TensorFlow: 使用深度学习破解字符验证码

TensorFlow: 使用深度学习破解字符验证码

        最近在研究破解验证码，然后网上开始找资料，然后找到一个帖子，用深度学习的方法去破解验证码，调试了几天，终于成功了，于是就记录下来。点击跳转帖子的原文地址

1.获取训练样本

        机器学习肯定需要大量的数据来做训练。如果要利用深度学习破解验证码，自然需要大量的验证码并且已经标识好的样本数据。这里就是用python的验证码库captcha来生成训练样本，既然用了captcha作为验证码训练样本，那么构建的模型也仅仅只能破解captcha库生成的验证码。

验证码生成工具类

from captcha.image import ImageCaptcha
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import random

# 验证码中的字符
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']


# 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():
    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 = np.array(captcha_image)
    return captcha_text, captcha_image


if __name__ == '__main__':
    # 测试
    text, image = gen_captcha_text_and_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()

2.CNN训练

from create_captcha import gen_captcha_text_and_image
from create_captcha import number
from create_captcha import alphabet
from create_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


# 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):
        idx = i * CHAR_SET_LEN + char2pos(c)
        vector[idx] = 1
    return vector


# 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)
    print("conv1 {}",format(conv1))

    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)
    print("conv2 {}", format(conv2))

    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)
    print("conv3 {}", format(conv3))

    # Fully connected layer
    w_d = tf.Variable(w_alpha * tf.random_normal([8 * 20 * 64, 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():
    # 定义CNN
    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(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(step, acc)
                # 如果准确率大于80%,保存模型,完成训练
                if acc > 0.9:
                    saver.save(sess, "./crack_capcha.model", global_step=step)
                    break

            step += 1


if __name__ == '__main__':
    # 训练模型函数 start
    train_crack_captcha_cnn()
    # 训练模型函数 end

3.调用模型

def crack_captcha(captcha_image):
    output = crack_captcha_cnn()

    saver = tf.train.Saver()
    with tf.Session() as sess:
        # 使用tf.train.latest_checkpoint（）来自动获取最后一次保存的模型
        saver.restore(sess, tf.train.latest_checkpoint('.'))

        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()
        vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)
        i = 0
        for n in text:
            vector[i * CHAR_SET_LEN + n] = 1
            i += 1
        return vec2text(vector)

if __name__ == '__main__':
    # 调用模型函数 start
    text, image = gen_captcha_text_and_image()
    image = convert2gray(image)
    image = image.flatten() / 255
    predict_text = crack_captcha(image)
    print("正确: {}  预测: {}".format(text, predict_text))
    # 调用模型函数 end

我引入的python包的版本，如下图：

如果比较懒的同学可以使用Anaconda安装python环境，直接在Anaconda Prompt的命令行pip install就可以了。

Anaconda教程

Anaconda安装教程
Anaconda切换环境

转载文章

http://blog.topspeedsnail.com/archives/10858