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Ruff format docstring Python code (#15792)

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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 01:08:07 +08:00 • committed by GitHub
parent c1882a4327
commit d27664216b
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63 changed files with 370 additions and 374 deletions
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10
ultralytics/trackers/utils/gmc.py
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@ -33,7 +33,7 @@ class GMC:
Examples:
Create a GMC object and apply it to a frame
>>> gmc = GMC(method='sparseOptFlow', downscale=2)
>>> gmc = GMC(method="sparseOptFlow", downscale=2)
>>> frame = np.array([[1, 2, 3], [4, 5, 6]])
>>> processed_frame = gmc.apply(frame)
>>> print(processed_frame)
@ -51,7 +51,7 @@ class GMC:
Examples:
Initialize a GMC object with the 'sparseOptFlow' method and a downscale factor of 2
>>> gmc = GMC(method='sparseOptFlow', downscale=2)
>>> gmc = GMC(method="sparseOptFlow", downscale=2)
"""
super().__init__()
@ -101,7 +101,7 @@ class GMC:
(np.ndarray): Processed frame with applied object detection.
Examples:
>>> gmc = GMC(method='sparseOptFlow')
>>> gmc = GMC(method="sparseOptFlow")
>>> raw_frame = np.random.rand(480, 640, 3)
>>> processed_frame = gmc.apply(raw_frame)
>>> print(processed_frame.shape)
@ -127,7 +127,7 @@ class GMC:
(np.ndarray): The processed frame with the applied ECC transformation.
Examples:
>>> gmc = GMC(method='ecc')
>>> gmc = GMC(method="ecc")
>>> processed_frame = gmc.applyEcc(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]))
>>> print(processed_frame)
[[1. 0. 0.]
@ -173,7 +173,7 @@ class GMC:
(np.ndarray): Processed frame.
Examples:
>>> gmc = GMC(method='orb')
>>> gmc = GMC(method="orb")
>>> raw_frame = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
>>> processed_frame = gmc.applyFeatures(raw_frame)
>>> print(processed_frame.shape)

2
ultralytics/trackers/utils/kalman_filter.py
View file

@ -268,7 +268,7 @@ class KalmanFilterXYAH:
>>> mean = np.array([0, 0, 1, 1, 0, 0, 0, 0])
>>> covariance = np.eye(8)
>>> measurements = np.array([[1, 1, 1, 1], [2, 2, 1, 1]])
>>> distances = kf.gating_distance(mean, covariance, measurements, only_position=False, metric='maha')
>>> distances = kf.gating_distance(mean, covariance, measurements, only_position=False, metric="maha")
"""
mean, covariance = self.project(mean, covariance)
if only_position:

2
ultralytics/trackers/utils/matching.py
View file

@ -121,7 +121,7 @@ def embedding_distance(tracks: list, detections: list, metric: str = "cosine") -
Compute the embedding distance between tracks and detections using cosine metric
>>> tracks = [STrack(...), STrack(...)] # List of track objects with embedding features
>>> detections = [BaseTrack(...), BaseTrack(...)] # List of detection objects with embedding features
>>> cost_matrix = embedding_distance(tracks, detections, metric='cosine')
>>> cost_matrix = embedding_distance(tracks, detections, metric="cosine")
"""
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float32)