使用Tensorflow构建和训练自己的CNN来做简单的验证码识别

        Tensorflow是目前最流行的深度学习框架，我们可以用它来搭建自己的卷积神经网络并训练自己的分类器，本文介绍怎样使用Tensorflow构建自己的CNN，怎样训练用于简单的验证码识别的分类器。本文假设你已经安装好了Tensorflow，了解过CNN的一些知识。

下面将分步介绍怎样获得训练数据，怎样使用tensorflow构建卷积神经网络，怎样训练，以及怎样测试训练出来的分类器

1. 准备训练样本

        使用Python的库captcha来生成我们需要的训练样本，代码如下：

import sys

import osimport shutilimport randomimport time#captcha是用于生成验证码图片的库，可以 pip install captcha 来安装它from captcha.image import ImageCaptcha#用于生成验证码的字符集CHAR_SET = ['0','1','2','3','4','5','6','7','8','9']#字符集的长度CHAR_SET_LEN = 10#验证码的长度，每个验证码由4个数字组成CAPTCHA_LEN = 4#验证码图片的存放路径CAPTCHA_IMAGE_PATH = 'E:/Tensorflow/captcha/images/'#用于模型测试的验证码图片的存放路径，它里面的验证码图片作为测试集TEST_IMAGE_PATH = 'E:/Tensorflow/captcha/test/'#用于模型测试的验证码图片的个数，从生成的验证码图片中取出来放入测试集中TEST_IMAGE_NUMBER = 50#生成验证码图片，4位的十进制数字可以有10000种验证码def generate_captcha_image(charSet = CHAR_SET, charSetLen=CHAR_SET_LEN, captchaImgPath=CAPTCHA_IMAGE_PATH):       k  = 0    total = 1    for i in range(CAPTCHA_LEN):        total *= charSetLen            for i in range(charSetLen):        for j in range(charSetLen):            for m in range(charSetLen):                for n in range(charSetLen):                    captcha_text = charSet[i] + charSet[j] + charSet[m] + charSet[n]                    image = ImageCaptcha()                    image.write(captcha_text, captchaImgPath + captcha_text + '.jpg')                    k += 1                    sys.stdout.write("\rCreating %d/%d" % (k, total))                    sys.stdout.flush()                    #从验证码的图片集中取出一部分作为测试集，这些图片不参加训练，只用于模型的测试                    def prepare_test_set():    fileNameList = []        for filePath in os.listdir(CAPTCHA_IMAGE_PATH):        captcha_name = filePath.split('/')[-1]        fileNameList.append(captcha_name)    random.seed(time.time())    random.shuffle(fileNameList)     for i in range(TEST_IMAGE_NUMBER):        name = fileNameList[i]        shutil.move(CAPTCHA_IMAGE_PATH + name, TEST_IMAGE_PATH + name)                        if __name__ == '__main__':    generate_captcha_image(CHAR_SET, CHAR_SET_LEN, CAPTCHA_IMAGE_PATH)    prepare_test_set()    sys.stdout.write("\nFinished")    sys.stdout.flush()  

运行上面的代码，可以生成验证码图片，

生成的验证码图片如下图所示：

2. 构建CNN，训练分类器

     代码如下：

import tensorflow as tfimport numpy as npfrom PIL import Imageimport osimport randomimport time#验证码图片的存放路径CAPTCHA_IMAGE_PATH = 'E:/Tensorflow/captcha/images/'#验证码图片的宽度CAPTCHA_IMAGE_WIDHT = 160#验证码图片的高度CAPTCHA_IMAGE_HEIGHT = 60CHAR_SET_LEN = 10CAPTCHA_LEN = 4#60%的验证码图片放入训练集中TRAIN_IMAGE_PERCENT = 0.6#训练集，用于训练的验证码图片的文件名TRAINING_IMAGE_NAME = []#验证集，用于模型验证的验证码图片的文件名

VALIDATION_IMAGE_NAME = []

#存放训练好的模型的路径MODEL_SAVE_PATH = 'E:/Tensorflow/captcha/models/'def get_image_file_name(imgPath=CAPTCHA_IMAGE_PATH):    fileName = []    total = 0    for filePath in os.listdir(imgPath):        captcha_name = filePath.split('/')[-1]        fileName.append(captcha_name)        total += 1    return fileName, total    #将验证码转换为训练时用的标签向量，维数是 40   #例如，如果验证码是 ‘0296’ ，则对应的标签是# [1 0 0 0 0 0 0 0 0 0#  0 0 1 0 0 0 0 0 0 0#  0 0 0 0 0 0 0 0 0 1#  0 0 0 0 0 0 1 0 0 0]def name2label(name):    label = np.zeros(CAPTCHA_LEN * CHAR_SET_LEN)    for i, c in enumerate(name):        idx = i*CHAR_SET_LEN + ord(c) - ord('0')        label[idx] = 1    return label    #取得验证码图片的数据以及它的标签        def get_data_and_label(fileName, filePath=CAPTCHA_IMAGE_PATH):    pathName = os.path.join(filePath, fileName)    img = Image.open(pathName)    #转为灰度图    img = img.convert("L")           image_array = np.array(img)        image_data = image_array.flatten()/255    image_label = name2label(fileName[0:CAPTCHA_LEN])    return image_data, image_label    #生成一个训练batch    def get_next_batch(batchSize=32, trainOrTest='train', step=0):    batch_data = np.zeros([batchSize, CAPTCHA_IMAGE_WIDHT*CAPTCHA_IMAGE_HEIGHT])    batch_label = np.zeros([batchSize, CAPTCHA_LEN * CHAR_SET_LEN])    fileNameList = TRAINING_IMAGE_NAME    if trainOrTest == 'validate':                fileNameList = VALIDATION_IMAGE_NAME            totalNumber = len(fileNameList)     indexStart = step*batchSize        for i in range(batchSize):        index = (i + indexStart) % totalNumber        name = fileNameList[index]                img_data, img_label = get_data_and_label(name)        batch_data[i, : ] = img_data        batch_label[i, : ] = img_label      return batch_data, batch_label    #构建卷积神经网络并训练def train_data_with_CNN():    #初始化权值    def weight_variable(shape, name='weight'):        init = tf.truncated_normal(shape, stddev=0.1)        var = tf.Variable(initial_value=init, name=name)        return var    #初始化偏置        def bias_variable(shape, name='bias'):        init = tf.constant(0.1, shape=shape)        var = tf.Variable(init, name=name)        return var    #卷积        def conv2d(x, W, name='conv2d'):        return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME', name=name)    #池化     def max_pool_2X2(x, name='maxpool'):        return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME', name=name)            #输入层    #请注意 X 的 name，在测试model时会用到它    X = tf.placeholder(tf.float32, [None, CAPTCHA_IMAGE_WIDHT * CAPTCHA_IMAGE_HEIGHT], name='data-input')    Y = tf.placeholder(tf.float32, [None, CAPTCHA_LEN * CHAR_SET_LEN], name='label-input')        x_input = tf.reshape(X, [-1, CAPTCHA_IMAGE_HEIGHT, CAPTCHA_IMAGE_WIDHT, 1], name='x-input')    #dropout,防止过拟合    #请注意 keep_prob 的 name，在测试model时会用到它    keep_prob = tf.placeholder(tf.float32, name='keep-prob')    #第一层卷积    W_conv1 = weight_variable([5,5,1,32], 'W_conv1')    B_conv1 = bias_variable([32], 'B_conv1')    conv1 = tf.nn.relu(conv2d(x_input, W_conv1, 'conv1') + B_conv1)    conv1 = max_pool_2X2(conv1, 'conv1-pool')    conv1 = tf.nn.dropout(conv1, keep_prob)    #第二层卷积    W_conv2 = weight_variable([5,5,32,64], 'W_conv2')    B_conv2 = bias_variable([64], 'B_conv2')    conv2 = tf.nn.relu(conv2d(conv1, W_conv2,'conv2') + B_conv2)    conv2 = max_pool_2X2(conv2, 'conv2-pool')    conv2 = tf.nn.dropout(conv2, keep_prob)    #第三层卷积    W_conv3 = weight_variable([5,5,64,64], 'W_conv3')    B_conv3 = bias_variable([64], 'B_conv3')    conv3 = tf.nn.relu(conv2d(conv2, W_conv3, 'conv3') + B_conv3)    conv3 = max_pool_2X2(conv3, 'conv3-pool')    conv3 = tf.nn.dropout(conv3, keep_prob)    #全链接层    #每次池化后，图片的宽度和高度均缩小为原来的一半，进过上面的三次池化，宽度和高度均缩小8倍    W_fc1 = weight_variable([20*8*64, 1024], 'W_fc1')    B_fc1 = bias_variable([1024], 'B_fc1')    fc1 = tf.reshape(conv3, [-1, 20*8*64])    fc1 = tf.nn.relu(tf.add(tf.matmul(fc1, W_fc1), B_fc1))    fc1 = tf.nn.dropout(fc1, keep_prob)    #输出层    W_fc2 = weight_variable([1024, CAPTCHA_LEN * CHAR_SET_LEN], 'W_fc2')    B_fc2 = bias_variable([CAPTCHA_LEN * CHAR_SET_LEN], 'B_fc2')    output = tf.add(tf.matmul(fc1, W_fc2), B_fc2, 'output')        loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=Y, logits=output))    optimizer = tf.train.AdamOptimizer(0.001).minimize(loss)        predict = tf.reshape(output, [-1, CAPTCHA_LEN, CHAR_SET_LEN], name='predict')    labels = tf.reshape(Y, [-1, CAPTCHA_LEN, CHAR_SET_LEN], name='labels')    #预测结果    #请注意 predict_max_idx 的 name，在测试model时会用到它    predict_max_idx = tf.argmax(predict, axis=2, name='predict_max_idx')    labels_max_idx = tf.argmax(labels, axis=2, name='labels_max_idx')    predict_correct_vec = tf.equal(predict_max_idx, labels_max_idx)    accuracy = tf.reduce_mean(tf.cast(predict_correct_vec, tf.float32))        saver = tf.train.Saver()    with tf.Session() as sess:        sess.run(tf.global_variables_initializer())        steps = 0        for epoch in range(6000):            train_data, train_label = get_next_batch(64, 'train', steps)            sess.run(optimizer, feed_dict={X : train_data, Y : train_label, keep_prob:0.75})            if steps % 100 == 0:                test_data, test_label = get_next_batch(100, 'validate', steps)                acc = sess.run(accuracy, feed_dict={X : test_data, Y : test_label, keep_prob:1.0})                print("steps=%d, accuracy=%f" % (steps, acc))                if acc > 0.99:                    saver.save(sess, MODEL_SAVE_PATH+"crack_captcha.model", global_step=steps)                    break            steps += 1if __name__ == '__main__':        image_filename_list, total = get_image_file_name(CAPTCHA_IMAGE_PATH)    random.seed(time.time())    #打乱顺序    random.shuffle(image_filename_list)    trainImageNumber = int(total * TRAIN_IMAGE_PERCENT)    #分成测试集    TRAINING_IMAGE_NAME = image_filename_list[ : trainImageNumber]    #和验证集    VALIDATION_IMAGE_NAME = image_filename_list[trainImageNumber : ]    train_data_with_CNN()        print('Training finished')

运行上面的代码，开始训练，训练要花些时间，如果没有GPU的话，会慢些，

训练完后，输出如下结果，经过4100次的迭代，训练出来的分类器模型在验证集上识别的准确率为99.5%

生成的模型文件如下，在模型测试时将用到这些文件

3. 测试模型

编写代码，对训练出来的模型进行测试

import tensorflow as tf

import numpy as npfrom PIL import Imageimport osimport matplotlib.pyplot as plt CAPTCHA_LEN = 4MODEL_SAVE_PATH = 'E:/Tensorflow/captcha/models/'TEST_IMAGE_PATH = 'E:/Tensorflow/captcha/test/'def get_image_data_and_name(fileName, filePath=TEST_IMAGE_PATH):    pathName = os.path.join(filePath, fileName)    img = Image.open(pathName)    #转为灰度图    img = img.convert("L")           image_array = np.array(img)        image_data = image_array.flatten()/255    image_name = fileName[0:CAPTCHA_LEN]    return image_data, image_namedef digitalStr2Array(digitalStr):    digitalList = []    for c in digitalStr:        digitalList.append(ord(c) - ord('0'))    return np.array(digitalList)def model_test():    nameList = []    for pathName in os.listdir(TEST_IMAGE_PATH):        nameList.append(pathName.split('/')[-1])    totalNumber = len(nameList)    #加载graph    saver = tf.train.import_meta_graph(MODEL_SAVE_PATH+"crack_captcha.model-4100.meta")    graph = tf.get_default_graph()    #从graph取得 tensor，他们的name是在构建graph时定义的(查看上面第2步里的代码)    input_holder = graph.get_tensor_by_name("data-input:0")    keep_prob_holder = graph.get_tensor_by_name("keep-prob:0")    predict_max_idx = graph.get_tensor_by_name("predict_max_idx:0")    with tf.Session() as sess:        saver.restore(sess, tf.train.latest_checkpoint(MODEL_SAVE_PATH))        count = 0        for fileName in nameList:            img_data, img_name = get_image_data_and_name(fileName, TEST_IMAGE_PATH)            predict = sess.run(predict_max_idx, feed_dict={input_holder:[img_data], keep_prob_holder : 1.0})                        filePathName = TEST_IMAGE_PATH + fileName            print(filePathName)            img = Image.open(filePathName)            plt.imshow(img)            plt.axis('off')            plt.show()            predictValue = np.squeeze(predict)            rightValue = digitalStr2Array(img_name)            if np.array_equal(predictValue, rightValue):                result = '正确'                count += 1            else:                 result = '错误'                        print('实际值：{}， 预测值：{}，测试结果：{}'.format(rightValue, predictValue, result))            print('\n')                    print('正确率：%.2f%%(%d/%d)' % (count*100/totalNumber, count, totalNumber))if __name__ == '__main__':    model_test()

对模型的测试结果如下，在测试集上识别的准确率为 94%

下面是两个识别错误的验证码