def _evaluate_predictions_on_coco(coco_gt,
coco_results,
iou_type,
kpt_oks_sigmas=None,
is_rela=False):
"""
Evaluate the coco results using COCOEval API.
"""
assert len(coco_results) > 0
if iou_type == "segm":
coco_results = copy.deepcopy(coco_results)
# When evaluating mask AP, if the results contain bbox, cocoapi will
# use the box area as the area of the instance, instead of the mask area.
# This leads to a different definition of small/medium/large.
# We remove the bbox field to let mask AP use mask area.
for c in coco_results:
c.pop("bbox", None)
if is_rela:
coco_dt = coco_gt.loadRes_rela(coco_results)
else:
coco_dt = coco_gt.loadRes(coco_results)
coco_eval = COCOeval(coco_gt, coco_dt, iou_type)
# Use the COCO default keypoint OKS sigmas unless overrides are specified
if kpt_oks_sigmas:
coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas)
if is_rela:
coco_eval.evaluate_rela()
else:
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
return coco_eval
