csv_to_concat_csv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder_path = \"./Dataset-copy/\"\n",
    "file_number = [0,1,2,3,4]\n",
    "file_number = list(map(str, file_number))\n",
    "# file_path = str(folder_path) + 'fold_' + str(i) + 'data.txt'\n",
    "\n",
    "csv_files = []\n",
    "for i in file_number:\n",
    "    file_path = str(folder_path) + 'fold_' + str(i) + '_data.txt'\n",
    "    csv_files.append(pd.read_csv(file_path, sep=\"\\t\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "concat_csv = pd.concat(csv_files)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "concat_csv.to_csv(\"train_faces_concatenated_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15554, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat_csv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3816, 12)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = str(folder_path) + 'fold_' + str(4) + '_data.txt'\n",
    "pd.read_csv(file_path, sep=\"\\t\").shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "concat_csv['path'] = concat_csv.apply(lambda x: folder_path+\"aligned/\"+x['user_id']+\"/landmark_aligned_face.%s.%s\"\n",
    "                                                                      %(x['face_id'], x['original_image']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>original_image</th>\n",
       "      <th>face_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>dx</th>\n",
       "      <th>dy</th>\n",
       "      <th>tilt_ang</th>\n",
       "      <th>fiducial_yaw_angle</th>\n",
       "      <th>fiducial_score</th>\n",
       "      <th>path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30601258@N03</td>\n",
       "      <td>10399646885_67c7d20df9_o.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>(25, 32)</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>414</td>\n",
       "      <td>1086</td>\n",
       "      <td>1383</td>\n",
       "      <td>-115</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>./Dataset-copy/aligned/30601258@N03/landmark_a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30601258@N03</td>\n",
       "      <td>10424815813_e94629b1ec_o.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>(25, 32)</td>\n",
       "      <td>m</td>\n",
       "      <td>301</td>\n",
       "      <td>105</td>\n",
       "      <td>640</td>\n",
       "      <td>641</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>./Dataset-copy/aligned/30601258@N03/landmark_a...</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>30601258@N03</td>\n",
       "      <td>10437979845_5985be4b26_o.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>(25, 32)</td>\n",
       "      <td>f</td>\n",
       "      <td>2395</td>\n",
       "      <td>876</td>\n",
       "      <td>771</td>\n",
       "      <td>771</td>\n",
       "      <td>175</td>\n",
       "      <td>-30</td>\n",
       "      <td>74</td>\n",
       "      <td>./Dataset-copy/aligned/30601258@N03/landmark_a...</td>\n",
       "    </tr>\n",
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       "      <td>30601258@N03</td>\n",
       "      <td>10437979845_5985be4b26_o.jpg</td>\n",
       "      <td>3</td>\n",
       "      <td>(25, 32)</td>\n",
       "      <td>m</td>\n",
       "      <td>752</td>\n",
       "      <td>1255</td>\n",
       "      <td>484</td>\n",
       "      <td>485</td>\n",
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       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>./Dataset-copy/aligned/30601258@N03/landmark_a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30601258@N03</td>\n",
       "      <td>11816644924_075c3d8d59_o.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>(25, 32)</td>\n",
       "      <td>m</td>\n",
       "      <td>175</td>\n",
       "      <td>80</td>\n",
       "      <td>769</td>\n",
       "      <td>768</td>\n",
       "      <td>-75</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>./Dataset-copy/aligned/30601258@N03/landmark_a...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id                original_image  face_id       age gender     x  \\\n",
       "0  30601258@N03  10399646885_67c7d20df9_o.jpg        1  (25, 32)      f     0   \n",
       "1  30601258@N03  10424815813_e94629b1ec_o.jpg        2  (25, 32)      m   301   \n",
       "2  30601258@N03  10437979845_5985be4b26_o.jpg        1  (25, 32)      f  2395   \n",
       "3  30601258@N03  10437979845_5985be4b26_o.jpg        3  (25, 32)      m   752   \n",
       "4  30601258@N03  11816644924_075c3d8d59_o.jpg        2  (25, 32)      m   175   \n",
       "\n",
       "      y    dx    dy  tilt_ang  fiducial_yaw_angle  fiducial_score  \\\n",
       "0   414  1086  1383      -115                  30              17   \n",
       "1   105   640   641         0                   0              94   \n",
       "2   876   771   771       175                 -30              74   \n",
       "3  1255   484   485       180                   0              47   \n",
       "4    80   769   768       -75                   0              34   \n",
       "\n",
       "                                                path  \n",
       "0  ./Dataset-copy/aligned/30601258@N03/landmark_a...  \n",
       "1  ./Dataset-copy/aligned/30601258@N03/landmark_a...  \n",
       "2  ./Dataset-copy/aligned/30601258@N03/landmark_a...  \n",
       "3  ./Dataset-copy/aligned/30601258@N03/landmark_a...  \n",
       "4  ./Dataset-copy/aligned/30601258@N03/landmark_a...  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat_csv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "concat_csv.to_csv(\"test_faces_concacted_data_with_path.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = str(folder_path) + 'fold_' + str(4) + '_data.txt'\n",
    "test_csv = pd.read_csv(file_path, sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_csv.to_csv(\"test_faces_concatenated_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_df(df):\n",
    "\n",
    "    dict_age = {'(0, 2)' : 0,\n",
    "                '(4, 6)' : 1,\n",
    "                '(8, 12)' : 2,\n",
    "                '(15, 20)' : 3,\n",
    "                '(25, 32)' : 4,\n",
    "                '(38, 43)' : 5,\n",
    "                '(48, 53)' : 6,\n",
    "                '(60, 100)' : 7}\n",
    "    \n",
    "    df['f'] = df.age.apply(lambda x: int(x in dict_age))\n",
    "    df = df[df.f == 1]\n",
    "    return df\n",
    "train_df = filter_df(pd.read_csv(\"train_faces_concactenated_data_with_path.csv\"))\n",
    "test_df = filter_df(pd.read_csv(\"test_faces_concactenated_data_with_path.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.to_csv(\"train_faces_concactenated_data_with_path_with_age_range.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df.to_csv(\"test_faces_concactenated_data_with_path_with_age_range.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = pd.read_csv(\"train_faces_concactenated_data_with_path_with_age_range.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13717, 16)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "857.3125"
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     "execution_count": 8,
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    "13717/16"
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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    {
     "data": {
      "text/plain": [
       "462.5"
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     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "3700/8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "test_images = np.array([read_and_resize(file_path) for file_path in test_df.path.values])\n",
    "test_labels = np.array([int(g == \"m\") for g in test_df.gender.values])"
   ]
  }
 ],
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