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/byte_tracker.py

@@ -8,10 +8,43 @@ from .utils.kalman_filter import KalmanFilterXYAH
 
 
 class STrack(BaseTrack):
+    """
+    Single object tracking representation that uses Kalman filtering for state estimation.
+
+    This class is responsible for storing all the information regarding individual tracklets and performs state updates
+    and predictions based on Kalman filter.
+
+    Attributes:
+        shared_kalman (KalmanFilterXYAH): Shared Kalman filter that is used across all STrack instances for prediction.
+        _tlwh (np.ndarray): Private attribute to store top-left corner coordinates and width and height of bounding box.
+        kalman_filter (KalmanFilterXYAH): Instance of Kalman filter used for this particular object track.
+        mean (np.ndarray): Mean state estimate vector.
+        covariance (np.ndarray): Covariance of state estimate.
+        is_activated (bool): Boolean flag indicating if the track has been activated.
+        score (float): Confidence score of the track.
+        tracklet_len (int): Length of the tracklet.
+        cls (any): Class label for the object.
+        idx (int): Index or identifier for the object.
+        frame_id (int): Current frame ID.
+        start_frame (int): Frame where the object was first detected.
+
+    Methods:
+        predict(): Predict the next state of the object using Kalman filter.
+        multi_predict(stracks): Predict the next states for multiple tracks.
+        multi_gmc(stracks, H): Update multiple track states using a homography matrix.
+        activate(kalman_filter, frame_id): Activate a new tracklet.
+        re_activate(new_track, frame_id, new_id): Reactivate a previously lost tracklet.
+        update(new_track, frame_id): Update the state of a matched track.
+        convert_coords(tlwh): Convert bounding box to x-y-angle-height format.
+        tlwh_to_xyah(tlwh): Convert tlwh bounding box to xyah format.
+        tlbr_to_tlwh(tlbr): Convert tlbr bounding box to tlwh format.
+        tlwh_to_tlbr(tlwh): Convert tlwh bounding box to tlbr format.
+    """
+
     shared_kalman = KalmanFilterXYAH()
 
     def __init__(self, tlwh, score, cls):
-        """wait activate."""
+        """Initialize new STrack instance."""
         self._tlwh = np.asarray(self.tlbr_to_tlwh(tlwh[:-1]), dtype=np.float32)
         self.kalman_filter = None
         self.mean, self.covariance = None, None
@@ -92,10 +125,11 @@ class STrack(BaseTrack):
 
     def update(self, new_track, frame_id):
         """
-        Update a matched track
-        :type new_track: STrack
-        :type frame_id: int
-        :return:
+        Update the state of a matched track.
+
+        Args:
+            new_track (STrack): The new track containing updated information.
+            frame_id (int): The ID of the current frame.
         """
         self.frame_id = frame_id
         self.tracklet_len += 1
@@ -116,9 +150,7 @@ class STrack(BaseTrack):
 
     @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()
@@ -128,17 +160,15 @@ class STrack(BaseTrack):
 
     @property
     def tlbr(self):
-        """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
-        `(top left, bottom right)`.
-        """
+        """Convert bounding box to format (min x, min y, max x, max y), i.e., (top left, bottom right)."""
         ret = self.tlwh.copy()
         ret[2:] += ret[:2]
         return ret
 
     @staticmethod
     def tlwh_to_xyah(tlwh):
-        """Convert bounding box to format `(center x, center y, aspect ratio,
-        height)`, where the aspect ratio is `width / height`.
+        """Convert bounding box to format (center x, center y, aspect ratio, height), where the aspect ratio is width /
+        height.
         """
         ret = np.asarray(tlwh).copy()
         ret[:2] += ret[2:] / 2
@@ -165,6 +195,33 @@ class STrack(BaseTrack):
 
 
 class BYTETracker:
+    """
+    BYTETracker: A tracking algorithm built on top of YOLOv8 for object detection and tracking.
+
+    The class is responsible for initializing, updating, and managing the tracks for detected objects in a video
+    sequence. It maintains the state of tracked, lost, and removed tracks over frames, utilizes Kalman filtering for
+    predicting the new object locations, and performs data association.
+
+    Attributes:
+        tracked_stracks (list[STrack]): List of successfully activated tracks.
+        lost_stracks (list[STrack]): List of lost tracks.
+        removed_stracks (list[STrack]): List of removed tracks.
+        frame_id (int): The current frame ID.
+        args (namespace): Command-line arguments.
+        max_time_lost (int): The maximum frames for a track to be considered as 'lost'.
+        kalman_filter (object): Kalman Filter object.
+
+    Methods:
+        update(results, img=None): Updates object tracker with new detections.
+        get_kalmanfilter(): Returns a Kalman filter object for tracking bounding boxes.
+        init_track(dets, scores, cls, img=None): Initialize object tracking with detections.
+        get_dists(tracks, detections): Calculates the distance between tracks and detections.
+        multi_predict(tracks): Predicts the location of tracks.
+        reset_id(): Resets the ID counter of STrack.
+        joint_stracks(tlista, tlistb): Combines two lists of stracks.
+        sub_stracks(tlista, tlistb): Filters out the stracks present in the second list from the first list.
+        remove_duplicate_stracks(stracksa, stracksb): Removes duplicate stracks based on IOU.
+    """
 
     def __init__(self, args, frame_rate=30):
         """Initialize a YOLOv8 object to track objects with given arguments and frame rate."""
@@ -234,8 +291,7 @@ class BYTETracker:
             else:
                 track.re_activate(det, self.frame_id, new_id=False)
                 refind_stracks.append(track)
-        # Step 3: Second association, with low score detection boxes
-        # association the untrack to the low score detections
+        # Step 3: Second association, with low score detection boxes association the untrack to the low score detections
         detections_second = self.init_track(dets_second, scores_second, cls_second, img)
         r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked]
         # TODO