def _evaluate_predictions_on_coco(coco_gt,
coco_results,
iou_type,
kpt_oks_sigmas=None):
# 1 vez al tener todas las predicciones
"""
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)
coco_dt = coco_gt.loadRes(coco_results)
coco_eval = COCOeval(coco_gt, coco_dt, iou_type)
if iou_type == "keypoints":
# Use the COCO default keypoint OKS sigmas unless overrides are specified
if kpt_oks_sigmas:
assert hasattr(coco_eval.params,
"kpt_oks_sigmas"), "pycocotools is too old!"
coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas)
# COCOAPI requires every detection and every gt to have keypoints, so
# we just take the first entry from both
num_keypoints_dt = len(coco_results[0]["keypoints"]) // 3
num_keypoints_gt = len(next(iter(
coco_gt.anns.values()))["keypoints"]) // 3
num_keypoints_oks = len(coco_eval.params.kpt_oks_sigmas)
assert num_keypoints_oks == num_keypoints_dt == num_keypoints_gt, (
f"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. "
f"Ground truth contains {num_keypoints_gt} keypoints. "
f"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. "
"They have to agree with each other. For meaning of OKS, please refer to "
"http://cocodataset.org/#keypoints-eval.")
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
return coco_eval
def _evaluate_predictions_on_coco(coco_gt,
coco_results,
iou_type,
kpt_oks_sigmas=None):
"""
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)
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 iou_type == "keypoints":
num_keypoints = len(coco_results[0]["keypoints"]) // 3
assert len(coco_eval.params.kpt_oks_sigmas) == num_keypoints, (
"[COCOEvaluator] The length of cfg.TEST.KEYPOINT_OKS_SIGMAS (default: 17) "
"must be equal to the number of keypoints. However the prediction has {} "
"keypoints! For more information please refer to "
"http://cocodataset.org/#keypoints-eval.".format(num_keypoints))
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
return coco_eval