# 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](./get-started/system-requirements.md).
- Install Docker: [Installation Guide](https://docs.docker.com/get-docker/).

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**Setup and First Use**: Include installation instructions, basic operation, and initial validation.
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## Set up and First Use

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**User Story 1**: Setting Up the Application
- **As a developer**, I want to set up the application in my environment, so that I can start exploring its functionality.

**Acceptance Criteria**:
1. Step-by-step instructions for downloading and installing the application.
2. Verification steps to ensure successful setup.
3. Troubleshooting tips for common installation issues.
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1. **Clone the Repository**:
   - Run:
     ```bash
     git clone https://github.com/open-edge-platform/edge-ai-suites.git
     cd edge-ai-suites/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:
     ```bash
     ./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:
     ```bash
     docker compose up -d
     ```

     <details>
     <summary>
     Check Status of Microservices
     </summary>

     - The application starts the following microservices.
     - To check if all microservices are in Running state:
       ```bash
       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)

     </details>

2. **Run Predefined Pipelines**:

   - Start video streams to run video inference pipelines:
     ```bash
     ./sample_start.sh
     ```
   - To check the status of the pipelines:
      ```bash
      ./sample_status.sh
      ```
     <details>
     <summary>
     Stop pipelines
     </summary>

     - To stop the pipelines without waiting for video streams to finish replay:
       ```bash
       ./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.
     </details>

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.
      ![Grafana Dashboard](./_assets/grafana-smart-parking.jpg)

### **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:
  ```bash
  docker compose down
  ```

## Other Deployment Options

- [Deploy Using Helm](./get-started/deploy-with-helm.md): Use Helm to deploy the application to a Kubernetes cluster for scalable and production-ready deployments.
- [Deploy with Edge Orchestrator](./get-started/deploy-with-edge-orchestrator.md): Use a simplified
edge application deployment process.

## Supporting Resources
- [Troubleshooting](./troubleshooting.md): Find detailed steps to resolve common issues during deployments.
- [DL Streamer Pipeline Server](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer-pipeline-server/index.html): Intel microservice based on Python for video ingestion and deep learning inferencing functions.


:::{toctree}
:hidden:

get-started/system-requirements.md
get-started/deploy-with-helm.md
get-started/deploy-with-edge-orchestrator.md

:::