Get Started#

The Smart Parking application uses AI-driven video analytics to optimize parking management. It provides a modular architecture that integrates seamlessly with various input sources and leverages AI models to deliver accurate and actionable insights.

By following this guide, you will learn how to:

Set up the sample application: Use Docker Compose to quickly deploy the application in your environment.
Run a predefined pipeline: Execute a pipeline to see smart parking application in action.
Access the application’s features and user interfaces: Explore the Grafana dashboard, Node-RED interface, and DL Streamer Pipeline Server to monitor, analyze and customize workflows.

Prerequisites#

Verify that your system meets the minimum requirements.
Install Docker: Installation Guide.

Set up and First Use#

Clone the Repository:

Run:

git clone https://github.com/open-edge-platform/edge-ai-suites.git
cd edge-ai-suites/metro-ai-suite/metro-vision-ai-app-recipe/

Setup Application and Download Assets:
- Use the installation script to configure the application and download required models:
```
./install.sh smart-parking
```
Specify Custom Host IP Address (Advanced Configuration)

For environments requiring a specific host IP address (such as when using Edge Manageability Toolkit or deploying across different network interfaces), you can explicitly specify the IP address:
```
./install.sh smart-parking <HOST_IP>
```
Replace <HOST_IP> with your target IP address.

Run the Application#

Start the Application:
- Download container images with Application microservices and run with Docker Compose:
```
docker compose up -d
```
  Check Status of Microservices
  - The application starts the following microservices.
  - To check if all microservices are in Running state:
    docker ps
  Expected Services:
  - Grafana Dashboard
  - DL Streamer Pipeline Server
  - MQTT Broker
  - Node-RED (for applications without Scenescape)
  - Scenescape services (for Smart Intersection only)
Run Predefined Pipelines:
- Start video streams to run video inference pipelines:
```
./sample_start.sh
```
  Check Status and Stop pipelines
  - To check the status:
    ./sample_status.sh
  - To stop the pipelines without waiting for video streams to finish replay:
    ./sample_stop.sh
View the Application Output:
- Open a browser and go to http://localhost:3000 to access the Grafana dashboard.
  - Change the localhost to your host IP if you are accessing it remotely.
- Log in with the following credentials:
  - Username: admin
  - Password: admin
- Check under the Dashboards section for the application-specific preloaded dashboard.
- Expected Results: The dashboard displays real-time video streams with AI overlays and detection metrics.

Access the Application and Components#

Grafana UI#

URL: http://localhost:3000
Log in with credentials:
- Username: admin
- Password: admin (You will be prompted to change it on first login.)
In Grafana UI, the dashboard displays the detected cars in the parking lot.

NodeRED UI#

URL: http://localhost:1880

DL Streamer Pipeline Server#

REST API: http://localhost:8080
- - Check Pipeline Status:
```
curl http://localhost:8080/pipelines
```
WebRTC: http://localhost:8889/object_detection_1

Stop the Application:#

To stop the application microservices, use the following command:
```
docker compose down
```

Other Deployment Option#

Choose one of the following methods to deploy the Smart Parking Sample Application:

Deploy Using Helm: Use Helm to deploy the application to a Kubernetes cluster for scalable and production-ready deployments.

Supporting Resources#

Troubleshooting Guide: Find detailed steps to resolve common issues during deployments.
DL Streamer Pipeline Server