Live classification model training (#18583)

* Implement model training via ZMQ and add model states to represent training

* Get model updates working

* Improve toasts and model state

* Clean up logging

* Add back in

frigate/util/classification.py

@@ -1,7 +1,7 @@
 """Util for classification models."""
 
-import logging
 import os
+import sys
 
 import cv2
 import numpy as np
@@ -50,7 +50,13 @@ def train_classification_model(model_name: str) -> bool:
         ]
     )
 
-    tf.get_logger().setLevel(logging.ERROR)
+    # 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(
@@ -112,3 +118,7 @@ def train_classification_model(model_name: str) -> bool:
     # write model
     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