【python+TensorFlow】验证码识别，机器深度学习

人工智能：

伴随计算机的发展，人工智能被推向了风口浪尖，成为计算机领域炙手可热的技术，任何一家互联网
公司都会或多或少的接触的这方面的技术。之前 阿尔法狗VS 柯洁，阿尔法狗3：0大胜，让大家更直观了解到机器学习，
了解到了人工智能，大量资金涌入人工智能，机器学习领域，成就了现在大家日常的生活中。

我们日常生活中也存在很多人工智能的产物，最简单的是现在日渐普及的 AI 美颜相机，智慧城市，智慧家，这都是机器学习 +优化算法 的产物。

验证码背景：

大家想必在生活中，会遇到各种验证码，为什么会产生验证码？为什么我们在注册各种app时候会受到各种验证短信? 其实不必要纠结，其实是为了反爬虫，反撞库的技术策略，有人会纠结，反爬虫跟验码有个毛关系，我可以负责人的告诉大家，其实验证码是一种安全策略，为了保证大家在正常使用中的信息安全，以及企业为了防止别人恶意攻击手段，别看小小验证码，在机器学习之前还是很安全的。他能判断出是正常用户、还是机器恶意攻击，正常用户可以正常输入所看到的验证码信息，而机器则识别不出来。 网站为了网站信息安全也会设置验证码，保护自身网站的安全性，在抵挡别人恶意撞库测试。

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验证码的形式多种多样，我们列举我们常见的几种验证码，供大家了解：

列表	类型	详细
1	滑动解锁	一键式解锁从左滑到右
2	滑动拼图	滑动拼接图片
3	验证码手动输入	输入正确验证码
4	汉字选取	选取提示所示汉字

为了方便我们模型测试，我这是生成的验证码,当然我们也可以用本地截取的验证码，我会在代码中注明如何使用本地验证码：

gen_captcha.py 文件

#coding=utf-8
from captcha.image import ImageCaptcha  # pip install captcha
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import random
import os,glob

# 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 =[]
'''

# DATA_DIR = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'data')
# DEFAULT_FONTS = [os.path.join(DATA_DIR, 'DroidSansMono.ttf')]
#
# class ImageCaptcha(ImageCaptcha):
#
#     def __init__(self):
#         # (self, width=180, height=67, fonts=None, font_sizes=None):
#         self._width = 180
#         self._height = 67
#         self._fonts = None or DEFAULT_FONTS
#         self._font_sizes = None or (42, 50, 56)
#         self._truefonts = []
# 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(180,67)
        # image = ImageCaptcha()

        captcha_text = random_captcha_text()
        captcha_text = ''.join(captcha_text)
        path = 'F://CNN_1/test/'
        print("-----------------"+captcha_text)
        captcha = image.generate(captcha_text)
        # image.write(captcha_text, path+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==(67,180,4):
            break

    return captcha_text, captcha_image


#读取本地打好标记的验证码
def get_name_and_image():
    list = glob.glob(r"C:\Users\Administrator\Desktop\1\*.png")
    num  =random.randint(0,len(list)-1)

    path = list[num]
    # print(path)
    p, image_text = path.split('_')
    captcha_text, type = image_text.split('.')

    captcha_image = Image.open(path)

    captcha_image = np.array(captcha_image)

    return captcha_text, captcha_image

if __name__ == '__main__':
  while 1:
    # 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()

上述代码中，有两种方式，1 生成验证码作为训练字库  2 另一种是自己打标记
这是我打好标记的验证码，如下

train.py 代码：

#coding=utf-8
from gen_captcha import gen_captcha_text_and_image,read_localhost_images,get_name_and_image
from gen_captcha import number
from gen_captcha import alphabet
from gen_captcha import ALPHABET
from PIL import Image
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 = 67
IMAGE_WIDTH = 180
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
"""
IMAGE_HEIGHT = 67 #图片高度
IMAGE_WIDTH = 180 #宽度
MAX_CAPTCHA = 4 #最大字符，验证码训练样本

# 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)  # 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, 4)
    def wrap_gen_captcha_text_and_image():
        while True:
            # text, image = gen_captcha_text_and_image()  #端对端自动生成验证码，可以不用打标记，直接使用
            # text, image = read_localhost_images()
            text, image = get_name_and_image()  #读取本地打好标记验证码
            # print(image.shape)
            if image.shape == (67, 180, 4):
                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([9 * 23 * 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)
    # 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 > 0.99:                  ##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。
                    print("保存模型......")
                    # joblib.dump(sess, "F:/model/rf_model.m")
                    saver.save(sess, "F:/CNN_2/model_3/crack_capcha.model", global_step=step)
                    print (time.time()-start_time)
                    break
            step += 1


train_crack_captcha_cnn()
#
# 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('.'))
# 		# saver.restore(sess, "F:/CNN_1/model/crack_capcha.model-10400")
# 		saver.restore(sess, "F:/CNN_2/model_2/crack_capcha.model-1700")
#
# 		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_list = sess.run(predict, feed_dict={X: [captcha_image]})
#
# 		text = text_list[0].tolist()
# 		vector = np.zeros(MAX_CAPTCHA*CHAR_SET_LEN)
# 		i = 0
# 		for n in text:
# 				vector[i*CHAR_SET_LEN + n] = 1
# 				i += 1
# 		return vec2text(vector)

# path = "C:/Users/Administrator/Desktop/text/text/2.png"
# image = Image.open('f://CNN_1/test/TUFK.jpg')
# image = Image.open('C:/Users/Administrator/Desktop/text/text/3.png')
# image = np.array(image)
# text = 'jvdf'
# image = convert2gray(image)
# image = image.flatten() / 255
# predict_text = crack_captcha(image)
# print("正确: {}  预测: {}".format(text, predict_text))

放两张训练图：

最后样本我训练结果是：99% 正确率
模型保存成功后是四个文件：

看下预测代码predict.py：

from train import crack_captcha_cnn
from train import  MAX_CAPTCHA,CHAR_SET_LEN,keep_prob,X
from train import  vec2text,convert2gray
from PIL import Image
import numpy as np
import tensorflow as tf
import glob,random




def predict_result(captcha_image):

    output = crack_captcha_cnn()

    saver = tf.train.Saver()
    # tf.reset_default_graph()

    with tf.Session() as sess:
        saver.restore(sess, "F:/CNN_2/model_3/crack_capcha.model-2000")
        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_list = sess.run(predict, feed_dict={X: [captcha_image]})

        text = text_list[0].tolist()
        vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)
        i = 0
        for n in text:
            vector[i * CHAR_SET_LEN + n] = 1
            i += 1
        return vec2text(vector)

def get_name_and_image():
    list1 = glob.glob(r"C:\Users\Administrator\Desktop\text\text\*.png")
    num  =random.randint(0,len(list1)-1)

    path = list1[num]
    print(path)
    p, image_text = path.split('_')
    captcha_text, type = image_text.split('.')

    captcha_image = Image.open(path)

    captcha_image = np.array(captcha_image)

    image = convert2gray(captcha_image)
    image = image.flatten() / 255

    return captcha_text, image
if __name__ =="__main__":
    text , image =get_name_and_image()
    predict_text = predict_result(image)
    print("正确: {}  预测: {}".format(text, predict_text))

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