ken 3 rokov pred
rodič
commit
404a89a271

11월이전/detect_label.ipynb → data/backup/detect_label.ipynb


11월이전/df_label_1025_1103.csv → data/backup/df_label_1025_1103.csv


11월이전/df_label_8030_0916.csv → data/backup/df_label_8030_0916.csv


11월이전/df_labeled.csv → data/backup/df_labeled.csv


11월이전/df_merge.csv → data/backup/df_merge.csv


11월이전/illumination.csv → data/backup/illumination.csv


11월이전/label_work.ipynb → data/backup/label_work.ipynb


11월이전/m3_0103.csv → data/backup/m3_0103.csv


11월이전/motion_door.csv → data/backup/motion_door.csv


11월이전/mr_0104.csv → data/backup/mr_0104.csv


11월이전/tableau_merge.csv → data/backup/tableau_merge.csv


11월이전/temperature_humidity.csv → data/backup/temperature_humidity.csv


11월이전/union_sensor.csv → data/backup/union_sensor.csv


dfs.csv → data/dfs.csv


m3_door_0104.csv → data/m3_door_0104.csv


m3_illumination_0104.csv → data/m3_illumination_0104.csv


m3_motion_0104.csv → data/m3_motion_0104.csv


m3_temperature_humidity_0104.csv → data/m3_temperature_humidity_0104.csv


mr_door_0104.csv → data/mr_door_0104.csv


mr_illumination_0104.csv → data/mr_illumination_0104.csv


mr_motion_0104.csv → data/mr_motion_0104.csv


mr_temperature_humidity_0104.csv → data/mr_temperature_humidity_0104.csv


11월이전/20210830_080618A.csv → data/video_label_m3/20210830_080618A.csv


11월이전/20211025_080015A.csv → data/video_label_m3/20211025_080015A.csv


11월이전/20211103_095316A.csv → data/video_label_m3/20211103_095316A.csv


11월이전/20211105_193120A.csv → data/video_label_m3/20211105_193120A.csv


11월이전/20211109_203305A.csv → data/video_label_m3/20211109_203305A.csv


11월이전/20211117_003420A.csv → data/video_label_m3/20211117_003420A.csv


11월이전/20211124_001105A.csv → data/video_label_m3/20211124_001105A.csv


+ 12 - 12
label_work.ipynb

@@ -124,19 +124,19 @@
     "    #            '00158d0006c9d5ed':'m3_illumination_0104.csv'}\n",
     "    # func = [preporc_il, preproc_th, preproc_motion, preproc_door]\n",
     "    \n",
-    "df =             preproc_th('mr_temperature_humidity_0104.csv', '00158d0006c9d545')\n",
-    "df =   df.join(preproc_door('mr_door_0104.csv',                 '00158d0005bb968a'))\n",
-    "df = df.join(preproc_motion('mr_motion_0104.csv',               '00158d0002cab225'))\n",
-    "df =     df.join(preproc_il('mr_illumination_0104.csv',         '00158d0005a9998b'))\n",
+    "df =             preproc_th('data/mr_temperature_humidity_0104.csv', '00158d0006c9d545')\n",
+    "df =   df.join(preproc_door('data/mr_door_0104.csv',                 '00158d0005bb968a'))\n",
+    "df = df.join(preproc_motion('data/mr_motion_0104.csv',               '00158d0002cab225'))\n",
+    "df =     df.join(preproc_il('data/mr_illumination_0104.csv',         '00158d0005a9998b'))\n",
     "df_rnd = df.copy()\n",
-    "df_rnd.to_csv('mr_0104.csv')\n",
+    "df_rnd.to_csv('data/mr_0104.csv')\n",
     "\n",
-    "df =             preproc_th('m3_temperature_humidity_0104.csv', '00158d00028d93d8')\n",
-    "df =   df.join(preproc_door('m3_door_0104.csv',                 '00158d0005bb96f3'))\n",
-    "df = df.join(preproc_motion('m3_motion_0104.csv',               '00158d0002d545b4'))\n",
-    "df =     df.join(preproc_il('m3_illumination_0104.csv',         '00158d0006c9d5ed'))\n",
+    "df =             preproc_th('data/m3_temperature_humidity_0104.csv', '00158d00028d93d8')\n",
+    "df =   df.join(preproc_door('data/m3_door_0104.csv',                 '00158d0005bb96f3'))\n",
+    "df = df.join(preproc_motion('data/m3_motion_0104.csv',               '00158d0002d545b4'))\n",
+    "df =     df.join(preproc_il('data/m3_illumination_0104.csv',         '00158d0006c9d5ed'))\n",
     "df_m3 = df.copy()\n",
-    "df_m3.to_csv('m3_0104.csv')"
+    "df_m3.to_csv('data/m3_0104.csv')"
    ]
   },
   {
@@ -191,12 +191,12 @@
     "import glob\n",
     "\n",
     "dfs = []\n",
-    "for elm in glob.glob('202111*.csv'):\n",
+    "for elm in glob.glob('data/video_label_m3/202111*.csv'):\n",
     "    print(elm)\n",
     "    dfs.append(pd.read_csv(elm))\n",
     "df_label = pd.concat(dfs, ignore_index=True)\n",
     "df_label.rename(columns={df_label.columns[1]:'timestamp', df_label.columns[2]:'label'}, inplace=True)\n",
-    "df_label.to_csv('dfs.csv')"
+    "df_label.to_csv('data/dfs.csv')"
    ]
   }
  ],