# Get Started

-   **Time to Complete:** 30 minutes
-   **Programming Language:**  Python 3

## Prerequisites

- [System Requirements](system-requirements.md)

## Setup the application

The following instructions assume Docker engine is correctly set up in the host system.
If not, follow the [installation guide for docker engine](https://docs.docker.com/engine/install/ubuntu/).

1. Clone the **edge-ai-suites** repository and change into industrial-edge-insights-vision directory. The directory contains the utility scripts required in the instructions that follows.

    ```bash
    git clone https://github.com/open-edge-platform/edge-ai-suites.git
    cd edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/
    ```

2.  Set app specific environment variable file

    ```bash
    cp .env_worker_safety_gear_detection .env
    ```

3.  Edit the `HOST_IP`, `MTX_WEBRTCICESERVERS2_0_USERNAME` and `MTX_WEBRTCICESERVERS2_0_PASSWORD` environment variables in the `.env` file as follows:

    ```bash
    HOST_IP=<HOST_IP>   # IP address of server where DLStreamer Pipeline Server is running.

    MTX_WEBRTCICESERVERS2_0_USERNAME=<username>  # WebRTC credentials e.g. intel1234
    MTX_WEBRTCICESERVERS2_0_PASSWORD=<password>

    # application directory
    SAMPLE_APP=worker-safety-gear-detection
    ```

4.  Install the pre-requisites. Run with sudo if needed.

    ```bash
    ./setup.sh
    ```

    This script sets up application pre-requisites, downloads artifacts, sets executable permissions for scripts etc. Downloaded resource directories are made available to the application via volume mounting in docker compose file automatically.

## Deploy the Application

5.  Start the Docker application:

    ```bash
    docker compose up -d
    ```

6.  Fetch the list of pipeline loaded available to launch

    ```bash
    ./sample_list.sh
    ```

    This lists the pipeline loaded in DL Streamer Pipeline Server.

    Example Output:

    ```bash
    # Example output for Worker Safety gear detection
    Environment variables loaded from [WORKDIR]/manufacturing-ai-suite/industrial-edge-insights-vision/.env
    Running sample app: worker-safety-gear-detection
    Checking status of dlstreamer-pipeline-server...
    Server reachable. HTTP Status Code: 200
    Loaded pipelines:
    [
        ...
        {
            "description": "DL Streamer Pipeline Server pipeline",
            "name": "user_defined_pipelines",
            "parameters": {
            "properties": {
                "detection-properties": {
                    "element": {
                        "format": "element-properties",
                        "name": "detection"
                    }
                }
            },
            "type": "object"
            },
            "type": "GStreamer",
            "version": "worker_safety_gear_detection"
        }
        ...
    ]
    ```
7.  Start the sample application with a pipeline.

    ```bash
    ./sample_start.sh -p worker_safety_gear_detection
    ```

    This command will look for the payload for the pipeline specified in the `-p` argument above, inside the `payload.json` file and launch a pipeline instance in DLStreamer Pipeline Server. Refer to the table, to learn about different available options.

    Output:

    ```bash
    # Example output for Worker Safety gear detection
    Environment variables loaded from [WORKDIR]/manufacturing-ai-suite/industrial-edge-insights-vision/.env
    Running sample app: worker-safety-gear-detection
    Checking status of dlstreamer-pipeline-server...
    Server reachable. HTTP Status Code: 200
    Loading payload from [WORKDIR]/manufacturing-ai-suite/industrial-edge-insights-vision/apps/worker-safety-gear-detection/payload.json
    Payload loaded successfully.
    Starting pipeline: worker_safety_gear_detection
    Launching pipeline: worker_safety_gear_detection
    Extracting payload for pipeline: worker_safety_gear_detection
    Found 1 payload(s) for pipeline: worker_safety_gear_detection
    Payload for pipeline 'worker_safety_gear_detection' {"source":{"uri":"file:///home/pipeline-server/resources/videos/Safety_Full_Hat_and_Vest.avi","type":"uri"},"destination":{"frame":{"type":"webrtc","peer-id":"worker_safety"}},"parameters":{"detection-properties":{"model":"/home/pipeline-server/resources/models/worker-safety-gear-detection/deployment/Detection/model/model.xml","device":"CPU"}}}
    Posting payload to REST server at http://10.223.23.156:8080/pipelines/user_defined_pipelines/worker_safety_gear_detection
    Payload for pipeline 'worker_safety_gear_detection' posted successfully. Response: "784b87b45d1511f08ab0da88aa49c01e"
    ```

    NOTE: This will start the pipeline. The inference stream can be viewed on WebRTC, in a browser, at the following url:

    ```sh
    http://<HOST_IP>:8889/worker_safety/
    ```

8.  Get the status of running pipeline instance(s).

    ```bash
    ./sample_status.sh
    ```

    This command lists the statuses of pipeline instances launched during the lifetime of sample application.

    Output:

    ```bash
    # Example output for Worker Safety gear detection
    Environment variables loaded from [WORKDIR]/manufacturing-ai-suite/industrial-edge-insights-vision/.env
    Running sample app: worker-safety-gear-detection
    [
    {
        "avg_fps": 30.036955894826452,
        "elapsed_time": 3.096184492111206,
        "id": "784b87b45d1511f08ab0da88aa49c01e",
        "message": "",
        "start_time": 1752100724.3075056,
        "state": "RUNNING"
    }
    ]
    ```

9.  Stop pipeline instances.

    ```bash
    ./sample_stop.sh
    ```

    This command will stop all instances that are currently in the `RUNNING` state and return their last status.

    Output:

    ```bash
    # Example output for Worker Safety gear detection
    No pipelines specified. Stopping all pipeline instances
    Environment variables loaded from [WORKDIR]/manufacturing-ai-suite/industrial-edge-insights-vision/.env
    Running sample app: worker-safety-gear-detection
    Checking status of dlstreamer-pipeline-server...
    Server reachable. HTTP Status Code: 200
    Instance list fetched successfully. HTTP Status Code: 200
    Found 1 running pipeline instances.
    Stopping pipeline instance with ID: 784b87b45d1511f08ab0da88aa49c01e
    Pipeline instance with ID '784b87b45d1511f08ab0da88aa49c01e' stopped successfully. Response: {
        "avg_fps": 29.985911953641363,
        "elapsed_time": 37.45091152191162,
        "id": "784b87b45d1511f08ab0da88aa49c01e",
        "message": "",
        "start_time": 1752100724.3075056,
        "state": "RUNNING"
    }
    ```

    To stop a specific instance, identify it with the `--id` argument.
    For example, `./sample_stop.sh --id 784b87b45d1511f08ab0da88aa49c01e`

10. Stop the Docker application.

    ```bash
    docker compose down -v
    ```

    This will bring down the services in the application and remove any volumes.


## Further Reading

- [Helm based deployment](how-to-deploy-using-helm-charts.md)
- [MLOps using Model Registry](how-to-enable-mlops.md)
- [Run multiple AI pipelines](how-to-run-multiple-ai-pipelines.md)
- [Publish frames to S3 storage pipelines](how-to-run-store-frames-in-s3.md)
- [View telemetry data in Open Telemetry](how-to-view-telemetry-data.md)
- [Publish metadata to OPCUA](how-to-use-opcua-publisher.md)

## Troubleshooting

- [Troubleshooting Guide](troubleshooting-guide.md)