code: def _create_anno(bb, lb, sc, img_id, anno_id, ar=None, crw=None):
      y_min = bb[0]
      x_min = bb[1]
      y_max = bb[2]
      x_max = bb[3]
      height = y_max - y_min
      width = x_max - x_min
      if ar is None:
          # We compute dummy area to pass to pycocotools.
          # Note that area dependent scores are ignored afterwards.
          ar = height * width
      if crw is None:
          crw = False
      # Rounding is done to make the result consistent with COCO.
      anno = {
          "image_id": img_id,
          "category_id": lb,
          "bbox": [
              np.round(x_min, 2),
              np.round(y_min, 2),
              np.round(width, 2),
              np.round(height, 2),
          ],
          "segmentation": [
              x_min,
              y_min,
              x_min,
              y_max,
              x_max,
              y_max,
              x_max,
              y_min,
          ],
          "area": ar,
          "id": anno_id,
          "iscrowd": crw,
      }
      if sc is not None:
          anno.update({"score": sc})
      return anno
  
file_path: chainercv/chainercv/evaluations/eval_detection_coco.py
imports: import contextlib
  import itertools
  import numpy
  import os
  import six
  import sys
label_desc: Use image node_id 2 for calling a global function with example usage: _create_anno(bb, lb, sc, img_id, anno_id, ar, crw) and returns: anno