{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abcdefghijklmnopqrstuvwxyz0123456789\n",
      "15 ['latin/BOOKOS.TTF', 'latin/Candarab.ttf', 'latin/corbeli.ttf', 'latin/ARIALN.TTF', 'latin/CALISTBI.TTF', 'latin/SCHLBKI.TTF', 'latin/CENTURY.TTF', 'latin/ARIALNBI.TTF', 'latin/consolai.ttf', 'latin/ANTQUAB.TTF', 'latin/verdana.ttf', 'latin/ARIALNB.TTF', 'latin/LBRITEI.TTF', 'latin/tahoma.ttf', 'latin/Candara.ttf']\n",
      "n_class:37, n_len:6\n",
      "15 latin/BOOKOS.TTF\n"
     ]
    }
   ],
   "source": [
    "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",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import string\n",
    "characters = string.ascii_lowercase + string.digits # 验证码字符集合数字+英文  全部转为小写格式\n",
    "print(characters)\n",
    "\n",
    "width, height, n_len, n_class = 200, 70, 6, len(characters) + 1  #图片宽、高，验证码最大长度，分类\n",
    "# width, height, n_len, n_class = 128, 64, 6, len(characters) + 1  #图片宽、高，验证码最大长度，分类\n",
    "# fonts=glob.glob('fonts/english/*')\n",
    "# fonts = glob.glob('latin2/*')\n",
    "# fonts.remove('latin2/ANTQUABI.TTF') # n,u 不清晰删除\n",
    "\n",
    "font_paths = glob.glob('latin/*')\n",
    "# font_list = ['ARIALN.TTF', 'ARIALNI.TTF', 'BKANT.TTF', 'calibrii.ttf', 'calibrili.ttf','Calibrib.ttf', 'CALISTI.TTF','cambriai.ttf','LSANS.TTF','CENSCBK.TTF']\n",
    "font_list = ['ANTQUAB.TTF', 'ARIALN.TTF', 'ARIALNB.TTF', 'ARIALNBI.TTF', 'BOOKOS.TTF','CALISTBI.TTF', 'Candara.ttf', 'Candarab.ttf','CENTURY.TTF','corbeli.ttf',\n",
    "             'consolai.ttf','LBRITEI.TTF','SCHLBKI.TTF','tahoma.ttf', 'verdana.ttf']\n",
    "\n",
    "fonts = []\n",
    "for font in font_paths:\n",
    "    if font.split('/')[-1] in font_list:\n",
    "        fonts.append(font)\n",
    "print(len(fonts), fonts[:])\n",
    "\n",
    "print('n_class:%d, n_len:%d'%(n_class, n_len))\n",
    "print(len(fonts), fonts[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image size (122, 48) (200, 70)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3600x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 2224
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random_color(rs, re, gs, ge, bs, be) (19, 16, 10)\n",
      "rand 0.3371096892297716\n"
     ]
    }
   ],
   "source": [
    "'''生成彩色图像'''\n",
    "def get_wavy_line(w = (0, 100),h = (30, 50)):\n",
    "    '''产生波浪线坐标'''\n",
    "    import random\n",
    "    n = 50\n",
    "    x = 0\n",
    "    y = random.randint(h[0],h[1])\n",
    "    flag = random.randint(0,2)\n",
    "    xy = [(x, y)]\n",
    "    while x < w[1]:\n",
    "        temp_y = random.randint(1, 3)\n",
    "        temp_x = random.randint(5, 10)\n",
    "        if flag == 0:\n",
    "            if y + temp_y > h[1]:\n",
    "                y -= temp_y\n",
    "                flag = 1\n",
    "            else:\n",
    "                y += temp_y\n",
    "        else:\n",
    "            if y - temp_y < h[0]:\n",
    "                y += temp_y\n",
    "                flag = 0\n",
    "            else:\n",
    "                y -= temp_y\n",
    "        x = x+temp_x if x+temp_x < w[1] else w[1]\n",
    "        xy.append((x, y))\n",
    "    return xy\n",
    "def Asin(x, A=8,w=0.05, b=6, k=40):\n",
    "    '''\n",
    "    y=Asin(ωx+φ)+k在直角坐标系上的图象\n",
    "    A——振幅，当物体作轨迹符合正弦曲线的直线往复运动时，其值为行程的1/2。\n",
    "    (ωx+φ)——相位，反映变量y所处的状态。\n",
    "    φ——初相，x=0时的相位；反映在坐标系上则为图像的左右移动。\n",
    "    k——偏距，反映在坐标系上则为图像的上移或下移。\n",
    "    ω——角速度， 控制正弦周期(单位弧度内震动的次数)。\n",
    "    '''\n",
    "    return A*math.sin(w*x+b)+k\n",
    "\n",
    "def random_xy(width,height):   \n",
    "    '''\n",
    "    随机位置函数，返回指定范围随机位置坐标\n",
    "    参数：width:图片宽，height:图片高\n",
    "    '''\n",
    "    x = random.randint(0, width)\n",
    "    y = random.randint(0, height)\n",
    "    return x, y\n",
    "def random_color(color_tuple):\n",
    "    '''\n",
    "    随机颜色函数，返回指定范围随机颜色值\n",
    "    参数：start:颜色最低值，end:颜色最高值\n",
    "    '''\n",
    "    if len(color_tuple)==2:\n",
    "        rs, re = color_tuple\n",
    "        gs = bs = rs\n",
    "        ge = be = re\n",
    "    else:\n",
    "        rs, re, gs, ge, bs, be = color_tuple\n",
    "    red = random.randint(rs, re)\n",
    "    green = random.randint(gs, ge)\n",
    "    blue = random.randint(bs, be)\n",
    "    return (red, green, blue)\n",
    "\n",
    "is_raw_size=False\n",
    "\n",
    "def gen_captcha(text, fig_size=(200,70), fonts=['fonts/ANTQUAB.TTF'],font_color=(10,100),same_color=1, font_size=(25, 35), rotate=0,\n",
    "                font_noise=0, offset_w=(0,0), offset_h=0, line=(0,0), shortline=(0,0), line_width=(0,1), line_color=(200,250), point=(0,500), \n",
    "                point_color=(150,250), frame_color=None, wavy=(0,0), bg=(200,255)):\n",
    "    '''\n",
    "    text:验证码文本\n",
    "    size:验证码图片宽高\n",
    "    fonts:字体列表，随机选择一个\n",
    "    font_noise: 字体散点干扰，0不加干扰，1加干扰\n",
    "    offset_hor: 左右偏移值\n",
    "    offset_var: 上下偏移值\n",
    "    fill:字体颜色范围\n",
    "    rotate:字体旋转角度\n",
    "    line:干扰线条数范围\n",
    "    point:干扰点数范围\n",
    "    wavy:波浪线数范围\n",
    "    color:干扰线、点 颜色\n",
    "    bg:背景色范围\n",
    "    '''\n",
    "    bg = random_color(bg)\n",
    "    img = Image.new(mode='RGB', size=fig_size, color=bg) #\n",
    "    draw = ImageDraw.Draw(im=img, mode='RGB') # im, mode=None\n",
    "    \n",
    "    font_path = random.choice(fonts)\n",
    "#     font_name = font_path.split('/')[-1][:-4]\n",
    "#     print('font_name:', font_name)\n",
    "    \n",
    "    font = ImageFont.truetype(font_path, size=random.randint(font_size[0], font_size[1])) # font=None, size=10, index=0, encoding=\"\"\n",
    "    rotate = random.randint(0, rotate)\n",
    "    def get_char_img(char,font,font_color,rotate,bg, font_noise=0):\n",
    "        '''\n",
    "        生成单个字符图片，随机颜色加随机旋转\n",
    "        \n",
    "        '''\n",
    "        w, h = draw.textsize(char, font=font)\n",
    "        im = Image.new('RGBA',(w,h), color=bg)\n",
    "        ImageDraw.Draw(im).text((0,0), char, font=font, fill=font_color)   \n",
    "        if rotate:\n",
    "            im = im.rotate(random.randint(-rotate, rotate),Image.BILINEAR,expand=1)\n",
    "            im = im.crop(im.getbbox())\n",
    "        if font_noise: \n",
    "            im_draw = ImageDraw.Draw(im)\n",
    "#             for i in range(random.randint(1,20)):\n",
    "            for i in range(random.randint(int(w*h*0.01),min(int(w*h*0.05), 5))):\n",
    "                im_draw.point(xy=(random.randint(0, w), random.randint(0, h)),fill=bg)\n",
    "\n",
    "        table = []\n",
    "        for i in range(256):\n",
    "            table.append(i * 97) # 5.97\n",
    "        mask = im.convert('L').point(table) \n",
    "        return (im, mask)\n",
    "    \n",
    "#     char_color = random.randint(font_color[0],font_color[1])\n",
    "    char_color = random_color(font_color)\n",
    "    if same_color:        \n",
    "        char_imgs = [get_char_img(char, font, font_color=char_color, rotate=rotate, bg=bg, font_noise=font_noise) for char in text]\n",
    "    else:\n",
    "#         char_imgs = [get_char_img(char, font, font_color=random.randint(font_color[0],font_color[1]), rotate=rotate, bg=bg, font_noise=font_noise) for char in text]\n",
    "        char_imgs = [get_char_img(char, font, font_color=random_color(font_color), rotate=rotate, bg=bg, font_noise=font_noise) for char in text]       \n",
    "    ws = [img[0].size[0] for img in char_imgs]\n",
    "    hs = [img[0].size[1] for img in char_imgs]\n",
    "    w = max(sum(ws), fig_size[0])\n",
    "    h = max(max(hs), fig_size[1])\n",
    "    if w>fig_size[0] or h>fig_size[1]:\n",
    "        img = Image.new('RGB',(w+6,h+6), color=bg)\n",
    "        draw = ImageDraw.Draw(im=img, mode='RGB')  # im, mode=None\n",
    "        w, h = img.size\n",
    "        fig_size = img.size\n",
    "        \n",
    "\n",
    "    # 短线\n",
    "    for i in range(random.randint(shortline[0], shortline[1])):\n",
    "        x0, y0 = random_xy(w, h)\n",
    "        x1 = x0 + random.randint(2, 5)\n",
    "        y1 = y0 + random.randint(2, 5)\n",
    "        draw.line(xy=((x0,y0),(x1,y1)),\n",
    "                  fill=random_color(line_color),\n",
    "                  width=random.randint(line_width[0], line_width[1])) # xy, fill=None, width=0\n",
    "        \n",
    "    if rotate:\n",
    "        temp_x = random.randint(int((fig_size[0]-sum(ws))/5), int((fig_size[0]-sum(ws))/2+1))\n",
    "        temp_y = random.randint(int((fig_size[1]-hs[0])/8), int((fig_size[1]-hs[0])/2+1))\n",
    "        for i in range(len(char_imgs)):\n",
    "            tmp_offset = random.randint(offset_w[0], offset_w[1]) if sum(ws)+(len(ws)-1)*offset_w[1]<fig_size[0] else 0\n",
    "            if i > 0:\n",
    "                temp_x = new_x if new_x+ws[i]<fig_size[0] else temp_x+ws[i-1]\n",
    "                temp_y = new_y if 0<new_y and new_y+hs[i]<fig_size[1] else random.randint(0, h-hs[i]+1) \n",
    "            img.paste(char_imgs[i][0], box=(temp_x, temp_y), mask=char_imgs[i][1]) \n",
    "            new_x = temp_x+ws[i]+tmp_offset         \n",
    "            new_y = temp_y+random.randint(-offset_h, offset_h)  \n",
    "       \n",
    "    # 直线\n",
    "    for i in range(random.randint(line[0], line[1])):\n",
    "        x0, y0 = random_xy(w, h)\n",
    "        x1, y1 = random_xy(w, h)\n",
    "        draw.line(xy=((x0,y0),(x1,y1)),\n",
    "#                   fill=random.randint(noise_color[0], noise_color[1]),\n",
    "                  fill=random_color(line_color),\n",
    "                  width=random.randint(line_width[0], line_width[1])) # xy, fill=None, width=0\n",
    "    # 散点\n",
    "    for i in range(random.randint(point[0], point[1])):\n",
    "        if font_color == point_color:\n",
    "            draw.point(xy=(random.randint(0, fig_size[0]), random.randint(0, fig_size[1])),\n",
    "                      fill=char_color)    \n",
    "        else:            \n",
    "            draw.point(xy=(random.randint(0, fig_size[0]), random.randint(0, fig_size[1])),\n",
    "                      fill=random_color(point_color))     \n",
    "    if random.random()>=0.5:\n",
    "        A_ = random.uniform(hs[1]*0.1,hs[1]*0.2)\n",
    "        w_ = math.pi*4/w#random.uniform(0.04, 0.06)\n",
    "        b_ = random.random()*math.pi\n",
    "        k_ = random.uniform(h*0.5, h*0.7)\n",
    "        # 波浪线\n",
    "        for _ in range(random.randint(wavy[0],wavy[1])):  \n",
    "            draw.line(xy=[(x, Asin(x, A_, w_, b_, k_)) for x in range(int(w))], \n",
    "                      fill=char_color, width=random.randint(line_width[0], line_width[1]))  \n",
    "    else:\n",
    "        # 波浪线\n",
    "        for _ in range(random.randint(wavy[0],wavy[1])):  \n",
    "            draw.line(xy=get_wavy_line(w = (0, w),h = (min(hs)-5, max(hs)+5)), \n",
    "                      fill=char_color, width=random.randint(line_width[0], line_width[1]))           \n",
    "    \n",
    "    # 边框\n",
    "    if frame_color!=None:\n",
    "        draw.line(xy=[(0,0),(0, h), (0, 0), (w, 0),(w-1,0),(w-1, h), (0,h-1),(w-1, h-1)], fill=random_color(frame_color))\n",
    "        \n",
    "    if not rotate:\n",
    "        temp_x = random.randint(int((fig_size[0]-sum(ws))/5), int((fig_size[0]-sum(ws))/2+1))\n",
    "        temp_y = random.randint(int((fig_size[1]-hs[0])/8), int((fig_size[1]-hs[0])/2+1))\n",
    "        for i in range(len(char_imgs)):\n",
    "            tmp_offset = random.randint(offset_w[0], offset_w[1]) if sum(ws)+(len(ws)-1)*offset_w[1]<fig_size[0] else 0\n",
    "            if i > 0:\n",
    "                temp_x = new_x if new_x+ws[i]<fig_size[0] else temp_x+ws[i-1]\n",
    "                temp_y = new_y if 0<new_y and new_y+hs[i]<fig_size[1] else temp_y   \n",
    "            if same_color:\n",
    "                draw.text((temp_x, temp_y), text[i], font=font, fill=char_color)\n",
    "            else:\n",
    "                draw.text((temp_x, temp_y), text[i], font=font, fill=random_color(font_color)) #\n",
    "            new_x = temp_x+ws[i]+tmp_offset         \n",
    "            new_y = temp_y+random.randint(-offset_h, offset_h)       \n",
    "\n",
    "        \n",
    "#     filename = font_name+\"_\"+str(uuid.uuid1())+\"_\"+text\n",
    "#     img.save('/data/python/lsm/gen_captcha/{}.jpg'.format(filename))\n",
    "#     return img \n",
    "    if is_raw_size:\n",
    "        return img\n",
    "    w, h = img.size\n",
    "    if w>width or h> height:\n",
    "        return img.resize((width, height), Image.BILINEAR)    \n",
    "    elif random.random() >0.5:\n",
    "        background = Image.new(mode='RGB', size=(width, height), color=bg)\n",
    "        background.paste(img, box=(0, 0)) \n",
    "        return background\n",
    "    else:\n",
    "        return img.resize((width, height), Image.BILINEAR)\n",
    "\n",
    "# # paths = 'FileInfo0508_2/*.jpg'\n",
    "# paths = '/data/esa_sdk/gan/english/*.jpg'\n",
    "# # paths = '/data/captcha/shensebeijingsandian/*.jpg'\n",
    "# # paths = '/data/captcha/shensexiansandian/*.jpg'\n",
    "# files =  glob.glob(paths)\n",
    "# img2 = Image.open(files[0])\n",
    "# img2 = Image.open('/data/captcha/captcha_sample/4.jpg')#.resize((200,70), Image.BILINEAR) #小图多种颜色字、干扰线 \n",
    "\n",
    "# img2 = Image.open('/data/captcha/label_english/90_38/0ef53417-3af2-11ec-b040-2cf05ded1cb1_aq4f.jpg')\n",
    "# random_str = 'Aq4f'\n",
    "# image = gen_captcha(random_str, fig_size=(90,38), fonts=fonts,font_color=(20,160,20,165,20,160),same_color=0, font_size=(15, 20), rotate=0,\n",
    "#                     font_noise=0,offset_w=(-1,3),offset_h=0, line=(100,200), line_width=(0,1), line_color=(170,230), point=(20,150),\n",
    "#                     point_color=(200,255),frame_color=(10,30),wavy=(0,0), bg=(255,255)).resize((width, height), Image.BILINEAR)\n",
    "\n",
    "# img2 = Image.open('/data/captcha/label_english/122_46/fe9bbdda-24ea-11ed-ab40-b4b5b67760ae_PDZWON.jpg')\n",
    "# random_str = 'PDZWON'\n",
    "\n",
    "imgs_122_46 = glob.glob('/data/captcha/label_english/122_46/*.jpg')[:800]\n",
    "img_path = random.choice(imgs_122_46)\n",
    "img2 = Image.open(img_path)\n",
    "random_str = img_path.split('/')[-1].split('_')[-1][:-4]\n",
    "\n",
    "image = gen_captcha(random_str, fig_size=(122,46), fonts=fonts,font_color=(5,160,5,150,5,160),same_color=0, font_size=(17, 20), rotate=10,\n",
    "                    font_noise=0,offset_w=(-1,3),offset_h=2, line=(0,3), line_width=(0,1), line_color=(200,250), point=(0,150),\n",
    "                    point_color=(200,255),frame_color=(200,250),wavy=(0,0), bg=(235,255)).resize((width, height), Image.BILINEAR)\n",
    "\n",
    "\n",
    "im = [image, img2]\n",
    "print('image size',img2.size, image.size)\n",
    "plt.figure(figsize=(50,10))\n",
    "for i in range(1,3):    \n",
    "    plt.subplot(2,2,i)\n",
    "    plt.imshow(im[i-1])\n",
    "plt.show()\n",
    "\n",
    "cl = (10,20)\n",
    "print('random_color(rs, re, gs, ge, bs, be)', random_color(cl))\n",
    "print('rand', random.random())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7524 /data/captcha/label_english/70_26/07c5530f-ce96-11ea-b53b-c81f66ef0810_9jyr.jpg\n",
      "14110 /data/captcha/label_english/52_21/169d7266-0a84-11eb-9a54-c81f66ef0810_2f37.jpg\n",
      "32514 /data/captcha/label_english/100_25/b2b2f39e-db79-11eb-a41f-c81f66ef0810_p24m.jpg\n",
      "2502 /data/captcha/shensexiansandian/a711d2ab4416673804f0415cb6bab36d_YDJP.jpg\n",
      "(200, 70)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ydzu')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 163,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 真实验证码加干扰 \n",
    "import re\n",
    "from PIL import ImageFilter\n",
    "len4_imgs = []\n",
    "len5_imgs = []\n",
    "\n",
    "filePath = 'FileInfo0508_2/*.jpg' # 波浪线验证码\n",
    "files = glob.glob(filePath)\n",
    "sp = min(int(len(files)*0.8), 3000)\n",
    "for path in files[:sp]:\n",
    "    label = path.split('_')[-1][:-4].lower().replace('1','l')\n",
    "    if len(label) ==5 and re.search('[0-9]', label)==None:\n",
    "        len5_imgs.append(path)\n",
    "    else:\n",
    "        print('label error', path)\n",
    "filePath = '/data/captcha/label_english/100_30/*.jpg'\n",
    "files = glob.glob(filePath)\n",
    "sp = min(int(len(files)*0.8), 3000)\n",
    "for path in files[:sp]:\n",
    "    label = path.split('_')[-1][:-4]\n",
    "    if len(label) ==5:\n",
    "        len5_imgs.append(path)\n",
    "    else:\n",
    "        print('label error', path)\n",
    "\n",
    "path1 = '/data/captcha/label_english/70_26/*.jpg'\n",
    "path2 = '/data/captcha/label_english/52_21/*.jpg'\n",
    "path3 = '/data/captcha/label_english/100_25/*.jpg'\n",
    "path4 = '/data/captcha/shensebeijingsandian/*.jpg' # 3,4一样图片类型\n",
    "path5 = '/data/captcha/shensexiansandian/*.jpg'\n",
    "path6 = '/data/esa_sdk/gan/english/*.jpg' # 数据已被删除\n",
    "path7 = '/data/captcha/label_english/90_38/*.jpg'\n",
    "for paths in [path1, path2, path3, path5]:  # path1, path2, path3, path4, path5, \n",
    "    files = glob.glob(paths)\n",
    "#     sp = int(len(files)*0.8)\n",
    "    sp = min(int(len(files)*0.8), 3000)\n",
    "    for path in files[:sp]:\n",
    "        label = path.split('_')[-1][:-4]\n",
    "        if len(label) ==4:\n",
    "            len4_imgs.append(path)\n",
    "        else:\n",
    "            print('label error', path)\n",
    "    print(len(files), files[0])\n",
    "\n",
    "random.shuffle(len4_imgs)\n",
    "random.shuffle(len5_imgs)\n",
    "    \n",
    "def rebuild_img(path):\n",
    "    '''\n",
    "    读取本地验证码图片进行随机加点噪声为新图片\n",
    "    参数：path：图片路径\n",
    "    返回：重组后图片    \n",
    "    '''\n",
    "    if re.search('FileInfo0508', path)!=None:\n",
    "        label = path.split('_')[-1][:-4].lower().replace('1','l')\n",
    "#         print(re.search('FileInfo0508', path))\n",
    "    else:\n",
    "        label = path.split('_')[-1][:-4]\n",
    "    crop_n = len(label) \n",
    "    img = Image.open(path)\n",
    "    img = img.convert('RGB')\n",
    "    w, h = img.size\n",
    "#     img2 = img2.resize((100,50), Image.BILINEAR)\n",
    "    draw = ImageDraw.Draw(img)\n",
    "    # gau = img.filter(ImageFilter.GaussianBlur(radius=2))\n",
    "# imgs.append((gau, 'gau'))\n",
    "# sharp = gau.filter(ImageFilter.UnsharpMask(radius=2, percent=150, threshold=3))\n",
    "# imgs.append((sharp, 'sharp'))\n",
    "# rank = img.filter(ImageFilter.RankFilter(size=3, rank=3))\n",
    "#     img = img.filter(ImageFilter.GaussianBlur(radius=2))\n",
    "#     img = img.filter(ImageFilter.UnsharpMask(radius=2, percent=150, threshold=3))\n",
    "    for _ in range(random.randint(20,250)):\n",
    "        draw.point(xy=(random_xy(w, h)),fill=random_color((70,220,20,255,70,220)))  \n",
    "    # 短线\n",
    "    for i in range(random.randint(10,100)):\n",
    "        x0, y0 = random_xy(w, h)\n",
    "        x1 = x0 + random.randint(2, 5)\n",
    "        y1 = y0 + random.randint(2, 5)\n",
    "        draw.line(xy=((x0,y0),(x1,y1)),\n",
    "                  fill=random_color((90,130)),\n",
    "                  width=random.randint(0,1)) # xy, fill=None, width=0        \n",
    "    for _ in range(random.randint(0, 3)):\n",
    "        draw.line(xy=(random_xy(w, h),random_xy(w, h)), fill=random_color((80, 250)), width=random.randint(0,2))\n",
    "    w, h = img.size\n",
    "    if w>width or h> height:\n",
    "        return img.resize((width, height), Image.BILINEAR), label.lower()    \n",
    "    elif random.random() >0.5:\n",
    "        background = Image.new(mode='RGB', size=(width, height), color=(255,255,255))\n",
    "        background.paste(img, box=(0, 0)) \n",
    "        return background, label.lower()\n",
    "    else:\n",
    "        return img.resize((width, height), Image.BILINEAR), label.lower()\n",
    "#     return img.resize((width, height), Image.BILINEAR), label.lower()\n",
    "filePath = 'FileInfo0508_2/*.jpg' # 波浪线验证码\n",
    "files = glob.glob(path7)\n",
    "img, label = rebuild_img(random.choice(files))\n",
    "print(img.size)\n",
    "plt.imshow(img)\n",
    "plt.title(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 150,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bgs = glob.glob('/data/captcha/crop_english/bg_70_25/*.jpg')\n",
    "crops = glob.glob('/data/captcha/crop_english/crop_70_25/*.jpg')\n",
    "def merge_img_7025():\n",
    "    img = Image.open(random.choice(bgs))\n",
    "    w, h = (37,12)\n",
    "    label = []\n",
    "    for i in range(4):\n",
    "        im_p = random.choice(crops)\n",
    "        lb = im_p.split('/')[-1].split('_')[0]\n",
    "        label.append(lb)\n",
    "        im = Image.open(im_p)\n",
    "        img.paste(im, (16+w//4*i,7+0)) # ,w//4*(i+1), h\n",
    "    w, h = img.size\n",
    "    draw = ImageDraw.Draw(img)        \n",
    "    for _ in range(random.randint(10,250)):\n",
    "        draw.point(xy=(random_xy(w, h)),fill=random_color((70,220,20,255,70,220)))            \n",
    "    return img.resize((width, height), Image.BILINEAR), ''.join(label)\n",
    "\n",
    "# img, label =  merge_img_7025()\n",
    "\n",
    "bgs2 = glob.glob('/data/captcha/crop_english/bg_90_38/*.jpg')\n",
    "crops2 = glob.glob('/data/captcha/crop_english/crop_90_38/*.jpg')\n",
    "def merge_img_9038():\n",
    "    img = Image.open(random.choice(bgs2))\n",
    "    w, h = (37,12)\n",
    "    label = []\n",
    "    for i in range(4):\n",
    "        im_p = random.choice(crops2)\n",
    "        lb = im_p.split('/')[-1].split('_')[0]\n",
    "        label.append(lb)\n",
    "        im = Image.open(im_p)\n",
    "        img.paste(im, (17*i+10,0+10)) \n",
    "    w, h = img.size\n",
    "    draw = ImageDraw.Draw(img)\n",
    "    for i in range(random.randint(0,20)):\n",
    "        x0, y0 = random_xy(w, h)\n",
    "        x1 = x0 + random.randint(2, 5)\n",
    "        y1 = y0 + random.randint(2, 5)\n",
    "        draw.line(xy=((x0,y0),(x1,y1)),\n",
    "                  fill=random_color((150,200)),\n",
    "                  width=random.randint(0,1)) # xy, fill=None, width=0            \n",
    "    return img.resize((width, height), Image.BILINEAR), ''.join(label)\n",
    "\n",
    "img, label =  merge_img_9038()\n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label:  w6bx\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 150,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''添加真实图片'''\n",
    "name_dic = dict()\n",
    "with open('/data/captcha/label_english/200_80/answer.txt', 'r', encoding='utf-8') as f:\n",
    "    name_labels = f.readlines()\n",
    "for it in name_labels:\n",
    "    k, v = it.strip().split('=')\n",
    "    name_dic[k] = v\n",
    "with open('/data/captcha/label_english/超级鹰导出图片-2023-04-03/超级鹰识别结果.txt', 'r', encoding='utf-8') as f:\n",
    "    name_labels = f.readlines()\n",
    "for it in name_labels:\n",
    "    k, v = it.strip().split('=')\n",
    "    name_dic[k] = v    \n",
    "\n",
    "imgs_200_80 = glob.glob('/data/captcha/label_english/200_80/*.jpg')[:2000]\n",
    "\n",
    "imgs_122_46 = glob.glob('/data/captcha/label_english/122_46/*.jpg')[:800]\n",
    "\n",
    "imgs_160_60 = glob.glob('/data/captcha/label_english/超级鹰导出图片-2023-04-03/*.jpg')[:400]\n",
    "\n",
    "def add_real_img(imgs_list):\n",
    "    path = random.choice(imgs_list)\n",
    "    if '122_46' in path:\n",
    "        label = path.split('/')[-1].split('_')[-1][:-4]\n",
    "    else:    \n",
    "        file_name = path.split('/')[-1][:-4]\n",
    "        label = name_dic[file_name]\n",
    "    img = Image.open(path)\n",
    "    img = img.convert('RGB')\n",
    "    w, h = img.size\n",
    "    draw = ImageDraw.Draw(img)\n",
    "    for _ in range(random.randint(20,250)):\n",
    "        draw.point(xy=(random_xy(w, h)),fill=random_color((100,220,120,255,100,220)))  \n",
    "    # 短线\n",
    "    for i in range(random.randint(10,100)):\n",
    "        x0, y0 = random_xy(w, h)\n",
    "        x1 = x0 + random.randint(2, 5)\n",
    "        y1 = y0 + random.randint(2, 5)\n",
    "        draw.line(xy=((x0,y0),(x1,y1)),\n",
    "                  fill=random_color((90,130)),\n",
    "                  width=random.randint(0,1)) # xy, fill=None, width=0        \n",
    "    for _ in range(random.randint(0, 3)):\n",
    "        draw.line(xy=(random_xy(w, h),random_xy(w, h)), fill=random_color((80, 250)), width=random.randint(0,2))\n",
    "        \n",
    "    w, h = img.size\n",
    "    if w>width or h> height:\n",
    "        return img.resize((width, height), Image.BILINEAR), label.lower()    \n",
    "    elif random.random() >0.5:\n",
    "        background = Image.new(mode='RGB', size=(width, height), color=(255,255,255))\n",
    "        background.paste(img, box=(0, 0)) \n",
    "        return background, label.lower()\n",
    "    else:\n",
    "        return img.resize((width, height), Image.BILINEAR), label.lower()\n",
    "#     return img.resize((width, height), Image.BILINEAR), label.lower()\n",
    "\n",
    "# img, label = add_real_img(imgs_200_80)\n",
    "# img, label = add_real_img(imgs_122_46)\n",
    "img, label = add_real_img(imgs_160_60)\n",
    "print('label: ', label)\n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大于\n",
      "[('3', 266), ('7', 263), ('6', 263), ('4', 258), ('2', 257), ('8', 251), ('5', 239), ('9', 238), ('q', 151), ('G', 133), ('N', 132), ('b', 129), ('B', 128), ('D', 127), ('x', 127), ('g', 127), ('c', 127), ('k', 126), ('d', 126), ('C', 126), ('U', 126), ('L', 125), ('y', 125), ('a', 123), ('S', 123), ('f', 123), ('T', 122), ('s', 122), ('r', 121), ('z', 121), ('v', 121), ('P', 121), ('J', 120), ('u', 118), ('p', 116), ('h', 116), ('t', 114), ('W', 114), ('E', 113), ('e', 111), ('Z', 111), ('m', 110), ('n', 110), ('w', 110), ('R', 109), ('V', 109), ('F', 108), ('H', 106), ('j', 105), ('A', 103), ('i', 102), ('M', 101), ('K', 98), ('1', 97), ('Y', 97), ('X', 97), ('Q', 97), ('0', 74), ('l', 55), ('o', 50), ('O', 36), ('I', 30)]\n",
      "2001 62 36\n",
      "label:  JHRW\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 167,
       "width": 370
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''保存生成图片到本地'''\n",
    "lower = 'abcdefghijkmnpqrstuvwxyz'\n",
    "upper = 'ABCDEFGHJKLMNPQRSTUVWXYZ'\n",
    "digit = '23456789'\n",
    "\n",
    "label_set = set()\n",
    "labels = []\n",
    "is_raw_size=True\n",
    "from collections import Counter\n",
    "\n",
    "# for gen_characters in [lower, upper, digit,lower+'lo', upper+'IO', digit+'01', lower+digit, upper+digit, lower+upper+digit, lower+upper+digit+'01IOlo']:    \n",
    "for i in range(10000):\n",
    "    gen_characters = random.choice([lower, upper, digit,lower+'lo', upper+'IO', digit+'01', lower+digit, upper+digit, lower+upper+digit, lower+upper+digit+'01IOlo']) \n",
    "    random_str = ''.join([random.choice(gen_characters) for j in range(4)])\n",
    "    if random_str in label_set:\n",
    "        continue\n",
    "    label_set.add(random_str)\n",
    "    labels.append(random_str)  \n",
    "    ty = '409'\n",
    "    fig_size='5221'\n",
    "    image = gen_captcha(random_str, fig_size=(52,21), fonts=fonts,font_color=(0,255,0,255,0,255),same_color=1, font_size=(16, 18), rotate=0,\n",
    "                                font_noise=0,offset_w=(0,1),offset_h=0, line=(0,0), line_width=(0,1), line_color=(90,150), point=(30,50),\n",
    "                                point_color=(0,255,0,255,0,255),frame_color=(150, 170),wavy=(0,0), bg=(255,255))\n",
    "    image.save('/data/captcha/generate_pic/{}_{}_{}.jpg'.format(ty, random_str, fig_size))\n",
    "    if len(labels)>2000:\n",
    "        c = Counter(''.join(labels))\n",
    "        if c.most_common()[-1][1] > 10:\n",
    "            print('大于')\n",
    "            print(c.most_common())\n",
    "            print(len(labels),len(c), len(characters))\n",
    "            break\n",
    "    \n",
    "print('label: ', random_str)\n",
    "plt.imshow(image)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1902_420x65', '1004_150x50', '1004_130x40', '4004_130x40', '1004_100x40', '1902_140x40', '4004_108x40', '3004_100x40', '1004_80x20', '4006_200x100', '1004_70x35', '1902_100x20', '3004_200x62'] []\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "from PIL import Image\n",
    "\n",
    "def organize_images_by_dimensions(source_dir, target_base_dir):\n",
    "    \"\"\"\n",
    "    按图片像素尺寸分类并移动到对应文件夹\n",
    "    :param source_dir: 源图片文件夹路径\n",
    "    :param target_base_dir: 目标分类文件夹的基路径\n",
    "    \"\"\"\n",
    "\n",
    "    # 支持的图片格式\n",
    "    supported_formats = ('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.webp')\n",
    "\n",
    "    # 遍历源文件夹\n",
    "    for root, _, files in os.walk(source_dir):\n",
    "        for filename in files:\n",
    "            if filename.lower().endswith(supported_formats):\n",
    "                filepath = os.path.join(root, filename)\n",
    "\n",
    "                try:\n",
    "                    # 获取图片尺寸\n",
    "                    with Image.open(filepath) as img:\n",
    "                        width, height = img.size\n",
    "\n",
    "                    # 确定分类\n",
    "                    category = filename.split('-')[0]\n",
    "                    # 创建目标文件夹（格式：尺寸分类_宽x高）\n",
    "                    target_dir = os.path.join(target_base_dir, f\"{category}_{width}x{height}\")\n",
    "                    os.makedirs(target_dir, exist_ok=True)\n",
    "\n",
    "                    # 移动文件（避免重名冲突）\n",
    "                    base_name, ext = os.path.splitext(filename)\n",
    "                    counter = 1\n",
    "                    while True:\n",
    "                        new_name = f\"{base_name}_{counter}{ext}\" if counter > 1 else filename\n",
    "                        target_path = os.path.join(target_dir, new_name)\n",
    "                        if not os.path.exists(target_path):\n",
    "                            shutil.move(filepath, target_path)\n",
    "#                             shutil.copy(filepath, target_path)\n",
    "                            print(f\"Moved: {filename} ({width}x{height}) -> {category}\")\n",
    "                            break\n",
    "                        counter += 1\n",
    "\n",
    "                except Exception as e:\n",
    "                    print(f\"Error processing {filename}: {str(e)}\")\n",
    "\n",
    "\n",
    "# 使用示例\n",
    "source_folder = \"/data/captcha/chaojiyingpic2\"  # 替换为实际图片文件夹路径 /data/captcha/chaojiyingpic\n",
    "target_folder = \"/data/captcha/chaojiyingpic\"  # 替换为目标分类文件夹路径\n",
    "# organize_images_by_dimensions(source_folder, target_folder)\n",
    "print(os.listdir(target_folder), os.listdir(source_folder))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/data/captcha/chaojiyingpic/1902_420x65 1120\n",
      "/data/captcha/chaojiyingpic/1004_130x40 318\n",
      "/data/captcha/chaojiyingpic/4004_130x40 1060\n",
      "/data/captcha/chaojiyingpic/1004_100x40 799\n",
      "/data/captcha/chaojiyingpic/1902_140x40 44\n",
      "/data/captcha/chaojiyingpic/4004_108x40 816\n",
      "/data/captcha/chaojiyingpic/3004_100x40 83\n",
      "/data/captcha/chaojiyingpic/4006_200x100 235\n",
      "/data/captcha/chaojiyingpic/1004_70x35 1392\n",
      "/data/captcha/chaojiyingpic/1902_100x20 131\n",
      "/data/captcha/chaojiyingpic/3004_200x62 116\n",
      "训练数据：5498, 测试数据：616\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 201,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label:  75cm\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 150,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''加入超级鹰数据'''\n",
    "chaojiying_train = []\n",
    "chaojiying_test = []\n",
    "target_folder = \"/data/captcha/chaojiyingpic\"\n",
    "for p in os.listdir(target_folder):\n",
    "    new_path = os.path.join(target_folder, p)\n",
    "    if os.path.isdir(new_path):\n",
    "        files = glob.glob('%s/*.jpg'%new_path)\n",
    "        if len(files)<10:\n",
    "            continue\n",
    "        print(new_path, len(files))\n",
    "        sp = int(len(files)*0.9)\n",
    "        n = 0\n",
    "        for path in files:\n",
    "            if path.endswith('1004-8268-1262409550172832926.jpg'):\n",
    "                print('打开异常图片')\n",
    "                continue\n",
    "            label = path.split('/')[-1].split('-')[1]\n",
    "            ty = path.split('/')[-1].split('-')[0]\n",
    "            if ty == 4006 and len(label)!=6: # 此类较多验证码标签错误\n",
    "                continue\n",
    "            if n < sp:\n",
    "                chaojiying_train.append(path)\n",
    "            else:\n",
    "                chaojiying_test.append(path)\n",
    "            n += 1   \n",
    "print('训练数据：%d, 测试数据：%d'%(len(chaojiying_train), len(chaojiying_test)))\n",
    "\n",
    "# file1 = glob.glob('/data/captcha/chaojiyingpic/4006*.jpg')\n",
    "# file2 = glob.glob('/data/captcha/chaojiyingpic/4004*.jpg')\n",
    "# file3 = glob.glob('/data/captcha/chaojiyingpic/3004*.jpg')\n",
    "# file4 = glob.glob('/data/captcha/chaojiyingpic/1004*.jpg')\n",
    "# file5 = glob.glob('/data/captcha/chao_241125_1004/1004*.jpg')\n",
    "# chaojiying_train = []\n",
    "# chaojiying_test = []\n",
    "# for file in [file1, file2, file3, file4, file5]:\n",
    "#     sp = int(len(file)*0.9)\n",
    "#     n = 0\n",
    "#     for path in file:\n",
    "#         if path.endswith('1004-8268-1262409550172832926.jpg'):\n",
    "#             print('打开异常图片')\n",
    "#             continue\n",
    "#         label = path.split('/')[-1].split('-')[1]\n",
    "#         ty = path.split('/')[-1].split('-')[0]\n",
    "#         if ty == 4006 and len(label)!=6: # 此类较多验证码标签错误\n",
    "#             continue\n",
    "#         if n < sp:\n",
    "#             chaojiying_train.append(path)\n",
    "#         else:\n",
    "#             chaojiying_test.append(path)\n",
    "#         n += 1\n",
    "# print('训练数据：%d, 测试数据：%d'%(len(chaojiying_train), len(chaojiying_test)))\n",
    "\n",
    "# for ty in ['4004', '1902', '1004', '4006', '3004']:\n",
    "#     files = glob.glob('/data/captcha/chaojiyingpic2/%s-*.jpg'%ty)\n",
    "#     sp = int(len(files)*0.9)\n",
    "#     n = 0\n",
    "#     for path in files:\n",
    "#         if path.endswith('1004-8268-1262409550172832926.jpg'):\n",
    "#             print('打开异常图片')\n",
    "#             continue\n",
    "#         label = path.split('/')[-1].split('-')[1]\n",
    "#         ty = path.split('/')[-1].split('-')[0]\n",
    "#         if ty == 4006 and len(label)!=6: # 此类较多验证码标签错误\n",
    "#             continue\n",
    "#         if n < sp:\n",
    "#             chaojiying_train.append(path)\n",
    "#         else:\n",
    "#             chaojiying_test.append(path)\n",
    "#         n += 1        \n",
    "# print('训练数据：%d, 测试数据：%d'%(len(chaojiying_train), len(chaojiying_test)))\n",
    "\n",
    "\n",
    "img = Image.open(random.choice(chaojiying_test))\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "\n",
    "def add_real_img2(imgs_list):\n",
    "    path = random.choice(imgs_list)   \n",
    "    label = path.split('/')[-1].split('-')[1]    \n",
    "    img = Image.open(path)\n",
    "    img = img.convert('RGB')\n",
    "    w, h = img.size\n",
    "    draw = ImageDraw.Draw(img)\n",
    "    for _ in range(random.randint(20,250)):\n",
    "        draw.point(xy=(random_xy(w, h)),fill=random_color((100,220,120,255,100,220)))  \n",
    "    # 短线\n",
    "    for i in range(random.randint(10,100)):\n",
    "        x0, y0 = random_xy(w, h)\n",
    "        x1 = x0 + random.randint(2, 5)\n",
    "        y1 = y0 + random.randint(2, 5)\n",
    "        draw.line(xy=((x0,y0),(x1,y1)),\n",
    "                  fill=random_color((90,130)),\n",
    "                  width=random.randint(0,1)) # xy, fill=None, width=0        \n",
    "    for _ in range(random.randint(0, 3)):\n",
    "        draw.line(xy=(random_xy(w, h),random_xy(w, h)), fill=random_color((80, 250)), width=random.randint(0,2))\n",
    "        \n",
    "    w, h = img.size\n",
    "    if w>width or h> height:\n",
    "        return img.resize((width, height), Image.BILINEAR), label.lower()    \n",
    "    elif random.random() >0.5:\n",
    "        background = Image.new(mode='RGB', size=(width, height), color=(255,255,255))\n",
    "        background.paste(img, box=(0, 0)) \n",
    "        return background, label.lower()\n",
    "    else:\n",
    "        return img.resize((width, height), Image.BILINEAR), label.lower()\n",
    "#     return img.resize((width, height), Image.BILINEAR), label.lower()\n",
    "\n",
    "# img, label = add_real_img(imgs_200_80)\n",
    "# img, label = add_real_img(imgs_122_46)\n",
    "img, label = add_real_img2(chaojiying_train)\n",
    "print('label: ', label)\n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.15.4\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'6276'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)\n",
    "path = '/data/captcha/chaojiyingpic2/4004-6276-1274716010172835011.jpg'\n",
    "path.split('/')[-1].split('-')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' 彩色图像生成 '''\n",
    "from tensorflow.keras.utils import Sequence\n",
    "from collections import Counter\n",
    "lower = 'abcdefghijkmnpqrstuvwxyz'\n",
    "upper = 'ABCDEFGHJKLMNPQRSTUVWXYZ'\n",
    "digit = '23456789'\n",
    "\n",
    "class CaptchaSequence(Sequence):\n",
    "    '''\n",
    "    继承Sequence的数据生成类，方便调用多CPU，加快生成训练及测试数据\n",
    "    参数：self.characters:验证码字符集合，self.batch_size：每批次样本数，self.steps：生成多少批数据，self.n_len：验证码长度，\n",
    "    self.width：图片宽度，self.height:图片高度，self.input_length：lstm time step长度，self.label_length：标签长度\n",
    "    返回：array类型训练或测试数据 \n",
    "    \n",
    "    '''\n",
    "    def __init__(self, characters, batch_size, steps, n_len=6, width=width, height=height, \n",
    "                 input_length=12, label_length=6, chars_len=(4, 6)): #  width=128, height=64, input_length=16, label_length=4\n",
    "        self.characters = characters\n",
    "        self.batch_size = batch_size\n",
    "        self.steps = steps\n",
    "        self.n_len = n_len\n",
    "        self.width = width\n",
    "        self.height = height\n",
    "        self.input_length = input_length\n",
    "        self.label_length = label_length\n",
    "        self.chars_len = chars_len\n",
    "#         self.label_length = self.n_len\n",
    "        self.n_class = len(characters)+1\n",
    "\n",
    "            \n",
    "    def __len__(self):\n",
    "        return self.steps\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        X = np.zeros((self.batch_size, self.height, self.width, 3), dtype=np.float32)\n",
    "        y = np.zeros((self.batch_size, self.n_len), dtype=np.uint8)\n",
    "\n",
    "        input_length = np.ones(self.batch_size)*self.input_length\n",
    "        label_length = np.ones(self.batch_size)*self.n_len \n",
    "\n",
    "        max_num_len = 65\n",
    "\n",
    "        for i in range(self.batch_size):\n",
    "#                 print('len 4',y.shape, i)\n",
    "            # 定义验证码字符集 (大写字母、小写字母、大写字母+数字) , string.ascii_lowercase+string.digits+string.ascii_uppercase\n",
    "#             gen_characters = random.choice([string.ascii_lowercase, string.ascii_uppercase,string.digits,string.ascii_lowercase+string.digits,\n",
    "#                                             string.ascii_uppercase+string.digits])           \n",
    "            gen_characters = random.choice([lower, upper, digit,lower+'lo', upper+'IO', digit+'01', lower+digit, upper+digit, lower+upper+digit, lower+upper+digit+'01IOlo']) \n",
    "\n",
    "            if i%max_num_len <= 1:    # line=(0,0), line_width=(0,1), point=(0,100),wavy=(0,0)            \n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(4)])\n",
    "                image = gen_captcha(random_str, fig_size=(52,21), fonts=fonts,font_color=(0,255,0,255,0,255),same_color=1, font_size=(16, 18), rotate=0,\n",
    "                                font_noise=0,offset_w=(0,1),offset_h=0, line=(0,0), line_width=(0,1), line_color=(90,150), point=(30,50),\n",
    "                                point_color=(0,255,0,255,0,255),frame_color=(150, 170),wavy=(0,0), bg=(255,255))\n",
    "\n",
    "            elif i%max_num_len <= 3: # line=(0,5), line_width=(0,1), point=(20,300),wavy=(0,0)\n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(4)])\n",
    "                image = gen_captcha(random_str, fig_size=(70,26), fonts=fonts,font_color=(20,130),same_color=0, font_size=(18, 22), rotate=0,\n",
    "                                    font_noise=0,offset_w=(-1,1),offset_h=3, line=(4,6), line_width=(0,1), line_color=(90,130), point=(300,500),\n",
    "                                    point_color=(220,255),frame_color=None,wavy=(0,0), bg=(235,255))\n",
    "\n",
    "            elif i%max_num_len <= 5: # line=(0,0), line_width=(0,2), point=(0,0),wavy=(1,1)\n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(4)])\n",
    "                image = gen_captcha(random_str, fig_size=(100,25), fonts=fonts,font_color=(150,250),same_color=0, font_size=(18, 22), rotate=0,\n",
    "                                    font_noise=0,offset_w=(3,5),offset_h=3, line=(0,0), line_width=(0,1), line_color=(60,130), point=(50,100),\n",
    "                                    point_color=(70,120,220,255,70,120),frame_color=None,wavy=(0,0), bg=(70,100,45,80,250,255))\n",
    "\n",
    "\n",
    "            elif i%max_num_len <= 7:  # line=(0,0), line_width=(0,1), point=(0,80),wavy=(0,0)\n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(4)])\n",
    "                image = gen_captcha(random_str, fig_size=(80,30), fonts=fonts,font_color=(30,90),same_color=0, font_size=(22, 25), rotate=0,\n",
    "                                    font_noise=0,offset_w=(1,3),offset_h=1, line=(50,100), line_width=(0,1), line_color=(200,250), point=(0,0),\n",
    "                                    point_color=(70,120,220,255,70,120),frame_color=(120,130),wavy=(0,0), bg=(250,255))\n",
    "\n",
    "            elif i%max_num_len<=9:\n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(4)])\n",
    "                image = gen_captcha(random_str, fig_size=(100,44), fonts=fonts,font_color=(20,150),same_color=0, font_size=(22, 25), rotate=20,\n",
    "                                    font_noise=0,offset_w=(-1,3),offset_h=8, line=(3,5), shortline=(150,250), line_width=(0,1), line_color=(150,250), point=(0,0),\n",
    "                                    point_color=(70,120,220,255,70,120),frame_color=(190,200),wavy=(0,0), bg=(240,255))\n",
    "\n",
    "\n",
    "            elif i%max_num_len<=11:\n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(4)])                      \n",
    "                image = gen_captcha(random_str, fig_size=(135,40), fonts=fonts,font_color=(0,80,0,70,100,200),same_color=0, font_size=(28, 30), rotate=20,\n",
    "                                    font_noise=0,offset_w=(1,3),offset_h=2, line=(3,5), shortline=(0,0), line_width=(1,3), line_color=(0,100,80,230,0,90), point=(180,250),\n",
    "                                    point_color=(70,220),frame_color=(100,150),wavy=(0,0), bg=(240,255))   \n",
    "\n",
    "            elif i%max_num_len<=13:\n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(4)])    \n",
    "                image = gen_captcha(random_str, fig_size=(90,38), fonts=fonts,font_color=(20,160,20,165,20,160),same_color=0, font_size=(15, 20), rotate=0,\n",
    "                font_noise=0,offset_w=(-1,3),offset_h=0, line=(100,200), line_width=(0,1), line_color=(170,230), point=(20,150),\n",
    "                point_color=(200,255),frame_color=(10,30),wavy=(0,0), bg=(255,255))\n",
    "\n",
    "            elif i%max_num_len<=15: \n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(4)])\n",
    "                tmp_w = random.randint(80,100)\n",
    "                tmp_h = random.randint(25, 35)\n",
    "                font_s = (int(tmp_h*0.8), int(tmp_h*0.9))\n",
    "                image = gen_captcha(random_str, fig_size=(tmp_w,tmp_h), fonts=fonts,font_color=(0,180),same_color=0, font_size=font_s, rotate=15,\n",
    "                                    font_noise=0,offset_w=(-2,1),offset_h=2, line=(0,0), shortline=(0,0), line_width=(0,1), line_color=(170,200), point=(0,0),\n",
    "                                    point_color=(250,255),frame_color=None,wavy=(0,0), bg=(150,255))\n",
    "            elif i%max_num_len<=17:\n",
    "                image, random_str = merge_img_7025()\n",
    "            elif i%max_num_len<=23:\n",
    "                image, random_str = merge_img_9038()                   \n",
    "\n",
    "            elif i%max_num_len<=30: # 加入真实验证码\n",
    "                image, random_str = rebuild_img(random.choice(len4_imgs))\n",
    "            elif i%max_num_len <= 35:\n",
    "                image, random_str = add_real_img(imgs_200_80)                \n",
    "                    \n",
    "            elif i%max_num_len <= 37: \n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(4)])\n",
    "                tmp_w = random.randint(80,100)\n",
    "                tmp_h = random.randint(25, 35)\n",
    "                font_s = (int(tmp_h*0.8), int(tmp_h*0.9))\n",
    "                image = gen_captcha(random_str, fig_size=(tmp_w,tmp_h), fonts=fonts,font_color=(0,180),same_color=0, font_size=font_s, rotate=15,\n",
    "                                    font_noise=0,offset_w=(-2,1),offset_h=2, line=(0,5), shortline=(0,100), line_width=(0,1), line_color=(10,200), point=(0,200),\n",
    "                                    point_color=(50,255),frame_color=None,wavy=(0,1), bg=(150,255)) \n",
    "                    \n",
    "            # 上面是4个字符验证码，下面是 5个字符      \n",
    "\n",
    "            elif i%max_num_len <= 39:  #  line=(0,6), line_width=(0,1), point=(0,500),wavy=(0,0)\n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(5)])\n",
    "                image = gen_captcha(random_str, fig_size=(200,70), fonts=fonts,font_color=(0,255,0,255,0,255),same_color=1, font_size=(50, 55), rotate=10,\n",
    "                                    font_noise=0,offset_w=(-2,1),offset_h=5, line=(0,0), shortline=(0,0), line_width=(2,2), line_color=(0,100,80,230,0,90), point=(0,50),\n",
    "                                    point_color=(250,255),frame_color=None,wavy=(1,1), bg=(255,255))\n",
    "\n",
    "            elif i%max_num_len <= 41: #   line=(0,3), line_width=(0,1), point=(0,200),wavy=(0,0)\n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(5)])\n",
    "                image = gen_captcha(random_str, fig_size=(100,30), fonts=fonts,font_color=(40,120),same_color=0, font_size=(25, 27), rotate=0,\n",
    "                                    font_noise=0,offset_w=(-3,-1),offset_h=2, line=(10,20), shortline=(200,250), line_width=(0,1), line_color=(170,200), point=(0,0),\n",
    "                                    point_color=(250,255),frame_color=(150,180),wavy=(0,0), bg=(250,255))\n",
    "\n",
    "            elif i%max_num_len <= 43: # line=(2,6), line_width=(0,2), point=(0,0),wavy=(0,0)\n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(5)])\n",
    "                image = gen_captcha(random_str, fig_size=(100,27), fonts=fonts,font_color=(20,80,100,120,20,80),same_color=0, font_size=(25, 27), rotate=0,\n",
    "                                    font_noise=0,offset_w=(2,2),offset_h=2, line=(0,0), shortline=(0,0), line_width=(0,1), line_color=(170,200), point=(500,800),\n",
    "                                    point_color=(250,255),frame_color=None,wavy=(0,0), bg=(210,240))                \n",
    "\n",
    "            elif i%max_num_len <= 45:\n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(5)])\n",
    "                tmp_w = random.randint(100,150)\n",
    "                tmp_h = random.randint(35, 45)\n",
    "                font_s = (int(tmp_h*0.8), int(tmp_h*0.9))\n",
    "                image = gen_captcha(random_str, fig_size=(tmp_w,tmp_h), fonts=fonts,font_color=(0,180),same_color=0, font_size=font_s, rotate=15,\n",
    "                                    font_noise=0,offset_w=(-2,1),offset_h=2, line=(0,0), shortline=(0,0), line_width=(0,1), line_color=(170,200), point=(0,0),\n",
    "                                    point_color=(250,255),frame_color=None,wavy=(0,0), bg=(150,255))                       \n",
    "\n",
    "            elif i%max_num_len<=47: #  line=(0,0), line_width=(0,1), point=(0,0),wavy=(0,0)     \n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(5)])\n",
    "                tmp_w = random.randint(100,150)\n",
    "                tmp_h = random.randint(35, 45)\n",
    "                font_s = (int(tmp_h*0.8), int(tmp_h*0.9))\n",
    "                image = gen_captcha(random_str, fig_size=(tmp_w,tmp_h), fonts=fonts,font_color=(0,180),same_color=0, font_size=font_s, rotate=15,\n",
    "                                    font_noise=0,offset_w=(-2,1),offset_h=2, line=(0,5), shortline=(0,100), line_width=(0,1), line_color=(10,200), point=(0,200),\n",
    "                                    point_color=(50,255),frame_color=None,wavy=(0,1), bg=(150,255)) \n",
    "\n",
    "            elif i%max_num_len<=49: # 加入真实验证码\n",
    "                image, random_str = rebuild_img(random.choice(len5_imgs))\n",
    "\n",
    "            elif i%max_num_len<=50:    \n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(5)])\n",
    "                tmp_w = random.randint(100,150)\n",
    "                tmp_h = random.randint(35, 45)\n",
    "                font_s = (int(tmp_h*0.8), int(tmp_h*0.9))\n",
    "                image = gen_captcha(random_str, fig_size=(tmp_w,tmp_h), fonts=fonts,font_color=(200,255),same_color=0, font_size=font_s, rotate=15,\n",
    "                                    font_noise=0,offset_w=(-2,3),offset_h=2, line=(0,5), shortline=(0,100), line_width=(0,1), line_color=(10,200), point=(0,200),\n",
    "                                    point_color=(50,155),frame_color=None,wavy=(0,1), bg=(0,255,0,250,0,250))  \n",
    "                \n",
    "            #下面是6个字符验证码 \n",
    "            elif i%max_num_len<=55: #  line=(0,0), line_width=(0,1), point=(0,0),wavy=(0,0)     \n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(6)])\n",
    "                image = gen_captcha(random_str, fig_size=(122,46), fonts=fonts,font_color=(5,160,5,150,5,160),same_color=0, font_size=(17, 20), rotate=10,\n",
    "                font_noise=0,offset_w=(-1,3),offset_h=2, line=(0,3), line_width=(0,1), line_color=(200,250), point=(0,150),\n",
    "                point_color=(200,255),frame_color=(200,250),wavy=(0,0), bg=(235,255)).resize((width, height), Image.BILINEAR)\n",
    "                \n",
    "            elif i%max_num_len<=57:\n",
    "                image, random_str = add_real_img(imgs_160_60)  \n",
    "            \n",
    "            elif i%max_num_len<=62:\n",
    "                image, random_str = add_real_img2(chaojiying_train)\n",
    "                \n",
    "            else : # 加入真实验证码\n",
    "                image, random_str = add_real_img(imgs_122_46)   \n",
    "\n",
    "            X[i] = np.array(image)/255.0\n",
    "            label = [self.characters.find(x) for x in random_str.lower()]  # 全部标签转换为小写\n",
    "            if len(random_str) < self.n_len:\n",
    "                label += [self.n_class]*(self.n_len-len(random_str))                \n",
    "            y[i] = label\n",
    "    \n",
    "#         return imgs# \n",
    "        return [X, y, input_length, label_length], np.ones(self.batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image size (200, 70)\n"
     ]
    },
    {
     "data": {
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AAAAAAAAAAAAAAAAAGM3kNQAAAAAAAAAAAAAAAAAAAAAAjGbyGgAAAAAAAAAAAAAAAAAAAAAARjN5DQAAAAAAAAAAAAAAAAAAAAAAo5m8BgAAAAAAAAAAAAAAAAAAAACA0UxeAwAAAAAAAAAAAAAAAAAAAADAaCavAQAAAAAAAAAAAAAAAAAAAABgNJPXAAAAAAAAAAAAAAAAAAAAAAAwmslrAAAAAAAAAAAAAAAAAAAAAAAY7f8CjFMxFzYDbCIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 3600x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 2263
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''生成灰度图像'''\n",
    "def get_wavy_line(w = (0, 100),h = (30, 50)):\n",
    "    '''产生波浪线坐标'''\n",
    "    import random\n",
    "    n = 50\n",
    "    x = 0\n",
    "    y = random.randint(h[0],h[1])\n",
    "    flag = random.randint(0,2)\n",
    "    xy = [(x, y)]\n",
    "    while x < w[1]:\n",
    "        temp_y = random.randint(1, 3)\n",
    "        temp_x = random.randint(5, 10)\n",
    "        if flag == 0:\n",
    "            if y + temp_y > h[1]:\n",
    "                y -= temp_y\n",
    "                flag = 1\n",
    "            else:\n",
    "                y += temp_y\n",
    "        else:\n",
    "            if y - temp_y < h[0]:\n",
    "                y += temp_y\n",
    "                flag = 0\n",
    "            else:\n",
    "                y -= temp_y\n",
    "        x = x+temp_x if x+temp_x < w[1] else w[1]\n",
    "        xy.append((x, y))\n",
    "    return xy\n",
    "def Asin(x, A=8,w=0.05, b=6, k=40):\n",
    "    '''\n",
    "    y=Asin(ωx+φ)+k在直角坐标系上的图象\n",
    "    A——振幅，当物体作轨迹符合正弦曲线的直线往复运动时，其值为行程的1/2。\n",
    "    (ωx+φ)——相位，反映变量y所处的状态。\n",
    "    φ——初相，x=0时的相位；反映在坐标系上则为图像的左右移动。\n",
    "    k——偏距，反映在坐标系上则为图像的上移或下移。\n",
    "    ω——角速度， 控制正弦周期(单位弧度内震动的次数)。\n",
    "    '''\n",
    "    return A*math.sin(w*x+b)+k\n",
    "\n",
    "def random_xy(width,height):   \n",
    "    '''\n",
    "    随机位置函数，返回指定范围随机位置坐标\n",
    "    参数：width:图片宽，height:图片高\n",
    "    '''\n",
    "    x = random.randint(0, width)\n",
    "    y = random.randint(0, height)\n",
    "    return x, y\n",
    "def random_color(start, end, opacity=None):\n",
    "    '''\n",
    "    随机颜色函数，返回指定范围随机颜色值\n",
    "    参数：start:颜色最低值，end:颜色最高值\n",
    "    '''\n",
    "    red = random.randint(start, end)\n",
    "    green = random.randint(start, end)\n",
    "    blue = random.randint(start, end)\n",
    "    if opacity is None:\n",
    "        return (red, green, blue)\n",
    "    return (red, green, blue, opacity)\n",
    "\n",
    "def gen_captcha(text, fig_size=(200,70), fonts=['fonts/ANTQUAB.TTF'],font_color=(10,100),same_color=1, font_size=(25, 35), rotate=0,\n",
    "                font_noise=0, line=(0,5), line_width=(1,2), point=(0,500),wavy=(0,0), noise_color=(150,250), bg=(200,255)):\n",
    "    '''\n",
    "    text:验证码文本\n",
    "    size:验证码图片宽高\n",
    "    fonts:字体列表，随机选择一个\n",
    "    font_noise: 字体散点干扰，0不加干扰，1加干扰\n",
    "    offset_hor: 左右偏移值\n",
    "    offset_var: 上下偏移值\n",
    "    fill:字体颜色范围\n",
    "    rotate:字体旋转角度\n",
    "    line:干扰线条数范围\n",
    "    point:干扰点数范围\n",
    "    wavy:波浪线数范围\n",
    "    color:干扰线、点 颜色\n",
    "    bg:背景色范围\n",
    "    '''\n",
    "    bg = random.randint(bg[0], bg[1])\n",
    "    img = Image.new(mode='L', size=fig_size, color=bg) #\n",
    "    draw = ImageDraw.Draw(im=img, mode='L') # im, mode=None\n",
    "    \n",
    "    font_path = random.choice(fonts)\n",
    "#     font_name = font_path.split('/')[-1][:-4]\n",
    "#     print('font_name:', font_name)\n",
    "    \n",
    "    font = ImageFont.truetype(font_path, size=random.randint(font_size[0], font_size[1])) # font=None, size=10, index=0, encoding=\"\"\n",
    "    rotate = random.randint(0, rotate)\n",
    "    def get_char_img(char,font,font_color,rotate,bg, font_noise=0):\n",
    "        '''\n",
    "        生成单个字符图片，随机颜色加随机旋转\n",
    "        \n",
    "        '''\n",
    "        w, h = draw.textsize(char, font=font)\n",
    "        im = Image.new('L',(w,h), color=bg)\n",
    "        ImageDraw.Draw(im).text((0,0), char, font=font, fill=font_color)   \n",
    "        if rotate:\n",
    "            im = im.rotate(random.randint(-rotate, rotate),Image.BILINEAR,expand=1)\n",
    "            im = im.crop(im.getbbox())\n",
    "        if font_noise: \n",
    "            im_draw = ImageDraw.Draw(im)\n",
    "#             for i in range(random.randint(1,20)):\n",
    "            for i in range(random.randint(int(w*h*0.01),int(w*h*0.05))):\n",
    "                im_draw.point(xy=(random.randint(0, w), random.randint(0, h)),fill=bg)\n",
    "\n",
    "        table = []\n",
    "        for i in range(256):\n",
    "            table.append(i * 97) # 5.97\n",
    "        mask = im.convert('L').point(table) \n",
    "        return (im, mask)\n",
    "    \n",
    "    char_color = random.randint(font_color[0],font_color[1])\n",
    "    if same_color:        \n",
    "        char_imgs = [get_char_img(char, font, font_color=char_color, rotate=rotate, bg=bg, font_noise=font_noise) for char in text]\n",
    "    else:\n",
    "        char_imgs = [get_char_img(char, font, font_color=random.randint(font_color[0],font_color[1]), rotate=rotate, bg=bg, font_noise=font_noise) for char in text]       \n",
    "    ws = [img[0].size[0] for img in char_imgs]\n",
    "    hs = [img[0].size[1] for img in char_imgs]\n",
    "    w = max(sum(ws), fig_size[0])\n",
    "    h = max(max(hs), fig_size[1])\n",
    "    if w>fig_size[0] or h>fig_size[1]:\n",
    "        img = Image.new('L',(w+6,h+6), color=bg)\n",
    "        draw = ImageDraw.Draw(im=img, mode='L')  # im, mode=None\n",
    "        fig_size = img.size\n",
    "\n",
    "#     if rotate:\n",
    "    temp_x = random.randint(int((fig_size[0]-sum(ws))/5), int((fig_size[0]-sum(ws))/2+1))\n",
    "    temp_y = random.randint(int((fig_size[1]-hs[0])/10), int((fig_size[1]-hs[0])/5+1))\n",
    "    for i in range(len(char_imgs)):\n",
    "        img.paste(char_imgs[i][0], box=(temp_x, temp_y), mask=char_imgs[i][1]) \n",
    "        new_x = temp_x+ws[i]+random.randint(-(ws[i]//8), (ws[i]//8)) #ws[i]//5\n",
    "        temp_x = new_x if new_x<fig_size[0] else temp_x+ws[i]+random.randint(-(ws[i]//5),0)\n",
    "        new_y = random.randint(0, min(h-hs[i], int(hs[i]*0.5)))\n",
    "        temp_y = new_y if 0<new_y and new_y+hs[i]<fig_size[1] else temp_y  \n",
    "       \n",
    "    # 直线\n",
    "    for i in range(random.randint(line[0], line[1])):\n",
    "        x0 = random.randint(0, fig_size[0])\n",
    "        x1 = random.randint(0, fig_size[0])\n",
    "        y0 = random.randint(0, fig_size[1])\n",
    "        y1 = random.randint(0, fig_size[1])\n",
    "        draw.line(xy=((x0,y0),(x1,y1)),\n",
    "                  fill=random.randint(noise_color[0], noise_color[1]),\n",
    "                  width=random.randint(line_width[0], line_width[1])) # xy, fill=None, width=0\n",
    "    # 散点\n",
    "    for i in range(random.randint(point[0], point[1])):\n",
    "        draw.point(xy=(random.randint(0, fig_size[0]), random.randint(0, fig_size[1])),\n",
    "                   fill=random.randint(noise_color[0], noise_color[1]))\n",
    "#     # 波浪线\n",
    "#     for _ in range(random.randint(wavy[0],wavy[1])):  \n",
    "#         draw.line(xy=get_wavy_line(w = (0, 200),h = (min(hs)-5, max(hs)+5)), \n",
    "#                   fill=char_color, width=random.randint(line_width[0], line_width[1]))              \n",
    "\n",
    "    A_ = random.uniform(hs[0]*0.1,hs[0]*0.4)\n",
    "    w_ = math.pi*4/w#random.uniform(0.04, 0.06)\n",
    "    b_ = random.random()*math.pi\n",
    "    k_ = random.uniform(h*0.5, h*0.7)\n",
    "\n",
    "    # 波浪线\n",
    "    for _ in range(random.randint(wavy[0],wavy[1])):  \n",
    "        draw.line(xy=[(x, Asin(x, A_, w_, b_, k_)) for x in range(int(w))], \n",
    "                  fill=char_color, width=random.randint(line_width[0], line_width[1]))    \n",
    "        \n",
    "#     filename = font_name+\"_\"+str(uuid.uuid1())+\"_\"+text\n",
    "#     img.save('/data/python/lsm/gen_captcha/{}.jpg'.format(filename))\n",
    "        \n",
    "    return img.resize((width, height), Image.BILINEAR)\n",
    "\n",
    "img2 = Image.open('/data/captcha/captcha_sample/1.jpg').resize((200,70), Image.BILINEAR) #小图多种颜色字、干扰线 \n",
    "\n",
    "random_str = 'RpH2'\n",
    "image = gen_captcha(random_str, fig_size=(200,50), fonts=fonts,font_color=(10,80),same_color=0, font_size=(15, 30), rotate=2,\n",
    "                    font_noise=0, line=(0,0), line_width=(0,1), point=(0,0),wavy=(0,0), noise_color=(200,230), bg=(200,240))\n",
    "\n",
    "im = [image, img2]\n",
    "print('image size',image.size)\n",
    "plt.figure(figsize=(50,10))\n",
    "for i in range(1,3):    \n",
    "    \n",
    "    plt.subplot(2,2,i)\n",
    "    plt.imshow(im[i-1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = CaptchaSequence(characters, batch_size=60, steps=10,input_length=12, label_length=6,chars_len=(4, 6))  # (characters, batch_size=128, steps=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60, 70, 200, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f56b34da128>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 150,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "l, _  = data[1]\n",
    "x = l[0]\n",
    "print(x.shape)\n",
    "idx = 58\n",
    "# plt.imshow(np.reshape(x[idx], (height, width)))\n",
    "\n",
    "# x = data[1]\n",
    "# idx = 8\n",
    "plt.imshow(x[idx])\n",
    "# len4_imgs[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 70, 200, 3)]      0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 70, 200, 32)       896       \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 70, 200, 32)       128       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu (LeakyReLU)      (None, 70, 200, 32)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 70, 200, 32)       9248      \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 70, 200, 32)       128       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 70, 200, 32)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 35, 100, 32)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 35, 100, 64)       18496     \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 35, 100, 64)       256       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 35, 100, 64)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 35, 100, 64)       36928     \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 35, 100, 64)       256       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 35, 100, 64)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 17, 50, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 17, 50, 128)       73856     \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 17, 50, 128)       512       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 17, 50, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 17, 50, 128)       147584    \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 17, 50, 128)       512       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)    (None, 17, 50, 128)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 8, 25, 128)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 8, 25, 256)        295168    \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 8, 25, 256)        1024      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_6 (LeakyReLU)    (None, 8, 25, 256)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 8, 25, 256)        590080    \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",
      "max_pooling2d_3 (MaxPooling2 (None, 4, 12, 256)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 4, 12, 256)        590080    \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 4, 12, 256)        1024      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_8 (LeakyReLU)    (None, 4, 12, 256)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 4, 12, 256)        590080    \n",
      "_________________________________________________________________\n",
      "batch_normalization_9 (Batch (None, 4, 12, 256)        1024      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_9 (LeakyReLU)    (None, 4, 12, 256)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 2, 12, 256)        0         \n",
      "_________________________________________________________________\n",
      "permute (Permute)            (None, 12, 2, 256)        0         \n",
      "_________________________________________________________________\n",
      "time_distributed (TimeDistri (None, 12, 512)           0         \n",
      "_________________________________________________________________\n",
      "bidirectional (Bidirectional (None, 12, 256)           492288    \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 12, 256)           295680    \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 12, 37)            9509      \n",
      "=================================================================\n",
      "Total params: 3,155,781\n",
      "Trainable params: 3,152,837\n",
      "Non-trainable params: 2,944\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# 定义网络\n",
    "from tensorflow.keras.models import *\n",
    "from tensorflow.keras.layers import *\n",
    "\n",
    "# 定义 CTC Loss\n",
    "import tensorflow.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",
    "        x = Conv2D(32*2**min(i, 3), kernel_size=3, padding='same', kernel_initializer='he_uniform')(x)  # 32*2**min(i, 3)\n",
    "        x = BatchNormalization()(x)\n",
    "#         x = Activation('relu')(x) # 20200729 relu 改LeakyReLU\n",
    "        x = LeakyReLU(0.01)(x)\n",
    "    x = MaxPooling2D(2 if i < 4 else (2, 1))(x)\n",
    "\n",
    "x = Permute((2, 1, 3))(x)\n",
    "x = TimeDistributed(Flatten())(x)\n",
    "rnn_size = 128 # 128  32\n",
    "\n",
    "x = Bidirectional(GRU(rnn_size, return_sequences=True))(x)\n",
    "x = Bidirectional(GRU(rnn_size, return_sequences=True))(x)  # 200epoch 0.0153 - val_loss: 0.0136\n",
    "\n",
    "x = Dense(n_class, activation='softmax')(x)\n",
    "base_model = Model(inputs=input_tensor, outputs=x)\n",
    "print(base_model.summary())\n",
    "\n",
    "labels = Input(name='the_labels', shape=[None], dtype='float32')\n",
    "input_length = Input(name='input_length', shape=[1], dtype='int64')\n",
    "label_length = Input(name='label_length', shape=[1], dtype='int64')\n",
    "loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([x, labels, input_length, label_length])\n",
    "model = Model(inputs=[input_tensor, labels, input_length, label_length], outputs=loss_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 7.8331Epoch 1/300\n",
      "1000/1000 [==============================] - 280s 280ms/step - loss: 7.8275 - val_loss: 2.8487\n",
      "Epoch 2/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 1.8288Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 1.8281 - val_loss: 2.0008\n",
      "Epoch 3/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 1.2126Epoch 1/300\n",
      "1000/1000 [==============================] - 269s 269ms/step - loss: 1.2126 - val_loss: 1.3810\n",
      "Epoch 4/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.9099Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.9097 - val_loss: 1.0598\n",
      "Epoch 5/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.7244Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.7245 - val_loss: 0.7570\n",
      "Epoch 6/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.6213Epoch 1/300\n",
      "1000/1000 [==============================] - 269s 269ms/step - loss: 0.6211 - val_loss: 0.6709\n",
      "Epoch 7/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5337Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.5336 - val_loss: 0.6363\n",
      "Epoch 8/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.4732Epoch 1/300\n",
      "1000/1000 [==============================] - 269s 269ms/step - loss: 0.4733 - val_loss: 0.5321\n",
      "Epoch 9/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.4304Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.4305 - val_loss: 0.5188\n",
      "Epoch 10/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.3920Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.3919 - val_loss: 0.5322\n",
      "Epoch 11/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.3621Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.3622 - val_loss: 0.4119\n",
      "Epoch 12/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.3417Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.3417 - val_loss: 0.4534\n",
      "Epoch 13/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.3176Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.3175 - val_loss: 0.4698\n",
      "Epoch 14/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.3054Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.3054 - val_loss: 0.3874\n",
      "Epoch 15/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2877Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.2876 - val_loss: 0.4453\n",
      "Epoch 16/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2699Epoch 1/300\n",
      "1000/1000 [==============================] - 269s 269ms/step - loss: 0.2699 - val_loss: 0.3406\n",
      "Epoch 17/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2568Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.2568 - val_loss: 0.3487\n",
      "Epoch 18/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2503Epoch 1/300\n",
      "1000/1000 [==============================] - 269s 269ms/step - loss: 0.2503 - val_loss: 0.2569\n",
      "Epoch 19/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2375Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.2374 - val_loss: 0.3040\n",
      "Epoch 20/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2338Epoch 1/300\n",
      "1000/1000 [==============================] - 269s 269ms/step - loss: 0.2337 - val_loss: 0.2552\n",
      "Epoch 21/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2242Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.2244 - val_loss: 0.2351\n",
      "Epoch 22/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2196Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.2196 - val_loss: 0.6589\n",
      "Epoch 23/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.2050Epoch 1/300\n",
      "1000/1000 [==============================] - 269s 269ms/step - loss: 0.2050 - val_loss: 0.2289\n",
      "Epoch 24/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1965Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1964 - val_loss: 0.2099\n",
      "Epoch 25/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1996Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1995 - val_loss: 0.2275\n",
      "Epoch 26/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1849Epoch 1/300\n",
      "1000/1000 [==============================] - 267s 267ms/step - loss: 0.1849 - val_loss: 0.2232\n",
      "Epoch 27/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1859Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1860 - val_loss: 0.2576\n",
      "Epoch 28/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1845Epoch 1/300\n",
      "1000/1000 [==============================] - 267s 267ms/step - loss: 0.1846 - val_loss: 0.2168\n",
      "Epoch 29/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1711Epoch 1/300\n",
      "1000/1000 [==============================] - 269s 269ms/step - loss: 0.1711 - val_loss: 0.1964\n",
      "Epoch 30/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1699Epoch 1/300\n",
      "1000/1000 [==============================] - 269s 269ms/step - loss: 0.1698 - val_loss: 0.1745\n",
      "Epoch 31/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1648Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1647 - val_loss: 0.1842\n",
      "Epoch 32/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1565Epoch 1/300\n",
      "1000/1000 [==============================] - 267s 267ms/step - loss: 0.1566 - val_loss: 0.1947\n",
      "Epoch 33/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1668Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1669 - val_loss: 0.1638\n",
      "Epoch 34/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1598Epoch 1/300\n",
      "1000/1000 [==============================] - 267s 267ms/step - loss: 0.1598 - val_loss: 0.1779\n",
      "Epoch 35/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1461Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1461 - val_loss: 0.1690\n",
      "Epoch 36/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1542Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1541 - val_loss: 0.1682\n",
      "Epoch 37/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1479Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1478 - val_loss: 0.2161\n",
      "Epoch 38/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1489Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1489 - val_loss: 0.1500\n",
      "Epoch 39/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1434Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1433 - val_loss: 0.1882\n",
      "Epoch 40/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1383Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1384 - val_loss: 0.1569\n",
      "Epoch 41/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1364Epoch 1/300\n",
      "1000/1000 [==============================] - 269s 269ms/step - loss: 0.1363 - val_loss: 0.1463\n",
      "Epoch 42/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1382Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1381 - val_loss: 0.1392\n",
      "Epoch 43/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1354Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1354 - val_loss: 0.1835\n",
      "Epoch 44/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1317Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1317 - val_loss: 0.1319\n",
      "Epoch 45/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1277Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1278 - val_loss: 0.1241\n",
      "Epoch 46/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1289Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1289 - val_loss: 0.1437\n",
      "Epoch 47/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1225Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1224 - val_loss: 0.1344\n",
      "Epoch 48/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1250Epoch 1/300\n",
      "1000/1000 [==============================] - 267s 267ms/step - loss: 0.1250 - val_loss: 0.1522\n",
      "Epoch 49/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1207Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1207 - val_loss: 0.1631\n",
      "Epoch 50/300\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.1163Epoch 1/300\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.1163 - val_loss: 0.1391\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nEpoch 30/300\\n 999/1000 [============================>.] - ETA: 0s - loss: 0.1605Epoch 1/300\\n1000/1000 [==============================] - 375s 375ms/step - loss: 0.1606 - val_loss: 0.1919'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping, CSVLogger, ModelCheckpoint\n",
    "from tensorflow.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_english4to6_ctc_best_1012.h5')\n",
    "\n",
    "train_data = CaptchaSequence(characters, batch_size=128, steps=1000,input_length=12, label_length=6,chars_len=(4, 6)) # (characters, batch_size=128, steps=1000)\n",
    "valid_data = CaptchaSequence(characters, batch_size=128, steps=100,input_length=12, label_length=6,chars_len=(4, 6))  # (characters, batch_size=128, steps=100)\n",
    "\n",
    "callbacks = [EarlyStopping(patience=5),ModelCheckpoint('gru_english4to6_ctc_best_20250415.h5', save_best_only=True)]\n",
    "model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=Adam(1e-3, amsgrad=True))\n",
    "model.fit_generator(train_data, epochs=300, validation_data=valid_data, workers=4, use_multiprocessing=True,\n",
    "                   callbacks=callbacks)\n",
    "'''\n",
    "Epoch 30/300\n",
    " 999/1000 [============================>.] - ETA: 0s - loss: 0.1605Epoch 1/300\n",
    "1000/1000 [==============================] - 375s 375ms/step - loss: 0.1606 - val_loss: 0.1919'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.0553Epoch 1/50\n",
      "1000/1000 [==============================] - 530s 530ms/step - loss: 0.0553 - val_loss: 0.0658\n",
      "Epoch 2/50\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.0563Epoch 1/50\n",
      "1000/1000 [==============================] - 517s 517ms/step - loss: 0.0562 - val_loss: 0.0615\n",
      "Epoch 3/50\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.0562Epoch 1/50\n",
      "1000/1000 [==============================] - 517s 517ms/step - loss: 0.0563 - val_loss: 0.0561\n",
      "Epoch 4/50\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.0563Epoch 1/50\n",
      "1000/1000 [==============================] - 517s 517ms/step - loss: 0.0563 - val_loss: 0.0514\n",
      "Epoch 5/50\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.0574Epoch 1/50\n",
      "1000/1000 [==============================] - 518s 518ms/step - loss: 0.0574 - val_loss: 0.0524\n",
      "Epoch 6/50\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.0554Epoch 1/50\n",
      "1000/1000 [==============================] - 517s 517ms/step - loss: 0.0554 - val_loss: 0.0512\n",
      "Epoch 7/50\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.0534Epoch 1/50\n",
      "1000/1000 [==============================] - 518s 518ms/step - loss: 0.0534 - val_loss: 0.0561\n",
      "Epoch 8/50\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.0539Epoch 1/50\n",
      "1000/1000 [==============================] - 517s 517ms/step - loss: 0.0539 - val_loss: 0.0513\n",
      "Epoch 9/50\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.0536Epoch 1/50\n",
      "1000/1000 [==============================] - 518s 518ms/step - loss: 0.0535 - val_loss: 0.0584\n",
      "Epoch 10/50\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.0550Epoch 1/50\n",
      "1000/1000 [==============================] - 517s 517ms/step - loss: 0.0550 - val_loss: 0.0540\n",
      "Epoch 11/50\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.0534Epoch 1/50\n",
      "1000/1000 [==============================] - 516s 516ms/step - loss: 0.0534 - val_loss: 0.0443\n",
      "Epoch 12/50\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.0538Epoch 1/50\n",
      "1000/1000 [==============================] - 517s 517ms/step - loss: 0.0538 - val_loss: 0.0465\n",
      "Epoch 13/50\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.0538Epoch 1/50\n",
      "1000/1000 [==============================] - 516s 516ms/step - loss: 0.0537 - val_loss: 0.0513\n",
      "Epoch 14/50\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.0519Epoch 1/50\n",
      "1000/1000 [==============================] - 517s 517ms/step - loss: 0.0519 - val_loss: 0.0549\n",
      "Epoch 15/50\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.0526Epoch 1/50\n",
      "1000/1000 [==============================] - 517s 517ms/step - loss: 0.0526 - val_loss: 0.0523\n",
      "Epoch 16/50\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.0510Epoch 1/50\n",
      "1000/1000 [==============================] - 516s 516ms/step - loss: 0.0510 - val_loss: 0.0522\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f54c826b5c0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping, CSVLogger, ModelCheckpoint\n",
    "from tensorflow.keras.optimizers import *\n",
    "import gc \n",
    "train_data = CaptchaSequence(characters, batch_size=256, steps=1000,input_length=12, label_length=6,chars_len=(4, 6)) # (characters, batch_size=128, steps=1000)\n",
    "valid_data = CaptchaSequence(characters, batch_size=128, steps=100,input_length=12, label_length=6,chars_len=(4, 6))  # (characters, batch_size=128, steps=100)\n",
    "\n",
    "# callbacks = [CSVLogger('ctc.csv', append=True), ModelCheckpoint('gru_english4to6_ctc_best_20220829.h5', save_best_only=True)]\n",
    "callbacks = [EarlyStopping(patience=5), CSVLogger('ctc.csv', append=True), ModelCheckpoint('gru_english4to6_ctc_best_20250415.h5', save_best_only=True)]\n",
    "# model.load_weights('gru_english4to6_ctc_best_5.h5')  # 以前英文数字模型预测\n",
    "# model.load_weights('gru_english4to6_ctc_best_1014.h5') # lose:0.0203 val_loss:0.012\n",
    "# model.load_weights('gru_english4to6_ctc_best_1102.h5') #  loss: 0.0178 - val_loss: 0.0120\n",
    "# model.load_weights('gru_DigitAndEnglist_base_model_20220628.h5') # loss: 0.0162 - val_loss: 0.0564\n",
    "# model.load_weights('gru_english4to6_ctc_best_20220829.h5') # loss: 0.0162 - val_loss: 0.0564\n",
    "model.load_weights('gru_english4to6_ctc_best_20250415.h5') # loss: 0.0162 - val_loss: 0.0564\n",
    "# gru_DigitAndEnglist_ctc_best.h5  mobilenet_DigitAndEnglist_ctc_best0930\n",
    "# callbacks = [CSVLogger('ctc.csv', append=True), ModelCheckpoint('DigitAndEnglist_cnn5gru_ctc_best2.h5', save_best_only=True)]\n",
    "# model.load_weights('DigitAndEnglist_cnn5gru_ctc_best2.h5')\n",
    "model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=Adam(1e-4, amsgrad=True))\n",
    "model.fit_generator(train_data, epochs=50, validation_data=valid_data, workers=4, use_multiprocessing=True,\n",
    "                    callbacks=callbacks)"
   ]
  },
  {
   "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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# base_model.save('gru_DigitAndEnglist_base_model1014.h5') # 保存基础模型，预测用\n",
    "# base_model.save('gru_DigitAndEnglist_base_model_1103.h5') # 保存基础模型，预测用\n",
    "# base_model.save('gru_DigitAndEnglist_base_model_20220829.h5') # 保存基础模型，预测用\n",
    "# base_model.save('gru_DigitAndEnglist_base_model_20230406.h5') # 保存基础模型，预测用\n",
    "x= base_model.output    # [batch_sizes, series_length, classes]\n",
    "input_length = Input(batch_shape=[None], dtype='int32')\n",
    "ctc_decode = K.ctc_decode(x, input_length=input_length * K.shape(x)[1])\n",
    "decode = K.function([base_model.input, input_length], [ctc_decode[0][0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "InternalError",
     "evalue": "tensorflow/core/kernels/cuda_solvers.cc:468: cuSolverDN call failed with status =7\n\t [[Node: bidirectional/forward_gru/recurrent_kernel/Initializer/Qr = Qr[T=DT_FLOAT, _class=[\"loc:@bidirectional/forward_gru/recurrent_kernel/Assign\"], full_matrices=false, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](bidirectional/forward_gru/recurrent_kernel/Initializer/random_normal/RandomStandardNormal)]]\n\nCaused by op 'bidirectional/forward_gru/recurrent_kernel/Initializer/Qr', defined at:\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/runpy.py\", line 170, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n    app.start()\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/kernelapp.py\", line 497, in start\n    self.io_loop.start()\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tornado/platform/asyncio.py\", line 132, in start\n    self.asyncio_loop.run_forever()\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/asyncio/base_events.py\", line 301, in run_forever\n    self._run_once()\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/asyncio/base_events.py\", line 1198, in _run_once\n    handle._run()\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/asyncio/events.py\", line 125, in _run\n    self._callback(*self._args)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tornado/platform/asyncio.py\", line 122, in _handle_events\n    handler_func(fileobj, events)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 450, in _handle_events\n    self._handle_recv()\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 480, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 432, in _run_callback\n    callback(*args, **kwargs)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 233, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/zmqshell.py\", line 537, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2662, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2785, in _run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2901, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2961, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-5-8c4b392b1ebf>\", line 31, in <module>\n    x = Bidirectional(GRU(rnn_size, return_sequences=True))(x)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/layers/wrappers.py\", line 471, in __call__\n    return super(Bidirectional, self).__call__(inputs, **kwargs)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/engine/base_layer.py\", line 728, in __call__\n    self.build(input_shapes)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/layers/wrappers.py\", line 593, in build\n    self.forward_layer.build(input_shape)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/utils/tf_utils.py\", line 148, in wrapper\n    output_shape = fn(instance, input_shape)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/layers/recurrent.py\", line 487, in build\n    self.cell.build(step_input_shape)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/utils/tf_utils.py\", line 148, in wrapper\n    output_shape = fn(instance, input_shape)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/layers/recurrent.py\", line 1267, in build\n    constraint=self.recurrent_constraint)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/engine/base_layer.py\", line 565, in add_weight\n    aggregation=aggregation)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/training/checkpointable/base.py\", line 535, in _add_variable_with_custom_getter\n    **kwargs_for_getter)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/engine/base_layer.py\", line 1918, in make_variable\n    aggregation=aggregation)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py\", line 2443, in variable\n    aggregation=aggregation)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py\", line 2425, in <lambda>\n    previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py\", line 2395, in default_variable_creator\n    constraint=constraint)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/resource_variable_ops.py\", line 312, in __init__\n    constraint=constraint)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/resource_variable_ops.py\", line 417, in _init_from_args\n    initial_value(), name=\"initial_value\", dtype=dtype)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/engine/base_layer.py\", line 1903, in <lambda>\n    shape, dtype=dtype, partition_info=partition_info)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/init_ops.py\", line 549, in __call__\n    q, r = gen_linalg_ops.qr(a, full_matrices=False)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/gen_linalg_ops.py\", line 1494, in qr\n    \"Qr\", input=input, full_matrices=full_matrices, name=name)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py\", line 787, in _apply_op_helper\n    op_def=op_def)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/util/deprecation.py\", line 454, in new_func\n    return func(*args, **kwargs)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 3155, in create_op\n    op_def=op_def)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 1717, in __init__\n    self._traceback = tf_stack.extract_stack()\n\nInternalError (see above for traceback): tensorflow/core/kernels/cuda_solvers.cc:468: cuSolverDN call failed with status =7\n\t [[Node: bidirectional/forward_gru/recurrent_kernel/Initializer/Qr = Qr[T=DT_FLOAT, _class=[\"loc:@bidirectional/forward_gru/recurrent_kernel/Assign\"], full_matrices=false, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](bidirectional/forward_gru/recurrent_kernel/Initializer/random_normal/RandomStandardNormal)]]\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInternalError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1277\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1278\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\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[0m\n\u001b[0m\u001b[1;32m   1279\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\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/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1262\u001b[0m       return self._call_tf_sessionrun(\n\u001b[0;32m-> 1263\u001b[0;31m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[1;32m   1264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m   1349\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1350\u001b[0;31m         run_metadata)\n\u001b[0m\u001b[1;32m   1351\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInternalError\u001b[0m: tensorflow/core/kernels/cuda_solvers.cc:468: cuSolverDN call failed with status =7\n\t [[Node: bidirectional/forward_gru/recurrent_kernel/Initializer/Qr = Qr[T=DT_FLOAT, _class=[\"loc:@bidirectional/forward_gru/recurrent_kernel/Assign\"], full_matrices=false, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](bidirectional/forward_gru/recurrent_kernel/Initializer/random_normal/RandomStandardNormal)]]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mInternalError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-ef9ed7e534e6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0mimg_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg2array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mout_pre\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mimg_arr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_arr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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[0m\u001b[1;32m     21\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcharacters\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mout_pre\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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     22\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\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/tensorflow/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2874\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'`inputs` should be a list or tuple.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2875\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2876\u001b[0;31m     \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\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   2877\u001b[0m     \u001b[0mfeed_arrays\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   2878\u001b[0m     \u001b[0marray_vals\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/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/backend.py\u001b[0m in \u001b[0;36mget_session\u001b[0;34m()\u001b[0m\n\u001b[1;32m    442\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_MANUAL_VAR_INIT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_default\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--> 444\u001b[0;31m       \u001b[0m_initialize_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    445\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    446\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/backend.py\u001b[0m in \u001b[0;36m_initialize_variables\u001b[0;34m(session)\u001b[0m\n\u001b[1;32m    673\u001b[0m       \u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_keras_initialized\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    674\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0muninitialized_vars\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 675\u001b[0;31m       \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariables_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariables_initializer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muninitialized_vars\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    676\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    875\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    876\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 877\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    878\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    879\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\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/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1098\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1099\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1100\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1101\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1102\u001b[0m       \u001b[0mresults\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/dl_nlp/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1270\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1271\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1272\u001b[0;31m                            run_metadata)\n\u001b[0m\u001b[1;32m   1273\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1274\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\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/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1289\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1290\u001b[0m           \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1291\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1292\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1293\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInternalError\u001b[0m: tensorflow/core/kernels/cuda_solvers.cc:468: cuSolverDN call failed with status =7\n\t [[Node: bidirectional/forward_gru/recurrent_kernel/Initializer/Qr = Qr[T=DT_FLOAT, _class=[\"loc:@bidirectional/forward_gru/recurrent_kernel/Assign\"], full_matrices=false, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](bidirectional/forward_gru/recurrent_kernel/Initializer/random_normal/RandomStandardNormal)]]\n\nCaused by op 'bidirectional/forward_gru/recurrent_kernel/Initializer/Qr', defined at:\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/runpy.py\", line 170, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n    app.start()\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/kernelapp.py\", line 497, in start\n    self.io_loop.start()\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tornado/platform/asyncio.py\", line 132, in start\n    self.asyncio_loop.run_forever()\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/asyncio/base_events.py\", line 301, in run_forever\n    self._run_once()\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/asyncio/base_events.py\", line 1198, in _run_once\n    handle._run()\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/asyncio/events.py\", line 125, in _run\n    self._callback(*self._args)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tornado/platform/asyncio.py\", line 122, in _handle_events\n    handler_func(fileobj, events)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 450, in _handle_events\n    self._handle_recv()\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 480, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 432, in _run_callback\n    callback(*args, **kwargs)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 233, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/zmqshell.py\", line 537, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2662, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2785, in _run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2901, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2961, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-5-8c4b392b1ebf>\", line 31, in <module>\n    x = Bidirectional(GRU(rnn_size, return_sequences=True))(x)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/layers/wrappers.py\", line 471, in __call__\n    return super(Bidirectional, self).__call__(inputs, **kwargs)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/engine/base_layer.py\", line 728, in __call__\n    self.build(input_shapes)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/layers/wrappers.py\", line 593, in build\n    self.forward_layer.build(input_shape)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/utils/tf_utils.py\", line 148, in wrapper\n    output_shape = fn(instance, input_shape)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/layers/recurrent.py\", line 487, in build\n    self.cell.build(step_input_shape)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/utils/tf_utils.py\", line 148, in wrapper\n    output_shape = fn(instance, input_shape)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/layers/recurrent.py\", line 1267, in build\n    constraint=self.recurrent_constraint)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/engine/base_layer.py\", line 565, in add_weight\n    aggregation=aggregation)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/training/checkpointable/base.py\", line 535, in _add_variable_with_custom_getter\n    **kwargs_for_getter)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/engine/base_layer.py\", line 1918, in make_variable\n    aggregation=aggregation)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py\", line 2443, in variable\n    aggregation=aggregation)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py\", line 2425, in <lambda>\n    previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py\", line 2395, in default_variable_creator\n    constraint=constraint)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/resource_variable_ops.py\", line 312, in __init__\n    constraint=constraint)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/resource_variable_ops.py\", line 417, in _init_from_args\n    initial_value(), name=\"initial_value\", dtype=dtype)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/engine/base_layer.py\", line 1903, in <lambda>\n    shape, dtype=dtype, partition_info=partition_info)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/init_ops.py\", line 549, in __call__\n    q, r = gen_linalg_ops.qr(a, full_matrices=False)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/gen_linalg_ops.py\", line 1494, in qr\n    \"Qr\", input=input, full_matrices=full_matrices, name=name)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py\", line 787, in _apply_op_helper\n    op_def=op_def)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/util/deprecation.py\", line 454, in new_func\n    return func(*args, **kwargs)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 3155, in create_op\n    op_def=op_def)\n  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 1717, in __init__\n    self._traceback = tf_stack.extract_stack()\n\nInternalError (see above for traceback): tensorflow/core/kernels/cuda_solvers.cc:468: cuSolverDN call failed with status =7\n\t [[Node: bidirectional/forward_gru/recurrent_kernel/Initializer/Qr = Qr[T=DT_FLOAT, _class=[\"loc:@bidirectional/forward_gru/recurrent_kernel/Assign\"], full_matrices=false, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](bidirectional/forward_gru/recurrent_kernel/Initializer/random_normal/RandomStandardNormal)]]\n"
     ]
    }
   ],
   "source": [
    "# img = Image.open('FileInfo0508/31c1f481-912a-11ea-b24d-408d5cd36814_cmftq.jpg') # 波浪线验证码\n",
    "# img = Image.open('/data/captcha/shensebeijingsandian/pgv4_d58a8328-c425-11ea-be07-ecf4bbc56acd.jpg') # 深色背景验证码\n",
    "# img = Image.open('/data/captcha/0ad9.jpg').resize((200,70), Image.BILINEAR) #小图噪点 \n",
    "imgs = glob.glob('/data/captcha/label_english/超级鹰导出图片-2023-04-03/*.jpg')[400:]\n",
    "img = Image.open(imgs[9])\n",
    "img = img.resize((width, height), Image.BILINEAR)\n",
    "def img2array(image, width=width,height=height):\n",
    "    X = np.zeros((1, height, width, 3))\n",
    "    image = image.convert('L')\n",
    "    px = [image.getpixel((x,2)) for x in range(image.size[0])]\n",
    "    c = Counter(px)\n",
    "    m = c.most_common()\n",
    "    bg = m[0][0]\n",
    "    bg_img = Image.new(mode='L', size=(width,height), color=bg)\n",
    "    bg_img.paste(image, box=(0, 0)) # \n",
    "    X[0] = np.expand_dims(np.array(bg_img)/255.0, axis=-1)\n",
    "    return X\n",
    "img_arr = img2array(img)\n",
    "\n",
    "out_pre = decode([img_arr, np.ones(img_arr.shape[0])])\n",
    "out = ''.join([characters[x] for x in out_pre[0][0]])\n",
    "plt.imshow(img)\n",
    "print('out', out)\n",
    "plt.title(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pred:3417\ttrue:3477\n",
      "pred:w9k\ttrue:w9ik\n",
      "pred:zfpl\ttrue:zfpi\n",
      "pred:y83m\ttrue:y83n\n",
      "pred:ekdrlo\ttrue:ekdrlq\n",
      "pred:4pgmn\ttrue:4pgnn\n",
      "pred:lutkp\ttrue:iutkp\n",
      "pred:qj3c\ttrue:qjsc\n",
      "pred:2nxj8\ttrue:2nzj8\n",
      "pred:egtk\ttrue:e9tk\n",
      "pred:xiyi\ttrue:xtyl\n",
      "pred:smao\ttrue:6mao\n",
      "pred:1407\ttrue:14b7\n",
      "pred:bcxxgm\ttrue:bcyxgm\n",
      "pred:xaxf\ttrue:xakf\n",
      "15\n",
      "总耗时： 18.56656241416931\n",
      "正确数：985， 错误数：15, 总样本：1000, 准确率：0.9850\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "data = CaptchaSequence(characters, batch_size=200, steps=5, input_length=12, chars_len=(6,6))\n",
    "# model.load_weights('gru_DigitAndEnglist_ctc_best_0927.h5') \n",
    "# model.load_weights('mobilenet_DigitAndEnglist_ctc_best0930.h5')\n",
    "# model.load_weights('mobilenet_DigitAndEnglist_ctc_best_32.h5')\n",
    "# model.load_weights('gru_english4to6_ctc_best_5.h5') \n",
    "model.load_weights('gru_english4to6_ctc_best_20250415.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)))\n",
    "# 正确数：952， 错误数：48, 总样本：1000, 准确率：0.9520"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'pre: f4aq, lab: ftaq')"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 163,
       "width": 370
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = -1\n",
    "# plt.imshow(err_img[i].reshape((height, width)))\n",
    "plt.imshow(err_img[i])\n",
    "plt.title(err_label[i])\n",
    "# idx = 8\n",
    "# img_arr = X_test[idx:idx+1]\n",
    "# out_pre = decode([img_arr, np.ones(img_arr.shape[0])])\n",
    "# out = ''.join([characters[x] for x in out_pre[0][0]])\n",
    "# y_true = ''.join([characters[x] for x in y_test[idx] if x < len(characters)])\n",
    "# plt.imshow(img_arr.reshape((height, width)))\n",
    "# print('out', out)\n",
    "# plt.title(out)\n",
    "# i = 9\n",
    "# print(model.layers[i].name)\n",
    "# model.layers[i].get_weights() # 打印某层权重\n",
    "# height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "qnthp qnrhp False\n",
      "sv8fk sv8ek False\n",
      "z9uet z8uet False\n",
      "jwvnd jwynd False\n",
      "mk6np mr6np False\n",
      "正确数：995, 总数：1000, 准确率：0.9950\n"
     ]
    }
   ],
   "source": [
    "'''预测真实验证码，统计准确率'''\n",
    "import re\n",
    "pos = neg = 0\n",
    "n = 0\n",
    "# model.load_weights('gru_english4to6_ctc_best_1014.h5')\n",
    "# model.load_weights('gru_english4to6_ctc_best_1102.h5')\n",
    "path51 = 'FileInfo0508_2/*.jpg' # 波浪线验证码 正确数：714, 总数：715, 准确率：0.9986\n",
    "#正确数：706, 总数：715, 准确率：0.9874\n",
    "path52 = '/data/captcha/label_english/100_30/*.jpg' #正确数：0, 总数：209, 准确率：0.0000  正确数：174, 总数：209, 准确率：0.8325\n",
    "# 正确数：168, 总数：209, 准确率：0.8038  正确数：976, 总数：1000, 准确率：0.9760\n",
    "path1 = '/data/captcha/label_english/70_26/*.jpg' #正确数：588, 总数：1505, 准确率：0.3907   正确数：1500, 总数：1505, 准确率：0.9967\n",
    "path2 = '/data/captcha/label_english/52_21/*.jpg' # 正确数：1122, 总数：2822, 准确率：0.3976  正确数：2761, 总数：2822, 准确率：0.9784\n",
    "path3 = '/data/captcha/label_english/100_25/*.jpg'  #正确数：6488, 总数：6503, 准确率：0.9977 正确数：6503, 总数：6503, 准确率：1.0000\n",
    "path4 = '/data/captcha/shensebeijingsandian/*.jpg' #正确数：543, 总数：544, 准确率：0.9982\n",
    "path5 = '/data/captcha/shensexiansandian/*.jpg'#正确数：499, 总数：501, 准确率：0.9960\n",
    "# 正确数：493, 总数：501, 准确率：0.9840\n",
    "path6 = '/data/esa_sdk/gan/english/*.jpg' #正确数：12, 总数：23, 准确率：0.5217   正确数：18, 总数：23, 准确率：0.7826\n",
    "# 正确数：18, 总数：23, 准确率：0.7826\n",
    "path7 = '/data/captcha/label_english/90_38/*.jpg'  #正确数：226, 总数：243, 准确率：0.9300\n",
    "path8 = '/data/captcha/label_english/70_25/*.jpg' #正确数：69, 总数：70, 准确率：0.9857 正确数：46, 总数：49, 准确率：0.9388\n",
    "\n",
    "# model.load_weights('gru_english4to6_ctc_best_20220829.h5') \n",
    "err_imgs = []\n",
    "err_labels = []\n",
    "files = glob.glob(path52)\n",
    "# files = glob.glob('/data/captcha/label_english/200_80/*.jpg')[2000:]\n",
    "# files = glob.glob('/data/captcha/label_english/122_46/*.jpg')[:]\n",
    "sp = int(len(files)*0.8)\n",
    "sp = min(int(len(files)*0.8), 3000)\n",
    "for file in files[:][:1000]:\n",
    "    try:\n",
    "        img = Image.open(file)\n",
    "    except:\n",
    "        print('打开错误：',file)\n",
    "        continue\n",
    "    if re.search('FileInfo0508', file)!=None:\n",
    "        label = file.split('_')[-1][:-4].lower().replace('1','l')\n",
    "    elif re.search('200_80', file):\n",
    "        file_name = file.split('/')[-1][:-4]\n",
    "        label = name_dic[file_name].lower()\n",
    "    else:\n",
    "        label = file.split('_')[-1][:-4].lower()\n",
    "#     label = file.split('\\\\')[-1].split('_')[-1][:-4]\n",
    "#     label = file.split('/')[-1].split('_')[0]\n",
    "    img = img.resize((width, height), Image.BILINEAR)\n",
    "\n",
    "#     X = np.zeros((1, height, width, 1))\n",
    "#     img = img.convert('L')\n",
    "#     X[0] = np.expand_dims(np.array(img)/255.0, axis=-1)\n",
    "    \n",
    "    X = np.zeros((1, height, width, 3))\n",
    "    img = img.convert('RGB')\n",
    "    X[0] = np.array(img)/255.0\n",
    "    \n",
    "    out_pre = decode([X, np.ones(X.shape[0])])\n",
    "    out = ''.join([characters[x] for x in out_pre[0][0]])\n",
    "    if label.lower() == out.lower():\n",
    "        pos += 1\n",
    "    else:\n",
    "        neg += 1\n",
    "        print(label, out, label==out)\n",
    "        err_imgs.append(img)\n",
    "        err_labels.append('label:'+label+'  pred:'+out)\n",
    "    n += 1\n",
    "#     if n > 100:\n",
    "#         break\n",
    "print('正确数：%d, 总数：%d, 准确率：%.4f'%(pos, pos+neg, pos/(pos+neg)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/data/python/lishimin/linuxPro/captcha_pro\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 163,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 57\n",
    "plt.imshow(err_imgs[i])\n",
    "plt.title(err_labels[i])\n",
    "import os\n",
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 /data/captcha/chaojiyingpic/1902_420x65/1902-8prl-2283617470172830860.jpg 8prl 8jnl False\n",
      "1 /data/captcha/chaojiyingpic/1902_420x65/1902-GQIM-1283417000172830836.jpg GQIM gqih False\n",
      "2 /data/captcha/chaojiyingpic/1902_420x65/1902-esdj-1284110410172830025.jpg esdj eeoj False\n",
      "3 /data/captcha/chaojiyingpic/1902_420x65/1902-62vu-1283708400172830356.jpg 62vu bzvu False\n",
      "4 /data/captcha/chaojiyingpic/1902_420x65/1902-UZWA-1283709480172830434.jpg UZWA u2wa False\n",
      "5 /data/captcha/chaojiyingpic/1902_420x65/1902-0c6f-2283411520172830492.jpg 0c6f dc6f False\n",
      "6 /data/captcha/chaojiyingpic/1902_420x65/1902-hu5l-1283516590172830162.jpg hu5l hubl False\n",
      "7 /data/captcha/chaojiyingpic/1902_420x65/1902-y2ev-2283619510172830030.jpg y2ev yzev False\n",
      "8 /data/captcha/chaojiyingpic/1902_420x65/1902-pjei-2283514270172830953.jpg pjei plei False\n",
      "9 /data/captcha/chaojiyingpic/1902_420x65/1902-n44w-2283417230172830918.jpg n44w m44w False\n",
      "10 /data/captcha/chaojiyingpic/1902_420x65/1902-ju0y-1283414180172830660.jpg ju0y juqy False\n",
      "11 /data/captcha/chaojiyingpic/1902_420x65/1902-inzq-2283409510172830371.jpg inzq inqq False\n",
      "12 /data/captcha/chaojiyingpic/1902_420x65/1902-GIUT-2283711290172830630.jpg GIUT g1ut False\n",
      "13 /data/captcha/chaojiyingpic/1902_420x65/1902-6um3-2283511500172830778.jpg 6um3 6uh3 False\n",
      "14 /data/captcha/chaojiyingpic/1902_420x65/1902-CC5W-2283416010172830775.jpg CC5W cgsw False\n",
      "15 /data/captcha/chaojiyingpic/1902_420x65/1902-hcdr-2284110220172831988.jpg hcdr hodr False\n",
      "16 /data/captcha/chaojiyingpic/1902_420x65/1902-zfmw-1283611210172830787.jpg zfmw zfww False\n",
      "17 /data/captcha/chaojiyingpic/1902_420x65/1902-nkm1-2283716070172830983.jpg nkm1 nkml False\n",
      "18 /data/captcha/chaojiyingpic/1902_420x65/1902-eupv-2283511520172830784.jpg eupv ejpv False\n",
      "19 /data/captcha/chaojiyingpic/1902_420x65/1902-6nkn-1283511420172830759.jpg 6nkn 6n4m False\n",
      "20 /data/captcha/chaojiyingpic/1902_420x65/1902-jhh0-2283812150172830679.jpg jhh0 jhh9 False\n",
      "21 /data/captcha/chaojiyingpic/1902_420x65/1902-nh1i-2283519310172830288.jpg nh1i nh1u False\n",
      "22 /data/captcha/chaojiyingpic/1902_420x65/1902-LWCI-1283713580172830756.jpg LWCI lwrd False\n",
      "23 /data/captcha/chaojiyingpic/1902_420x65/1902-mqv1-1283209320172830899.jpg mqv1 mq1 False\n",
      "24 /data/captcha/chaojiyingpic/1004_130x40/1004-ejrl-2262315190172831854.jpg ejrl ejrj False\n",
      "25 /data/captcha/chaojiyingpic/1004_130x40/1004-gmre-1274813240172835640.jpg gmre gmpe False\n",
      "26 /data/captcha/chaojiyingpic/4004_130x40/4004-9159-1284015520172831198.jpg 9159 gr59 False\n",
      "27 /data/captcha/chaojiyingpic/4004_130x40/4004-1360-2274812460172835624.jpg 1360 136c False\n",
      "28 /data/captcha/chaojiyingpic/4004_130x40/4004-7008-2274808310172835482.jpg 7008 7co8 False\n",
      "29 /data/captcha/chaojiyingpic/4004_130x40/4004-1754-1283219110172830126.jpg 1754 7754 False\n",
      "30 /data/captcha/chaojiyingpic/4004_130x40/4004-0844-2274709460172834535.jpg 0844 084m False\n",
      "31 /data/captcha/chaojiyingpic/4004_130x40/4004-2391-1283319560172830797.jpg 2391 z3gi False\n",
      "32 /data/captcha/chaojiyingpic/4004_130x40/4004-3710-1283206410172830845.jpg 3710 3718 False\n",
      "33 /data/captcha/chaojiyingpic/4004_130x40/4004-6495-2283516160172830121.jpg 6495 6499 False\n",
      "34 /data/captcha/chaojiyingpic/4004_130x40/4004-1608-2283806220172830413.jpg 1608 1698 False\n",
      "35 /data/captcha/chaojiyingpic/4004_130x40/4004-2215-2283613220172830199.jpg 2215 zei5 False\n",
      "36 /data/captcha/chaojiyingpic/4004_130x40/4004-4322-2283506160172830454.jpg 4322 2322 False\n",
      "37 /data/captcha/chaojiyingpic/4004_130x40/4004-0924-1274813310172835649.jpg 0924 mg2m False\n",
      "38 /data/captcha/chaojiyingpic/4004_130x40/4004-8171-1274711310172834688.jpg 8171 8174 False\n",
      "39 /data/captcha/chaojiyingpic/4004_130x40/4004-3129-1284111020172830053.jpg 3129 3124 False\n",
      "40 /data/captcha/chaojiyingpic/1004_100x40/1004-7wf6-2262323080172832351.jpg 7wf6 7wy6 False\n",
      "41 /data/captcha/chaojiyingpic/1004_100x40/1004-e2m8-1262315380172831880.jpg e2m8 e2m3 False\n",
      "42 /data/captcha/chaojiyingpic/1004_100x40/1004-q6dd-2262300080172832406.jpg q6dd qbdd False\n",
      "43 /data/captcha/chaojiyingpic/1004_100x40/1004-mg4y-1262323230172832363.jpg mg4y mu4y False\n",
      "44 /data/captcha/chaojiyingpic/1902_140x40/1902-zxj67-1274712360172834778.jpg zxj67 zv67 False\n",
      "45 /data/captcha/chaojiyingpic/3004_100x40/3004-pdnf-2262320330172832185.jpg pdnf pdne False\n",
      "46 /data/captcha/chaojiyingpic/4006_200x100/4006-158361-2262308190172831408.jpg 158361 158401 False\n",
      "47 /data/captcha/chaojiyingpic/4006_200x100/4006-145301-1262406050172832705.jpg 145301 145381 False\n",
      "48 /data/captcha/chaojiyingpic/4006_200x100/4006-181226-1274702460172835361.jpg 181226 187226 False\n",
      "49 /data/captcha/chaojiyingpic/4006_200x100/4006-557174-2274806040172835427.jpg 557174 557171 False\n",
      "50 /data/captcha/chaojiyingpic/4006_200x100/4006-755596-2262410370172832955.jpg 755596 759696 False\n",
      "51 /data/captcha/chaojiyingpic/4006_200x100/4006-220931-2262322470172832333.jpg 220931 120931 False\n",
      "52 /data/captcha/chaojiyingpic/4006_200x100/4006-381182-1262405000172832648.jpg 381182 381189 False\n",
      "53 /data/captcha/chaojiyingpic/4006_200x100/4006-51852-1262407070172832770.jpg 51852 648572 False\n",
      "54 /data/captcha/chaojiyingpic/4006_200x100/4006-202944-2262311240172831616.jpg 202944 262244 False\n",
      "55 /data/captcha/chaojiyingpic/1004_70x35/1004-mwdlx-2270011500172836568.jpg mwdlx mwdx False\n",
      "56 /data/captcha/chaojiyingpic/1004_70x35/1004-v5nd-2270011540172830287.jpg v5nd v5no False\n",
      "57 /data/captcha/chaojiyingpic/1004_70x35/1004-n9x5-2270011580172830645.jpg n9x5 n9t5 False\n",
      "58 /data/captcha/chaojiyingpic/1004_70x35/1004-9wf7-1270012110172831522.jpg 9wf7 9wfz False\n",
      "59 /data/captcha/chaojiyingpic/1004_70x35/1004-mv3m-1270012010172830915.jpg mv3m mvbm False\n",
      "60 /data/captcha/chaojiyingpic/1004_70x35/1004-jf2c-2270012000172830833.jpg jf2c lf2c False\n",
      "61 /data/captcha/chaojiyingpic/1004_70x35/1004-3mm-1270012120172831607.jpg 3mm 3mfm False\n",
      "62 /data/captcha/chaojiyingpic/1004_70x35/1004-komw-2270011570172830549.jpg komw komm False\n",
      "63 /data/captcha/chaojiyingpic/1004_70x35/1004-sbao-1270012050172831203.jpg sbao sbaq False\n",
      "64 /data/captcha/chaojiyingpic/1004_70x35/1004-cb7k-2270011510172830019.jpg cb7k c37k False\n",
      "65 /data/captcha/chaojiyingpic/1004_70x35/1004-9hou-2270012080172831320.jpg 9hou 9h6u False\n",
      "66 /data/captcha/chaojiyingpic/1004_70x35/1004-N5UR-2270011570172830578.jpg N5UR n5un False\n",
      "67 /data/captcha/chaojiyingpic/1004_70x35/1004-wxzp-2270012020172830979.jpg wxzp wkzp False\n",
      "68 /data/captcha/chaojiyingpic/1004_70x35/1004-smsk-1270011520172830122.jpg smsk smbk False\n",
      "69 /data/captcha/chaojiyingpic/3004_200x62/3004-miii-1274701560172835344.jpg miii miji False\n",
      "70 /data/captcha/chaojiyingpic/3004_200x62/3004-ducc-1274704250172834192.jpg ducc ducg False\n",
      "正确数：545, 总数：616, 准确率：0.8847\n",
      "类别：/data/captcha/chaojiyingpic/1902_420x65/1902, 总数：112, acc：0.7857\n",
      "类别：/data/captcha/chaojiyingpic/1004_130x40/1004, 总数：32, acc：0.9375\n",
      "类别：/data/captcha/chaojiyingpic/4004_130x40/4004, 总数：106, acc：0.8679\n",
      "类别：/data/captcha/chaojiyingpic/1004_100x40/1004, 总数：80, acc：0.9500\n",
      "类别：/data/captcha/chaojiyingpic/1902_140x40/1902, 总数：5, acc：0.8000\n",
      "类别：/data/captcha/chaojiyingpic/4004_108x40/4004, 总数：82, acc：1.0000\n",
      "类别：/data/captcha/chaojiyingpic/3004_100x40/3004, 总数：9, acc：0.8889\n",
      "类别：/data/captcha/chaojiyingpic/4006_200x100/4006, 总数：24, acc：0.6250\n",
      "类别：/data/captcha/chaojiyingpic/1004_70x35/1004, 总数：140, acc：0.9000\n",
      "类别：/data/captcha/chaojiyingpic/1902_100x20/1902, 总数：14, acc：1.0000\n",
      "类别：/data/captcha/chaojiyingpic/3004_200x62/3004, 总数：12, acc：0.8333\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\n正确数：547, 总数：616, 准确率：0.8880\\n类别：/data/captcha/chaojiyingpic/1902_420x65/1902, 总数：112, acc：0.7589\\n类别：/data/captcha/chaojiyingpic/1004_130x40/1004, 总数：32, acc：0.9375\\n类别：/data/captcha/chaojiyingpic/4004_130x40/4004, 总数：106, acc：0.9057\\n类别：/data/captcha/chaojiyingpic/1004_100x40/1004, 总数：80, acc：0.9750\\n类别：/data/captcha/chaojiyingpic/1902_140x40/1902, 总数：5, acc：0.8000\\n类别：/data/captcha/chaojiyingpic/4004_108x40/4004, 总数：82, acc：0.9878\\n类别：/data/captcha/chaojiyingpic/3004_100x40/3004, 总数：9, acc：0.7778\\n类别：/data/captcha/chaojiyingpic/4006_200x100/4006, 总数：24, acc：0.6250\\n类别：/data/captcha/chaojiyingpic/1004_70x35/1004, 总数：140, acc：0.8929\\n类别：/data/captcha/chaojiyingpic/1902_100x20/1902, 总数：14, acc：1.0000\\n类别：/data/captcha/chaojiyingpic/3004_200x62/3004, 总数：12, acc：1.0000\\n'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''测试超级鹰验证码'''\n",
    "err_imgs = []\n",
    "err_labels = []\n",
    "pos = neg = 0\n",
    "metrics = {}\n",
    "for path in chaojiying_test:\n",
    "    if path.endswith('1004-8268-1262409550172832926.jpg'):\n",
    "        print('打开异常图片')\n",
    "        continue\n",
    "    label = path.split('/')[-1].split('-')[1]\n",
    "    ty = path.split('/')[-1].split('-')[0]\n",
    "    if ty == 4006 and len(label)!=6: # 此类较多验证码标签错误\n",
    "        continue\n",
    "    try:\n",
    "        img = Image.open(path)\n",
    "    except:\n",
    "        print('打开错误：',path)\n",
    "        continue\n",
    "    img = img.resize((width, height), Image.BILINEAR)   \n",
    "    X = np.zeros((1, height, width, 3))\n",
    "    img = img.convert('RGB')\n",
    "    X[0] = np.array(img)/255.0\n",
    "    \n",
    "    out_pre = decode([X, np.ones(X.shape[0])])\n",
    "    out = ''.join([characters[x] for x in out_pre[0][0]])\n",
    "    \n",
    "    k = path.split('-')[0]\n",
    "    if k not in metrics:\n",
    "        metrics[k] = {'pos': 0, 'neg': 0}\n",
    "    \n",
    "    if label.lower() == out.lower():\n",
    "        pos += 1\n",
    "        metrics[k]['pos'] += 1\n",
    "    else:        \n",
    "        print(neg, path, label, out, label==out)\n",
    "        neg += 1\n",
    "        err_imgs.append(img)\n",
    "        err_labels.append('label:'+label+'  pred:'+out)\n",
    "        metrics[k]['neg'] += 1\n",
    "    n += 1\n",
    "#     if n > 100:\n",
    "#         break\n",
    "print('正确数：%d, 总数：%d, 准确率：%.4f'%(pos, pos+neg, pos/(pos+neg)))   \n",
    "for k, v in metrics.items():\n",
    "    print('类别：%s, 总数：%d, acc：%.4f'%(k, (v['pos']+v['neg']), v['pos']/(v['pos']+v['neg'])))\n",
    "'''\n",
    "正确数：547, 总数：616, 准确率：0.8880\n",
    "类别：/data/captcha/chaojiyingpic/1902_420x65/1902, 总数：112, acc：0.7589\n",
    "类别：/data/captcha/chaojiyingpic/1004_130x40/1004, 总数：32, acc：0.9375\n",
    "类别：/data/captcha/chaojiyingpic/4004_130x40/4004, 总数：106, acc：0.9057\n",
    "类别：/data/captcha/chaojiyingpic/1004_100x40/1004, 总数：80, acc：0.9750\n",
    "类别：/data/captcha/chaojiyingpic/1902_140x40/1902, 总数：5, acc：0.8000\n",
    "类别：/data/captcha/chaojiyingpic/4004_108x40/4004, 总数：82, acc：0.9878\n",
    "类别：/data/captcha/chaojiyingpic/3004_100x40/3004, 总数：9, acc：0.7778\n",
    "类别：/data/captcha/chaojiyingpic/4006_200x100/4006, 总数：24, acc：0.6250\n",
    "类别：/data/captcha/chaojiyingpic/1004_70x35/1004, 总数：140, acc：0.8929\n",
    "类别：/data/captcha/chaojiyingpic/1902_100x20/1902, 总数：14, acc：1.0000\n",
    "类别：/data/captcha/chaojiyingpic/3004_200x62/3004, 总数：12, acc：1.0000\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/data/python/lishimin/linuxPro/captcha_pro\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 137,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''展示错误样例'''\n",
    "path = '/data/captcha/chaojiyingpic/3004_200x62/3004-miii-1274701560172835344.jpg'\n",
    "img = Image.open(path)\n",
    "plt.imshow(img)\n",
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "# files = glob.glob('/data/captcha/shuziyingwen/*.jpg')\n",
    "\n",
    "# num = 0\n",
    "# lb_dic = {}\n",
    "# with open('/data/captcha/shuziyingwen/answer.txt', encoding='utf-8') as f:\n",
    "#     lines = f.readlines()\n",
    "#     for line in lines:\n",
    "#         name, lb = line.strip().split('=')\n",
    "#         lb_dic[name] = lb\n",
    "# print(num)\n",
    "# print(len(lines), len(files))  \n",
    "# # file_path = random.choice(files)\n",
    "# # filename = file_path.split('/')[-1][:-4]\n",
    "# # while filename not in lb_dic:\n",
    "# #     print('不在字典')\n",
    "# #     file_path = random.choice(files)\n",
    "# #     filename = file_path.split('/')[-1][:-4]\n",
    "# # random_str = lb_dic.get(filename, '')\n",
    "# # print(random_str)\n",
    "# for file in files:\n",
    "#     filename = file_path.split('/')[-1][:-4]\n",
    "#     if filename not in lb_dic:\n",
    "#         print('not in ')\n",
    "        \n",
    "# file_path = random.choice(files)\n",
    "# filename = file_path.split('/')[-1][:-4]\n",
    "# random_str = lb_dic.get(filename, '')\n",
    "# image = Image.open(file_path).convert('L').resize((width, height), Image.BILINEAR)\n",
    "# plt.imshow(image)\n",
    "\n",
    "# file_path = random.choice(files)\n",
    "# for file_path in files:\n",
    "#     try:\n",
    "#         filename = file_path.split('/')[-1][:-4]\n",
    "#         random_str = lb_dic.get(filename, '')\n",
    "#         image = Image.open(file_path).convert('L').resize((width, height), Image.BILINEAR)\n",
    "#     except:\n",
    "#         print(filename)\n",
    "#         os.remove(file_path)\n",
    "# plt.imshow(image)\n",
    "# layers = model.layers\n",
    "# for i in range(len(layers)):\n",
    "#     if i> 14:\n",
    "#         print(layers[i].name)\n",
    "#         layers[i].trainable = False\n",
    "#     print(layers[i].trainable)\n",
    "import os\n",
    "# paths = ['/data/captcha/label_english/100_30/*.jpg','FileInfo0508_2/*.jpg']\n",
    "# files = glob.glob(paths[6%len(paths)])\n",
    "# file_path = random.choice(files)\n",
    "# random_str = file_path.split('/')[-1].split('_')[-1][:-4]\n",
    "# print(file_path, os.path.exists(file_path), random_str)\n",
    "\n",
    "for file in glob.glob('/data/captcha/shensexiansandian/*.jpg'):\n",
    "#     print(file)\n",
    "#     break\n",
    "    name = file.split('/')[-1]\n",
    "    print(name)\n",
    "    tmp = name.split('_')\n",
    "#     newname = tmp[1][:-4]+'_'+tmp[0]+'.jpg'\n",
    "    newname = tmp[0]+'_'+tmp[1][:-4].upper()+'.jpg'\n",
    "    print(newname)\n",
    "    os.rename(file, '/data/captcha/shensexiansandian/'+newname)\n",
    "# img = Image.open(file)\n",
    "# plt.imshow(img)\n",
    "\n",
    "# file = '/data/captcha/shensebeijingsandian/117d3695-b4aa-11ea-8217-5254009c362b_kdea.jpg'\n",
    "# name = file.split('/')[-1]\n",
    "# tmp = name.split('_')\n",
    "# newname = tmp[0]+'_'+tmp[1][:-4].upper()+'.jpg'\n",
    "# print(newname)"
   ]
  }
 ],
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