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

frigate/config/semantic_search.py

@@ -4,7 +4,17 @@ from pydantic import Field
 
 from .base import FrigateBaseModel
 
-__all__ = ["SemanticSearchConfig"]
+__all__ = ["FaceRecognitionConfig", "SemanticSearchConfig"]
+
+
+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 SemanticSearchConfig(FrigateBaseModel):
@@ -12,6 +22,9 @@ class SemanticSearchConfig(FrigateBaseModel):
     reindex: Optional[bool] = Field(
         default=False, title="Reindex all detections on startup."
     )
+    face_recognition: FaceRecognitionConfig = Field(
+        default_factory=FaceRecognitionConfig, title="Face recognition config."
+    )
     model_size: str = Field(
         default="small", title="The size of the embeddings model used."
     )