🚘 DeepParking
Find the closest vacant parking spot, every time.
DeepParking is an open-source solution for detecting vacant parking spots in indoor parking garages, and delivering real-time notifications to nearby drivers. Low-cost cameras are mounted throughout garages and mapped to locations of available parking spaces. State-of-the-art deep learning algorithms detect occupied spaces, and write the results to a Redis cache. As drivers enter the garage, they are routed to closest available free parking space.
With DeepParking, you'll never have to waste time hunting for a parking spot again!
📖 Technical Stack
DeepParking prides itself on being a 100% open-source solution, from algorithm to web application.
Web Application
Angular version 6.0.8.
Deep Learning: Object Detection
Leverages the state-of-the-art YOLO v3 real-time object detection system.
Backends
Written in Python 3.6.5 using Flask
Persistent Store
Redis - A blazing fast in-memory database with disk persistence.
Scalable Deployment
Docker, Kubernetes
📹 Hardware Configuration
For optimal results with DeepParking, we recommend using a camera configuration similar to the one shown below. No more than five parking spots should be visible for any single captured frame; a single camera with one degree of rotation can be used to monitor 10 parking spots. It is recommended that an image for each 5-space frame is captured every 15 to 20 seconds.
Estimated cost for 100-car garage: $150
Usage
DeepPark is made up of the following components:
- Camera Gateway API - Receives images from cameras, performs vacant spot analysis on the images and pushes the results to Redis
- Spots API - External facing API that is used by the Web App to provide vacant parking spots info
- Web App - A web application for drivers that shows information about vacant parking spots
- Redis - stores results about parking areas, cameras and vacant spots for realtime querying
In order to set-up an environment locally, edit the area.json file that contains metadata about the parking lot and start a local Redis instance.
Run the Python setup script:
$ python3 ./setup/setup_area.py --redis-host=localhost
The same script can be run on any accessible redis host to set-up the environment.
Running on Kubernetes
DeepPark runs natively on Kubernetes. To install DeepPark, simply run:
$ kubectl create -f ./setup/kubernetes
Thats it! Wait for the following deployments to appear as running:
- camera-gateway-deployment
- camera-tester-deployment
- redis-deployment
- spots-deployment
Setup the parking lot on Redis
Forward traffic from the Redis service:
$ kubectl port-forward svc/redis-svc 6379:6379
Edit the area.json to your liking and run the setup script:
$ python3 ./setup/setup_area.py --redis-host=localhost
Deploying the driver app
Grab the Spots API External IP:
$ kubectl get svc spots-api-svc
Take the Area ID you created earlier and edit the API_URL and AREA_ID env variables in setup/kubernetes/driver_app. Deploy the app:
$ kubectl create -f ./setup/kubernetes/driver_app
Wait for the External IP to show up:
$ kubectl get svc driverapp-svc
Point your mobile device browser to the IP address.
Running the Camera Tester
DeepPark allows you to mimic a camera by running a simple web app locally that sends images to the Camera Gateway API. Simply open camera_tester/index.html on a local device (or behind an SSL public internet endpoint) and input the following values in the UI:
- Camera Gateway Address - The public ip of the Camera Gateway API
- Area ID - The Area ID you created in setup/area.json
- Camera ID - The ID of the camera you will be sending photos as, created in setup/area.json
Get the Camera Gateway External IP with:
$ kubectl get svc camera-gateway-svc
Note: The Camera Gateway API uses port 8080.
Done!
👍 Project Team
- Haishi Bai (Principal Software Engineer, Office of the Azure CTO)
- Yaron Schneider (Senior Software Engineer, Office of the Azure CTO)
- Paige Bailey (Senior Cloud Developer Advocate, Cloud+AI)