fetching_pedestrian_crossing_images.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fetching the pedestrian crossing of selected region from helsinki city. https://kartta.hel.fi/\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Follow the steps to get the images from the database\n",
    "- Go to the lnk above. And go to **layers** ==> **Pedestrian and bicycle paths** ==>  **Routes** ==> click on **Pedestrian crossings**  \n",
    "- Click on **Download** button which takes you to **Data Exporter**. Select **Draw Search Area** and **Inside Search Area**.     Draw the box of desired size on map.\n",
    "- Finally select the format you need. For example **GeoPackage** and click **Download**\n",
    "   (**Note:** Excel file don't retrive any coordinates information)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the required geopandas library and dependencies(fiona). I am importing pandas which is handy for data cleaning\n",
    "import geopandas\n",
    "import fiona\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25497295.9682035 6672929.268 25497296.3567031 6672912.646\n",
      "25497172.2023273 6673374.486 25497179.4363201 6673370.764\n",
      "25497334.3101652 6672931.001 25497334.2901652 6672914.92\n",
      "25497361.1891383 6672770.811 25497354.3851451 6672776.495\n"
     ]
    }
   ],
   "source": [
    "# Reading geaopakage file format\n",
    "file=geopandas.read_file(\"C:/Users/35841/Downloads/_20191205_145349/klinj_suojatie.gpkg\")\n",
    "df=pd.DataFrame(file)\n",
    "df.geometry=df.geometry.astype(\"str\")\n",
    "df[\"geometry\"]=df[\"geometry\"].str.replace(\"LINESTRING \",\"\").str.replace(\",\",\"\").str.replace(\"(\",\"\").str.replace(\")\",\"\")\n",
    "print(df.loc[0,\"geometry\"])\n",
    "print(df.loc[1,\"geometry\"])\n",
    "print(df.loc[2,\"geometry\"])\n",
    "print(df.loc[3,\"geometry\"])\n",
    "# insconsistent order of minx and maxx\n",
    "# inconsistent order of miny and maxy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[[\"min_x\",\"max_y\",\"max_x\",\"min_y\",\"a\",\"b\"]]=df[\"geometry\"].str.split(\" \",expand=True)\n",
    "df=df.drop(columns=[\"a\",\"b\",\"geometry\"])\n",
    "df[\"id\"]=df[\"gml_id\"].str.replace(\"klinj_suojatie.\",\"\")"
   ]
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   "execution_count": 4,
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       "      <td>klinj_suojatie.96058</td>\n",
       "      <td>96058</td>\n",
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       "      <td>12</td>\n",
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       "      <td>96574</td>\n",
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       "                    gml_id     id vaylatyyppi     luokka  featurecode  \\\n",
       "0     klinj_suojatie.16704  16704    Suojatie  Suojatiet           12   \n",
       "1     klinj_suojatie.16713  16713    Suojatie  Suojatiet           12   \n",
       "2     klinj_suojatie.16741  16741    Suojatie  Suojatiet           12   \n",
       "3     klinj_suojatie.16908  16908    Suojatie  Suojatiet           12   \n",
       "4     klinj_suojatie.16910  16910    Suojatie  Suojatiet           12   \n",
       "...                    ...    ...         ...        ...          ...   \n",
       "1388  klinj_suojatie.96058  96058    Suojatie  Suojatiet           12   \n",
       "1389  klinj_suojatie.96553  96553    Suojatie  Suojatiet           12   \n",
       "1390  klinj_suojatie.96573  96573    Suojatie  Suojatiet           12   \n",
       "1391  klinj_suojatie.96574  96574    Suojatie  Suojatiet           12   \n",
       "1392  klinj_suojatie.96579  96579    Suojatie  Suojatiet           12   \n",
       "\n",
       "     html_color         min_x        max_y         max_x        min_y  \n",
       "0        400040  2.549730e+07  6672929.268  2.549730e+07  6672912.646  \n",
       "1        400040  2.549717e+07  6673374.486  2.549718e+07  6673370.764  \n",
       "2        400040  2.549733e+07  6672931.001  2.549733e+07  6672914.920  \n",
       "3        400040  2.549736e+07  6672770.811  2.549735e+07  6672776.495  \n",
       "4        400040  2.549735e+07  6672758.490  2.549734e+07  6672759.135  \n",
       "...         ...           ...          ...           ...          ...  \n",
       "1388     400040  2.549739e+07  6672374.249  2.549738e+07  6672370.090  \n",
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       "\n",
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   ],
   "source": [
    "df[\"min_x\"]=df[\"min_x\"].astype(float)\n",
    "df[\"min_y\"]=df[\"min_y\"].astype(float)\n",
    "df[\"max_x\"]=df[\"max_x\"].astype(float)\n",
    "df[\"max_y\"]=df[\"max_y\"].astype(float)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fixing inconsistencies \n",
    "df.min_x, df.max_x = np.where(df.min_x > df.max_x, [df.max_x, df.min_x], [df.min_x, df.max_x])\n",
    "df.min_y, df.max_y = np.where(df.min_y > df.max_y, [df.max_y, df.min_y], [df.min_y, df.max_y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WMS api allows you to download the images \n",
    "from owslib.wms import WebMapService\n",
    "import os\n",
    "\n",
    "wms = WebMapService('https://kartta.hel.fi/ws/geoserver/avoindata/wms', version='1.3.0')\n",
    "for i in range(0,len(df)):\n",
    "    img=wms.getmap(layers=[\"Ortoilmakuva_2019_5cm\"],srs=\"EPSG:3879\",bbox=(df.loc[i,\"min_x\"],df.loc[i,\"min_y\"],df.loc[i,\"max_x\"],df.loc[i,\"max_y\"]),size=(100,100),format='image/png',transparent=True)\n",
    "    out = open(\"images/phase2_crossroad_images_1/\" + str(df.loc[i,\"id\"])+\".png\", 'wb')\n",
    "    out.write(img.read())\n",
    "    out.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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