def test_seg_show():
import mmcv
import tempfile
from os import path as osp
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
root_path = './tests/data/scannet'
ann_file = './tests/data/scannet/scannet_infos.pkl'
scannet_dataset = ScanNetSegDataset(
data_root=root_path, ann_file=ann_file, scene_idxs=[0])
result = dict(
semantic_mask=torch.tensor([
13, 5, 1, 2, 6, 2, 13, 1, 14, 2, 0, 0, 5, 5, 3, 0, 1, 14, 0, 0, 0,
18, 6, 15, 13, 0, 2, 4, 0, 3, 16, 6, 13, 5, 13, 0, 0, 0, 0, 1, 7,
3, 19, 12, 8, 0, 11, 0, 0, 1, 2, 13, 17, 1, 1, 1, 6, 2, 13, 19, 4,
17, 0, 14, 1, 7, 2, 1, 7, 2, 0, 5, 17, 5, 0, 0, 3, 6, 5, 11, 1, 13,
13, 2, 3, 1, 0, 13, 19, 1, 14, 5, 3, 1, 13, 1, 2, 3, 2, 1
]).long())
results = [result]
scannet_dataset.show(results, temp_dir, show=False)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
def test_seg_show():
import mmcv
import tempfile
from os import path as osp
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
root_path = './tests/data/scannet'
ann_file = './tests/data/scannet/scannet_infos.pkl'
scannet_dataset = ScanNetSegDataset(data_root=root_path,
ann_file=ann_file,
scene_idxs=[0])
result = dict(semantic_mask=torch.tensor([
13, 5, 1, 2, 6, 2, 13, 1, 14, 2, 0, 0, 5, 5, 3, 0, 1, 14, 0, 0, 0, 18,
6, 15, 13, 0, 2, 4, 0, 3, 16, 6, 13, 5, 13, 0, 0, 0, 0, 1, 7, 3, 19,
12, 8, 0, 11, 0, 0, 1, 2, 13, 17, 1, 1, 1, 6, 2, 13, 19, 4, 17, 0, 14,
1, 7, 2, 1, 7, 2, 0, 5, 17, 5, 0, 0, 3, 6, 5, 11, 1, 13, 13, 2, 3, 1,
0, 13, 19, 1, 14, 5, 3, 1, 13, 1, 2, 3, 2, 1
]).long())
results = [result]
scannet_dataset.show(results, temp_dir, show=False)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
# test show with pipeline
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
class_names = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'otherfurniture')
eval_pipeline = [
dict(type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True),
dict(type='PointSegClassMapping',
valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24,
28, 33, 34, 36, 39)),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
]
scannet_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()