Skip to main content

🌈 Vancouver Watching with AI.

Project description

🌈 Vancouver Watching (vanwatch)

vanwatch 🌈 discovers and ingests images from traffic cameras in an area and then runs YOLO 🚀, OpenAI Vision, and other vision algo to extract information about urban activity at scale.

pip install vancouver-watching
 > vanwatch help
vanwatch conda create [validate,~recreate]
 . create conda environment.
vanwatch conda validate
 . validate conda environment.
vanwatch discover \
	[area=<area>,~upload] \
	[-|<object-name>] \
	[<args>]
 . discover area -> <object-name>.
vanwatch ingest \
	area=<area>,count=<count>,dryrun,gif,model=<model-id>,~process,publish,~upload \
	-|<object-name> \
	[<args>]
 . ingest <area> -> <object-name>.
vanwatch list [area=<area>,discovery|ingest,published] \
	[--count <count>] \
	[--delim space] \
	[--log 0] \
	[--offset <offset>]
 . list objects from area.
2 area(s): iran,vancouver
vanwatch list areas
 . list areas.
vanwatch vision "prompt" \
	[area=<area>,offset=<1>,auto|low|high,dryrun,~upload] \
	Davie,Bute \
	[--verbose 1]
 . openai_commands vision: prompt @ <area>/intersection.
vanwatch process \
	count=<count>,~download,gif,model=<model-id>,publish,~upload \
	.|<object-name> \
	[--detect_objects 0] \
	[--do_dryrun 1] \
	[--overwrite 1] \
	[--verbose 1]
 . process <object-name>.
vanwatch pylint
 . pylint vancouver_watching.
vanwatch update|update_cache \
	area=<vancouver>,overwrite,process,~publish,refresh,~upload \
	[--verbose 1]
 . update QGIS cache.
vancouver_watching test \
	[dryrun,~ingest,~list,~process,upload]
 . test vancouver_watching.

last build 🔗 image

discover and Ingest an Area

image

to see the list of areas supported by vanwatch type in,

vanwatch list areas

to discover the available cameras in an area type in,

vanwatch discover area=vancouver

you have generated a geojson of traffic images in the City of Vancouver. Now, you can ingest the traffic images from this area and detect people and cars in them,

vanwatch ingest area=vancouver,count=2,publish

image

model: https://hub.ultralytics.com/models/R6nMlK6kQjSsQ76MPqQM?tab=preview

image

image

image

dataset: vanwatch-cache-2024-02-28-21-04-19-26236.tar.gz (details).


PyPI version

to use on AWS SageMaker replace <plugin-name> with vanwatch and follow these instructions.

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

vancouver_watching-3.391.1.tar.gz (18.1 kB view details)

Uploaded Source

Built Distribution

vancouver_watching-3.391.1-py3-none-any.whl (25.8 kB view details)

Uploaded Python 3

File details

Details for the file vancouver_watching-3.391.1.tar.gz.

File metadata

  • Download URL: vancouver_watching-3.391.1.tar.gz
  • Upload date:
  • Size: 18.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.19

File hashes

Hashes for vancouver_watching-3.391.1.tar.gz
Algorithm Hash digest
SHA256 003c6a056eaf44d7403239ee21e53a842490102ddbb43b6bcc10f673e23292b7
MD5 a67de7af6801d315ea3ad43bbb54bcaf
BLAKE2b-256 aa1a69aad52d761f8c95e3eb4fabd17ba58f273eccbdf3d6d058f77e18ce33d8

See more details on using hashes here.

File details

Details for the file vancouver_watching-3.391.1-py3-none-any.whl.

File metadata

File hashes

Hashes for vancouver_watching-3.391.1-py3-none-any.whl
Algorithm Hash digest
SHA256 a10f584888f77e9a564052d561274f4e5f6231bdd3be810114e50ed6e749108c
MD5 50e3aaddb51db41dd658fc92f379235c
BLAKE2b-256 67ccf5391e3af01d4bbaff1ff80bc70e52c55304cb664a2d0b26f1e8bcd2c137

See more details on using hashes here.

Supported by

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