本次验证码识别项目是基于TensorFlow和captcha库，通过卷积神经网络训练来实现的一个简单的验证码识别。由于本人设备条件有限，本次实验只针对数字验证码进行识别，有条件的同学可以对代码进行简单修改加入大小写英文字母的识别。
首先打开Anaconda Prompt进入到自己配置的TensorFlow环境中，然后输入pip install captcha回车即可安装capthca库，由于本人已经安装过所以显示会不一样。
安装好后captcha库后先测试一下生成验证码
首先导入所需的库和生成验证码的数据集，然后定义构造验证码和生成验证码的函数；生成的验证码是在number、alphabet、ALPHABET中随机抽选出来的。
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'] # 数字+大小写英文字母 # def random_captcha_text(char_set=number+alphabet+ALPHABET, captcha_size=4): def random_captcha_text(char_set=number, captcha_size=4): # 构造captcha_text(lsit类型) 然后循环4次从char_set中选4个元素放进captcha_text 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(): # 定义captcha库中的ImageCaptcha()类 image = ImageCaptcha() # 调用random_captcha_text函数，生成长度为4的验证码 captcha_text = random_captcha_text() # 将lsit转换成字符串 captcha_text = ''.join(captcha_text) # 生成验证码图像 captcha = image.generate(captcha_text) # 将验证码图像保存为np.array格式（TensorFlow网络可接受的格式） 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()代码运行结果：
由于设备条件有限，准确率设置为80%时保存模型，CPU环境下大概一个小时完成训练。
训练结果：
测试结果： 由下图可以看出，80%准确率的模型大部分的数字还是可以识别出来的，但还是会出现个别数字识别错误。
Captcha Generate.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): # 构造captcha_text lsit 然后循环4次从char_set中选4个元素放进captcha_text 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(): # 定义captcha库中的ImageCaptcha()类 image = ImageCaptcha() # 将lsit转换成字符串 captcha_text = random_captcha_text() captcha_text = ''.join(captcha_text) # 生成图像验证码 captcha = image.generate(captcha_text) # 将验证码图保存为np.array格式 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()Captcha Recognition.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, captcha_size=4): # 构造captcha_text(lsit类型) 然后循环4次从char_set中选4个元素放进captcha_text 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(): # 定义captcha库中的ImageCaptcha()类 image = ImageCaptcha() # 调用random_captcha_text函数，生成长度为4的验证码 captcha_text = random_captcha_text() # 将lsit转换成字符串 captcha_text = ''.join(captcha_text) # 生成验证码图像 captcha = image.generate(captcha_text) # 将验证码图像保存为np.array格式（TensorFlow网络可接受的格式） captcha_image = Image.open(captcha) captcha_image = np.array(captcha_image) return captcha_text, captcha_image # 将彩色图像转化为灰色图像 def convert2gray(img): if len(img.shape) > 2: gray = np.mean(img, -1) 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) for i, c in enumerate(text): idx = i * CHAR_SET_LEN + int(c) vector[idx] = 1 return vector # 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 # 让值在0~1之间 batch_y[i, :] = text2vec(text) return batch_x, batch_y # 定义卷积神经网络 def crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1): x = tf.reshape(X, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1]) # 卷积层1 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) # 卷积层2 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) # 卷积层3 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) # 全连接层 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]]) 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) return out # 训练 def train_crack_captcha_cnn(): output = crack_captcha_cnn() loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=Y, logits=output)) 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}) # 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 %d, training accuracy %g" % (step, acc)) # 如果准确率大于80%,保存模型,完成训练 if acc > 0.80: saver.save(sess, "./model/crack_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-2900") 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() return text if __name__ == '__main__': train = 1 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) # 验证码长度为4 print("验证码文本最长字符数", MAX_CAPTCHA) char_set = number CHAR_SET_LEN = len(char_set) # 数字集长度为10 X = tf.placeholder(tf.float32, [None, IMAGE_HEIGHT * IMAGE_WIDTH]) # 60*160 Y = tf.placeholder(tf.float32, [None, MAX_CAPTCHA * CHAR_SET_LEN]) # 4*10 keep_prob = tf.placeholder(tf.float32) # dropout 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))GitHub地址：https://github.com/WellTung666/Captcha-Recognition
