def test_show():
import mmcv
import tempfile
from os import path as osp
from mmdet3d.core.bbox import LiDARInstance3DBoxes
temp_dir = tempfile.mkdtemp()
data_root, ann_file, classes, pts_prefix,\
pipeline, modality, split = _generate_kitti_dataset_config()
kitti_dataset = KittiDataset(data_root,
ann_file,
split=split,
modality=modality,
pipeline=pipeline)
boxes_3d = LiDARInstance3DBoxes(
torch.tensor(
[[46.1218, -4.6496, -0.9275, 0.5316, 1.4442, 1.7450, 1.1749],
[33.3189, 0.1981, 0.3136, 0.5656, 1.2301, 1.7985, 1.5723],
[46.1366, -4.6404, -0.9510, 0.5162, 1.6501, 1.7540, 1.3778],
[33.2646, 0.2297, 0.3446, 0.5746, 1.3365, 1.7947, 1.5430],
[58.9079, 16.6272, -1.5829, 1.5656, 3.9313, 1.4899, 1.5505]]))
scores_3d = torch.tensor([0.1815, 0.1663, 0.5792, 0.2194, 0.2780])
labels_3d = torch.tensor([0, 0, 1, 1, 2])
result = dict(boxes_3d=boxes_3d, scores_3d=scores_3d, labels_3d=labels_3d)
results = [result]
kitti_dataset.show(results, temp_dir)
pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj')
gt_file_path = osp.join(temp_dir, '000000', '000000_gt.ply')
pred_file_path = osp.join(temp_dir, '000000', '000000_pred.ply')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
def test_show():
import mmcv
import tempfile
from os import path as osp
from mmdet3d.core.bbox import LiDARInstance3DBoxes
temp_dir = tempfile.mkdtemp()
data_root = 'tests/data/kitti'
ann_file = 'tests/data/kitti/kitti_infos_train.pkl'
modality = {'use_lidar': True, 'use_camera': False}
split = 'training'
file_client_args = dict(backend='disk')
point_cloud_range = [0, -40, -3, 70.4, 40, 1]
class_names = ['Pedestrian', 'Cyclist', 'Car']
pipeline = [
dict(type='LoadPointsFromFile',
load_dim=4,
use_dim=4,
file_client_args=file_client_args),
dict(type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D'),
dict(type='PointsRangeFilter',
point_cloud_range=point_cloud_range),
dict(type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
])
]
kitti_dataset = KittiDataset(data_root,
ann_file,
split=split,
modality=modality,
pipeline=pipeline)
boxes_3d = LiDARInstance3DBoxes(
torch.tensor(
[[46.1218, -4.6496, -0.9275, 0.5316, 1.4442, 1.7450, 1.1749],
[33.3189, 0.1981, 0.3136, 0.5656, 1.2301, 1.7985, 1.5723],
[46.1366, -4.6404, -0.9510, 0.5162, 1.6501, 1.7540, 1.3778],
[33.2646, 0.2297, 0.3446, 0.5746, 1.3365, 1.7947, 1.5430],
[58.9079, 16.6272, -1.5829, 1.5656, 3.9313, 1.4899, 1.5505]]))
scores_3d = torch.tensor([0.1815, 0.1663, 0.5792, 0.2194, 0.2780])
labels_3d = torch.tensor([0, 0, 1, 1, 2])
result = dict(boxes_3d=boxes_3d, scores_3d=scores_3d, labels_3d=labels_3d)
results = [result]
kitti_dataset.show(results, temp_dir)
pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj')
gt_file_path = osp.join(temp_dir, '000000', '000000_gt.ply')
pred_file_path = osp.join(temp_dir, '000000', '000000_pred.ply')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
def test_show():
import mmcv
from os import path as osp
from mmdet3d.core.bbox import LiDARInstance3DBoxes
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
data_root, ann_file, classes, pts_prefix, \
pipeline, modality, split = _generate_kitti_dataset_config()
kitti_dataset = KittiDataset(data_root,
ann_file,
split=split,
modality=modality,
pipeline=pipeline)
boxes_3d = LiDARInstance3DBoxes(
torch.tensor(
[[46.1218, -4.6496, -0.9275, 0.5316, 1.4442, 1.7450, 1.1749],
[33.3189, 0.1981, 0.3136, 0.5656, 1.2301, 1.7985, 1.5723],
[46.1366, -4.6404, -0.9510, 0.5162, 1.6501, 1.7540, 1.3778],
[33.2646, 0.2297, 0.3446, 0.5746, 1.3365, 1.7947, 1.5430],
[58.9079, 16.6272, -1.5829, 1.5656, 3.9313, 1.4899, 1.5505]]))
scores_3d = torch.tensor([0.1815, 0.1663, 0.5792, 0.2194, 0.2780])
labels_3d = torch.tensor([0, 0, 1, 1, 2])
result = dict(boxes_3d=boxes_3d, scores_3d=scores_3d, labels_3d=labels_3d)
results = [result]
kitti_dataset.show(results, temp_dir, show=False)
pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj')
gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj')
pred_file_path = osp.join(temp_dir, '000000', '000000_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
eval_pipeline = [
dict(type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4),
dict(type='DefaultFormatBundle3D',
class_names=classes,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
kitti_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj')
gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj')
pred_file_path = osp.join(temp_dir, '000000', '000000_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 multi-modality show
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
_, _, _, _, multi_modality_pipeline, modality, _ = \
_generate_kitti_multi_modality_dataset_config()
kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix,
multi_modality_pipeline, classes, modality)
kitti_dataset.show(results, temp_dir, show=False)
pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj')
gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj')
pred_file_path = osp.join(temp_dir, '000000', '000000_pred.obj')
img_file_path = osp.join(temp_dir, '000000', '000000_img.png')
img_pred_path = osp.join(temp_dir, '000000', '000000_pred.png')
img_gt_file = osp.join(temp_dir, '000000', '000000_gt.png')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
mmcv.check_file_exist(img_file_path)
mmcv.check_file_exist(img_pred_path)
mmcv.check_file_exist(img_gt_file)
tmp_dir.cleanup()
# test multi-modality show with pipeline
eval_pipeline = [
dict(type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4),
dict(type='LoadImageFromFile'),
dict(type='DefaultFormatBundle3D',
class_names=classes,
with_label=False),
dict(type='Collect3D', keys=['points', 'img'])
]
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
kitti_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj')
gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj')
pred_file_path = osp.join(temp_dir, '000000', '000000_pred.obj')
img_file_path = osp.join(temp_dir, '000000', '000000_img.png')
img_pred_path = osp.join(temp_dir, '000000', '000000_pred.png')
img_gt_file = osp.join(temp_dir, '000000', '000000_gt.png')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
mmcv.check_file_exist(img_file_path)
mmcv.check_file_exist(img_pred_path)
mmcv.check_file_exist(img_gt_file)
tmp_dir.cleanup()
