Advanced EVAM configuration#

Basic configuration needs that described [here]. For EIS deployments, there are more fields applicable and they are described in this section.

Here is a sample EIS config file.

{
    "config": {
        "logging": {
            "C_LOG_LEVEL": "INFO",
            "PY_LOG_LEVEL": "INFO"
        },
        "cert_type": [
            "grpc"
        ],
        "pipelines": [
            {
                "name": "edge_video_analytics_results",
                "source": "gstreamer",
                "queue_maxsize": 50,
                "pipeline": "multifilesrc loop=TRUE location=/home/pipeline-server/resources/videos/anomalib_pcb_test.avi name=source ! h264parse ! decodebin ! queue max-size-buffers=10 ! videoconvert ! video/x-raw,format=RGB ! udfloader name=udfloader ! appsink name=destination",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "udfloader": {
                            "element": {
                                "name": "udfloader",
                                "property": "config",
                                "format": "json"
                            },
                            "type": "object"
                        }
                    }
                },
                "auto_start": true,
                "udfs": {
                    "udfloader": [
                        {
                            "device": "CPU",
                            "task": "classification",
                            "inferencer": "openvino_nomask",
                            "model_metadata": "/home/pipeline-server/udfs/python/anomalib_udf/stfpm/metadata.json",
                            "name": "python.anomalib_udf.inference",
                            "type": "python",
                            "visualize": "false",
                            "weights": "/home/pipeline-server/udfs/python/anomalib_udf/stfpm/model.onnx"
                        }
                    ]
                }
            }
        ]
    },
    "interfaces": {
        "Clients": [
            {
                "EndPoint": "multimodal-data-visualization-streaming:65138",
                "Name": "visualizer",
                "Topics": [
                    "edge_video_analytics_results"
                ],
                "Type": "grpc",
                "overlay_annotation": "true"
            },
            {
                "EndPoint": "ia-datastore:65137",
                "Name": "datastore",
                "Topics": [
                    "edge_video_analytics_results"
                ],
                "Type": "grpc"
            }
        ]
    }
}

Fields in config section.

Parameter	Description

Fields within pipelines section.

Parameter	Description
`name`	Name of the pipeline.
`source`	Source of the frames. This should be `"gstreamer"` or `"image-ingestor"`.
`pipeline`	DLStreamer pipeline description.
`parameters`	Optional JSON object specifying pipeline parameters that can be customized when the pipeline is launched
`auto_start`	The Boolean flag for whether to start the pipeline on EVAM start up.
`udfs`	UDF config parameters
`tags`	Additional information to store with frame metadata. e.g. camera location/orientation of video input.
`publish_raw_frame`	The Boolean flag for whether to publish raw frame.
`encoding`	Encodes the image in jpeg or png format.
`mqtt_publisher`	Publishes frame/metadata to mqtt broker.
`convert_metadata_to_dcaas_format`	Converts inference results to DCaaS standardized format.

Note:

For jpeg encoding type, level is the quality from 0 to 100. A higher value means better quality.
For png encoding type, level is the compression level from 0 to 9. A higher value means a smaller size and longer compression time.

Encoding elements can be used in the pipeline as an alternative to specifying the encoding parameters. Refer to the below pipeline for using jpegenc in config.json.

"pipeline": "multifilesrc loop=FALSE stop-index=0 location=/home/pipeline-server/resources/pcb_d2000.avi name=source ! h264parse ! decodebin ! videoconvert ! video/x-raw,format=BGR ! udfloader name=udfloader ! jpegenc ! appsink name=destination",

Refer to the below pipeline for using pngenc in config.json.

"pipeline": "multifilesrc loop=FALSE stop-index=0 location=/home/pipeline-server/resources/pcb_d2000.avi name=source ! h264parse ! decodebin ! videoconvert ! video/x-raw,format=BGR ! udfloader name=udfloader ! videoconvert ! pngenc ! appsink name=destination",

convert_metadata_to_dcaas_format, when set to true in config.json converts the metadata to DCaaS compatible format. Currently this has been tested for gvadetect element used in the pipeline. Refer to the below pipeline for example,

"pipeline": "multifilesrc loop=TRUE stop-index=0 location=/home/pipeline-server/resources/classroom.avi name=source ! h264parse ! decodebin ! queue max-size-buffers=10 ! videoconvert ! video/x-raw,format=BGR ! gvadetect model=/home/pipeline-server/models/object_detection/person/FP32/person-detection-retail-0013.xml model-proc=/home/pipeline-server/models/object_detection/person/person-detection-retail-0013.json ! queue ! jpegenc ! appsink name=destination",

For MQTT publishing,
- Refer to the document here for details on prerequisites, configuration, filtering and error handling.
- MQTT publishing can be enabled along with EII Message Bus publishing.

Interface Configuration#

By default, EVAM is configured to publish frame and metadata over the EIS message bus. The interfaces configuration for publisher is shown below -

    "interfaces": {
        "Clients": [
            {
                "EndPoint": "multimodal-data-visualization-streaming:65138",
                "Name": "visualizer",
                "Topics": [
                    "edge_video_analytics_results"
                ],
                "Type": "grpc",
                "overlay_annotation": "true"
            }
        ]
    }

Key	Type	Required (Mandatory)	Description
`Clients`	`array`	Yes	Entire client interface will be added with in the array. Multiple server endpoints can be added by adding elements in the array.
`Name`	`string`	Yes	Name of different publisher interfaces
`Type`	`string`	Yes	Specifies protocol on which data will be published. Currently `grpc` is only supported.
`EndPoint`	`string` or `object`	Yes	Refers to server endpoint which is the container name and the port number. In `network` mode, use host IP instead of container name. For example, when using network cameras. Refer to Different ways of specifying endpoint
`Topics`	`array`	Yes	Specifying the topics on which data will be published on. Multiple elements in this array can denote multiple topics published on the same endpoint
`overlay_annotation`	`string`	No	Can be `true` or `false`(also `false` when key is omitted). Specifies whether to use overlay annotations using annotation data from a UDF. For this to work, the UDF must provide annotation data as part of metadata.