Use other logging redirect class

frigate/data_processing/common/face/model.py

@@ -11,7 +11,7 @@ from scipy import stats
 from frigate.config import FrigateConfig
 from frigate.const import MODEL_CACHE_DIR
 from frigate.embeddings.onnx.face_embedding import ArcfaceEmbedding, FaceNetEmbedding
-from frigate.log import redirect_stdout_to_logpipe
+from frigate.log import redirect_stdout_to_logger
 
 logger = logging.getLogger(__name__)
 
@@ -38,7 +38,7 @@ class FaceRecognizer(ABC):
     def classify(self, face_image: np.ndarray) -> tuple[str, float] | None:
         pass
 
-    @redirect_stdout_to_logpipe(__name__, logging.DEBUG)
+    @redirect_stdout_to_logger(__name__, logging.DEBUG)
     def init_landmark_detector(self) -> None:
         landmark_model = os.path.join(MODEL_CACHE_DIR, "facedet/landmarkdet.yaml")
 

frigate/detectors/plugins/cpu_tfl.py

@@ -5,6 +5,7 @@ from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig
+from frigate.log import redirect_stdout_to_logger
 
 from ..detector_utils import tflite_detect_raw, tflite_init
 
@@ -27,6 +28,7 @@ class CpuDetectorConfig(BaseDetectorConfig):
 class CpuTfl(DetectionApi):
     type_key = DETECTOR_KEY
 
+    @redirect_stdout_to_logger(__name__, logging.DEBUG)
     def __init__(self, detector_config: CpuDetectorConfig):
         interpreter = Interpreter(
             model_path=detector_config.model.path,

frigate/embeddings/onnx/face_embedding.py

@@ -6,7 +6,7 @@ import os
 import numpy as np
 
 from frigate.const import MODEL_CACHE_DIR
-from frigate.log import redirect_stdout_to_logpipe
+from frigate.log import redirect_stdout_to_logger
 from frigate.util.downloader import ModelDownloader
 
 from .base_embedding import BaseEmbedding
@@ -54,7 +54,7 @@ class FaceNetEmbedding(BaseEmbedding):
             self._load_model_and_utils()
             logger.debug(f"models are already downloaded for {self.model_name}")
 
-    @redirect_stdout_to_logpipe(__name__, logging.DEBUG)
+    @redirect_stdout_to_logger(__name__, logging.DEBUG)
     def _load_model_and_utils(self):
         if self.runner is None:
             if self.downloader:

frigate/events/audio.py

@@ -37,7 +37,7 @@ from frigate.data_processing.real_time.audio_transcription import (
     AudioTranscriptionRealTimeProcessor,
 )
 from frigate.ffmpeg_presets import parse_preset_input
-from frigate.log import LogPipe, redirect_stdout_to_logpipe
+from frigate.log import LogPipe, redirect_stdout_to_logger
 from frigate.object_detection.base import load_labels
 from frigate.util.builtin import get_ffmpeg_arg_list
 from frigate.util.process import FrigateProcess
@@ -423,7 +423,7 @@ class AudioEventMaintainer(threading.Thread):
 
 
 class AudioTfl:
-    @redirect_stdout_to_logpipe(__name__, logging.DEBUG)
+    @redirect_stdout_to_logger(__name__, logging.DEBUG)
     def __init__(self, stop_event: threading.Event, num_threads=2):
         self.stop_event = stop_event
         self.num_threads = num_threads

frigate/log.py

@@ -1,5 +1,6 @@
 # In log.py
 import atexit
+import io
 import logging
 import os
 import sys
@@ -10,7 +11,7 @@ from functools import wraps
 from logging.handlers import QueueHandler, QueueListener
 from multiprocessing.managers import SyncManager
 from queue import Queue
-from typing import Deque, Optional
+from typing import Any, Callable, Deque, Optional
 
 from frigate.util.builtin import clean_camera_user_pass
 
@@ -138,12 +139,61 @@ class LogPipe(threading.Thread):
         os.close(self.fdWrite)
 
 
-def redirect_stdout_to_logpipe(log_name: str, level: int):
-    def decorator(func):
+class LogRedirect(io.StringIO):
+    """
+    A custom file-like object to capture stdout and process it.
+    It extends io.StringIO to capture output and then processes it
+    line by line.
+    """
+
+    def __init__(self, logger_instance: logging.Logger, level: int):
+        super().__init__()
+        self.logger = logger_instance
+        self.log_level = level
+        self.buffer = []
+
+    def write(self, s):
+        if not isinstance(s, str):
+            s = str(s)
+
+        self.buffer.append(s)
+
+        # Process output line by line if a newline is present
+        if "\n" in s:
+            full_output = "".join(self.buffer)
+            lines = full_output.splitlines(keepends=True)
+            self.buffer = []
+
+            for line in lines:
+                if line.endswith("\n"):
+                    self._process_line(line.rstrip("\n"))
+                else:
+                    self.buffer.append(line)
+
+    def _process_line(self, line):
+        self.logger.log(self.log_level, line)
+
+    def flush(self):
+        if self.buffer:
+            full_output = "".join(self.buffer)
+            self.buffer = []
+            if full_output:  # Only process if there's content
+                self._process_line(full_output)
+
+    def __enter__(self):
+        """Context manager entry point."""
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """Context manager exit point. Ensures buffered content is flushed."""
+        self.flush()
+
+
+def redirect_stdout_to_logger(log_name: str, level: int) -> Any:
+    def decorator(func: Callable):
         @wraps(func)
         def wrapper(*args, **kwargs):
-            current_log_pipe = LogPipe(log_name, logging.ERROR)
-            current_log_pipe.run()
+            current_log_pipe = LogRedirect(log_name, logging.ERROR)
 
             old_stdout = sys.stdout
             old_stderr = sys.stderr
@@ -151,13 +201,11 @@ def redirect_stdout_to_logpipe(log_name: str, level: int):
             sys.stderr = current_log_pipe
 
             try:
-                print()
                 result = func(*args, **kwargs)
             finally:
                 sys.stdout = old_stdout
                 sys.stderr = old_stderr
-                current_log_pipe.dump()
-                current_log_pipe.close()
+                current_log_pipe.flush()
 
             return result
 

frigate/util/classification.py

@@ -1,7 +1,7 @@
 """Util for classification models."""
 
+import logging
 import os
-import sys
 
 import cv2
 import numpy as np
@@ -9,6 +9,7 @@ import numpy as np
 from frigate.comms.embeddings_updater import EmbeddingsRequestEnum, EmbeddingsRequestor
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.const import CLIPS_DIR, MODEL_CACHE_DIR, UPDATE_MODEL_STATE
+from frigate.log import redirect_stdout_to_logger
 from frigate.types import ModelStatusTypesEnum
 from frigate.util.process import FrigateProcess
 
@@ -36,6 +37,7 @@ def __generate_representative_dataset_factory(dataset_dir: str):
     return generate_representative_dataset
 
 
+@redirect_stdout_to_logger(__name__, logging.DEBUG)
 def __train_classification_model(model_name: str) -> bool:
     """Train a classification model."""
 
@@ -55,14 +57,6 @@ def __train_classification_model(model_name: str) -> bool:
         ]
     )
 
-    # TF and Keras are very loud with logging
-    # we want to avoid these logs so we
-    # temporarily redirect stdout / stderr
-    original_stdout = sys.stdout
-    original_stderr = sys.stderr
-    sys.stdout = open(os.devnull, "w")
-    sys.stderr = open(os.devnull, "w")
-
     # Start with imagenet base model with 35% of channels in each layer
     base_model = MobileNetV2(
         input_shape=(224, 224, 3),
@@ -124,10 +118,6 @@ def __train_classification_model(model_name: str) -> bool:
     with open(os.path.join(model_dir, "model.tflite"), "wb") as f:
         f.write(tflite_model)
 
-    # restore original stdout / stderr
-    sys.stdout = original_stdout
-    sys.stderr = original_stderr
-
 
 @staticmethod
 def kickoff_model_training(