#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

# 驗(yàn)證碼中的字符, 就不用漢字了

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 =[]
'''

# 驗(yàn)證碼一般都無(wú)視大小寫；驗(yàn)證碼長(zhǎng)度4個(gè)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


# 生成字符對(duì)應(yīng)的驗(yàn)證碼
def gen_captcha_text_and_image():
  while(1):
    image = ImageCaptcha()

    captcha_text = random_captcha_text()
    captcha_text = ''.join(captcha_text)

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

    captcha_image = Image.open(captcha)
    #captcha_image.show()
    captcha_image = np.array(captcha_image)
    if captcha_image.shape==(60,160,3):
      break

  return captcha_text, captcha_image






if __name__ == '__main__':
  # 測(cè)試
  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()

#coding=utf-8
from gen_captcha import gen_captcha_text_and_image
from gen_captcha import number
from gen_captcha import alphabet
from gen_captcha import ALPHABET

import numpy as np
import tensorflow as tf

"""
text, image = gen_captcha_text_and_image()
print "驗(yàn)證碼圖像channel:", image.shape # (60, 160, 3)
# 圖像大小
IMAGE_HEIGHT = 60
IMAGE_WIDTH = 160
MAX_CAPTCHA = len(text)
print  "驗(yàn)證碼文本最長(zhǎng)字符數(shù)", MAX_CAPTCHA # 驗(yàn)證碼最長(zhǎng)4字符; 我全部固定為4,可以不固定. 如果驗(yàn)證碼長(zhǎng)度小于4，用'_'補(bǔ)齊
"""
IMAGE_HEIGHT = 60
IMAGE_WIDTH = 160
MAX_CAPTCHA = 4

# 把彩色圖像轉(zhuǎn)為灰度圖像（色彩對(duì)識(shí)別驗(yàn)證碼沒有什么用）
def convert2gray(img):
  if len(img.shape) > 2:
    gray = np.mean(img, -1)
    # 上面的轉(zhuǎn)法較快，正規(guī)轉(zhuǎn)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在圖像大小是2的倍數(shù)時(shí)性能最高, 如果你用的圖像大小不是2的倍數(shù)，可以在圖像邊緣補(bǔ)無(wú)用像素。
np.pad(image,((2,3),(2,2)), 'constant', constant_values=(255,)) # 在圖像上補(bǔ)2行，下補(bǔ)3行，左補(bǔ)2行，右補(bǔ)2行
"""

# 文本轉(zhuǎn)向量
char_set = number + alphabet + ALPHABET + ['_'] # 如果驗(yàn)證碼長(zhǎng)度小于4, '_'用來(lái)補(bǔ)齊
CHAR_SET_LEN = len(char_set)


def text2vec(text):
  text_len = len(text)
  if text_len > MAX_CAPTCHA:
    raise ValueError('驗(yàn)證碼最長(zhǎng)4個(gè)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_')

# 向量轉(zhuǎn)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編碼 每63個(gè)編碼一個(gè)字符，這樣順利有，字符也有
vec = text2vec("F5Sd")
text = vec2text(vec)
print(text) # F5Sd
vec = text2vec("SFd5")
text = vec2text(vec)
print(text) # SFd5
"""


# 生成一個(gè)訓(xùn)練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])

  # 有時(shí)生成圖像大小不是(60, 160, 3)
  def wrap_gen_captcha_text_and_image():
    while True:
      text, image = gen_captcha_text_and_image()
      if image.shape == (60, 160, 3):
        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為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


# 定義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([8 * 32 * 40, 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


# 訓(xùn)練
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))
  # 最后一層用來(lái)分類的softmax和sigmoid有什么不同？
  # optimizer 為了加快訓(xùn)練 learning_rate應(yīng)該開始大，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計(jì)算一次準(zhǔn)確率
      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次的準(zhǔn)確率為%s'%(step, acc)
        # 如果準(zhǔn)確率大于50%,保存模型,完成訓(xùn)練
        if acc > 0.9:         ##我這里設(shè)了0.9，設(shè)得越大訓(xùn)練要花的時(shí)間越長(zhǎng)，如果設(shè)得過(guò)于接近1，很難達(dá)到。如果使用cpu，花的時(shí)間很長(zhǎng)，cpu占用很高電腦發(fā)燙。
          saver.save(sess, "crack_capcha.model", global_step=step)
          print time.time()-start_time
          break

      step += 1


train_crack_captcha_cnn()

output = crack_captcha_cnn()
saver = tf.train.Saver()
sess = tf.Session()
saver.restore(sess, tf.train.latest_checkpoint('.'))

while(1):
  

  text, image = gen_captcha_text_and_image()
  image = convert2gray(image)
  image = image.flatten() / 255

  predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
  text_list = sess.run(predict, feed_dict={X: [image], keep_prob: 1})
  predict_text = text_list[0].tolist()

  vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)
  i = 0
  for t in predict_text:
    vector[i * 63 + t] = 1
    i += 1
    # break



  print("正確: {} 預(yù)測(cè): {}".format(text, vec2text(vector)))