statics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "def length_statics(data_pre):\n",
    "    segments=np.load(data_pre+'segments.npy',allow_pickle=True)\n",
    "    durations=np.load(data_pre+'duration.npy',allow_pickle=True)\n",
    "    print('-'*10)\n",
    "    plt.hist(x=durations,bins=100,color='yellow',edgecolor='black')\n",
    "    plt.xlabel('video durations')\n",
    "    plt.ylabel('frequency')\n",
    "    plt.title('Distribution of video length')\n",
    "    plt.show()\n",
    "    plt.clf()\n",
    "    plt.close()\n",
    "\n",
    "    print('-'*10)\n",
    "\n",
    "    abs_length=[]\n",
    "    rel_length=[]\n",
    "    for (dur,segs) in zip(durations,segments):\n",
    "        if type(dur)==type(np.array([0])):\n",
    "            dur=dur[0]\n",
    "\n",
    "        for seg in segs:\n",
    "            s,e=seg[0],seg[1]\n",
    "            seg_len=e-s\n",
    "            abs_length.append(seg_len)\n",
    "            rel_length.append(seg_len/dur)\n",
    "\n",
    "    print('-'*10)\n",
    "    plt.hist(x=abs_length,bins=100,color='steelblue',edgecolor='black')\n",
    "    plt.xlabel('segment absolute durations')\n",
    "    plt.ylabel('frequency')\n",
    "    plt.title('Distribution of segment absolute length')\n",
    "    plt.show()\n",
    "    plt.clf()\n",
    "    plt.close()\n",
    "\n",
    "    print('-'*10)\n",
    "\n",
    "    print('-'*10)\n",
    "    plt.hist(x=rel_length,bins=100,color='green',edgecolor='black')\n",
    "    plt.xlabel('segment relative durations')\n",
    "    plt.ylabel('frequency')\n",
    "    plt.title('Distribution of segment relative length')\n",
    "    plt.show()\n",
    "    plt.clf()\n",
    "    plt.close()\n",
    "\n",
    "    print('-'*10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% for ANT\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../ActivityNet1.2-Annotations/\n",
      "----------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n",
      "----------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n",
      "----------\n"
     ]
    },
    {
     "data": {
      "image/png": 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dKOktTYzNzMxarMrdR4dExP21voiIdzU4HjMza6MqxUfH9D9vACBpG0mnNC8kMzNrlypJYb/8RDIA+bbU/ZsWkZmZtU2VpNAjaUx/T346ecwg45uZWZeqUqdwHrBA0vdJDdS9n7VNX5uZ2QgyZFKIiC9LupP0FLKAL0TEFU2PzMzMWq5S20cRcTlweZNj6VoTJk+gb1kfAOMnjeeRpY+0OSIzs/UzZJ2CpHfl9yk/KekpSU9LeqoVwXWLvmV9MBuYTZEczMy6UZUrhS8D74yIe5sdjJmZtVeVu4/6nBDMzEaHKlcKiyRdCFwC9DdxQURc3KygzMysPaokha2A54C/Kw0LwEnBzGyEqXJL6lGtCMTMzNqvyt1Hr5S0QNJduX8XSZ9ufmhmZtZqVSqavwucDPwVitdszmxmUGZm1h5VksIWEXHTgGGrmhFMV+kBSfjNpGY2klRJCo9KejmpchlJBwPLmxpVN1hN8cCamdlIUeXuo+OBM4FXS1oGLAEOb2pUZmbWFlXuProf2FvSi4CNIuLp5odlZmbtUOUdzZ8Z0A9ARHy+STGZmVmbVCk+erbUvRlwAOBmL8zMRqAqxUenlfslfRWY37SIzMysbarcfTTQFsDLGh2ImZm1X5U6hTvJt6MCPUAv4PoEM7MRqEqdwgGl7lWkprT98JqZ2QhUJSkMvAV1q/JTvBHxeEMjMjOztqmSFG4FdgCeAAS8GHgwfxe4fmFdPWtv2/X7ms2s21SpaP456XWc4yLiJaTipIsjYlpEOCEMVGr+wu9rNrNuUyUpvCEiftbfExGXA3/TvJDMzKxdqhQfPZrfn3AeqbjocOCxpkZlZmZtUeVK4T2k21B/kj+9eZiZmY0wVZ5ofhz4sKQtI+KZFsRkZmZtUuV1nG+WdA9wT+7fVdIZFabbQdLVku6VdLekD+fh20q6UtLv899tStOcLGmxpPsk7bMB62VmZuuhSvHR14F9yPUIEXE78PYK060CToyI1wB7AMdL2hE4CVgQEdOBBbmf/N1MYCdgX+AMST3DW50OU3o724TJE9odjZnZkCq1fRQRDw0YtLrCNMsj4tbc/TSpZdVJwIHA3DzaXOCg3H0gcEFEPB8RS4DFwO5V4utYvj3VzLpMlaTwkKQ3AyFpU0n/yjCbzpY0FXgtcCMwPiKWQ0ocwHZ5tElAOfkszcMGzutYSYskLVq5cuVwwjAzsyFUSQrHkV7JOYl0oN4t91ciaUvgIuAjEfHUYKPWGBYvGBBxZkTMiIgZvb29VcMwM7MKBr37KJfpfyMiDlufmUvahJQQ/k9EXJwH90maGBHLJU0EVuThS0nNafSbDDy8Pss1M7P1M+iVQkSsBnolbTrcGSs1APQ94N6I+Frpq/nArNw9C7i0NHympDGSpgHTgZuGu1wzM1t/VZ5ofgD4jaT5lF7NOeBAX8tbgCOAOyXdlod9EjgVmCfpaFLDeofk+d0taR7p1tdVwPE5KZmZWYvUTQqSzo2II4BDSbelbgSMrTrjiLiW2vUEAHvVmWYOMKfqMszMrLEGu1J4vaQppLP5/2xRPKPOhMkTittV3dS2mbXbYEnh26Rms6cBi0rDhd+jMHx13rPQt6wvPcsA9M32swxm1l51k0JEnA6cLum/IuIDLYxpZOp/kA0f/M2scw35nIITgpnZ6FGpmYuRasLkCUXbRGZmNsqTQlGeP7vNgZiZdYhRnRTMzGxdTgpmZlZwUjAzs4KTgpmZFZwUzMys4KRgZmYFJ4VO4nc6m1mbVWk621rFTWGYWZv5SsHMzApOCmZmVnBSMDOzgusU2qH0bgUzs07iK4V26K9Qnt3eMMzMBnJSMDOzgpOCmZkVnBTMzKzgpGBmZgUnBTOzNim/ErhTmrbxLalmZm1SvBKYzmnaxlcKZmZWcFLoVG4x1czawEmhU5UecOtb1hmXlWa2rnKdwEg5gXOdgpnZeirXCUDn1AtsCF8pmJlZwUnBzKxRRkBdoJOCmVlWriPoGdMz/AN8nbrATnweoR4nhW5QOvtYrx+qmVVS1BHMhjV/WTPkAX595tvpN444KXSD0tlHvR+qmbVG+QDfCuuThDaE7z4yM2ulYb5ka507nGYPMmKD+Eqhm42ASi2zrlD6X9tgHf6SraYlBUlnS1oh6a7SsG0lXSnp9/nvNqXvTpa0WNJ9kvZpVlwjygio1DLrCutzIG9kImmhZl4pnAPsO2DYScCCiJgOLMj9SNoRmAnslKc5Q1JPE2Mb0bqpUsus3ZpWZt/hVwT1NC0pRMQ1wOMDBh8IzM3dc4GDSsMviIjnI2IJsBjYvVmxjUhdelZi1m6trjiuq0OKg1tdpzA+IpYD5L/b5eGTgIdK4y3Nw6yqLj0rMbOsQ9o765SK5lqnt1FzROlYSYskLVq5cmWTwzIzG11anRT6JE0EyH9X5OFLgR1K400GHq41g4g4MyJmRMSM3t7epgZrZjbatDopzAdm5e5ZwKWl4TMljZE0DZgO3NTi2Czz3UvWzer9fv27rqZpD69J+iGwJzBO0lLgs8CpwDxJRwMPAocARMTdkuYB9wCrgOMjYnWzYhtVSg/KjJ80nkeWPjLkJJ34ikCzqur9fv27rqZpSSEi3lPnq73qjD8HmNOseEat/sor/I9g1jWG+dRzI3VKRbO1wiC3vLW6fRWzlqh3q3an38LdxrsJnRRGk0Fueatyr3Yzympdzmu1VPldVPrt1Du4+hbuupwUrLJ6T0pXeYK63j+wn77ufs1I7FV+F/7tNIeTgrWE/4G722AvqPe+HVncdLY11nrc7WSdbyS+oN5q85WCrZ96FXUNelS/YWXK1jbeh93JSWG0Kh3U1+sOjCoVdRtwh8dIKFMe7Qe8dfbPI30ueuoSTgqjVfmgPrsFy6hiQ24TrNDCZKsP0t1ywGvJdilfQZYSxDoq7P+6t053+i2mXcRJwTrHhtwmWKHYqlsO0lU1qnim5dtlA24TrXvrtG8xbRgnBRucz8A6VrOL2PxA4+jkpGCDa8YZ2ChPNN1SATvoA42t3Iej/PfSak4K1nodfKk/2P349cardNAuHdjqnb2X51mlknbYGvlmr1buww7+vYxEfk7BusqEyRMaXu79gnnOXttZ7378Ybe4WWqYsN7BbZ1nAcrjDLNRw7rbyI0jWgVOCtb5BrYYOXvA3w1U92BcVSsf2KvQeuYGr4+Nai4+ss63Abe2tqQ8fkMe2BtueXm9bTHc+TSrnN7l/13PScFGnka+AL3ZCaZR5eXDnU+zyuld/t/1XHxkNhiXw9so46RgVlUb34Zl1ipOCjayNfJAXuEOoq7nxDfquU7BRjaXcQ+Pt9eo5ysFG518RmxWk68UbHTyGbFZTb5SMGskX4FYl/OVglkj+QrEupyTgpmZFZwUzMys4KRgZmYFJwUzMys4KZiZWcFJwczMCk4KZmZWcFIwM7OCk4KZmRWcFMzMrOCkYGZmBScFMzMrOCmYmVmh45KCpH0l3SdpsaST2h2Pmdlo0lFJQVIP8L+B/YAdgfdI2rG9UZmZjR4dlRSA3YHFEXF/RPwFuAA4sM0xmZmNGoqIdsdQkHQwsG9EHJP7jwDeGBEnlMY5Fjg2974KuG8DFjkOeHQDpu82o219wes8Goy29YUNX+cpEdFb64tOex1nrfcYrpO1IuJM4MyGLExaFBEzGjGvbjDa1he8zqPBaFtfaO46d1rx0VJgh1L/ZODhNsViZjbqdFpSuBmYLmmapE2BmcD8NsdkZjZqdFTxUUSsknQCcAXQA5wdEXc3cZENKYbqIqNtfcHrPBqMtvWFJq5zR1U0m5lZe3Va8ZGZmbWRk4KZmRVGfFIYqtkMJafn7++Q9Lp2xNlIFdb5sLyud0i6TtKu7Yizkao2jyLpDZJW52diulaV9ZW0p6TbJN0t6VetjrHRKvyut5b0U0m353U+qh1xNoqksyWtkHRXne+bc+yKiBH7IVVW/zfwMmBT4HZgxwHj7A9cTnpGYg/gxnbH3YJ1fjOwTe7ebzSsc2m8q4CfAQe3O+4m7+MXA/cAL83927U77has8yeBL+XuXuBxYNN2x74B6/x24HXAXXW+b8qxa6RfKVRpNuNA4AeR3AC8WNLEVgfaQEOuc0RcFxFP5N4bSM+DdLOqzaN8ELgIWNHK4Jqgyvq+F7g4Ih4EiIjRsM4BjJUkYEtSUljV2jAbJyKuIa1DPU05do30pDAJeKjUvzQPG+443WS463M06Wyjmw25zpImAf8AfLuFcTVLlX38SmAbSQsl3SLpfS2LrjmqrPO3gNeQHni9E/hwRKxpTXht0ZRjV0c9p9AEQzabUXGcblJ5fST9T1JSeGtTI2q+Kuv8DeATEbE6nUh2tSrruzHwemAvYHPgekk3RMTvmh1ck1RZ532A24C/BV4OXCnp1xHxVJNja5emHLtGelKo0mzGSGtao9L6SNoFOAvYLyIea1FszVJlnWcAF+SEMA7YX9KqiLikJRE2VtXf9aMR8SzwrKRrgF2Bbk0KVdb5KODUSAXuiyUtAV4N3NSaEFuuKceukV58VKXZjPnA+3JN/h7AkxGxvNWBNtCQ6yzppcDFwBFdfOZYNuQ6R8S0iJgaEVOBHwP/0qUJAar9ri8F3iZpY0lbAG8E7m1xnI1UZZ0fJF0ZIWk8qRXl+1saZWs15dg1oq8Uok6zGZKOy99/m3Qnyv7AYuA50tlG16q4zp8BXgKckc+cV0UXtzJZcZ1HjCrrGxH3Svo5cAewBjgrImre2tgNKu7jLwDnSLqTVLTyiYjo2ia1Jf0Q2BMYJ2kp8FlgE2juscvNXJiZWWGkFx+ZmdkwOCmYmVnBScHMzApOCmZmVnBSMDOzgpOCjRqSpkp6bwPmMeitnQOXI2mGpNM3ZLl1lrNQUkNuJZa0m6T9S/1/P1hrszZyOSnYaDKV1FDcoCT1NHI5EbEoIj60gfPcYJIGey5pN9I97wBExPyIOLXpQVnHcVKwhpH0Ikn/N7dnf5ekQ/Pw10v6VW6Y7Yr+lhzzuw3ukHS9pK/0n4FLOlLSJblt/CWSTpD0MUm/lXSDpG3zeC+X9PM8319LenUefk5uZ/46Sfdr7bsTTiU95XubpI8OiH1PSVdLOh+4U1JPjunmHOM/11jfqXm5t+bPm2stJ8/7MkkbSXpA0otL81gsabykXkkX5eXdLOktNZa3uaQLcjwXkto06v/umVL3wZLOKW2Lr0m6GviSpN3zdvlt/vuq/ITw54FDc8yH5n3wrTyPKZIW5OUuUHoivu52ljRR0jV5XndJelulH5B1hna3Ge7PyPkA/wh8t9S/NekJzOuA3jzsUNLTqAB3AW/O3aeS240HjiQ9pTmW1C7+k8Bx+buvAx/J3QuA6bn7jcBVufsc4Eekk54dSU0uQ3o69LI6se8JPAtMy/3HAp/O3WOARcA00lVAf5xbAJvl7unAolrLKfcD3wSOKsX8y9x9PvDW3P1S4N4aMX6stO12ITULPSP3P1Ma72DgnNK2uAzoyf1bARvn7r2Bi0rb/FuleRT9wE+BWbn7/cAlQ2znE4FP5e4eYGy7f5v+VP+M6GYurOXuBL4q6Uukg+CvJe0M7ExqsRLSQWJ5PlseGxHX5WnPBw4ozevqiHgaeFrSk6QDU/8ydpG0JellQT/S2lZPx5SmvyRSs8n3KLWDU8VNEbEkd/9dXk7/VcbWpAN/ua2oTYBvSdoNWE1qrnooF5KaGfk+qf2eC/PwvYEdS+uylaSxeRv0eztwOkBE3CHpjorr9aOIWF1aj7mSppNa1NykwvRvAt6Vu88Fvlz6rtZ2vhk4W9Im+fvbKsZpHcBJwRomIn4n6fWksukvSvoF8BPg7oh4U3lcSdsMMbvnS91rSv1rSL/bjYA/RsRuFaav2lb2swOm+WBEXFEeQdLUUu9HgT5S66MbAX+usIzrgVdI6gUOAk7JwzcC3hQRfxpi+nrt0pSHbzbgu/J6fYGUcP8hr8vCCjEPtqwXbOeIuEbS24F3AOdK+kpE/GA9lmNt4DoFaxhJ2wPPRcR5wFdJrxK8D+iV9KY8ziaSdor05renlVp3hHTWXFmkNvKXSDokz1ca+l3TT5OKpKq4AvhAPttF0islvWjAOFsDy/OZ8hGkq6BBlxMRQUqUXyMVEfU3W/4L4IT+8fLVx0DXAIfl73cmFSH165P0GkkbkV4mVM/WwLLcfWRp+GDb5jrW7p/DgGsHmT+SpgArIuK7wPdIvwPrEk4K1kj/A7hJ0m3Ap4BTIr068WBSJeftpJeg9FfIHg2cKel60lnmk8Nc3mHA0Xm+d1P7FZxldwCrlCrCPzrEuGeR3nF8q1IF+Hd44ZX1GcAsSTeQio76z8iHWs6FwOGsLToC+BAwI1fm3gMcV2O6/wK2zMVGH2fd9wScRKo7uAoYrPnkL5Ou4n7D2iQGcDWp+Oo25RsEBsR2VF7uEcCHB5k/pDqU2yT9llTP9M0hxrcO4lZSrW0kbRkRz+Tuk4CJETHUAcfMmsh1CtZO75B0Mul3+AfWLc4wszbwlYKZmRVcp2BmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlb4/4HRemAwGvpcAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n"
     ]
    }
   ],
   "source": [
    "data_pre='../ActivityNet1.2-Annotations/'\n",
    "print(data_pre)\n",
    "length_statics(data_pre)\n",
    "'''\n",
    "Conclusion:\n",
    "1. ActivityNet is long, and the lengths of the action instances are long too.\n",
    "2. This is suitable for anchor based model, but how to find out the proper one is an important problem\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% for THUMOS14\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../Thumos14reduced-Annotations/\n",
      "----------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n",
      "----------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n",
      "----------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n"
     ]
    }
   ],
   "source": [
    "data_pre='../Thumos14reduced-Annotations/'\n",
    "print(data_pre)\n",
    "length_statics(data_pre)\n",
    "'''\n",
    "Conclusions:\n",
    "1. Action instances of Thumos14 are short, below 0.2* video length.\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./Dataset/ActivityNet1.3-Annotations/\n",
      "----------\n"
     ]
    },
    {
     "data": {
      "image/png": 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x8TFt27Y1f/nLX8yECRPKXUl49uxZ86c//cl07NjR+Pn5mQYNGpjrrrvOTJkyxXz33XdWXXFxsXn22WdN27Ztjbe3twkNDTVjx441+/fv/9l9qOxqx+TkZDN48GDTpEkT4+3tba666iozePBgp59DZd+5itZ59OhRM3HiRNOoUSMTEBBg+vXrZz755BMjybzwwgtOn3/yySdNVFSU8fDwcBq/0u9sWb169TK9evX62X0FLpbDmJ+5RAcAgBqwevVqjRkzRh9//HG5qwGBuoSwBACocWvWrNHBgwfVoUMHeXh46JNPPtHixYvVqVMn69YCQF3FOUsAgBoXFBSktWvXat68eTp16pQiIyP1wAMPaN68ee7uGvCzmFkCAACwwa0DAAAAbBCWAAAAbBCWAAAAbHCCdxWVlJTo0KFDCgoKuqSb2AEAgNpjjNGJEycUFRXldANUVxCWqujQoUPlno4OAADqh/3795d7fmJVEZaqqPRW+/v371fDhg3d3BsAAFAVx48fV/PmzS/pkTmEpSoqPfTWsGFDwhIAAPXMpZxCwwneAAAANghLAAAANghLAAAANghLAAAANghLAAAANtwalv79739r6NChioqKksPh0N///nen5cYYxcXFKSoqSv7+/urdu7d2797tVFNYWKhp06YpNDRUgYGBGjZsmA4cOOBUk5eXp3Hjxik4OFjBwcEaN26cjh07VsN7BwAALgduDUunTp1Sx44d9dJLL1W4fNGiRYqPj9dLL72kHTt2KCIiQv369dOJEyesmtjYWK1fv15r167V9u3bdfLkSQ0ZMkTFxcVWzejRo7Vz505t3LhRGzdu1M6dOzVu3Lga3z8AAHAZMHWEJLN+/XrrfUlJiYmIiDALFy602s6cOWOCg4PNq6++aowx5tixY8bb29usXbvWqjl48KDx8PAwGzduNMYYs2fPHiPJfPLJJ1ZNamqqkWT+85//VLl/+fn5RpLJz8+/2F0EAAC1rDp+f9fZc5b27dun7Oxs9e/f32rz9fVVr169lJKSIklKS0vT2bNnnWqioqIUExNj1aSmpio4OFjdunWzam655RYFBwdbNRUpLCzU8ePHnV4AAODKU2fDUnZ2tiQpPDzcqT08PNxalp2dLR8fHzVu3Ni2JiwsrNz6w8LCrJqKLFiwwDrHKTg4mOfCAQBwhaqzYalU2duTG2N+9pblZWsqqv+59Tz55JPKz8+3Xvv373ex5wAA4HJQZ8NSRESEJJWb/cnJybFmmyIiIlRUVKS8vDzbmp9++qnc+g8fPlxu1upCvr6+1nPgeB4cAABXrjobllq3bq2IiAglJSVZbUVFRUpOTlb37t0lSV26dJG3t7dTTVZWlnbt2mXV3HrrrcrPz9dnn31m1Xz66afKz8+3agAAACrj5c6Nnzx5Uv/973+t9/v27dPOnTvVpEkTtWjRQrGxsZo/f77atGmjNm3aaP78+QoICNDo0aMlScHBwZo8ebJmzZqlkJAQNWnSRLNnz1aHDh3Ut29fSVJ0dLQGDhyoX/3qV3rttdckSb/+9a81ZMgQtWvXrvZ3GgAA1CtuDUuff/65br/9duv9zJkzJUkTJkzQihUrNGfOHJ0+fVpTp05VXl6eunXrpk2bNikoKMj6zPPPPy8vLy+NHDlSp0+fVp8+fbRixQp5enpaNW+++aYeffRR66q5YcOGVXpvp8tdZmamcnNzndpCQ0PVokULN/UIAIC6zWGMMe7uRH1w/PhxBQcHKz8/v96ev5SZmano6HYqKDjj1B4Q4Kf09L0EJgDAZac6fn/X2XOWUP1yc3NVUHBGiYlSWtr5V2KiVFBwptxsEwAAOM+th+HgHtHRUufO7u4FAAD1AzNLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANghLAAAANrzc3QFcvMzMTOXm5lrvQ0ND1aJFCzf2CACAyw9hqZ7KzMxUdHQ7FRScsdoCAvyUnr6XwAQAQDXiMFw9lZubq4KCM0pMlNLSpMREqaDgjNNMEwAAuHTMLNVz0dFS586Xvp709HTrzxzOAwDg/xCWrnBZWZKHhzR27FirjcN5AAD8Hw7DXeGOHZNKSsThPAAAKsHMEiRV3+E8AAAuN8wsAQAA2CAsAQAA2CAsAQAA2CAsAQAA2OAEb9SYso9jkbiHEwCg/iEs1RNlg8eFN5Gsiyp6HIvEPZwAAPUPYakeqCx41GUXPo4lOvp8W3q6NHbs+Xs4EZYAAPUFYakeqCh4/POf0u9+595+VQX3bwIA1HeEpXrkwuBRx4/CAQBw2eBqOAAAABuEJQAAABuEJQAAABucs4QKlb01AfdHAgBcqQhLcJKVJXl4SGPHjnVq5/5IAIArFWHpMnfhzSyrciPLY8ekkhJxfyQAAP4/wtJl7FJuZsn9kQAAOI8TvC9jF97MMi1NeuYZd/cIAID6h7B0BSidJWrd2t09AQCg/iEsAQAA2KjTYencuXP67W9/q9atW8vf319XX321/vCHP6ikpMSqMcYoLi5OUVFR8vf3V+/evbV7926n9RQWFmratGkKDQ1VYGCghg0bpgMHDtT27gAAgHqoToelZ599Vq+++qpeeuklpaena9GiRVq8eLGWLl1q1SxatEjx8fF66aWXtGPHDkVERKhfv346ceKEVRMbG6v169dr7dq12r59u06ePKkhQ4aouLjYHbsFAADqkTp9NVxqaqruvPNODR48WJLUqlUrrVmzRp9//rmk87NKS5Ys0VNPPaURI0ZIklauXKnw8HCtXr1aU6ZMUX5+vpYvX66//vWv6tu3ryQpMTFRzZs31+bNmzVgwAD37BwAAKgX6vTMUs+ePbVlyxZ9++23kqSvvvpK27dv16BBgyRJ+/btU3Z2tvr37299xtfXV7169VJKSookKS0tTWfPnnWqiYqKUkxMjFVTkcLCQh0/ftzpBQAArjx1embp8ccfV35+vq677jp5enqquLhYf/zjH3X//fdLkrKzsyVJ4eHhTp8LDw9XRkaGVePj46PGjRuXqyn9fEUWLFigp59+ujp3BwAA1EN1embprbfeUmJiolavXq0vvvhCK1eu1J/+9CetXLnSqc7hcDi9N8aUayvr52qefPJJ5efnW6/9+/df/I4AAIB6q07PLD322GN64oknNGrUKElShw4dlJGRoQULFmjChAmKiIiQdH72KDIy0vpcTk6ONdsUERGhoqIi5eXlOc0u5eTkqHv37pVu29fXV76+vjWxW/XWhY9L4cG6AIArRZ2eWSooKJCHh3MXPT09rVsHtG7dWhEREUpKSrKWFxUVKTk52QpCXbp0kbe3t1NNVlaWdu3aZRuW8H8ufLhuly5d1KVLF0VHt1NmZqa7uwYAQI2r0zNLQ4cO1R//+Ee1aNFC119/vb788kvFx8dr0qRJks4ffouNjdX8+fPVpk0btWnTRvPnz1dAQIBGjx4tSQoODtbkyZM1a9YshYSEqEmTJpo9e7Y6dOhgXR0He2UfrsuDdQEAV5I6HZaWLl2q3/3ud5o6dapycnIUFRWlKVOm6Pe//71VM2fOHJ0+fVpTp05VXl6eunXrpk2bNikoKMiqef755+Xl5aWRI0fq9OnT6tOnj1asWCFPT0937Fa9xcN1AQBXojodloKCgrRkyRItWbKk0hqHw6G4uDjFxcVVWuPn56elS5c63cwSAACgKur0OUsAAADuRlgCAACwUacPw8F1F17ef+GfAQDAxSEsXSYuvLwfAABUHw7DXSYuvLw/Le3865ln3N0rAADqP2aWLjMXXt7PUTgAAC4dM0sAAAA2CEsAAAA2CEsAAAA2CEsAAAA2CEsAAAA2CEsAAAA2uHUALlrZO4SHhoaqRYsWbuoNAAA1g7AEl1V2t/CAAD+lp+91S2DKzMxUbm6uUxvhDQBQHQhLddSFv/zr2jPeLrxbeHT0+bb0dGns2DPKzc2t9YCSmZmp6Oh2Kig449Tu5+erd955V5GRkVYbAQoA4CrCUh1U2S//uubCu4W7U25urgoKzjiFt48+kmbOLNSQIUOcat05+wUAqJ8IS3VQ2V/+//yn9LvfubtXNaM6D5+VfdRLXZr9AgDUX4SlOqz0l38dOwpXbSqbQavO2Z+6MvsFAKi/uHUA3ObCGbS0tPOvxESpoOBMudkmAADchZkluB2zPwCAuoywhFpXenVfXbvKDwCAihCWUGsquz9TRS4MUlzuDwBwJ8ISak3Z+zNVdJVfRYGKy/0BAO7ECd6odaXnKLVuXX7ZhYGKE74BAHUBM0uokzjpGwBQVzCzBAAAYIOwBAAAYIOwBAAAYINzllAvlL0nE7cTAADUFsIS6rTK7s3E7QQAALWFw3Co08reSoDbCQAAahszS6gXuJUAAMBdCEuot3jGHACgNhCWUK1qI8C48ow5AAAuFWEJ1aI2A0xVnjEHAEB14QRvVIuyJ2I/80zNb9PuGXMAAFQXwhKqFQEGAHC5ISwBAADYICwBAADYICwBAADY4Go4XNEyMzPL3Qmc584BAC5EWMIVKzMzU9HR7VRQcMapnefOAQAuxGE4XLFyc3NVUHCG584BAGwxs4QrHs+dAwDYYWYJAADABmEJAADABmEJAADABmEJAADABmEJAADABmEJAADABrcOwBUnPT3d6b8AANghLOGKkZUleXhIY8eOdXdXAAD1CIfhcMU4dkwqKZF1x+5nnnF3jwAA9QFhCVec0jt2t27t7p4AAOoDwhIAAIANwhIAAIANwhIAAIANroYDKnDhbQVCQ0PVokULN/YGAOBOhCXgAhXdXiAgwE/p6XsJTABwheIwHHCBsrcXSEyUCgrOKDc3191dAwC4SZ0PSwcPHtTYsWMVEhKigIAA3XjjjUpLS7OWG2MUFxenqKgo+fv7q3fv3tq9e7fTOgoLCzVt2jSFhoYqMDBQw4YN04EDB2p7V1CPlN5eIDra3T0BALiby2Fp3759NdGPCuXl5alHjx7y9vbWv/71L+3Zs0fPPfecGjVqZNUsWrRI8fHxeumll7Rjxw5FRESoX79+OnHihFUTGxur9evXa+3atdq+fbtOnjypIUOGqLi4uNb2BQAA1E8un7N07bXX6rbbbtPkyZN1zz33yM/Pryb6JUl69tln1bx5cyUkJFhtrVq1sv5sjNGSJUv01FNPacSIEZKklStXKjw8XKtXr9aUKVOUn5+v5cuX669//av69u0rSUpMTFTz5s21efNmDRgwoMb6DwAA6j+XZ5a++uorderUSbNmzVJERISmTJmizz77rCb6pg0bNqhr16669957FRYWpk6dOumNN96wlu/bt0/Z2dnq37+/1ebr66tevXopJSVFkpSWlqazZ8861URFRSkmJsaqqUhhYaGOHz/u9AIAAFcel8NSTEyM4uPjdfDgQSUkJCg7O1s9e/bU9ddfr/j4eB0+fLjaOvfDDz9o2bJlatOmjT744AM99NBDevTRR7Vq1SpJUnZ2tiQpPDzc6XPh4eHWsuzsbPn4+Khx48aV1lRkwYIFCg4Otl7Nmzevtv0CAAD1x0Wf4O3l5aXhw4fr7bff1rPPPqvvv/9es2fPVrNmzTR+/HhlZWVdcudKSkrUuXNnzZ8/X506ddKUKVP0q1/9SsuWLXOqczgcTu+NMeXayvq5mieffFL5+fnWa//+/Re/IwAAoN666LD0+eefa+rUqYqMjFR8fLxmz56t77//Xlu3btXBgwd15513XnLnIiMj1b59e6e26OhoZWZmSpIiIiIkqdwMUU5OjjXbFBERoaKiIuXl5VVaUxFfX181bNjQ6QUAAK48Loel+Ph4dejQQd27d9ehQ4e0atUqZWRkaN68eWrdurV69Oih1157TV988cUld65Hjx7au3evU9u3336rli1bSpJat26tiIgIJSUlWcuLioqUnJys7t27S5K6dOkib29vp5qsrCzt2rXLqgEAAKiMy1fDLVu2TJMmTdLEiROtmZ2yWrRooeXLl19y52bMmKHu3btr/vz5GjlypD777DO9/vrrev311yWdP/wWGxur+fPnq02bNmrTpo3mz5+vgIAAjR49WpIUHBysyZMna9asWQoJCVGTJk00e/ZsdejQwbo6DgAAoDIuh6XvvvvuZ2t8fHw0YcKEi+rQhW666SatX79eTz75pP7whz+odevWWrJkicaMGWPVzJkzR6dPn9bUqVOVl5enbt26adOmTQoKCrJqnn/+eXl5eWnkyJE6ffq0+vTpoxUrVsjT0/OS+wgAAC5vLoelhIQENWjQQPfee69T+9/+9jcVFBRUS0i60JAhQzRkyJBKlzscDsXFxSkuLq7SGj8/Py1dulRLly6t1r4BAIDLn8vnLC1cuFChoaHl2sPCwjR//vxq6RQAAEBd4XJYysjIUOvWrcu1t2zZ0rpKDQAA4HLh8mG4sLAwff31106PHZHO39k7JCSkuvoF1Cnp6elO70NDQ9WiRQs39QYAUJtcDkujRo3So48+qqCgIN12222SpOTkZE2fPl2jRo2q9g4C7pSVJXl4SGPHjnVqDwjwU3r6XgITAFwBXA5L8+bNU0ZGhvr06SMvr/MfLykp0fjx4zlnCZedY8ekkhIpMVGKjj7flp4ujR17Rrm5uYQlALgCuByWfHx89NZbb+mZZ57RV199JX9/f3Xo0MG6USRwOYqOljp3dncvAADu4HJYKtW2bVu1bdu2OvsCAABQ57gcloqLi7VixQpt2bJFOTk5KikpcVq+devWauscAACAu7kclqZPn64VK1Zo8ODBiomJkcPhqIl+AQAA1Akuh6W1a9fq7bff1qBBg2qiPwAAAHWKyzel9PHx0bXXXlsTfQEAAKhzXA5Ls2bN0gsvvCBjTE30BwAAoE5x+TDc9u3btW3bNv3rX//S9ddfL29vb6fl69atq7bOAQAAuJvLYalRo0YaPnx4TfQFAACgznE5LCUkJNREPwAAAOokl89ZkqRz585p8+bNeu2113TixAlJ0qFDh3Ty5Mlq7RwAAIC7uTyzlJGRoYEDByozM1OFhYXq16+fgoKCtGjRIp05c0avvvpqTfQTAADALVyeWZo+fbq6du2qvLw8+fv7W+3Dhw/Xli1bqrVzAAAA7nZRV8N9/PHH8vHxcWpv2bKlDh48WG0dAwAAqAtcnlkqKSlRcXFxufYDBw4oKCioWjoFAABQV7gclvr166clS5ZY7x0Oh06ePKm5c+fyCBQAAHDZcfkw3PPPP6/bb79d7du315kzZzR69Gh99913Cg0N1Zo1a2qijwAAAG7jcliKiorSzp07tWbNGn3xxRcqKSnR5MmTNWbMGKcTvgEAAC4HLoclSfL399ekSZM0adKk6u4PAABAneJyWFq1apXt8vHjx190ZwAAAOoal8PS9OnTnd6fPXtWBQUF8vHxUUBAAGEJAABcVly+Gi4vL8/pdfLkSe3du1c9e/bkBG8AAHDZuahnw5XVpk0bLVy4sNysEwAAQH1XLWFJkjw9PXXo0KHqWh0AAECd4PI5Sxs2bHB6b4xRVlaWXnrpJfXo0aPaOgYAAFAXuByW7rrrLqf3DodDTZs21R133KHnnnuuuvoFAABQJ7gclkpKSmqiHwAAAHVStZ2zBAAAcDlyeWZp5syZVa6Nj493dfUAAAB1isth6csvv9QXX3yhc+fOqV27dpKkb7/9Vp6enurcubNV53A4qq+XAAAAbuJyWBo6dKiCgoK0cuVKNW7cWNL5G1VOnDhRv/jFLzRr1qxq7yQAAIC7uHzO0nPPPacFCxZYQUmSGjdurHnz5nE1HAAAuOy4HJaOHz+un376qVx7Tk6OTpw4US2dAgAAqCtcDkvDhw/XxIkT9c477+jAgQM6cOCA3nnnHU2ePFkjRoyoiT4CAAC4jcvnLL366quaPXu2xo4dq7Nnz55fiZeXJk+erMWLF1d7BwEAANzJ5bAUEBCgV155RYsXL9b3338vY4yuvfZaBQYG1kT/AAAA3Oqib0qZlZWlrKwstW3bVoGBgTLGVGe/AAAA6gSXw9KRI0fUp08ftW3bVoMGDVJWVpYk6cEHH+S2AQAA4LLjcliaMWOGvL29lZmZqYCAAKv9vvvu08aNG6u1cwAAAO7m8jlLmzZt0gcffKBmzZo5tbdp00YZGRnV1jEAAIC6wOWZpVOnTjnNKJXKzc2Vr69vtXQKAACgrnA5LN12221atWqV9d7hcKikpESLFy/W7bffXq2dAwAAcDeXD8MtXrxYvXv31ueff66ioiLNmTNHu3fv1tGjR/Xxxx/XRB8BAADcxuWZpfbt2+vrr7/WzTffrH79+unUqVMaMWKEvvzyS11zzTU10UcAAAC3cWlm6ezZs+rfv79ee+01Pf300zXVJwAAgDrDpZklb29v7dq1Sw6Ho6b6AwAAUKe4fBhu/PjxWr58eU30BQAAoM5x+QTvoqIi/fnPf1ZSUpK6du1a7plw8fHx1dY5AAAAd6tSWPr6668VExMjDw8P7dq1S507d5Ykffvtt051HJ4DAACXmyqFpU6dOikrK0thYWHKyMjQjh07FBISUtN9AwAAcLsqnbPUqFEj7du3T5L0448/qqSkpEY7BQAAUFdUaWbp7rvvVq9evRQZGSmHw6GuXbvK09OzwtoffvihWjsIAADgTlUKS6+//rpGjBih//73v3r00Uf1q1/9SkFBQTXdNwAAALer8tVwAwcOlCSlpaVp+vTphCUAAHBFcPnWAQkJCTXRDwAAgDrJ5ZtSutOCBQvkcDgUGxtrtRljFBcXp6ioKPn7+6t3797avXu30+cKCws1bdo0hYaGKjAwUMOGDdOBAwdqufcAAKA+qjdhaceOHXr99dd1ww03OLUvWrRI8fHxeumll7Rjxw5FRESoX79+OnHihFUTGxur9evXa+3atdq+fbtOnjypIUOGqLi4uLZ3AwAA1DP1IiydPHlSY8aM0RtvvKHGjRtb7cYYLVmyRE899ZRGjBihmJgYrVy5UgUFBVq9erUkKT8/X8uXL9dzzz2nvn37qlOnTkpMTNQ333yjzZs3u2uXAABAPVEvwtLDDz+swYMHq2/fvk7t+/btU3Z2tvr372+1+fr6qlevXkpJSZF0/oT0s2fPOtVERUUpJibGqgEAAKiMyyd417a1a9cqLS1Nn3/+ebll2dnZkqTw8HCn9vDwcGVkZFg1Pj4+TjNSpTWln69IYWGhCgsLrffHjx+/6H0AAAD1V52eWdq/f7+mT5+uN998U35+fpXWlX0mnTHmZ59T93M1CxYsUHBwsPVq3ry5a50HAACXhTodltLS0pSTk6MuXbrIy8tLXl5eSk5O1osvvigvLy9rRqnsDFFOTo61LCIiQkVFRcrLy6u0piJPPvmk8vPzrdf+/furee8AAEB9UKfDUp8+ffTNN99o586d1qtr164aM2aMdu7cqauvvloRERFKSkqyPlNUVKTk5GR1795dktSlSxd5e3s71WRlZWnXrl1WTUV8fX3VsGFDpxcAALjy1OlzloKCghQTE+PUFhgYqJCQEKs9NjZW8+fPV5s2bdSmTRvNnz9fAQEBGj16tCQpODhYkydP1qxZsxQSEqImTZpo9uzZ6tChQ7kTxgEAAMqq02GpKubMmaPTp09r6tSpysvLU7du3bRp0yanx7E8//zz8vLy0siRI3X69Gn16dNHK1asqPRhwAAAAKXqXVj68MMPnd47HA7FxcUpLi6u0s/4+flp6dKlWrp0ac12DgAAXHbq9DlLAAAA7kZYAgAAsFHvDsNdjjIzM5Wbm2u9T09Pd2NvAADAhQhLbpaZmano6HYqKDjj7q4AAIAKcBjOzXJzc1VQcEaJiVJa2vnXM8+4u1cAAKAUM0t1RHS01Lnz+T9zFA4AgLqDmSUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbhCUAAAAbXu7uAFBfpaenW38ODQ1VixYt3NgbAEBNISwBLsrKkjw8pLFjx1ptAQF+Sk/fS2ACgMsQh+EAFx07JpWUSImJUlra+f8WFJxRbm6uu7sGAKgBzCwBFyk6Wurc2d29AADUNGaWAAAAbBCWAAAAbBCWAAAAbBCWAAAAbBCWAAAAbBCWAAAAbBCWAAAAbBCWAAAAbBCWAAAAbBCWAAAAbBCWAAAAbNTpsLRgwQLddNNNCgoKUlhYmO666y7t3bvXqcYYo7i4OEVFRcnf31+9e/fW7t27nWoKCws1bdo0hYaGKjAwUMOGDdOBAwdqc1cAAEA9VafDUnJysh5++GF98sknSkpK0rlz59S/f3+dOnXKqlm0aJHi4+P10ksvaceOHYqIiFC/fv104sQJqyY2Nlbr16/X2rVrtX37dp08eVJDhgxRcXGxO3YLAADUI17u7oCdjRs3Or1PSEhQWFiY0tLSdNttt8kYoyVLluipp57SiBEjJEkrV65UeHi4Vq9erSlTpig/P1/Lly/XX//6V/Xt21eSlJiYqObNm2vz5s0aMGBAre8XAACoP+r0zFJZ+fn5kqQmTZpIkvbt26fs7Gz179/fqvH19VWvXr2UkpIiSUpLS9PZs2edaqKiohQTE2PVVKSwsFDHjx93egEAgCtPvQlLxhjNnDlTPXv2VExMjCQpOztbkhQeHu5UGx4ebi3Lzs6Wj4+PGjduXGlNRRYsWKDg4GDr1bx58+rcHQAAUE/Um7D0yCOP6Ouvv9aaNWvKLXM4HE7vjTHl2sr6uZonn3xS+fn51mv//v0X13EAAFCv1YuwNG3aNG3YsEHbtm1Ts2bNrPaIiAhJKjdDlJOTY802RUREqKioSHl5eZXWVMTX11cNGzZ0egEAgCtPnQ5Lxhg98sgjWrdunbZu3arWrVs7LW/durUiIiKUlJRktRUVFSk5OVndu3eXJHXp0kXe3t5ONVlZWdq1a5dVAwAAUJk6fTXcww8/rNWrV+sf//iHgoKCrBmk4OBg+fv7y+FwKDY2VvPnz1ebNm3Upk0bzZ8/XwEBARo9erRVO3nyZM2aNUshISFq0qSJZs+erQ4dOlhXxwEAAFSmToelZcuWSZJ69+7t1J6QkKAHHnhAkjRnzhydPn1aU6dOVV5enrp166ZNmzYpKCjIqn/++efl5eWlkSNH6vTp0+rTp49WrFghT0/P2toVAABQT9XpsGSM+dkah8OhuLg4xcXFVVrj5+enpUuXaunSpdXYOwAAcCWo0+csAQAAuBthCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwAZhCQAAwMYVFZZeeeUVtW7dWn5+furSpYs++ugjd3cJAADUcVdMWHrrrbcUGxurp556Sl9++aV+8Ytf6Je//KUyMzPd3TUAAFCHebm7A7UlPj5ekydP1oMPPihJWrJkiT744AMtW7ZMCxYscHPvcCXIzMxUbm6uU1toaKhatGjhph4BAKriighLRUVFSktL0xNPPOHU3r9/f6WkpLipV7jcpKenO70vLCyUr6+vJCkrK0v33nu3Tp8udKrx8/PVO++8q8jIyHKfqWg9ldVUJXSVDWtV2VZF6y27nuoKfFUJkzUZON25X7WprvWnJlW0r1X5jqN21NTfuZpwRYSl3NxcFRcXKzw83Kk9PDxc2dnZFX6msLBQhYX/94stPz9fknT8+PFq7dvJkyclSWlp0v//o0p/55a2lX1Pjes1Nbnu1FTJ4ZDGjh2rC3l4SCUlTk167DGpefPzf969W3r99UINGTLE9jNl2yqq8fPz0apVidZ33MPDQyUXFP3000+aMGGsTp8ucmlbZddb0XrK1lS0/bLvy7ZVtN6y665KTVW2VdXxqa39utg+Xw79qc6aqu7rz33H3b0fV0pNRT8ff39f7diRpual/5OsJqW/t40xF78ScwU4ePCgkWRSUlKc2ufNm2fatWtX4Wfmzp1rJPHixYsXL168LoPX/v37LzpHXBEzS6GhofL09Cw3i5STk1NutqnUk08+qZkzZ1rvS0pKdPToUYWEhMjhcFRb344fP67mzZtr//79atiwYbWtFxVjvGsfY177GPPaxXjXPlfG3BijEydOKCoq6qK3d0WEJR8fH3Xp0kVJSUkaPny41Z6UlKQ777yzws/4+vqWO5+jUaNGNdbHhg0b8pesFjHetY8xr32Mee1ivGtfVcc8ODj4krZzRYQlSZo5c6bGjRunrl276tZbb9Xrr7+uzMxMPfTQQ+7uGgAAqMOumLB033336ciRI/rDH/6grKwsxcTE6J///Kdatmzp7q4BAIA67IoJS5I0depUTZ061d3dcOLr66u5c+eWO+SHmsF41z7GvPYx5rWL8a59tT3mDmMu5Vo6AACAy9sV87gTAACAi0FYAgAAsEFYAgAAsEFYAgAAsEFYcqNXXnlFrVu3lp+fn7p06aKPPvrI3V2qlxYsWKCbbrpJQUFBCgsL01133aW9e/c61RhjFBcXp6ioKPn7+6t3797avXu3U01hYaGmTZum0NBQBQYGatiwYTpw4EBt7kq9tGDBAjkcDsXGxlptjHf1O3jwoMaOHauQkBAFBAToxhtvVFpamrWcMa9e586d029/+1u1bt1a/v7+uvrqq/WHP/zB6flmjPml+fe//62hQ4cqKipKDodDf//7352WV9f45uXlady4cQoODlZwcLDGjRunY8eOudbZi35QCi7J2rVrjbe3t3njjTfMnj17zPTp001gYKDJyMhwd9fqnQEDBpiEhASza9cus3PnTjN48GDTokULc/LkSatm4cKFJigoyLz77rvmm2++Mffdd5+JjIw0x48ft2oeeughc9VVV5mkpCTzxRdfmNtvv9107NjRnDt3zh27VS989tlnplWrVuaGG24w06dPt9oZ7+p19OhR07JlS/PAAw+YTz/91Ozbt89s3rzZ/Pe//7VqGPPqNW/ePBMSEmLee+89s2/fPvO3v/3NNGjQwCxZssSqYcwvzT//+U/z1FNPmXfffddIMuvXr3daXl3jO3DgQBMTE2NSUlJMSkqKiYmJMUOGDHGpr4QlN7n55pvNQw895NR23XXXmSeeeMJNPbp85OTkGEkmOTnZGGNMSUmJiYiIMAsXLrRqzpw5Y4KDg82rr75qjDHm2LFjxtvb26xdu9aqOXjwoPHw8DAbN26s3R2oJ06cOGHatGljkpKSTK9evaywxHhXv8cff9z07Nmz0uWMefUbPHiwmTRpklPbiBEjzNixY40xjHl1KxuWqmt89+zZYySZTz75xKpJTU01ksx//vOfKvePw3BuUFRUpLS0NPXv39+pvX///kpJSXFTry4f+fn5kqQmTZpIkvbt26fs7Gyn8fb19VWvXr2s8U5LS9PZs2edaqKiohQTE8PPpBIPP/ywBg8erL59+zq1M97Vb8OGDeratavuvfdehYWFqVOnTnrjjTes5Yx59evZs6e2bNmib7/9VpL01Vdfafv27Ro0aJAkxrymVdf4pqamKjg4WN26dbNqbrnlFgUHB7v0M7ii7uBdV+Tm5qq4uFjh4eFO7eHh4crOznZTry4PxhjNnDlTPXv2VExMjCRZY1rReGdkZFg1Pj4+aty4cbkafiblrV27Vmlpafr888/LLWO8q98PP/ygZcuWaebMmfrNb36jzz77TI8++qh8fX01fvx4xrwGPP7448rPz9d1110nT09PFRcX649//KPuv/9+SXzPa1p1jW92drbCwsLKrT8sLMylnwFhyY0cDofTe2NMuTa45pFHHtHXX3+t7du3l1t2MePNz6S8/fv3a/r06dq0aZP8/PwqrWO8q09JSYm6du2q+fPnS5I6deqk3bt3a9myZRo/frxVx5hXn7feekuJiYlavXq1rr/+eu3cuVOxsbGKiorShAkTrDrGvGZVx/hWVO/qz4DDcG4QGhoqT0/Pcqk2JyenXIpG1U2bNk0bNmzQtm3b1KxZM6s9IiJCkmzHOyIiQkVFRcrLy6u0BuelpaUpJydHXbp0kZeXl7y8vJScnKwXX3xRXl5e1ngx3tUnMjJS7du3d2qLjo5WZmamJL7jNeGxxx7TE088oVGjRqlDhw4aN26cZsyYoQULFkhizGtadY1vRESEfvrpp3LrP3z4sEs/A8KSG/j4+KhLly5KSkpyak9KSlL37t3d1Kv6yxijRx55ROvWrdPWrVvVunVrp+WtW7dWRESE03gXFRUpOTnZGu8uXbrI29vbqSYrK0u7du3iZ1JGnz599M0332jnzp3Wq2vXrhozZox27typq6++mvGuZj169Ch3O4xvv/1WLVu2lMR3vCYUFBTIw8P5V6Snp6d16wDGvGZV1/jeeuutys/P12effWbVfPrpp8rPz3ftZ1D1c9VRnUpvHbB8+XKzZ88eExsbawIDA82PP/7o7q7VO//zP/9jgoODzYcffmiysrKsV0FBgVWzcOFCExwcbNatW2e++eYbc//991d4CWqzZs3M5s2bzRdffGHuuOMOLvGtoguvhjOG8a5un332mfHy8jJ//OMfzXfffWfefPNNExAQYBITE60axrx6TZgwwVx11VXWrQPWrVtnQkNDzZw5c6waxvzSnDhxwnz55Zfmyy+/NJJMfHy8+fLLL61b6FTX+A4cONDccMMNJjU11aSmppoOHTpw64D65OWXXzYtW7Y0Pj4+pnPnztal7nCNpApfCQkJVk1JSYmZO3euiYiIML6+vua2224z33zzjdN6Tp8+bR555BHTpEkT4+/vb4YMGWIyMzNreW/qp7JhifGufv/7v/9rYmJijK+vr7nuuuvM66+/7rScMa9ex48fN9OnTzctWrQwfn5+5uqrrzZPPfWUKSwstGoY80uzbdu2Cv/fPWHCBGNM9Y3vkSNHzJgxY0xQUJAJCgoyY8aMMXl5eS711WGMMRcxQwYAAHBF4JwlAAAAG4QlAAAAG4QlAAAAG4QlAAAAG4QlAAAAG4QlAAAAG4QlAAAAG4QlANUuLi5ON954o23NAw88oLvuuqtW+nOhFStWqFGjRrW+3Qu1atVKS5YscWsfAFQdYQlAtZs9e7a2bNni7m64XWXBbMeOHfr1r39d+x0CcFG83N0BAJefBg0aqEGDBu7uRo0pKiqSj4/PRX++adOm1dgbADWNmSUALnnttdd01VVXWU9fLzVs2DBNmDBBUvnDcMXFxZo5c6YaNWqkkJAQzZkzR2WftGSM0aJFi3T11VfL399fHTt21DvvvONUk5ycrJtvvlm+vr6KjIzUE088oXPnztn2d8WKFWrRooUCAgI0fPhwHTlyxGl5RYcDY2Nj1bt3b+t979699cgjj2jmzJkKDQ1Vv379JEnx8fHq0KGDAgMD1bx5c02dOlUnT56UJH344YeaOHGi8vPz5XA45HA4FBcXJ6n8YbjMzEzdeeedatCggRo2bKiRI0fqp59+spaXjudf//pXtWrVSsHBwRo1apROnDhh1bzzzjvq0KGD/P39FRISor59++rUqVO2YwOgaghLAFxy7733Kjc3V9u2bbPa8vLy9MEHH2jMmDEVfua5557TX/7yFy1fvlzbt2/X0aNHtX79eqea3/72t0pISNCyZcu0e/duzZgxQ2PHjlVycrIk6eDBgxo0aJBuuukmffXVV1q2bJmWL1+uefPmVdrXTz/9VJMmTdLUqVO1c+dO3X777bb1dlauXCkvLy99/PHHeu211yRJHh4eevHFF7Vr1y6tXLlSW7du1Zw5cyRJ3bt315IlS9SwYUNlZWUpKytLs2fPLrdeY4zuuusuHT16VMnJyUpKStL333+v++67z6nu+++/19///ne99957eu+995ScnKyFCxdKkrKysnT//fdr0qRJSk9P14cffqgRI0aUC6QALtLFPSsYwJVs2LBhZtKkSdb71157zURERJhz584ZY4yZO3eu6dixo7U8MjLSLFy40Hp/9uxZ06xZM3PnnXcaY4w5efKk8fPzMykpKU7bmTx5srn//vuNMcb85je/Me3atTMlJSXW8pdfftk0aNDAFBcXV9jP+++/3wwcONCp7b777jPBwcHW+wkTJlj9KDV9+nTTq1cv632vXr3MjTfeWPFgXODtt982ISEh1vuEhASnbZVq2bKlef75540xxmzatMl4eno6PSl99+7dRpL57LPPjDHnxzMgIMAcP37cqnnsscdMt27djDHGpKWlGUnmxx9//Nk+AnAdM0sAXDZmzBi9++67KiwslCS9+eabGjVqlDw9PcvV5ufnKysrS7feeqvV5uXlpa5du1rv9+zZozNnzqhfv37W+U4NGjTQqlWr9P3330uS0tPTdeutt8rhcFif69Gjh06ePKkDBw5U2M/Sz1yo7PuqurC/pbZt26Z+/frpqquuUlBQkMaPH68jR464dPgrPT1dzZs3V/Pmza229u3bq1GjRkpPT7faWrVqpaCgIOt9ZGSkcnJyJEkdO3ZUnz591KFDB91777164403lJeXdzG7CaAChCUALhs6dKhKSkr0/vvva//+/froo480duzYi15f6flP77//vnbu3Gm99uzZY523ZIxxCkqlbZLKtZddbsfDw6Nc3dmzZ8vVBQYGOr3PyMjQoEGDFBMTo3fffVdpaWl6+eWXK/18ZSrar4ravb29nZY7HA5r3Dw9PZWUlKR//etfat++vZYuXap27dpp3759Ve4HgMoRlgC4zN/fXyNGjNCbb76pNWvWqG3bturSpUuFtcHBwYqMjNQnn3xitZ07d05paWnW+/bt28vX11eZmZm69tprnV6lMy7t27dXSkqKU7BJSUlRUFCQrrrqqgq33b59e6ftSir3vmnTpsrKynJq27lz58+Oweeff65z587pueee0y233KK2bdvq0KFDTjU+Pj4qLi62XU/79u2VmZmp/fv3W2179uxRfn6+oqOjf7YfpRwOh3r06KGnn35aX375pXx8fMqdFwbg4nDrAAAXZcyYMRo6dKh27979s7NK06dP18KFC9WmTRtFR0crPj5ex44ds5YHBQVp9uzZmjFjhkpKStSzZ08dP35cKSkpatCggSZMmKCpU6dqyZIlmjZtmh555BHt3btXc+fO1cyZM+XhUfG/+x599FF1795dixYt0l133aVNmzZp48aNTjV33HGHFi9erFWrVunWW29VYmKidu3apU6dOtnu0zXXXKNz585p6dKlGjp0qD7++GO9+uqrTjWtWrXSyZMntWXLFnXs2FEBAQEKCAhwqunbt69uuOEGjRkzRkuWLNG5c+c0depU9erVq8JDfxX59NNPtWXLFvXv319hYWH69NNPdfjwYZfCFgAbbjxfCkA9du7cORMZGWkkme+//95pWdkTvM+ePWumT59uGjZsaBo1amRmzpxpxo8f73RidUlJiXnhhRdMu3btjLe3t2natKkZMGCASU5Otmo+/PBDc9NNNxkfHx8TERFhHn/8cXP27Fnbfi5fvtw0a9bM+Pv7m6FDh5o//elP5U66/v3vf2/Cw8NNcHCwmTFjhnnkkUfKneA9ffr0cuuOj483kZGRxt/f3wwYMMCsWrXKSDJ5eXlWzUMPPWRCQkKMJDN37lxjjPMJ3sYYk5GRYYYNG2YCAwNNUFCQuffee012dnal42mMMc8//7xp2bKlMcaYPXv2mAEDBpimTZsaX19f07ZtW7N06VLbcQFQdQ5juLYUAACgMpyzBAAAYIOwBAAAYIOwBAAAYIOwBAAAYIOwBAAAYIOwBAAAYIOwBAAAYIOwBAAAYIOwBAAAYIOwBAAAYIOwBAAAYIOwBAAAYOP/AVBRwDrxKQE0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n",
      "----------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n",
      "----------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nConclusion:\\n1. ActivityNet is long, and the lengths of the action instances are long too.\\n2. This is suitable for anchor based model, but how to find out the proper one is an important problem\\n'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pre='./Dataset/ActivityNet1.3-Annotations/'\n",
    "print(data_pre)\n",
    "length_statics(data_pre)\n",
    "'''\n",
    "Conclusion:\n",
    "1. ActivityNet is long, and the lengths of the action instances are long too.\n",
    "2. This is suitable for anchor based model, but how to find out the proper one is an important problem\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train 1 action number: 179, train 2 action number: 21\n",
      "the percent of 2 action is: 0.105\n",
      "test 1 action number: 181, test 2 action number: 29\n",
      "the percent of 2 action is: 0.1380952380952381\n",
      "all dataset the percent of 2 action: 0.12195121951219512\n",
      "the average number of action categories per video: 1.1219512195121952\n",
      "average action instance per video: 15.411622276029055\n"
     ]
    }
   ],
   "source": [
    "# statistic the average number of action category for different dataset\n",
    "import numpy as np\n",
    "\n",
    "labels_all = np.load('./Dataset/Thumos14reduced-Annotations/labels_all.npy',allow_pickle=True)\n",
    "labels = np.load('./Dataset/Thumos14reduced-Annotations/labels.npy',allow_pickle=True)\n",
    "subset = np.load('./Dataset/Thumos14reduced-Annotations/subset.npy',allow_pickle=True)\n",
    "segments = np.load('./Dataset/Thumos14reduced-Annotations/segments.npy',allow_pickle=True)\n",
    "train_idx = []\n",
    "test_idx = []\n",
    "for i,item in enumerate(subset):\n",
    "    if item.decode(\"utf-8\")=='validation':\n",
    "        train_idx.append(i)\n",
    "    elif item.decode(\"utf-8\") == 'test':\n",
    "        test_idx.append(i)\n",
    "\n",
    "\n",
    "train_action_classes = dict()\n",
    "test_action_classes = dict()\n",
    "for id in train_idx:\n",
    "    action_classes = len(labels_all[id])\n",
    "    if action_classes not in train_action_classes.keys():\n",
    "        train_action_classes[action_classes]=set()\n",
    "    train_action_classes[action_classes].add(id)\n",
    "for id in test_idx:\n",
    "    action_classes = len(labels_all[id])\n",
    "    if action_classes not in test_action_classes.keys():\n",
    "        test_action_classes[action_classes]=set()\n",
    "    test_action_classes[action_classes].add(id)\n",
    "\n",
    "\n",
    "print(f\"train 1 action number: {len(train_action_classes[1])}, train 2 action number: {len(train_action_classes[2])}\")\n",
    "print(f\"the percent of 2 action is: {len(train_action_classes[2])/(len(train_action_classes[1])+len(train_action_classes[2]))}\")\n",
    "\n",
    "print(f\"test 1 action number: {len(test_action_classes[1])}, test 2 action number: {len(test_action_classes[2])}\")\n",
    "print(f\"the percent of 2 action is: {len(test_action_classes[2])/(len(test_action_classes[1])+len(test_action_classes[2]))}\")\n",
    "\n",
    "print(f\"all dataset the percent of 2 action: {(len(train_action_classes[2])+len(test_action_classes[2]))/(len(train_action_classes[1])+len(train_action_classes[2])+len(test_action_classes[1])+len(test_action_classes[2]))}\")\n",
    "\n",
    "print(f\"the average number of action categories per video: {(179+181+21*2+29*2)/(179+21+181+29)}\")\n",
    "\n",
    "action_instance_number = 0\n",
    "for seg in segments:\n",
    "    action_instance_number+=len(seg)\n",
    "print(f\"average action instance per video: {action_instance_number/413}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train 1 action number: 9028, train 2 action number: 4\n",
      "the percent of 2 action is: 0.0004428697962798937\n",
      "test 1 action number: 4421, test 2 action number: 6\n",
      "the percent of 2 action is: 0.0013553196295459679\n",
      "all dataset the percent of 2 action: 0.0007429972509101717\n",
      "the average number of action categories per video: 1.00074299725091\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "labels_all = np.load('./Dataset/ActivityNet1.3-Annotations/labels_all.npy',allow_pickle=True)\n",
    "labels = np.load('./Dataset/ActivityNet1.3-Annotations/labels.npy',allow_pickle=True)\n",
    "subset = np.load('./Dataset/ActivityNet1.3-Annotations/subset.npy',allow_pickle=True)\n",
    "train_idx = []\n",
    "test_idx = []\n",
    "for i,item in enumerate(subset):\n",
    "    if item=='train':\n",
    "        train_idx.append(i)\n",
    "    elif item == 'val':\n",
    "        test_idx.append(i)\n",
    "\n",
    "train_action_classes = dict()\n",
    "test_action_classes = dict()\n",
    "for id in train_idx:\n",
    "    action_classes = len(labels_all[id])\n",
    "    if action_classes not in train_action_classes.keys():\n",
    "        train_action_classes[action_classes]=set()\n",
    "    train_action_classes[action_classes].add(id)\n",
    "for id in test_idx:\n",
    "    action_classes = len(labels_all[id])\n",
    "    if action_classes not in test_action_classes.keys():\n",
    "        test_action_classes[action_classes]=set()\n",
    "    test_action_classes[action_classes].add(id)\n",
    "\n",
    "\n",
    "print(f\"train 1 action number: {len(train_action_classes[1])}, train 2 action number: {len(train_action_classes[2])}\")\n",
    "print(f\"the percent of 2 action is: {len(train_action_classes[2])/(len(train_action_classes[1])+len(train_action_classes[2]))}\")\n",
    "\n",
    "print(f\"test 1 action number: {len(test_action_classes[1])}, test 2 action number: {len(test_action_classes[2])}\")\n",
    "print(f\"the percent of 2 action is: {len(test_action_classes[2])/(len(test_action_classes[1])+len(test_action_classes[2]))}\")\n",
    "\n",
    "print(f\"all dataset the percent of 2 action: {(len(train_action_classes[2])+len(test_action_classes[2]))/(len(train_action_classes[1])+len(train_action_classes[2])+len(test_action_classes[1])+len(test_action_classes[2]))}\")\n",
    "\n",
    "print(f\"the average number of action categories per video: {(9028+4421+4*2+6*2)/(9028+4421+4+6)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compting:\n",
    "- The Percentage of action Snippet exhibiting Overlapping subtle actions (PSO) \n",
    "- Average Relative Duration of action instances to the whole video (ARD)\n",
    "- The Average number of action Instances per Video (AIV)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{b'Applying sunscreen': 0, b'Archery': 1, b'Arm wrestling': 2, b'Assembling bicycle': 3, b'BMX': 4, b'Baking cookies': 5, b'Ballet': 6, b'Bathing dog': 7, b'Baton twirling': 8, b'Beach soccer': 9, b'Beer pong': 10, b'Belly dance': 11, b'Blow-drying hair': 12, b'Blowing leaves': 13, b'Braiding hair': 14, b'Breakdancing': 15, b'Brushing hair': 16, b'Brushing teeth': 17, b'Building sandcastles': 18, b'Bullfighting': 19, b'Bungee jumping': 20, b'Calf roping': 21, b'Camel ride': 22, b'Canoeing': 23, b'Capoeira': 24, b'Carving jack-o-lanterns': 25, b'Changing car wheel': 26, b'Cheerleading': 27, b'Chopping wood': 28, b'Clean and jerk': 29, b'Cleaning shoes': 30, b'Cleaning sink': 31, b'Cleaning windows': 32, b'Clipping cat claws': 33, b'Cricket': 34, b'Croquet': 35, b'Cumbia': 36, b'Curling': 37, b'Cutting the grass': 38, b'Decorating the Christmas tree': 39, b'Disc dog': 40, b'Discus throw': 41, b'Dodgeball': 42, b'Doing a powerbomb': 43, b'Doing crunches': 44, b'Doing fencing': 45, b'Doing karate': 46, b'Doing kickboxing': 47, b'Doing motocross': 48, b'Doing nails': 49, b'Doing step aerobics': 50, b'Drinking beer': 51, b'Drinking coffee': 52, b'Drum corps': 53, b'Elliptical trainer': 54, b'Fixing bicycle': 55, b'Fixing the roof': 56, b'Fun sliding down': 57, b'Futsal': 58, b'Gargling mouthwash': 59, b'Getting a haircut': 60, b'Getting a piercing': 61, b'Getting a tattoo': 62, b'Grooming dog': 63, b'Grooming horse': 64, b'Hammer throw': 65, b'Hand car wash': 66, b'Hand washing clothes': 67, b'Hanging wallpaper': 68, b'Having an ice cream': 69, b'High jump': 70, b'Hitting a pinata': 71, b'Hopscotch': 72, b'Horseback riding': 73, b'Hula hoop': 74, b'Hurling': 75, b'Ice fishing': 76, b'Installing carpet': 77, b'Ironing clothes': 78, b'Javelin throw': 79, b'Kayaking': 80, b'Kite flying': 81, b'Kneeling': 82, b'Knitting': 83, b'Laying tile': 84, b'Layup drill in basketball': 85, b'Long jump': 86, b'Longboarding': 87, b'Making a cake': 88, b'Making a lemonade': 89, b'Making a sandwich': 90, b'Making an omelette': 91, b'Mixing drinks': 92, b'Mooping floor': 93, b'Mowing the lawn': 94, b'Paintball': 95, b'Painting': 96, b'Painting fence': 97, b'Painting furniture': 98, b'Peeling potatoes': 99, b'Ping-pong': 100, b'Plastering': 101, b'Plataform diving': 102, b'Playing accordion': 103, b'Playing badminton': 104, b'Playing bagpipes': 105, b'Playing beach volleyball': 106, b'Playing blackjack': 107, b'Playing congas': 108, b'Playing drums': 109, b'Playing field hockey': 110, b'Playing flauta': 111, b'Playing guitarra': 112, b'Playing harmonica': 113, b'Playing ice hockey': 114, b'Playing kickball': 115, b'Playing lacrosse': 116, b'Playing piano': 117, b'Playing polo': 118, b'Playing pool': 119, b'Playing racquetball': 120, b'Playing rubik cube': 121, b'Playing saxophone': 122, b'Playing squash': 123, b'Playing ten pins': 124, b'Playing violin': 125, b'Playing water polo': 126, b'Pole vault': 127, b'Polishing forniture': 128, b'Polishing shoes': 129, b'Powerbocking': 130, b'Preparing pasta': 131, b'Preparing salad': 132, b'Putting in contact lenses': 133, b'Putting on makeup': 134, b'Putting on shoes': 135, b'Rafting': 136, b'Raking leaves': 137, b'Removing curlers': 138, b'Removing ice from car': 139, b'Riding bumper cars': 140, b'River tubing': 141, b'Rock climbing': 142, b'Rock-paper-scissors': 143, b'Rollerblading': 144, b'Roof shingle removal': 145, b'Rope skipping': 146, b'Running a marathon': 147, b'Sailing': 148, b'Scuba diving': 149, b'Sharpening knives': 150, b'Shaving': 151, b'Shaving legs': 152, b'Shot put': 153, b'Shoveling snow': 154, b'Shuffleboard': 155, b'Skateboarding': 156, b'Skiing': 157, b'Slacklining': 158, b'Smoking a cigarette': 159, b'Smoking hookah': 160, b'Snatch': 161, b'Snow tubing': 162, b'Snowboarding': 163, b'Spinning': 164, b'Spread mulch': 165, b'Springboard diving': 166, b'Starting a campfire': 167, b'Sumo': 168, b'Surfing': 169, b'Swimming': 170, b'Swinging at the playground': 171, b'Table soccer': 172, b'Tai chi': 173, b'Tango': 174, b'Tennis serve with ball bouncing': 175, b'Throwing darts': 176, b'Trimming branches or hedges': 177, b'Triple jump': 178, b'Tug of war': 179, b'Tumbling': 180, b'Using parallel bars': 181, b'Using the balance beam': 182, b'Using the monkey bar': 183, b'Using the pommel horse': 184, b'Using the rowing machine': 185, b'Using uneven bars': 186, b'Vacuuming floor': 187, b'Volleyball': 188, b'Wakeboarding': 189, b'Walking the dog': 190, b'Washing dishes': 191, b'Washing face': 192, b'Washing hands': 193, b'Waterskiing': 194, b'Waxing skis': 195, b'Welding': 196, b'Windsurfing': 197, b'Wrapping presents': 198, b'Zumba': 199}\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "dataset_name = \"ActivityNet1.3\" # MultiThumos, ActivityNet1.3, Thumos14reduced\n",
    "\n",
    "labels_all = np.load('./Dataset/'+ dataset_name + '-Annotations/labels_all.npy',allow_pickle=True) # video-level label\n",
    "labels = np.load('./Dataset/'+ dataset_name + '-Annotations/labels.npy',allow_pickle=True) # instance-level label\n",
    "subset = np.load('./Dataset/'+ dataset_name + '-Annotations/subset.npy',allow_pickle=True)\n",
    "segments = np.load('./Dataset/'+ dataset_name + '-Annotations/segments.npy', allow_pickle=True)\n",
    "duration = np.load('./Dataset/'+ dataset_name + '-Annotations/duration.npy', allow_pickle=True)\n",
    "classlist = np.load('./Dataset/'+ dataset_name + '-Annotations/classlist.npy', allow_pickle=True)\n",
    "videoname = np.load('./Dataset/'+ dataset_name + '-Annotations/videoname.npy', allow_pickle=True)\n",
    "\n",
    "if 'Thumos' in dataset_name:\n",
    "    classlist_dict = {classname.decode():idx for idx, classname in enumerate(classlist) }\n",
    "    videoname_dict = {vn.decode():idx for idx, vn in enumerate(videoname)}\n",
    "else: \n",
    "    classlist_dict = {classname:idx for idx, classname in enumerate(classlist) }\n",
    "    videoname_dict = {vn:idx for idx, vn in enumerate(videoname)}\n",
    "video_num = len(labels)\n",
    "print(classlist_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17999/17999 [01:00<00:00, 297.33it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The PSO of dataset ActivityNet1.3 is 0.00033619954789370406.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "PSO\n",
    "'''\n",
    "PSO_list = []\n",
    "category_num = 200\n",
    "video_length = 10000\n",
    "\n",
    "for idx in tqdm(range(video_num)):\n",
    "    # compute the percentage of single video    \n",
    "    ## convert the annotation to a 2D array TxC\n",
    "    matrix = np.zeros((video_length,category_num)) # [T,C]\n",
    "    video_second = duration[idx]\n",
    "\n",
    "    if len(segments[idx])==0: # don't process test set in activityNet1.3 \n",
    "        continue \n",
    "    for seg, label in zip(segments[idx],labels[idx]):\n",
    "        ## convert the timestamps to time index\n",
    "        start_idx, end_idx = int((seg[0]/video_second)*video_length), int((seg[1]/video_second)*video_length)\n",
    "        # print(f\"start_idx:{start_idx}, end_idx:{end_idx}\")\n",
    "        cls_idx = classlist_dict[label]\n",
    "        # print(f\"cls_idx:{cls_idx}, label:{label}\")\n",
    "        matrix[start_idx:end_idx,cls_idx] = 1\n",
    "        # print(matrix[:,cls_idx])\n",
    "    matrix_sum = matrix.sum(axis=1) # [T]\n",
    "    ## count the number of action snippet\n",
    "    # num_action_snippet = np.nonzero(matrix_sum)\n",
    "    num_action_snippet = np.count_nonzero(matrix_sum)\n",
    "    # print(f\"num_action_snippet:{num_action_snippet}\")\n",
    "\n",
    "    ## count the number of overlapping snippet\n",
    "    num_overlapping_snippet = len(np.where(matrix_sum>1)[0])\n",
    "    # print(f\"num_overlapping_snippet:{num_overlapping_snippet}\")\n",
    "\n",
    "    if num_action_snippet !=0:\n",
    "        pso = num_overlapping_snippet/num_action_snippet\n",
    "    else:\n",
    "        pso = 0\n",
    "    PSO_list.append(pso)\n",
    "PSO_list = np.array(PSO_list)\n",
    "# print(f\"The PSO_list of dataset {dataset_name} is {PSO_list}.\")\n",
    "PSO = PSO_list.mean()\n",
    "print(f\"The PSO of dataset {dataset_name} is {PSO}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 412/412 [00:00<00:00, 21678.44it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ARD of dataset Thumos14reduced is 0.05849929674785926.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "ARD\n",
    "'''\n",
    "ARD_list = []\n",
    "for idx in tqdm(range(video_num)):\n",
    "    video_ARD_list = []\n",
    "    video_second = duration[idx]\n",
    "    if len(segments[idx])==0: # don't process test set in activityNet1.3 \n",
    "        continue \n",
    "    for seg, label in zip(segments[idx],labels[idx]):\n",
    "        instance_duration = seg[1]-seg[0]\n",
    "        percentage = instance_duration/video_second\n",
    "        video_ARD_list.append(percentage)\n",
    "    video_ARD_list = np.array(video_ARD_list)\n",
    "    video_ARD = video_ARD_list.mean()\n",
    "    ARD_list.append(video_ARD)\n",
    "ARD_list = np.array(ARD_list)\n",
    "# print(f\"The ARD_list of dataset {dataset_name} is {ARD_list}.\")\n",
    "ARD = ARD_list.mean()\n",
    "print(f\"The ARD of dataset {dataset_name} is {ARD}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17999/17999 [00:00<00:00, 1199715.18it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The AIV of dataset ActivityNet1.3 is 1.5416449959135152.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "AIV\n",
    "'''\n",
    "AIV_list = []\n",
    "for idx in tqdm(range(video_num)):\n",
    "    if len(segments[idx])==0: # don't process test set in activityNet1.3 \n",
    "        continue \n",
    "    AIV_list.append(len(segments[idx]))\n",
    "AIV_list = np.array(AIV_list)\n",
    "# print(f\"The AIV_list of dataset {dataset_name} is {AIV_list}.\")\n",
    "AIV = AIV_list.mean()\n",
    "print(f\"The AIV of dataset {dataset_name} is {AIV}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "video_second:[528.7153514]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the information for specify video\n",
    "import seaborn as sns\n",
    "idx = videoname_dict['video_test_0001495']\n",
    "video_second = duration[idx]\n",
    "print(f\"video_second:{video_second}\")\n",
    "\n",
    "# print(segments[idx])\n",
    "# print(labels[idx])\n",
    "\n",
    "matrix = np.zeros((1000,65)) # [T,C]\n",
    "\n",
    "for seg, label in zip(segments[idx],labels[idx]):\n",
    "    start_idx, end_idx = int((seg[0]/video_second)*video_length), int((seg[1]/video_second)*video_length)\n",
    "    cls_idx = classlist_dict[label]\n",
    "    matrix[start_idx:end_idx,cls_idx] = 1\n",
    "sns.heatmap(matrix.transpose(1,0),cmap='Reds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'LongJump', b'Run',b'Jump'\n",
      "['OneRaisedArmCelebrate', 'Sit', 'Walk', 'Jump', 'BodyContract', 'FistPump', 'ClapHands', 'Throw', 'StandUp', 'Run', 'LongJump']\n"
     ]
    }
   ],
   "source": [
    "print(f\"{classlist[13]}, {classlist[24]},{classlist[25]}\")\n",
    "print(labels_all[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17999/17999 [00:00<00:00, 817974.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of long duraction videos:860\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# statistic the video in which the action duraction all exceed 3 minutes, save as a list\n",
    "long_vn_list = []\n",
    "for idx in tqdm(range(video_num)):\n",
    "    if subset[idx] != \"val\": # only choose the subset of \"val\"\n",
    "        continue \n",
    "    flag = True\n",
    "    for seg, label in zip(segments[idx],labels[idx]):\n",
    "        instance_duration = seg[1]-seg[0]\n",
    "        if instance_duration<120: # 这里写的是排除的视频要去\n",
    "            flag = False\n",
    "            break\n",
    "    if flag:\n",
    "        long_vn_list.append(videoname[idx])\n",
    "print(f\"number of long duraction videos:{len(long_vn_list)}\")\n",
    "np.save(\"./long_vn_list.npy\",long_vn_list)\n",
    "with open(\"./long_vn_list.txt\",\"w\") as f:\n",
    "    for line in long_vn_list:\n",
    "        f.write(line+'\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17999/17999 [00:00<00:00, 947087.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of sparse action videos:3340\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# statistic the video that only contains one action instance, save as a list\n",
    "sparse_vn_list = []\n",
    "for idx in tqdm(range(video_num)):\n",
    "    if subset[idx] != \"val\": # only choose the subset of \"val\"\n",
    "        continue \n",
    "\n",
    "    if len(segments[idx])==1:\n",
    "        sparse_vn_list.append(videoname[idx])\n",
    "    # if len(segments[idx])==1:\n",
    "    #     instance_duration = segments[idx][0][1]-segments[idx][0][0]\n",
    "    #     if instance_duration<200:\n",
    "    #         sparse_vn_list.append(videoname[idx])\n",
    "print(f\"number of sparse action videos:{len(sparse_vn_list)}\")\n",
    "np.save(\"./sparse_vn_list.npy\",sparse_vn_list)\n",
    "with open(\"./sparse_vn_list.txt\",\"w\") as f:\n",
    "    for line in sparse_vn_list:\n",
    "        f.write(line+'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "# 示例数据\n",
    "x = [1.0,1.0,0.5,0.5,0.1,0.1,1.5,0.5,0.1,1.5,0.1,0.5,1.0]\n",
    "y = [0.1,0.5,0.1,1.0,0.5,1.0,1.0,0.1,1.5,0.1,0.5,1.5,1.0]\n",
    "z = [0.5,0.1,1.0,0.1,1.0,0.5,0.5,1.5,1.0,0.5,1.5,0.1,1.0]\n",
    "v = [48.2,46.9,48.0,46.2,46.6,45.8,11.1,11.1,11.1,11.1,11.1,11.1,48.6]\n",
    "# 创建 3D 图\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# 绘制 3D 散点图\n",
    "ax.scatter(x, y, z, c=v, cmap='viridis')\n",
    "\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')\n",
    "ax.set_zlabel('z')\n",
    "ax.set_title('v')\n",
    "\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Torch",
   "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": "3.7.12"
  },
  "vscode": {
   "interpreter": {
    "hash": "833de89dedaa4e45b6cb498e61e87b478122704d639ae44ceaef6e0b9dd856c7"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}