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