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
[docs]@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.ndarray |
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.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:
(tuple | list | np.ndarray | 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:
point (tuple | list | np.ndarray |
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.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:
(tuple | list | np.ndarray | 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.
# TODO: LIDAR
# only implemented provided Rt matrix in cam-depth conversion
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]])
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]])
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]])
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]])
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]])
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]])
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[:, 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