验证码的生成与识别
目录
1. 验证码的制作
2. 卷积神经网络结构
3. 训练参数保存与使用
4. 注意事项
5. 代码实现(python3.5)
6. 运行结果以及分析
1. 验证码的制作
深度学习一个必要的前提就是需要大量的训练样本数据, 毫不夸张的说, 训练样本数据的多少直接决定模型的预测准确度. 而本节的训练样本数据 (验证码: 字母和数字组成) 通过调用 Image 模块 (图像处理库) 中相关函数生成.
安装: pip install pillow
验证码生成步骤: 随机在字母和数字中选择 4 个字符 -> 创建背景图片 -> 添加噪声 -> 字符扭曲
具体样本如下所示:
对于上图的验证码, 如果用传统方式破解, 其步骤一般是:
图片分割: 采用分割算法分割出每一个字符;
字符识别: 由分割出的每个字符图片, 根据 OCR 光学字符识别出每个字符图片对应的字符;
难点在于: 对于图片字符有黏连(2 个, 3 个, 或者 4 个全部黏连), 图片是无法完全分割出来的, 也就是说, 即使分割出来了, 字符识别基本上都是错误的, 特别对于人眼都无法分辨的验证码, 用传统的这种破解方法, 成功率基本上是极其低的.
黏连验证码
人眼几乎无法分辨验证码
第一张是 0ymo or 0ynb ? 第二张是 7e9l or 1e9l ?
对于以上传统方法破解验证码的短板, 我们采用深度学习之卷积神经网络来进行破解.
2. 卷积神经网络结构
前向传播组成: 3 个卷积层(3*3*1*32,3*3*32*64,3*3*64*64),3 个池化层, 4 个 dropout 防过拟合层, 2 个全连接层((8*20*64,1024),(1024, MAX_CAPTCHA*CHAR_SET_LEN])),4 个 Relu 激活函数.
反向传播组成: 计算损失(sigmoid 交叉熵), 计算梯度, 目标预测, 计算准确率, 参数更新.
tensorboard 生成结构图 (图片可能不是很清楚, 在图片位置点击鼠标右键 -> 在新标签页面打开图片, 就可以放缩图片了.)
这里特别要注意数据流的变化:
- (?,60,160,1) + conv1->(?,60,160,32)+ relu ->(?,60,160,32) + pool1 ->(?,30,80,32) + dropout -> (?,30,80,32)
- + conv2->(?,30,80,64) + relu ->(?,30,80,64) + pool2 ->(?,15,40,64) + dropout -> (?,15,40,64)
- + conv3->(?,15,40,64) + relu ->(?,15,40,64) + pool3 ->(?,8,20,64) + dropout -> (?,8,20,64)
- + fc1 ->(?,1024) + relu ->(?,1024) + dropout ->(?,1024)
- + fc2 ->(?,MAX_CAPTCHA*CHAR_SET_LEN)
只要把握住一点, 卷积过程跟全连接运算是不一样的.
卷积过程: 矩阵对应位置相乘再相加, 要求相乘的两个矩阵宽, 高必须相同(比如大小都是 m * n), 得到结果就是一个数值.
全连接(矩阵乘法): 它要求第一个矩阵的列和第二个矩阵的行必须相同, 比如矩阵 A 大小 m * n, 矩阵 B 大小 n* k, 红色部分必须相同, 得到结果大小就是 m * k.
3. 训练参数保存与使用
参数保存:
tensorflow 对于参数保存功能已帮我们做好了, 我们只要直接使用就可以了. 使用也很简单, 就两步, 获取保存对象, 调用保存方法.
获取保存对象:
saver = tf.train.Saver()
调用保存方法:
saver.save(sess, "./model/crack_capcha.model99", global_step=step)
global_step=step : 在保存文件时, 会统计运行了多少次.
参数使用:
获取保存对象 ->获取最后一次生成文件的路径 ->导入参数到 session 会话中
获取保存对象与参数保存是一样的.
获取最后一次生成文件的路径: 在参数保存时会生成一个 checkpoint 文件(我的是在 model 文件下), 里面会记录最后一次生成文件的文件名. model 文件
checkpoint 内容
导入参数到 session 会话中: 首先要开启 session 会话, 然后调用保存对象的 restore 方法即可.
saver.restore(sess, checkpoint.model_checkpoint_path)
4. 注意事项
1. 在 session 调用 run 方法时, 一定不能遗漏某个操作结果对应的参数赋值, 这表述比较绕口, 我们来看下面的例子.
_, loss_ = sess.run([optimizer, loss], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75})
X: 输入数据, Y: 标签数据, keep_prob: 防过拟合概率因子(超参), 这些参数在获取损失函数 loss, 计算梯度 optimizer 时被用到,
在 tensorflow 的 CNN 中只是作为占位符处理的, 所以在 session 调用 run 方法时, 一定要对这些参数赋值, 并用 feed_dict 作为字典参数传入, 注意大小写也要相同.
2. 在训练前需要将文本转为向量, 在预测判断是否准确时需要将向量转为文本字符串.
这里的样例总长度 63: 数字 10 个(0-9), 小写字母 26(a-z), 大写字母 26(A-Z),'_': 如果不够 4 个字符, 用来补齐.
向量长度范围: 字符 4*(10 + 26 + 26 + 1) = 252
文本转向量: 通过某种规则(char2pos), 计算字符数值, 然后根据该字符在 4 个字符中的位置, 计算向量索引
idx = i * CHAR_SET_LEN + char2pos(c)
向量转文本: 跟文本转向量操作相反(vec2text)
5. 代码实现(python3.5)
在 letterAndNumber.py 文件中, train = 0 表示训练, 1 表示预测.
在训练时, 采用的 batch_size = 64, 每训练 100 次计算一次准确率, 如果准确率大于 0.8, 就将参数保存到 model 文件中, 准确率大于 0.9, 在保存参数的同时结束训练.
在预测时, 随机采用 100 幅图片, 观察其准确率; 另外, 对于之前展示的黏连验证码, 人眼不能较好分辨的验证码, 单独进行识别.
letterAndNumber.py
- import numpy as np
- import tensorflow as tf
- from captcha.image import ImageCaptcha
- import numpy as np
- import matplotlib.pyplot as plt
- from PIL import Image
- import random
- number = ['0','1','2','3','4','5','6','7','8','9']
- alphabet = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']
- ALPHABET = ['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z']
- def random_captcha_text(char_set=number+alphabet+ALPHABET, captcha_size=4):
- #def random_captcha_text(char_set=number, captcha_size=4):
- captcha_text = []
- for i in range(captcha_size):
- c = random.choice(char_set)
- captcha_text.append(c)
- return captcha_text
- def gen_captcha_text_and_image(i = 0):
- # 创建图像实例对象
- image = ImageCaptcha()
- # 随机选择 4 个字符
- captcha_text = random_captcha_text()
- # array 转化为 string
- captcha_text = ''.join(captcha_text)
- # 生成验证码
- captcha = image.generate(captcha_text)
- if i%100 == 0 :
- image.write(captcha_text, "./generateImage/" + captcha_text + '.jpg')
- captcha_image = Image.open(captcha)
- captcha_image = np.array(captcha_image)
- return captcha_text, captcha_image
- def convert2gray(img):
- if len(img.shape)> 2:
- gray = np.mean(img, -1)
- # 上面的转法较快, 正规转法如下
- # r, g, b = img[:,:,0], img[:,:,1], img[:,:,2]
- # gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
- return gray
- else:
- return img
- # 文本转向量
- 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):
- if c =='_':
- k = 62
- return k
- k = ord(c)-48
- if k> 9:
- k = ord(c) - 55
- if k> 35:
- k = ord(c) - 61
- if k> 61:
- raise ValueError('No Map')
- return k
- for i, c in enumerate(text):
- #idx = i * CHAR_SET_LEN + int(c)
- idx = i * CHAR_SET_LEN + char2pos(c)
- vector[idx] = 1
- return vector
- # 向量转回文本
- def vec2text(vec):
- char_pos = vec[0]
- text=[]
- for i, c in enumerate(char_pos):
- char_at_pos = i #c/63
- char_idx = c % CHAR_SET_LEN
- if char_idx <10:
- char_code = char_idx + ord('0')
- elif char_idx <36:
- char_code = char_idx - 10 + ord('A')
- elif char_idx < 62:
- char_code = char_idx- 36 + ord('a')
- elif char_idx == 62:
- char_code = ord('_')
- else:
- raise ValueError('error')
- text.append(chr(char_code))
- """
- text=[]
- char_pos = vec.nonzero()[0]
- for i, c in enumerate(char_pos):
- number = i % 10
- text.append(str(number))
- """ return"".join(text)
- """
- #向量 (大小 MAX_CAPTCHA*CHAR_SET_LEN) 用 0,1 编码 每 63 个编码一个字符, 这样顺利有, 字符也有
- vec = text2vec("F5Sd")
- text = vec2text(vec)
- print(text) # F5Sd
- vec = text2vec("SFd5")
- text = vec2text(vec)
- print(text) # SFd5
- """
- # 生成一个训练 batch
- def get_next_batch(batch_size=128):
- batch_x = np.zeros([batch_size, IMAGE_HEIGHT*IMAGE_WIDTH])
- batch_y = np.zeros([batch_size, MAX_CAPTCHA*CHAR_SET_LEN])
- # 有时生成图像大小不是(60, 160, 3)
- def wrap_gen_captcha_text_and_image(i):
- while True:
- text, image = gen_captcha_text_and_image(i)
- if image.shape == (60, 160, 3):
- return text, image
- for i in range(batch_size):
- text, image = wrap_gen_captcha_text_and_image(i)
- image = convert2gray(image)
- batch_x[i,:] = image.flatten() / 255 # (image.flatten()-128)/128 mean 为 0
- batch_y[i,:] = text2vec(text)
- return batch_x, batch_y
- # 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])
- #w_c1_alpha = np.sqrt(2.0/(IMAGE_HEIGHT*IMAGE_WIDTH)) #
- #w_c2_alpha = np.sqrt(2.0/(3*3*32))
- #w_c3_alpha = np.sqrt(2.0/(3*3*64))
- #w_d1_alpha = np.sqrt(2.0/(8*32*64))
- #out_alpha = np.sqrt(2.0/1024)
- # 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]))
- # 卷积 + Relu 激活函数
- conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1))
- # 池化
- conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
- # dropout 防止过拟合
- conv1 = tf.nn.dropout(conv1, rate = 1 - 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]))
- # 卷积 + Relu 激活函数
- conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, w_c2, strides=[1, 1, 1, 1], padding='SAME'), b_c2))
- # 池化
- conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
- # dropout 防止过拟合
- conv2 = tf.nn.dropout(conv2, rate = 1 - 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]))
- # 卷积 + Relu 激活函数
- conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, w_c3, strides=[1, 1, 1, 1], padding='SAME'), b_c3))
- # 池化
- conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
- # dropout 防止过拟合
- conv3 = tf.nn.dropout(conv3, rate = 1 - keep_prob)
- # Fully connected layer
- w_d = tf.Variable(w_alpha*tf.random_normal([8*20*64, 1024]))
- b_d = tf.Variable(b_alpha*tf.random_normal([1024]))
- dense = tf.reshape(conv3, [-1, w_d.get_shape().as_list()[0]])
- # 全连接 + Relu
- dense = tf.nn.relu(tf.add(tf.matmul(dense, w_d), b_d))
- dense = tf.nn.dropout(dense, rate = 1 - 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]))
- # 全连接
- out = tf.add(tf.matmul(dense, w_out), b_out)
- return out
- # 训练
- def train_crack_captcha_cnn():
- output = crack_captcha_cnn()
- # 计算损失
- loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits= output, labels= Y))
- # 计算梯度
- optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
- # 目标预测
- predict = tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN])
- # 目标预测最大值
- max_idx_p = tf.argmax(predict, 2)
- # 真实标签最大值
- 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)
- # 准确率
- accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
- saver = tf.train.Saver()
- with tf.Session() as sess:
- # 打印 tensorboard 流程图
- tf.summary.FileWriter("./tensorboard/", sess.graph)
- sess.run(tf.global_variables_initializer())
- step = 0
- while True:
- batch_x, batch_y = get_next_batch(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(100)
- acc = sess.run(accuracy, feed_dict={X: batch_x_test, Y: batch_y_test, keep_prob: 1.})
- print(step, acc)
- # 如果准确率大于 80%, 保存模型, 完成训练
- if acc> 0.90:
- saver.save(sess, "./model/crack_capcha.model99", global_step=step)
- break
- if acc> 0.80:
- saver.save(sess, "./model/crack_capcha.model88", global_step=step)
- step += 1
- def crack_captcha(captcha_image, output):
- saver = tf.train.Saver()
- with tf.Session() as sess:
- sess.run(tf.initialize_all_variables())
- # 获取训练后的参数
- checkpoint = tf.train.get_checkpoint_state("model")
- if checkpoint and checkpoint.model_checkpoint_path:
- saver.restore(sess, checkpoint.model_checkpoint_path)
- print("Successfully loaded:", checkpoint.model_checkpoint_path)
- else:
- print("Could not find old network weights")
- 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()
- text = vec2text(text_list)
- return text
- if __name__ == '__main__':
- train = 0 # 0: 训练 1: 预测
- if train == 0:
- number = ['0','1','2','3','4','5','6','7','8','9']
- alphabet = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']
- ALPHABET = ['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z']
- text, image = gen_captcha_text_and_image()
- print("验证码图像 channel:", image.shape) # (60, 160, 3)
- # 图像大小
- IMAGE_HEIGHT = 60
- IMAGE_WIDTH = 160
- MAX_CAPTCHA = len(text)
- print("验证码文本最长字符数", MAX_CAPTCHA)
- # 文本转向量
- char_set = number + alphabet + ALPHABET + ['_'] # 如果验证码长度小于 4, '_'用来补齐
- #char_set = number
- CHAR_SET_LEN = len(char_set)
- # placeholder 占位符, 作用域: 整个页面, 不需要声明时初始化
- 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
- train_crack_captcha_cnn()
- # 预测时需要将训练的变量初始化, 且只能初始化一次.
- if train == 1:
- # 自然计数
- step = 0
- # 正确预测计数
- rightCnt = 0
- # 设置测试次数
- count = 100
- number = ['0','1','2','3','4','5','6','7','8','9']
- alphabet = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']
- ALPHABET = ['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z']
- IMAGE_HEIGHT = 60
- IMAGE_WIDTH = 160
- char_set = number + alphabet + ALPHABET + ['_']
- CHAR_SET_LEN = len(char_set)
- MAX_CAPTCHA = 4 # len(text)
- # placeholder 占位符, 作用域: 整个页面, 不需要声明时初始化
- 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
- output = crack_captcha_cnn()
- saver = tf.train.Saver()
- with tf.Session() as sess:
- sess.run(tf.global_variables_initializer())
- # 获取训练后参数路径
- checkpoint = tf.train.get_checkpoint_state("model")
- if checkpoint and checkpoint.model_checkpoint_path:
- saver.restore(sess, checkpoint.model_checkpoint_path)
- print("Successfully loaded:", checkpoint.model_checkpoint_path)
- else:
- print("Could not find old network weights.")
- while True:
- # image = Image.open("D:/Project/python/myProject/CNN/tensorflow/captchaIdentify/11/0sHB.jpg")
- # image = np.array(image)
- # text = '0sHB'
- text, image = gen_captcha_text_and_image()
- # f = plt.figure()
- # ax = f.add_subplot(111)
- # ax.text(0.1, 0.9,text, ha='center', va='center', transform=ax.transAxes)
- # plt.imshow(image)
- #
- # plt.show()
- image = convert2gray(image)
- image = image.flatten() / 255
- predict = tf.math.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
- text_list = sess.run(predict, feed_dict= { X: [image], keep_prob : 1})
- predict_text = vec2text(text_list)
- predict_text = crack_captcha(image, output)
- # predict_text_list = [str(x) for x in predict_text]
- # predict_text_new = ''.join(predict_text_list)
- print("step:{} 真实值: {} 预测: {} 预测结果: {}".format(str(step), text, predict_text, "正确" if text.lower()==predict_text.lower() else "错误"))
- if text.lower()==predict_text.lower():
- rightCnt += 1
- if step == count - 1:
- print("测试总数: {} 测试准确率: {}".format(str(count), str(rightCnt/count)))
- break
- step += 1
- View Code
captchaIdentify.py
- import tensorflow as tf
- from captcha.image import ImageCaptcha
- import numpy as np
- import matplotlib.pyplot as plt
- from PIL import Image
- import random
- number = ['0','1','2','3','4','5','6','7','8','9']
- alphabet = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']
- ALPHABET = ['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z']
- def random_captcha_text(char_set=number+alphabet+ALPHABET, captcha_size=4):
- captcha_text = []
- for i in range(captcha_size):
- c = random.choice(char_set)
- captcha_text.append(c)
- return captcha_text
- def gen_captcha_text_and_image():
- image = ImageCaptcha()
- captcha_text = random_captcha_text()
- captcha_text = ''.join(captcha_text)
- captcha = image.generate(captcha_text)
- #image.write(captcha_text, captcha_text + '.jpg')
- captcha_image = Image.open(captcha)
- captcha_image = np.array(captcha_image)
- return captcha_text, captcha_image
- if __name__ == '__main__':
- text, image = gen_captcha_text_and_image()
- f = plt.figure()
- ax = f.add_subplot(111)
- ax.text(0.1, 0.9,text, ha='center', va='center', transform=ax.transAxes)
- plt.imshow(image)
- plt.show()
- View Code
6. 运行结果以及分析
随机采用 100 幅图片, 运行结果如下:
黏连验证码
运行结果
人眼较难识别验证码
运行结果
结果分析: 随机选取 100 张验证码测试, 准确率有 73%, 这个准确率在同类型的验证码中已经比较可观了. 当然, 可以在训练时将测试准确率继续提高, 比如 0.95 或更高, 这样, 在预测时的准确率应该还会有提升的, 大家有兴趣的话可以试试.
不要让懒惰占据你的大脑, 不要让妥协拖垮了你的人生. 青春就是一张票, 能不能赶上时代的快车, 你的步伐就掌握在你的脚下.
来源: https://www.cnblogs.com/further-further-further/p/10755361.html