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Don't run classification on stationary objects and set a maximum number of classifications

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This commit is contained in:
Nicolas Mowen 2025-10-26 07:17:52 -06:00
parent 840d567d22
commit 0468b13725
1 changed files with 19 additions and 5 deletions
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24
frigate/data_processing/real_time/custom_classification.py
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@ -34,6 +34,8 @@ except ModuleNotFoundError:
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
MAX_OBJECT_CLASSIFICATIONS = 16
class CustomStateClassificationProcessor(RealTimeProcessorApi): class CustomStateClassificationProcessor(RealTimeProcessorApi):
def __init__( def __init__(
@ -396,6 +398,18 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
if obj_data.get("end_time") is not None: if obj_data.get("end_time") is not None:
return return
if obj_data.get("stationary"):
return
object_id = obj_data["id"]
if (
object_id in self.classification_history
and len(self.classification_history[object_id])
>= MAX_OBJECT_CLASSIFICATIONS
):
return
now = datetime.datetime.now().timestamp() now = datetime.datetime.now().timestamp()
x, y, x2, y2 = calculate_region( x, y, x2, y2 = calculate_region(
frame.shape, frame.shape,
@ -427,7 +441,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
write_classification_attempt( write_classification_attempt(
self.train_dir, self.train_dir,
cv2.cvtColor(crop, cv2.COLOR_RGB2BGR), cv2.cvtColor(crop, cv2.COLOR_RGB2BGR),
obj_data["id"], object_id,
now, now,
"unknown", "unknown",
0.0, 0.0,
@ -448,7 +462,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
write_classification_attempt( write_classification_attempt(
self.train_dir, self.train_dir,
cv2.cvtColor(crop, cv2.COLOR_RGB2BGR), cv2.cvtColor(crop, cv2.COLOR_RGB2BGR),
obj_data["id"], object_id,
now, now,
self.labelmap[best_id], self.labelmap[best_id],
score, score,
@ -461,7 +475,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
sub_label = self.labelmap[best_id] sub_label = self.labelmap[best_id]
consensus_label, consensus_score = self.get_weighted_score( consensus_label, consensus_score = self.get_weighted_score(
obj_data["id"], sub_label, score, now object_id, sub_label, score, now
) )
if consensus_label is not None: if consensus_label is not None:
@ -470,7 +484,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
== ObjectClassificationType.sub_label == ObjectClassificationType.sub_label
): ):
self.sub_label_publisher.publish( self.sub_label_publisher.publish(
(obj_data["id"], consensus_label, consensus_score), (object_id, consensus_label, consensus_score),
EventMetadataTypeEnum.sub_label, EventMetadataTypeEnum.sub_label,
) )
elif ( elif (
@ -479,7 +493,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
): ):
self.sub_label_publisher.publish( self.sub_label_publisher.publish(
( (
obj_data["id"], object_id,
self.model_config.name, self.model_config.name,
consensus_label, consensus_label,
consensus_score, consensus_score,