利用Tensorflow构建和训练自己的CNN来做简单的验证码识别方式

技术  /  管理员 发布于 7年前   132

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 os import shutil import random import 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 tf import numpy as np from PIL import Image import os import random import 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 = 60  CHAR_SET_LEN = 10 CAPTCHA_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 += 1  if __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 np from PIL import Image import os import matplotlib.pyplot as plt   CAPTCHA_LEN = 4  MODEL_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_name  def 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

以上这篇利用Tensorflow构建和训练自己的CNN来做简单的验证码识别方式就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。