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Adjust number of required faces based on detection type

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This commit is contained in:
Nicolas Mowen 2025-03-26 06:20:05 -06:00
parent 0a0880c21f
commit fc7f9cd1a8
1 changed files with 32 additions and 31 deletions
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63
frigate/data_processing/real_time/face.py
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@ -36,36 +36,6 @@ MAX_DETECTION_HEIGHT = 1080
MIN_MATCHING_FACES = 2
def weighted_average_by_area(results_list: list[tuple[str, float, int]]):
if len(results_list) < 3:
return "unknown", 0.0
score_count = {}
weighted_scores = {}
total_face_areas = {}
for name, score, face_area in results_list:
if name not in weighted_scores:
score_count[name] = 1
weighted_scores[name] = 0.0
total_face_areas[name] = 0.0
else:
score_count[name] += 1
weighted_scores[name] += score * face_area
total_face_areas[name] += face_area
prominent_name = max(score_count)
# if a single name is not prominent in the history then we are not confident
if score_count[prominent_name] / len(results_list) < 0.65:
return "unknown", 0.0
return prominent_name, weighted_scores[prominent_name] / total_face_areas[
prominent_name
]
class FaceRealTimeProcessor(RealTimeProcessorApi):
def __init__(
self,
@ -288,7 +258,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
self.person_face_history[id].append(
(sub_label, score, face_frame.shape[0] * face_frame.shape[1])
)
(weighted_sub_label, weighted_score) = weighted_average_by_area(
(weighted_sub_label, weighted_score) = self.weighted_average_by_area(
self.person_face_history[id]
)
@ -415,3 +385,34 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
def expire_object(self, object_id: str):
if object_id in self.person_face_history:
self.person_face_history.pop(object_id)
def weighted_average_by_area(self, results_list: list[tuple[str, float, int]]):
min_faces = 1 if self.requires_face_detection else 3
if len(results_list) < min_faces:
return "unknown", 0.0
score_count = {}
weighted_scores = {}
total_face_areas = {}
for name, score, face_area in results_list:
if name not in weighted_scores:
score_count[name] = 1
weighted_scores[name] = 0.0
total_face_areas[name] = 0.0
else:
score_count[name] += 1
weighted_scores[name] += score * face_area
total_face_areas[name] += face_area
prominent_name = max(score_count)
# if a single name is not prominent in the history then we are not confident
if score_count[prominent_name] / len(results_list) < 0.65:
return "unknown", 0.0
return prominent_name, weighted_scores[prominent_name] / total_face_areas[
prominent_name
]