Skip to main content

An LPR data analysis package!

Project description

LAPIN

CI - Test

What it is

A framework for the analysis of on street parking occupancy via Licence Plate Recognition (LPR) data.

Getting started

Requirements

Install Lapin python's requirments with conda :

conda env create --name <YOUR_ENV_NAME> -f environment.yml

Optional

You may need to have a docker installation available on your machine. See Valhalla mapmatching.

Configuration

Project configuration

Create a config file for your project . You can create a blank one by running :

python -m lapin -c

Mapmatching configuration

You have the choice between two mapmatching engine : OSRM and Valhalla. The main difference being that while using Valhalla you can do the matching directly on the Montreal Geobase. Doing so improve the accuracy of the positionning of the plate on the geobase. Thus improving the quality of the results.

Valhalla

To use valhalla, you'll need to compute the OSM network from the geobase file. Then create the valhalla graph with valhalla engine and the OSM network. The step are the following :

  1. Create the OSM graph
python -m lapin --generate-graph
  1. Generate Valhalla's graph
sudo docker run --rm --name valhalla_gis-ops -p 8002:8002 -v $PWD/data/network/valhalla:/custom_files -e tile ghcr.io/gis-ops/docker-valhalla/valhalla:latest'
  1. Specify the use of valhalla in lapin/__main__.py line 113-115.
    matcher_host='<PATH_TO_LAPIN>/lapin/data/network/valhalla/valhalla_tiles.tar',
    matcher_client='valhalla',
    matcher_kwargs={'service_limits':{"trace": {"max_shape": 26000}}}, # your desired config
OSRM

To use OSRM simply identify a valid OSRM instance.

  1. Specify the use of OSRM in lapin/__main__.py line 113-115.
    matcher_host=<ADRESS_TO_OSRM_INSTANCE>,
    matcher_client='osrm',
    matcher_kwargs={},

Note : the instance must be launched with a sufficiently large max-matching-size parameter (e.g. 100000)

Lauching an analysis

Then excecute the package with the following command :

python -m lapin --conf-file <PATH_TO_YOUR_CONF_FILE>

Installing the module

Clone the repo and install the lapin package.

cd <repo_dir>
pip install .

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

onstreet_parking_study-1.2.10.tar.gz (218.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

onstreet_parking_study-1.2.10-py3-none-any.whl (234.2 kB view details)

Uploaded Python 3

File details

Details for the file onstreet_parking_study-1.2.10.tar.gz.

File metadata

  • Download URL: onstreet_parking_study-1.2.10.tar.gz
  • Upload date:
  • Size: 218.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.3

File hashes

Hashes for onstreet_parking_study-1.2.10.tar.gz
Algorithm Hash digest
SHA256 ac1a584bc2b0c5337cf351d604ce09254f108c0facf9bd04a6fe4a8529da7b33
MD5 ab0b44865ff417210974d48f0d1d8e59
BLAKE2b-256 47b8d219e5b267610c1af49f438c062289e44be690c094d1c53a944d319d9587

See more details on using hashes here.

File details

Details for the file onstreet_parking_study-1.2.10-py3-none-any.whl.

File metadata

File hashes

Hashes for onstreet_parking_study-1.2.10-py3-none-any.whl
Algorithm Hash digest
SHA256 5457f195aa187f44fd5bb6f010825b27f74bf4eef9d6abf32763311b705c2856
MD5 a18e47488bd8abee7d5252b686fd3835
BLAKE2b-256 5f242315812408f008aa6762e235de925f164e8b1aaa5be7a1fd39fb1a9c37ab

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page