{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0123456789+?-×/= 17\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "# os.environ['CUDA_VISIBLE_DEVICES'] = ''\n",
    "\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 string\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "characters = '0123456789+?-×/='  # 验证码字符集合\n",
    "\n",
    "width, height, n_len, n_class = 200, 64, 12, len(characters) + 1   #图片宽、高，验证码最大长度，分类类别：字符集+1个空值\n",
    "\n",
    "font_paths = glob.glob('latin/*')\n",
    "#  '/usr/share/fonts/opentype/noto/NotoSerifCJK-Bold.ttc', , '/usr/share/fonts/truetype/arphic/ukai.ttc' '/usr/share/fonts/truetype/arphic/uming.ttc', 'latin/arialbi.ttf',\n",
    "fonts = [ '/usr/share/fonts/opentype/malayalam/Manjari-Regular.otf', '/usr/share/fonts/opentype/malayalam/Manjari-Thin.otf', '/usr/share/fonts/opentype/noto/NotoSerifCJK-Regular.ttc', '/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc', '/usr/share/fonts/opentype/noto/NotoSansCJK-Bold.ttc']\n",
    "fonts2 = ['latin/segoeuil.ttf', 'latin/verdana.ttf', 'latin/calibri.ttf', 'latin/SIMLI.TTF', 'latin/verdanai.ttf', 'latin/framd.ttf', 'latin/ariali.ttf', 'latin/LSANS.TTF']\n",
    "fonts = fonts+fonts2\n",
    "print(characters, n_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "部分变量未定义，跳过删除步骤\n",
      "TF1.15 GPU内存释放完成！\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import gc\n",
    "\n",
    "# ========== 1. 核心：清除TF1.x的计算图和会话（最关键） ==========\n",
    "# 关闭所有活跃的Session（TF1.x中Session是GPU资源占用的核心）\n",
    "try:\n",
    "    tf.get_default_session().close()\n",
    "except:\n",
    "    pass  # 无活跃Session时跳过\n",
    "\n",
    "# 重置默认计算图（清除所有节点/变量）\n",
    "tf.reset_default_graph()\n",
    "\n",
    "# ========== 2. 释放GPU显存（TF1.15专属方式） ==========\n",
    "# 重新配置GPU显存策略：设置按需分配，释放未使用的显存\n",
    "config = tf.ConfigProto()\n",
    "# 关键：允许TF动态分配GPU显存，而非一次性占满\n",
    "config.gpu_options.allow_growth = True  \n",
    "# 可选：限制GPU显存占用比例（如仅用50%），按需调整\n",
    "# config.gpu_options.per_process_gpu_memory_fraction = 0.5\n",
    "\n",
    "# 重新初始化Session，覆盖旧的显存配置\n",
    "sess = tf.Session(config=config)\n",
    "tf.keras.backend.set_session(sess)  # 兼容Keras（TF1.15的Keras基于TF1.x）\n",
    "\n",
    "# ========== 3. 手动删除大对象 + 强制垃圾回收 ==========\n",
    "# 替换为你实际的模型/数据变量名（如model、train_data、valid_data）\n",
    "try:\n",
    "    del model, train_data, valid_data  \n",
    "except NameError:\n",
    "    print(\"部分变量未定义，跳过删除步骤\")\n",
    "\n",
    "# 强制Python垃圾回收，释放内存\n",
    "gc.collect()\n",
    "\n",
    "print(\"TF1.15 GPU内存释放完成！\")\n",
    "\n",
    "# paths = glob.glob('/data/captcha/arithmetic/160_60/*.jpg') # 100_26 70_25 100_40  330_69  160_60  146_46\n",
    "# i = 12\n",
    "# img = Image.open(paths[i])\n",
    "# img2 = img.resize((width, height), Image.BILINEAR)\n",
    "\n",
    "# plt.imshow(img)\n",
    "# plt.show()\n",
    "# plt.imshow(img2)\n",
    "# plt.show()\n",
    "# text = '70/10+0×20=?'\n",
    "# re.split('(\\+|\\-|\\*|×|/|=)', text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5+5+0=?\n",
      "image size (200, 64) (200, 64)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 3600x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 2328
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random_color(rs, re, gs, ge, bs, be) (18, 12, 20)\n"
     ]
    }
   ],
   "source": [
    "'''生成彩色图像'''\n",
    "import re\n",
    "\n",
    "def get_arith(top=9, i=None, que_mark=True, numOfas=1):\n",
    "    '''生成带等号问号+x-三种算法，返回公式及求解答案\n",
    "    i: 第几个位置为问号\n",
    "    numOfas：算术符号数量\n",
    "    que_mark：True公式包含问号，False不包含'''\n",
    "    a = random.randint(0,top)\n",
    "    sign = random.choice(['+','*','-','/']) if top<10 else random.choice(['+','-'])\n",
    "    b = random.randint(0,top) if sign!='/' else random.randint(1, top) \n",
    "    if sign=='/':\n",
    "        answer = int(eval(('%d*%d'%(a,b))))  if sign!='/' else int(eval(('%d*%d'%(a,b))))\n",
    "        a, answer = answer, a\n",
    "    elif sign=='-':\n",
    "        b = random.randint(0,a)\n",
    "        answer = int(eval('%d%s%d'%(a,sign,b)))\n",
    "    else:\n",
    "        answer = int(eval('%d%s%d'%(a,sign,b)))    \n",
    "     \n",
    "    if numOfas==2:        \n",
    "        sign2 = random.choice(['+','-'])\n",
    "        c = random.randint(0,top) if sign2=='+' else random.randint(0,answer)\n",
    "        answer = int(eval('%d%s%d%s%d'%(a,sign,b,sign2,c)))\n",
    "        if sign=='*' and random.random()>0.5:\n",
    "            sign = '×'   \n",
    "#         arith = '%d%s%d%s%d=%d'%(a,sign,b,sign2,c,answer)\n",
    "        a = str(a)\n",
    "        b = str(b)\n",
    "        c = str(c)\n",
    "        answer = str(answer)\n",
    "        l = [a,b,c,answer]\n",
    "        if que_mark:\n",
    "            i = random.choice([0,1,2,3]) if i==None else i\n",
    "            question = l[i]\n",
    "            l[i] = '?'\n",
    "            arith = '%s%s%s%s%s=%s'%(l[0],sign,l[1],sign2,l[2],l[3])\n",
    "            return arith, question\n",
    "        arith = '%s%s%s%s%s='%(l[0],sign,l[1],sign2,l[2])\n",
    "        return arith, answer\n",
    "    else:\n",
    "        if sign=='*' and random.random()>0.5:\n",
    "            sign = '×'         \n",
    "#         arith = '%d%s%d=%d'%(a,sign,b,answer)\n",
    "        a = str(a)\n",
    "        b = str(b)\n",
    "        answer = str(answer)\n",
    "        l = [a,b,answer]        \n",
    "        if que_mark:\n",
    "            i = random.choice([0,1,2]) if i==None else i\n",
    "            question = l[i]\n",
    "            l[i] = '?'\n",
    "            arith = '%s%s%s=%s'%(l[0],sign,l[1], l[2])\n",
    "            return arith, question\n",
    "        arith = '%s%s%s='%(l[0],sign,l[1])\n",
    "        return arith, answer\n",
    "\n",
    "\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",
    "def gen_captcha(text, fig_size=(200,70), fonts=['fonts/ANTQUAB.TTF'],font_color=(10,100),same_color=1, font_size=(25, 35), rotate=(0,0),\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_path = 'latin/verdana.ttf'\n",
    "#     print('font_path:',font_path)\n",
    "#     font_name = font_path.split('/')[-1][:-4]\n",
    "#     print('font_name:', font_name)\n",
    "    rotate = (rotate, rotate) if isinstance(rotate, int) else rotate\n",
    "    font = ImageFont.truetype(font_path, size=random.randint(font_size[0], font_size[1])) # font=None, size=10, index=0, encoding=\"\"\n",
    "    \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",
    "#         print('get_char_img', char)\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!=(0, 0) and char not in ['+','-','×']:\n",
    "            im = im.rotate(random.randint(rotate[0], rotate[1]),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",
    "    \n",
    "    # 解决两位数问题\n",
    "    chars = re.split('(\\+|\\-|\\*|×|/|=)', text)\n",
    "    char_imgs = []\n",
    "    char_list = []\n",
    "    if same_color: \n",
    "        for char in chars:\n",
    "            char_list.append(char)\n",
    "            char_imgs.append(get_char_img(char, font, font_color=char_color, rotate=rotate, bg=bg, font_noise=font_noise))\n",
    "    else:\n",
    "        for char in chars:\n",
    "            char_list.append(char)\n",
    "            char_imgs.append(get_char_img(char, font, font_color=random_color(font_color), rotate=rotate, bg=bg, font_noise=font_noise))\n",
    "\n",
    "\n",
    "#     re_s = re.search('(\\d+|\\?)(\\+|-|\\*|×)(\\d+|\\?)(=)(-?\\d+|\\?)?', text)\n",
    "#     if re_s:\n",
    "# #         print(re_s.group(0))\n",
    "#         char_imgs = []\n",
    "#         char_list = []\n",
    "#         if same_color: \n",
    "#             for i in range(1,6):\n",
    "#                 if re_s.group(i)!=None:\n",
    "#                     char_list.append(re_s.group(i))\n",
    "#                     char_imgs.append(get_char_img(re_s.group(i), font, font_color=char_color, rotate=rotate, bg=bg, font_noise=font_noise))\n",
    "#         else:\n",
    "#             for i in range(1,6):\n",
    "#                 if re_s.group(i)!=None:\n",
    "#                     char_list.append(re_s.group(i))\n",
    "#                     char_imgs.append(get_char_img(re_s.group(i), font, font_color=random_color(font_color), rotate=rotate, bg=bg, font_noise=font_noise))\n",
    "#     else:\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!=(0, 0):\n",
    "        temp_x = random.randint(0, min(50,int((fig_size[0]-sum(ws))/2+1))) #int((fig_size[0]-sum(ws))/5)\n",
    "        temp_y = random.randint(int((fig_size[1]-hs[0])/8), int((fig_size[1]-hs[0])/2+1))\n",
    "#         print('len(char_imgs):',len(char_imgs))\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 rotate==(0, 0):            \n",
    "        temp_x = random.randint(0, min(50, int((fig_size[0]-sum(ws))/2+1))) #int((fig_size[0]-sum(ws))/5)\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),char_list[i], font=font, fill=char_color)\n",
    "#                 if re_s:\n",
    "#                     draw.text((temp_x, temp_y),char_list[i], font=font, fill=char_color)\n",
    "#                 else:\n",
    "#                     draw.text((temp_x, temp_y), text[i], font=font, fill=char_color)\n",
    "            else:\n",
    "                draw.text((temp_x, temp_y), char_list[i], font=font, fill=random_color(font_color))        \n",
    "#                 if re_s:\n",
    "#                     draw.text((temp_x, temp_y), char_list[i], font=font, fill=random_color(font_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",
    "    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",
    "# img2 = Image.open('/data/captcha/label_english/90_38/0ef53417-3af2-11ec-b040-2cf05ded1cb1_aq4f.jpg')\n",
    "\n",
    "# img2 = Image.open('/data/captcha/arithmetic/100_26/19c3254f-f699-11ea-bae3-c81f66ef0810.jpg')\n",
    "# random_str, question = get_arith(top=9)\n",
    "# image = gen_captcha(random_str, fig_size=(100,26), fonts=fonts,font_color=(20,230,20,230,20,230),same_color=1, font_size=(15, 20), rotate=0,\n",
    "#                     font_noise=0,offset_w=(-1,3),offset_h=0, line=(0,0), shortline=(10,20), line_width=(0,1), line_color=(100,150), point=(0,0),\n",
    "#                     point_color=(0,0),frame_color=(120,150),wavy=(0,0), bg=(255,255))\n",
    "\n",
    "# img2 = Image.open('/data/captcha/arithmetic/70_25/0.jpg')#.resize((width, height), Image.BILINEAR)\n",
    "# # random_str, question = get_arith(top=99, i=2)\n",
    "# random_str = '56-33=?'\n",
    "# image = gen_captcha(random_str, fig_size=(70,25), fonts=fonts,font_color=(30,200),same_color=1, font_size=(12, 15), rotate=0,\n",
    "#                     font_noise=0,offset_w=(-1,1),offset_h=0, line=(0,0), shortline=(200,290), line_width=(0,1), line_color=(170,190), point=(200,300),\n",
    "#                     point_color=(150,200),frame_color=None,wavy=(0,0), bg=(200,255))\n",
    "\n",
    "# img2 = Image.open('/data/captcha/arithmetic/100_40/b48fc250-f979-11e9-b970-408d5cd36814_5.jpg')#.resize((width, height), Image.BILINEAR)\n",
    "# random_str, question = get_arith(top=9, que_mark=False)\n",
    "\n",
    "# # random_str = '56*33=?'\n",
    "# image = gen_captcha(random_str, fig_size=(100,40), fonts=fonts,font_color=(0,0),same_color=1, font_size=(20, 25), rotate=0,\n",
    "#                     font_noise=0,offset_w=(-1,1),offset_h=0, line=(3,3), shortline=(0,0), line_width=(0,1), line_color=(0,0), point=(0,0),\n",
    "#                     point_color=(200,250),frame_color=None,wavy=(0,0), bg=(250,255))\n",
    "\n",
    "# img2 = Image.open('/data/captcha/arithmetic/330_69/1.jpg').resize((width, height), Image.BILINEAR)\n",
    "# random_str, question = get_arith(top=99, i=2)\n",
    "# # random_str = '56-33=?'\n",
    "# print(random_str)\n",
    "# image = gen_captcha(random_str, fig_size=(330, 69), fonts=fonts,font_color=(0,250,0,250,0,250),same_color=0, font_size=(35, 40), rotate=0,\n",
    "#                     font_noise=0,offset_w=(5,5),offset_h=0, line=(3,6), shortline=(0,5), line_width=(1,2), line_color=(150,230), point=(30,130),\n",
    "#                     point_color=(50,230),frame_color=None,wavy=(0,0), bg=(255,255))\n",
    "\n",
    "# img2 = Image.open('/data/captcha/arithmetic/160_60/fff3b9c4-ec7e-11ec-9150-b4b5b67760ae_6.jpg').resize((width, height), Image.BILINEAR)\n",
    "# random_str, question = get_arith(top=9, i=2)\n",
    "# # random_str = '56-33=?'\n",
    "# print(random_str)\n",
    "# image = gen_captcha(random_str, fig_size=(160, 60), fonts=fonts,font_color=(0,250,0,250,0,250),same_color=1, font_size=(35, 40), rotate=(20,30),\n",
    "#                     font_noise=0,offset_w=(-6,-2),offset_h=0, line=(0,0), shortline=(0,0), line_width=(1,2), line_color=(150,230), point=(0,0),\n",
    "#                     point_color=(50,230),frame_color=(120,150),wavy=(0,0), bg=(190,250))\n",
    "\n",
    "img2 = Image.open('/data/captcha/arithmetic/146_46/2a0d8c9a-ee09-11ec-b07d-b4b5b67760ae.jpg').resize((width, height), Image.BILINEAR)\n",
    "random_str, question = get_arith(top=9, i=3, numOfas=2)\n",
    "print(random_str)\n",
    "image = gen_captcha(random_str, fig_size=(146, 46), fonts=fonts,font_color=(0,250,0,250,0,250),same_color=1, font_size=(25, 30), rotate=(0,0),\n",
    "                    font_noise=0,offset_w=(2,5),offset_h=0, line=(0,0), shortline=(0,0), line_width=(1,2), line_color=(150,230), point=(10,50),\n",
    "                    point_color=(150,230),frame_color=(150,200),wavy=(0,0), bg=(220,250))\n",
    "\n",
    "# random_str, question = get_arith(top=9)\n",
    "# tmp_w = random.randint(70,100)\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=(200,250),same_color=0, font_size=font_s, rotate=20,\n",
    "#                     font_noise=0,offset_w=(-2,1),offset_h=2, line=(0,5), shortline=(0,100), line_width=(0,1), line_color=(10,150), point=(0,200),\n",
    "#                     point_color=(50,255),frame_color=None,wavy=(0,0), bg=(10,150)) \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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4+64=?\n",
      "4\n",
      "+\n",
      "64\n",
      "=\n",
      "?\n"
     ]
    }
   ],
   "source": [
    "# char = 'w'\n",
    "# font = ImageFont.truetype(fonts[-3], size=15) \n",
    "# print(font.getsize(char))\n",
    "# random.random()>0.3\n",
    "re_s = re.search('(\\d+|\\?)(\\+|-|\\*|×)(\\d+|\\?)(=)(\\d+|\\?)?', random_str)\n",
    "print(random_str)\n",
    "for i in range(1,6):\n",
    "#     print(i)\n",
    "    if re_s.group(i)!=None:\n",
    "        print(re_s.group(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5×9=?\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 140,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 重组验证码\n",
    "\n",
    "crops = glob.glob('/data/captcha/arithmetic/crop_70_25/crop_num/*.jpg')\n",
    "bgs_7025 = glob.glob('/data/captcha/arithmetic/crop_70_25/crop_bg/*.jpg')\n",
    "def merge_img_7025():\n",
    "#     if random.random()>0.4:\n",
    "#         img = Image.new(mode='RGB', size=((70,25)), color=(255,255,255))\n",
    "#     else:\n",
    "    img = Image.open(random.choice(bgs_7025))\n",
    "    w, h = img.size\n",
    "    draw = ImageDraw.Draw(img)   \n",
    "        \n",
    "    w0 = random.randint(0,4)\n",
    "    h0 = random.randint(1,5)\n",
    "    label = []\n",
    "    range_num = random.randint(1,2)\n",
    "    for i in range(range_num):\n",
    "        im_p = random.choice(crops)\n",
    "        lb = im_p.split('/')[-1].split('_')[0]\n",
    "        if lb=='0' and range_num == 2 and i==0:\n",
    "            continue\n",
    "        label.append(lb)\n",
    "        im = Image.open(im_p)\n",
    "        w, h = im.size        \n",
    "        img.paste(im, (w0,h0)) # ,w//4*(i+1), h\n",
    "        w0 += w\n",
    "        \n",
    "    fh = glob.glob('/data/captcha/arithmetic/crop_70_25/crop_sign/jiajiancheng/*.jpg')\n",
    "    im_p = random.choice(fh)\n",
    "    lb = im_p.split('/')[-1].split('_')[0]\n",
    "    if lb == 'jia':\n",
    "        lb = '+'\n",
    "    elif lb == 'jian':\n",
    "        lb = '-'\n",
    "    elif lb == 'cheng':\n",
    "        lb = '×'\n",
    "    label.append(lb)\n",
    "    im = Image.open(im_p)\n",
    "    w, h = im.size        \n",
    "    img.paste(im, (w0,h0)) # ,w//4*(i+1), h\n",
    "    w0 += w  \n",
    "    \n",
    "    range_num = random.randint(1,2)\n",
    "    for i in range(range_num):\n",
    "        im_p = random.choice(crops)\n",
    "        lb = im_p.split('/')[-1].split('_')[0]\n",
    "        if lb=='0' and range_num == 2 and i==0:\n",
    "            continue\n",
    "        label.append(lb)\n",
    "        im = Image.open(im_p)\n",
    "        w, h = im.size        \n",
    "        img.paste(im, (w0,h0)) # ,w//4*(i+1), h\n",
    "        w0 += w  \n",
    "        \n",
    "    fh = glob.glob('/data/captcha/arithmetic/crop_70_25/crop_sign/denghao/*.jpg')\n",
    "    im_p = random.choice(fh)\n",
    "    lb = im_p.split('/')[-1].split('_')[0]\n",
    "    if lb == 'deng':\n",
    "        lb = '='\n",
    "    label.append(lb)\n",
    "    im = Image.open(im_p)\n",
    "    w, h = im.size        \n",
    "    img.paste(im, (w0,h0)) # ,w//4*(i+1), h\n",
    "    w0 += w   \n",
    "    \n",
    "    fh = glob.glob('/data/captcha/arithmetic/crop_70_25/crop_sign/wenhao/*.jpg')\n",
    "    im_p = random.choice(fh)\n",
    "    lb = im_p.split('/')[-1].split('_')[0]\n",
    "    if lb == 'wen':\n",
    "        lb = '?'\n",
    "    label.append(lb)\n",
    "    im = Image.open(im_p)\n",
    "    w, h = im.size        \n",
    "    img.paste(im, (w0,h0)) # ,w//4*(i+1), h\n",
    "    w0 += w   \n",
    "    w, h = img.size    \n",
    "    for i in range(0,2):\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((200,250)),\n",
    "                  width=1) # xy, fill=None, width=0        \n",
    "    for _ in range(random.randint(0,10)):\n",
    "        draw.point(xy=(random_xy(w, h)),fill=random_color((180,250)))            \n",
    "\n",
    "    return img.resize((width, height), Image.BILINEAR), ''.join(label)\n",
    "\n",
    "img, label =  merge_img_7025()\n",
    "print(label)\n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6-2=?\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 140,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''添加真实验证码'''\n",
    "imgs_60 = glob.glob('/data/captcha/arithmetic/160_60/*.jpg')  #变形字体\n",
    "imgs_46 = glob.glob('/data/captcha/arithmetic/146_46/*.jpg') #两个算术符号\n",
    "name_label_dic = dict()                    \n",
    "with open('/data/captcha/arithmetic/146_46/answer.txt', 'r', encoding='utf-8') as f:  #/data/captcha/arithmetic/160_60/answer.txt\n",
    "    lines = f.readlines()\n",
    "    for line in lines:\n",
    "        f_n, q, a = line.strip().split('\\t')  \n",
    "        name_label_dic[f_n] = q\n",
    "                    \n",
    "with open('/data/captcha/arithmetic/160_60/answer.txt', 'r', encoding='utf-8') as f:  #/data/captcha/arithmetic/160_60/answer.txt\n",
    "    lines = f.readlines()\n",
    "    for line in lines:\n",
    "        f_n, q, a = line.strip().split('\\t') \n",
    "        if f_n in name_label_dic:\n",
    "            print('file 已存在：', f_n)\n",
    "            continue\n",
    "        name_label_dic[f_n] = q                    \n",
    "\n",
    "def get_real_img(imgs):\n",
    "    num = len(imgs)\n",
    "    im_p = random.choice(imgs[:int(0.9*num)])\n",
    "    file_name = im_p.split('/')[-1]\n",
    "    label = name_label_dic[file_name]\n",
    "    img = Image.open(im_p)  \n",
    "    w, h = img.size                \n",
    "    draw = ImageDraw.Draw(img)   \n",
    "    for i in range(2,20):\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((200,250)),\n",
    "                  width=1) # xy, fill=None, width=0        \n",
    "    for _ in range(random.randint(20,500)):\n",
    "        draw.point(xy=(random_xy(w, h)),fill=random_color((180,250)))            \n",
    "\n",
    "    return img.resize((width, height), Image.BILINEAR), label\n",
    "img, label =  get_real_img(imgs_60)  #imgs_46 imgs_60\n",
    "print(label)\n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2+87\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 140,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 200, 4) RGBA\n",
      "异常格式\n"
     ]
    }
   ],
   "source": [
    "'''添加AI标注验证码'''\n",
    "\n",
    "def get_files_one_level(target_path, file_names={}):\n",
    "    \"\"\"\n",
    "    获取指定路径下一级的文件\n",
    "    :param target_path: 目标路径（绝对/相对路径）\n",
    "    :return: 文件列表\n",
    "    \"\"\"\n",
    "    # 校验路径是否存在\n",
    "    if not os.path.exists(target_path):\n",
    "        print(f\"错误：路径 {target_path} 不存在\")\n",
    "        return [], []\n",
    "\n",
    "    file_list = []  # 存储文件\n",
    "\n",
    "    # 遍历路径下的所有条目\n",
    "    for item in os.listdir(target_path):\n",
    "        # 拼接完整路径\n",
    "        if item in file_names:\n",
    "            item_path = os.path.join(target_path, item)\n",
    "            # 判断是文件夹还是文件\n",
    "            if os.path.isfile(item_path):\n",
    "                file_list.append(item_path)\n",
    "                # file_list.append(item)\n",
    "    return file_list\n",
    "\n",
    "ai_lab_dic = {}\n",
    "with open('/data/captcha/arich_capt_ai/ai_answer.txt', 'r', encoding='utf-8') as f:\n",
    "    text = f.read().strip()\n",
    "    for line in text.split('\\n'):\n",
    "        k, v = line.strip().split('\\t')\n",
    "        # print(k, v)\n",
    "        ai_lab_dic[k] = v\n",
    "\n",
    "ai_files1 = get_files_one_level(r'/data/captcha/arich_capt_ai/6001_120x26', ai_lab_dic)\n",
    "ai_files2 = get_files_one_level(r'/data/captcha/arich_capt_ai/6001_130x48', ai_lab_dic)\n",
    "ai_files3 = get_files_one_level(r'/data/captcha/arich_capt_ai/6001_150x50', ai_lab_dic)\n",
    "\n",
    "def get_ai_label_img(imgs):\n",
    "    num = len(imgs)\n",
    "    im_p = random.choice(imgs[:int(0.9*num)])\n",
    "    file_name = im_p.split('/')[-1]\n",
    "    label = ai_lab_dic[file_name]\n",
    "    img = Image.open(im_p)\n",
    "    if img.mode != \"RGB\":\n",
    "        img = img.convert(\"RGB\")\n",
    "    w, h = img.size                \n",
    "    draw = ImageDraw.Draw(img)   \n",
    "    for i in range(2,20):\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((200,250)),\n",
    "                  width=1) # xy, fill=None, width=0        \n",
    "    for _ in range(random.randint(20,500)):\n",
    "        draw.point(xy=(random_xy(w, h)),fill=random_color((180,250)))            \n",
    "\n",
    "    return img.resize((width, height), Image.BILINEAR), label\n",
    "img, label =  get_ai_label_img(ai_files3)  #imgs_46 imgs_60\n",
    "print(label)\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "print(np.array(img).shape, img.mode)\n",
    "if img.mode == \"RGBA\":\n",
    "    print('异常格式')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "''' 彩色图像生成 '''\n",
    "from keras.utils import Sequence # tensorflow.\n",
    "from collections import Counter\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=n_len, 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",
    "#         self.n_class = -2\n",
    "#         self.generator = ImageCaptcha(width=width, height=height, font_sizes=(12,20,18,25))\n",
    "#         self.fonts_list = glob.glob('/usr/share/fonts/WindowsFonts/fonts/*.ttf')\n",
    "            \n",
    "    def __len__(self):\n",
    "        return self.steps\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        batch_label_length = random.choice([4,5,4,4])\n",
    "#         imgs = []\n",
    "#         print('batch_label_length',batch_label_length)\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",
    "#         print(y)\n",
    "#         y = np.zeros((self.batch_size, batch_label_length), dtype=np.uint8)\n",
    "        input_length = np.ones(self.batch_size)*self.input_length\n",
    "        label_length = np.ones(self.batch_size)*self.n_len \n",
    "#         label_length = np.ones(self.batch_size)*batch_label_length\n",
    "        max_num = 85\n",
    "        for i in range(self.batch_size):\n",
    "\n",
    "            if i%max_num <= 3:    # line=(0,0), line_width=(0,1), point=(0,100),wavy=(0,0)            \n",
    "                random_str, question = get_arith(top=9, i=1)\n",
    "                image = gen_captcha(random_str, fig_size=(100,26), fonts=fonts,font_color=(20,230,20,230,20,230),same_color=1, font_size=(15, 20), rotate=0,\n",
    "                                    font_noise=0,offset_w=(-1,3),offset_h=0, line=(0,0), shortline=(10,20), line_width=(0,1), line_color=(100,150), point=(0,0),\n",
    "                                    point_color=(0,0),frame_color=(120,150),wavy=(0,0), bg=(255,255))\n",
    "\n",
    "\n",
    "            elif i%max_num <= 6: # line=(0,5), line_width=(0,1), point=(20,300),wavy=(0,0)\n",
    "                random_str, question = get_arith(top=99, i=2)\n",
    "                image = gen_captcha(random_str, fig_size=(70,25), fonts=fonts,font_color=(70,100),same_color=1, font_size=(12, 15), rotate=0,\n",
    "                    font_noise=0,offset_w=(-1,0),offset_h=0, line=(0,0), shortline=(150,200), line_width=(0,1), line_color=(180,230), point=(200,300),\n",
    "                    point_color=(200,250),frame_color=None,wavy=(0,0), bg=(210,255))\n",
    "\n",
    "            elif i%max_num <= 9: # line=(0,0), line_width=(0,2), point=(0,0),wavy=(1,1)\n",
    "                random_str, question = get_arith(top=9)\n",
    "                image = gen_captcha(random_str, fig_size=(100,26), fonts=fonts,font_color=(20,230,20,230,20,230),same_color=1, font_size=(15, 20), rotate=0,\n",
    "                                    font_noise=0,offset_w=(-1,3),offset_h=0, line=(0,0), shortline=(0,0), line_width=(0,1), line_color=(100,150), point=(0,0),\n",
    "                                    point_color=(0,0),frame_color=(120,150),wavy=(0,0), bg=(255,255))\n",
    "\n",
    "            elif i%max_num <= 12:  # line=(0,0), line_width=(0,1), point=(0,80),wavy=(0,0)\n",
    "                random_str, question = get_arith(top=99, i=2)\n",
    "                image = gen_captcha(random_str, fig_size=(70,25), fonts=fonts,font_color=(10,230,10,230,10,230),same_color=0, font_size=(12, 15), rotate=0,\n",
    "                                    font_noise=0,offset_w=(-1,1),offset_h=0, line=(0,0), shortline=(0,0), line_width=(0,1), line_color=(150,200), point=(0,0),\n",
    "                                    point_color=(0,0),frame_color=None,wavy=(0,0), bg=(150,255))\n",
    "                \n",
    "            elif i%max_num<=15:\n",
    "                random_str, question = get_arith(top=9, que_mark=False)\n",
    "                image = gen_captcha(random_str, fig_size=(100,40), fonts=fonts,font_color=(0,0),same_color=1, font_size=(20, 25), rotate=0,\n",
    "                                    font_noise=0,offset_w=(-1,1),offset_h=0, line=(3,3), shortline=(0,0), line_width=(0,1), line_color=(0,0), point=(0,0),\n",
    "                                    point_color=(200,250),frame_color=None,wavy=(0,0), bg=(250,255))\n",
    "\n",
    "            elif i%max_num<=20:\n",
    "                random_str, question = get_arith(top=99, i=2)\n",
    "                image = gen_captcha(random_str, fig_size=(330, 69), fonts=fonts,font_color=(10,250,10,250,10,250),same_color=0, font_size=(35, 40), rotate=30,\n",
    "                    font_noise=0,offset_w=(5,5),offset_h=0, line=(3,6), shortline=(0,5), line_width=(1,2), line_color=(150,230), point=(30,130),\n",
    "                    point_color=(50,230),frame_color=None,wavy=(0,0), bg=(255,255))\n",
    "                \n",
    "            elif i%max_num<=30:\n",
    "                random_str, question = get_arith(top=9)\n",
    "                tmp_w = random.randint(70,100)\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=(200,250),same_color=0, font_size=font_s, rotate=20,\n",
    "                                    font_noise=0,offset_w=(-2,1),offset_h=2, line=(0,5), shortline=(0,100), line_width=(0,1), line_color=(10,150), point=(0,200),\n",
    "                                    point_color=(50,255),frame_color=None,wavy=(0,0), bg=(10,150))    \n",
    "            elif i%max_num<=35:\n",
    "                random_str, question = get_arith(top=9, i=2)                \n",
    "                image = gen_captcha(random_str, fig_size=(160, 60), fonts=fonts,font_color=(0,250,0,250,0,250),same_color=1, font_size=(35, 40), rotate=(20,30),\n",
    "                    font_noise=0,offset_w=(-6,-2),offset_h=0, line=(0,0), shortline=(0,0), line_width=(1,2), line_color=(150,230), point=(0,0),\n",
    "                    point_color=(50,230),frame_color=(120,150),wavy=(0,0), bg=(190,250))\n",
    "            elif i%max_num<=40:\n",
    "                random_str, question = get_arith(top=9, i=3, numOfas=2)\n",
    "                image = gen_captcha(random_str, fig_size=(146, 46), fonts=fonts,font_color=(0,250,0,250,0,250),same_color=1, font_size=(25, 30), rotate=(0,0),\n",
    "                    font_noise=0,offset_w=(-2,3),offset_h=0, line=(0,0), shortline=(0,0), line_width=(1,2), line_color=(150,230), point=(10,50),\n",
    "                    point_color=(150,230),frame_color=(150,200),wavy=(0,0), bg=(220,250))\n",
    "\n",
    "            elif i%max_num<=45:\n",
    "                image, random_str =  merge_img_7025()\n",
    "            elif i%max_num<=50:                \n",
    "                image, random_str =  get_real_img(imgs_46)  #imgs_46 imgs_60\n",
    "            elif i%max_num<=55:                \n",
    "                image, random_str =  get_real_img(imgs_60)  #imgs_46 imgs_60\n",
    "            elif i%max_num<=60:                \n",
    "                image, random_str =  get_ai_label_img(ai_files1)\n",
    "            elif i%max_num<=65:                \n",
    "                image, random_str =  get_ai_label_img(ai_files2)\n",
    "            elif i%max_num<=70:                \n",
    "                image, random_str =  get_ai_label_img(ai_files3)\n",
    "                \n",
    "            else: \n",
    "                random_str, question = get_arith(top=99, i=2)\n",
    "                tmp_w = random.randint(70,100)\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=20,\n",
    "                                    font_noise=0,offset_w=(2,5),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=(0,0), bg=(150,255)) \n",
    "                \n",
    "            if image.mode == 'RGBA':\n",
    "                X[i] = X[i] = np.array(image)[:,:,:3]/255.0\n",
    "            else:\n",
    "                X[i] = np.array(image)/255.0\n",
    "            random_str = random_str.replace('*', '×') \n",
    "            label = [self.characters.find(x) for x in random_str]  # 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": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "a = re.search('(\\d+|\\?)(\\+|-|\\*|×)(\\d+|\\?)(=)(-?\\d+|\\?)?', '2-?=-7')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('6*23=?', 138)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_arith(top=99, i=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = CaptchaSequence(characters, batch_size=64, steps=2,input_length=12, label_length=10,chars_len=(5, 5))  # (characters, batch_size=128, steps=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 32, 100, 3)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 9600 into shape (32,100)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-58-d4499b13cc21>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# x = data[1]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mreshape\u001b[0;34m(a, newshape, order)\u001b[0m\n\u001b[1;32m    255\u001b[0m            [5, 6]])\n\u001b[1;32m    256\u001b[0m     \"\"\"\n\u001b[0;32m--> 257\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'reshape'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[0;31m# An AttributeError occurs if the object does not have\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 9600 into shape (32,100)"
     ]
    }
   ],
   "source": [
    "l, _  = data[1]\n",
    "x = l[0]\n",
    "print(x.shape)\n",
    "idx =18\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": 7,
   "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, 64, 200, 3)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 64, 200, 32)       896       \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 64, 200, 32)       128       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 64, 200, 32)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 64, 200, 32)       1056      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 64, 200, 32)       128       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 64, 200, 32)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 32, 100, 32)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 32, 100, 64)       18496     \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 32, 100, 64)       256       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 32, 100, 64)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 32, 100, 64)       4160      \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 32, 100, 64)       256       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 32, 100, 64)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 16, 50, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 16, 50, 128)       73856     \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 16, 50, 128)       512       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)    (None, 16, 50, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 16, 50, 128)       16512     \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 16, 50, 128)       512       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_6 (LeakyReLU)    (None, 16, 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)        65792     \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, 25, 256)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 4, 25, 256)        590080    \n",
      "_________________________________________________________________\n",
      "batch_normalization_9 (Batch (None, 4, 25, 256)        1024      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_9 (LeakyReLU)    (None, 4, 25, 256)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 4, 25, 256)        65792     \n",
      "_________________________________________________________________\n",
      "batch_normalization_10 (Batc (None, 4, 25, 256)        1024      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_10 (LeakyReLU)   (None, 4, 25, 256)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 2, 25, 256)        0         \n",
      "_________________________________________________________________\n",
      "permute_1 (Permute)          (None, 25, 2, 256)        0         \n",
      "_________________________________________________________________\n",
      "time_distributed_1 (TimeDist (None, 25, 512)           0         \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 25, 256)           492288    \n",
      "_________________________________________________________________\n",
      "bidirectional_2 (Bidirection (None, 25, 256)           295680    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 25, 17)            4369      \n",
      "=================================================================\n",
      "Total params: 1,930,033\n",
      "Trainable params: 1,927,089\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",
    "from keras.models import *\n",
    "from keras.layers import *\n",
    "\n",
    "# 定义 CTC Loss\n",
    "import keras.backend as K\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",
    "        kernel_size = 3 if j==0 else 1\n",
    "        x = Conv2D(32*2**min(i, 3), kernel_size=kernel_size, 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 < 3 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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-72-b54dab4a343e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'ctc'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e-3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mamsgrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m model.fit_generator(train_data, epochs=100, validation_data=valid_data, workers=4, use_multiprocessing=True,\n\u001b[0;32m---> 16\u001b[0;31m                    callbacks=callbacks)\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/tf1.15/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1294\u001b[0m         \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1295\u001b[0m         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1296\u001b[0;31m         steps_name='steps_per_epoch')\n\u001b[0m\u001b[1;32m   1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m   def evaluate_generator(self,\n",
      "\u001b[0;32m~/anaconda3/envs/tf1.15/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mmodel_iteration\u001b[0;34m(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs)\u001b[0m\n\u001b[1;32m    219\u001b[0m     \u001b[0mstep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mtarget_steps\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m       \u001b[0mbatch_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_next_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mbatch_data\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_dataset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf1.15/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_generator.py\u001b[0m in \u001b[0;36m_get_next_batch\u001b[0;34m(generator)\u001b[0m\n\u001b[1;32m    361\u001b[0m   \u001b[0;34m\"\"\"Retrieves the next batch of input data.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 363\u001b[0;31m     \u001b[0mgenerator_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    364\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    365\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf1.15/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    781\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    782\u001b[0m       \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 783\u001b[0;31m         \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    784\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask_done\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    785\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf1.15/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    637\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 638\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    639\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf1.15/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    633\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    634\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 635\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    637\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf1.15/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    549\u001b[0m             \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    552\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf1.15/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    293\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from keras.callbacks import EarlyStopping, CSVLogger, ModelCheckpoint\n",
    "from keras.optimizers import *\n",
    "import gc \n",
    "\n",
    "# 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_arithmetic_ctc_best_20220617.h5')\n",
    "\n",
    "train_data = CaptchaSequence(characters, batch_size=128, steps=1000,input_length=25, label_length=12,chars_len=(4, 6)) # (characters, batch_size=128, steps=1000)\n",
    "valid_data = CaptchaSequence(characters, batch_size=128, steps=100,input_length=25, label_length=12,chars_len=(4, 6))  # (characters, batch_size=128, steps=100)\n",
    "\n",
    "callbacks = [EarlyStopping(patience=5),ModelCheckpoint('gru_arithmetic_ctc_best_20260309.h5', save_best_only=True)] # gru_arithmetic_ctc_best_20220617\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=100, validation_data=valid_data, workers=4, use_multiprocessing=True,\n",
    "                   callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/optimizers.py:757: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:977: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:964: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
      "\n",
      "Epoch 1/100\n",
      "1000/1000 [==============================] - 580s 580ms/step - loss: 0.1263 - val_loss: 0.0394\n",
      "Epoch 2/100\n",
      "1000/1000 [==============================] - 578s 578ms/step - loss: 0.0208 - val_loss: 0.0144\n",
      "Epoch 3/100\n",
      "1000/1000 [==============================] - 578s 578ms/step - loss: 0.0113 - val_loss: 0.0093\n",
      "Epoch 4/100\n",
      "1000/1000 [==============================] - 582s 582ms/step - loss: 0.0084 - val_loss: 0.0103\n",
      "Epoch 5/100\n",
      "1000/1000 [==============================] - 579s 579ms/step - loss: 0.0066 - val_loss: 0.0026\n",
      "Epoch 6/100\n",
      "1000/1000 [==============================] - 580s 580ms/step - loss: 0.0050 - val_loss: 0.0083\n",
      "Epoch 7/100\n",
      "1000/1000 [==============================] - 580s 580ms/step - loss: 0.0048 - val_loss: 0.0053\n",
      "Epoch 8/100\n",
      "1000/1000 [==============================] - 577s 577ms/step - loss: 0.0043 - val_loss: 0.0014\n",
      "Epoch 9/100\n",
      "1000/1000 [==============================] - 576s 576ms/step - loss: 0.0039 - val_loss: 0.0116\n",
      "Epoch 10/100\n",
      "1000/1000 [==============================] - 580s 580ms/step - loss: 0.0033 - val_loss: 0.0020\n",
      "Epoch 11/100\n",
      "1000/1000 [==============================] - 576s 576ms/step - loss: 0.0040 - val_loss: 8.5879e-04\n",
      "Epoch 12/100\n",
      "1000/1000 [==============================] - 580s 580ms/step - loss: 0.0032 - val_loss: 8.2029e-04\n",
      "Epoch 13/100\n",
      "1000/1000 [==============================] - 586s 586ms/step - loss: 0.0035 - val_loss: 0.0017\n",
      "Epoch 14/100\n",
      "1000/1000 [==============================] - 586s 586ms/step - loss: 0.0032 - val_loss: 0.0045\n",
      "Epoch 15/100\n",
      "1000/1000 [==============================] - 584s 584ms/step - loss: 0.0030 - val_loss: 0.0015\n",
      "Epoch 16/100\n",
      "1000/1000 [==============================] - 581s 581ms/step - loss: 0.0026 - val_loss: 0.0020\n",
      "Epoch 17/100\n",
      "1000/1000 [==============================] - 579s 579ms/step - loss: 0.0025 - val_loss: 0.0021\n",
      "Epoch 18/100\n",
      "1000/1000 [==============================] - 578s 578ms/step - loss: 0.0025 - val_loss: 0.0052\n",
      "Epoch 19/100\n",
      "1000/1000 [==============================] - 576s 576ms/step - loss: 0.0027 - val_loss: 0.0031\n",
      "Epoch 20/100\n",
      "1000/1000 [==============================] - 579s 579ms/step - loss: 0.0025 - val_loss: 0.0028\n",
      "Epoch 21/100\n",
      "1000/1000 [==============================] - 577s 577ms/step - loss: 0.0021 - val_loss: 0.0044\n",
      "Epoch 22/100\n",
      "1000/1000 [==============================] - 577s 577ms/step - loss: 0.0026 - val_loss: 0.0012\n",
      "Epoch 23/100\n",
      "1000/1000 [==============================] - 579s 579ms/step - loss: 0.0023 - val_loss: 3.5396e-04\n",
      "Epoch 24/100\n",
      "1000/1000 [==============================] - 578s 578ms/step - loss: 0.0025 - val_loss: 7.9764e-04\n",
      "Epoch 25/100\n",
      "1000/1000 [==============================] - 580s 580ms/step - loss: 0.0026 - val_loss: 0.0028\n",
      "Epoch 26/100\n",
      "1000/1000 [==============================] - 580s 580ms/step - loss: 0.0021 - val_loss: 0.0013\n",
      "Epoch 27/100\n",
      "1000/1000 [==============================] - 579s 579ms/step - loss: 0.0021 - val_loss: 4.9656e-04\n",
      "Epoch 28/100\n",
      "1000/1000 [==============================] - 581s 581ms/step - loss: 0.0023 - val_loss: 0.0094\n",
      "Epoch 29/100\n",
      "1000/1000 [==============================] - 583s 583ms/step - loss: 0.0021 - val_loss: 0.0015\n",
      "Epoch 30/100\n",
      "1000/1000 [==============================] - 581s 581ms/step - loss: 0.0021 - val_loss: 0.0016\n",
      "Epoch 31/100\n",
      "1000/1000 [==============================] - 583s 583ms/step - loss: 0.0023 - val_loss: 0.0041\n",
      "Epoch 32/100\n",
      "1000/1000 [==============================] - 581s 581ms/step - loss: 0.0020 - val_loss: 0.0041\n",
      "Epoch 33/100\n",
      "1000/1000 [==============================] - 577s 577ms/step - loss: 0.0024 - val_loss: 6.3811e-04\n",
      "Epoch 34/100\n",
      "1000/1000 [==============================] - 576s 576ms/step - loss: 0.0020 - val_loss: 3.2414e-04\n",
      "Epoch 35/100\n",
      "1000/1000 [==============================] - 582s 582ms/step - loss: 0.0020 - val_loss: 0.0058\n",
      "Epoch 36/100\n",
      "1000/1000 [==============================] - 580s 580ms/step - loss: 0.0023 - val_loss: 9.2458e-04\n",
      "Epoch 37/100\n",
      "1000/1000 [==============================] - 586s 586ms/step - loss: 0.0018 - val_loss: 3.5871e-04\n",
      "Epoch 38/100\n",
      "1000/1000 [==============================] - 581s 581ms/step - loss: 0.0018 - val_loss: 0.0041\n",
      "Epoch 39/100\n",
      "1000/1000 [==============================] - 577s 577ms/step - loss: 0.0026 - val_loss: 7.2181e-04\n",
      "Epoch 40/100\n",
      "1000/1000 [==============================] - 578s 578ms/step - loss: 0.0022 - val_loss: 9.8201e-04\n",
      "Epoch 41/100\n",
      "1000/1000 [==============================] - 579s 579ms/step - loss: 0.0021 - val_loss: 0.0011\n",
      "Epoch 42/100\n",
      "1000/1000 [==============================] - 572s 572ms/step - loss: 0.0022 - val_loss: 4.1537e-04\n",
      "Epoch 43/100\n",
      "1000/1000 [==============================] - 574s 574ms/step - loss: 0.0017 - val_loss: 0.0014\n",
      "Epoch 44/100\n",
      "1000/1000 [==============================] - 574s 574ms/step - loss: 0.0019 - val_loss: 0.0026\n",
      "Epoch 45/100\n",
      "1000/1000 [==============================] - 572s 572ms/step - loss: 0.0020 - val_loss: 8.7554e-04\n",
      "Epoch 46/100\n",
      "1000/1000 [==============================] - 573s 573ms/step - loss: 0.0017 - val_loss: 8.3964e-04\n",
      "Epoch 47/100\n",
      "1000/1000 [==============================] - 574s 574ms/step - loss: 0.0018 - val_loss: 9.4485e-04\n",
      "Epoch 48/100\n",
      "1000/1000 [==============================] - 575s 575ms/step - loss: 0.0019 - val_loss: 0.0020\n",
      "Epoch 49/100\n",
      "1000/1000 [==============================] - 575s 575ms/step - loss: 0.0015 - val_loss: 2.6012e-04\n",
      "Epoch 50/100\n",
      "1000/1000 [==============================] - 574s 574ms/step - loss: 0.0015 - val_loss: 8.4379e-04\n",
      "Epoch 51/100\n",
      "1000/1000 [==============================] - 578s 578ms/step - loss: 0.0017 - val_loss: 0.0028\n",
      "Epoch 52/100\n",
      "1000/1000 [==============================] - 576s 576ms/step - loss: 0.0014 - val_loss: 0.0046\n",
      "Epoch 53/100\n",
      "1000/1000 [==============================] - 579s 579ms/step - loss: 0.0014 - val_loss: 0.0012\n",
      "Epoch 54/100\n",
      "1000/1000 [==============================] - 573s 573ms/step - loss: 0.0014 - val_loss: 9.4104e-04\n",
      "Epoch 55/100\n",
      "1000/1000 [==============================] - 573s 573ms/step - loss: 0.0014 - val_loss: 7.8984e-04\n",
      "Epoch 56/100\n",
      "1000/1000 [==============================] - 571s 571ms/step - loss: 0.0017 - val_loss: 1.4760e-04\n",
      "Epoch 57/100\n",
      "1000/1000 [==============================] - 577s 577ms/step - loss: 0.0014 - val_loss: 0.0029\n",
      "Epoch 58/100\n",
      "1000/1000 [==============================] - 573s 573ms/step - loss: 0.0018 - val_loss: 0.0045\n",
      "Epoch 59/100\n",
      "1000/1000 [==============================] - 573s 573ms/step - loss: 0.0017 - val_loss: 0.0051\n",
      "Epoch 60/100\n",
      "1000/1000 [==============================] - 573s 573ms/step - loss: 0.0017 - val_loss: 2.7455e-04\n",
      "Epoch 61/100\n",
      "1000/1000 [==============================] - 573s 573ms/step - loss: 0.0015 - val_loss: 3.9017e-04\n",
      "Epoch 62/100\n",
      "1000/1000 [==============================] - 574s 574ms/step - loss: 0.0015 - val_loss: 2.4035e-04\n",
      "Epoch 63/100\n",
      "1000/1000 [==============================] - 573s 573ms/step - loss: 0.0020 - val_loss: 0.0031\n",
      "Epoch 64/100\n",
      "1000/1000 [==============================] - 574s 574ms/step - loss: 0.0015 - val_loss: 0.0018\n",
      "Epoch 65/100\n",
      "1000/1000 [==============================] - 574s 574ms/step - loss: 0.0013 - val_loss: 3.4714e-04\n",
      "Epoch 66/100\n",
      "1000/1000 [==============================] - 570s 570ms/step - loss: 0.0015 - val_loss: 0.0053\n",
      "Epoch 67/100\n",
      "1000/1000 [==============================] - 572s 572ms/step - loss: 0.0020 - val_loss: 4.9530e-04\n",
      "Epoch 68/100\n",
      "1000/1000 [==============================] - 573s 573ms/step - loss: 0.0019 - val_loss: 3.7684e-04\n",
      "Epoch 69/100\n",
      "1000/1000 [==============================] - 575s 575ms/step - loss: 0.0016 - val_loss: 0.0016\n",
      "Epoch 70/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000/1000 [==============================] - 572s 572ms/step - loss: 0.0014 - val_loss: 8.6671e-04\n",
      "Epoch 71/100\n",
      "1000/1000 [==============================] - 573s 573ms/step - loss: 0.0019 - val_loss: 1.8328e-04\n",
      "Epoch 72/100\n",
      "1000/1000 [==============================] - 572s 572ms/step - loss: 0.0015 - val_loss: 0.0047\n",
      "Epoch 73/100\n",
      "1000/1000 [==============================] - 570s 570ms/step - loss: 0.0015 - val_loss: 4.9386e-04\n",
      "Epoch 74/100\n",
      "1000/1000 [==============================] - 572s 572ms/step - loss: 0.0016 - val_loss: 0.0015\n",
      "Epoch 75/100\n",
      "1000/1000 [==============================] - 573s 573ms/step - loss: 0.0013 - val_loss: 5.5904e-04\n",
      "Epoch 76/100\n",
      "1000/1000 [==============================] - 577s 577ms/step - loss: 0.0015 - val_loss: 2.7603e-04\n",
      "Epoch 77/100\n",
      "1000/1000 [==============================] - 572s 572ms/step - loss: 0.0019 - val_loss: 0.0030\n",
      "Epoch 78/100\n",
      "1000/1000 [==============================] - 574s 574ms/step - loss: 0.0020 - val_loss: 0.0019\n",
      "Epoch 79/100\n",
      "1000/1000 [==============================] - 575s 575ms/step - loss: 0.0013 - val_loss: 4.7126e-04\n",
      "Epoch 80/100\n",
      "1000/1000 [==============================] - 574s 574ms/step - loss: 0.0014 - val_loss: 4.5830e-04\n",
      "Epoch 81/100\n",
      "1000/1000 [==============================] - 573s 573ms/step - loss: 0.0014 - val_loss: 1.6494e-04\n",
      "Epoch 82/100\n",
      "1000/1000 [==============================] - 573s 573ms/step - loss: 0.0014 - val_loss: 1.9709e-04\n",
      "Epoch 83/100\n",
      "1000/1000 [==============================] - 574s 574ms/step - loss: 0.0018 - val_loss: 0.0059\n",
      "Epoch 84/100\n",
      "1000/1000 [==============================] - 575s 575ms/step - loss: 0.0016 - val_loss: 0.0019\n",
      "Epoch 85/100\n",
      "1000/1000 [==============================] - 574s 574ms/step - loss: 0.0014 - val_loss: 0.0037\n",
      "Epoch 86/100\n",
      "1000/1000 [==============================] - 574s 574ms/step - loss: 0.0016 - val_loss: 2.1207e-04\n",
      "Epoch 87/100\n",
      "1000/1000 [==============================] - 572s 572ms/step - loss: 0.0016 - val_loss: 4.4403e-04\n",
      "Epoch 88/100\n",
      "1000/1000 [==============================] - 574s 574ms/step - loss: 0.0014 - val_loss: 0.0027\n",
      "Epoch 89/100\n",
      "1000/1000 [==============================] - 574s 574ms/step - loss: 0.0013 - val_loss: 7.4963e-04\n",
      "Epoch 90/100\n",
      "1000/1000 [==============================] - 574s 574ms/step - loss: 0.0013 - val_loss: 1.4529e-04\n",
      "Epoch 91/100\n",
      " 592/1000 [================>.............] - ETA: 3:47 - loss: 0.0017"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process ForkPoolWorker-1048:\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/PIL/ImageFont.py\", line 677, in getmask2\n",
      "    text, im.id, mode, direction, features, language, stroke_width, ink\n",
      "Process ForkPoolWorker-1046:\n",
      "Process ForkPoolWorker-1045:\n",
      "Process ForkPoolWorker-1047:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/multiprocessing/pool.py\", line 119, in worker\n",
      "    result = (True, func(*args, **kwds))\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/multiprocessing/pool.py\", line 119, in worker\n",
      "    result = (True, func(*args, **kwds))\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/utils/data_utils.py\", line 401, in get_index\n",
      "    return _SHARED_SEQUENCES[uid][i]\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/utils/data_utils.py\", line 401, in get_index\n",
      "    return _SHARED_SEQUENCES[uid][i]\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"<ipython-input-6-de132363d14e>\", line 64, in __getitem__\n",
      "    point_color=(0,0),frame_color=(120,150),wavy=(0,0), bg=(255,255))\n",
      "  File \"<ipython-input-6-de132363d14e>\", line 58, in __getitem__\n",
      "    point_color=(200,250),frame_color=None,wavy=(0,0), bg=(210,255))\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/multiprocessing/pool.py\", line 119, in worker\n",
      "    result = (True, func(*args, **kwds))\n",
      "  File \"<ipython-input-2-b401e0775457>\", line 184, in gen_captcha\n",
      "    char_imgs.append(get_char_img(char, font, font_color=char_color, rotate=rotate, bg=bg, font_noise=font_noise))\n",
      "  File \"<ipython-input-2-b401e0775457>\", line 184, in gen_captcha\n",
      "    char_imgs.append(get_char_img(char, font, font_color=char_color, rotate=rotate, bg=bg, font_noise=font_noise))\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/utils/data_utils.py\", line 401, in get_index\n",
      "    return _SHARED_SEQUENCES[uid][i]\n",
      "  File \"<ipython-input-2-b401e0775457>\", line 158, in get_char_img\n",
      "    ImageDraw.Draw(im).text((0,0), char, font=font, fill=font_color)\n",
      "  File \"<ipython-input-6-de132363d14e>\", line 70, in __getitem__\n",
      "    point_color=(0,0),frame_color=None,wavy=(0,0), bg=(150,255))\n",
      "  File \"<ipython-input-2-b401e0775457>\", line 158, in get_char_img\n",
      "    ImageDraw.Draw(im).text((0,0), char, font=font, fill=font_color)\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/PIL/ImageDraw.py\", line 463, in text\n",
      "    draw_text(ink)\n",
      "  File \"<ipython-input-2-b401e0775457>\", line 188, in gen_captcha\n",
      "    char_imgs.append(get_char_img(char, font, font_color=random_color(font_color), rotate=rotate, bg=bg, font_noise=font_noise))\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/PIL/ImageDraw.py\", line 463, in text\n",
      "    draw_text(ink)\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/PIL/ImageDraw.py\", line 418, in draw_text\n",
      "    **kwargs,\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/PIL/ImageDraw.py\", line 418, in draw_text\n",
      "    **kwargs,\n",
      "  File \"<ipython-input-2-b401e0775457>\", line 158, in get_char_img\n",
      "    ImageDraw.Draw(im).text((0,0), char, font=font, fill=font_color)\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/PIL/ImageFont.py\", line 677, in getmask2\n",
      "    text, im.id, mode, direction, features, language, stroke_width, ink\n",
      "KeyboardInterrupt\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/PIL/ImageDraw.py\", line 463, in text\n",
      "    draw_text(ink)\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/PIL/ImageDraw.py\", line 418, in draw_text\n",
      "    **kwargs,\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/multiprocessing/pool.py\", line 119, in worker\n",
      "    result = (True, func(*args, **kwds))\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/PIL/ImageFont.py\", line 670, in getmask2\n",
      "    text, mode, direction, features, language, anchor\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/utils/data_utils.py\", line 401, in get_index\n",
      "    return _SHARED_SEQUENCES[uid][i]\n",
      "KeyboardInterrupt\n",
      "  File \"<ipython-input-6-de132363d14e>\", line 91, in __getitem__\n",
      "    point_color=(50,255),frame_color=None,wavy=(0,0), bg=(10,150))\n",
      "  File \"<ipython-input-2-b401e0775457>\", line 188, in gen_captcha\n",
      "    char_imgs.append(get_char_img(char, font, font_color=random_color(font_color), rotate=rotate, bg=bg, font_noise=font_noise))\n",
      "  File \"<ipython-input-2-b401e0775457>\", line 156, in get_char_img\n",
      "    w, h = draw.textsize(char, font=font)\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/PIL/ImageDraw.py\", line 563, in textsize\n",
      "    return font.getsize(text, direction, features, language, stroke_width)\n",
      "  File \"/home/python/anaconda3/envs/tf1.15/lib/python3.6/site-packages/PIL/ImageFont.py\", line 432, in getsize\n",
      "    size, offset = self.font.getsize(text, \"L\", direction, features, language)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-81e0a92f05ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'ctc'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e-4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mamsgrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m model.fit_generator(train_data, epochs=100, validation_data=valid_data, workers=4, use_multiprocessing=True,\n\u001b[0;32m---> 17\u001b[0;31m                     callbacks=callbacks)\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name +\n\u001b[1;32m     90\u001b[0m                               '` call to the Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   2192\u001b[0m                 \u001b[0mbatch_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2193\u001b[0m                 \u001b[0;32mwhile\u001b[0m \u001b[0msteps_done\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2194\u001b[0;31m                     \u001b[0mgenerator_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2196\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__len__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf1.15/lib/python3.6/site-packages/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 578\u001b[0;31m                 \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    579\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask_done\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf1.15/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    637\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 638\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    639\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf1.15/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    633\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    634\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 635\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    637\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf1.15/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    549\u001b[0m             \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    552\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf1.15/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    293\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from keras.callbacks import EarlyStopping, CSVLogger, ModelCheckpoint\n",
    "from keras.optimizers import *\n",
    "import gc \n",
    "train_data = CaptchaSequence(characters, batch_size=128, steps=1000,input_length=25, label_length=12,chars_len=(4, 6)) # (characters, batch_size=128, steps=1000)\n",
    "valid_data = CaptchaSequence(characters, batch_size=128, steps=10,input_length=25, label_length=12,chars_len=(4, 6))  # (characters, batch_size=128, steps=100)\n",
    "\n",
    "callbacks = [CSVLogger('ctc.csv', append=True), ModelCheckpoint('gru_arithmetic_ctc_best_20260309.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_1104.h5') # 1104 卷积核 3 5 ，1105卷积核3 1\n",
    "model.load_weights('gru_arithmetic_ctc_best_20220617.h5')\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",
    "model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=Adam(1e-4, amsgrad=True))\n",
    "model.fit_generator(train_data, epochs=100, validation_data=valid_data, workers=4, use_multiprocessing=True,\n",
    "                    callbacks=callbacks)"
   ]
  },
  {
   "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": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# base_model.save('gru_arithmetic_base_model_20220620.h5') # 保存基础模型，预测用\n",
    "\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]])\n",
    "\n",
    "def decode_arith(arith = '2×?=12'):\n",
    "    arith = arith.replace('×', '*')\n",
    "    items = re.split('=', arith)\n",
    "    if len(items)==2:\n",
    "        if items[-1] in ['?', '']:\n",
    "            return eval(items[0])\n",
    "        l = re.split('-|\\+|\\*', items[0])\n",
    "        signs = re.findall('-|\\+|\\*', items[0])\n",
    "        if len(l)==2 and len(signs)==1:\n",
    "            if l[1] == '?':\n",
    "                if signs[0] == '+':\n",
    "                    return eval('%s-%s'%(items[-1], l[0]))\n",
    "                elif signs[0] == '-':\n",
    "                    return eval('%s-%s'%(l[0],items[-1]))\n",
    "                elif signs[0] == '*':\n",
    "                    return eval('%s/%s'%(items[-1], l[0]))                   \n",
    "            elif l[0] == '?':\n",
    "                if signs[0] == '+':\n",
    "                    return eval('%s-%s'%(items[-1], l[1]))\n",
    "                elif signs[0] == '-':\n",
    "                    return eval('%s+%s'%(l[1],items[-1]))\n",
    "                elif signs[0] == '*':\n",
    "                    return eval('%s/%s'%(items[-1], l[1]))  \n",
    "    return ''\n",
    "def decode_arith(arith = '2×?=12'):\n",
    "    arith = arith.replace('×', '*')\n",
    "    if re.search('^(\\d+|\\?)([\\+\\-\\*/](\\d+|\\?))+=(\\d+|\\?)?$', arith) and len(re.findall('\\?', arith))<=1:\n",
    "        if arith[-1] == '?':\n",
    "            answer = str(int(eval(arith[:-2])))\n",
    "        elif arith[-1] == '=':\n",
    "            answer = str(int(eval(arith[:-1])))    \n",
    "        elif re.search('^(\\d+|\\?)[\\+\\-\\*/](\\d+|\\?)=\\d+$', arith):\n",
    "            a,sign,b,_,quest = re.split('(\\+|\\-|\\*|×|/|=)', arith)\n",
    "            if a=='?':\n",
    "                if sign==\"+\":\n",
    "                    sign = '-'\n",
    "                elif sign=='-':\n",
    "                    sign = '+'\n",
    "                elif sign==\"*\":\n",
    "                    sign = '/'\n",
    "                elif sign=='/':\n",
    "                    sign = '*'\n",
    "                a, quest = quest, a\n",
    "            elif b == '?':\n",
    "                if sign==\"+\":\n",
    "                    sign = '-'  \n",
    "                    b, quest = quest, b\n",
    "                    a, b = b, a                \n",
    "                elif sign=='-':\n",
    "                    b, quest = quest, b                \n",
    "                elif sign==\"*\":\n",
    "                    sign = '/'\n",
    "                    b, quest = quest, b\n",
    "                    a, b = b, a                   \n",
    "                elif sign=='/':\n",
    "                    b, quest = quest, b   \n",
    "            else:\n",
    "                print('公式出错:', arith)\n",
    "            answer = str(int(eval('%s%s%s'%(a,sign,b)))) \n",
    "        else:\n",
    "            print('公式出错:', arith)\n",
    "    elif re.search('^\\d+[\\+\\-\\*/]\\d+$', arith):\n",
    "        answer = str(int(eval(arith)))            \n",
    "    else:\n",
    "        answer = ''\n",
    "    return answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "out ccntq\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ccntq')"
      ]
     },
     "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": 163,
       "width": 370
      },
      "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",
    "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": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "总耗时： 36.182950258255005\n",
      "正确数：1000， 错误数：0, 总样本：1000, 准确率：1.0000\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "data = CaptchaSequence(characters, batch_size=200, steps=5, input_length=25, 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_5.h5') \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)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'pre: 51-1+5=?, lab: 5-1+5=?')"
      ]
     },
     "execution_count": 15,
     "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": 153,
       "width": 370
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 3\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\n",
    "# # model.load_weights('gru_english4to6_ctc_best_1105.h5')\n",
    "# # model.load_weights('gru_arithmetic_ctc_best_1108.h5')\n",
    "# paths = glob.glob('/data/captcha/arithmetic/330_69/*.jpg') # 100_26 70_25 100_40  330_69\n",
    "\n",
    "# i = 12\n",
    "# img = Image.open(paths[i])\n",
    "# img2 = img.resize((width, height), Image.BILINEAR)\n",
    "# # img_arr = [np.array(img2)/255.0]\n",
    "# # out_pre = decode([img_arr, np.ones((1,))])\n",
    "# # out = ''.join([characters[x] for x in out_pre[0][0]])\n",
    "# # print('out', out)\n",
    "# plt.imshow(img)\n",
    "# plt.show()\n",
    "# plt.imshow(img2)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正确数：99, 总数：99, 准确率：1.0000\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_arithmetic_ctc_best_20220617.h5')\n",
    "model.load_weights('gru_arithmetic_ctc_best_20260309.h5')\n",
    "\n",
    "# path3 = '/data/captcha/arithmetic/100_40/*.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",
    "# path3 = '/data/captcha/arithmetic/100_26/*.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",
    "\n",
    "# path3 = '/data/captcha/arithmetic/160_60/*.jpg'\n",
    "path3 = '/data/captcha/arithmetic/146_46/*.jpg'\n",
    "with open('/data/captcha/arithmetic/146_46/answer.txt', 'r', encoding='utf-8') as f:\n",
    "    lines = f.readlines()\n",
    "d = dict()\n",
    "for line in lines:\n",
    "    f_n, q, a = line.strip().split('\\t')  \n",
    "    d[f_n] = a\n",
    "err_imgs = []\n",
    "err_labels = []\n",
    "files = glob.glob(path3)\n",
    "sp = int(len(files)*0.9)\n",
    "# sp = min(int(len(files)*0.8), 3000)\n",
    "for file in files[sp:]:\n",
    "    name = file.split('/')[-1]\n",
    "    if name not in d:\n",
    "        print(name)\n",
    "        continue\n",
    "    try:\n",
    "        img = Image.open(file)\n",
    "    except:\n",
    "        print('打开错误：',file)\n",
    "        continue\n",
    "\n",
    "#     label = file.split('_')[-1][:-4].lower()  # 答案在文件名_分割的情况\n",
    "    label = d[name]  #答案在answer.txt 文件中的情况\n",
    "\n",
    "    img = img.resize((width, height), Image.BILINEAR)\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",
    "    \n",
    "    try:\n",
    "        gs = out\n",
    "        out = decode_arith(arith = out)\n",
    "        out = str(int(out))\n",
    "    except:\n",
    "        print('计算错误:输出公式：',gs)\n",
    "    \n",
    "    if label == out:\n",
    "        pos += 1\n",
    "    else:\n",
    "        neg += 1\n",
    "        print('预测错误',label, out, label==out)\n",
    "        err_imgs.append(img)\n",
    "        err_labels.append('label:%s  pred:%s  pred_gs:%s'%(label,out,gs))\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)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测错误 90+1 90+7 False\n",
      "预测错误 1+0 7+0 False\n",
      "预测错误 96+0 96-0 False\n",
      "预测错误 72-34 72-4 False\n",
      "预测错误 13+67 18+67 False\n",
      "预测错误 88-8 88-80 False\n",
      "预测错误 74+12 14+12 False\n",
      "预测错误 11+71 11+1 False\n",
      "预测错误 21-8 27-8 False\n",
      "正确数：86, 总数：95, 准确率：0.9053\n"
     ]
    }
   ],
   "source": [
    "'''预测AI标注验证码'''\n",
    "err_imgs = []\n",
    "err_labels = []\n",
    "pos = neg = 0\n",
    "files = ai_files3\n",
    "sp = int(len(files)*0.9)\n",
    "# sp = min(int(len(files)*0.8), 3000)\n",
    "for file in files[sp:]:\n",
    "    name = file.split('/')[-1]\n",
    "    if name not in ai_lab_dic:\n",
    "        print(name)\n",
    "        continue\n",
    "    try:\n",
    "        img = Image.open(file)\n",
    "    except:\n",
    "        print('打开错误：',file)\n",
    "        continue\n",
    "    label = ai_lab_dic[name]\n",
    "    img = img.resize((width, height), Image.BILINEAR)\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",
    "    \n",
    "    try:\n",
    "        gs = out\n",
    "        answer = decode_arith(arith = out)\n",
    "#         print(gs, answer)\n",
    "    except:\n",
    "        print('计算错误:输出公式：',gs)\n",
    "    \n",
    "    if label == out:\n",
    "        pos += 1\n",
    "#         print('预测正确',label, out, label==out)\n",
    "    else:\n",
    "        neg += 1\n",
    "        print('预测错误',label, out, label==out)\n",
    "        err_imgs.append(img)\n",
    "        err_labels.append('label:%s  pred:%s  pred_gs:%s'%(label,out,gs))\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)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 153,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 153,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 153,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 153,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 153,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 153,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 153,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 153,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 153,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "for i in range(min(10, len(err_imgs))):\n",
    "    plt.imshow(err_imgs[i])\n",
    "    plt.title(err_labels[i])\n",
    "    plt.show()\n",
    "    time.sleep(0.5)\n",
    "    if i > 10:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/data/python/lishimin/linuxPro/captcha_pro\n"
     ]
    }
   ],
   "source": [
    "# decode_arith('10×?=60')\n",
    "# base_model.save('gru_arithmetic_base_model_20260310.h5') # 保存基础模型，预测用\n",
    "print(os.getcwd())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}