How to autostart pipelines#

Steps#

DL Streamer Pipeline Server allows autostarting pipelines provided all the necessary payload information such as source, destination, model parameters etc are available in the configuration file.

There are 2 ways to enable autostarting the pipeline. First way is to provide all the necessary payload information while defining the pipeline itself. Second way is to provide the information under the "pipelines" config with "payload" as the keyword.

Autostart for a pipeline can be enabled by setting the flag auto_start to true. This would start an instance of pipeline immediately as soon as DL Streamer Pipeline Server container is up.

Method 1 - Specifying all the information in the pipeline itself -#

Replace the following sections in [WORKDIR]/configs/default/config.json with the following Note: Follow instruction in the Prerequisite section to create a sample configuration file.

  • replace "pipeline" section with

    "pipeline": "multifilesrc location=/home/pipeline-server/resources/videos/warehouse.avi name=source  ! decodebin ! videoconvert ! gvadetect model=/home/pipeline-server/resources/models/geti/pallet_defect_detection/deployment/Detection/model/model.xml name=detection ! queue ! gvawatermark ! gvafpscounter ! gvametaconvert add-empty-results=true name=metaconvert ! gvametapublish file-format=json-lines file-path=/tmp/results.jsonl name=destination ! appsink name=appsink",

  • set "auto_start"to "true".

    Notice that we have inlined model xml path (in gvadetect element) and metadata publish file (in gvametapublish element) into the pipeline string.

  • After making changes to config.json, make sure to save it and restart DL Streamer Pipeline Server. Ensure that the changes made to the config.json are reflected in the container by volume mounting (as mentioned above) it.

    cd [WORKDIR]/docker/    
docker compose up

    We should see the metadata results in /tmp/results.jsonl file like the following snippet.

    {"objects":[{"detection":{"bounding_box":{"x_max":0.2760509825893678,"x_min":0.0009660996147431433,"y_max":0.5821986049413681,"y_min":0.23702500760555267},"confidence":0.8490034937858582,"label":"box","label_id":0},"h":166,"region_id":4602,"roi_type":"box","w":176,"x":1,"y":114},{"detection":{"bounding_box":{"x_max":0.18180961161851883,"x_min":0.051308222115039825,"y_max":0.4810962677001953,"y_min":0.3541457951068878},"confidence":0.7778390645980835,"label":"defect","label_id":2},"h":61,"region_id":4603,"roi_type":"defect","w":84,"x":33,"y":170}],"resolution":{"height":480,"width":640},"tags":{},"timestamp":96862470800}
{"objects":[{"detection":{"bounding_box":{"x_max":0.2759847411653027,"x_min":0.0009118685266003013,"y_max":0.5828713774681091,"y_min":0.2364599108695984},"confidence":0.8393885493278503,"label":"box","label_id":0},"h":166,"region_id":4606,"roi_type":"box","w":176,"x":1,"y":114},{"detection":{"bounding_box":{"x_max":0.18369046971201897,"x_min":0.044871505349874496,"y_max":0.480486124753952,"y_min":0.34511199593544006},"confidence":0.7414445281028748,"label":"defect","label_id":2},"h":65,"region_id":4607,"roi_type":"defect","w":89,"x":29,"y":166}],"resolution":{"height":480,"width":640},"tags":{},"timestamp":96895871652}

    Note If "auto_start"to "false", then you can start the pipeline manually by sending the curl request as shown below. In this case, curl request need not have any parameters as all the required parameters to start the pipeline is already mentioned in the pipeline config above.

    curl http://localhost:8080/pipelines/user_defined_pipelines/pallet_defect_detection -X POST -H 'Content-Type: application/json' -d '{}'

Method 2 - Add REST payload under "payload" section of the pipeline config.#

  • In case you have provided placeholders in you pipeline configuration, we can also plug in the REST payload in the pipeline configuration and make use of the auto_start feature to start DL Streamer Pipeline Server with this pipeline with the payload provided.

    The REST payload can be added inside a payload key section and the autostart key is then set true.

    Following is a sample config with payload provided and auto_start set to true

    {
    "config": {
        "logging": {
            "C_LOG_LEVEL": "INFO",
            "PY_LOG_LEVEL": "INFO"
        },
        "pipelines": [
            {
                "name": "pallet_defect_detection",
                "source": "gstreamer",
                "queue_maxsize": 50,
                "pipeline": "{auto_source} name=source  ! decodebin ! videoconvert ! gvadetect name=detection ! queue ! gvawatermark ! gvafpscounter ! gvametaconvert add-empty-results=true name=metaconvert ! gvametapublish name=destination ! appsink name=appsink",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "detection-properties": {
                                "element": {
                                "name": "detection",
                                "format": "element-properties"
                                }
                        }
                    }
                },
                "auto_start": true,
                "payload": {
                    "source": {
                        "uri": "file:///home/pipeline-server/resources/videos/warehouse.avi",
                        "type": "uri"
                    },
                    "destination": {
                        "metadata": {
                            "type": "file",
                            "path": "/tmp/results.jsonl",
                            "format": "json-lines"
                        },
                        "frame": {
                            "type": "rtsp",
                            "path": "pallet_defect_detection"
                        }
                    },
                    "parameters": {
                        "detection-properties": {
                            "model": "/home/pipeline-server/resources/models/geti/pallet_defect_detection/deployment/Detection/model/model.xml",
                            "device": "CPU"
                        }
                    }
                }
            }
        ]
    }
}

  • Ensure that the changes made to the config.json are reflected in the container by volume mounting (as described here).

  • Start DL Streamer Pipeline Server

        cd [WORKDIR]/docker/
    docker compose up

    The pipeline would start automatically as soon as DL Streamer Pipeline Server starts.