Fixes (#18117)

* face library i18n fixes

* face library i18n fixes

* add ability to use ctrl/cmd S to save in the config editor

* Use datetime as ID

* Update metrics inference speed to start with 0 ms

* fix android formatted thumbnail

* ensure role is comma separated and stripped correctly

* improve face library deletion

- add a confirmation dialog
- add ability to select all / delete faces in collections

* Implement lazy loading for video previews

* Force GPU for large embedding model

* GPU is required

* settings i18n fixes

* Don't delete train tab

* webpush debugging logs

* Fix incorrectly copying zones

* copy path data

* Ensure that cache dir exists for Frigate+

* face docs update

* Add description to upload image step to clarify the image

* Clean up

---------

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

frigate/data_processing/common/license_plate/mixin.py

@@ -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):

frigate/data_processing/real_time/face.py

@@ -5,9 +5,7 @@ import datetime
 import json
 import logging
 import os
-import random
 import shutil
-import string
 from typing import Optional
 
 import cv2
@@ -27,7 +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
@@ -58,6 +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 = {
@@ -155,9 +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."""
@@ -343,11 +340,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
 
             return {"success": True, "score": score, "face_name": sub_label}
         elif topic == EmbeddingsRequestEnum.register_face.value:
-            rand_id = "".join(
-                random.choices(string.ascii_lowercase + string.digits, k=6)
-            )
             label = request_data["face_name"]
-            id = f"{label}-{rand_id}"
 
             if request_data.get("cropped"):
                 thumbnail = request_data["image"]
@@ -376,7 +369,9 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
 
             # write face to library
             folder = os.path.join(FACE_DIR, label)
-            file = os.path.join(folder, f"{id}.webp")
+            file = os.path.join(
+                folder, f"{label}_{datetime.datetime.now().timestamp()}.webp"
+            )
             os.makedirs(folder, exist_ok=True)
 
             # save face image

frigate/data_processing/types.py

@@ -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)