def evaluate(
self,
results,
metric=None,
iou_thr=(0.25, 0.5),
logger=None,
show=False,
out_dir=None,
):
"""Evaluate.
Evaluation in indoor protocol.
Args:
results (list[dict]): List of results.
metric (str | list[str]): Metrics to be evaluated.
iou_thr (list[float]): AP IoU thresholds.
show (bool): Whether to visualize.
Default: False.
out_dir (str): Path to save the visualization results.
Default: None.
Returns:
dict: Evaluation results.
"""
from mmdet3d.core.evaluation import indoor_eval
assert isinstance(
results, list
), f"Expect results to be list, got {type(results)}."
assert len(results) > 0, "Expect length of results > 0."
assert len(results) == len(self.data_infos)
assert isinstance(
results[0], dict
), f"Expect elements in results to be dict, got {type(results[0])}."
gt_annos = [info["annos"] for info in self.data_infos]
label2cat = {i: cat_id for i, cat_id in enumerate(self.CLASSES)}
ret_dict = indoor_eval(
gt_annos,
results,
iou_thr,
label2cat,
logger=logger,
box_type_3d=self.box_type_3d,
box_mode_3d=self.box_mode_3d,
)
if show:
self.show(results, out_dir)
return ret_dict
def evaluate(gt_infos, pre_dict, part_name_list,
iou_th=(0.25, 0.5, 0.75, 0.9)):
from mmdet3d.core.evaluation import indoor_eval
from mmdet3d.core.bbox.structures.box_3d_mode import Box3DMode, DepthInstance3DBoxes
gt_annos = [info['annos'] for info in gt_infos]
label2cat = {i: cat_id for i, cat_id in enumerate(part_name_list)}
ret_dict = indoor_eval(gt_annos,
pre_dict,
iou_th,
label2cat,
logger=None,
box_type_3d=DepthInstance3DBoxes,
box_mode_3d=Box3DMode.DEPTH)
#print(ret_dict)
#exit(0)
info_image = generate_infos(gt_annos, pre_dict, iou_th,
DepthInstance3DBoxes, Box3DMode.DEPTH)
return ret_dict, info_image