mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-26 19:06:11 +01:00
775 lines
28 KiB
Python
775 lines
28 KiB
Python
"""Maintain embeddings in SQLite-vec."""
|
|
|
|
import base64
|
|
import logging
|
|
import os
|
|
import threading
|
|
from multiprocessing.synchronize import Event as MpEvent
|
|
from typing import Optional
|
|
|
|
import cv2
|
|
import numpy as np
|
|
import requests
|
|
from peewee import DoesNotExist
|
|
from playhouse.sqliteq import SqliteQueueDatabase
|
|
|
|
from frigate.comms.embeddings_updater import EmbeddingsRequestEnum, EmbeddingsResponder
|
|
from frigate.comms.event_metadata_updater import (
|
|
EventMetadataSubscriber,
|
|
EventMetadataTypeEnum,
|
|
)
|
|
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, 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.types import TrackedObjectUpdateTypesEnum
|
|
from frigate.util.builtin import serialize
|
|
from frigate.util.image import SharedMemoryFrameManager, area, calculate_region
|
|
|
|
from .embeddings import Embeddings
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
REQUIRED_FACES = 2
|
|
MAX_THUMBNAILS = 10
|
|
|
|
|
|
class EmbeddingMaintainer(threading.Thread):
|
|
"""Handle embedding queue and post event updates."""
|
|
|
|
def __init__(
|
|
self,
|
|
db: SqliteQueueDatabase,
|
|
config: FrigateConfig,
|
|
stop_event: MpEvent,
|
|
) -> None:
|
|
super().__init__(name="embeddings_maintainer")
|
|
self.config = config
|
|
self.embeddings = Embeddings(config, db)
|
|
|
|
# Check if we need to re-index events
|
|
if config.semantic_search.reindex:
|
|
self.embeddings.reindex()
|
|
|
|
self.event_subscriber = EventUpdateSubscriber()
|
|
self.event_end_subscriber = EventEndSubscriber()
|
|
self.event_metadata_subscriber = EventMetadataSubscriber(
|
|
EventMetadataTypeEnum.regenerate_description
|
|
)
|
|
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: dict[str, list[any]] = {}
|
|
self.genai_client = get_genai_client(config)
|
|
|
|
# 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():
|
|
self._process_requests()
|
|
self._process_updates()
|
|
self._process_finalized()
|
|
self._process_event_metadata()
|
|
|
|
self.event_subscriber.stop()
|
|
self.event_end_subscriber.stop()
|
|
self.event_metadata_subscriber.stop()
|
|
self.embeddings_responder.stop()
|
|
self.requestor.stop()
|
|
logger.info("Exiting embeddings maintenance...")
|
|
|
|
def _process_requests(self) -> None:
|
|
"""Process embeddings requests"""
|
|
|
|
def _handle_request(topic: str, data: dict[str, any]) -> str:
|
|
try:
|
|
if topic == EmbeddingsRequestEnum.embed_description.value:
|
|
return serialize(
|
|
self.embeddings.embed_description(
|
|
data["id"], data["description"]
|
|
),
|
|
pack=False,
|
|
)
|
|
elif topic == EmbeddingsRequestEnum.embed_thumbnail.value:
|
|
thumbnail = base64.b64decode(data["thumbnail"])
|
|
return serialize(
|
|
self.embeddings.embed_thumbnail(data["id"], thumbnail),
|
|
pack=False,
|
|
)
|
|
elif topic == EmbeddingsRequestEnum.generate_search.value:
|
|
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}")
|
|
|
|
self.embeddings_responder.check_for_request(_handle_request)
|
|
|
|
def _process_updates(self) -> None:
|
|
"""Process event updates"""
|
|
update = self.event_subscriber.check_for_update(timeout=0.01)
|
|
|
|
if update is None:
|
|
return
|
|
|
|
source_type, _, camera, frame_name, data = update
|
|
|
|
if not camera or source_type != EventTypeEnum.tracked_object:
|
|
return
|
|
|
|
camera_config = self.config.cameras[camera]
|
|
|
|
# no need to process updated objects if face recognition, lpr, genai are disabled
|
|
if (
|
|
not camera_config.genai.enabled
|
|
and not self.face_recognition_enabled
|
|
and not self.lpr_config.enabled
|
|
):
|
|
return
|
|
|
|
# Create our own thumbnail based on the bounding box and the frame time
|
|
try:
|
|
yuv_frame = self.frame_manager.get(frame_name, camera_config.frame_shape_yuv)
|
|
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_name)
|
|
|
|
def _process_finalized(self) -> None:
|
|
"""Process the end of an event."""
|
|
while True:
|
|
ended = self.event_end_subscriber.check_for_update(timeout=0.01)
|
|
|
|
if ended == None:
|
|
break
|
|
|
|
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)
|
|
except DoesNotExist:
|
|
continue
|
|
|
|
# Skip the event if not an object
|
|
if event.data.get("type") != "object":
|
|
continue
|
|
|
|
# Extract valid thumbnail
|
|
thumbnail = base64.b64decode(event.thumbnail)
|
|
|
|
# Embed the thumbnail
|
|
self._embed_thumbnail(event_id, thumbnail)
|
|
|
|
if (
|
|
camera_config.genai.enabled
|
|
and self.genai_client is not None
|
|
and event.data.get("description") is None
|
|
and (
|
|
not camera_config.genai.objects
|
|
or event.label in camera_config.genai.objects
|
|
)
|
|
and (
|
|
not camera_config.genai.required_zones
|
|
or set(event.zones) & set(camera_config.genai.required_zones)
|
|
)
|
|
):
|
|
if event.has_snapshot and camera_config.genai.use_snapshot:
|
|
with open(
|
|
os.path.join(CLIPS_DIR, f"{event.camera}-{event.id}.jpg"),
|
|
"rb",
|
|
) as image_file:
|
|
snapshot_image = image_file.read()
|
|
|
|
img = cv2.imdecode(
|
|
np.frombuffer(snapshot_image, dtype=np.int8),
|
|
cv2.IMREAD_COLOR,
|
|
)
|
|
|
|
# crop snapshot based on region before sending off to genai
|
|
height, width = img.shape[:2]
|
|
x1_rel, y1_rel, width_rel, height_rel = event.data["region"]
|
|
|
|
x1, y1 = int(x1_rel * width), int(y1_rel * height)
|
|
cropped_image = img[
|
|
y1 : y1 + int(height_rel * height),
|
|
x1 : x1 + int(width_rel * width),
|
|
]
|
|
|
|
_, buffer = cv2.imencode(".jpg", cropped_image)
|
|
snapshot_image = buffer.tobytes()
|
|
|
|
embed_image = (
|
|
[snapshot_image]
|
|
if event.has_snapshot and camera_config.genai.use_snapshot
|
|
else (
|
|
[thumbnail for data in self.tracked_events[event_id]]
|
|
if len(self.tracked_events.get(event_id, [])) > 0
|
|
else [thumbnail]
|
|
)
|
|
)
|
|
|
|
# Generate the description. Call happens in a thread since it is network bound.
|
|
threading.Thread(
|
|
target=self._embed_description,
|
|
name=f"_embed_description_{event.id}",
|
|
daemon=True,
|
|
args=(
|
|
event,
|
|
embed_image,
|
|
),
|
|
).start()
|
|
|
|
# Delete tracked events based on the event_id
|
|
if event_id in self.tracked_events:
|
|
del self.tracked_events[event_id]
|
|
|
|
def _process_event_metadata(self):
|
|
# Check for regenerate description requests
|
|
(topic, event_id, source) = self.event_metadata_subscriber.check_for_update(
|
|
timeout=0.01
|
|
)
|
|
|
|
if topic is None:
|
|
return
|
|
|
|
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 = license_plates[0], confidences[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_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
|
|
|
|
# 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 (
|
|
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} "
|
|
f"vs Previous: length={len(prev_plate)}, avg_conf={prev_avg_confidence:.2f}"
|
|
)
|
|
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,
|
|
}
|
|
|
|
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)
|
|
region = calculate_region(
|
|
frame.shape, box[0], box[1], box[2], box[3], height, multiplier=1.4
|
|
)
|
|
frame = frame[region[1] : region[3], region[0] : region[2]]
|
|
width = int(height * frame.shape[1] / frame.shape[0])
|
|
frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
|
|
ret, jpg = cv2.imencode(".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), 100])
|
|
|
|
if ret:
|
|
return jpg.tobytes()
|
|
|
|
return None
|
|
|
|
def _embed_thumbnail(self, event_id: str, thumbnail: bytes) -> None:
|
|
"""Embed the thumbnail for an event."""
|
|
self.embeddings.embed_thumbnail(event_id, thumbnail)
|
|
|
|
def _embed_description(self, event: Event, thumbnails: list[bytes]) -> None:
|
|
"""Embed the description for an event."""
|
|
camera_config = self.config.cameras[event.camera]
|
|
|
|
description = self.genai_client.generate_description(
|
|
camera_config, thumbnails, event
|
|
)
|
|
|
|
if not description:
|
|
logger.debug("Failed to generate description for %s", event.id)
|
|
return
|
|
|
|
# fire and forget description update
|
|
self.requestor.send_data(
|
|
UPDATE_EVENT_DESCRIPTION,
|
|
{
|
|
"type": TrackedObjectUpdateTypesEnum.description,
|
|
"id": event.id,
|
|
"description": description,
|
|
},
|
|
)
|
|
|
|
# Embed the description
|
|
self.embeddings.embed_description(event.id, description)
|
|
|
|
logger.debug(
|
|
"Generated description for %s (%d images): %s",
|
|
event.id,
|
|
len(thumbnails),
|
|
description,
|
|
)
|
|
|
|
def handle_regenerate_description(self, event_id: str, source: str) -> None:
|
|
try:
|
|
event: Event = Event.get(Event.id == event_id)
|
|
except DoesNotExist:
|
|
logger.error(f"Event {event_id} not found for description regeneration")
|
|
return
|
|
|
|
camera_config = self.config.cameras[event.camera]
|
|
if not camera_config.genai.enabled or self.genai_client is None:
|
|
logger.error(f"GenAI not enabled for camera {event.camera}")
|
|
return
|
|
|
|
thumbnail = base64.b64decode(event.thumbnail)
|
|
|
|
logger.debug(
|
|
f"Trying {source} regeneration for {event}, has_snapshot: {event.has_snapshot}"
|
|
)
|
|
|
|
if event.has_snapshot and source == "snapshot":
|
|
with open(
|
|
os.path.join(CLIPS_DIR, f"{event.camera}-{event.id}.jpg"),
|
|
"rb",
|
|
) as image_file:
|
|
snapshot_image = image_file.read()
|
|
img = cv2.imdecode(
|
|
np.frombuffer(snapshot_image, dtype=np.int8), cv2.IMREAD_COLOR
|
|
)
|
|
|
|
# crop snapshot based on region before sending off to genai
|
|
height, width = img.shape[:2]
|
|
x1_rel, y1_rel, width_rel, height_rel = event.data["region"]
|
|
|
|
x1, y1 = int(x1_rel * width), int(y1_rel * height)
|
|
cropped_image = img[
|
|
y1 : y1 + int(height_rel * height), x1 : x1 + int(width_rel * width)
|
|
]
|
|
|
|
_, buffer = cv2.imencode(".jpg", cropped_image)
|
|
snapshot_image = buffer.tobytes()
|
|
|
|
embed_image = (
|
|
[snapshot_image]
|
|
if event.has_snapshot and source == "snapshot"
|
|
else (
|
|
[thumbnail for data in self.tracked_events[event_id]]
|
|
if len(self.tracked_events.get(event_id, [])) > 0
|
|
else [thumbnail]
|
|
)
|
|
)
|
|
|
|
self._embed_description(event, embed_image)
|