Get Started#

  • Time to Complete: 30 minutes

  • Programming Language: Python 3

Prerequisites#

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 at docker.com.

  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.

    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

    cp .env_pallet_defect_detection .env

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

    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=pallet-defect-detection

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

    ./setup.sh

    This script sets up application pre-requisites, download 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#

  1. Start the Docker application:

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

sudo systemctl start docker

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

  1. Fetch the list of pipeline loaded available to launch:

    ./sample_list.sh

    This lists the pipeline loaded in DL Streamer Pipeline Server.

    Example Output:

    # Example output for Pallet Defect Detection
Environment variables loaded from /home/intel/OEP/edge-ai-suites/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"
    }
    ...
]

  2. Start the sample application with a pipeline.

    ./sample_start.sh -p pallet_defect_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:

    # Example output for Pallet Defect Detection
Environment variables loaded from /home/intel/OEP/edge-ai-suites/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 /home/intel/OEP/edge-ai-suites/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 http://<HOST_IP>:8080/pipelines/user_defined_pipelines/pallet_defect_detection
Payload for pipeline 'pallet_defect_detection' posted successfully. Response: "4b36b3ce52ad11f0ad60863f511204e2"

    NOTE: This will start the pipeline. To view the inference stream on WebRTC, open a browser and navigate to http://<HOST_IP>:8889/pdd/ for Pallet Defect Detection

  3. Get the status of running pipeline instance(s):

    ./sample_status.sh

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

    Output:

    # Example output for Pallet Defect Detection
Environment variables loaded from /home/intel/OEP/edge-ai-suites/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"
}
]

  4. Stop pipeline instances.

    ./sample_stop.sh

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

    Output:

    # Example output for Pallet Defect Detection
No pipelines specified. Stopping all pipeline instances
Environment variables loaded from /home/intel/OEP/edge-ai-suites/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

  5. Stop the Docker application.

    docker compose down -v

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

Further Reading#

Troubleshooting#