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Use different consolidation requirement depending on label (#8249)

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Nicolas Mowen 2023-10-20 17:29:52 -06:00 committed by GitHub
parent ee1e1b748c
commit e80b6d9e5b
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2 changed files with 15 additions and 6 deletions
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7
frigate/const.py
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@ -12,7 +12,7 @@ FRIGATE_LOCALHOST = "http://127.0.0.1:5000"
PLUS_ENV_VAR = "PLUS_API_KEY" PLUS_ENV_VAR = "PLUS_API_KEY"
PLUS_API_HOST = "https://api.frigate.video" PLUS_API_HOST = "https://api.frigate.video"
# Attributes # Attribute & Object Consts
ATTRIBUTE_LABEL_MAP = { ATTRIBUTE_LABEL_MAP = {
"person": ["face", "amazon"], "person": ["face", "amazon"],
@ -21,6 +21,11 @@ ATTRIBUTE_LABEL_MAP = {
ALL_ATTRIBUTE_LABELS = [ ALL_ATTRIBUTE_LABELS = [
item for sublist in ATTRIBUTE_LABEL_MAP.values() for item in sublist item for sublist in ATTRIBUTE_LABEL_MAP.values() for item in sublist
] ]
LABEL_CONSOLIDATION_MAP = {
"car": 0.8,
"face": 0.5,
}
LABEL_CONSOLIDATION_DEFAULT = 0.9
# Audio Consts # Audio Consts

14
frigate/util/object.py
View File

@ -9,6 +9,7 @@ import numpy as np
from peewee import DoesNotExist from peewee import DoesNotExist
from frigate.config import DetectConfig, ModelConfig from frigate.config import DetectConfig, ModelConfig
from frigate.const import LABEL_CONSOLIDATION_DEFAULT, LABEL_CONSOLIDATION_MAP
from frigate.detectors.detector_config import PixelFormatEnum from frigate.detectors.detector_config import PixelFormatEnum
from frigate.models import Event, Regions, Timeline from frigate.models import Event, Regions, Timeline
from frigate.util.image import ( from frigate.util.image import (
@ -426,18 +427,21 @@ def get_consolidated_object_detections(detected_object_groups):
sorted_by_area = sorted(group, key=lambda g: g[3]) sorted_by_area = sorted(group, key=lambda g: g[3])
for current_detection_idx in range(0, len(sorted_by_area)): for current_detection_idx in range(0, len(sorted_by_area)):
current_detection = sorted_by_area[current_detection_idx][2] current_detection = sorted_by_area[current_detection_idx]
current_label = current_detection[0]
current_box = current_detection[2]
overlap = 0 overlap = 0
for to_check_idx in range( for to_check_idx in range(
min(current_detection_idx + 1, len(sorted_by_area)), min(current_detection_idx + 1, len(sorted_by_area)),
len(sorted_by_area), len(sorted_by_area),
): ):
to_check = sorted_by_area[to_check_idx][2] to_check = sorted_by_area[to_check_idx][2]
intersect_box = intersection(current_detection, to_check) intersect_box = intersection(current_box, to_check)
# if 90% of smaller detection is inside of another detection, consolidate # if 90% of smaller detection is inside of another detection, consolidate
if ( if intersect_box is not None and area(intersect_box) / area(
intersect_box is not None current_box
and area(intersect_box) / area(current_detection) > 0.9 ) > LABEL_CONSOLIDATION_MAP.get(
current_label, LABEL_CONSOLIDATION_DEFAULT
): ):
overlap = 1 overlap = 1
break break