keras入门（三）搭建CNN模型破解网站验证码

Love The Way You Lie 2022-04-17 07:11 180阅读 0赞

### 项目介绍 ###

在文章[CNN大战验证码][CNN]中，我们利用TensorFlow搭建了简单的CNN模型来破解某个网站的验证码。验证码如下：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2pjbGlhbjkx_size_16_color_FFFFFF_t_70][]

在本文中，我们将会用Keras来搭建一个稍微复杂的CNN模型来破解以上的验证码。

### 数据集 ###

对于验证码图片的处理过程在本文中将不再具体叙述，有兴趣的读者可以参考文章[CNN大战验证码][CNN]。  
  在这个项目中，我们现在的样本一共是1668个样本，每个样本都是一个字符图片，字符图片的大小为16\*20。样本的特征为字符图片的像素，0代表白色，1代表黑色，每个样本为320个特征，取值为0或1，特征变量名称为v1到v320，样本的类别标签即为该字符。整个数据集的部分如下：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2pjbGlhbjkx_size_16_color_FFFFFF_t_70 1][]

### CNN模型 ###

利用Keras可以快速方便地搭建CNN模型，本文搭建的CNN模型如下：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2pjbGlhbjkx_size_16_color_FFFFFF_t_70 2][]

将数据集分为训练集和测试集，占比为8：2，该模型训练的代码如下：

# -*- coding: utf-8 -*-
    import numpy as np
    import pandas as pd
    from sklearn.model_selection import train_test_split
    from matplotlib import pyplot as plt
    
    from keras.utils import np_utils, plot_model
    from keras.models import Sequential
    from keras.layers.core import Dense, Dropout, Activation, Flatten
    from keras.callbacks import EarlyStopping
    from keras.layers import Conv2D, MaxPooling2D
    
    # 读取数据
    df = pd.read_csv('F://verifycode_data/data.csv')
    
    # 标签值
    vals = range(31)
    keys = ['1','2','3','4','5','6','7','8','9','A','B','C','D','E','F','G','H','J','K','L','N','P','Q','R','S','T','U','V','X','Y','Z']
    label_dict = dict(zip(keys, vals))
    
    x_data = df[['v'+str(i+1) for i in range(320)]]
    y_data = pd.DataFrame({ 'label':df['label']})
    y_data['class'] = y_data['label'].apply(lambda x: label_dict[x])
    
    # 将数据分为训练集和测试集
    X_train, X_test, Y_train, Y_test = train_test_split(x_data, y_data['class'], test_size=0.3, random_state=42)
    x_train = np.array(X_train).reshape((1167, 20, 16, 1))
    x_test = np.array(X_test).reshape((501, 20, 16, 1))
    
    # 对标签值进行one-hot encoding
    n_classes = 31
    y_train = np_utils.to_categorical(Y_train, n_classes)
    y_val = np_utils.to_categorical(Y_test, n_classes)
    
    input_shape = x_train[0].shape
    
    # CNN模型
    model = Sequential()
    
    # 卷积层和池化层
    model.add(Conv2D(32, kernel_size=(3, 3), input_shape=input_shape, padding='same'))
    model.add(Activation('relu'))
    model.add(Conv2D(32, kernel_size=(3, 3), padding='same'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
    
    # Dropout层
    model.add(Dropout(0.25))
    
    model.add(Conv2D(64, kernel_size=(3, 3), padding='same'))
    model.add(Activation('relu'))
    model.add(Conv2D(64, kernel_size=(3, 3), padding='same'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
    
    model.add(Dropout(0.25))
    
    model.add(Conv2D(128, kernel_size=(3, 3), padding='same'))
    model.add(Activation('relu'))
    model.add(Conv2D(128, kernel_size=(3, 3), padding='same'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
    
    model.add(Dropout(0.25))
    
    model.add(Flatten())
    
    # 全连接层
    model.add(Dense(256, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(128, activation='relu'))
    model.add(Dense(n_classes, activation='softmax'))
    
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    
    # plot model
    plot_model(model, to_file=r'./model.png', show_shapes=True)
    
    # 模型训练
    callbacks = [EarlyStopping(monitor='val_acc', patience=5, verbose=1)]
    batch_size = 64
    n_epochs = 100
    history = model.fit(x_train, y_train, batch_size=batch_size, epochs=n_epochs, \
                        verbose=1, validation_data=(x_test, y_val), callbacks=callbacks)
    
    mp = 'F://verifycode_data/verifycode_Keras.h5'
    model.save(mp)
    
    # 绘制验证集上的准确率曲线
    val_acc = history.history['val_acc']
    plt.plot(range(len(val_acc)), val_acc, label='CNN model')
    plt.title('Validation accuracy on verifycode dataset')
    plt.xlabel('epochs')
    plt.ylabel('accuracy')
    plt.legend()
    plt.show()

在上述代码中，我们训练模型的时候采用了early stopping技巧。early stopping是用于提前停止训练的callbacks。具体地，可以达到当训练集上的loss不在减小（即减小的程度小于某个阈值）的时候停止继续训练。

### 模型训练 ###

运行上述模型训练代码，输出的结果如下：

......(忽略之前的输出)
    Epoch 22/100
    
      64/1167 [>.............................] - ETA: 3s - loss: 0.0399 - acc: 1.0000
     128/1167 [==>...........................] - ETA: 3s - loss: 0.1195 - acc: 0.9844
     192/1167 [===>..........................] - ETA: 2s - loss: 0.1085 - acc: 0.9792
     256/1167 [=====>........................] - ETA: 2s - loss: 0.1132 - acc: 0.9727
     320/1167 [=======>......................] - ETA: 2s - loss: 0.1045 - acc: 0.9750
     384/1167 [========>.....................] - ETA: 2s - loss: 0.1006 - acc: 0.9740
     448/1167 [==========>...................] - ETA: 2s - loss: 0.1522 - acc: 0.9643
     512/1167 [============>.................] - ETA: 1s - loss: 0.1450 - acc: 0.9648
     576/1167 [=============>................] - ETA: 1s - loss: 0.1368 - acc: 0.9653
     640/1167 [===============>..............] - ETA: 1s - loss: 0.1353 - acc: 0.9641
     704/1167 [=================>............] - ETA: 1s - loss: 0.1280 - acc: 0.9659
     768/1167 [==================>...........] - ETA: 1s - loss: 0.1243 - acc: 0.9674
     832/1167 [====================>.........] - ETA: 0s - loss: 0.1577 - acc: 0.9639
     896/1167 [======================>.......] - ETA: 0s - loss: 0.1488 - acc: 0.9665
     960/1167 [=======================>......] - ETA: 0s - loss: 0.1488 - acc: 0.9656
    1024/1167 [=========================>....] - ETA: 0s - loss: 0.1427 - acc: 0.9668
    1088/1167 [==========================>...] - ETA: 0s - loss: 0.1435 - acc: 0.9669
    1152/1167 [============================>.] - ETA: 0s - loss: 0.1383 - acc: 0.9688
    1167/1167 [==============================] - 4s 3ms/step - loss: 0.1380 - acc: 0.9683 - val_loss: 0.0835 - val_acc: 0.9760
    Epoch 00022: early stopping

可以看到，一共训练了21次，最近一次的训练后，在测试集上的准确率为96.83%。在测试集的准确率曲线如下图：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2pjbGlhbjkx_size_16_color_FFFFFF_t_70 3][]

### 模型预测 ###

模型训练完后，我们对新的验证码进行预测。新的100张验证码如下图：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2pjbGlhbjkx_size_16_color_FFFFFF_t_70 4][]

使用训练好的CNN模型，对这些新的验证码进行预测，预测的Python代码如下：

# -*- coding: utf-8 -*-
    
    import os
    import cv2
    import numpy as np
    
    def split_picture(imagepath):
    
        # 以灰度模式读取图片
        gray = cv2.imread(imagepath, 0)
    
        # 将图片的边缘变为白色
        height, width = gray.shape
        for i in range(width):
            gray[0, i] = 255
            gray[height-1, i] = 255
        for j in range(height):
            gray[j, 0] = 255
            gray[j, width-1] = 255
    
        # 中值滤波
        blur = cv2.medianBlur(gray, 3) #模板大小3*3
    
        # 二值化
        ret,thresh1 = cv2.threshold(blur, 200, 255, cv2.THRESH_BINARY)
    
        # 提取单个字符
        chars_list = []
        image, contours, hierarchy = cv2.findContours(thresh1, 2, 2)
        for cnt in contours:
            # 最小的外接矩形
            x, y, w, h = cv2.boundingRect(cnt)
            if x != 0 and y != 0 and w*h >= 100:
                chars_list.append((x,y,w,h))
    
        sorted_chars_list = sorted(chars_list, key=lambda x:x[0])
        for i,item in enumerate(sorted_chars_list):
            x, y, w, h = item
            cv2.imwrite('F://test_verifycode/chars/%d.jpg'%(i+1), thresh1[y:y+h, x:x+w])
    
    def remove_edge_picture(imagepath):
    
        image = cv2.imread(imagepath, 0)
        height, width = image.shape
        corner_list = [image[0,0] < 127,
                       image[height-1, 0] < 127,
                       image[0, width-1]<127,
                       image[ height-1, width-1] < 127
                       ]
        if sum(corner_list) >= 3:
            os.remove(imagepath)
    
    def resplit_with_parts(imagepath, parts):
        image = cv2.imread(imagepath, 0)
        os.remove(imagepath)
        height, width = image.shape
    
        file_name = imagepath.split('/')[-1].split(r'.')[0]
        # 将图片重新分裂成parts部分
        step = width//parts     # 步长
        start = 0             # 起始位置
        for i in range(parts):
            cv2.imwrite('F://test_verifycode/chars/%s.jpg'%(file_name+'-'+str(i)), \
                        image[:, start:start+step])
            start += step
    
    def resplit(imagepath):
    
        image = cv2.imread(imagepath, 0)
        height, width = image.shape
    
        if width >= 64:
            resplit_with_parts(imagepath, 4)
        elif width >= 48:
            resplit_with_parts(imagepath, 3)
        elif width >= 26:
            resplit_with_parts(imagepath, 2)
    
    # rename and convert to 16*20 size
    def convert(dir, file):
    
        imagepath = dir+'/'+file
        # 读取图片
        image = cv2.imread(imagepath, 0)
        # 二值化
        ret, thresh = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)
        img = cv2.resize(thresh, (16, 20), interpolation=cv2.INTER_AREA)
        # 保存图片
        cv2.imwrite('%s/%s' % (dir, file), img)
    
    # 读取图片的数据，并转化为0-1值
    def Read_Data(dir, file):
    
        imagepath = dir+'/'+file
        # 读取图片
        image = cv2.imread(imagepath, 0)
        # 二值化
        ret, thresh = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)
        # 显示图片
        bin_values = [1 if pixel==255 else 0 for pixel in thresh.ravel()]
    
        return bin_values
    
    def predict(VerifyCodePath):
    
        dir = 'F://test_verifycode/chars'
        files = os.listdir(dir)
    
        # 清空原有的文件
        if files:
            for file in files:
                os.remove(dir + '/' + file)
    
        split_picture(VerifyCodePath)
    
        files = os.listdir(dir)
        if not files:
            print('查看的文件夹为空！')
        else:
    
            # 去除噪声图片
            for file in files:
                remove_edge_picture(dir + '/' + file)
    
            # 对黏连图片进行重分割
            for file in os.listdir(dir):
                resplit(dir + '/' + file)
    
            # 将图片统一调整至16*20大小
            for file in os.listdir(dir):
                convert(dir, file)
    
            # 图片中的字符代表的向量
            files = sorted(os.listdir(dir), key=lambda x: x[0])
            table = np.array([Read_Data(dir, file) for file in files]).reshape(-1,20,16,1)
    
            # 模型保存地址
            mp = 'F://verifycode_data/verifycode_Keras.h5'
            # 载入模型
            from keras.models import load_model
            cnn = load_model(mp)
            # 模型预测
            y_pred = cnn.predict(table)
            predictions = np.argmax(y_pred, axis=1)
    
            # 标签字典
            keys = range(31)
            vals = ['1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K', 'L', 'N',
                    'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'X', 'Y', 'Z']
            label_dict = dict(zip(keys, vals))
    
            return ''.join([label_dict[pred] for pred in predictions])
    
    def main():
    
        dir = 'F://VerifyCode/'
        correct = 0
        for i, file in enumerate(os.listdir(dir)):
            true_label = file.split('.')[0]
            VerifyCodePath = dir+file
            pred = predict(VerifyCodePath)
    
            if true_label == pred:
                correct += 1
            print(i+1, (true_label, pred), true_label == pred, correct)
    
        total = len(os.listdir(dir))
        print('\n总共图片：%d张\n识别正确：%d张\n识别准确率:%.2f%%.'\
              %(total, correct, correct*100/total))
    
    main()

以下是该CNN模型的预测结果：

Using TensorFlow backend.
    2018-10-25 15:13:50.390130: I C:\tf_jenkins\workspace\rel-win\M\windows\PY\35\tensorflow\core\platform\cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
    1 ('ZK6N', 'ZK6N') True 1
    2 ('4JPX', '4JPX') True 2
    3 ('5GP5', '5GP5') True 3
    4 ('5RQ8', '5RQ8') True 4
    5 ('5TQP', '5TQP') True 5
    6 ('7S62', '7S62') True 6
    7 ('8R2Z', '8R2Z') True 7
    8 ('8RFV', '8RFV') True 8
    9 ('9BBT', '9BBT') True 9
    10 ('9LNE', '9LNE') True 10
    11 ('67UH', '67UH') True 11
    12 ('74UK', '74UK') True 12
    13 ('A5T2', 'A5T2') True 13
    14 ('AHYV', 'AHYV') True 14
    15 ('ASEY', 'ASEY') True 15
    16 ('B371', 'B371') True 16
    17 ('CCQL', 'CCQL') True 17
    18 ('CFD5', 'GFD5') False 17
    19 ('CJLJ', 'CJLJ') True 18
    20 ('D4QV', 'D4QV') True 19
    21 ('DFQ8', 'DFQ8') True 20
    22 ('DP18', 'DP18') True 21
    23 ('E3HC', 'E3HC') True 22
    24 ('E8VB', 'E8VB') True 23
    25 ('DE1U', 'DE1U') True 24
    26 ('FK1R', 'FK1R') True 25
    27 ('FK91', 'FK91') True 26
    28 ('FSKP', 'FSKP') True 27
    29 ('FVZP', 'FVZP') True 28
    30 ('GC6H', 'GC6H') True 29
    31 ('GH62', 'GH62') True 30
    32 ('H9FQ', 'H9FQ') True 31
    33 ('H67Q', 'H67Q') True 32
    34 ('HEKC', 'HEKC') True 33
    35 ('HV2B', 'HV2B') True 34
    36 ('J65Z', 'J65Z') True 35
    37 ('JZCX', 'JZCX') True 36
    38 ('KH5D', 'KH5D') True 37
    39 ('KXD2', 'KXD2') True 38
    40 ('1GDH', '1GDH') True 39
    41 ('LCL3', 'LCL3') True 40
    42 ('LNZR', 'LNZR') True 41
    43 ('LZU5', 'LZU5') True 42
    44 ('N5AK', 'N5AK') True 43
    45 ('N5Q3', 'N5Q3') True 44
    46 ('N96Z', 'N96Z') True 45
    47 ('NCDG', 'NCDG') True 46
    48 ('NELS', 'NELS') True 47
    49 ('P96U', 'P96U') True 48
    50 ('PD42', 'PD42') True 49
    51 ('PECG', 'PEQG') False 49
    52 ('PPZF', 'PPZF') True 50
    53 ('PUUL', 'PUUL') True 51
    54 ('Q2DN', 'D2DN') False 51
    55 ('QCQ9', 'QCQ9') True 52
    56 ('QDB1', 'QDBJ') False 52
    57 ('QZUD', 'QZUD') True 53
    58 ('R3T5', 'R3T5') True 54
    59 ('S1YT', 'S1YT') True 55
    60 ('SP7L', 'SP7L') True 56
    61 ('SR2K', 'SR2K') True 57
    62 ('SUP5', 'SVP5') False 57
    63 ('T2SP', 'T2SP') True 58
    64 ('U6V9', 'U6V9') True 59
    65 ('UC9P', 'UC9P') True 60
    66 ('UFYD', 'UFYD') True 61
    67 ('V9NJ', 'V9NH') False 61
    68 ('V35X', 'V35X') True 62
    69 ('V98F', 'V98F') True 63
    70 ('VD28', 'VD28') True 64
    71 ('YGHE', 'YGHE') True 65
    72 ('YNKD', 'YNKD') True 66
    73 ('YVXV', 'YVXV') True 67
    74 ('ZFBS', 'ZFBS') True 68
    75 ('ET6X', 'ET6X') True 69
    76 ('TKVC', 'TKVC') True 70
    77 ('2UCU', '2UCU') True 71
    78 ('HNBK', 'HNBK') True 72
    79 ('X8FD', 'X8FD') True 73
    80 ('ZGNX', 'ZGNX') True 74
    81 ('LQCU', 'LQCU') True 75
    82 ('JNZY', 'JNZVY') False 75
    83 ('RX34', 'RX34') True 76
    84 ('811E', '811E') True 77
    85 ('ETDX', 'ETDX') True 78
    86 ('4CPR', '4CPR') True 79
    87 ('FE91', 'FE91') True 80
    88 ('B7XH', 'B7XH') True 81
    89 ('1RUA', '1RUA') True 82
    90 ('UBCX', 'UBCX') True 83
    91 ('KVT5', 'KVT5') True 84
    92 ('HZ3A', 'HZ3A') True 85
    93 ('3XLR', '3XLR') True 86
    94 ('VC7T', 'VC7T') True 87
    95 ('7PG1', '7PQ1') False 87
    96 ('4F21', '4F21') True 88
    97 ('3HLJ', '3HLJ') True 89
    98 ('1KT7', '1KT7') True 90
    99 ('1RHE', '1RHE') True 91
    100 ('1TTA', '1TTA') True 92
    
    总共图片：100张
    识别正确：92张
    识别准确率:92.00%.

可以看到，该训练后的CNN模型，其预测新验证的准确率在90%以上。

### 总结 ###

在文章[CNN大战验证码][CNN]中，笔者使用TensorFlow搭建了CNN模型，代码较长，训练时间在两个小时以上，而使用Keras搭建该模型，代码简洁，且使用early stopping技巧后能缩短训练时间，同时保证模型的准确率，由此可见Keras的优势所在。  
  该项目已开源，Github地址为：[https://github.com/percent4/CNN\_4\_Verifycode。][https_github.com_percent4_CNN_4_Verifycode]

> 注意：本人现已开通微信公众号： Python爬虫与算法（微信号为：easy\_web\_scrape）， 欢迎大家关注哦~~

[CNN]: https://www.jianshu.com/p/0287bcd24f78
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[https_github.com_percent4_CNN_4_Verifycode]: https://github.com/percent4/CNN_4_Verifycode%E3%80%82