Ruff format docstring Python code (#15792)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

ultralytics/trackers/byte_tracker.py

@@ -42,7 +42,7 @@ class STrack(BaseTrack):
 
     Examples:
         Initialize and activate a new track
-        >>> track = STrack(xywh=[100, 200, 50, 80, 0], score=0.9, cls='person')
+        >>> track = STrack(xywh=[100, 200, 50, 80, 0], score=0.9, cls="person")
         >>> track.activate(kalman_filter=KalmanFilterXYAH(), frame_id=1)
     """
 
@@ -61,7 +61,7 @@ class STrack(BaseTrack):
         Examples:
             >>> xywh = [100.0, 150.0, 50.0, 75.0, 1]
             >>> score = 0.9
-            >>> cls = 'person'
+            >>> cls = "person"
             >>> track = STrack(xywh, score, cls)
         """
         super().__init__()

ultralytics/trackers/utils/gmc.py

@@ -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)

ultralytics/trackers/utils/kalman_filter.py

@@ -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:

ultralytics/trackers/utils/matching.py

@@ -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)