Increase ruff coverage (#23644)

* Pin ruff

* Add python upgrade fixes

This enables python upgrade checks in ruff to look for deprecated types and patterns. This namely fixes:
- usage of deprecated `Typing` which is now built in
- some specific exceptions which are caught and have new aliases

Some specific UP checks were also ignored as they are stylistic / unimportant and likely to cause bugs

* Remove async blocking calls

Use asyncio.to_thread on two remaining blocking calls to fix hanging event thread loop. Enable this specific rule to block it in the future.

* Use proper logging mechanism

* Correctly format logs

* Raise with context

When raising an exception include the from context to improve debugging

* Cleanup

frigate/detectors/detection_api.py

@@ -1,6 +1,5 @@
 import logging
 from abc import ABC, abstractmethod
-from typing import List
 
 import numpy as np
 
@@ -11,7 +10,7 @@ logger = logging.getLogger(__name__)
 
 class DetectionApi(ABC):
     type_key: str
-    supported_models: List[ModelTypeEnum]
+    supported_models: list[ModelTypeEnum]
 
     @abstractmethod
     def __init__(self, detector_config: BaseDetectorConfig):

frigate/detectors/detection_runners.py

@@ -491,7 +491,7 @@ class RKNNModelRunner(BaseModelRunner):
 
         except ImportError:
             logger.error("RKNN Lite not available")
-            raise ImportError("RKNN Lite not available")
+            raise ImportError("RKNN Lite not available") from None
         except Exception as e:
             logger.error(f"Error loading RKNN model: {e}")
             raise

frigate/detectors/detector_config.py

@@ -3,7 +3,7 @@ import json
 import logging
 import os
 from enum import Enum
-from typing import Any, Dict, Optional, Tuple
+from typing import Any
 
 import requests
 from pydantic import BaseModel, ConfigDict, Field
@@ -45,12 +45,12 @@ class ModelTypeEnum(str, Enum):
 
 
 class ModelConfig(BaseModel):
-    path: Optional[str] = Field(
+    path: str | None = Field(
         None,
         title="Custom object detector model path",
         description="Path to a custom detection model file (or plus://<model_id> for Frigate+ models).",
     )
-    labelmap_path: Optional[str] = Field(
+    labelmap_path: str | None = Field(
         None,
         title="Label map for custom object detector",
         description="Path to a labelmap file that maps numeric classes to string labels for the detector.",
@@ -65,12 +65,12 @@ class ModelConfig(BaseModel):
         title="Object detection model input height",
         description="Height of the model input tensor in pixels.",
     )
-    labelmap: Dict[int, str] = Field(
+    labelmap: dict[int, str] = Field(
         default_factory=dict,
         title="Labelmap customization",
         description="Overrides or remapping entries to merge into the standard labelmap.",
     )
-    attributes_map: Dict[str, list[str]] = Field(
+    attributes_map: dict[str, list[str]] = Field(
         default=DEFAULT_ATTRIBUTE_LABEL_MAP,
         title="Map of object labels to their attribute labels",
         description="Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate']).",
@@ -95,18 +95,18 @@ class ModelConfig(BaseModel):
         title="Object Detection Model Type",
         description="Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization.",
     )
-    _merged_labelmap: Optional[Dict[int, str]] = PrivateAttr()
-    _colormap: Dict[int, Tuple[int, int, int]] = PrivateAttr()
+    _merged_labelmap: dict[int, str] | None = PrivateAttr()
+    _colormap: dict[int, tuple[int, int, int]] = PrivateAttr()
     _all_attributes: list[str] = PrivateAttr()
     _all_attribute_logos: list[str] = PrivateAttr()
     _model_hash: str = PrivateAttr()
 
     @property
-    def merged_labelmap(self) -> Dict[int, str]:
+    def merged_labelmap(self) -> dict[int, str]:
         return self._merged_labelmap
 
     @property
-    def colormap(self) -> Dict[int, Tuple[int, int, int]]:
+    def colormap(self) -> dict[int, tuple[int, int, int]]:
         return self._colormap
 
     @property
@@ -171,7 +171,7 @@ class ModelConfig(BaseModel):
             with open(model_info_path, "w") as f:
                 json.dump(model_info, f)
         else:
-            with open(model_info_path, "r") as f:
+            with open(model_info_path) as f:
                 model_info: dict[str, Any] = json.load(f)
 
         if detector and detector not in model_info["supportedDetectors"]:
@@ -240,12 +240,12 @@ class BaseDetectorConfig(BaseModel):
         title="Detector Type",
         description="Type of detector to use for object detection (for example 'cpu', 'edgetpu', 'openvino').",
     )
-    model: Optional[ModelConfig] = Field(
+    model: ModelConfig | None = Field(
         default=None,
         title="Detector specific model configuration",
         description="Detector-specific model configuration options (path, input size, etc.).",
     )
-    model_path: Optional[str] = Field(
+    model_path: str | None = Field(
         default=None,
         title="Detector specific model path",
         description="File path to the detector model binary if required by the chosen detector.",

frigate/detectors/detector_types.py

@@ -2,10 +2,9 @@ import importlib
 import logging
 import pkgutil
 from enum import Enum
-from typing import Union
+from typing import Annotated, Union
 
 from pydantic import Field
-from typing_extensions import Annotated
 
 from . import plugins
 from .detection_api import DetectionApi
@@ -37,6 +36,6 @@ class StrEnum(str, Enum):
 DetectorTypeEnum = StrEnum("DetectorTypeEnum", {k: k for k in api_types})
 
 DetectorConfig = Annotated[
-    Union[tuple(BaseDetectorConfig.__subclasses__())],
+    Union[tuple(BaseDetectorConfig.__subclasses__())],  # noqa: UP007
     Field(discriminator="type"),
 ]

frigate/detectors/plugins/axengine.py

@@ -39,8 +39,7 @@ class Axengine(DetectionApi):
         try:
             import axengine as axe
         except ModuleNotFoundError:
-            raise ImportError("AXEngine is not installed.")
-            return
+            raise ImportError("AXEngine is not installed.") from None
 
         logger.info("__init__ axengine")
         super().__init__(config)

frigate/detectors/plugins/cpu_tfl.py

@@ -1,7 +1,7 @@
 import logging
+from typing import Literal
 
 from pydantic import ConfigDict, Field
-from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig

frigate/detectors/plugins/deepstack.py

@@ -1,11 +1,11 @@
 import io
 import logging
+from typing import Literal
 
 import numpy as np
 import requests
 from PIL import Image
 from pydantic import ConfigDict, Field
-from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig

frigate/detectors/plugins/degirum.py

@@ -1,9 +1,9 @@
 import logging
 import queue
+from typing import Literal
 
 import numpy as np
 from pydantic import ConfigDict, Field
-from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig
@@ -46,7 +46,7 @@ class DGDetector(DetectionApi):
         try:
             import degirum as dg
         except ModuleNotFoundError:
-            raise ImportError("Unable to import DeGirum detector.")
+            raise ImportError("Unable to import DeGirum detector.") from None
 
         self._queue = queue.Queue()
         self._zoo = dg.connect(

frigate/detectors/plugins/edgetpu_tfl.py

@@ -1,11 +1,11 @@
 import logging
 import math
 import os
+from typing import Literal
 
 import cv2
 import numpy as np
 from pydantic import ConfigDict, Field
-from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum

frigate/detectors/plugins/hailo8l.py

@@ -4,12 +4,11 @@ import subprocess
 import threading
 import urllib.request
 from functools import partial
-from typing import Dict, List, Optional, Tuple
+from typing import Literal
 
 import cv2
 import numpy as np
 from pydantic import ConfigDict, Field
-from typing_extensions import Literal
 
 from frigate.const import MODEL_CACHE_DIR
 from frigate.detectors.detection_api import DetectionApi
@@ -83,8 +82,8 @@ class HailoAsyncInference:
         input_store: RequestStore,
         output_store: ResponseStore,
         batch_size: int = 1,
-        input_type: Optional[str] = None,
-        output_type: Optional[Dict[str, str]] = None,
+        input_type: str | None = None,
+        output_type: dict[str, str] | None = None,
         send_original_frame: bool = False,
     ) -> None:
         # when importing hailo it activates the driver
@@ -125,9 +124,9 @@ class HailoAsyncInference:
     def callback(
         self,
         completion_info,
-        bindings_list: List,
-        input_batch: List,
-        request_ids: List[int],
+        bindings_list: list,
+        input_batch: list,
+        request_ids: list[int],
     ):
         if completion_info.exception:
             logger.error(f"Inference error: {completion_info.exception}")
@@ -163,7 +162,7 @@ class HailoAsyncInference:
             }
         return configured_infer_model.create_bindings(output_buffers=output_buffers)
 
-    def get_input_shape(self) -> Tuple[int, ...]:
+    def get_input_shape(self) -> tuple[int, ...]:
         return self.hef.get_input_vstream_infos()[0].shape
 
     def run(self) -> None:
@@ -304,7 +303,7 @@ class HailoDetector(DetectionApi):
             urllib.request.urlretrieve(url, destination)
             logger.debug(f"Downloaded model to {destination}")
         except Exception as e:
-            raise RuntimeError(f"Failed to download model from {url}: {str(e)}")
+            raise RuntimeError(f"Failed to download model from {url}: {str(e)}") from e
 
     def check_and_prepare(self) -> str:
         if not os.path.exists(self.cache_dir):
@@ -350,7 +349,7 @@ class HailoDetector(DetectionApi):
             if not self.inference_thread.is_alive():
                 raise RuntimeError(
                     "HailoRT inference thread has stopped, restart required."
-                )
+                ) from None
 
             return np.zeros((20, 6), dtype=np.float32)
 

frigate/detectors/plugins/memryx.py

@@ -5,11 +5,11 @@ import shutil
 import urllib.request
 import zipfile
 from queue import Queue
+from typing import Literal
 
 import cv2
 import numpy as np
 from pydantic import BaseModel, ConfigDict, Field
-from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import (
@@ -61,7 +61,7 @@ class MemryXDetector(DetectionApi):
         except ModuleNotFoundError:
             raise ImportError(
                 "MemryX SDK is not installed. Install it and set up MIX environment."
-            )
+            ) from None
             return
 
         # Initialize stop_event as None, will be set later by set_stop_event()

frigate/detectors/plugins/onnx.py

@@ -1,8 +1,8 @@
 import logging
+from typing import Literal
 
 import numpy as np
 from pydantic import ConfigDict, Field
-from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detection_runners import get_optimized_runner

frigate/detectors/plugins/openvino.py

@@ -1,9 +1,9 @@
 import logging
+from typing import Literal
 
 import numpy as np
 import openvino as ov
 from pydantic import ConfigDict, Field
-from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detection_runners import OpenVINOModelRunner

frigate/detectors/plugins/rknn.py

@@ -90,7 +90,7 @@ class Rknn(DetectionApi):
             with open("/proc/device-tree/compatible") as file:
                 soc = file.read().split(",")[-1].strip("\x00")
         except FileNotFoundError:
-            raise Exception("Make sure to run docker in privileged mode.")
+            raise Exception("Make sure to run docker in privileged mode.") from None
 
         if soc not in SUPPORTED_RK_SOCS:
             raise Exception(

frigate/detectors/plugins/synaptics.py

@@ -1,9 +1,9 @@
 import logging
 import os
+from typing import Literal
 
 import numpy as np
 from pydantic import ConfigDict
-from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import (

frigate/detectors/plugins/teflon_tfl.py

@@ -1,7 +1,7 @@
 import logging
+from typing import Literal
 
 from pydantic import ConfigDict
-from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig

frigate/detectors/plugins/tensorrt.py

@@ -14,8 +14,9 @@ try:
 except ModuleNotFoundError:
     TRT_SUPPORT = False
 
+from typing import Literal
+
 from pydantic import ConfigDict, Field
-from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig
@@ -58,7 +59,7 @@ class TensorRTDetectorConfig(BaseDetectorConfig):
     )
 
 
-class HostDeviceMem(object):
+class HostDeviceMem:
     """Simple helper data class that's a little nicer to use than a 2-tuple."""
 
     def __init__(self, host_mem, device_mem, nbytes, size):

frigate/detectors/plugins/zmq_ipc.py

@@ -1,12 +1,11 @@
 import json
 import logging
 import os
-from typing import Any, List
+from typing import Any, Literal
 
 import numpy as np
 import zmq
 from pydantic import ConfigDict, Field
-from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig
@@ -274,7 +273,7 @@ class ZmqIpcDetector(DetectionApi):
         }
         return json.dumps(header).encode("utf-8")
 
-    def _decode_response(self, frames: List[bytes]) -> np.ndarray:
+    def _decode_response(self, frames: list[bytes]) -> np.ndarray:
         try:
             if len(frames) == 1:
                 # Single-frame raw float32 (20x6)