class_docstr: This class represents a bounding box detection in a single image.
  
  Args:
      tlwh (Tensor): Bounding box in format `(top left x, top left y,
          width, height)`.
      score (Tensor): Bounding box confidence score.
      feature (Tensor): A feature vector that describes the object 
          contained in this image.
      cls_id (Tensor): Bounding box category id.
class_docstr_tok: ["This","class","represents","a","bounding","box","detection","in","a","single","image",".","Args",":","tlwh","(","Tensor",")",":","Bounding","box","in","format","`","(","top","left","x",",","top","left","y",",","width",",","height",")","`",".","score","(","Tensor",")",":","Bounding","box","confidence","score",".","feature","(","Tensor",")",":","A","feature","vector","that","describes","the","object","contained","in","this","image",".","cls_id","(","Tensor",")",":","Bounding","box","category","id","."]
code: def __init__(self, tlwh, score, feature, cls_id):
      self.tlwh = np.asarray(tlwh, dtype=np.float32)
      self.score = float(score)
      self.feature = np.asarray(feature, dtype=np.float32)
      self.cls_id = int(cls_id)
  
imports: import os
  import cv2
  import time
  import numpy
  import collections
  import math
label_desc: Use image node_id 1 to create a new Detection object from inherited base classes: object with example: obj = Detection(tlwh, score, feature, cls_id)