Source code for mmdet3d.datasets.s3dis_dataset
# Copyright (c) OpenMMLab. All rights reserved.
from os import path as osp
import numpy as np
from mmdet3d.core import show_seg_result
from mmdet3d.core.bbox import DepthInstance3DBoxes
from mmseg.datasets import DATASETS as SEG_DATASETS
from .builder import DATASETS
from .custom_3d import Custom3DDataset
from .custom_3d_seg import Custom3DSegDataset
from .pipelines import Compose
[docs]@DATASETS.register_module()
class S3DISDataset(Custom3DDataset):
r"""S3DIS Dataset for Detection Task.
This class is the inner dataset for S3DIS. Since S3DIS has 6 areas, we
often train on 5 of them and test on the remaining one. The one for
test is Area_5 as suggested in `GSDN <https://arxiv.org/abs/2006.12356>`_.
To concatenate 5 areas during training
`mmdet.datasets.dataset_wrappers.ConcatDataset` should be used.
Args:
data_root (str): Path of dataset root.
ann_file (str): Path of annotation file.
pipeline (list[dict], optional): Pipeline used for data processing.
Defaults to None.
classes (tuple[str], optional): Classes used in the dataset.
Defaults to None.
modality (dict, optional): Modality to specify the sensor data used
as input. Defaults to None.
box_type_3d (str, optional): Type of 3D box of this dataset.
Based on the `box_type_3d`, the dataset will encapsulate the box
to its original format then converted them to `box_type_3d`.
Defaults to 'Depth' in this dataset. Available options includes
- 'LiDAR': Box in LiDAR coordinates.
- 'Depth': Box in depth coordinates, usually for indoor dataset.
- 'Camera': Box in camera coordinates.
filter_empty_gt (bool, optional): Whether to filter empty GT.
Defaults to True.
test_mode (bool, optional): Whether the dataset is in test mode.
Defaults to False.
"""
CLASSES = ('table', 'chair', 'sofa', 'bookcase', 'board')
def __init__(self,
data_root,
ann_file,
pipeline=None,
classes=None,
modality=None,
box_type_3d='Depth',
filter_empty_gt=True,
test_mode=False,
*kwargs):
super().__init__(
data_root=data_root,
ann_file=ann_file,
pipeline=pipeline,
classes=classes,
modality=modality,
box_type_3d=box_type_3d,
filter_empty_gt=filter_empty_gt,
test_mode=test_mode,
*kwargs)
[docs] def get_ann_info(self, index):
"""Get annotation info according to the given index.
Args:
index (int): Index of the annotation data to get.
Returns:
dict: annotation information consists of the following keys:
- gt_bboxes_3d (:obj:`DepthInstance3DBoxes`):
3D ground truth bboxes
- gt_labels_3d (np.ndarray): Labels of ground truths.
- pts_instance_mask_path (str): Path of instance masks.
- pts_semantic_mask_path (str): Path of semantic masks.
"""
# Use index to get the annos, thus the evalhook could also use this api
info = self.data_infos[index]
if info['annos']['gt_num'] != 0:
gt_bboxes_3d = info['annos']['gt_boxes_upright_depth'].astype(
np.float32) # k, 6
gt_labels_3d = info['annos']['class'].astype(np.int64)
else:
gt_bboxes_3d = np.zeros((0, 6), dtype=np.float32)
gt_labels_3d = np.zeros((0, ), dtype=np.int64)
# to target box structure
gt_bboxes_3d = DepthInstance3DBoxes(
gt_bboxes_3d,
box_dim=gt_bboxes_3d.shape[-1],
with_yaw=False,
origin=(0.5, 0.5, 0.5)).convert_to(self.box_mode_3d)
pts_instance_mask_path = osp.join(self.data_root,
info['pts_instance_mask_path'])
pts_semantic_mask_path = osp.join(self.data_root,
info['pts_semantic_mask_path'])
anns_results = dict(
gt_bboxes_3d=gt_bboxes_3d,
gt_labels_3d=gt_labels_3d,
pts_instance_mask_path=pts_instance_mask_path,
pts_semantic_mask_path=pts_semantic_mask_path)
return anns_results
[docs] def get_data_info(self, index):
"""Get data info according to the given index.
Args:
index (int): Index of the sample data to get.
Returns:
dict: Data information that will be passed to the data
preprocessing pipelines. It includes the following keys:
- pts_filename (str): Filename of point clouds.
- file_name (str): Filename of point clouds.
- ann_info (dict): Annotation info.
"""
info = self.data_infos[index]
pts_filename = osp.join(self.data_root, info['pts_path'])
input_dict = dict(pts_filename=pts_filename)
if not self.test_mode:
annos = self.get_ann_info(index)
input_dict['ann_info'] = annos
if self.filter_empty_gt and ~(annos['gt_labels_3d'] != -1).any():
return None
return input_dict
def _build_default_pipeline(self):
"""Build the default pipeline for this dataset."""
pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='DefaultFormatBundle3D',
class_names=self.CLASSES,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
return Compose(pipeline)
class _S3DISSegDataset(Custom3DSegDataset):
r"""S3DIS Dataset for Semantic Segmentation Task.
This class is the inner dataset for S3DIS. Since S3DIS has 6 areas, we
often train on 5 of them and test on the remaining one.
However, there is not a fixed train-test split of S3DIS. People often test
on Area_5 as suggested by `SEGCloud <https://arxiv.org/abs/1710.07563>`_.
But many papers also report the average results of 6-fold cross validation
over the 6 areas (e.g. `DGCNN <https://arxiv.org/abs/1801.07829>`_).
Therefore, we use an inner dataset for one area, and further use a dataset
wrapper to concat all the provided data in different areas.
Args:
data_root (str): Path of dataset root.
ann_file (str): Path of annotation file.
pipeline (list[dict], optional): Pipeline used for data processing.
Defaults to None.
classes (tuple[str], optional): Classes used in the dataset.
Defaults to None.
palette (list[list[int]], optional): The palette of segmentation map.
Defaults to None.
modality (dict, optional): Modality to specify the sensor data used
as input. Defaults to None.
test_mode (bool, optional): Whether the dataset is in test mode.
Defaults to False.
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.
scene_idxs (np.ndarray | str, optional): Precomputed index to load
data. For scenes with many points, we may sample it several times.
Defaults to None.
"""
CLASSES = ('ceiling', 'floor', 'wall', 'beam', 'column', 'window', 'door',
'table', 'chair', 'sofa', 'bookcase', 'board', 'clutter')
VALID_CLASS_IDS = tuple(range(13))
ALL_CLASS_IDS = tuple(range(14)) # possibly with 'stair' class
PALETTE = [[0, 255, 0], [0, 0, 255], [0, 255, 255], [255, 255, 0],
[255, 0, 255], [100, 100, 255], [200, 200, 100],
[170, 120, 200], [255, 0, 0], [200, 100, 100], [10, 200, 100],
[200, 200, 200], [50, 50, 50]]
def __init__(self,
data_root,
ann_file,
pipeline=None,
classes=None,
palette=None,
modality=None,
test_mode=False,
ignore_index=None,
scene_idxs=None,
**kwargs):
super().__init__(
data_root=data_root,
ann_file=ann_file,
pipeline=pipeline,
classes=classes,
palette=palette,
modality=modality,
test_mode=test_mode,
ignore_index=ignore_index,
scene_idxs=scene_idxs,
**kwargs)
def get_ann_info(self, index):
"""Get annotation info according to the given index.
Args:
index (int): Index of the annotation data to get.
Returns:
dict: annotation information consists of the following keys:
- pts_semantic_mask_path (str): Path of semantic masks.
"""
# Use index to get the annos, thus the evalhook could also use this api
info = self.data_infos[index]
pts_semantic_mask_path = osp.join(self.data_root,
info['pts_semantic_mask_path'])
anns_results = dict(pts_semantic_mask_path=pts_semantic_mask_path)
return anns_results
def _build_default_pipeline(self):
"""Build the default pipeline for this dataset."""
pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True),
dict(
type='PointSegClassMapping',
valid_cat_ids=self.VALID_CLASS_IDS,
max_cat_id=np.max(self.ALL_CLASS_IDS)),
dict(
type='DefaultFormatBundle3D',
with_label=False,
class_names=self.CLASSES),
dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
]
return Compose(pipeline)
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)
def get_scene_idxs(self, scene_idxs):
"""Compute scene_idxs for data sampling.
We sample more times for scenes with more points.
"""
# when testing, we load one whole scene every time
if not self.test_mode and scene_idxs is None:
raise NotImplementedError(
'please provide re-sampled scene indexes for training')
return super().get_scene_idxs(scene_idxs)
[docs]@DATASETS.register_module()
@SEG_DATASETS.register_module()
class S3DISSegDataset(_S3DISSegDataset):
r"""S3DIS Dataset for Semantic Segmentation Task.
This class serves as the API for experiments on the S3DIS Dataset.
It wraps the provided datasets of different areas.
We don't use `mmdet.datasets.dataset_wrappers.ConcatDataset` because we
need to concat the `scene_idxs` of different areas.
Please refer to the `google form <https://docs.google.com/forms/d/e/1FAIpQL
ScDimvNMCGhy_rmBA2gHfDu3naktRm6A8BPwAWWDv-Uhm6Shw/viewform?c=0&w=1>`_ for
data downloading.
Args:
data_root (str): Path of dataset root.
ann_files (list[str]): Path of several annotation files.
pipeline (list[dict], optional): Pipeline used for data processing.
Defaults to None.
classes (tuple[str], optional): Classes used in the dataset.
Defaults to None.
palette (list[list[int]], optional): The palette of segmentation map.
Defaults to None.
modality (dict, optional): Modality to specify the sensor data used
as input. Defaults to None.
test_mode (bool, optional): Whether the dataset is in test mode.
Defaults to False.
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.
scene_idxs (list[np.ndarray] | list[str], optional): Precomputed index
to load data. For scenes with many points, we may sample it several
times. Defaults to None.
"""
def __init__(self,
data_root,
ann_files,
pipeline=None,
classes=None,
palette=None,
modality=None,
test_mode=False,
ignore_index=None,
scene_idxs=None,
**kwargs):
# make sure that ann_files and scene_idxs have same length
ann_files = self._check_ann_files(ann_files)
scene_idxs = self._check_scene_idxs(scene_idxs, len(ann_files))
# initialize some attributes as datasets[0]
super().__init__(
data_root=data_root,
ann_file=ann_files[0],
pipeline=pipeline,
classes=classes,
palette=palette,
modality=modality,
test_mode=test_mode,
ignore_index=ignore_index,
scene_idxs=scene_idxs[0],
**kwargs)
datasets = [
_S3DISSegDataset(
data_root=data_root,
ann_file=ann_files[i],
pipeline=pipeline,
classes=classes,
palette=palette,
modality=modality,
test_mode=test_mode,
ignore_index=ignore_index,
scene_idxs=scene_idxs[i],
**kwargs) for i in range(len(ann_files))
]
# data_infos and scene_idxs need to be concat
self.concat_data_infos([dst.data_infos for dst in datasets])
self.concat_scene_idxs([dst.scene_idxs for dst in datasets])
# set group flag for the sampler
if not self.test_mode:
self._set_group_flag()
[docs] def concat_data_infos(self, data_infos):
"""Concat data_infos from several datasets to form self.data_infos.
Args:
data_infos (list[list[dict]])
"""
self.data_infos = [
info for one_data_infos in data_infos for info in one_data_infos
]
[docs] def concat_scene_idxs(self, scene_idxs):
"""Concat scene_idxs from several datasets to form self.scene_idxs.
Needs to manually add offset to scene_idxs[1, 2, ...].
Args:
scene_idxs (list[np.ndarray])
"""
self.scene_idxs = np.array([], dtype=np.int32)
offset = 0
for one_scene_idxs in scene_idxs:
self.scene_idxs = np.concatenate(
[self.scene_idxs, one_scene_idxs + offset]).astype(np.int32)
offset = np.unique(self.scene_idxs).max() + 1
@staticmethod
def _duplicate_to_list(x, num):
"""Repeat x `num` times to form a list."""
return [x for _ in range(num)]
def _check_ann_files(self, ann_file):
"""Make ann_files as list/tuple."""
# ann_file could be str
if not isinstance(ann_file, (list, tuple)):
ann_file = self._duplicate_to_list(ann_file, 1)
return ann_file
def _check_scene_idxs(self, scene_idx, num):
"""Make scene_idxs as list/tuple."""
if scene_idx is None:
return self._duplicate_to_list(scene_idx, num)
# scene_idx could be str, np.ndarray, list or tuple
if isinstance(scene_idx, str): # str
return self._duplicate_to_list(scene_idx, num)
if isinstance(scene_idx[0], str): # list of str
return scene_idx
if isinstance(scene_idx[0], (list, tuple, np.ndarray)): # list of idx
return scene_idx
# single idx
return self._duplicate_to_list(scene_idx, num)