【python+TensorFlow】验证码识别，机器深度学习

忘是亡心i 2022-05-12 11:46 274阅读 0赞

##  ##

### 人工智能： ###

伴随计算机的发展，人工智能被推向了风口浪尖，成为计算机领域炙手可热的技术，任何一家互联网
    公司都会或多或少的接触的这方面的技术。之前 阿尔法狗VS 柯洁，阿尔法狗3：0大胜，让大家更直观了解到机器学习，
    了解到了人工智能，大量资金涌入人工智能，机器学习领域，成就了现在大家日常的生活中。

我们日常生活中也存在很多人工智能的产物，最简单的是现在日渐普及的 AI 美颜相机，智慧城市，智慧家，这都是机器学习 +优化算法 的产物。

### 验证码背景： ###

大家想必在生活中，会遇到各种验证码，为什么会产生验证码？为什么我们在注册各种app时候会受到各种验证短信? 其实不必要纠结，其实是为了反爬虫，反撞库的技术策略，有人会纠结，反爬虫跟验码有个毛关系，我可以负责人的告诉大家，其实验证码是一种安全策略，为了保证大家在正常使用中的信息安全，以及企业为了防止别人恶意攻击手段，别看小小验证码，在机器学习之前还是很安全的。他能判断出是正常用户、还是机器恶意攻击，正常用户可以正常输入所看到的验证码信息，而机器则识别不出来。 网站为了网站信息安全也会设置验证码，保护自身网站的安全性，在抵挡别人恶意撞库测试。

--------------------

验证码的形式多种多样，我们列举我们常见的几种验证码，供大家了解：

<table> 
 <thead> 
  <tr> 
   <th>列表</th> 
   <th align="center">类型</th> 
   <th align="right">详细</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>1</td> 
   <td align="center">滑动解锁</td> 
   <td align="right">一键式解锁从左滑到右</td> 
  </tr> 
  <tr> 
   <td>2</td> 
   <td align="center">滑动拼图</td> 
   <td align="right">滑动拼接图片</td> 
  </tr> 
  <tr> 
   <td>3</td> 
   <td align="center">验证码手动输入</td> 
   <td align="right">输入正确验证码</td> 
  </tr> 
  <tr> 
   <td>4</td> 
   <td align="center">汉字选取</td> 
   <td align="right">选取提示所示汉字</td> 
  </tr> 
 </tbody> 
</table>

![这里写图片描述][70]

![这里写图片描述][70 1]

为了方便我们模型测试，我这是生成的验证码,当然我们也可以用本地截取的验证码，我会在代码中注明如何使用本地验证码：

gen\_captcha.py 文件

#coding=utf-8
    from captcha.image import ImageCaptcha  # pip install captcha
    import numpy as np
    import matplotlib.pyplot as plt
    from PIL import Image
    import random
    import os,glob
    
    # 验证码中的字符, 就不用汉字了
    
    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']
    '''
    number=['0','1','2','3','4','5','6','7','8','9']
    alphabet =[]
    ALPHABET =[]
    '''
    
    # DATA_DIR = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'data')
    # DEFAULT_FONTS = [os.path.join(DATA_DIR, 'DroidSansMono.ttf')]
    #
    # class ImageCaptcha(ImageCaptcha):
    #
    #     def __init__(self):
    #         # (self, width=180, height=67, fonts=None, font_sizes=None):
    #         self._width = 180
    #         self._height = 67
    #         self._fonts = None or DEFAULT_FONTS
    #         self._font_sizes = None or (42, 50, 56)
    #         self._truefonts = []
    # 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():
    
        while(1):
            image = ImageCaptcha(180,67)
            # image = ImageCaptcha()
    
            captcha_text = random_captcha_text()
            captcha_text = ''.join(captcha_text)
            path = 'F://CNN_1/test/'
            print("-----------------"+captcha_text)
            captcha = image.generate(captcha_text)
            # image.write(captcha_text, path+captcha_text + '.jpg')  # 写到文件
    
            captcha_image = Image.open(captcha)
            #captcha_image.show()
            captcha_image = np.array(captcha_image)
            if captcha_image.shape==(67,180,4):
                break
    
        return captcha_text, captcha_image
    
    
    #读取本地打好标记的验证码
    def get_name_and_image():
        list = glob.glob(r"C:\Users\Administrator\Desktop\1\*.png")
        num  =random.randint(0,len(list)-1)
    
        path = list[num]
        # print(path)
        p, image_text = path.split('_')
        captcha_text, type = image_text.split('.')
    
        captcha_image = Image.open(path)
    
        captcha_image = np.array(captcha_image)
    
        return captcha_text, captcha_image
    
    if __name__ == '__main__':
      while 1:
        # 测试
        text, image = gen_captcha_text_and_image()
        # print image
        gray = np.mean(image, -1)
        # print gray
    
        # print image.shape
        # print gray.shape
        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()

上述代码中，有两种方式，1 生成验证码作为训练字库  2 另一种是自己打标记
    这是我打好标记的验证码，如下

![这里写图片描述][70 2]

[train.py][] 代码：

#coding=utf-8
    from gen_captcha import gen_captcha_text_and_image,read_localhost_images,get_name_and_image
    from gen_captcha import number
    from gen_captcha import alphabet
    from gen_captcha import ALPHABET
    from PIL import Image
    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 = 67
    IMAGE_WIDTH = 180
    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
    """
    IMAGE_HEIGHT = 67 #图片高度
    IMAGE_WIDTH = 180 #宽度
    MAX_CAPTCHA = 4 #最大字符，验证码训练样本
    
    # 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):
            #print text
            idx = i * CHAR_SET_LEN + char2pos(c)
            #print i,CHAR_SET_LEN,char2pos(c),idx
            vector[idx] = 1
        return vector
    
    #print text2vec('1aZ_')
    
    # 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, 4)
        def wrap_gen_captcha_text_and_image():
            while True:
                # text, image = gen_captcha_text_and_image()  #端对端自动生成验证码，可以不用打标记，直接使用
                # text, image = read_localhost_images()
                text, image = get_name_and_image()  #读取本地打好标记验证码
                # print(image.shape)
                if image.shape == (67, 180, 4):
                    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)
    
        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)
    
        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)
    
        # Fully connected layer
        w_d = tf.Variable(w_alpha * tf.random_normal([9 * 23 * 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():
        import time
        start_time=time.time()
        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 (time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time())),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 (u'***************************************************************第%s次的准确率为%s'%(step, acc))
                    # 如果准确率大于50%,保存模型,完成训练
                    if acc > 0.99:                  ##我这里设了0.9，设得越大训练要花的时间越长，如果设得过于接近1，很难达到。如果使用cpu，花的时间很长，cpu占用很高电脑发烫。
                        print("保存模型......")
                        # joblib.dump(sess, "F:/model/rf_model.m")
                        saver.save(sess, "F:/CNN_2/model_3/crack_capcha.model", global_step=step)
                        print (time.time()-start_time)
                        break
                step += 1
    
    
    train_crack_captcha_cnn()
    #
    # def crack_captcha(captcha_image):
    # 	output = crack_captcha_cnn()
    #
    # 	saver = tf.train.Saver()
    # 	with tf.Session() as sess:
    # 		# saver.restore(sess, tf.train.latest_checkpoint('.'))
    # 		# saver.restore(sess, "F:/CNN_1/model/crack_capcha.model-10400")
    # 		saver.restore(sess, "F:/CNN_2/model_2/crack_capcha.model-1700")
    #
    # 		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_list = sess.run(predict, feed_dict={X: [captcha_image]})
    #
    # 		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)
    
    # path = "C:/Users/Administrator/Desktop/text/text/2.png"
    # image = Image.open('f://CNN_1/test/TUFK.jpg')
    # image = Image.open('C:/Users/Administrator/Desktop/text/text/3.png')
    # image = np.array(image)
    # text = 'jvdf'
    # image = convert2gray(image)
    # image = image.flatten() / 255
    # predict_text = crack_captcha(image)
    # print("正确: {}  预测: {}".format(text, predict_text))

放两张训练图：

![这里写图片描述][70 3]

![这里写图片描述][70 4]

最后样本我训练结果是：99% 正确率  
模型保存成功后是四个文件：  
![这里写图片描述][70 5]

[看下预测代码predict.py][predict.py]：

from train import crack_captcha_cnn
    from train import  MAX_CAPTCHA,CHAR_SET_LEN,keep_prob,X
    from train import  vec2text,convert2gray
    from PIL import Image
    import numpy as np
    import tensorflow as tf
    import glob,random
    
    
    
    
    def predict_result(captcha_image):
    
        output = crack_captcha_cnn()
    
        saver = tf.train.Saver()
        # tf.reset_default_graph()
    
        with tf.Session() as sess:
            saver.restore(sess, "F:/CNN_2/model_3/crack_capcha.model-2000")
            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_list = sess.run(predict, feed_dict={X: [captcha_image]})
    
            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)
    
    def get_name_and_image():
        list1 = glob.glob(r"C:\Users\Administrator\Desktop\text\text\*.png")
        num  =random.randint(0,len(list1)-1)
    
        path = list1[num]
        print(path)
        p, image_text = path.split('_')
        captcha_text, type = image_text.split('.')
    
        captcha_image = Image.open(path)
    
        captcha_image = np.array(captcha_image)
    
        image = convert2gray(captcha_image)
        image = image.flatten() / 255
    
        return captcha_text, image
    if __name__ =="__main__":
        text , image =get_name_and_image()
        predict_text = predict_result(image)
        print("正确: {}  预测: {}".format(text, predict_text))

作者声明：

本文章仅用作技术交流，不用于任何非法商业活动，转载请注明出处。

如果您觉得本文确实帮助了您，可以微信扫一扫，进行小额的打赏和鼓励，谢谢 \_  
![这里写图片描述][70 6]

[70]: /images/20220512/8537e207c0f04bd1804b58bc4eb54389.png
[70 1]: /images/20220512/9efba2437bcb4f269767511db2e4ec75.png
[70 2]: /images/20220512/a7aa05e0844e4b39a5ba2de1d1f114a2.png
[train.py]: http://train.py
[70 3]: /images/20220512/b87e5e35d69e488d8b149922da85e95b.png
[70 4]: /images/20220512/11d31d94c45747fba8fc8fd693fb3721.png
[70 5]: /images/20220512/98ad4a6355b54451ade33696dce9a7d1.png
[predict.py]: http://xn--predict-nw3kw6d870moowapscl65n.py
[70 6]: /images/20220512/b5c542cd855b403385f41d2ba7168e69.png