RNN入门（二）识别验证码
介绍
作为RNN的第二个demo，笔者将会介绍RNN模型在识别验证码方面的应用。
我们的验证码及样本数据集来自于博客： CNN大战验证码,在这篇博客中，我们已经准备好了所需的样本数据集，不需要在辛辛苦苦地再弄一遍，直接调用data.csv就可以进行建模了。
RNN模型
用TensorFlow搭建简单RNN模型，因为是多分类问题，所以在最后的输出部分再加一softmax层，损失函数采用对数损失函数，optimizer选择RMSPropOptimizer。以下是RNN模型的完整Python代码(TensorFlow_RNN.py)：
# -*- coding: utf-8 -*-
import tensorflow as tf
import logging
# 设置日志
logging.basicConfig(level = logging.INFO, format='%(asctime)s - %(levelname)s: %(message)s')
logger = logging.getLogger(__name__)
# RNN类
class RNN:
# 初始化
# 参数说明： element_size: 元素大小
# time_steps: 序列大小
# num_classes: 目标变量的类别总数
# batch_size: 图片总数
# hidden_layer_size: 隐藏层的神经元个数
# epoch: 训练次数
# learning_rate: 用RMSProp优化时的学习率
# save_model_path: 模型保存地址
def __init__(self, element_size, time_steps, num_classes, batch_size, hidden_layer_size = 150,
epoch = 1000, learning_rate=0.001, save_model_path = r'./logs/RNN_train.ckpt'):
self.epoch = epoch
self.learning_rate = learning_rate
self.save_model_path = save_model_path
# 设置RNN结构
self.element_size = element_size
self.time_steps = time_steps
self.num_classes = num_classes
self.batch_size = batch_size
self.hidden_layer_size = hidden_layer_size
# 输入向量和输出向量
self._inputs = tf.placeholder(tf.float32, shape=[None, self.time_steps, self.element_size], name='inputs')
self.y = tf.placeholder(tf.float32, shape=[None, self.num_classes], name='inputs')
# 利用TensorFlow的内置函数BasicRNNCell, dynamic_rnn来构建RNN的基本模块
rnn_cell = tf.contrib.rnn.BasicRNNCell(self.hidden_layer_size)
outputs, _ = tf.nn.dynamic_rnn(rnn_cell, self._inputs, dtype=tf.float32)
Wl = tf.Variable(tf.truncated_normal([self.hidden_layer_size, self.num_classes], mean=0, stddev=.01))
bl = tf.Variable(tf.truncated_normal([self.num_classes], mean=0, stddev=.01))
def get_linear_layer(vector):
return tf.matmul(vector, Wl) + bl
# 取输出的向量outputs中的最后一个向量最为最终输出
last_rnn_output = outputs[:, -1, :]
self.final_output = get_linear_layer(last_rnn_output)
# 定义损失函数并用RMSProp优化
softmax = tf.nn.softmax_cross_entropy_with_logits(logits=self.final_output, labels=self.y)
self.cross_entropy = tf.reduce_mean(softmax)
self.train_model = tf.train.RMSPropOptimizer(self.learning_rate, 0.9).minimize(self.cross_entropy)
self.saver = tf.train.Saver()
logger.info('Initialize RNN model...')
# 模型训练
def train(self, x_data, y_data):
logger.info('Training RNN model...')
with tf.Session() as sess:
# 对所有变量进行初始化
sess.run(tf.global_variables_initializer())
# 进行迭代学习
feed_dict = {self._inputs: x_data, self.y: y_data}
for i in range(self.epoch + 1):
sess.run(self.train_model, feed_dict=feed_dict)
if i % int(self.epoch / 50) == 0:
# to see the step improvement
print('已训练%d次, loss: %s.' % (i, sess.run(self.cross_entropy, feed_dict=feed_dict)))
# 保存RNN模型
logger.info('Saving RNN model...')
self.saver.save(sess, self.save_model_path)
# 对新数据进行预测
def predict(self, data):
with tf.Session() as sess:
logger.info('Restoring RNN model...')
self.saver.restore(sess, self.save_model_path)
predict = sess.run(self.final_output, feed_dict={self._inputs: data})
return predict
模型训练
对样本数据集data.csv进行RNN建模，将数据集分为训练集和测试集，各占70%和30%.因为图片的大小为16*20,所以在将图片看成序列时，序列的长度为20，每一时刻的向量含有16个元素，共有31个目标类，取隐藏层大小为300，总共训练1000次。 完整的Python代码如下：
# -*- coding: utf-8 -*-
"""
数字字母识别
利用RNN对验证码的数据集进行多分类
"""
from TensorFlow_RNN import RNN
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import LabelBinarizer
CSV_FILE_PATH = 'F://验证码识别/data.csv' # CSV 文件路径
df = pd.read_csv(CSV_FILE_PATH) # 读取CSV文件
# 数据集的特征
features = ['v'+str(i+1) for i in range(16*20)]
labels = df['label'].unique()
# 对样本的真实标签进行标签二值化
lb = LabelBinarizer()
lb.fit(labels)
y_ture = pd.DataFrame(lb.transform(df['label']), columns=['y'+str(i) for i in range(31)])
y_bin_columns = list(y_ture.columns)
for col in y_bin_columns:
df[col] = y_ture[col]
# 将数据集分为训练集和测试集，训练集70%, 测试集30%
x_train, x_test, y_train, y_test = train_test_split(df[features], df[y_bin_columns], \
train_size = 0.7, test_size=0.3, random_state=123)
# 构建RNN网络
# 模型保存地址
MODEL_SAVE_PATH = 'F:///验证码识别/logs/RNN_train.ckpt'
# RNN初始化
element_size = 16
time_steps = 20
num_classes = 31
hidden_layer_size = 300
batch_size = 960
new_x_train = np.array(x_train).reshape((-1, time_steps, element_size))
new_x_test = np.array(x_test).reshape((-1, time_steps, element_size))
rnn = RNN(element_size=element_size,
time_steps=time_steps,
num_classes=num_classes,
batch_size=batch_size,
hidden_layer_size= hidden_layer_size,
epoch=1000,
save_model_path=MODEL_SAVE_PATH,
)
# 训练RNN
rnn.train(new_x_train, y_train)
# 预测数据
y_pred = rnn.predict(new_x_test)
# 预测分类
label = '123456789ABCDEFGHJKLNPQRSTUVXYZ'
prediction = []
for pred in y_pred:
label = labels[list(pred).index(max(pred))]
prediction.append(label)
# 计算预测的准确率
x_test['prediction'] = prediction
x_test['label'] = df['label'][y_test.index]
print(x_test.head())
accuracy = accuracy_score(x_test['prediction'], x_test['label'])
print('CNN的预测准确率为%.2f%%.'%(accuracy*100))
以下是模型训练的结果：
2018-09-26 11:18:12,339 - INFO: Initialize RNN model...
2018-09-26 11:18:12,340 - INFO: Training RNN model...
已训练0次, loss: 3.43417.
已训练20次, loss: 3.42695.
已训练40次, loss: 3.40638.
已训练60次, loss: 3.33286.
已训练80次, loss: 2.78305.
已训练100次, loss: 2.33391.
已训练120次, loss: 1.15871.
已训练140次, loss: 0.659932.
已训练160次, loss: 0.566225.
已训练180次, loss: 0.397372.
已训练200次, loss: 0.317218.
已训练220次, loss: 0.346782.
已训练240次, loss: 0.639625.
已训练260次, loss: 0.0575929.
已训练280次, loss: 0.100429.
已训练300次, loss: 0.0347529.
已训练320次, loss: 0.0189503.
已训练340次, loss: 0.0265893.
已训练360次, loss: 0.0151181.
已训练380次, loss: 1.18662.
已训练400次, loss: 0.0164317.
已训练420次, loss: 0.00819814.
已训练440次, loss: 0.0041992.
已训练460次, loss: 0.0206414.
已训练480次, loss: 0.00826409.
已训练500次, loss: 0.00398952.
已训练520次, loss: 0.00214751.
已训练540次, loss: 0.0365587.
已训练560次, loss: 0.00738376.
已训练580次, loss: 0.00302118.
已训练600次, loss: 0.00161713.
已训练620次, loss: 0.000885372.
已训练640次, loss: 1.24874.
已训练660次, loss: 0.00601175.
已训练680次, loss: 0.0023275.
已训练700次, loss: 0.00121995.
已训练720次, loss: 0.000705643.
已训练740次, loss: 0.000407971.
已训练760次, loss: 0.000219642.
已训练780次, loss: 0.0889083.
已训练800次, loss: 0.00395974.
已训练820次, loss: 0.00131215.
已训练840次, loss: 0.000631665.
已训练860次, loss: 0.000342329.
已训练880次, loss: 0.000191806.
已训练900次, loss: 0.000108547.
已训练920次, loss: 6.29806e-05.
已训练940次, loss: 3.99281e-05.
已训练960次, loss: 0.0124334.
已训练980次, loss: 0.00142853.
2018-09-26 11:26:08,302 - INFO: Saving RNN model...
已训练1000次, loss: 0.000571731.
2018-09-26 11:26:08,761 - INFO: Restoring RNN model...
INFO:tensorflow:Restoring parameters from F:///验证码识别/logs/RNN_train.ckpt
2018-09-26 11:26:08,761 - INFO: Restoring parameters from F:///验证码识别/logs/RNN_train.ckpt
v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 ... v313 v314 v315 v316 \
657 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1
18 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1
700 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1
221 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1
1219 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1
v317 v318 v319 v320 prediction label
657 1 1 1 1 G G
18 1 1 1 1 1 1
700 1 1 1 1 H H
221 1 1 1 1 5 5
1219 1 1 1 1 V V
[5 rows x 322 columns]
CNN的预测准确率为93.69%.
总共的训练时间为8分钟，在测试集上的准确为93.69%.与CNN相比，测试集上的准确率略高，训练时间却明显减少，因为CNN训练1000次的时间为75分钟。总的来说，该RNN模型在这个数据集的表现优于之前的CNN模型。
模型预测
接着，我们利用刚才训练好的CNN模型，对新验证码进行识别，看看模型的识别效果。
笔者采集了50张新验证码，如下：
完整的预测新验证码的Python脚本如下：
# -*- coding: utf-8 -*-
"""
利用训练好的RNN模型对验证码进行识别
（共训练960条数据，训练1000次测试集上的准确率为95.15%.）
"""
import os
import cv2
import pandas as pd
import numpy as np
from TensorFlow_RNN import RNN
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://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://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(rnn, VerifyCodePath, time_steps, element_size):
dir = 'F://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 = [Read_Data(dir, file) for file in files]
test_data = pd.DataFrame(table, columns=['v%d' % i for i in range(1, 321)])
new_test_data = np.array(test_data).reshape((-1, time_steps, element_size))
y_pred = rnn.predict(new_test_data)
# 预测分类
prediction = []
labels = '123456789ABCDEFGHJKLNPQRSTUVXYZ'
for pred in y_pred:
label = labels[list(pred).index(max(pred))]
prediction.append(label)
TRUE_LABEL = VerifyCodePath.split('/')[-1].split(r'.')[0]
return TRUE_LABEL, ''.join(prediction)
def main():
# 创建RNN预测模型
# 模型保存地址
MODEL_SAVE_PATH = 'F:///验证码识别/logs/RNN_train.ckpt'
# RNN初始化
element_size = 16
time_steps = 20
num_classes = 31
batch_size = 4
hidden_layer_size = 300
rnn = RNN(element_size=element_size,
time_steps=time_steps,
num_classes=num_classes,
batch_size=batch_size,
hidden_layer_size=hidden_layer_size,
epoch=1000,
save_model_path=MODEL_SAVE_PATH,
)
# 预测验证码
pred_list = []
dir = 'F://VerifyCode/'
for file in os.listdir(dir):
VerifyCodePath = dir+file
label, prediction = predict(rnn, VerifyCodePath, time_steps, element_size)
pred_list.append((label, prediction))
# print('真实值为：%s, 预测结果为: %s.'%(label, prediction))
# 统计预测正确的验证码的数量及准确率
total_images = len(pred_list)
correct_pred = sum([1 if x[0] == x[1] else 0 for x in pred_list])
accuracy = correct_pred*100/total_images
print("\n一共有%d张图片，识别正确的图片为%d张，\n"
"RNN的预测准确率为%.2f%%."
%(total_images, correct_pred, accuracy))
main()
输出的结果如下：
一共有50张图片，识别正确的图片为45张，
RNN的预测准确率为90.00%.
识别的效果相当可以。
总结
对于用RNN识别图像，有时候其表现不会比CNN模型差，在训练时间上有明显改善。
笔者将会持续RNN方面的研究，欢迎大家关注~
注意：本人现已开通微信公众号： 轻松学会Python爬虫（微信号为：easy_web_scrape）， 欢迎大家关注哦~~
