Bugfix: Tracker reset fails

[LinasKo]

Description

Resetting the tracker would fail. This was noticed on Develop.

Type of change

Please delete options that are not relevant.

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• This change requires a documentation update

How has this change been tested, please provide a testcase or example of how you tested the change?

YOUR_ANSWER

Any specific deployment considerations

For example, documentation changes, usability, usage/costs, secrets, etc.

Docs

• Docs updated? What were the changes:

[LinasKo]

Noticed when running test code:

import sys
import os
import cv2
import time
import numpy as np

import supervision as sv
from ultralytics import YOLO
import torch
from torch.nn import functional as F


VIDEO_ROOT = "people_walking.mp4"
VIDEO_OUT = "people_walking_out.mp4"


device = "cuda" if torch.cuda.is_available() else "cpu"
# model = YOLO('yolov8x-seg.pt').to(device)
model = YOLO("yolov8n.pt").to(device)

mask_annotator = sv.MaskAnnotator()
box_annotator = sv.BoxAnnotator(thickness=1)
track_annotator = sv.TraceAnnotator()
label_annotator = sv.LabelAnnotator()

video_info = sv.VideoInfo.from_video_path(video_path=VIDEO_ROOT)
frame_generator = sv.get_video_frames_generator(source_path=VIDEO_ROOT)
byte_tracker = sv.ByteTrack(minimum_consecutive_frames=10)
byte_tracker.reset()

with sv.VideoSink(VIDEO_OUT, video_info=video_info) as sink:

    for frame in frame_generator:

        result = model(frame, imgsz = 1280,verbose=False)[0]
        pre_track_detections = sv.Detections.from_ultralytics(result)
        # pre_track_detections = detections.copy()
        detections = byte_tracker.update_with_detections(pre_track_detections)
        try:
          print(f"largest detection id: {np.max(detections.tracker_id)}")
          print(f"number of detections: {detections.tracker_id.shape[0]}")
          # print(f"pre track detections: {pre_track_detections}")
          print('YOLO bbox: ', result.boxes.cpu().xyxy if len(result.boxes.cpu().xyxy)>0 else [])
          print(f"post track detections: {detections}")

        except ValueError:
          pass

        labels = [
            f"#{tracker_id} {model.model.names[class_id]} {confidence:0.2f}"
            for _, _, confidence, class_id, tracker_id, _
            in detections
        ]

        annotated_frame = box_annotator.annotate(
            scene=frame.copy(),
            detections=detections)
        annotated_frame = track_annotator.annotate(
            scene=annotated_frame.copy(),
            detections=detections)
        annotated_frame = mask_annotator.annotate(
            scene=annotated_frame.copy(),
            detections=detections)
        annotated_frame = label_annotator.annotate(
            scene=annotated_frame.copy(),
            labels=labels,
            detections=detections
        )


        sink.write_frame(frame=annotated_frame)