Tensorflow简单验证码识别应用

简单的Tensorflow验证码识别应用，供大家参考，具体内容如下

1.Tensorflow的安装方式简单,在此就不赘述了.

2.训练集训练集以及测试及如下(纯手工打造,所以数量不多):

3.实现代码部分(参考了网上的一些实现来完成的)

main.py(主要的神经网络代码)

    from gen_check_code import gen_captcha_text_and_image_new,gen_captcha_text_and_image
    from gen_check_code import number
    from test_check_code import get_test_captcha_text_and_image
    import numpy as np
    import tensorflow as tf

    text, image = gen_captcha_text_and_image_new()
    print("验证码图像channel:", image.shape) # (60, 160, 3) 
    # 图像大小 
    IMAGE_HEIGHT = image.shape[0]
    IMAGE_WIDTH = image.shape[1]
    image_shape = image.shape
    MAX_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


    # 把彩色图像转为灰度图像（色彩对识别验证码没有什么用）
    # 度化是将三分量转化成一样数值的过程
    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
      # int gray = (int) (0.3 * r + 0.59 * g + 0.11 * 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 
    """


    char_set = number # 如果验证码长度小于4, '_'用来补齐
    CHAR_SET_LEN = len(char_set)

    # 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):
      try:
       k = ord(c)-ord('0')
      except:
       raise ValueError('No Map')
      return k

     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):
      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)


    # 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_new()

       if image.shape == image_shape:
        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)

     # 定义三层的卷积神经网络

     # 定义第一层的卷积神经网络
     # 定义第一层权重
     w_c1 = tf.Variable(w_alpha * tf.random_normal([3, 3, 1, 32]))
     # 定义第一层的偏置
     b_c1 = tf.Variable(b_alpha * tf.random_normal([32]))
     # 定义第一层的激励函数
     conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1))
     # conv1 为输入 ksize 表示使用2*2池化，即将2*2的色块转化成1*1的色块
     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, keep_prob)

     # 定义第二层的卷积神经网络
     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)

     # 定义第三层的卷积神经网络
     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
     # 随机生成权重
     w_d = tf.Variable(w_alpha * tf.random_normal([1536, 1024]))
     # 随机生成偏置
     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():
     # 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()
     # 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(output, 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(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50%,保存模型,完成训练
         if acc > 0.99:
          saver.save(sess, "./crack_capcha.model", global_step=step)
          break
        step += 1

    ## 训练(如果要训练则去掉下面一行的注释)
    train_crack_captcha_cnn()


    def crack_captcha():
     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)
      count = 0
      # 因为测试集共40个...写的很草率
      for i in range(40):
       text, image = get_test_captcha_text_and_image(i)
       image = convert2gray(image)
       captcha_image = image.flatten() / 255
       text_list = sess.run(predict, feed_dict={X: [captcha_image], keep_prob: 1})
       predict_text = text_list[0].tolist()
       predict_text = str(predict_text)
       predict_text = predict_text.replace("[", "").replace("]", "").replace(",", "").replace(" ","")
       if text == predict_text:
        count += 1
        check_result = "，预测结果正确"
       else:
        check_result = "，预测结果不正确"
        print("正确: {} 预测: {}".format(text, predict_text) + check_result)

      print("正确率:" + str(count) + "/40")
    # 测试(如果要测试则去掉下面一行的注释)
    # crack_captcha()

gen_check_code.py(得到训练集输入,需要注意修改root_dir为训练集的输入文件夹,下同)

    from captcha.image import ImageCaptcha # pip install captcha
    import numpy as np
    from PIL import Image
    import random
    # import matplotlib.pyplot as plt
    import os
    from random import choice

    # 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']

    root_dir = "d:\\train"

    # 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, 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():
     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 = np.array(captcha_image)
     return captcha_text, captcha_image


    def gen_list():
     img_list = []
     for parent, dirnames, filenames in os.walk(root_dir): # 三个参数：分别返回1.父目录 2.所有文件夹名字（不含路径） 3.所有文件名字
      for filename in filenames: # 输出文件信息
       img_list.append(filename.replace(".gif",""))
       # print("parent is:" + parent)
       # print("filename is:" + filename)
       # print("the full name of the file is:" + os.path.join(parent, filename)) # 输出文件路径信息
     return img_list
    img_list = gen_list()
    def gen_captcha_text_and_image_new():
     img = choice(img_list)
     captcha_image = Image.open(root_dir + "\\" + img + ".gif")
     captcha_image = np.array(captcha_image)
     return img, captcha_image


    # if __name__ == '__main__':
    #  # 测试
    #  # 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()
    #  #
    #
    #  text, image = gen_captcha_text_and_image_new()
    #
    #  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()

test_check_code.py(得到测试集输入)

    from captcha.image import ImageCaptcha # pip install captcha
    import numpy as np
    from PIL import Image
    import random
    import matplotlib.pyplot as plt
    import os
    from random import choice


    root_dir = "d:\\test"



    img_list = []
    def gen_list():

     for parent, dirnames, filenames in os.walk(root_dir): # 三个参数：分别返回1.父目录 2.所有文件夹名字（不含路径） 3.所有文件名字
      for filename in filenames: # 输出文件信息
       img_list.append(filename.replace(".gif",""))
       # print("parent is:" + parent)
       # print("filename is:" + filename)
       # print("the full name of the file is:" + os.path.join(parent, filename)) # 输出文件路径信息
     return img_list

    img_list = gen_list()
    def get_test_captcha_text_and_image(i=None):
     img = img_list[i]
     captcha_image = Image.open(root_dir + "\\" + img + ".gif")
     captcha_image = np.array(captcha_image)
     return img, captcha_image

4.效果

在测试集上的识别率

5.相关文件下载

训练集以及测试集 下载

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脚本之家。