Add docformatter to pre-commit (#5279)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>

ultralytics/trackers/bot_sort.py

@@ -12,6 +12,33 @@ from .utils.kalman_filter import KalmanFilterXYWH
 
 
 class BOTrack(STrack):
+    """
+    An extended version of the STrack class for YOLOv8, adding object tracking features.
+
+    Attributes:
+        shared_kalman (KalmanFilterXYWH): A shared Kalman filter for all instances of BOTrack.
+        smooth_feat (np.ndarray): Smoothed feature vector.
+        curr_feat (np.ndarray): Current feature vector.
+        features (deque): A deque to store feature vectors with a maximum length defined by `feat_history`.
+        alpha (float): Smoothing factor for the exponential moving average of features.
+        mean (np.ndarray): The mean state of the Kalman filter.
+        covariance (np.ndarray): The covariance matrix of the Kalman filter.
+
+    Methods:
+        update_features(feat): Update features vector and smooth it using exponential moving average.
+        predict(): Predicts the mean and covariance using Kalman filter.
+        re_activate(new_track, frame_id, new_id): Reactivates a track with updated features and optionally new ID.
+        update(new_track, frame_id): Update the YOLOv8 instance with new track and frame ID.
+        tlwh: Property that gets the current position in tlwh format `(top left x, top left y, width, height)`.
+        multi_predict(stracks): Predicts the mean and covariance of multiple object tracks using shared Kalman filter.
+        convert_coords(tlwh): Converts tlwh bounding box coordinates to xywh format.
+        tlwh_to_xywh(tlwh): Convert bounding box to xywh format `(center x, center y, width, height)`.
+
+    Usage:
+        bo_track = BOTrack(tlwh, score, cls, feat)
+        bo_track.predict()
+        bo_track.update(new_track, frame_id)
+    """
     shared_kalman = KalmanFilterXYWH()
 
     def __init__(self, tlwh, score, cls, feat=None, feat_history=50):
@@ -59,9 +86,7 @@ class BOTrack(STrack):
 
     @property
     def tlwh(self):
-        """Get current position in bounding box format `(top left x, top left y,
-        width, height)`.
-        """
+        """Get current position in bounding box format `(top left x, top left y, width, height)`."""
         if self.mean is None:
             return self._tlwh.copy()
         ret = self.mean[:4].copy()
@@ -90,15 +115,37 @@ class BOTrack(STrack):
 
     @staticmethod
     def tlwh_to_xywh(tlwh):
-        """Convert bounding box to format `(center x, center y, width,
-        height)`.
-        """
+        """Convert bounding box to format `(center x, center y, width, height)`."""
         ret = np.asarray(tlwh).copy()
         ret[:2] += ret[2:] / 2
         return ret
 
 
 class BOTSORT(BYTETracker):
+    """
+    An extended version of the BYTETracker class for YOLOv8, designed for object tracking with ReID and GMC algorithm.
+
+    Attributes:
+        proximity_thresh (float): Threshold for spatial proximity (IoU) between tracks and detections.
+        appearance_thresh (float): Threshold for appearance similarity (ReID embeddings) between tracks and detections.
+        encoder (object): Object to handle ReID embeddings, set to None if ReID is not enabled.
+        gmc (GMC): An instance of the GMC algorithm for data association.
+        args (object): Parsed command-line arguments containing tracking parameters.
+
+    Methods:
+        get_kalmanfilter(): Returns an instance of KalmanFilterXYWH for object tracking.
+        init_track(dets, scores, cls, img): Initialize track with detections, scores, and classes.
+        get_dists(tracks, detections): Get distances between tracks and detections using IoU and (optionally) ReID.
+        multi_predict(tracks): Predict and track multiple objects with YOLOv8 model.
+
+    Usage:
+        bot_sort = BOTSORT(args, frame_rate)
+        bot_sort.init_track(dets, scores, cls, img)
+        bot_sort.multi_predict(tracks)
+
+    Note:
+        The class is designed to work with the YOLOv8 object detection model and supports ReID only if enabled via args.
+    """
 
     def __init__(self, args, frame_rate=30):
         """Initialize YOLOv8 object with ReID module and GMC algorithm."""