强智教务系统验证码识别 Tensorflow CNN
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 /WindrunnerMax/SWVerifyCode 如果觉得不错，点个star吧 😃
目录结构
准备工作
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 requestsimport cv2import numpy as npimport sysfrom Convert import Convertclass GetImg(object):"""docstring for GetImg"""def __init__(self):super(GetImg, self).__init__()def run(self):count = 1cvt = 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 += 1cv2.imwrite("TrainImg/%s.jpg" % (mark),img)if __name__ == '__main__':GetImg().run()
Convert.py预处理图片
import cv2import numpy as npclass 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 th1def 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] = 255return imgdef 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 = 0if img[x, y - 1] > 245:count = count + 1if img[x, y + 1] > 245:count = count + 1if img[x - 1, y] > 245:count = count + 1if img[x + 1, y] > 245:count = count + 1if count > 2:img[x, y] = 255return imgdef 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] = 0elif 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] = 0else: # 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] = 0elif 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] = 0elif 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] = 0else: # 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] = 0else: # 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] = 0elif 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] = 0else: # 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] = 0return 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
训练
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 npimport tensorflow as tfimport cv2import osimport randomimport time# numbernumber = ['1', '2', '3', 'b', 'c', 'm', 'n', 'v', 'x', 'z']# 图像大小IMAGE_HEIGHT = 22 # 80IMAGE_WIDTH = 62 # 160MAX_CAPTCHA = 4char_set = numberCHAR_SET_LEN = len(char_set) #10image_filename_list = []total = 0train_path = "TrainImg/"valid_path = "TestImg/"model_path = "model/"def get_image_file_name(imgFilePath):fileName = []total = 0for filePath in os.listdir(imgFilePath):captcha_name = filePath.split('/')[-1]fileName.append(captcha_name)total += 1random.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 = -1for index, item in enumerate(char_set):if c == item : return indexif(k == -1) : raise ValueError('字符数组中不存在字符' + c);return -1for i, c in enumerate(text):idx = i * CHAR_SET_LEN + char2pos(c)vector[idx] = 1return 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batchdef 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, imagefor listNum in os.walk(imageFilePath):passimageAmount = 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0batch_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CNNdef 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*1024dense = 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 = 0while 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)breakstep += 1def 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 = 0for n in text:vector[i * CHAR_SET_LEN + n] = 1i += 1return vec2text(vector)if __name__ == '__main__':train_crack_captcha_cnn()print ("FINISH")# print(vec2text(text2vec("123z")))
测试集测试数据准确率
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 npimport tensorflow as tfimport cv2import osimport randomimport timeimport sys# numbernumber = ['1', '2', '3', 'b', 'c', 'm', 'n', 'v', 'x', 'z']# 图像大小IMAGE_HEIGHT = 22 # 80IMAGE_WIDTH = 62 # 160MAX_CAPTCHA = 4char_set = numberCHAR_SET_LEN = len(char_set) #10valid_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CNNdef crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1):x = tf.reshape(X, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1])# 3 conv layerw_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 layerw_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 = 0for n in text:vector[i * CHAR_SET_LEN + n] = 1i += 1return 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 = 0acceptCount = 0for image_ in image_list:count += 1text_ = 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() / 255text_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 = 0for n in text:vector[i * CHAR_SET_LEN + n] = 1i += 1predict_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)
自动获取训练集
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 npimport tensorflow as tfimport cv2import osimport randomimport timeimport structimport requestsfrom Convert import Convertimport reimport socket# numbernumber = ['1', '2', '3', 'b', 'c', 'm', 'n', 'v', 'x', 'z']# 图像大小IMAGE_HEIGHT = 22 # 80IMAGE_WIDTH = 62 # 160MAX_CAPTCHA = 4char_set = numberCHAR_SET_LEN = len(char_set) #10model_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CNNdef crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1):x = tf.reshape(X, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1])# 3 conv layerw_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 layerw_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 = 0for n in text:vector[i * CHAR_SET_LEN + n] = 1i += 1return 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 = 1acceptCount = 1headers = {}while True:try:IP = socket.inet_ntoa(struct.pack('>I', random.randint(1, 0xffffffff)))headers['X-FORWARDED-FOR'] = IPheaders['CLIENT-IP'] = IPreq = 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() / 255text_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 = 0for n in text:vector[i * CHAR_SET_LEN + n] = 1i += 1predict_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 += 1cv2.imwrite("TrainImg/%s.jpg" % (predict_text),img)count += 1time.sleep(0.3) #稍微延时一下except Exception as e:print(e)pass
训练完成
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
/WindrunnerMax/SWVerifyCode
/windrunner_Max/IMGPATH/tree/master/DL/SW
