data-preprocessing.ipynb

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  {
   "cell_type": "code",
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   "metadata": {
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   "source": [
    "import os\n",
    "from fnmatch import fnmatch\n",
    "import pandas as pd\n",
    "import cv2\n",
    "\n",
    "def convert_bytes(num):\n",
    "    \"\"\"\n",
    "    this function will convert bytes to MB.... GB... etc\n",
    "    \"\"\"\n",
    "    for x in ['bytes', 'KB', 'MB', 'GB', 'TB']:\n",
    "        if num < 1024.0:\n",
    "            return \"%3.1f %s\" % (num, x)\n",
    "        num /= 1024.0\n",
    "\n",
    "\n",
    "def file_size(file_path):\n",
    "    \"\"\"\n",
    "    this function will return the file size\n",
    "    \"\"\"\n",
    "    if os.path.isfile(file_path):\n",
    "        file_info = os.stat(file_path)\n",
    "        #return convert_bytes(file_info.st_size)\n",
    "        return file_info.st_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "root = '/home/mayank/Desktop/gender-bias-detection/official-trailers/'\n",
    "pattern = \"*.mp4\"\n",
    "\n",
    "columns = ['path', 'filename','movie-name', 'year-of-release', 'size', 'trailer', 'movie']\n",
    "\n",
    "df = pd.DataFrame(columns=columns)\n",
    "\n",
    "for path, subdirs, files in os.walk(root):\n",
    "    for name in files:\n",
    "        if fnmatch(name, pattern):\n",
    "            \n",
    "            # filename = os.path.join(path, name)\n",
    "            # print('filename', filename)\n",
    "            # print('filename type', type(filename))\n",
    "            # print('path', path)\n",
    "            # print('path type', type(path))\n",
    "            # print('files', files)\n",
    "            # print('files type', type(files))\n",
    "            # print('files[0]', files[0])\n",
    "            # break\n",
    "            \n",
    "            p = os.path.join(path, name)\n",
    "            filename = files[0]\n",
    "            year = filename.split('-')[0]\n",
    "            movie_name = filename.split('-')[1]\n",
    "            movie_name = movie_name.split('.')[0]\n",
    "            size = file_size(p)\n",
    "            if (size < 5e+7) :\n",
    "                trailer = 1\n",
    "                movie = 0\n",
    "            else :\n",
    "                trailer = 0\n",
    "                movie = 1\n",
    "                 \n",
    "            df.loc[df.shape[0]] = [p, filename, movie_name, year, size, trailer, movie] \n",
    "           \n",
    "            \n",
    "df.head()\n",
    "\n",
    "df.to_csv('/home/mayank/Desktop/gender-bias-detection/trailers_info.csv')"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "('Movie', '2009-all_the_best:_fun_begins')\n",
      "('count', 120)\n",
      "('Movie', '2009-all_the_best:_fun_begins')\n",
      "('new_frames_folder_path', '/home/mayank/Desktop/gender-bias-detection/trailer-frames/2017-jagga_jasoos/')\n",
      "('row0', '/home/mayank/Desktop/gender-bias-detection/official-trailers/2017-jagga_jasoos/2017-jagga_jasoos.mp4')\n",
      "('count', 0)\n",
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      "('Movie', '2017-jagga_jasoos')\n",
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      "('Movie', '2017-jagga_jasoos')\n",
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      "('Movie', '2017-jagga_jasoos')\n",
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      "('Movie', '2017-jagga_jasoos')\n",
      "('count', 48)\n",
      "('Movie', '2017-jagga_jasoos')\n",
      "('count', 49)\n",
      "('Movie', '2017-jagga_jasoos')\n",
      "('count', 50)\n",
      "('Movie', '2017-jagga_jasoos')\n",
      "('count', 51)\n",
      "('Movie', '2017-jagga_jasoos')\n",
      "('count', 52)\n",
      "('Movie', '2017-jagga_jasoos')\n",
      "('count', 53)\n",
      "('Movie', '2017-jagga_jasoos')\n"
     ]
    }
   ],
   "source": [
    "base_path = '/home/mayank/Desktop/gender-bias-detection/trailer-frames/'\n",
    "\n",
    "frames_processed = 0 \n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    if (row[5] == 1):\n",
    "        #print('index', index)\n",
    "        #print('row', row[1].split('.')[0])\n",
    "        folder_path = row[1].split('.')[0]\n",
    "        new_frames_folder_path = base_path + folder_path + \"/\"\n",
    "        \n",
    "        print('new_frames_folder_path', new_frames_folder_path)\n",
    "    \n",
    "        if not os.path.exists(new_frames_folder_path):\n",
    "            os.makedirs(new_frames_folder_path)\n",
    "        \n",
    "        print('row0', row[0])\n",
    "        vidcap = cv2.VideoCapture(row[0])\n",
    "        success,image = vidcap.read()\n",
    "        count = 0\n",
    "        success = True\n",
    "        while success:\n",
    "            success,image = vidcap.read()\n",
    "  \n",
    "            if success :\n",
    "                #print ('Read a new frame: ', success)\n",
    "                frames_processed = frames_processed + 1\n",
    "                if (frames_processed % 25 == 0):\n",
    "                    cv2.imwrite(new_frames_folder_path + \"frame%d.jpg\" % count, image)     # save frame as JPEG file\n",
    "                    print('count', count)\n",
    "                    print('Movie', row[1].split('.')[0])\n",
    "        \n",
    "                    count += 1"
   ]
  }
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