INCLab_fixPositionWithGrid.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fd303709",
   "metadata": {},
   "source": [
    "# fixing position by Grid Table\n",
    "- result_name = \"csv file that only interpolated\"\n",
    "- df_result_raw = \"csv file that done by Grid Table\"\n",
    "- steps = how big cells are\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1bfef28a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "date =\"0507\" #0611 0514 0507\n",
    "#result_name = \"Result-0709-#2_enter_10min_late-Fin.csv\"\n",
    "#result_name = \"Result-0604-#10-21120_25179-Fin.csv\"\n",
    "result_name =\"Result-\"+date+\"-Fin.csv\"\n",
    "df_result_raw = \"withGrid_\"+date+\".csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5d3e2ee3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(result_name)\n",
    "\n",
    "# pd.options.display.max_rows = 60\n",
    "# pd.options.display.max_columns = 20\n",
    "df_x=df[['1_X', '2_X',  '3_X', '4_X', '5_X',  '6_X', '7_X', '8_X', \n",
    "       '9_X', '10_X', '11_X',  '12_X', '13_X', '14_X', '15_X', '16_X']]\n",
    "df_y=df[[ '1_Y',  '2_Y',  '3_Y', '4_Y', '5_Y',  '6_Y',  '7_Y', '8_Y',\n",
    "       '9_Y','10_Y', '11_Y',  '12_Y', '13_Y', '14_Y', '15_Y',  '16_Y']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "967b4308",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1_X\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\82108\\AppData\\Local\\Temp/ipykernel_4940/1898572540.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_x[i][index]=c\n"
     ]
    }
   ],
   "source": [
    "#Griding X position\n",
    "\n",
    "\n",
    "#1920×1080\n",
    "steps=2\n",
    "x_grid =np.arange(0, 1920, step=steps)\n",
    "y_grid =np.arange(0, 1080, step=steps)\n",
    "# 이전 프레임의 값을 고정해두고, 그 값이 속해있던 그리드를 벗어나면 그 값으로 다시 초기화 하는 방식.\n",
    "for i in df_x.columns:\n",
    "    c=0\n",
    "    g=0\n",
    "    print(i)\n",
    "    for index in range(df[\"1_X\"].size): \n",
    "        if(index==0):\n",
    "            for grid in x_grid:\n",
    "                if (df_x[i][index]>=grid and df_x[i][index]<(grid+steps)):\n",
    "                    c=df_x[i][index]\n",
    "                    g=grid\n",
    "                    break\n",
    "        else:\n",
    "            for grid in x_grid:\n",
    "                if (df_x[i][index]>=grid and df_x[i][index]<(grid+steps)):\n",
    "                    if (g==grid) :\n",
    "                        df_x[i][index]=c\n",
    "                        break\n",
    "                    else :\n",
    "                        c=df_x[i][index]\n",
    "                        g=grid\n",
    "                        break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dbcfb8c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1_Y\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\82108\\AppData\\Local\\Temp/ipykernel_2160/1009793327.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_y[i][index]=c\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2_Y\n",
      "3_Y\n",
      "4_Y\n",
      "5_Y\n",
      "6_Y\n",
      "7_Y\n",
      "8_Y\n",
      "9_Y\n",
      "10_Y\n",
      "11_Y\n",
      "12_Y\n",
      "13_Y\n",
      "14_Y\n",
      "15_Y\n",
      "16_Y\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Griding Y position\n",
    "\n",
    "for i in df_y.columns:\n",
    "    c=0\n",
    "    g=0\n",
    "    print(i)\n",
    "    for index in range(df[\"1_Y\"].size):\n",
    "        if(index==0):\n",
    "            for grid in y_grid:\n",
    "                if (df_y[i][index]>=grid and df_y[i][index]<(grid+steps)):\n",
    "                    c=df_y[i][index]\n",
    "                    g=grid\n",
    "                    break\n",
    "        else:\n",
    "            for grid in y_grid:\n",
    "                if (df_y[i][index]>=grid and df_y[i][index]<(grid+steps)):\n",
    "                    if (g==grid) :\n",
    "                        df_y[i][index]=c\n",
    "                        break\n",
    "                    else :\n",
    "                        c=df_y[i][index]\n",
    "                        g=grid\n",
    "                        break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c8f382b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       frameNum     1_X    1_Y     2_X    2_Y          3_X         3_Y  \\\n",
      "0            76  1127.0  810.0  1233.0  383.0  1301.000000  530.000000   \n",
      "1            77  1127.0  810.0  1233.0  385.0  1303.000000  532.000000   \n",
      "2            78  1127.0  810.0  1242.0  405.0  1303.000000  531.000000   \n",
      "3            79  1127.0  810.0  1239.0  400.0  1305.000000  534.000000   \n",
      "4            80  1127.0  810.0  1233.0  382.0  1307.000000  540.000000   \n",
      "...         ...     ...    ...     ...    ...          ...         ...   \n",
      "62273     62349  1380.0  539.0   986.0  717.0  1302.867821  740.535805   \n",
      "62274     62350  1380.0  539.0   991.0  718.0  1306.650718  740.651820   \n",
      "62275     62351  1380.0  539.0  1007.0  720.0  1310.433616  740.767835   \n",
      "62276     62352  1380.0  539.0  1012.0  721.0  1314.216513  740.883850   \n",
      "62277     62353  1379.0  540.0  1003.0  719.0  1318.000000  741.000000   \n",
      "\n",
      "          4_X    4_Y     5_X  ...    nc7    nc8    nc9   nc10   nc11   nc12  \\\n",
      "0      1344.0  600.0  1371.0  ...   True  False  False  False  False  False   \n",
      "1      1344.0  600.0  1371.0  ...   True  False  False  False  False  False   \n",
      "2      1344.0  601.0  1371.0  ...   True  False  False  False  False  False   \n",
      "3      1343.0  601.0  1370.0  ...   True  False  False  False  False  False   \n",
      "4      1342.0  599.0  1370.0  ...   True  False  False  False  False  False   \n",
      "...       ...    ...     ...  ...    ...    ...    ...    ...    ...    ...   \n",
      "62273  1347.0  607.0   506.0  ...  False  False  False  False   True  False   \n",
      "62274  1347.0  607.0   510.0  ...  False  False  False  False   True  False   \n",
      "62275  1346.0  604.0   505.0  ...  False  False  False  False   True  False   \n",
      "62276  1347.0  604.0   501.0  ...  False  False  False  False   True  False   \n",
      "62277  1349.0  608.0   501.0  ...  False  False  False  False   True  False   \n",
      "\n",
      "        nc13   nc14  nc15  nc16  \n",
      "0      False  False  True  True  \n",
      "1      False  False  True  True  \n",
      "2      False  False  True  True  \n",
      "3      False  False  True  True  \n",
      "4      False  False  True  True  \n",
      "...      ...    ...   ...   ...  \n",
      "62273  False  False  True  True  \n",
      "62274  False  False  True  True  \n",
      "62275  False  False  True  True  \n",
      "62276  False  False  True  True  \n",
      "62277  False  False  True  True  \n",
      "\n",
      "[62278 rows x 49 columns]\n"
     ]
    }
   ],
   "source": [
    "#concat X and Y position, also adding frameNum\n",
    "result = pd.concat([df_x,df_y],axis=1)\n",
    "result[\"frameNum\"] = df[\"frameNum\"]\n",
    "result = result.reindex(columns=[\"frameNum\",\"1_X\", \"1_Y\",\"2_X\", \"2_Y\" \n",
    "                                     ,\"3_X\", \"3_Y\" ,\"4_X\", \"4_Y\" \n",
    "                                     ,\"5_X\", \"5_Y\" ,\"6_X\", \"6_Y\" \n",
    "                                     ,\"7_X\", \"7_Y\" ,\"8_X\", \"8_Y\" \n",
    "                                     ,\"9_X\", \"9_Y\" ,\"10_X\", \"10_Y\" \n",
    "                                     ,\"11_X\", \"11_Y\" ,\"12_X\", \"12_Y\" \n",
    "                                     ,\"13_X\", \"13_Y\" ,\"14_X\", \"14_Y\" \n",
    "                                     ,\"15_X\", \"15_Y\" ,\"16_X\", \"16_Y\" ])\n",
    "result[[\"nc1\", \"nc2\", \"nc3\", \"nc4\", \"nc5\", \"nc6\", \"nc7\", \"nc8\", \n",
    "        \"nc9\", \"nc10\", \"nc11\", \"nc12\", \"nc13\", \"nc14\", \"nc15\", \"nc16\"]]= df[[\"nc1\", \"nc2\", \"nc3\", \"nc4\", \"nc5\", \"nc6\", \"nc7\", \"nc8\", \n",
    "                                                                             \"nc9\", \"nc10\", \"nc11\", \"nc12\", \"nc13\", \"nc14\", \"nc15\", \"nc16\"]]\n",
    "print(result)\n",
    "result.to_csv(df_result_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c8caa3bf",
   "metadata": {},
   "outputs": [
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62277</th>\n",
       "      <td>62353</td>\n",
       "      <td>1379.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>719.0</td>\n",
       "      <td>1318.000000</td>\n",
       "      <td>741.000000</td>\n",
       "      <td>1349.0</td>\n",
       "      <td>608.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62278 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       frameNum     1_X    1_Y     2_X    2_Y          3_X         3_Y  \\\n",
       "0            76  1127.0  810.0  1233.0  383.0  1301.000000  530.000000   \n",
       "1            77  1127.0  810.0  1233.0  385.0  1303.000000  532.000000   \n",
       "2            78  1127.0  810.0  1242.0  405.0  1303.000000  531.000000   \n",
       "3            79  1127.0  810.0  1239.0  400.0  1305.000000  534.000000   \n",
       "4            80  1127.0  810.0  1233.0  382.0  1307.000000  540.000000   \n",
       "...         ...     ...    ...     ...    ...          ...         ...   \n",
       "62273     62349  1380.0  539.0   986.0  717.0  1302.867821  740.535805   \n",
       "62274     62350  1380.0  539.0   991.0  718.0  1306.650718  740.651820   \n",
       "62275     62351  1380.0  539.0  1007.0  720.0  1310.433616  740.767835   \n",
       "62276     62352  1380.0  539.0  1012.0  721.0  1314.216513  740.883850   \n",
       "62277     62353  1379.0  540.0  1003.0  719.0  1318.000000  741.000000   \n",
       "\n",
       "          4_X    4_Y     5_X  ...    nc7    nc8    nc9   nc10   nc11   nc12  \\\n",
       "0      1344.0  600.0  1371.0  ...   True  False  False  False  False  False   \n",
       "1      1344.0  600.0  1371.0  ...   True  False  False  False  False  False   \n",
       "2      1344.0  601.0  1371.0  ...   True  False  False  False  False  False   \n",
       "3      1343.0  601.0  1370.0  ...   True  False  False  False  False  False   \n",
       "4      1342.0  599.0  1370.0  ...   True  False  False  False  False  False   \n",
       "...       ...    ...     ...  ...    ...    ...    ...    ...    ...    ...   \n",
       "62273  1347.0  607.0   506.0  ...  False  False  False  False   True  False   \n",
       "62274  1347.0  607.0   510.0  ...  False  False  False  False   True  False   \n",
       "62275  1346.0  604.0   505.0  ...  False  False  False  False   True  False   \n",
       "62276  1347.0  604.0   501.0  ...  False  False  False  False   True  False   \n",
       "62277  1349.0  608.0   501.0  ...  False  False  False  False   True  False   \n",
       "\n",
       "        nc13   nc14  nc15  nc16  \n",
       "0      False  False  True  True  \n",
       "1      False  False  True  True  \n",
       "2      False  False  True  True  \n",
       "3      False  False  True  True  \n",
       "4      False  False  True  True  \n",
       "...      ...    ...   ...   ...  \n",
       "62273  False  False  True  True  \n",
       "62274  False  False  True  True  \n",
       "62275  False  False  True  True  \n",
       "62276  False  False  True  True  \n",
       "62277  False  False  True  True  \n",
       "\n",
       "[62278 rows x 49 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show result dataframe\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "289a7d46",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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