get-started.md

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

1. Clone the Suite:

   Go to the target directory of your choice and clone the suite. If you want to clone a specific release branch, replace main with the desired tag. To learn more on partial cloning, check the Repository Cloning guide.

   git clone --filter=blob:none --sparse --branch main https://github.com/open-edge-platform/edge-ai-suites.git
   cd edge-ai-suites
   git sparse-checkout set metro-ai-suite
   cd metro-ai-suite/metro-vision-ai-app-recipe/

2. Setup Application and Download Assets:

   • Use the installation script to configure the application and download required models:

     ./install.sh smart-parking

Note: 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

1. 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 Intel® SceneScape)
     • Intel® SceneScape services (for Smart Intersection only)

2. Run Predefined Pipelines:

   • Start video streams to run video inference pipelines:

     ./sample_start.sh

   • To check the status of the pipelines:

     ./sample_status.sh

     Stop pipelines

     - To stop the pipelines without waiting for video streams to finish replay:

     ./sample_stop.sh

     Note: This will stop all the pipelines and the streams. DO NOT run this if you want to see smart parking detection.

3. View the Application Output:

   • Open a browser and go to https://localhost/grafana 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

Nginx Dashboard

• URL: https://localhost

Grafana UI

• URL: https://localhost/grafana
• 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: https://localhost/nodered/

DL Streamer Pipeline Server

• REST API: https://localhost/api/pipelines/status
• WebRTC: https://localhost/mediamtx/object_detection_1/

Stop the Application

• To stop the application microservices, use the following command:

  docker compose down

Other Deployment Options

• Deploy Using Helm: Use Helm to deploy the application to a Kubernetes cluster for scalable and production-ready deployments.
• Deploy with Edge Orchestrator: Use a simplified edge application deployment process.

Supporting Resources

• Troubleshooting: Find detailed steps to resolve common issues during deployments.
• DL Streamer Pipeline Server: Intel microservice based on Python for video ingestion and deep learning inferencing functions.