Mirrors/blakeblackshear.frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2026-02-20 13:54:36 +01:00
Code Issues Projects Releases Wiki Activity

Face recognition backend (#14495)

Browse Source 
* 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
This commit is contained in:
Nicolas Mowen
2024-10-22 16:05:48 -06:00
committed by Blake Blackshear
parent 0e1139a7a4
commit aa19ec3ddb
13 changed files with 365 additions and 45 deletions
Download Patch File Download Diff File Expand all files Collapse all files

17
frigate/db/sqlitevecq.py
View File

@@ -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[128] distance_metric=cosine
);
""")