def show_result_meshlab(data, result, out_dir):
"""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.
"""
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.'
pred_bboxes = result[0]['boxes_3d'].tensor.numpy()
# for now we convert points into depth mode
if data['img_metas'][0][0]['box_mode_3d'] != Box3DMode.DEPTH:
points = points[..., [1, 0, 2]]
points[..., 0] *= -1
pred_bboxes = Box3DMode.convert(pred_bboxes,
data['img_metas'][0][0]['box_mode_3d'],
Box3DMode.DEPTH)
pred_bboxes[..., 2] += pred_bboxes[..., 5] / 2
else:
pred_bboxes[..., 2] += pred_bboxes[..., 5] / 2
show_result(points, None, pred_bboxes, out_dir, file_name)
def show_results(self, data, result, out_dir):
"""Results visualization.
Args:
data (dict): Input points and the information of the sample.
result (dict): Prediction results.
out_dir (str): Output directory of visualization result.
"""
for batch_id in range(len(result)):
if isinstance(data["points"][0], DC):
points = data["points"][0]._data[0][batch_id].numpy()
elif mmcv.is_list_of(data["points"][0], torch.Tensor):
points = data["points"][0][batch_id]
else:
ValueError(
f"Unsupported data type {type(data['points'][0])} "
f"for visualization!"
)
if isinstance(data["img_metas"][0], DC):
pts_filename = data["img_metas"][0]._data[0][batch_id]["pts_filename"]
box_mode_3d = data["img_metas"][0]._data[0][batch_id]["box_mode_3d"]
elif mmcv.is_list_of(data["img_metas"][0], dict):
pts_filename = data["img_metas"][0][batch_id]["pts_filename"]
box_mode_3d = data["img_metas"][0][batch_id]["box_mode_3d"]
else:
ValueError(
f"Unsupported data type {type(data['img_metas'][0])} "
f"for visualization!"
)
file_name = osp.split(pts_filename)[-1].split(".")[0]
assert out_dir is not None, "Expect out_dir, got none."
inds = result[batch_id]["pts_bbox"]["scores_3d"] > 0.1
pred_bboxes = copy.deepcopy(
result[batch_id]["pts_bbox"]["boxes_3d"][inds].tensor.numpy()
)
# for now we convert points into depth mode
if box_mode_3d == Box3DMode.DEPTH:
pred_bboxes[..., 2] += pred_bboxes[..., 5] / 2
elif (box_mode_3d == Box3DMode.CAM) or (box_mode_3d == Box3DMode.LIDAR):
points = points[..., [1, 0, 2]]
points[..., 0] *= -1
pred_bboxes = Box3DMode.convert(
pred_bboxes, box_mode_3d, Box3DMode.DEPTH
)
pred_bboxes[..., 2] += pred_bboxes[..., 5] / 2
else:
ValueError(
f"Unsupported box_mode_3d {box_mode_3d} for convertion!")
show_result(points, None, pred_bboxes, out_dir, file_name)
def show_results(self, data, result, out_dir):
"""Results visualization.
Args:
data (dict): Input points and the information of the sample.
result (dict): Prediction results.
out_dir (str): Output directory of visualization result.
"""
for batch_id in range(len(result)):
if isinstance(data['points'][0], DC):
points = data['points'][0]._data[0][batch_id].numpy()
elif mmcv.is_list_of(data['points'][0], torch.Tensor):
points = data['points'][0][batch_id]
else:
ValueError(f"Unsupported data type {type(data['points'][0])} "
f'for visualization!')
if isinstance(data['img_metas'][0], DC):
pts_filename = data['img_metas'][0]._data[0][batch_id][
'pts_filename']
box_mode_3d = data['img_metas'][0]._data[0][batch_id][
'box_mode_3d']
elif mmcv.is_list_of(data['img_metas'][0], dict):
pts_filename = data['img_metas'][0][batch_id]['pts_filename']
box_mode_3d = data['img_metas'][0][batch_id]['box_mode_3d']
else:
ValueError(
f"Unsupported data type {type(data['img_metas'][0])} "
f'for visualization!')
file_name = osp.split(pts_filename)[-1].split('.')[0]
assert out_dir is not None, 'Expect out_dir, got none.'
inds = result[batch_id]['pts_bbox']['scores_3d'] > 0.1
pred_bboxes = result[batch_id]['pts_bbox']['boxes_3d'][inds]
# for now we convert points and bbox into depth mode
if (box_mode_3d == Box3DMode.CAM) or (box_mode_3d
== Box3DMode.LIDAR):
points = Coord3DMode.convert_point(points, Coord3DMode.LIDAR,
Coord3DMode.DEPTH)
pred_bboxes = Box3DMode.convert(pred_bboxes, box_mode_3d,
Box3DMode.DEPTH)
elif box_mode_3d != Box3DMode.DEPTH:
ValueError(
f'Unsupported box_mode_3d {box_mode_3d} for convertion!')
pred_bboxes = pred_bboxes.tensor.cpu().numpy()
show_result(points, None, pred_bboxes, out_dir, file_name)
def show(self, results, out_dir, show=True, pipeline=None):
"""Results visualization.
Args:
results (list[dict]): List of bounding boxes results.
out_dir (str): Output directory of visualization result.
show (bool): Visualize the results online.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
"""
assert out_dir is not None, 'Expect out_dir, got none.'
pipeline = self._get_pipeline(pipeline)
for i, result in enumerate(results):
data_info = self.data_infos[i]
pts_path = data_info['pts_path']
file_name = osp.split(pts_path)[-1].split('.')[0]
points, img_metas, img, calib = self._extract_data(
i, pipeline, ['points', 'img_metas', 'img', 'calib'])
# scale colors to [0, 255]
points = points.numpy()
points[:, 3:] *= 255
gt_bboxes = self.get_ann_info(i)['gt_bboxes_3d'].tensor.numpy()
pred_bboxes = result['boxes_3d'].tensor.numpy()
show_result(points, gt_bboxes.copy(), pred_bboxes.copy(), out_dir,
file_name, show)
# multi-modality visualization
if self.modality['use_camera'] and 'calib' in data_info.keys():
img = img.numpy()
# need to transpose channel to first dim
img = img.transpose(1, 2, 0)
pred_bboxes = DepthInstance3DBoxes(
pred_bboxes, origin=(0.5, 0.5, 0))
gt_bboxes = DepthInstance3DBoxes(
gt_bboxes, origin=(0.5, 0.5, 0))
show_multi_modality_result(
img,
gt_bboxes,
pred_bboxes,
calib,
out_dir,
file_name,
depth_bbox=True,
img_metas=img_metas,
show=show)
def show(self, results, out_dir, show=True):
"""Results visualization.
Args:
results (list[dict]): List of bounding boxes results.
out_dir (str): Output directory of visualization result.
show (bool): Visualize the results online.
"""
assert out_dir is not None, 'Expect out_dir, got none.'
for i, result in enumerate(results):
data_info = self.data_infos[i]
pts_path = data_info['pts_path']
file_name = osp.split(pts_path)[-1].split('.')[0]
if hasattr(self, 'pipeline'):
example = self.prepare_test_data(i)
else:
example = None
points = np.fromfile(osp.join(self.data_root, pts_path),
dtype=np.float32).reshape(-1, 6)
points[:, 3:] *= 255
gt_bboxes = self.get_ann_info(i)['gt_bboxes_3d'].tensor.numpy()
pred_bboxes = result['boxes_3d'].tensor.numpy()
show_result(points, gt_bboxes.copy(), pred_bboxes.copy(), out_dir,
file_name, show)
# multi-modality visualization
if self.modality['use_camera'] and example is not None and \
'calib' in data_info.keys():
img = mmcv.imread(example['img_metas']._data['filename'])
pred_bboxes = DepthInstance3DBoxes(pred_bboxes,
origin=(0.5, 0.5, 0))
gt_bboxes = DepthInstance3DBoxes(gt_bboxes,
origin=(0.5, 0.5, 0))
show_multi_modality_result(
img,
gt_bboxes,
pred_bboxes,
example['calib'],
out_dir,
file_name,
depth_bbox=True,
img_metas=example['img_metas']._data,
show=show)
def show(self, results, out_dir, show=True):
"""Results visualization.
Args:
results (list[dict]): List of bounding boxes results.
out_dir (str): Output directory of visualization result.
show (bool): Visualize the results online.
"""
assert out_dir is not None, 'Expect out_dir, got none.'
for i, result in enumerate(results):
data_info = self.data_infos[i]
pts_path = data_info['pts_path']
file_name = osp.split(pts_path)[-1].split('.')[0]
points = np.fromfile(osp.join(self.data_root, pts_path),
dtype=np.float32).reshape(-1, 6)
gt_bboxes = self.get_ann_info(i)['gt_bboxes_3d'].tensor
pred_bboxes = result['boxes_3d'].tensor.numpy()
show_result(points, gt_bboxes, pred_bboxes, out_dir, file_name,
show)
def show_results(self, data, result, out_dir):
"""Results visualization.
Args:
data (dict): Input points and the information of the sample.
result (dict): Prediction results.
out_dir (str): Output directory of visualization result.
"""
if isinstance(data['points'][0], DC):
points = data['points'][0]._data[0][0].numpy()
elif mmcv.is_list_of(data['points'][0], torch.Tensor):
points = data['points'][0][0]
else:
ValueError(f"Unsupported data type {type(data['points'][0])} "
f'for visualization!')
if isinstance(data['img_metas'][0], DC):
pts_filename = data['img_metas'][0]._data[0][0]['pts_filename']
box_mode_3d = data['img_metas'][0]._data[0][0]['box_mode_3d']
elif mmcv.is_list_of(data['img_metas'][0], dict):
pts_filename = data['img_metas'][0][0]['pts_filename']
box_mode_3d = data['img_metas'][0][0]['box_mode_3d']
else:
ValueError(f"Unsupported data type {type(data['img_metas'][0])} "
f'for visualization!')
file_name = osp.split(pts_filename)[-1].split('.')[0]
assert out_dir is not None, 'Expect out_dir, got none.'
pred_bboxes = copy.deepcopy(result['boxes_3d'].tensor.numpy())
# for now we convert points into depth mode
if box_mode_3d == Box3DMode.DEPTH:
pred_bboxes[..., 2] += pred_bboxes[..., 5] / 2
elif box_mode_3d == Box3DMode.CAM or box_mode_3d == Box3DMode.LIDAR:
points = points[..., [1, 0, 2]]
points[..., 0] *= -1
pred_bboxes = Box3DMode.convert(pred_bboxes, box_mode_3d,
Box3DMode.DEPTH)
pred_bboxes[..., 2] += pred_bboxes[..., 5] / 2
else:
ValueError(
f'Unsupported box_mode_3d {box_mode_3d} for convertion!')
show_result(points, None, pred_bboxes, out_dir, file_name)
def show(self, results, out_dir):
"""Results visualization.
Args:
results (list[dict]): List of bounding boxes results.
out_dir (str): Output directory of visualization result.
"""
assert out_dir is not None, 'Expect out_dir, got none.'
for i, result in enumerate(results):
data_info = self.data_infos[i]
pts_path = data_info['pts_path']
file_name = osp.split(pts_path)[-1].split('.')[0]
points = np.fromfile(osp.join(self.data_root, pts_path),
dtype=np.float32).reshape(-1, 6)
gt_bboxes = np.pad(data_info['annos']['gt_boxes_upright_depth'],
((0, 0), (0, 1)), 'constant')
pred_bboxes = result['boxes_3d'].tensor.numpy()
pred_bboxes[..., 2] += pred_bboxes[..., 5] / 2
show_result(points, gt_bboxes, pred_bboxes, out_dir, file_name)
def show_det_result_meshlab(data,
result,
out_dir,
score_thr=0.0,
show=False,
snapshot=False):
"""Show 3D detection result by meshlab."""
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]
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)
return file_name
def show(self, results, out_dir, show=True, pipeline=None):
"""Results visualization.
Args:
results (list[dict]): List of bounding boxes results.
out_dir (str): Output directory of visualization result.
show (bool): Visualize the results online.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
"""
assert out_dir is not None, 'Expect out_dir, got none.'
pipeline = self._get_pipeline(pipeline)
for i, result in enumerate(results):
data_info = self.data_infos[i]
pts_path = data_info['pts_path']
file_name = osp.split(pts_path)[-1].split('.')[0]
points = self._extract_data(i, pipeline, 'points').numpy()
gt_bboxes = self.get_ann_info(i)['gt_bboxes_3d'].tensor.numpy()
pred_bboxes = result['boxes_3d'].tensor.numpy()
show_result(points, gt_bboxes, pred_bboxes, out_dir, file_name,
show)
def show_results(self, data, result, out_dir, show=False, score_thr=None):
"""Results visualization.
Args:
data (list[dict]): Input points and the information of the sample.
result (list[dict]): Prediction results.
out_dir (str): Output directory of visualization result.
show (bool, optional): Determines whether you are
going to show result by open3d.
Defaults to False.
score_thr (float, optional): Score threshold of bounding boxes.
Default to None.
"""
for batch_id in range(len(result)):
if isinstance(data['points'][0], DC):
points = data['points'][0]._data[0][batch_id].numpy()
elif mmcv.is_list_of(data['points'][0], torch.Tensor):
points = data['points'][0][batch_id]
else:
ValueError(f"Unsupported data type {type(data['points'][0])} "
f'for visualization!')
if isinstance(data['img_metas'][0], DC):
pts_filename = data['img_metas'][0]._data[0][batch_id][
'pts_filename']
box_mode_3d = data['img_metas'][0]._data[0][batch_id][
'box_mode_3d']
elif mmcv.is_list_of(data['img_metas'][0], dict):
pts_filename = data['img_metas'][0][batch_id]['pts_filename']
box_mode_3d = data['img_metas'][0][batch_id]['box_mode_3d']
else:
ValueError(
f"Unsupported data type {type(data['img_metas'][0])} "
f'for visualization!')
file_name = osp.split(pts_filename)[-1].split('.')[0]
assert out_dir is not None, 'Expect out_dir, got none.'
pred_bboxes = result[batch_id]['boxes_3d']
pred_labels = result[batch_id]['labels_3d']
if score_thr is not None:
mask = result[batch_id]['scores_3d'] > score_thr
pred_bboxes = pred_bboxes[mask]
pred_labels = pred_labels[mask]
# for now we convert points and bbox into depth mode
if (box_mode_3d == Box3DMode.CAM) or (box_mode_3d
== Box3DMode.LIDAR):
points = Coord3DMode.convert_point(points, Coord3DMode.LIDAR,
Coord3DMode.DEPTH)
pred_bboxes = Box3DMode.convert(pred_bboxes, box_mode_3d,
Box3DMode.DEPTH)
elif box_mode_3d != Box3DMode.DEPTH:
ValueError(
f'Unsupported box_mode_3d {box_mode_3d} for convertion!')
pred_bboxes = pred_bboxes.tensor.cpu().numpy()
show_result(points,
None,
pred_bboxes,
out_dir,
file_name,
show=show,
pred_labels=pred_labels)
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
