利用Tensorflow构建和训练自己的CNN来做简单的验证码识别方式

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服务器之家。

原文链接：https://blog.csdn.net/maliao1123/article/details/79415828