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"]
docstr: pretty print result
  Parameters
  ----------
      success_ret: returned dict from function eval
docstr_tok: ["pretty","print","result","Parameters","--","--","--","--","--","success_ret",":","returned","dict","from","function","eval"]
code: def show_result(
      self,
      success_ret,
      precision_ret=None,
      norm_precision_ret=None,
      show_video_level=False,
      helight_threshold=0.6,
  ):
      """pretty print result
      Parameters
      ----------
          success_ret: returned dict from function eval
      """
      colorama = try_import_colorama()
      tracker_auc = {}
      for tracker_name in success_ret.keys():
          auc = np.mean(list(success_ret[tracker_name].values()))
          tracker_auc[tracker_name] = auc
      tracker_auc_ = sorted(
          tracker_auc.items(), key=lambda x: x[1], reverse=True
      )[:20]
      tracker_names = [x[0] for x in tracker_auc_]
  
      tracker_name_len = max(
          (max([len(x) for x in success_ret.keys()]) + 2), 12
      )
      header = (
          "|{:^" + str(tracker_name_len) + "}|{:^9}|{:^16}|{:^11}|"
      ).format("Tracker name", "Success", "Norm Precision", "Precision")
      formatter = (
          "|{:^"
          + str(tracker_name_len)
          + "}|{:^9.3f}|{:^16.3f}|{:^11.3f}|"
      )
      print("-" * len(header))
      print(header)
      print("-" * len(header))
      for tracker_name in tracker_names:
          success = tracker_auc[tracker_name]
          if precision_ret is not None:
              precision = np.mean(
                  list(precision_ret[tracker_name].values()), axis=0
              )[20]
          else:
              precision = 0
          if norm_precision_ret is not None:
              norm_precision = np.mean(
                  list(norm_precision_ret[tracker_name].values()),
                  axis=0,
              )[20]
          else:
              norm_precision = 0
          print(
              formatter.format(
                  tracker_name, success, norm_precision, precision
              )
          )
      print("-" * len(header))
  
      if (
          show_video_level
          and len(success_ret) < 10
          and precision_ret is not None
          and len(precision_ret) < 10
      ):
          print("\n\n")
          header1 = "|{:^21}|".format("Tracker name")
          header2 = "|{:^21}|".format("Video name")
          for tracker_name in success_ret.keys():
              header1 += ("{:^21}|").format(tracker_name)
              header2 += "{:^9}|{:^11}|".format("success", "precision")
          print("-" * len(header1))
          print(header1)
          print("-" * len(header1))
          print(header2)
          print("-" * len(header1))
          videos = list(success_ret[tracker_name].keys())
          for video in videos:
              row = "|{:^21}|".format(video)
              for tracker_name in success_ret.keys():
                  success = np.mean(success_ret[tracker_name][video])
                  precision = np.mean(
                      precision_ret[tracker_name][video]
                  )
                  success_str = "{:^9.3f}".format(success)
                  if success < helight_threshold:
                      row += f"{colorama.Fore.RED}{success_str}{colorama.Style.RESET_ALL}|"
                  else:
                      row += success_str + "|"
                  precision_str = "{:^11.3f}".format(precision)
                  if precision < helight_threshold:
                      row += f"{colorama.Fore.RED}{precision_str}{colorama.Style.RESET_ALL}|"
                  else:
                      row += precision_str + "|"
              print(row)
          print("-" * len(header1))
  
file_path: gluon-cv/gluoncv/utils/metrics/tracking.py
imports: import numpy
  from ..filesystem import try_import_colorama
label_desc: Use image node_id 7 for calling the OPEBenchmark obj's underlying member method code with example usage: obj.show_result(success_ret, precision_ret, norm_precision_ret, show_video_level, helight_threshold) without return types