python使用tensorflow深度学习识别验证码

Python  /  管理员 发布于 7年前   276

本文介绍了python使用tensorflow深度学习识别验证码 ，分享给大家，具体如下：

除了传统的PIL包处理图片,然后用pytessert+OCR识别意外,还可以使用tessorflow训练来识别验证码。

此篇代码大部分是转载的，只改了很少地方。

代码是运行在linux环境，tessorflow没有支持windows的python 2.7。

gen_captcha.py代码。

#coding=utf-8from captcha.image import ImageCaptcha # pip install captchaimport numpy as npimport matplotlib.pyplot as pltfrom PIL import Imageimport random# 验证码中的字符, 就不用汉字了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']'''number=['0','1','2','3','4','5','6','7','8','9']alphabet =[]ALPHABET =[]'''# 验证码一般都无视大小写；验证码长度4个字符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# 生成字符对应的验证码def gen_captcha_text_and_image():  while(1):    image = ImageCaptcha()    captcha_text = random_captcha_text()    captcha_text = ''.join(captcha_text)    captcha = image.generate(captcha_text)    #image.write(captcha_text, captcha_text + '.jpg') # 写到文件    captcha_image = Image.open(captcha)    #captcha_image.show()    captcha_image = np.array(captcha_image)    if captcha_image.shape==(60,160,3):      break  return captcha_text, captcha_imageif __name__ == '__main__':  # 测试  text, image = gen_captcha_text_and_image()  print image  gray = np.mean(image, -1)  print gray  print image.shape  print gray.shape  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()

train.py代码。

#coding=utf-8from gen_captcha import gen_captcha_text_and_imagefrom gen_captcha import numberfrom gen_captcha import alphabetfrom gen_captcha import ALPHABETimport numpy as npimport tensorflow as tf"""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 = 60IMAGE_WIDTH = 160MAX_CAPTCHA = len(text)print  "验证码文本最长字符数", MAX_CAPTCHA # 验证码最长4字符; 我全部固定为4,可以不固定. 如果验证码长度小于4，用'_'补齐"""IMAGE_HEIGHT = 60IMAGE_WIDTH = 160MAX_CAPTCHA = 4# 把彩色图像转为灰度图像（色彩对识别验证码没有什么用）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"""cnn在图像大小是2的倍数时性能最高, 如果你用的图像大小不是2的倍数，可以在图像边缘补无用像素。np.pad(image,((2,3),(2,2)), 'constant', constant_values=(255,)) # 在图像上补2行，下补3行，左补2行，右补2行"""# 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_LEN = len(char_set)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):    #print text    idx = i * CHAR_SET_LEN + char2pos(c)    #print i,CHAR_SET_LEN,char2pos(c),idx    vector[idx] = 1  return vector#print text2vec('1aZ_')# 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 - 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))  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) # F5Sdvec = text2vec("SFd5")text = vec2text(vec)print(text) # SFd5"""# 生成一个训练batchdef 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####################################################################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])  # 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]))  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([8 * 32 * 40, 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# 训练def train_crack_captcha_cnn():  import time  start_time=time.time()  output = crack_captcha_cnn()  # loss  #loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(output, Y))  loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=Y))  # 最后一层用来分类的softmax和sigmoid有什么不同？  # optimizer 为了加快训练 learning_rate应该开始大，然后慢慢衰  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(64)      _, loss_ = sess.run([optimizer, loss], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75})      print time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time())),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 u'***************************************************************第%s次的准确率为%s'%(step, acc)        # 如果准确率大于50%,保存模型,完成训练        if acc > 0.9:         ##我这里设了0.9，设得越大训练要花的时间越长，如果设得过于接近1，很难达到。如果使用cpu，花的时间很长，cpu占用很高电脑发烫。          saver.save(sess, "crack_capcha.model", global_step=step)          print time.time()-start_time          break      step += 1train_crack_captcha_cnn()

测试代码：

output = crack_captcha_cnn()saver = tf.train.Saver()sess = tf.Session()saver.restore(sess, tf.train.latest_checkpoint('.'))while(1):    text, image = gen_captcha_text_and_image()  image = convert2gray(image)  image = image.flatten() / 255  predict = tf.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 = text_list[0].tolist()  vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)  i = 0  for t in predict_text:    vector[i * 63 + t] = 1    i += 1    # break  print("正确: {} 预测: {}".format(text, vec2text(vector)))

如果想要快点测试代码效果，验证码的字符不要设置太多，例如0123这几个数字就可以了。

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。