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More stationary cleanup (#20229)
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Browse Source 
* Always return false for active objects

* Cleanup
This commit is contained in:
Nicolas Mowen 2025-09-26 07:23:29 -06:00 committed by GitHub
parent b8b07ee6e1
commit e6cbc93703
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GPG Key ID: B5690EEEBB952194
1 changed files with 21 additions and 21 deletions
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42
frigate/track/norfair_tracker.py
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@ -330,6 +330,18 @@ class NorfairTracker(ObjectTracker):
stationary: bool, stationary: bool,
yuv_frame: np.ndarray | None, yuv_frame: np.ndarray | None,
) -> bool: ) -> bool:
def reset_position(xmin: int, ymin: int, xmax: int, ymax: int) -> None:
self.positions[id] = {
"xmins": [xmin],
"ymins": [ymin],
"xmaxs": [xmax],
"ymaxs": [ymax],
"xmin": xmin,
"ymin": ymin,
"xmax": xmax,
"ymax": ymax,
}
xmin, ymin, xmax, ymax = box xmin, ymin, xmax, ymax = box
position = self.positions[id] position = self.positions[id]
self.stationary_box_history[id].append(box) self.stationary_box_history[id].append(box)
@ -360,17 +372,11 @@ class NorfairTracker(ObjectTracker):
if not self.stationary_classifier.evaluate( if not self.stationary_classifier.evaluate(
id, yuv_frame, cast(tuple[int, int, int, int], tuple(box)) id, yuv_frame, cast(tuple[int, int, int, int], tuple(box))
): ):
self.positions[id] = { reset_position(xmin, ymin, xmax, ymax)
"xmins": [xmin],
"ymins": [ymin],
"xmaxs": [xmax],
"ymaxs": [ymax],
"xmin": xmin,
"ymin": ymin,
"xmax": xmax,
"ymax": ymax,
}
return False return False
else:
reset_position(xmin, ymin, xmax, ymax)
return False
threshold = ( threshold = (
THRESHOLD_STATIONARY_CHECK_IOU if stationary else THRESHOLD_ACTIVE_CHECK_IOU THRESHOLD_STATIONARY_CHECK_IOU if stationary else THRESHOLD_ACTIVE_CHECK_IOU
@ -391,22 +397,16 @@ class NorfairTracker(ObjectTracker):
# if the median iou drops below the threshold # if the median iou drops below the threshold
# assume object is no longer stationary # assume object is no longer stationary
if median_iou < threshold: if median_iou < threshold:
# Before flipping to active, check with classifier if we have YUV frame # If we have a yuv_frame to check before flipping to active, check with classifier if we have YUV frame
if stationary and yuv_frame is not None: if stationary and yuv_frame is not None:
if not self.stationary_classifier.evaluate( if not self.stationary_classifier.evaluate(
id, yuv_frame, cast(tuple[int, int, int, int], tuple(box)) id, yuv_frame, cast(tuple[int, int, int, int], tuple(box))
): ):
self.positions[id] = { reset_position(xmin, ymin, xmax, ymax)
"xmins": [xmin],
"ymins": [ymin],
"xmaxs": [xmax],
"ymaxs": [ymax],
"xmin": xmin,
"ymin": ymin,
"xmax": xmax,
"ymax": ymax,
}
return False return False
else:
reset_position(xmin, ymin, xmax, ymax)
return False
# if there are more than 5 and less than 10 entries for the position, add the bounding box # if there are more than 5 and less than 10 entries for the position, add the bounding box
# and recompute the position box # and recompute the position box