Openpoiservice (ops) is a flask application which hosts a highly customizable points of interest database derived from OpenStreetMap.org data and thereby exploits its notion of tags...
OpenStreetMap tags consisting of a key and value describe specific features of map elements (nodes, ways, or relations) or changesets. Both items are free format text fields, but often represent numeric or other structured items.
This service consumes OSM tags on nodes, ways and relations by grouping them into predefined categories.
If it picks up an OSM object tagged with one of the osm keys defined in categories.yml it will import this
point of interest with specific additional tags which may be defined in ops_settings.yml. Any additional tag,
for instance wheelchair or smoking may then be used to query the service via the API after import.
For instance, if you want to request all pois accessible by wheelchair within a geometry, you could add then add
wheelchair: ['yes', 'dedicated] in filters within the body of your HTTP POST request.
You may pass 3 different types of geometry within the request to the database. Currently, "Point" and "LineString" with a corresponding and buffer are supported as well as a polygon. Points of interest will be returned within the given geometry.
You can control the maximum size of geometries and further restrictions in the settings file of this service.
The osm file(s) to be imported are parsed several times to extract points of interest from relations (osm_type 3), ways (osm_type 2) and nodes (osm_type 1) in order. Which type the specific point of interest originated from will be returned within the response - this will help you find the object directly on OpenStreetMap.org.
You can either run openpoiservice on your host machine in a virtual environment or simply with Docker. The Dockerfile provided installs a WSGI server (gunicorn) which starts the flask service on port 5000.
As this service makes use of the python collections library, in particular the notion of deque's and its functions it only supports python 3.5 and greater.
This application uses a psql/postgis setup for storing the points of interest. We highly recommend using this docker container.
Please consider the following technical requirements for parsing & importing osm files.
| Region | Memory |
|---|---|
| Germany | 8 GB |
| Europe | 32 GB |
| Planet | 128 GB |
Note: Openpoiservice will import any osm pbf file located in the osm folder or subdirectory within. This way you can split the planet file into smaller regions (e.g. download from Geofabrik, scraper script for the download links to be found in the osm folder) and use a smaller instance to import the global data set (as long as the OSM files don't exceed 5 GB of disk space, 16 GB of memory will suffice to import the entire planet).
Make your necessary changes to the settings in the file ops_settings_docker.yml and to categories if you need inside categories_docker.yml. These files are mounted as volumes to the docker container.
If you are planning to import a different osm file, please download it to the osm folder (any folder within will be scanned
for osm files) as this will be a shared volume.
This docker-compose will allow you to run openpoiservice with psql/postgis image. This will allow you to deploy this project fast.
Important : The database is not exposed, you won't be able to access it from outside the container. If you want to acces it simply adds those lines to the database definition inside the docker-compose-with-postgis.yml:
ports:
- <PORT YOU WANT>:5432Don't forget to change the host name and port inside ops_settings_docker.yml by the one given to docker container for database.
- Hostname default value :
psql_postgis_db - Port default value :
5432
Notes : If openpoiservice can't connect to the database, it's probably because you don't have the same settings inside ops_settings_docker.yml and docker-compose-with-postgis.yml.
Command to use to run all-in-one docker container
docker-compose -f /path/to/docker-compose.yml up api -dThis will only run openpoiservice inside a container, meaning that you will need to handle the database yourself and connect it to this container.
docker-compose -f /path/to/docker-compose-standalone.yml up api -dOnce the container is built you can either, create the empty database:
$ docker exec -it container_name /ops_venv/bin/python manage.py create-dbDelete the database:
$ docker exec -it container_name /ops_venv/bin/python manage.py drop-dbOr import the OSM data:
$ docker exec -it container_name /ops_venv/bin/python manage.py import-dataYou can initialize POI database with docker service init
docker-compose -f /path/to/docker-compose.yml up initOr updating POI database
docker-compose -f /path/to/docker-compose.yml up updateThis repository uses imposm.parser to parse the
OpenStreetMap pbf files which uses google's protobuf library under its hood.
The imposm.parser requirement will not build with pip unless you are running protobuf 3.0.0.
To this end, please make sure that you are running the aforementioned version of protobuf if pip install -r requirements.txt fails (install protobuf from source)
Update openpoiservice/server/ops_settings.yml with your necessary settings and then run one of the following
commands.
[
$ export APP_SETTINGS="openpoiservice.server.config.ProductionConfig|DevelopmentConfig"]
$ python manage.py create-db$ python manage.py drop-db$ python manage.py import-data$ python manage.py runPer default you can access the application at the address http://localhost:5000/
Want to specify a different port?
$ python manage.py run -h 0.0.0.0 -p 8080
$ export TESTING="True" && python manage.py testopenpoiservice/server/categories/categories.yml is a list of (note: not all!) OpenStreetMap tags with arbitrary category IDs.
If you keep the structure as follows, you can manipulate this list as you wish.
transport:
id: 580
children:
aeroway:
aerodrome: 581
aeroport: 582
helipad: 598
heliport: 599
amenity:
bicycle_parking: 583
sustenance:
id: 560
children:
amenity:
bar: 561
bbq: 562
...Openpoiservice uses this mapping while it imports pois from the OpenStreetMap data and assigns the custom category IDs accordingly.
column_mappings in openpoiservice/server/ops_settings.yml controls which OSM information will be considered in the database and also if
these may be queried by the user via the API , e.g.
wheelchair:
smoking:
fees:For instance means that the OpenStreetMap tag wheelchair will be considered
during import and save to the database. A user may then add a list of common values in the filters object wheelchair: ['yes', 'dedicated', ...]
which correspond to the OSM common values of the tag itself, e.g.
https://wiki.openstreetmap.org/wiki/Key:wheelchair.
The documentation for this flask service is provided via flasgger and can be
accessed via http://localhost:5000/apidocs/.
Generally you have three different request types pois, stats and
list.
Using request=pois in the POST body will return a GeoJSON FeatureCollection
in your specified bounding box or geometry.
Using request=stats will do the same but group by the categories, ultimately
returning a JSON object with the absolute numbers of pois of a certain group.
Finally, request=list will return a JSON object generated from
openpoiservice/server/categories/categories.yml.
The default base url is http://localhost:5000/.
The openpoiservice holds the endpoint /pois:
| Method allowed | Parameter | Values [optional] |
|---|---|---|
| POST | request | pois, stats, list |
| geometry | bbox, geojson, buffer | |
| filter | category_group_ids, category_ids, [name, wheelchair, smoking, fee] | |
| limit | integer | |
| sortby | category, distance |
curl -X POST \
http://localhost:5000/pois \
-H 'Content-Type: application/json' \
-d '{
"request": "pois",
"geometry": {
"bbox": [
[8.8034, 53.0756],
[8.7834, 53.0456]
],
"geojson": {
"type": "Point",
"coordinates": [8.8034, 53.0756]
},
"buffer": 250
}
}'curl -X POST \
http://localhost:5000/pois \
-H 'Content-Type: application/json' \
-d '{
"request": "pois",
"geometry": {
"bbox": [
[8.8034, 53.0756],
[8.7834, 53.0456]
],
"geojson": {
"type": "Point",
"coordinates": [8.8034, 53.0756]
},
"buffer": 100
},
"limit": 200,
"filters": {
"category_ids": [180, 245]
}
}'curl -X POST \
http://localhost:5000/pois \
-H 'Content-Type: application/json' \
-d '{
"request": "pois",
"geometry": {
"bbox": [
[8.8034, 53.0756],
[8.7834, 53.0456]
],
"geojson": {
"type": "Point",
"coordinates": [8.8034, 53.0756]
},
"buffer": 100
},
"limit": 200,
"filters": {
"category_group_ids": [160]
}
}'curl -X POST \
http://129.206.7.157:5005/pois \
-H 'Content-Type: application/json' \
-d '{
"request": "stats",
"geometry": {
"bbox": [
[8.8034, 53.0756],
[8.7834, 53.0456]
],
"geojson": {
"type": "Point",
"coordinates": [8.8034, 53.0756]
},
"buffer": 100
}
}'curl -X POST \
http://127.0.0.1:5000/pois \
-H 'content-type: application/json' \
-d '{
"request": "list"
}'
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