1、申 请 I D ：BigDaddyN0tail
2、个人邮箱：program_design@163.com
3、原创技术文章：编程代码 基于tensorflow识别图像验证码
1.前两个函数使用python生成验证码 一共有10的4次方种可能
2.使用CNN进行训练
2.网络结构：三成卷积 + 池化 + 全连接
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()
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吾爱游客
发表于 2018-9-30 11:23
发表于 2018-9-30 16:45
