In this article, we will use Keras to build a slightly more complex model of CNN to break above the verification code. Codes are as follows:
!
data set
In this project, we are now a total sample is 1668 samples, each sample is a character picture, the picture size of the character is 16 * 20. Wherein the sample image is a character pixel, 0 represents a white, a black representative of each sample to 320 wherein, a value of 0 or 1, wherein v1 is a variable name to V320, the sample is the character class label. Portion of the entire data set as follows:
CNN model
Keras can use to quickly and easily build a CNN model, we built CNN model is as follows:
The data set into training and test sets, accounting for 8: 2, the model training code is as follows:
# -*- 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()
In the above code, we train the model when using early stopping skills. early stopping is stopped prematurely callbacks for training. In particular, the loss can be achieved when the training set is not reduced (i.e., degree of reduction is less than a certain threshold) when the train is stopped continues.
Model training
Results of running the above model training code and output as follows:
......(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
一号学习基地已满 二号学习基地群号906407826 海量精品资料免费领取
You can see, training a total of 21 times, after a recent training, the accuracy rate of 96.83% on the test set. In the test set accuracy graph below:
Model predictions
After training model, we predict the new code. 100 new codes as shown below:
Use trained CNN model, these new code are forecasted Python code as follows:
# -*- 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('
总共图片：%d张
识别正确：%d张
识别准确率:%.2f%%.'
%(total, correct, correct*100/total))
main()
The following is the result of the prediction model of CNN:
sing 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
Total Pictures: 100
correctly identify: 92
recognition accuracy rate: 92.00%
can be seen, after the training CNN model that predicts the new verification accuracy rate of 90%.