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Add classification object debug logs

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This commit is contained in:
Nicolas Mowen 2026-01-28 12:44:27 -07:00
parent 789a7d12a2
commit b0df44e8bc
1 changed files with 30 additions and 1 deletions
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31
frigate/data_processing/real_time/custom_classification.py
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@ -419,14 +419,19 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
""" """
if object_id not in self.classification_history: if object_id not in self.classification_history:
self.classification_history[object_id] = [] self.classification_history[object_id] = []
logger.debug(f"Created new classification history for {object_id}")
self.classification_history[object_id].append( self.classification_history[object_id].append(
(current_label, current_score, current_time) (current_label, current_score, current_time)
) )
history = self.classification_history[object_id] history = self.classification_history[object_id]
logger.debug(
f"History for {object_id}: {len(history)} entries, latest=({current_label}, {current_score})"
)
if len(history) < 3: if len(history) < 3:
logger.debug(f"History for {object_id} has {len(history)} entries, need at least 3")
return None, 0.0 return None, 0.0
label_counts = {} label_counts = {}
@ -445,14 +450,25 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
best_count = label_counts[best_label] best_count = label_counts[best_label]
consensus_threshold = total_attempts * 0.6 consensus_threshold = total_attempts * 0.6
logger.debug(
f"Consensus calc for {object_id}: label_counts={label_counts}, "
f"best_label={best_label}, best_count={best_count}, "
f"total={total_attempts}, threshold={consensus_threshold}"
)
if best_count < consensus_threshold: if best_count < consensus_threshold:
logger.debug(
f"No consensus for {object_id}: {best_count} < {consensus_threshold}"
)
return None, 0.0 return None, 0.0
avg_score = sum(label_scores[best_label]) / len(label_scores[best_label]) avg_score = sum(label_scores[best_label]) / len(label_scores[best_label])
if best_label == "none": if best_label == "none":
logger.debug(f"Filtering 'none' label for {object_id}")
return None, 0.0 return None, 0.0
logger.debug(f"Consensus reached for {object_id}: {best_label} with avg_score={avg_score}")
return best_label, avg_score return best_label, avg_score
def process_frame(self, obj_data, frame): def process_frame(self, obj_data, frame):
@ -560,17 +576,30 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
) )
if score < self.model_config.threshold: if score < self.model_config.threshold:
logger.debug(f"Score {score} is less than threshold.") logger.debug(
f"{self.model_config.name}: Score {score} < threshold {self.model_config.threshold} for {object_id}, skipping"
)
return return
sub_label = self.labelmap[best_id] sub_label = self.labelmap[best_id]
logger.debug(
f"{self.model_config.name}: Object {object_id} (label={obj_data['label']}) passed threshold with sub_label={sub_label}, score={score}"
)
consensus_label, consensus_score = self.get_weighted_score( consensus_label, consensus_score = self.get_weighted_score(
object_id, sub_label, score, now object_id, sub_label, score, now
) )
logger.debug(
f"{self.model_config.name}: get_weighted_score returned consensus_label={consensus_label}, consensus_score={consensus_score} for {object_id}"
)
if consensus_label is not None: if consensus_label is not None:
camera = obj_data["camera"] camera = obj_data["camera"]
logger.info(
f"{self.model_config.name}: Publishing sub_label={consensus_label} for {obj_data['label']} object {object_id} on {camera}"
)
if ( if (
self.model_config.object_config.classification_type self.model_config.object_config.classification_type