{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abcdefghijklmnopqrstuvwxyz0123456789\n",
      "15 ['latin/CENTURY.TTF', 'latin/SCHLBKI.TTF', 'latin/corbeli.ttf', 'latin/ARIALNBI.TTF', 'latin/tahoma.ttf', 'latin/ANTQUAB.TTF', 'latin/ARIALN.TTF', 'latin/Candarab.ttf', 'latin/verdana.ttf', 'latin/BOOKOS.TTF', 'latin/Candara.ttf', 'latin/consolai.ttf', 'latin/LBRITEI.TTF', 'latin/CALISTBI.TTF', 'latin/ARIALNB.TTF']\n",
      "n_class:37, n_len:6\n",
      "15 latin/CENTURY.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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image size (90, 38) (200, 70)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 3600x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 2203
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random_color(rs, re, gs, ge, bs, be) (12, 17, 19)\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",
    "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",
    "    return img.resize((width, height), Image.BILINEAR)\n",
    "\n",
    "# # paths = 'FileInfo0508_2/*.jpg'\n",
    "# paths = '/data/esa_sdk/gan/english/*.jpg'\n",
    "# # paths = '/data/captcha/shensebeijingsandian/*.jpg'\n",
    "# # paths = '/data/captcha/shensexiansandian/*.jpg'\n",
    "# files =  glob.glob(paths)\n",
    "# img2 = Image.open(files[0])\n",
    "# img2 = Image.open('/data/captcha/captcha_sample/4.jpg')#.resize((200,70), Image.BILINEAR) #小图多种颜色字、干扰线 \n",
    "img2 = Image.open('/data/captcha/label_english/90_38/0ef53417-3af2-11ec-b040-2cf05ded1cb1_aq4f.jpg')\n",
    "\n",
    "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",
    "\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",
    "    \n",
    "    plt.subplot(2,2,i)\n",
    "    plt.imshow(im[i-1])\n",
    "plt.show()\n",
    "\n",
    "cl = (10,20)\n",
    "print('random_color(rs, re, gs, ge, bs, be)', random_color(cl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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",
      "111 /data/esa_sdk/gan/english/805bc78f-fbbd-11e9-9bc7-408d5cd36814_9tcl.jpg\n",
      "(200, 70)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'qnks')"
      ]
     },
     "execution_count": 38,
     "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": [
    "# 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'\n",
    "path7 = '/data/captcha/label_english/90_38/*.jpg'\n",
    "for paths in [path1, path2, path3, path5, path6]:  # 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",
    "    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": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 150,
       "width": 370
      },
      "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": 187,
   "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",
    "#         self.n_class = -2\n",
    "#         self.generator = ImageCaptcha(width=width, height=height, font_sizes=(12,20,18,25))\n",
    "#         self.fonts_list = glob.glob('/usr/share/fonts/WindowsFonts/fonts/*.ttf')\n",
    "            \n",
    "    def __len__(self):\n",
    "        return self.steps\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        batch_label_length = random.choice([4,5,4,4])\n",
    "#         imgs = []\n",
    "#         print('batch_label_length',batch_label_length)\n",
    "        X = np.zeros((self.batch_size, self.height, self.width, 3), dtype=np.float32)\n",
    "        y = np.zeros((self.batch_size, self.n_len), dtype=np.uint8)\n",
    "#         print(y)\n",
    "#         y = np.zeros((self.batch_size, batch_label_length), dtype=np.uint8)\n",
    "        input_length = np.ones(self.batch_size)*self.input_length\n",
    "        label_length = np.ones(self.batch_size)*self.n_len \n",
    "#         label_length = np.ones(self.batch_size)*batch_label_length\n",
    "\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 batch_label_length == 4:\n",
    "                if i%20 <= 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(batch_label_length)])\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%20 <= 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(batch_label_length)])\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%20 <= 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(batch_label_length)])\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%20 <= 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(batch_label_length)])\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%20<=9:\n",
    "                    random_str = ''.join([random.choice(gen_characters) for j in range(batch_label_length)])\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%20<=11:\n",
    "                    random_str = ''.join([random.choice(gen_characters) for j in range(batch_label_length)])                      \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%20<=13:\n",
    "                    random_str = ''.join([random.choice(gen_characters) for j in range(batch_label_length)])    \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%20<=15: \n",
    "                    random_str = ''.join([random.choice(gen_characters) for j in range(batch_label_length)])\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%30<=17:\n",
    "                    image, random_str = merge_img_7025()\n",
    "                elif i%30<=23:\n",
    "                    image, random_str = merge_img_9038()                    \n",
    "\n",
    "\n",
    "                elif i%35<=30: # 加入真实验证码\n",
    "                    image, random_str = rebuild_img(random.choice(len4_imgs))\n",
    "                    \n",
    "                else: \n",
    "                    random_str = ''.join([random.choice(gen_characters) for j in range(batch_label_length)])\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",
    "            else:         \n",
    "                \n",
    "                if i%10 <= 1:  #  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(batch_label_length)])\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%10 <= 3: #   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(batch_label_length)])\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%10 <= 5: # 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(batch_label_length)])\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%15 <= 9:\n",
    "                    random_str = ''.join([random.choice(gen_characters) for j in range(batch_label_length)])\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%15<=11: #  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(batch_label_length)])\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%20<=17: # 加入真实验证码\n",
    "                    image, random_str = rebuild_img(random.choice(len5_imgs))\n",
    "                    \n",
    "                else:    \n",
    "                    random_str = ''.join([random.choice(gen_characters) for j in range(batch_label_length)])\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",
    "            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": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/data/captcha/rph2.jpg'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-dc78607a2886>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    164\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBILINEAR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m \u001b[0mimg2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/data/captcha/rph2.jpg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m70\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBILINEAR\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#小图多种颜色字、干扰线\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[0mrandom_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'RpH2'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode)\u001b[0m\n\u001b[1;32m   2632\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2633\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2634\u001b[0;31m         \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2635\u001b[0m         \u001b[0mexclusive_fp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/data/captcha/rph2.jpg'"
     ]
    }
   ],
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''生成灰度图像'''\n",
    "from tensorflow.keras.utils import Sequence\n",
    "from collections import Counter\n",
    "\n",
    "class CaptchaSequence(Sequence):\n",
    "    '''\n",
    "    继承Sequence的数据生成类，方便调用多CPU，加快生成训练及测试数据\n",
    "    参数：self.characters:验证码字符集合，self.batch_size：每批次样本数，self.steps：生成多少批数据，self.n_len：验证码长度，\n",
    "    self.width：图片宽度，self.height:图片高度，self.input_length：lstm time step长度，self.label_length：标签长度\n",
    "    返回：array类型训练或测试数据 \n",
    "    \n",
    "    '''\n",
    "    def __init__(self, characters, batch_size, steps, n_len=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",
    "#         self.n_class = -2\n",
    "#         self.generator = ImageCaptcha(width=width, height=height, font_sizes=(12,20,18,25))\n",
    "#         self.fonts_list = glob.glob('/usr/share/fonts/WindowsFonts/fonts/*.ttf')\n",
    "            \n",
    "    def __len__(self):\n",
    "        return self.steps\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        batch_label_length = random.choice([4,5,4,4])\n",
    "#         imgs = []\n",
    "#         print('batch_label_length',batch_label_length)\n",
    "        X = np.zeros((self.batch_size, self.height, self.width, 3), dtype=np.float32)\n",
    "        y = np.zeros((self.batch_size, self.n_len), dtype=np.uint8)\n",
    "#         print(y)\n",
    "#         y = np.zeros((self.batch_size, batch_label_length), dtype=np.uint8)\n",
    "        input_length = np.ones(self.batch_size)*self.input_length\n",
    "        label_length = np.ones(self.batch_size)*self.n_len \n",
    "#         label_length = np.ones(self.batch_size)*batch_label_length\n",
    "\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",
    "#             if i % 10 == 0:                     \n",
    "#                 image, random_str = rebuild_img(random.choice(len4_imgs)) \n",
    "#             elif i % 10 == 1:\n",
    "#                 image, random_str = rebuild_img(random.choice(len5_imgs)) \n",
    "            if batch_label_length == 4:\n",
    "                if i%20 <= 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(batch_label_length)])\n",
    "                    image = gen_captcha(random_str, fig_size=(70,26), fonts=fonts,font_color=(80,130),same_color=0, font_size=(15, 22), rotate=0,\n",
    "                                        font_noise=0, line=(0,3), line_width=(0,1), point=(0,100),wavy=(0,0), noise_color=(130,250), bg=(230,255))\n",
    "\n",
    "                elif i%20 <= 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(batch_label_length)])\n",
    "                    image = gen_captcha(random_str, fig_size=(52,21), fonts=fonts,font_color=(50,120),same_color=1, font_size=(12, 15), rotate=0,\n",
    "                                        font_noise=0, line=(0,5), line_width=(0,1), point=(0,50),wavy=(0,0), noise_color=(200,230), bg=(240,250))\n",
    "\n",
    "\n",
    "                elif i%20 <= 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(batch_label_length)])\n",
    "                    image = gen_captcha(random_str, fig_size=(80,35), fonts=fonts,font_color=(10,80),same_color=0, font_size=(20, 30), rotate=5,\n",
    "                        font_noise=0, line=(0,2), line_width=(0,1), point=(0,80),wavy=(0,0), noise_color=(150,200), bg=(180,240)) \n",
    "                    \n",
    "                elif i%20 <= 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(batch_label_length)])\n",
    "                    image = gen_captcha(random_str, fig_size=(100,40), fonts=fonts,font_color=(230,250),same_color=1, font_size=(25, 35), rotate=20,\n",
    "                        font_noise=0, line=(0,0), line_width=(0,1), point=(0,200),wavy=(0,0), noise_color=(120,200), bg=(20,140))\n",
    "\n",
    "                elif i%20<12:\n",
    "                    random_str = ''.join([random.choice(gen_characters) for j in range(batch_label_length)])\n",
    "                    tmp_w = random.randint(100,200)\n",
    "                    tmp_h = random.randint(25, 50)\n",
    "                    font_s = (int(tmp_h*0.6), int(tmp_h*0.8))\n",
    "                    image = gen_captcha(random_str, fig_size=(tmp_w,tmp_h), fonts=fonts,font_color=(10,180),same_color=0, font_size=font_s, rotate=15,\n",
    "                                        font_noise=0, line=(0,0), line_width=(0,0), point=(0,0),wavy=(0,0), noise_color=(170,230), bg=(220,250))\n",
    " \n",
    "                    \n",
    "                else: \n",
    "#                     paths = ['/data/captcha/label_english/52_21/*.jpg',\n",
    "#                              '/data/captcha/label_english/70_26/*.jpg',\n",
    "#                              '/data/captcha/label_english/100_25/*.jpg',\n",
    "#                             '/data/captcha/shensebeijingsandian/*.jpg',\n",
    "#                             '/data/captcha/shensexiansandian/*jpg']\n",
    "#                     files = glob.glob(paths[i%len(paths)])\n",
    "#                     file_path = random.choice(files)\n",
    "#                     random_str = file_path.split('/')[-1].split('_')[-1][:-4]\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",
    "\n",
    "                    random_str = ''.join([random.choice(gen_characters) for j in range(batch_label_length)])\n",
    "                    tmp_w = random.randint(100,200)\n",
    "                    tmp_h = random.randint(25, 50)\n",
    "                    font_s = (int(tmp_h*0.6), int(tmp_h*0.8))\n",
    "#                     image = gen_captcha(random_str, fig_size=(tmp_w,tmp_h), fonts=fonts,font_color=(10,180),same_color=0, font_size=font_s, rotate=15,\n",
    "#                                         font_noise=0, line=(0,5), line_width=(0,1), point=(0,200),wavy=(0,1), noise_color=(170,230), bg=(220,250))\n",
    "                    image = gen_captcha(random_str, fig_size=(tmp_w,tmp_h), fonts=fonts,font_color=(50,150),same_color=1, font_size=font_s, rotate=20,\n",
    "                                        font_noise=0,offset_w=(0,1),offset_h=0, line=(0,6), line_width=(0,1), line_color=(90,150), point=(0,250),\n",
    "                                        point_color=(20,200),wavy=(0,1), bg=(155,255))\n",
    "                    \n",
    "            else:           \n",
    "                \n",
    "                if i%10 <= 1:  #  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(batch_label_length)])\n",
    "                    image = gen_captcha(random_str, fig_size=(100,25), fonts=fonts,font_color=(50,120),same_color=1, font_size=(15, 20), rotate=0,\n",
    "                                        font_noise=0, line=(0,5), line_width=(0,1), point=(0,100),wavy=(0,0), noise_color=(200,230), bg=(240,250))\n",
    "\n",
    "                elif i%10 <= 3: #   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(batch_label_length)])\n",
    "                    image = gen_captcha(random_str, fig_size=(100,40), fonts=fonts,font_color=(0,60),same_color=0, font_size=(20, 30), rotate=2,\n",
    "                                        font_noise=0, line=(0,5), line_width=(0,1), point=(0,20),wavy=(0,0), noise_color=(80,130), bg=(200,250))\n",
    "\n",
    "                elif i%10 <= 5: # 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(batch_label_length)])\n",
    "                    image = gen_captcha(random_str, fig_size=(135,45), fonts=fonts,font_color=(100,180),same_color=1, font_size=(25, 35), rotate=3,\n",
    "                                        font_noise=0, line=(0,5), line_width=(0,2), point=(0,80),wavy=(0,0), noise_color=(200,230), bg=(200,250))\n",
    "                elif i%10 <= 7:  #  line=(0,0), line_width=(0,1), point=(0,0),wavy=(1,1)\n",
    "                    random_str = ''.join([random.choice(gen_characters) for j in range(batch_label_length)])\n",
    "                    image = gen_captcha(random_str, fig_size=(200,70), fonts=fonts,font_color=(200,250),same_color=0, font_size=(30, 45), rotate=2,\n",
    "                                        font_noise=0, line=(0,0), line_width=(0,1), point=(0,0),wavy=(1,1), noise_color=(180,230), bg=(40,150))\n",
    "\n",
    "                elif i%15 <= 9:\n",
    "                    random_str = ''.join([random.choice(gen_characters) for j in range(batch_label_length)])\n",
    "                    tmp_w = random.randint(100,200)\n",
    "                    tmp_h = random.randint(25, 50)\n",
    "                    font_s = (int(tmp_h*0.6), int(tmp_h*0.8))\n",
    "                    image = gen_captcha(random_str, fig_size=(tmp_w,tmp_h), fonts=fonts,font_color=(10,180),same_color=0, font_size=font_s, rotate=20,\n",
    "                                    font_noise=0, line=(0,0), line_width=(0,0), point=(0,0),wavy=(0,0), noise_color=(150,230), bg=(200,250))\n",
    "                        \n",
    "                    \n",
    "                else: #  line=(0,0), line_width=(0,1), point=(0,0),wavy=(0,0)\n",
    "#                     paths = ['/data/captcha/label_english/100_30/*.jpg','FileInfo0508_2/*.jpg']\n",
    "#                     files = glob.glob(paths[i%len(paths)])\n",
    "#                     file_path = random.choice(files)\n",
    "#                     random_str = file_path.split('/')[-1].split('_')[-1][:-4]\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",
    "    \n",
    "                    random_str = ''.join([random.choice(gen_characters) for j in range(batch_label_length)])\n",
    "                    tmp_w = random.randint(100,200)\n",
    "                    tmp_h = random.randint(25, 50)\n",
    "                    font_s = (int(tmp_h*0.6), int(tmp_h*0.8))\n",
    "                    image = gen_captcha(random_str, fig_size=(tmp_w,tmp_h), fonts=fonts,font_color=(10,180),same_color=0, font_size=font_s, rotate=20,\n",
    "                                    font_noise=0, line=(0,3), line_width=(0,0), point=(0,100),wavy=(0,1), noise_color=(150,230), bg=(200,250))\n",
    "                                        \n",
    "                    \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=(self.width,self.height), color=bg)\n",
    "#             bg_img.paste(image, box=(0, 0)) # \n",
    "# #             if bg<120:\n",
    "# #                 image = image.filter(ImageFilter.ModeFilter(size=3))\n",
    "# #             else:\n",
    "# #                 image = image.filter(ImageFilter.MedianFilter(size=3))\n",
    "\n",
    "#             X[i] = np.expand_dims(np.array(bg_img)/255.0, axis=-1)\n",
    "\n",
    "            X[i] = np.array(image)/255.0\n",
    "#             X[i] = np.expand_dims(np.array(image)/255.0, axis=-1)\n",
    "            label = [self.characters.find(x) for x in random_str] \n",
    "            if len(random_str) < self.n_len:\n",
    "                label += [self.n_class]*(self.n_len-len(random_str))                \n",
    "            y[i] = label\n",
    "#             imgs.append(bg_img)\n",
    "    \n",
    "#         return imgs# \n",
    "        return [X, y, input_length, label_length], np.ones(self.batch_size)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = CaptchaSequence(characters, batch_size=16, steps=10,input_length=12, label_length=6,chars_len=(5, 5))  # (characters, batch_size=128, steps=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 70, 200, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8560abb710>"
      ]
     },
     "execution_count": 21,
     "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": 370
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "l, _  = data[1]\n",
    "x = l[0]\n",
    "print(x.shape)\n",
    "idx = 15\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": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/initializers.py:104: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with distribution=normal is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "`normal` is a deprecated alias for `truncated_normal`\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 64, 128, 3)   0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 32, 64, 32)   896         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 32, 64, 32)   128         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu (LeakyReLU)         (None, 32, 64, 32)   0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 16, 64, 32)   0           leaky_re_lu[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand (Conv2D)         (None, 16, 64, 192)  6144        max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 16, 64, 192)  768         block_1_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 16, 64, 192)  0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise (DepthwiseCon (None, 8, 32, 192)   1728        activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 8, 32, 192)   768         block_1_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 8, 32, 192)   0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_1_project (Conv2D)        (None, 8, 32, 24)    4608        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 8, 32, 24)    96          block_1_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 4, 32, 24)    0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand (Conv2D)         (None, 4, 32, 144)   3456        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 4, 32, 144)   576         block_2_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 4, 32, 144)   0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise (DepthwiseCon (None, 4, 32, 144)   1296        activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 4, 32, 144)   576         block_2_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 4, 32, 144)   0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_2_project (Conv2D)        (None, 4, 32, 24)    3456        activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 4, 32, 24)    96          block_2_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_2_add (Add)               (None, 4, 32, 24)    0           max_pooling2d_1[0][0]            \n",
      "                                                                 batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand (Conv2D)         (None, 4, 32, 144)   3456        block_2_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 4, 32, 144)   576         block_3_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 4, 32, 144)   0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise (DepthwiseCon (None, 4, 32, 144)   1296        activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 4, 32, 144)   576         block_3_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 4, 32, 144)   0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_3_project (Conv2D)        (None, 4, 32, 32)    4608        activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 4, 32, 32)    128         block_3_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand (Conv2D)         (None, 4, 32, 192)   6144        batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 4, 32, 192)   768         block_4_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 4, 32, 192)   0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise (DepthwiseCon (None, 4, 32, 192)   1728        activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 4, 32, 192)   768         block_4_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 4, 32, 192)   0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_4_project (Conv2D)        (None, 4, 32, 32)    6144        activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 4, 32, 32)    128         block_4_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_4_add (Add)               (None, 4, 32, 32)    0           batch_normalization_9[0][0]      \n",
      "                                                                 batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand (Conv2D)         (None, 4, 32, 192)   6144        block_4_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 4, 32, 192)   768         block_5_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 4, 32, 192)   0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise (DepthwiseCon (None, 4, 32, 192)   1728        activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 4, 32, 192)   768         block_5_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 4, 32, 192)   0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_5_project (Conv2D)        (None, 4, 32, 32)    6144        activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 4, 32, 32)    128         block_5_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_5_add (Add)               (None, 4, 32, 32)    0           block_4_add[0][0]                \n",
      "                                                                 batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand (Conv2D)         (None, 4, 32, 192)   6144        block_5_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 4, 32, 192)   768         block_6_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 4, 32, 192)   0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise (DepthwiseCon (None, 4, 32, 192)   1728        activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 4, 32, 192)   768         block_6_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 4, 32, 192)   0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_6_project (Conv2D)        (None, 4, 32, 64)    12288       activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 4, 32, 64)    256         block_6_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand (Conv2D)         (None, 4, 32, 384)   24576       batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 4, 32, 384)   1536        block_7_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 4, 32, 384)   0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise (DepthwiseCon (None, 4, 32, 384)   3456        activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 4, 32, 384)   1536        block_7_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 4, 32, 384)   0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_7_project (Conv2D)        (None, 4, 32, 64)    24576       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 4, 32, 64)    256         block_7_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_7_add (Add)               (None, 4, 32, 64)    0           batch_normalization_18[0][0]     \n",
      "                                                                 batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand (Conv2D)         (None, 4, 32, 384)   24576       block_7_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 4, 32, 384)   1536        block_8_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 4, 32, 384)   0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise (DepthwiseCon (None, 4, 32, 384)   3456        activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 4, 32, 384)   1536        block_8_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 4, 32, 384)   0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_8_project (Conv2D)        (None, 4, 32, 64)    24576       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 4, 32, 64)    256         block_8_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_8_add (Add)               (None, 4, 32, 64)    0           block_7_add[0][0]                \n",
      "                                                                 batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand (Conv2D)         (None, 4, 32, 384)   24576       block_8_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 4, 32, 384)   1536        block_9_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 4, 32, 384)   0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise (DepthwiseCon (None, 4, 32, 384)   3456        activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 4, 32, 384)   1536        block_9_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 4, 32, 384)   0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_9_project (Conv2D)        (None, 4, 32, 64)    24576       activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 4, 32, 64)    256         block_9_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_9_add (Add)               (None, 4, 32, 64)    0           block_8_add[0][0]                \n",
      "                                                                 batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand (Conv2D)        (None, 4, 32, 384)   24576       block_9_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 4, 32, 384)   1536        block_10_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 4, 32, 384)   0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise (DepthwiseCo (None, 4, 32, 384)   3456        activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 4, 32, 384)   1536        block_10_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 4, 32, 384)   0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_10_project (Conv2D)       (None, 4, 32, 96)    36864       activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 4, 32, 96)    384         block_10_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand (Conv2D)        (None, 4, 32, 576)   55296       batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 4, 32, 576)   2304        block_11_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 4, 32, 576)   0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise (DepthwiseCo (None, 4, 32, 576)   5184        activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, 4, 32, 576)   2304        block_11_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 4, 32, 576)   0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_11_project (Conv2D)       (None, 4, 32, 96)    55296       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, 4, 32, 96)    384         block_11_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_11_add (Add)              (None, 4, 32, 96)    0           batch_normalization_30[0][0]     \n",
      "                                                                 batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand (Conv2D)        (None, 4, 32, 576)   55296       block_11_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, 4, 32, 576)   2304        block_12_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 4, 32, 576)   0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise (DepthwiseCo (None, 4, 32, 576)   5184        activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, 4, 32, 576)   2304        block_12_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 4, 32, 576)   0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_12_project (Conv2D)       (None, 4, 32, 96)    55296       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, 4, 32, 96)    384         block_12_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_12_add (Add)              (None, 4, 32, 96)    0           block_11_add[0][0]               \n",
      "                                                                 batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand (Conv2D)        (None, 4, 32, 576)   55296       block_12_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, 4, 32, 576)   2304        block_13_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 4, 32, 576)   0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise (DepthwiseCo (None, 4, 32, 576)   5184        activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, 4, 32, 576)   2304        block_13_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 4, 32, 576)   0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_13_project (Conv2D)       (None, 4, 32, 160)   92160       activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, 4, 32, 160)   640         block_13_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand (Conv2D)        (None, 4, 32, 960)   153600      batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, 4, 32, 960)   3840        block_14_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 4, 32, 960)   0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise (DepthwiseCo (None, 4, 32, 960)   8640        activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, 4, 32, 960)   3840        block_14_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 4, 32, 960)   0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_14_project (Conv2D)       (None, 4, 32, 160)   153600      activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, 4, 32, 160)   640         block_14_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_14_add (Add)              (None, 4, 32, 160)   0           batch_normalization_39[0][0]     \n",
      "                                                                 batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand (Conv2D)        (None, 4, 32, 960)   153600      block_14_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, 4, 32, 960)   3840        block_15_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 4, 32, 960)   0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise (DepthwiseCo (None, 4, 32, 960)   8640        activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, 4, 32, 960)   3840        block_15_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 4, 32, 960)   0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_15_project (Conv2D)       (None, 4, 32, 160)   153600      activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, 4, 32, 160)   640         block_15_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_15_add (Add)              (None, 4, 32, 160)   0           block_14_add[0][0]               \n",
      "                                                                 batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand (Conv2D)        (None, 4, 32, 960)   153600      block_15_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, 4, 32, 960)   3840        block_16_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 4, 32, 960)   0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise (DepthwiseCo (None, 4, 32, 960)   8640        activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNo (None, 4, 32, 960)   3840        block_16_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 4, 32, 960)   0           batch_normalization_47[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_16_project (Conv2D)       (None, 4, 32, 320)   307200      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, 4, 32, 320)   1280        block_16_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "Conv_1 (Conv2D)                 (None, 4, 32, 256)   81920       batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, 4, 32, 256)   1024        Conv_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "out_relu (ReLU)                 (None, 4, 32, 256)   0           batch_normalization_49[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "permute (Permute)               (None, 32, 4, 256)   0           out_relu[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "time_distributed (TimeDistribut (None, 32, 1024)     0           permute[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional (Bidirectional)   (None, 32, 512)      2623488     time_distributed[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 32, 37)       18981       bidirectional[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 4,576,261\n",
      "Trainable params: 4,543,909\n",
      "Non-trainable params: 32,352\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\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 relu6(x):\n",
    "    return tf.keras.backend.relu(x, max_value=6)\n",
    "\n",
    "def inverted_res_block(input_tensor, expansion, stride, filters, block_id):\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "\n",
    "    in_channels = tf.keras.backend.int_shape(input_tensor)[channel_axis]\n",
    "    pointwise_filters = int(filters)\n",
    "    x = input_tensor\n",
    "    prefix = 'block_{}_'.format(block_id)\n",
    "\n",
    "    if block_id:\n",
    "        # Expand\n",
    "        x = tf.keras.layers.Conv2D(\n",
    "            expansion * in_channels,\n",
    "            kernel_size=1,\n",
    "            padding='same',\n",
    "            use_bias=False,\n",
    "            activation=None,\n",
    "            name=prefix + 'expand'\n",
    "        )(x)\n",
    "        x = tf.keras.layers.BatchNormalization()(x)\n",
    "\n",
    "        x = tf.keras.layers.Activation(relu6)(x)\n",
    "\n",
    "    else:\n",
    "        prefix = 'expanded_conv_'\n",
    "\n",
    "    # Depthwise\n",
    "    x = tf.keras.layers.DepthwiseConv2D(\n",
    "        kernel_size=3,\n",
    "        strides=stride,\n",
    "        activation=None,\n",
    "        use_bias=False,\n",
    "        padding='same',\n",
    "        name=prefix + 'depthwise'\n",
    "    )(x)\n",
    "    x = tf.keras.layers.BatchNormalization()(x)\n",
    "\n",
    "    x =tf.keras.layers.Activation(relu6)(x)\n",
    "\n",
    "    # Project\n",
    "    x = tf.keras.layers.Conv2D(\n",
    "        pointwise_filters,\n",
    "        kernel_size=1,\n",
    "        padding='same',\n",
    "        use_bias=False,\n",
    "        activation=None,\n",
    "        name=prefix + 'project'\n",
    "    )(x)\n",
    "    x = tf.keras.layers.BatchNormalization()(x)\n",
    "\n",
    "    if in_channels == pointwise_filters and stride == 1:\n",
    "        return tf.keras.layers.Add(name=prefix + 'add')([input_tensor, x])\n",
    "    return x\n",
    "\n",
    "def first_layer(inputs):\n",
    "    x = tf.keras.layers.Conv2D(\n",
    "        filters=32,\n",
    "        kernel_size=(3, 3),\n",
    "        strides=(2, 2),\n",
    "        padding='same',\n",
    "        kernel_initializer='he_normal',\n",
    "        name='conv1')(inputs)\n",
    "    x = tf.keras.layers.BatchNormalization()(x)\n",
    "    x = tf.keras.layers.LeakyReLU(0.01)(x)\n",
    "    return x\n",
    "\n",
    "last_block_filters = 256\n",
    "def pwise_block(inputs):\n",
    "    x = tf.keras.layers.Conv2D(\n",
    "        last_block_filters,\n",
    "        kernel_size=1,\n",
    "        use_bias=False,\n",
    "        name='Conv_1')(inputs)\n",
    "    x = tf.keras.layers.BatchNormalization()(x)\n",
    "    x = tf.keras.layers.ReLU(6., name='out_relu')(x)\n",
    "    return x\n",
    "\n",
    "input_tensor = tf.keras.layers.Input((height, width, 3))\n",
    "x = first_layer(input_tensor)\n",
    "x = tf.keras.layers.MaxPooling2D(pool_size=(2, 1))(x)\n",
    "x = inverted_res_block(x, filters=24, stride=2, expansion=6, block_id=1)\n",
    "x = tf.keras.layers.MaxPooling2D(pool_size=(2, 1))(x)\n",
    "x = inverted_res_block(x, filters=24, stride=1, expansion=6, block_id=2)\n",
    "x = inverted_res_block(x, filters=32, stride=1, expansion=6, block_id=3)\n",
    "x = inverted_res_block(x, filters=32, stride=1, expansion=6, block_id=4)\n",
    "x = inverted_res_block(x, filters=32, stride=1, expansion=6, block_id=5)\n",
    "x = inverted_res_block(x, filters=64, stride=1, expansion=6, block_id=6)\n",
    "x = inverted_res_block(x, filters=64, stride=1, expansion=6, block_id=7)\n",
    "x = inverted_res_block(x, filters=64, stride=1, expansion=6, block_id=8)\n",
    "x = inverted_res_block(x, filters=64, stride=1, expansion=6, block_id=9)\n",
    "x = inverted_res_block(x, filters=96, stride=1, expansion=6, block_id=10)\n",
    "x = inverted_res_block(x, filters=96, stride=1, expansion=6, block_id=11)\n",
    "x = inverted_res_block(x, filters=96, stride=1, expansion=6, block_id=12)\n",
    "x = inverted_res_block(x, filters=160, stride=1, expansion=6, block_id=13)\n",
    "x = inverted_res_block(x, filters=160, stride=1, expansion=6, block_id=14)\n",
    "x = inverted_res_block(x, filters=160, stride=1, expansion=6, block_id=15)\n",
    "x = inverted_res_block(x, filters=320, stride=1, expansion=6, block_id=16)\n",
    "\n",
    "x = pwise_block(x)\n",
    "x = tf.keras.layers.Permute((2, 1, 3))(x)\n",
    "x = tf.keras.layers.TimeDistributed(\n",
    "    layer=tf.keras.layers.Flatten(),\n",
    ")(inputs=x)\n",
    "x = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(256, return_sequences=True))(x)\n",
    "x = tf.keras.layers.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",
    "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",
    "loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([x, labels, input_length, label_length])\n",
    "\n",
    "model = Model(inputs=[input_tensor, labels, input_length, label_length], outputs=loss_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "1000/1000 [==============================] - 353s 353ms/step - loss: 6.2151 - val_loss: 1.1167\n",
      "Epoch 2/300\n",
      "1000/1000 [==============================] - 357s 357ms/step - loss: 0.5092 - val_loss: 1.0623\n",
      "Epoch 3/300\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.3337 - val_loss: 0.3593\n",
      "Epoch 4/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.2491 - val_loss: 0.3317\n",
      "Epoch 5/300\n",
      "1000/1000 [==============================] - 354s 354ms/step - loss: 0.2124 - val_loss: 0.3163\n",
      "Epoch 6/300\n",
      "1000/1000 [==============================] - 332s 332ms/step - loss: 0.1859 - val_loss: 0.4006\n",
      "Epoch 7/300\n",
      "1000/1000 [==============================] - 364s 364ms/step - loss: 0.1670 - val_loss: 0.2722\n",
      "Epoch 8/300\n",
      "1000/1000 [==============================] - 372s 372ms/step - loss: 0.1468 - val_loss: 0.2371\n",
      "Epoch 9/300\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.1306 - val_loss: 0.2704\n",
      "Epoch 10/300\n",
      "1000/1000 [==============================] - 368s 368ms/step - loss: 0.1200 - val_loss: 0.1695\n",
      "Epoch 11/300\n",
      "1000/1000 [==============================] - 371s 371ms/step - loss: 0.1102 - val_loss: 0.2073\n",
      "Epoch 12/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.1057 - val_loss: 0.2047\n",
      "Epoch 13/300\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.1017 - val_loss: 0.1251\n",
      "Epoch 14/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0936 - val_loss: 0.1215\n",
      "Epoch 15/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0871 - val_loss: 0.1827\n",
      "Epoch 16/300\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0890 - val_loss: 0.1239\n",
      "Epoch 17/300\n",
      "1000/1000 [==============================] - 325s 325ms/step - loss: 0.0883 - val_loss: 0.1155\n",
      "Epoch 18/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0791 - val_loss: 0.1220\n",
      "Epoch 19/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0747 - val_loss: 0.1054\n",
      "Epoch 20/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0763 - val_loss: 0.1644\n",
      "Epoch 21/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0795 - val_loss: 0.1667\n",
      "Epoch 22/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0791 - val_loss: 0.1091\n",
      "Epoch 23/300\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0718 - val_loss: 0.1315\n",
      "Epoch 24/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0690 - val_loss: 0.1009\n",
      "Epoch 25/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0660 - val_loss: 0.2051\n",
      "Epoch 26/300\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0648 - val_loss: 0.1053\n",
      "Epoch 27/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0587 - val_loss: 0.0987\n",
      "Epoch 28/300\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0602 - val_loss: 0.0918\n",
      "Epoch 29/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0555 - val_loss: 0.0723\n",
      "Epoch 30/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0561 - val_loss: 0.0923\n",
      "Epoch 31/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0583 - val_loss: 0.1309\n",
      "Epoch 32/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0596 - val_loss: 0.1987\n",
      "Epoch 33/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0548 - val_loss: 0.1870\n",
      "Epoch 34/300\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0561 - val_loss: 0.0692\n",
      "Epoch 35/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0564 - val_loss: 0.1072\n",
      "Epoch 36/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0549 - val_loss: 0.0978\n",
      "Epoch 37/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0576 - val_loss: 0.0872\n",
      "Epoch 38/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0548 - val_loss: 0.1256\n",
      "Epoch 39/300\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0516 - val_loss: 0.0796\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f8549601cf8>"
      ]
     },
     "execution_count": 35,
     "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_1012.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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1000/1000 [==============================] - 273s 273ms/step - loss: 0.0409 - val_loss: 0.0729\n",
      "Epoch 2/100\n",
      "1000/1000 [==============================] - 260s 260ms/step - loss: 0.0320 - val_loss: 0.0668\n",
      "Epoch 3/100\n",
      "1000/1000 [==============================] - 259s 259ms/step - loss: 0.0279 - val_loss: 0.0708\n",
      "Epoch 4/100\n",
      "1000/1000 [==============================] - 260s 260ms/step - loss: 0.0276 - val_loss: 0.0273\n",
      "Epoch 5/100\n",
      "1000/1000 [==============================] - 259s 259ms/step - loss: 0.0253 - val_loss: 0.0398\n",
      "Epoch 6/100\n",
      "1000/1000 [==============================] - 260s 260ms/step - loss: 0.0256 - val_loss: 0.0628\n",
      "Epoch 7/100\n",
      "1000/1000 [==============================] - 259s 259ms/step - loss: 0.0237 - val_loss: 0.0723\n",
      "Epoch 8/100\n",
      "1000/1000 [==============================] - 260s 260ms/step - loss: 0.0230 - val_loss: 0.0584\n",
      "Epoch 9/100\n",
      "1000/1000 [==============================] - 260s 260ms/step - loss: 0.0224 - val_loss: 0.0315\n",
      "Epoch 10/100\n",
      "1000/1000 [==============================] - 259s 259ms/step - loss: 0.0225 - val_loss: 0.0245\n",
      "Epoch 11/100\n",
      "1000/1000 [==============================] - 259s 259ms/step - loss: 0.0208 - val_loss: 0.0475\n",
      "Epoch 12/100\n",
      "1000/1000 [==============================] - 260s 260ms/step - loss: 0.0208 - val_loss: 0.0493\n",
      "Epoch 13/100\n",
      "1000/1000 [==============================] - 259s 259ms/step - loss: 0.0207 - val_loss: 0.0576\n",
      "Epoch 14/100\n",
      "1000/1000 [==============================] - 260s 260ms/step - loss: 0.0203 - val_loss: 0.0579\n",
      "Epoch 15/100\n",
      "1000/1000 [==============================] - 260s 260ms/step - loss: 0.0210 - val_loss: 0.0534\n",
      "Epoch 16/100\n",
      "1000/1000 [==============================] - 259s 259ms/step - loss: 0.0198 - val_loss: 0.0519\n",
      "Epoch 17/100\n",
      "1000/1000 [==============================] - 260s 260ms/step - loss: 0.0203 - val_loss: 0.0179\n",
      "Epoch 18/100\n",
      "1000/1000 [==============================] - 259s 259ms/step - loss: 0.0200 - val_loss: 0.0594\n",
      "Epoch 19/100\n",
      "1000/1000 [==============================] - 259s 259ms/step - loss: 0.0191 - val_loss: 0.0784\n",
      "Epoch 20/100\n",
      "1000/1000 [==============================] - 259s 259ms/step - loss: 0.0189 - val_loss: 0.0211\n",
      "Epoch 21/100\n",
      "1000/1000 [==============================] - 260s 260ms/step - loss: 0.0178 - val_loss: 0.0120\n",
      "Epoch 22/100\n",
      " 305/1000 [========>.....................] - ETA: 2:58 - loss: 0.0203"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process ForkPoolWorker-2811:\n",
      "Process ForkPoolWorker-2809:\n",
      "Process ForkPoolWorker-2808:\n",
      "Process ForkPoolWorker-2810:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/process.py\", line 254, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/process.py\", line 254, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/process.py\", line 254, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/process.py\", line 254, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/pool.py\", line 119, in worker\n",
      "    result = (True, func(*args, **kwds))\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/queues.py\", line 343, in get\n",
      "    res = self._reader.recv_bytes()\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/utils/data_utils.py\", line 432, in get_index\n",
      "    return _SHARED_SEQUENCES[uid][i]\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"<ipython-input-187-fe0c1ccb6bb1>\", line 109, in __getitem__\n",
      "    image, random_str = merge_img_7025()\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/queues.py\", line 342, in get\n",
      "    with self._rlock:\n",
      "  File \"<ipython-input-181-2e9e0150b6ee>\", line 16, in merge_img_7025\n",
      "    draw.point(xy=(random_xy(w, h)),fill=random_color((70,220,20,255,70,220)))\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-188-d6508eab4db6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'ctc'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e-4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mamsgrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m model.fit_generator(train_data, epochs=100, validation_data=valid_data, workers=4, use_multiprocessing=True,\n\u001b[0;32m---> 16\u001b[0;31m                     callbacks=callbacks)\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1777\u001b[0m         \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1778\u001b[0m         \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1779\u001b[0;31m         initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1780\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1781\u001b[0m   def evaluate_generator(self,\n",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m    209\u001b[0m           \u001b[0mbatch_logs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m         \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0mbatch_index\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\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/callbacks.py\u001b[0m in \u001b[0;36mon_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0mt_before_callbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m       \u001b[0mcallback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_delta_ts_batch_end\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mt_before_callbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0mdelta_t_median\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_delta_ts_batch_end\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/callbacks.py\u001b[0m in \u001b[0;36mon_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m    343\u001b[0m     \u001b[0;31m# will be handled by on_epoch_end.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseen\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_epoch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/utils/generic_utils.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, current, values)\u001b[0m\n\u001b[1;32m    433\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    434\u001b[0m       \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 435\u001b[0;31m       \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\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    436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\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/ipykernel/iostream.py\u001b[0m in \u001b[0;36mflush\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    347\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpub_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    348\u001b[0m                 \u001b[0;31m# and give a timeout to avoid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 349\u001b[0;31m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mevt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush_timeout\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    350\u001b[0m                     \u001b[0;31m# write directly to __stderr__ instead of warning because\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    351\u001b[0m                     \u001b[0;31m# if this is happening sys.stderr may be the problem.\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/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    554\u001b[0m             \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    555\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 556\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    557\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    558\u001b[0m         \u001b[0;32mfinally\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/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    295\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 297\u001b[0;31m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    298\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/queues.py\", line 342, in get\n",
      "    with self._rlock:\n",
      "  File \"<ipython-input-2-07993984e3f3>\", line 59, in random_color\n",
      "    green = random.randint(gs, ge)\n",
      "KeyboardInterrupt\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/random.py\", line 218, in randint\n",
      "    return self.randrange(a, b+1)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/random.py\", line 194, in randrange\n",
      "    return istart + self._randbelow(width)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n"
     ]
    }
   ],
   "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=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=10,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_1102.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')\n",
    "# gru_DigitAndEnglist_ctc_best.h5  mobilenet_DigitAndEnglist_ctc_best0930\n",
    "# callbacks = [CSVLogger('ctc.csv', append=True), ModelCheckpoint('DigitAndEnglist_cnn5gru_ctc_best2.h5', save_best_only=True)]\n",
    "# model.load_weights('DigitAndEnglist_cnn5gru_ctc_best2.h5')\n",
    "model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=Adam(1e-4, amsgrad=True))\n",
    "model.fit_generator(train_data, epochs=100, validation_data=valid_data, workers=4, use_multiprocessing=True,\n",
    "                    callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 准确率回调函数\n",
    "from tqdm import tqdm\n",
    "\n",
    "def evaluate(model, batch_size=128, steps=1):\n",
    "    '''\n",
    "    准确率验证函数，每批次的验证码长度必须一致\n",
    "    '''    \n",
    "    batch_acc = 0\n",
    "    valid_data = CaptchaSequence(characters, batch_size, steps)\n",
    "    for i in range(len(valid_data)):\n",
    "        [X_test, y_test, _, _], _ = valid_data[i]\n",
    "        y_pred = base_model.predict(X_test)\n",
    "        shape = y_pred.shape\n",
    "    #         out = K.get_value(K.ctc_decode(y_pred, input_length=np.ones(shape[0])*shape[1],)[0][0])[:, :4]\n",
    "        out = K.get_value(K.ctc_decode(y_pred, input_length=np.ones(shape[0])*shape[1],)[0][0])[:, :]\n",
    "    #     print(y_test)\n",
    "    #     print(type(y_test))\n",
    "    #     print(y_test[y_test<10, axis=1])\n",
    "    #     print(out)\n",
    "        if out.shape[1] >= 4:\n",
    "            batch_acc += (y_test[:,:out.shape[1]] == out).all(axis=1).mean()\n",
    "    return batch_acc / steps\n",
    "evaluate(base_model,batch_size=256, steps=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "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",
    "\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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "out ccntq\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ccntq')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 163,
       "width": 370
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = Image.open('FileInfo0508/31c1f481-912a-11ea-b24d-408d5cd36814_cmftq.jpg') # 波浪线验证码\n",
    "# img = Image.open('/data/captcha/shensebeijingsandian/pgv4_d58a8328-c425-11ea-be07-ecf4bbc56acd.jpg') # 深色背景验证码\n",
    "# img = Image.open('/data/captcha/0ad9.jpg').resize((200,70), Image.BILINEAR) #小图噪点 \n",
    "img = img.resize((width, height), Image.BILINEAR)\n",
    "def img2array(image, width=width,height=height):\n",
    "    X = np.zeros((1, height, width, 3))\n",
    "    image = image.convert('L')\n",
    "    px = [image.getpixel((x,2)) for x in range(image.size[0])]\n",
    "    c = Counter(px)\n",
    "    m = c.most_common()\n",
    "    bg = m[0][0]\n",
    "    bg_img = Image.new(mode='L', size=(width,height), color=bg)\n",
    "    bg_img.paste(image, box=(0, 0)) # \n",
    "    X[0] = np.expand_dims(np.array(bg_img)/255.0, axis=-1)\n",
    "    return X\n",
    "img_arr = img2array(img)\n",
    "\n",
    "out_pre = decode([img_arr, np.ones(img_arr.shape[0])])\n",
    "out = ''.join([characters[x] for x in out_pre[0][0]])\n",
    "plt.imshow(img)\n",
    "print('out', out)\n",
    "plt.title(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pred:peas\ttrue:pea6\n",
      "pred:txpo\ttrue:txpq\n",
      "pred:3aef\ttrue:35ef\n",
      "pred:ulul\ttrue:ului\n",
      "pred:fqrj\ttrue:pqrj\n",
      "pred:pytf\ttrue:pylf\n",
      "pred:blxv\ttrue:bixv\n",
      "pred:wigx\ttrue:wlgx\n",
      "8\n",
      "总耗时： 14.96862506866455\n",
      "正确数：992， 错误数：8, 总样本：1000, 准确率：0.9920\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",
    "pos = neg = 0\n",
    "t1 = time.time()\n",
    "err_img = []\n",
    "err_label = []\n",
    "for i in range(len(data)): \n",
    "    flag = False\n",
    "    [X_test, y_test, input_len, label_len], _  = data[i]\n",
    "    for idx in range(len(X_test)):\n",
    "        in_data = X_test[idx:idx+1]\n",
    "        out_pre = decode([in_data, np.ones(in_data.shape[0])])\n",
    "#         print(out_pre)\n",
    "        out = ''.join([characters[x] for x in out_pre[0][0]])  \n",
    "            \n",
    "        y_true = ''.join([characters[x] for x in y_test[idx] if x < len(characters)])\n",
    "#         print('out', out, y_true)\n",
    "        if out != y_true:\n",
    "            err_img.append(X_test[idx])\n",
    "            err_label.append('pre: %s, lab: %s'%(out, y_true))\n",
    "            print('pred:' + str(out) + '\\ttrue:' + str(y_true))\n",
    "            neg += 1\n",
    "            flag = True\n",
    "        else:\n",
    "            pos += 1 \n",
    "print(len(err_img))\n",
    "\n",
    "t2 = time.time()\n",
    "print('总耗时：',t2-t1)\n",
    "print('正确数：%d， 错误数：%d, 总样本：%d, 准确率：%.4f'%(pos,neg,pos+neg, pos/(pos+neg)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# i = -2\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": 216,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8wyx swyx False\n",
      "qgck ogck False\n",
      "t86o tg6o False\n",
      "kfhq krhq False\n",
      "ukm3 wkm3 False\n",
      "gd6v gd5v False\n",
      "auf7 auft False\n",
      "sydd sydj False\n",
      "e1qs e10s False\n",
      "qrh9 qrh9h False\n",
      "gnk0 gnko False\n",
      "du4u uu4u False\n",
      "85ud s5ud False\n",
      "ox6b ex6b False\n",
      "j4y0 j4yo False\n",
      "aoye a0ye False\n",
      "63h2 g3h2 False\n",
      "6jqq 6jq9 False\n",
      "vtnv vknv False\n",
      "qcwj ocwj False\n",
      "正确数：223, 总数：243, 准确率：0.9177\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\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'\n",
    "path8 = '/data/captcha/label_english/70_25/*.jpg'\n",
    "\n",
    "# model.load_weights('gru_english4to6_ctc_best_5.h5') \n",
    "err_imgs = []\n",
    "err_labels = []\n",
    "files = glob.glob(path7)\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",
    "    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": 213,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# import time\n",
    "# for i in range(len(err_imgs)):\n",
    "#     plt.imshow(err_imgs[i])\n",
    "#     plt.title(err_labels[i])\n",
    "#     plt.show()\n",
    "#     time.sleep(0.5)\n",
    "#     if i > 10:\n",
    "#         break"
   ]
  },
  {
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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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