CNN破解简单验证码(Tensorflow实现)
使用CNN破解一下自己生成的图片验证码,因为电脑性能不行,只破解四位的数字验证码,代码实现中可以对符号、字符和数字混合的验证码进行破解,原理相同,有高性能GPU的童鞋可以试试玩玩。CNN使用简单的三层卷积,人懒结构手绘如下图:
生成验证码的代码,使用了第三方库:
- #coding=utf-8
- import tensorflow as tf
- import numpy as np
- import matplotlib.pyplot as plt
- #conda install Pillow
- from PIL import Image
- import random
- #pip install captcha 安装验证码库
- from captcha.image import ImageCaptcha
- #本代码生成验证码图片
- 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():
- #构造captcha对象
- image = ImageCaptcha()
- captcha_text = random_captcha_text()
- #list->string
- captcha_text = ''.join(captcha_text)
- #生成图像验证码
- captcha = image.generate(captcha_text)
- #image.write(captcha_text, captcha_text + '.jpg')
- captcha_image = Image.open(captcha)
- #转换为numpu array格式
- captcha_image = np.array(captcha_image)
- #返回Label和验证码
- 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()
生成的效果如图:
验证码识别代码:
- #coding=utf-8
- 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):
- #生成验证码文本
- 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
- #生成验证码图片(H*W*Chanel)和标签
- 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
- 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
- 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)
- 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):
- 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 www.thd178.com/- 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():
- while True:
- text, image = gen_captcha_text_and_image()
- if image.shape == (60, 160, 3):
- return text, image
- for i in range(batch_size):
- text, image = wrap_gen_captcha_text_and_image()
- 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
- # w_alpha, b_alpha传入一个很小的值作为初始化值
- def crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1):
- #传入的X为[batch_size,H,W],需要转换为Tensorflow格式[batch_size,H,W,Chanel]
- 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
- #filter:3*3,输入通道1(灰度图),输出(特征图):32
- 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_www.tkcyl1.com 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, www.huachengj1980.com/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([8*20*64, 1024]))
- b_d = tf.Variable(b_alpha*tf.random_normal([1024]))
- #卷积结果扁平化
- 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_www.chaoyueyule.com CAPTCHA*CHAR_SET_LEN]))
- out = tf.add(tf.matmul(dense, w_out), b_out)
- return out
- # 训练
- def train_crack_captcha_cnn():
- #三层CNN预测输出
- output = crack_captcha_cnn()
- loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=Y))
- 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_www.536611.cn 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(www.wanmeiyuele.cn ))
- step = 0
- while True:
- batch_x, batch_y = get_next_batch(64)
- _, loss_ = sess.run([optimizer, loss], www.636591.cn 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 % 10 == 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50%,保存模型,完成训练
- if acc > 0.50:
- saver.save(sess, "./model/crack_www.douniu157.com capcha.model", global_step=step)
- break
- step += 1
- def crack_captcha(captcha_image):
- output = crack_captcha_cnn()
- saver = tf.train.Saver()
- with tf.Session() as sess:
- saver.restore(sess, "./model/crack_capcha.model-810")
- 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(www.cnzhaotai.com)
- return text
- if __name__ == '__main__':
- #定义train变量,train=0:网络测试,train=1:网络训练
- train = 0
- if train == 0:
- number = ['0','1','2','3','4','5','6','7','8','9']
- #没有GPU,为加快训练速度,暂时只训练仅含数字的验证码
- #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)
- #验证码识别中,颜色用处不大,因此将彩色图转换为灰度图,加快训练速度
- X = tf.placeholder(tf.float32, [None, IMAGE_HEIGHT*IMAGE_WIDTH]) #60*160
- #四位验证码:采用四组10位的one-hot
- Y = tf.placeholder(tf.float32, [None, MAX_CAPTCHA*CHAR_SET_LEN]) #4*10
- # dropout
- keep_prob = tf.placeholder(tf.float32)
- train_crack_captcha_cnn()
- if train == 1:
- number = ['0','1','2','3','4','5','6','7','8','9']
- IMAGE_HEIGHT = 60
- IMAGE_WIDTH = 160
- char_set = number
- CHAR_SET_LEN = len(char_set)
- 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()
- MAX_CAPTCHA = len(text)
- image = convert2gray(image)
- image = image.flatten() / 255
- 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
- predict_text = crack_captcha(image)
- print("正确: {} 预测: {}".format(text, predict_text))
转载于:https://www.cnblogs.com/qwangxiao/p/9119447.html
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