code: def get_det_poly_res(
      bboxes, bbox_nums, image_id, label_to_cat_id_map, bias=0
  ):
      det_res = []
      k = 0
      for i in range(len(bbox_nums)):
          cur_image_id = int(image_id[i][0])
          det_nums = bbox_nums[i]
          for j in range(det_nums):
              dt = bboxes[k]
              k = k + 1
              (
                  num_id,
                  score,
                  x1,
                  y1,
                  x2,
                  y2,
                  x3,
                  y3,
                  x4,
                  y4,
              ) = dt.tolist()
              if int(num_id) < 0:
                  continue
              category_id = label_to_cat_id_map[int(num_id)]
              rbox = [x1, y1, x2, y2, x3, y3, x4, y4]
              dt_res = {
                  "image_id": cur_image_id,
                  "category_id": category_id,
                  "bbox": rbox,
                  "score": score,
              }
              det_res.append(dt_res)
      return det_res
  
file_path: PaddleDetection/ppdet/metrics/json_results.py
imports: import six
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: get_det_poly_res(bboxes, bbox_nums, image_id, label_to_cat_id_map, bias) and returns: det_res