HongChuang/ultralytics-ascend
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Ruff Docstring formatting (#15793)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-08-25 04:27:55 +08:00 • committed by GitHub
parent d27664216b
commit 776ca86369
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60 changed files with 241 additions and 309 deletions
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4
ultralytics/trackers/utils/matching.py
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@ -37,7 +37,6 @@ def linear_assignment(cost_matrix: np.ndarray, thresh: float, use_lap: bool = Tr
>>> thresh = 5.0
>>> matched_indices, unmatched_a, unmatched_b = linear_assignment(cost_matrix, thresh, use_lap=True)
"""
if cost_matrix.size == 0:
return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))
@ -80,7 +79,6 @@ def iou_distance(atracks: list, btracks: list) -> np.ndarray:
>>> btracks = [np.array([5, 5, 15, 15]), np.array([25, 25, 35, 35])]
>>> cost_matrix = iou_distance(atracks, btracks)
"""
if atracks and isinstance(atracks[0], np.ndarray) or btracks and isinstance(btracks[0], np.ndarray):
atlbrs = atracks
btlbrs = btracks
@ -123,7 +121,6 @@ def embedding_distance(tracks: list, detections: list, metric: str = "cosine") -
>>> detections = [BaseTrack(...), BaseTrack(...)] # List of detection objects with embedding features
>>> cost_matrix = embedding_distance(tracks, detections, metric="cosine")
"""
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float32)
if cost_matrix.size == 0:
return cost_matrix
@ -152,7 +149,6 @@ def fuse_score(cost_matrix: np.ndarray, detections: list) -> np.ndarray:
>>> detections = [BaseTrack(score=np.random.rand()) for _ in range(10)]
>>> fused_matrix = fuse_score(cost_matrix, detections)
"""
if cost_matrix.size == 0:
return cost_matrix
iou_sim = 1 - cost_matrix