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_results(self, data, result, out_dir, show=False, score_thr=None):
"""Results visualization.
Args:
data (list[dict]): Input images 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.
TODO: implement score_thr of single_stage_mono3d.
score_thr (float, optional): Score threshold of bounding boxes.
Default to None.
Not implemented yet, but it is here for unification.
"""
for batch_id in range(len(result)):
if isinstance(data['img_metas'][0], DC):
img_filename = data['img_metas'][0]._data[0][batch_id][
'filename']
cam2img = data['img_metas'][0]._data[0][batch_id]['cam2img']
elif mmcv.is_list_of(data['img_metas'][0], dict):
img_filename = data['img_metas'][0][batch_id]['filename']
cam2img = data['img_metas'][0][batch_id]['cam2img']
else:
ValueError(
f"Unsupported data type {type(data['img_metas'][0])} "
f'for visualization!')
img = mmcv.imread(img_filename)
file_name = osp.split(img_filename)[-1].split('.')[0]
assert out_dir is not None, 'Expect out_dir, got none.'
pred_bboxes = result[batch_id]['img_bbox']['boxes_3d']
assert isinstance(pred_bboxes, CameraInstance3DBoxes), \
f'unsupported predicted bbox type {type(pred_bboxes)}'
show_multi_modality_result(img,
None,
pred_bboxes,
cam2img,
out_dir,
file_name,
'camera',
show=show)
def show_results(self, data, result, out_dir):
"""Results visualization.
Args:
data (list[dict]): Input images and the information of the sample.
result (list[dict]): Prediction results.
out_dir (str): Output directory of visualization result.
"""
for batch_id in range(len(result)):
if isinstance(data['img_metas'][0], DC):
img_filename = data['img_metas'][0]._data[0][batch_id][
'filename']
cam_intrinsic = data['img_metas'][0]._data[0][batch_id][
'cam_intrinsic']
elif mmcv.is_list_of(data['img_metas'][0], dict):
img_filename = data['img_metas'][0][batch_id]['filename']
cam_intrinsic = data['img_metas'][0][batch_id]['cam_intrinsic']
else:
ValueError(
f"Unsupported data type {type(data['img_metas'][0])} "
f'for visualization!')
img = mmcv.imread(img_filename)
file_name = osp.split(img_filename)[-1].split('.')[0]
assert out_dir is not None, 'Expect out_dir, got none.'
pred_bboxes = result[batch_id]['img_bbox']['boxes_3d']
assert isinstance(pred_bboxes, CameraInstance3DBoxes), \
f'unsupported predicted bbox type {type(pred_bboxes)}'
# TODO: remove the hack of box from NuScenesMonoDataset
pred_bboxes = mono_cam_box2vis(pred_bboxes)
show_multi_modality_result(img,
None,
pred_bboxes,
cam_intrinsic,
out_dir,
file_name,
'camera',
show=True)
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 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
