Fix various typing issues (#18187)

* Fix the `Any` typing hint treewide

There has been confusion between the Any type[1] and the any function[2]
in typing hints.

[1] https://docs.python.org/3/library/typing.html#typing.Any
[2] https://docs.python.org/3/library/functions.html#any

* Fix typing for various frame_shape members

Frame shapes are most likely defined by height and width, so a single int
cannot express that.

* Wrap gpu stats functions in Optional[]

These can return `None`, so they need to be `Type | None`, which is what
`Optional` expresses very nicely.

* Fix return type in get_latest_segment_datetime

Returns a datetime object, not an integer.

* Make the return type of FrameManager.write optional

This is necessary since the SharedMemoryFrameManager.write function can
return None.

* Fix total_seconds() return type in get_tz_modifiers

The function returns a float, not an int.

https://docs.python.org/3/library/datetime.html#datetime.timedelta.total_seconds

* Account for floating point results in to_relative_box

Because the function uses division the return types may either be int or
float.

* Resolve ruff deprecation warning

The config has been split into formatter and linter, and the global
options are deprecated.

frigate/data_processing/real_time/api.py

@@ -2,6 +2,7 @@
 
 import logging
 from abc import ABC, abstractmethod
+from typing import Any
 
 import numpy as np
 
@@ -24,7 +25,7 @@ class RealTimeProcessorApi(ABC):
         pass
 
     @abstractmethod
-    def process_frame(self, obj_data: dict[str, any], frame: np.ndarray) -> None:
+    def process_frame(self, obj_data: dict[str, Any], frame: np.ndarray) -> None:
         """Processes the frame with object data.
         Args:
             obj_data (dict): containing data about focused object in frame.
@@ -37,8 +38,8 @@ class RealTimeProcessorApi(ABC):
 
     @abstractmethod
     def handle_request(
-        self, topic: str, request_data: dict[str, any]
-    ) -> dict[str, any] | None:
+        self, topic: str, request_data: dict[str, Any]
+    ) -> dict[str, Any] | None:
         """Handle metadata requests.
         Args:
             topic (str): topic that dictates what work is requested.

frigate/data_processing/real_time/bird.py

@@ -2,6 +2,7 @@
 
 import logging
 import os
+from typing import Any
 
 import cv2
 import numpy as np
@@ -35,8 +36,8 @@ class BirdRealTimeProcessor(RealTimeProcessorApi):
         super().__init__(config, metrics)
         self.interpreter: Interpreter = None
         self.sub_label_publisher = sub_label_publisher
-        self.tensor_input_details: dict[str, any] = None
-        self.tensor_output_details: dict[str, any] = None
+        self.tensor_input_details: dict[str, Any] = None
+        self.tensor_output_details: dict[str, Any] = None
         self.detected_birds: dict[str, float] = {}
         self.labelmap: dict[int, str] = {}
 

frigate/data_processing/real_time/face.py

@@ -6,7 +6,7 @@ import json
 import logging
 import os
 import shutil
-from typing import Optional
+from typing import Any, Optional
 
 import cv2
 import numpy as np
@@ -157,7 +157,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
         self.faces_per_second.update()
         self.inference_speed.update(duration)
 
-    def process_frame(self, obj_data: dict[str, any], frame: np.ndarray):
+    def process_frame(self, obj_data: dict[str, Any], frame: np.ndarray):
         """Look for faces in image."""
         self.metrics.face_rec_fps.value = self.faces_per_second.eps()
         camera = obj_data["camera"]
@@ -198,7 +198,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
                 logger.debug("Not processing due to hitting max rec attempts.")
                 return
 
-        face: Optional[dict[str, any]] = None
+        face: Optional[dict[str, Any]] = None
 
         if self.requires_face_detection:
             logger.debug("Running manual face detection.")
@@ -238,7 +238,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
                 logger.debug("No attributes to parse.")
                 return
 
-            attributes: list[dict[str, any]] = obj_data.get("current_attributes", [])
+            attributes: list[dict[str, Any]] = obj_data.get("current_attributes", [])
             for attr in attributes:
                 if attr.get("label") != "face":
                     continue
@@ -323,7 +323,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
 
         self.__update_metrics(datetime.datetime.now().timestamp() - start)
 
-    def handle_request(self, topic, request_data) -> dict[str, any] | None:
+    def handle_request(self, topic, request_data) -> dict[str, Any] | None:
         if topic == EmbeddingsRequestEnum.clear_face_classifier.value:
             self.recognizer.clear()
         elif topic == EmbeddingsRequestEnum.recognize_face.value:

frigate/data_processing/real_time/license_plate.py

@@ -2,6 +2,7 @@
 
 import json
 import logging
+from typing import Any
 
 import numpy as np
 
@@ -30,7 +31,7 @@ class LicensePlateRealTimeProcessor(LicensePlateProcessingMixin, RealTimeProcess
         sub_label_publisher: EventMetadataPublisher,
         metrics: DataProcessorMetrics,
         model_runner: LicensePlateModelRunner,
-        detected_license_plates: dict[str, dict[str, any]],
+        detected_license_plates: dict[str, dict[str, Any]],
     ):
         self.requestor = requestor
         self.detected_license_plates = detected_license_plates
@@ -43,14 +44,14 @@ class LicensePlateRealTimeProcessor(LicensePlateProcessingMixin, RealTimeProcess
 
     def process_frame(
         self,
-        obj_data: dict[str, any],
+        obj_data: dict[str, Any],
         frame: np.ndarray,
         dedicated_lpr: bool | None = False,
     ):
         """Look for license plates in image."""
         self.lpr_process(obj_data, frame, dedicated_lpr)
 
-    def handle_request(self, topic, request_data) -> dict[str, any] | None:
+    def handle_request(self, topic, request_data) -> dict[str, Any] | None:
         return
 
     def expire_object(self, object_id: str, camera: str):