Python搭建Keras CNN模型破解網(wǎng)站驗證碼的實現(xiàn)

更新時間：2020年04月07日 10:31:28 作者：不脫發(fā)的程序猿

這篇文章主要介紹了Python搭建Keras CNN模型破解網(wǎng)站驗證碼的實現(xiàn)，文中通過示例代碼介紹的非常詳細，對大家的學習或者工作具有一定的參考學習價值，需要的朋友們下面隨著小編來一起學習學習吧

在本項目中，將會用Keras來搭建一個稍微復(fù)雜的CNN模型來破解以上的驗證碼。驗證碼如下：

利用Keras可以快速方便地搭建CNN模型，本項目搭建的CNN模型如下：

將數(shù)據(jù)集分為訓練集和測試集，占比為8：2，該模型訓練的代碼如下：

# -*- 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
 
# 讀取數(shù)據(jù)
df = pd.read_csv('./data.csv')
 
# 標簽值
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])
 
# 將數(shù)據(jù)分為訓練集和測試集
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))
 
# 對標簽值進行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()
 
# 卷積層和池化層
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層
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())
 
# 全連接層
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)
 
# 模型訓練
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 = './verifycode_Keras.h5'
model.save(mp)
 
# 繪制驗證集上的準確率曲線
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()

在上述代碼中，訓練模型的時候采用了early stopping技巧。early stopping是用于提前停止訓練的callbacks。具體地，可以達到當訓練集上的loss不在減?。礈p小的程度小于某個閾值）的時候停止繼續(xù)訓練。

運行上述模型訓練代碼，輸出的結(jié)果如下：

......(忽略之前的輸出)
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，花費幾分鐘，一共訓練了21次，最近一次的訓練后，在測試集上的準確率為96.83%。在測試集的準確率曲線如下圖：

模型訓練完后，我們對新的驗證碼進行預(yù)測。新的100張驗證碼如下圖：

使用訓練好的CNN模型，對這些新的驗證碼進行預(yù)測，預(yù)測的Python代碼如下：

# -*- coding: utf-8 -*-
 
import os
import cv2
import numpy as np
 
def split_picture(imagepath):
 
  # 以灰度模式讀取圖片
  gray = cv2.imread(imagepath, 0)
 
  # 將圖片的邊緣變?yōu)榘咨?
  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
 
  # 中值濾波
  blur = cv2.medianBlur(gray, 3) #模板大小3*3
 
  # 二值化
  ret,thresh1 = cv2.threshold(blur, 200, 255, cv2.THRESH_BINARY)
 
  # 提取單個字符
  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('test_verifycode/%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]
  # 將圖片重新分裂成parts部分
  step = width//parts   # 步長
  start = 0       # 起始位置
  for i in range(parts):
    cv2.imwrite('./test_verifycode/%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
  # 讀取圖片
  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)
  # 保存圖片
  cv2.imwrite('%s/%s' % (dir, file), img)
 
# 讀取圖片的數(shù)據(jù)，并轉(zhuǎn)化為0-1值
def Read_Data(dir, file):
 
  imagepath = dir+'/'+file
  # 讀取圖片
  image = cv2.imread(imagepath, 0)
  # 二值化
  ret, thresh = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)
  # 顯示圖片
  bin_values = [1 if pixel==255 else 0 for pixel in thresh.ravel()]
 
  return bin_values
 
def predict(VerifyCodePath):
 
  dir = './test_verifycode'
  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('查看的文件夾為空！')
  else:
 
    # 去除噪聲圖片
    for file in files:
      remove_edge_picture(dir + '/' + file)
 
    # 對黏連圖片進行重分割
    for file in os.listdir(dir):
      resplit(dir + '/' + file)
 
    # 將圖片統(tǒng)一調(diào)整至16*20大小
    for file in os.listdir(dir):
      convert(dir, file)
 
    # 圖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 = './verifycode_Keras.h5'
    # 載入模型
    from keras.models import load_model
    cnn = load_model(mp)
    # 模型預(yù)測
    y_pred = cnn.predict(table)
    predictions = np.argmax(y_pred, axis=1)
 
    # 標簽字典
    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 = './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總共圖片：%d張\n識別正確：%d張\n識別準確率:%.2f%%.'\
     %(total, correct, correct*100/total))
 
main()

以下是該CNN模型的預(yù)測結(jié)果：

Using TensorFlow backend.
2018-10-25 15:13:50.390130: I C: f_jenkinsworkspace
el-winMwindowsPY35 ensorflowcoreplatformcpu_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

總共圖片：100張
識別正確：92張
識別準確率:92.00%.

可以看到，該訓練后的CNN模型，其預(yù)測新驗證的準確率在90%以上。

Demo及數(shù)據(jù)集下載網(wǎng)站：CNN_4_Verifycode_jb51.rar

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