diff --git a/docs/docs/configuration/license_plate_recognition.md b/docs/docs/configuration/license_plate_recognition.md index 47dcfaa09..e4c8c1167 100644 --- a/docs/docs/configuration/license_plate_recognition.md +++ b/docs/docs/configuration/license_plate_recognition.md @@ -63,8 +63,8 @@ Fine-tune the LPR feature using these optional parameters: - **`detection_threshold`**: License plate object detection confidence score required before recognition runs. - Default: `0.7` - Note: This is field only applies to the standalone license plate detection model, `min_score` should be used to filter for models that have license plate detection built in. -- **`min_area`**: Defines the minimum size (in pixels) a license plate must be before recognition runs. - - Default: `1000` pixels. +- **`min_area`**: Defines the minimum area (in pixels) a license plate must be before recognition runs. + - Default: `1000` pixels. Note: this is intentionally set very low as it is an _area_ measurement (length x width). For reference, 1000 pixels represents a ~32x32 pixel square in your camera image. - Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant plates. ### Recognition @@ -94,7 +94,7 @@ These configuration parameters are available at the global level of your config. ```yaml lpr: enabled: True - min_area: 1500 # Ignore plates smaller than 1500 pixels + min_area: 1500 # Ignore plates with an area (length x width) smaller than 1500 pixels min_plate_length: 4 # Only recognize plates with 4 or more characters known_plates: Wife's Car: @@ -111,7 +111,7 @@ lpr: ```yaml lpr: enabled: True - min_area: 4000 # Run recognition on larger plates only + min_area: 4000 # Run recognition on larger plates only (4000 pixels represents a 63x63 pixel square in your image) recognition_threshold: 0.85 format: "^[A-Z]{2} [A-Z][0-9]{4}$" # Only recognize plates that are two letters, followed by a space, followed by a single letter and 4 numbers match_distance: 1 # Allow one character variation in plate matching @@ -175,6 +175,7 @@ When using `type: "lpr"` for a camera, a non-standard object detection pipeline - Snapshots will always be saved. - Tracked objects are retained according to your retain settings for `record` and `snapshots`. - Zones and object masks cannot be used. +- Debug view may not show `license_plate` bounding boxes, even if you are using a Frigate+ model for your standard object detection pipeline. - The `frigate/events` MQTT topic will not publish tracked object updates, though `frigate/reviews` will if recordings are enabled. ::: diff --git a/web/src/components/input/InputWithTags.tsx b/web/src/components/input/InputWithTags.tsx index c82b60b04..13977b8a7 100644 --- a/web/src/components/input/InputWithTags.tsx +++ b/web/src/components/input/InputWithTags.tsx @@ -806,7 +806,7 @@ export default function InputWithTags({ className="inline-flex items-center whitespace-nowrap rounded-full bg-green-100 px-2 py-0.5 text-sm capitalize text-green-800" > {t("filter.label." + filterType)}:{" "} - {formatFilterValues(filterType, value)} + {value.replaceAll("_", " ")}