CNN识别验证码1
2024-10-21 11:58:19
之前学习python的时候，想尝试用requests实现自动登陆，但是现在网站登陆都会有验证码保护，主要是为了防止暴力破解，任意用户注册。最近接触深度学习，cnn能够进行图像识别，能够进行验证码识别。
主要步骤：
1、采样制作样本文件
2、根据样本文件类型创建识别模型
3、对样本文件分为训练样本和测试样本来训练识别模型
4、保存识别模型和验证
第一步生成验证码，保存文件为generate_captcha.py：
#-*- coding:utf-8 -*-
from captcha.image import ImageCaptcha
from PIL import Image
import numpy as np
import random
import string class generateCaptcha():
def __init__(self,
width = 160,#验证码图片的宽
height = 60,#验证码图片的高
char_num = 4,#验证码字符个数
characters = string.digits + string.ascii_uppercase + string.ascii_lowercase):#验证码组成，数字+大写字母+小写字母
self.width = width
self.height = height
self.char_num = char_num
self.characters = characters
self.classes = len(characters) #10+26+26=62 def gen_captcha(self,batch_size = 50):
X = np.zeros([batch_size,self.height,self.width,1])
img = np.zeros((self.height,self.width),dtype=np.uint8)
Y = np.zeros([batch_size,self.char_num,self.classes])
image = ImageCaptcha(width = self.width,height = self.height) while True:
for i in range(batch_size):
captcha_str = ''.join(random.sample(self.characters,self.char_num))
img = image.generate_image(captcha_str).convert('L')
img = np.array(img.getdata())
X[i] = np.reshape(img,[self.height,self.width,1])/255.0
for j,ch in enumerate(captcha_str):
Y[i,j,self.characters.find(ch)] = 1
Y = np.reshape(Y,(batch_size,self.char_num*self.classes))
yield X,Y def decode_captcha(self,y):
y = np.reshape(y,(len(y),self.char_num,self.classes))
return ''.join(self.characters[x] for x in np.argmax(y,axis = 2)[0,:]) def get_parameter(self):
return self.width,self.height,self.char_num,self.characters,self.classes def gen_test_captcha(self):
image = ImageCaptcha(width = self.width,height = self.height)
captcha_str = ''.join(random.sample(self.characters,self.char_num))
img = image.generate_image(captcha_str)
img.save(captcha_str + '.jpg') X = np.zeros([1,self.height,self.width,1])
Y = np.zeros([1,self.char_num,self.classes])
img = img.convert('L')
img = np.array(img.getdata())
X[0] = np.reshape(img,[self.height,self.width,1])/255.0
for j,ch in enumerate(captcha_str):
Y[0,j,self.characters.find(ch)] = 1
Y = np.reshape(Y,(1,self.char_num*self.classes))
return X,Y
返回参数的含义：
- X：一个 mini-batch 的训练数据，其 shape 为 [ batch_size, height, width, 1 ]，batch_size 表示每批次多少个训练数据，height 表示验证码图片的高，width 表示验证码图片的宽，1 表示图片的通道。
- Y：X 中每个训练数据属于哪一类验证码，其形状为 [ batch_size, class ] ，对验证码中每个字符进行 One-Hot 编码，所以 class 大小为 4*62。
调用这个类的时候会生成这样的验证码图片：
第二步创建识别模型
这里用到了 5 层网络，前 3 层为卷积层，第 4、5 层为全连接层。对 4 层隐藏层都进行 dropout。网络结构如下所示： input——>conv——>pool——>dropout——>conv——>pool——>dropout——>conv——>pool——>dropout——>fully connected layer——>dropout——>fully connected layer——>output
主要代码（captcha_model.py）：
# -*- coding: utf-8 -*
import tensorflow as tf
import math class captchaModel():
def __init__(self,
width = 160,
height = 60,
char_num = 4,
classes = 62):
self.width = width
self.height = height
self.char_num = char_num
self.classes = classes def conv2d(self,x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') #步长是1，卷积的时候图片大小没有缩小。最大池化的时候图片减为一半。 def max_pool_2x2(self,x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], #用2*2的过滤器
strides=[1, 2, 2, 1], padding='SAME') #最大池化步长是2 def weight_variable(self,shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial) def bias_variable(self,shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial) def create_model(self,x_images,keep_prob):
#first layer
w_conv1 = self.weight_variable([5, 5, 1, 32]) #通过过滤器计算权重值5*5*32
b_conv1 = self.bias_variable([32]) #32是[5,5,1,32]中的32的输出。a[1]=Relu(w[1]a[0]+b[1]),因为w[1]a[0]是32，矩阵相加。
h_conv1 = tf.nn.relu(tf.nn.bias_add(self.conv2d(x_images, w_conv1), b_conv1))
h_pool1 = self.max_pool_2x2(h_conv1)
h_dropout1 = tf.nn.dropout(h_pool1,keep_prob)
conv_width = math.ceil(self.width/2)
conv_height = math.ceil(self.height/2) #second layer
w_conv2 = self.weight_variable([5, 5, 32, 64])
b_conv2 = self.bias_variable([64])
h_conv2 = tf.nn.relu(tf.nn.bias_add(self.conv2d(h_dropout1, w_conv2), b_conv2))
h_pool2 = self.max_pool_2x2(h_conv2)
h_dropout2 = tf.nn.dropout(h_pool2,keep_prob)
conv_width = math.ceil(conv_width/2)
conv_height = math.ceil(conv_height/2) #third layer
w_conv3 = self.weight_variable([5, 5, 64, 64])
b_conv3 = self.bias_variable([64])
h_conv3 = tf.nn.relu(tf.nn.bias_add(self.conv2d(h_dropout2, w_conv3), b_conv3))
h_pool3 = self.max_pool_2x2(h_conv3)
h_dropout3 = tf.nn.dropout(h_pool3,keep_prob)
conv_width = math.ceil(conv_width/2)
conv_height = math.ceil(conv_height/2) #first fully layer
conv_width = int(conv_width)
conv_height = int(conv_height)
w_fc1 = self.weight_variable([64*conv_width*conv_height,1024]) #64*20*8
b_fc1 = self.bias_variable([1024])
h_dropout3_flat = tf.reshape(h_dropout3,[-1,64*conv_width*conv_height])
h_fc1 = tf.nn.relu(tf.nn.bias_add(tf.matmul(h_dropout3_flat, w_fc1), b_fc1))
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) #second fully layer
w_fc2 = self.weight_variable([1024,self.char_num*self.classes])
b_fc2 = self.bias_variable([self.char_num*self.classes])
y_conv = tf.add(tf.matmul(h_fc1_drop, w_fc2), b_fc2) return y_conv
第三步，有了样本和模型可以训练识别模型
每 100 次循环采用 100 个测试样本检查识别准确度，当准确度大于 99% 时，训练结束，采用 GPU 需要 4-5 个小时左右，CPU 大概需要 20 个小时左右。
主要代码（train_captcha.py）：
#-*- coding:utf-8 -*-
import tensorflow as tf
import numpy as np
import string
import generate_captcha
import captcha_model if __name__ == '__main__':
captcha = generate_captcha.generateCaptcha()
width,height,char_num,characters,classes = captcha.get_parameter() x = tf.placeholder(tf.float32, [None, height,width,1]) #占位符
y_ = tf.placeholder(tf.float32, [None, char_num*classes])
keep_prob = tf.placeholder(tf.float32) model = captcha_model.captchaModel(width,height,char_num,classes)
y_conv = model.create_model(x,keep_prob)
cross_entropy = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y_,logits=y_conv))#將tensor取平均，第二個參數代表沿著那一維取平均
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) #优化器 predict = tf.reshape(y_conv, [-1,char_num, classes]) #y_conv通过模型获得的
real = tf.reshape(y_,[-1,char_num, classes])
correct_prediction = tf.equal(tf.argmax(predict,2), tf.argmax(real,2))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction) saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
step = 1
while True:
batch_x,batch_y = next(captcha.gen_captcha(64))
_,loss = sess.run([train_step,cross_entropy],feed_dict={x: batch_x, y_: batch_y, keep_prob: 0.75})
print ('step:%d,loss:%f' % (step,loss))
if step % 100 == 0:
batch_x_test,batch_y_test = next(captcha.gen_captcha(100))
acc = sess.run(accuracy, feed_dict={x: batch_x_test, y_: batch_y_test, keep_prob: 1.})
print ('###############################################step:%d,accuracy:%f' % (step,acc))
if acc > 0.99: #准确率
saver.save(sess,"capcha_model.ckpt") #保存模型文件
break
step += 1
进行训练，查看训练过程(这里为了快速得到模型，我们将acc改的很小。训练模型也不准确)：
当训练好后会生成以下模型文件，其中checkpoint保存模型路劲和一些模型信息，方便下次训练：
第四步，进行预测：
主要代码（predict_captcha.py）：
#-*- coding:utf-8 -*-
from PIL import Image, ImageFilter
import tensorflow as tf
import numpy as np
import string
import sys
import generate_captcha
import captcha_model if __name__ == '__main__':
captcha = generate_captcha.generateCaptcha()
width,height,char_num,characters,classes = captcha.get_parameter() gray_image = Image.open('./captcha/0hwn.jpg').convert('L') #要预测的验证码图片位置
# gray_image = Image.open(sys.argv[1]).convert('L')
img = np.array(gray_image.getdata())
test_x = np.reshape(img,[height,width,1])/255.0
x = tf.placeholder(tf.float32, [None, height,width,1])
keep_prob = tf.placeholder(tf.float32) model = captcha_model.captchaModel(width,height,char_num,classes)
y_conv = model.create_model(x,keep_prob)
predict = tf.argmax(tf.reshape(y_conv, [-1,char_num, classes]),2)
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.95)
with tf.Session(config=tf.ConfigProto(log_device_placement=False,gpu_options=gpu_options)) as sess:
sess.run(init_op)
saver.restore(sess, "./capcha_model.ckpt") #训练好模型文件的位置
pre_list = sess.run(predict,feed_dict={x: [test_x], keep_prob: 1})
for i in pre_list:
s = ''
for j in i:
s += characters[j]
print(s)
查看结果：
参考链接：
https://xianzhi.aliyun.com/forum/topic/1470/
https://xianzhi.aliyun.com/forum/topic/1505/
https://xianzhi.aliyun.com/forum/topic/1552/
https://cloud.tencent.com/developer/labs/lab/10325
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