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Unverified Commit 20b163a4 authored by encore-zhou's avatar encore-zhou Committed by GitHub
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[Feature]: Add points structure (#196) 

* add h3d backbone

* add h3d backbone

* add h3dnet

* modify scannet config

* fix bugs for proposal refine

* fix bugs for test backbone

* add primitive head test

* modify h3dhead

* modify h3d head

* update loss weight config

* fix bugs for h3d head loss

* modify h3d head get targets function

* update h3dnet base config

* modify weighted loss

* Revert "Merge branch 'h3d_u2' into 'master'"

This reverts merge request !5

* add points structure

* modify points rotation and add coord_3d_mode unittest

* modify docstring
parent 37ce1871
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mmdet3d/core/__init__.py
View file @ 20b163a4
from .anchor import * # noqa: F401, F403 from .anchor import * # noqa: F401, F403
from .bbox import * # noqa: F401, F403 from .bbox import * # noqa: F401, F403
from .evaluation import * # noqa: F401, F403 from .evaluation import * # noqa: F401, F403
from .points import * # noqa: F401, F403
from .post_processing import * # noqa: F401, F403 from .post_processing import * # noqa: F401, F403
from .utils import * # noqa: F401, F403 from .utils import * # noqa: F401, F403
from .visualizer import * # noqa: F401, F403 from .visualizer import * # noqa: F401, F403
......
mmdet3d/core/bbox/__init__.py
View file @ 20b163a4
...@@ -7,9 +7,9 @@ from .samplers import (BaseSampler, CombinedSampler, ...@@ -7,9 +7,9 @@ from .samplers import (BaseSampler, CombinedSampler,
InstanceBalancedPosSampler, IoUBalancedNegSampler, InstanceBalancedPosSampler, IoUBalancedNegSampler,
PseudoSampler, RandomSampler, SamplingResult) PseudoSampler, RandomSampler, SamplingResult)
from .structures import (BaseInstance3DBoxes, Box3DMode, CameraInstance3DBoxes, from .structures import (BaseInstance3DBoxes, Box3DMode, CameraInstance3DBoxes,
DepthInstance3DBoxes, LiDARInstance3DBoxes, Coord3DMode, DepthInstance3DBoxes,
get_box_type, limit_period, points_cam2img, LiDARInstance3DBoxes, get_box_type, limit_period,
xywhr2xyxyr) points_cam2img, xywhr2xyxyr)
from .transforms import bbox3d2result, bbox3d2roi, bbox3d_mapping_back from .transforms import bbox3d2result, bbox3d2roi, bbox3d_mapping_back
__all__ = [ __all__ = [
...@@ -21,5 +21,5 @@ __all__ = [ ...@@ -21,5 +21,5 @@ __all__ = [
'LiDARInstance3DBoxes', 'CameraInstance3DBoxes', 'bbox3d2roi', 'LiDARInstance3DBoxes', 'CameraInstance3DBoxes', 'bbox3d2roi',
'bbox3d2result', 'DepthInstance3DBoxes', 'BaseInstance3DBoxes', 'bbox3d2result', 'DepthInstance3DBoxes', 'BaseInstance3DBoxes',
'bbox3d_mapping_back', 'xywhr2xyxyr', 'limit_period', 'points_cam2img', 'bbox3d_mapping_back', 'xywhr2xyxyr', 'limit_period', 'points_cam2img',
'get_box_type' 'get_box_type', 'Coord3DMode'
] ]
mmdet3d/core/bbox/structures/__init__.py
View file @ 20b163a4
from .base_box3d import BaseInstance3DBoxes from .base_box3d import BaseInstance3DBoxes
from .box_3d_mode import Box3DMode from .box_3d_mode import Box3DMode
from .cam_box3d import CameraInstance3DBoxes from .cam_box3d import CameraInstance3DBoxes
from .coord_3d_mode import Coord3DMode
from .depth_box3d import DepthInstance3DBoxes from .depth_box3d import DepthInstance3DBoxes
from .lidar_box3d import LiDARInstance3DBoxes from .lidar_box3d import LiDARInstance3DBoxes
from .utils import (get_box_type, limit_period, points_cam2img, from .utils import (get_box_type, limit_period, points_cam2img,
...@@ -9,5 +10,6 @@ from .utils import (get_box_type, limit_period, points_cam2img, ...@@ -9,5 +10,6 @@ from .utils import (get_box_type, limit_period, points_cam2img,
__all__ = [ __all__ = [
'Box3DMode', 'BaseInstance3DBoxes', 'LiDARInstance3DBoxes', 'Box3DMode', 'BaseInstance3DBoxes', 'LiDARInstance3DBoxes',
'CameraInstance3DBoxes', 'DepthInstance3DBoxes', 'xywhr2xyxyr', 'CameraInstance3DBoxes', 'DepthInstance3DBoxes', 'xywhr2xyxyr',
'get_box_type', 'rotation_3d_in_axis', 'limit_period', 'points_cam2img' 'get_box_type', 'rotation_3d_in_axis', 'limit_period', 'points_cam2img',
'Coord3DMode'
] ]
mmdet3d/core/bbox/structures/coord_3d_mode.py 0 → 100644
View file @ 20b163a4
import numpy as np
import torch
from enum import IntEnum, unique
from mmdet3d.core.points import (BasePoints, CameraPoints, DepthPoints,
LiDARPoints)
from .base_box3d import BaseInstance3DBoxes
from .cam_box3d import CameraInstance3DBoxes
from .depth_box3d import DepthInstance3DBoxes
from .lidar_box3d import LiDARInstance3DBoxes
@unique
class Coord3DMode(IntEnum):
r"""Enum of different ways to represent a box
and point cloud.
Coordinates in LiDAR:
.. code-block:: none
up z
^ x front
| /
| /
left y <------ 0
The relative coordinate of bottom center in a LiDAR box is (0.5, 0.5, 0),
and the yaw is around the z axis, thus the rotation axis=2.
Coordinates in camera:
.. code-block:: none
z front
/
/
0 ------> x right
|
|
v
down y
The relative coordinate of bottom center in a CAM box is [0.5, 1.0, 0.5],
and the yaw is around the y axis, thus the rotation axis=1.
Coordinates in Depth mode:
.. code-block:: none
up z
^ y front
| /
| /
0 ------> x right
The relative coordinate of bottom center in a DEPTH box is (0.5, 0.5, 0),
and the yaw is around the z axis, thus the rotation axis=2.
"""
LIDAR = 0
CAM = 1
DEPTH = 2
@staticmethod
def convert(input, src, dst, rt_mat=None):
"""Convert boxes or points from `src` mode to `dst` mode."""
if isinstance(input, BaseInstance3DBoxes):
return Coord3DMode.convert_box(input, src, dst, rt_mat=rt_mat)
elif isinstance(input, BasePoints):
return Coord3DMode.convert_point(input, src, dst, rt_mat=rt_mat)
else:
raise NotImplementedError
@staticmethod
def convert_box(box, src, dst, rt_mat=None):
"""Convert boxes from `src` mode to `dst` mode.
Args:
box (tuple | list | np.dnarray |
torch.Tensor | BaseInstance3DBoxes):
Can be a k-tuple, k-list or an Nxk array/tensor, where k = 7.
src (:obj:`CoordMode`): The src Box mode.
dst (:obj:`CoordMode`): The target Box mode.
rt_mat (np.dnarray | torch.Tensor): The rotation and translation
matrix between different coordinates. Defaults to None.
The conversion from `src` coordinates to `dst` coordinates
usually comes along the change of sensors, e.g., from camera
to LiDAR. This requires a transformation matrix.
Returns:
(tuple | list | np.dnarray | torch.Tensor | BaseInstance3DBoxes): \
The converted box of the same type.
"""
if src == dst:
return box
is_numpy = isinstance(box, np.ndarray)
is_Instance3DBoxes = isinstance(box, BaseInstance3DBoxes)
single_box = isinstance(box, (list, tuple))
if single_box:
assert len(box) >= 7, (
'CoordMode.convert takes either a k-tuple/list or '
'an Nxk array/tensor, where k >= 7')
arr = torch.tensor(box)[None, :]
else:
# avoid modifying the input box
if is_numpy:
arr = torch.from_numpy(np.asarray(box)).clone()
elif is_Instance3DBoxes:
arr = box.tensor.clone()
else:
arr = box.clone()
# convert box from `src` mode to `dst` mode.
x_size, y_size, z_size = arr[..., 3:4], arr[..., 4:5], arr[..., 5:6]
if src == Coord3DMode.LIDAR and dst == Coord3DMode.CAM:
if rt_mat is None:
rt_mat = arr.new_tensor([[0, -1, 0], [0, 0, -1], [1, 0, 0]])
xyz_size = torch.cat([y_size, z_size, x_size], dim=-1)
elif src == Coord3DMode.CAM and dst == Coord3DMode.LIDAR:
if rt_mat is None:
rt_mat = arr.new_tensor([[0, 0, 1], [-1, 0, 0], [0, -1, 0]])
xyz_size = torch.cat([z_size, x_size, y_size], dim=-1)
elif src == Coord3DMode.DEPTH and dst == Coord3DMode.CAM:
if rt_mat is None:
rt_mat = arr.new_tensor([[1, 0, 0], [0, 0, 1], [0, -1, 0]])
xyz_size = torch.cat([x_size, z_size, y_size], dim=-1)
elif src == Coord3DMode.CAM and dst == Coord3DMode.DEPTH:
if rt_mat is None:
rt_mat = arr.new_tensor([[1, 0, 0], [0, 0, -1], [0, 1, 0]])
xyz_size = torch.cat([x_size, z_size, y_size], dim=-1)
elif src == Coord3DMode.LIDAR and dst == Coord3DMode.DEPTH:
if rt_mat is None:
rt_mat = arr.new_tensor([[0, -1, 0], [1, 0, 0], [0, 0, 1]])
xyz_size = torch.cat([y_size, x_size, z_size], dim=-1)
elif src == Coord3DMode.DEPTH and dst == Coord3DMode.LIDAR:
if rt_mat is None:
rt_mat = arr.new_tensor([[0, 1, 0], [-1, 0, 0], [0, 0, 1]])
xyz_size = torch.cat([y_size, x_size, z_size], dim=-1)
else:
raise NotImplementedError(
f'Conversion from Coord3DMode {src} to {dst} '
'is not supported yet')
if not isinstance(rt_mat, torch.Tensor):
rt_mat = arr.new_tensor(rt_mat)
if rt_mat.size(1) == 4:
extended_xyz = torch.cat(
[arr[:, :3], arr.new_ones(arr.size(0), 1)], dim=-1)
xyz = extended_xyz @ rt_mat.t()
else:
xyz = arr[:, :3] @ rt_mat.t()
remains = arr[..., 6:]
arr = torch.cat([xyz[:, :3], xyz_size, remains], dim=-1)
# convert arr to the original type
original_type = type(box)
if single_box:
return original_type(arr.flatten().tolist())
if is_numpy:
return arr.numpy()
elif is_Instance3DBoxes:
if dst == Coord3DMode.CAM:
target_type = CameraInstance3DBoxes
elif dst == Coord3DMode.LIDAR:
target_type = LiDARInstance3DBoxes
elif dst == Coord3DMode.DEPTH:
target_type = DepthInstance3DBoxes
else:
raise NotImplementedError(
f'Conversion to {dst} through {original_type}'
' is not supported yet')
return target_type(
arr, box_dim=arr.size(-1), with_yaw=box.with_yaw)
else:
return arr
@staticmethod
def convert_point(point, src, dst, rt_mat=None):
"""Convert points from `src` mode to `dst` mode.
Args:
box (tuple | list | np.dnarray |
torch.Tensor | BasePoints):
Can be a k-tuple, k-list or an Nxk array/tensor.
src (:obj:`CoordMode`): The src Point mode.
dst (:obj:`CoordMode`): The target Point mode.
rt_mat (np.dnarray | torch.Tensor): The rotation and translation
matrix between different coordinates. Defaults to None.
The conversion from `src` coordinates to `dst` coordinates
usually comes along the change of sensors, e.g., from camera
to LiDAR. This requires a transformation matrix.
Returns:
(tuple | list | np.dnarray | torch.Tensor | BasePoints): \
The converted point of the same type.
"""
if src == dst:
return point
is_numpy = isinstance(point, np.ndarray)
is_InstancePoints = isinstance(point, BasePoints)
single_point = isinstance(point, (list, tuple))
if single_point:
assert len(point) >= 3, (
'CoordMode.convert takes either a k-tuple/list or '
'an Nxk array/tensor, where k >= 3')
arr = torch.tensor(point)[None, :]
else:
# avoid modifying the input point
if is_numpy:
arr = torch.from_numpy(np.asarray(point)).clone()
elif is_InstancePoints:
arr = point.tensor.clone()
else:
arr = point.clone()
# convert point from `src` mode to `dst` mode.
if rt_mat is not None:
if not isinstance(rt_mat, torch.Tensor):
rt_mat = arr.new_tensor(rt_mat)
if src == Coord3DMode.LIDAR and dst == Coord3DMode.CAM:
rt_mat = arr.new_tensor([[0, -1, 0], [0, 0, -1], [1, 0, 0]])
elif src == Coord3DMode.CAM and dst == Coord3DMode.LIDAR:
rt_mat = arr.new_tensor([[0, 0, 1], [-1, 0, 0], [0, -1, 0]])
elif src == Coord3DMode.DEPTH and dst == Coord3DMode.CAM:
rt_mat = arr.new_tensor([[1, 0, 0], [0, 0, 1], [0, -1, 0]])
elif src == Coord3DMode.CAM and dst == Coord3DMode.DEPTH:
rt_mat = arr.new_tensor([[1, 0, 0], [0, 0, -1], [0, 1, 0]])
elif src == Coord3DMode.LIDAR and dst == Coord3DMode.DEPTH:
rt_mat = arr.new_tensor([[0, -1, 0], [1, 0, 0], [0, 0, 1]])
elif src == Coord3DMode.DEPTH and dst == Coord3DMode.LIDAR:
rt_mat = arr.new_tensor([[0, 1, 0], [-1, 0, 0], [0, 0, 1]])
else:
raise NotImplementedError(
f'Conversion from Coord3DMode {src} to {dst} '
'is not supported yet')
if rt_mat.size(1) == 4:
extended_xyz = torch.cat(
[arr[:, :3], arr.new_ones(arr.size(0), 1)], dim=-1)
xyz = extended_xyz @ rt_mat.t()
else:
xyz = arr[:, :3] @ rt_mat.t()
remains = arr[..., 3:]
arr = torch.cat([xyz[:, :3], remains], dim=-1)
# convert arr to the original type
original_type = type(point)
if single_point:
return original_type(arr.flatten().tolist())
if is_numpy:
return arr.numpy()
elif is_InstancePoints:
if dst == Coord3DMode.CAM:
target_type = CameraPoints
elif dst == Coord3DMode.LIDAR:
target_type = LiDARPoints
elif dst == Coord3DMode.DEPTH:
target_type = DepthPoints
else:
raise NotImplementedError(
f'Conversion to {dst} through {original_type}'
' is not supported yet')
return target_type(
arr,
points_dim=arr.size(-1),
attribute_dims=point.attribute_dims)
else:
return arr
mmdet3d/core/points/__init__.py 0 → 100644
View file @ 20b163a4
from .base_points import BasePoints
from .cam_points import CameraPoints
from .depth_points import DepthPoints
from .lidar_points import LiDARPoints
__all__ = ['BasePoints', 'CameraPoints', 'DepthPoints', 'LiDARPoints']
mmdet3d/core/points/base_points.py 0 → 100644
View file @ 20b163a4
import torch
from abc import abstractmethod
class BasePoints(object):
"""Base class for Points.
Args:
tensor (torch.Tensor | np.ndarray | list): a N x points_dim matrix.
points_dim (int): Number of the dimension of a point.
Each row is (x, y, z). Default to 3.
attribute_dims (dict): Dictinory to indicate the meaning of extra
dimension. Default to None.
Attributes:
tensor (torch.Tensor): Float matrix of N x points_dim.
points_dim (int): Integer indicating the dimension of a point.
Each row is (x, y, z, ...).
attribute_dims (bool): Dictinory to indicate the meaning of extra
dimension. Default to None.
"""
def __init__(self, tensor, points_dim=3, attribute_dims=None):
if isinstance(tensor, torch.Tensor):
device = tensor.device
else:
device = torch.device('cpu')
tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)
if tensor.numel() == 0:
# Use reshape, so we don't end up creating a new tensor that
# does not depend on the inputs (and consequently confuses jit)
tensor = tensor.reshape((0, points_dim)).to(
dtype=torch.float32, device=device)
assert tensor.dim() == 2 and tensor.size(-1) == \
points_dim, tensor.size()
self.tensor = tensor
self.points_dim = points_dim
self.attribute_dims = attribute_dims
@property
def coord(self):
"""torch.Tensor: Coordinates of each point with size (N, 3)."""
return self.tensor[:, :3]
@property
def height(self):
"""torch.Tensor: A vector with height of each point."""
if self.attribute_dims is not None and \
'height' in self.attribute_dims.keys():
return self.tensor[:, self.attribute_dims['height']]
else:
return None
@property
def color(self):
"""torch.Tensor: A vector with color of each point."""
if self.attribute_dims is not None and \
'color' in self.attribute_dims.keys():
return self.tensor[:, self.attribute_dims['color']]
else:
return None
def shuffle(self):
"""Shuffle the points."""
self.tensor = self.tensor[torch.randperm(
self.__len__(), device=self.tensor.device)]
def rotate(self, rotation, axis=2):
"""Rotate points with the given rotation matrix or angle.
Args:
rotation (float, np.ndarray, torch.Tensor): Rotation matrix
or angle.
axis (int): Axis to rotate at. Defaults to 2.
"""
if not isinstance(rotation, torch.Tensor):
rotation = self.tensor.new_tensor(rotation)
assert rotation.shape == torch.Size([3, 3]) or \
rotation.numel() == 1
if rotation.numel() == 1:
rot_sin = torch.sin(rotation)
rot_cos = torch.cos(rotation)
if axis == 1:
rot_mat_T = rotation.new_tensor([[rot_cos, 0, -rot_sin],
[0, 1, 0],
[rot_sin, 0, rot_cos]])
elif axis == 2 or axis == -1:
rot_mat_T = rotation.new_tensor([[rot_cos, -rot_sin, 0],
[rot_sin, rot_cos, 0],
[0, 0, 1]])
elif axis == 0:
rot_mat_T = rotation.new_tensor([[0, rot_cos, -rot_sin],
[0, rot_sin, rot_cos],
[1, 0, 0]])
else:
raise ValueError('axis should in range')
rot_mat_T = rot_mat_T.T
elif rotation.numel() == 9:
rot_mat_T = rotation
else:
raise NotImplementedError
self.tensor[:, :3] = self.tensor[:, :3] @ rot_mat_T
@abstractmethod
def flip(self, bev_direction='horizontal'):
"""Flip the points in BEV along given BEV direction."""
pass
def translate(self, trans_vector):
"""Translate points with the given translation vector.
Args:
trans_vector (np.ndarray, torch.Tensor): Translation
vector of size 3 or nx3.
"""
if not isinstance(trans_vector, torch.Tensor):
trans_vector = self.tensor.new_tensor(trans_vector)
trans_vector = trans_vector.squeeze(0)
if trans_vector.dim() == 1:
assert trans_vector.shape[0] == 3
elif trans_vector.dim() == 2:
assert trans_vector.shape[0] == self.tensor.shape[0] and \
trans_vector.shape[1] == 3
else:
raise NotImplementedError(
'Unsupported translation vector of shape {}'.format(
trans_vector.shape))
self.tensor[:, :3] += trans_vector
def in_range_3d(self, point_range):
"""Check whether the points are in the given range.
Args:
point_range (list | torch.Tensor): The range of point
(x_min, y_min, z_min, x_max, y_max, z_max)
Note:
In the original implementation of SECOND, checking whether
a box in the range checks whether the points are in a convex
polygon, we try to reduce the burden for simpler cases.
Returns:
torch.Tensor: A binary vector indicating whether each point is \
inside the reference range.
"""
in_range_flags = ((self.tensor[:, 0] > point_range[0])
& (self.tensor[:, 1] > point_range[1])
& (self.tensor[:, 2] > point_range[2])
& (self.tensor[:, 0] < point_range[3])
& (self.tensor[:, 1] < point_range[4])
& (self.tensor[:, 2] < point_range[5]))
return in_range_flags
@abstractmethod
def in_range_bev(self, point_range):
"""Check whether the points are in the given range.
Args:
point_range (list | torch.Tensor): The range of point
in order of (x_min, y_min, x_max, y_max).
Returns:
torch.Tensor: Indicating whether each point is inside \
the reference range.
"""
pass
@abstractmethod
def convert_to(self, dst, rt_mat=None):
"""Convert self to ``dst`` mode.
Args:
dst (:obj:`CoordMode`): The target Box mode.
rt_mat (np.ndarray | torch.Tensor): The rotation and translation
matrix between different coordinates. Defaults to None.
The conversion from `src` coordinates to `dst` coordinates
usually comes along the change of sensors, e.g., from camera
to LiDAR. This requires a transformation matrix.
Returns:
:obj:`BasePoints`: The converted box of the same type \
in the `dst` mode.
"""
pass
def scale(self, scale_factor):
"""Scale the points with horizontal and vertical scaling factors.
Args:
scale_factors (float): Scale factors to scale the points.
"""
self.tensor[:, :3] *= scale_factor
def __getitem__(self, item):
"""
Note:
The following usage are allowed:
1. `new_points = points[3]`:
return a `Points` that contains only one point.
2. `new_points = points[2:10]`:
return a slice of points.
3. `new_points = points[vector]`:
where vector is a torch.BoolTensor with `length = len(points)`.
Nonzero elements in the vector will be selected.
Note that the returned Points might share storage with this Points,
subject to Pytorch's indexing semantics.
Returns:
:obj:`BaseInstancesPints`: A new object of \
:class:`BaseInstancesPints` after indexing.
"""
original_type = type(self)
if isinstance(item, int):
return original_type(
self.tensor[item].view(1, -1),
points_dim=self.points_dim,
attribute_dims=self.attribute_dims)
p = self.tensor[item]
assert p.dim() == 2, \
f'Indexing on Points with {item} failed to return a matrix!'
return original_type(
p, points_dim=self.points_dim, attribute_dims=self.attribute_dims)
def __len__(self):
"""int: Number of points in the current object."""
return self.tensor.shape[0]
def __repr__(self):
"""str: Return a strings that describes the object."""
return self.__class__.__name__ + '(\n ' + str(self.tensor) + ')'
@classmethod
def cat(cls, points_list):
"""Concatenate a list of Points into a single Points.
Args:
points_list (list[:obj:`BaseInstancesPoints`]): List of points.
Returns:
:obj:`BaseInstancesPoints`: The concatenated Points.
"""
assert isinstance(points_list, (list, tuple))
if len(points_list) == 0:
return cls(torch.empty(0))
assert all(isinstance(points, cls) for points in points_list)
# use torch.cat (v.s. layers.cat)
# so the returned points never share storage with input
cat_points = cls(
torch.cat([p.tensor for p in points_list], dim=0),
points_dim=points_list[0].tensor.shape[1],
attribute_dims=points_list[0].attribute_dims)
return cat_points
def to(self, device):
"""Convert current points to a specific device.
Args:
device (str | :obj:`torch.device`): The name of the device.
Returns:
:obj:`BasePoints`: A new boxes object on the \
specific device.
"""
original_type = type(self)
return original_type(
self.tensor.to(device),
points_dim=self.points_dim,
attribute_dims=self.attribute_dims)
def clone(self):
"""Clone the Points.
Returns:
:obj:`BasePoints`: Box object with the same properties \
as self.
"""
original_type = type(self)
return original_type(
self.tensor.clone(),
points_dim=self.points_dim,
attribute_dims=self.attribute_dims)
@property
def device(self):
"""str: The device of the points are on."""
return self.tensor.device
def __iter__(self):
"""Yield a point as a Tensor of shape (4,) at a time.
Returns:
torch.Tensor: A point of shape (4,).
"""
yield from self.tensor
def new_point(self, data):
"""Create a new point object with data.
The new point and its tensor has the similar properties \
as self and self.tensor, respectively.
Args:
data (torch.Tensor | numpy.array | list): Data to be copied.
Returns:
:obj:`BasePoints`: A new point object with ``data``, \
the object's other properties are similar to ``self``.
"""
new_tensor = self.tensor.new_tensor(data) \
if not isinstance(data, torch.Tensor) else data.to(self.device)
original_type = type(self)
return original_type(
new_tensor,
points_dim=self.points_dim,
attribute_dims=self.attribute_dims)
mmdet3d/core/points/cam_points.py 0 → 100644
View file @ 20b163a4
from .base_points import BasePoints
class CameraPoints(BasePoints):
"""Points of instances in CAM coordinates.
Args:
tensor (torch.Tensor | np.ndarray | list): a N x points_dim matrix.
points_dim (int): Number of the dimension of a point.
Each row is (x, y, z). Default to 3.
attribute_dims (dict): Dictinory to indicate the meaning of extra
dimension. Default to None.
Attributes:
tensor (torch.Tensor): Float matrix of N x points_dim.
points_dim (int): Integer indicating the dimension of a point.
Each row is (x, y, z, ...).
attribute_dims (bool): Dictinory to indicate the meaning of extra
dimension. Default to None.
"""
def __init__(self, tensor, points_dim=3, attribute_dims=None):
super(CameraPoints, self).__init__(
tensor, points_dim=points_dim, attribute_dims=attribute_dims)
def flip(self, bev_direction='horizontal'):
"""Flip the boxes in BEV along given BEV direction."""
if bev_direction == 'horizontal':
self.tensor[:, 0] = -self.tensor[:, 0]
elif bev_direction == 'vertical':
self.tensor[:, 2] = -self.tensor[:, 2]
def in_range_bev(self, point_range):
"""Check whether the points are in the given range.
Args:
point_range (list | torch.Tensor): The range of point
in order of (x_min, y_min, x_max, y_max).
Returns:
torch.Tensor: Indicating whether each point is inside \
the reference range.
"""
in_range_flags = ((self.tensor[:, 0] > point_range[0])
& (self.tensor[:, 2] > point_range[1])
& (self.tensor[:, 0] < point_range[2])
& (self.tensor[:, 2] < point_range[3]))
return in_range_flags
def convert_to(self, dst, rt_mat=None):
"""Convert self to ``dst`` mode.
Args:
dst (:obj:`CoordMode`): The target Point mode.
rt_mat (np.ndarray | torch.Tensor): The rotation and translation
matrix between different coordinates. Defaults to None.
The conversion from `src` coordinates to `dst` coordinates
usually comes along the change of sensors, e.g., from camera
to LiDAR. This requires a transformation matrix.
Returns:
:obj:`BasePoints`: The converted point of the same type \
in the `dst` mode.
"""
from mmdet3d.core.bbox import Coord3DMode
return Coord3DMode.convert_point(
point=self, src=Coord3DMode.CAM, dst=dst, rt_mat=rt_mat)
mmdet3d/core/points/depth_points.py 0 → 100644
View file @ 20b163a4
from .base_points import BasePoints
class DepthPoints(BasePoints):
"""Points of instances in DEPTH coordinates.
Args:
tensor (torch.Tensor | np.ndarray | list): a N x points_dim matrix.
points_dim (int): Number of the dimension of a point.
Each row is (x, y, z). Default to 3.
attribute_dims (dict): Dictinory to indicate the meaning of extra
dimension. Default to None.
Attributes:
tensor (torch.Tensor): Float matrix of N x points_dim.
points_dim (int): Integer indicating the dimension of a point.
Each row is (x, y, z, ...).
attribute_dims (bool): Dictinory to indicate the meaning of extra
dimension. Default to None.
"""
def __init__(self, tensor, points_dim=3, attribute_dims=None):
super(DepthPoints, self).__init__(
tensor, points_dim=points_dim, attribute_dims=attribute_dims)
def flip(self, bev_direction='horizontal'):
"""Flip the boxes in BEV along given BEV direction."""
if bev_direction == 'horizontal':
self.tensor[:, 0] = -self.tensor[:, 0]
elif bev_direction == 'vertical':
self.tensor[:, 1] = -self.tensor[:, 1]
def in_range_bev(self, point_range):
"""Check whether the points are in the given range.
Args:
point_range (list | torch.Tensor): The range of point
in order of (x_min, y_min, x_max, y_max).
Returns:
torch.Tensor: Indicating whether each point is inside \
the reference range.
"""
in_range_flags = ((self.tensor[:, 0] > point_range[0])
& (self.tensor[:, 1] > point_range[1])
& (self.tensor[:, 0] < point_range[2])
& (self.tensor[:, 1] < point_range[3]))
return in_range_flags
def convert_to(self, dst, rt_mat=None):
"""Convert self to ``dst`` mode.
Args:
dst (:obj:`CoordMode`): The target Point mode.
rt_mat (np.ndarray | torch.Tensor): The rotation and translation
matrix between different coordinates. Defaults to None.
The conversion from `src` coordinates to `dst` coordinates
usually comes along the change of sensors, e.g., from camera
to LiDAR. This requires a transformation matrix.
Returns:
:obj:`BasePoints`: The converted point of the same type \
in the `dst` mode.
"""
from mmdet3d.core.bbox import Coord3DMode
return Coord3DMode.convert_point(
point=self, src=Coord3DMode.DEPTH, dst=dst, rt_mat=rt_mat)
mmdet3d/core/points/lidar_points.py 0 → 100644
View file @ 20b163a4
from .base_points import BasePoints
class LiDARPoints(BasePoints):
"""Points of instances in LIDAR coordinates.
Args:
tensor (torch.Tensor | np.ndarray | list): a N x points_dim matrix.
points_dim (int): Number of the dimension of a point.
Each row is (x, y, z). Default to 3.
attribute_dims (dict): Dictinory to indicate the meaning of extra
dimension. Default to None.
Attributes:
tensor (torch.Tensor): Float matrix of N x points_dim.
points_dim (int): Integer indicating the dimension of a point.
Each row is (x, y, z, ...).
attribute_dims (bool): Dictinory to indicate the meaning of extra
dimension. Default to None.
"""
def __init__(self, tensor, points_dim=3, attribute_dims=None):
super(LiDARPoints, self).__init__(
tensor, points_dim=points_dim, attribute_dims=attribute_dims)
def flip(self, bev_direction='horizontal'):
"""Flip the boxes in BEV along given BEV direction."""
if bev_direction == 'horizontal':
self.tensor[:, 1] = -self.tensor[:, 1]
elif bev_direction == 'vertical':
self.tensor[:, 0] = -self.tensor[:, 0]
def in_range_bev(self, point_range):
"""Check whether the points are in the given range.
Args:
point_range (list | torch.Tensor): The range of point
in order of (x_min, y_min, x_max, y_max).
Returns:
torch.Tensor: Indicating whether each point is inside \
the reference range.
"""
in_range_flags = ((self.tensor[:, 0] > point_range[0])
& (self.tensor[:, 1] > point_range[1])
& (self.tensor[:, 0] < point_range[2])
& (self.tensor[:, 1] < point_range[3]))
return in_range_flags
def convert_to(self, dst, rt_mat=None):
"""Convert self to ``dst`` mode.
Args:
dst (:obj:`CoordMode`): The target Point mode.
rt_mat (np.ndarray | torch.Tensor): The rotation and translation
matrix between different coordinates. Defaults to None.
The conversion from `src` coordinates to `dst` coordinates
usually comes along the change of sensors, e.g., from camera
to LiDAR. This requires a transformation matrix.
Returns:
:obj:`BasePoints`: The converted point of the same type \
in the `dst` mode.
"""
from mmdet3d.core.bbox import Coord3DMode
return Coord3DMode.convert_point(
point=self, src=Coord3DMode.LIDAR, dst=dst, rt_mat=rt_mat)
tests/test_coord_3d_mode.py 0 → 100644
View file @ 20b163a4
import numpy as np
import torch
from mmdet3d.core.bbox import (CameraInstance3DBoxes, Coord3DMode,
DepthInstance3DBoxes, LiDARInstance3DBoxes)
from mmdet3d.core.points import CameraPoints, DepthPoints, LiDARPoints
def test_points_conversion():
"""Test the conversion of points between different modes."""
points_np = np.array([[
-5.24223238e+00, 4.00209696e+01, 2.97570381e-01, 0.6666, 0.1956,
0.4974, 0.9409
],
[
-2.66751588e+01, 5.59499564e+00, -9.14345860e-01,
0.1502, 0.3707, 0.1086, 0.6297
],
[
-5.80979675e+00, 3.54092357e+01, 2.00889888e-01,
0.6565, 0.6248, 0.6954, 0.2538
],
[
-3.13086877e+01, 1.09007628e+00, -1.94612112e-01,
0.2803, 0.0258, 0.4896, 0.3269
]],
dtype=np.float32)
# test CAM to LIDAR and DEPTH
cam_points = CameraPoints(
points_np,
points_dim=7,
attribute_dims=dict(color=[3, 4, 5], height=6))
convert_lidar_points = cam_points.convert_to(Coord3DMode.LIDAR)
expected_tensor = torch.tensor([[
2.9757e-01, 5.2422e+00, -4.0021e+01, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-9.1435e-01, 2.6675e+01, -5.5950e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
2.0089e-01, 5.8098e+00, -3.5409e+01,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-1.9461e-01, 3.1309e+01, -1.0901e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
lidar_point_tensor = Coord3DMode.convert_point(cam_points.tensor,
Coord3DMode.CAM,
Coord3DMode.LIDAR)
assert torch.allclose(expected_tensor, convert_lidar_points.tensor, 1e-4)
assert torch.allclose(lidar_point_tensor, convert_lidar_points.tensor,
1e-4)
convert_depth_points = cam_points.convert_to(Coord3DMode.DEPTH)
expected_tensor = torch.tensor([[
-5.2422e+00, -2.9757e-01, 4.0021e+01, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-2.6675e+01, 9.1435e-01, 5.5950e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
-5.8098e+00, -2.0089e-01, 3.5409e+01,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-3.1309e+01, 1.9461e-01, 1.0901e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
depth_point_tensor = Coord3DMode.convert_point(cam_points.tensor,
Coord3DMode.CAM,
Coord3DMode.DEPTH)
assert torch.allclose(expected_tensor, convert_depth_points.tensor, 1e-4)
assert torch.allclose(depth_point_tensor, convert_depth_points.tensor,
1e-4)
# test LIDAR to CAM and DEPTH
lidar_points = LiDARPoints(
points_np,
points_dim=7,
attribute_dims=dict(color=[3, 4, 5], height=6))
convert_cam_points = lidar_points.convert_to(Coord3DMode.CAM)
expected_tensor = torch.tensor([[
-4.0021e+01, -2.9757e-01, -5.2422e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-5.5950e+00, 9.1435e-01, -2.6675e+01,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
-3.5409e+01, -2.0089e-01, -5.8098e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-1.0901e+00, 1.9461e-01, -3.1309e+01,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
cam_point_tensor = Coord3DMode.convert_point(lidar_points.tensor,
Coord3DMode.LIDAR,
Coord3DMode.CAM)
assert torch.allclose(expected_tensor, convert_cam_points.tensor, 1e-4)
assert torch.allclose(cam_point_tensor, convert_cam_points.tensor, 1e-4)
convert_depth_points = lidar_points.convert_to(Coord3DMode.DEPTH)
expected_tensor = torch.tensor([[
-4.0021e+01, -5.2422e+00, 2.9757e-01, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-5.5950e+00, -2.6675e+01, -9.1435e-01,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
-3.5409e+01, -5.8098e+00, 2.0089e-01,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-1.0901e+00, -3.1309e+01, -1.9461e-01,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
depth_point_tensor = Coord3DMode.convert_point(lidar_points.tensor,
Coord3DMode.LIDAR,
Coord3DMode.DEPTH)
assert torch.allclose(expected_tensor, convert_depth_points.tensor, 1e-4)
assert torch.allclose(depth_point_tensor, convert_depth_points.tensor,
1e-4)
# test DEPTH to CAM and LIDAR
depth_points = DepthPoints(
points_np,
points_dim=7,
attribute_dims=dict(color=[3, 4, 5], height=6))
convert_cam_points = depth_points.convert_to(Coord3DMode.CAM)
expected_tensor = torch.tensor([[
-5.2422e+00, 2.9757e-01, -4.0021e+01, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-2.6675e+01, -9.1435e-01, -5.5950e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
-5.8098e+00, 2.0089e-01, -3.5409e+01,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-3.1309e+01, -1.9461e-01, -1.0901e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
cam_point_tensor = Coord3DMode.convert_point(depth_points.tensor,
Coord3DMode.DEPTH,
Coord3DMode.CAM)
assert torch.allclose(expected_tensor, convert_cam_points.tensor, 1e-4)
assert torch.allclose(cam_point_tensor, convert_cam_points.tensor, 1e-4)
convert_lidar_points = depth_points.convert_to(Coord3DMode.LIDAR)
expected_tensor = torch.tensor([[
4.0021e+01, 5.2422e+00, 2.9757e-01, 6.6660e-01, 1.9560e-01, 4.9740e-01,
9.4090e-01
],
[
5.5950e+00, 2.6675e+01, -9.1435e-01,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
3.5409e+01, 5.8098e+00, 2.0089e-01,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
1.0901e+00, 3.1309e+01, -1.9461e-01,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
lidar_point_tensor = Coord3DMode.convert_point(depth_points.tensor,
Coord3DMode.DEPTH,
Coord3DMode.LIDAR)
assert torch.allclose(lidar_point_tensor, convert_lidar_points.tensor,
1e-4)
assert torch.allclose(lidar_point_tensor, convert_lidar_points.tensor,
1e-4)
def test_boxes_conversion():
# test CAM to LIDAR and DEPTH
cam_boxes = CameraInstance3DBoxes(
[[1.7802081, 2.516249, -1.7501148, 1.75, 3.39, 1.65, 1.48],
[8.959413, 2.4567227, -1.6357126, 1.54, 4.01, 1.57, 1.62],
[28.2967, -0.5557558, -1.303325, 1.47, 2.23, 1.48, -1.57],
[26.66902, 21.82302, -1.736057, 1.56, 3.48, 1.4, -1.69],
[31.31978, 8.162144, -1.6217787, 1.74, 3.77, 1.48, 2.79]])
convert_lidar_boxes = Coord3DMode.convert(cam_boxes, Coord3DMode.CAM,
Coord3DMode.LIDAR)
expected_tensor = torch.tensor(
[[-1.7501, -1.7802, -2.5162, 1.6500, 1.7500, 3.3900, 1.4800],
[-1.6357, -8.9594, -2.4567, 1.5700, 1.5400, 4.0100, 1.6200],
[-1.3033, -28.2967, 0.5558, 1.4800, 1.4700, 2.2300, -1.5700],
[-1.7361, -26.6690, -21.8230, 1.4000, 1.5600, 3.4800, -1.6900],
[-1.6218, -31.3198, -8.1621, 1.4800, 1.7400, 3.7700, 2.7900]])
assert torch.allclose(expected_tensor, convert_lidar_boxes.tensor, 1e-3)
convert_depth_boxes = Coord3DMode.convert(cam_boxes, Coord3DMode.CAM,
Coord3DMode.DEPTH)
expected_tensor = torch.tensor(
[[1.7802, 1.7501, 2.5162, 1.7500, 1.6500, 3.3900, 1.4800],
[8.9594, 1.6357, 2.4567, 1.5400, 1.5700, 4.0100, 1.6200],
[28.2967, 1.3033, -0.5558, 1.4700, 1.4800, 2.2300, -1.5700],
[26.6690, 1.7361, 21.8230, 1.5600, 1.4000, 3.4800, -1.6900],
[31.3198, 1.6218, 8.1621, 1.7400, 1.4800, 3.7700, 2.7900]])
assert torch.allclose(expected_tensor, convert_depth_boxes.tensor, 1e-3)
# test LIDAR to CAM and DEPTH
lidar_boxes = LiDARInstance3DBoxes(
[[1.7802081, 2.516249, -1.7501148, 1.75, 3.39, 1.65, 1.48],
[8.959413, 2.4567227, -1.6357126, 1.54, 4.01, 1.57, 1.62],
[28.2967, -0.5557558, -1.303325, 1.47, 2.23, 1.48, -1.57],
[26.66902, 21.82302, -1.736057, 1.56, 3.48, 1.4, -1.69],
[31.31978, 8.162144, -1.6217787, 1.74, 3.77, 1.48, 2.79]])
convert_cam_boxes = Coord3DMode.convert(lidar_boxes, Coord3DMode.LIDAR,
Coord3DMode.CAM)
expected_tensor = torch.tensor(
[[-2.5162, 1.7501, 1.7802, 3.3900, 1.6500, 1.7500, 1.4800],
[-2.4567, 1.6357, 8.9594, 4.0100, 1.5700, 1.5400, 1.6200],
[0.5558, 1.3033, 28.2967, 2.2300, 1.4800, 1.4700, -1.5700],
[-21.8230, 1.7361, 26.6690, 3.4800, 1.4000, 1.5600, -1.6900],
[-8.1621, 1.6218, 31.3198, 3.7700, 1.4800, 1.7400, 2.7900]])
assert torch.allclose(expected_tensor, convert_cam_boxes.tensor, 1e-3)
convert_depth_boxes = Coord3DMode.convert(lidar_boxes, Coord3DMode.LIDAR,
Coord3DMode.DEPTH)
expected_tensor = torch.tensor(
[[-2.5162, 1.7802, -1.7501, 3.3900, 1.7500, 1.6500, 1.4800],
[-2.4567, 8.9594, -1.6357, 4.0100, 1.5400, 1.5700, 1.6200],
[0.5558, 28.2967, -1.3033, 2.2300, 1.4700, 1.4800, -1.5700],
[-21.8230, 26.6690, -1.7361, 3.4800, 1.5600, 1.4000, -1.6900],
[-8.1621, 31.3198, -1.6218, 3.7700, 1.7400, 1.4800, 2.7900]])
assert torch.allclose(expected_tensor, convert_depth_boxes.tensor, 1e-3)
# test DEPTH to CAM and LIDAR
depth_boxes = DepthInstance3DBoxes(
[[1.7802081, 2.516249, -1.7501148, 1.75, 3.39, 1.65, 1.48],
[8.959413, 2.4567227, -1.6357126, 1.54, 4.01, 1.57, 1.62],
[28.2967, -0.5557558, -1.303325, 1.47, 2.23, 1.48, -1.57],
[26.66902, 21.82302, -1.736057, 1.56, 3.48, 1.4, -1.69],
[31.31978, 8.162144, -1.6217787, 1.74, 3.77, 1.48, 2.79]])
convert_cam_boxes = Coord3DMode.convert(depth_boxes, Coord3DMode.DEPTH,
Coord3DMode.CAM)
expected_tensor = torch.tensor(
[[1.7802, -1.7501, -2.5162, 1.7500, 1.6500, 3.3900, 1.4800],
[8.9594, -1.6357, -2.4567, 1.5400, 1.5700, 4.0100, 1.6200],
[28.2967, -1.3033, 0.5558, 1.4700, 1.4800, 2.2300, -1.5700],
[26.6690, -1.7361, -21.8230, 1.5600, 1.4000, 3.4800, -1.6900],
[31.3198, -1.6218, -8.1621, 1.7400, 1.4800, 3.7700, 2.7900]])
assert torch.allclose(expected_tensor, convert_cam_boxes.tensor, 1e-3)
convert_lidar_boxes = Coord3DMode.convert(depth_boxes, Coord3DMode.DEPTH,
Coord3DMode.LIDAR)
expected_tensor = torch.tensor(
[[2.5162, -1.7802, -1.7501, 3.3900, 1.7500, 1.6500, 1.4800],
[2.4567, -8.9594, -1.6357, 4.0100, 1.5400, 1.5700, 1.6200],
[-0.5558, -28.2967, -1.3033, 2.2300, 1.4700, 1.4800, -1.5700],
[21.8230, -26.6690, -1.7361, 3.4800, 1.5600, 1.4000, -1.6900],
[8.1621, -31.3198, -1.6218, 3.7700, 1.7400, 1.4800, 2.7900]])
assert torch.allclose(expected_tensor, convert_lidar_boxes.tensor, 1e-3)
tests/test_points.py 0 → 100644
View file @ 20b163a4
import numpy as np
import torch
from mmdet3d.core.points import (BasePoints, CameraPoints, DepthPoints,
LiDARPoints)
def test_base_points():
# test empty initialization
empty_boxes = []
points = BasePoints(empty_boxes)
assert points.tensor.shape[0] == 0
assert points.tensor.shape[1] == 3
# Test init with origin
points_np = np.array([[-5.24223238e+00, 4.00209696e+01, 2.97570381e-01],
[-2.66751588e+01, 5.59499564e+00, -9.14345860e-01],
[-5.80979675e+00, 3.54092357e+01, 2.00889888e-01],
[-3.13086877e+01, 1.09007628e+00, -1.94612112e-01]],
dtype=np.float32)
base_points = BasePoints(points_np, points_dim=3)
assert base_points.tensor.shape[0] == 4
# Test init with color and height
points_np = np.array([[
-5.24223238e+00, 4.00209696e+01, 2.97570381e-01, 0.6666, 0.1956,
0.4974, 0.9409
],
[
-2.66751588e+01, 5.59499564e+00, -9.14345860e-01,
0.1502, 0.3707, 0.1086, 0.6297
],
[
-5.80979675e+00, 3.54092357e+01, 2.00889888e-01,
0.6565, 0.6248, 0.6954, 0.2538
],
[
-3.13086877e+01, 1.09007628e+00, -1.94612112e-01,
0.2803, 0.0258, 0.4896, 0.3269
]],
dtype=np.float32)
base_points = BasePoints(
points_np,
points_dim=7,
attribute_dims=dict(color=[3, 4, 5], height=6))
expected_tensor = torch.tensor([[
-5.24223238e+00, 4.00209696e+01, 2.97570381e-01, 0.6666, 0.1956,
0.4974, 0.9409
],
[
-2.66751588e+01, 5.59499564e+00,
-9.14345860e-01, 0.1502, 0.3707,
0.1086, 0.6297
],
[
-5.80979675e+00, 3.54092357e+01,
2.00889888e-01, 0.6565, 0.6248, 0.6954,
0.2538
],
[
-3.13086877e+01, 1.09007628e+00,
-1.94612112e-01, 0.2803, 0.0258,
0.4896, 0.3269
]])
assert torch.allclose(expected_tensor, base_points.tensor)
assert torch.allclose(expected_tensor[:, :3], base_points.coord)
assert torch.allclose(expected_tensor[:, 3:6], base_points.color)
assert torch.allclose(expected_tensor[:, 6], base_points.height)
# test points clone
new_base_points = base_points.clone()
assert torch.allclose(new_base_points.tensor, base_points.tensor)
# test points shuffle
new_base_points.shuffle()
assert new_base_points.tensor.shape == torch.Size([4, 7])
# test points rotation
rot_mat = torch.tensor([[0.93629336, -0.27509585, 0.21835066],
[0.28962948, 0.95642509, -0.03695701],
[-0.19866933, 0.0978434, 0.97517033]])
base_points.rotate(rot_mat)
expected_tensor = torch.tensor([[
6.6239e+00, 3.9748e+01, -2.3335e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-2.3174e+01, 1.2600e+01, -6.9230e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
4.7760e+00, 3.5484e+01, -2.3813e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-2.8960e+01, 9.6364e+00, -7.0663e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, base_points.tensor, 1e-3)
new_base_points = base_points.clone()
new_base_points.rotate(0.1, axis=2)
expected_tensor = torch.tensor([[
2.6226e+00, 4.0211e+01, -2.3335e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-2.4316e+01, 1.0224e+01, -6.9230e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
1.2096e+00, 3.5784e+01, -2.3813e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-2.9777e+01, 6.6971e+00, -7.0663e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, new_base_points.tensor, 1e-3)
# test points translation
translation_vector = torch.tensor([0.93629336, -0.27509585, 0.21835066])
base_points.translate(translation_vector)
expected_tensor = torch.tensor([[
7.5602e+00, 3.9473e+01, -2.1152e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-2.2237e+01, 1.2325e+01, -6.7046e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
5.7123e+00, 3.5209e+01, -2.1629e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-2.8023e+01, 9.3613e+00, -6.8480e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, base_points.tensor, 1e-4)
# test points filter
point_range = [-10, -40, -10, 10, 40, 10]
in_range_flags = base_points.in_range_3d(point_range)
expected_flags = torch.tensor([True, False, True, False])
assert torch.all(in_range_flags == expected_flags)
# test points scale
base_points.scale(1.2)
expected_tensor = torch.tensor([[
9.0722e+00, 4.7368e+01, -2.5382e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-2.6685e+01, 1.4790e+01, -8.0455e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
6.8547e+00, 4.2251e+01, -2.5955e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-3.3628e+01, 1.1234e+01, -8.2176e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, base_points.tensor, 1e-3)
# test get_item
expected_tensor = torch.tensor(
[[-26.6848, 14.7898, -8.0455, 0.1502, 0.3707, 0.1086, 0.6297]])
assert torch.allclose(expected_tensor, base_points[1].tensor, 1e-4)
expected_tensor = torch.tensor(
[[-26.6848, 14.7898, -8.0455, 0.1502, 0.3707, 0.1086, 0.6297],
[6.8547, 42.2509, -2.5955, 0.6565, 0.6248, 0.6954, 0.2538]])
assert torch.allclose(expected_tensor, base_points[1:3].tensor, 1e-4)
mask = torch.tensor([True, False, True, False])
expected_tensor = torch.tensor(
[[9.0722, 47.3678, -2.5382, 0.6666, 0.1956, 0.4974, 0.9409],
[6.8547, 42.2509, -2.5955, 0.6565, 0.6248, 0.6954, 0.2538]])
assert torch.allclose(expected_tensor, base_points[mask].tensor, 1e-4)
# test length
assert len(base_points) == 4
# test repr
expected_repr = 'BasePoints(\n '\
'tensor([[ 9.0722e+00, 4.7368e+01, -2.5382e+00, '\
'6.6660e-01, 1.9560e-01,\n 4.9740e-01, '\
'9.4090e-01],\n '\
'[-2.6685e+01, 1.4790e+01, -8.0455e+00, 1.5020e-01, '\
'3.7070e-01,\n '\
'1.0860e-01, 6.2970e-01],\n '\
'[ 6.8547e+00, 4.2251e+01, -2.5955e+00, 6.5650e-01, '\
'6.2480e-01,\n '\
'6.9540e-01, 2.5380e-01],\n '\
'[-3.3628e+01, 1.1234e+01, -8.2176e+00, 2.8030e-01, '\
'2.5800e-02,\n '\
'4.8960e-01, 3.2690e-01]]))'
assert expected_repr == str(base_points)
# test concatenate
base_points_clone = base_points.clone()
cat_points = BasePoints.cat([base_points, base_points_clone])
assert torch.allclose(cat_points.tensor[:len(base_points)],
base_points.tensor)
# test iteration
for i, point in enumerate(base_points):
assert torch.allclose(point, base_points.tensor[i])
# test new_point
new_points = base_points.new_point([[1, 2, 3, 4, 5, 6, 7]])
assert torch.allclose(
new_points.tensor,
torch.tensor([[1, 2, 3, 4, 5, 6, 7]], dtype=base_points.tensor.dtype))
def test_cam_points():
# test empty initialization
empty_boxes = []
points = CameraPoints(empty_boxes)
assert points.tensor.shape[0] == 0
assert points.tensor.shape[1] == 3
# Test init with origin
points_np = np.array([[-5.24223238e+00, 4.00209696e+01, 2.97570381e-01],
[-2.66751588e+01, 5.59499564e+00, -9.14345860e-01],
[-5.80979675e+00, 3.54092357e+01, 2.00889888e-01],
[-3.13086877e+01, 1.09007628e+00, -1.94612112e-01]],
dtype=np.float32)
cam_points = CameraPoints(points_np, points_dim=3)
assert cam_points.tensor.shape[0] == 4
# Test init with color and height
points_np = np.array([[
-5.24223238e+00, 4.00209696e+01, 2.97570381e-01, 0.6666, 0.1956,
0.4974, 0.9409
],
[
-2.66751588e+01, 5.59499564e+00, -9.14345860e-01,
0.1502, 0.3707, 0.1086, 0.6297
],
[
-5.80979675e+00, 3.54092357e+01, 2.00889888e-01,
0.6565, 0.6248, 0.6954, 0.2538
],
[
-3.13086877e+01, 1.09007628e+00, -1.94612112e-01,
0.2803, 0.0258, 0.4896, 0.3269
]],
dtype=np.float32)
cam_points = CameraPoints(
points_np,
points_dim=7,
attribute_dims=dict(color=[3, 4, 5], height=6))
expected_tensor = torch.tensor([[
-5.24223238e+00, 4.00209696e+01, 2.97570381e-01, 0.6666, 0.1956,
0.4974, 0.9409
],
[
-2.66751588e+01, 5.59499564e+00,
-9.14345860e-01, 0.1502, 0.3707,
0.1086, 0.6297
],
[
-5.80979675e+00, 3.54092357e+01,
2.00889888e-01, 0.6565, 0.6248, 0.6954,
0.2538
],
[
-3.13086877e+01, 1.09007628e+00,
-1.94612112e-01, 0.2803, 0.0258,
0.4896, 0.3269
]])
assert torch.allclose(expected_tensor, cam_points.tensor)
assert torch.allclose(expected_tensor[:, :3], cam_points.coord)
assert torch.allclose(expected_tensor[:, 3:6], cam_points.color)
assert torch.allclose(expected_tensor[:, 6], cam_points.height)
# test points clone
new_cam_points = cam_points.clone()
assert torch.allclose(new_cam_points.tensor, cam_points.tensor)
# test points shuffle
new_cam_points.shuffle()
assert new_cam_points.tensor.shape == torch.Size([4, 7])
# test points rotation
rot_mat = torch.tensor([[0.93629336, -0.27509585, 0.21835066],
[0.28962948, 0.95642509, -0.03695701],
[-0.19866933, 0.0978434, 0.97517033]])
cam_points.rotate(rot_mat)
expected_tensor = torch.tensor([[
6.6239e+00, 3.9748e+01, -2.3335e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-2.3174e+01, 1.2600e+01, -6.9230e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
4.7760e+00, 3.5484e+01, -2.3813e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-2.8960e+01, 9.6364e+00, -7.0663e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, cam_points.tensor, 1e-3)
new_cam_points = cam_points.clone()
new_cam_points.rotate(0.1, axis=2)
expected_tensor = torch.tensor([[
2.6226e+00, 4.0211e+01, -2.3335e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-2.4316e+01, 1.0224e+01, -6.9230e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
1.2096e+00, 3.5784e+01, -2.3813e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-2.9777e+01, 6.6971e+00, -7.0663e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, new_cam_points.tensor, 1e-3)
# test points translation
translation_vector = torch.tensor([0.93629336, -0.27509585, 0.21835066])
cam_points.translate(translation_vector)
expected_tensor = torch.tensor([[
7.5602e+00, 3.9473e+01, -2.1152e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-2.2237e+01, 1.2325e+01, -6.7046e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
5.7123e+00, 3.5209e+01, -2.1629e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-2.8023e+01, 9.3613e+00, -6.8480e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, cam_points.tensor, 1e-4)
# test points filter
point_range = [-10, -40, -10, 10, 40, 10]
in_range_flags = cam_points.in_range_3d(point_range)
expected_flags = torch.tensor([True, False, True, False])
assert torch.all(in_range_flags == expected_flags)
# test points scale
cam_points.scale(1.2)
expected_tensor = torch.tensor([[
9.0722e+00, 4.7368e+01, -2.5382e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-2.6685e+01, 1.4790e+01, -8.0455e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
6.8547e+00, 4.2251e+01, -2.5955e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-3.3628e+01, 1.1234e+01, -8.2176e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, cam_points.tensor, 1e-3)
# test get_item
expected_tensor = torch.tensor(
[[-26.6848, 14.7898, -8.0455, 0.1502, 0.3707, 0.1086, 0.6297]])
assert torch.allclose(expected_tensor, cam_points[1].tensor, 1e-4)
expected_tensor = torch.tensor(
[[-26.6848, 14.7898, -8.0455, 0.1502, 0.3707, 0.1086, 0.6297],
[6.8547, 42.2509, -2.5955, 0.6565, 0.6248, 0.6954, 0.2538]])
assert torch.allclose(expected_tensor, cam_points[1:3].tensor, 1e-4)
mask = torch.tensor([True, False, True, False])
expected_tensor = torch.tensor(
[[9.0722, 47.3678, -2.5382, 0.6666, 0.1956, 0.4974, 0.9409],
[6.8547, 42.2509, -2.5955, 0.6565, 0.6248, 0.6954, 0.2538]])
assert torch.allclose(expected_tensor, cam_points[mask].tensor, 1e-4)
# test length
assert len(cam_points) == 4
# test repr
expected_repr = 'CameraPoints(\n '\
'tensor([[ 9.0722e+00, 4.7368e+01, -2.5382e+00, '\
'6.6660e-01, 1.9560e-01,\n 4.9740e-01, '\
'9.4090e-01],\n '\
'[-2.6685e+01, 1.4790e+01, -8.0455e+00, 1.5020e-01, '\
'3.7070e-01,\n '\
'1.0860e-01, 6.2970e-01],\n '\
'[ 6.8547e+00, 4.2251e+01, -2.5955e+00, 6.5650e-01, '\
'6.2480e-01,\n '\
'6.9540e-01, 2.5380e-01],\n '\
'[-3.3628e+01, 1.1234e+01, -8.2176e+00, 2.8030e-01, '\
'2.5800e-02,\n '\
'4.8960e-01, 3.2690e-01]]))'
assert expected_repr == str(cam_points)
# test concatenate
cam_points_clone = cam_points.clone()
cat_points = CameraPoints.cat([cam_points, cam_points_clone])
assert torch.allclose(cat_points.tensor[:len(cam_points)],
cam_points.tensor)
# test iteration
for i, point in enumerate(cam_points):
assert torch.allclose(point, cam_points.tensor[i])
# test new_point
new_points = cam_points.new_point([[1, 2, 3, 4, 5, 6, 7]])
assert torch.allclose(
new_points.tensor,
torch.tensor([[1, 2, 3, 4, 5, 6, 7]], dtype=cam_points.tensor.dtype))
# test in_range_bev
point_bev_range = [-10, -10, 10, 10]
in_range_flags = cam_points.in_range_bev(point_bev_range)
expected_flags = torch.tensor([True, False, True, False])
assert torch.all(in_range_flags == expected_flags)
# test flip
cam_points.flip(bev_direction='horizontal')
expected_tensor = torch.tensor([[
-9.0722e+00, 4.7368e+01, -2.5382e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
2.6685e+01, 1.4790e+01, -8.0455e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
-6.8547e+00, 4.2251e+01, -2.5955e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
3.3628e+01, 1.1234e+01, -8.2176e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, cam_points.tensor, 1e-4)
cam_points.flip(bev_direction='vertical')
expected_tensor = torch.tensor([[
-9.0722e+00, 4.7368e+01, 2.5382e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
2.6685e+01, 1.4790e+01, 8.0455e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
-6.8547e+00, 4.2251e+01, 2.5955e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
3.3628e+01, 1.1234e+01, 8.2176e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, cam_points.tensor, 1e-4)
def test_lidar_points():
# test empty initialization
empty_boxes = []
points = LiDARPoints(empty_boxes)
assert points.tensor.shape[0] == 0
assert points.tensor.shape[1] == 3
# Test init with origin
points_np = np.array([[-5.24223238e+00, 4.00209696e+01, 2.97570381e-01],
[-2.66751588e+01, 5.59499564e+00, -9.14345860e-01],
[-5.80979675e+00, 3.54092357e+01, 2.00889888e-01],
[-3.13086877e+01, 1.09007628e+00, -1.94612112e-01]],
dtype=np.float32)
lidar_points = LiDARPoints(points_np, points_dim=3)
assert lidar_points.tensor.shape[0] == 4
# Test init with color and height
points_np = np.array([[
-5.24223238e+00, 4.00209696e+01, 2.97570381e-01, 0.6666, 0.1956,
0.4974, 0.9409
],
[
-2.66751588e+01, 5.59499564e+00, -9.14345860e-01,
0.1502, 0.3707, 0.1086, 0.6297
],
[
-5.80979675e+00, 3.54092357e+01, 2.00889888e-01,
0.6565, 0.6248, 0.6954, 0.2538
],
[
-3.13086877e+01, 1.09007628e+00, -1.94612112e-01,
0.2803, 0.0258, 0.4896, 0.3269
]],
dtype=np.float32)
lidar_points = LiDARPoints(
points_np,
points_dim=7,
attribute_dims=dict(color=[3, 4, 5], height=6))
expected_tensor = torch.tensor([[
-5.24223238e+00, 4.00209696e+01, 2.97570381e-01, 0.6666, 0.1956,
0.4974, 0.9409
],
[
-2.66751588e+01, 5.59499564e+00,
-9.14345860e-01, 0.1502, 0.3707,
0.1086, 0.6297
],
[
-5.80979675e+00, 3.54092357e+01,
2.00889888e-01, 0.6565, 0.6248, 0.6954,
0.2538
],
[
-3.13086877e+01, 1.09007628e+00,
-1.94612112e-01, 0.2803, 0.0258,
0.4896, 0.3269
]])
assert torch.allclose(expected_tensor, lidar_points.tensor)
assert torch.allclose(expected_tensor[:, :3], lidar_points.coord)
assert torch.allclose(expected_tensor[:, 3:6], lidar_points.color)
assert torch.allclose(expected_tensor[:, 6], lidar_points.height)
# test points clone
new_lidar_points = lidar_points.clone()
assert torch.allclose(new_lidar_points.tensor, lidar_points.tensor)
# test points shuffle
new_lidar_points.shuffle()
assert new_lidar_points.tensor.shape == torch.Size([4, 7])
# test points rotation
rot_mat = torch.tensor([[0.93629336, -0.27509585, 0.21835066],
[0.28962948, 0.95642509, -0.03695701],
[-0.19866933, 0.0978434, 0.97517033]])
lidar_points.rotate(rot_mat)
expected_tensor = torch.tensor([[
6.6239e+00, 3.9748e+01, -2.3335e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-2.3174e+01, 1.2600e+01, -6.9230e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
4.7760e+00, 3.5484e+01, -2.3813e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-2.8960e+01, 9.6364e+00, -7.0663e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, lidar_points.tensor, 1e-3)
new_lidar_points = lidar_points.clone()
new_lidar_points.rotate(0.1, axis=2)
expected_tensor = torch.tensor([[
2.6226e+00, 4.0211e+01, -2.3335e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-2.4316e+01, 1.0224e+01, -6.9230e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
1.2096e+00, 3.5784e+01, -2.3813e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-2.9777e+01, 6.6971e+00, -7.0663e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, new_lidar_points.tensor, 1e-3)
# test points translation
translation_vector = torch.tensor([0.93629336, -0.27509585, 0.21835066])
lidar_points.translate(translation_vector)
expected_tensor = torch.tensor([[
7.5602e+00, 3.9473e+01, -2.1152e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-2.2237e+01, 1.2325e+01, -6.7046e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
5.7123e+00, 3.5209e+01, -2.1629e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-2.8023e+01, 9.3613e+00, -6.8480e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, lidar_points.tensor, 1e-4)
# test points filter
point_range = [-10, -40, -10, 10, 40, 10]
in_range_flags = lidar_points.in_range_3d(point_range)
expected_flags = torch.tensor([True, False, True, False])
assert torch.all(in_range_flags == expected_flags)
# test points scale
lidar_points.scale(1.2)
expected_tensor = torch.tensor([[
9.0722e+00, 4.7368e+01, -2.5382e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-2.6685e+01, 1.4790e+01, -8.0455e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
6.8547e+00, 4.2251e+01, -2.5955e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-3.3628e+01, 1.1234e+01, -8.2176e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, lidar_points.tensor, 1e-3)
# test get_item
expected_tensor = torch.tensor(
[[-26.6848, 14.7898, -8.0455, 0.1502, 0.3707, 0.1086, 0.6297]])
assert torch.allclose(expected_tensor, lidar_points[1].tensor, 1e-4)
expected_tensor = torch.tensor(
[[-26.6848, 14.7898, -8.0455, 0.1502, 0.3707, 0.1086, 0.6297],
[6.8547, 42.2509, -2.5955, 0.6565, 0.6248, 0.6954, 0.2538]])
assert torch.allclose(expected_tensor, lidar_points[1:3].tensor, 1e-4)
mask = torch.tensor([True, False, True, False])
expected_tensor = torch.tensor(
[[9.0722, 47.3678, -2.5382, 0.6666, 0.1956, 0.4974, 0.9409],
[6.8547, 42.2509, -2.5955, 0.6565, 0.6248, 0.6954, 0.2538]])
assert torch.allclose(expected_tensor, lidar_points[mask].tensor, 1e-4)
# test length
assert len(lidar_points) == 4
# test repr
expected_repr = 'LiDARPoints(\n '\
'tensor([[ 9.0722e+00, 4.7368e+01, -2.5382e+00, '\
'6.6660e-01, 1.9560e-01,\n 4.9740e-01, '\
'9.4090e-01],\n '\
'[-2.6685e+01, 1.4790e+01, -8.0455e+00, 1.5020e-01, '\
'3.7070e-01,\n '\
'1.0860e-01, 6.2970e-01],\n '\
'[ 6.8547e+00, 4.2251e+01, -2.5955e+00, 6.5650e-01, '\
'6.2480e-01,\n '\
'6.9540e-01, 2.5380e-01],\n '\
'[-3.3628e+01, 1.1234e+01, -8.2176e+00, 2.8030e-01, '\
'2.5800e-02,\n '\
'4.8960e-01, 3.2690e-01]]))'
assert expected_repr == str(lidar_points)
# test concatenate
lidar_points_clone = lidar_points.clone()
cat_points = LiDARPoints.cat([lidar_points, lidar_points_clone])
assert torch.allclose(cat_points.tensor[:len(lidar_points)],
lidar_points.tensor)
# test iteration
for i, point in enumerate(lidar_points):
assert torch.allclose(point, lidar_points.tensor[i])
# test new_point
new_points = lidar_points.new_point([[1, 2, 3, 4, 5, 6, 7]])
assert torch.allclose(
new_points.tensor,
torch.tensor([[1, 2, 3, 4, 5, 6, 7]], dtype=lidar_points.tensor.dtype))
# test in_range_bev
point_bev_range = [-30, -40, 30, 40]
in_range_flags = lidar_points.in_range_bev(point_bev_range)
expected_flags = torch.tensor([False, True, False, False])
assert torch.all(in_range_flags == expected_flags)
# test flip
lidar_points.flip(bev_direction='horizontal')
expected_tensor = torch.tensor([[
9.0722e+00, -4.7368e+01, -2.5382e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-2.6685e+01, -1.4790e+01, -8.0455e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
6.8547e+00, -4.2251e+01, -2.5955e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-3.3628e+01, -1.1234e+01, -8.2176e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, lidar_points.tensor, 1e-4)
lidar_points.flip(bev_direction='vertical')
expected_tensor = torch.tensor([[
-9.0722e+00, -4.7368e+01, -2.5382e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
2.6685e+01, -1.4790e+01, -8.0455e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
-6.8547e+00, -4.2251e+01, -2.5955e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
3.3628e+01, -1.1234e+01, -8.2176e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, lidar_points.tensor, 1e-4)
def test_depth_points():
# test empty initialization
empty_boxes = []
points = DepthPoints(empty_boxes)
assert points.tensor.shape[0] == 0
assert points.tensor.shape[1] == 3
# Test init with origin
points_np = np.array([[-5.24223238e+00, 4.00209696e+01, 2.97570381e-01],
[-2.66751588e+01, 5.59499564e+00, -9.14345860e-01],
[-5.80979675e+00, 3.54092357e+01, 2.00889888e-01],
[-3.13086877e+01, 1.09007628e+00, -1.94612112e-01]],
dtype=np.float32)
depth_points = DepthPoints(points_np, points_dim=3)
assert depth_points.tensor.shape[0] == 4
# Test init with color and height
points_np = np.array([[
-5.24223238e+00, 4.00209696e+01, 2.97570381e-01, 0.6666, 0.1956,
0.4974, 0.9409
],
[
-2.66751588e+01, 5.59499564e+00, -9.14345860e-01,
0.1502, 0.3707, 0.1086, 0.6297
],
[
-5.80979675e+00, 3.54092357e+01, 2.00889888e-01,
0.6565, 0.6248, 0.6954, 0.2538
],
[
-3.13086877e+01, 1.09007628e+00, -1.94612112e-01,
0.2803, 0.0258, 0.4896, 0.3269
]],
dtype=np.float32)
depth_points = DepthPoints(
points_np,
points_dim=7,
attribute_dims=dict(color=[3, 4, 5], height=6))
expected_tensor = torch.tensor([[
-5.24223238e+00, 4.00209696e+01, 2.97570381e-01, 0.6666, 0.1956,
0.4974, 0.9409
],
[
-2.66751588e+01, 5.59499564e+00,
-9.14345860e-01, 0.1502, 0.3707,
0.1086, 0.6297
],
[
-5.80979675e+00, 3.54092357e+01,
2.00889888e-01, 0.6565, 0.6248, 0.6954,
0.2538
],
[
-3.13086877e+01, 1.09007628e+00,
-1.94612112e-01, 0.2803, 0.0258,
0.4896, 0.3269
]])
assert torch.allclose(expected_tensor, depth_points.tensor)
assert torch.allclose(expected_tensor[:, :3], depth_points.coord)
assert torch.allclose(expected_tensor[:, 3:6], depth_points.color)
assert torch.allclose(expected_tensor[:, 6], depth_points.height)
# test points clone
new_depth_points = depth_points.clone()
assert torch.allclose(new_depth_points.tensor, depth_points.tensor)
# test points shuffle
new_depth_points.shuffle()
assert new_depth_points.tensor.shape == torch.Size([4, 7])
# test points rotation
rot_mat = torch.tensor([[0.93629336, -0.27509585, 0.21835066],
[0.28962948, 0.95642509, -0.03695701],
[-0.19866933, 0.0978434, 0.97517033]])
depth_points.rotate(rot_mat)
expected_tensor = torch.tensor([[
6.6239e+00, 3.9748e+01, -2.3335e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-2.3174e+01, 1.2600e+01, -6.9230e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
4.7760e+00, 3.5484e+01, -2.3813e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-2.8960e+01, 9.6364e+00, -7.0663e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, depth_points.tensor, 1e-3)
new_depth_points = depth_points.clone()
new_depth_points.rotate(0.1, axis=2)
expected_tensor = torch.tensor([[
2.6226e+00, 4.0211e+01, -2.3335e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-2.4316e+01, 1.0224e+01, -6.9230e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
1.2096e+00, 3.5784e+01, -2.3813e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-2.9777e+01, 6.6971e+00, -7.0663e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, new_depth_points.tensor, 1e-3)
# test points translation
translation_vector = torch.tensor([0.93629336, -0.27509585, 0.21835066])
depth_points.translate(translation_vector)
expected_tensor = torch.tensor([[
7.5602e+00, 3.9473e+01, -2.1152e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-2.2237e+01, 1.2325e+01, -6.7046e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
5.7123e+00, 3.5209e+01, -2.1629e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-2.8023e+01, 9.3613e+00, -6.8480e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, depth_points.tensor, 1e-4)
# test points filter
point_range = [-10, -40, -10, 10, 40, 10]
in_range_flags = depth_points.in_range_3d(point_range)
expected_flags = torch.tensor([True, False, True, False])
assert torch.all(in_range_flags == expected_flags)
# test points scale
depth_points.scale(1.2)
expected_tensor = torch.tensor([[
9.0722e+00, 4.7368e+01, -2.5382e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
-2.6685e+01, 1.4790e+01, -8.0455e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
6.8547e+00, 4.2251e+01, -2.5955e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
-3.3628e+01, 1.1234e+01, -8.2176e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, depth_points.tensor, 1e-3)
# test get_item
expected_tensor = torch.tensor(
[[-26.6848, 14.7898, -8.0455, 0.1502, 0.3707, 0.1086, 0.6297]])
assert torch.allclose(expected_tensor, depth_points[1].tensor, 1e-4)
expected_tensor = torch.tensor(
[[-26.6848, 14.7898, -8.0455, 0.1502, 0.3707, 0.1086, 0.6297],
[6.8547, 42.2509, -2.5955, 0.6565, 0.6248, 0.6954, 0.2538]])
assert torch.allclose(expected_tensor, depth_points[1:3].tensor, 1e-4)
mask = torch.tensor([True, False, True, False])
expected_tensor = torch.tensor(
[[9.0722, 47.3678, -2.5382, 0.6666, 0.1956, 0.4974, 0.9409],
[6.8547, 42.2509, -2.5955, 0.6565, 0.6248, 0.6954, 0.2538]])
assert torch.allclose(expected_tensor, depth_points[mask].tensor, 1e-4)
# test length
assert len(depth_points) == 4
# test repr
expected_repr = 'DepthPoints(\n '\
'tensor([[ 9.0722e+00, 4.7368e+01, -2.5382e+00, '\
'6.6660e-01, 1.9560e-01,\n 4.9740e-01, '\
'9.4090e-01],\n '\
'[-2.6685e+01, 1.4790e+01, -8.0455e+00, 1.5020e-01, '\
'3.7070e-01,\n '\
'1.0860e-01, 6.2970e-01],\n '\
'[ 6.8547e+00, 4.2251e+01, -2.5955e+00, 6.5650e-01, '\
'6.2480e-01,\n '\
'6.9540e-01, 2.5380e-01],\n '\
'[-3.3628e+01, 1.1234e+01, -8.2176e+00, 2.8030e-01, '\
'2.5800e-02,\n '\
'4.8960e-01, 3.2690e-01]]))'
assert expected_repr == str(depth_points)
# test concatenate
depth_points_clone = depth_points.clone()
cat_points = DepthPoints.cat([depth_points, depth_points_clone])
assert torch.allclose(cat_points.tensor[:len(depth_points)],
depth_points.tensor)
# test iteration
for i, point in enumerate(depth_points):
assert torch.allclose(point, depth_points.tensor[i])
# test new_point
new_points = depth_points.new_point([[1, 2, 3, 4, 5, 6, 7]])
assert torch.allclose(
new_points.tensor,
torch.tensor([[1, 2, 3, 4, 5, 6, 7]], dtype=depth_points.tensor.dtype))
# test in_range_bev
point_bev_range = [-30, -40, 30, 40]
in_range_flags = depth_points.in_range_bev(point_bev_range)
expected_flags = torch.tensor([False, True, False, False])
assert torch.all(in_range_flags == expected_flags)
# test flip
depth_points.flip(bev_direction='horizontal')
expected_tensor = torch.tensor([[
-9.0722e+00, 4.7368e+01, -2.5382e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
2.6685e+01, 1.4790e+01, -8.0455e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
-6.8547e+00, 4.2251e+01, -2.5955e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
3.3628e+01, 1.1234e+01, -8.2176e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, depth_points.tensor, 1e-4)
depth_points.flip(bev_direction='vertical')
expected_tensor = torch.tensor([[
-9.0722e+00, -4.7368e+01, -2.5382e+00, 6.6660e-01, 1.9560e-01,
4.9740e-01, 9.4090e-01
],
[
2.6685e+01, -1.4790e+01, -8.0455e+00,
1.5020e-01, 3.7070e-01, 1.0860e-01,
6.2970e-01
],
[
-6.8547e+00, -4.2251e+01, -2.5955e+00,
6.5650e-01, 6.2480e-01, 6.9540e-01,
2.5380e-01
],
[
3.3628e+01, -1.1234e+01, -8.2176e+00,
2.8030e-01, 2.5800e-02, 4.8960e-01,
3.2690e-01
]])
assert torch.allclose(expected_tensor, depth_points.tensor, 1e-4)
test_base_points()
test_cam_points()
test_depth_points()
test_lidar_points()
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