{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      " * Serving Flask app \"__main__\" (lazy loading)\n",
      " * Environment: production\n",
      "   WARNING: Do not use the development server in a production environment.\n",
      "   Use a production WSGI server instead.\n",
      " * Debug mode: on\n"
     ]
    },
    {
     "ename": "OSError",
     "evalue": "[Errno 99] Cannot assign requested address",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-a6dc78a3d939>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m'验证码文件未上传'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'__main__'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m     \u001b[0mapp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"192.168.2.101\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/flask/app.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, host, port, debug, load_dotenv, **options)\u001b[0m\n\u001b[1;32m    941\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    942\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 943\u001b[0;31m             \u001b[0mrun_simple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    944\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    945\u001b[0m             \u001b[0;31m# reset the first request information if the development server\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/werkzeug/serving.py\u001b[0m in \u001b[0;36mrun_simple\u001b[0;34m(hostname, port, application, use_reloader, use_debugger, use_evalex, extra_files, reloader_interval, reloader_type, threaded, processes, request_handler, static_files, passthrough_errors, ssl_context)\u001b[0m\n\u001b[1;32m    793\u001b[0m             \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maddress_family\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSOCK_STREAM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    794\u001b[0m             \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetsockopt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSOL_SOCKET\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSO_REUSEADDR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 795\u001b[0;31m             \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_sockaddr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhostname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maddress_family\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    796\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'set_inheritable'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    797\u001b[0m                 \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_inheritable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: [Errno 99] Cannot assign requested address"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras import models \n",
    "import tensorflow.keras.backend as K\n",
    "from flask import request, Flask\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "import numpy as np\n",
    "import base64\n",
    "import string\n",
    "\n",
    "characters = string.digits\n",
    "base_model =  models.load_model('digit_base_model.h5')\n",
    "app = Flask(__name__)\n",
    "\n",
    "\n",
    "def predit_img(img):\n",
    "    img_arr = np.array(img.resize((100,50), Image.BILINEAR))/255.0\n",
    "    if img_arr.shape != (50,100,3):\n",
    "        return '图片必须为rgb格式'\n",
    "    X_test = np.array([img_arr])\n",
    "    print(X_test.shape)    \n",
    "    y_pred = base_model.predict(X_test)\n",
    "    print('y_pred',y_pred)\n",
    "    out_pre = K.get_value(K.ctc_decode(y_pred, input_length=np.ones(y_pred.shape[0])*y_pred.shape[1])[0][0])[:, :6]\n",
    "    print('out_pre', out_pre.shape)\n",
    "    out = ''.join([characters[x] for x in out_pre[0]])  \n",
    "    print('out',out.shape)\n",
    "    return out\n",
    "    \n",
    "@app.route(\"/upload\", methods=[\"POST\"])\n",
    "def upload():\n",
    "    \"\"\"接受前端传送来的文件\"\"\"\n",
    "    code_type = request.form.get('code')\n",
    "    base64pic = request.form.get('base64pic')\n",
    "    file_obj = request.files.get(\"pic\")\n",
    "    if base64pic is not None:\n",
    "        try:\n",
    "            src = base64.b64decode(base64pic.split(',')[-1])\n",
    "            img = Image.open(BytesIO(src))\n",
    "            pre = predit_img(img)\n",
    "            return \"上传文件成功 ,验证码结果为 %s\" % pre\n",
    "        except:\n",
    "            return '文件格式错误，请检查是否为base64文件'\n",
    "    if file_obj is not None:\n",
    "        try:\n",
    "            img = Image.open(file_obj)\n",
    "            pre = predit_img(img)\n",
    "            return \"上传文件成功 ,验证码结果为 %s\" % pre\n",
    "        except:\n",
    "            return '文件格式错误，请检查是否为验证码图片'\n",
    "\n",
    "    return '验证码文件未上传'\n",
    "if __name__ == '__main__':\n",
    "    app.run(\"192.168.2.101\", port=5000, debug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras import models \n",
    "import tensorflow.keras.backend as K\n",
    "from flask import request, Flask\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import base64\n",
    "import string\n",
    "import os\n",
    "os.environ['CUDA_DEVICE_ORDER'] = \"PIC_BUS_ID\"\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = \"-1\"\n",
    "\n",
    "characters = string.digits\n",
    "\n",
    "english_characters = string.ascii_lowercase + string.digits  # 20200728 更新为全部小写多种验证码\n",
    "english_base_model = models.load_model('gru_DigitAndEnglist_base_model1014.h5') # 20200518 新增  20200728 更新为全部小写多种验证码\n",
    "english_input = english_base_model.output\n",
    "english_input_length = tf.keras.Input(batch_shape=[None], dtype='int32')\n",
    "english_decode = K.ctc_decode(y_pred=english_input, input_length=english_input_length * K.shape(english_input)[1])\n",
    "english_decode = K.function([english_base_model.input, english_input_length], [english_decode[0][0]])\n",
    "app = Flask(__name__)\n",
    "width = 200\n",
    "height = 70\n",
    "\n",
    "def predict_english(img):\n",
    "    img_arr = np.array(img.resize((200, 70), Image.NEAREST)) / 255.0\n",
    "    X_test = np.array([img_arr])\n",
    "    with graph.as_default():\n",
    "        out_pre = english_decode([X_test, np.ones(X_test.shape[0])])[0]\n",
    "    out = ''.join([english_characters[x] for x in out_pre[0]])\n",
    "    return out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * Serving Flask app \"__main__\" (lazy loading)\n",
      " * Environment: production\n",
      "   WARNING: Do not use the development server in a production environment.\n",
      "   Use a production WSGI server instead.\n",
      " * Debug mode: on\n"
     ]
    },
    {
     "ename": "OSError",
     "evalue": "[Errno 98] Address already in use",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-b35140b2b5d5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;31m#     handler.setFormatter(logging_format)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[0;31m#     app.logger.addHandler(handler)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m     \u001b[0mapp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"127.0.0.1\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5011\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#http://192.168.2.102\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     84\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'flash running'\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/flask/app.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, host, port, debug, load_dotenv, **options)\u001b[0m\n\u001b[1;32m    941\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    942\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 943\u001b[0;31m             \u001b[0mrun_simple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    944\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    945\u001b[0m             \u001b[0;31m# reset the first request information if the development server\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/werkzeug/serving.py\u001b[0m in \u001b[0;36mrun_simple\u001b[0;34m(hostname, port, application, use_reloader, use_debugger, use_evalex, extra_files, reloader_interval, reloader_type, threaded, processes, request_handler, static_files, passthrough_errors, ssl_context)\u001b[0m\n\u001b[1;32m    793\u001b[0m             \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maddress_family\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSOCK_STREAM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    794\u001b[0m             \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetsockopt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSOL_SOCKET\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSO_REUSEADDR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 795\u001b[0;31m             \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_sockaddr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhostname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maddress_family\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    796\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'set_inheritable'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    797\u001b[0m                 \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_inheritable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: [Errno 98] Address already in use"
     ]
    }
   ],
   "source": [
    "# from predict_model import *\n",
    "from flask import request, Flask, jsonify\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "import base64\n",
    "import time\n",
    "import logging\n",
    "\n",
    "coun_dic = {'shuzi':{'total_num':0, 'neg_num':0}, 'suanshu':{'total_num':0, 'neg_num':0}\n",
    "               ,'yingwen':{'total_num':0, 'neg_num':0},'hanzi':{'total_num':0, 'neg_num':0}}\n",
    "\n",
    "app = Flask(__name__)\n",
    "\n",
    "@app.route(\"/upload\", methods=[\"POST\"])\n",
    "def upload():\n",
    "    start_time = time.time()\n",
    "    \"\"\"receive image and predict \"\"\"\n",
    "    code_type = request.form.get('code')\n",
    "    base64pic = request.form.get('base64pic')\n",
    "    file_obj = request.files.get(\"pic\")\n",
    "    data = {'success':False}\n",
    "    if code_type is None or str(code_type) not in ['shuzi', 'suanshu','yingwen','hanzi']:\n",
    "        data = {'errorinfo':'please check you param:code, code must be in shuzi/suanshu/yingwen/hanzi'}\n",
    "        return jsonify(data)\n",
    "    if base64pic is not None:\n",
    "        try:\n",
    "            src = base64.b64decode(base64pic.split(',')[-1])\n",
    "            img = Image.open(BytesIO(src))\n",
    "            if img.mode != \"RGB\":\n",
    "                img = img.convert(\"RGB\")\n",
    "#             if code_type == 'shuzi':\n",
    "#                 pre = predict_digit(img)\n",
    "#             elif code_type == 'suanshu':\n",
    "#                 pre = predict_arith(img)\n",
    "#                 pre = str(eval(pre))\n",
    "#             elif code_type == 'hanzi':\n",
    "#                 pre = predict_chinese(img)\n",
    "#             elif code_type == 'yingwen':\n",
    "            pre = predict_english(img)\n",
    "            data['predict'] = pre\n",
    "            data['success'] = True\n",
    "            coun_dic[code_type]['total_num'] +=1\n",
    "            app.logger.info(\"success ,use time:%.4f\" %(time.time() - start_time))\n",
    "            return jsonify(data)\n",
    "        except:\n",
    "            app.logger.info(\"except error,use time:%.4f\" %(time.time() - start_time))\n",
    "            return jsonify(data)\n",
    "    if file_obj is not None:\n",
    "        try:\n",
    "            img = Image.open(file_obj)\n",
    "            if img.mode != \"RGB\":\n",
    "                img = img.convert(\"RGB\")\n",
    "#             if code_type == 'shuzi':\n",
    "#                 pre = predict_digit(img)\n",
    "#             elif code_type == 'suanshu':\n",
    "#                 pre = predict_arith(img)\n",
    "#                 pre = str(eval(pre))\n",
    "#             elif code_type == 'hanzi':\n",
    "#                 pre = predict_chinese(img)\n",
    "#             elif code_type == 'yingwen':\n",
    "            pre = predict_english(img)\n",
    "            data['success'] = True\n",
    "            data['predict'] = pre\n",
    "            coun_dic[code_type]['total_num'] += 1\n",
    "            app.logger.info(\"success ,use time:%.4f\" %(time.time() - start_time))\n",
    "            # print('graph_node_num',len(tf.get_default_graph().as_graph_def().node))\n",
    "            return jsonify(data)\n",
    "        except:\n",
    "            app.logger.info(\"except error, use time:%.4f\" %(time.time() - start_time))\n",
    "            return jsonify(data)\n",
    "\n",
    "    return 'please check you post '\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "#     handler = logging.FileHandler('flask.log', encoding='UTF-8')\n",
    "#     app.logger.setLevel(\"INFO\")\n",
    "#     logging_format = logging.Formatter(\n",
    "#         '%(asctime)s - %(levelname)s - %(filename)s -%(lineno)s - %(message)s'\n",
    "#     )\n",
    "#     handler.setFormatter(logging_format)\n",
    "#     app.logger.addHandler(handler)\n",
    "    app.run(\"127.0.0.1\", port=5011, debug=True) #http://192.168.2.102\n",
    "    print('flash running')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * Serving Flask app \"__main__\" (lazy loading)\n",
      " * Environment: production\n",
      "   WARNING: Do not use the development server in a production environment.\n",
      "   Use a production WSGI server instead.\n",
      " * Debug mode: off\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " * Running on http://192.168.2.101:5025/ (Press CTRL+C to quit)\n",
      "[2019-12-19 17:54:13,477] ERROR in app: Exception on /upload [POST]\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/flask/app.py\", line 2292, in wsgi_app\n",
      "    response = self.full_dispatch_request()\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/flask/app.py\", line 1815, in full_dispatch_request\n",
      "    rv = self.handle_user_exception(e)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/flask/app.py\", line 1718, in handle_user_exception\n",
      "    reraise(exc_type, exc_value, tb)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/flask/_compat.py\", line 35, in reraise\n",
      "    raise value\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/flask/app.py\", line 1813, in full_dispatch_request\n",
      "    rv = self.dispatch_request()\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/flask/app.py\", line 1799, in dispatch_request\n",
      "    return self.view_functions[rule.endpoint](**req.view_args)\n",
      "  File \"<ipython-input-9-71305679478d>\", line 100, in upload\n",
      "    pre = predict_chinese(img)\n",
      "NameError: name 'predict_chinese' is not defined\n",
      "192.168.2.2 - - [19/Dec/2019 17:54:13] \"\u001b[1m\u001b[35mPOST /upload HTTP/1.1\u001b[0m\" 500 -\n"
     ]
    }
   ],
   "source": [
    "from predict_model import *\n",
    "from flask import request, Flask, jsonify\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "import base64\n",
    "import time\n",
    "\n",
    "total_num = 0\n",
    "neg_num = 0\n",
    "\n",
    "app = Flask(__name__)\n",
    "\n",
    "@app.route(\"/getlog\", methods=[\"POST\"])\n",
    "def get_acc():\n",
    "    global total_num\n",
    "    global neg_num\n",
    "    data = {'total_num':total_num, 'neg_numative':neg_num}\n",
    "    clear = request.form.get('clear_log', 'no')\n",
    "    if clear == 'yes':\n",
    "        with open('upload_num_log.txt', 'a', encoding='utf=8') as f:\n",
    "            f.write('total_number：%d,\\t error_number：%d\\n'%(total_num, neg_num))\n",
    "        total_num = 0\n",
    "        neg_num = 0\n",
    "    return jsonify(data)\n",
    "\n",
    "@app.route(\"/errorlog\", methods=[\"POST\"])\n",
    "def save_error():\n",
    "    \"\"\"receive not success image and save \"\"\"\n",
    "    global total_num\n",
    "    global neg_num\n",
    "    code_type = request.form.get('code', 'unknow')\n",
    "    base64pic = request.form.get('base64pic')\n",
    "    file_obj = request.files.get(\"pic\")\n",
    "    data = {'save_success':False}\n",
    "    if base64pic is not None:\n",
    "        try:\n",
    "            src = base64.b64decode(base64pic.split(',')[-1])\n",
    "            img = Image.open(BytesIO(src))\n",
    "            time_tr = time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.localtime())\n",
    "            img.save('pic/'+str(code_type)+'_'+time_tr+'.jpg')\n",
    "            data['save_success'] = True\n",
    "            neg_num += 1\n",
    "            return jsonify(data)\n",
    "        except:\n",
    "            return jsonify(data)\n",
    "    if file_obj is not None:\n",
    "        try:\n",
    "            img = Image.open(file_obj)\n",
    "            time_tr = time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.localtime())\n",
    "            img.save('pic/'+str(code_type)+'_'+time_tr+'.jpg')\n",
    "            data['save_success'] = True\n",
    "            neg_num += 1\n",
    "            return jsonify(data)\n",
    "        except:\n",
    "            return jsonify(data)\n",
    "\n",
    "@app.route(\"/upload\", methods=[\"POST\"])\n",
    "def upload():\n",
    "    global total_num\n",
    "    global neg_num\n",
    "    \"\"\"receive image and predict \"\"\"\n",
    "    code_type = request.form.get('code')\n",
    "    base64pic = request.form.get('base64pic')\n",
    "    file_obj = request.files.get(\"pic\")\n",
    "    data = {'success':False}\n",
    "#     print(type(code_type))\n",
    "    if code_type is None or str(code_type) not in ['shuzi', 'suanshu','yingwen','hanzi']:\n",
    "        data = {'errorinfo':'please check you param:code, code must be in shuzi/suanshu/yingwen/hanzi'}\n",
    "        return jsonify(data)\n",
    "    if base64pic is not None:\n",
    "        try:\n",
    "            src = base64.b64decode(base64pic.split(',')[-1])\n",
    "            img = Image.open(BytesIO(src))\n",
    "            if img.mode != \"RGB\":\n",
    "                img = img.convert(\"RGB\")\n",
    "            if code_type == 'shuzi':\n",
    "                pre = predict_digit(img)\n",
    "            elif code_type == 'suanshu':\n",
    "                pre = predict_arith(img)\n",
    "                pre = str(eval(pre))\n",
    "            elif code_type == 'hanzi':\n",
    "                pre = predict_chinese(img)\n",
    "            data['predict'] = pre\n",
    "            data['success'] = True\n",
    "            total_num += 1\n",
    "            return jsonify(data)\n",
    "        except:\n",
    "            return jsonify(data)\n",
    "    if file_obj is not None:\n",
    "#         try:\n",
    "        img = Image.open(file_obj)\n",
    "        if img.mode != \"RGB\":\n",
    "            img = img.convert(\"RGB\")\n",
    "        if code_type == 'shuzi':\n",
    "            pre = predict_digit(img)\n",
    "        elif code_type == 'suanshu':\n",
    "            pre = predict_arith(img)\n",
    "            pre = str(eval(pre))\n",
    "        elif code_type == 'hanzi':\n",
    "            pre = predict_chinese(img)\n",
    "        data['success'] = True\n",
    "        data['predict'] = pre\n",
    "        total_num += 1\n",
    "        return jsonify(data)\n",
    "#         except:\n",
    "#             return jsonify(data)\n",
    "\n",
    "    return 'please check you post '\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run(\"192.168.2.101\", port=5025, debug=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abcdefghijklmnopqrstuvwxyz0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ\n",
      "25 latin2/ARIALNI.TTF\n"
     ]
    }
   ],
   "source": [
    "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 + string.ascii_uppercase # 验证码字符集合数字+英文\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",
    "print(len(fonts), fonts[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image size (100, 26) (128, 64)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 3600x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 2280
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random_color(rs, re, gs, ge, bs, be) (19, 19, 11)\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/arithmetic/100_26/19c3254f-f699-11ea-bae3-c81f66ef0810.jpg')\n",
    "\n",
    "random_str = '2×?=12'\n",
    "image = gen_captcha(random_str, fig_size=(100, 26), fonts=fonts,font_color=(100,150,20,80,20,80),same_color=0, font_size=(20, 23), rotate=0,\n",
    "                    font_noise=0,offset_w=(-1,3),offset_h=0, line=(0,0),shortline=(10,20), line_width=(0,1), line_color=(120,150), point=(0,0),\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": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "def decode_arith(arith = '2×?=12'):\n",
    "    arith = arith.replace('×', '*')\n",
    "    items = re.split('=', arith)\n",
    "    if len(items)==2:\n",
    "        if items[-1] in ['?', '']:\n",
    "            return eval(items[0])\n",
    "        l = re.split('-|\\+|\\*', items[0])\n",
    "        print(l)\n",
    "        signs = re.findall('-|\\+|\\*', items[0])\n",
    "        if len(l)==2 and len(signs)==1:\n",
    "            if l[1] == '?':\n",
    "                if signs[0] == '+':\n",
    "                    return eval('%s-%s'%(items[-1], l[0]))\n",
    "                elif signs[0] == '-':\n",
    "                    return eval('%s-%s'%(l[0],items[-1]))\n",
    "                elif signs[0] == '*':\n",
    "                    return eval('%s/%s'%(items[-1], l[0]))                   \n",
    "            elif l[0] == '?':\n",
    "                if signs[0] == '+':\n",
    "                    return eval('%s-%s'%(items[-1], l[1]))\n",
    "                elif signs[0] == '-':\n",
    "                    return eval('%s+%s'%(l[1],items[-1]))\n",
    "                elif signs[0] == '*':\n",
    "                    return eval('%s/%s'%(items[-1], l[1]))  \n",
    "    return ''\n",
    "decode_arith('5×5=?')                           \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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([5])\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=(0,235,0,235,0,235),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=(0,235,0,235,0,235),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=(60,130),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=(60,130), 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<=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",
    "                    \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=(0,250,0,250,0,250),same_color=1, font_size=(50, 55), rotate=10,\n",
    "                                        font_noise=0,offset_w=(-3,1),offset_h=2, line=(0,0), shortline=(0,0), line_width=(2,2), line_color=(0,100,80,230,0,90), point=(300,450),\n",
    "                                        point_color=(250,255),frame_color=None,wavy=(1,1), bg=(250,255))\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=(-8,-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",
    "                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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = CaptchaSequence(characters, batch_size=20, steps=1,input_length=32, label_length=6,chars_len=(5, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 64, 128, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fbc84cba048>"
      ]
     },
     "execution_count": 168,
     "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": 200,
       "width": 370
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "font_type = ['ARIALN.TTF', 'ARIALNI.TTF', 'BKANT.TTF', 'Calibril.ttf', 'calibrili.ttf', 'CALISTI.TTF']\n",
    "l, _  = data[1]\n",
    "x = l[0]\n",
    "print(x.shape)\n",
    "idx = 13\n",
    "plt.imshow(x[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random_str 2a5e\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3600x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 530
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_str = '2a5e'\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",
    "image = gen_captcha(random_str, fig_size=(200,70), fonts=fonts,font_color=(0,250,0,250,0,250),same_color=1, font_size=(50, 55), rotate=10,\n",
    "                    font_noise=0,offset_w=(-3,1),offset_h=2, line=(0,0), shortline=(0,0), line_width=(2,2), line_color=(0,100,80,230,0,90), point=(300,450),\n",
    "                    point_color=(250,255),frame_color=None,wavy=(1,1), bg=(250,255))\n",
    "im = [image]\n",
    "print('random_str', random_str)\n",
    "plt.figure(figsize=(50,10))\n",
    "for i in range(len(im)):      \n",
    "    plt.subplot(2,2,i+1)\n",
    "    plt.imshow(im[i])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image, ImageFont, ImageDraw\n",
    "# fonts = glob.glob('latin2/*')\n",
    "# fonts = glob.glob('fonts/english/*')\n",
    "# fonts = glob.glob('latin/*')\n",
    "# fonts = glob.glob('fonts/*.TTF')\n",
    "# fonts = ['/usr/share/fonts/truetype/arphic/uming.ttc', '/usr/share/fonts/truetype/arphic/ukai.ttc', '/usr/share/fonts/opentype/malayalam/Manjari-Regular.otf', '/usr/share/fonts/opentype/malayalam/Manjari-Thin.otf', '/usr/share/fonts/opentype/noto/NotoSerifCJK-Regular.ttc', '/usr/share/fonts/opentype/noto/NotoSerifCJK-Bold.ttc', '/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc', '/usr/share/fonts/opentype/noto/NotoSansCJK-Bold.ttc']\n",
    "# fonts2 = ['latin/segoeuil.ttf', 'latin/verdana.ttf', 'latin/calibri.ttf', 'latin/SIMLI.TTF', 'latin/verdanai.ttf', 'latin/framd.ttf', 'latin/arialbi.ttf', 'latin/ariali.ttf', 'latin/LSANS.TTF']\n",
    "# fonts = fonts+fonts2\n",
    "# fonts = sorted(fonts)\n",
    "fonts = [ '/usr/share/fonts/opentype/malayalam/Manjari-Regular.otf', '/usr/share/fonts/opentype/malayalam/Manjari-Thin.otf', '/usr/share/fonts/opentype/noto/NotoSerifCJK-Regular.ttc', '/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc', '/usr/share/fonts/opentype/noto/NotoSansCJK-Bold.ttc']\n",
    "fonts2 = ['latin/segoeuil.ttf', 'latin/verdana.ttf', 'latin/calibri.ttf', 'latin/SIMLI.TTF', 'latin/verdanai.ttf', 'latin/framd.ttf', 'latin/ariali.ttf', 'latin/LSANS.TTF']\n",
    "fonts = fonts+fonts2\n",
    "fonts = sorted(fonts)\n",
    "# text = '3fxw'\n",
    "text = '14+2*?=32'\n",
    "# text='56-33=?'\n",
    "# img2 = Image.open('/data/captcha/arithmetic/100_26/19c3254f-f699-11ea-bae3-c81f66ef0810.jpg')\n",
    "img2 = Image.open('/data/captcha/arithmetic/70_25/0.jpg')\n",
    "imgs = [img2]\n",
    "names = ['lizi']\n",
    "for font_path in fonts:\n",
    "    if os.path.isdir(font_path):\n",
    "        continue\n",
    "    img = Image.new(mode='RGB', size=(70, 25), color=(255,255,255)) #\n",
    "    draw = ImageDraw.Draw(im=img, mode='RGB') # im, mode=None\n",
    "    name = font_path.split('/')[-1]   \n",
    "    font = ImageFont.truetype(font_path, size=15) # font=None, size=10, index=0, encoding=\"\"\n",
    "    draw.text((3, 2), text, font=font, fill=(0,255,255))\n",
    "    imgs.append(img)\n",
    "    names.append(name)\n",
    "plt.figure(figsize=(20,30))\n",
    "for i in range(len(imgs)):      \n",
    "    plt.subplot(len(imgs)//4+1,4,i+1)\n",
    "    plt.imshow(imgs[i])\n",
    "    plt.title(names[i])\n",
    "plt.show()\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 7 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 415,
       "width": 1151
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# font_paths = glob.glob('latin/*')\n",
    "\n",
    "# font_list = ['framd.ttf','segoeuil.ttf','verdana.ttf','verdanai.ttf','LSANS.TTF', 'arialbi.ttf','ariali.ttf','calibri.ttf', 'SIMLI.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",
    "# # # font_list = ['ARIALN.TTF', 'ARIALNI.TTF', 'BKANT.TTF', 'calibrii.ttf', 'calibrili.ttf','Calibrib.ttf', 'CALISTI.TTF','cambriai.ttf','LSANS.TTF','CENSCBK.TTF']\n",
    "\n",
    "# fonts = []\n",
    "# for font in font_paths:\n",
    "#     if font.split('/')[-1] in font_list:\n",
    "#         fonts.append(font)\n",
    "\n",
    "# fonts = sorted(fonts)\n",
    "# print(len(fonts), len(font_list), fonts)\n",
    "# text = '3fxw'\n",
    "text='56×33=12'\n",
    "# imgs = []\n",
    "# names = []\n",
    "# for font_path in fonts:\n",
    "#     if os.path.isdir(font_path):\n",
    "#         continue\n",
    "#     img = Image.new(mode='RGB', size=(100, 30), color=(255,255,255)) #\n",
    "#     draw = ImageDraw.Draw(im=img, mode='RGB') # im, mode=None\n",
    "#     name = font_path.split('/')[-1]   \n",
    "#     font = ImageFont.truetype(font_path, size=20) # font=None, size=10, index=0, encoding=\"\"\n",
    "#     draw.text((10, 2), text, font=font, fill=(0,255,255))\n",
    "#     imgs.append(img)\n",
    "#     names.append(name)\n",
    "# plt.figure(figsize=(20,20))\n",
    "# for i in range(len(imgs)):      \n",
    "#     plt.subplot(len(imgs)//4+1,4,i+1)\n",
    "#     plt.imshow(imgs[i])\n",
    "#     plt.title(names[i])\n",
    "# plt.show()\n",
    "\n",
    "imgs = []\n",
    "names = []\n",
    "paths = []\n",
    "for root,dirs, files in os.walk('/usr/share/fonts/opentype/', topdown=True):\n",
    "#     print(root)\n",
    "#     print(dirs)\n",
    "#     print(files)\n",
    "    for file in files:\n",
    "        font_path = os.path.join(root, file)\n",
    "        img = Image.new(mode='RGB', size=(100, 30), color=(255,255,255)) #\n",
    "        draw = ImageDraw.Draw(im=img, mode='RGB') # im, mode=None \n",
    "        try:\n",
    "            font = ImageFont.truetype(font_path, size=20) # font=None, size=10, index=0, encoding=\"\"\n",
    "            draw.text((10, 2), text, font=font, fill=(0,255,255))\n",
    "            imgs.append(img)\n",
    "            names.append(len(paths))\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "            print('err',font_path)\n",
    "            continue\n",
    "        paths.append(font_path)\n",
    "plt.figure(figsize=(20,10))\n",
    "for i in range(len(imgs)):      \n",
    "    plt.subplot(len(imgs)//4+1,4,i+1)\n",
    "    plt.imshow(imgs[i])\n",
    "    plt.title(names[i])\n",
    "plt.show()        \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/usr/share/fonts/opentype/malayalam/Manjari-Regular.otf', '/usr/share/fonts/opentype/malayalam/Manjari-Thin.otf', '/usr/share/fonts/opentype/noto/NotoSerifCJK-Regular.ttc', '/usr/share/fonts/opentype/noto/NotoSerifCJK-Bold.ttc', '/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc', '/usr/share/fonts/opentype/noto/NotoSansCJK-Bold.ttc\n"
     ]
    }
   ],
   "source": [
    "paths\n",
    "# font_paths = ['/usr/share/fonts/truetype/arphic/uming.ttc', '/usr/share/fonts/truetype/arphic/ukai.ttc']\n",
    "\n",
    "print(\"', '\".join(paths[1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "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",
      "2716 /data/captcha/shensebeijingsandian/90f2c39e-c08a-11ea-adc0-ecf4bbc56acd_9ESN.jpg\n",
      "2502 /data/captcha/shensexiansandian/a711d2ab4416673804f0415cb6bab36d_YDJP.jpg\n",
      "111 /data/esa_sdk/gan/english/805bc78f-fbbd-11e9-9bc7-408d5cd36814_9tcl.jpg\n",
      "47579\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 180,
       "width": 370
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fonts.remove('latin2/ANTQUABI.TTF')\n",
    "# 'latin2/ANTQUABI.TTF' in fonts\n",
    "# fonts\n",
    "# filePath = '/data/captcha/label_english/100_30/*.jpg'\n",
    "filePath = 'FileInfo0508_2/*.jpg' # 波浪线验证码\n",
    "\n",
    "# filePath = '/data/captcha/label_english/70_26/*.jpg'\n",
    "# filePath = '/data/captcha/label_english/52_21/*.jpg'\n",
    "# filePath = '/data/captcha/label_english/100_25/*.jpg'\n",
    "# filePath = '/data/captcha/shensebeijingsandian/*.jpg'\n",
    "# filePath = '/data/captcha/shensexiansandian/*.jpg'\n",
    "# filePath = '/data/esa_sdk/gan/english/*.jpg'\n",
    "\n",
    "import re\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 = int(len(files)*0.8)\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 = int(len(files)*0.8)\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'\n",
    "path5 = '/data/captcha/shensexiansandian/*.jpg'\n",
    "path6 = '/data/esa_sdk/gan/english/*.jpg'\n",
    "for paths in [path1, path2, path3, path4, path5, path6]:\n",
    "    files = glob.glob(paths)\n",
    "    sp = int(len(files)*0.8)\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",
    "img = Image.open(files[0])\n",
    "plt.imshow(img)\n",
    "print(len(len4_imgs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x2880 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 155,
       "width": 860
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import ImageFilter\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",
    "    for _ in range(random.randint(20,250)):\n",
    "        draw.point(xy=(random_xy(w, h)),fill=random_color((125, 255)))  \n",
    "    for _ in range(random.randint(0, 3)):\n",
    "        draw.line(xy=(random_xy(w, h),random_xy(w, h)), fill=random_color((120, 250)), width=random.randint(0,1))\n",
    "    return img.resize((width, height), Image.BILINEAR), label.lower()\n",
    "\n",
    "files = glob.glob(path2)\n",
    "img = Image.open(files[0]).convert('RGB')\n",
    "rebiuld, label = rebuild_img(files[0])\n",
    "\n",
    "# fil = img.filter(ImageFilter.GaussianBlur(radius=1))\n",
    "\n",
    "# fil = img.filter(ImageFilter.UnsharpMask(radius=1, percent=150, threshold=5))\n",
    "\n",
    "# fil = img.filter(ImageFilter.RankFilter(size=3, rank=3))\n",
    "\n",
    "fil = img.filter(ImageFilter.MedianFilter(size=3))\n",
    "\n",
    "fil = img.filter(ImageFilter.ModeFilter(size=3))\n",
    "\n",
    "\n",
    "# print(img.size)\n",
    "# plt.imshow(img)\n",
    "# plt.title(label)\n",
    "imgs = [img,rebiuld, fil]\n",
    "plt.figure(figsize=(20,40))\n",
    "for i in range(len(imgs)):      \n",
    "    plt.subplot(len(imgs)//4+1,4,i+1)\n",
    "    plt.imshow(imgs[i])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 100,
       "width": 370
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 7, 8, 9, 17, 18, 26, 27, 34, 35, 36, 43, 44, 45, 46, 55, 56, 63]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 169
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 186
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 186
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 169
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 169
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 203
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 169
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import glob\n",
    "# import re\n",
    "# path7 = '/data/captcha/label_english/90_38/*.jpg'\n",
    "\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[:]:\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",
    "#     if len(label)!=4:\n",
    "#         print(file)\n",
    "#     break\n",
    "# from PIL import ImageFilter\n",
    "# img2 = img.filter(ImageFilter.MedianFilter(size=3))\n",
    "# img2 = img2.filter(ImageFilter.MedianFilter(size=3))\n",
    "# img2 = img.filter(ImageFilter.ModeFilter(size=3))\n",
    "img2 = Image.open('/data/captcha/arithmetic/70_25/0.jpg')\n",
    "# img2 = img2.crop((6,4,70,17))\n",
    "# w0,h = img2.size\n",
    "# w = w0//7\n",
    "# for i in range(7):\n",
    "#     if i<=4:\n",
    "#         im = img2.crop((w*i, 0, w*(i+1), h))\n",
    "#     else:\n",
    "#         im = img2.crop((w*i+1, 0, min(w0,w*(i+1)+2), h))\n",
    "#     plt.imshow(im)\n",
    "#     plt.show()\n",
    "# plt.imshow(img2)\n",
    "# plt.show()\n",
    "img2 = img2.crop((6,4,70,17))\n",
    "w, h = img2.size\n",
    "ws = []\n",
    "for i in range(w):\n",
    "    white_num = 0\n",
    "    for j in range(h):\n",
    "        if sum(img2.getpixel((i, j)))>400:\n",
    "            img2.putpixel((i, j), (255, 255, 255))\n",
    "            white_num += 1\n",
    "    if white_num == h:\n",
    "        ws.append(i)\n",
    "plt.imshow(img2)\n",
    "plt.show()\n",
    "print(ws)\n",
    "for i in range(len(ws)-1):\n",
    "    if ws[i+1]-ws[i]>3:\n",
    "        im = img2.crop((ws[i], 0, min(ws[i+1]+1, w), h))\n",
    "        plt.imshow(im)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GYS7\n"
     ]
    }
   ],
   "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",
    "    return img, ''.join(label)\n",
    "\n",
    "img, label =  merge_img_7025()\n",
    "\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 162,
       "width": 370
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xep4\n"
     ]
    }
   ],
   "source": [
    "bgs = glob.glob('/data/captcha/crop_english/bg_90_38/*.jpg')\n",
    "crops = glob.glob('/data/captcha/crop_english/crop_90_38/*.jpg')\n",
    "def merge_img_9038():\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, (17*i+10,0+10)) \n",
    "\n",
    "    return img, ''.join(label)\n",
    "\n",
    "img, label =  merge_img_9038()\n",
    "\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "print(label)\n",
    "\n",
    "# path = 'E:\\验证码标注数据/crop_english/bg_90_38/*.jpg'\n",
    "# files = glob.glob(path)\n",
    "# img = Image.open(files[0])\n",
    "# # img2 = img.crop((10,10,80,30))\n",
    "# plt.imshow(img)\n",
    "# plt.show()\n",
    "# for i in range(4):\n",
    "#     im = Image.open(random.choice(glob.glob('E:/验证码标注数据/crop_english/crop_90_38/*.jpg')))\n",
    "# #     im = img2.crop((17*i,0, 17*(i+1), 20))\n",
    "#     img.paste(im,(17*i+10,0+10))\n",
    "# plt.imshow(img)\n",
    "# plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}