Configuring udfloader element

The udfloader element has a single configurable property with the name config. This field expects a JSON string as an input. They key/value pairs in the JSON string should correspond to the constructor arguments of the UDFs class. Currently only atomic data types are supported.

Sample UDF config:

The UDF config should be passed to the udfs key in the EIS config file. The below example configures the udfloader element to load and execute a single UDF, which in this case happens to be the geti_udf.

{
      "udfs": [
          {
              "name": "python.geti_udf.geti_udf",
              "type": "python",
              "device": "CPU",
              "visualize": "false",
              "deployment": "./resources/geti/pallet_defect_detection/deployment",
              "metadata_converter": "null",
          }
      ]
}

In order to chain multiple UDFs, simply provide the configs for UDFs as additional entries in the udfs array in the EIS config file as shown below:

  {
      "udfs": [
          {
              "name": "udf1",
              "type": "python",
              "arg1": "value1",
              "arg2": "value2"
          },
          {
              "name": "udf2",
              "type": "python",
              "arg1": "value1",
              "arg2": "value2"
          }
      ]
  }

Pallet Defect Detection

This Geti™ udf supports deploying a project for local inference with OpenVINO™ using Intel® Geti™ SDK Python package. It uses a Geti™ based Pallet Defect Detection model.

Refer to the below config for the default config used for this Geti UDF:

 "udfs": [
            {
                "name": "python.geti_udf.geti_udf",
                "type": "python",
                "device": "CPU",
                "deployment": "./resources/geti/pallet_defect_detection/deployment",
                "visualize": "false",
                "metadata_converter": "null"
            }
        ]

Add Label

This is a dummy udf for providing sample classification (anomalib usecase) data.

Add Label UDF is developed to facilitate anomalib training data collection where one is pointing camera on anomalous or non-anomalous product. While you point a camera to a given category of scene, default label value which is a configurable field ("anomalous": "true" or "anomalous": "false") in this UDF is applied to every frame. This process needs to be repeated to every type of class ("anomalous": "true" or "anomalous": "false") that you want to capture from the scene for training.

The following example shows the configuration for add label UDF. Either set "anomalous": "true" or "anomalous": "false"

    "udfs": [
        {
            "type": "python",
            "name": "python.add_label",
            "anomalous": "true"
        }
    ]

There is no AI model involved. It is a simple UDF script that labels the data with default label values and saves in DataStore which is further expected to be consumed by Visualizer microservice and Intel® Edge Data Collection microservice.

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