Merge pull request #1 from weitheng/0.16

0.16 Alpha 30th Oct

.cspell/frigate-dictionary.txt

@@ -2,6 +2,7 @@ aarch
 absdiff
 airockchip
 Alloc
+alpr
 Amcrest
 amdgpu
 analyzeduration
@@ -60,6 +61,7 @@ dsize
 dtype
 ECONNRESET
 edgetpu
+facenet
 fastapi
 faststart
 fflags
@@ -113,6 +115,8 @@ itemsize
 Jellyfin
 jetson
 jetsons
+jina
+jinaai
 joserfc
 jsmpeg
 jsonify
@@ -186,6 +190,7 @@ openai
 opencv
 openvino
 OWASP
+paddleocr
 paho
 passwordless
 popleft
@@ -305,4 +310,4 @@ yolo
 yolonas
 yolox
 zeep
-zerolatency
+zerolatency

Makefile

@@ -1,7 +1,7 @@
 default_target: local
 
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.15.0
+VERSION = 0.16.0
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
 BOARDS= #Initialized empty

docker/main/requirements-wheels.txt

@@ -8,6 +8,8 @@ imutils == 0.5.*
 joserfc == 1.0.*
 pathvalidate == 3.2.*
 markupsafe == 2.1.*
+python-multipart == 0.0.12
+# General
 mypy == 1.6.1
 numpy == 1.26.*
 onvif_zeep == 0.2.12
@@ -43,3 +45,6 @@ openai == 1.51.*
 # push notifications
 py-vapid == 1.9.*
 pywebpush == 2.0.*
+# alpr
+pyclipper == 1.3.*
+shapely == 2.0.*

docker/main/rootfs/usr/local/nginx/conf/nginx.conf

@@ -246,6 +246,8 @@ http {
             proxy_no_cache $should_not_cache;
             add_header X-Cache-Status $upstream_cache_status;
 
+            client_max_body_size 10M;
+
             location /api/vod/ {
                 include auth_request.conf;
                 proxy_pass http://frigate_api/vod/;

docs/docs/configuration/face_recognition.md

@@ -0,0 +1,21 @@
+---
+id: face_recognition
+title: Face Recognition
+---
+
+Face recognition allows people to be assigned names and when their face is recognized Frigate will assign the person's name as a sub label. This information is included in the UI, filters, as well as in notifications.
+
+Frigate has support for FaceNet to create face embeddings, which runs locally. Embeddings are then saved to Frigate's database.
+
+## Minimum System Requirements
+
+Face recognition works by running a large AI model locally on your system. Systems without a GPU will not run Face Recognition reliably or at all.
+
+## Configuration
+
+Face recognition is disabled by default and requires semantic search to be enabled, face recognition must be enabled in your config file before it can be used. Semantic Search and face recognition are global configuration settings.
+
+```yaml
+face_recognition:
+  enabled: true
+```

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.

docs/docs/configuration/reference.md

@@ -522,6 +522,14 @@ semantic_search:
   # NOTE: small model runs on CPU and large model runs on GPU
   model_size: "small"
 
+# Optional: Configuration for face recognition capability
+face_recognition:
+  # Optional: Enable semantic search (default: shown below)
+  enabled: False
+  # Optional: Set the model size used for embeddings. (default: shown below)
+  # NOTE: small model runs on CPU and large model runs on GPU
+  model_size: "small"
+
 # Optional: Configuration for AI generated tracked object descriptions
 # NOTE: Semantic Search must be enabled for this to do anything.
 # WARNING: Depending on the provider, this will send thumbnails over the internet

docs/sidebars.ts

@@ -36,6 +36,8 @@ const sidebars: SidebarsConfig = {
       'Semantic Search': [
         'configuration/semantic_search',
         'configuration/genai',
+        'configuration/face_recognition',
+        'configuration/license_plate_recognition',
       ],
       Cameras: [
         'configuration/cameras',

frigate/api/classification.py

@@ -0,0 +1,56 @@
+"""Object classification APIs."""
+
+import logging
+
+from fastapi import APIRouter, Request, UploadFile
+from fastapi.responses import JSONResponse
+
+from frigate.api.defs.tags import Tags
+from frigate.embeddings import EmbeddingsContext
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(tags=[Tags.events])
+
+
+@router.get("/faces")
+def get_faces():
+    return JSONResponse(content={"message": "there are faces"})
+
+
+@router.post("/faces/{name}")
+async def register_face(request: Request, name: str, file: UploadFile):
+    # if not file.content_type.startswith("image"):
+    #    return JSONResponse(
+    #        status_code=400,
+    #        content={
+    #            "success": False,
+    #            "message": "Only an image can be used to register a face.",
+    #        },
+    #    )
+
+    context: EmbeddingsContext = request.app.embeddings
+    context.register_face(name, await file.read())
+    return JSONResponse(
+        status_code=200,
+        content={"success": True, "message": "Successfully registered face."},
+    )
+
+
+@router.delete("/faces")
+def deregister_faces(request: Request, body: dict = None):
+    json: dict[str, any] = body or {}
+    list_of_ids = json.get("ids", "")
+
+    if not list_of_ids or len(list_of_ids) == 0:
+        return JSONResponse(
+            content=({"success": False, "message": "Not a valid list of ids"}),
+            status_code=404,
+        )
+
+    context: EmbeddingsContext = request.app.embeddings
+    context.delete_face_ids(list_of_ids)
+    return JSONResponse(
+        content=({"success": True, "message": "Successfully deleted faces."}),
+        status_code=200,
+    )

frigate/api/defs/events_body.py

@@ -8,6 +8,9 @@ class EventsSubLabelBody(BaseModel):
     subLabelScore: Optional[float] = Field(
         title="Score for sub label", default=None, gt=0.0, le=1.0
     )
+    camera: Optional[str] = Field(
+        title="Camera this object is detected on.", default=None
+    )
 
 
 class EventsDescriptionBody(BaseModel):

frigate/api/defs/tags.py

@@ -10,4 +10,5 @@ class Tags(Enum):
     review = "Review"
     export = "Export"
     events = "Events"
+    classification = "classification"
     auth = "Auth"

frigate/api/event.py

@@ -890,38 +890,59 @@ def set_sub_label(
     try:
         event: Event = Event.get(Event.id == event_id)
     except DoesNotExist:
+        if not body.camera:
+            return JSONResponse(
+                content=(
+                    {
+                        "success": False,
+                        "message": "Event "
+                        + event_id
+                        + " not found and camera is not provided.",
+                    }
+                ),
+                status_code=404,
+            )
+
+        event = None
+
+    if request.app.detected_frames_processor:
+        tracked_obj: TrackedObject = (
+            request.app.detected_frames_processor.camera_states[
+                event.camera if event else body.camera
+            ].tracked_objects.get(event_id)
+        )
+    else:
+        tracked_obj = None
+
+    if not event and not tracked_obj:
         return JSONResponse(
-            content=({"success": False, "message": "Event " + event_id + " not found"}),
+            content=(
+                {"success": False, "message": "Event " + event_id + " not found."}
+            ),
             status_code=404,
         )
 
     new_sub_label = body.subLabel
     new_score = body.subLabelScore
 
-    if not event.end_time:
+    if tracked_obj:
-        # update tracked object
+        tracked_obj.obj_data["sub_label"] = (new_sub_label, new_score)
-        tracked_obj: TrackedObject = (
-            request.app.detected_frames_processor.camera_states[
-                event.camera
-            ].tracked_objects.get(event.id)
-        )
-
-        if tracked_obj:
-            tracked_obj.obj_data["sub_label"] = (new_sub_label, new_score)
 
         # update timeline items
         Timeline.update(
             data=Timeline.data.update({"sub_label": (new_sub_label, new_score)})
         ).where(Timeline.source_id == event_id).execute()
 
-    event.sub_label = new_sub_label
+    if event:
+        event.sub_label = new_sub_label
 
-    if new_score:
+        if new_score:
-        data = event.data
+            data = event.data
-        data["sub_label_score"] = new_score
+            data["sub_label_score"] = new_score
-        event.data = data
+            event.data = data
+
+        event.save()
 
-    event.save()
     return JSONResponse(
         content=(
             {

frigate/api/fastapi_app.py

@@ -11,7 +11,16 @@ from starlette_context import middleware, plugins
 from starlette_context.plugins import Plugin
 
 from frigate.api import app as main_app
-from frigate.api import auth, event, export, media, notification, preview, review
+from frigate.api import (
+    auth,
+    classification,
+    event,
+    export,
+    media,
+    notification,
+    preview,
+    review,
+)
 from frigate.api.auth import get_jwt_secret, limiter
 from frigate.comms.event_metadata_updater import (
     EventMetadataPublisher,
@@ -95,6 +104,7 @@ def create_fastapi_app(
     # Routes
     # Order of include_router matters: https://fastapi.tiangolo.com/tutorial/path-params/#order-matters
     app.include_router(auth.router)
+    app.include_router(classification.router)
     app.include_router(review.router)
     app.include_router(main_app.router)
     app.include_router(preview.router)

frigate/comms/embeddings_updater.py

@@ -12,6 +12,7 @@ class EmbeddingsRequestEnum(Enum):
     embed_description = "embed_description"
     embed_thumbnail = "embed_thumbnail"
     generate_search = "generate_search"
+    register_face = "register_face"
 
 
 class EmbeddingsResponder:
@@ -22,7 +23,7 @@ class EmbeddingsResponder:
 
     def check_for_request(self, process: Callable) -> None:
         while True:  # load all messages that are queued
-            has_message, _, _ = zmq.select([self.socket], [], [], 0.1)
+            has_message, _, _ = zmq.select([self.socket], [], [], 0.01)
 
             if not has_message:
                 break

frigate/config/camera/objects.py

@@ -1,6 +1,6 @@
 from typing import Any, Optional, Union
 
-from pydantic import Field, field_serializer
+from pydantic import Field, PrivateAttr, field_serializer
 
 from ..base import FrigateBaseModel
 
@@ -53,3 +53,20 @@ class ObjectConfig(FrigateBaseModel):
         default_factory=dict, title="Object filters."
     )
     mask: Union[str, list[str]] = Field(default="", title="Object mask.")
+    _all_objects: list[str] = PrivateAttr()
+
+    @property
+    def all_objects(self) -> list[str]:
+        return self._all_objects
+
+    def parse_all_objects(self, cameras):
+        if "_all_objects" in self:
+            return
+
+        # get list of unique enabled labels for tracking
+        enabled_labels = set(self.track)
+
+        for camera in cameras.values():
+            enabled_labels.update(camera.objects.track)
+
+        self._all_objects = list(enabled_labels)

frigate/config/config.py

@@ -56,7 +56,11 @@ from .logger import LoggerConfig
 from .mqtt import MqttConfig
 from .notification import NotificationConfig
 from .proxy import ProxyConfig
-from .semantic_search import SemanticSearchConfig
+from .semantic_search import (
+    FaceRecognitionConfig,
+    LicensePlateRecognitionConfig,
+    SemanticSearchConfig,
+)
 from .telemetry import TelemetryConfig
 from .tls import TlsConfig
 from .ui import UIConfig
@@ -159,6 +163,16 @@ class RestreamConfig(BaseModel):
     model_config = ConfigDict(extra="allow")
 
 
+def verify_semantic_search_dependent_configs(config: FrigateConfig) -> None:
+    """Verify that semantic search is enabled if required features are enabled."""
+    if not config.semantic_search.enabled:
+        if config.genai.enabled:
+            raise ValueError("Genai requires semantic search to be enabled.")
+
+        if config.face_recognition.enabled:
+            raise ValueError("Face recognition requires semantic to be enabled.")
+
+
 def verify_config_roles(camera_config: CameraConfig) -> None:
     """Verify that roles are setup in the config correctly."""
     assigned_roles = list(
@@ -316,6 +330,13 @@ class FrigateConfig(FrigateBaseModel):
     semantic_search: SemanticSearchConfig = Field(
         default_factory=SemanticSearchConfig, title="Semantic search configuration."
     )
+    face_recognition: FaceRecognitionConfig = Field(
+        default_factory=FaceRecognitionConfig, title="Face recognition config."
+    )
+    lpr: LicensePlateRecognitionConfig = Field(
+        default_factory=LicensePlateRecognitionConfig,
+        title="License Plate recognition config.",
+    )
     ui: UIConfig = Field(default_factory=UIConfig, title="UI configuration.")
 
     # Detector config
@@ -574,13 +595,8 @@ class FrigateConfig(FrigateBaseModel):
             verify_autotrack_zones(camera_config)
             verify_motion_and_detect(camera_config)
 
-        # get list of unique enabled labels for tracking
+        self.objects.parse_all_objects(self.cameras)
-        enabled_labels = set(self.objects.track)
+        self.model.create_colormap(sorted(self.objects.all_objects))
-
-        for camera in self.cameras.values():
-            enabled_labels.update(camera.objects.track)
-
-        self.model.create_colormap(sorted(enabled_labels))
         self.model.check_and_load_plus_model(self.plus_api)
 
         for key, detector in self.detectors.items():
@@ -621,6 +637,7 @@ class FrigateConfig(FrigateBaseModel):
             detector_config.model.compute_model_hash()
             self.detectors[key] = detector_config
 
+        verify_semantic_search_dependent_configs(self)
         return self
 
     @field_validator("cameras")

frigate/config/semantic_search.py

@@ -1,10 +1,14 @@
-from typing import Optional
+from typing import Dict, List, Optional
 
 from pydantic import Field
 
 from .base import FrigateBaseModel
 
-__all__ = ["SemanticSearchConfig"]
+__all__ = [
+    "FaceRecognitionConfig",
+    "SemanticSearchConfig",
+    "LicensePlateRecognitionConfig",
+]
 
 
 class SemanticSearchConfig(FrigateBaseModel):
@@ -15,3 +19,28 @@ class SemanticSearchConfig(FrigateBaseModel):
     model_size: str = Field(
         default="small", title="The size of the embeddings model used."
     )
+
+
+class FaceRecognitionConfig(FrigateBaseModel):
+    enabled: bool = Field(default=False, title="Enable face recognition.")
+    threshold: float = Field(
+        default=0.9, title="Face similarity score required to be considered a match."
+    )
+    min_area: int = Field(
+        default=500, title="Min area of face box to consider running face recognition."
+    )
+
+
+class LicensePlateRecognitionConfig(FrigateBaseModel):
+    enabled: bool = Field(default=False, title="Enable license plate recognition.")
+    threshold: float = Field(
+        default=0.9,
+        title="License plate confidence score required to be added to the object as a sub label.",
+    )
+    min_area: int = Field(
+        default=500,
+        title="Min area of license plate to consider running license plate recognition.",
+    )
+    known_plates: Optional[Dict[str, List[str]]] = Field(
+        default={}, title="Known plates to track."
+    )

frigate/const.py

@@ -5,8 +5,9 @@ DEFAULT_DB_PATH = f"{CONFIG_DIR}/frigate.db"
 MODEL_CACHE_DIR = f"{CONFIG_DIR}/model_cache"
 BASE_DIR = "/media/frigate"
 CLIPS_DIR = f"{BASE_DIR}/clips"
-RECORD_DIR = f"{BASE_DIR}/recordings"
 EXPORT_DIR = f"{BASE_DIR}/exports"
+FACE_DIR = f"{CLIPS_DIR}/faces"
+RECORD_DIR = f"{BASE_DIR}/recordings"
 BIRDSEYE_PIPE = "/tmp/cache/birdseye"
 CACHE_DIR = "/tmp/cache"
 FRIGATE_LOCALHOST = "http://127.0.0.1:5000"

frigate/db/sqlitevecq.py

@@ -29,6 +29,10 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
         ids = ",".join(["?" for _ in event_ids])
         self.execute_sql(f"DELETE FROM vec_descriptions WHERE id IN ({ids})", event_ids)
 
+    def delete_embeddings_face(self, face_ids: list[str]) -> None:
+        ids = ",".join(["?" for _ in face_ids])
+        self.execute_sql(f"DELETE FROM vec_faces WHERE id IN ({ids})", face_ids)
+
     def drop_embeddings_tables(self) -> None:
         self.execute_sql("""
             DROP TABLE vec_descriptions;
@@ -36,8 +40,11 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
         self.execute_sql("""
             DROP TABLE vec_thumbnails;
         """)
+        self.execute_sql("""
+            DROP TABLE vec_faces;
+        """)
 
-    def create_embeddings_tables(self) -> None:
+    def create_embeddings_tables(self, face_recognition: bool) -> None:
         """Create vec0 virtual table for embeddings"""
         self.execute_sql("""
             CREATE VIRTUAL TABLE IF NOT EXISTS vec_thumbnails USING vec0(
@@ -51,3 +58,11 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
                 description_embedding FLOAT[768] distance_metric=cosine
             );
         """)
+
+        if face_recognition:
+            self.execute_sql("""
+                CREATE VIRTUAL TABLE IF NOT EXISTS vec_faces USING vec0(
+                    id TEXT PRIMARY KEY,
+                    face_embedding FLOAT[512] distance_metric=cosine
+                );
+            """)

frigate/embeddings/__init__.py

@@ -1,5 +1,6 @@
 """SQLite-vec embeddings database."""
 
+import base64
 import json
 import logging
 import multiprocessing as mp
@@ -189,6 +190,28 @@ class EmbeddingsContext:
 
         return results
 
+    def register_face(self, face_name: str, image_data: bytes) -> None:
+        self.requestor.send_data(
+            EmbeddingsRequestEnum.register_face.value,
+            {
+                "face_name": face_name,
+                "image": base64.b64encode(image_data).decode("ASCII"),
+            },
+        )
+
+    def get_face_ids(self, name: str) -> list[str]:
+        sql_query = f"""
+            SELECT
+                id
+            FROM vec_descriptions
+            WHERE id LIKE '%{name}%'
+        """
+
+        return self.db.execute_sql(sql_query).fetchall()
+
+    def delete_face_ids(self, ids: list[str]) -> None:
+        self.db.delete_embeddings_face(ids)
+
     def update_description(self, event_id: str, description: str) -> None:
         self.requestor.send_data(
             EmbeddingsRequestEnum.embed_description.value,

frigate/embeddings/alpr/alpr.py

@@ -0,0 +1,808 @@
+import logging
+import math
+from typing import List, Tuple
+
+import cv2
+import numpy as np
+from pyclipper import ET_CLOSEDPOLYGON, JT_ROUND, PyclipperOffset
+from shapely.geometry import Polygon
+
+from frigate.comms.inter_process import InterProcessRequestor
+from frigate.config.semantic_search import LicensePlateRecognitionConfig
+from frigate.embeddings.embeddings import Embeddings
+
+logger = logging.getLogger(__name__)
+
+MIN_PLATE_LENGTH = 3
+
+
+class LicensePlateRecognition:
+    def __init__(
+        self,
+        config: LicensePlateRecognitionConfig,
+        requestor: InterProcessRequestor,
+        embeddings: Embeddings,
+    ):
+        self.lpr_config = config
+        self.requestor = requestor
+        self.embeddings = embeddings
+        self.detection_model = self.embeddings.lpr_detection_model
+        self.classification_model = self.embeddings.lpr_classification_model
+        self.recognition_model = self.embeddings.lpr_recognition_model
+        self.ctc_decoder = CTCDecoder()
+
+        self.batch_size = 6
+
+        # Detection specific parameters
+        self.min_size = 3
+        self.max_size = 960
+        self.box_thresh = 0.8
+        self.mask_thresh = 0.8
+
+        if self.lpr_config.enabled:
+            # all models need to be loaded to run LPR
+            self.detection_model._load_model_and_utils()
+            self.classification_model._load_model_and_utils()
+            self.recognition_model._load_model_and_utils()
+
+    def detect(self, image: np.ndarray) -> List[np.ndarray]:
+        """
+        Detect possible license plates in the input image by first resizing and normalizing it,
+        running a detection model, and filtering out low-probability regions.
+
+        Args:
+            image (np.ndarray): The input image in which license plates will be detected.
+
+        Returns:
+            List[np.ndarray]: A list of bounding box coordinates representing detected license plates.
+        """
+        h, w = image.shape[:2]
+
+        if sum([h, w]) < 64:
+            image = self.zero_pad(image)
+
+        resized_image = self.resize_image(image)
+        normalized_image = self.normalize_image(resized_image)
+
+        outputs = self.detection_model([normalized_image])[0]
+        outputs = outputs[0, :, :]
+
+        boxes, _ = self.boxes_from_bitmap(outputs, outputs > self.mask_thresh, w, h)
+        return self.filter_polygon(boxes, (h, w))
+
+    def classify(
+        self, images: List[np.ndarray]
+    ) -> Tuple[List[np.ndarray], List[Tuple[str, float]]]:
+        """
+        Classify the orientation or category of each detected license plate.
+
+        Args:
+            images (List[np.ndarray]): A list of images of detected license plates.
+
+        Returns:
+            Tuple[List[np.ndarray], List[Tuple[str, float]]]: A tuple of rotated/normalized plate images
+                                                            and classification results with confidence scores.
+        """
+        num_images = len(images)
+        indices = np.argsort([x.shape[1] / x.shape[0] for x in images])
+
+        for i in range(0, num_images, self.batch_size):
+            norm_images = []
+            for j in range(i, min(num_images, i + self.batch_size)):
+                norm_img = self._preprocess_classification_image(images[indices[j]])
+                norm_img = norm_img[np.newaxis, :]
+                norm_images.append(norm_img)
+
+        outputs = self.classification_model(norm_images)
+
+        return self._process_classification_output(images, outputs)
+
+    def recognize(
+        self, images: List[np.ndarray]
+    ) -> Tuple[List[str], List[List[float]]]:
+        """
+        Recognize the characters on the detected license plates using the recognition model.
+
+        Args:
+            images (List[np.ndarray]): A list of images of license plates to recognize.
+
+        Returns:
+            Tuple[List[str], List[List[float]]]: A tuple of recognized license plate texts and confidence scores.
+        """
+        input_shape = [3, 48, 320]
+        num_images = len(images)
+
+        # sort images by aspect ratio for processing
+        indices = np.argsort(np.array([x.shape[1] / x.shape[0] for x in images]))
+
+        for index in range(0, num_images, self.batch_size):
+            input_h, input_w = input_shape[1], input_shape[2]
+            max_wh_ratio = input_w / input_h
+            norm_images = []
+
+            # calculate the maximum aspect ratio in the current batch
+            for i in range(index, min(num_images, index + self.batch_size)):
+                h, w = images[indices[i]].shape[0:2]
+                max_wh_ratio = max(max_wh_ratio, w * 1.0 / h)
+
+            # preprocess the images based on the max aspect ratio
+            for i in range(index, min(num_images, index + self.batch_size)):
+                norm_image = self._preprocess_recognition_image(
+                    images[indices[i]], max_wh_ratio
+                )
+                norm_image = norm_image[np.newaxis, :]
+                norm_images.append(norm_image)
+
+        outputs = self.recognition_model(norm_images)
+        return self.ctc_decoder(outputs)
+
+    def process_license_plate(
+        self, image: np.ndarray
+    ) -> Tuple[List[str], List[float], List[int]]:
+        """
+        Complete pipeline for detecting, classifying, and recognizing license plates in the input image.
+
+        Args:
+            image (np.ndarray): The input image in which to detect, classify, and recognize license plates.
+
+        Returns:
+            Tuple[List[str], List[float], List[int]]: Detected license plate texts, confidence scores, and areas of the plates.
+        """
+        if (
+            self.detection_model.runner is None
+            or self.classification_model.runner is None
+            or self.recognition_model.runner is None
+        ):
+            # we might still be downloading the models
+            logger.debug("Model runners not loaded")
+            return [], [], []
+
+        plate_points = self.detect(image)
+        if len(plate_points) == 0:
+            return [], [], []
+
+        plate_points = self.sort_polygon(list(plate_points))
+        plate_images = [self._crop_license_plate(image, x) for x in plate_points]
+        rotated_images, _ = self.classify(plate_images)
+
+        # keep track of the index of each image for correct area calc later
+        sorted_indices = np.argsort([x.shape[1] / x.shape[0] for x in rotated_images])
+        reverse_mapping = {
+            idx: original_idx for original_idx, idx in enumerate(sorted_indices)
+        }
+
+        results, confidences = self.recognize(rotated_images)
+
+        if results:
+            license_plates = [""] * len(rotated_images)
+            average_confidences = [[0.0]] * len(rotated_images)
+            areas = [0] * len(rotated_images)
+
+            # map results back to original image order
+            for i, (plate, conf) in enumerate(zip(results, confidences)):
+                original_idx = reverse_mapping[i]
+
+                height, width = rotated_images[original_idx].shape[:2]
+                area = height * width
+
+                average_confidence = conf
+
+                # set to True to write each cropped image for debugging
+                if False:
+                    save_image = cv2.cvtColor(
+                        rotated_images[original_idx], cv2.COLOR_RGB2BGR
+                    )
+                    filename = f"/config/plate_{original_idx}_{plate}_{area}.jpg"
+                    cv2.imwrite(filename, save_image)
+
+                license_plates[original_idx] = plate
+                average_confidences[original_idx] = average_confidence
+                areas[original_idx] = area
+
+            # Filter out plates that have a length of less than 3 characters
+            # Sort by area, then by plate length, then by confidence all desc
+            sorted_data = sorted(
+                [
+                    (plate, conf, area)
+                    for plate, conf, area in zip(
+                        license_plates, average_confidences, areas
+                    )
+                    if len(plate) >= MIN_PLATE_LENGTH
+                ],
+                key=lambda x: (x[2], len(x[0]), x[1]),
+                reverse=True,
+            )
+
+            if sorted_data:
+                return map(list, zip(*sorted_data))
+
+        return [], [], []
+
+    def resize_image(self, image: np.ndarray) -> np.ndarray:
+        """
+        Resize the input image while maintaining the aspect ratio, ensuring dimensions are multiples of 32.
+
+        Args:
+            image (np.ndarray): The input image to resize.
+
+        Returns:
+            np.ndarray: The resized image.
+        """
+        h, w = image.shape[:2]
+        ratio = min(self.max_size / max(h, w), 1.0)
+        resize_h = max(int(round(int(h * ratio) / 32) * 32), 32)
+        resize_w = max(int(round(int(w * ratio) / 32) * 32), 32)
+        return cv2.resize(image, (resize_w, resize_h))
+
+    def normalize_image(self, image: np.ndarray) -> np.ndarray:
+        """
+        Normalize the input image by subtracting the mean and multiplying by the standard deviation.
+
+        Args:
+            image (np.ndarray): The input image to normalize.
+
+        Returns:
+            np.ndarray: The normalized image, transposed to match the model's expected input format.
+        """
+        mean = np.array([123.675, 116.28, 103.53]).reshape(1, -1).astype("float64")
+        std = 1 / np.array([58.395, 57.12, 57.375]).reshape(1, -1).astype("float64")
+
+        image = image.astype("float32")
+        cv2.subtract(image, mean, image)
+        cv2.multiply(image, std, image)
+        return image.transpose((2, 0, 1))[np.newaxis, ...]
+
+    def boxes_from_bitmap(
+        self, output: np.ndarray, mask: np.ndarray, dest_width: int, dest_height: int
+    ) -> Tuple[np.ndarray, List[float]]:
+        """
+        Process the binary mask to extract bounding boxes and associated confidence scores.
+
+        Args:
+            output (np.ndarray): Output confidence map from the model.
+            mask (np.ndarray): Binary mask of detected regions.
+            dest_width (int): Target width for scaling the box coordinates.
+            dest_height (int): Target height for scaling the box coordinates.
+
+        Returns:
+            Tuple[np.ndarray, List[float]]: Array of bounding boxes and list of corresponding scores.
+        """
+
+        mask = (mask * 255).astype(np.uint8)
+        height, width = mask.shape
+        outs = cv2.findContours(mask, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+
+        # handle different return values of findContours between OpenCV versions
+        contours = outs[0] if len(outs) == 2 else outs[1]
+
+        boxes = []
+        scores = []
+
+        for index in range(len(contours)):
+            contour = contours[index]
+
+            # get minimum bounding box (rotated rectangle) around the contour and the smallest side length.
+            points, min_side = self.get_min_boxes(contour)
+
+            if min_side < self.min_size:
+                continue
+
+            points = np.array(points)
+
+            score = self.box_score(output, contour)
+            if self.box_thresh > score:
+                continue
+
+            polygon = Polygon(points)
+            distance = polygon.area / polygon.length
+
+            # Use pyclipper to shrink the polygon slightly based on the computed distance.
+            offset = PyclipperOffset()
+            offset.AddPath(points, JT_ROUND, ET_CLOSEDPOLYGON)
+            points = np.array(offset.Execute(distance * 1.5)).reshape((-1, 1, 2))
+
+            # get the minimum bounding box around the shrunken polygon.
+            box, min_side = self.get_min_boxes(points)
+
+            if min_side < self.min_size + 2:
+                continue
+
+            box = np.array(box)
+
+            # normalize and clip box coordinates to fit within the destination image size.
+            box[:, 0] = np.clip(np.round(box[:, 0] / width * dest_width), 0, dest_width)
+            box[:, 1] = np.clip(
+                np.round(box[:, 1] / height * dest_height), 0, dest_height
+            )
+
+            boxes.append(box.astype("int32"))
+            scores.append(score)
+
+        return np.array(boxes, dtype="int32"), scores
+
+    @staticmethod
+    def get_min_boxes(contour: np.ndarray) -> Tuple[List[Tuple[float, float]], float]:
+        """
+        Calculate the minimum bounding box (rotated rectangle) for a given contour.
+
+        Args:
+            contour (np.ndarray): The contour points of the detected shape.
+
+        Returns:
+            Tuple[List[Tuple[float, float]], float]: A list of four points representing the
+            corners of the bounding box, and the length of the shortest side.
+        """
+        bounding_box = cv2.minAreaRect(contour)
+        points = sorted(cv2.boxPoints(bounding_box), key=lambda x: x[0])
+        index_1, index_4 = (0, 1) if points[1][1] > points[0][1] else (1, 0)
+        index_2, index_3 = (2, 3) if points[3][1] > points[2][1] else (3, 2)
+        box = [points[index_1], points[index_2], points[index_3], points[index_4]]
+        return box, min(bounding_box[1])
+
+    @staticmethod
+    def box_score(bitmap: np.ndarray, contour: np.ndarray) -> float:
+        """
+        Calculate the average score within the bounding box of a contour.
+
+        Args:
+            bitmap (np.ndarray): The output confidence map from the model.
+            contour (np.ndarray): The contour of the detected shape.
+
+        Returns:
+            float: The average score of the pixels inside the contour region.
+        """
+        h, w = bitmap.shape[:2]
+        contour = contour.reshape(-1, 2)
+        x1, y1 = np.clip(contour.min(axis=0), 0, [w - 1, h - 1])
+        x2, y2 = np.clip(contour.max(axis=0), 0, [w - 1, h - 1])
+        mask = np.zeros((y2 - y1 + 1, x2 - x1 + 1), dtype=np.uint8)
+        cv2.fillPoly(mask, [contour - [x1, y1]], 1)
+        return cv2.mean(bitmap[y1 : y2 + 1, x1 : x2 + 1], mask)[0]
+
+    @staticmethod
+    def expand_box(points: List[Tuple[float, float]]) -> np.ndarray:
+        """
+        Expand a polygonal shape slightly by a factor determined by the area-to-perimeter ratio.
+
+        Args:
+            points (List[Tuple[float, float]]): Points of the polygon to expand.
+
+        Returns:
+            np.ndarray: Expanded polygon points.
+        """
+        polygon = Polygon(points)
+        distance = polygon.area / polygon.length
+        offset = PyclipperOffset()
+        offset.AddPath(points, JT_ROUND, ET_CLOSEDPOLYGON)
+        expanded = np.array(offset.Execute(distance * 1.5)).reshape((-1, 2))
+        return expanded
+
+    def filter_polygon(
+        self, points: List[np.ndarray], shape: Tuple[int, int]
+    ) -> np.ndarray:
+        """
+        Filter a set of polygons to include only valid ones that fit within an image shape
+        and meet size constraints.
+
+        Args:
+            points (List[np.ndarray]): List of polygons to filter.
+            shape (Tuple[int, int]): Shape of the image (height, width).
+
+        Returns:
+            np.ndarray: List of filtered polygons.
+        """
+        height, width = shape
+        return np.array(
+            [
+                self.clockwise_order(point)
+                for point in points
+                if self.is_valid_polygon(point, width, height)
+            ]
+        )
+
+    @staticmethod
+    def is_valid_polygon(point: np.ndarray, width: int, height: int) -> bool:
+        """
+        Check if a polygon is valid, meaning it fits within the image bounds
+        and has sides of a minimum length.
+
+        Args:
+            point (np.ndarray): The polygon to validate.
+            width (int): Image width.
+            height (int): Image height.
+
+        Returns:
+            bool: Whether the polygon is valid or not.
+        """
+        return (
+            point[:, 0].min() >= 0
+            and point[:, 0].max() < width
+            and point[:, 1].min() >= 0
+            and point[:, 1].max() < height
+            and np.linalg.norm(point[0] - point[1]) > 3
+            and np.linalg.norm(point[0] - point[3]) > 3
+        )
+
+    @staticmethod
+    def clockwise_order(point: np.ndarray) -> np.ndarray:
+        """
+        Arrange the points of a polygon in clockwise order based on their angular positions
+        around the polygon's center.
+
+        Args:
+            point (np.ndarray): Array of points of the polygon.
+
+        Returns:
+            np.ndarray: Points ordered in clockwise direction.
+        """
+        center = point.mean(axis=0)
+        return point[
+            np.argsort(np.arctan2(point[:, 1] - center[1], point[:, 0] - center[0]))
+        ]
+
+    @staticmethod
+    def sort_polygon(points):
+        """
+        Sort polygons based on their position in the image. If polygons are close in vertical
+        position (within 10 pixels), sort them by horizontal position.
+
+        Args:
+            points: List of polygons to sort.
+
+        Returns:
+            List: Sorted list of polygons.
+        """
+        points.sort(key=lambda x: (x[0][1], x[0][0]))
+        for i in range(len(points) - 1):
+            for j in range(i, -1, -1):
+                if abs(points[j + 1][0][1] - points[j][0][1]) < 10 and (
+                    points[j + 1][0][0] < points[j][0][0]
+                ):
+                    temp = points[j]
+                    points[j] = points[j + 1]
+                    points[j + 1] = temp
+                else:
+                    break
+        return points
+
+    @staticmethod
+    def zero_pad(image: np.ndarray) -> np.ndarray:
+        """
+        Apply zero-padding to an image, ensuring its dimensions are at least 32x32.
+        The padding is added only if needed.
+
+        Args:
+            image (np.ndarray): Input image.
+
+        Returns:
+            np.ndarray: Zero-padded image.
+        """
+        h, w, c = image.shape
+        pad = np.zeros((max(32, h), max(32, w), c), np.uint8)
+        pad[:h, :w, :] = image
+        return pad
+
+    @staticmethod
+    def _preprocess_classification_image(image: np.ndarray) -> np.ndarray:
+        """
+        Preprocess a single image for classification by resizing, normalizing, and padding.
+
+        This method resizes the input image to a fixed height of 48 pixels while adjusting
+        the width dynamically up to a maximum of 192 pixels. The image is then normalized and
+        padded to fit the required input dimensions for classification.
+
+        Args:
+            image (np.ndarray): Input image to preprocess.
+
+        Returns:
+            np.ndarray: Preprocessed and padded image.
+        """
+        # fixed height of 48, dynamic width up to 192
+        input_shape = (3, 48, 192)
+        input_c, input_h, input_w = input_shape
+
+        h, w = image.shape[:2]
+        ratio = w / h
+        resized_w = min(input_w, math.ceil(input_h * ratio))
+
+        resized_image = cv2.resize(image, (resized_w, input_h))
+
+        # handle single-channel images (grayscale) if needed
+        if input_c == 1 and resized_image.ndim == 2:
+            resized_image = resized_image[np.newaxis, :, :]
+        else:
+            resized_image = resized_image.transpose((2, 0, 1))
+
+        # normalize
+        resized_image = (resized_image.astype("float32") / 255.0 - 0.5) / 0.5
+
+        padded_image = np.zeros((input_c, input_h, input_w), dtype=np.float32)
+        padded_image[:, :, :resized_w] = resized_image
+
+        return padded_image
+
+    def _process_classification_output(
+        self, images: List[np.ndarray], outputs: List[np.ndarray]
+    ) -> Tuple[List[np.ndarray], List[Tuple[str, float]]]:
+        """
+        Process the classification model output by matching labels with confidence scores.
+
+        This method processes the outputs from the classification model and rotates images
+        with high confidence of being labeled "180". It ensures that results are mapped to
+        the original image order.
+
+        Args:
+            images (List[np.ndarray]): List of input images.
+            outputs (List[np.ndarray]): Corresponding model outputs.
+
+        Returns:
+            Tuple[List[np.ndarray], List[Tuple[str, float]]]: A tuple of processed images and
+            classification results (label and confidence score).
+        """
+        labels = ["0", "180"]
+        results = [["", 0.0]] * len(images)
+        indices = np.argsort(np.array([x.shape[1] / x.shape[0] for x in images]))
+
+        outputs = np.stack(outputs)
+
+        outputs = [
+            (labels[idx], outputs[i, idx])
+            for i, idx in enumerate(outputs.argmax(axis=1))
+        ]
+
+        for i in range(0, len(images), self.batch_size):
+            for j in range(len(outputs)):
+                label, score = outputs[j]
+                results[indices[i + j]] = [label, score]
+                if "180" in label and score >= self.lpr_config.threshold:
+                    images[indices[i + j]] = cv2.rotate(images[indices[i + j]], 1)
+
+        return images, results
+
+    def _preprocess_recognition_image(
+        self, image: np.ndarray, max_wh_ratio: float
+    ) -> np.ndarray:
+        """
+        Preprocess an image for recognition by dynamically adjusting its width.
+
+        This method adjusts the width of the image based on the maximum width-to-height ratio
+        while keeping the height fixed at 48 pixels. The image is then normalized and padded
+        to fit the required input dimensions for recognition.
+
+        Args:
+            image (np.ndarray): Input image to preprocess.
+            max_wh_ratio (float): Maximum width-to-height ratio for resizing.
+
+        Returns:
+            np.ndarray: Preprocessed and padded image.
+        """
+        # fixed height of 48, dynamic width based on ratio
+        input_shape = [3, 48, 320]
+        input_h, input_w = input_shape[1], input_shape[2]
+
+        assert image.shape[2] == input_shape[0], "Unexpected number of image channels."
+
+        # dynamically adjust input width based on max_wh_ratio
+        input_w = int(input_h * max_wh_ratio)
+
+        # check for model-specific input width
+        model_input_w = self.recognition_model.runner.ort.get_inputs()[0].shape[3]
+        if isinstance(model_input_w, int) and model_input_w > 0:
+            input_w = model_input_w
+
+        h, w = image.shape[:2]
+        aspect_ratio = w / h
+        resized_w = min(input_w, math.ceil(input_h * aspect_ratio))
+
+        resized_image = cv2.resize(image, (resized_w, input_h))
+        resized_image = resized_image.transpose((2, 0, 1))
+        resized_image = (resized_image.astype("float32") / 255.0 - 0.5) / 0.5
+
+        padded_image = np.zeros((input_shape[0], input_h, input_w), dtype=np.float32)
+        padded_image[:, :, :resized_w] = resized_image
+
+        return padded_image
+
+    @staticmethod
+    def _crop_license_plate(image: np.ndarray, points: np.ndarray) -> np.ndarray:
+        """
+        Crop the license plate from the image using four corner points.
+
+        This method crops the region containing the license plate by using the perspective
+        transformation based on four corner points. If the resulting image is significantly
+        taller than wide, the image is rotated to the correct orientation.
+
+        Args:
+            image (np.ndarray): Input image containing the license plate.
+            points (np.ndarray): Four corner points defining the plate's position.
+
+        Returns:
+            np.ndarray: Cropped and potentially rotated license plate image.
+        """
+        assert len(points) == 4, "shape of points must be 4*2"
+        points = points.astype(np.float32)
+        crop_width = int(
+            max(
+                np.linalg.norm(points[0] - points[1]),
+                np.linalg.norm(points[2] - points[3]),
+            )
+        )
+        crop_height = int(
+            max(
+                np.linalg.norm(points[0] - points[3]),
+                np.linalg.norm(points[1] - points[2]),
+            )
+        )
+        pts_std = np.float32(
+            [[0, 0], [crop_width, 0], [crop_width, crop_height], [0, crop_height]]
+        )
+        matrix = cv2.getPerspectiveTransform(points, pts_std)
+        image = cv2.warpPerspective(
+            image,
+            matrix,
+            (crop_width, crop_height),
+            borderMode=cv2.BORDER_REPLICATE,
+            flags=cv2.INTER_CUBIC,
+        )
+        height, width = image.shape[0:2]
+        if height * 1.0 / width >= 1.5:
+            image = np.rot90(image, k=3)
+        return image
+
+
+class CTCDecoder:
+    """
+    A decoder for interpreting the output of a CTC (Connectionist Temporal Classification) model.
+
+    This decoder converts the model's output probabilities into readable sequences of characters
+    while removing duplicates and handling blank tokens. It also calculates the confidence scores
+    for each decoded character sequence.
+    """
+
+    def __init__(self):
+        """
+        Initialize the CTCDecoder with a list of characters and a character map.
+
+        The character set includes digits, letters, special characters, and a "blank" token
+        (used by the CTC model for decoding purposes). A character map is created to map
+        indices to characters.
+        """
+        self.characters = [
+            "blank",
+            "0",
+            "1",
+            "2",
+            "3",
+            "4",
+            "5",
+            "6",
+            "7",
+            "8",
+            "9",
+            ":",
+            ";",
+            "<",
+            "=",
+            ">",
+            "?",
+            "@",
+            "A",
+            "B",
+            "C",
+            "D",
+            "E",
+            "F",
+            "G",
+            "H",
+            "I",
+            "J",
+            "K",
+            "L",
+            "M",
+            "N",
+            "O",
+            "P",
+            "Q",
+            "R",
+            "S",
+            "T",
+            "U",
+            "V",
+            "W",
+            "X",
+            "Y",
+            "Z",
+            "[",
+            "\\",
+            "]",
+            "^",
+            "_",
+            "`",
+            "a",
+            "b",
+            "c",
+            "d",
+            "e",
+            "f",
+            "g",
+            "h",
+            "i",
+            "j",
+            "k",
+            "l",
+            "m",
+            "n",
+            "o",
+            "p",
+            "q",
+            "r",
+            "s",
+            "t",
+            "u",
+            "v",
+            "w",
+            "x",
+            "y",
+            "z",
+            "{",
+            "|",
+            "}",
+            "~",
+            "!",
+            '"',
+            "#",
+            "$",
+            "%",
+            "&",
+            "'",
+            "(",
+            ")",
+            "*",
+            "+",
+            ",",
+            "-",
+            ".",
+            "/",
+            " ",
+            " ",
+        ]
+        self.char_map = {i: char for i, char in enumerate(self.characters)}
+
+    def __call__(
+        self, outputs: List[np.ndarray]
+    ) -> Tuple[List[str], List[List[float]]]:
+        """
+        Decode a batch of model outputs into character sequences and their confidence scores.
+
+        The method takes the output probability distributions for each time step and uses
+        the best path decoding strategy. It then merges repeating characters and ignores
+        blank tokens. Confidence scores for each decoded character are also calculated.
+
+        Args:
+            outputs (List[np.ndarray]): A list of model outputs, where each element is
+                                        a probability distribution for each time step.
+
+        Returns:
+            Tuple[List[str], List[List[float]]]: A tuple of decoded character sequences
+                                                and confidence scores for each sequence.
+        """
+        results = []
+        confidences = []
+        for output in outputs:
+            seq_log_probs = np.log(output + 1e-8)
+            best_path = np.argmax(seq_log_probs, axis=1)
+
+            merged_path = []
+            merged_probs = []
+            for t, char_index in enumerate(best_path):
+                if char_index != 0 and (t == 0 or char_index != best_path[t - 1]):
+                    merged_path.append(char_index)
+                    merged_probs.append(seq_log_probs[t, char_index])
+
+            result = "".join(self.char_map[idx] for idx in merged_path)
+            results.append(result)
+
+            confidence = np.exp(merged_probs).tolist()
+            confidences.append(confidence)
+
+        return results, confidences

frigate/embeddings/embeddings.py

@@ -3,15 +3,18 @@
 import base64
 import logging
 import os
+import random
+import string
 import time
 
 from numpy import ndarray
 from playhouse.shortcuts import model_to_dict
 
 from frigate.comms.inter_process import InterProcessRequestor
-from frigate.config.semantic_search import SemanticSearchConfig
+from frigate.config import FrigateConfig
 from frigate.const import (
     CONFIG_DIR,
+    FACE_DIR,
     UPDATE_EMBEDDINGS_REINDEX_PROGRESS,
     UPDATE_MODEL_STATE,
 )
@@ -59,23 +62,25 @@ def get_metadata(event: Event) -> dict:
 class Embeddings:
     """SQLite-vec embeddings database."""
 
-    def __init__(
+    def __init__(self, config: FrigateConfig, db: SqliteVecQueueDatabase) -> None:
-        self, config: SemanticSearchConfig, db: SqliteVecQueueDatabase
-    ) -> None:
         self.config = config
         self.db = db
         self.requestor = InterProcessRequestor()
 
         # Create tables if they don't exist
-        self.db.create_embeddings_tables()
+        self.db.create_embeddings_tables(self.config.face_recognition.enabled)
 
         models = [
             "jinaai/jina-clip-v1-text_model_fp16.onnx",
             "jinaai/jina-clip-v1-tokenizer",
             "jinaai/jina-clip-v1-vision_model_fp16.onnx"
-            if config.model_size == "large"
+            if config.semantic_search.model_size == "large"
             else "jinaai/jina-clip-v1-vision_model_quantized.onnx",
             "jinaai/jina-clip-v1-preprocessor_config.json",
+            "facenet-facenet.onnx",
+            "paddleocr-onnx-detection.onnx",
+            "paddleocr-onnx-classification.onnx",
+            "paddleocr-onnx-recognition.onnx",
         ]
 
         for model in models:
@@ -94,7 +99,7 @@ class Embeddings:
             download_urls={
                 "text_model_fp16.onnx": "https://huggingface.co/jinaai/jina-clip-v1/resolve/main/onnx/text_model_fp16.onnx",
             },
-            model_size=config.model_size,
+            model_size=config.semantic_search.model_size,
             model_type=ModelTypeEnum.text,
             requestor=self.requestor,
             device="CPU",
@@ -102,7 +107,7 @@ class Embeddings:
 
         model_file = (
             "vision_model_fp16.onnx"
-            if self.config.model_size == "large"
+            if self.config.semantic_search.model_size == "large"
             else "vision_model_quantized.onnx"
         )
 
@@ -115,12 +120,69 @@ class Embeddings:
             model_name="jinaai/jina-clip-v1",
             model_file=model_file,
             download_urls=download_urls,
-            model_size=config.model_size,
+            model_size=config.semantic_search.model_size,
             model_type=ModelTypeEnum.vision,
             requestor=self.requestor,
-            device="GPU" if config.model_size == "large" else "CPU",
+            device="GPU" if config.semantic_search.model_size == "large" else "CPU",
         )
 
+        self.face_embedding = None
+
+        if self.config.face_recognition.enabled:
+            self.face_embedding = GenericONNXEmbedding(
+                model_name="facenet",
+                model_file="facenet.onnx",
+                download_urls={
+                    "facenet.onnx": "https://github.com/NickM-27/facenet-onnx/releases/download/v1.0/facenet.onnx",
+                    "facedet.onnx": "https://github.com/opencv/opencv_zoo/raw/refs/heads/main/models/face_detection_yunet/face_detection_yunet_2023mar_int8.onnx",
+                },
+                model_size="large",
+                model_type=ModelTypeEnum.face,
+                requestor=self.requestor,
+                device="GPU",
+            )
+
+        self.lpr_detection_model = None
+        self.lpr_classification_model = None
+        self.lpr_recognition_model = None
+
+        if self.config.lpr.enabled:
+            self.lpr_detection_model = GenericONNXEmbedding(
+                model_name="paddleocr-onnx",
+                model_file="detection.onnx",
+                download_urls={
+                    "detection.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/detection.onnx"
+                },
+                model_size="large",
+                model_type=ModelTypeEnum.alpr_detect,
+                requestor=self.requestor,
+                device="CPU",
+            )
+
+            self.lpr_classification_model = GenericONNXEmbedding(
+                model_name="paddleocr-onnx",
+                model_file="classification.onnx",
+                download_urls={
+                    "classification.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/classification.onnx"
+                },
+                model_size="large",
+                model_type=ModelTypeEnum.alpr_classify,
+                requestor=self.requestor,
+                device="CPU",
+            )
+
+            self.lpr_recognition_model = GenericONNXEmbedding(
+                model_name="paddleocr-onnx",
+                model_file="recognition.onnx",
+                download_urls={
+                    "recognition.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/recognition.onnx"
+                },
+                model_size="large",
+                model_type=ModelTypeEnum.alpr_recognize,
+                requestor=self.requestor,
+                device="CPU",
+            )
+
     def embed_thumbnail(
         self, event_id: str, thumbnail: bytes, upsert: bool = True
     ) -> ndarray:
@@ -215,12 +277,40 @@ class Embeddings:
 
         return embeddings
 
+    def embed_face(self, label: str, thumbnail: bytes, upsert: bool = False) -> ndarray:
+        embedding = self.face_embedding(thumbnail)[0]
+
+        if upsert:
+            rand_id = "".join(
+                random.choices(string.ascii_lowercase + string.digits, k=6)
+            )
+            id = f"{label}-{rand_id}"
+
+            # write face to library
+            folder = os.path.join(FACE_DIR, label)
+            file = os.path.join(folder, f"{id}.webp")
+            os.makedirs(folder, exist_ok=True)
+
+            # save face image
+            with open(file, "wb") as output:
+                output.write(thumbnail)
+
+            self.db.execute_sql(
+                """
+                INSERT OR REPLACE INTO vec_faces(id, face_embedding)
+                VALUES(?, ?)
+                """,
+                (id, serialize(embedding)),
+            )
+
+        return embedding
+
     def reindex(self) -> None:
         logger.info("Indexing tracked object embeddings...")
 
         self.db.drop_embeddings_tables()
         logger.debug("Dropped embeddings tables.")
-        self.db.create_embeddings_tables()
+        self.db.create_embeddings_tables(self.config.face_recognition.enabled)
         logger.debug("Created embeddings tables.")
 
         # Delete the saved stats file

frigate/embeddings/functions/onnx.py

@@ -31,11 +31,16 @@ warnings.filterwarnings(
 disable_progress_bar()
 logger = logging.getLogger(__name__)
 
+FACE_EMBEDDING_SIZE = 160
+
 
 class ModelTypeEnum(str, Enum):
     face = "face"
     vision = "vision"
     text = "text"
+    alpr_detect = "alpr_detect"
+    alpr_classify = "alpr_classify"
+    alpr_recognize = "alpr_recognize"
 
 
 class GenericONNXEmbedding:
@@ -47,7 +52,7 @@ class GenericONNXEmbedding:
         model_file: str,
         download_urls: Dict[str, str],
         model_size: str,
-        model_type: str,
+        model_type: ModelTypeEnum,
         requestor: InterProcessRequestor,
         tokenizer_file: Optional[str] = None,
         device: str = "AUTO",
@@ -57,7 +62,7 @@ class GenericONNXEmbedding:
         self.tokenizer_file = tokenizer_file
         self.requestor = requestor
         self.download_urls = download_urls
-        self.model_type = model_type  # 'text' or 'vision'
+        self.model_type = model_type
         self.model_size = model_size
         self.device = device
         self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name)
@@ -87,12 +92,13 @@ class GenericONNXEmbedding:
                 files_names,
                 ModelStatusTypesEnum.downloaded,
             )
-            self._load_model_and_tokenizer()
+            self._load_model_and_utils()
             logger.debug(f"models are already downloaded for {self.model_name}")
 
     def _download_model(self, path: str):
         try:
             file_name = os.path.basename(path)
+
             if file_name in self.download_urls:
                 ModelDownloader.download_from_url(self.download_urls[file_name], path)
             elif (
@@ -101,6 +107,7 @@ class GenericONNXEmbedding:
             ):
                 if not os.path.exists(path + "/" + self.model_name):
                     logger.info(f"Downloading {self.model_name} tokenizer")
+
                 tokenizer = AutoTokenizer.from_pretrained(
                     self.model_name,
                     trust_remote_code=True,
@@ -125,14 +132,23 @@ class GenericONNXEmbedding:
                 },
             )
 
-    def _load_model_and_tokenizer(self):
+    def _load_model_and_utils(self):
         if self.runner is None:
             if self.downloader:
                 self.downloader.wait_for_download()
             if self.model_type == ModelTypeEnum.text:
                 self.tokenizer = self._load_tokenizer()
-            else:
+            elif self.model_type == ModelTypeEnum.vision:
                 self.feature_extractor = self._load_feature_extractor()
+            elif self.model_type == ModelTypeEnum.face:
+                self.feature_extractor = []
+            elif self.model_type == ModelTypeEnum.alpr_detect:
+                self.feature_extractor = []
+            elif self.model_type == ModelTypeEnum.alpr_classify:
+                self.feature_extractor = []
+            elif self.model_type == ModelTypeEnum.alpr_recognize:
+                self.feature_extractor = []
+
             self.runner = ONNXModelRunner(
                 os.path.join(self.download_path, self.model_file),
                 self.device,
@@ -172,23 +188,73 @@ class GenericONNXEmbedding:
                 self.feature_extractor(images=image, return_tensors="np")
                 for image in processed_images
             ]
+        elif self.model_type == ModelTypeEnum.face:
+            if isinstance(raw_inputs, list):
+                raise ValueError("Face embedding does not support batch inputs.")
+
+            pil = self._process_image(raw_inputs)
+
+            # handle images larger than input size
+            width, height = pil.size
+            if width != FACE_EMBEDDING_SIZE or height != FACE_EMBEDDING_SIZE:
+                if width > height:
+                    new_height = int(((height / width) * FACE_EMBEDDING_SIZE) // 4 * 4)
+                    pil = pil.resize((FACE_EMBEDDING_SIZE, new_height))
+                else:
+                    new_width = int(((width / height) * FACE_EMBEDDING_SIZE) // 4 * 4)
+                    pil = pil.resize((new_width, FACE_EMBEDDING_SIZE))
+
+            og = np.array(pil).astype(np.float32)
+
+            # Image must be FACE_EMBEDDING_SIZExFACE_EMBEDDING_SIZE
+            og_h, og_w, channels = og.shape
+            frame = np.full(
+                (FACE_EMBEDDING_SIZE, FACE_EMBEDDING_SIZE, channels),
+                (0, 0, 0),
+                dtype=np.float32,
+            )
+
+            # compute center offset
+            x_center = (FACE_EMBEDDING_SIZE - og_w) // 2
+            y_center = (FACE_EMBEDDING_SIZE - og_h) // 2
+
+            # copy img image into center of result image
+            frame[y_center : y_center + og_h, x_center : x_center + og_w] = og
+
+            frame = np.expand_dims(frame, axis=0)
+            return [{"input_2": frame}]
+        elif self.model_type == ModelTypeEnum.alpr_detect:
+            preprocessed = []
+            for x in raw_inputs:
+                preprocessed.append(x)
+            return [{"x": preprocessed[0]}]
+        elif self.model_type == ModelTypeEnum.alpr_classify:
+            processed = []
+            for img in raw_inputs:
+                processed.append({"x": img})
+            return processed
+        elif self.model_type == ModelTypeEnum.alpr_recognize:
+            processed = []
+            for img in raw_inputs:
+                processed.append({"x": img})
+            return processed
         else:
             raise ValueError(f"Unable to preprocess inputs for {self.model_type}")
 
-    def _process_image(self, image):
+    def _process_image(self, image, output: str = "RGB") -> Image.Image:
         if isinstance(image, str):
             if image.startswith("http"):
                 response = requests.get(image)
-                image = Image.open(BytesIO(response.content)).convert("RGB")
+                image = Image.open(BytesIO(response.content)).convert(output)
         elif isinstance(image, bytes):
-            image = Image.open(BytesIO(image)).convert("RGB")
+            image = Image.open(BytesIO(image)).convert(output)
 
         return image
 
     def __call__(
         self, inputs: Union[List[str], List[Image.Image], List[str]]
     ) -> List[np.ndarray]:
-        self._load_model_and_tokenizer()
+        self._load_model_and_utils()
         if self.runner is None or (
             self.tokenizer is None and self.feature_extractor is None
         ):

frigate/embeddings/maintainer.py

@@ -9,6 +9,7 @@ from typing import Optional
 
 import cv2
 import numpy as np
+import requests
 from peewee import DoesNotExist
 from playhouse.sqliteq import SqliteQueueDatabase
 
@@ -20,17 +21,19 @@ from frigate.comms.event_metadata_updater import (
 from frigate.comms.events_updater import EventEndSubscriber, EventUpdateSubscriber
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.config import FrigateConfig
-from frigate.const import CLIPS_DIR, UPDATE_EVENT_DESCRIPTION
+from frigate.const import CLIPS_DIR, FRIGATE_LOCALHOST, UPDATE_EVENT_DESCRIPTION
+from frigate.embeddings.alpr.alpr import LicensePlateRecognition
 from frigate.events.types import EventTypeEnum
 from frigate.genai import get_genai_client
 from frigate.models import Event
 from frigate.util.builtin import serialize
-from frigate.util.image import SharedMemoryFrameManager, calculate_region
+from frigate.util.image import SharedMemoryFrameManager, area, calculate_region
 
 from .embeddings import Embeddings
 
 logger = logging.getLogger(__name__)
 
+REQUIRED_FACES = 2
 MAX_THUMBNAILS = 10
 
 
@@ -45,7 +48,7 @@ class EmbeddingMaintainer(threading.Thread):
     ) -> None:
         super().__init__(name="embeddings_maintainer")
         self.config = config
-        self.embeddings = Embeddings(config.semantic_search, db)
+        self.embeddings = Embeddings(config, db)
 
         # Check if we need to re-index events
         if config.semantic_search.reindex:
@@ -58,12 +61,43 @@ class EmbeddingMaintainer(threading.Thread):
         )
         self.embeddings_responder = EmbeddingsResponder()
         self.frame_manager = SharedMemoryFrameManager()
+
+        # set face recognition conditions
+        self.face_recognition_enabled = self.config.face_recognition.enabled
+        self.requires_face_detection = "face" not in self.config.objects.all_objects
+        self.detected_faces: dict[str, float] = {}
+
         # create communication for updating event descriptions
         self.requestor = InterProcessRequestor()
         self.stop_event = stop_event
-        self.tracked_events = {}
+        self.tracked_events: dict[str, list[any]] = {}
         self.genai_client = get_genai_client(config.genai)
 
+        # set license plate recognition conditions
+        self.lpr_config = self.config.lpr
+        self.requires_license_plate_detection = (
+            "license_plate" not in self.config.objects.all_objects
+        )
+        self.detected_license_plates: dict[str, dict[str, any]] = {}
+
+        if self.lpr_config.enabled:
+            self.license_plate_recognition = LicensePlateRecognition(
+                self.lpr_config, self.requestor, self.embeddings
+            )
+
+    @property
+    def face_detector(self) -> cv2.FaceDetectorYN:
+        # Lazily create the classifier.
+        if "face_detector" not in self.__dict__:
+            self.__dict__["face_detector"] = cv2.FaceDetectorYN.create(
+                "/config/model_cache/facenet/facedet.onnx",
+                config="",
+                input_size=(320, 320),
+                score_threshold=0.8,
+                nms_threshold=0.3,
+            )
+        return self.__dict__["face_detector"]
+
     def run(self) -> None:
         """Maintain a SQLite-vec database for semantic search."""
         while not self.stop_event.is_set():
@@ -82,7 +116,7 @@ class EmbeddingMaintainer(threading.Thread):
     def _process_requests(self) -> None:
         """Process embeddings requests"""
 
-        def _handle_request(topic: str, data: str) -> str:
+        def _handle_request(topic: str, data: dict[str, any]) -> str:
             try:
                 if topic == EmbeddingsRequestEnum.embed_description.value:
                     return serialize(
@@ -101,6 +135,35 @@ class EmbeddingMaintainer(threading.Thread):
                     return serialize(
                         self.embeddings.text_embedding([data])[0], pack=False
                     )
+                elif topic == EmbeddingsRequestEnum.register_face.value:
+                    if data.get("cropped"):
+                        self.embeddings.embed_face(
+                            data["face_name"],
+                            base64.b64decode(data["image"]),
+                            upsert=True,
+                        )
+                        return True
+                    else:
+                        img = cv2.imdecode(
+                            np.frombuffer(
+                                base64.b64decode(data["image"]), dtype=np.uint8
+                            ),
+                            cv2.IMREAD_COLOR,
+                        )
+                        face_box = self._detect_face(img)
+
+                        if not face_box:
+                            return False
+
+                        face = img[face_box[1] : face_box[3], face_box[0] : face_box[2]]
+                        ret, webp = cv2.imencode(
+                            ".webp", face, [int(cv2.IMWRITE_WEBP_QUALITY), 100]
+                        )
+                        self.embeddings.embed_face(
+                            data["face_name"], webp.tobytes(), upsert=True
+                        )
+
+                    return False
             except Exception as e:
                 logger.error(f"Unable to handle embeddings request {e}")
 
@@ -108,7 +171,7 @@ class EmbeddingMaintainer(threading.Thread):
 
     def _process_updates(self) -> None:
         """Process event updates"""
-        update = self.event_subscriber.check_for_update(timeout=0.1)
+        update = self.event_subscriber.check_for_update(timeout=0.01)
 
         if update is None:
             return
@@ -119,41 +182,55 @@ class EmbeddingMaintainer(threading.Thread):
             return
 
         camera_config = self.config.cameras[camera]
-        # no need to save our own thumbnails if genai is not enabled
+
-        # or if the object has become stationary
+        # no need to process updated objects if face recognition, lpr, genai are disabled
         if (
             not camera_config.genai.enabled
-            or self.genai_client is None
+            and not self.face_recognition_enabled
-            or data["stationary"]
+            and not self.lpr_config.enabled
         ):
             return
 
-        if data["id"] not in self.tracked_events:
-            self.tracked_events[data["id"]] = []
-
         # Create our own thumbnail based on the bounding box and the frame time
         try:
             frame_id = f"{camera}{data['frame_time']}"
             yuv_frame = self.frame_manager.get(frame_id, camera_config.frame_shape_yuv)
-
-            if yuv_frame is not None:
-                data["thumbnail"] = self._create_thumbnail(yuv_frame, data["box"])
-
-                # Limit the number of thumbnails saved
-                if len(self.tracked_events[data["id"]]) >= MAX_THUMBNAILS:
-                    # Always keep the first thumbnail for the event
-                    self.tracked_events[data["id"]].pop(1)
-
-                self.tracked_events[data["id"]].append(data)
-
-                self.frame_manager.close(frame_id)
         except FileNotFoundError:
             pass
 
+        if yuv_frame is None:
+            logger.debug(
+                "Unable to process object update because frame is unavailable."
+            )
+            return
+
+        if self.face_recognition_enabled:
+            self._process_face(data, yuv_frame)
+
+        if self.lpr_config.enabled:
+            self._process_license_plate(data, yuv_frame)
+
+        # no need to save our own thumbnails if genai is not enabled
+        # or if the object has become stationary
+        if self.genai_client is not None and not data["stationary"]:
+            if data["id"] not in self.tracked_events:
+                self.tracked_events[data["id"]] = []
+
+            data["thumbnail"] = self._create_thumbnail(yuv_frame, data["box"])
+
+            # Limit the number of thumbnails saved
+            if len(self.tracked_events[data["id"]]) >= MAX_THUMBNAILS:
+                # Always keep the first thumbnail for the event
+                self.tracked_events[data["id"]].pop(1)
+
+            self.tracked_events[data["id"]].append(data)
+
+        self.frame_manager.close(frame_id)
+
     def _process_finalized(self) -> None:
         """Process the end of an event."""
         while True:
-            ended = self.event_end_subscriber.check_for_update(timeout=0.1)
+            ended = self.event_end_subscriber.check_for_update(timeout=0.01)
 
             if ended == None:
                 break
@@ -161,6 +238,12 @@ class EmbeddingMaintainer(threading.Thread):
             event_id, camera, updated_db = ended
             camera_config = self.config.cameras[camera]
 
+            if event_id in self.detected_faces:
+                self.detected_faces.pop(event_id)
+
+            if event_id in self.detected_license_plates:
+                self.detected_license_plates.pop(event_id)
+
             if updated_db:
                 try:
                     event: Event = Event.get(Event.id == event_id)
@@ -243,7 +326,7 @@ class EmbeddingMaintainer(threading.Thread):
     def _process_event_metadata(self):
         # Check for regenerate description requests
         (topic, event_id, source) = self.event_metadata_subscriber.check_for_update(
-            timeout=0.1
+            timeout=0.01
         )
 
         if topic is None:
@@ -252,6 +335,347 @@ class EmbeddingMaintainer(threading.Thread):
         if event_id:
             self.handle_regenerate_description(event_id, source)
 
+    def _search_face(self, query_embedding: bytes) -> list[tuple[str, float]]:
+        """Search for the face most closely matching the embedding."""
+        sql_query = f"""
+            SELECT
+                id,
+                distance
+            FROM vec_faces
+            WHERE face_embedding MATCH ?
+                AND k = {REQUIRED_FACES} ORDER BY distance
+        """
+        return self.embeddings.db.execute_sql(sql_query, [query_embedding]).fetchall()
+
+    def _detect_face(self, input: np.ndarray) -> tuple[int, int, int, int]:
+        """Detect faces in input image."""
+        self.face_detector.setInputSize((input.shape[1], input.shape[0]))
+        faces = self.face_detector.detect(input)
+
+        if faces[1] is None:
+            return None
+
+        face = None
+
+        for _, potential_face in enumerate(faces[1]):
+            raw_bbox = potential_face[0:4].astype(np.uint16)
+            x: int = max(raw_bbox[0], 0)
+            y: int = max(raw_bbox[1], 0)
+            w: int = raw_bbox[2]
+            h: int = raw_bbox[3]
+            bbox = (x, y, x + w, y + h)
+
+            if face is None or area(bbox) > area(face):
+                face = bbox
+
+        return face
+
+    def _process_face(self, obj_data: dict[str, any], frame: np.ndarray) -> None:
+        """Look for faces in image."""
+        id = obj_data["id"]
+
+        # don't run for non person objects
+        if obj_data.get("label") != "person":
+            logger.debug("Not a processing face for non person object.")
+            return
+
+        # don't overwrite sub label for objects that have a sub label
+        # that is not a face
+        if obj_data.get("sub_label") and id not in self.detected_faces:
+            logger.debug(
+                f"Not processing face due to existing sub label: {obj_data.get('sub_label')}."
+            )
+            return
+
+        face: Optional[dict[str, any]] = None
+
+        if self.requires_face_detection:
+            logger.debug("Running manual face detection.")
+            person_box = obj_data.get("box")
+
+            if not person_box:
+                return None
+
+            rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420)
+            left, top, right, bottom = person_box
+            person = rgb[top:bottom, left:right]
+            face = self._detect_face(person)
+
+            if not face:
+                logger.debug("Detected no faces for person object.")
+                return
+
+            face_frame = person[face[1] : face[3], face[0] : face[2]]
+            face_frame = cv2.cvtColor(face_frame, cv2.COLOR_RGB2BGR)
+        else:
+            # don't run for object without attributes
+            if not obj_data.get("current_attributes"):
+                logger.debug("No attributes to parse.")
+                return
+
+            attributes: list[dict[str, any]] = obj_data.get("current_attributes", [])
+            for attr in attributes:
+                if attr.get("label") != "face":
+                    continue
+
+                if face is None or attr.get("score", 0.0) > face.get("score", 0.0):
+                    face = attr
+
+            # no faces detected in this frame
+            if not face:
+                return
+
+            face_box = face.get("box")
+
+            # check that face is valid
+            if not face_box or area(face_box) < self.config.face_recognition.min_area:
+                logger.debug(f"Invalid face box {face}")
+                return
+
+            face_frame = cv2.cvtColor(frame, cv2.COLOR_YUV2BGR_I420)
+            face_frame = face_frame[
+                face_box[1] : face_box[3], face_box[0] : face_box[2]
+            ]
+
+        ret, webp = cv2.imencode(
+            ".webp", face_frame, [int(cv2.IMWRITE_WEBP_QUALITY), 100]
+        )
+
+        if not ret:
+            logger.debug("Not processing face due to error creating cropped image.")
+            return
+
+        embedding = self.embeddings.embed_face("unknown", webp.tobytes(), upsert=False)
+        query_embedding = serialize(embedding)
+        best_faces = self._search_face(query_embedding)
+        logger.debug(f"Detected best faces for person as: {best_faces}")
+
+        if not best_faces or len(best_faces) < REQUIRED_FACES:
+            logger.debug(f"{len(best_faces)} < {REQUIRED_FACES} min required faces.")
+            return
+
+        sub_label = str(best_faces[0][0]).split("-")[0]
+        avg_score = 0
+
+        for face in best_faces:
+            score = 1.0 - face[1]
+
+            if face[0].split("-")[0] != sub_label:
+                logger.debug("Detected multiple faces, result is not valid.")
+                return
+
+            avg_score += score
+
+        avg_score = round(avg_score / REQUIRED_FACES, 2)
+
+        if avg_score < self.config.face_recognition.threshold or (
+            id in self.detected_faces and avg_score <= self.detected_faces[id]
+        ):
+            logger.debug(
+                f"Recognized face score {avg_score} is less than threshold ({self.config.face_recognition.threshold}) / previous face score ({self.detected_faces.get(id)})."
+            )
+            return
+
+        resp = requests.post(
+            f"{FRIGATE_LOCALHOST}/api/events/{id}/sub_label",
+            json={
+                "camera": obj_data.get("camera"),
+                "subLabel": sub_label,
+                "subLabelScore": avg_score,
+            },
+        )
+
+        if resp.status_code == 200:
+            self.detected_faces[id] = avg_score
+
+    def _detect_license_plate(self, input: np.ndarray) -> tuple[int, int, int, int]:
+        """Return the dimensions of the input image as [x, y, width, height]."""
+        height, width = input.shape[:2]
+        return (0, 0, width, height)
+
+    def _process_license_plate(
+        self, obj_data: dict[str, any], frame: np.ndarray
+    ) -> None:
+        """Look for license plates in image."""
+        id = obj_data["id"]
+
+        # don't run for non car objects
+        if obj_data.get("label") != "car":
+            logger.debug("Not a processing license plate for non car object.")
+            return
+
+        # don't run for stationary car objects
+        if obj_data.get("stationary") == True:
+            logger.debug("Not a processing license plate for a stationary car object.")
+            return
+
+        # don't overwrite sub label for objects that have a sub label
+        # that is not a license plate
+        if obj_data.get("sub_label") and id not in self.detected_license_plates:
+            logger.debug(
+                f"Not processing license plate due to existing sub label: {obj_data.get('sub_label')}."
+            )
+            return
+
+        license_plate: Optional[dict[str, any]] = None
+
+        if self.requires_license_plate_detection:
+            logger.debug("Running manual license_plate detection.")
+            car_box = obj_data.get("box")
+
+            if not car_box:
+                return None
+
+            rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420)
+            left, top, right, bottom = car_box
+            car = rgb[top:bottom, left:right]
+            license_plate = self._detect_license_plate(car)
+
+            if not license_plate:
+                logger.debug("Detected no license plates for car object.")
+                return
+
+            license_plate_frame = car[
+                license_plate[1] : license_plate[3], license_plate[0] : license_plate[2]
+            ]
+            license_plate_frame = cv2.cvtColor(license_plate_frame, cv2.COLOR_RGB2BGR)
+        else:
+            # don't run for object without attributes
+            if not obj_data.get("current_attributes"):
+                logger.debug("No attributes to parse.")
+                return
+
+            attributes: list[dict[str, any]] = obj_data.get("current_attributes", [])
+            for attr in attributes:
+                if attr.get("label") != "license_plate":
+                    continue
+
+                if license_plate is None or attr.get("score", 0.0) > license_plate.get(
+                    "score", 0.0
+                ):
+                    license_plate = attr
+
+            # no license plates detected in this frame
+            if not license_plate:
+                return
+
+            license_plate_box = license_plate.get("box")
+
+            # check that license plate is valid
+            if (
+                not license_plate_box
+                or area(license_plate_box) < self.config.lpr.min_area
+            ):
+                logger.debug(f"Invalid license plate box {license_plate}")
+                return
+
+            license_plate_frame = cv2.cvtColor(frame, cv2.COLOR_YUV2BGR_I420)
+            license_plate_frame = license_plate_frame[
+                license_plate_box[1] : license_plate_box[3],
+                license_plate_box[0] : license_plate_box[2],
+            ]
+
+        # run detection, returns results sorted by confidence, best first
+        license_plates, confidences, areas = (
+            self.license_plate_recognition.process_license_plate(license_plate_frame)
+        )
+
+        logger.debug(f"Text boxes: {license_plates}")
+        logger.debug(f"Confidences: {confidences}")
+        logger.debug(f"Areas: {areas}")
+
+        if license_plates:
+            for plate, confidence, text_area in zip(license_plates, confidences, areas):
+                avg_confidence = (
+                    (sum(confidence) / len(confidence)) if confidence else 0
+                )
+
+                logger.debug(
+                    f"Detected text: {plate} (average confidence: {avg_confidence:.2f}, area: {text_area} pixels)"
+                )
+        else:
+            # no plates found
+            logger.debug("No text detected")
+            return
+
+        top_plate, top_char_confidences, top_area = (
+            license_plates[0],
+            confidences[0],
+            areas[0],
+        )
+        avg_confidence = (
+            (sum(top_char_confidences) / len(top_char_confidences))
+            if top_char_confidences
+            else 0
+        )
+
+        # Check if we have a previously detected plate for this ID
+        if id in self.detected_license_plates:
+            prev_plate = self.detected_license_plates[id]["plate"]
+            prev_char_confidences = self.detected_license_plates[id]["char_confidences"]
+            prev_area = self.detected_license_plates[id]["area"]
+            prev_avg_confidence = (
+                (sum(prev_char_confidences) / len(prev_char_confidences))
+                if prev_char_confidences
+                else 0
+            )
+
+            # Define conditions for keeping the previous plate
+            shorter_than_previous = len(top_plate) < len(prev_plate)
+            lower_avg_confidence = avg_confidence <= prev_avg_confidence
+            smaller_area = top_area < prev_area
+
+            # Compare character-by-character confidence where possible
+            min_length = min(len(top_plate), len(prev_plate))
+            char_confidence_comparison = sum(
+                1
+                for i in range(min_length)
+                if top_char_confidences[i] <= prev_char_confidences[i]
+            )
+            worse_char_confidences = char_confidence_comparison >= min_length / 2
+
+            if (shorter_than_previous or smaller_area) and (
+                lower_avg_confidence and worse_char_confidences
+            ):
+                logger.debug(
+                    f"Keeping previous plate. New plate stats: "
+                    f"length={len(top_plate)}, avg_conf={avg_confidence:.2f}, area={top_area} "
+                    f"vs Previous: length={len(prev_plate)}, avg_conf={prev_avg_confidence:.2f}, area={prev_area}"
+                )
+                return
+
+        # Check against minimum confidence threshold
+        if avg_confidence < self.lpr_config.threshold:
+            logger.debug(
+                f"Average confidence {avg_confidence} is less than threshold ({self.lpr_config.threshold})"
+            )
+            return
+
+        # Determine subLabel based on known plates
+        # Default to the detected plate, use label name if there's a match
+        sub_label = top_plate
+        for label, plates in self.lpr_config.known_plates.items():
+            if top_plate in plates:
+                sub_label = label
+                break
+
+        # Send the result to the API
+        resp = requests.post(
+            f"{FRIGATE_LOCALHOST}/api/events/{id}/sub_label",
+            json={
+                "camera": obj_data.get("camera"),
+                "subLabel": sub_label,
+                "subLabelScore": avg_confidence,
+            },
+        )
+
+        if resp.status_code == 200:
+            self.detected_license_plates[id] = {
+                "plate": top_plate,
+                "char_confidences": top_char_confidences,
+                "area": top_area,
+            }
+
     def _create_thumbnail(self, yuv_frame, box, height=500) -> Optional[bytes]:
         """Return jpg thumbnail of a region of the frame."""
         frame = cv2.cvtColor(yuv_frame, cv2.COLOR_YUV2BGR_I420)

frigate/util/downloader.py

@@ -101,7 +101,7 @@ class ModelDownloader:
         self.download_complete.set()
 
     @staticmethod
-    def download_from_url(url: str, save_path: str, silent: bool = False):
+    def download_from_url(url: str, save_path: str, silent: bool = False) -> Path:
         temporary_filename = Path(save_path).with_name(
             os.path.basename(save_path) + ".part"
         )
@@ -125,6 +125,8 @@ class ModelDownloader:
         if not silent:
             logger.info(f"Downloading complete: {url}")
 
+        return Path(save_path)
+
     @staticmethod
     def mark_files_state(
         requestor: InterProcessRequestor,