CNN卷积神经网络实现验证码识别（准确率达99%）

 基于python生成验证码，并用CNN进行训练识别，验证码四位，如果由数字，小写字母，大写字母组成，那么cpu要跑很久很久，所以这里的验证码只包含了四位数字，一共有10*10*10*10个可能的数据。通过卷积神经模型，准确率达到了99%，大约迭代1200次左右，运行时间不算太长！！下面附上代码和效果图！！！！具体的细节就不介绍了，最近有点忙～～～，有问题可以评论！！！我会解答！！

另外附上我另一个博客地址，我会尽力经常更新，因为楼主还在上学，如果比较忙的话，可能更新会慢点！点击打开链接

import tensorflow as tffrom captcha.image import  ImageCaptchaimport numpy as npimport matplotlib.pyplot as pltfrom PIL import  Imageimport randomnumber=['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):    captcha_text=[]    for i in range(captcha_size):        c=random.choice(char_set)        captcha_text.append(c)    return captcha_textdef gen_captcha_text_image():    image=ImageCaptcha()    captcha_text=random_captcha_text()    captcha_text=''.join(captcha_text)    captcha=image.generate(captcha_text)    captcha_image=Image.open(captcha)    captcha_image=np.array(captcha_image)    return captcha_text,captcha_imagedef convert2gray(img):    if len(img.shape)>2:        r, g, b = img[:, :, 0], img[:, :, 1], img[:, :, 2]        gray = 0.2989 * r + 0.5870 * g + 0.1140 * b        return gray    else:        return imgdef 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 + char2pos(c)        vector[idx] = 1    return vectordef 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])    def wrap_gen_captcha_text_and_image():        while True:            text, image = gen_captcha_text_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        batch_y[i, :] = text2vec(text)    return batch_x, batch_ydef cnn_structure(w_alpha=0.01, b_alpha=0.1):    x = tf.reshape(X, shape=[-1, image_height, image_width, 1])    wc1=tf.get_variable(name='wc1',shape=[3,3,1,32],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer())    #wc1 = tf.Variable(w_alpha * tf.random_normal([3, 3, 1, 32]))    bc1 = tf.Variable(b_alpha * tf.random_normal([32]))    conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, wc1, strides=[1, 1, 1, 1], padding='SAME'), bc1))    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)    wc2=tf.get_variable(name='wc2',shape=[3,3,32,64],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer())   # wc2 = tf.Variable(w_alpha * tf.random_normal([3, 3, 32, 64]))    bc2 = tf.Variable(b_alpha * tf.random_normal([64]))    conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, wc2, strides=[1, 1, 1, 1], padding='SAME'), bc2))    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)    wc3=tf.get_variable(name='wc3',shape=[3,3,64,128],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer())    #wc3 = tf.Variable(w_alpha * tf.random_normal([3, 3, 64, 128]))    bc3 = tf.Variable(b_alpha * tf.random_normal([128]))    conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, wc3, strides=[1, 1, 1, 1], padding='SAME'), bc3))    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)    wd1=tf.get_variable(name='wd1',shape=[8*20*128,1024],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer())    #wd1 = tf.Variable(w_alpha * tf.random_normal([7*20*128,1024]))    bd1 = tf.Variable(b_alpha * tf.random_normal([1024]))    dense = tf.reshape(conv3, [-1, wd1.get_shape().as_list()[0]])    dense = tf.nn.relu(tf.add(tf.matmul(dense, wd1), bd1))    dense = tf.nn.dropout(dense, keep_prob)    wout=tf.get_variable('name',shape=[1024,max_captcha * char_set_len],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer())    #wout = tf.Variable(w_alpha * tf.random_normal([1024, max_captcha * char_set_len]))    bout = tf.Variable(b_alpha * tf.random_normal([max_captcha * char_set_len]))    out = tf.add(tf.matmul(dense, wout), bout)    return outdef train_cnn():    output=cnn_structure()    cost=tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output,labels=Y))    optimizer=tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)    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:        init = tf.global_variables_initializer()        sess.run(init)        step = 0        while True:            batch_x, batch_y = get_next_batch(100)            _, cost_= sess.run([optimizer, cost], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75})            print(step, cost_)            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)                if acc > 0.99:                    saver.save(sess, "./model/crack_capcha.model", global_step=step)                    break            step += 1def crack_captcha(captcha_image):    output = cnn_structure()    saver = tf.train.Saver()    with tf.Session() as sess:        saver.restore(sess, "./model/crack_capcha.model-1200")        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 textif __name__=='__main__':    train=1    if train==0:        text,image=gen_captcha_text_image()        print("验证码大小：",image.shape)#(60,160,3)        image_height=60        image_width=160        max_captcha=len(text)        print("验证码文本最长字符数",max_captcha)        char_set=number        char_set_len=len(char_set)        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)        train_cnn()    if train == 1:        image_height = 60        image_width = 160        char_set = number        char_set_len = len(char_set)        text, image = gen_captcha_text_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)        predict_text = crack_captcha(image)        print("正确: {}  预测: {}".format(text, predict_text))        plt.show()

以上就是代码加上效果图，如果有好的建议可以在下面评论 ！！！