Overview

This node uses a cloud based service to read license plates from vehicles that are detected using the Vehicle & License Plate models.

This node requires AI Model Node (Vehicle Detection model) -> Model Inference Node (License Plate model) -> Track Objects Node prior to it in order to function properly using the "LPR & Make/Model/Color" recognition mode.

Inputs & Outputs

Inputs : 1, Media Format : Raw Video
Outputs : 1, Media Format: Raw Video
Output Metadata: license-plate results, counts, and entered/exited deltas under nodes.<node_id>, plus associated object attributes.

Properties

Property	Description	Type	Default	Required
`geographies`	Geographies for recognition	`string`	`us, us-ca`	No
`objects_to_track`	ex. `car`, `vehicle`, `truck`. Classes to be considered on the node. Leave blank to consider all.	model-labels	`car,vehicle,truck`	No
`display_info`	Display LPR info on video?	`bool`	`true`	No
`recognition_mode`	Continue performing additional recognition attempts until the above info is found. Options: LPR and make/model/color (`lpr_mmc`); LPR only (`lpr_only`); Make/model/color Only (`mmc_only`).	`enum`	`lpr_mmc`	No
`legacy_meta`	If enabled, add this node's metadata under 'lpr' top level key for backward compatibility	`bool`	`false`	No
`requests_buffer`	Time that the object must be present before the initial LPR request and between subsequent retries. Unit: seconds.	`float`	`1.0`	Yes
`presence_buffer`	Time during which objects that leave and re-enter the scene will be ignored. Increase to reduce duplicate alerts for a given object. Unit: seconds.	`float`	`5.0`	Yes

Output Metadata

The fields below are declared by this node's metadata schema; the JSON values are representative examples.

Path	Type	Description
`nodes.<node_id>.license_plates_entered_delta`	`integer`	Number of new license plates detected since last frame
`nodes.<node_id>.license_plates_exited_delta`	`integer`	Number of license plates that left since last frame
`nodes.<node_id>.license_plates_count`	`integer`	Total number of license plates in the view right now
`nodes.<node_id>.license_plates.<object_id>.plate.type`	`string`	Recognition result category for the plate.
`nodes.<node_id>.license_plates.<object_id>.plate.score`	`number`	Overall plate-recognition confidence.
`nodes.<node_id>.license_plates.<object_id>.plate.props.plate[].value`	`string`	Recognized plate text.
`nodes.<node_id>.license_plates.<object_id>.plate.props.plate[].score`	`number`	Confidence of this plate-text candidate.
`nodes.<node_id>.license_plates.<object_id>.plate.props.region[].value`	`string`	Recognized plate region.
`nodes.<node_id>.license_plates.<object_id>.plate.props.region[].score`	`number`	Confidence of this region candidate.
`nodes.<node_id>.license_plates.<object_id>.vehicle.type`	`string`	Recognized vehicle type.
`nodes.<node_id>.license_plates.<object_id>.vehicle.score`	`number`	Overall vehicle-recognition confidence.
`nodes.<node_id>.license_plates.<object_id>.vehicle.props.make_model[].make`	`string`	Recognized vehicle make.
`nodes.<node_id>.license_plates.<object_id>.vehicle.props.make_model[].model`	`string`	Recognized vehicle model.
`nodes.<node_id>.license_plates.<object_id>.vehicle.props.make_model[].score`	`number`	Confidence of this make/model candidate.
`nodes.<node_id>.license_plates.<object_id>.vehicle.props.orientation[].value`	`string`	Recognized vehicle orientation.
`nodes.<node_id>.license_plates.<object_id>.vehicle.props.orientation[].score`	`number`	Confidence of this orientation candidate.
`nodes.<node_id>.license_plates.<object_id>.vehicle.props.color[].value`	`string`	Recognized vehicle color.
`nodes.<node_id>.license_plates.<object_id>.vehicle.props.color[].score`	`number`	Confidence of this color candidate.
`nodes.<node_id>.error`	`string`	Most recent processing or recognition error, if any.
`nodes.<node_id>.type`	`string`	Identifies the node type that produced this metadata.

JSON example

{
  "nodes": {
    "annotate_lpr1": {
      "error": "value",
      "license_plates": {
        "object_id": {
          "plate": {
            "props": {
              "plate": [
                {
                  "score": 0.0,
                  "value": "value"
                }
              ],
              "region": [
                {
                  "score": 0.0,
                  "value": "value"
                }
              ]
            },
            "score": 0.0,
            "type": "value"
          },
          "vehicle": {
            "props": {
              "color": [
                {
                  "score": 0.0,
                  "value": "value"
                }
              ],
              "make_model": [
                {
                  "make": "value",
                  "model": "value",
                  "score": 0.0
                }
              ],
              "orientation": [
                {
                  "score": 0.0,
                  "value": "value"
                }
              ]
            },
            "score": 0.0,
            "type": "value"
          }
        }
      },
      "license_plates_count": 0,
      "license_plates_entered_delta": 0,
      "license_plates_exited_delta": 0,
      "type": "annotate_lpr"
    }
  }
}

Object labels and attributes

Object labels/classes added: None.
Object attribute labels/classes added: Recognized plate text (10300), vehicle make (10301), model (10302), and color (10303).

The following information is added to the detected object's "attributes" array:

"class_id" field	"label" field	"probability" field
10300	The recognized license plate (string)	LP recognition confidence
10301	Vehicle's make	LP recognition confidence
10302	Vehicle's model	LP recognition confidence
10303	Vehicle's color	LP recognition confidence

"objects": [{
    "id": 5750484150146564100,
    "label": "car",
    "class_id": 0,
    "probability": 0.98,
    "rect": {
        "width": 47,
        "top": 201,
        "left": 656,
        "height": 25.
    },
    "attributes": [{
        "label": "ABC1234",
        "class_id": 10500,
        "probability": 1.0,
    },
    {
        "label": "Tesla",
        "class_id": 10501,
        "probability": 1.0,
    },
    {
        "label": "Model S",
        "class_id": 10502,
        "probability": 1.0,
    },
    {
        "label": "Red",
        "class_id": 10503,
        "probability": 1.0,
    }]
}]