class_docstr: This class represents a bounding box detection in a single image.
  
  Args:
      tlwh (ndarray): Bounding box in format `(top left x, top left y,
          width, height)`.
      confidence (ndarray): Detector confidence score.
      feature (Tensor): A feature vector that describes the object
          contained in this image.
class_docstr_tok: ["This","class","represents","a","bounding","box","detection","in","a","single","image",".","Args",":","tlwh","(","ndarray",")",":","Bounding","box","in","format","`","(","top","left","x",",","top","left","y",",","width",",","height",")","`",".","confidence","(","ndarray",")",":","Detector","confidence","score",".","feature","(","Tensor",")",":","A","feature","vector","that","describes","the","object","contained","in","this","image","."]
docstr: Convert bounding box to format `(center x, center y, aspect ratio,
  height)`, where the aspect ratio is `width / height`.
docstr_tok: ["Convert","bounding","box","to","format","`","(","center","x",",","center","y",",","aspect","ratio",",","height",")","`",",","where","the","aspect","ratio","is","`","width","\/","height","`","."]
code: def to_xyah(self):
      """
      Convert bounding box to format `(center x, center y, aspect ratio,
      height)`, where the aspect ratio is `width / height`.
      """
      ret = self.tlwh.copy()
      ret[:2] += ret[2:] / 2
      ret[2] /= ret[3]
      return ret
  
imports: import os
  import cv2
  import time
  import paddle
  import numpy
label_desc: Use image node_id 3 for calling the Detection obj's underlying member method code with example usage: obj.to_xyah() and returns: ret