def show_proj_det_result_meshlab(data,
result,
out_dir,
score_thr=0.0,
show=False,
snapshot=False):
"""Show result of projecting 3D bbox to 2D image by meshlab."""
assert 'img' in data.keys(), 'image data is not provided for visualization'
img_filename = data['img_metas'][0][0]['filename']
file_name = osp.split(img_filename)[-1].split('.')[0]
# read from file because img in data_dict has undergone pipeline transform
img = mmcv.imread(img_filename)
if 'pts_bbox' in result[0].keys():
result[0] = result[0]['pts_bbox']
elif 'img_bbox' in result[0].keys():
result[0] = result[0]['img_bbox']
pred_bboxes = result[0]['boxes_3d'].tensor.numpy()
pred_scores = result[0]['scores_3d'].numpy()
# filter out low score bboxes for visualization
if score_thr > 0:
inds = pred_scores > score_thr
pred_bboxes = pred_bboxes[inds]
box_mode = data['img_metas'][0][0]['box_mode_3d']
if box_mode == Box3DMode.LIDAR:
if 'lidar2img' not in data['img_metas'][0][0]:
raise NotImplementedError(
'LiDAR to image transformation matrix is not provided')
show_bboxes = LiDARInstance3DBoxes(pred_bboxes, origin=(0.5, 0.5, 0))
show_multi_modality_result(img,
None,
show_bboxes,
data['img_metas'][0][0]['lidar2img'],
out_dir,
file_name,
box_mode='lidar',
show=show)
elif box_mode == Box3DMode.DEPTH:
show_bboxes = DepthInstance3DBoxes(pred_bboxes, origin=(0.5, 0.5, 0))
show_multi_modality_result(img,
None,
show_bboxes,
None,
out_dir,
file_name,
box_mode='depth',
img_metas=data['img_metas'][0][0],
show=show)
elif box_mode == Box3DMode.CAM:
if 'cam2img' not in data['img_metas'][0][0]:
raise NotImplementedError(
'camera intrinsic matrix is not provided')
show_bboxes = CameraInstance3DBoxes(pred_bboxes,
box_dim=pred_bboxes.shape[-1],
origin=(0.5, 1.0, 0.5))
show_multi_modality_result(img,
None,
show_bboxes,
data['img_metas'][0][0]['cam2img'],
out_dir,
file_name,
box_mode='camera',
show=show)
else:
raise NotImplementedError(
f'visualization of {box_mode} bbox is not supported')
return file_name
def test_global_rot_scale_trans():
angle = 0.78539816
scale = [0.95, 1.05]
trans_std = 1.0
# rot_range should be a number or seq of numbers
with pytest.raises(AssertionError):
global_rot_scale_trans = GlobalRotScaleTrans(rot_range='0.0')
# scale_ratio_range should be seq of numbers
with pytest.raises(AssertionError):
global_rot_scale_trans = GlobalRotScaleTrans(scale_ratio_range=1.0)
# translation_std should be a positive number or seq of positive numbers
with pytest.raises(AssertionError):
global_rot_scale_trans = GlobalRotScaleTrans(translation_std='0.0')
with pytest.raises(AssertionError):
global_rot_scale_trans = GlobalRotScaleTrans(translation_std=-1.0)
global_rot_scale_trans = GlobalRotScaleTrans(rot_range=angle,
scale_ratio_range=scale,
translation_std=trans_std,
shift_height=False)
np.random.seed(0)
points = np.fromfile('tests/data/scannet/points/scene0000_00.bin',
np.float32).reshape(-1, 6)
annos = mmcv.load('tests/data/scannet/scannet_infos.pkl')
info = annos[0]
gt_bboxes_3d = info['annos']['gt_boxes_upright_depth']
depth_points = DepthPoints(points.copy(),
points_dim=6,
attribute_dims=dict(color=[3, 4, 5]))
gt_bboxes_3d = DepthInstance3DBoxes(gt_bboxes_3d.copy(),
box_dim=gt_bboxes_3d.shape[-1],
with_yaw=False,
origin=(0.5, 0.5, 0.5))
input_dict = dict(points=depth_points.clone(),
bbox3d_fields=['gt_bboxes_3d'],
gt_bboxes_3d=gt_bboxes_3d.clone())
input_dict = global_rot_scale_trans(input_dict)
trans_depth_points = input_dict['points']
trans_bboxes_3d = input_dict['gt_bboxes_3d']
noise_rot = 0.07667607233534723
scale_factor = 1.021518936637242
trans_factor = np.array([0.97873798, 2.2408932, 1.86755799])
true_depth_points = depth_points.clone()
true_bboxes_3d = gt_bboxes_3d.clone()
true_depth_points, noise_rot_mat_T = true_bboxes_3d.rotate(
noise_rot, true_depth_points)
true_bboxes_3d.scale(scale_factor)
true_bboxes_3d.translate(trans_factor)
true_depth_points.scale(scale_factor)
true_depth_points.translate(trans_factor)
assert torch.allclose(trans_depth_points.tensor,
true_depth_points.tensor,
atol=1e-6)
assert torch.allclose(trans_bboxes_3d.tensor,
true_bboxes_3d.tensor,
atol=1e-6)
assert input_dict['pcd_scale_factor'] == scale_factor
assert torch.allclose(input_dict['pcd_rotation'],
noise_rot_mat_T,
atol=1e-6)
assert np.allclose(input_dict['pcd_trans'], trans_factor)
repr_str = repr(global_rot_scale_trans)
expected_repr_str = f'GlobalRotScaleTrans(rot_range={[-angle, angle]},' \
f' scale_ratio_range={scale},' \
f' translation_std={[trans_std for _ in range(3)]},' \
f' shift_height=False)'
assert repr_str == expected_repr_str
# points with shift_height but no bbox
global_rot_scale_trans = GlobalRotScaleTrans(rot_range=angle,
scale_ratio_range=scale,
translation_std=trans_std,
shift_height=True)
# points should have height attribute when shift_height=True
with pytest.raises(AssertionError):
input_dict = global_rot_scale_trans(input_dict)
np.random.seed(0)
shift_height = points[:, 2:3] * 0.99
points = np.concatenate([points, shift_height], axis=1)
depth_points = DepthPoints(points.copy(),
points_dim=7,
attribute_dims=dict(color=[3, 4, 5], height=6))
input_dict = dict(points=depth_points.clone(), bbox3d_fields=[])
input_dict = global_rot_scale_trans(input_dict)
trans_depth_points = input_dict['points']
true_shift_height = shift_height * scale_factor
assert np.allclose(
trans_depth_points.tensor.numpy(),
np.concatenate([true_depth_points.tensor.numpy(), true_shift_height],
axis=1),
atol=1e-6)
def show_result_meshlab(data,
result,
out_dir,
score_thr=0.0,
show=False,
snapshot=False):
"""Show result by meshlab.
Args:
data (dict): Contain data from pipeline.
result (dict): Predicted result from model.
out_dir (str): Directory to save visualized result.
score_thr (float): Minimum score of bboxes to be shown. Default: 0.0
show (bool): Visualize the results online. Defaults to False.
snapshot (bool): Whether to save the online results. Defaults to False.
"""
points = data['points'][0][0].cpu().numpy()
pts_filename = data['img_metas'][0][0]['pts_filename']
file_name = osp.split(pts_filename)[-1].split('.')[0]
assert out_dir is not None, 'Expect out_dir, got none.'
if 'pts_bbox' in result[0].keys():
pred_bboxes = result[0]['pts_bbox']['boxes_3d'].tensor.numpy()
pred_scores = result[0]['pts_bbox']['scores_3d'].numpy()
else:
pred_bboxes = result[0]['boxes_3d'].tensor.numpy()
pred_scores = result[0]['scores_3d'].numpy()
# filter out low score bboxes for visualization
if score_thr > 0:
inds = pred_scores > score_thr
pred_bboxes = pred_bboxes[inds]
# for now we convert points into depth mode
box_mode = data['img_metas'][0][0]['box_mode_3d']
if box_mode != Box3DMode.DEPTH:
points = points[..., [1, 0, 2]]
points[..., 0] *= -1
show_bboxes = Box3DMode.convert(pred_bboxes, box_mode, Box3DMode.DEPTH)
else:
show_bboxes = deepcopy(pred_bboxes)
show_result(points,
None,
show_bboxes,
out_dir,
file_name,
show=show,
snapshot=snapshot)
if 'img' not in data.keys():
return out_dir, file_name
# multi-modality visualization
# project 3D bbox to 2D image plane
if box_mode == Box3DMode.LIDAR:
if 'lidar2img' not in data['img_metas'][0][0]:
raise NotImplementedError(
'LiDAR to image transformation matrix is not provided')
show_bboxes = LiDARInstance3DBoxes(pred_bboxes, origin=(0.5, 0.5, 0))
img = mmcv.imread(data['img_metas'][0][0]['filename'])
show_multi_modality_result(img,
None,
show_bboxes,
data['img_metas'][0][0]['lidar2img'],
out_dir,
file_name,
show=show)
elif box_mode == Box3DMode.DEPTH:
if 'calib' not in data.keys():
raise NotImplementedError(
'camera calibration information is not provided')
show_bboxes = DepthInstance3DBoxes(pred_bboxes, origin=(0.5, 0.5, 0))
img = mmcv.imread(data['img_metas'][0][0]['filename'])
show_multi_modality_result(img,
None,
show_bboxes,
data['calib'][0],
out_dir,
file_name,
depth_bbox=True,
img_metas=data['img_metas'][0][0],
show=show)
else:
raise NotImplementedError(
f'visualization of {box_mode} bbox is not supported')
return out_dir, file_name
