TensorFlow - 深度学习破解验证码

简介：验证码主要用于防刷，传统的验证码识别算法一般需要把验证码分割为单个字符，然后逐个识别，如果字符之间相互重叠，传统的算法就然并卵了，本文采用cnn对验证码进行整体识别。

主要涉及：

• 1.captcha库生成验证码
• 2.如何将验证码识别问题转化为分类问题
• 3.可以训练自己的验证码识别模型

一.安装 captcha 库

sudo pip install captcha

生成验证码训练数据

所有的模型训练，数据是王道，本文采用 captcha 库生成验证码，captcha 可以生成语音和图片验证码，我们采用生成图片验证码功能，验证码是由数字、大写字母、小写字母组成（当然你也可以根据自己的需求调整，比如添加一些特殊字符），长度为 4，所以总共有 62^4 种组合验证码。

验证码生成器

采用 python 中生成器方式来生成我们的训练数据，这样的好处是，不需要提前生成大量的数据，训练过程中生成数据，并且可以无限生成数据。

示例代码：

在 /home/ubuntu 目录下创建源文件 generate_captcha.py，内容可参考：

示例代码：

/home/ubuntu/generate_captcha.py

#!/usr/bin/python

# -*- 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)

    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')

然后执行:

cd /home/ubuntu;

python

import generate_captcha

g = generate_captcha.generateCaptcha()

g.gen_test_captcha()

执行结果：

在 /home/ubuntu 目录下查看生成的验证码，jpg 格式的图片可以点击查看。

二.验证码识别模型

将验证码识别问题转化为分类问题，总共 62^4 种类型，采用 4 个 one-hot 编码分别表示 4 个字符取值。

cnn 验证码识别模型

3 层隐藏层、2 层全连接层，对每层都进行 dropout。input——>conv——>pool——>dropout——>conv——>pool——>dropout——>conv——>pool——>dropout——>fully connected layer——>dropout——>fully connected layer——>output

示例代码：

现在您可以在 /home/ubuntu 目录下创建源文件 captcha_model.py，内容可参考：

示例代码：

/home/ubuntu/captcha_model.py

#!/usr/bin/python

# -*- 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')

    def max_pool_2x2(self,x):

        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],

                              strides=[1, 2, 2, 1], padding='SAME')

    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])

        b_conv1 = self.bias_variable([32])

        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])

        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

训练 cnn 验证码识别模型

每批次采用 64 个训练样本，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 5-6 个小时左右，CPU 大概需要 20 个小时左右。

注：作为实验，你可以通过调整 train_captcha.py 文件中 if acc > 0.99: 代码行的准确度节省训练时间(比如将 0.99 为 0.01)；同时，我们已经通过长时间的训练得到了一个训练集，可以通过如下命令将训练集下载到本地。

wget http://tensorflow-1253902462.cosgz.myqcloud.com/captcha/capcha_model.zip

unzip capcha_model.zip

现在可以在 /home/ubuntu 目录下创建源文件 train_captcha.py，内容可参考：

示例代码：/home/ubuntu/train_captcha.py

#!/usr/bin/python

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])

    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))

    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

    predict = tf.reshape(y_conv, [-1,char_num, classes])

    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 = 0

        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:

                    saver.save(sess,"capcha_model.ckpt")

                    break

            step += 1

然后执行:

cd /home/ubuntu;

python train_captcha.py

执行结果：

step:75173,loss:0.010555

step:75174,loss:0.009410

step:75175,loss:0.009978

step:75176,loss:0.008089

step:75177,loss:0.009949

step:75178,loss:0.010126

step:75179,loss:0.009584

step:75180,loss:0.012272

step:75181,loss:0.010157

step:75182,loss:0.009529

step:75183,loss:0.007636

step:75184,loss:0.009058

step:75185,loss:0.010061

step:75186,loss:0.009941

step:75187,loss:0.009339

step:75188,loss:0.009685

step:75189,loss:0.009879

step:75190,loss:0.007799

step:75191,loss:0.010866

step:75192,loss:0.009838

step:75193,loss:0.010931

step:75194,loss:0.012859

step:75195,loss:0.008747

step:75196,loss:0.009147

step:75197,loss:0.009351

step:75198,loss:0.009746

step:75199,loss:0.010014

step:75200,loss:0.009024

###############################################step:75200,accuracy:0.992500

三.测试 cnn 验证码识别模型

示例代码：

现在您可以在 /home/ubuntu 目录下创建源文件 predict_captcha.py，内容可参考：

示例代码：/home/ubuntu/predict_captcha.py

#!/usr/bin/python

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(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")

        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

然后执行:

cd /home/ubuntu;

python predict_captcha.py Kz2J.jpg

执行结果：

Kz2J

注:因为实验时间的限制，你可能调整了准确度导致执行结果不符合预期，属于正常情况。

在训练时间足够长的情况下，你可以采用验证码生成器生成测试数据，cnn 训练出来的验证码识别模型还是很强大的，大小写的 z 都可以区分，甚至有时候人都无法区分，该模型也可以正确的识别。

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