docstr: return metrics {'heman':0,
      'thr 0.3':'precision: 0 recall: 0 hmean: 0',
      'thr 0.4':'precision: 0 recall: 0 hmean: 0',
      'thr 0.5':'precision: 0 recall: 0 hmean: 0',
      'thr 0.6':'precision: 0 recall: 0 hmean: 0',
      'thr 0.7':'precision: 0 recall: 0 hmean: 0',
      'thr 0.8':'precision: 0 recall: 0 hmean: 0',
      'thr 0.9':'precision: 0 recall: 0 hmean: 0',
      }
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code: def get_metric(self):
      """
      return metrics {'heman':0,
          'thr 0.3':'precision: 0 recall: 0 hmean: 0',
          'thr 0.4':'precision: 0 recall: 0 hmean: 0',
          'thr 0.5':'precision: 0 recall: 0 hmean: 0',
          'thr 0.6':'precision: 0 recall: 0 hmean: 0',
          'thr 0.7':'precision: 0 recall: 0 hmean: 0',
          'thr 0.8':'precision: 0 recall: 0 hmean: 0',
          'thr 0.9':'precision: 0 recall: 0 hmean: 0',
          }
      """
      metrics = {}
      hmean = 0
      for score_thr in self.results.keys():
          metric = self.evaluator.combine_results(
              self.results[score_thr]
          )
          # for key, value in metric.items():
          #     metrics['{}_{}'.format(key, score_thr)] = value
          metric_str = (
              "precision:{:.5f} recall:{:.5f} hmean:{:.5f}".format(
                  metric["precision"], metric["recall"], metric["hmean"]
              )
          )
          metrics["thr {}".format(score_thr)] = metric_str
          hmean = max(hmean, metric["hmean"])
      metrics["hmean"] = hmean
  
      self.reset()
      return metrics
  
file_path: PaddleOCR/ppocr/metrics/det_metric.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from .eval_det_iou import DetectionIoUEvaluator
label_desc: Use image node_id 3 for calling the DetFCEMetric obj's underlying member method code with example usage: obj.get_metric() and returns: metrics