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import warnings
from typing import Iterable, List, Optional, Tuple, Union
import numpy as np
import torch
from detrsmpl.utils.transforms import ee_to_rotmat, rotmat_to_ee
CAMERA_CONVENTIONS = {
'pytorch3d': {
'axis': '-xyz',
'left_mm_extrinsic': False,
'view_to_world': False,
'left_mm_intrinsic': True,
},
'pyrender': {
'axis': 'xy-z',
'left_mm_extrinsic': True,
'view_to_world': False,
'left_mm_intrinsic': True,
},
'opengl': {
'axis': 'xy-z',
'left_mm_extrinsic': True,
'view_to_world': False,
'left_mm_intrinsic': True,
},
'open3d': {
'axis': 'x-yz',
'left_mm_extrinsic': False,
'view_to_world': False,
'left_mm_intrinsic': False,
},
'opencv': {
'axis': 'x-yz',
'left_mm_extrinsic': True,
'view_to_world': True,
'left_mm_intrinsic': True,
},
'unity': {
'axis': 'xyz',
'left_mm_extrinsic': True,
'view_to_world': False,
'left_mm_intrinsic': True,
},
'blender': {
'axis': 'xy-z',
'left_mm_extrinsic': True,
'view_to_world': False,
'left_mm_intrinsic': True,
},
'maya': {
'axis': 'xy-z',
'left_mm_extrinsic': True,
'view_to_world': False,
'left_mm_intrinsic': True,
}
}
def enc_camera_convention(convention, camera_conventions=CAMERA_CONVENTIONS):
"""convert camera convention to axis direction and order."""
if convention in camera_conventions:
convention = camera_conventions[convention]['axis']
else:
assert set(convention).issubset(
{'x', 'y', 'z', '+',
'-'}), 'Wrong convention string, choose either in'
f'set({camera_conventions.keys()}) or define by xyz.'
sign = [1, 1, 1]
convention = '_' + convention
count = 0
axis_order = ''
for i in range(len(convention)):
if convention[i] in 'xyz':
axis_order += convention[i]
if convention[i - 1] == '-':
sign[count] *= -1
count += 1
return sign, axis_order
def convert_camera_matrix(
K: Optional[Union[torch.Tensor, np.ndarray]] = None,
R: Optional[Union[torch.Tensor, np.ndarray]] = None,
T: Optional[Union[torch.Tensor, np.ndarray]] = None,
is_perspective: bool = True,
convention_src: str = 'opencv',
convention_dst: str = 'pytorch3d',
in_ndc_src: bool = True,
in_ndc_dst: bool = True,
resolution_src: Optional[Union[int, Tuple[int, int], torch.Tensor,
np.ndarray]] = None,
resolution_dst: Optional[Union[int, Tuple[int, int], torch.Tensor,
np.ndarray]] = None,
camera_conventions: dict = CAMERA_CONVENTIONS,
) -> Tuple[Union[torch.Tensor, np.ndarray], Union[torch.Tensor, np.ndarray],
Union[torch.Tensor, np.ndarray]]:
"""Convert the intrinsic matrix K and extrinsic matrix [R|T] from source
convention to destination convention.
Args:
K (Union[torch.Tensor, np.ndarray]): Intrinsic matrix,
shape should be (batch_size, 4, 4) or (batch_size, 3, 3).
Will be ignored if None.
R (Optional[Union[torch.Tensor, np.ndarray]], optional):
Extrinsic rotation matrix. Shape should be (batch_size, 3, 3).
Will be identity if None.
Defaults to None.
T (Optional[Union[torch.Tensor, np.ndarray]], optional):
Extrinsic translation matrix. Shape should be (batch_size, 3).
Will be zeros if None.
Defaults to None.
is_perspective (bool, optional): whether is perspective projection.
Defaults to True.
_____________________________________________________________________
# Camera dependent args
convention_src (str, optional): convention of source camera,
convention_dst (str, optional): convention of destination camera,
We define the convention of cameras by the order of right, front and
up.
E.g., the first one is pyrender and its convention should be
'+x+z+y'. '+' could be ignored.
The second one is opencv and its convention should be '+x-z-y'.
The third one is pytorch3d and its convention should be '-xzy'.
opengl(pyrender) opencv pytorch3d
y z y
| / |
| / |
|_______x /________x x________ |
/ | /
/ | /
z / y | z /
in_ndc_src (bool, optional): Whether is the source camera defined
in ndc.
Defaults to True.
in_ndc_dst (bool, optional): Whether is the destination camera defined
in ndc.
Defaults to True.
in camera_convention, we define these args as:
1). `left_mm_ex` means extrinsic matrix `K` is left matrix
multiplcation defined.
2). `left_mm_in` means intrinsic matrix [`R`| `T`] is left
matrix multiplcation defined.
3) `view_to_world` means extrinsic matrix [`R`| `T`] is defined
as view to world.
resolution_src (Optional[Union[int, Tuple[int, int], torch.Tensor,
np.ndarray]], optional):
Source camera image size of (height, width).
Required if defined in screen.
Will be square if int.
Shape should be (2,) if `array` or `tensor`.
Defaults to None.
resolution_dst (Optional[Union[int, Tuple[int, int], torch.Tensor,
np.ndarray]], optional):
Destination camera image size of (height, width).
Required if defined in screen.
Will be square if int.
Shape should be (2,) if `array` or `tensor`.
Defaults to None.
camera_conventions: (dict, optional): `dict` containing
pre-defined camera convention information.
Defaults to CAMERA_CONVENTIONS.
Raises:
TypeError: K, R, T should all be `torch.Tensor` or `np.ndarray`.
Returns:
Tuple[Union[torch.Tensor, None], Union[torch.Tensor, None],
Union[torch.Tensor, None]]:
Converted K, R, T matrix of `tensor`.
"""
convention_dst = convention_dst.lower()
convention_src = convention_src.lower()
assert convention_dst in CAMERA_CONVENTIONS
assert convention_src in CAMERA_CONVENTIONS
left_mm_ex_src = CAMERA_CONVENTIONS[convention_src].get(
'left_mm_extrinsic', True)
view_to_world_src = CAMERA_CONVENTIONS[convention_src].get(
'view_to_world', False)
left_mm_in_src = CAMERA_CONVENTIONS[convention_src].get(
'left_mm_intrinsic', False)
left_mm_ex_dst = CAMERA_CONVENTIONS[convention_dst].get(
'left_mm_extrinsic', True)
view_to_world_dst = CAMERA_CONVENTIONS[convention_dst].get(
'view_to_world', False)
left_mm_in_dst = CAMERA_CONVENTIONS[convention_dst].get(
'left_mm_intrinsic', False)
sign_src, axis_src = enc_camera_convention(convention_src,
camera_conventions)
sign_dst, axis_dst = enc_camera_convention(convention_dst,
camera_conventions)
sign = torch.Tensor(sign_dst) / torch.Tensor(sign_src)
type_ = []
for x in [K, R, T]:
if x is not None:
type_.append(type(x))
if len(type_) > 0:
if not all(x == type_[0] for x in type_):
raise TypeError('Input type should be the same.')
use_numpy = False
if np.ndarray in type_:
use_numpy = True
# convert raw matrix to tensor
if isinstance(K, np.ndarray):
new_K = torch.Tensor(K)
elif K is None:
new_K = None
elif isinstance(K, torch.Tensor):
new_K = K.clone()
else:
raise TypeError(
f'K should be `torch.Tensor` or `numpy.ndarray`, type(K): '
f'{type(K)}')
if isinstance(R, np.ndarray):
new_R = torch.Tensor(R).view(-1, 3, 3)
elif R is None:
new_R = torch.eye(3, 3)[None]
elif isinstance(R, torch.Tensor):
new_R = R.clone().view(-1, 3, 3)
else:
raise TypeError(
f'R should be `torch.Tensor` or `numpy.ndarray`, type(R): '
f'{type(R)}')
if isinstance(T, np.ndarray):
new_T = torch.Tensor(T).view(-1, 3)
elif T is None:
new_T = torch.zeros(1, 3)
elif isinstance(T, torch.Tensor):
new_T = T.clone().view(-1, 3)
else:
raise TypeError(
f'T should be `torch.Tensor` or `numpy.ndarray`, type(T): '
f'{type(T)}')
if axis_dst != axis_src:
new_R = ee_to_rotmat(rotmat_to_ee(new_R, convention=axis_src),
convention=axis_dst)
# convert extrinsic to world_to_view
if view_to_world_src is True:
new_R, new_T = convert_world_view(new_R, new_T)
# right mm to left mm
if (not left_mm_ex_src) and left_mm_ex_dst:
new_R *= sign.to(new_R.device)
new_R = new_R.permute(0, 2, 1)
# left mm to right mm
elif left_mm_ex_src and (not left_mm_ex_dst):
new_R = new_R.permute(0, 2, 1)
new_R *= sign.to(new_R.device)
# right_mm to right mm
elif (not left_mm_ex_dst) and (not left_mm_ex_src):
new_R *= sign.to(new_R.device)
# left mm to left mm
elif left_mm_ex_src and left_mm_ex_dst:
new_R *= sign.view(3, 1).to(new_R.device)
new_T *= sign.to(new_T.device)
# convert extrinsic to as definition
if view_to_world_dst is True:
new_R, new_T = convert_world_view(new_R, new_T)
# in ndc or in screen
if in_ndc_dst is False and in_ndc_src is True:
assert resolution_dst is not None, \
'dst in screen, should specify resolution_dst.'
if in_ndc_src is False and in_ndc_dst is True:
assert resolution_src is not None, \
'src in screen, should specify resolution_dst.'
if resolution_dst is None:
resolution_dst = 2.0
if resolution_src is None:
resolution_src = 2.0
if new_K is not None:
if left_mm_in_src is False and left_mm_in_dst is True:
new_K = new_K.permute(0, 2, 1)
if new_K.shape[-2:] == (3, 3):
new_K = convert_K_3x3_to_4x4(new_K, is_perspective)
# src in ndc, dst in screen
if in_ndc_src is True and (in_ndc_dst is False):
new_K = convert_ndc_to_screen(K=new_K,
is_perspective=is_perspective,
sign=sign.to(new_K.device),
resolution=resolution_dst)
# src in screen, dst in ndc
elif in_ndc_src is False and in_ndc_dst is True:
new_K = convert_screen_to_ndc(K=new_K,
is_perspective=is_perspective,
sign=sign.to(new_K.device),
resolution=resolution_src)
# src in ndc, dst in ndc
elif in_ndc_src is True and in_ndc_dst is True:
if is_perspective:
new_K[:, 0, 2] *= sign[0].to(new_K.device)
new_K[:, 1, 2] *= sign[1].to(new_K.device)
else:
new_K[:, 0, 3] *= sign[0].to(new_K.device)
new_K[:, 1, 3] *= sign[1].to(new_K.device)
# src in screen, dst in screen
else:
pass
if left_mm_in_src is True and left_mm_in_dst is False:
new_K = new_K.permute(0, 2, 1)
num_batch = max(new_K.shape[0], new_R.shape[0], new_T.shape[0])
if new_K.shape[0] == 1:
new_K = new_K.repeat(num_batch, 1, 1)
if new_R.shape[0] == 1:
new_R = new_R.repeat(num_batch, 1, 1)
if new_T.shape[0] == 1:
new_T = new_T.repeat(num_batch, 1)
if use_numpy:
if isinstance(new_K, torch.Tensor):
new_K = new_K.cpu().numpy()
if isinstance(new_R, torch.Tensor):
new_R = new_R.cpu().numpy()
if isinstance(new_T, torch.Tensor):
new_T = new_T.cpu().numpy()
return new_K, new_R, new_T
def convert_K_3x3_to_4x4(
K: Union[torch.Tensor, np.ndarray],
is_perspective: bool = True) -> Union[torch.Tensor, np.ndarray]:
"""Convert opencv 3x3 intrinsic matrix to 4x4.
Args:
K (Union[torch.Tensor, np.ndarray]):
Input 3x3 intrinsic matrix, left mm defined.
[[fx, 0, px],
[0, fy, py],
[0, 0, 1]]
is_perspective (bool, optional): whether is perspective projection.
Defaults to True.
Raises:
TypeError: K is not `Tensor` or `array`.
ValueError: Shape is not (batch, 3, 3) or (3, 3)
Returns:
Union[torch.Tensor, np.ndarray]:
Output intrinsic matrix.
for perspective:
[[fx, 0, px, 0],
[0, fy, py, 0],
[0, 0, 0, 1],
[0, 0, 1, 0]]
for orthographics:
[[fx, 0, 0, px],
[0, fy, 0, py],
[0, 0, 1, 0],
[0, 0, 0, 1]]
"""
if isinstance(K, torch.Tensor):
K = K.clone()
elif isinstance(K, np.ndarray):
K = K.copy()
else:
raise TypeError('K should be `torch.Tensor` or `numpy.ndarray`, '
f'type(K): {type(K)}.')
if K.shape[-2:] == (4, 4):
warnings.warn(
f'shape of K already is {K.shape}, will pass converting.')
return K
use_numpy = False
if K.ndim == 2:
K = K[None].reshape(-1, 3, 3)
elif K.ndim == 3:
K = K.reshape(-1, 3, 3)
else:
raise ValueError(f'Wrong ndim of K: {K.ndim}')
if isinstance(K, np.ndarray):
use_numpy = True
if is_perspective:
if use_numpy:
K_ = np.zeros((K.shape[0], 4, 4))
else:
K_ = torch.zeros(K.shape[0], 4, 4)
K_[:, :2, :3] = K[:, :2, :3]
K_[:, 3, 2] = 1
K_[:, 2, 3] = 1
else:
if use_numpy:
K_ = np.eye(4, 4)[None].repeat(K.shape[0], 0)
else:
K_ = torch.eye(4, 4)[None].repeat(K.shape[0], 1, 1)
K_[:, :2, :2] = K[:, :2, :2]
K_[:, :2, 3:] = K[:, :2, 2:]
return K_
def convert_K_4x4_to_3x3(
K: Union[torch.Tensor, np.ndarray],
is_perspective: bool = True) -> Union[torch.Tensor, np.ndarray]:
"""Convert opencv 4x4 intrinsic matrix to 3x3.
Args:
K (Union[torch.Tensor, np.ndarray]):
Input 4x4 intrinsic matrix, left mm defined.
for perspective:
[[fx, 0, px, 0],
[0, fy, py, 0],
[0, 0, 0, 1],
[0, 0, 1, 0]]
for orthographics:
[[fx, 0, 0, px],
[0, fy, 0, py],
[0, 0, 1, 0],
[0, 0, 0, 1]]
is_perspective (bool, optional): whether is perspective projection.
Defaults to True.
Raises:
TypeError: type K should be `Tensor` or `array`.
ValueError: Shape is not (batch, 3, 3) or (3, 3).
Returns:
Union[torch.Tensor, np.ndarray]:
Output 3x3 intrinsic matrix, left mm defined.
[[fx, 0, px],
[0, fy, py],
[0, 0, 1]]
"""
if isinstance(K, torch.Tensor):
K = K.clone()
elif isinstance(K, np.ndarray):
K = K.copy()
else:
raise TypeError('K should be `torch.Tensor` or `numpy.ndarray`, '
f'type(K): {type(K)}.')
if K.shape[-2:] == (3, 3):
warnings.warn(
f'shape of K already is {K.shape}, will pass converting.')
return K
use_numpy = True if isinstance(K, np.ndarray) else False
if K.ndim == 2:
K = K[None].reshape(-1, 4, 4)
elif K.ndim == 3:
K = K.reshape(-1, 4, 4)
else:
raise ValueError(f'Wrong ndim of K: {K.ndim}')
if use_numpy:
K_ = np.eye(3, 3)[None].repeat(K.shape[0], 0)
else:
K_ = torch.eye(3, 3)[None].repeat(K.shape[0], 1, 1)
if is_perspective:
K_[:, :2, :3] = K[:, :2, :3]
else:
K_[:, :2, :2] = K[:, :2, :2]
K_[:, :2, 2:3] = K[:, :2, 3:4]
return K_
def convert_ndc_to_screen(
K: Union[torch.Tensor, np.ndarray],
resolution: Union[int, Tuple[int, int], List[int], torch.Tensor,
np.ndarray],
sign: Optional[Iterable[int]] = None,
is_perspective: bool = True) -> Union[torch.Tensor, np.ndarray]:
"""Convert intrinsic matrix from ndc to screen.
Args:
K (Union[torch.Tensor, np.ndarray]):
Input 4x4 intrinsic matrix, left mm defined.
resolution (Union[int, Tuple[int, int], torch.Tensor, np.ndarray]):
(height, width) of image.
sign (Optional[Union[Iterable[int]]], optional): xyz axis sign.
Defaults to None.
is_perspective (bool, optional): whether is perspective projection.
Defaults to True.
Raises:
TypeError: K should be Tensor or array.
ValueError: shape of K should be (batch, 4, 4)
Returns:
Union[torch.Tensor, np.ndarray]: output intrinsic matrix.
"""
sign = [1, 1, 1] if sign is None else sign
if isinstance(K, torch.Tensor):
K = K.clone()
elif isinstance(K, np.ndarray):
K = K.copy()
else:
raise TypeError(
f'K should be `torch.Tensor` or `np.ndarray`, type(K): {type(K)}')
if K.ndim == 2:
K = K[None].reshape(-1, 4, 4)
elif K.ndim == 3:
K = K.reshape(-1, 4, 4)
else:
raise ValueError(f'Wrong ndim of K: {K.ndim}')
if isinstance(resolution, (int, float)):
w_dst = h_dst = resolution
elif isinstance(resolution, (list, tuple)):
h_dst, w_dst = resolution
elif isinstance(resolution, (torch.Tensor, np.ndarray)):
resolution = resolution.reshape(-1, 2)
h_dst, w_dst = resolution[:, 0], resolution[:, 1]
aspect_ratio = w_dst / h_dst
K[:, 0, 0] *= w_dst / 2
K[:, 1, 1] *= h_dst / 2
if aspect_ratio > 1:
K[:, 0, 0] /= aspect_ratio
else:
K[:, 1, 1] *= aspect_ratio
if is_perspective:
K[:, 0, 2] *= sign[0]
K[:, 1, 2] *= sign[1]
K[:, 0, 2] = (K[:, 0, 2] + 1) * (w_dst / 2)
K[:, 1, 2] = (K[:, 1, 2] + 1) * (h_dst / 2)
else:
K[:, 0, 3] *= sign[0]
K[:, 1, 3] *= sign[1]
K[:, 0, 3] = (K[:, 0, 3] + 1) * (w_dst / 2)
K[:, 1, 3] = (K[:, 1, 3] + 1) * (h_dst / 2)
return K
def convert_screen_to_ndc(
K: Union[torch.Tensor, np.ndarray],
resolution: Union[int, Tuple[int, int], torch.Tensor, np.ndarray],
sign: Optional[Iterable[int]] = None,
is_perspective: bool = True) -> Union[torch.Tensor, np.ndarray]:
"""Convert intrinsic matrix from screen to ndc.
Args:
K (Union[torch.Tensor, np.ndarray]): input intrinsic matrix.
resolution (Union[int, Tuple[int, int], torch.Tensor, np.ndarray]):
(height, width) of image.
sign (Optional[Union[Iterable[int]]], optional): xyz axis sign.
Defaults to None.
is_perspective (bool, optional): whether is perspective projection.
Defaults to True.
Raises:
TypeError: K should be Tensor or array.
ValueError: shape of K should be (batch, 4, 4)
Returns:
Union[torch.Tensor, np.ndarray]: output intrinsic matrix.
"""
if sign is None:
sign = [1, 1, 1]
if isinstance(K, torch.Tensor):
K = K.clone()
elif isinstance(K, np.ndarray):
K = K.copy()
else:
raise TypeError(
f'K should be `torch.Tensor` or `np.ndarray`, type(K): {type(K)}')
if K.ndim == 2:
K = K[None].reshape(-1, 4, 4)
elif K.ndim == 3:
K = K.reshape(-1, 4, 4)
else:
raise ValueError(f'Wrong ndim of K: {K.ndim}')
if isinstance(resolution, (int, float)):
w_src = h_src = resolution
elif isinstance(resolution, (list, tuple)):
h_src, w_src = resolution
elif isinstance(resolution, (torch.Tensor, np.ndarray)):
resolution = resolution.reshape(-1, 2)
h_src, w_src = resolution[:, 0], resolution[:, 1]
aspect_ratio = w_src / h_src
K[:, 0, 0] /= w_src / 2
K[:, 1, 1] /= h_src / 2
if aspect_ratio > 1:
K[:, 0, 0] *= aspect_ratio
else:
K[:, 1, 1] /= aspect_ratio
if is_perspective:
K[:, 0, 2] = K[:, 0, 2] / (w_src / 2) - 1
K[:, 1, 2] = K[:, 1, 2] / (h_src / 2) - 1
K[:, 0, 2] *= sign[0]
K[:, 1, 2] *= sign[1]
else:
K[:, 0, 3] = K[:, 0, 3] / (w_src / 2) - 1
K[:, 1, 3] = K[:, 1, 3] / (h_src / 2) - 1
K[:, 0, 3] *= sign[0]
K[:, 1, 3] *= sign[1]
return K
def convert_world_view(
R: Union[torch.Tensor, np.ndarray], T: Union[torch.Tensor, np.ndarray]
) -> Tuple[Union[torch.Tensor, np.ndarray], Union[torch.Tensor, np.ndarray]]:
"""Convert between view_to_world and world_to_view defined extrinsic
matrix.
Args:
R (Union[torch.Tensor, np.ndarray]): extrinsic rotation matrix.
shape should be (batch, 3, 4)
T (Union[torch.Tensor, np.ndarray]): extrinsic translation matrix.
Raises:
TypeError: R and T should be of the same type.
Returns:
Tuple[Union[torch.Tensor, np.ndarray], Union[torch.Tensor,
np.ndarray]]: output R, T.
"""
if not (type(R) is type(T)):
raise TypeError(
f'R: {type(R)}, T: {type(T)} should have the same type.')
if isinstance(R, torch.Tensor):
R = R.clone()
T = T.clone()
R = R.permute(0, 2, 1)
T = -(R @ T.view(-1, 3, 1)).view(-1, 3)
elif isinstance(R, np.ndarray):
R = R.copy()
T = T.copy()
R = R.transpose(0, 2, 1)
T = -(R @ T.reshape(-1, 3, 1)).reshape(-1, 3)
else:
raise TypeError(f'R: {type(R)}, T: {type(T)} should be torch.Tensor '
f'or numpy.ndarray.')
return R, T
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