一直都是使用API取得数据，但是API提供的数据较少，且为了防止API关闭，先把验证码问题解决
使用Tensorflow训练模型，强智教务系统的验证码还是比较简单的，不用CNN一样可以识别的很好，使用CNN也算是重新温习一下CNN
训练集3109张，测试集128张，训练1600次，ACC为99%，实际测试准确率98%左右
代码与模型 https://github.com/WindrunnerMax/SWVerifyCode
首先需要手打码，自动获取验证码，并在powershell手动输入验证码，注意目录中的文件夹需要手动建立
引入Convert是为了处理图片，二值化并降噪
手工打200多张验证码，然后开始训练，设置的停止条件低一些，ACC到50%就停止训练
获取一定准确率的模型后再自动获取训练集，再训练，重复过程提高准确率
我重复上述过程三次，最后一次吃了个饭，得到了大约3000张图片的训练集
GetImg.py手动打码
import requests
import cv2
import numpy as np
import sys
from Convert import Convert
class GetImg(object):
"""docstring for GetImg"""
def __init__(self):
super(GetImg, self).__init__()
def run(self):
count = 1
cvt = Convert()
while True:
print("第",count,"张")
req = requests.get("http://xxxxxx/verifycode.servlet")
with open("pv.jpg",'wb') as fb:
fb.write(req.content)
img = cvt.run(req.content)
cv2.imwrite("v.jpg",img)
mark = input()
if mark == "" : continue;
count += 1
cv2.imwrite("TrainImg/%s.jpg" % (mark),img)
if __name__ == '__main__':
GetImg().run()Convert.py预处理图片
import cv2
import numpy as np
class Convert(object):
"""docstring for Convert"""
def __init__(self):
super(Convert, self).__init__()
def _get_dynamic_binary_image(self,img):
'''
自适应阀值二值化
'''
img = cv2.imdecode(np.frombuffer(img, np.uint8), cv2.IMREAD_COLOR)
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
th1 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 21, 1)
return th1
def clear_border(self,img):
'''去除边框
'''
h, w = img.shape[:2]
for y in range(0, w):
for x in range(0, h):
# if y ==0 or y == w -1 or y == w - 2:
if y < 4 or y > w -4:
img[x, y] = 255
# if x == 0 or x == h - 1 or x == h - 2:
if x < 4 or x > h - 4:
img[x, y] = 255
return img
def interference_line(self,img):
'''
干扰线降噪
'''
h, w = img.shape[:2]
# ！！！opencv矩阵点是反的
# img[1,2] 1:图片的高度，2：图片的宽度
for y in range(1, w - 1):
for x in range(1, h - 1):
count = 0
if img[x, y - 1] > 245:
count = count + 1
if img[x, y + 1] > 245:
count = count + 1
if img[x - 1, y] > 245:
count = count + 1
if img[x + 1, y] > 245:
count = count + 1
if count > 2:
img[x, y] = 255
return img
def interference_point(self,img, x = 0, y = 0):
"""点降噪
9邻域框,以当前点为中心的田字框,黑点个数
:param x:
:param y:
:return:
"""
# todo 判断图片的长宽度下限
cur_pixel = img[x,y]# 当前像素点的值
height,width = img.shape[:2]
for y in range(0, width - 1):
for x in range(0, height - 1):
if y == 0: # 第一行
if x == 0: # 左上顶点,4邻域
# 中心点旁边3个点
sum = int(cur_pixel) \
+ int(img[x, y + 1]) \
+ int(img[x + 1, y]) \
+ int(img[x + 1, y + 1])
if sum <= 2 * 245:
img[x, y] = 0
elif x == height - 1: # 右上顶点
sum = int(cur_pixel) \
+ int(img[x, y + 1]) \
+ int(img[x - 1, y]) \
+ int(img[x - 1, y + 1])
if sum <= 2 * 245:
img[x, y] = 0
else: # 最上非顶点,6邻域
sum = int(img[x - 1, y]) \
+ int(img[x - 1, y + 1]) \
+ int(cur_pixel) \
+ int(img[x, y + 1]) \
+ int(img[x + 1, y]) \
+ int(img[x + 1, y + 1])
if sum <= 3 * 245:
img[x, y] = 0
elif y == width - 1: # 最下面一行
if x == 0: # 左下顶点
# 中心点旁边3个点
sum = int(cur_pixel) \
+ int(img[x + 1, y]) \
+ int(img[x + 1, y - 1]) \
+ int(img[x, y - 1])
if sum <= 2 * 245:
img[x, y] = 0
elif x == height - 1: # 右下顶点
sum = int(cur_pixel) \
+ int(img[x, y - 1]) \
+ int(img[x - 1, y]) \
+ int(img[x - 1, y - 1])
if sum <= 2 * 245:
img[x, y] = 0
else: # 最下非顶点,6邻域
sum = int(cur_pixel) \
+ int(img[x - 1, y]) \
+ int(img[x + 1, y]) \
+ int(img[x, y - 1]) \
+ int(img[x - 1, y - 1]) \
+ int(img[x + 1, y - 1])
if sum <= 3 * 245:
img[x, y] = 0
else: # y不在边界
if x == 0: # 左边非顶点
sum = int(img[x, y - 1]) \
+ int(cur_pixel) \
+ int(img[x, y + 1]) \
+ int(img[x + 1, y - 1]) \
+ int(img[x + 1, y]) \
+ int(img[x + 1, y + 1])
if sum <= 3 * 245:
img[x, y] = 0
elif x == height - 1: # 右边非顶点
sum = int(img[x, y - 1]) \
+ int(cur_pixel) \
+ int(img[x, y + 1]) \
+ int(img[x - 1, y - 1]) \
+ int(img[x - 1, y]) \
+ int(img[x - 1, y + 1])
if sum <= 3 * 245:
img[x, y] = 0
else: # 具备9领域条件的
sum = int(img[x - 1, y - 1]) \
+ int(img[x - 1, y]) \
+ int(img[x - 1, y + 1]) \
+ int(img[x, y - 1]) \
+ int(cur_pixel) \
+ int(img[x, y + 1]) \
+ int(img[x + 1, y - 1]) \
+ int(img[x + 1, y]) \
+ int(img[x + 1, y + 1])
if sum <= 4 * 245:
img[x, y] = 0
return img
def run(self,img):
# 自适应阈值二值化
img = self._get_dynamic_binary_image(img)
# 去除边框
img = self.clear_border(img)
# 对图片进行干扰线降噪
img = self.interference_line(img)
# 对图片进行点降噪
img = self.interference_point(img)
return img我们强智的验证码只有['1', '2', '3', 'b', 'c', 'm', 'n', 'v', 'x', 'z']字符
图像大小为 22 * 62
学习率设置为 0.01
keep_prob为0.75
具体定义CNN在crack_captcha_cnn方法注释给出
根据重复的训练，估算 0.01 的学习率多少次训练会收敛，ACC能够达到多少，这里设置ACC大于99%停止
CNNTrain.py 训练
#!/usr/bin/python
# -*- coding: utf-8 -*-
#构建CNN，训练分类器
import numpy as np
import tensorflow as tf
import cv2
import os
import random
import time
# number
number = ['1', '2', '3', 'b', 'c', 'm', 'n', 'v', 'x', 'z']
# 图像大小
IMAGE_HEIGHT = 22 # 80
IMAGE_WIDTH = 62 # 160
MAX_CAPTCHA = 4
char_set = number
CHAR_SET_LEN = len(char_set) #10
image_filename_list = []
total = 0
train_path = "TrainImg/"
valid_path = "TestImg/"
model_path = "model/"
def get_image_file_name(imgFilePath):
fileName = []
total = 0
for filePath in os.listdir(imgFilePath):
captcha_name = filePath.split('/')[-1]
fileName.append(captcha_name)
total += 1
random.seed(time.time())
# 打乱顺序
random.shuffle(fileName)
return fileName, total
# 获取训练数据的名称列表
image_filename_list, total = get_image_file_name(train_path)
# 获取测试数据的名称列表
image_filename_list_valid, total = get_image_file_name(valid_path)
# 读取图片和标签
def gen_captcha_text_and_image(imageFilePath, image_filename_list, imageAmount):
num = random.randint(0, imageAmount - 1)
img = cv2.imread(os.path.join(imageFilePath, image_filename_list[num]), 0)
img = cv2.resize(img, (IMAGE_WIDTH, IMAGE_HEIGHT))
img = np.float32(img)
text = image_filename_list[num].split('.')[0]
return text, img
# 文本转向量
# 例如，如果验证码是 ‘0296’ ，则对应的标签是
# [1 0 0 0 0 0 0 0 0 0
# 0 0 1 0 0 0 0 0 0 0
# 0 0 0 0 0 0 0 0 0 1
# 0 0 0 0 0 0 1 0 0 0]
def text2vec(text):
text_len = len(text)
if text_len > MAX_CAPTCHA:
raise ValueError('验证码最长4个字符')
vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)
def char2pos(c):
k = -1
for index, item in enumerate(char_set):
if c == item : return index
if(k == -1) : raise ValueError('字符数组中不存在字符' + c);
return -1
for i, c in enumerate(text):
idx = i * CHAR_SET_LEN + char2pos(c)
vector[idx] = 1
return vector
# 向量转回文本
def vec2text(vec):
char_pos = vec.nonzero()[0]
text = []
for i, c in enumerate(char_pos):
text.append(char_set[c % CHAR_SET_LEN])
return "".join(text)
# 生成一个训练batch
def get_next_batch(imageFilePath, image_filename_list=None, batch_size=128):
batch_x = np.zeros([batch_size, IMAGE_HEIGHT * IMAGE_WIDTH])
batch_y = np.zeros([batch_size, MAX_CAPTCHA * CHAR_SET_LEN])
def wrap_gen_captcha_text_and_image(imageFilePath, imageAmount):
while True:
text, image = gen_captcha_text_and_image(imageFilePath, image_filename_list, imageAmount)
if image.shape == (IMAGE_HEIGHT, IMAGE_WIDTH):
return text, image
for listNum in os.walk(imageFilePath):
pass
imageAmount = len(listNum[2])
for i in range(batch_size):
text, image = wrap_gen_captcha_text_and_image(imageFilePath, imageAmount)
batch_x[i, :] = image.flatten() / 255 # (image.flatten()-128)/128 mean为0
batch_y[i, :] = text2vec(text)
return batch_x, batch_y
####################################################################
# 占位符，X和Y分别是输入训练数据和其标签，标签转换成8*10的向量
X = tf.placeholder(tf.float32, [None, IMAGE_HEIGHT * IMAGE_WIDTH])
Y = tf.placeholder(tf.float32, [None, MAX_CAPTCHA * CHAR_SET_LEN])
# 声明dropout占位符变量
keep_prob = tf.placeholder(tf.float32) # dropout
# 定义CNN
def crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1):
# 把 X reshape 成 IMAGE_HEIGHT*IMAGE_WIDTH*1的格式,输入的是灰度图片，所有通道数是1;
# shape 里的-1表示数量不定，根据实际情况获取，这里为每轮迭代输入的图像数量（batchsize）的大小;
x = tf.reshape(X, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1])
# 搭建第一层卷积层
# shape[3, 3, 1, 32]里前两个参数表示卷积核尺寸大小，即patch;
# 第三个参数是图像通道数，第四个参数是该层卷积核的数量，有多少个卷积核就会输出多少个卷积特征图像
w_c1 = tf.Variable(w_alpha * tf.random_normal([3, 3, 1, 32]))
# 每个卷积核都配置一个偏置量，该层有多少个输出，就应该配置多少个偏置量
b_c1 = tf.Variable(b_alpha * tf.random_normal([32]))
# 图片和卷积核卷积，并加上偏执量，卷积结果28x28x32
# tf.nn.conv2d() 函数实现卷积操作
# tf.nn.conv2d()中的padding用于设置卷积操作对边缘像素的处理方式，在tf中有VALID和SAME两种模式
# padding='SAME'会对图像边缘补0,完成图像上所有像素（特别是边缘象素）的卷积操作
# padding='VALID'会直接丢弃掉图像边缘上不够卷积的像素
# strides：卷积时在图像每一维的步长，是一个一维的向量，长度4，并且strides[0]=strides[3]=1
# tf.nn.bias_add() 函数的作用是将偏置项b_c1加到卷积结果value上去;
# 注意这里的偏置项b_c1必须是一维的，并且数量一定要与卷积结果value最后一维数量相同
# tf.nn.relu() 函数是relu激活函数，实现输出结果的非线性转换，即features=max(features, 0)，输出tensor的形状和输入一致
conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1))
# tf.nn.max_pool()函数实现最大池化操作，进一步提取图像的抽象特征，并且降低特征维度
# ksize=[1, 2, 2, 1]定义最大池化操作的核尺寸为2*2, 池化结果14x14x32 卷积结果乘以池化卷积核
conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
# tf.nn.dropout是tf里为了防止或减轻过拟合而使用的函数，一般用在全连接层;
# Dropout机制就是在不同的训练过程中根据一定概率（大小可以设置，一般情况下训练推荐0.5）随机扔掉（屏蔽）一部分神经元，
# 不参与本次神经网络迭代的计算（优化）过程，权重保留但不做更新;
# tf.nn.dropout()中 keep_prob用于设置概率，需要是一个占位变量，在执行的时候具体给定数值
conv1 = tf.nn.dropout(conv1, keep_prob)
# 原图像HEIGHT = 22 WIDTH = 62，经过神经网络第一层卷积（图像尺寸不变、特征×32）、池化（图像尺寸缩小一半，特征不变）之后;
# 输出大小为 11*31*32
# 搭建第二层卷积层
w_c2 = tf.Variable(w_alpha * tf.random_normal([3, 3, 32, 64]))
b_c2 = tf.Variable(b_alpha * tf.random_normal([64]))
conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, w_c2, strides=[1, 1, 1, 1], padding='SAME'), b_c2))
conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
conv2 = tf.nn.dropout(conv2, keep_prob)
# 经过神经网络第二层运算后输出为 6*16*64 (30*80的图像经过2*2的卷积核池化，padding为SAME，输出维度是6*16)
# 搭建第三层卷积层
w_c3 = tf.Variable(w_alpha * tf.random_normal([3, 3, 64, 64]))
b_c3 = tf.Variable(b_alpha * tf.random_normal([64]))
conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, w_c3, strides=[1, 1, 1, 1], padding='SAME'), b_c3))
conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
conv3 = tf.nn.dropout(conv3, keep_prob)
# 原图像HEIGHT = 22 WIDTH = 62，经过神经网络第一层后输出大小为 11*31*32
# 经过神经网络第二层运算后输出为 6*16*64 ; 经过第三层输出为 3*8*64，这个参数很重要，决定量后边全连接层的维度
# print(conv3)
# 搭建全连接层
# 二维张量，第一个参数3*8*64的patch，这个参数由最后一层卷积层的输出决定，第二个参数代表卷积个数共1024个，即输出为1024个特征
w_d = tf.Variable(w_alpha * tf.random_normal([3 * 8 * 64, 1024]))
# 偏置项为1维，个数跟卷积核个数保持一致
b_d = tf.Variable(b_alpha * tf.random_normal([1024]))
# w_d.get_shape()作用是把张量w_d的形状转换为元组tuple的形式，w_d.get_shape().as_list()是把w_d转为元组再转为list形式
# w_d 的 形状是[ 8 * 20 * 64, 1024]，w_d.get_shape().as_list()结果为 8*20*64=10240 ;
# 所以tf.reshape(conv3, [-1, w_d.get_shape().as_list()[0]])的作用是把最后一层隐藏层的输出转换成一维的形式
dense = tf.reshape(conv3, [-1, w_d.get_shape().as_list()[0]])
# tf.matmul(dense, w_d)函数是矩阵相乘，输出维度是 -1*1024
dense = tf.nn.relu(tf.add(tf.matmul(dense, w_d), b_d))
dense = tf.nn.dropout(dense, keep_prob)
# 经过全连接层之后，输出为 一维，1024个向量
# w_out定义成一个形状为 [1024, 8 * 10] = [1024, 80]
w_out = tf.Variable(w_alpha * tf.random_normal([1024, MAX_CAPTCHA * CHAR_SET_LEN]))
b_out = tf.Variable(b_alpha * tf.random_normal([MAX_CAPTCHA * CHAR_SET_LEN]))
# out 的输出为 8*10 的向量， 8代表识别结果的位数，10是每一位上可能的结果（0到9）
out = tf.add(tf.matmul(dense, w_out), b_out)
# out = tf.nn.softmax(out)
# 输出神经网络在当前参数下的预测值
return out
# 训练
def train_crack_captcha_cnn():
output = crack_captcha_cnn()
# loss
# loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(output, Y))
# tf.nn.sigmoid_cross_entropy_with_logits()函数计算交叉熵,输出的是一个向量而不是数;
# 交叉熵刻画的是实际输出（概率）与期望输出（概率）的距离，也就是交叉熵的值越小，两个概率分布就越接近
# tf.reduce_mean()函数求矩阵的均值
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=Y))
# optimizer 为了加快训练 learning_rate应该开始大，然后慢慢减小
# tf.train.AdamOptimizer（）函数实现了Adam算法的优化器
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
predict = tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN])
max_idx_p = tf.argmax(predict, 2)
max_idx_l = tf.argmax(tf.reshape(Y, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
correct_pred = tf.equal(max_idx_p, max_idx_l)
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
step = 0
while True:
batch_x, batch_y = get_next_batch(train_path, image_filename_list, 64)
_, loss_ = sess.run([optimizer, loss], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75})
print(step, loss_)
# 每100 step计算一次准确率
if step % 100 == 0:
batch_x_test, batch_y_test = get_next_batch(valid_path, image_filename_list_valid, 128)
acc = sess.run(accuracy, feed_dict={X: batch_x_test, Y: batch_y_test, keep_prob: 1.})
print("Predict: " + str(step) + " " + str(acc))
# 训练结束条件
if acc > 0.99 or step > 3000:
saver.save(sess, model_path, global_step=step)
break
step += 1
def predict_captcha(captcha_image):
output = crack_captcha_cnn()
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('.'))
predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
text_list = sess.run(predict, feed_dict={X: [captcha_image], keep_prob: 1})
text = text_list[0].tolist()
vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)
i = 0
for n in text:
vector[i * CHAR_SET_LEN + n] = 1
i += 1
return vec2text(vector)
if __name__ == '__main__':
train_crack_captcha_cnn()
print ("FINISH")
# print(vec2text(text2vec("123z")))
训练集3109张，测试集128张，训练1600次，测试集准确率大约96%
Spot.py测试数据集
import numpy as np
import tensorflow as tf
import cv2
import os
import random
import time
import sys
# number
number = ['1', '2', '3', 'b', 'c', 'm', 'n', 'v', 'x', 'z']
# 图像大小
IMAGE_HEIGHT = 22 # 80
IMAGE_WIDTH = 62 # 160
MAX_CAPTCHA = 4
char_set = number
CHAR_SET_LEN = len(char_set) #10
valid_path = "TestImg/"
model_path = "model/"
X = tf.placeholder(tf.float32, [None, IMAGE_HEIGHT * IMAGE_WIDTH])
Y = tf.placeholder(tf.float32, [None, MAX_CAPTCHA * CHAR_SET_LEN])
keep_prob = tf.placeholder(tf.float32) # dropout
# 定义CNN
def crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1):
x = tf.reshape(X, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1])
# 3 conv layer
w_c1 = tf.Variable(w_alpha * tf.random_normal([3, 3, 1, 32]))
b_c1 = tf.Variable(b_alpha * tf.random_normal([32]))
conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1))
conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
conv1 = tf.nn.dropout(conv1, keep_prob)
w_c2 = tf.Variable(w_alpha * tf.random_normal([3, 3, 32, 64]))
b_c2 = tf.Variable(b_alpha * tf.random_normal([64]))
conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, w_c2, strides=[1, 1, 1, 1], padding='SAME'), b_c2))
conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
conv2 = tf.nn.dropout(conv2, keep_prob)
w_c3 = tf.Variable(w_alpha * tf.random_normal([3, 3, 64, 64]))
b_c3 = tf.Variable(b_alpha * tf.random_normal([64]))
conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, w_c3, strides=[1, 1, 1, 1], padding='SAME'), b_c3))
conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
conv3 = tf.nn.dropout(conv3, keep_prob)
# Fully connected layer
w_d = tf.Variable(w_alpha * tf.random_normal([3 * 8 * 64, 1024]))
b_d = tf.Variable(b_alpha * tf.random_normal([1024]))
dense = tf.reshape(conv3, [-1, w_d.get_shape().as_list()[0]])
dense = tf.nn.relu(tf.add(tf.matmul(dense, w_d), b_d))
dense = tf.nn.dropout(dense, keep_prob)
w_out = tf.Variable(w_alpha * tf.random_normal([1024, MAX_CAPTCHA * CHAR_SET_LEN]))
b_out = tf.Variable(b_alpha * tf.random_normal([MAX_CAPTCHA * CHAR_SET_LEN]))
out = tf.add(tf.matmul(dense, w_out), b_out)
# out = tf.nn.softmax(out)
return out
# 向量转回文本
def vec2text(vec):
char_pos = vec.nonzero()[0]
text = []
for i, c in enumerate(char_pos):
text.append(char_set[c % CHAR_SET_LEN])
return "".join(text)
def predict_captcha(captcha_image):
output = crack_captcha_cnn()
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint(model_path))
predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
text_list = sess.run(predict, feed_dict={X: [captcha_image], keep_prob: 1})
text = text_list[0].tolist()
vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)
i = 0
for n in text:
vector[i * CHAR_SET_LEN + n] = 1
i += 1
return vec2text(vector)
if not os.path.exists(valid_path):
print('Image does not exist, please check!, path:"{}"'.format(os.path.abspath(valid_pathb)))
sys.exit()
image_list = os.listdir(valid_path)
output = crack_captcha_cnn()
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint(model_path))
predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
count = 0
acceptCount = 0
for image_ in image_list:
count += 1
text_ = image_.split('.')[0]
image_p = os.path.join(valid_path, image_)
# 单张图片预测
image = np.float32(cv2.imread(image_p, 0))
image = image.flatten() / 255
text_list = sess.run(predict, feed_dict={X: [image], keep_prob: 1})
text = text_list[0].tolist()
vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)
i = 0
for n in text:
vector[i * CHAR_SET_LEN + n] = 1
i += 1
predict_text= vec2text(vector)
print("真实值: {0} 预测值: {1}".format(text_, predict_text),text_ == predict_text)
if text_ == predict_text: acceptCount += 1;
print("测试集准群率",acceptCount,count,acceptCount/count)
直接使用爬虫爬取图片，并模拟登录验证是否正确即可
AutoGetImg.py自动获取训练集
import numpy as np
import tensorflow as tf
import cv2
import os
import random
import time
import struct
import requests
from Convert import Convert
import re
import socket
# number
number = ['1', '2', '3', 'b', 'c', 'm', 'n', 'v', 'x', 'z']
# 图像大小
IMAGE_HEIGHT = 22 # 80
IMAGE_WIDTH = 62 # 160
MAX_CAPTCHA = 4
char_set = number
CHAR_SET_LEN = len(char_set) #10
model_path = "model/"
X = tf.placeholder(tf.float32, [None, IMAGE_HEIGHT * IMAGE_WIDTH])
Y = tf.placeholder(tf.float32, [None, MAX_CAPTCHA * CHAR_SET_LEN])
keep_prob = tf.placeholder(tf.float32) # dropout
# 定义CNN
def crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1):
x = tf.reshape(X, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1])
# 3 conv layer
w_c1 = tf.Variable(w_alpha * tf.random_normal([3, 3, 1, 32]))
b_c1 = tf.Variable(b_alpha * tf.random_normal([32]))
conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1))
conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
conv1 = tf.nn.dropout(conv1, keep_prob)
w_c2 = tf.Variable(w_alpha * tf.random_normal([3, 3, 32, 64]))
b_c2 = tf.Variable(b_alpha * tf.random_normal([64]))
conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, w_c2, strides=[1, 1, 1, 1], padding='SAME'), b_c2))
conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
conv2 = tf.nn.dropout(conv2, keep_prob)
w_c3 = tf.Variable(w_alpha * tf.random_normal([3, 3, 64, 64]))
b_c3 = tf.Variable(b_alpha * tf.random_normal([64]))
conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, w_c3, strides=[1, 1, 1, 1], padding='SAME'), b_c3))
conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
conv3 = tf.nn.dropout(conv3, keep_prob)
# Fully connected layer
w_d = tf.Variable(w_alpha * tf.random_normal([3 * 8 * 64, 1024]))
b_d = tf.Variable(b_alpha * tf.random_normal([1024]))
dense = tf.reshape(conv3, [-1, w_d.get_shape().as_list()[0]])
dense = tf.nn.relu(tf.add(tf.matmul(dense, w_d), b_d))
dense = tf.nn.dropout(dense, keep_prob)
w_out = tf.Variable(w_alpha * tf.random_normal([1024, MAX_CAPTCHA * CHAR_SET_LEN]))
b_out = tf.Variable(b_alpha * tf.random_normal([MAX_CAPTCHA * CHAR_SET_LEN]))
out = tf.add(tf.matmul(dense, w_out), b_out)
# out = tf.nn.softmax(out)
return out
# 向量转回文本
def vec2text(vec):
char_pos = vec.nonzero()[0]
text = []
for i, c in enumerate(char_pos):
text.append(char_set[c % 10])
return "".join(text)
def predict_captcha(captcha_image):
output = crack_captcha_cnn()
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint(model_path))
predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
text_list = sess.run(predict, feed_dict={X: [captcha_image], keep_prob: 1})
text = text_list[0].tolist()
vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)
i = 0
for n in text:
vector[i * CHAR_SET_LEN + n] = 1
i += 1
return vec2text(vector)
if __name__ == '__main__':
output = crack_captcha_cnn()
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint(model_path))
predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
cvt = Convert()
session = requests.Session()
count = 1
acceptCount = 1
headers = {}
while True:
try:
IP = socket.inet_ntoa(struct.pack('>I', random.randint(1, 0xffffffff)))
headers['X-FORWARDED-FOR'] = IP
headers['CLIENT-IP'] = IP
req = session.get("http://xxxxxxxx/jsxsd/",headers = headers)
req = session.get("http://xxxxxxxx/jsxsd/verifycode.servlet",headers = headers)
img = cvt.run(req.content)
cv2.imwrite("vvvv.jpg",img)
image = np.float32(img)
image = image.flatten() / 255
text_list = sess.run(predict, feed_dict={X: [image], keep_prob: 1})
text = text_list[0].tolist()
vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)
i = 0
for n in text:
vector[i * CHAR_SET_LEN + n] = 1
i += 1
predict_text= vec2text(vector)
# predict_text = input()
print(predict_text)
params={
"encoded": "MjAyMDE2MTIyMzU=%%%MjAyMDE2MTIyMzU=",
"RANDOMCODE": predict_text
}
req = session.post("http://xxxxxxxx/jsxsd/xk/LoginToXk",data=params,headers = headers)
if not re.search("验证码错误", req.text) :
print("Load",acceptCount,count,acceptCount/count)
acceptCount += 1
cv2.imwrite("TrainImg/%s.jpg" % (predict_text),img)
count += 1
time.sleep(0.3) #稍微延时一下
except Exception as e:
print(e)
pass
训练集3109张，测试集128张，训练1600次，ACC为99%，实际测试准确率98%左右
提供代码与模型
https://github.com/WindrunnerMax/SWVerifyCode
https://gitee.com/windrunner_Max/IMGPATH/tree/master/DL/SW