class_docstr: SiamRPN OPEBenchmark have eval_success, precision to select.
  eval_success is  distance between the center point of the predicted position
  precision is Compute overlap ratio between two rects through thresholds_overlap
  Parameters
  ----------
      dataset
          dataset Benchmark
class_docstr_tok: ["SiamRPN","OPEBenchmark","have","eval_success",",","precision","to","select",".","eval_success","is","distance","between","the","center","point","of","the","predicted","position","precision","is","Compute","overlap","ratio","between","two","rects","through","thresholds_overlap","Parameters","--","--","--","--","--","dataset","dataset","Benchmark"]
code: def convert_bb_to_center(self, bboxes):
      return np.array(
          [
              (bboxes[:, 0] + (bboxes[:, 2] - 1) / 2),
              (bboxes[:, 1] + (bboxes[:, 3] - 1) / 2),
          ]
      ).T
  
file_path: gluon-cv/gluoncv/utils/metrics/tracking.py
imports: import numpy
  from ..filesystem import try_import_colorama
label_desc: Use image node_id 2 for calling the OPEBenchmark obj's underlying member method code with example usage: obj.convert_bb_to_center(bboxes) and returns: np