administrator/frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2025-12-06 13:34:13 +03:00
Code Issues Actions 1 Packages Projects Releases Wiki Activity

Fix mypy

Browse Source
This commit is contained in:
Nicolas Mowen 2025-09-24 14:56:11 -06:00
parent 6548eb7260
commit b3ba6d8e06
1 changed files with 10 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

16
frigate/track/norfair_tracker.py
View File

@ -1,7 +1,7 @@
import logging
import random
import string
from typing import Any, Sequence
from typing import Any, Sequence, cast
import cv2
import numpy as np
@ -89,7 +89,7 @@ class StationaryMotionClassifier:
crop_resized = cv2.resize(
crop, (self.CROP_SIZE, self.CROP_SIZE), interpolation=cv2.INTER_AREA
)
return gaussian_filter(crop_resized, sigma=0.5)
return cast(np.ndarray[Any, Any], gaussian_filter(crop_resized, sigma=0.5))
def ensure_anchor(
self, id: str, yuv_frame: np.ndarray, median_box: tuple[int, int, int, int]
@ -478,13 +478,17 @@ class NorfairTracker(ObjectTracker):
if id not in self.stationary_classifier.anchor_crops and len(history) >= 5:
stability_iou = intersection_over_union(avg_box, median_box)
if stability_iou >= 0.7:
self.stationary_classifier.ensure_anchor(id, yuv_frame, median_box)
self.stationary_classifier.ensure_anchor(
id, yuv_frame, cast(tuple[int, int, int, int], median_box)
)
# object has minimal or zero iou
# assume object is active
if avg_iou < THRESHOLD_KNOWN_ACTIVE_IOU:
if stationary:
if not self.stationary_classifier.evaluate(id, yuv_frame, tuple(box)):
if stationary and yuv_frame is not None:
if not self.stationary_classifier.evaluate(
id, yuv_frame, cast(tuple[int, int, int, int], tuple(box))
):
self.positions[id] = {
"xmins": [xmin],
"ymins": [ymin],
@ -519,7 +523,7 @@ class NorfairTracker(ObjectTracker):
# Before flipping to active, check with classifier if we have YUV frame
if stationary and yuv_frame is not None:
if not self.stationary_classifier.evaluate(
id, yuv_frame, tuple(box)
id, yuv_frame, cast(tuple[int, int, int, int], tuple(box))
):
self.positions[id] = {
"xmins": [xmin],