深度学习——基于Tensorflow的CAPTCHA注册码识别实验

深度学习——基于Tensorflow的CAPTCHA注册码识别实验

本次人工智能实验3的内容就是验证码识别技术，因为实验时间有限，本人又是生生得拿自己电脑CPU来跑，所以训练数据集选择比较小，只选择了0-9的数字，4位为一组，如果条件允许，你?️可用的云平台、GPU之类的可以考虑加入大写字母、小写字母
提前声明一下，本代码是将三色图转换成灰度图处理，再进行识别，节省了时间，在正确率达到80%的时候就break掉啦，有更高要求或者需求不一样的童鞋可以自己加以更改。
	下面是代码部分：生成验证码图片 文件名：gen_captcha.py

    """
    function:识别captcha验证码技术
    生成验证码函数
    """
    from captcha.image import ImageCaptcha  # pip install captcha_recognize
    import numpy as np
    import matplotlib.pyplot as plt
    from PIL import Image
    import 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']
    # 验证码一般都无视大小写；验证码长度4个字符 这里为节省演示时间 将alphabet ALPHABET注释掉
    # def random_captcha_text(char_set=number+alphabet+ALPHABET, captcha_size=4):
    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)  # 用字符['m', 'i', 'R', 'd']转化为miRd
    
        captcha = image.generate(captcha_text)   # 将captcha_text转化为图片
        # image.write(captcha_text, captcha_text + '.jpg')  # 写到文件
        captcha_image = Image.open(captcha)
        # captcha_image.show() 测试是否成功生成验证码
        captcha_image = np.array(captcha_image)
    
        return captcha_text, 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)

训练加验证部分 train_captcha.py

# -*- coding: utf-8 -*-
# @Time    : 2018/10/31 下午8:18
# @Author  : xuef
# @FileName: train_captcha.py
# @Software: PyCharm
# @Blog    ：https://blog.csdn.net/weixin_42118777/article
"""
function:识别captcha验证码技术
训练函数
"""
from captcha_recognize.gen_captcha import gen_captcha_text_and_image
from captcha_recognize.gen_captcha import number
from captcha_recognize.gen_captcha import alphabet
from captcha_recognize.gen_captcha import ALPHABET

import numpy as np
import 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 = 60
IMAGE_WIDTH = 160
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）
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 这
# 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)
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 + char2pos(c)
        vector[idx] = 1
    return vector
# 向量转回文本
def vec2text(vec):
    char_pos = vec.nonzero()[0]   # 显示vec中非0值的位置
    text=[]
    for i, c in enumerate(char_pos):   # enumerate(char_pos)将字符串分离成位置和字符
        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)  # 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

####################################################################

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)

    # 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():
    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(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 > 0.8:
                    saver.save(sess, "./crack_capcha.model", global_step=step)
                    break

            step += 1
#训练
train_crack_captcha_cnn()
#验证
def crack_captcha(captcha_image):
    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)
        text_list = sess.run(predict, feed_dict={X: [captcha_image], keep_prob: 1})
        print(text_list)
        text = text_list[0].tolist()
        print(text)
        vector = np.zeros(MAX_CAPTCHA*CHAR_SET_LEN)
        i = 0
        for n in text:
                vector[i*CHAR_SET_LEN + n] = 1
                i += 1
        print(vector)
        return vec2text(vector)



text, image = gen_captcha_text_and_image()
image = convert2gray(image)
image = image.flatten() / 255
predict_text = crack_captcha(image)
print("正确: {}  预测: {}".format(text, predict_text))

附上git地址，有详细的介绍以及源码：
验证码识别：https://github.com/Arfer-ustc/captcha