Tensorflow简单验证码识别应用

from gen_check_code import gen_captcha_text_and_image_new,gen_captcha_text_and_image
from gen_check_code import number
from test_check_code import get_test_captcha_text_and_image
import numpy as np
import tensorflow as tf

text,image = gen_captcha_text_and_image_new()
print("验证码图像channel:",image.shape) # (60,160,3) 
# 图像大小 
IMAGE_HEIGHT = image.shape[0]
IMAGE_WIDTH = image.shape[1]
image_shape = image.shape
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）
# 度化是将三分量转化成一样数值的过程
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],2] 
  # gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
  # int gray = (int) (0.3 * r + 0.59 * g + 0.11 * 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 
"""


char_set = number # 如果验证码长度小于4,'_'用来补齐
CHAR_SET_LEN = len(char_set)

# 文本转向量
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):
  try:
   k = ord(c)-ord('0')
  except:
   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]
 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)


# 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(60,3) 
 def wrap_gen_captcha_text_and_image():
  while True:
   text,image = gen_captcha_text_and_image_new()

   if image.shape == image_shape:
    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,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)

 # 定义三层的卷积神经网络

 # 定义第一层的卷积神经网络
 # 定义第一层权重
 w_c1 = tf.Variable(w_alpha * tf.random_normal([3,3,1,32]))
 # 定义第一层的偏置
 b_c1 = tf.Variable(b_alpha * tf.random_normal([32]))
 # 定义第一层的激励函数
 conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x,w_c1,strides=[1,padding='SAME'),b_c1))
 # conv1 为输入 ksize 表示使用2*2池化，即将2*2的色块转化成1*1的色块
 conv1 = tf.nn.max_pool(conv1,ksize=[1,2,padding='SAME')
 # dropout防止过拟合。
 conv1 = tf.nn.dropout(conv1,keep_prob)

 # 定义第二层的卷积神经网络
 w_c2 = tf.Variable(w_alpha * tf.random_normal([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,b_c2))
 conv2 = tf.nn.max_pool(conv2,padding='SAME')
 conv2 = tf.nn.dropout(conv2,keep_prob)

 # 定义第三层的卷积神经网络
 w_c3 = tf.Variable(w_alpha * tf.random_normal([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,b_c3))
 conv3 = tf.nn.max_pool(conv3,padding='SAME')
 conv3 = tf.nn.dropout(conv3,keep_prob)

 # Fully connected layer
 # 随机生成权重
 w_d = tf.Variable(w_alpha * tf.random_normal([1536,1024]))
 # 随机生成偏置
 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():
 # X = tf.placeholder(tf.float32,IMAGE_HEIGHT * IMAGE_WIDTH])
 # Y = tf.placeholder(tf.float32,MAX_CAPTCHA * CHAR_SET_LEN])
 # keep_prob = tf.placeholder(tf.float32) # dropout
 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(output,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,MAX_CAPTCHA,CHAR_SET_LEN])
 max_idx_p = tf.argmax(predict,2)
 max_idx_l = tf.argmax(tf.reshape(Y,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 > 0.99:
      saver.save(sess,"./crack_capcha.model",global_step=step)
      break
    step += 1

## 训练(如果要训练则去掉下面一行的注释)
train_crack_captcha_cnn()


def crack_captcha():
 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,2)
  count = 0
  # 因为测试集共40个...写的很草率
  for i in range(40):
   text,image = get_test_captcha_text_and_image(i)
   image = convert2gray(image)
   captcha_image = image.flatten() / 255
   text_list = sess.run(predict,feed_dict={X: [captcha_image],keep_prob: 1})
   predict_text = text_list[0].tolist()
   predict_text = str(predict_text)
   predict_text = predict_text.replace("[","").replace("]","").replace(",","").replace(" ","")
   if text == predict_text:
    count += 1
    check_result = "，预测结果正确"
   else:
    check_result = "，预测结果不正确"
    print("正确: {} 预测: {}".format(text,predict_text) + check_result)

  print("正确率:" + str(count) + "/40")
# 测试(如果要测试则去掉下面一行的注释)
# crack_captcha()

from captcha.image import ImageCaptcha # pip install captcha
import numpy as np
from PIL import Image
import random
# import matplotlib.pyplot as plt
import os
from random import choice

# 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']

root_dir = "d:train"

# 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,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)

 captcha = image.generate(captcha_text)
 # image.write(captcha_text,captcha_text + '.jpg') # 写到文件

 captcha_image = Image.open(captcha)
 captcha_image = np.array(captcha_image)
 return captcha_text,captcha_image


def gen_list():
 img_list = []
 for parent,dirnames,filenames in os.walk(root_dir): # 三个参数：分别返回1.父目录 2.所有文件夹名字（不含路径） 3.所有文件名字
  for filename in filenames: # 输出文件信息
   img_list.append(filename.replace(".gif",""))
   # print("parent is:" + parent)
   # print("filename is:" + filename)
   # print("the full name of the file is:" + os.path.join(parent,filename)) # 输出文件路径信息
 return img_list
img_list = gen_list()
def gen_captcha_text_and_image_new():
 img = choice(img_list)
 captcha_image = Image.open(root_dir + "" + img + ".gif")
 captcha_image = np.array(captcha_image)
 return img,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)
#  # plt.show()
#  #
#
#  text,image = gen_captcha_text_and_image_new()
#
#  f = plt.figure()
#  ax = f.add_subplot(111)
#  ax.text(0.1,transform=ax.transAxes)
#  plt.imshow(image)
#  plt.show()

from captcha.image import ImageCaptcha # pip install captcha
import numpy as np
from PIL import Image
import random
import matplotlib.pyplot as plt
import os
from random import choice


root_dir = "d:test"



img_list = []
def gen_list():

 for parent,filename)) # 输出文件路径信息
 return img_list

img_list = gen_list()
def get_test_captcha_text_and_image(i=None):
 img = img_list[i]
 captcha_image = Image.open(root_dir + "" + img + ".gif")
 captcha_image = np.array(captcha_image)
 return img,captcha_image