nik/frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2026-03-04 06:33:45 +00:00
Code Issues Packages Projects Releases Wiki Activity

Update metrics inference speed to start with 0 ms

Browse Source
This commit is contained in:
Nicolas Mowen 2025-05-08 08:39:11 -06:00
parent 325a150cc4
commit b8f6a5d6b6
5 changed files with 46 additions and 50 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

26
frigate/data_processing/common/license_plate/mixin.py
View File

@ -25,7 +25,7 @@ from frigate.comms.event_metadata_updater import (
from frigate.const import CLIPS_DIR
from frigate.embeddings.onnx.lpr_embedding import LPR_EMBEDDING_SIZE
from frigate.types import TrackedObjectUpdateTypesEnum
from frigate.util.builtin import EventsPerSecond
from frigate.util.builtin import EventsPerSecond, InferenceSpeed
from frigate.util.image import area
logger = logging.getLogger(__name__)
@ -36,8 +36,10 @@ WRITE_DEBUG_IMAGES = False
class LicensePlateProcessingMixin:
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.plate_rec_speed = InferenceSpeed(self.metrics.alpr_speed)
self.plates_rec_second = EventsPerSecond()
self.plates_rec_second.start()
self.plate_det_speed = InferenceSpeed(self.metrics.yolov9_lpr_speed)
self.plates_det_second = EventsPerSecond()
self.plates_det_second.start()
self.event_metadata_publisher = EventMetadataPublisher()
@ -1157,22 +1159,6 @@ class LicensePlateProcessingMixin:
# 5. Return True if previous plate scores higher
return prev_score > curr_score
def __update_yolov9_metrics(self, duration: float) -> None:
"""
Update inference metrics.
"""
self.metrics.yolov9_lpr_speed.value = (
self.metrics.yolov9_lpr_speed.value * 9 + duration
) / 10
def __update_lpr_metrics(self, duration: float) -> None:
"""
Update inference metrics.
"""
self.metrics.alpr_speed.value = (
self.metrics.alpr_speed.value * 9 + duration
) / 10
def _generate_plate_event(self, camera: str, plate: str, plate_score: float) -> str:
"""Generate a unique ID for a plate event based on camera and text."""
now = datetime.datetime.now().timestamp()
@ -1228,7 +1214,7 @@ class LicensePlateProcessingMixin:
f"{camera}: YOLOv9 LPD inference time: {(datetime.datetime.now().timestamp() - yolov9_start) * 1000:.2f} ms"
)
self.plates_det_second.update()
self.__update_yolov9_metrics(
self.plate_det_speed.update(
datetime.datetime.now().timestamp() - yolov9_start
)
@ -1319,7 +1305,7 @@ class LicensePlateProcessingMixin:
f"{camera}: YOLOv9 LPD inference time: {(datetime.datetime.now().timestamp() - yolov9_start) * 1000:.2f} ms"
)
self.plates_det_second.update()
self.__update_yolov9_metrics(
self.plate_det_speed.update(
datetime.datetime.now().timestamp() - yolov9_start
)
@ -1433,7 +1419,7 @@ class LicensePlateProcessingMixin:
camera, id, license_plate_frame
)
self.plates_rec_second.update()
self.__update_lpr_metrics(datetime.datetime.now().timestamp() - start)
self.plate_rec_speed.update(datetime.datetime.now().timestamp() - start)
if license_plates:
for plate, confidence, text_area in zip(license_plates, confidences, areas):

7
frigate/data_processing/real_time/face.py
View File

@ -25,7 +25,7 @@ from frigate.data_processing.common.face.model import (
FaceRecognizer,
)
from frigate.types import TrackedObjectUpdateTypesEnum
from frigate.util.builtin import EventsPerSecond
from frigate.util.builtin import EventsPerSecond, InferenceSpeed
from frigate.util.image import area
from ..types import DataProcessorMetrics
@ -56,6 +56,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
self.person_face_history: dict[str, list[tuple[str, float, int]]] = {}
self.recognizer: FaceRecognizer | None = None
self.faces_per_second = EventsPerSecond()
self.inference_speed = InferenceSpeed(self.metrics.face_rec_speed)
download_path = os.path.join(MODEL_CACHE_DIR, "facedet")
self.model_files = {
@ -153,9 +154,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
def __update_metrics(self, duration: float) -> None:
self.faces_per_second.update()
self.metrics.face_rec_speed.value = (
self.metrics.face_rec_speed.value * 9 + duration
) / 10
self.inference_speed.update(duration)
def process_frame(self, obj_data: dict[str, any], frame: np.ndarray):
"""Look for faces in image."""

12
frigate/data_processing/types.py
View File

@ -7,7 +7,9 @@ from multiprocessing.sharedctypes import Synchronized
class DataProcessorMetrics:
image_embeddings_speed: Synchronized
image_embeddings_eps: Synchronized
text_embeddings_speed: Synchronized
text_embeddings_eps: Synchronized
face_rec_speed: Synchronized
face_rec_fps: Synchronized
alpr_speed: Synchronized
@ -16,15 +18,15 @@ class DataProcessorMetrics:
yolov9_lpr_pps: Synchronized
def __init__(self):
self.image_embeddings_speed = mp.Value("d", 0.01)
self.image_embeddings_speed = mp.Value("d", 0.0)
self.image_embeddings_eps = mp.Value("d", 0.0)
self.text_embeddings_speed = mp.Value("d", 0.01)
self.text_embeddings_speed = mp.Value("d", 0.0)
self.text_embeddings_eps = mp.Value("d", 0.0)
self.face_rec_speed = mp.Value("d", 0.01)
self.face_rec_speed = mp.Value("d", 0.0)
self.face_rec_fps = mp.Value("d", 0.0)
self.alpr_speed = mp.Value("d", 0.01)
self.alpr_speed = mp.Value("d", 0.0)
self.alpr_pps = mp.Value("d", 0.0)
self.yolov9_lpr_speed = mp.Value("d", 0.01)
self.yolov9_lpr_speed = mp.Value("d", 0.0)
self.yolov9_lpr_pps = mp.Value("d", 0.0)

23
frigate/embeddings/embeddings.py
View File

@ -21,7 +21,7 @@ from frigate.data_processing.types import DataProcessorMetrics
from frigate.db.sqlitevecq import SqliteVecQueueDatabase
from frigate.models import Event
from frigate.types import ModelStatusTypesEnum
from frigate.util.builtin import EventsPerSecond, serialize
from frigate.util.builtin import EventsPerSecond, InferenceSpeed, serialize
from frigate.util.path import get_event_thumbnail_bytes
from .onnx.jina_v1_embedding import JinaV1ImageEmbedding, JinaV1TextEmbedding
@ -75,8 +75,10 @@ class Embeddings:
self.metrics = metrics
self.requestor = InterProcessRequestor()
self.image_inference_speed = InferenceSpeed(self.metrics.image_embeddings_speed)
self.image_eps = EventsPerSecond()
self.image_eps.start()
self.text_inference_speed = InferenceSpeed(self.metrics.text_embeddings_speed)
self.text_eps = EventsPerSecond()
self.text_eps.start()
@ -183,10 +185,7 @@ class Embeddings:
(event_id, serialize(embedding)),
)
duration = datetime.datetime.now().timestamp() - start
self.metrics.image_embeddings_speed.value = (
self.metrics.image_embeddings_speed.value * 9 + duration
) / 10
self.image_inference_speed.update(datetime.datetime.now().timestamp() - start)
self.image_eps.update()
return embedding
@ -220,9 +219,7 @@ class Embeddings:
)
duration = datetime.datetime.now().timestamp() - start
self.metrics.text_embeddings_speed.value = (
self.metrics.text_embeddings_speed.value * 9 + (duration / len(ids))
) / 10
self.text_inference_speed.update(duration / len(ids))
return embeddings
@ -241,10 +238,7 @@ class Embeddings:
(event_id, serialize(embedding)),
)
duration = datetime.datetime.now().timestamp() - start
self.metrics.text_embeddings_speed.value = (
self.metrics.text_embeddings_speed.value * 9 + duration
) / 10
self.text_inference_speed.update(datetime.datetime.now().timestamp() - start)
self.text_eps.update()
return embedding
@ -276,10 +270,7 @@ class Embeddings:
items,
)
duration = datetime.datetime.now().timestamp() - start
self.metrics.text_embeddings_speed.value = (
self.metrics.text_embeddings_speed.value * 9 + (duration / len(ids))
) / 10
self.text_inference_speed.update(datetime.datetime.now().timestamp() - start)
return embeddings

28
frigate/util/builtin.py
View File

@ -11,6 +11,7 @@ import shlex
import struct
import urllib.parse
from collections.abc import Mapping
from multiprocessing.sharedctypes import Synchronized
from pathlib import Path
from typing import Any, Optional, Tuple, Union
from zoneinfo import ZoneInfoNotFoundError
@ -26,16 +27,16 @@ logger = logging.getLogger(__name__)
class EventsPerSecond:
def __init__(self, max_events=1000, last_n_seconds=10):
def __init__(self, max_events=1000, last_n_seconds=10) -> None:
self._start = None
self._max_events = max_events
self._last_n_seconds = last_n_seconds
self._timestamps = []
def start(self):
def start(self) -> None:
self._start = datetime.datetime.now().timestamp()
def update(self):
def update(self) -> None:
now = datetime.datetime.now().timestamp()
if self._start is None:
self._start = now
@ -45,7 +46,7 @@ class EventsPerSecond:
self._timestamps = self._timestamps[(1 - self._max_events) :]
self.expire_timestamps(now)
def eps(self):
def eps(self) -> float:
now = datetime.datetime.now().timestamp()
if self._start is None:
self._start = now
@ -58,12 +59,29 @@ class EventsPerSecond:
return len(self._timestamps) / seconds
# remove aged out timestamps
def expire_timestamps(self, now):
def expire_timestamps(self, now: float) -> None:
threshold = now - self._last_n_seconds
while self._timestamps and self._timestamps[0] < threshold:
del self._timestamps[0]
class InferenceSpeed:
def __init__(self, metric: Synchronized) -> None:
self.__metric = metric
self.__initialized = False
def update(self, inference_time: float) -> None:
if not self.__initialized:
self.__metric.value = inference_time
self.__initialized = True
return
self.__metric.value = (self.__metric.value * 9 + inference_time) / 10
def current(self) -> float:
return self.__metric.value
def deep_merge(dct1: dict, dct2: dict, override=False, merge_lists=False) -> dict:
"""
:param dct1: First dict to merge