import numpy as np
import warnings
from mmcv import is_tuple_of
from mmcv.utils import build_from_cfg
from mmdet3d.core import VoxelGenerator
from mmdet3d.core.bbox import (CameraInstance3DBoxes, DepthInstance3DBoxes,
LiDARInstance3DBoxes, box_np_ops)
from mmdet.datasets.builder import PIPELINES
from mmdet.datasets.pipelines import RandomFlip
from ..builder import OBJECTSAMPLERS
from .data_augment_utils import noise_per_object_v3_
[docs]@PIPELINES.register_module()
class RandomDropPointsColor(object):
r"""Randomly set the color of points to all zeros.
Once this transform is executed, all the points' color will be dropped.
Refer to `PAConv <https://github.com/CVMI-Lab/PAConv/blob/main/scene_seg/
util/transform.py#L223>`_ for more details.
Args:
drop_ratio (float): The probability of dropping point colors.
Defaults to 0.2.
"""
def __init__(self, drop_ratio=0.2):
assert isinstance(drop_ratio, (int, float)) and 0 <= drop_ratio <= 1, \
f'invalid drop_ratio value {drop_ratio}'
self.drop_ratio = drop_ratio
def __call__(self, input_dict):
"""Call function to drop point colors.
Args:
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after color dropping, \
'points' key is updated in the result dict.
"""
points = input_dict['points']
assert points.attribute_dims is not None and \
'color' in points.attribute_dims, \
'Expect points have color attribute'
# this if-expression is a bit strange
# `RandomDropPointsColor` is used in training 3D segmentor PAConv
# we discovered in our experiments that, using
# `if np.random.rand() > 1.0 - self.drop_ratio` consistently leads to
# better results than using `if np.random.rand() < self.drop_ratio`
# so we keep this hack in our codebase
if np.random.rand() > 1.0 - self.drop_ratio:
points.color = points.color * 0.0
return input_dict
def __repr__(self):
"""str: Return a string that describes the module."""
repr_str = self.__class__.__name__
repr_str += f'(drop_ratio={self.drop_ratio})'
return repr_str
[docs]@PIPELINES.register_module()
class RandomFlip3D(RandomFlip):
"""Flip the points & bbox.
If the input dict contains the key "flip", then the flag will be used,
otherwise it will be randomly decided by a ratio specified in the init
method.
Args:
sync_2d (bool, optional): Whether to apply flip according to the 2D
images. If True, it will apply the same flip as that to 2D images.
If False, it will decide whether to flip randomly and independently
to that of 2D images. Defaults to True.
flip_ratio_bev_horizontal (float, optional): The flipping probability
in horizontal direction. Defaults to 0.0.
flip_ratio_bev_vertical (float, optional): The flipping probability
in vertical direction. Defaults to 0.0.
"""
def __init__(self,
sync_2d=True,
flip_ratio_bev_horizontal=0.0,
flip_ratio_bev_vertical=0.0,
**kwargs):
super(RandomFlip3D, self).__init__(
flip_ratio=flip_ratio_bev_horizontal, **kwargs)
self.sync_2d = sync_2d
self.flip_ratio_bev_vertical = flip_ratio_bev_vertical
if flip_ratio_bev_horizontal is not None:
assert isinstance(
flip_ratio_bev_horizontal,
(int, float)) and 0 <= flip_ratio_bev_horizontal <= 1
if flip_ratio_bev_vertical is not None:
assert isinstance(
flip_ratio_bev_vertical,
(int, float)) and 0 <= flip_ratio_bev_vertical <= 1
[docs] def random_flip_data_3d(self, input_dict, direction='horizontal'):
"""Flip 3D data randomly.
Args:
input_dict (dict): Result dict from loading pipeline.
direction (str): Flip direction. Default: horizontal.
Returns:
dict: Flipped results, 'points', 'bbox3d_fields' keys are \
updated in the result dict.
"""
assert direction in ['horizontal', 'vertical']
if len(input_dict['bbox3d_fields']) == 0: # test mode
input_dict['bbox3d_fields'].append('empty_box3d')
input_dict['empty_box3d'] = input_dict['box_type_3d'](
np.array([], dtype=np.float32))
assert len(input_dict['bbox3d_fields']) == 1
for key in input_dict['bbox3d_fields']:
if 'points' in input_dict:
input_dict['points'] = input_dict[key].flip(
direction, points=input_dict['points'])
else:
input_dict[key].flip(direction)
if 'centers2d' in input_dict:
assert self.sync_2d is True and direction == 'horizontal', \
'Only support sync_2d=True and horizontal flip with images'
w = input_dict['ori_shape'][1]
input_dict['centers2d'][..., 0] = \
w - input_dict['centers2d'][..., 0]
# need to modify the horizontal position of camera center
# along u-axis in the image (flip like centers2d)
# ['cam_intrinsic'][0][2] = c_u
# see more details and examples at
# https://github.com/open-mmlab/mmdetection3d/pull/744
input_dict['cam_intrinsic'][0][2] = \
w - input_dict['cam_intrinsic'][0][2]
def __call__(self, input_dict):
"""Call function to flip points, values in the ``bbox3d_fields`` and \
also flip 2D image and its annotations.
Args:
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Flipped results, 'flip', 'flip_direction', \
'pcd_horizontal_flip' and 'pcd_vertical_flip' keys are added \
into result dict.
"""
# filp 2D image and its annotations
super(RandomFlip3D, self).__call__(input_dict)
if self.sync_2d:
input_dict['pcd_horizontal_flip'] = input_dict['flip']
input_dict['pcd_vertical_flip'] = False
else:
if 'pcd_horizontal_flip' not in input_dict:
flip_horizontal = True if np.random.rand(
) < self.flip_ratio else False
input_dict['pcd_horizontal_flip'] = flip_horizontal
if 'pcd_vertical_flip' not in input_dict:
flip_vertical = True if np.random.rand(
) < self.flip_ratio_bev_vertical else False
input_dict['pcd_vertical_flip'] = flip_vertical
if 'transformation_3d_flow' not in input_dict:
input_dict['transformation_3d_flow'] = []
if input_dict['pcd_horizontal_flip']:
self.random_flip_data_3d(input_dict, 'horizontal')
input_dict['transformation_3d_flow'].extend(['HF'])
if input_dict['pcd_vertical_flip']:
self.random_flip_data_3d(input_dict, 'vertical')
input_dict['transformation_3d_flow'].extend(['VF'])
return input_dict
def __repr__(self):
"""str: Return a string that describes the module."""
repr_str = self.__class__.__name__
repr_str += f'(sync_2d={self.sync_2d},'
repr_str += f' flip_ratio_bev_vertical={self.flip_ratio_bev_vertical})'
return repr_str
[docs]@PIPELINES.register_module()
class RandomJitterPoints(object):
"""Randomly jitter point coordinates.
Different from the global translation in ``GlobalRotScaleTrans``, here we \
apply different noises to each point in a scene.
Args:
jitter_std (list[float]): The standard deviation of jittering noise.
This applies random noise to all points in a 3D scene, which is \
sampled from a gaussian distribution whose standard deviation is \
set by ``jitter_std``. Defaults to [0.01, 0.01, 0.01]
clip_range (list[float] | None): Clip the randomly generated jitter \
noise into this range. If None is given, don't perform clipping.
Defaults to [-0.05, 0.05]
Note:
This transform should only be used in point cloud segmentation tasks \
because we don't transform ground-truth bboxes accordingly.
For similar transform in detection task, please refer to `ObjectNoise`.
"""
def __init__(self,
jitter_std=[0.01, 0.01, 0.01],
clip_range=[-0.05, 0.05]):
seq_types = (list, tuple, np.ndarray)
if not isinstance(jitter_std, seq_types):
assert isinstance(jitter_std, (int, float)), \
f'unsupported jitter_std type {type(jitter_std)}'
jitter_std = [jitter_std, jitter_std, jitter_std]
self.jitter_std = jitter_std
if clip_range is not None:
if not isinstance(clip_range, seq_types):
assert isinstance(clip_range, (int, float)), \
f'unsupported clip_range type {type(clip_range)}'
clip_range = [-clip_range, clip_range]
self.clip_range = clip_range
def __call__(self, input_dict):
"""Call function to jitter all the points in the scene.
Args:
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after adding noise to each point, \
'points' key is updated in the result dict.
"""
points = input_dict['points']
jitter_std = np.array(self.jitter_std, dtype=np.float32)
jitter_noise = \
np.random.randn(points.shape[0], 3) * jitter_std[None, :]
if self.clip_range is not None:
jitter_noise = np.clip(jitter_noise, self.clip_range[0],
self.clip_range[1])
points.translate(jitter_noise)
return input_dict
def __repr__(self):
"""str: Return a string that describes the module."""
repr_str = self.__class__.__name__
repr_str += f'(jitter_std={self.jitter_std},'
repr_str += f' clip_range={self.clip_range})'
return repr_str
[docs]@PIPELINES.register_module()
class ObjectSample(object):
"""Sample GT objects to the data.
Args:
db_sampler (dict): Config dict of the database sampler.
sample_2d (bool): Whether to also paste 2D image patch to the images
This should be true when applying multi-modality cut-and-paste.
Defaults to False.
"""
def __init__(self, db_sampler, sample_2d=False):
self.sampler_cfg = db_sampler
self.sample_2d = sample_2d
if 'type' not in db_sampler.keys():
db_sampler['type'] = 'DataBaseSampler'
self.db_sampler = build_from_cfg(db_sampler, OBJECTSAMPLERS)
[docs] @staticmethod
def remove_points_in_boxes(points, boxes):
"""Remove the points in the sampled bounding boxes.
Args:
points (:obj:`BasePoints`): Input point cloud array.
boxes (np.ndarray): Sampled ground truth boxes.
Returns:
np.ndarray: Points with those in the boxes removed.
"""
masks = box_np_ops.points_in_rbbox(points.coord.numpy(), boxes)
points = points[np.logical_not(masks.any(-1))]
return points
def __call__(self, input_dict):
"""Call function to sample ground truth objects to the data.
Args:
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after object sampling augmentation, \
'points', 'gt_bboxes_3d', 'gt_labels_3d' keys are updated \
in the result dict.
"""
gt_bboxes_3d = input_dict['gt_bboxes_3d']
gt_labels_3d = input_dict['gt_labels_3d']
# change to float for blending operation
points = input_dict['points']
if self.sample_2d:
img = input_dict['img']
gt_bboxes_2d = input_dict['gt_bboxes']
# Assume for now 3D & 2D bboxes are the same
sampled_dict = self.db_sampler.sample_all(
gt_bboxes_3d.tensor.numpy(),
gt_labels_3d,
gt_bboxes_2d=gt_bboxes_2d,
img=img)
else:
sampled_dict = self.db_sampler.sample_all(
gt_bboxes_3d.tensor.numpy(), gt_labels_3d, img=None)
if sampled_dict is not None:
sampled_gt_bboxes_3d = sampled_dict['gt_bboxes_3d']
sampled_points = sampled_dict['points']
sampled_gt_labels = sampled_dict['gt_labels_3d']
gt_labels_3d = np.concatenate([gt_labels_3d, sampled_gt_labels],
axis=0)
gt_bboxes_3d = gt_bboxes_3d.new_box(
np.concatenate(
[gt_bboxes_3d.tensor.numpy(), sampled_gt_bboxes_3d]))
points = self.remove_points_in_boxes(points, sampled_gt_bboxes_3d)
# check the points dimension
points = points.cat([sampled_points, points])
if self.sample_2d:
sampled_gt_bboxes_2d = sampled_dict['gt_bboxes_2d']
gt_bboxes_2d = np.concatenate(
[gt_bboxes_2d, sampled_gt_bboxes_2d]).astype(np.float32)
input_dict['gt_bboxes'] = gt_bboxes_2d
input_dict['img'] = sampled_dict['img']
input_dict['gt_bboxes_3d'] = gt_bboxes_3d
input_dict['gt_labels_3d'] = gt_labels_3d.astype(np.long)
input_dict['points'] = points
return input_dict
def __repr__(self):
"""str: Return a string that describes the module."""
repr_str = self.__class__.__name__
repr_str += f' sample_2d={self.sample_2d},'
repr_str += f' data_root={self.sampler_cfg.data_root},'
repr_str += f' info_path={self.sampler_cfg.info_path},'
repr_str += f' rate={self.sampler_cfg.rate},'
repr_str += f' prepare={self.sampler_cfg.prepare},'
repr_str += f' classes={self.sampler_cfg.classes},'
repr_str += f' sample_groups={self.sampler_cfg.sample_groups}'
return repr_str
[docs]@PIPELINES.register_module()
class ObjectNoise(object):
"""Apply noise to each GT objects in the scene.
Args:
translation_std (list[float], optional): Standard deviation of the
distribution where translation noise are sampled from.
Defaults to [0.25, 0.25, 0.25].
global_rot_range (list[float], optional): Global rotation to the scene.
Defaults to [0.0, 0.0].
rot_range (list[float], optional): Object rotation range.
Defaults to [-0.15707963267, 0.15707963267].
num_try (int, optional): Number of times to try if the noise applied is
invalid. Defaults to 100.
"""
def __init__(self,
translation_std=[0.25, 0.25, 0.25],
global_rot_range=[0.0, 0.0],
rot_range=[-0.15707963267, 0.15707963267],
num_try=100):
self.translation_std = translation_std
self.global_rot_range = global_rot_range
self.rot_range = rot_range
self.num_try = num_try
def __call__(self, input_dict):
"""Call function to apply noise to each ground truth in the scene.
Args:
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after adding noise to each object, \
'points', 'gt_bboxes_3d' keys are updated in the result dict.
"""
gt_bboxes_3d = input_dict['gt_bboxes_3d']
points = input_dict['points']
# TODO: check this inplace function
numpy_box = gt_bboxes_3d.tensor.numpy()
numpy_points = points.tensor.numpy()
noise_per_object_v3_(
numpy_box,
numpy_points,
rotation_perturb=self.rot_range,
center_noise_std=self.translation_std,
global_random_rot_range=self.global_rot_range,
num_try=self.num_try)
input_dict['gt_bboxes_3d'] = gt_bboxes_3d.new_box(numpy_box)
input_dict['points'] = points.new_point(numpy_points)
return input_dict
def __repr__(self):
"""str: Return a string that describes the module."""
repr_str = self.__class__.__name__
repr_str += f'(num_try={self.num_try},'
repr_str += f' translation_std={self.translation_std},'
repr_str += f' global_rot_range={self.global_rot_range},'
repr_str += f' rot_range={self.rot_range})'
return repr_str
[docs]@PIPELINES.register_module()
class GlobalAlignment(object):
"""Apply global alignment to 3D scene points by rotation and translation.
Args:
rotation_axis (int): Rotation axis for points and bboxes rotation.
Note:
We do not record the applied rotation and translation as in \
GlobalRotScaleTrans. Because usually, we do not need to reverse \
the alignment step.
For example, ScanNet 3D detection task uses aligned ground-truth \
bounding boxes for evaluation.
"""
def __init__(self, rotation_axis):
self.rotation_axis = rotation_axis
def _trans_points(self, input_dict, trans_factor):
"""Private function to translate points.
Args:
input_dict (dict): Result dict from loading pipeline.
trans_factor (np.ndarray): Translation vector to be applied.
Returns:
dict: Results after translation, 'points' is updated in the dict.
"""
input_dict['points'].translate(trans_factor)
def _rot_points(self, input_dict, rot_mat):
"""Private function to rotate bounding boxes and points.
Args:
input_dict (dict): Result dict from loading pipeline.
rot_mat (np.ndarray): Rotation matrix to be applied.
Returns:
dict: Results after rotation, 'points' is updated in the dict.
"""
# input should be rot_mat_T so I transpose it here
input_dict['points'].rotate(rot_mat.T)
def _check_rot_mat(self, rot_mat):
"""Check if rotation matrix is valid for self.rotation_axis.
Args:
rot_mat (np.ndarray): Rotation matrix to be applied.
"""
is_valid = np.allclose(np.linalg.det(rot_mat), 1.0)
valid_array = np.zeros(3)
valid_array[self.rotation_axis] = 1.0
is_valid &= (rot_mat[self.rotation_axis, :] == valid_array).all()
is_valid &= (rot_mat[:, self.rotation_axis] == valid_array).all()
assert is_valid, f'invalid rotation matrix {rot_mat}'
def __call__(self, input_dict):
"""Call function to shuffle points.
Args:
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after global alignment, 'points' and keys in \
input_dict['bbox3d_fields'] are updated in the result dict.
"""
assert 'axis_align_matrix' in input_dict['ann_info'].keys(), \
'axis_align_matrix is not provided in GlobalAlignment'
axis_align_matrix = input_dict['ann_info']['axis_align_matrix']
assert axis_align_matrix.shape == (4, 4), \
f'invalid shape {axis_align_matrix.shape} for axis_align_matrix'
rot_mat = axis_align_matrix[:3, :3]
trans_vec = axis_align_matrix[:3, -1]
self._check_rot_mat(rot_mat)
self._rot_points(input_dict, rot_mat)
self._trans_points(input_dict, trans_vec)
return input_dict
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(rotation_axis={self.rotation_axis})'
return repr_str
[docs]@PIPELINES.register_module()
class GlobalRotScaleTrans(object):
"""Apply global rotation, scaling and translation to a 3D scene.
Args:
rot_range (list[float]): Range of rotation angle.
Defaults to [-0.78539816, 0.78539816] (close to [-pi/4, pi/4]).
scale_ratio_range (list[float]): Range of scale ratio.
Defaults to [0.95, 1.05].
translation_std (list[float]): The standard deviation of translation
noise. This applies random translation to a scene by a noise, which
is sampled from a gaussian distribution whose standard deviation
is set by ``translation_std``. Defaults to [0, 0, 0]
shift_height (bool): Whether to shift height.
(the fourth dimension of indoor points) when scaling.
Defaults to False.
"""
def __init__(self,
rot_range=[-0.78539816, 0.78539816],
scale_ratio_range=[0.95, 1.05],
translation_std=[0, 0, 0],
shift_height=False):
seq_types = (list, tuple, np.ndarray)
if not isinstance(rot_range, seq_types):
assert isinstance(rot_range, (int, float)), \
f'unsupported rot_range type {type(rot_range)}'
rot_range = [-rot_range, rot_range]
self.rot_range = rot_range
assert isinstance(scale_ratio_range, seq_types), \
f'unsupported scale_ratio_range type {type(scale_ratio_range)}'
self.scale_ratio_range = scale_ratio_range
if not isinstance(translation_std, seq_types):
assert isinstance(translation_std, (int, float)), \
f'unsupported translation_std type {type(translation_std)}'
translation_std = [
translation_std, translation_std, translation_std
]
assert all([std >= 0 for std in translation_std]), \
'translation_std should be positive'
self.translation_std = translation_std
self.shift_height = shift_height
def _trans_bbox_points(self, input_dict):
"""Private function to translate bounding boxes and points.
Args:
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after translation, 'points', 'pcd_trans' \
and keys in input_dict['bbox3d_fields'] are updated \
in the result dict.
"""
translation_std = np.array(self.translation_std, dtype=np.float32)
trans_factor = np.random.normal(scale=translation_std, size=3).T
input_dict['points'].translate(trans_factor)
input_dict['pcd_trans'] = trans_factor
for key in input_dict['bbox3d_fields']:
input_dict[key].translate(trans_factor)
def _rot_bbox_points(self, input_dict):
"""Private function to rotate bounding boxes and points.
Args:
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after rotation, 'points', 'pcd_rotation' \
and keys in input_dict['bbox3d_fields'] are updated \
in the result dict.
"""
rotation = self.rot_range
noise_rotation = np.random.uniform(rotation[0], rotation[1])
# if no bbox in input_dict, only rotate points
if len(input_dict['bbox3d_fields']) == 0:
rot_mat_T = input_dict['points'].rotate(noise_rotation)
input_dict['pcd_rotation'] = rot_mat_T
return
# rotate points with bboxes
for key in input_dict['bbox3d_fields']:
if len(input_dict[key].tensor) != 0:
points, rot_mat_T = input_dict[key].rotate(
noise_rotation, input_dict['points'])
input_dict['points'] = points
input_dict['pcd_rotation'] = rot_mat_T
def _scale_bbox_points(self, input_dict):
"""Private function to scale bounding boxes and points.
Args:
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after scaling, 'points'and keys in \
input_dict['bbox3d_fields'] are updated in the result dict.
"""
scale = input_dict['pcd_scale_factor']
points = input_dict['points']
points.scale(scale)
if self.shift_height:
assert 'height' in points.attribute_dims.keys(), \
'setting shift_height=True but points have no height attribute'
points.tensor[:, points.attribute_dims['height']] *= scale
input_dict['points'] = points
for key in input_dict['bbox3d_fields']:
input_dict[key].scale(scale)
def _random_scale(self, input_dict):
"""Private function to randomly set the scale factor.
Args:
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after scaling, 'pcd_scale_factor' are updated \
in the result dict.
"""
scale_factor = np.random.uniform(self.scale_ratio_range[0],
self.scale_ratio_range[1])
input_dict['pcd_scale_factor'] = scale_factor
def __call__(self, input_dict):
"""Private function to rotate, scale and translate bounding boxes and \
points.
Args:
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after scaling, 'points', 'pcd_rotation',
'pcd_scale_factor', 'pcd_trans' and keys in \
input_dict['bbox3d_fields'] are updated in the result dict.
"""
if 'transformation_3d_flow' not in input_dict:
input_dict['transformation_3d_flow'] = []
self._rot_bbox_points(input_dict)
if 'pcd_scale_factor' not in input_dict:
self._random_scale(input_dict)
self._scale_bbox_points(input_dict)
self._trans_bbox_points(input_dict)
input_dict['transformation_3d_flow'].extend(['R', 'S', 'T'])
return input_dict
def __repr__(self):
"""str: Return a string that describes the module."""
repr_str = self.__class__.__name__
repr_str += f'(rot_range={self.rot_range},'
repr_str += f' scale_ratio_range={self.scale_ratio_range},'
repr_str += f' translation_std={self.translation_std},'
repr_str += f' shift_height={self.shift_height})'
return repr_str
[docs]@PIPELINES.register_module()
class PointShuffle(object):
"""Shuffle input points."""
def __call__(self, input_dict):
"""Call function to shuffle points.
Args:
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after filtering, 'points', 'pts_instance_mask' \
and 'pts_semantic_mask' keys are updated in the result dict.
"""
idx = input_dict['points'].shuffle()
idx = idx.numpy()
pts_instance_mask = input_dict.get('pts_instance_mask', None)
pts_semantic_mask = input_dict.get('pts_semantic_mask', None)
if pts_instance_mask is not None:
input_dict['pts_instance_mask'] = pts_instance_mask[idx]
if pts_semantic_mask is not None:
input_dict['pts_semantic_mask'] = pts_semantic_mask[idx]
return input_dict
def __repr__(self):
return self.__class__.__name__
[docs]@PIPELINES.register_module()
class ObjectRangeFilter(object):
"""Filter objects by the range.
Args:
point_cloud_range (list[float]): Point cloud range.
"""
def __init__(self, point_cloud_range):
self.pcd_range = np.array(point_cloud_range, dtype=np.float32)
def __call__(self, input_dict):
"""Call function to filter objects by the range.
Args:
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after filtering, 'gt_bboxes_3d', 'gt_labels_3d' \
keys are updated in the result dict.
"""
# Check points instance type and initialise bev_range
if isinstance(input_dict['gt_bboxes_3d'],
(LiDARInstance3DBoxes, DepthInstance3DBoxes)):
bev_range = self.pcd_range[[0, 1, 3, 4]]
elif isinstance(input_dict['gt_bboxes_3d'], CameraInstance3DBoxes):
bev_range = self.pcd_range[[0, 2, 3, 5]]
gt_bboxes_3d = input_dict['gt_bboxes_3d']
gt_labels_3d = input_dict['gt_labels_3d']
mask = gt_bboxes_3d.in_range_bev(bev_range)
gt_bboxes_3d = gt_bboxes_3d[mask]
# mask is a torch tensor but gt_labels_3d is still numpy array
# using mask to index gt_labels_3d will cause bug when
# len(gt_labels_3d) == 1, where mask=1 will be interpreted
# as gt_labels_3d[1] and cause out of index error
gt_labels_3d = gt_labels_3d[mask.numpy().astype(np.bool)]
# limit rad to [-pi, pi]
gt_bboxes_3d.limit_yaw(offset=0.5, period=2 * np.pi)
input_dict['gt_bboxes_3d'] = gt_bboxes_3d
input_dict['gt_labels_3d'] = gt_labels_3d
return input_dict
def __repr__(self):
"""str: Return a string that describes the module."""
repr_str = self.__class__.__name__
repr_str += f'(point_cloud_range={self.pcd_range.tolist()})'
return repr_str
[docs]@PIPELINES.register_module()
class PointsRangeFilter(object):
"""Filter points by the range.
Args:
point_cloud_range (list[float]): Point cloud range.
"""
def __init__(self, point_cloud_range):
self.pcd_range = np.array(point_cloud_range, dtype=np.float32)
def __call__(self, input_dict):
"""Call function to filter points by the range.
Args:
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after filtering, 'points', 'pts_instance_mask' \
and 'pts_semantic_mask' keys are updated in the result dict.
"""
points = input_dict['points']
points_mask = points.in_range_3d(self.pcd_range)
clean_points = points[points_mask]
input_dict['points'] = clean_points
points_mask = points_mask.numpy()
pts_instance_mask = input_dict.get('pts_instance_mask', None)
pts_semantic_mask = input_dict.get('pts_semantic_mask', None)
if pts_instance_mask is not None:
input_dict['pts_instance_mask'] = pts_instance_mask[points_mask]
if pts_semantic_mask is not None:
input_dict['pts_semantic_mask'] = pts_semantic_mask[points_mask]
return input_dict
def __repr__(self):
"""str: Return a string that describes the module."""
repr_str = self.__class__.__name__
repr_str += f'(point_cloud_range={self.pcd_range.tolist()})'
return repr_str
[docs]@PIPELINES.register_module()
class ObjectNameFilter(object):
"""Filter GT objects by their names.
Args:
classes (list[str]): List of class names to be kept for training.
"""
def __init__(self, classes):
self.classes = classes
self.labels = list(range(len(self.classes)))
def __call__(self, input_dict):
"""Call function to filter objects by their names.
Args:
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after filtering, 'gt_bboxes_3d', 'gt_labels_3d' \
keys are updated in the result dict.
"""
gt_labels_3d = input_dict['gt_labels_3d']
gt_bboxes_mask = np.array([n in self.labels for n in gt_labels_3d],
dtype=np.bool_)
input_dict['gt_bboxes_3d'] = input_dict['gt_bboxes_3d'][gt_bboxes_mask]
input_dict['gt_labels_3d'] = input_dict['gt_labels_3d'][gt_bboxes_mask]
return input_dict
def __repr__(self):
"""str: Return a string that describes the module."""
repr_str = self.__class__.__name__
repr_str += f'(classes={self.classes})'
return repr_str
[docs]@PIPELINES.register_module()
class IndoorPointSample(object):
"""Indoor point sample.
Sampling data to a certain number.
Args:
name (str): Name of the dataset.
num_points (int): Number of points to be sampled.
"""
def __init__(self, num_points):
self.num_points = num_points
[docs] def points_random_sampling(self,
points,
num_samples,
replace=None,
return_choices=False):
"""Points random sampling.
Sample points to a certain number.
Args:
points (np.ndarray | :obj:`BasePoints`): 3D Points.
num_samples (int): Number of samples to be sampled.
replace (bool): Whether the sample is with or without replacement.
Defaults to None.
return_choices (bool): Whether return choice. Defaults to False.
Returns:
tuple[np.ndarray] | np.ndarray:
- points (np.ndarray | :obj:`BasePoints`): 3D Points.
- choices (np.ndarray, optional): The generated random samples.
"""
if replace is None:
replace = (points.shape[0] < num_samples)
choices = np.random.choice(
points.shape[0], num_samples, replace=replace)
if return_choices:
return points[choices], choices
else:
return points[choices]
def __call__(self, results):
"""Call function to sample points to in indoor scenes.
Args:
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after sampling, 'points', 'pts_instance_mask' \
and 'pts_semantic_mask' keys are updated in the result dict.
"""
points = results['points']
points, choices = self.points_random_sampling(
points, self.num_points, return_choices=True)
results['points'] = points
pts_instance_mask = results.get('pts_instance_mask', None)
pts_semantic_mask = results.get('pts_semantic_mask', None)
if pts_instance_mask is not None:
pts_instance_mask = pts_instance_mask[choices]
results['pts_instance_mask'] = pts_instance_mask
if pts_semantic_mask is not None:
pts_semantic_mask = pts_semantic_mask[choices]
results['pts_semantic_mask'] = pts_semantic_mask
return results
def __repr__(self):
"""str: Return a string that describes the module."""
repr_str = self.__class__.__name__
repr_str += f'(num_points={self.num_points})'
return repr_str
[docs]@PIPELINES.register_module()
class IndoorPatchPointSample(object):
r"""Indoor point sample within a patch. Modified from `PointNet++ <https://
github.com/charlesq34/pointnet2/blob/master/scannet/scannet_dataset.py>`_.
Sampling data to a certain number for semantic segmentation.
Args:
num_points (int): Number of points to be sampled.
block_size (float, optional): Size of a block to sample points from.
Defaults to 1.5.
sample_rate (float, optional): Stride used in sliding patch generation.
This parameter is unused in `IndoorPatchPointSample` and thus has
been deprecated. We plan to remove it in the future.
Defaults to None.
ignore_index (int, optional): Label index that won't be used for the
segmentation task. This is set in PointSegClassMapping as neg_cls.
If not None, will be used as a patch selection criterion.
Defaults to None.
use_normalized_coord (bool, optional): Whether to use normalized xyz as
additional features. Defaults to False.
num_try (int, optional): Number of times to try if the patch selected
is invalid. Defaults to 10.
enlarge_size (float | None, optional): Enlarge the sampled patch to
[-block_size / 2 - enlarge_size, block_size / 2 + enlarge_size] as
an augmentation. If None, set it as 0. Defaults to 0.2.
min_unique_num (int | None, optional): Minimum number of unique points
the sampled patch should contain. If None, use PointNet++'s method
to judge uniqueness. Defaults to None.
eps (float, optional): A value added to patch boundary to guarantee
points coverage. Defaults to 1e-2.
Note:
This transform should only be used in the training process of point
cloud segmentation tasks. For the sliding patch generation and
inference process in testing, please refer to the `slide_inference`
function of `EncoderDecoder3D` class.
"""
def __init__(self,
num_points,
block_size=1.5,
sample_rate=None,
ignore_index=None,
use_normalized_coord=False,
num_try=10,
enlarge_size=0.2,
min_unique_num=None,
eps=1e-2):
self.num_points = num_points
self.block_size = block_size
self.ignore_index = ignore_index
self.use_normalized_coord = use_normalized_coord
self.num_try = num_try
self.enlarge_size = enlarge_size if enlarge_size is not None else 0.0
self.min_unique_num = min_unique_num
self.eps = eps
if sample_rate is not None:
warnings.warn(
"'sample_rate' has been deprecated and will be removed in "
'the future. Please remove them from your code.')
def _input_generation(self, coords, patch_center, coord_max, attributes,
attribute_dims, point_type):
"""Generating model input.
Generate input by subtracting patch center and adding additional \
features. Currently support colors and normalized xyz as features.
Args:
coords (np.ndarray): Sampled 3D Points.
patch_center (np.ndarray): Center coordinate of the selected patch.
coord_max (np.ndarray): Max coordinate of all 3D Points.
attributes (np.ndarray): features of input points.
attribute_dims (dict): Dictionary to indicate the meaning of extra
dimension.
point_type (type): class of input points inherited from BasePoints.
Returns:
:obj:`BasePoints`: The generated input data.
"""
# subtract patch center, the z dimension is not centered
centered_coords = coords.copy()
centered_coords[:, 0] -= patch_center[0]
centered_coords[:, 1] -= patch_center[1]
if self.use_normalized_coord:
normalized_coord = coords / coord_max
attributes = np.concatenate([attributes, normalized_coord], axis=1)
if attribute_dims is None:
attribute_dims = dict()
attribute_dims.update(
dict(normalized_coord=[
attributes.shape[1], attributes.shape[1] +
1, attributes.shape[1] + 2
]))
points = np.concatenate([centered_coords, attributes], axis=1)
points = point_type(
points, points_dim=points.shape[1], attribute_dims=attribute_dims)
return points
def _patch_points_sampling(self, points, sem_mask):
"""Patch points sampling.
First sample a valid patch.
Then sample points within that patch to a certain number.
Args:
points (:obj:`BasePoints`): 3D Points.
sem_mask (np.ndarray): semantic segmentation mask for input points.
Returns:
tuple[:obj:`BasePoints`, np.ndarray] | :obj:`BasePoints`:
- points (:obj:`BasePoints`): 3D Points.
- choices (np.ndarray): The generated random samples.
"""
coords = points.coord.numpy()
attributes = points.tensor[:, 3:].numpy()
attribute_dims = points.attribute_dims
point_type = type(points)
coord_max = np.amax(coords, axis=0)
coord_min = np.amin(coords, axis=0)
for _ in range(self.num_try):
# random sample a point as patch center
cur_center = coords[np.random.choice(coords.shape[0])]
# boundary of a patch, which would be enlarged by
# `self.enlarge_size` as an augmentation
cur_max = cur_center + np.array(
[self.block_size / 2.0, self.block_size / 2.0, 0.0])
cur_min = cur_center - np.array(
[self.block_size / 2.0, self.block_size / 2.0, 0.0])
cur_max[2] = coord_max[2]
cur_min[2] = coord_min[2]
cur_choice = np.sum(
(coords >= (cur_min - self.enlarge_size)) *
(coords <= (cur_max + self.enlarge_size)),
axis=1) == 3
if not cur_choice.any(): # no points in this patch
continue
cur_coords = coords[cur_choice, :]
cur_sem_mask = sem_mask[cur_choice]
point_idxs = np.where(cur_choice)[0]
mask = np.sum(
(cur_coords >= (cur_min - self.eps)) * (cur_coords <=
(cur_max + self.eps)),
axis=1) == 3
# two criteria for patch sampling, adopted from PointNet++
# 1. selected patch should contain enough unique points
if self.min_unique_num is None:
# use PointNet++'s method as default
# [31, 31, 62] are just some big values used to transform
# coords from 3d array to 1d and then check their uniqueness
# this is used in all the ScanNet code following PointNet++
vidx = np.ceil(
(cur_coords[mask, :] - cur_min) / (cur_max - cur_min) *
np.array([31.0, 31.0, 62.0]))
vidx = np.unique(vidx[:, 0] * 31.0 * 62.0 + vidx[:, 1] * 62.0 +
vidx[:, 2])
flag1 = len(vidx) / 31.0 / 31.0 / 62.0 >= 0.02
else:
# if `min_unique_num` is provided, directly compare with it
flag1 = mask.sum() >= self.min_unique_num
# 2. selected patch should contain enough annotated points
if self.ignore_index is None:
flag2 = True
else:
flag2 = np.sum(cur_sem_mask != self.ignore_index) / \
len(cur_sem_mask) >= 0.7
if flag1 and flag2:
break
# sample idx to `self.num_points`
if point_idxs.size >= self.num_points:
# no duplicate in sub-sampling
choices = np.random.choice(
point_idxs, self.num_points, replace=False)
else:
# do not use random choice here to avoid some points not counted
dup = np.random.choice(point_idxs.size,
self.num_points - point_idxs.size)
idx_dup = np.concatenate(
[np.arange(point_idxs.size),
np.array(dup)], 0)
choices = point_idxs[idx_dup]
# construct model input
points = self._input_generation(coords[choices], cur_center, coord_max,
attributes[choices], attribute_dims,
point_type)
return points, choices
def __call__(self, results):
"""Call function to sample points to in indoor scenes.
Args:
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after sampling, 'points', 'pts_instance_mask' \
and 'pts_semantic_mask' keys are updated in the result dict.
"""
points = results['points']
assert 'pts_semantic_mask' in results.keys(), \
'semantic mask should be provided in training and evaluation'
pts_semantic_mask = results['pts_semantic_mask']
points, choices = self._patch_points_sampling(points,
pts_semantic_mask)
results['points'] = points
results['pts_semantic_mask'] = pts_semantic_mask[choices]
pts_instance_mask = results.get('pts_instance_mask', None)
if pts_instance_mask is not None:
results['pts_instance_mask'] = pts_instance_mask[choices]
return results
def __repr__(self):
"""str: Return a string that describes the module."""
repr_str = self.__class__.__name__
repr_str += f'(num_points={self.num_points},'
repr_str += f' block_size={self.block_size},'
repr_str += f' ignore_index={self.ignore_index},'
repr_str += f' use_normalized_coord={self.use_normalized_coord},'
repr_str += f' num_try={self.num_try},'
repr_str += f' enlarge_size={self.enlarge_size},'
repr_str += f' min_unique_num={self.min_unique_num},'
repr_str += f' eps={self.eps})'
return repr_str
[docs]@PIPELINES.register_module()
class BackgroundPointsFilter(object):
"""Filter background points near the bounding box.
Args:
bbox_enlarge_range (tuple[float], float): Bbox enlarge range.
"""
def __init__(self, bbox_enlarge_range):
assert (is_tuple_of(bbox_enlarge_range, float)
and len(bbox_enlarge_range) == 3) \
or isinstance(bbox_enlarge_range, float), \
f'Invalid arguments bbox_enlarge_range {bbox_enlarge_range}'
if isinstance(bbox_enlarge_range, float):
bbox_enlarge_range = [bbox_enlarge_range] * 3
self.bbox_enlarge_range = np.array(
bbox_enlarge_range, dtype=np.float32)[np.newaxis, :]
def __call__(self, input_dict):
"""Call function to filter points by the range.
Args:
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after filtering, 'points', 'pts_instance_mask' \
and 'pts_semantic_mask' keys are updated in the result dict.
"""
points = input_dict['points']
gt_bboxes_3d = input_dict['gt_bboxes_3d']
# avoid groundtruth being modified
gt_bboxes_3d_np = gt_bboxes_3d.tensor.clone().numpy()
gt_bboxes_3d_np[:, :3] = gt_bboxes_3d.gravity_center.clone().numpy()
enlarged_gt_bboxes_3d = gt_bboxes_3d_np.copy()
enlarged_gt_bboxes_3d[:, 3:6] += self.bbox_enlarge_range
points_numpy = points.tensor.clone().numpy()
foreground_masks = box_np_ops.points_in_rbbox(
points_numpy, gt_bboxes_3d_np, origin=(0.5, 0.5, 0.5))
enlarge_foreground_masks = box_np_ops.points_in_rbbox(
points_numpy, enlarged_gt_bboxes_3d, origin=(0.5, 0.5, 0.5))
foreground_masks = foreground_masks.max(1)
enlarge_foreground_masks = enlarge_foreground_masks.max(1)
valid_masks = ~np.logical_and(~foreground_masks,
enlarge_foreground_masks)
input_dict['points'] = points[valid_masks]
pts_instance_mask = input_dict.get('pts_instance_mask', None)
if pts_instance_mask is not None:
input_dict['pts_instance_mask'] = pts_instance_mask[valid_masks]
pts_semantic_mask = input_dict.get('pts_semantic_mask', None)
if pts_semantic_mask is not None:
input_dict['pts_semantic_mask'] = pts_semantic_mask[valid_masks]
return input_dict
def __repr__(self):
"""str: Return a string that describes the module."""
repr_str = self.__class__.__name__
repr_str += f'(bbox_enlarge_range={self.bbox_enlarge_range.tolist()})'
return repr_str
[docs]@PIPELINES.register_module()
class VoxelBasedPointSampler(object):
"""Voxel based point sampler.
Apply voxel sampling to multiple sweep points.
Args:
cur_sweep_cfg (dict): Config for sampling current points.
prev_sweep_cfg (dict): Config for sampling previous points.
time_dim (int): Index that indicate the time dimention
for input points.
"""
def __init__(self, cur_sweep_cfg, prev_sweep_cfg=None, time_dim=3):
self.cur_voxel_generator = VoxelGenerator(**cur_sweep_cfg)
self.cur_voxel_num = self.cur_voxel_generator._max_voxels
self.time_dim = time_dim
if prev_sweep_cfg is not None:
assert prev_sweep_cfg['max_num_points'] == \
cur_sweep_cfg['max_num_points']
self.prev_voxel_generator = VoxelGenerator(**prev_sweep_cfg)
self.prev_voxel_num = self.prev_voxel_generator._max_voxels
else:
self.prev_voxel_generator = None
self.prev_voxel_num = 0
def _sample_points(self, points, sampler, point_dim):
"""Sample points for each points subset.
Args:
points (np.ndarray): Points subset to be sampled.
sampler (VoxelGenerator): Voxel based sampler for
each points subset.
point_dim (int): The dimention of each points
Returns:
np.ndarray: Sampled points.
"""
voxels, coors, num_points_per_voxel = sampler.generate(points)
if voxels.shape[0] < sampler._max_voxels:
padding_points = np.zeros([
sampler._max_voxels - voxels.shape[0], sampler._max_num_points,
point_dim
],
dtype=points.dtype)
padding_points[:] = voxels[0]
sample_points = np.concatenate([voxels, padding_points], axis=0)
else:
sample_points = voxels
return sample_points
def __call__(self, results):
"""Call function to sample points from multiple sweeps.
Args:
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after sampling, 'points', 'pts_instance_mask' \
and 'pts_semantic_mask' keys are updated in the result dict.
"""
points = results['points']
original_dim = points.shape[1]
# TODO: process instance and semantic mask while _max_num_points
# is larger than 1
# Extend points with seg and mask fields
map_fields2dim = []
start_dim = original_dim
points_numpy = points.tensor.numpy()
extra_channel = [points_numpy]
for idx, key in enumerate(results['pts_mask_fields']):
map_fields2dim.append((key, idx + start_dim))
extra_channel.append(results[key][..., None])
start_dim += len(results['pts_mask_fields'])
for idx, key in enumerate(results['pts_seg_fields']):
map_fields2dim.append((key, idx + start_dim))
extra_channel.append(results[key][..., None])
points_numpy = np.concatenate(extra_channel, axis=-1)
# Split points into two part, current sweep points and
# previous sweeps points.
# TODO: support different sampling methods for next sweeps points
# and previous sweeps points.
cur_points_flag = (points_numpy[:, self.time_dim] == 0)
cur_sweep_points = points_numpy[cur_points_flag]
prev_sweeps_points = points_numpy[~cur_points_flag]
if prev_sweeps_points.shape[0] == 0:
prev_sweeps_points = cur_sweep_points
# Shuffle points before sampling
np.random.shuffle(cur_sweep_points)
np.random.shuffle(prev_sweeps_points)
cur_sweep_points = self._sample_points(cur_sweep_points,
self.cur_voxel_generator,
points_numpy.shape[1])
if self.prev_voxel_generator is not None:
prev_sweeps_points = self._sample_points(prev_sweeps_points,
self.prev_voxel_generator,
points_numpy.shape[1])
points_numpy = np.concatenate(
[cur_sweep_points, prev_sweeps_points], 0)
else:
points_numpy = cur_sweep_points
if self.cur_voxel_generator._max_num_points == 1:
points_numpy = points_numpy.squeeze(1)
results['points'] = points.new_point(points_numpy[..., :original_dim])
# Restore the correspoinding seg and mask fields
for key, dim_index in map_fields2dim:
results[key] = points_numpy[..., dim_index]
return results
def __repr__(self):
"""str: Return a string that describes the module."""
def _auto_indent(repr_str, indent):
repr_str = repr_str.split('\n')
repr_str = [' ' * indent + t + '\n' for t in repr_str]
repr_str = ''.join(repr_str)[:-1]
return repr_str
repr_str = self.__class__.__name__
indent = 4
repr_str += '(\n'
repr_str += ' ' * indent + f'num_cur_sweep={self.cur_voxel_num},\n'
repr_str += ' ' * indent + f'num_prev_sweep={self.prev_voxel_num},\n'
repr_str += ' ' * indent + f'time_dim={self.time_dim},\n'
repr_str += ' ' * indent + 'cur_voxel_generator=\n'
repr_str += f'{_auto_indent(repr(self.cur_voxel_generator), 8)},\n'
repr_str += ' ' * indent + 'prev_voxel_generator=\n'
repr_str += f'{_auto_indent(repr(self.prev_voxel_generator), 8)})'
return repr_str