mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-08-02 17:12:16 +03:00
Docs refactor (#22703)
CI / AMD64 Build (push) Waiting to run
CI / ARM Build (push) Waiting to run
CI / Jetson Jetpack 6 (push) Waiting to run
CI / AMD64 Extra Build (push) Blocked by required conditions
CI / ARM Extra Build (push) Blocked by required conditions
CI / Synaptics Build (push) Blocked by required conditions
CI / Assemble and push default build (push) Blocked by required conditions
CI / AMD64 Build (push) Waiting to run
CI / ARM Build (push) Waiting to run
CI / Jetson Jetpack 6 (push) Waiting to run
CI / AMD64 Extra Build (push) Blocked by required conditions
CI / ARM Extra Build (push) Blocked by required conditions
CI / Synaptics Build (push) Blocked by required conditions
CI / Assemble and push default build (push) Blocked by required conditions
* add generation script a script to read yaml code blocks from docs markdown files and generate corresponding "Frigate UI" tab instructions based on the json schema, i18n, section configs (hidden fields), and nav mappings * first pass * components * add to gitignore * second pass * fix broken anchors * fixes * clean up tabs * version bump * tweaks * remove role mapping config from ui
This commit is contained in:
Josh Hawkins
@@ -3,6 +3,10 @@ id: license_plate_recognition
|
||||
title: License Plate Recognition (LPR)
|
||||
---
|
||||
|
||||
import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
|
||||
Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) name as a `sub_label` to tracked objects of type `car` or `motorcycle`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
|
||||
|
||||
LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. When a vehicle becomes stationary, LPR continues to run for a short time after to attempt recognition.
|
||||
@@ -34,14 +38,35 @@ License plate recognition works by running AI models locally on your system. The
|
||||
|
||||
## Configuration
|
||||
|
||||
License plate recognition is disabled by default. Enable it in your config file:
|
||||
License plate recognition is disabled by default and must be enabled before it can be used.
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
|
||||
|
||||
- Set **Enable LPR** to on
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
lpr:
|
||||
enabled: True
|
||||
```
|
||||
|
||||
Like other enrichments in Frigate, LPR **must be enabled globally** to use the feature. You should disable it for specific cameras at the camera level if you don't want to run LPR on cars on those cameras:
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
Like other enrichments in Frigate, LPR **must be enabled globally** to use the feature. Disable it for specific cameras at the camera level if you don't want to run LPR on cars on those cameras.
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > License plate recognition" /> for the desired camera and disable the **Enable LPR** toggle.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml {4,5}
|
||||
cameras:
|
||||
@@ -51,65 +76,144 @@ cameras:
|
||||
enabled: False
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
For non-dedicated LPR cameras, ensure that your camera is configured to detect objects of type `car` or `motorcycle`, and that a car or motorcycle is actually being detected by Frigate. Otherwise, LPR will not run.
|
||||
|
||||
Like the other real-time processors in Frigate, license plate recognition runs on the camera stream defined by the `detect` role in your config. To ensure optimal performance, select a suitable resolution for this stream in your camera's firmware that fits your specific scene and requirements.
|
||||
|
||||
## Advanced Configuration
|
||||
|
||||
Fine-tune the LPR feature using these optional parameters at the global level of your config. The only optional parameters that can be set at the camera level are `enabled`, `min_area`, and `enhancement`.
|
||||
Fine-tune the LPR feature using these optional parameters. The only optional parameters that can be set at the camera level are `enabled`, `min_area`, and `enhancement`.
|
||||
|
||||
### Detection
|
||||
|
||||
- **`detection_threshold`**: License plate object detection confidence score required before recognition runs.
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
|
||||
|
||||
- **Detection threshold**: License plate object detection confidence score required before recognition runs. This field only applies to the standalone license plate detection model; `threshold` and `min_score` object filters should be used for models like Frigate+ that have license plate detection built in.
|
||||
- Default: `0.7`
|
||||
- Note: This is field only applies to the standalone license plate detection model, `threshold` and `min_score` object filters should be used for models like Frigate+ that have license plate detection built in.
|
||||
- **`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.
|
||||
- **`device`**: Device to use to run license plate detection _and_ recognition models.
|
||||
- **Minimum plate area**: Minimum area (in pixels) a license plate must be before recognition runs. This 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.
|
||||
- Default: `1000` pixels
|
||||
- **Device**: Device to use to run license plate detection _and_ recognition models. Auto-selected by Frigate and can be `CPU`, `GPU`, or the GPU's device number. For users without a model that detects license plates natively, using a GPU may increase performance of the YOLOv9 license plate detector model. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation.
|
||||
- Default: `None`
|
||||
- This is auto-selected by Frigate and can be `CPU`, `GPU`, or the GPU's device number. For users without a model that detects license plates natively, using a GPU may increase performance of the YOLOv9 license plate detector model. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation. However, for users who run a model that detects `license_plate` natively, there is little to no performance gain reported with running LPR on GPU compared to the CPU.
|
||||
- **`model_size`**: The size of the model used to identify regions of text on plates.
|
||||
- **Model size**: The size of the model used to identify regions of text on plates. The `small` model is fast and identifies groups of Latin and Chinese characters. The `large` model identifies Latin characters only, and uses an enhanced text detector to find characters on multi-line plates. If your country or region does not use multi-line plates, you should use the `small` model.
|
||||
- Default: `small`
|
||||
- This can be `small` or `large`.
|
||||
- The `small` model is fast and identifies groups of Latin and Chinese characters.
|
||||
- The `large` model identifies Latin characters only, and uses an enhanced text detector to find characters on multi-line plates. It is significantly slower than the `small` model.
|
||||
- If your country or region does not use multi-line plates, you should use the `small` model as performance is much better for single-line plates.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
lpr:
|
||||
enabled: True
|
||||
detection_threshold: 0.7
|
||||
min_area: 1000
|
||||
device: CPU
|
||||
model_size: small
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
### Recognition
|
||||
|
||||
- **`recognition_threshold`**: Recognition confidence score required to add the plate to the object as a `recognized_license_plate` and/or `sub_label`.
|
||||
- Default: `0.9`.
|
||||
- **`min_plate_length`**: Specifies the minimum number of characters a detected license plate must have to be added as a `recognized_license_plate` and/or `sub_label` to an object.
|
||||
- Use this to filter out short, incomplete, or incorrect detections.
|
||||
- **`format`**: A regular expression defining the expected format of detected plates. Plates that do not match this format will be discarded.
|
||||
- `"^[A-Z]{1,3} [A-Z]{1,2} [0-9]{1,4}$"` matches plates like "B AB 1234" or "M X 7"
|
||||
- `"^[A-Z]{2}[0-9]{2} [A-Z]{3}$"` matches plates like "AB12 XYZ" or "XY68 ABC"
|
||||
- Websites like https://regex101.com/ can help test regular expressions for your plates.
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
|
||||
|
||||
- **Recognition threshold**: Recognition confidence score required to add the plate to the object as a `recognized_license_plate` and/or `sub_label`.
|
||||
- Default: `0.9`
|
||||
- **Min plate length**: Minimum number of characters a detected license plate must have to be added as a `recognized_license_plate` and/or `sub_label`. Use this to filter out short, incomplete, or incorrect detections.
|
||||
- **Plate format regex**: A regular expression defining the expected format of detected plates. Plates that do not match this format will be discarded. Websites like https://regex101.com/ can help test regular expressions for your plates.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
lpr:
|
||||
enabled: True
|
||||
recognition_threshold: 0.9
|
||||
min_plate_length: 4
|
||||
format: "^[A-Z]{2}[0-9]{2} [A-Z]{3}$"
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
### Matching
|
||||
|
||||
- **`known_plates`**: List of strings or regular expressions that assign custom a `sub_label` to `car` and `motorcycle` objects when a recognized plate matches a known value.
|
||||
- These labels appear in the UI, filters, and notifications.
|
||||
- Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
|
||||
- **`match_distance`**: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate.
|
||||
- For example, setting `match_distance: 1` allows a plate `ABCDE` to match `ABCBE` or `ABCD`.
|
||||
- This parameter will _not_ operate on known plates that are defined as regular expressions. You should define the full string of your plate in `known_plates` in order to use `match_distance`.
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
|
||||
|
||||
- **Known plates**: Assign custom `sub_label` values to `car` and `motorcycle` objects when a recognized plate matches a known value. These labels appear in the UI, filters, and notifications. Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
|
||||
- **Match distance**: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate. For example, setting to `1` allows a plate `ABCDE` to match `ABCBE` or `ABCD`. This parameter will _not_ operate on known plates that are defined as regular expressions.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
lpr:
|
||||
enabled: True
|
||||
match_distance: 1
|
||||
known_plates:
|
||||
Wife's Car:
|
||||
- "ABC-1234"
|
||||
Johnny:
|
||||
- "J*N-*234"
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
### Image Enhancement
|
||||
|
||||
- **`enhancement`**: A value between 0 and 10 that adjusts the level of image enhancement applied to captured license plates before they are processed for recognition. This preprocessing step can sometimes improve accuracy but may also have the opposite effect.
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
|
||||
|
||||
- **Enhancement level**: A value between 0 and 10 that adjusts the level of image enhancement applied to captured license plates before they are processed for recognition. Higher values increase contrast, sharpen details, and reduce noise, but excessive enhancement can blur or distort characters. This setting is best adjusted at the camera level if running LPR on multiple cameras.
|
||||
- Default: `0` (no enhancement)
|
||||
- Higher values increase contrast, sharpen details, and reduce noise, but excessive enhancement can blur or distort characters, actually making them much harder for Frigate to recognize.
|
||||
- This setting is best adjusted at the camera level if running LPR on multiple cameras.
|
||||
- If Frigate is already recognizing plates correctly, leave this setting at the default of `0`. However, if you're experiencing frequent character issues or incomplete plates and you can already easily read the plates yourself, try increasing the value gradually, starting at 5 and adjusting as needed. You should see how different enhancement levels affect your plates. Use the `debug_save_plates` configuration option (see below).
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
lpr:
|
||||
enabled: True
|
||||
enhancement: 1
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
If Frigate is already recognizing plates correctly, leave enhancement at the default of `0`. However, if you're experiencing frequent character issues or incomplete plates and you can already easily read the plates yourself, try increasing the value gradually, starting at 3 and adjusting as needed. Use the `debug_save_plates` configuration option (see below) to see how different enhancement levels affect your plates.
|
||||
|
||||
### Normalization Rules
|
||||
|
||||
- **`replace_rules`**: List of regex replacement rules to normalize detected plates. These rules are applied sequentially and are applied _before_ the `format` regex, if specified. Each rule must have a `pattern` (which can be a string or a regex) and `replacement` (a string, which also supports [backrefs](https://docs.python.org/3/library/re.html#re.sub) like `\1`). These rules are useful for dealing with common OCR issues like noise characters, separators, or confusions (e.g., 'O'→'0').
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
These rules must be defined at the global level of your `lpr` config.
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
|
||||
|
||||
Under **Replacement rules**, add regex rules to normalize detected plate strings before matching. Rules fire in order. For example:
|
||||
|
||||
| Pattern | Replacement | Description |
|
||||
| ---------------- | ----------- | -------------------------------------------------- |
|
||||
| `[%#*?]` | _(empty)_ | Remove noise symbols |
|
||||
| `[= ]` | `-` | Normalize `=` or space to dash |
|
||||
| `O` | `0` | Swap `O` to `0` (common OCR error) |
|
||||
| `I` | `1` | Swap `I` to `1` |
|
||||
| `(\w{3})(\w{3})` | `\1-\2` | Split 6 chars into groups (e.g., ABC123 → ABC-123) |
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
lpr:
|
||||
@@ -126,6 +230,11 @@ lpr:
|
||||
replacement: '\1-\2'
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
These rules must be defined at the global level of your `lpr` config.
|
||||
|
||||
- Rules fire in order: In the example above: clean noise first, then separators, then swaps, then splits.
|
||||
- Backrefs (`\1`, `\2`) allow dynamic replacements (e.g., capture groups).
|
||||
- Any changes made by the rules are printed to the LPR debug log.
|
||||
@@ -133,13 +242,50 @@ lpr:
|
||||
|
||||
### Debugging
|
||||
|
||||
- **`debug_save_plates`**: Set to `True` to save captured text on plates for debugging. These images are stored in `/media/frigate/clips/lpr`, organized into subdirectories by `<camera>/<event_id>`, and named based on the capture timestamp.
|
||||
- These saved images are not full plates but rather the specific areas of text detected on the plates. It is normal for the text detection model to sometimes find multiple areas of text on the plate. Use them to analyze what text Frigate recognized and how image enhancement affects detection.
|
||||
- **Note:** Frigate does **not** automatically delete these debug images. Once LPR is functioning correctly, you should disable this option and manually remove the saved files to free up storage.
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
|
||||
|
||||
- **Save debug plates**: Set to on to save captured text on plates for debugging. These images are stored in `/media/frigate/clips/lpr`, organized into subdirectories by `<camera>/<event_id>`, and named based on the capture timestamp.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
lpr:
|
||||
enabled: True
|
||||
debug_save_plates: True
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
The saved images are not full plates but rather the specific areas of text detected on the plates. It is normal for the text detection model to sometimes find multiple areas of text on the plate. Use them to analyze what text Frigate recognized and how image enhancement affects detection.
|
||||
|
||||
**Note:** Frigate does **not** automatically delete these debug images. Once LPR is functioning correctly, you should disable this option and manually remove the saved files to free up storage.
|
||||
|
||||
## Configuration Examples
|
||||
|
||||
These configuration parameters are available at the global level of your config. The only optional parameters that should be set at the camera level are `enabled`, `min_area`, and `enhancement`.
|
||||
These configuration parameters are available at the global level. The only optional parameters that should be set at the camera level are `enabled`, `min_area`, and `enhancement`.
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
|
||||
|
||||
| Field | Description |
|
||||
| ------------------------------ | ----------------------------------------------------------------------------------------------------- |
|
||||
| **Enable LPR** | Set to on |
|
||||
| **Minimum plate area** | Set to `1500` — ignore plates with an area (length x width) smaller than 1500 pixels |
|
||||
| **Min plate length** | Set to `4` — only recognize plates with 4 or more characters |
|
||||
| **Known plates > Wife's Car** | `ABC-1234`, `ABC-I234` (accounts for potential confusion between the number one and capital letter I) |
|
||||
| **Known plates > Johnny** | `J*N-*234` (matches JHN-1234 and JMN-I234; `*` matches any number of characters) |
|
||||
| **Known plates > Sally** | `[S5]LL 1234` (matches both SLL 1234 and 5LL 1234) |
|
||||
| **Known plates > Work Trucks** | `EMP-[0-9]{3}[A-Z]` (matches plates like EMP-123A, EMP-456Z) |
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
lpr:
|
||||
@@ -158,28 +304,21 @@ lpr:
|
||||
- "EMP-[0-9]{3}[A-Z]" # Matches plates like EMP-123A, EMP-456Z
|
||||
```
|
||||
|
||||
```yaml
|
||||
lpr:
|
||||
enabled: True
|
||||
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
|
||||
replace_rules:
|
||||
- pattern: "O"
|
||||
replacement: "0" # Replace the letter O with the number 0 in every plate
|
||||
known_plates:
|
||||
Delivery Van:
|
||||
- "RJ K5678"
|
||||
- "UP A1234"
|
||||
Supervisor:
|
||||
- "MN D3163"
|
||||
```
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
:::note
|
||||
|
||||
If a camera is configured to detect `car` or `motorcycle` but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > License plate recognition" /> for the desired camera and disable the **Enable LPR** toggle.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
cameras:
|
||||
side_yard:
|
||||
@@ -188,13 +327,16 @@ cameras:
|
||||
...
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
:::
|
||||
|
||||
## Dedicated LPR Cameras
|
||||
|
||||
Dedicated LPR cameras are single-purpose cameras with powerful optical zoom to capture license plates on distant vehicles, often with fine-tuned settings to capture plates at night.
|
||||
|
||||
To mark a camera as a dedicated LPR camera, add `type: "lpr"` the camera configuration.
|
||||
To mark a camera as a dedicated LPR camera, set `type: "lpr"` in the camera configuration.
|
||||
|
||||
:::note
|
||||
|
||||
@@ -210,6 +352,55 @@ Users running a Frigate+ model (or any model that natively detects `license_plat
|
||||
|
||||
An example configuration for a dedicated LPR camera using a `license_plate`-detecting model:
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" /> and set **Enable LPR** to on. Set **Device** to `CPU` (can also be `GPU` if available).
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> and add your camera streams.
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Object detection" />.
|
||||
|
||||
| Field | Description |
|
||||
| --------------------------------- | ------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| **Enable object detection** | Set to on |
|
||||
| **Detect FPS** | Set to `5`. Increase to `10` if vehicles move quickly across your frame. Higher than 10 is unnecessary and is not recommended. |
|
||||
| **Minimum initialization frames** | Set to `2` |
|
||||
| **Detect width** | Set to `1920` |
|
||||
| **Detect height** | Set to `1080` |
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Objects" />.
|
||||
|
||||
| Field | Description |
|
||||
| ---------------------------------------------- | ------------------- |
|
||||
| **Objects to track** | Add `license_plate` |
|
||||
| **Object filters > License Plate > Threshold** | Set to `0.7` |
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Motion detection" />.
|
||||
|
||||
| Field | Description |
|
||||
| -------------------- | --------------------------------------------------------------------- |
|
||||
| **Motion threshold** | Set to `30` |
|
||||
| **Contour area** | Set to `60`. Use an increased value to tune out small motion changes. |
|
||||
| **Improve contrast** | Set to off |
|
||||
|
||||
Also add a motion mask over your camera's timestamp so it is not incorrectly detected as a license plate.
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Recording" />.
|
||||
|
||||
| Field | Description |
|
||||
| -------------------- | -------------------------------------------------------- |
|
||||
| **Enable recording** | Set to on. Disable recording if you only want snapshots. |
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Snapshots" />.
|
||||
|
||||
| Field | Description |
|
||||
| -------------------- | ----------- |
|
||||
| **Enable snapshots** | Set to on |
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
# LPR global configuration
|
||||
lpr:
|
||||
@@ -248,6 +439,9 @@ cameras:
|
||||
- license_plate
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
With this setup:
|
||||
|
||||
- License plates are treated as normal objects in Frigate.
|
||||
@@ -259,10 +453,65 @@ With this setup:
|
||||
|
||||
### Using the Secondary LPR Pipeline (Without Frigate+)
|
||||
|
||||
If you are not running a Frigate+ model, you can use Frigate’s built-in secondary dedicated LPR pipeline. In this mode, Frigate bypasses the standard object detection pipeline and runs a local license plate detector model on the full frame whenever motion activity occurs.
|
||||
If you are not running a Frigate+ model, you can use Frigate's built-in secondary dedicated LPR pipeline. In this mode, Frigate bypasses the standard object detection pipeline and runs a local license plate detector model on the full frame whenever motion activity occurs.
|
||||
|
||||
An example configuration for a dedicated LPR camera using the secondary pipeline:
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" /> and set **Enable LPR** to on. Set **Device** to `CPU` (can also be `GPU` if available and the correct Docker image is used). Set **Detection threshold** to `0.7` (change if necessary).
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > License plate recognition" /> for your dedicated LPR camera.
|
||||
|
||||
| Field | Description |
|
||||
| --------------------- | -------------------------------------------------------------------------------- |
|
||||
| **Enable LPR** | Set to on |
|
||||
| **Enhancement level** | Set to `3` (optional — enhances the image before trying to recognize characters) |
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> and add your camera streams.
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Object detection" />.
|
||||
|
||||
| Field | Description |
|
||||
| --------------------------- | ---------------------------------------------------------------------------------------------------------------------------- |
|
||||
| **Enable object detection** | Set to off — disables Frigate's standard object detection pipeline |
|
||||
| **Detect FPS** | Set to `5`. Increase if necessary, though high values may slow down Frigate's enrichments pipeline and use considerable CPU. |
|
||||
| **Detect width** | Set to `1920` (recommended value, but depends on your camera) |
|
||||
| **Detect height** | Set to `1080` (recommended value, but depends on your camera) |
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Objects" />.
|
||||
|
||||
| Field | Description |
|
||||
| -------------------- | -------------------------------------------------------------------------------------- |
|
||||
| **Objects to track** | Set to an empty list — required when not using a Frigate+ model for dedicated LPR mode |
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Motion detection" />.
|
||||
|
||||
| Field | Description |
|
||||
| -------------------- | --------------------------------------------------------------------- |
|
||||
| **Motion threshold** | Set to `30` |
|
||||
| **Contour area** | Set to `60`. Use an increased value to tune out small motion changes. |
|
||||
| **Improve contrast** | Set to off |
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and add a motion mask over your camera's timestamp so it is not incorrectly detected as a license plate.
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Recording" />.
|
||||
|
||||
| Field | Description |
|
||||
| -------------------- | -------------------------------------------------------- |
|
||||
| **Enable recording** | Set to on. Disable recording if you only want snapshots. |
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Review" />.
|
||||
|
||||
| Field | Description |
|
||||
| ----------------------------------------- | --------------- |
|
||||
| **Detections config > Enable detections** | Set to on |
|
||||
| **Detections config > Retain > Default** | Set to `7` days |
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
# LPR global configuration
|
||||
lpr:
|
||||
@@ -299,6 +548,9 @@ cameras:
|
||||
default: 7
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
With this setup:
|
||||
|
||||
- The standard object detection pipeline is bypassed. Any detected license plates on dedicated LPR cameras are treated similarly to manual events in Frigate. You must **not** specify `license_plate` as an object to track.
|
||||
@@ -377,12 +629,27 @@ Start with ["Why isn't my license plate being detected and recognized?"](#why-is
|
||||
1. Start with a simplified LPR config.
|
||||
- Remove or comment out everything in your LPR config, including `min_area`, `min_plate_length`, `format`, `known_plates`, or `enhancement` values so that the only values left are `enabled` and `debug_save_plates`. This will run LPR with Frigate's default values.
|
||||
|
||||
```yaml
|
||||
lpr:
|
||||
enabled: true
|
||||
device: CPU
|
||||
debug_save_plates: true
|
||||
```
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
|
||||
|
||||
- Set **Enable LPR** to on
|
||||
- Set **Device** to `CPU`
|
||||
- Set **Save debug plates** to on
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
lpr:
|
||||
enabled: true
|
||||
device: CPU
|
||||
debug_save_plates: true
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
2. Enable debug logs to see exactly what Frigate is doing.
|
||||
- Enable debug logs for LPR by adding `frigate.data_processing.common.license_plate: debug` to your `logger` configuration. These logs are _very_ verbose, so only keep this enabled when necessary. Restart Frigate after this change.
|
||||
@@ -391,7 +658,7 @@ Start with ["Why isn't my license plate being detected and recognized?"](#why-is
|
||||
logger:
|
||||
default: info
|
||||
logs:
|
||||
# highlight-next-line
|
||||
# highlight-next-line
|
||||
frigate.data_processing.common.license_plate: debug
|
||||
```
|
||||
|
||||
|
||||
Reference in New Issue
Block a user