mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
cedd082b30
* 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
69 lines
2.6 KiB
Python
69 lines
2.6 KiB
Python
import sqlite3
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from playhouse.sqliteq import SqliteQueueDatabase
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class SqliteVecQueueDatabase(SqliteQueueDatabase):
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def __init__(self, *args, load_vec_extension: bool = False, **kwargs) -> None:
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self.load_vec_extension: bool = load_vec_extension
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# no extension necessary, sqlite will load correctly for each platform
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self.sqlite_vec_path = "/usr/local/lib/vec0"
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super().__init__(*args, **kwargs)
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def _connect(self, *args, **kwargs) -> sqlite3.Connection:
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conn: sqlite3.Connection = super()._connect(*args, **kwargs)
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if self.load_vec_extension:
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self._load_vec_extension(conn)
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return conn
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def _load_vec_extension(self, conn: sqlite3.Connection) -> None:
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conn.enable_load_extension(True)
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conn.load_extension(self.sqlite_vec_path)
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conn.enable_load_extension(False)
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def delete_embeddings_thumbnail(self, event_ids: list[str]) -> None:
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ids = ",".join(["?" for _ in event_ids])
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self.execute_sql(f"DELETE FROM vec_thumbnails WHERE id IN ({ids})", event_ids)
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def delete_embeddings_description(self, event_ids: list[str]) -> None:
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ids = ",".join(["?" for _ in event_ids])
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self.execute_sql(f"DELETE FROM vec_descriptions WHERE id IN ({ids})", event_ids)
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def delete_embeddings_face(self, face_ids: list[str]) -> None:
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ids = ",".join(["?" for _ in face_ids])
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self.execute_sql(f"DELETE FROM vec_faces WHERE id IN ({ids})", face_ids)
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def drop_embeddings_tables(self) -> None:
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self.execute_sql("""
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DROP TABLE vec_descriptions;
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""")
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self.execute_sql("""
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DROP TABLE vec_thumbnails;
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""")
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self.execute_sql("""
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DROP TABLE vec_faces;
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""")
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def create_embeddings_tables(self, face_recognition: bool) -> None:
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"""Create vec0 virtual table for embeddings"""
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self.execute_sql("""
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CREATE VIRTUAL TABLE IF NOT EXISTS vec_thumbnails USING vec0(
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id TEXT PRIMARY KEY,
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thumbnail_embedding FLOAT[768] distance_metric=cosine
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);
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""")
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self.execute_sql("""
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CREATE VIRTUAL TABLE IF NOT EXISTS vec_descriptions USING vec0(
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id TEXT PRIMARY KEY,
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description_embedding FLOAT[768] distance_metric=cosine
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);
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""")
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if face_recognition:
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self.execute_sql("""
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CREATE VIRTUAL TABLE IF NOT EXISTS vec_faces USING vec0(
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id TEXT PRIMARY KEY,
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face_embedding FLOAT[128] distance_metric=cosine
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);
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""")
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