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Fix mypy

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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
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16
frigate/track/norfair_tracker.py
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@ -1,7 +1,7 @@
import logging import logging
import random import random
import string import string
from typing import Any, Sequence from typing import Any, Sequence, cast
import cv2 import cv2
import numpy as np import numpy as np
@ -89,7 +89,7 @@ class StationaryMotionClassifier:
crop_resized = cv2.resize( crop_resized = cv2.resize(
crop, (self.CROP_SIZE, self.CROP_SIZE), interpolation=cv2.INTER_AREA 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( def ensure_anchor(
self, id: str, yuv_frame: np.ndarray, median_box: tuple[int, int, int, int] 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: if id not in self.stationary_classifier.anchor_crops and len(history) >= 5:
stability_iou = intersection_over_union(avg_box, median_box) stability_iou = intersection_over_union(avg_box, median_box)
if stability_iou >= 0.7: 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 # object has minimal or zero iou
# assume object is active # assume object is active
if avg_iou < THRESHOLD_KNOWN_ACTIVE_IOU: if avg_iou < THRESHOLD_KNOWN_ACTIVE_IOU:
if stationary: if stationary and yuv_frame is not None:
if not self.stationary_classifier.evaluate(id, yuv_frame, tuple(box)): if not self.stationary_classifier.evaluate(
id, yuv_frame, cast(tuple[int, int, int, int], tuple(box))
):
self.positions[id] = { self.positions[id] = {
"xmins": [xmin], "xmins": [xmin],
"ymins": [ymin], "ymins": [ymin],
@ -519,7 +523,7 @@ class NorfairTracker(ObjectTracker):
# Before flipping to active, check with classifier if we have YUV frame # 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, tuple(box) id, yuv_frame, cast(tuple[int, int, int, int], tuple(box))
): ):
self.positions[id] = { self.positions[id] = {
"xmins": [xmin], "xmins": [xmin],