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
468 lines
17 KiB
Python
468 lines
17 KiB
Python
"""Maintain embeddings in SQLite-vec."""
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import base64
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import logging
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import os
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import threading
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from multiprocessing.synchronize import Event as MpEvent
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from typing import Optional
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import cv2
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import numpy as np
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import requests
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from peewee import DoesNotExist
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from playhouse.sqliteq import SqliteQueueDatabase
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from frigate.comms.embeddings_updater import EmbeddingsRequestEnum, EmbeddingsResponder
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from frigate.comms.event_metadata_updater import (
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EventMetadataSubscriber,
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EventMetadataTypeEnum,
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)
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from frigate.comms.events_updater import EventEndSubscriber, EventUpdateSubscriber
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from frigate.comms.inter_process import InterProcessRequestor
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from frigate.config import FrigateConfig
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from frigate.const import CLIPS_DIR, FRIGATE_LOCALHOST, UPDATE_EVENT_DESCRIPTION
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from frigate.events.types import EventTypeEnum
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from frigate.genai import get_genai_client
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from frigate.models import Event
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from frigate.types import TrackedObjectUpdateTypesEnum
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from frigate.util.builtin import serialize
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from frigate.util.image import SharedMemoryFrameManager, area, calculate_region
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from .embeddings import Embeddings
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logger = logging.getLogger(__name__)
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MAX_THUMBNAILS = 10
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class EmbeddingMaintainer(threading.Thread):
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"""Handle embedding queue and post event updates."""
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def __init__(
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self,
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db: SqliteQueueDatabase,
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config: FrigateConfig,
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stop_event: MpEvent,
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) -> None:
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super().__init__(name="embeddings_maintainer")
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self.config = config
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self.embeddings = Embeddings(config.semantic_search, db)
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# Check if we need to re-index events
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if config.semantic_search.reindex:
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self.embeddings.reindex()
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self.event_subscriber = EventUpdateSubscriber()
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self.event_end_subscriber = EventEndSubscriber()
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self.event_metadata_subscriber = EventMetadataSubscriber(
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EventMetadataTypeEnum.regenerate_description
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)
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self.embeddings_responder = EmbeddingsResponder()
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self.frame_manager = SharedMemoryFrameManager()
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# set face recognition conditions
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self.face_recognition_enabled = (
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self.config.semantic_search.face_recognition.enabled
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)
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self.requires_face_detection = "face" not in self.config.model.all_attributes
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# create communication for updating event descriptions
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self.requestor = InterProcessRequestor()
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self.stop_event = stop_event
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self.tracked_events: dict[str, list[any]] = {}
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self.genai_client = get_genai_client(config)
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def run(self) -> None:
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"""Maintain a SQLite-vec database for semantic search."""
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while not self.stop_event.is_set():
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self._process_requests()
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self._process_updates()
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self._process_finalized()
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self._process_event_metadata()
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self.event_subscriber.stop()
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self.event_end_subscriber.stop()
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self.event_metadata_subscriber.stop()
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self.embeddings_responder.stop()
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self.requestor.stop()
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logger.info("Exiting embeddings maintenance...")
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def _process_requests(self) -> None:
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"""Process embeddings requests"""
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def _handle_request(topic: str, data: str) -> str:
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try:
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if topic == EmbeddingsRequestEnum.embed_description.value:
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return serialize(
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self.embeddings.embed_description(
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data["id"], data["description"]
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),
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pack=False,
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)
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elif topic == EmbeddingsRequestEnum.embed_thumbnail.value:
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thumbnail = base64.b64decode(data["thumbnail"])
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return serialize(
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self.embeddings.embed_thumbnail(data["id"], thumbnail),
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pack=False,
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)
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elif topic == EmbeddingsRequestEnum.generate_search.value:
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return serialize(
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self.embeddings.text_embedding([data])[0], pack=False
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)
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elif topic == EmbeddingsRequestEnum.register_face.value:
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self.embeddings.embed_face(
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data["face_name"],
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base64.b64decode(data["image"]),
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upsert=True,
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)
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return None
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except Exception as e:
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logger.error(f"Unable to handle embeddings request {e}")
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self.embeddings_responder.check_for_request(_handle_request)
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def _process_updates(self) -> None:
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"""Process event updates"""
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update = self.event_subscriber.check_for_update(timeout=0.01)
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if update is None:
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return
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source_type, _, camera, frame_name, data = update
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if not camera or source_type != EventTypeEnum.tracked_object:
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return
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camera_config = self.config.cameras[camera]
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# no need to process updated objects if face recognition and genai are disabled
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if not camera_config.genai.enabled and not self.face_recognition_enabled:
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return
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# Create our own thumbnail based on the bounding box and the frame time
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try:
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yuv_frame = self.frame_manager.get(frame_name, camera_config.frame_shape_yuv)
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except FileNotFoundError:
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pass
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if yuv_frame is None:
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logger.debug(
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"Unable to process object update because frame is unavailable."
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)
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return
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if self.face_recognition_enabled:
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self._process_face(data, yuv_frame)
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# no need to save our own thumbnails if genai is not enabled
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# or if the object has become stationary
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if self.genai_client is not None and not data["stationary"]:
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if data["id"] not in self.tracked_events:
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self.tracked_events[data["id"]] = []
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data["thumbnail"] = self._create_thumbnail(yuv_frame, data["box"])
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# Limit the number of thumbnails saved
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if len(self.tracked_events[data["id"]]) >= MAX_THUMBNAILS:
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# Always keep the first thumbnail for the event
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self.tracked_events[data["id"]].pop(1)
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self.tracked_events[data["id"]].append(data)
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self.frame_manager.close(frame_name)
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def _process_finalized(self) -> None:
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"""Process the end of an event."""
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while True:
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ended = self.event_end_subscriber.check_for_update(timeout=0.01)
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if ended == None:
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break
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event_id, camera, updated_db = ended
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camera_config = self.config.cameras[camera]
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if updated_db:
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try:
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event: Event = Event.get(Event.id == event_id)
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except DoesNotExist:
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continue
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# Skip the event if not an object
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if event.data.get("type") != "object":
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continue
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# Extract valid thumbnail
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thumbnail = base64.b64decode(event.thumbnail)
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# Embed the thumbnail
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self._embed_thumbnail(event_id, thumbnail)
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if (
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camera_config.genai.enabled
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and self.genai_client is not None
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and event.data.get("description") is None
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and (
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not camera_config.genai.objects
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or event.label in camera_config.genai.objects
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)
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and (
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not camera_config.genai.required_zones
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or set(event.zones) & set(camera_config.genai.required_zones)
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)
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):
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if event.has_snapshot and camera_config.genai.use_snapshot:
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with open(
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os.path.join(CLIPS_DIR, f"{event.camera}-{event.id}.jpg"),
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"rb",
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) as image_file:
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snapshot_image = image_file.read()
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img = cv2.imdecode(
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np.frombuffer(snapshot_image, dtype=np.int8),
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cv2.IMREAD_COLOR,
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)
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# crop snapshot based on region before sending off to genai
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height, width = img.shape[:2]
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x1_rel, y1_rel, width_rel, height_rel = event.data["region"]
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x1, y1 = int(x1_rel * width), int(y1_rel * height)
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cropped_image = img[
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y1 : y1 + int(height_rel * height),
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x1 : x1 + int(width_rel * width),
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]
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_, buffer = cv2.imencode(".jpg", cropped_image)
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snapshot_image = buffer.tobytes()
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embed_image = (
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[snapshot_image]
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if event.has_snapshot and camera_config.genai.use_snapshot
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else (
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[thumbnail for data in self.tracked_events[event_id]]
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if len(self.tracked_events.get(event_id, [])) > 0
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else [thumbnail]
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)
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)
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# Generate the description. Call happens in a thread since it is network bound.
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threading.Thread(
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target=self._embed_description,
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name=f"_embed_description_{event.id}",
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daemon=True,
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args=(
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event,
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embed_image,
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),
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).start()
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# Delete tracked events based on the event_id
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if event_id in self.tracked_events:
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del self.tracked_events[event_id]
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def _process_event_metadata(self):
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# Check for regenerate description requests
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(topic, event_id, source) = self.event_metadata_subscriber.check_for_update(
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timeout=0.01
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)
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if topic is None:
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return
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if event_id:
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self.handle_regenerate_description(event_id, source)
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def _search_face(self, query_embedding: bytes) -> list:
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"""Search for the face most closely matching the embedding."""
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sql_query = """
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SELECT
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id,
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distance
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FROM vec_faces
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WHERE face_embedding MATCH ?
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AND k = 10 ORDER BY distance
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"""
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return self.embeddings.db.execute_sql(sql_query, [query_embedding]).fetchall()
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def _process_face(self, obj_data: dict[str, any], frame: np.ndarray) -> None:
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"""Look for faces in image."""
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# don't run for non person objects
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if obj_data.get("label") != "person":
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logger.debug("Not a processing face for non person object.")
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return
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# don't overwrite sub label for objects that have one
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if obj_data.get("sub_label"):
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logger.debug(
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f"Not processing face due to existing sub label: {obj_data.get('sub_label')}."
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)
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return
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face: Optional[dict[str, any]] = None
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if self.requires_face_detection:
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# TODO run cv2 face detection
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pass
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else:
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# don't run for object without attributes
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if not obj_data.get("current_attributes"):
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logger.debug("No attributes to parse.")
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return
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attributes: list[dict[str, any]] = obj_data.get("current_attributes", [])
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for attr in attributes:
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if attr.get("label") != "face":
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continue
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if face is None or attr.get("score", 0.0) > face.get("score", 0.0):
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face = attr
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# no faces detected in this frame
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if not face:
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return
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face_box = face.get("box")
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# check that face is valid
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if (
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not face_box
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or area(face_box) < self.config.semantic_search.face_recognition.min_area
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):
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logger.debug(f"Invalid face box {face}")
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return
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face_frame = cv2.cvtColor(frame, cv2.COLOR_YUV2BGR_I420)
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face_frame = face_frame[face_box[1] : face_box[3], face_box[0] : face_box[2]]
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ret, jpg = cv2.imencode(
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".webp", face_frame, [int(cv2.IMWRITE_WEBP_QUALITY), 100]
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)
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if not ret:
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logger.debug("Not processing face due to error creating cropped image.")
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return
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embedding = self.embeddings.embed_face("unknown", jpg.tobytes(), upsert=False)
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query_embedding = serialize(embedding)
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best_faces = self._search_face(query_embedding)
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logger.debug(f"Detected best faces for person as: {best_faces}")
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if not best_faces:
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return
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sub_label = str(best_faces[0][0]).split("-")[0]
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score = 1.0 - best_faces[0][1]
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if score < self.config.semantic_search.face_recognition.threshold:
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return None
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requests.post(
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f"{FRIGATE_LOCALHOST}/api/events/{obj_data['id']}/sub_label",
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json={"subLabel": sub_label, "subLabelScore": score},
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)
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def _create_thumbnail(self, yuv_frame, box, height=500) -> Optional[bytes]:
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"""Return jpg thumbnail of a region of the frame."""
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frame = cv2.cvtColor(yuv_frame, cv2.COLOR_YUV2BGR_I420)
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region = calculate_region(
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frame.shape, box[0], box[1], box[2], box[3], height, multiplier=1.4
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)
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frame = frame[region[1] : region[3], region[0] : region[2]]
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width = int(height * frame.shape[1] / frame.shape[0])
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frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
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ret, jpg = cv2.imencode(".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), 100])
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if ret:
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return jpg.tobytes()
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return None
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def _embed_thumbnail(self, event_id: str, thumbnail: bytes) -> None:
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"""Embed the thumbnail for an event."""
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self.embeddings.embed_thumbnail(event_id, thumbnail)
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def _embed_description(self, event: Event, thumbnails: list[bytes]) -> None:
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"""Embed the description for an event."""
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camera_config = self.config.cameras[event.camera]
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description = self.genai_client.generate_description(
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camera_config, thumbnails, event
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)
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if not description:
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logger.debug("Failed to generate description for %s", event.id)
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return
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# fire and forget description update
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self.requestor.send_data(
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UPDATE_EVENT_DESCRIPTION,
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{
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"type": TrackedObjectUpdateTypesEnum.description,
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"id": event.id,
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"description": description,
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},
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)
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# Embed the description
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self.embeddings.embed_description(event.id, description)
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logger.debug(
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"Generated description for %s (%d images): %s",
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event.id,
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len(thumbnails),
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description,
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)
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def handle_regenerate_description(self, event_id: str, source: str) -> None:
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try:
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event: Event = Event.get(Event.id == event_id)
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except DoesNotExist:
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logger.error(f"Event {event_id} not found for description regeneration")
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return
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camera_config = self.config.cameras[event.camera]
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if not camera_config.genai.enabled or self.genai_client is None:
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logger.error(f"GenAI not enabled for camera {event.camera}")
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return
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thumbnail = base64.b64decode(event.thumbnail)
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logger.debug(
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f"Trying {source} regeneration for {event}, has_snapshot: {event.has_snapshot}"
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)
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if event.has_snapshot and source == "snapshot":
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with open(
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os.path.join(CLIPS_DIR, f"{event.camera}-{event.id}.jpg"),
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"rb",
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) as image_file:
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snapshot_image = image_file.read()
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img = cv2.imdecode(
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np.frombuffer(snapshot_image, dtype=np.int8), cv2.IMREAD_COLOR
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)
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# crop snapshot based on region before sending off to genai
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height, width = img.shape[:2]
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x1_rel, y1_rel, width_rel, height_rel = event.data["region"]
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x1, y1 = int(x1_rel * width), int(y1_rel * height)
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cropped_image = img[
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y1 : y1 + int(height_rel * height), x1 : x1 + int(width_rel * width)
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]
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_, buffer = cv2.imencode(".jpg", cropped_image)
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snapshot_image = buffer.tobytes()
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embed_image = (
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[snapshot_image]
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if event.has_snapshot and source == "snapshot"
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else (
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[thumbnail for data in self.tracked_events[event_id]]
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if len(self.tracked_events.get(event_id, [])) > 0
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else [thumbnail]
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)
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)
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self._embed_description(event, embed_image)
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