,32,64],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer())
|
|
# wc2 = tf.Variable(w_alpha * tf.random_normal([3, 3, 32, 64]))
|
|
bc2 = tf.Variable(b_alpha * tf.random_normal([64]))
|
|
conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, wc2, strides=[1, 1, 1, 1], padding='SAME'), bc2))
|
|
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)
|
|
|
|
wc3=tf.get_variable(name='wc3',shape=[3,3,64,128],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer())
|
|
#wc3 = tf.Variable(w_alpha * tf.random_normal([3, 3, 64, 128]))
|
|
bc3 = tf.Variable(b_alpha * tf.random_normal([128]))
|
|
conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, wc3, strides=[1, 1, 1, 1], padding='SAME'), bc3))
|
|
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)
|
|
|
|
|
|
wd1=tf.get_variable(name='wd1',shape=[8*20*128,1024],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer())
|
|
#wd1 = tf.Variable(w_alpha * tf.random_normal([7*20*128,1024]))
|
|
bd1 = tf.Variable(b_alpha * tf.random_normal([1024]))
|
|
dense = tf.reshape(conv3, [-1, wd1.get_shape().as_list()[0]])
|
|
dense = tf.nn.relu(tf.add(tf.matmul(dense, wd1), bd1))
|
|
dense = tf.nn.dropout(dense, keep_prob)
|
|
|
|
wout=tf.get_variable('name',shape=[1024,max_captcha * char_set_len],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer())
|
|
#wout = tf.Variable(w_alpha * tf.random_normal([1024, max_captcha * char_set_len]))
|
|
bout = tf.Variable(b_alpha * tf.random_normal([max_captcha * char_set_len]))
|
|
out = tf.add(tf.matmul(dense, wout), bout)
|
|
return out
|
|
|
|
def train_cnn():
|
|
output=cnn_structure()
|
|
cost=tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output,labels=Y))
|
|
optimizer=tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
|
|
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:
|
|
init = tf.global_variables_initializer()
|
|
sess.run(init)
|
|
step = 0
|
|
while True:
|
|
batch_x, batch_y = get_next_batch(100)
|
|
_, cost_= sess.run([optimizer, cost], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75})
|
|
print(step, cost_)
|
|
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)
|
|
if acc > 0.99:
|
|
saver.save(sess, "./model/crack_capcha.model", global_step=step)
|
|
break
|
|
step += 1
|
|
|
|
|
|
def crack_captcha(captcha_image):
|
|
output = cnn_structure()
|
|
|
|
saver = tf.train.Saver()
|
|
with tf.Session() as sess:
|
|
saver.restore(sess, "./model/crack_capcha.model-1200")
|
|
|
|
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__':
|
|
train=1
|
|
if train==0:
|
|
text,image=gen_captcha_text_image()
|
|
print("验证码大小：",image.shape)#(60,160,3)
|
|
|
|
image_height=60
|
|
image_width=160
|
|
max_captcha=len(text)
|
|
print("验证码文本最长字符数",max_captcha)
|
|
char_set=number
|
|
char_set_len=len(char_set)
|
|
|
|
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)
|
|
train_cnn()
|
|
|
|
if train == 1:
|
|
image_height = 60
|
|
image_width = 160
|
|
char_set = number
|
|
char_set_len = len(char_set)
|
|
|
|
text, image = gen_captcha_text_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)
|
|
|
|
predict_text = crack_captcha(image)
|
|
print("正确: {} 预测: {}".format(text, predict_text))
|
|
|
|
|
|
plt.show() |