License plate recognition (ALPR) backend (#14564)

* Update version * Face recognition backend (#14495) * Add basic config and face recognition table * Reconfigure updates processing to handle face * Crop frame to face box * Implement face embedding calculation * Get matching face embeddings * Add support face recognition based on existing faces * Use arcface face embeddings instead of generic embeddings model * Add apis for managing faces * Implement face uploading API * Build out more APIs * Add min area config * Handle larger images * Add more debug logs * fix calculation * Reduce timeout * Small tweaks * Use webp images * Use facenet model * Improve face recognition (#14537) * Increase requirements for face to be set * Manage faces properly * Add basic docs * Simplify * Separate out face recognition frome semantic search * Update docs * Formatting * Fix access (#14540) * Face detection (#14544) * Add support for face detection * Add support for detecting faces during registration * Set body size to be larger * Undo * Update version * Face recognition backend (#14495) * Add basic config and face recognition table * Reconfigure updates processing to handle face * Crop frame to face box * Implement face embedding calculation * Get matching face embeddings * Add support face recognition based on existing faces * Use arcface face embeddings instead of generic embeddings model * Add apis for managing faces * Implement face uploading API * Build out more APIs * Add min area config * Handle larger images * Add more debug logs * fix calculation * Reduce timeout * Small tweaks * Use webp images * Use facenet model * Improve face recognition (#14537) * Increase requirements for face to be set * Manage faces properly * Add basic docs * Simplify * Separate out face recognition frome semantic search * Update docs * Formatting * Fix access (#14540) * Face detection (#14544) * Add support for face detection * Add support for detecting faces during registration * Set body size to be larger * Undo * initial foundation for alpr with paddleocr * initial foundation for alpr with paddleocr * initial foundation for alpr with paddleocr * config * config * lpr maintainer * clean up * clean up * fix processing * don't process for stationary cars * fix order * fixes * check for known plates * improved length and character by character confidence * model fixes and small tweaks * docs * placeholder for non frigate+ model lp detection --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
2024-10-26 12:07:45 -05:00
parent 8dc9c2c9ed
commit 3ed4fb87ef
10 changed files with 1151 additions and 9 deletions
--- a/docs/docs/configuration/license_plate_recognition.md
+++ b/docs/docs/configuration/license_plate_recognition.md
@@ -0,0 +1,48 @@
+---
+id: license_plate_recognition
+title: License Plate Recognition (LPR)
+---
+
+Frigate can recognize license plates on vehicles and automatically add the detected characters as a `sub_label` to objects that are of type `car`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street with a dedicated LPR camera.
+
+Users running a Frigate+ model should ensure that `license_plate` is added to the [list of objects to track](https://docs.frigate.video/plus/#available-label-types) either globally or for a specific camera. This will improve the accuracy and performance of the LPR model.
+
+LPR is most effective when the vehicle’s license plate is fully visible to the camera. For moving vehicles, Frigate will attempt to read the plate continuously, refining its detection and keeping the most confident result. LPR will not run on stationary vehicles.
+
+## Minimum System Requirements
+
+License plate recognition works by running AI models locally on your system. The models are relatively lightweight and run on your CPU. At least 4GB of RAM is required.
+
+## Configuration
+
+License plate recognition is disabled by default. Enable it in your config file:
+
+```yaml
+lpr:
+  enabled: true
+```
+
+## Advanced Configuration
+
+Several options are available to fine-tune the LPR feature. For example, you can adjust the `min_area` setting, which defines the minimum size in pixels a license plate must be before LPR runs. The default is 500 pixels.
+
+Additionally, you can define `known_plates`, allowing Frigate to label tracked vehicles with custom sub_labels when a recognized plate is detected. This information is then accessible in the UI, filters, and notifications.
+
+```yaml
+lpr:
+  enabled: true
+  min_area: 500
+  known_plates:
+    Wife's Car:
+      - "ABC-1234"
+      - "ABC-I234"
+    Johnny:
+      - "JHN-1234"
+      - "JMN-1234"
+      - "JHN-I234"
+    Sally:
+      - "SLL-1234"
+      - "5LL-1234"
+```
+
+In this example, "Wife's Car" will appear as the label for any vehicle matching the plate "ABC-1234." The model might occasionally interpret the digit 1 as a capital I (e.g., "ABC-I234"), so both variations are listed. Similarly, multiple possible variations are specified for Johnny and Sally.
--- a/docs/sidebars.ts
+++ b/docs/sidebars.ts
@@ -37,6 +37,7 @@ const sidebars: SidebarsConfig = {
        'configuration/semantic_search',
        'configuration/genai',
        'configuration/face_recognition',
+        'configuration/license_plate_recognition',
      ],
      Cameras: [
        'configuration/cameras',