mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-04-27 17:17:40 +03:00
Use other logging redirect class
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parent
a490767f91
commit
4701bedde4
@ -38,7 +38,7 @@ class FaceRecognizer(ABC):
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def classify(self, face_image: np.ndarray) -> tuple[str, float] | None:
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pass
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@redirect_stdout_to_logger(__name__, logging.DEBUG)
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@redirect_stdout_to_logger(logger, logging.DEBUG)
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def init_landmark_detector(self) -> None:
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landmark_model = os.path.join(MODEL_CACHE_DIR, "facedet/landmarkdet.yaml")
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@ -28,7 +28,7 @@ class CpuDetectorConfig(BaseDetectorConfig):
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class CpuTfl(DetectionApi):
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type_key = DETECTOR_KEY
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@redirect_stdout_to_logger(__name__, logging.DEBUG)
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@redirect_stdout_to_logger(logger, logging.DEBUG)
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def __init__(self, detector_config: CpuDetectorConfig):
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interpreter = Interpreter(
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model_path=detector_config.model.path,
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@ -54,7 +54,7 @@ class FaceNetEmbedding(BaseEmbedding):
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self._load_model_and_utils()
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logger.debug(f"models are already downloaded for {self.model_name}")
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@redirect_stdout_to_logger(__name__, logging.DEBUG)
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@redirect_stdout_to_logger(logger, logging.DEBUG)
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def _load_model_and_utils(self):
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if self.runner is None:
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if self.downloader:
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@ -49,6 +49,9 @@ except ModuleNotFoundError:
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from tensorflow.lite.python.interpreter import Interpreter
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logger = logging.getLogger(__name__)
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def get_ffmpeg_command(ffmpeg: FfmpegConfig) -> list[str]:
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ffmpeg_input: CameraInput = [i for i in ffmpeg.inputs if "audio" in i.roles][0]
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input_args = get_ffmpeg_arg_list(ffmpeg.global_args) + (
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@ -423,7 +426,7 @@ class AudioEventMaintainer(threading.Thread):
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class AudioTfl:
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@redirect_stdout_to_logger(__name__, logging.DEBUG)
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@redirect_stdout_to_logger(logger, logging.DEBUG)
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def __init__(self, stop_event: threading.Event, num_threads=2):
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self.stop_event = stop_event
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self.num_threads = num_threads
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@ -189,11 +189,11 @@ class LogRedirect(io.StringIO):
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self.flush()
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def redirect_stdout_to_logger(log_name: str, level: int) -> Any:
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def redirect_stdout_to_logger(logger: logging.Logger, level: int) -> Any:
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def decorator(func: Callable):
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@wraps(func)
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def wrapper(*args, **kwargs):
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current_log_pipe = LogRedirect(log_name, logging.ERROR)
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current_log_pipe = LogRedirect(logger, logging.ERROR)
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old_stdout = sys.stdout
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old_stderr = sys.stderr
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@ -17,6 +17,8 @@ BATCH_SIZE = 16
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EPOCHS = 50
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LEARNING_RATE = 0.001
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logger = logging.getLogger(__name__)
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def __generate_representative_dataset_factory(dataset_dir: str):
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def generate_representative_dataset():
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@ -37,7 +39,7 @@ def __generate_representative_dataset_factory(dataset_dir: str):
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return generate_representative_dataset
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@redirect_stdout_to_logger(__name__, logging.DEBUG)
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@redirect_stdout_to_logger(logger, logging.DEBUG)
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def __train_classification_model(model_name: str) -> bool:
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"""Train a classification model."""
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