lint fixed

docs/tracker/tracker_bytetrack_detections.py

@@ -2,8 +2,8 @@ from tqdm.notebook import tqdm
 from ultralytics import YOLO
 
 import supervision as sv
-from supervision.detection.annotate import BoxAnnotator
 from supervision import ByteTrack
+from supervision.detection.annotate import BoxAnnotator
 from supervision.utils.video import VideoInfo, VideoSink, get_video_frames_generator
 
 model = YOLO("yolov5s.pt")

docs/tracker/tracker_bytetrack_numpy.py

@@ -3,12 +3,8 @@ from tqdm.notebook import tqdm
 from ultralytics import YOLO
 
 import supervision as sv
+from supervision import ByteTrack, detections2boxes, match_detections_with_tracks
 from supervision.detection.annotate import BoxAnnotator
-from supervision import (
-    ByteTrack,
-    detections2boxes,
-    match_detections_with_tracks,
-)
 from supervision.utils.video import VideoInfo, VideoSink, get_video_frames_generator
 
 model = YOLO("yolov5s.pt")

supervision/tracker/byte_tracker/byte_tracker.py

@@ -156,7 +156,13 @@ def detections2boxes(detections: Detections) -> np.ndarray:
     Returns:
         np.ndarray: An array containing the bounding boxes' coordinates (xyxy) and their corresponding confidences.
     """
-    return np.hstack((detections.xyxy, detections.confidence[:, np.newaxis], detections.class_id[:, np.newaxis]))
+    return np.hstack(
+        (
+            detections.xyxy,
+            detections.confidence[:, np.newaxis],
+            detections.class_id[:, np.newaxis],
+        )
+    )
 
 
 # converts List[strack] into format that can be consumed by match_detections_with_tracks function

supervision/tracker/byte_tracker/matching.py

@@ -1,12 +1,12 @@
-from typing import Tuple, List, Optional
+from typing import List, Optional, Tuple
 
 import numpy as np
 import scipy
 from scipy.optimize import linear_sum_assignment
 from scipy.spatial.distance import cdist
 
-from supervision.tracker.byte_tracker import kalman_filter
 from supervision.detection.utils import box_iou_batch
+from supervision.tracker.byte_tracker import kalman_filter
 
 
 def merge_matches(m1, m2, shape) -> Tuple[List, tuple, tuple]:
@@ -26,7 +26,9 @@ def merge_matches(m1, m2, shape) -> Tuple[List, tuple, tuple]:
     return match, unmatched_O, unmatched_Q
 
 
-def _indices_to_matches(cost_matrix: np.ndarray, indices: np.ndarray, thresh: float) -> Tuple[np.ndarray, tuple, tuple]:
+def _indices_to_matches(
+    cost_matrix: np.ndarray, indices: np.ndarray, thresh: float
+) -> Tuple[np.ndarray, tuple, tuple]:
     matched_cost = cost_matrix[tuple(zip(*indices))]
     matched_mask = matched_cost <= thresh
 
@@ -36,7 +38,9 @@ def _indices_to_matches(cost_matrix: np.ndarray, indices: np.ndarray, thresh: fl
     return matches, unmatched_a, unmatched_b
 
 
-def linear_assignment(cost_matrix: np.ndarray, thresh: float) -> [np.ndarray, Tuple[int], Tuple[int, int]]:
+def linear_assignment(
+    cost_matrix: np.ndarray, thresh: float
+) -> [np.ndarray, Tuple[int], Tuple[int, int]]:
     """
     Simple linear assignment
     :type cost_matrix: np.ndarray
@@ -100,7 +104,7 @@ def v_iou_distance(atracks: List, btracks: List) -> np.ndarray:
     else:
         atlbrs = [track.tlwh_to_tlbr(track.pred_bbox) for track in atracks]
         btlbrs = [track.tlwh_to_tlbr(track.pred_bbox) for track in btracks]
-    _ious =box_iou_batch(np.asarray(atlbrs), np.asarray(btlbrs))
+    _ious = box_iou_batch(np.asarray(atlbrs), np.asarray(btlbrs))
     cost_matrix = 1 - _ious
 
     return cost_matrix
@@ -129,7 +133,13 @@ def embedding_distance(tracks: List, detections: List, metric="cosine") -> np.nd
     return cost_matrix
 
 
-def gate_cost_matrix(kf, cost_matrix: np.ndarray, tracks: List, detections: np.ndarray, only_position=False) -> np.ndarray:
+def gate_cost_matrix(
+    kf,
+    cost_matrix: np.ndarray,
+    tracks: List,
+    detections: np.ndarray,
+    only_position=False,
+) -> np.ndarray:
     if cost_matrix.size == 0:
         return cost_matrix
     gating_dim = 2 if only_position else 4
@@ -143,7 +153,14 @@ def gate_cost_matrix(kf, cost_matrix: np.ndarray, tracks: List, detections: np.n
     return cost_matrix
 
 
-def fuse_motion(kf, cost_matrix: np.ndarray, tracks: List, detections: np.ndarray, only_position=False, lambda_=0.98) -> np.ndarray:
+def fuse_motion(
+    kf,
+    cost_matrix: np.ndarray,
+    tracks: List,
+    detections: np.ndarray,
+    only_position=False,
+    lambda_=0.98,
+) -> np.ndarray:
     if cost_matrix.size == 0:
         return cost_matrix
     gating_dim = 2 if only_position else 4
@@ -158,7 +175,9 @@ def fuse_motion(kf, cost_matrix: np.ndarray, tracks: List, detections: np.ndarra
     return cost_matrix
 
 
-def fuse_iou(cost_matrix: np.ndarray, tracks: List, detections: np.ndarray) -> np.ndarray:
+def fuse_iou(
+    cost_matrix: np.ndarray, tracks: List, detections: np.ndarray
+) -> np.ndarray:
     if cost_matrix.size == 0:
         return cost_matrix
     reid_sim = 1 - cost_matrix