Python搭建Keras CNN模型破解网站验证码的实现

砸漏

发布于 2020-10-27 14:50:26

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发布于 2020-10-27 14:50:26

文章被收录于专栏：恩蓝脚本

在本项目中，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。验证码如下：

利用Keras可以快速方便地搭建CNN模型，本项目搭建的CNN模型如下：

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('./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 = './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，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。

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，花费几分钟，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：

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：

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('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]
# 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('./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
# 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 = './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 = './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 = './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: 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 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。

Demo及数据集下载网站：CNN_4_Verifycode_jb51.rar

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