def show_seg_result_meshlab(data,
result,
out_dir,
palette,
show=False,
snapshot=False):
"""Show 3D segmentation 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]
pred_seg = result[0]['semantic_mask'].numpy()
if palette is None:
# generate random color map
max_idx = pred_seg.max()
palette = np.random.randint(0, 256, size=(max_idx + 1, 3))
palette = np.array(palette).astype(np.int)
show_seg_result(points,
None,
pred_seg,
out_dir,
file_name,
palette=palette,
show=show,
snapshot=snapshot)
return file_name
def show_results(self,
data,
result,
palette=None,
out_dir=None,
ignore_index=None):
"""Results visualization.
Args:
data (list[dict]): Input points and the information of the sample.
result (list[dict]): Prediction results.
palette (list[list[int]]] | np.ndarray | None): The palette of
segmentation map. If None is given, random palette will be
generated. Default: None
out_dir (str): Output directory of visualization result.
ignore_index (int, optional): The label index to be ignored, e.g.
unannotated points. If None is given, set to len(self.CLASSES).
Defaults to None.
"""
assert out_dir is not None, 'Expect out_dir, got none.'
if palette is None:
if self.PALETTE is None:
palette = np.random.randint(0,
255,
size=(len(self.CLASSES), 3))
else:
palette = self.PALETTE
palette = np.array(palette)
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']
elif mmcv.is_list_of(data['img_metas'][0], dict):
pts_filename = data['img_metas'][0][batch_id]['pts_filename']
else:
ValueError(
f"Unsupported data type {type(data['img_metas'][0])} "
f'for visualization!')
file_name = osp.split(pts_filename)[-1].split('.')[0]
pred_sem_mask = result[batch_id]['semantic_mask'].cpu().numpy()
show_seg_result(points,
None,
pred_sem_mask,
out_dir,
file_name,
palette,
ignore_index,
show=True)
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)
sem_mask_path = data_info['pts_semantic_mask_path']
gt_sem_mask = self.convert_to_label(
osp.join(self.data_root, sem_mask_path))
pred_sem_mask = result['semantic_mask'].numpy()
show_seg_result(points, gt_sem_mask,
pred_sem_mask, out_dir, file_name,
np.array(self.PALETTE), self.ignore_index, show)
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, gt_sem_mask = self._extract_data(
i, pipeline, ['points', 'pts_semantic_mask'], load_annos=True)
points = points.numpy()
pred_sem_mask = result['semantic_mask'].numpy()
show_seg_result(points, gt_sem_mask,
pred_sem_mask, out_dir, file_name,
np.array(self.PALETTE), self.ignore_index, show)
