Overview

This node uses Platerecognizer's Snapshot API 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 : None

Properties

Property	Description	Type	Default	Required
`pr_mode`	Platerecognizer API type. Options: `snapshot_cloud` (API hosted by Platerecognizer), `snapshot_onprem` (API hosted by you using Platerecognizer docker SDK)	enum	`snapshot_cloud`	No
`pr_api_key`	Platerecognizer snapshot API Token from https://platerecognizer.com/snapshot/. Conditional on `pr_mode` being `snapshot_cloud`	string	null	Yes
`pr_host`	Platerecognizer On-premise host URL. Conditional on `pr_mode` being `snapshot_onprem`	string	`http://localhost:8080`	Yes
`geographies`	Geographies to consider for license plate recognition. Leave blank to consider all.	string	`us`	No
`objects_to_track`	Objects to perform LPR. Example: `car`, `vehicle`, `truck`. Unset means all. Accepted formats: `object_label`: any object of this type, with or without a classifier attribute. Example: `car` `object_label.class_type`: any object of this type that has a specific classifier attribute. Example: `car.red` `object_label.`: any object of this type that has at least one classifier attribute. Example: `car.` (this will match `car.red`, `car.yellow`, etc)	model-label	`car,vehicle,truck`	No
`display_info`	Display LPR info on video?	bool	true	No
`recognition_mode`	Recognition mode. Options: `lpr_mmc` (LPR & Make/Model/Color). perform recognition once we find a valid association between a Vehicle and a License Plate. `lpr_only` (LPR Only). Will perform recognition attempts using the detected License Plate image crop. `mmc_only` (Make/Model/Color Only). Performs recognition attempts using the detected Vehicle image crop.	enum	`lpr_mmc`	No
`requests_buffer`	License plate recognition buffer time in seconds. The interval (in seconds) before perform the initial LPR request and between subsequent retries.	float	1.0	Yes
`presence_buffer`	Duplicate rejection buffer time in seconds	float	5.0	Yes
`min_dims`	Min object size for lookup attempt in pixels	number	20	No
`max_attempts`	Max object lookup attempts	number	5	No

Metadata

Metadata Property	Description
`nodes.<node_id>`	License plate information as described in the JSON below. `<node_id>` for License Plate Detection Nodes is of the form `annotate_lprX` (ex. `annotate_lpr1`)

"nodes":{
    "annotate_lpr*":{
        "type":"annotate_lpr",
        "license_plates_entered_delta":"<int>",
        "license_plates_exited_delta":"<int>",
        "license_plates_count":"<int>",
        "license_plates":{
            "<object_tracking_id>": {
                "plate":{
                    "type":"Plate",
                    "score":<float>,
                    "props":{
                        "plate":[
                            {
                            "value":"<license_plate_number>",
                            "score":<float>
                            }
                        ],
                        "region":[
                            {
                            "value":"<region>",
                            "score":<float>
                            }
                        ]
                    }
                },
                "vehicle":{
                    "type":"<vehicle_type>",
                    "score":<float>,
                    "props":{
                        "make_model":[
                            {
                            "make":"<make>",
                            "model":"<model>",
                            "score":<float>
                            }
                        ],
                        "orientation":[
                            {
                            "value":"<orientation>",
                            "score":<float>
                            }
                        ],
                        "color":[
                            {
                            "value":"<color>",
                            "score":<float>
                            }
                        ]
                    }
                }
            }
        }
    }
}

Format

Key	Type	Description
`license_plates_entered_delta`	Integer	Number of new license plates detected since last frame
`license_plates_exited_delta`	Integer	Number of license plates that left since last frame
`license_plates_count`	Integer	Total number of license plates in the view right now
`license_plates`	List of object IDs	Information about the license plate, make, model & color of each vehicle. `object_tracking_id` : Unique identifier for a specific object, as specified by `object.id` property (see Model Inference Node ) The `"plate"` and `"recognition"` fields contents may not be present depending on the current recognition results. Please check the `json` example for more details.

Objects metadata augmentation

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": 10300,
        "probability": 1.0,
    },
    {
        "label": "Tesla",
        "class_id": 10301,
        "probability": 1.0,
    },
    {
        "label": "Model S",
        "class_id": 10302,
        "probability": 1.0,
    },
    {
        "label": "Red",
        "class_id": 10303,
        "probability": 1.0,
    }]
}]