# Get Started

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

## Prerequisites

- [System Requirements](./get-started/vision-system-requirements.md)

## Set up 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 follow.

   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](https://docs.openedgeplatform.intel.com/dev/OEP-articles/contribution-guide.html#repository-cloning-partial-cloning).

   ```bash
   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 manufacturing-ai-suite
   cd manufacturing-ai-suite/industrial-edge-insights-vision
   ```

2. Set the application-specific environment variable file:

    ::::{tab-set} 
    :::{tab-item}  **Pallet Defect Detection**
    :sync: pallet-detect 

   ```bash
   cp .env_pallet-defect-detection .env
   ```

    ::: 
    :::{tab-item}  **PCB Anomaly Detection**
    :sync: pcb-detect 

   ```bash
   cp .env_pcb-anomaly-detection .env
   ```

   
   :::
   ::::
   

3. Edit the following environment variables in the `.env` file:

    ::::{tab-set} 
    :::{tab-item}  **Pallet Defect Detection**
    :sync: pallet-detect 

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

   MINIO_ACCESS_KEY=   # MinIO service & client access key e.g. intel1234
   MINIO_SECRET_KEY=   # MinIO service & client secret key e.g. intel1234

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

   # application directory
   SAMPLE_APP=pallet-defect-detection
   ```

    ::: 
    :::{tab-item}  **PCB Anomaly Detection**
    :sync: pcb-detect 

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

   MINIO_ACCESS_KEY=   # MinIO service & client access key e.g. intel1234
   MINIO_SECRET_KEY=   # MinIO service & client secret key e.g. intel1234

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

   # application directory
   SAMPLE_APP=pcb-anomaly-detection
   ```

   
   :::
   ::::
   

4. Install the prerequisites. Run with sudo if needed.

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

   This script sets up the application prerequisites, 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.

   > **Note:** For the Pallet Defect Detection application, the setup script downloads a pre-trained detection model by default. If you want to train and use your own custom model, see [Generate a Model with Geti™](./pallet-defect-detection/how-to-guides/generate-model-with-geti.md).

## Deploy the Application

1. Start the Docker application:

   The Docker daemon service should start automatically at boot. If not, you can start it manually:

   ```bash
   sudo systemctl start docker
   ```

    > **Note:** If you are running multiple instances of the application, start the services using `./run.sh up` instead.

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

2. 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 for Pallet Defect Detection:

   ```bash
   Environment variables loaded from [WORKDIR]/manufacturing-ai-suite/industrial-edge-insights-vision/.env
   Running sample app: pallet-defect-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": "pallet_defect_detection"
       }
       ...
   ]
   ```

3. Start the sample application with a pipeline:

    ::::{tab-set} 
    :::{tab-item}  **Pallet Defect Detection**
    :sync: pallet-detect 

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

    ::: 
    :::{tab-item}  **PCB Anomaly Detection**
    :sync: pcb-detect 

   ```bash
   ./sample_start.sh -p pcb_anomaly_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 DL Streamer Pipeline Server.

   > **IMPORTANT:** Before you run `sample_start.sh` script, make sure that
   > `jq` is installed on your system. See the
   > [troubleshooting guide](./troubleshooting.md#unable-to-parse-json-payload-due-to-missing-jq-package)
   > for more details.

   Example Output for Pallet Defect Detection:

   ```bash
   Environment variables loaded from [WORKDIR]/manufacturing-ai-suite/industrial-edge-insights-vision/.env
   Running sample app: pallet-defect-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/pallet-defect-detection/payload.json
   Payload loaded successfully.
   Starting pipeline: pallet_defect_detection
   Launching pipeline: pallet_defect_detection
   Extracting payload for pipeline: pallet_defect_detection
   Found 1 payload(s) for pipeline: pallet_defect_detection
   Payload for pipeline 'pallet_defect_detection' {"source":{"uri":"file:///home/pipeline-server/resources/videos/warehouse.avi","type":"uri"},"destination":{"frame":{"type":"webrtc","peer-id":"pdd"}},"parameters":{"detection-properties":{"model":"/home/pipeline-server/resources/models/pallet-defect-detection/model.xml","device":"CPU"}}}
   Posting payload to REST server at https://<HOST_IP>/api/pipelines/user_defined_pipelines/pallet_defect_detection
   Payload for pipeline 'pallet_defect_detection' posted successfully. Response: "4b36b3ce52ad11f0ad60863f511204e2"
   ```

   > **Note:** The pipeline uses the pre-trained model downloaded during setup. To replace it with a custom model trained on your own data using Geti™, follow [Generate a Model with Geti™](./pallet-defect-detection/how-to-guides/generate-model-with-geti.md) and replace the `model.xml` and `model.bin` files in your resources accordingly.

   > **Note:** This will start the pipeline. To view the inference stream on WebRTC, open a browser and navigate to the application URL below.
   > If you are running multiple instances of the application, provide the `NGINX_HTTPS_PORT` number in the URL for the application instance, i.e., replace `<HOST_IP>` with `<HOST_IP>:<NGINX_HTTPS_PORT>`.
   > If you are running a single instance and using an `NGINX_HTTPS_PORT` other than the default 443, replace 443 with `<NGINX_HTTPS_PORT>`.

    ::::{tab-set} 
    :::{tab-item}  **Pallet Defect Detection**
    :sync: pallet-detect 

   ```text
   https://<HOST_IP>/mediamtx/pdd/
   ```

    ::: 
    :::{tab-item}  **PCB Anomaly Detection**
    :sync: pcb-detect 

   ```text
   https://<HOST_IP>/mediamtx/anomaly/
   ```

   
   :::
   ::::
   

4. 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 the sample application.

   Example Output for Pallet Defect Detection:

   ```bash
   Environment variables loaded from [WORKDIR]/manufacturing-ai-suite/industrial-edge-insights-vision/.env
   Running sample app: pallet-defect-detection
   [
   {
       "avg_fps": 30.00446179356829,
       "elapsed_time": 36.927825689315796,
       "id": "4b36b3ce52ad11f0ad60863f511204e2",
       "message": "",
       "start_time": 1750956469.620569,
       "state": "RUNNING"
   }
   ]
   ```

5. 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.

   Example Output for Pallet Defect Detection:

   ```bash
   No pipelines specified. Stopping all pipeline instances
   Environment variables loaded from [WORKDIR]/manufacturing-ai-suite/industrial-edge-insights-vision/.env
   Running sample app: pallet-defect-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: 4b36b3ce52ad11f0ad60863f511204e2
   Pipeline instance with ID '4b36b3ce52ad11f0ad60863f511204e2' stopped successfully. Response: {
   "avg_fps": 30.002200575353214,
   "elapsed_time": 63.72864031791687,
   "id": "4b36b3ce52ad11f0ad60863f511204e2",
   "message": "",
   "start_time": 1750956469.620569,
   "state": "RUNNING"
   }
   ```

   To stop a specific instance, identify it with the `--id` argument.
   For example, `./sample_stop.sh --id 4b36b3ce52ad11f0ad60863f511204e2`

6. Stop the Docker application.

    > **Note:** If you are running multiple instances of the application, stop the services using `./run.sh down` instead.

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

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

## Further Reading

- [Deploy with Helm](./get-started/deploy-with-helm.md)
- [Deploy multiple instances with Helm](./get-started/deploy-multiple-instances-with-helm.md)
- [Enable MLOps](./how-to-guides/enable-mlops.md)
- [Run multiple AI pipelines](./how-to-guides/run-multiple-ai-pipelines.md)
- [Publish frames to S3 storage pipelines](./how-to-guides/store-frames-in-s3.md)
- [View telemetry data in Open Telemetry](./how-to-guides/view-telemetry-data.md)
- [Publish metadata to OPCUA](./how-to-guides/use-opcua-publisher.md)
- For the Pallet Defect Detection application, see [Generate a model with Geti™](./pallet-defect-detection/how-to-guides/generate-model-with-geti.md)
- For the Pallet Defect Detection application, see [Integrate Balluff SDK with supported cameras](./pallet-defect-detection/how-to-guides/integrate-balluff-sdk.md)
- For the Pallet Defect Detection application, see [Integrate pylon SDK for Basler camera support](./pallet-defect-detection/how-to-guides/integrate-pylon-sdk.md)

## Troubleshooting

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


:::{toctree}
:hidden:

Vision System Requirements <./get-started/vision-system-requirements.md>
./get-started/environment-variables
./get-started/deploy-with-helm
./get-started/deploy-multiple-instances-with-helm

:::