{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.\n",
      "abcdefghijklmnopqrstuvwxyz0123456789\n",
      "15 ['latin/BOOKOS.TTF', 'latin/Candarab.ttf', 'latin/corbeli.ttf', 'latin/ARIALN.TTF', 'latin/CALISTBI.TTF', 'latin/SCHLBKI.TTF', 'latin/CENTURY.TTF', 'latin/ARIALNBI.TTF', 'latin/consolai.ttf', 'latin/ANTQUAB.TTF', 'latin/verdana.ttf', 'latin/ARIALNB.TTF', 'latin/LBRITEI.TTF', 'latin/tahoma.ttf', 'latin/Candara.ttf']\n",
      "n_class:37, n_len:6\n",
      "15 latin/BOOKOS.TTF\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "from captcha.image import ImageCaptcha\n",
    "from PIL import Image, ImageFont, ImageDraw\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random\n",
    "import uuid\n",
    "import math\n",
    "import glob\n",
    "import re\n",
    "from PIL import ImageFilter\n",
    "import shutil\n",
    "from collections import Counter\n",
    "from keras.utils import Sequence\n",
    "from keras.models import *\n",
    "from keras.layers import *\n",
    "import keras.backend as K\n",
    "from keras.callbacks import EarlyStopping, CSVLogger, ModelCheckpoint\n",
    "from keras.optimizers import *\n",
    "import gc \n",
    "\n",
    "import os\n",
    "import shutil\n",
    "from PIL import Image\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import string\n",
    "characters = string.ascii_lowercase + string.digits # 验证码字符集合数字+英文  全部转为小写格式\n",
    "print(characters)\n",
    "\n",
    "width, height, n_len, n_class = 200, 70, 6, len(characters) + 1  #图片宽、高，验证码最大长度，分类\n",
    "# width, height, n_len, n_class = 128, 64, 6, len(characters) + 1  #图片宽、高，验证码最大长度，分类\n",
    "# fonts=glob.glob('fonts/english/*')\n",
    "# fonts = glob.glob('latin2/*')\n",
    "# fonts.remove('latin2/ANTQUABI.TTF') # n,u 不清晰删除\n",
    "\n",
    "font_paths = glob.glob('latin/*')\n",
    "# font_list = ['ARIALN.TTF', 'ARIALNI.TTF', 'BKANT.TTF', 'calibrii.ttf', 'calibrili.ttf','Calibrib.ttf', 'CALISTI.TTF','cambriai.ttf','LSANS.TTF','CENSCBK.TTF']\n",
    "font_list = ['ANTQUAB.TTF', 'ARIALN.TTF', 'ARIALNB.TTF', 'ARIALNBI.TTF', 'BOOKOS.TTF','CALISTBI.TTF', 'Candara.ttf', 'Candarab.ttf','CENTURY.TTF','corbeli.ttf',\n",
    "             'consolai.ttf','LBRITEI.TTF','SCHLBKI.TTF','tahoma.ttf', 'verdana.ttf']\n",
    "\n",
    "fonts = []\n",
    "for font in font_paths:\n",
    "    if font.split('/')[-1] in font_list:\n",
    "        fonts.append(font)\n",
    "print(len(fonts), fonts[:])\n",
    "\n",
    "print('n_class:%d, n_len:%d'%(n_class, n_len))\n",
    "print(len(fonts), fonts[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.15.4\n",
      "3.6.13 |Anaconda, Inc.| (default, Jun  4 2021, 14:25:59) \n",
      "[GCC 7.5.0]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import sys\n",
    "print(tf.__version__)  # 应 >= 2.0.0\n",
    "print(sys.version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image size (122, 48) (200, 70)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3600x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 2224
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random_color(rs, re, gs, ge, bs, be) (11, 12, 11)\n",
      "rand 0.9227165293303677\n"
     ]
    }
   ],
   "source": [
    "'''生成彩色图像'''\n",
    "def get_wavy_line(w = (0, 100),h = (30, 50)):\n",
    "    '''产生波浪线坐标'''\n",
    "    import random\n",
    "    n = 50\n",
    "    x = 0\n",
    "    y = random.randint(h[0],h[1])\n",
    "    flag = random.randint(0,2)\n",
    "    xy = [(x, y)]\n",
    "    while x < w[1]:\n",
    "        temp_y = random.randint(1, 3)\n",
    "        temp_x = random.randint(5, 10)\n",
    "        if flag == 0:\n",
    "            if y + temp_y > h[1]:\n",
    "                y -= temp_y\n",
    "                flag = 1\n",
    "            else:\n",
    "                y += temp_y\n",
    "        else:\n",
    "            if y - temp_y < h[0]:\n",
    "                y += temp_y\n",
    "                flag = 0\n",
    "            else:\n",
    "                y -= temp_y\n",
    "        x = x+temp_x if x+temp_x < w[1] else w[1]\n",
    "        xy.append((x, y))\n",
    "    return xy\n",
    "def Asin(x, A=8,w=0.05, b=6, k=40):\n",
    "    '''\n",
    "    y=Asin(ωx+φ)+k在直角坐标系上的图象\n",
    "    A——振幅，当物体作轨迹符合正弦曲线的直线往复运动时，其值为行程的1/2。\n",
    "    (ωx+φ)——相位，反映变量y所处的状态。\n",
    "    φ——初相，x=0时的相位；反映在坐标系上则为图像的左右移动。\n",
    "    k——偏距，反映在坐标系上则为图像的上移或下移。\n",
    "    ω——角速度， 控制正弦周期(单位弧度内震动的次数)。\n",
    "    '''\n",
    "    return A*math.sin(w*x+b)+k\n",
    "\n",
    "def random_xy(width,height):   \n",
    "    '''\n",
    "    随机位置函数，返回指定范围随机位置坐标\n",
    "    参数：width:图片宽，height:图片高\n",
    "    '''\n",
    "    x = random.randint(0, width)\n",
    "    y = random.randint(0, height)\n",
    "    return x, y\n",
    "def random_color(color_tuple):\n",
    "    '''\n",
    "    随机颜色函数，返回指定范围随机颜色值\n",
    "    参数：start:颜色最低值，end:颜色最高值\n",
    "    '''\n",
    "    if len(color_tuple)==2:\n",
    "        rs, re = color_tuple\n",
    "        gs = bs = rs\n",
    "        ge = be = re\n",
    "    else:\n",
    "        rs, re, gs, ge, bs, be = color_tuple\n",
    "    red = random.randint(rs, re)\n",
    "    green = random.randint(gs, ge)\n",
    "    blue = random.randint(bs, be)\n",
    "    return (red, green, blue)\n",
    "\n",
    "is_raw_size=False\n",
    "\n",
    "def gen_captcha(text, fig_size=(200,70), fonts=['fonts/ANTQUAB.TTF'],font_color=(10,100),same_color=1, font_size=(25, 35), rotate=0,\n",
    "                font_noise=0, offset_w=(0,0), offset_h=0, line=(0,0), shortline=(0,0), line_width=(0,1), line_color=(200,250), point=(0,500), \n",
    "                point_color=(150,250), frame_color=None, wavy=(0,0), bg=(200,255)):\n",
    "    '''\n",
    "    text:验证码文本\n",
    "    size:验证码图片宽高\n",
    "    fonts:字体列表，随机选择一个\n",
    "    font_noise: 字体散点干扰，0不加干扰，1加干扰\n",
    "    offset_hor: 左右偏移值\n",
    "    offset_var: 上下偏移值\n",
    "    fill:字体颜色范围\n",
    "    rotate:字体旋转角度\n",
    "    line:干扰线条数范围\n",
    "    point:干扰点数范围\n",
    "    wavy:波浪线数范围\n",
    "    color:干扰线、点 颜色\n",
    "    bg:背景色范围\n",
    "    '''\n",
    "    bg = random_color(bg)\n",
    "    img = Image.new(mode='RGB', size=fig_size, color=bg) #\n",
    "    draw = ImageDraw.Draw(im=img, mode='RGB') # im, mode=None\n",
    "    \n",
    "    font_path = random.choice(fonts)\n",
    "#     font_name = font_path.split('/')[-1][:-4]\n",
    "#     print('font_name:', font_name)\n",
    "    \n",
    "    font = ImageFont.truetype(font_path, size=random.randint(font_size[0], font_size[1])) # font=None, size=10, index=0, encoding=\"\"\n",
    "    rotate = random.randint(0, rotate)\n",
    "    def get_char_img(char,font,font_color,rotate,bg, font_noise=0):\n",
    "        '''\n",
    "        生成单个字符图片，随机颜色加随机旋转\n",
    "        \n",
    "        '''\n",
    "        w, h = draw.textsize(char, font=font)\n",
    "        im = Image.new('RGBA',(w,h), color=bg)\n",
    "        ImageDraw.Draw(im).text((0,0), char, font=font, fill=font_color)   \n",
    "        if rotate:\n",
    "            im = im.rotate(random.randint(-rotate, rotate),Image.BILINEAR,expand=1)\n",
    "            im = im.crop(im.getbbox())\n",
    "        if font_noise: \n",
    "            im_draw = ImageDraw.Draw(im)\n",
    "#             for i in range(random.randint(1,20)):\n",
    "            for i in range(random.randint(int(w*h*0.01),min(int(w*h*0.05), 5))):\n",
    "                im_draw.point(xy=(random.randint(0, w), random.randint(0, h)),fill=bg)\n",
    "\n",
    "        table = []\n",
    "        for i in range(256):\n",
    "            table.append(i * 97) # 5.97\n",
    "        mask = im.convert('L').point(table) \n",
    "        return (im, mask)\n",
    "    \n",
    "#     char_color = random.randint(font_color[0],font_color[1])\n",
    "    char_color = random_color(font_color)\n",
    "    if same_color:        \n",
    "        char_imgs = [get_char_img(char, font, font_color=char_color, rotate=rotate, bg=bg, font_noise=font_noise) for char in text]\n",
    "    else:\n",
    "#         char_imgs = [get_char_img(char, font, font_color=random.randint(font_color[0],font_color[1]), rotate=rotate, bg=bg, font_noise=font_noise) for char in text]\n",
    "        char_imgs = [get_char_img(char, font, font_color=random_color(font_color), rotate=rotate, bg=bg, font_noise=font_noise) for char in text]       \n",
    "    ws = [img[0].size[0] for img in char_imgs]\n",
    "    hs = [img[0].size[1] for img in char_imgs]\n",
    "    w = max(sum(ws), fig_size[0])\n",
    "    h = max(max(hs), fig_size[1])\n",
    "    if w>fig_size[0] or h>fig_size[1]:\n",
    "        img = Image.new('RGB',(w+6,h+6), color=bg)\n",
    "        draw = ImageDraw.Draw(im=img, mode='RGB')  # im, mode=None\n",
    "        w, h = img.size\n",
    "        fig_size = img.size\n",
    "        \n",
    "\n",
    "    # 短线\n",
    "    for i in range(random.randint(shortline[0], shortline[1])):\n",
    "        x0, y0 = random_xy(w, h)\n",
    "        x1 = x0 + random.randint(2, 5)\n",
    "        y1 = y0 + random.randint(2, 5)\n",
    "        draw.line(xy=((x0,y0),(x1,y1)),\n",
    "                  fill=random_color(line_color),\n",
    "                  width=random.randint(line_width[0], line_width[1])) # xy, fill=None, width=0\n",
    "        \n",
    "    if rotate:\n",
    "        temp_x = random.randint(int((fig_size[0]-sum(ws))/5), int((fig_size[0]-sum(ws))/2+1))\n",
    "        temp_y = random.randint(int((fig_size[1]-hs[0])/8), int((fig_size[1]-hs[0])/2+1))\n",
    "        for i in range(len(char_imgs)):\n",
    "            tmp_offset = random.randint(offset_w[0], offset_w[1]) if sum(ws)+(len(ws)-1)*offset_w[1]<fig_size[0] else 0\n",
    "            if i > 0:\n",
    "                temp_x = new_x if new_x+ws[i]<fig_size[0] else temp_x+ws[i-1]\n",
    "                temp_y = new_y if 0<new_y and new_y+hs[i]<fig_size[1] else random.randint(0, h-hs[i]+1) \n",
    "            img.paste(char_imgs[i][0], box=(temp_x, temp_y), mask=char_imgs[i][1]) \n",
    "            new_x = temp_x+ws[i]+tmp_offset         \n",
    "            new_y = temp_y+random.randint(-offset_h, offset_h)  \n",
    "       \n",
    "    # 直线\n",
    "    for i in range(random.randint(line[0], line[1])):\n",
    "        x0, y0 = random_xy(w, h)\n",
    "        x1, y1 = random_xy(w, h)\n",
    "        draw.line(xy=((x0,y0),(x1,y1)),\n",
    "#                   fill=random.randint(noise_color[0], noise_color[1]),\n",
    "                  fill=random_color(line_color),\n",
    "                  width=random.randint(line_width[0], line_width[1])) # xy, fill=None, width=0\n",
    "    # 散点\n",
    "    for i in range(random.randint(point[0], point[1])):\n",
    "        if font_color == point_color:\n",
    "            draw.point(xy=(random.randint(0, fig_size[0]), random.randint(0, fig_size[1])),\n",
    "                      fill=char_color)    \n",
    "        else:            \n",
    "            draw.point(xy=(random.randint(0, fig_size[0]), random.randint(0, fig_size[1])),\n",
    "                      fill=random_color(point_color))     \n",
    "    if random.random()>=0.5:\n",
    "        A_ = random.uniform(hs[1]*0.1,hs[1]*0.2)\n",
    "        w_ = math.pi*4/w#random.uniform(0.04, 0.06)\n",
    "        b_ = random.random()*math.pi\n",
    "        k_ = random.uniform(h*0.5, h*0.7)\n",
    "        # 波浪线\n",
    "        for _ in range(random.randint(wavy[0],wavy[1])):  \n",
    "            draw.line(xy=[(x, Asin(x, A_, w_, b_, k_)) for x in range(int(w))], \n",
    "                      fill=char_color, width=random.randint(line_width[0], line_width[1]))  \n",
    "    else:\n",
    "        # 波浪线\n",
    "        for _ in range(random.randint(wavy[0],wavy[1])):  \n",
    "            draw.line(xy=get_wavy_line(w = (0, w),h = (min(hs)-5, max(hs)+5)), \n",
    "                      fill=char_color, width=random.randint(line_width[0], line_width[1]))           \n",
    "    \n",
    "    # 边框\n",
    "    if frame_color!=None:\n",
    "        draw.line(xy=[(0,0),(0, h), (0, 0), (w, 0),(w-1,0),(w-1, h), (0,h-1),(w-1, h-1)], fill=random_color(frame_color))\n",
    "        \n",
    "    if not rotate:\n",
    "        temp_x = random.randint(int((fig_size[0]-sum(ws))/5), int((fig_size[0]-sum(ws))/2+1))\n",
    "        temp_y = random.randint(int((fig_size[1]-hs[0])/8), int((fig_size[1]-hs[0])/2+1))\n",
    "        for i in range(len(char_imgs)):\n",
    "            tmp_offset = random.randint(offset_w[0], offset_w[1]) if sum(ws)+(len(ws)-1)*offset_w[1]<fig_size[0] else 0\n",
    "            if i > 0:\n",
    "                temp_x = new_x if new_x+ws[i]<fig_size[0] else temp_x+ws[i-1]\n",
    "                temp_y = new_y if 0<new_y and new_y+hs[i]<fig_size[1] else temp_y   \n",
    "            if same_color:\n",
    "                draw.text((temp_x, temp_y), text[i], font=font, fill=char_color)\n",
    "            else:\n",
    "                draw.text((temp_x, temp_y), text[i], font=font, fill=random_color(font_color)) #\n",
    "            new_x = temp_x+ws[i]+tmp_offset         \n",
    "            new_y = temp_y+random.randint(-offset_h, offset_h)       \n",
    "\n",
    "        \n",
    "#     filename = font_name+\"_\"+str(uuid.uuid1())+\"_\"+text\n",
    "#     img.save('/data/python/lsm/gen_captcha/{}.jpg'.format(filename))\n",
    "#     return img \n",
    "    if is_raw_size:\n",
    "        return img\n",
    "    w, h = img.size\n",
    "    if w>width or h> height:\n",
    "        return img.resize((width, height), Image.BILINEAR)    \n",
    "    elif random.random() >0.5:\n",
    "        background = Image.new(mode='RGB', size=(width, height), color=bg)\n",
    "        background.paste(img, box=(0, 0)) \n",
    "        return background\n",
    "    else:\n",
    "        return img.resize((width, height), Image.BILINEAR)\n",
    "\n",
    "# # paths = 'FileInfo0508_2/*.jpg'\n",
    "# paths = '/data/esa_sdk/gan/english/*.jpg'\n",
    "# # paths = '/data/captcha/shensebeijingsandian/*.jpg'\n",
    "# # paths = '/data/captcha/shensexiansandian/*.jpg'\n",
    "# files =  glob.glob(paths)\n",
    "# img2 = Image.open(files[0])\n",
    "# img2 = Image.open('/data/captcha/captcha_sample/4.jpg')#.resize((200,70), Image.BILINEAR) #小图多种颜色字、干扰线 \n",
    "\n",
    "# img2 = Image.open('/data/captcha/label_english/90_38/0ef53417-3af2-11ec-b040-2cf05ded1cb1_aq4f.jpg')\n",
    "# random_str = 'Aq4f'\n",
    "# image = gen_captcha(random_str, fig_size=(90,38), fonts=fonts,font_color=(20,160,20,165,20,160),same_color=0, font_size=(15, 20), rotate=0,\n",
    "#                     font_noise=0,offset_w=(-1,3),offset_h=0, line=(100,200), line_width=(0,1), line_color=(170,230), point=(20,150),\n",
    "#                     point_color=(200,255),frame_color=(10,30),wavy=(0,0), bg=(255,255)).resize((width, height), Image.BILINEAR)\n",
    "\n",
    "# img2 = Image.open('/data/captcha/label_english/122_46/fe9bbdda-24ea-11ed-ab40-b4b5b67760ae_PDZWON.jpg')\n",
    "# random_str = 'PDZWON'\n",
    "\n",
    "imgs_122_46 = glob.glob('/data/captcha/label_english/122_46/*.jpg')[:800]\n",
    "img_path = random.choice(imgs_122_46)\n",
    "img2 = Image.open(img_path)\n",
    "random_str = img_path.split('/')[-1].split('_')[-1][:-4]\n",
    "\n",
    "image = gen_captcha(random_str, fig_size=(122,46), fonts=fonts,font_color=(5,160,5,150,5,160),same_color=0, font_size=(17, 20), rotate=10,\n",
    "                    font_noise=0,offset_w=(-1,3),offset_h=2, line=(0,3), line_width=(0,1), line_color=(200,250), point=(0,150),\n",
    "                    point_color=(200,255),frame_color=(200,250),wavy=(0,0), bg=(235,255)).resize((width, height), Image.BILINEAR)\n",
    "\n",
    "\n",
    "im = [image, img2]\n",
    "print('image size',img2.size, image.size)\n",
    "plt.figure(figsize=(50,10))\n",
    "for i in range(1,3):    \n",
    "    plt.subplot(2,2,i)\n",
    "    plt.imshow(im[i-1])\n",
    "plt.show()\n",
    "\n",
    "cl = (10,20)\n",
    "print('random_color(rs, re, gs, ge, bs, be)', random_color(cl))\n",
    "print('rand', random.random())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7524 /data/captcha/label_english/70_26/07c5530f-ce96-11ea-b53b-c81f66ef0810_9jyr.jpg\n",
      "14110 /data/captcha/label_english/52_21/169d7266-0a84-11eb-9a54-c81f66ef0810_2f37.jpg\n",
      "32514 /data/captcha/label_english/100_25/b2b2f39e-db79-11eb-a41f-c81f66ef0810_p24m.jpg\n",
      "2502 /data/captcha/shensexiansandian/a711d2ab4416673804f0415cb6bab36d_YDJP.jpg\n",
      "(200, 70)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'nv7a')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 163,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 真实验证码加干扰 \n",
    "\n",
    "len4_imgs = []\n",
    "len5_imgs = []\n",
    "\n",
    "filePath = 'FileInfo0508_2/*.jpg' # 波浪线验证码\n",
    "files = glob.glob(filePath)\n",
    "sp = min(int(len(files)*0.8), 3000)\n",
    "for path in files[:sp]:\n",
    "    label = path.split('_')[-1][:-4].lower().replace('1','l')\n",
    "    if len(label) ==5 and re.search('[0-9]', label)==None:\n",
    "        len5_imgs.append(path)\n",
    "    else:\n",
    "        print('label error', path)\n",
    "filePath = '/data/captcha/label_english/100_30/*.jpg'\n",
    "files = glob.glob(filePath)\n",
    "sp = min(int(len(files)*0.8), 3000)\n",
    "for path in files[:sp]:\n",
    "    label = path.split('_')[-1][:-4]\n",
    "    if len(label) ==5:\n",
    "        len5_imgs.append(path)\n",
    "    else:\n",
    "        print('label error', path)\n",
    "\n",
    "path1 = '/data/captcha/label_english/70_26/*.jpg'\n",
    "path2 = '/data/captcha/label_english/52_21/*.jpg'\n",
    "path3 = '/data/captcha/label_english/100_25/*.jpg'\n",
    "path4 = '/data/captcha/shensebeijingsandian/*.jpg' # 3,4一样图片类型\n",
    "path5 = '/data/captcha/shensexiansandian/*.jpg'\n",
    "path6 = '/data/esa_sdk/gan/english/*.jpg' # 数据已被删除\n",
    "path7 = '/data/captcha/label_english/90_38/*.jpg'\n",
    "for paths in [path1, path2, path3, path5]:  # path1, path2, path3, path4, path5, \n",
    "    files = glob.glob(paths)\n",
    "#     sp = int(len(files)*0.8)\n",
    "    sp = min(int(len(files)*0.8), 3000)\n",
    "    for path in files[:sp]:\n",
    "        label = path.split('_')[-1][:-4]\n",
    "        if len(label) ==4:\n",
    "            len4_imgs.append(path)\n",
    "        else:\n",
    "            print('label error', path)\n",
    "    print(len(files), files[0])\n",
    "\n",
    "random.shuffle(len4_imgs)\n",
    "random.shuffle(len5_imgs)\n",
    "    \n",
    "def rebuild_img(path):\n",
    "    '''\n",
    "    读取本地验证码图片进行随机加点噪声为新图片\n",
    "    参数：path：图片路径\n",
    "    返回：重组后图片    \n",
    "    '''\n",
    "    if re.search('FileInfo0508', path)!=None:\n",
    "        label = path.split('_')[-1][:-4].lower().replace('1','l')\n",
    "#         print(re.search('FileInfo0508', path))\n",
    "    else:\n",
    "        label = path.split('_')[-1][:-4]\n",
    "    crop_n = len(label) \n",
    "    img = Image.open(path)\n",
    "    img = img.convert('RGB')\n",
    "    w, h = img.size\n",
    "#     img2 = img2.resize((100,50), Image.BILINEAR)\n",
    "    draw = ImageDraw.Draw(img)\n",
    "    # gau = img.filter(ImageFilter.GaussianBlur(radius=2))\n",
    "# imgs.append((gau, 'gau'))\n",
    "# sharp = gau.filter(ImageFilter.UnsharpMask(radius=2, percent=150, threshold=3))\n",
    "# imgs.append((sharp, 'sharp'))\n",
    "# rank = img.filter(ImageFilter.RankFilter(size=3, rank=3))\n",
    "#     img = img.filter(ImageFilter.GaussianBlur(radius=2))\n",
    "#     img = img.filter(ImageFilter.UnsharpMask(radius=2, percent=150, threshold=3))\n",
    "    for _ in range(random.randint(20,250)):\n",
    "        draw.point(xy=(random_xy(w, h)),fill=random_color((70,220,20,255,70,220)))  \n",
    "    # 短线\n",
    "    for i in range(random.randint(10,100)):\n",
    "        x0, y0 = random_xy(w, h)\n",
    "        x1 = x0 + random.randint(2, 5)\n",
    "        y1 = y0 + random.randint(2, 5)\n",
    "        draw.line(xy=((x0,y0),(x1,y1)),\n",
    "                  fill=random_color((90,130)),\n",
    "                  width=random.randint(0,1)) # xy, fill=None, width=0        \n",
    "    for _ in range(random.randint(0, 3)):\n",
    "        draw.line(xy=(random_xy(w, h),random_xy(w, h)), fill=random_color((80, 250)), width=random.randint(0,2))\n",
    "    w, h = img.size\n",
    "    if w>width or h> height:\n",
    "        return img.resize((width, height), Image.BILINEAR), label.lower()    \n",
    "    elif random.random() >0.5:\n",
    "        background = Image.new(mode='RGB', size=(width, height), color=(255,255,255))\n",
    "        background.paste(img, box=(0, 0)) \n",
    "        return background, label.lower()\n",
    "    else:\n",
    "        return img.resize((width, height), Image.BILINEAR), label.lower()\n",
    "#     return img.resize((width, height), Image.BILINEAR), label.lower()\n",
    "filePath = 'FileInfo0508_2/*.jpg' # 波浪线验证码\n",
    "files = glob.glob(path7)\n",
    "img, label = rebuild_img(random.choice(files))\n",
    "print(img.size)\n",
    "plt.imshow(img)\n",
    "plt.title(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 150,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bgs = glob.glob('/data/captcha/crop_english/bg_70_25/*.jpg')\n",
    "crops = glob.glob('/data/captcha/crop_english/crop_70_25/*.jpg')\n",
    "def merge_img_7025():\n",
    "    img = Image.open(random.choice(bgs))\n",
    "    w, h = (37,12)\n",
    "    label = []\n",
    "    for i in range(4):\n",
    "        im_p = random.choice(crops)\n",
    "        lb = im_p.split('/')[-1].split('_')[0]\n",
    "        label.append(lb)\n",
    "        im = Image.open(im_p)\n",
    "        img.paste(im, (16+w//4*i,7+0)) # ,w//4*(i+1), h\n",
    "    w, h = img.size\n",
    "    draw = ImageDraw.Draw(img)        \n",
    "    for _ in range(random.randint(10,250)):\n",
    "        draw.point(xy=(random_xy(w, h)),fill=random_color((70,220,20,255,70,220)))            \n",
    "    return img.resize((width, height), Image.BILINEAR), ''.join(label)\n",
    "\n",
    "# img, label =  merge_img_7025()\n",
    "\n",
    "bgs2 = glob.glob('/data/captcha/crop_english/bg_90_38/*.jpg')\n",
    "crops2 = glob.glob('/data/captcha/crop_english/crop_90_38/*.jpg')\n",
    "def merge_img_9038():\n",
    "    img = Image.open(random.choice(bgs2))\n",
    "    w, h = (37,12)\n",
    "    label = []\n",
    "    for i in range(4):\n",
    "        im_p = random.choice(crops2)\n",
    "        lb = im_p.split('/')[-1].split('_')[0]\n",
    "        label.append(lb)\n",
    "        im = Image.open(im_p)\n",
    "        img.paste(im, (17*i+10,0+10)) \n",
    "    w, h = img.size\n",
    "    draw = ImageDraw.Draw(img)\n",
    "    for i in range(random.randint(0,20)):\n",
    "        x0, y0 = random_xy(w, h)\n",
    "        x1 = x0 + random.randint(2, 5)\n",
    "        y1 = y0 + random.randint(2, 5)\n",
    "        draw.line(xy=((x0,y0),(x1,y1)),\n",
    "                  fill=random_color((150,200)),\n",
    "                  width=random.randint(0,1)) # xy, fill=None, width=0            \n",
    "    return img.resize((width, height), Image.BILINEAR), ''.join(label)\n",
    "\n",
    "img, label =  merge_img_9038()\n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label:  4ean\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 150,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''添加真实图片'''\n",
    "name_dic = dict()\n",
    "with open('/data/captcha/label_english/200_80/answer.txt', 'r', encoding='utf-8') as f:\n",
    "    name_labels = f.readlines()\n",
    "for it in name_labels:\n",
    "    k, v = it.strip().split('=')\n",
    "    name_dic[k] = v\n",
    "with open('/data/captcha/label_english/超级鹰导出图片-2023-04-03/超级鹰识别结果.txt', 'r', encoding='utf-8') as f:\n",
    "    name_labels = f.readlines()\n",
    "for it in name_labels:\n",
    "    k, v = it.strip().split('=')\n",
    "    name_dic[k] = v    \n",
    "\n",
    "imgs_200_80 = glob.glob('/data/captcha/label_english/200_80/*.jpg')[:2000]\n",
    "\n",
    "imgs_122_46 = glob.glob('/data/captcha/label_english/122_46/*.jpg')[:800]\n",
    "\n",
    "imgs_160_60 = glob.glob('/data/captcha/label_english/超级鹰导出图片-2023-04-03/*.jpg')[:400]\n",
    "\n",
    "def add_real_img(imgs_list):\n",
    "    path = random.choice(imgs_list)\n",
    "    if '122_46' in path:\n",
    "        label = path.split('/')[-1].split('_')[-1][:-4]\n",
    "    else:    \n",
    "        file_name = path.split('/')[-1][:-4]\n",
    "        label = name_dic[file_name]\n",
    "    img = Image.open(path)\n",
    "    img = img.convert('RGB')\n",
    "    w, h = img.size\n",
    "    draw = ImageDraw.Draw(img)\n",
    "    for _ in range(random.randint(20,250)):\n",
    "        draw.point(xy=(random_xy(w, h)),fill=random_color((100,220,120,255,100,220)))  \n",
    "    # 短线\n",
    "    for i in range(random.randint(10,100)):\n",
    "        x0, y0 = random_xy(w, h)\n",
    "        x1 = x0 + random.randint(2, 5)\n",
    "        y1 = y0 + random.randint(2, 5)\n",
    "        draw.line(xy=((x0,y0),(x1,y1)),\n",
    "                  fill=random_color((90,130)),\n",
    "                  width=random.randint(0,1)) # xy, fill=None, width=0        \n",
    "    for _ in range(random.randint(0, 3)):\n",
    "        draw.line(xy=(random_xy(w, h),random_xy(w, h)), fill=random_color((80, 250)), width=random.randint(0,2))\n",
    "        \n",
    "    w, h = img.size\n",
    "    if w>width or h> height:\n",
    "        return img.resize((width, height), Image.BILINEAR), label.lower()    \n",
    "    elif random.random() >0.5:\n",
    "        background = Image.new(mode='RGB', size=(width, height), color=(255,255,255))\n",
    "        background.paste(img, box=(0, 0)) \n",
    "        return background, label.lower()\n",
    "    else:\n",
    "        return img.resize((width, height), Image.BILINEAR), label.lower()\n",
    "#     return img.resize((width, height), Image.BILINEAR), label.lower()\n",
    "\n",
    "# img, label = add_real_img(imgs_200_80)\n",
    "# img, label = add_real_img(imgs_122_46)\n",
    "img, label = add_real_img(imgs_160_60)\n",
    "print('label: ', label)\n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大于\n",
      "[('3', 266), ('7', 263), ('6', 263), ('4', 258), ('2', 257), ('8', 251), ('5', 239), ('9', 238), ('q', 151), ('G', 133), ('N', 132), ('b', 129), ('B', 128), ('D', 127), ('x', 127), ('g', 127), ('c', 127), ('k', 126), ('d', 126), ('C', 126), ('U', 126), ('L', 125), ('y', 125), ('a', 123), ('S', 123), ('f', 123), ('T', 122), ('s', 122), ('r', 121), ('z', 121), ('v', 121), ('P', 121), ('J', 120), ('u', 118), ('p', 116), ('h', 116), ('t', 114), ('W', 114), ('E', 113), ('e', 111), ('Z', 111), ('m', 110), ('n', 110), ('w', 110), ('R', 109), ('V', 109), ('F', 108), ('H', 106), ('j', 105), ('A', 103), ('i', 102), ('M', 101), ('K', 98), ('1', 97), ('Y', 97), ('X', 97), ('Q', 97), ('0', 74), ('l', 55), ('o', 50), ('O', 36), ('I', 30)]\n",
      "2001 62 36\n",
      "label:  JHRW\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 167,
       "width": 370
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''保存生成图片到本地'''\n",
    "lower = 'abcdefghijkmnpqrstuvwxyz'\n",
    "upper = 'ABCDEFGHJKLMNPQRSTUVWXYZ'\n",
    "digit = '23456789'\n",
    "\n",
    "label_set = set()\n",
    "labels = []\n",
    "is_raw_size=True\n",
    "\n",
    "# for gen_characters in [lower, upper, digit,lower+'lo', upper+'IO', digit+'01', lower+digit, upper+digit, lower+upper+digit, lower+upper+digit+'01IOlo']:    \n",
    "for i in range(10000):\n",
    "    gen_characters = random.choice([lower, upper, digit,lower+'lo', upper+'IO', digit+'01', lower+digit, upper+digit, lower+upper+digit, lower+upper+digit+'01IOlo']) \n",
    "    random_str = ''.join([random.choice(gen_characters) for j in range(4)])\n",
    "    if random_str in label_set:\n",
    "        continue\n",
    "    label_set.add(random_str)\n",
    "    labels.append(random_str)  \n",
    "    ty = '409'\n",
    "    fig_size='5221'\n",
    "    image = gen_captcha(random_str, fig_size=(52,21), fonts=fonts,font_color=(0,255,0,255,0,255),same_color=1, font_size=(16, 18), rotate=0,\n",
    "                                font_noise=0,offset_w=(0,1),offset_h=0, line=(0,0), line_width=(0,1), line_color=(90,150), point=(30,50),\n",
    "                                point_color=(0,255,0,255,0,255),frame_color=(150, 170),wavy=(0,0), bg=(255,255))\n",
    "    image.save('/data/captcha/generate_pic/{}_{}_{}.jpg'.format(ty, random_str, fig_size))\n",
    "    if len(labels)>2000:\n",
    "        c = Counter(''.join(labels))\n",
    "        if c.most_common()[-1][1] > 10:\n",
    "            print('大于')\n",
    "            print(c.most_common())\n",
    "            print(len(labels),len(c), len(characters))\n",
    "            break\n",
    "    \n",
    "print('label: ', random_str)\n",
    "plt.imshow(image)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1902_420x65', '1004_150x50', '1004_130x40', '4004_130x40', '1004_100x40', '1902_140x40', '4004_73x27', '4004_108x40', '3004_100x40', '1004_80x20', '4006_200x100', '1902_150x40', '4004_110x35', '1004_70x35', '1004_80x30', '1902_100x20', '6001_150x50', '3004_120x40', '3004_200x62', '1004_150x40', '1005_120x50'] ['1005', '4004', '1004', '3004', '6001', '2002', '1902']\n"
     ]
    }
   ],
   "source": [
    "'''按图片像素尺寸分类并移动到对应文件夹'''\n",
    "\n",
    "def organize_images_by_dimensions(source_dir, target_base_dir):\n",
    "    \"\"\"\n",
    "    按图片像素尺寸分类并移动到对应文件夹\n",
    "    :param source_dir: 源图片文件夹路径\n",
    "    :param target_base_dir: 目标分类文件夹的基路径\n",
    "    \"\"\"\n",
    "\n",
    "    # 支持的图片格式\n",
    "    supported_formats = ('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.webp')\n",
    "\n",
    "    # 遍历源文件夹\n",
    "    for root, _, files in os.walk(source_dir):\n",
    "        for filename in files:\n",
    "            if filename.lower().endswith(supported_formats):\n",
    "                filepath = os.path.join(root, filename)\n",
    "\n",
    "                try:\n",
    "                    # 获取图片尺寸\n",
    "                    with Image.open(filepath) as img:\n",
    "                        width, height = img.size\n",
    "\n",
    "                    # 确定分类\n",
    "                    category = filename.split('-')[0]\n",
    "                    # 创建目标文件夹（格式：尺寸分类_宽x高）\n",
    "                    target_dir = os.path.join(target_base_dir, f\"{category}_{width}x{height}\")\n",
    "                    os.makedirs(target_dir, exist_ok=True)\n",
    "\n",
    "                    # 移动文件（避免重名冲突）\n",
    "                    base_name, ext = os.path.splitext(filename)\n",
    "                    counter = 1\n",
    "                    while True:\n",
    "                        new_name = f\"{base_name}_{counter}{ext}\" if counter > 1 else filename\n",
    "                        target_path = os.path.join(target_dir, new_name)\n",
    "                        if not os.path.exists(target_path):\n",
    "                            shutil.move(filepath, target_path)\n",
    "#                             shutil.copy(filepath, target_path)\n",
    "                            print(f\"Moved: {filename} ({width}x{height}) -> {category}\")\n",
    "                            break\n",
    "                        counter += 1\n",
    "\n",
    "                except Exception as e:\n",
    "                    print(f\"Error processing {filename}: {str(e)}\")\n",
    "\n",
    "\n",
    "# 使用示例\n",
    "source_folder = \"/data/captcha/验证码\"# \"/data/captcha/chaojiyingpic2\"  # 替换为实际图片文件夹路径 /data/captcha/chaojiyingpic\n",
    "target_folder = \"/data/captcha/chaojiyingpic\"  # 替换为目标分类文件夹路径\n",
    "# organize_images_by_dimensions(source_folder, target_folder)\n",
    "print(os.listdir(target_folder), os.listdir(source_folder))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/data/captcha/chaojiyingpic/1902_420x65 1120\n",
      "/data/captcha/chaojiyingpic/1004_130x40 318\n",
      "/data/captcha/chaojiyingpic/4004_130x40 1215\n",
      "/data/captcha/chaojiyingpic/1004_100x40 802\n",
      "/data/captcha/chaojiyingpic/1902_140x40 44\n",
      "/data/captcha/chaojiyingpic/4004_73x27 148\n",
      "/data/captcha/chaojiyingpic/4004_108x40 816\n",
      "/data/captcha/chaojiyingpic/3004_100x40 148\n",
      "/data/captcha/chaojiyingpic/4006_200x100 235\n",
      "/data/captcha/chaojiyingpic/1902_150x40 1450\n",
      "/data/captcha/chaojiyingpic/4004_110x35 1696\n",
      "/data/captcha/chaojiyingpic/1004_70x35 1392\n",
      "/data/captcha/chaojiyingpic/1004_80x30 1013\n",
      "/data/captcha/chaojiyingpic/1902_100x20 131\n",
      "/data/captcha/chaojiyingpic/3004_120x40 1293\n",
      "/data/captcha/chaojiyingpic/3004_200x62 189\n",
      "/data/captcha/chaojiyingpic/1004_150x40 89\n",
      "/data/captcha/chaojiyingpic/1005_120x50 101\n",
      "训练数据：10972, 测试数据：1228\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 86,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label:  cu4y\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 150,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''加入超级鹰数据'''\n",
    "chaojiying_train = []\n",
    "chaojiying_test = []\n",
    "target_folder = \"/data/captcha/chaojiyingpic\"\n",
    "for p in os.listdir(target_folder):\n",
    "    new_path = os.path.join(target_folder, p)\n",
    "    if os.path.isdir(new_path):\n",
    "        files = glob.glob('%s/*.jpg'%new_path)\n",
    "        if len(files)<10:\n",
    "            continue\n",
    "        print(new_path, len(files))\n",
    "        sp = int(len(files)*0.9)\n",
    "        n = 0\n",
    "        for path in files:\n",
    "            if path.endswith('1004-8268-1262409550172832926.jpg'):\n",
    "                print('打开异常图片')\n",
    "                continue\n",
    "            label = path.split('/')[-1].split('-')[1]\n",
    "            ty = path.split('/')[-1].split('-')[0]\n",
    "            if ty == 4006 and len(label)!=6: # 此类较多验证码标签错误\n",
    "                continue\n",
    "            if n < sp:\n",
    "                chaojiying_train.append(path)\n",
    "            else:\n",
    "                chaojiying_test.append(path)\n",
    "            n += 1   \n",
    "print('训练数据：%d, 测试数据：%d'%(len(chaojiying_train), len(chaojiying_test)))\n",
    "\n",
    "# file1 = glob.glob('/data/captcha/chaojiyingpic/4006*.jpg')\n",
    "# file2 = glob.glob('/data/captcha/chaojiyingpic/4004*.jpg')\n",
    "# file3 = glob.glob('/data/captcha/chaojiyingpic/3004*.jpg')\n",
    "# file4 = glob.glob('/data/captcha/chaojiyingpic/1004*.jpg')\n",
    "# file5 = glob.glob('/data/captcha/chao_241125_1004/1004*.jpg')\n",
    "# chaojiying_train = []\n",
    "# chaojiying_test = []\n",
    "# for file in [file1, file2, file3, file4, file5]:\n",
    "#     sp = int(len(file)*0.9)\n",
    "#     n = 0\n",
    "#     for path in file:\n",
    "#         if path.endswith('1004-8268-1262409550172832926.jpg'):\n",
    "#             print('打开异常图片')\n",
    "#             continue\n",
    "#         label = path.split('/')[-1].split('-')[1]\n",
    "#         ty = path.split('/')[-1].split('-')[0]\n",
    "#         if ty == 4006 and len(label)!=6: # 此类较多验证码标签错误\n",
    "#             continue\n",
    "#         if n < sp:\n",
    "#             chaojiying_train.append(path)\n",
    "#         else:\n",
    "#             chaojiying_test.append(path)\n",
    "#         n += 1\n",
    "# print('训练数据：%d, 测试数据：%d'%(len(chaojiying_train), len(chaojiying_test)))\n",
    "\n",
    "# for ty in ['4004', '1902', '1004', '4006', '3004']:\n",
    "#     files = glob.glob('/data/captcha/chaojiyingpic2/%s-*.jpg'%ty)\n",
    "#     sp = int(len(files)*0.9)\n",
    "#     n = 0\n",
    "#     for path in files:\n",
    "#         if path.endswith('1004-8268-1262409550172832926.jpg'):\n",
    "#             print('打开异常图片')\n",
    "#             continue\n",
    "#         label = path.split('/')[-1].split('-')[1]\n",
    "#         ty = path.split('/')[-1].split('-')[0]\n",
    "#         if ty == 4006 and len(label)!=6: # 此类较多验证码标签错误\n",
    "#             continue\n",
    "#         if n < sp:\n",
    "#             chaojiying_train.append(path)\n",
    "#         else:\n",
    "#             chaojiying_test.append(path)\n",
    "#         n += 1        \n",
    "# print('训练数据：%d, 测试数据：%d'%(len(chaojiying_train), len(chaojiying_test)))\n",
    "\n",
    "\n",
    "img = Image.open(random.choice(chaojiying_test))\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "\n",
    "def add_real_img2(imgs_list):\n",
    "    path = random.choice(imgs_list)   \n",
    "    label = path.split('/')[-1].split('-')[1]    \n",
    "    img = Image.open(path)\n",
    "    img = img.convert('RGB')\n",
    "    w, h = img.size\n",
    "    draw = ImageDraw.Draw(img)\n",
    "    for _ in range(random.randint(20,250)):\n",
    "        draw.point(xy=(random_xy(w, h)),fill=random_color((100,220,120,255,100,220)))  \n",
    "    # 短线\n",
    "    for i in range(random.randint(10,100)):\n",
    "        x0, y0 = random_xy(w, h)\n",
    "        x1 = x0 + random.randint(2, 5)\n",
    "        y1 = y0 + random.randint(2, 5)\n",
    "        draw.line(xy=((x0,y0),(x1,y1)),\n",
    "                  fill=random_color((90,130)),\n",
    "                  width=random.randint(0,1)) # xy, fill=None, width=0        \n",
    "    for _ in range(random.randint(0, 3)):\n",
    "        draw.line(xy=(random_xy(w, h),random_xy(w, h)), fill=random_color((80, 250)), width=random.randint(0,2))\n",
    "        \n",
    "    w, h = img.size\n",
    "    if w>width or h> height:\n",
    "        return img.resize((width, height), Image.BILINEAR), label.lower()    \n",
    "    elif random.random() >0.5:\n",
    "        background = Image.new(mode='RGB', size=(width, height), color=(255,255,255))\n",
    "        background.paste(img, box=(0, 0)) \n",
    "        return background, label.lower()\n",
    "    else:\n",
    "        return img.resize((width, height), Image.BILINEAR), label.lower()\n",
    "#     return img.resize((width, height), Image.BILINEAR), label.lower()\n",
    "\n",
    "# img, label = add_real_img(imgs_200_80)\n",
    "# img, label = add_real_img(imgs_122_46)\n",
    "img, label = add_real_img2(chaojiying_train)\n",
    "print('label: ', label)\n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.\n",
      "1.15.4\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'6276'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)\n",
    "path = '/data/captcha/chaojiyingpic2/4004-6276-1274716010172835011.jpg'\n",
    "path.split('/')[-1].split('-')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' 彩色图像生成 '''\n",
    "lower = 'abcdefghijkmnpqrstuvwxyz'\n",
    "upper = 'ABCDEFGHJKLMNPQRSTUVWXYZ'\n",
    "digit = '23456789'\n",
    "\n",
    "class CaptchaSequence(Sequence):\n",
    "    '''\n",
    "    继承Sequence的数据生成类，方便调用多CPU，加快生成训练及测试数据\n",
    "    参数：self.characters:验证码字符集合，self.batch_size：每批次样本数，self.steps：生成多少批数据，self.n_len：验证码长度，\n",
    "    self.width：图片宽度，self.height:图片高度，self.input_length：lstm time step长度，self.label_length：标签长度\n",
    "    返回：array类型训练或测试数据 \n",
    "    \n",
    "    '''\n",
    "    def __init__(self, characters, batch_size, steps, n_len=6, width=width, height=height, \n",
    "                 input_length=12, label_length=6, chars_len=(4, 6)): #  width=128, height=64, input_length=16, label_length=4\n",
    "        self.characters = characters\n",
    "        self.batch_size = batch_size\n",
    "        self.steps = steps\n",
    "        self.n_len = n_len\n",
    "        self.width = width\n",
    "        self.height = height\n",
    "        self.input_length = input_length\n",
    "        self.label_length = label_length\n",
    "        self.chars_len = chars_len\n",
    "#         self.label_length = self.n_len\n",
    "        self.n_class = len(characters)+1\n",
    "\n",
    "            \n",
    "    def __len__(self):\n",
    "        return self.steps\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        X = np.zeros((self.batch_size, self.height, self.width, 3), dtype=np.float32)\n",
    "        y = np.zeros((self.batch_size, self.n_len), dtype=np.uint8)\n",
    "\n",
    "        input_length = np.ones(self.batch_size)*self.input_length\n",
    "        label_length = np.ones(self.batch_size)*self.n_len \n",
    "\n",
    "        max_num_len = 65\n",
    "\n",
    "        for i in range(self.batch_size):\n",
    "#                 print('len 4',y.shape, i)\n",
    "            # 定义验证码字符集 (大写字母、小写字母、大写字母+数字) , string.ascii_lowercase+string.digits+string.ascii_uppercase\n",
    "#             gen_characters = random.choice([string.ascii_lowercase, string.ascii_uppercase,string.digits,string.ascii_lowercase+string.digits,\n",
    "#                                             string.ascii_uppercase+string.digits])           \n",
    "            gen_characters = random.choice([lower, upper, digit,lower+'lo', upper+'IO', digit+'01', lower+digit, upper+digit, lower+upper+digit, lower+upper+digit+'01IOlo']) \n",
    "\n",
    "            if i%max_num_len <= 1:    # line=(0,0), line_width=(0,1), point=(0,100),wavy=(0,0)            \n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(4)])\n",
    "                image = gen_captcha(random_str, fig_size=(52,21), fonts=fonts,font_color=(0,255,0,255,0,255),same_color=1, font_size=(16, 18), rotate=0,\n",
    "                                font_noise=0,offset_w=(0,1),offset_h=0, line=(0,0), line_width=(0,1), line_color=(90,150), point=(30,50),\n",
    "                                point_color=(0,255,0,255,0,255),frame_color=(150, 170),wavy=(0,0), bg=(255,255))\n",
    "\n",
    "            elif i%max_num_len <= 3: # line=(0,5), line_width=(0,1), point=(20,300),wavy=(0,0)\n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(4)])\n",
    "                image = gen_captcha(random_str, fig_size=(70,26), fonts=fonts,font_color=(20,130),same_color=0, font_size=(18, 22), rotate=0,\n",
    "                                    font_noise=0,offset_w=(-1,1),offset_h=3, line=(4,6), line_width=(0,1), line_color=(90,130), point=(300,500),\n",
    "                                    point_color=(220,255),frame_color=None,wavy=(0,0), bg=(235,255))\n",
    "\n",
    "            elif i%max_num_len <= 5: # line=(0,0), line_width=(0,2), point=(0,0),wavy=(1,1)\n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(4)])\n",
    "                image = gen_captcha(random_str, fig_size=(100,25), fonts=fonts,font_color=(150,250),same_color=0, font_size=(18, 22), rotate=0,\n",
    "                                    font_noise=0,offset_w=(3,5),offset_h=3, line=(0,0), line_width=(0,1), line_color=(60,130), point=(50,100),\n",
    "                                    point_color=(70,120,220,255,70,120),frame_color=None,wavy=(0,0), bg=(70,100,45,80,250,255))\n",
    "\n",
    "\n",
    "            elif i%max_num_len <= 7:  # line=(0,0), line_width=(0,1), point=(0,80),wavy=(0,0)\n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(4)])\n",
    "                image = gen_captcha(random_str, fig_size=(80,30), fonts=fonts,font_color=(30,90),same_color=0, font_size=(22, 25), rotate=0,\n",
    "                                    font_noise=0,offset_w=(1,3),offset_h=1, line=(50,100), line_width=(0,1), line_color=(200,250), point=(0,0),\n",
    "                                    point_color=(70,120,220,255,70,120),frame_color=(120,130),wavy=(0,0), bg=(250,255))\n",
    "\n",
    "            elif i%max_num_len<=9:\n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(4)])\n",
    "                image = gen_captcha(random_str, fig_size=(100,44), fonts=fonts,font_color=(20,150),same_color=0, font_size=(22, 25), rotate=20,\n",
    "                                    font_noise=0,offset_w=(-1,3),offset_h=8, line=(3,5), shortline=(150,250), line_width=(0,1), line_color=(150,250), point=(0,0),\n",
    "                                    point_color=(70,120,220,255,70,120),frame_color=(190,200),wavy=(0,0), bg=(240,255))\n",
    "\n",
    "\n",
    "            elif i%max_num_len<=11:\n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(4)])                      \n",
    "                image = gen_captcha(random_str, fig_size=(135,40), fonts=fonts,font_color=(0,80,0,70,100,200),same_color=0, font_size=(28, 30), rotate=20,\n",
    "                                    font_noise=0,offset_w=(1,3),offset_h=2, line=(3,5), shortline=(0,0), line_width=(1,3), line_color=(0,100,80,230,0,90), point=(180,250),\n",
    "                                    point_color=(70,220),frame_color=(100,150),wavy=(0,0), bg=(240,255))   \n",
    "\n",
    "            elif i%max_num_len<=13:\n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(4)])    \n",
    "                image = gen_captcha(random_str, fig_size=(90,38), fonts=fonts,font_color=(20,160,20,165,20,160),same_color=0, font_size=(15, 20), rotate=0,\n",
    "                font_noise=0,offset_w=(-1,3),offset_h=0, line=(100,200), line_width=(0,1), line_color=(170,230), point=(20,150),\n",
    "                point_color=(200,255),frame_color=(10,30),wavy=(0,0), bg=(255,255))\n",
    "\n",
    "            elif i%max_num_len<=15: \n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(4)])\n",
    "                tmp_w = random.randint(80,100)\n",
    "                tmp_h = random.randint(25, 35)\n",
    "                font_s = (int(tmp_h*0.8), int(tmp_h*0.9))\n",
    "                image = gen_captcha(random_str, fig_size=(tmp_w,tmp_h), fonts=fonts,font_color=(0,180),same_color=0, font_size=font_s, rotate=15,\n",
    "                                    font_noise=0,offset_w=(-2,1),offset_h=2, line=(0,0), shortline=(0,0), line_width=(0,1), line_color=(170,200), point=(0,0),\n",
    "                                    point_color=(250,255),frame_color=None,wavy=(0,0), bg=(150,255))\n",
    "            elif i%max_num_len<=17:\n",
    "                image, random_str = merge_img_7025()\n",
    "            elif i%max_num_len<=23:\n",
    "                image, random_str = merge_img_9038()                   \n",
    "\n",
    "            elif i%max_num_len<=30: # 加入真实验证码\n",
    "                image, random_str = rebuild_img(random.choice(len4_imgs))\n",
    "            elif i%max_num_len <= 35:\n",
    "                image, random_str = add_real_img(imgs_200_80)                \n",
    "                    \n",
    "            elif i%max_num_len <= 37: \n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(4)])\n",
    "                tmp_w = random.randint(80,100)\n",
    "                tmp_h = random.randint(25, 35)\n",
    "                font_s = (int(tmp_h*0.8), int(tmp_h*0.9))\n",
    "                image = gen_captcha(random_str, fig_size=(tmp_w,tmp_h), fonts=fonts,font_color=(0,180),same_color=0, font_size=font_s, rotate=15,\n",
    "                                    font_noise=0,offset_w=(-2,1),offset_h=2, line=(0,5), shortline=(0,100), line_width=(0,1), line_color=(10,200), point=(0,200),\n",
    "                                    point_color=(50,255),frame_color=None,wavy=(0,1), bg=(150,255)) \n",
    "                    \n",
    "            # 上面是4个字符验证码，下面是 5个字符      \n",
    "\n",
    "            elif i%max_num_len <= 39:  #  line=(0,6), line_width=(0,1), point=(0,500),wavy=(0,0)\n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(5)])\n",
    "                image = gen_captcha(random_str, fig_size=(200,70), fonts=fonts,font_color=(0,255,0,255,0,255),same_color=1, font_size=(50, 55), rotate=10,\n",
    "                                    font_noise=0,offset_w=(-2,1),offset_h=5, line=(0,0), shortline=(0,0), line_width=(2,2), line_color=(0,100,80,230,0,90), point=(0,50),\n",
    "                                    point_color=(250,255),frame_color=None,wavy=(1,1), bg=(255,255))\n",
    "\n",
    "            elif i%max_num_len <= 41: #   line=(0,3), line_width=(0,1), point=(0,200),wavy=(0,0)\n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(5)])\n",
    "                image = gen_captcha(random_str, fig_size=(100,30), fonts=fonts,font_color=(40,120),same_color=0, font_size=(25, 27), rotate=0,\n",
    "                                    font_noise=0,offset_w=(-3,-1),offset_h=2, line=(10,20), shortline=(200,250), line_width=(0,1), line_color=(170,200), point=(0,0),\n",
    "                                    point_color=(250,255),frame_color=(150,180),wavy=(0,0), bg=(250,255))\n",
    "\n",
    "            elif i%max_num_len <= 43: # line=(2,6), line_width=(0,2), point=(0,0),wavy=(0,0)\n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(5)])\n",
    "                image = gen_captcha(random_str, fig_size=(100,27), fonts=fonts,font_color=(20,80,100,120,20,80),same_color=0, font_size=(25, 27), rotate=0,\n",
    "                                    font_noise=0,offset_w=(2,2),offset_h=2, line=(0,0), shortline=(0,0), line_width=(0,1), line_color=(170,200), point=(500,800),\n",
    "                                    point_color=(250,255),frame_color=None,wavy=(0,0), bg=(210,240))                \n",
    "\n",
    "            elif i%max_num_len <= 45:\n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(5)])\n",
    "                tmp_w = random.randint(100,150)\n",
    "                tmp_h = random.randint(35, 45)\n",
    "                font_s = (int(tmp_h*0.8), int(tmp_h*0.9))\n",
    "                image = gen_captcha(random_str, fig_size=(tmp_w,tmp_h), fonts=fonts,font_color=(0,180),same_color=0, font_size=font_s, rotate=15,\n",
    "                                    font_noise=0,offset_w=(-2,1),offset_h=2, line=(0,0), shortline=(0,0), line_width=(0,1), line_color=(170,200), point=(0,0),\n",
    "                                    point_color=(250,255),frame_color=None,wavy=(0,0), bg=(150,255))                       \n",
    "\n",
    "            elif i%max_num_len<=47: #  line=(0,0), line_width=(0,1), point=(0,0),wavy=(0,0)     \n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(5)])\n",
    "                tmp_w = random.randint(100,150)\n",
    "                tmp_h = random.randint(35, 45)\n",
    "                font_s = (int(tmp_h*0.8), int(tmp_h*0.9))\n",
    "                image = gen_captcha(random_str, fig_size=(tmp_w,tmp_h), fonts=fonts,font_color=(0,180),same_color=0, font_size=font_s, rotate=15,\n",
    "                                    font_noise=0,offset_w=(-2,1),offset_h=2, line=(0,5), shortline=(0,100), line_width=(0,1), line_color=(10,200), point=(0,200),\n",
    "                                    point_color=(50,255),frame_color=None,wavy=(0,1), bg=(150,255)) \n",
    "\n",
    "            elif i%max_num_len<=49: # 加入真实验证码\n",
    "                image, random_str = rebuild_img(random.choice(len5_imgs))\n",
    "\n",
    "            elif i%max_num_len<=50:    \n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(5)])\n",
    "                tmp_w = random.randint(100,150)\n",
    "                tmp_h = random.randint(35, 45)\n",
    "                font_s = (int(tmp_h*0.8), int(tmp_h*0.9))\n",
    "                image = gen_captcha(random_str, fig_size=(tmp_w,tmp_h), fonts=fonts,font_color=(200,255),same_color=0, font_size=font_s, rotate=15,\n",
    "                                    font_noise=0,offset_w=(-2,3),offset_h=2, line=(0,5), shortline=(0,100), line_width=(0,1), line_color=(10,200), point=(0,200),\n",
    "                                    point_color=(50,155),frame_color=None,wavy=(0,1), bg=(0,255,0,250,0,250))  \n",
    "                \n",
    "            #下面是6个字符验证码 \n",
    "            elif i%max_num_len<=55: #  line=(0,0), line_width=(0,1), point=(0,0),wavy=(0,0)     \n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(6)])\n",
    "                image = gen_captcha(random_str, fig_size=(122,46), fonts=fonts,font_color=(5,160,5,150,5,160),same_color=0, font_size=(17, 20), rotate=10,\n",
    "                font_noise=0,offset_w=(-1,3),offset_h=2, line=(0,3), line_width=(0,1), line_color=(200,250), point=(0,150),\n",
    "                point_color=(200,255),frame_color=(200,250),wavy=(0,0), bg=(235,255)).resize((width, height), Image.BILINEAR)\n",
    "                \n",
    "            elif i%max_num_len<=57:\n",
    "                image, random_str = add_real_img(imgs_160_60)  \n",
    "            \n",
    "            elif i%max_num_len<=62:\n",
    "                image, random_str = add_real_img2(chaojiying_train)\n",
    "                \n",
    "            else : # 加入真实验证码\n",
    "                image, random_str = add_real_img(imgs_122_46)   \n",
    "\n",
    "            X[i] = np.array(image)/255.0\n",
    "            label = [self.characters.find(x) for x in random_str.lower()]  # 全部标签转换为小写\n",
    "            if len(random_str) < self.n_len:\n",
    "                label += [self.n_class]*(self.n_len-len(random_str))                \n",
    "            y[i] = label\n",
    "    \n",
    "#         return imgs# \n",
    "        return [X, y, input_length, label_length], np.ones(self.batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image size (200, 70)\n"
     ]
    },
    {
     "data": {
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AAAAAAAAAAAAAAAAAGM3kNQAAAAAAAAAAAAAAAAAAAAAAjGbyGgAAAAAAAAAAAAAAAAAAAAAARjN5DQAAAAAAAAAAAAAAAAAAAAAAo5m8BgAAAAAAAAAAAAAAAAAAAACA0UxeAwAAAAAAAAAAAAAAAAAAAADAaCavAQAAAAAAAAAAAAAAAAAAAABgNJPXAAAAAAAAAAAAAAAAAAAAAAAwmslrAAAAAAAAAAAAAAAAAAAAAAAY7f8CjFMxFzYDbCIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 3600x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 2263
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''生成灰度图像'''\n",
    "def get_wavy_line(w = (0, 100),h = (30, 50)):\n",
    "    '''产生波浪线坐标'''\n",
    "    import random\n",
    "    n = 50\n",
    "    x = 0\n",
    "    y = random.randint(h[0],h[1])\n",
    "    flag = random.randint(0,2)\n",
    "    xy = [(x, y)]\n",
    "    while x < w[1]:\n",
    "        temp_y = random.randint(1, 3)\n",
    "        temp_x = random.randint(5, 10)\n",
    "        if flag == 0:\n",
    "            if y + temp_y > h[1]:\n",
    "                y -= temp_y\n",
    "                flag = 1\n",
    "            else:\n",
    "                y += temp_y\n",
    "        else:\n",
    "            if y - temp_y < h[0]:\n",
    "                y += temp_y\n",
    "                flag = 0\n",
    "            else:\n",
    "                y -= temp_y\n",
    "        x = x+temp_x if x+temp_x < w[1] else w[1]\n",
    "        xy.append((x, y))\n",
    "    return xy\n",
    "def Asin(x, A=8,w=0.05, b=6, k=40):\n",
    "    '''\n",
    "    y=Asin(ωx+φ)+k在直角坐标系上的图象\n",
    "    A——振幅，当物体作轨迹符合正弦曲线的直线往复运动时，其值为行程的1/2。\n",
    "    (ωx+φ)——相位，反映变量y所处的状态。\n",
    "    φ——初相，x=0时的相位；反映在坐标系上则为图像的左右移动。\n",
    "    k——偏距，反映在坐标系上则为图像的上移或下移。\n",
    "    ω——角速度， 控制正弦周期(单位弧度内震动的次数)。\n",
    "    '''\n",
    "    return A*math.sin(w*x+b)+k\n",
    "\n",
    "def random_xy(width,height):   \n",
    "    '''\n",
    "    随机位置函数，返回指定范围随机位置坐标\n",
    "    参数：width:图片宽，height:图片高\n",
    "    '''\n",
    "    x = random.randint(0, width)\n",
    "    y = random.randint(0, height)\n",
    "    return x, y\n",
    "def random_color(start, end, opacity=None):\n",
    "    '''\n",
    "    随机颜色函数，返回指定范围随机颜色值\n",
    "    参数：start:颜色最低值，end:颜色最高值\n",
    "    '''\n",
    "    red = random.randint(start, end)\n",
    "    green = random.randint(start, end)\n",
    "    blue = random.randint(start, end)\n",
    "    if opacity is None:\n",
    "        return (red, green, blue)\n",
    "    return (red, green, blue, opacity)\n",
    "\n",
    "def gen_captcha(text, fig_size=(200,70), fonts=['fonts/ANTQUAB.TTF'],font_color=(10,100),same_color=1, font_size=(25, 35), rotate=0,\n",
    "                font_noise=0, line=(0,5), line_width=(1,2), point=(0,500),wavy=(0,0), noise_color=(150,250), bg=(200,255)):\n",
    "    '''\n",
    "    text:验证码文本\n",
    "    size:验证码图片宽高\n",
    "    fonts:字体列表，随机选择一个\n",
    "    font_noise: 字体散点干扰，0不加干扰，1加干扰\n",
    "    offset_hor: 左右偏移值\n",
    "    offset_var: 上下偏移值\n",
    "    fill:字体颜色范围\n",
    "    rotate:字体旋转角度\n",
    "    line:干扰线条数范围\n",
    "    point:干扰点数范围\n",
    "    wavy:波浪线数范围\n",
    "    color:干扰线、点 颜色\n",
    "    bg:背景色范围\n",
    "    '''\n",
    "    bg = random.randint(bg[0], bg[1])\n",
    "    img = Image.new(mode='L', size=fig_size, color=bg) #\n",
    "    draw = ImageDraw.Draw(im=img, mode='L') # im, mode=None\n",
    "    \n",
    "    font_path = random.choice(fonts)\n",
    "#     font_name = font_path.split('/')[-1][:-4]\n",
    "#     print('font_name:', font_name)\n",
    "    \n",
    "    font = ImageFont.truetype(font_path, size=random.randint(font_size[0], font_size[1])) # font=None, size=10, index=0, encoding=\"\"\n",
    "    rotate = random.randint(0, rotate)\n",
    "    def get_char_img(char,font,font_color,rotate,bg, font_noise=0):\n",
    "        '''\n",
    "        生成单个字符图片，随机颜色加随机旋转\n",
    "        \n",
    "        '''\n",
    "        w, h = draw.textsize(char, font=font)\n",
    "        im = Image.new('L',(w,h), color=bg)\n",
    "        ImageDraw.Draw(im).text((0,0), char, font=font, fill=font_color)   \n",
    "        if rotate:\n",
    "            im = im.rotate(random.randint(-rotate, rotate),Image.BILINEAR,expand=1)\n",
    "            im = im.crop(im.getbbox())\n",
    "        if font_noise: \n",
    "            im_draw = ImageDraw.Draw(im)\n",
    "#             for i in range(random.randint(1,20)):\n",
    "            for i in range(random.randint(int(w*h*0.01),int(w*h*0.05))):\n",
    "                im_draw.point(xy=(random.randint(0, w), random.randint(0, h)),fill=bg)\n",
    "\n",
    "        table = []\n",
    "        for i in range(256):\n",
    "            table.append(i * 97) # 5.97\n",
    "        mask = im.convert('L').point(table) \n",
    "        return (im, mask)\n",
    "    \n",
    "    char_color = random.randint(font_color[0],font_color[1])\n",
    "    if same_color:        \n",
    "        char_imgs = [get_char_img(char, font, font_color=char_color, rotate=rotate, bg=bg, font_noise=font_noise) for char in text]\n",
    "    else:\n",
    "        char_imgs = [get_char_img(char, font, font_color=random.randint(font_color[0],font_color[1]), rotate=rotate, bg=bg, font_noise=font_noise) for char in text]       \n",
    "    ws = [img[0].size[0] for img in char_imgs]\n",
    "    hs = [img[0].size[1] for img in char_imgs]\n",
    "    w = max(sum(ws), fig_size[0])\n",
    "    h = max(max(hs), fig_size[1])\n",
    "    if w>fig_size[0] or h>fig_size[1]:\n",
    "        img = Image.new('L',(w+6,h+6), color=bg)\n",
    "        draw = ImageDraw.Draw(im=img, mode='L')  # im, mode=None\n",
    "        fig_size = img.size\n",
    "\n",
    "#     if rotate:\n",
    "    temp_x = random.randint(int((fig_size[0]-sum(ws))/5), int((fig_size[0]-sum(ws))/2+1))\n",
    "    temp_y = random.randint(int((fig_size[1]-hs[0])/10), int((fig_size[1]-hs[0])/5+1))\n",
    "    for i in range(len(char_imgs)):\n",
    "        img.paste(char_imgs[i][0], box=(temp_x, temp_y), mask=char_imgs[i][1]) \n",
    "        new_x = temp_x+ws[i]+random.randint(-(ws[i]//8), (ws[i]//8)) #ws[i]//5\n",
    "        temp_x = new_x if new_x<fig_size[0] else temp_x+ws[i]+random.randint(-(ws[i]//5),0)\n",
    "        new_y = random.randint(0, min(h-hs[i], int(hs[i]*0.5)))\n",
    "        temp_y = new_y if 0<new_y and new_y+hs[i]<fig_size[1] else temp_y  \n",
    "       \n",
    "    # 直线\n",
    "    for i in range(random.randint(line[0], line[1])):\n",
    "        x0 = random.randint(0, fig_size[0])\n",
    "        x1 = random.randint(0, fig_size[0])\n",
    "        y0 = random.randint(0, fig_size[1])\n",
    "        y1 = random.randint(0, fig_size[1])\n",
    "        draw.line(xy=((x0,y0),(x1,y1)),\n",
    "                  fill=random.randint(noise_color[0], noise_color[1]),\n",
    "                  width=random.randint(line_width[0], line_width[1])) # xy, fill=None, width=0\n",
    "    # 散点\n",
    "    for i in range(random.randint(point[0], point[1])):\n",
    "        draw.point(xy=(random.randint(0, fig_size[0]), random.randint(0, fig_size[1])),\n",
    "                   fill=random.randint(noise_color[0], noise_color[1]))\n",
    "#     # 波浪线\n",
    "#     for _ in range(random.randint(wavy[0],wavy[1])):  \n",
    "#         draw.line(xy=get_wavy_line(w = (0, 200),h = (min(hs)-5, max(hs)+5)), \n",
    "#                   fill=char_color, width=random.randint(line_width[0], line_width[1]))              \n",
    "\n",
    "    A_ = random.uniform(hs[0]*0.1,hs[0]*0.4)\n",
    "    w_ = math.pi*4/w#random.uniform(0.04, 0.06)\n",
    "    b_ = random.random()*math.pi\n",
    "    k_ = random.uniform(h*0.5, h*0.7)\n",
    "\n",
    "    # 波浪线\n",
    "    for _ in range(random.randint(wavy[0],wavy[1])):  \n",
    "        draw.line(xy=[(x, Asin(x, A_, w_, b_, k_)) for x in range(int(w))], \n",
    "                  fill=char_color, width=random.randint(line_width[0], line_width[1]))    \n",
    "        \n",
    "#     filename = font_name+\"_\"+str(uuid.uuid1())+\"_\"+text\n",
    "#     img.save('/data/python/lsm/gen_captcha/{}.jpg'.format(filename))\n",
    "        \n",
    "    return img.resize((width, height), Image.BILINEAR)\n",
    "\n",
    "img2 = Image.open('/data/captcha/captcha_sample/1.jpg').resize((200,70), Image.BILINEAR) #小图多种颜色字、干扰线 \n",
    "\n",
    "random_str = 'RpH2'\n",
    "image = gen_captcha(random_str, fig_size=(200,50), fonts=fonts,font_color=(10,80),same_color=0, font_size=(15, 30), rotate=2,\n",
    "                    font_noise=0, line=(0,0), line_width=(0,1), point=(0,0),wavy=(0,0), noise_color=(200,230), bg=(200,240))\n",
    "\n",
    "im = [image, img2]\n",
    "print('image size',image.size)\n",
    "plt.figure(figsize=(50,10))\n",
    "for i in range(1,3):    \n",
    "    \n",
    "    plt.subplot(2,2,i)\n",
    "    plt.imshow(im[i-1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = CaptchaSequence(characters, batch_size=60, steps=10,input_length=12, label_length=6,chars_len=(4, 6))  # (characters, batch_size=128, steps=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60, 70, 200, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1f149c7b70>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 150,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "l, _  = data[1]\n",
    "x = l[0]\n",
    "print(x.shape)\n",
    "idx = 58\n",
    "# plt.imshow(np.reshape(x[idx], (height, width)))\n",
    "\n",
    "# x = data[1]\n",
    "# idx = 8\n",
    "plt.imshow(x[idx])\n",
    "# len4_imgs[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:68: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:508: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3837: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:168: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:175: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:180: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:184: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:193: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:200: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:1801: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:127: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3661: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 70, 200, 3)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 70, 200, 32)       896       \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 70, 200, 32)       128       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 70, 200, 32)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 70, 200, 32)       9248      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 70, 200, 32)       128       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 70, 200, 32)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 35, 100, 32)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 35, 100, 64)       18496     \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 35, 100, 64)       256       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 35, 100, 64)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 35, 100, 64)       36928     \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 35, 100, 64)       256       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 35, 100, 64)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 17, 50, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 17, 50, 128)       73856     \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 17, 50, 128)       512       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)    (None, 17, 50, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 17, 50, 128)       147584    \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 17, 50, 128)       512       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_6 (LeakyReLU)    (None, 17, 50, 128)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 8, 25, 128)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 8, 25, 256)        295168    \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 8, 25, 256)        1024      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_7 (LeakyReLU)    (None, 8, 25, 256)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 8, 25, 256)        590080    \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 8, 25, 256)        1024      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_8 (LeakyReLU)    (None, 8, 25, 256)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 4, 12, 256)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 4, 12, 256)        590080    \n",
      "_________________________________________________________________\n",
      "batch_normalization_9 (Batch (None, 4, 12, 256)        1024      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_9 (LeakyReLU)    (None, 4, 12, 256)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 4, 12, 256)        590080    \n",
      "_________________________________________________________________\n",
      "batch_normalization_10 (Batc (None, 4, 12, 256)        1024      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_10 (LeakyReLU)   (None, 4, 12, 256)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 2, 12, 256)        0         \n",
      "_________________________________________________________________\n",
      "permute_1 (Permute)          (None, 12, 2, 256)        0         \n",
      "_________________________________________________________________\n",
      "time_distributed_1 (TimeDist (None, 12, 512)           0         \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 12, 256)           492288    \n",
      "_________________________________________________________________\n",
      "bidirectional_2 (Bidirection (None, 12, 256)           295680    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 12, 37)            9509      \n",
      "=================================================================\n",
      "Total params: 3,155,781\n",
      "Trainable params: 3,152,837\n",
      "Non-trainable params: 2,944\n",
      "_________________________________________________________________\n",
      "None\n",
      "WARNING:tensorflow:From /home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3952: The name tf.log is deprecated. Please use tf.math.log instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 定义网络\n",
    "\n",
    "# 定义 CTC Loss\n",
    "\n",
    "def ctc_lambda_func(args):\n",
    "    '''\n",
    "        定义ctc损失函数\n",
    "        参数：y_pred:预测值,labels:标签，input_length:lstm tiemstep,label_length:标签长度\n",
    "    '''    \n",
    "    y_pred, labels, input_length, label_length = args\n",
    "    return K.ctc_batch_cost(labels, y_pred, input_length, label_length)\n",
    "\n",
    "input_tensor = Input((height, width, 3))\n",
    "x = input_tensor\n",
    "\n",
    "for i, n_cnn in enumerate([2, 2, 2, 2, 2]):   \n",
    "    for j in range(n_cnn):\n",
    "        x = Conv2D(32*2**min(i, 3), kernel_size=3, padding='same', kernel_initializer='he_uniform')(x)  # 32*2**min(i, 3)\n",
    "        x = BatchNormalization()(x)\n",
    "#         x = Activation('relu')(x) # 20200729 relu 改LeakyReLU\n",
    "        x = LeakyReLU(0.01)(x)\n",
    "    x = MaxPooling2D(2 if i < 4 else (2, 1))(x)\n",
    "\n",
    "x = Permute((2, 1, 3))(x)\n",
    "x = TimeDistributed(Flatten())(x)\n",
    "rnn_size = 128 # 128  32\n",
    "\n",
    "x = Bidirectional(GRU(rnn_size, return_sequences=True))(x)\n",
    "x = Bidirectional(GRU(rnn_size, return_sequences=True))(x)  # 200epoch 0.0153 - val_loss: 0.0136\n",
    "\n",
    "x = Dense(n_class, activation='softmax')(x)\n",
    "base_model = Model(inputs=input_tensor, outputs=x)\n",
    "print(base_model.summary())\n",
    "\n",
    "labels = Input(name='the_labels', shape=[None], dtype='float32')\n",
    "input_length = Input(name='input_length', shape=[1], dtype='int64')\n",
    "label_length = Input(name='label_length', shape=[1], dtype='int64')\n",
    "loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([x, labels, input_length, label_length])\n",
    "model = Model(inputs=[input_tensor, labels, input_length, label_length], outputs=loss_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 6.8734Epoch 1/300\n",
      "1000/1000 [==============================] - 286s 286ms/step - loss: 6.8698 - val_loss: 3.2382\n",
      "Epoch 2/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 1.8097Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 1.8096 - val_loss: 1.7810\n",
      "Epoch 3/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 1.2344Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 1.2340 - val_loss: 1.2269\n",
      "Epoch 4/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.9508Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.9507 - val_loss: 1.0153\n",
      "Epoch 5/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.7724Epoch 1/300\n",
      "1000/1000 [==============================] - 267s 267ms/step - loss: 0.7723 - val_loss: 1.1388\n",
      "Epoch 6/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.6630Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.6630 - val_loss: 0.8267\n",
      "Epoch 7/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5764Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.5763 - val_loss: 0.6892\n",
      "Epoch 8/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5143Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.5142 - val_loss: 0.5660\n",
      "Epoch 9/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.4750Epoch 1/300\n",
      "1000/1000 [==============================] - 269s 269ms/step - loss: 0.4749 - val_loss: 0.5425\n",
      "Epoch 10/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.4285Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.4284 - val_loss: 0.5129\n",
      "Epoch 11/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.4021Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.4020 - val_loss: 0.6387\n",
      "Epoch 12/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.3861Epoch 1/300\n",
      "1000/1000 [==============================] - 269s 269ms/step - loss: 0.3860 - val_loss: 0.4498\n",
      "Epoch 13/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.3549Epoch 1/300\n",
      "1000/1000 [==============================] - 269s 269ms/step - loss: 0.3548 - val_loss: 0.4089\n",
      "Epoch 14/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.3394Epoch 1/300\n",
      "1000/1000 [==============================] - 269s 269ms/step - loss: 0.3393 - val_loss: 0.4065\n",
      "Epoch 15/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.3291Epoch 1/300\n",
      "1000/1000 [==============================] - 267s 267ms/step - loss: 0.3291 - val_loss: 0.6685\n",
      "Epoch 16/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.3076Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.3076 - val_loss: 0.3908\n",
      "Epoch 17/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2894Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.2893 - val_loss: 0.3812\n",
      "Epoch 18/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2809Epoch 1/300\n",
      "1000/1000 [==============================] - 269s 269ms/step - loss: 0.2809 - val_loss: 0.2938\n",
      "Epoch 19/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2635Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.2639 - val_loss: 0.3321\n",
      "Epoch 20/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2639Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.2639 - val_loss: 0.3076\n",
      "Epoch 21/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2494Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.2493 - val_loss: 0.4833\n",
      "Epoch 22/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2434Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.2433 - val_loss: 0.3487\n",
      "Epoch 23/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2392Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.2392 - val_loss: 0.2933\n",
      "Epoch 24/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2265Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.2266 - val_loss: 0.2886\n",
      "Epoch 25/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2289Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.2288 - val_loss: 0.2698\n",
      "Epoch 26/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2235Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.2235 - val_loss: 0.2303\n",
      "Epoch 27/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2113Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.2113 - val_loss: 0.2884\n",
      "Epoch 28/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2056Epoch 1/300\n",
      "1000/1000 [==============================] - 267s 267ms/step - loss: 0.2054 - val_loss: 0.3072\n",
      "Epoch 29/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1995Epoch 1/300\n",
      "1000/1000 [==============================] - 267s 267ms/step - loss: 0.1994 - val_loss: 0.2545\n",
      "Epoch 30/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1964Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1967 - val_loss: 0.3611\n",
      "Epoch 31/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1976Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1976 - val_loss: 0.1984\n",
      "Epoch 32/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1927Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1928 - val_loss: 0.2128\n",
      "Epoch 33/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1810Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1810 - val_loss: 0.1986\n",
      "Epoch 34/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1830Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1832 - val_loss: 0.2082\n",
      "Epoch 35/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1753Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1752 - val_loss: 0.2063\n",
      "Epoch 36/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1764Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1764 - val_loss: 0.6058\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nEpoch 30/300\\n 999/1000 [============================>.] - ETA: 0s - loss: 0.1605Epoch 1/300\\n1000/1000 [==============================] - 375s 375ms/step - loss: 0.1606 - val_loss: 0.1919'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model.load_weights('gru_DigitAndEnglist_ctc_best.h5')  # gru_DigitAndEnglist_ctc_best_0924\n",
    "# model.load_weights('gru_DigitAndEnglist_ctc_best_0927.h5') #DigitAndEnglist_cnn5gru_ctc_best2.h5  DigitAndEnglist_cnn5gru_ctc_best\n",
    "# 'mobilenet_DigitAndEnglist_ctc_best_32.h5' 损失下降到0.2左右 准确率97 \n",
    "# model.load_weights('gru_english4to6_ctc_best_1012.h5')\n",
    "\n",
    "train_data = CaptchaSequence(characters, batch_size=128, steps=1000,input_length=12, label_length=6,chars_len=(4, 6)) # (characters, batch_size=128, steps=1000)\n",
    "valid_data = CaptchaSequence(characters, batch_size=128, steps=100,input_length=12, label_length=6,chars_len=(4, 6))  # (characters, batch_size=128, steps=100)\n",
    "\n",
    "callbacks = [EarlyStopping(patience=5),ModelCheckpoint('gru_english4to6_ctc_best_20250826.h5', save_best_only=True)]\n",
    "model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=Adam(1e-3, amsgrad=True))\n",
    "model.fit_generator(train_data, epochs=300, validation_data=valid_data, workers=4, use_multiprocessing=True,\n",
    "                   callbacks=callbacks)\n",
    "'''\n",
    "Epoch 30/300\n",
    " 999/1000 [============================>.] - ETA: 0s - loss: 0.1605Epoch 1/300\n",
    "1000/1000 [==============================] - 375s 375ms/step - loss: 0.1606 - val_loss: 0.1919'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准备训练\n",
      "Epoch 1/50\n",
      "1000/1000 [==============================] - 505s 505ms/step - loss: 0.1415 - val_loss: 0.1406\n",
      "Epoch 2/50\n",
      "1000/1000 [==============================] - 498s 498ms/step - loss: 0.1271 - val_loss: 0.1299\n",
      "Epoch 3/50\n",
      "1000/1000 [==============================] - 499s 499ms/step - loss: 0.1176 - val_loss: 0.1085\n",
      "Epoch 4/50\n",
      "1000/1000 [==============================] - 495s 495ms/step - loss: 0.1116 - val_loss: 0.1156\n",
      "Epoch 5/50\n",
      "1000/1000 [==============================] - 498s 498ms/step - loss: 0.1112 - val_loss: 0.1114\n",
      "Epoch 6/50\n",
      "1000/1000 [==============================] - 499s 499ms/step - loss: 0.1068 - val_loss: 0.1054\n",
      "Epoch 7/50\n",
      "1000/1000 [==============================] - 499s 499ms/step - loss: 0.1066 - val_loss: 0.1083\n",
      "Epoch 8/50\n",
      "1000/1000 [==============================] - 498s 498ms/step - loss: 0.1022 - val_loss: 0.1091\n",
      "Epoch 9/50\n",
      "1000/1000 [==============================] - 498s 498ms/step - loss: 0.0998 - val_loss: 0.1041\n",
      "Epoch 10/50\n",
      "1000/1000 [==============================] - 499s 499ms/step - loss: 0.1008 - val_loss: 0.1055\n",
      "Epoch 11/50\n",
      "1000/1000 [==============================] - 499s 499ms/step - loss: 0.0977 - val_loss: 0.0993\n",
      "Epoch 12/50\n",
      "1000/1000 [==============================] - 499s 499ms/step - loss: 0.0960 - val_loss: 0.0985\n",
      "Epoch 13/50\n",
      "1000/1000 [==============================] - 498s 498ms/step - loss: 0.0950 - val_loss: 0.0978\n",
      "Epoch 14/50\n",
      "1000/1000 [==============================] - 500s 500ms/step - loss: 0.0940 - val_loss: 0.0999\n",
      "Epoch 15/50\n",
      "1000/1000 [==============================] - 498s 498ms/step - loss: 0.0917 - val_loss: 0.0969\n",
      "Epoch 16/50\n",
      "1000/1000 [==============================] - 500s 500ms/step - loss: 0.0917 - val_loss: 0.0931\n",
      "Epoch 17/50\n",
      "1000/1000 [==============================] - 500s 500ms/step - loss: 0.0893 - val_loss: 0.0904\n",
      "Epoch 18/50\n",
      "1000/1000 [==============================] - 500s 500ms/step - loss: 0.0890 - val_loss: 0.0896\n",
      "Epoch 19/50\n",
      "1000/1000 [==============================] - 499s 499ms/step - loss: 0.0886 - val_loss: 0.0952\n",
      "Epoch 20/50\n",
      "1000/1000 [==============================] - 493s 493ms/step - loss: 0.0878 - val_loss: 0.0804\n",
      "Epoch 21/50\n",
      "1000/1000 [==============================] - 498s 498ms/step - loss: 0.0854 - val_loss: 0.0878\n",
      "Epoch 22/50\n",
      "1000/1000 [==============================] - 499s 499ms/step - loss: 0.0853 - val_loss: 0.0826\n",
      "Epoch 23/50\n",
      "1000/1000 [==============================] - 499s 499ms/step - loss: 0.0864 - val_loss: 0.0963\n",
      "Epoch 24/50\n",
      "1000/1000 [==============================] - 500s 500ms/step - loss: 0.0833 - val_loss: 0.0825\n",
      "Epoch 25/50\n",
      "1000/1000 [==============================] - 498s 498ms/step - loss: 0.0838 - val_loss: 0.0947\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Epoch 16/50\\n 999/1000 [============================>.] - ETA: 0s - loss: 0.0510Epoch 1/50\\n1000/1000 [==============================] - 516s 516ms/step - loss: 0.0510 - val_loss: 0.0522\\nEpoch 34/50\\n 999/1000 [============================>.] - ETA: 0s - loss: 0.0805Epoch 1/50\\n1000/1000 [==============================] - 504s 504ms/step - loss: 0.0805 - val_loss: 0.0912\\n'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = CaptchaSequence(characters, batch_size=256, steps=1000,input_length=12, label_length=6,chars_len=(4, 6)) # (characters, batch_size=128, steps=1000)\n",
    "valid_data = CaptchaSequence(characters, batch_size=128, steps=100,input_length=12, label_length=6,chars_len=(4, 6))  # (characters, batch_size=128, steps=100)\n",
    "\n",
    "# callbacks = [CSVLogger('ctc.csv', append=True), ModelCheckpoint('gru_english4to6_ctc_best_20220829.h5', save_best_only=True)]\n",
    "callbacks = [EarlyStopping(patience=5), CSVLogger('ctc.csv', append=True), ModelCheckpoint('gru_english4to6_ctc_best_20250826-2.h5', save_best_only=True)]\n",
    "# model.load_weights('gru_english4to6_ctc_best_5.h5')  # 以前英文数字模型预测\n",
    "# model.load_weights('gru_english4to6_ctc_best_1014.h5') # lose:0.0203 val_loss:0.012\n",
    "# model.load_weights('gru_english4to6_ctc_best_1102.h5') #  loss: 0.0178 - val_loss: 0.0120\n",
    "# model.load_weights('gru_DigitAndEnglist_base_model_20220628.h5') # loss: 0.0162 - val_loss: 0.0564\n",
    "# model.load_weights('gru_english4to6_ctc_best_20220829.h5') # loss: 0.0162 - val_loss: 0.0564\n",
    "model.load_weights('gru_english4to6_ctc_best_20250826.h5') # loss: 0.0162 - val_loss: 0.0564\n",
    "# gru_DigitAndEnglist_ctc_best.h5  mobilenet_DigitAndEnglist_ctc_best0930\n",
    "# callbacks = [CSVLogger('ctc.csv', append=True), ModelCheckpoint('DigitAndEnglist_cnn5gru_ctc_best2.h5', save_best_only=True)]\n",
    "# model.load_weights('DigitAndEnglist_cnn5gru_ctc_best2.h5')\n",
    "print('准备训练')\n",
    "model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=Adam(1e-4, amsgrad=True))\n",
    "model.fit_generator(train_data, epochs=50, validation_data=valid_data, workers=4, use_multiprocessing=True,\n",
    "                    callbacks=callbacks)\n",
    "'''Epoch 16/50\n",
    " 999/1000 [============================>.] - ETA: 0s - loss: 0.0510Epoch 1/50\n",
    "1000/1000 [==============================] - 516s 516ms/step - loss: 0.0510 - val_loss: 0.0522\n",
    "Epoch 34/50\n",
    " 999/1000 [============================>.] - ETA: 0s - loss: 0.0805Epoch 1/50\n",
    "1000/1000 [==============================] - 504s 504ms/step - loss: 0.0805 - val_loss: 0.0912\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 准确率回调函数\n",
    "from tqdm import tqdm\n",
    "\n",
    "def evaluate(model, batch_size=128, steps=1):\n",
    "    '''\n",
    "    准确率验证函数，每批次的验证码长度必须一致\n",
    "    '''    \n",
    "    batch_acc = 0\n",
    "    valid_data = CaptchaSequence(characters, batch_size, steps)\n",
    "    for i in range(len(valid_data)):\n",
    "        [X_test, y_test, _, _], _ = valid_data[i]\n",
    "        y_pred = base_model.predict(X_test)\n",
    "        shape = y_pred.shape\n",
    "    #         out = K.get_value(K.ctc_decode(y_pred, input_length=np.ones(shape[0])*shape[1],)[0][0])[:, :4]\n",
    "        out = K.get_value(K.ctc_decode(y_pred, input_length=np.ones(shape[0])*shape[1],)[0][0])[:, :]\n",
    "    #     print(y_test)\n",
    "    #     print(type(y_test))\n",
    "    #     print(y_test[y_test<10, axis=1])\n",
    "    #     print(out)\n",
    "        if out.shape[1] >= 4:\n",
    "            batch_acc += (y_test[:,:out.shape[1]] == out).all(axis=1).mean()\n",
    "    return batch_acc / steps\n",
    "evaluate(base_model,batch_size=256, steps=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# base_model.save('gru_DigitAndEnglist_base_model1014.h5') # 保存基础模型，预测用\n",
    "# base_model.save('gru_DigitAndEnglist_base_model_1103.h5') # 保存基础模型，预测用\n",
    "# base_model.save('gru_DigitAndEnglist_base_model_20220829.h5') # 保存基础模型，预测用\n",
    "# base_model.save('gru_DigitAndEnglist_base_model_20230406.h5') # 保存基础模型，预测用\n",
    "x= base_model.output    # [batch_sizes, series_length, classes]\n",
    "input_length = Input(batch_shape=[None], dtype='int32')\n",
    "ctc_decode = K.ctc_decode(x, input_length=input_length * K.shape(x)[1])\n",
    "decode = K.function([base_model.input, input_length], [ctc_decode[0][0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "out 5ec6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '5ec6')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QjhdQv6iKnogLIYQQQgjRABqICyGEEEII0QADIU3pV8sjnpYcM2ZMaR1bYW3atKmIX3nllVI5lqZ4WUSMbm+XdqlqW8ZT0tyePjMpT0lfd911RewlQCkLxBgvvPBCaZnP8WOPPVbEjz76aKncI488UsTz5s0r4tmzZ5fKcf9JZeCMSVjEa8gOcfiM5nGkJCx8bbKsBChLz26//fYivvHGG0vl+JpLyUVYKjZz5swiPvnkk0vleHnq1KmldWPHji1ivjZT1zDTy/1n0K+zQc8KWjejeYx6Ii6EEEIIIUQDaCAuhBBCCCFEAwyENGUQ2HvvvUvLkyZNKuKnn366iFPSlE66pjRJzC0hNSXN7eTL8TrOmLl27dpSuQcffLCI2VWh07DEaMmSJaV1Dz30UBHzlLmf/mbnlUMPPbSIJ06cWCrH/Y6dV1JT4YMwrSmaoR23o5T07OWXXy5ilqIAwL/9278V8fXXX1/EK1euLJXz99zY7/J1wRl2ly5dWirH95kFCxaU1s2aNauI2YXFZ/GsKimLOSZVdUgSQuwePREXQgghhBCiATQQF0IIIYQQogFGPBA3s4lm9jEz+4mZrTKzF8xsm5ndbGYfNbOWv2FmC8zsWjPbkm9zv5ldYmZvbFVeCCGEEEKIfqIOjfj7AXwDwAYAiwCsA3AwgPcA+AcAbzez9wcSlZnZbwC4EsBOAD8EsAXABQC+BGBhvs++o5P2QimNeEqvyzZ4zz33XBGzNhIA9tij+14nGKkWsd2Mmdw2rMdmXSfwek1pN8Ca9sWLFxcxZ+YEypn9OBvnKaecUirH9mm+DzKxPujPYVPa036xMKubXrA3a+cdEL6mq17fv/zlL0vlFi1aVMSrVq1quT1Q7vt8H/W/y1pytkpcv359qdyPfvSjIl6xYkVp3TnnnFPErB/3Nod8rXKd2s2sWbWcrrPdM2jHC3S2X/RCe9YxuloB4EIAPw0hFHcWM/sLAHcCeC+yQfmV+fcHAPg2gFcAnBVCuDv//nMAbgDwPjO7KIRweQ11E0IIIYQQoisZsTQlhHBDCOEaHoTn328E8M188Sxa9T4ABwG4fGgQnpffCeCv8sVPjLReQgghhBBCdDOd1hu8lH/yXN3Q3NnPWpRfDGAHgAVmtncI4cUWZTpGL091eFkAW1elpCk8Hbpjx44i9lk2J0yYUMRsU5eiqfZMTUnzb6Vsyzh+6aWXSuV46vmpp54q4scff7xUbvPmzcOpNoDXt23MEtBPa8eyfaakHsyzzz5bWr7jjjuKeMOGDdHf5X625557VvotPqZ2pShVt6t6/HXQ1H2hbjlBJ2m3jarWr6p9Yer65vvgPffcU8QsRQHKNoW8D3+/3W+//YqYrxefqZMz87KEzNfv+eefL2LO3guU7WhZanjuueeWyh1yyCFFvP/++xfxXnvtVSrH1yq3mT/G2HlNWTT2Gr08PmgHyYhGj44NxM1sDwAfyhd50D2US7ssbgMQQnjZzFYDOAbADADLd/Mb90RWzRlebYUQQgghhBhdOmlf+PcAjgVwbQjhevp+XP657fWblL4f36F6CSGEEEII0TgdeSJuZp8G8BkADwH4YCd+AwBCCPMjv38PgHmd+t1ugaeB/JQiZ0CsKiXhqVIvqxg3blwRV91fN8LTqylpCss7/NRwrJ02bdpUKsdTyCnYtWDatGmldUcffXQR83SyPz88Tb5u3boiriNbKk9D+37GMh1up5TLTixb33Co212Ft2nX6aGqDKRKHYZDO7KNbqRdSVFMjuIzWsYyZrIMBCjLzW688cYi9q5I7DjF/ZilKEA5a+2MGTOK+OCDD47u78477yxilon5+vp7zLJly4qYpXI+6+95551XxMccc0wRs3QGAMaMGVPE7UgSU65IdcgY+kU+MZrH0Y3HXze91i9qfyJuZp8E8BUAywCcHULwub2HnniPQ2uGvt9ad92EEEIIIYToFmodiJvZJQC+BmApskH4xhbFHs4/Z7XYfg8A05G93PlonXUTQgghhBCim6htIG5mf44sIc99yAbhmyJFb8g/z2+x7gwAYwHcOtqOKUIIIYQQQowmtWjE82Q8/wXAPQDOayFHYa4A8P8AuMjMvkYJfcYA+Hxe5ht11KtOul3D1a59IcPaQ9ZJAq/XLo+UOtqzqo6Ut0tl1ItpxL3WlDXi3E5et802aCn43Hl96dixY1uW8/rSAw88sIhnzXptssnrWh988MEi5vPdrs6YdeGsu02d33bXxerot/Hndbi/lbJcS+mYu8G+sG4deDvX2HD21+52sXWpjLh8HXNf9dadnCWT373wemz+rX333beIp0+fXio3c+bMIp4yZUoRH3DAAaVy3Bb8To7XbbNmfOPG8qQz35u2bHntz7C3XuRrn20O580rv1rF9eX7T9V78aBZ/olqqF+UGfFA3Mw+jGwQ/gqAXwL4dIvGWxNCuAwAQgjPmtnvIRuQ32hmlyNLcX8hMmvDK5ClvRdCCCGEEKJvqeOJ+NB//98I4JJImZsAXDa0EEK4yszOBPCXAN4LYAyAVQD+GMBXQ7e/3i+EEEIIIcQIGfFAPIRwKYBL29juFgDvGOnvt0M/Tnd4acqkSZOKuKo0hac1n3766dK62HR/N7Zl1WnsqlPXKftCntbmrHZ+fynYEtBbhPE+2N7Ml+N98FS2l7ocdthhRcxT8Gx5CJSPhdvJW71x23BchzQlJTnhtvB15+Ni6ZDfH9tBHn/88UXspQD77LNPpfpVpY59MKlskjG4fwNl2cVjjz1WxF76wP2R7zGzZ88uleN+x/efTtwvYte0v75j1zRntASAhx56qIg5q6zv+wxff9wuQDlzJV+3/r4Ss0A89thjS+W4vj47J/d3PlfexpQzhnKdfHbcmBzO25gyfBztPlOru590UgrRjX8DR1MW243HXzedPsZOJvQRQgghhBBCRNBAXAghhBBCiAbQQFwIIYQQQogG6EiKe7F76tZweY14LMV9ynYqZV9Y1RKu20lpxGMpsL2Wk20JWavs9ZpV9ZGp88O/ndKIc0p51nmyPhUAJkyYUMTjx48v4sMPP7xU7sknn2y5P6+T5WPmuvr3Eng51ZdS+ult27YV8fLly4v4lltuKZVjja9/14Fhy0fWzJ544omlcrzMdm6+frEU4O1avVVNB57SiPMy6/5ZBw4Av/rVr4p46dKlRezvA7w/7j/e3vSEE04oYtY4c5sD8fPtjzdl0RjThft3NPia5n7L/Qoop4aPvSvh65TSWXPf37lzZxH7+0psf/yOAgAceeSRRczWg0D53uTtFhm+Lvh68eeHl/l8+74+Whaf/aRHlh67Nd1uFd0J9ERcCCGEEEKIBtBAXAghhBBCiAaQNKVP8FOFLEngqU0vGeDpW5YdVLUv7AWq2pvF7Au95ISnfFkuUtWu0BOzKATK2fHYtsxPf7PdGZ/7lDyGrQy9NIVtGblOLG0B4nIelsoA5WOsmgnSZyblKfRrr722iFlWApSt2nj63//W+vXri/iBBx5o+TtA2cLu1FNPLeI3velNpXIxm7oUVa3e2rXk5HOydu3aIr7ppptK5a6//voiZtmK79Ox+vrj5QyNZ5xxRhG/+c1vLpVjqQ+3n79PVZWmcH1TtqPct7w0hZd5f/53Y5Iybw0Zyz6bkpfFYqBsr+n7IN+3vZ0qw8fC14uXLLF9JctqvH1hVYlRO3KrbpEPCNEp9ERcCCGEEEKIBtBAXAghhBBCiAaQNCVBL73V7H+XpQv85vvmzZtL5Vh2kJKmxKQF7dav09sxMVcJP9VcNbMmT9Fy7Kekq5Jqd54m53Plp6snT55cxIceemgR+yx/PB3O9fXODCwZGDt2bBHvu+++kaMo78+3RSy7q5+e57ZeuXJlad0NN9xQxIsXLy5iPwU/UhmVl6bwNfPoo48W8YUXXlgqd/TRRxcxS4VSLhrtUlVuxbKDJUuWFLGXpqxevbqIU3KeqrCTzYMPPljECxcuLJU799xzi/iYY44p4nHjxpXKsRTCtx/XMdUH+bi4z6ScR1LHH8sYmpKX8bXu+z5fgxz77Lh87fsssFOnTi1idrzxx8F98oADDihivtZ9uVTb8rFwH0ydq16Wn/Rafeumk5lKBxE9ERdCCCGEEKIBNBAXQgghhBCiATQQF0IIIYQQogEGQiM+iDol1uaxTnjdunWlcrHMkF5L3g32hamsge1kHkxl1kxl6ON13GbtasRjOn1PSt/OetAnnniiiGfMmFEqx1kOx4wZE90fH2M7bZay0YttD5QtGm+//fbSukWLFhWx1/Uy3Bf4OkhZCvI59uebLdy4Tvx+AAC8973vLeLZs2cXsbd8ZN1tuzrZqpZ9rM++7bbbipgzkwKvt+iMEdNFp/oFn9Of//znpXJcj7PPPruIvZactc8+WyzXidvCHxNfW9xOqXdAUhpxPkbext87Y7aJvn6cHZnf8zjqqKNK5Vgz7rX0nHWTLUj9cXCWTLYxnTVrVqnc9OnT0YqURjyVRbcdbXGn9cjdNj4YzePotmPvBL3QL/REXAghhBBCiAbQQFwIIYQQQogGGAhpSt30wtQRTxWyNMVnRGNS0pSYpVkd0zd1t2dVy7WUHCMls+ApZG4zP8VdNYMkr2s3Oyfvg6UADz/8cKkcT42ffPLJRczHBFSfGm5H9pOS8DzyyCNFzFaBwOszIMZg67eZM2cW8bRp00rleAr9/vvvL2LOpAmUZQdsB8hSD6As+3rnO99ZxKeddlqpHGdDZJlKytawqtWmz0bKlo/33ntvEfu+GsPLefhewlZ33naTs89y3b30itvsmmuuKeIVK1aUyi1YsKCI2eYQKFt3srWf74N8rXKdfLtze6auAz4HfLxe/sdtyO2eutY5w6VvC85O6vv0wQcfXMTvete7ithf33zuWOrDVoZAWb6Wsi/kdufjSsnBUlK7GL0spejlulelF8ZG3YieiAshhBBCCNEAGogLIYQQQgjRAJKmEH011UHTrZx9zU9RMrEMdUBZ7sAuEKn9dUt7Vs2sWVWawlOvPHXrZT/tZiUcKVxfnjIHgDVr1hQxn7u3vOUtpXI8Xc19yU/jx+Q3qTZLuXxwVsv169dH98G/6/sgSxc42+Xhhx9eKsfT6byNl8Tcc889RczZPr3MguUEV199dfQ42BHkuOOOK+JUBsWUNIXrsXbt2lK5TZs2FTG781TNGOldd3h54sSJReyvdXbuYdlPSvLG8hbfb1mewVIUoOzswQ4jvhzLLlISFs4eWzULKp8P76YTc5dJnQN2BfKZOnmd76sxKZa/N8XuzSm3Iz7GlPQsdY+tei/u5N+OXnDR6CTKitl96Im4EEIIIYQQDaCBuBBCCCGEEA2ggbgQQgghhBANII14F1KHhiuWWTNlX8h4DSDrN4844ogiTmnER5N2rLBS+sWURpxt0Ngqr9224PPozw/rhjkbnrcFY1s9zqjn9apsb8d6Z2+lxrrUuXPnFjHbnvm6M77NYueH3z0AgMcff7yI+Zj8dty/WacPlNuJMw/688M6YW5nPl6/P9aZL1mypFSO7RX5OLzGlzXjS5cuLeJ58+aVyrHNodePc7vz/jnDKlBu31TWVt4f92mfFZTt8Tgjo29bXsdZMb2dJi9zXb1GnJe9zpzbkzN18rsxQDlbJZ9HrwNnbXlVjTiTykJc1RY0lr0XSJ9Tvvb5/QDuS0D5nPC174+X+0JML+7rm9LBt3OfTtGNOubRsuLrxmOvm0GxQ9QTcSGEEEIIIRpAA3EhhBBCCCEaYCClKYMwdRTLrFlVPpGSpvB0qM/EVpU62nO07AFTv8NSEs6SCMSt/fz0L5+rAw88sLSOp9N5nZewsASFp+43btxYKsfT1WyDxjIVoDwNzXXnqWqg3J9S9oVMyuqN5She0sHwb/mp+5hdnJ/GZ+s8jv155Gn9lLXdfffdV8Tctj7rJMsHWFbhreg4gyLbMAJl+QTXnX8XKB9z6pwwKakCy3m433qpFEuCWN7i+zfLR1atWlXE3vKRJVXe8nLLli1FzP2H5UFAuX1ZmuJlG1wnvs58X23n/sPtxG3pl7mcP6d8Hn0mVbaN5Lbw9wFuC77HTJkypVSOZYgsZ/Hnm68ZjlPSFNEf9NrYqBvRE3EhhBBCCCEaQANxIYQQQgghGmAgpSmDQDuZNRn/Vjy7MaTcF/qF1Nv9PC3L07BeLsJtncoEyXKPQw45pLSOXSrYscTvg5d5H+z44eEpfS8D4alrLwVgWDLBdWUJh4f353+X+11qGpvXpabnGV8nXuZ28g4lfH5YisXSEQ9LfbyTCUscNmzYUMRPPvlkqRwv+31wJlCWgfi24PsA99uUqw3Xz58f3v/27duL2GfiZZkFXxc+2yW3O/cfzsYJlDOGejcdliZx//HOK3zf4uuWpXu+jiyl8dIUv1wFbhfOXguU+xY7AbEbj1/254fbgmPfZpxhl88By1T88pw5c4qYM5gC5YyrvI2/J3K/475Zh4RFcgfRq+iJuBBCCCGEEA2ggbgQQgghhBANoIG4EEIIIYQQDSCNeJ/CermJEycWsdcWxyz2qtoXDgJee8jLrCfmdgbK7cRaSa9V5n14fTKfL9aXels5rlNMa+qXH3jggSL2FnusPWW9OOvKgbItGmump0+fXirHx8XaVdYZA+V28n01ZWfILFu2rIhXrFhRxL7d+XxNmzatiL1On9+xYF2v198vXLiwZTluZ6BszRfTNwPAI488UsS+nVg/7jOBMjGLwZTun/H9gs9/6v2DmNbfa3+5T3Of8eeKtdpscwiUdfasYfc2lHy++bfYyhAoX0tsbci2k0D5+q6qaeZz4K1fWVvN/cdbBbIto3+vIJZV1/ctXma7U5/plq/9hx56qIi9Rvzkk08u4lNOOaWIUxlh+biqtl/qXjyc7UZarik6fRzdfvx10057dqKN9ERcCCGEEEKIBtBAXAghhBBCiAaQNKVmumXqiKULPM3JFnhAeWqYp6u9NIXt03wmw04Sk86kytXRttx+XgbCyyxj8NP9nFEwJjEB0hkP26kTx376m5e5TnfddVepHE95szWdlyrccMMNRcxT5izTAMp2eyw/8Zk6/TR8O3B7xuzcgPI0PMsdOJsgABx//PFFPGvWrCL21xK350knndTye6DcntxmXj7A/clniWRrPpZjnHbaaaVy3BeqZtZkfEZGthjkTIu+7/O1mroeuRxfF14exLINLyW5++67i5jlPCwpAspyCs4g6eUTLDnhvsCWf0DZRrCq1IflVd7ykWVjfO/w1wS3hc+6+dhjjxUxy0q85CR2r/d9hPsW1933C2+vOQRLvoDyNROzMvTrqt7bm5JV9JoMJCUVq1outp3vq3xNd3Js1OuSGj0RF0IIIYQQogE0EBdCCCGEEKIBJE1J0OvTHUPwcXinA54q5KlWP03Ob9a345oymm2ZerM+JvXwyzG3CSCeTdNPhbPsgh0M/DR2SjIQq5N3hOB1vI1vC96Os2L6cpzZkKe7/XQ69xN2KPFZB1nqwvIOnoL3yz7zIPfPdjLvVcVn5mQ5DjuyzJ8/v1SOswuyzICdN4DyufrVr35VxCwlANJSMZYkPPzww0Xs5TK8zM4j3m0j1p6pDLvcL2bOnNly+93Bx8V9y9eH+6eXDsXa2ktOOGMmX7f+fhZzfPFyGd6O+0hKusfH668RPqcsIfMOMrwPzqrqt2NnGO80s27duiJm2Yqve6wt/D1s9erVRcxyNS+NO//884vY33+ZmKQhtY2nX5xS2sFfP3xtsdzIZ5/lZY69BCpWju/tQPm+wPefXmA0+4WeiAshhBBCCNEAGogLIYQQQgjRABqICyGEEEII0QDSiHc5dVsjeU0h6w9Zg9st9oV1MFILQK/HZv0iW9P5LJYxey6vr/Q63Cp19xaIvBzTi/v9cTmfKY/rzvXlrJCemC0fULY+4+yM3uaQrelYg+z32U4mPq85599iOzavh2QtL9fBl2P9OGuVvbaYdbMnnHBCEfvjTVnicZ/h6/aee+4plZs3b14Rs0Z606ZNpXJV25O34/b02tCq/TH2PkPKLs3vg7WnfD9LvSsS0z63Wh7CvwPCfZozn1a9P/ostaznZ3tFfy9OvVPC9yp+34L7AVC2E2X9+PLly0vlfD+JwW3B+/DvlCxYsKCI+R2aqu/r+GOP3c88sXcRgLhFo89my+VicWqd31/st/z+YutS5XwWYn6fgY8/lXE1Fvtlbtvf+Z3fKZV7z3veU8R1a8T76R0APREXQgghhBCiATQQF0IIIYQQogEkTSG6JStmJ/H2hd7iawg/Bfjss88WMU+J+Skrnh7sxvZMWWFx3Xlq3UtT2PqMpSl+yjM2xe2nFNn6bOvWraV13L4sLeCshr6OqXMQazMvq+GMeP64mA0bNrSsq7eE42Pk/XmpFMs4Jk2aVFrHWR15ytz31aqZ47jNWCLisxCyHIWPw0sLeGqYy3n7Oe4/3GbDkXzFjpH37Zd5atjLZbi+LInx7cd15G289eLs2bOLmNvZS6pSVptV4e14//7eFJuS978bq5PPkMrSD5YC+P7I9m4pC0C+9lkq5e0aq7YT3wf89c2yopS8g2UrqSywvMzXAWc6BYDvfve7Rcz3GN9vuW049vcVlo3564fXpexjeZn7yGiWi22TWuf/fnHf99cZX4MsCfJ9mscDXM5nQOZl7lte4ujlgDG6YWzUJHoiLoQQQgghRANoIC6EEEIIIUQDaCAuhBBCCCFEA0gjPmD4lOIxjbiHdWps2+bTNHs9bCeJ6cpS6bHr0Iizri5lg8aaRW4zTi8NlHXWmzdvLq1jTSBrcn3q6Fj67na1d6zt4zTFvs343QHWwnqdLB8Ha7CXLFlSKsf79/pC1sVzv/M2grFz4vsq68xZM+1T0vM1w+fKp4nntmC7T47bxWs++TrjurOOHihrOblf+HcMuM1Y+55674GPl60bAeDQQw8tYr7HeE1qVY14SvfPfS1lBRrbn7d6Y602W0P6fua3G8LfL1K2jExMW+3PAbehb7PY/lMWjXxv9ynPvSa7Crxvb9l33XXXFTFfV16bXlUjnrr/MrH3CID4Oz9etx5b5/+Gxvbn+37VcrHfTZVL/RZrulPH2E6d/P1n4sSJ6BWa1Kl35Im4mX3AzEL+72ORMu8ysxvNbJuZPW9md5jZhztRHyGEEEIIIbqN2gfiZjYFwNcBPJ8o80kA1wA4FsD3AXwbwGEALjOzL9RdJyGEEEIIIbqNWqUplj3b/w6AzQB+DOBPWpSZBuALALYAOCmEsCb//r8AuAvAZ8zsyhDCbXXWTWRw9jbg9VNOVXj44YeL+MgjjyytY9uyVKazdmh36oinv1MZ+mKZK1PSFJ4W91OjvA+e8l29enWpHMtRvLUhT73yPrwNJdf9sMMOK2JvO8X1jcV+fzGZClCW2bCFnZ+S5rbhqXaWegBlmYW3beNpTpY++H7BEgKe1vb2Ziz14XMyY8aMUjnOwMmWiv4c8LFwHbyEgY+f28X3M55C9jaPXA9uJ3++ef98PfopY24nbk9v0chtGMs4CgArVqwoYpbR+Gl8b6s3hJcgxH7XL3Ps2z22juUnQFlyxOfR9+nYte/rXjVrKR8j18EfR0rqEpNqpLJxpuR6vMzn0d+nYn3aS4W4P/H90Wct5f7N0gcvpeD+k7LYS1nxxfZR1bLP9+FYOf+3t+rvcjn+2+MlNlXppK3woNsQtkvdT8Q/DeAcAB8BsD1S5ncB7A3g60ODcAAIITwD4O/yxY/XXC8hhBBCCCG6itoG4mY2F8DfA/hKCGFxoug5+efPWqy7zpURQgghhBCiL6lFmmJmewD4HoB1AP5iN8WHtAsr/IoQwgYz2w7gCDMbG0LY4cv0Ep3OGNnO/v20VzvSlJtvvrmI/VQz75+n9FNOAlWp6hCQ2nfMQQUoT/WlZBs83ZqSpvA0Kq/zU+E8ZZ7KEsnygaVLl5bK8XZ8HN4BhM9DKmNmbIrbT4ced9xxRcwuGl4+UNXNgqfk/VQuuyyw/Mb3rccee6xlnVLZANeuXVvEqel5njKfM2dOqRz3d86MyBIYoCxx4Lb1x8vyET91z645fO599j4+xtT5ZqkPX8OcWREoy6h4f37f999/fxHzcfn+zbIn3gefN6B8zfh1vJwqxzITlnm14wwyHFLSDyYmJfHZdvn8eJeqmENNyimErx9/LbGcic+Pz6TKTkiprKUss5g/f34Rn3nmmaVyc+fOLWKWgKUyQ3vZSsyRx5+Duv/eStKxe7ox63aT1KUR/88A3gzgtBBCa0+n1xgSO26LrN8GYN+8XHIgbmb3RFbNiXwvhBBCCCFEVzBiaYqZvRXZU/Av6gVLIYQQQgghqjGiJ+K5JOWfkclMPldxs20AJiF74r25xfrdPTEvCCHMb/V9/qR8XsX6CCGEEEIIMeqMVJqyH4BZebwzosv5tpl9G9lLnJcAeBjZQHwWgNITdDM7FJksZX0n9eGDrOHylnBsF8f6Pa93Zu0pW5VdeeWVpXLr168v4g996ENF7LXKMduyTlA1s2ZMC53K5Jey6mJN6oMPPljEPnsd6xz9PlhPzBpNb2nGmlzGvxPA579qtkJe568D1kyzbttnIfQ2eEP44+Xj8hZprCfm/sN92MNabdaxAmW7OI7XrFlTKsf9hDW0Kb0q2zx6O8RYu6cs5vz54b7Adfe6aG5DbltvLcp6dM6Ol7JejOmCgbKuedGiRUV8223lSVM+Zu7rqbZol7rv4dwvYn0EKLc1r/Ntxlr1lG59ypQpRXziiSeW1k2fPr2IWSOeyqyZes+F13E/S2UR5j7oy3F/4vr5a4nfB+HryrdtrK5A//zNHk0GeWyUIvVuWR2MdCD+IoB/jKybh0w3fjOywffQHfgGAAsBnA83EAfwdiojhBBCCCFE3zKigXj+YmYshf2lyAbi3w0h/AOt+g6APwPwSTP7DiX0mYDXHFe+OZJ6CSGEEEII0e3UmlmzCiGE1Wb2pwC+CuBuM/shgF0A3gfgCOilzyQjnTo68MADS+tOO+20IubsfV4+8Ytf/KKI2cLMW/HddNNNRbxy5coivvjii0vlTjnllCL2Wf6qEmuLlM0hr6ua+TM1XcsxHy8A3HHHHUXM2Uj98Z533nlF7Ker77nnNWOg5cuXR+vI0iGWwXg7uwsuuKCIeco3dYypTH5cju37vGVfTJriYXmCz4TJ5ytlqchWfGzzxzIVoGzBxu3uZQGPPPJIEbMt48knn1wqx1Ptqeu0qu0ml/NtsWnTpiLm4/Bynpg1pu/7POXPfcbbEqZsPWPEJECeqhkoq+KlZ7H+7q02/TUzhD8/fC9l+ZHPNhzbh7/HsiRqyZIl0X1wOS/xY/kW1ymVgTMWA+VzHJPiAPH+njqn3Ff9OeB+x+fDS1O4vqk6paSGsW06TSdlIIMgF2F63eZw1AfiABBC+JqZrQHwJwA+hMy9ZRmAvwohfLeJOgkhhBBCCDGadGwgHkK4FMClifXXALimU78vhBBCCCFEN9PIE/FeoR+njnz2sWOPPbaI2enBT0mfccYZRfyTn/ykiO++++5SOZatcIbDb33rW6VyLNU499xzo3VKZaJrB96fn1rndTx96evA0/qcCfLOO+8slWNZCUskLrzwwlI5djpITX+z9OO+++4rlWOXCpZW+MyIV1xxRREvXLiwiNnxBChP5cYy1Hl4nZ8yZ/cW78TBcNumpBpcPz+dzvVluZXv+1zHmPwEKE+N8/m+6667SuXYzSK2PRB3hvGyEj6P/npkiUfVzKwcp/ZXt2NJSgrAUgM+V9OmTSuVY3ce77DB27E0w2edZNnTvffeW8T+Hubldq3q6n+LXT74eyAujfNZiWMuJ16Sxv3JO/zE3B34HgOUr4VUxlXuC9xvq2bO9fdOvg9we/o+x/2Rr1NfLuX4wqScn2J0+9/yJlGW0Xqpd5QjhBBCCCGEqIQG4kIIIYQQQjSABuJCCCGEEEI0wEBoxAdRc1TV2o91lBx7vR1rlVnn57WcixcvLuJly5YVMWfjBIB///d/L+KnnnqqtI716GeeeWYRjx07tlSuHZ1aSisY01f6cqw1ZbtGrxlmbeNZZ51VxHPmzCmVY72m10ByJkxud9akAmXNOLe112OvXr26iFnX6TP0TZ06tYir2vLVkX0sZS/JunDWl3odKh8znyufdZK1wKyTTWUhZG2s79N+/0N4/Sz/VizeXZ1Gim8zPscc+2uOlzn2GVy5LfhdEW/Zx23DumDWfQPACSecUMQ+OzDft7hf+D7I76WwzjpmV+j34duCdetsSerfRahqn8r3C25PX79169YVMfdvIP6ug+9b/L4J1zf1Xgbfp/mdFCCeCdT3M75vcZtxn/P15Thlr5i6buu+froR6bFb0wvWhnoiLoQQQgghRANoIC6EEEIIIUQDDIQ0pVvopakj/7s8fTl37twiZls+ADjooINaxmzlB5SnOW+77bboOs7QyJIVoJxB0dvlMbFpSS8D4alcnlr3WSJvvfXWIr7llluiv/uWt7yliE899dQi9lPcPPXs68RT7TwV7qUAvMyZNb19IR8XW5/5KV+eeuZpbH++ue48Te6t+GL2Zr6fpSzNnnvuuSKOyU9S9fAyHV7Hx5GaxuY6+en42PR8ilj2P6DcFr79Yu2Z2j/LAg455JBSOe5bfC356yomTfF9mtuaZW0++yxnCOXzwRIqoCxd8NZ5Bx98cBGz3GPDhg2lcvfff38R83Xhzxu3GUsruI2AchvyMXqZRQwvTeFlltv4DK58L/b2hXx9c8ZVD987WSLiLRpZvsX7Y5taoCwf4Tbz/YctPidPnlzEvs34OuO+XtWes9P0o7Vxk/TS2KgT6Im4EEIIIYQQDaCBuBBCCCGEEA0gaYoYNjwl5B0M2O2A13kXBM5sx9kKAeCBBx4oYnam8NOhnIGTp1c9PE3O09BePsGODjwl6+vHchTeH8tPAOD0008vYp6G9RKJlMwgNt3qpSmzZ88uYp4O9nIHztLHx7t27dpSOZ7i3rhxYxGz3MjXnWUGqanrFFzO74PPCdePJStAdZnJSEk5TNSxv3ZcaLy0gLNQzpgxo4iPPPLIUjk+ryyRSGWV5dj/Li+znMlLEFguwhIwL0154oknipjdT4ByVlj+XXZrAcr3Er72UzI8lvN4hyiWd3CWTC85iZFqW143a9asUjm+pn17rlixooj5GuHvgfL1zn3E74/3wddfKisxy2q8BIr7IP+uv0/F3FBSzihVSW3TT3IH0TvoibgQQgghhBANoIG4EEIIIYQQDaCBuBBCCCGEEA0gjTghe6E4VY95//33L+J58+YVMWdqBICrrrqqiG+++ebSOrbkYt3xD37wg1I5tsJKacTZio/1xN4GjYlpYYFyBsCZM2cW8UknnVQqx7pt/i2/P9ZXet1oLCuo1znydtwuXrvL+4vpSYGyPttrtevE9ys+P7FMlXX9FrcZt5NvM9YMs5bV7++ZZ54p4pQlJcPnkfvVcOD+xDZ6QNlqlPW5vlwsI6U/Rv6tmM2fX8daat/3+Xf5vZEtW7aUyvG7GJw9stVyFbh+XhfNdojcftOnTy+VY/14KtNrrD2rlvNtxnVibTpQfj9k6dKlReyvJW5PfgekXVgXzu8GHX300aVy/HeAt0m9b1FVI95uZs06MgL3Ep0c5wxC+3UCPREXQgghhBCiATQQF0IIIYQQogEkTelhun3qiKde2b4PAC6++OIi9lZqLFu56667ipin+4GyHRlLWLwVFi+zlRpLOICyvIWnnX3WQLZS42yfPGUMlKeUuQ6+fmzZV1W2kpI78D5YFgAAv/Zrv1bEbIHINnJAWWaR+q2RUse+/RR/rM285ISPn+UIHANlCza24fQZGdnWkvumt6uMWbOlSFnd8RT/UUcdVSrHfZLlKFX7WUrOU7W+vA+2GgTKFnZ8j1i0aFGpHFsb+nZvpw25L3hLzhNPPLGIjznmmCL2EhbeX1XLx6oyiKrrvKUiS1VYpsKyH6AsAeP7T6otY8cLlCUn3H7eepH7akwO5ZcHQe7Q7X/Le41ea089ERdCCCGEEKIBNBAXQgghhBCiASRNqchoTnUMwpQTT/OyvAMoT1//+Mc/LuKf/exnpXLHHXdcEZ999tlF/OY3v7lUjqfkU9OhVafdWSITczUBylO+KTlCO9nhfN35tzjbpZdFxBwSvPxm+/btRZxyl2kKlvd4BxCWHHHss/yxFKmqwwKfR58R9Zxzzinihx56qIhXrVpVKscyKj5vvv9xPbys5vDDDy/i+fPnF7HPdMvyG3Z/qZoxMyUZSFG1HNePHTbYfQkoS9R81k3uq9wv/Hlk2QZLdryzB8uPuM1Sji8xGZpfTkku6nAK4T7N90R/jEuWLCliltp52Q/XkdvP748lcCw/SbVFypkqJvVJydCqyls6/Xd5EP5+M50cGw1KW+qJuBBCCCGEEA2ggbgQQgghhBANoIG4EEIIIYQQDTCQGvFB0R0N0e0aLq9/5QyAbHO4cOHCUjnWkrPlHGsU/f5Tmu4YVbXf/P3u9sGk9OOsR2et9tq1a0vl1q1bV8QbNmwoYrZ9A4AdO3YUMetBvQ48dSwjhc+BP/escfXWi3y+WQvNVpNAWXfM7yJ4vSrXg4/XH3tsnbde5P0ff/zxRextMp966qmWsbfnZN2+t1TkZdZT87ED8Yyhvt1j2t1U9kfG99uYlj51HXCdWAMPlHXbbK0JlDO/Pv/880XM9n1A+V0Ctk305WJtkcq4ynGqbatqxFP3FW5P/54Cw33f28eeeuqpRcxtxvcHv3/uW/4ey7+Vaou6+2PVrMSp7/txPKD306rRLVp1PREXQgghhBCiATQQF0IIIYQQogEGUprSDWjqKA5Pc/IUtbep4ylQLztgRtpmKZvD1L55qpllBzyVDgBr1qwpYpaYAMD69euLmG3vtm3bVirHmfJS1oN1Z8nk4+epa85gCpSlAHweWYoClGUmXnLC26Uyn8bOT0r2w9P/XiLCy7Ft/O9yf/TSB24L7t9eZsDT9V4KwG2dkk/wckxK4culssBye3Jf8v2KjyVmremXeR/+OFia4uU33BdYbuUt8WL3C38Nx9rTtxmf15g0w/9W6t4Rk6P4fpbqgzHplG9Pvj65PX3fj2X9Tdlf8vH7bKRVJSyxPujPaUrqM1rZOUfzb/kg/P0fxHbRE3EhhBBCCCEaQANxIYQQQgghGkADcSGEEEIIIRpgIDTi0nDVy2i2C2/ntcAjpWqdvP51165dRfzkk08WMdsGAuXU0byOtwHKaefZzg4o68lZ+13VerEqXnvJbc32eJzm2i9z7FOUsyaVNdJeB85aaH++Wcsa0+cC1TXiMV04n1+/jjXIXk/Lv5tKXc+aV26z1DlNaXJ5f15ry20Ti4G4XjelyU1pxGNtm9Igp6whGa9b537C/aJqe6ZS13Pstf68nOqPMY24p6pGPNUHR/r3x9fP96chfJvxMfLxez0/L8euZyCu5/e/y8t1a8T1t3zk2w1CG9aBnogLIYQQQgjRABqICyGEEEII0QADIU0RYgiermapBwBs3bq1iDl735YtW0rlOFslZ7j01oO8jqUpPLXs61QHPK3r5R0sheB13h6PpSRsHedtCXk5ZScZs4RLTfdXndb2dnGx6eqq0hRfd5aqpDL58e9WtTmMZVP0dU/9VlWLvZR8gpd5f6lMhlx3b0vIx5yyFOR2Sk1j8zr/W1yP1PnmdSmLxpjNo++PsbZNWUO2I01JSaB8e8b6Z+o8pjJ1xtrWtxkfY1VpSjv9MXVfSbWtJBKim9ETcSGEEEIIIRpAA3EhhBBCCCEaQNKULkdvNcfhaVSWD+zYsaNUjiUoHHPWSgB45JFHinjFihVFvGrVqlI5lpl42cFI8eeDp2Vj0gy/zK4kkydPLpU79NBDi3jSpElF7CUnMQcQf7wxd4uUUwhP3XtpStVjTEkBYtPVKWlKavo7JmNISVi4DlWzH6akFCmXipgsAIhP//t253Upl4qq0hSWU/A+vCNNTEaUkrD49uSyVZ1SUucx5i6TkqakMmvy+Uk5eXBfSEmlYo45frlquVh2U6DcnrH2A+qXpsSca/zvpjKVxtq6qQycVVHW7Wp0eowzmu2pJ+JCCCGEEEI0gAbiQgghhBBCNIAG4kIIIYQQQjSANOJt0Auao17C6zpjdlpeh7pt27YiXrNmTREvXbq0VO6BBx4o4uXLl7fcHuis3julr2Qtp9dUssZ71qxZRTx79uxSuRkzZhQx2w16rTZbycVioKzlbUcjXlXTXNWi0C/HNM1+OaURr6rJraoRj60bTY14Svud0ojHMmtWtS/0/SDWnlU1zb7NUhpxvi/E7PZ83atmI03pmGPvKXiNOP9W6m8AtyHXydsX1t2eqQypTDsacd9mMY14Hf0xta5ulKmzNRobtYeeiAshhBBCCNEAGogLIYQQQgjRAJKmEIMwPTKaU0epbXgK+amnniqtW7lyZRGzrMRLTliO8uyzzxZxauqa19Wd0dIfL2ernDZtWhEfe+yxpXK8fOSRR5bWTZw4sYiryixito5+Hyl7M54aTklTYlKAVCZI3ncqo56XpsQs4lLT5KlMi9xmVa3eqsoCYu3nl1NSgFR7VrUvjLWZl2NUlQLE2tO3baw9/e/GsoL6fhuTUgBxaYqn6nmMyUx828bKpfpFVftCPqaqsiS/zHXy8paq8iimqjQllek1Jlvx5WLtmbIvTPXVfvzbLmvjemnyePVEXAghhBBCiAbQQFwIIYQQQogGkDSlC+mlqSPvtsEyE85IyXITv27jxo2ldSwz4SyZPmMm/7Z3VBkpPB3ss07OnDmziNnJhGMAmDJlShFPmDChiFmyAsTdQHw9eNo4JXfguKrMwk9xx/bn27kdaUpq6roOKUAsW6Mn1p5eChCTWaSm51kK4NssJqVIuaZUbc+UA0hV+URM2uPr0Y40JfW73Ga+X6SkKal6tFOnWCbHduUTsf6YkqZw7PdX9fpOScqq9kGG61u1/1SVQPlyMfeX4chPYtd+1XLtIkeV+hgUFxY9ERdCCCGEEKIBNBAXQgghhBCiATQQF0IIIYQQogEGUiM+WhquXtApsSbwhRdeKOInn3yyVG7dunUt48cff7xUjrd75plnWsYAsHXr1iLeuXNnaV0n9d4HHXRQEU+dOrVU7k1velMRs7778MMPL5U78MADi5i13xwDwP7771/EXlPJpHSZVa3FYnpir3eOrfP615guPKXPTWmaY9Zn/ner6kbbtTRrp+4xbXHKVo6PK2W3V9W+MHUeU1r/2LqUjrmqJjelLebtqlrxpez2UtkfY9dIqu5121Wm+gXXo6pGnI+j6rn3y6msxLH2rKqxT9lppto21gdT/aKqDjz197YOK8Ne+Hs+UjQ2aoZan4ib2dvM7CdmttHMXjSzJ8zsejN7R4uyC8zsWjPbYmYvmNn9ZnaJmb2x1b6FEEIIIYToJ2p7Im5m/xXAnwJYD+B/A3gawEEA5gM4C8C1VPY3AFwJYCeAHwLYAuACAF8CsBDA++uqlxBCCCGEEN1ILQNxM/s9ZIPw7wL4/RDCLrd+T4oPAPBtAK8AOCuEcHf+/ecA3ADgfWZ2UQjh8jrq1m/EpnT8dC1b/W3atKmIveSElzn2loKxdVu2bIn+bh2ZK/l4vYxh0qRJRXzIIYe0jFPrUuUmT55cxOPHj4/WqY4ptjqm9mLT7n5KOjZtnJJPpDIXxuQdKdu7mPWer1NKPpGank/JTGKk5DyxaXxfjo8lJaWomgmyqtQnJQWoKquJ7S/Vz6pKqnh/VWU6/jhSMpiYvKfqNVJVZpGytUztr50+WFX249fFrumU7WhV+8KU5Ct2/CnJSVUJXeo66PfsmZ5esjYeTUYzy2gn2nbE0hQz2xvA3wJYhxaDcAAIIbDg733InpRfPjQIz8vsBPBX+eInRlovIYQQQgghupk6noifi2xg/WUAr5rZOwEci0x2cmcI4TZX/pz882ct9rUYwA4AC8xs7xDCiy3KCCGEEEII0fPUMRB/S/65E8C9yAbhBWa2GMD7QghDKRdn558r/I5CCC+b2WoAxwCYAWB5DfXrWzZv3lzEq1evLq3jzJWPPfZYEbPjiV/HWTF37XrdxEatjB07tojHjRtXWhdzJWHHEwA44ogjipgdUNj9xJfj3+10hrU6iE1Dt+MGArQnTUlNXcemslNTyClZSdV1qWnt2O/6toi1Z0pWw+3kj5H3V9XloyopuUNVyUBKclK1PZmqbhYpKUVMtjIcaUpVCVxVaUrVfhYrl2rbdkhlXK3anim3o6qkznesb6X6Y1XpVTfel4WokzoG4kNi2j8FsAzA6QDuAzAdwBcAnAfgR8he2ASAoVHXtsj+hr4fv7sfNrN7Iqvm7G5bIYQQQgghmqQO+8KhfbwM4MIQws0hhOdDCA8A+E1kLipnmtmpNfyWEEIIIYQQfUEdT8S35p/3hhDW8IoQwg4zux7ARwGcDOA2vPbEu6xHeI2h77dG1vP+57f6Pn9SPm932wshhBBCCNEUdQzEH84/t0bWD6VU3IfKnwRgFoCStMTM9kAmaXkZwKM11G3UGU17oTVr1hTx1VdfXVp3/fXXF3EdNoIM6/f23nvv0jrWYHO87777lsoddthhRTxjxozSulmzZhXxkUceWcSs9QbKdnFVs6o1RaoOKU13VVh7mdKSx7S2XkPKGt2q+tx2MuBVtdHz21XVkLaTec8Ta8+U1jvVtrHrMaUFrqqhTWl3U1rlkdpztauLjmV1rEPT7Im1Z9X3GaoeY6ffPUlp+KvaErajq099X7U/xta1a0tYd9uOZpbIbvi71GmUdbMadUhTfgEgADjazFrtb+jlzaG3CW/IP89vUfYMAGMB3CrHFCGEEEII0c+MeCAeQlgL4BoAUwH8Ea8zs/MA/Dqyp+VDdoVXIMu6eZGZnURlxwD4fL74jZHWSwghhBBCiG6mrhT3fwjgzQD+e+4jfi8yicm7kWXQ/FgIYRsAhBCezTNxXgHgRjO7HFmK+wuRWRtegSztfeN0+xQJ2/5xlklg5JZeqQx9+++/fxF7q8C5c+cW8XHHHVfExxxzTKkc2xL6jJlV6loH3TKlWLecqarMoqodYmx7v9zOdHJKflJ1WrtquVQ9qma4ZKrKfqpKCVLUIQVoR5oymhnrmHalFFXrUEc/66Q0pWrf9FTNktkO7bRnu9KUGO1aQXb73/I6kKymXkbzGOuQpiCEsB7AfABfB3AUsifjZyF7Ur4whHClK38VgDORJfB5L4BPAXgJwB8DuCjUfQcRQgghhBCiy6jriTjyhD2fyv9VKX8LgHfU9ftCCCGEEEL0ErUNxMXuqXvq6NBDDy3imTNnRrdLuXJMnDix5T68lISX2clk/PjxpXLsZJKSulSdYhxNF5puoO7jTTlx1FGunTq1I5fwy3Wf73akOaPZ56r+Vsp9IvV9J6+zOqRSI3X5SJVLbdNOhseqbduuk1LV7eqgk+1Zdx26nUH7WzYcOimr6YX2rEWaIoQQQgghhBgeGogLIYQQQgjRABqICyGEEEII0QDSiBO9pjnaZ599ithrxC+44IIiPuigg4rYZ7HkDJdsh8hZMf0y/27Kmq1b2mmkdLpf1KGPi2lDU/rSdvTOdWS5q8N6MPW7nbSLS9Wv6jloh3Y14lX319S9rx2ryar7a7fcSOs0muc7td1o1mM0rfPq3l831L0bUbuMHnoiLoQQQgghRANoIC6EEEIIIUQDSJrSw7AsZOrUqaV1F110URGzrMTbDfK6XpOZaOrsNZpqi7rtzer4rZEymjKITmxX5z668VyNZrbPbpBPdMs5aOoa7td7NtPL/aIb6bV+pifiQgghhBBCNIAG4kIIIYQQQjSABuJCCCGEEEI0gDTiHWQ0NVz77bdfaXnWrFm11qmTdGOdOsloalzr3vdo/la3aLCb0hbX3bbdfp318vnuZbrxvtKNDFq/6CRqyzJ6Ii6EEEIIIUQDaCAuhBBCCCFEA0iaIoQQPcygTN8OF7WLEKIX0BNxIYQQQgghGkADcSGEEEIIIRpgIKQp69atKy0vW7Zs2PvodieKuvfdC281d4OrxGgefze4G3T6eAftOhvpNsPZrhv6aje687TLaNWpE8fRbXXvhr5Z13bdcI775TrrluPgdStXrmyrTin0RFwIIYQQQogG0EBcCCGEEEKIBtBAXAghhBBCiAawEELTdagdM7sHwLym6yGEEEIIIQaCX4UQ5g93Iz0RF0IIIYQQogH6dSA+rekKCCGEEEKIgWFaOxv1q33hs/nnmPzzoaYq0kfMyT/VlvWg9qwXtWd9qC3rRe1ZL2rPelF71sM0vDb2HBZ9qREfIteKox3NjiijtqwXtWe9qD3rQ21ZL2rPelF71ovas3n6VZoihBBCCCFEV6OBuBBCCCGEEA2ggbgQQgghhBANoIG4EEIIIYQQDaCBuBBCCCGEEA3Q164pQgghhBBCdCt6Ii6EEEIIIUQDaCAuhBBCCCFEA2ggLoQQQgghRANoIC6EEEIIIUQDaCAuhBBCCCFEA2ggLoQQQgghRANoIC6EEEIIIUQD9OVA3MyOMLN/MrMnzOxFM1tjZl82swlN163bMLOJZvYxM/uJma0ysxfMbJuZ3WxmHzWzN7jy08wsJP5d3tSxdAt5f4u1z8bINgvM7Foz25Kfg/vN7BIze+No17+bMLOLd9Pfgpm9QuUHvn+a2fvM7Gtm9kszezY/7u/vZpth9z8ze5eZ3ZjfL543szvM7MP1H1GzDKc9zewoM/tzM7vBzB4zs11m9qSZXW1mZ0e22V0f/3hnj3B0GWZ7tn09m9mHzezOvG9uy/vquzp3ZM0wzPa8rML99Bdum4Hqn02wR9MVqBszmwngVgCTAVwN4CEAJwP4IwDnm9nCEMLmBqvYbbwfwDcAbACwCMA6AAcDeA+AfwDwdjN7f3h95qclAK5qsb+lnatqT7ENwJdbfP+8/8LMfgPAlQB2AvghgC0ALgDwJQALkZ2jQeU+AH8TWXc6gHMAXNdi3SD3z78CcAKyvrYewJxU4Xb6n5l9EsDXAGwG8H0AuwC8D8BlZnZcCOFP6jqYLmA47fl/AfhtAMsAXIusLWcDuBDAhWb2RyGEr0a2vRpZf/fc3V61u5Zh9c+cYV3PZvYFAJ/J9/9tAHsBuAjANWb2qRDC14df7a5lOO15FYA1kXUfBDADre+nwOD0z9EnhNBX/wBcDyAA+JT7/r/n33+z6Tp20z9kA5kLALzBfX8IskF5APBe+n5a/t1lTde9W/8hu9GtqVj2AACbALwI4CT6fgyy/1AGABc1fUzd+A/AbXn7XEjfDXz/BHA2gKMAGICz8vb4fqTssPtf3sY7kQ3Cp9H3EwCsyrc5tel2aKg9Lwbw5hbfn4nsPysvAji0xTYBwMVNH2sXtuewr2cAC/JtVgGY4Pa1Oe+705puhybaM7GP8QB25P1zkls3UP2ziX99JU3Jn4afh2wg9D/c6r8GsB3AB81s31GuWtcSQrghhHBNCOFV9/1GAN/MF88a9YoNDu8DcBCAy0MIxZOFEMJOZE86AOATTVSsmzGz4wCcAuBxAD9tuDpdRQhhUQhhZcj/iu6Gdvrf7wLYG8DXQwhraJtnAPxdvtg309XDac8QwmUhhHtbfH8TgBuRPZldUH8te4dh9s92GOp7f5v3yaHfXYNsXLA3gI906LdHnZra84MA9gHw4xDC0zVVTVSk36QpQxq8n7cYWD5nZrcgG6ifAuAXfmPxOl7KP19use4wM/uPACYie8pwWwjh/lGrWfezt5l9AMBUZP8BvB/A4hDCK67cOfnnz1rsYzGypxQLzGzvEMKLHatt7/H7+ec/tmhTQP2zKu30v9Q217ky4jVS91MAONHMLkE2G/E4gEUhhPWjUbEeYDjX8+765+fyMn9dey17l9/LP/9Xooz6Z4fot4H47PxzRWT9SmQD8VnQQDyJme0B4EP5Yqsb2rn5P97mRgAfDiGs62zteoJDAHzPfbfazD6SPx0bItpnQwgvm9lqAMcg0+4t70hNewwz2wfABwC8guw9hlaof1ajnf6X2maDmW0HcISZjQ0h7OhAnXsOM3sTgLch+4/N4kixP3LLr5jZPwC4JJ+hGGQqXc/5bPfhAJ4PIWxosZ+V+eesDtWz5zCzUwEcB2BFCGFRoqj6Z4foK2kKgHH557bI+qHvx3e+Kj3P3wM4FsC1IYTr6fsdyF5Imo9MEzoBmf5xETIJyy8k/cF3kP3RPQTAvshuct9CplG8zsxOoLLqs8Pnt5C1x89CCI+5deqfw6Od/ld1m3GR9QOFme0N4AfIJBGXslwiZzWATyH7D86+AA5D1sfXAPiPAP5p1CrbfQz3etb9dPgMzS5+O7Je/bPD9NtAXNSAmX0a2RvnDyHTjhWEEDaFEP5zCOFXIYSt+b/FyGYa7gBwJICPjXqlu4gQwt/k2vsnQwg7QghLQwgfR/bC8D4ALm22hj3P0B+Ob/kV6p+im8jtH7+HzH3mhwC+4MuEEG4KIXw9hLAiv19sCCH8CJnU8hkAv+P+8z4w6HruLGY2DtmgeheAy1qVUf/sPP02EN/dk5ih77d2viq9SW5L9hVk9ltnhxC2VNkuhPAyXpMJnNGh6vU6Qy+/cvuozw4DMzsG2ctu65HZw1VC/TNKO/2v6jaxp5IDQT4I/z4y+8f/F8AHhvNCXT7bM9TH1WeJxPWs++nw+ACAsWjjJU31z/rot4H4w/lnTP91VP4Z05APNPmLGF9D5s16du6cMhyeyj819d+aVu0T7bO5Tn86spe7Hu1s1XqG3b2kmUL98/W00/9S2xyKrH3XD7I+3Mz2BPCvyLyr/wXAf8gHj8NFfTbO69omhLAd2YuE++V90aMxQJmhlzRfN7tYEfXPGui3gfjQiwbn2eszQu6PbHpwB4DbR7ti3Y6Z/TmyBB73IRuEb2pjN6fknxo0tqZV+9yQf57fovwZyJ5W3CrHFMDMxiCTSr0C4B/b2IX65+tpp/+ltnm7KzNwmNleAH6E7En4PwP4YBv/aRzirfmn+uzriV3P6p8VMLO3IksEtCKEcGObu1H/rIG+GoiHEB4B8HNkL8X9oVv9N8j+1/a9/H/NIsfMPofs5cx7ALwtNUVlZvP8f3Ly798G4P/MF5PptPsZM5vb6mVAM5sGYCibG7fPFQCeBnCRmZ1E5ccA+Hy++I3O1LbneD+yl7Wua/GSJgD1zzZop/99B1nij0/m/XpomwkA/iJf/CYGkPzFzJ8A+A1k/1n8iLfSbbHNSS2+e4OZfRbAqcjOTyvnqr6nzet5qO/9Zd4nh7aZhmxc8CKyPjzoDM0upiwL1T9HAeucp34ztEhxvxzZ/9rORjYdtSAoxX2BmX0Y2UsaryCTpbTSda4JIVyWl78R2fTerch0ugBwPF7zbv1cCOHzfgeDgpldiuxF18UA1gJ4DsBMAO9E5r96LYDfDCHsom3ejWxAtBPA5cjSYl+I7C31KwD8VgeTX/QMZvZLAKchy6R5TaTMjRjw/pn3p3fni4cA+HVkT6x+mX/3dKAU9O30PzP7FICvIvN0/iFeS3F/BIAvhj5KcT+c9jSz7yDLRPg0gP+JLCOh50Z+AmlmAZkccAkyWcU4ZLO3xyKbwf3NEMLPazykRhlme96INq5nM/sigD/Ot7kCWSKl30bmQ95XKe6He73n2xwA4AlkFtZH7Obh20D1z0YIXZDes+5/AKYg+x/vBmR/INYC+DIo3a3+FW11KbI/Fql/N1L5jwL4/5BZFz2P7OnCOmR/jE9v+nia/ofMWutfkTnObEWWxOMpAP+GzJfdItstRDZIfwbACwAeQPbE541NH1M3/AMwN++Lj6XaRP2z0jW9psU2w+5/AC4AcBOy/2xuB3AXMl/nxtugqfZElj1zd/fTS93+/1vejk8g+8/Qjvz+8XUAM5o+/obbs+3rGdl/iO7K++ZzeRu/q+njb7I9aZtP5Ov+tcL+B6p/NvGv756ICyGEEEII0Qv0lUZcCCGEEEKIXkEDcSGEEEIIIRpAA3EhhBBCCCEaQANxIYQQQgghGkADcSGEEEIIIRpAA3EhhBBCCCEaQANxIYQQQgghGkADcSGEEEIIIRpAA3EhhBBCCCEaQANxIYQQQgghGkADcSGEEEIIIRpAA3EhhBBCCCEaQANxIYQQQgghGkADcSGEEEIIIRpAA3EhhBBCCCEaQANxIYQQQgghGkADcSGEEEIIIRrg/we98J7peWCcWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 163,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# img = Image.open('FileInfo0508/31c1f481-912a-11ea-b24d-408d5cd36814_cmftq.jpg') # 波浪线验证码\n",
    "# img = Image.open('/data/captcha/shensebeijingsandian/pgv4_d58a8328-c425-11ea-be07-ecf4bbc56acd.jpg') # 深色背景验证码\n",
    "# img = Image.open('/data/captcha/0ad9.jpg').resize((200,70), Image.BILINEAR) #小图噪点 \n",
    "imgs = glob.glob('/data/captcha/label_english/超级鹰导出图片-2023-04-03/*.jpg')[400:]\n",
    "img = Image.open(imgs[9])\n",
    "img = img.resize((width, height), Image.BILINEAR)\n",
    "def img2array(image, width=width,height=height):\n",
    "    X = np.zeros((1, height, width, 3))\n",
    "    image = image.convert('L')\n",
    "    px = [image.getpixel((x,2)) for x in range(image.size[0])]\n",
    "    c = Counter(px)\n",
    "    m = c.most_common()\n",
    "    bg = m[0][0]\n",
    "    bg_img = Image.new(mode='L', size=(width,height), color=bg)\n",
    "    bg_img.paste(image, box=(0, 0)) # \n",
    "    X[0] = np.expand_dims(np.array(bg_img)/255.0, axis=-1)\n",
    "    return X\n",
    "img_arr = img2array(img)\n",
    "\n",
    "out_pre = decode([img_arr, np.ones(img_arr.shape[0])])\n",
    "out = ''.join([characters[x] for x in out_pre[0][0]])\n",
    "plt.imshow(img)\n",
    "print('out', out)\n",
    "plt.title(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pred:tmh4\ttrue:tm14\n",
      "pred:1885\ttrue:1385\n",
      "pred:hn67j\ttrue:hn6yj\n",
      "pred:ekbo1\ttrue:ekb01\n",
      "pred:zl2f\ttrue:zt2f\n",
      "pred:dnvom\ttrue:jnvom\n",
      "pred:ljkz\ttrue:1jkz\n",
      "pred:ddei\ttrue:vdei\n",
      "pred:c782\ttrue:c182\n",
      "pred:oi6sk\ttrue:0i6sk\n",
      "pred:1894\ttrue:1694\n",
      "pred:redr\ttrue:iedr\n",
      "pred:bgvsk\ttrue:bqvsk\n",
      "pred:qifv\ttrue:qlfv\n",
      "pred:ayi4\ttrue:ayl4\n",
      "pred:mer9\ttrue:m8r9\n",
      "pred:805a\ttrue:305a\n",
      "pred:amype3\ttrue:anype3\n",
      "pred:e907\ttrue:e967\n",
      "pred:25598\ttrue:255598\n",
      "pred:djga\ttrue:diga\n",
      "pred:99283\ttrue:95283\n",
      "pred:uwm\ttrue:uwzm\n",
      "pred:6nwh\ttrue:6nwn\n",
      "24\n",
      "总耗时： 15.334752559661865\n",
      "正确数：976， 错误数：24, 总样本：1000, 准确率：0.9760\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "data = CaptchaSequence(characters, batch_size=200, steps=5, input_length=12, chars_len=(6,6))\n",
    "# model.load_weights('gru_DigitAndEnglist_ctc_best_0927.h5') \n",
    "# model.load_weights('mobilenet_DigitAndEnglist_ctc_best0930.h5')\n",
    "# model.load_weights('mobilenet_DigitAndEnglist_ctc_best_32.h5')\n",
    "model.load_weights('gru_english4to6_ctc_best_20250826-2.h5') \n",
    "# model.load_weights('gru_english4to6_ctc_best_20250516.h5')  #正确数：963， 错误数：37, 总样本：1000, 准确率：0.9630\n",
    "pos = neg = 0\n",
    "t1 = time.time()\n",
    "err_img = []\n",
    "err_label = []\n",
    "for i in range(len(data)): \n",
    "    flag = False\n",
    "    [X_test, y_test, input_len, label_len], _  = data[i]\n",
    "    for idx in range(len(X_test)):\n",
    "        in_data = X_test[idx:idx+1]\n",
    "        out_pre = decode([in_data, np.ones(in_data.shape[0])])\n",
    "#         print(out_pre)\n",
    "        out = ''.join([characters[x] for x in out_pre[0][0]])  \n",
    "            \n",
    "        y_true = ''.join([characters[x] for x in y_test[idx] if x < len(characters)])\n",
    "#         print('out', out, y_true)\n",
    "        if out != y_true:\n",
    "            err_img.append(X_test[idx])\n",
    "            err_label.append('pre: %s, lab: %s'%(out, y_true))\n",
    "            print('pred:' + str(out) + '\\ttrue:' + str(y_true))\n",
    "            neg += 1\n",
    "            flag = True\n",
    "        else:\n",
    "            pos += 1 \n",
    "print(len(err_img))\n",
    "\n",
    "t2 = time.time()\n",
    "print('总耗时：',t2-t1)\n",
    "print('正确数：%d， 错误数：%d, 总样本：%d, 准确率：%.4f'%(pos,neg,pos+neg, pos/(pos+neg)))\n",
    "# 正确数：952， 错误数：48, 总样本：1000, 准确率：0.9520"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'pre: f4aq, lab: ftaq')"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 163,
       "width": 370
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = -1\n",
    "# plt.imshow(err_img[i].reshape((height, width)))\n",
    "plt.imshow(err_img[i])\n",
    "plt.title(err_label[i])\n",
    "# idx = 8\n",
    "# img_arr = X_test[idx:idx+1]\n",
    "# out_pre = decode([img_arr, np.ones(img_arr.shape[0])])\n",
    "# out = ''.join([characters[x] for x in out_pre[0][0]])\n",
    "# y_true = ''.join([characters[x] for x in y_test[idx] if x < len(characters)])\n",
    "# plt.imshow(img_arr.reshape((height, width)))\n",
    "# print('out', out)\n",
    "# plt.title(out)\n",
    "# i = 9\n",
    "# print(model.layers[i].name)\n",
    "# model.layers[i].get_weights() # 打印某层权重\n",
    "# height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jmev4 jmev3 False\n",
      "m22oh m22ch False\n",
      "svuav svuaw False\n",
      "ajmxs ajmks False\n",
      "xcpws xcpvs False\n",
      "qnthp qnrhp False\n",
      "z9uet z8uet False\n",
      "swav5 svav5 False\n",
      "99wuv 99wdv False\n",
      "正确数：991, 总数：1000, 准确率：0.9910\n"
     ]
    }
   ],
   "source": [
    "'''预测真实验证码，统计准确率'''\n",
    "import re\n",
    "pos = neg = 0\n",
    "n = 0\n",
    "# model.load_weights('gru_english4to6_ctc_best_1014.h5')\n",
    "# model.load_weights('gru_english4to6_ctc_best_1102.h5')\n",
    "path51 = 'FileInfo0508_2/*.jpg' # 波浪线验证码 正确数：714, 总数：715, 准确率：0.9986\n",
    "#正确数：706, 总数：715, 准确率：0.9874\n",
    "path52 = '/data/captcha/label_english/100_30/*.jpg' #正确数：0, 总数：209, 准确率：0.0000  正确数：174, 总数：209, 准确率：0.8325\n",
    "# 正确数：168, 总数：209, 准确率：0.8038  正确数：976, 总数：1000, 准确率：0.9760\n",
    "path1 = '/data/captcha/label_english/70_26/*.jpg' #正确数：588, 总数：1505, 准确率：0.3907   正确数：1500, 总数：1505, 准确率：0.9967\n",
    "path2 = '/data/captcha/label_english/52_21/*.jpg' # 正确数：1122, 总数：2822, 准确率：0.3976  正确数：2761, 总数：2822, 准确率：0.9784\n",
    "path3 = '/data/captcha/label_english/100_25/*.jpg'  #正确数：6488, 总数：6503, 准确率：0.9977 正确数：6503, 总数：6503, 准确率：1.0000\n",
    "path4 = '/data/captcha/shensebeijingsandian/*.jpg' #正确数：543, 总数：544, 准确率：0.9982\n",
    "path5 = '/data/captcha/shensexiansandian/*.jpg'#正确数：499, 总数：501, 准确率：0.9960\n",
    "# 正确数：493, 总数：501, 准确率：0.9840\n",
    "path6 = '/data/esa_sdk/gan/english/*.jpg' #正确数：12, 总数：23, 准确率：0.5217   正确数：18, 总数：23, 准确率：0.7826\n",
    "# 正确数：18, 总数：23, 准确率：0.7826\n",
    "path7 = '/data/captcha/label_english/90_38/*.jpg'  #正确数：226, 总数：243, 准确率：0.9300\n",
    "path8 = '/data/captcha/label_english/70_25/*.jpg' #正确数：69, 总数：70, 准确率：0.9857 正确数：46, 总数：49, 准确率：0.9388\n",
    "\n",
    "# model.load_weights('gru_english4to6_ctc_best_20220829.h5') \n",
    "err_imgs = []\n",
    "err_labels = []\n",
    "files = glob.glob(path52)\n",
    "# files = glob.glob('/data/captcha/label_english/200_80/*.jpg')[2000:]\n",
    "# files = glob.glob('/data/captcha/label_english/122_46/*.jpg')[:]\n",
    "sp = int(len(files)*0.8)\n",
    "sp = min(int(len(files)*0.8), 3000)\n",
    "for file in files[:][:1000]:\n",
    "    try:\n",
    "        img = Image.open(file)\n",
    "    except:\n",
    "        print('打开错误：',file)\n",
    "        continue\n",
    "    if re.search('FileInfo0508', file)!=None:\n",
    "        label = file.split('_')[-1][:-4].lower().replace('1','l')\n",
    "    elif re.search('200_80', file):\n",
    "        file_name = file.split('/')[-1][:-4]\n",
    "        label = name_dic[file_name].lower()\n",
    "    else:\n",
    "        label = file.split('_')[-1][:-4].lower()\n",
    "#     label = file.split('\\\\')[-1].split('_')[-1][:-4]\n",
    "#     label = file.split('/')[-1].split('_')[0]\n",
    "    img = img.resize((width, height), Image.BILINEAR)\n",
    "\n",
    "#     X = np.zeros((1, height, width, 1))\n",
    "#     img = img.convert('L')\n",
    "#     X[0] = np.expand_dims(np.array(img)/255.0, axis=-1)\n",
    "    \n",
    "    X = np.zeros((1, height, width, 3))\n",
    "    img = img.convert('RGB')\n",
    "    X[0] = np.array(img)/255.0\n",
    "    \n",
    "    out_pre = decode([X, np.ones(X.shape[0])])\n",
    "    out = ''.join([characters[x] for x in out_pre[0][0]])\n",
    "    if label.lower() == out.lower():\n",
    "        pos += 1\n",
    "    else:\n",
    "        neg += 1\n",
    "        print(label, out, label==out)\n",
    "        err_imgs.append(img)\n",
    "        err_labels.append('label:'+label+'  pred:'+out)\n",
    "    n += 1\n",
    "#     if n > 100:\n",
    "#         break\n",
    "print('正确数：%d, 总数：%d, 准确率：%.4f'%(pos, pos+neg, pos/(pos+neg))) # 正确数：988, 总数：1000, 准确率：0.9880"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/data/python/lishimin/linuxPro/captcha_pro\n"
     ]
    },
    {
     "data": {
      "image/png": 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7cQ/yvRZPTSmt7F/qzrIbkLsI3JX8g+Xve9wkOYztynSY9JBWO+0sey755mvIvTF1s3eZXsnMj0tp3jMQlyZfq8eSv+1cUC55v7fCNjaiS95p5FEP31CentLRu8TSMn1SRDy538YjYvN+y6tKKW1XMfXgRWXedn3qtITcfzpUPBseEU8ETib3a/0Z4J8rlut7v00JnFoDH13QHnBFxLodvdV086YyvZ21b278Qpk+MCKe1qXsy8g31N3CTOpA3V4VefTETi8k/5C8g5mbKgextEwPioiH91uxvQ2WHlzOKE/f3OP4ztb7TVcppaWztNHW1ZPftc1f2r6NiNiGHITfizzi6pNSSn+hgohYj3ws9yT/eN47pdSvD/n2srO10xcwc5XgnI5ls5XdgZkeX9YoW3pdOq08fUNErBF7lLP7ryxPv9CjlxtpXjIQlybfd8v0HRHx9FY3geWy9qnkniX63ZAGOZh/T0S8rgSFRMR9yIOlPAhYBXygvUBK6dvkf/gBfDUi3hQRd95wF3mwmmdExDcYYDCTqDiyZg2eS75SsJrcY8ls9dqN3PPE+sBJwIsHCAheEBFfjYintQeEEbF+Ce7PYuaHVOdNqNsAP4mIF0fE1m1l14mIR0TE58n9aQMck1Jq7zKOlNJPyTfzQr7Jbp9Sft2I+CdmBrU5KqV0TcXXM6jFwLcj4qFl33eJiAOZyYv/9JBpE58mj9i5GDgjIl4WEZu2FkbElhHxgog4i7V/aB5BPiv+BPJxuWcpsyQi3ge8nJkfuWuJ6iNrDqSkJf0/corLhcATO9/TPmXXBf4LeDI55eUpKaVBrjScHRFvjYgHR1t3oxGxbUQcDhxfZp0PfLOj7Ecj4qMRsWu5ytIqu1lEHERu4xuUenW7n+Jw8lnxR5Lfj7u19k3+ntmW/MPig13KSvNXmoDOzH348LHGACIHdczfgjUH37iNHEAk8hnSg8jDUa8xsEgpe0SZ/xnyP7tW+evbtnc7+QaxbnXaiJkBRloDflxPvkmwfSCPEzrKHcQIA/r0OUY9B2Dpsu73y7qnVdz2GW3b/xM577bX47k9Xm/rcSO5V5vb2+atAl7dZb/bdZS9pex/VecxBhb1qPum5DSO1ro3d5Q/tVfZEdtsa/v/WPaZyMHUrW3LzqPLADG92nuX9e5BTm1obW81OR3jpo7jc3iXsm/qaLvXtb0nH+pXB2YZ0KdPfY+gz4A+5JFEW3VaMUs7O7qj7OM72km/sj/usu/lbeVvK2208zj+CNiyS9mlHe/Bdaz5PZLIN1k+ts+xOZAcjLfejxvayt4E7FV3G/XhY9Ifdl8oTbiU0nUR8WjymdR9gXuS/wmfARyZUvp+RBwx22bIN32+lhw03o/8T/T75JtAf9Rj3zcDz4yIp5J7QngUuWu4O8g/Di4AvgWcMsJLrF25TN7qB73qTZrtVwhnu/m1sxu2b5IvrT+BfOb7nuR0kBvJXcqdQR4Y6bes7Ury2fsnkM8WbkXurm8V8BtyIHtCSun7vSqTUroxInYFXk/u0/l+5GD4p+QA/lMppXFe7v8BuW28g/xDazHwa3Ku/b+l3I3jUFJK10TuY/u55P7cdyb/OL2NPOrrj8jdUn6jS9l/j9wl35vJNzUuIv9g+XhK6cS6z3ZX1N7ONi2PXpb0KbuY/veGdOvC8CDy2fTHk6/EtD7Ly8mf5ZPJw9t367XlA+Tc7b3I7eue5BSuPwK/JH8Gjk+9B/8ipfSZiPg/8g+kx5Pfx9+Tr/q9P6V0aZ/XI81LMd7vZknSfBV5MCmA7VM93VdK0oJijrgkSZLUAANxSZIkqQEG4pIkSVIDDMQlSZKkBnizpiRJktQAz4hLkiRJDTAQlyRJkhrQaCAeEVtHxPERcWVE3BoRyyPiI+1DREuSJEnzUWM54hFxX/JobPcAvk4eIe2RwJ7kEdl2Syld20jlJEmSpDFr8oz4f5CD8NemlJ6RUnprSmkv4CjgAcC/Nlg3SZIkaawaOSNezoZfCiwH7ptSuqNt2SbAVUAA90gp3TzE9i8DNi3blyRJksZlO+DGlNL2gxZcVH9dKtmzTE9vD8IBUkp/iYjvA3sDjwa+N8T2NwW2KA9JkiRp4jQViD+gTC/usfwSciB+f/oE4hFxfo9Fi4evmiRJkjSQ5cMUaipHfEmZruixvDV/s/FXRZIkSZp7TZ0Rr0VKaedu88uZ8p3muDqSpA477P6WkbdxyVkfrKEmkjR5mjoj3jrjvaTH8tb8G8ZfFUmSJGnuNRWI/7pM799j+Q5l2iuHXJIkSZpqTQXiZ5bp3hGxRh1K94W7ASuBH851xSRJkqS50EggnlL6DXA6ud/FV3csfhewEXDiMH2IS5IkSdOgyZs1/5k8xP1HI+IJwK+AR5H7GL8YeHuDdZMkSZLGqrEh7stZ8V2ApeQA/I3AfYGjgUenlK5tqm6SJEnSuDUyxP242X2hJE2eql0Z2l2hqvjgiZfc+fdbDtihz5rSnLigV7fa/TR2RlySJElayAzEJUmSpAaYmiJJkiZeeyrKbExVUQNMTZEkSZKmhYG4JEmS1AADcUmSJKkB5ohLkqSpY/eFmjDmiEuSJEnTwkBckiRJaoCpKZK0ABz+yZ+PvI13veLhNdREkuYlU1MkSZKkaWEgLkmSJDVgUdMVkCRJmgvvPf7CSusd9uIHj7kmUuYZcUmSJKkBBuKSJElSAwzEJUmSpAbYfaEkLTBVuzK0u0LNB1XzwvsxZ1wV2H2hJEmSNC0MxCVJkqQG2H2hJC0wppxI0mTwjLgkSZLUAANxSZIkqQEG4pIkSVID7L5QkiRJGo3dF0qSJEnTwkBckiRJaoCBuCRJktQAA3FJkiSpAQbikiRJUgMMxCVJkqQGGIhLkiRJDTAQlyRJkhpgIC5JkiQ1wEBckiRJaoCBuCRJktQAA3FJkiSpAQbikiRJUgMMxCVJkqQGGIhLkiRJDVjUdAUkSZJms9EOBzRdhbXcfMmJTVdBU84z4pIkSVIDDMQlSZKkBhiIS5IkSQ0wR1yS1LjDP/nzSuu96xUPH3NN5of2fOppy2Oe5rpLg/KMuCRJktQAA3FJkiSpAZFSaroOtYuI84Gdmq6HJKm3quko/cz3VJXOLvvaUzWG7c5v0tI9JrFbwmFN2rHVnLogpbTzoIU8Iy5JkiQ1wEBckiRJaoC9pmiiefe8pGlSR5rFuL/rqtax7nqMMwWljrrWUb9e2/D/l3rxjLgkSZLUAANxSZIkqQEjB+IRcdeIeGlEfDUiLo2IWyJiRUScGxEviYiu+4iIXSPitIi4rpT5RUQcEhHrjlonSZIkadLVkSO+P/AJ4CrgTOBy4J7As4DjgKdExP6prZ/EiHg68GVgFfBF4DpgX+AoYLeyTc1jw+Ti9evGS9Jk6nefx5FHHnnn34ceemil7U1Kd4XDfIf163qwareEw+R3j6MLxFENU7+5/M7v3Fevevh/SaOqIxC/GNgP+GZK6Y7WzIh4G/Aj4NnkoPzLZf6mwKeA1cAeKaWflPnvAM4AnhMRz0spnVRD3SRJkqSJNHJqSkrpjJTSqe1BeJl/NXBsebpH26LnAHcHTmoF4WX9VcBh5emrRq2XJEmSNMnG3X3hX8v09rZ5e5Xpt7usfzawEtg1ItZPKd06zsppvKa568FRLzt3bsNLmZokw6YCjDONoT3lZC7TJXbY/S2V1rvyyivHXJMZVb8T5tOIlL00mY4izYWxBeIRsQj4p/K0Peh+QJle3FkmpXR7RFwGPAS4D/CrWfZxfo9FDxystpIkSdLcGmf3hR8AHgqcllL6Ttv8JWW6oke51vzNxlQvSZIkqXHR1plJfRuNeC1wNHARsFtK6bq2ZRcDOwA7pJQu7VL2+8CuwK4ppfOG3P/5wE7DlNVo5jKlo982hmHvAZrP6m7fTfXKUTVdpt9npGo6Sj+XnPXBrnWYlM9mHd/Fc7Xffpo6ntMwQqomzgUppZ0HLVT7GfGIeA05CL8Q2LM9CC9aZ7yX0F1r/g11102SJEmaFLUG4hFxCHAM8EtyEH51l9V+Xab371J+EbA9+ebO39ZZN0mSJGmS1BaIR8RbyAPy/IwchF/TY9UzyvTJXZY9HtgQ+IE9pkiSJGk+q6XXlDIYz7uB84G9u6SjtDsF+CDwvIg4pm1An8XAe8s6n6ijXpp7deeGTkL3XHXksPczzCh6c5l3O4ldL05CPv+kaKq7war6HbOq+d2T8Dr6mcR2MU0m8fgN024HWW8SX7OaMXIgHhEHkoPw1cA5wGsjonO15SmlpQAppRsj4mXkgHxZRJxEHuJ+P3LXhqeQh72XJEmS5q06zohvX6brAof0WOcsYGnrSUrpaxGxO/B24NnAYuBS4A3AR9M4unKRJEmSJshYui9smt0Xzp2ql94GSWmYj91uTXr6QB3qfg/qSJeZhO2NQ1NtdZya6vaualeG7d0VTpthu5cc5wif05yasdBeryqbjO4LJUmSJM3OQFySJElqgIG4JEmS1ABzxDWScQ8DPExu9TjzGvvta9hcyzrymJsyat7jpLyOcZqGfPFJz19dCK+xbnOZx7wQcqZHbYPT9no1FHPEJUmSpGlhIC5JkiQ1oJaRNaVBDXOZrqnuzap2vTjspcd+5ereVy/Ddtk37PbrNOmpH+MeXW8SRz6t2zDdS85XdXeFWncbWWjvhzQqz4hLkiRJDTAQlyRJkhpgryka2DCjaXo3fjPGfRl/1BSjYS+tN/UeT3pawHy1kI/tsJ/TcaYs1VGnaTYpI+xq4thriiRJkjQtDMQlSZKkBhiIS5IkSQ2w+0J1NQ3dTtlN1viMM7exaneNk9gV3zB1ryOXfCGYr6NnVh0d1+8zaWHyjLgkSZLUAANxSZIkqQF2X6g7NTVyZVULvcusqhZyV29NGvXzsxDfg17HbJAu4Sahu7hxpiINeyyG3f4wqqbf1L3fueTnWxXYfaEkSZI0LQzEJUmSpAbYa4oqqXvETI3GXhUmz6i9XkxiLzH9jDMdY5AeReZrKsRcmcuRkiWtzTPikiRJUgMMxCVJkqQGGIhLkiRJDbD7Qt1p0ke2W+jdF9aRy1m1uzjVp2pO8yS+B/PpXoS6j/uoXQXWcU/AMN8JTZrENj6MSe9OU42x+0JJkiRpWhiIS5IkSQ2w+0KNZFIuvU3iZdg6VH0tVdeblPdrPvKS9OyqXsavo4vCYb8Hqr6Po77HdbSRqtsY9rhrRq9j4zHTqDwjLkmSJDXAQFySJElqgL2mLHBVe9GYxMvuo/ZaMImGvSTfy6S/3mk27l6GxtmzR9X9zqVhR8icq/rO18/SXPa8Mk3HcKG9XtXCXlMkSZKkaWEgLkmSJDXAQFySJElqgDniutMgeZmTYL7kTNfxOiYxh3+hqbs9zsdu0YZ9vXPZfWFV8/FzVvcxm7ZjNMyImXWMkKp5wxxxSZIkaVoYiEuSJEkNcGRN3anf5bZRL7uP4/Jdr+031b1ZP+NOHfFy6Nyro11NQtscRNV21ut1vff4C9d4ftiLHzzSfqrudxBV01tG3de4v6cmMX3CFDppbZ4RlyRJkhpgIC5JkiQ1YN72mrLO+lvstMG2TwW8BFbVML0RjOPy6lzta9ztYi7TDmzjg5umtJC639+5TFXoTEfppVeayrAmYYTQTlXrNJe9v9RRj2keAXjS/49oqthriiRJkjQtDMQlSZKkBhiIS5IkSQ1YEDninXrldA2Tv7dQVB1VbBjDjmA2V+oYxXLY/MphcjTni3G0rXHuq6qF/J5Og17vSd0jLQ77/2aa7m2oWx2fCUdcbd7+r/tCpfVOPvr5Y65J7cwRlyRJkqaFgbgkSZLUgAWZmlKVKSyjmcRuDqteQn7Anv9SqQ5XXHHFwPUb1kJoT3V0fTbOEQ+HSSNaCO/bNBs2HayXSXy/636Nk2KY1+HnuxlV01H6mYJUFVNTJEmSpGlhIC5JkiQ1wEBckiRJasCipiswLnfcet3AuVv9cszGnUc3V11mzaV+dZiE+nWqmhfebuutt77z73754u0GyWmehONUd67/XL6mSey+UM2r+7t+Ej6nUH/uct2vaxI+Z8O+972WjTs3ver2+qkaX1Qt31R7n6+5+WM5Ix4RL4yIVB4v7bHO0yJiWUSsiIibIuJ/IuLAcdRHkiRJmjS1B+IRsQ3wMeCmPuu8BjgVeCjwOeBTwL2ApRFxZN11kiRJkiZNrd0XRkQA3wW2B74CHAq8LKV0XNs62wEXATcDO6eUlpf5mwM/Bu4L7JpSOm+EepwP7DRs+dkMO0piU+bTJZwqhj3u7Wkmw6iamtJPv/eq6mW5uewCcJhRIielK8dxfj4X2mduGtT9fo/zPR6mO9bOZcOYhJSDSTVqd8bTYNQUmapl9tlnn0rrnXbaaQPXoWET0X3ha4G9gBeRA+1uXgysD3ysFYQDpJSuB95Xnr6y5npJkiRJE6W2QDwiHgR8ADg6pXR2n1X3KtNvd1n2rY51JEmSpHmpltSUiFgE/BDYBHhESumWiDgCOJy1U1P+BNwNuFtK6dou27oJ2AjYKKW0csj6jDU1ZRxGTScY9vLYQhgtbJhjUzVNpY50lHbjGIG01/abuqRaR/pNHaYpVUHVjHs0316GadPjHu1ymkeJrOOYNfVdV/eIz1X2M5u5ii/mMmVwUtpqh6FSU+rqvvCdwI7AY1NKt8yy7pIyXdFj+QpyIL4E6BuIl4C7mwfOUgdJkiSpUSOnpkTEo4C3AR8a5QZLSZIkaSEZ6Yx4SUn5LHAx8I6KxVaQU1OWAGulpjD7GfM79boEMI2pKZIkSVpYRsoRj4jNgOsrrn50SumQiDgX2I0uXRRGxFbAlcAVKaVtRqjXvA3EhxmBs6px5pxXLdNZro7RQ+dT91KjmoS8yaYMmyc7TaPZzqU6ujebxLzoUXN3q9ZpHDnso77GSfea951Red2PvW18fT4MMwpzp1+f+f6By4x7FOZxdlm4QL47G8kRvxX4dI9lO5Hzxs8Ffg20gu4zyIH4k9vmtTylbR1JkiRp3hopEC83ZvYawv4IciD+mfZeU4ATgDcDr4mIEzoG9HlbWefYUeolSZIkTbpaR9ZcY8M9ui8syw4GPkrOEf8icBvwHGBr8k2fh46473mbmjKqcY9qWPdl3UnQ1KXrOgybvjNfLhUu9NEzX/nu0yutd+w79x5pP+Nut011SzhMnZpKselWx/lgkHSUXupOU5mE1JRxvNdVU1+rlJmt3DzVaPeFA0kpHRMRy4FDgX8i995yIXBYSukzTdRJkiRJmktjC8RTSkcAR/RZfipw6rj2L0mSJE2ysaWmNMnUlPpVvSQ2zKWzaU5bqWoBXqKbOONOy6rbML1eVE1F6WfUNBUY/VgPm5oyCZ+zhZAuMpcmMTWlXdU0lWFSUTR1hkpNGXlAH0mSJEmDMxCXJEmSGmAgLkmSJDXAHHFNrWFGGW0qb9080ebN5ciaczlyY7sDDhh9G3XkiFc1afndmmyTMrKm1IM54pIkSdK0MBCXJEmSGtDIgD5SHapeyh7mkvcwZey2TE078cTebW4SR2T0M6JBmG6i+cgz4pIkSVIDDMQlSZKkBthriqR5a5pHbR1nDy2SpNrZa4okSZI0LQzEJUmSpAYYiEuSJEkNMEdc0oLU1IiZVbcnSZoq5ohLkiRJ08JAXJIkSWqAqSmSJGkth3/y5yOVf9crHl5TTaSpYGqKJEmSNC0MxCVJkqQGGIhLkiRJDTAQlyRJkhpgIC5JkiQ1wEBckiRJaoDdF0qSpL6qdmVol4VawOy+UJIkSZoWBuKSJElSA0xNkSRJkkZjaookSZI0LQzEJUmSpAYYiEuSJEkNMBCXJEmSGmAgLkmSJDXAQFySJElqwKKmKyBJkiRV9TePeX2l9X533lFjrsnoPCMuSZIkNcBAXJIkSWqAqSkL3EvfeVql9Y579z5jrokkSVJ3VdNR+pWZxFQVz4hLkiRJDTAQlyRJkhpgIC5JkiQ1YEHkiO//ui+MvI2Tj35+DTVpXtWc8H7lzBeXJEkanWfEJUmSpAYYiEuSJEkNWBCpKZIkSZpe7V0POrKmJEmSpJEYiEuSJEkNiJRS03WoXUScD+zUa3nVXlTmS08p/bQfi4XweiVJksbggpTSzoMW8oy4JEmS1AADcUmSJKkBBuKSJElSAxZkjvhCZn68JElS7ZrPEY+IJ0TEVyPi6oi4NSKujIjvRMRaY6JHxK4RcVpEXBcRt0TELyLikIhYt846SZIkSZOotgF9IuLfgDcBVwDfAP4M3B3YGdgDOK1t3acDXwZWAV8ErgP2BY4CdgP2r6tekiRJ0iSqJTUlIl4G/CfwGeDlKaXbOpbfJaX01/L3psClwBJgt5TST8r8xcAZwGOA56eUThqhPqamSNIQnn3w5yut9+VjXjDmmkjSVGkmNSUi1gf+FbicLkE4QCsIL55DPlN+UisIL+usAg4rT181ar0kSZKkSVZHasoTyYH1R4A7IuKpwEPJaSc/Simd17H+XmX67S7bOhtYCewaEeunlG6toX6SJEnSxKkjEP+7Ml0F/JQchN8pIs4GnpNS+lOZ9YAyvbhzQyml2yPiMuAhwH2AX9VQP0lSRe0pJ51pKqajSFK96gjE71GmbwIuBB4H/AzYHjgS2Bs4mXzDJuTccIAVPbbXmr/ZbDsuueDdPHC2spIkSVKT6ui+sLWN24H9UkrnppRuSin9L/BMci8qu0fEY2rYlyRJkjQv1HFG/IYy/WlKaXn7gpTSyoj4DvAS4JHAecyc8V5Cd635N/RY3r79rnen2muKJEmSJl0dgfivy/SGHsuvL9MN2tbfBbg/sEZqSUQsIqe03A78toa6SZKGZE64JI1XHakp3wMS8OCI6La91s2bl5XpGWX65C7rPh7YEPiBPaZIkiRpPhs5EE8p/Q44FdgWeF37sojYG3gS+Wx5q7vCU8ijbj4vInZpW3cx8N7y9BOj1kuSJEmaZHUNcf9qYEfgw6Uf8Z+SU0yeAawGXppSWgGQUrqxjMR5CrAsIk4iD3G/H7lrw1PIw95LkiRJ81YtQ9wDRMTdgXeSA+qtgBuBc4D3p5R+1GX93YC3k4e0X0we9v544KMppdUj1sWbNSVJkjRXhhrivrZAfJIYiEuSJGkODRWI13GzpiRJkqQBGYhLkiRJDTAQlyRJkhpgIC5JkiQ1oK7uCyVJkqS1vPSdp428jePevU8NNZk8nhGXJEmSGmAgLkmSJDXAQFySJElqgIG4JEmS1AADcUmSJKkBBuKSJElSAyKl1HQdahcR5wM7NV0PSZKkJj374M9XWu/Lx7xgzDXJqnZlOIXdFV6QUtp50EKeEZckSZIaYCAuSZIkNcDUFEmSpAWgPU1lrlJRFhBTUyRJkqRpYSAuSZIkNcDUFEmSJGk0pqZIkiRJ08JAXJIkSWqAgbgkSZLUAANxSZIkqQEG4pIkSVID5msgvl3TFZAkSdKCsd0whRbVXIlJcWOZLi7Ti5qqyDzywDL1WNbD41kvj2d9PJb18njWy+NZL49nPbZjJvYcyLzsR7yl9CfOMP06ak0ey3p5POvl8ayPx7JeHs96eTzr5fFs3nxNTZEkSZImmoG4JEmS1AADcUmSJKkBBuKSJElSAwzEJUmSpAbM615TJEmSpEnlGXFJkiSpAQbikiRJUgMMxCVJkqQGGIhLkiRJDTAQlyRJkhpgIC5JkiQ1wEBckiRJasC8DMQjYuuIOD4iroyIWyNieUR8JCI2b7pukyYi7hoRL42Ir0bEpRFxS0SsiIhzI+IlEbFOx/rbRUTq8zipqdcyKUp763V8ru5RZteIOC0irivvwS8i4pCIWHeu6z9JIuKgWdpbiojVbesv+PYZEc+JiGMi4pyIuLG87s/NUmbg9hcRT4uIZeX74qaI+J+IOLD+V9SsQY5nROwQEW+JiDMi4vcRcVtE/DEivh4Re/YoM1sbf+V4X+HcGvB4Dv15jogDI+JHpW2uKG31aeN7Zc0Y8HgurfB9+r2OMguqfTZhUdMVqFtE3Bf4AXAP4OvARcAjgdcBT46I3VJK1zZYxUmzP/AJ4CrgTOBy4J7As4DjgKdExP5p7ZGffg58rcv2fjm+qk6VFcBHusy/qXNGRDwd+DKwCvgicB2wL3AUsBv5PVqofga8q8eyxwF7Ad/qsmwht8/DgIeT29oVwAP7rTxM+4uI1wDHANcCnwNuA54DLI2Iv00pHVrXi5kAgxzP9wDPBS4ETiMfywcA+wH7RcTrUkof7VH26+T23uknw1V7Yg3UPouBPs8RcSTwxrL9TwHrAc8DTo2Ig1NKHxu82hNrkOP5NWB5j2UHAPeh+/cpLJz2OfdSSvPqAXwHSMDBHfM/XOYf23QdJ+lBDmT2BdbpmL8lOShPwLPb5m9X5i1tuu6T+iB/0S2vuO6mwDXArcAubfMXk39QJuB5Tb+mSXwA55Xjs1/bvAXfPoE9gR2AAPYox+NzPdYduP2VY7yKHIRv1zZ/c+DSUuYxTR+Hho7nQcCOXebvTv6xciuwVZcyCTio6dc6gcdz4M8zsGspcymwece2ri1td7umj0MTx7PPNjYDVpb2ebeOZQuqfTbxmFepKeVs+N7kQOjjHYsPB24GDoiIjea4ahMrpXRGSunUlNIdHfOvBo4tT/eY84otHM8B7g6clFK688xCSmkV+UwHwKuaqNgki4i/BR4N/AH4ZsPVmSgppTNTSpek8l90FsO0vxcD6wMfSyktbytzPfC+8nTeXK4e5HimlJamlH7aZf5ZwDLymdld66/l9BiwfQ6j1fb+tbTJ1n6Xk+OC9YEXjWnfc66m43kAsAHwlZTSn2uqmiqab6kprRy807sEln+JiO+TA/VHA9/rLKy1/LVMb++y7F4R8QrgruSzDOellH4xZzWbfOtHxAuBbck/AH8BnJ1SWt2x3l5l+u0u2zibfJZi14hYP6V069hqO31eXqaf7nJMwfZZ1TDtr1+Zb3Wsoxn9vk8BHhERh5CvRvwBODOldMVcVGwKDPJ5nq19vqOsc3jttZxeLyvT/+yzju1zTOZbIP6AMr24x/JLyIH4/TEQ7ysiFgH/VJ52+0J7Ynm0l1kGHJhSuny8tZsKWwIndsy7LCJeVM6OtfRssyml2yPiMuAh5Ny9X42lplMmIjYAXgisJt/H0I3ts5ph2l+/MldFxM3A1hGxYUpp5RjqPHUi4m+AJ5B/2JzdY7XXdTxfHRHHAYeUKxQLWaXPc7nafW/gppTSVV22c0mZ3n9M9Zw6EfEY4G+Bi1NKZ/ZZ1fY5JvMqNQVYUqYreixvzd9s/FWZeh8AHgqcllL6Ttv8leQbknYm54RuTs5/PJOcwvI9U384gfxPd0tgI/KX3CfJOYrfioiHt61rmx3cP5CPx7dTSr/vWGb7HMww7a9qmSU9li8oEbE+8HlySsQR7ekSxWXAweQfOBsB9yK38eXAK4Dj56yyk2fQz7Pfp4NrXV38VI/lts8xm2+BuGoQEa8l33F+ETl37E4ppWtSSu9MKV2QUrqhPM4mX2n4H+B+wEvnvNITJKX0rpJ7/8eU0sqU0i9TSq8k3zC8AXBEszWceq1/HJ/sXGD71CQp3T+eSO595ovAkZ3rpJTOSil9LKV0cfm+uCqldDI51fJ64PkdP94XDD/P4xURS8hB9W3A0m7r2D7Hb74F4rOdiWnNv2H8VZlOpVuyo8ndb+2ZUrquSrmU0u3MpAk8fkzVm3atm1/bj49tdgAR8RDyzW5XkLuHq8T22dMw7a9qmV5nJReEEoR/jtz945eAFw5yQ1252tNq47bZNn0+z36fDuaFwIYMcZOm7bM+8y0Q/3WZ9sr/2qFMe+WQL2jlRoxjyH2z7ll6ThnEn8rUS//ddTs+PdtsydPfnnxz12/HW7WpMdtNmv3YPtc2TPvrV2Yr8vG9YiHnh0fEXYAvkPuu/i/gH0vwOCjbbG9rHZuU0s3kGwk3Lm2xkzHAmlo3aa51dbEi22cN5lsg3rrRYO9Ye0TITciXB1cCP5zrik26iHgLeQCPn5GD8GuG2Myjy9Sgsbtux+eMMn1yl/UfTz5b8QN7TIGIWExOlVoNfHqITdg+1zZM++tX5ikd6yw4EbEecDL5TPhngQOG+NHY8qgytc2urdfn2fZZQUQ8ijwQ0MUppWVDbsb2WYN5FYinlH4DnE6+Ke7VHYvfRf7VdmL51awiIt5BvjnzfOAJ/S5RRcROnT9yyvwnAK8vT/sOpz2fRcSDut0MGBHbAa3R3NqPzynAn4HnRcQubesvBt5bnn5iPLWdOvuTb9b6VpebNAHb5xCGaX8nkAf+eE1p160ymwNvK0+PZQEqN2Z+FXg6+cfiizq70u1SZpcu89aJiH8BHkN+f7r1XDXvDfl5brW9t5c22SqzHTkuuJXchhe61tXFfl0W2j7nQIyvT/1mdBni/lfkX217ki9H7Zoc4v5OEXEg+SaN1eS0lG55nctTSkvL+svIl/d+QM7TBXgYM323viOl9N7ODSwUEXEE+UbXs4HfAX8B7gs8ldz/6mnAM1NKt7WVeQY5IFoFnEQeFns/8l3qpwD/MMbBL6ZGRJwDPJY8kuapPdZZxgJvn6U9PaM83RJ4EvmM1Tll3p9T2xD0w7S/iDgY+Ci5T+cvMjPE/dbAh9I8GuJ+kOMZESeQRyL8M/Af5BEJOy1rPwMZEYmcDvhzclrFEvLV24eSr+A+M6V0eo0vqVEDHs9lDPF5jogPAW8oZU4hD6T0XHI/5PNqiPtBP++lzKbAleQurLee5eTbgmqfjUgTMLxn3Q9gG/Iv3qvI/yB+B3yEtuFufdx5rI4g/7Po91jWtv5LgP8md110E/nswuXkf8aPa/r1NP0gd631BXKPMzeQB/H4E/Bdcr/s0aPcbuQg/XrgFuB/yWd81m36NU3CA3hQaYu/73dMbJ+VPtPLu5QZuP0B+wJnkX9s3gz8mNyvc+PHoKnjSR49c7bv0yM6tv/v5TheSf4xtLJ8f3wMuE/Tr7/h4zn055n8g+jHpW3+pRzjpzX9+ps8nm1lXlWWfaHC9hdU+2ziMe/OiEuSJEnTYF7liEuSJEnTwkBckiRJaoCBuCRJktQAA3FJkiSpAQbikiRJUgMMxCVJkqQGGIhLkiRJDTAQlyRJkhpgIC5JkiQ1wEBckiRJaoCBuCRJktQAA3FJkiSpAQbikiRJUgMMxCVJkqQGGIhLkiRJDTAQlyRJkhpgIC5JkiQ14P8Dv4x0G9BvZXoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 163,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 57\n",
    "plt.imshow(err_imgs[i])\n",
    "plt.title(err_labels[i])\n",
    "import os\n",
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 /data/captcha/chaojiyingpic/1902_420x65/1902-8prl-2283617470172830860.jpg 8prl 8pnl False\n",
      "1 /data/captcha/chaojiyingpic/1902_420x65/1902-GQIM-1283417000172830836.jpg GQIM gqtm False\n",
      "2 /data/captcha/chaojiyingpic/1902_420x65/1902-porv-2274711080172834654.jpg porv p0rv False\n",
      "3 /data/captcha/chaojiyingpic/1902_420x65/1902-AYJN-2274709110172834513.jpg AYJN ayjw False\n",
      "4 /data/captcha/chaojiyingpic/1902_420x65/1902-62vu-1283708400172830356.jpg 62vu bzvu False\n",
      "5 /data/captcha/chaojiyingpic/1902_420x65/1902-wohp-1283415440172830761.jpg wohp wobg False\n",
      "6 /data/captcha/chaojiyingpic/1902_420x65/1902-0c6f-2283411520172830492.jpg 0c6f dc6f False\n",
      "7 /data/captcha/chaojiyingpic/1902_420x65/1902-hu5l-1283516590172830162.jpg hu5l husi False\n",
      "8 /data/captcha/chaojiyingpic/1902_420x65/1902-pjei-2283514270172830953.jpg pjei plei False\n",
      "9 /data/captcha/chaojiyingpic/1902_420x65/1902-uqi4-2283217320172830083.jpg uqi4 uqt4 False\n",
      "10 /data/captcha/chaojiyingpic/1902_420x65/1902-n44w-2283417230172830918.jpg n44w m44w False\n",
      "11 /data/captcha/chaojiyingpic/1902_420x65/1902-lhmm-1274710360172834583.jpg lhmm lhmn False\n",
      "12 /data/captcha/chaojiyingpic/1902_420x65/1902-ju0y-1283414180172830660.jpg ju0y juqy False\n",
      "13 /data/captcha/chaojiyingpic/1902_420x65/1902-inzq-2283409510172830371.jpg inzq ircq False\n",
      "14 /data/captcha/chaojiyingpic/1902_420x65/1902-CC5W-2283416010172830775.jpg CC5W ccsw False\n",
      "15 /data/captcha/chaojiyingpic/1902_420x65/1902-fiai-1283717470172830052.jpg fiai ftai False\n",
      "16 /data/captcha/chaojiyingpic/1902_420x65/1902-ujhu-1283414080172830644.jpg ujhu ujmu False\n",
      "17 /data/captcha/chaojiyingpic/1902_420x65/1902-zfmw-1283611210172830787.jpg zfmw zfnw False\n",
      "18 /data/captcha/chaojiyingpic/1902_420x65/1902-eupv-2283511520172830784.jpg eupv bjpv False\n",
      "19 /data/captcha/chaojiyingpic/1902_420x65/1902-6nkn-1283511420172830759.jpg 6nkn 6rn False\n",
      "20 /data/captcha/chaojiyingpic/1902_420x65/1902-jhh0-2283812150172830679.jpg jhh0 jnq False\n",
      "21 /data/captcha/chaojiyingpic/1902_420x65/1902-uuuh-1283719310172830117.jpg uuuh uuduh False\n",
      "22 /data/captcha/chaojiyingpic/1902_420x65/1902-nh1i-2283519310172830288.jpg nh1i nh1u False\n",
      "23 /data/captcha/chaojiyingpic/1902_420x65/1902-LWCI-1283713580172830756.jpg LWCI lwcd False\n",
      "24 /data/captcha/chaojiyingpic/1902_420x65/1902-oxlr-1283811000172830608.jpg oxlr oxtr False\n",
      "25 /data/captcha/chaojiyingpic/1004_130x40/1004-ejrl-2262315190172831854.jpg ejrl ejrj False\n",
      "26 /data/captcha/chaojiyingpic/4004_130x40/4004-1890-1283422180172830193.jpg 1890 4890 False\n",
      "27 /data/captcha/chaojiyingpic/4004_130x40/4004-1360-2274812460172835624.jpg 1360 1136c False\n",
      "28 /data/captcha/chaojiyingpic/4004_130x40/4004-7008-2274808310172835482.jpg 7008 7c98 False\n",
      "29 /data/captcha/chaojiyingpic/4004_130x40/4004-1247-2283622220172830115.jpg 1247 1217 False\n",
      "30 /data/captcha/chaojiyingpic/4004_130x40/4004-9143-2283222560172830223.jpg 9143 3143 False\n",
      "31 /data/captcha/chaojiyingpic/4004_130x40/4004-2391-1283319560172830797.jpg 2391 23gv False\n",
      "32 /data/captcha/chaojiyingpic/4004_130x40/4004-1604-2274600310172834015.jpg 1604 4604 False\n",
      "33 /data/captcha/chaojiyingpic/4004_130x40/4004-4817-1283620370172830057.jpg 4817 4811 False\n",
      "34 /data/captcha/chaojiyingpic/4004_130x40/4004-1118-1296601370172831222.jpg 1118 1718 False\n",
      "35 /data/captcha/chaojiyingpic/4004_130x40/4004-6495-2283516160172830121.jpg 6495 649 False\n",
      "36 /data/captcha/chaojiyingpic/4004_130x40/4004-4217-2274704460172834216.jpg 4217 2217 False\n",
      "37 /data/captcha/chaojiyingpic/4004_130x40/4004-5856-1283517020172830167.jpg 5856 5866 False\n",
      "38 /data/captcha/chaojiyingpic/4004_130x40/4004-5721-1284018220172831324.jpg 5721 5711 False\n",
      "39 /data/captcha/chaojiyingpic/4004_130x40/4004-3817-2283408410172830309.jpg 3817 3877 False\n",
      "40 /data/captcha/chaojiyingpic/4004_130x40/4004-1814-2296706370172831518.jpg 1814 7814 False\n",
      "41 /data/captcha/chaojiyingpic/4004_130x40/4004-1752-1274601460172834073.jpg 1752 7752 False\n",
      "42 /data/captcha/chaojiyingpic/4004_130x40/4004-4001-1296519220172831302.jpg 4001 44000 False\n",
      "43 /data/captcha/chaojiyingpic/4004_130x40/4004-2212-2284004370172830745.jpg 2212 221e False\n",
      "44 /data/captcha/chaojiyingpic/4004_130x40/4004-4602-2296704370172831397.jpg 4602 4502 False\n",
      "45 /data/captcha/chaojiyingpic/4004_130x40/4004-0126-1284016370172831227.jpg 0126 0726 False\n",
      "46 /data/captcha/chaojiyingpic/4004_130x40/4004-1019-2296608070172832495.jpg 1019 1013 False\n",
      "47 /data/captcha/chaojiyingpic/1004_100x40/1004-7wf6-2262323080172832351.jpg 7wf6 7wy6 False\n",
      "48 /data/captcha/chaojiyingpic/1004_100x40/1004-6YTE-1262301380172832490.jpg 6YTE 6y7e False\n",
      "49 /data/captcha/chaojiyingpic/1004_100x40/1004-lu27-1262405380172832681.jpg lu27 lu2j False\n",
      "50 /data/captcha/chaojiyingpic/4004_108x40/4004-5161-2283211000172830942.jpg 5161 5761 False\n",
      "51 /data/captcha/chaojiyingpic/3004_100x40/3004-hsqe-1296521340172831475.jpg hsqe hs9e False\n",
      "52 /data/captcha/chaojiyingpic/3004_100x40/3004-a993-2274602050172834096.jpg a993 aqq3 False\n",
      "53 /data/captcha/chaojiyingpic/4006_200x100/4006-158361-2262308190172831408.jpg 158361 158461 False\n",
      "54 /data/captcha/chaojiyingpic/4006_200x100/4006-908004-1262321450172832264.jpg 908004 909908 False\n",
      "55 /data/captcha/chaojiyingpic/4006_200x100/4006-145301-1262406050172832705.jpg 145301 145381 False\n",
      "56 /data/captcha/chaojiyingpic/4006_200x100/4006-985098-1262302570172832554.jpg 985098 989098 False\n",
      "57 /data/captcha/chaojiyingpic/4006_200x100/4006-755596-2262410370172832955.jpg 755596 755606 False\n",
      "58 /data/captcha/chaojiyingpic/4006_200x100/4006-220931-2262322470172832333.jpg 220931 720931 False\n",
      "59 /data/captcha/chaojiyingpic/4006_200x100/4006-381182-1262405000172832648.jpg 381182 38112 False\n",
      "60 /data/captcha/chaojiyingpic/4006_200x100/4006-51852-1262407070172832770.jpg 51852 618572 False\n",
      "61 /data/captcha/chaojiyingpic/4006_200x100/4006-202944-2262311240172831616.jpg 202944 232844 False\n",
      "62 /data/captcha/chaojiyingpic/1902_150x40/1902-cyjae-1296500320172831829.jpg cyjae cyj9e False\n",
      "63 /data/captcha/chaojiyingpic/1902_150x40/1902-m9jp-1296601170172831186.jpg m9jp dm9jp False\n",
      "64 /data/captcha/chaojiyingpic/1902_150x40/1902-ejdlg-1296519340172831318.jpg ejdlg eldlg False\n",
      "65 /data/captcha/chaojiyingpic/1902_150x40/1902-0ym4q-2296602180172831261.jpg 0ym4q oym4q False\n",
      "66 /data/captcha/chaojiyingpic/1902_150x40/1902-hopha-2296517550172830167.jpg hopha h0pha False\n",
      "67 /data/captcha/chaojiyingpic/1902_150x40/1902-w4wh0-1296501140172831874.jpg w4wh0 w4who False\n",
      "68 /data/captcha/chaojiyingpic/1902_150x40/1902-delox-1296705400172831472.jpg delox del0x False\n",
      "69 /data/captcha/chaojiyingpic/1902_150x40/1902-mx79d-1296518410172831254.jpg mx79d mx790 False\n",
      "70 /data/captcha/chaojiyingpic/1902_150x40/1902-fcmfi-2296622430172830714.jpg fcmfi fcmfl False\n",
      "71 /data/captcha/chaojiyingpic/1902_150x40/1902-m85x-2296612110172832799.jpg m85x mb5ox False\n",
      "72 /data/captcha/chaojiyingpic/1902_150x40/1902-vs3t-2296709170172831712.jpg vs3t qvs3t False\n",
      "73 /data/captcha/chaojiyingpic/1902_150x40/1902-kyxc-2296522100172831541.jpg kyxc skyxc False\n",
      "74 /data/captcha/chaojiyingpic/1902_150x40/1902-d5yx-1296601160172831178.jpg d5yx 0d5yx False\n",
      "75 /data/captcha/chaojiyingpic/4004_110x35/4004-7820-2296518190172830899.jpg 7820 1820 False\n",
      "76 /data/captcha/chaojiyingpic/4004_110x35/4004-1724-1296518040172830417.jpg 1724 7724 False\n",
      "77 /data/captcha/chaojiyingpic/4004_110x35/4004-3786-2296518000172830323.jpg 3786 3186 False\n",
      "78 /data/captcha/chaojiyingpic/4004_110x35/4004-3541-2296518280172831214.jpg 3541 3547 False\n",
      "79 /data/captcha/chaojiyingpic/4004_110x35/4004-7460-1296517560172830206.jpg 7460 1450 False\n",
      "80 /data/captcha/chaojiyingpic/4004_110x35/4004-3196-2296518210172830983.jpg 3196 3796 False\n",
      "81 /data/captcha/chaojiyingpic/4004_110x35/4004-6111-2296517590172830302.jpg 6111 6711 False\n",
      "82 /data/captcha/chaojiyingpic/4004_110x35/4004-1160-2296518230172831051.jpg 1160 7160 False\n",
      "83 /data/captcha/chaojiyingpic/1004_70x35/1004-mwdlx-2270011500172836568.jpg mwdlx nwdx False\n",
      "84 /data/captcha/chaojiyingpic/1004_70x35/1004-v5nd-2270011540172830287.jpg v5nd v5no False\n",
      "85 /data/captcha/chaojiyingpic/1004_70x35/1004-n9x5-2270011580172830645.jpg n9x5 n9t5 False\n",
      "86 /data/captcha/chaojiyingpic/1004_70x35/1004-9wf7-1270012110172831522.jpg 9wf7 9wfz False\n",
      "87 /data/captcha/chaojiyingpic/1004_70x35/1004-p96a-2270011570172830596.jpg p96a pg6a False\n",
      "88 /data/captcha/chaojiyingpic/1004_70x35/1004-jf2c-2270012000172830833.jpg jf2c lf2c False\n",
      "89 /data/captcha/chaojiyingpic/1004_70x35/1004-prja-2270012000172830822.jpg prja phja False\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90 /data/captcha/chaojiyingpic/1004_70x35/1004-hgfr-1270011560172830531.jpg hgfr h6fr False\n",
      "91 /data/captcha/chaojiyingpic/1004_70x35/1004-3mm-1270012120172831607.jpg 3mm 3mn False\n",
      "92 /data/captcha/chaojiyingpic/1004_70x35/1004-ggzn-2270011580172830653.jpg ggzn ggza False\n",
      "93 /data/captcha/chaojiyingpic/1004_70x35/1004-ccnn-1270011510172830013.jpg ccnn dcnn False\n",
      "94 /data/captcha/chaojiyingpic/1004_70x35/1004-sbao-1270012050172831203.jpg sbao sbaq False\n",
      "95 /data/captcha/chaojiyingpic/1004_70x35/1004-cb7k-2270011510172830019.jpg cb7k c37k False\n",
      "96 /data/captcha/chaojiyingpic/1004_70x35/1004-9hou-2270012080172831320.jpg 9hou 9h8u False\n",
      "97 /data/captcha/chaojiyingpic/1004_70x35/1004-g34y-1270011510172830051.jpg g34y gj4y False\n",
      "98 /data/captcha/chaojiyingpic/1004_70x35/1004-3ct9-2270012090172831434.jpg 3ct9 3ctf9 False\n",
      "99 /data/captcha/chaojiyingpic/1004_70x35/1004-9jag-2270012110172831558.jpg 9jag 9uag False\n",
      "100 /data/captcha/chaojiyingpic/1004_70x35/1004-N5UR-2270011570172830578.jpg N5UR n5un False\n",
      "101 /data/captcha/chaojiyingpic/1004_70x35/1004-wxzp-2270012020172830979.jpg wxzp wkzp False\n",
      "102 /data/captcha/chaojiyingpic/1004_70x35/1004-do5b-1270011500172836598.jpg do5b d65b False\n",
      "103 /data/captcha/chaojiyingpic/1004_80x30/1004-dkms-1296604210172832216.jpg dkms 8km3 False\n",
      "104 /data/captcha/chaojiyingpic/1004_80x30/1004-yeu5-2296503120172832006.jpg yeu5 heu5 False\n",
      "105 /data/captcha/chaojiyingpic/1004_80x30/1004-6dtk-1296503590172832152.jpg 6dtk eutk False\n",
      "106 /data/captcha/chaojiyingpic/1004_80x30/1004-6R89-2296522300172831596.jpg 6R89 6r99 False\n",
      "107 /data/captcha/chaojiyingpic/1004_80x30/1004-76ta-2296709170172831709.jpg 76ta zgta False\n",
      "108 /data/captcha/chaojiyingpic/1004_80x30/1004-pwcd-1296522310172831600.jpg pwcd nwcd False\n",
      "109 /data/captcha/chaojiyingpic/1004_80x30/1004-OV5W-2296600190172831063.jpg OV5W cv5w False\n",
      "110 /data/captcha/chaojiyingpic/1004_80x30/1004-nrfr-2296522580172831678.jpg nrfr nnfr False\n",
      "111 /data/captcha/chaojiyingpic/1004_80x30/1004-6R89-1296618150172830417.jpg 6R89 6r99 False\n",
      "112 /data/captcha/chaojiyingpic/1004_80x30/1004-6vl6-1296617490172830315.jpg 6vl6 bvl6 False\n",
      "113 /data/captcha/chaojiyingpic/1902_100x20/1902-hrbqei-2274709260172834525.jpg hrbqei hirbqei False\n",
      "114 /data/captcha/chaojiyingpic/1004_150x40/1004-6e2q-2296621190172830663.jpg 6e2q l6e2q False\n",
      "115 /data/captcha/chaojiyingpic/1004_150x40/1004-zi9gg-2296706540172831525.jpg zi9gg z59gg False\n",
      "116 /data/captcha/chaojiyingpic/1004_150x40/1004-aggio-1296602370172831298.jpg aggio aggi0 False\n",
      "117 /data/captcha/chaojiyingpic/1005_120x50/1005-ADKOP-1296620520172830638.jpg ADKOP adeop False\n",
      "118 /data/captcha/chaojiyingpic/1005_120x50/1005-uayyd-1296600380172831127.jpg uayyd uayye False\n",
      "119 /data/captcha/chaojiyingpic/1005_120x50/1005-dtgee-1296501090172831868.jpg dtgee dtge9 False\n",
      "120 /data/captcha/chaojiyingpic/1005_120x50/1005-jpbtc-1296706220172831512.jpg jpbtc jp3tc False\n",
      "121 /data/captcha/chaojiyingpic/1005_120x50/1005-snmru-1296623570172830979.jpg snmru qnmru False\n",
      "122 /data/captcha/chaojiyingpic/1005_120x50/1005-cyvvp-2296612100172832795.jpg cyvvp ejunp False\n",
      "123 /data/captcha/chaojiyingpic/1005_120x50/1005-adypq-2296608550172832566.jpg adypq aypq False\n",
      "124 /data/captcha/chaojiyingpic/1005_120x50/1005-gengr-2296614270172832997.jpg gengr aenat False\n",
      "125 /data/captcha/chaojiyingpic/1005_120x50/1005-jrwgs-2296707380172831571.jpg jrwgs jreqs False\n",
      "126 /data/captcha/chaojiyingpic/1005_120x50/1005-xqipv-2296708430172831649.jpg xqipv x4py False\n",
      "正确数：1101, 总数：1228, 准确率：0.8966\n",
      "类别：/data/captcha/chaojiyingpic/1902_420x65/1902, 总数：112, acc：0.7768\n",
      "类别：/data/captcha/chaojiyingpic/1004_130x40/1004, 总数：32, acc：0.9688\n",
      "类别：/data/captcha/chaojiyingpic/4004_130x40/4004, 总数：122, acc：0.8279\n",
      "类别：/data/captcha/chaojiyingpic/1004_100x40/1004, 总数：81, acc：0.9630\n",
      "类别：/data/captcha/chaojiyingpic/1902_140x40/1902, 总数：5, acc：1.0000\n",
      "类别：/data/captcha/chaojiyingpic/4004_73x27/4004, 总数：15, acc：1.0000\n",
      "类别：/data/captcha/chaojiyingpic/4004_108x40/4004, 总数：82, acc：0.9878\n",
      "类别：/data/captcha/chaojiyingpic/3004_100x40/3004, 总数：15, acc：0.8667\n",
      "类别：/data/captcha/chaojiyingpic/4006_200x100/4006, 总数：24, acc：0.6250\n",
      "类别：/data/captcha/chaojiyingpic/1902_150x40/1902, 总数：145, acc：0.9103\n",
      "类别：/data/captcha/chaojiyingpic/4004_110x35/4004, 总数：170, acc：0.9529\n",
      "类别：/data/captcha/chaojiyingpic/1004_70x35/1004, 总数：140, acc：0.8571\n",
      "类别：/data/captcha/chaojiyingpic/1004_80x30/1004, 总数：102, acc：0.9020\n",
      "类别：/data/captcha/chaojiyingpic/1902_100x20/1902, 总数：14, acc：0.9286\n",
      "类别：/data/captcha/chaojiyingpic/3004_120x40/3004, 总数：130, acc：1.0000\n",
      "类别：/data/captcha/chaojiyingpic/3004_200x62/3004, 总数：19, acc：1.0000\n",
      "类别：/data/captcha/chaojiyingpic/1004_150x40/1004, 总数：9, acc：0.6667\n",
      "类别：/data/captcha/chaojiyingpic/1005_120x50/1005, 总数：11, acc：0.0909\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\n正确数：547, 总数：616, 准确率：0.8880\\n类别：/data/captcha/chaojiyingpic/1902_420x65/1902, 总数：112, acc：0.7589\\n类别：/data/captcha/chaojiyingpic/1004_130x40/1004, 总数：32, acc：0.9375\\n类别：/data/captcha/chaojiyingpic/4004_130x40/4004, 总数：106, acc：0.9057\\n类别：/data/captcha/chaojiyingpic/1004_100x40/1004, 总数：80, acc：0.9750\\n类别：/data/captcha/chaojiyingpic/1902_140x40/1902, 总数：5, acc：0.8000\\n类别：/data/captcha/chaojiyingpic/4004_108x40/4004, 总数：82, acc：0.9878\\n类别：/data/captcha/chaojiyingpic/3004_100x40/3004, 总数：9, acc：0.7778\\n类别：/data/captcha/chaojiyingpic/4006_200x100/4006, 总数：24, acc：0.6250\\n类别：/data/captcha/chaojiyingpic/1004_70x35/1004, 总数：140, acc：0.8929\\n类别：/data/captcha/chaojiyingpic/1902_100x20/1902, 总数：14, acc：1.0000\\n类别：/data/captcha/chaojiyingpic/3004_200x62/3004, 总数：12, acc：1.0000\\n\\n正确数：657, 总数：746, 准确率：0.8807\\n类别：/data/captcha/chaojiyingpic/1902_420x65/1902, 总数：112, acc：0.7054\\n类别：/data/captcha/chaojiyingpic/1004_130x40/1004, 总数：32, acc：0.9688\\n类别：/data/captcha/chaojiyingpic/4004_130x40/4004, 总数：106, acc：0.8396\\n类别：/data/captcha/chaojiyingpic/1004_100x40/1004, 总数：80, acc：0.9375\\n类别：/data/captcha/chaojiyingpic/1902_140x40/1902, 总数：5, acc：0.6000\\n类别：/data/captcha/chaojiyingpic/4004_108x40/4004, 总数：82, acc：0.9878\\n类别：/data/captcha/chaojiyingpic/3004_100x40/3004, 总数：9, acc：0.8889\\n类别：/data/captcha/chaojiyingpic/4006_200x100/4006, 总数：24, acc：0.5417  # 超级鹰错误较多\\n类别：/data/captcha/chaojiyingpic/1004_70x35/1004, 总数：140, acc：0.8714\\n类别：/data/captcha/chaojiyingpic/1902_100x20/1902, 总数：14, acc：1.0000\\n类别：/data/captcha/chaojiyingpic/3004_120x40/3004, 总数：130, acc：1.0000\\n类别：/data/captcha/chaojiyingpic/3004_200x62/3004, 总数：12, acc：1.0000\\n'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''测试超级鹰验证码'''\n",
    "err_imgs = []\n",
    "err_labels = []\n",
    "pos = neg = 0\n",
    "metrics = {}\n",
    "for path in chaojiying_test:\n",
    "    if path.endswith('1004-8268-1262409550172832926.jpg'):\n",
    "        print('打开异常图片')\n",
    "        continue\n",
    "    label = path.split('/')[-1].split('-')[1]\n",
    "    ty = path.split('/')[-1].split('-')[0]\n",
    "    if ty == 4006 and len(label)!=6: # 此类较多验证码标签错误\n",
    "        continue\n",
    "    try:\n",
    "        img = Image.open(path)\n",
    "    except:\n",
    "        print('打开错误：',path)\n",
    "        continue\n",
    "    img = img.resize((width, height), Image.BILINEAR)   \n",
    "    X = np.zeros((1, height, width, 3))\n",
    "    img = img.convert('RGB')\n",
    "    X[0] = np.array(img)/255.0\n",
    "    \n",
    "    out_pre = decode([X, np.ones(X.shape[0])])\n",
    "    out = ''.join([characters[x] for x in out_pre[0][0]])\n",
    "    \n",
    "    k = path.split('-')[0]\n",
    "    if k not in metrics:\n",
    "        metrics[k] = {'pos': 0, 'neg': 0}\n",
    "    \n",
    "    if label.lower() == out.lower():\n",
    "        pos += 1\n",
    "        metrics[k]['pos'] += 1\n",
    "    else:        \n",
    "        print(neg, path, label, out, label==out)\n",
    "        neg += 1\n",
    "        err_imgs.append(img)\n",
    "        err_labels.append('label:'+label+'  pred:'+out)\n",
    "        metrics[k]['neg'] += 1\n",
    "    n += 1\n",
    "#     if n > 100:\n",
    "#         break\n",
    "print('正确数：%d, 总数：%d, 准确率：%.4f'%(pos, pos+neg, pos/(pos+neg)))   \n",
    "for k, v in metrics.items():\n",
    "    print('类别：%s, 总数：%d, acc：%.4f'%(k, (v['pos']+v['neg']), v['pos']/(v['pos']+v['neg'])))\n",
    "'''\n",
    "正确数：547, 总数：616, 准确率：0.8880\n",
    "类别：/data/captcha/chaojiyingpic/1902_420x65/1902, 总数：112, acc：0.7589\n",
    "类别：/data/captcha/chaojiyingpic/1004_130x40/1004, 总数：32, acc：0.9375\n",
    "类别：/data/captcha/chaojiyingpic/4004_130x40/4004, 总数：106, acc：0.9057\n",
    "类别：/data/captcha/chaojiyingpic/1004_100x40/1004, 总数：80, acc：0.9750\n",
    "类别：/data/captcha/chaojiyingpic/1902_140x40/1902, 总数：5, acc：0.8000\n",
    "类别：/data/captcha/chaojiyingpic/4004_108x40/4004, 总数：82, acc：0.9878\n",
    "类别：/data/captcha/chaojiyingpic/3004_100x40/3004, 总数：9, acc：0.7778\n",
    "类别：/data/captcha/chaojiyingpic/4006_200x100/4006, 总数：24, acc：0.6250\n",
    "类别：/data/captcha/chaojiyingpic/1004_70x35/1004, 总数：140, acc：0.8929\n",
    "类别：/data/captcha/chaojiyingpic/1902_100x20/1902, 总数：14, acc：1.0000\n",
    "类别：/data/captcha/chaojiyingpic/3004_200x62/3004, 总数：12, acc：1.0000\n",
    "\n",
    "正确数：657, 总数：746, 准确率：0.8807\n",
    "类别：/data/captcha/chaojiyingpic/1902_420x65/1902, 总数：112, acc：0.7054\n",
    "类别：/data/captcha/chaojiyingpic/1004_130x40/1004, 总数：32, acc：0.9688\n",
    "类别：/data/captcha/chaojiyingpic/4004_130x40/4004, 总数：106, acc：0.8396\n",
    "类别：/data/captcha/chaojiyingpic/1004_100x40/1004, 总数：80, acc：0.9375\n",
    "类别：/data/captcha/chaojiyingpic/1902_140x40/1902, 总数：5, acc：0.6000\n",
    "类别：/data/captcha/chaojiyingpic/4004_108x40/4004, 总数：82, acc：0.9878\n",
    "类别：/data/captcha/chaojiyingpic/3004_100x40/3004, 总数：9, acc：0.8889\n",
    "类别：/data/captcha/chaojiyingpic/4006_200x100/4006, 总数：24, acc：0.5417  # 超级鹰错误较多\n",
    "类别：/data/captcha/chaojiyingpic/1004_70x35/1004, 总数：140, acc：0.8714\n",
    "类别：/data/captcha/chaojiyingpic/1902_100x20/1902, 总数：14, acc：1.0000\n",
    "类别：/data/captcha/chaojiyingpic/3004_120x40/3004, 总数：130, acc：1.0000\n",
    "类别：/data/captcha/chaojiyingpic/3004_200x62/3004, 总数：12, acc：1.0000\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/data/python/lishimin/linuxPro/captcha_pro\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 86,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''展示错误样例'''\n",
    "path = '/data/captcha/chaojiyingpic/1902_420x65/1902-8prl-2283617470172830860.jpg'\n",
    "img = Image.open(path)\n",
    "plt.imshow(img)\n",
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    }
   ],
   "source": [
    "# files = glob.glob('/data/captcha/shuziyingwen/*.jpg')\n",
    "\n",
    "# num = 0\n",
    "# lb_dic = {}\n",
    "# with open('/data/captcha/shuziyingwen/answer.txt', encoding='utf-8') as f:\n",
    "#     lines = f.readlines()\n",
    "#     for line in lines:\n",
    "#         name, lb = line.strip().split('=')\n",
    "#         lb_dic[name] = lb\n",
    "# print(num)\n",
    "# print(len(lines), len(files))  \n",
    "# # file_path = random.choice(files)\n",
    "# # filename = file_path.split('/')[-1][:-4]\n",
    "# # while filename not in lb_dic:\n",
    "# #     print('不在字典')\n",
    "# #     file_path = random.choice(files)\n",
    "# #     filename = file_path.split('/')[-1][:-4]\n",
    "# # random_str = lb_dic.get(filename, '')\n",
    "# # print(random_str)\n",
    "# for file in files:\n",
    "#     filename = file_path.split('/')[-1][:-4]\n",
    "#     if filename not in lb_dic:\n",
    "#         print('not in ')\n",
    "        \n",
    "# file_path = random.choice(files)\n",
    "# filename = file_path.split('/')[-1][:-4]\n",
    "# random_str = lb_dic.get(filename, '')\n",
    "# image = Image.open(file_path).convert('L').resize((width, height), Image.BILINEAR)\n",
    "# plt.imshow(image)\n",
    "\n",
    "# file_path = random.choice(files)\n",
    "# for file_path in files:\n",
    "#     try:\n",
    "#         filename = file_path.split('/')[-1][:-4]\n",
    "#         random_str = lb_dic.get(filename, '')\n",
    "#         image = Image.open(file_path).convert('L').resize((width, height), Image.BILINEAR)\n",
    "#     except:\n",
    "#         print(filename)\n",
    "#         os.remove(file_path)\n",
    "# plt.imshow(image)\n",
    "# layers = model.layers\n",
    "# for i in range(len(layers)):\n",
    "#     if i> 14:\n",
    "#         print(layers[i].name)\n",
    "#         layers[i].trainable = False\n",
    "#     print(layers[i].trainable)\n",
    "import os\n",
    "# paths = ['/data/captcha/label_english/100_30/*.jpg','FileInfo0508_2/*.jpg']\n",
    "# files = glob.glob(paths[6%len(paths)])\n",
    "# file_path = random.choice(files)\n",
    "# random_str = file_path.split('/')[-1].split('_')[-1][:-4]\n",
    "# print(file_path, os.path.exists(file_path), random_str)\n",
    "\n",
    "for file in glob.glob('/data/captcha/shensexiansandian/*.jpg'):\n",
    "#     print(file)\n",
    "#     break\n",
    "    name = file.split('/')[-1]\n",
    "    print(name)\n",
    "    tmp = name.split('_')\n",
    "#     newname = tmp[1][:-4]+'_'+tmp[0]+'.jpg'\n",
    "    newname = tmp[0]+'_'+tmp[1][:-4].upper()+'.jpg'\n",
    "    print(newname)\n",
    "    os.rename(file, '/data/captcha/shensexiansandian/'+newname)\n",
    "# img = Image.open(file)\n",
    "# plt.imshow(img)\n",
    "\n",
    "# file = '/data/captcha/shensebeijingsandian/117d3695-b4aa-11ea-8217-5254009c362b_kdea.jpg'\n",
    "# name = file.split('/')[-1]\n",
    "# tmp = name.split('_')\n",
    "# newname = tmp[0]+'_'+tmp[1][:-4].upper()+'.jpg'\n",
    "# print(newname)"
   ]
  }
 ],
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