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/utils/gmc.py

@@ -9,6 +9,29 @@ from ultralytics.utils import LOGGER
 
 
 class GMC:
+    """
+    Generalized Motion Compensation (GMC) class for tracking and object detection in video frames.
+
+    This class provides methods for tracking and detecting objects based on several tracking algorithms including ORB,
+    SIFT, ECC, and Sparse Optical Flow. It also supports downscaling of  frames for computational efficiency.
+
+    Attributes:
+        method (str): The method used for tracking. Options include 'orb', 'sift', 'ecc', 'sparseOptFlow', 'none'.
+        downscale (int): Factor by which to downscale the frames for processing.
+        prevFrame (np.array): Stores the previous frame for tracking.
+        prevKeyPoints (list): Stores the keypoints from the previous frame.
+        prevDescriptors (np.array): Stores the descriptors from the previous frame.
+        initializedFirstFrame (bool): Flag to indicate if the first frame has been processed.
+
+    Methods:
+        __init__(self, method='sparseOptFlow', downscale=2): Initializes a GMC object with the specified method
+                                                              and downscale factor.
+        apply(self, raw_frame, detections=None): Applies the chosen method to a raw frame and optionally uses
+                                                 provided detections.
+        applyEcc(self, raw_frame, detections=None): Applies the ECC algorithm to a raw frame.
+        applyFeatures(self, raw_frame, detections=None): Applies feature-based methods like ORB or SIFT to a raw frame.
+        applySparseOptFlow(self, raw_frame, detections=None): Applies the Sparse Optical Flow method to a raw frame.
+    """
 
     def __init__(self, method='sparseOptFlow', downscale=2):
         """Initialize a video tracker with specified parameters."""

ultralytics/trackers/utils/kalman_filter.py

@@ -8,8 +8,8 @@ class KalmanFilterXYAH:
     """
     For bytetrack. A simple Kalman filter for tracking bounding boxes in image space.
 
-    The 8-dimensional state space (x, y, a, h, vx, vy, va, vh) contains the bounding box center position (x, y),
-    aspect ratio a, height h, and their respective velocities.
+    The 8-dimensional state space (x, y, a, h, vx, vy, va, vh) contains the bounding box center position (x, y), aspect
+    ratio a, height h, and their respective velocities.
 
     Object motion follows a constant velocity model. The bounding box location (x, y, a, h) is taken as direct
     observation of the state space (linear observation model).
@@ -182,8 +182,8 @@ class KalmanFilterXYAH:
     def gating_distance(self, mean, covariance, measurements, only_position=False, metric='maha'):
         """
         Compute gating distance between state distribution and measurements. A suitable distance threshold can be
-        obtained from `chi2inv95`. If `only_position` is False, the chi-square distribution has 4 degrees of
-        freedom, otherwise 2.
+        obtained from `chi2inv95`. If `only_position` is False, the chi-square distribution has 4 degrees of freedom,
+        otherwise 2.
 
         Parameters
         ----------
@@ -223,8 +223,8 @@ class KalmanFilterXYWH(KalmanFilterXYAH):
     """
     For BoT-SORT. A simple Kalman filter for tracking bounding boxes in image space.
 
-    The 8-dimensional state space (x, y, w, h, vx, vy, vw, vh) contains the bounding box center position (x, y),
-    width w, height h, and their respective velocities.
+    The 8-dimensional state space (x, y, w, h, vx, vy, vw, vh) contains the bounding box center position (x, y), width
+    w, height h, and their respective velocities.
 
     Object motion follows a constant velocity model. The bounding box location (x, y, w, h) is taken as direct
     observation of the state space (linear observation model).