def test_remove_points_in_boxes():
points = np.array([[68.1370, 3.3580, 2.5160, 0.0000],
[67.6970, 3.5500, 2.5010, 0.0000],
[67.6490, 3.7600, 2.5000, 0.0000],
[66.4140, 3.9010, 2.4590, 0.0000],
[66.0120, 4.0850, 2.4460, 0.0000],
[65.8340, 4.1780, 2.4400, 0.0000],
[65.8410, 4.3860, 2.4400, 0.0000],
[65.7450, 4.5870, 2.4380, 0.0000],
[65.5510, 4.7800, 2.4320, 0.0000],
[65.4860, 4.9820, 2.4300, 0.0000]])
boxes = np.array(
[[30.0285, 10.5110, -1.5304, 0.5100, 0.8700, 1.6000, 1.6400],
[7.8369, 1.6053, -1.5605, 0.5800, 1.2300, 1.8200, -3.1000],
[10.8740, -1.0827, -1.3310, 0.6000, 0.5200, 1.7100, 1.3500],
[14.9783, 2.2466, -1.4950, 0.6100, 0.7300, 1.5300, -1.9200],
[11.0656, 0.6195, -1.5202, 0.6600, 1.0100, 1.7600, -1.4600],
[10.5994, -7.9049, -1.4980, 0.5300, 1.9600, 1.6800, 1.5600],
[28.7068, -8.8244, -1.1485, 0.6500, 1.7900, 1.7500, 3.1200],
[20.2630, 5.1947, -1.4799, 0.7300, 1.7600, 1.7300, 1.5100],
[18.2496, 3.1887, -1.6109, 0.5600, 1.6800, 1.7100, 1.5600],
[7.7396, -4.3245, -1.5801, 0.5600, 1.7900, 1.8000, -0.8300]])
points = LiDARPoints(points, points_dim=4)
points = ObjectSample.remove_points_in_boxes(points, boxes)
assert points.tensor.numpy().shape == (10, 4)
def test_object_sample():
db_sampler = mmcv.ConfigDict({
'data_root': './tests/data/kitti/',
'info_path': './tests/data/kitti/kitti_dbinfos_train.pkl',
'rate': 1.0,
'prepare': {
'filter_by_difficulty': [-1],
'filter_by_min_points': {
'Pedestrian': 10
}
},
'classes': ['Pedestrian', 'Cyclist', 'Car'],
'sample_groups': {
'Pedestrian': 6
}
})
object_sample = ObjectSample(db_sampler)
points = np.fromfile(
'./tests/data/kitti/training/velodyne_reduced/000000.bin',
np.float32).reshape(-1, 4)
annos = mmcv.load('./tests/data/kitti/kitti_infos_train.pkl')
info = annos[0]
rect = info['calib']['R0_rect'].astype(np.float32)
Trv2c = info['calib']['Tr_velo_to_cam'].astype(np.float32)
annos = info['annos']
loc = annos['location']
dims = annos['dimensions']
rots = annos['rotation_y']
gt_names = annos['name']
gt_bboxes_3d = np.concatenate([loc, dims, rots[..., np.newaxis]],
axis=1).astype(np.float32)
gt_bboxes_3d = CameraInstance3DBoxes(gt_bboxes_3d).convert_to(
Box3DMode.LIDAR, np.linalg.inv(rect @ Trv2c))
CLASSES = ('car', 'pedestrian', 'cyclist')
gt_labels = []
for cat in gt_names:
if cat in CLASSES:
gt_labels.append(CLASSES.index(cat))
else:
gt_labels.append(-1)
input_dict = dict(
points=points, gt_bboxes_3d=gt_bboxes_3d, gt_labels_3d=gt_labels)
input_dict = object_sample(input_dict)
points = input_dict['points']
gt_bboxes_3d = input_dict['gt_bboxes_3d']
gt_labels_3d = input_dict['gt_labels_3d']
expected_gt_bboxes_3d = torch.tensor(
[[8.7314, -1.8559, -1.5997, 0.4800, 1.2000, 1.8900, 0.0100],
[8.7314, -1.8559, -1.5997, 0.4800, 1.2000, 1.8900, 0.0100]])
expected_gt_labels_3d = np.array([-1, 0])
repr_str = repr(object_sample)
expected_repr_str = 'ObjectSample'
assert repr_str == expected_repr_str
assert points.shape == (1177, 4)
assert torch.allclose(gt_bboxes_3d.tensor, expected_gt_bboxes_3d)
assert np.all(gt_labels_3d == expected_gt_labels_3d)
def test_object_sample():
import pickle
db_sampler = mmcv.ConfigDict({
'data_root': './tests/data/kitti/',
'info_path': './tests/data/kitti/kitti_dbinfos_train.pkl',
'rate': 1.0,
'prepare': {
'filter_by_difficulty': [-1],
'filter_by_min_points': {
'Pedestrian': 10
}
},
'classes': ['Pedestrian', 'Cyclist', 'Car'],
'sample_groups': {
'Pedestrian': 6
}
})
with open('./tests/data/kitti/kitti_dbinfos_train.pkl', 'rb') as f:
db_infos = pickle.load(f)
np.random.seed(0)
object_sample = ObjectSample(db_sampler)
points = np.fromfile(
'./tests/data/kitti/training/velodyne_reduced/000000.bin',
np.float32).reshape(-1, 4)
annos = mmcv.load('./tests/data/kitti/kitti_infos_train.pkl')
info = annos[0]
annos = info['annos']
gt_names = annos['name']
gt_bboxes_3d = db_infos['Pedestrian'][0]['box3d_lidar']
gt_bboxes_3d = LiDARInstance3DBoxes([gt_bboxes_3d])
CLASSES = ('Car', 'Pedestrian', 'Cyclist')
gt_labels = []
for cat in gt_names:
if cat in CLASSES:
gt_labels.append(CLASSES.index(cat))
else:
gt_labels.append(-1)
input_dict = dict(points=points,
gt_bboxes_3d=gt_bboxes_3d,
gt_labels_3d=gt_labels)
input_dict = object_sample(input_dict)
points = input_dict['points']
gt_bboxes_3d = input_dict['gt_bboxes_3d']
gt_labels_3d = input_dict['gt_labels_3d']
repr_str = repr(object_sample)
expected_repr_str = 'ObjectSample sample_2d=False, ' \
'data_root=./tests/data/kitti/, ' \
'info_path=./tests/data/kitti/kitti' \
'_dbinfos_train.pkl, rate=1.0, ' \
'prepare={\'filter_by_difficulty\': [-1], ' \
'\'filter_by_min_points\': {\'Pedestrian\': 10}}, ' \
'classes=[\'Pedestrian\', \'Cyclist\', \'Car\'], ' \
'sample_groups={\'Pedestrian\': 6}'
assert repr_str == expected_repr_str
assert points.shape == (1177, 4)
assert gt_bboxes_3d.tensor.shape == (2, 7)
assert np.all(gt_labels_3d == [1, 0])
def test_object_sample():
db_sampler = mmcv.ConfigDict({
'data_root': './tests/data/kitti/',
'info_path': './tests/data/kitti/kitti_dbinfos_train.pkl',
'rate': 1.0,
'prepare': {
'filter_by_difficulty': [-1],
'filter_by_min_points': {
'Pedestrian': 10
}
},
'classes': ['Pedestrian', 'Cyclist', 'Car'],
'sample_groups': {
'Pedestrian': 6
}
})
np.random.seed(0)
object_sample = ObjectSample(db_sampler)
points = np.fromfile(
'./tests/data/kitti/training/velodyne_reduced/000000.bin',
np.float32).reshape(-1, 4)
annos = mmcv.load('./tests/data/kitti/kitti_infos_train.pkl')
info = annos[0]
rect = info['calib']['R0_rect'].astype(np.float32)
Trv2c = info['calib']['Tr_velo_to_cam'].astype(np.float32)
annos = info['annos']
loc = annos['location']
dims = annos['dimensions']
rots = annos['rotation_y']
gt_names = annos['name']
gt_bboxes_3d = np.concatenate([loc, dims, rots[..., np.newaxis]],
axis=1).astype(np.float32)
gt_bboxes_3d = CameraInstance3DBoxes(gt_bboxes_3d).convert_to(
Box3DMode.LIDAR, np.linalg.inv(rect @ Trv2c))
CLASSES = ('Pedestrian', 'Cyclist', 'Car')
gt_labels = []
for cat in gt_names:
if cat in CLASSES:
gt_labels.append(CLASSES.index(cat))
else:
gt_labels.append(-1)
gt_labels = np.array(gt_labels, dtype=np.long)
points = LiDARPoints(points, points_dim=4)
input_dict = dict(points=points,
gt_bboxes_3d=gt_bboxes_3d,
gt_labels_3d=gt_labels)
input_dict = object_sample(input_dict)
points = input_dict['points']
gt_bboxes_3d = input_dict['gt_bboxes_3d']
gt_labels_3d = input_dict['gt_labels_3d']
repr_str = repr(object_sample)
expected_repr_str = 'ObjectSample sample_2d=False, ' \
'data_root=./tests/data/kitti/, ' \
'info_path=./tests/data/kitti/kitti' \
'_dbinfos_train.pkl, rate=1.0, ' \
'prepare={\'filter_by_difficulty\': [-1], ' \
'\'filter_by_min_points\': {\'Pedestrian\': 10}}, ' \
'classes=[\'Pedestrian\', \'Cyclist\', \'Car\'], ' \
'sample_groups={\'Pedestrian\': 6}'
assert repr_str == expected_repr_str
assert points.tensor.numpy().shape == (800, 4)
assert gt_bboxes_3d.tensor.shape == (1, 7)
assert np.all(gt_labels_3d == [0])