This repository is Geographica version of libosrmc, focused only on Python 3 binding (osrmcpy). This repository is a fork of https://github.com/daniel-j-h/libosrmc , which is a C wrapper around the C++ libosrm library, useful for writing FFI bindings and guaranteeing ABI stability.
First you must build base Docker image:
$ cd docker
$ docker build --pull -t geographica/osrmcpy:latest .
You must build your Docker container with Docker-Compose:
$ docker-compose build osrmcpy-jupyter
This container exposes the internal port 8888 to the host port 8889.
You must build your Docker container with Docker-Compose:
$ docker-compose build osrmcpy
This container exposes the internal port 5000 to the host port 5050.
Up Docker container:
$ docker-compose up osrmcpy-jupyter
And and use these examples through JupyterLab in http://localhost:8888:
- notebooks/osrmcpy_compute_matrix.ipynb.
- notebooks/osrmcpy_compute_route.ipynb.
- notebooks/osrmcpy_compute_nearest.ipynb.
Before to use examples you must prepare test datasets (see Data processing).
There are two preprocessing pipelines to use OSRM:
- MLD: Multi-Level Dijkstra.
- CH: Contraction Hierarchies. You should use CH when performance is important.
Preprocessing pipelines to generate test data:
- CH pipeline example:
$ docker-compose exec osrmcpy bash -c 'cd data && ./preprocessing_monaco.sh'
- MLD pipeline example:
$ docker-compose exec osrmcpy bash -c 'cd data && ./preprocessing_monaco.sh MLD'
You have scripts in data folder to generate test datasets for three locations:
- Monaco (little)
- Berlin (medium)
- Ireland (moderately large)
- Spain (large)
- France (extra large)
Here is the information related to the performance procesing the data for France
8 x Intel(R) Core(TM) i7-8550U CPU @ 1.80GHz
Mem: 16GB
Swap: 2GB
france-latest.osm.pbf 3.43G
--Processing - osm-extract...
RAM: peak bytes used: 12157050880
time: 13m23.498s
--Processing - osrm-contract...
RAM: peak bytes used: 5705818112
time: 40m27.110s
france_osrm_ch/ 7.5G
Launch the API pointing to an osrm file
$ docker-compose run --service-ports osrmcpy osrm-routed ./data/osrm/france_osrm_ch/france-latest.osrm
Example
http://localhost:5050/table/v1/car/3.081427,48.809148;3.243775,48.811817;3.228700,48.759358;3.230363,48.800354?sources=0&annotations=distance,duration
First you need to build and install osrm-backend.
Second you need to build and install C/C++ interface to OSRM.
$ cd libosrmc
$ make
$ sudo make install
$ sudo ldconfig
This compiles the libosrmc.so shared object and installs it into /usr/local (you may have to export LD_LIBRARY_PATH="/usr/local/lib") or install to /usr/lib.
The library's interface osrmc.h gets installed into /usr/local/include/osrmc/osrmc.h.
You can modify defaults via config.mk.
Last you can install Python3 binding:
$ python -m pip install .
You can test it with this examples:
- osrmcpy/examples/osrm_python3_matrix.py.
- osrmcpy/examples/osrm_python3_route.py.
- osrmcpy/examples/osrm_python3_nearest.py.
Copyright © 2018 Cayetano Benavent - Geographica (Python3 binding; more functionalities to C/C++ Interface)
Copyright © 2016 Daniel J. Hofmann (Creator of libosrmc project)
Distributed under the MIT License (MIT).
libosrm 5.22 version
make
sudo make install
sudo ldconfig
make clean
make && sudo make install && sudo ldconfig && make clean
ls /usr/local/lib/
sudo apt-get install python3-setuptools
sudo apt install python3-pip
python3 -m pip install .
LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib python3 osrm_python3_route.py /mnt/fd68f224-e30e-4ea0-8bd8-7f2a4c4d5ab0/nurgasemetey-environment/geo-data/turkey2/turkey-latest.osrm
python setup.py sdist
twine upload dist/*