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import math
from typing import Iterable, List, Optional, Tuple, Union
import numpy as np
import torch
from pytorch3d.renderer import cameras
from pytorch3d.structures import Meshes
from pytorch3d.transforms import Transform3d
from detrsmpl.core.conventions.cameras.convert_convention import (
convert_camera_matrix,
convert_ndc_to_screen,
convert_screen_to_ndc,
convert_world_view,
)
from detrsmpl.utils.transforms import ee_to_rotmat
from .builder import CAMERAS
class MMCamerasBase(cameras.CamerasBase):
"""Inherited from Pytorch3D CamerasBase and provide some new functions."""
def __init__(self, **kwargs) -> None:
"""Initialize your cameras with `build_cameras` following:
1): provide `K`, `R`, `T`, `resolution`/`image_size`, `in_ndc`
directly.
`K` should be shape of (N, 3, 3) or (N, 4, 4).
`R` should be shape of (N, 3, 3).
`T` should be shape of (N, 3).
2): if `K` is not provided, will use `get_default_projection_matrix`
to generate K from camera intrinsic parameters.
E.g., you can pass `focal_length`, `principal_point` for
perspective camers.
If these args are not provided, will use default values.
3): if `R` is not provided, will use Identity matrix as default.
4): if `T` is not provided, will use zeros matrix as default.
5): `convention` means your source parameter camera convention.
This mainly depends on how you get the matrixs. E.g., you get the
`K` `R`, `T` by calibration with opencv, you should set
`convention = opencv`. To figure out your camera convention,
please see the definition of its extrinsic and intrinsic matrixs.
For projection and rendering, the matrixs will be converted to
`pytorch3d` finally since the `transforms3d` called in rendering
and projection are defined as `pytorch3d` convention.
6): `image_size` equals `resolution`.
7): `in_ndc` could be set for 'PerspectiveCameras' and
'OrthographicCameras', other cameras are fixed for this arg.
`in_ndc = True` means your projection matrix is defined as `camera
space to NDC space`. Under this cirecumstance you need to set
`image_size` or `resolution` (they are equal) when you need to do
`transform_points_screen`. You can also override resolution
in `transform_points_screen` function.
`in_ndc = False` means your projections matrix is defined as
`cameras space to screen space`. Under this cirecumstance you do
not need to set `image_size` or `resolution` (they are equal) when
you need to do `transform_points_screen` since the projection
matrix is defined as view space to screen space.
"""
for k in kwargs:
if isinstance(kwargs.get(k), np.ndarray):
kwargs.update({k: torch.Tensor(kwargs[k])})
convention = kwargs.pop('convention', 'pytorch3d').lower()
in_ndc = kwargs.pop('in_ndc', kwargs.get('_in_ndc'))
kwargs.update(_in_ndc=in_ndc)
is_perspective = kwargs.get('_is_perspective')
kwargs.pop('is_perspective', None)
image_size = kwargs.get('image_size', kwargs.get('resolution', None))
if image_size is not None:
if isinstance(image_size, (int, float)):
image_size = (image_size, image_size)
if isinstance(image_size, (tuple, list)):
image_size = torch.Tensor(image_size)
if isinstance(image_size, torch.Tensor):
if image_size.numel() == 1:
image_size = image_size.repeat(2)
image_size = image_size.view(-1, 2)
if kwargs.get('K') is None:
focal_length = kwargs.get('focal_length', None)
if focal_length is not None:
if not isinstance(focal_length, Iterable):
focal_length = [focal_length, focal_length]
if not torch.is_tensor(focal_length):
focal_length = torch.FloatTensor(focal_length).view(-1, 2)
elif focal_length.numel() == 1:
focal_length = focal_length.repeat(2).view(-1, 2)
kwargs.update(focal_length=focal_length)
principal_point = kwargs.get('principal_point', None)
if principal_point is not None:
if isinstance(principal_point, (tuple, list)):
principal_point = torch.FloatTensor(principal_point)
principal_point = principal_point.view(-1, 2)
kwargs.update(principal_point=principal_point)
K = self.get_default_projection_matrix(**kwargs)
K, _, _ = convert_camera_matrix(K=K,
is_perspective=is_perspective,
convention_src='pytorch3d',
convention_dst='pytorch3d',
in_ndc_src=in_ndc,
in_ndc_dst=in_ndc,
resolution_dst=image_size,
resolution_src=image_size)
kwargs.update(K=K)
K, R, T = convert_camera_matrix(K=kwargs.get('K'),
R=kwargs.get('R', None),
T=kwargs.get('T', None),
convention_src=convention,
convention_dst='pytorch3d',
is_perspective=is_perspective,
in_ndc_src=in_ndc,
in_ndc_dst=in_ndc,
resolution_src=image_size,
resolution_dst=image_size)
if image_size is not None:
if image_size.shape[0] == 1:
image_size = image_size.repeat(K.shape[0], 1)
kwargs.update(image_size=image_size)
kwargs.update(resolution=image_size)
kwargs.update(K=K, R=R, T=T)
super().__init__(**kwargs)
def get_camera_plane_normals(self, **kwargs) -> torch.Tensor:
"""Get the identity normal vector which stretchs out of the camera
plane.
Could pass `R` to override the camera extrinsic rotation matrix.
Returns:
torch.Tensor: shape will be (N, 3)
"""
normals = torch.Tensor([0, 0, 1]).view(1, 3).to(self.device)
w2v_trans = self.get_world_to_view_transform(**kwargs)
normals = w2v_trans.inverse().transform_normals(normals)
return normals.view(-1, 3)
def compute_depth_of_points(self, points: torch.Tensor) -> torch.Tensor:
"""Compute depth of points to the camera plane.
Args:
points ([torch.Tensor]): shape should be (batch_size, ..., 3).
Returns:
torch.Tensor: shape will be (batch_size, 1)
"""
world_to_view_transform = self.get_world_to_view_transform()
world_to_view_points = world_to_view_transform.transform_points(
points.to(self.device))
return world_to_view_points[..., 2:3]
def compute_normal_of_meshes(self, meshes: Meshes) -> torch.Tensor:
"""Compute normal of meshes in the camera view.
Args:
points ([torch.Tensor]): shape should be (batch_size, 3).
Returns:
torch.Tensor: shape will be (batch_size, 1)
"""
world_to_view_transform = self.get_world_to_view_transform()
world_to_view_normals = world_to_view_transform.transform_normals(
meshes.verts_normals_padded().to(self.device))
return world_to_view_normals
def __repr__(self):
"""Rewrite __repr__
Returns:
str: print the information of cameras (N, in_ndc, device).
"""
main_str = super().__repr__()
main_str = main_str.split(')')[0]
main_str += f'N: {self.__len__()}, in_ndc: {self.in_ndc()}, '
main_str += f'device: {self.device})'
return main_str
def get_image_size(self):
"""Returns the image size, if provided, expected in the form of
(height, width) The image size is used for conversion of projected
points to screen coordinates."""
if hasattr(self, 'image_size'):
image_size = self.image_size
if hasattr(self, 'resolution'):
if self.resolution is not None:
image_size = self.resolution
else:
image_size = None
return image_size
def __getitem__(
self, index: Union[slice, int, torch.Tensor, List,
Tuple]) -> 'MMCamerasBase':
"""Slice the cameras by batch dim.
Args:
index (Union[slice, int, torch.Tensor, List, Tuple]):
index for slicing.
Returns:
MMCamerasBase: sliced cameras.
"""
if isinstance(index, int):
index = [index]
return self.__class__(K=self.K[index],
R=self.R[index],
T=self.T[index],
image_size=self.get_image_size()[index]
if self.get_image_size() is not None else None,
in_ndc=self.in_ndc(),
convention='pytorch3d',
device=self.device)
def extend(self, N) -> 'MMCamerasBase':
"""Create new camera class which contains each input camera N times.
Args:
N: number of new copies of each camera.
Returns:
MMCamerasBase object.
"""
return self.__class__(K=self.K.repeat(N, 1, 1),
R=self.R.repeat(N, 1, 1),
T=self.T.repeat(N, 1),
image_size=self.get_image_size(),
in_ndc=self.in_ndc(),
convention='pytorch3d',
device=self.device)
def extend_(self, N):
"""extend camera inplace."""
self.K = self.K.repeat(N, 1, 1)
self.R = self.R.repeat(N, 1, 1)
self.T = self.T.repeat(N, 1)
self._N = self._N * N
@classmethod
def get_default_projection_matrix(cls, ):
"""Class method. Calculate the projective transformation matrix by
default parameters.
Args:
**kwargs: parameters for the projection can be passed in as keyword
arguments to override the default values set in `__init__`.
Return:
a `torch.Tensor` which represents a batch of projection matrices K
of shape (N, 4, 4)
"""
raise NotImplementedError()
def to_screen_(self, **kwargs) -> 'MMCamerasBase':
"""Convert to screen inplace."""
if self.in_ndc():
if self.get_image_size() is None:
self.image_size = kwargs.get('image_size')
else:
self.image_size = self.get_image_size()
self.K = convert_ndc_to_screen(K=self.K,
resolution=self.image_size,
is_perspective=self._is_perspective)
self._in_ndc = False
else:
print('Redundant operation, already in screen.')
def to_ndc_(self, **kwargs) -> 'MMCamerasBase':
"""Convert to ndc inplace."""
if self.in_ndc():
print('Redundant operation, already in ndc.')
else:
if self.get_image_size() is None:
self.image_size = kwargs.get('image_size')
else:
self.image_size = self.get_image_size()
self.K = convert_screen_to_ndc(K=self.K,
resolution=self.image_size,
is_perspective=self._is_perspective)
self._in_ndc = True
def to_screen(self, **kwargs) -> 'MMCamerasBase':
"""Convert to screen."""
if self.in_ndc():
if self.get_image_size() is None:
self.image_size = kwargs.get('image_size')
else:
self.image_size = self.get_image_size()
K = convert_ndc_to_screen(K=self.K,
resolution=self.image_size,
is_perspective=self._is_perspective)
return self.__class__(K=K,
R=self.R,
T=self.T,
in_ndc=False,
resolution=self.image_size)
else:
print('Redundant operation, already in screen.')
def to_ndc(self, **kwargs) -> 'MMCamerasBase':
"""Convert to ndc."""
if self.in_ndc():
print('Redundant operation, already in ndc.')
else:
if self.get_image_size() is None:
self.image_size = kwargs.get('image_size')
else:
self.image_size = self.get_image_size()
K = convert_screen_to_ndc(K=self.K,
resolution=self.image_size,
is_perspective=self._is_perspective)
return self.__class__(K=K,
R=self.R,
T=self.T,
in_ndc=True,
resolution=self.image_size)
def detach(self) -> 'MMCamerasBase':
image_size = self.image_size.detach(
) if self.image_size is not None else None
return self.__class__(K=self.K.detach(),
R=self.R.detach(),
T=self.T.detach(),
in_ndc=self.in_ndc(),
device=self.device,
resolution=image_size)
def concat(self, others) -> 'MMCamerasBase':
if isinstance(others, type(self)):
others = [others]
else:
raise TypeError('Could only concat with same type cameras.')
return concat_cameras([self] + others)
@CAMERAS.register_module(name=('WeakPerspectiveCameras', 'WeakPerspective',
'weakperspective'))
class WeakPerspectiveCameras(MMCamerasBase):
"""Inherited from [Pytorch3D cameras](https://github.com/facebookresearch/
pytorch3d/blob/main/pytorch3d/renderer/cameras.py) and mimiced the code
style. And re-inmplemented functions: compute_projection_matrix,
get_projection_transform, unproject_points, is_perspective, in_ndc for
render.
K modified from [VIBE](https://github.com/mkocabas/VIBE/blob/master/
lib/utils/renderer.py) and changed to opencv convention.
Original license please see docs/additional_license/md.
This intrinsic matrix is orthographics indeed, but could serve as
weakperspective for single smpl mesh.
"""
def __init__(
self,
scale_x: Union[torch.Tensor, float] = 1.0,
scale_y: Union[torch.Tensor, float] = 1.0,
transl_x: Union[torch.Tensor, float] = 0.0,
transl_y: Union[torch.Tensor, float] = 0.0,
znear: Union[torch.Tensor, float] = -1.0,
aspect_ratio: Union[torch.Tensor, float] = 1.0,
K: Optional[torch.Tensor] = None,
R: Optional[torch.Tensor] = None,
T: Optional[torch.Tensor] = None,
device: Union[torch.device, str] = 'cpu',
convention: str = 'pytorch3d',
**kwargs,
):
"""Initialize. If K is provided, don't need scale_x, scale_y, transl_x,
transl_y, znear, aspect_ratio.
Args:
scale_x (Union[torch.Tensor, float], optional):
Scale in x direction.
Defaults to 1.0.
scale_y (Union[torch.Tensor, float], optional):
Scale in y direction.
Defaults to 1.0.
transl_x (Union[torch.Tensor, float], optional):
Translation in x direction.
Defaults to 0.0.
transl_y (Union[torch.Tensor, float], optional):
Translation in y direction.
Defaults to 0.0.
znear (Union[torch.Tensor, float], optional):
near clipping plane of the view frustrum.
Defaults to -1.0.
aspect_ratio (Union[torch.Tensor, float], optional):
aspect ratio of the image pixels. 1.0 indicates square pixels.
Defaults to 1.0.
K (Optional[torch.Tensor], optional): Intrinsic matrix of shape
(N, 4, 4). If provided, don't need scale_x, scale_y, transl_x,
transl_y, znear, aspect_ratio.
Defaults to None.
R (Optional[torch.Tensor], optional):
Rotation matrix of shape (N, 3, 3).
Defaults to None.
T (Optional[torch.Tensor], optional):
Translation matrix of shape (N, 3).
Defaults to None.
device (Union[torch.device, str], optional):
torch device. Defaults to 'cpu'.
"""
kwargs.update(
_in_ndc=True,
_is_perspective=False,
)
kwargs.pop('in_ndc', None)
kwargs.pop('is_perspective', None)
super().__init__(scale_x=scale_x,
scale_y=scale_y,
transl_x=transl_x,
transl_y=transl_y,
znear=znear,
aspect_ratio=aspect_ratio,
K=K,
R=R,
T=T,
device=device,
convention=convention,
**kwargs)
@staticmethod
def convert_orig_cam_to_matrix(
orig_cam: torch.Tensor,
**kwargs) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Compute intrinsic camera matrix from orig_cam parameter of smpl.
.. code-block:: python
r > 1::
K = [[sx*r, 0, 0, tx*sx*r],
[0, sy, 0, ty*sy],
[0, 0, 1, 0],
[0, 0, 0, 1]]
or r < 1::
K = [[sx, 0, 0, tx*sx],
[0, sy/r, 0, ty*sy/r],
[0, 0, 1, 0],
[0, 0, 0, 1],]
rotation matrix: (N, 3, 3)::
[[1, 0, 0],
[0, 1, 0],
[0, 0, 1]]
translation matrix: (N, 3)::
[0, 0, -znear]
Args:
orig_cam (torch.Tensor): shape should be (N, 4).
znear (Union[torch.Tensor, float], optional):
near clipping plane of the view frustrum.
Defaults to 0.0.
aspect_ratio (Union[torch.Tensor, float], optional):
aspect ratio of the image pixels. 1.0 indicates square pixels.
Defaults to 1.0.
Returns:
Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
opencv intrinsic matrix: (N, 4, 4)
"""
znear = kwargs.get('znear', -1.0)
aspect_ratio = kwargs.get('aspect_ratio', 1.0)
_N = orig_cam.shape[0]
scale_x, scale_y, transl_x, transl_y = orig_cam[:, 0], orig_cam[:, 1],\
orig_cam[:, 2], orig_cam[:, 3]
K = torch.zeros((_N, 4, 4), dtype=torch.float32)
if aspect_ratio >= 1.0:
K[:, 0, 0] = scale_x * aspect_ratio
K[:, 1, 1] = scale_y
K[:, 0, 3] = transl_x * scale_x * aspect_ratio
K[:, 1, 3] = transl_y * scale_y
else:
K[:, 0, 0] = scale_x
K[:, 1, 1] = scale_y / aspect_ratio
K[:, 0, 3] = transl_x * scale_x
K[:, 1, 3] = transl_y * scale_y / aspect_ratio
K[:, 3, 3] = 1
K[:, 2, 2] = 1
R = torch.eye(3, 3)[None].repeat(_N, 1, 1)
T = torch.zeros(_N, 3)
T[:, 2] = znear
return K, R, T
@staticmethod
def convert_K_to_orig_cam(
K: torch.Tensor,
aspect_ratio: Union[torch.Tensor, float] = 1.0,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Compute intrinsic camera matrix from pred camera parameter of smpl.
Args:
K (torch.Tensor):
opencv orthographics intrinsic matrix: (N, 4, 4)
.. code-block:: python
K = [[sx*r, 0, 0, tx*sx*r],
[0, sy, 0, ty*sy],
[0, 0, 1, 0],
[0, 0, 0, 1],]
aspect_ratio (Union[torch.Tensor, float], optional):
aspect ratio of the image pixels. 1.0 indicates square pixels.
Defaults to 1.0.
Returns:
orig_cam (torch.Tensor): shape should be (N, 4).
"""
_N = K.shape[0]
s_x = K[:, 0, 0] / aspect_ratio
s_y = K[:, 1, 1] / aspect_ratio
t_x = K[:, 0, 3] / (aspect_ratio * s_x)
t_y = K[:, 1, 3] / s_y
orig_cam = torch.cat([s_x, s_y, t_x, t_y], -1).view(_N, 4)
return orig_cam
@classmethod
def get_default_projection_matrix(cls, **args):
"""Class method. Calculate the projective transformation matrix by
default parameters.
Args:
**kwargs: parameters for the projection can be passed in as keyword
arguments to override the default values set in `__init__`.
Return:
a `torch.Tensor` which represents a batch of projection matrices K
of shape (N, 4, 4)
"""
orig_cam = args.get('orig_cam', None)
scale_x = args.get('scale_x', 1.0)
scale_y = args.get('scale_y', 1.0)
transl_x = args.get('transl_x', 0.0)
transl_y = args.get('transl_y', 0.0)
aspect_ratio = args.get('aspect_ratio', 1.0)
batch_size = args.get('batch_size', 1)
device = args.get('device', 'cpu')
if orig_cam is not None:
K, _, _ = cls.convert_orig_cam_to_matrix(orig_cam, **args)
else:
K = torch.zeros((1, 4, 4), dtype=torch.float32)
K[:, 0, 0] = scale_x * aspect_ratio
K[:, 1, 1] = scale_y
K[:, 3, 3] = 1
K[:, 0, 3] = transl_x * scale_x * aspect_ratio
K[:, 1, 3] = transl_y * scale_y
K[:, 2, 2] = 1
K = K.repeat(batch_size, 1, 1).to(device)
return K
def compute_projection_matrix(self, scale_x, scale_y, transl_x, transl_y,
aspect_ratio) -> torch.Tensor:
"""Compute the calibration matrix K of shape (N, 4, 4)
Args:
scale_x (Union[torch.Tensor, float], optional):
Scale in x direction.
scale_y (Union[torch.Tensor, float], optional):
Scale in y direction.
transl_x (Union[torch.Tensor, float], optional):
Translation in x direction.
transl_y (Union[torch.Tensor, float], optional):
Translation in y direction.
aspect_ratio (Union[torch.Tensor, float], optional):
aspect ratio of the image pixels. 1.0 indicates square pixels.
Returns:
torch.FloatTensor of the calibration matrix with shape (N, 4, 4)
"""
K = torch.zeros((self._N, 4, 4),
dtype=torch.float32,
device=self.device)
K[:, 0, 0] = scale_x * aspect_ratio
K[:, 1, 1] = scale_y
K[:, 3, 3] = 1
K[:, 0, 3] = transl_x * scale_x * aspect_ratio
K[:, 1, 3] = transl_y * scale_y
K[:, 2, 2] = 1
return K
def get_projection_transform(self, **kwargs) -> Transform3d:
"""Calculate the orthographic projection matrix. Use column major
order.
Args:
**kwargs: parameters for the projection can be passed in to
override the default values set in __init__.
Return:
a Transform3d object which represents a batch of projection
matrices of shape (N, 4, 4)
"""
K = kwargs.get('K', self.K)
if K is not None:
if K.shape != (self._N, 4, 4):
msg = f'Expected K to have shape of ({self._N}, 4, 4)'
raise ValueError(msg)
else:
K = self.compute_projection_matrix(
kwargs.get('scale_x', self.scale_x),
kwargs.get('scale_y', self.scale_y),
kwargs.get('transl_x', self.trans_x),
kwargs.get('transl_y', self.trans_y),
kwargs.get('aspect_ratio', self.aspect_ratio))
transform = Transform3d(matrix=K.transpose(1, 2).contiguous(),
device=self.device)
return transform
def unproject_points(self,
xy_depth: torch.Tensor,
world_coordinates: bool = True,
**kwargs) -> torch.Tensor:
"""Sends points from camera coordinates (NDC or screen) back to camera
view or world coordinates depending on the `world_coordinates` boolean
argument of the function."""
if world_coordinates:
to_camera_transform = self.get_full_projection_transform(**kwargs)
else:
to_camera_transform = self.get_projection_transform(**kwargs)
unprojection_transform = to_camera_transform.inverse()
return unprojection_transform.transform_points(xy_depth)
def is_perspective(self):
"""Boolean of whether is perspective."""
return False
def in_ndc(self):
"""Boolean of whether in NDC."""
return True
def to_ndc_(self, **kwargs):
"""Not implemented."""
raise NotImplementedError()
def to_screen_(self, **kwargs):
"""Not implemented."""
raise NotImplementedError()
def to_ndc(self, **kwargs):
"""Not implemented."""
raise NotImplementedError()
def to_screen(self, **kwargs):
"""Not implemented."""
raise NotImplementedError()
@CAMERAS.register_module(name=('PerspectiveCameras', 'perspective',
'Perspective'))
class PerspectiveCameras(cameras.PerspectiveCameras, MMCamerasBase):
"""Inherited from Pytorch3D `PerspectiveCameras`."""
def __init__(
self,
focal_length=1.0,
principal_point=((0.0, 0.0), ),
R: Optional[torch.Tensor] = None,
T: Optional[torch.Tensor] = None,
K: Optional[torch.Tensor] = None,
device: Union[torch.device, str] = 'cpu',
in_ndc: bool = True,
convention: str = 'pytorch3d',
image_size: Optional[Union[List, Tuple, torch.Tensor]] = None,
**kwargs,
) -> None:
"""
Args:
focal_length (float, torch.Tensor, optional): Defaults to 1.0.
principal_point (tuple, optional): Defaults to ((0.0, 0.0), ).
R (Optional[torch.Tensor], optional): Defaults to None.
T (Optional[torch.Tensor], optional): Defaults to None.
K (Optional[torch.Tensor], optional): Defaults to None.
device (Union[torch.device, str], optional): Defaults to 'cpu'.
in_ndc (bool, optional): Defaults to True.
convention (str, optional): Defaults to 'pytorch3d'.
image_size (Optional[Union[List, Tuple, torch.Tensor]], optional):
Defaults to None.
"""
if image_size is not None:
kwargs.update({'image_size': image_size})
kwargs.update(
_in_ndc=in_ndc,
_is_perspective=True,
)
kwargs.pop('is_perspective', None)
kwargs.pop('in_ndc', None)
super(cameras.PerspectiveCameras,
self).__init__(device=device,
focal_length=focal_length,
principal_point=principal_point,
R=R,
T=T,
K=K,
convention=convention,
**kwargs)
if image_size is not None:
if (self.image_size < 1).any(): # pyre-ignore
raise ValueError('Image_size provided has invalid values')
else:
self.image_size = None
def __getitem__(self, index: Union[slice, int, torch.Tensor, List, Tuple]):
"""Slice the cameras by batch dim.
Args:
index (Union[slice, int, torch.Tensor, List, Tuple]):
index for slicing.
Returns:
MMCamerasBase: sliced cameras.
"""
return super(cameras.PerspectiveCameras, self).__getitem__(index)
@classmethod
def get_default_projection_matrix(cls, **args) -> torch.Tensor:
"""Class method. Calculate the projective transformation matrix by
default parameters.
Args:
**kwargs: parameters for the projection can be passed in as keyword
arguments to override the default values set in `__init__`.
Return:
a `torch.Tensor` which represents a batch of projection matrices K
of shape (N, 4, 4)
"""
batch_size = args.get('batch_size', 1)
device = args.get('device', 'cpu')
focal_length = args.get('focal_length')
principal_point = args.get('principal_point')
return cameras._get_sfm_calibration_matrix(
N=batch_size,
device=device,
focal_length=focal_length,
principal_point=principal_point,
orthographic=False)
def get_ndc_camera_transform(self, **kwargs) -> Transform3d:
kwargs.pop('cameras', None)
return super().get_ndc_camera_transform(**kwargs)
def transform_points_screen(self,
points,
eps: Optional[float] = None,
**kwargs) -> torch.Tensor:
kwargs.pop('cameras', None)
return super().transform_points_screen(points, eps, **kwargs)
@CAMERAS.register_module(name=('FoVPerspectiveCameras', 'FoVPerspective',
'fovperspective'))
class FoVPerspectiveCameras(cameras.FoVPerspectiveCameras, MMCamerasBase):
"""Inherited from Pytorch3D `FoVPerspectiveCameras`."""
def __init__(
self,
znear=1.0,
zfar=100.0,
aspect_ratio=1.0,
fov=60.0,
degrees: bool = True,
R: Optional[torch.Tensor] = None,
T: Optional[torch.Tensor] = None,
K: Optional[torch.Tensor] = None,
device: Union[torch.device, str] = 'cpu',
convention: str = 'pytorch3d',
**kwargs,
) -> None:
"""Initialize a camera.
Args:
znear (float, optional): Defaults to 1.0.
zfar (float, optional): Defaults to 100.0.
aspect_ratio (float, optional): Defaults to 1.0.
fov (float, optional): Defaults to 60.0.
degrees (bool, optional): Defaults to True.
R (Optional[torch.Tensor], optional): Defaults to None.
T (Optional[torch.Tensor], optional): Defaults to None.
K (Optional[torch.Tensor], optional): Defaults to None.
device (Union[torch.device, str], optional): Defaults to 'cpu'.
convention (str, optional): Defaults to 'pytorch3d'.
"""
kwargs.update(
_in_ndc=True,
_is_perspective=True,
)
kwargs.pop('in_ndc', None)
kwargs.pop('is_perspective', None)
super(cameras.FoVPerspectiveCameras, self).__init__(
device=device,
znear=znear,
zfar=zfar,
aspect_ratio=aspect_ratio,
fov=fov,
R=R,
T=T,
K=K,
convention=convention,
**kwargs,
)
self.degrees = degrees
def __getitem__(self, index: Union[slice, int, torch.Tensor, List, Tuple]):
"""Slice the cameras by batch dim.
Args:
index (Union[slice, int, torch.Tensor, List, Tuple]):
index for slicing.
Returns:
MMCamerasBase: sliced cameras.
"""
return super(cameras.FoVPerspectiveCameras, self).__getitem__(index)
def get_ndc_camera_transform(self, **kwargs) -> Transform3d:
kwargs.pop('cameras', None)
return super().get_ndc_camera_transform(**kwargs)
def transform_points_screen(self,
points,
eps: Optional[float] = None,
**kwargs) -> torch.Tensor:
kwargs.pop('cameras', None)
return super().transform_points_screen(points, eps, **kwargs)
@classmethod
def get_default_projection_matrix(cls, **args) -> torch.Tensor:
"""Class method. Calculate the projective transformation matrix by
default parameters.
Args:
**kwargs: parameters for the projection can be passed in as keyword
arguments to override the default values set in `__init__`.
Return:
a `torch.Tensor` which represents a batch of projection matrices K
of shape (N, 4, 4)
"""
znear = args.get('znear', 1.0)
zfar = args.get('zfar', 100.0)
aspect_ratio = args.get('aspect_ratio', 1.0)
fov = args.get('fov', 60.0)
degrees = args.get('degrees', True)
batch_size = args.get('batch_size', 1)
K = torch.zeros((1, 4, 4), dtype=torch.float32)
if degrees:
fov = (math.pi / 180) * fov
if not torch.is_tensor(fov):
fov = torch.tensor(fov)
tanHalfFov = torch.tan((fov / 2))
max_y = tanHalfFov * znear
min_y = -max_y
max_x = max_y * aspect_ratio
min_x = -max_x
z_sign = 1.0
K[:, 0, 0] = 2.0 * znear / (max_x - min_x)
K[:, 1, 1] = 2.0 * znear / (max_y - min_y)
K[:, 0, 2] = (max_x + min_x) / (max_x - min_x)
K[:, 1, 2] = (max_y + min_y) / (max_y - min_y)
K[:, 3, 2] = z_sign
K[:, 2, 2] = z_sign * zfar / (zfar - znear)
K[:, 2, 3] = -(zfar * znear) / (zfar - znear)
K = K.repeat(batch_size, 1, 1)
return K
def to_ndc_(self, **kwargs):
"""Not implemented."""
raise NotImplementedError()
def to_screen_(self, **kwargs):
"""Not implemented."""
raise NotImplementedError()
def to_ndc(self, **kwargs):
"""Not implemented."""
raise NotImplementedError()
def to_screen(self, **kwargs):
"""Not implemented."""
raise NotImplementedError()
@CAMERAS.register_module(name=('OrthographicCameras', 'Orthographic',
'orthographic'))
class OrthographicCameras(cameras.OrthographicCameras, MMCamerasBase):
"""Inherited from Pytorch3D `OrthographicCameras`."""
def __init__(
self,
focal_length=1.0,
principal_point=((0.0, 0.0), ),
R: Optional[torch.Tensor] = None,
T: Optional[torch.Tensor] = None,
K: Optional[torch.Tensor] = None,
device: Union[torch.Tensor, str] = 'cpu',
in_ndc: bool = True,
image_size: Optional[torch.Tensor] = None,
convention: str = 'pytorch3d',
**kwargs,
) -> None:
"""Initialize OrthographicCameras.
Args:
focal_length (float, optional): Defaults to 1.0.
principal_point (tuple, optional): Defaults to ((0.0, 0.0), ).
R (Optional[torch.Tensor], optional): Defaults to None.
T (Optional[torch.Tensor], optional): Defaults to None.
K (Optional[torch.Tensor], optional): Defaults to None.
device (Union[torch.Tensor, str], optional): Defaults to 'cpu'.
in_ndc (bool, optional): Defaults to True.
image_size (Optional[torch.Tensor], optional): Defaults to None.
convention (str, optional): Defaults to 'pytorch3d'.
Raises:
ValueError: [description]
"""
if image_size is not None:
kwargs.update({'image_size': image_size})
kwargs.update(
_is_perspective=False,
_in_ndc=in_ndc,
)
kwargs.pop('is_perspective', None)
kwargs.pop('in_ndc', None)
super(cameras.OrthographicCameras,
self).__init__(device=device,
focal_length=focal_length,
principal_point=principal_point,
R=R,
T=T,
K=K,
convention=convention,
**kwargs)
if image_size is not None:
if (self.image_size < 1).any(): # pyre-ignore
raise ValueError('Image_size provided has invalid values')
else:
self.image_size = None
def get_ndc_camera_transform(self, **kwargs) -> Transform3d:
kwargs.pop('cameras', None)
return super().get_ndc_camera_transform(**kwargs)
def transform_points_screen(self,
points,
eps: Optional[float] = None,
**kwargs) -> torch.Tensor:
kwargs.pop('cameras', None)
return super().transform_points_screen(points, eps, **kwargs)
def __getitem__(self, index: Union[slice, int, torch.Tensor, List, Tuple]):
"""Slice the cameras by batch dim.
Args:
index (Union[slice, int, torch.Tensor, List, Tuple]):
index for slicing.
Returns:
MMCamerasBase: sliced cameras.
"""
return super(cameras.OrthographicCameras, self).__getitem__(index)
@classmethod
def get_default_projection_matrix(cls, **args) -> torch.Tensor:
"""Class method. Calculate the projective transformation matrix by
default parameters.
.. code-block:: python
fx = focal_length[:,0]
fy = focal_length[:,1]
px = principal_point[:,0]
py = principal_point[:,1]
K = [[fx, 0, 0, px],
[0, fy, 0, py],
[0, 0, 1, 0],
[0, 0, 0, 1],]
Args:
**kwargs: parameters for the projection can be passed in as keyword
arguments to override the default values.
Return:
a `torch.Tensor` which represents a batch of projection matrices K
of shape (N, 4, 4)
"""
batch_size = args.get('batch_size', 1)
device = args.get('device', 'cpu')
focal_length = args.get('focal_length')
principal_point = args.get('principal_point')
return cameras._get_sfm_calibration_matrix(
N=batch_size,
device=device,
focal_length=focal_length,
principal_point=principal_point,
orthographic=True)
@CAMERAS.register_module(name=('FoVOrthographicCameras', 'FoVOrthographic',
'fovorthographic'))
class FoVOrthographicCameras(cameras.FoVOrthographicCameras, MMCamerasBase):
"""Inherited from Pytorch3D `FoVOrthographicCameras`."""
def __init__(
self,
znear: Union[torch.Tensor, int, float] = 1.0,
zfar: Union[torch.Tensor, int, float] = 100.0,
max_y: Union[torch.Tensor, int, float] = 1.0,
min_y: Union[torch.Tensor, int, float] = -1.0,
max_x: Union[torch.Tensor, int, float] = 1.0,
min_x: Union[torch.Tensor, int, float] = -1.0,
scale_xyz: Union[Iterable[float],
Iterable[int]] = ((1.0, 1.0, 1.0), ), # (1, 3)
R: Optional[torch.Tensor] = None,
T: Optional[torch.Tensor] = None,
K: Optional[torch.Tensor] = None,
device: Union[torch.device, str] = 'cpu',
convention: str = 'pytorch3d',
**kwargs):
"""reimplemented __init__, add `convention`.
Args:
znear (Union[torch.Tensor, int, float], optional):
Defaults to 1.0.
zfar (Union[torch.Tensor, int, float], optional):
Defaults to 100.0.
max_y (Union[torch.Tensor, int, float], optional):
Defaults to 1.0.
min_y (Union[torch.Tensor, int, float], optional):
Defaults to -1.0.
max_x (Union[torch.Tensor, int, float], optional):
Defaults to 1.0.
min_x (Union[torch.Tensor, int, float], optional):
Defaults to -1.0.
scale_xyz (Union[Iterable[float], Iterable[int]], optional):
Defaults to ((1.0, 1.0, 1.0), ).
T (Optional[torch.Tensor], optional): Defaults to None.
K (Optional[torch.Tensor], optional): Defaults to None.
device (Union[torch.device, str], optional): Defaults to 'cpu'.
convention (str, optional): Defaults to 'pytorch3d'.
"""
kwargs.update(_is_perspective=False, _in_ndc=True)
kwargs.pop('in_ndc', None)
kwargs.pop('is_perspective', None)
super(cameras.FoVOrthographicCameras,
self).__init__(device=device,
znear=znear,
zfar=zfar,
max_y=max_y,
min_y=min_y,
max_x=max_x,
min_x=min_x,
scale_xyz=scale_xyz,
R=R,
T=T,
K=K,
convention=convention,
**kwargs)
def __getitem__(self, index: Union[slice, int, torch.Tensor, List, Tuple]):
"""Slice the cameras by batch dim.
Args:
index (Union[slice, int, torch.Tensor, List, Tuple]):
index for slicing.
Returns:
MMCamerasBase: sliced cameras.
"""
return super(cameras.FoVOrthographicCameras, self).__getitem__(index)
@classmethod
def get_default_projection_matrix(cls, **args) -> torch.Tensor:
"""Class method. Calculate the projective transformation matrix by
default parameters.
.. code-block:: python
scale_x = 2 / (max_x - min_x)
scale_y = 2 / (max_y - min_y)
scale_z = 2 / (far-near)
mid_x = (max_x + min_x) / (max_x - min_x)
mix_y = (max_y + min_y) / (max_y - min_y)
mid_z = (far + near) / (far - near)
K = [[scale_x, 0, 0, -mid_x],
[0, scale_y, 0, -mix_y],
[0, 0, -scale_z, -mid_z],
[0, 0, 0, 1],]
Args:
**kwargs: parameters for the projection can be passed in as keyword
arguments to override the default values.
Return:
a `torch.Tensor` which represents a batch of projection matrices K
of shape (N, 4, 4)
"""
znear = args.get('znear', 1.0)
zfar = args.get('zfar', 100.0)
max_y = args.get('max_y', 1.0)
min_y = args.get('min_y', -1.0)
max_x = args.get('max_x', 1.0)
min_x = args.get('min_x', -1.0)
scale_xyz = args.get(
'scale_xyz',
((1.0, 1.0, 1.0), ),
)
batch_size = args.get('batch_size', 1)
K = torch.zeros((1, 4, 4), dtype=torch.float32)
ones = torch.ones((1), dtype=torch.float32)
z_sign = +1.0
if not isinstance(scale_xyz, torch.Tensor):
scale_xyz = torch.Tensor(scale_xyz)
K[:, 0, 0] = (2.0 / (max_x - min_x)) * scale_xyz[:, 0]
K[:, 1, 1] = (2.0 / (max_y - min_y)) * scale_xyz[:, 1]
K[:, 0, 3] = -(max_x + min_x) / (max_x - min_x)
K[:, 1, 3] = -(max_y + min_y) / (max_y - min_y)
K[:, 3, 3] = ones
# NOTE: This maps the z coordinate to the range [0, 1] and replaces the
# the OpenGL z normalization to [-1, 1]
K[:, 2, 2] = z_sign * (1.0 / (zfar - znear)) * scale_xyz[:, 2]
K[:, 2, 3] = -znear / (zfar - znear)
K = K.repeat(batch_size, 1, 1)
return K
def to_ndc_(self, **kwargs):
"""Not implemented."""
raise NotImplementedError()
def to_screen_(self, **kwargs):
"""Not implemented."""
raise NotImplementedError()
def to_ndc(self, **kwargs):
"""Not implemented."""
raise NotImplementedError()
def to_screen(self, **kwargs):
"""Not implemented."""
raise NotImplementedError()
def get_ndc_camera_transform(self, **kwargs) -> Transform3d:
kwargs.pop('cameras', None)
return super().get_ndc_camera_transform(**kwargs)
def transform_points_screen(self,
points,
eps: Optional[float] = None,
**kwargs) -> torch.Tensor:
kwargs.pop('cameras', None)
return super().transform_points_screen(points, eps, **kwargs)
def concat_cameras(cameras_list: List[MMCamerasBase]) -> MMCamerasBase:
"""Concat a list of cameras of the same type.
Args:
cameras_list (List[cameras.CamerasBase]): a list of cameras.
Returns:
MMCamerasBase: the returned cameras concated following the batch
dim.
"""
K = []
R = []
T = []
is_perspective = cameras_list[0].is_perspective()
in_ndc = cameras_list[0].in_ndc()
cam_cls = type(cameras_list[0])
image_size = cameras_list[0].get_image_size()
device = cameras_list[0].device
for cam in cameras_list:
assert type(cam) is cam_cls
assert cam.in_ndc() is in_ndc
assert cam.is_perspective() is is_perspective
assert cam.device is device
K.append(cam.K)
R.append(cam.R)
T.append(cam.T)
K = torch.cat(K)
R = torch.cat(R)
T = torch.cat(T)
concated_cameras = cam_cls(K=K,
R=R,
T=T,
device=device,
is_perspective=is_perspective,
in_ndc=in_ndc,
image_size=image_size)
return concated_cameras
def compute_orbit_cameras(
K: Union[torch.Tensor, np.ndarray, None] = None,
elev: float = 0,
azim: float = 0,
dist: float = 2.7,
at: Union[torch.Tensor, List, Tuple] = (0, 0, 0),
batch_size: int = 1,
orbit_speed: Union[float, Tuple[float, float]] = 0,
dist_speed: Optional[float] = 0,
convention: str = 'pytorch3d',
) -> Union[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Generate a sequence of moving cameras following an orbit.
Args:
K (Union[torch.Tensor, np.ndarray, None], optional):
Intrinsic matrix. Will generate a default K if None.
Defaults to None.
elev (float, optional): This is the angle between the
vector from the object to the camera, and the horizontal
plane y = 0 (xz-plane).
Defaults to 0.
azim (float, optional): angle in degrees or radians. The vector
from the object to the camera is projected onto a horizontal
plane y = 0. azim is the angle between the projected vector and a
reference vector at (0, 0, 1) on the reference plane (the
horizontal plane).
Defaults to 0.
dist (float, optional): distance of the camera from the object.
Defaults to 2.7.
at (Union[torch.Tensor, List, Tuple], optional):
the position of the object(s) in world coordinates.
Defaults to (0, 0, 0).
batch_size (int, optional): number of frames. Defaults to 1.
orbit_speed (Union[float, Tuple[float, float]], optional):
degree speed of camera moving along the orbit.
Could be one or two number. One number for only elev speed,
two number for both.
Defaults to 0.
dist_speed (Optional[float], optional):
speed of camera moving along the center line.
Defaults to 0.
convention (str, optional): Camera convention. Defaults to 'pytorch3d'.
Returns:
Union[torch.Tensor, torch.Tensor, torch.Tensor]: computed K, R, T.
"""
if not isinstance(orbit_speed, Iterable):
orbit_speed = (orbit_speed, 0.0)
if not isinstance(at, torch.Tensor):
at = torch.Tensor(at)
at = at.view(1, 3)
if batch_size > 1 and orbit_speed[0] != 0:
azim = torch.linspace(azim, azim + batch_size * orbit_speed[0],
batch_size)
if batch_size > 1 and orbit_speed[1] != 0:
elev = torch.linspace(elev, elev + batch_size * orbit_speed[1],
batch_size)
if batch_size > 1 and dist_speed != 0:
dist = torch.linspace(dist, dist + batch_size * dist_speed, batch_size)
if convention == 'opencv':
rotation_compensate = ee_to_rotmat(
torch.Tensor([math.pi, 0, 0]).view(1, 3))
at = rotation_compensate.permute(0, 2, 1) @ at.view(-1, 3, 1)
at = at.view(1, 3)
R, T = cameras.look_at_view_transform(dist=dist,
elev=elev,
azim=azim,
at=at)
if K is None:
K = FoVPerspectiveCameras.get_default_projection_matrix(
batch_size=batch_size)
if convention == 'opencv':
rotation_compensate = ee_to_rotmat(
torch.Tensor([math.pi, 0, 0]).view(1, 3))
R = rotation_compensate.permute(0, 2, 1) @ R
return K, R, T
def compute_direction_cameras(
K: Union[torch.Tensor, np.ndarray, None] = None,
at: Union[torch.Tensor, List, Tuple, None] = None,
eye: Union[torch.Tensor, List, Tuple, None] = None,
plane: Union[Iterable[torch.Tensor], None] = None,
dist: float = 1.0,
batch_size: int = 1,
dist_speed: float = 0.0,
z_vec: Union[torch.Tensor, List, Tuple, None] = None,
y_vec: Union[torch.Tensor, List, Tuple] = (0, 1, 0),
convention: str = 'pytorch3d',
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Generate a sequence of moving cameras along a direction.
We need a `z_vec`, `y_vec` to generate `x_vec` so as to get the `R` matrix.
And we need `eye` as `T` matrix.
`K` matrix could be set or use default.
We recommend `y_vec` as default (0, 1, 0), and it will be orthogonal
decomposed. The `x_vec` will be generated by cross production from
`y_vec` and `x_vec`.
You can set `z_vec` by: 1. set `at`, `dist`, `dist_speed`, `plane`,
`batch_size` to get `eye`, then get `z_vec`.
2. set `at`, `eye` directly and get `z_vec`.
3. set `z_vec` directly and:
1). set `eye` and `dist`.
2). set `at`, `dist`, `dist_speed`,
`batch_size` then get `eye`.
When we have `eye`, `z_vec`, `y_vec`, we will have `R` and `T`.
Args:
K (Union[torch.Tensor, np.ndarray, None], optional):
Intrinsic matrix. Will generate a default K if None.
Defaults to None.
at (Union[torch.Tensor, List, Tuple], optional):
the position of the object(s) in world coordinates.
Required.
Defaults to None.
eye (Union[torch.Tensor, List, Tuple], optional):
the position of the camera(s) in world coordinates.
If eye is not None, it will override the camera position derived
from plane, dist, dist_speed.
Defaults to None.
plane (Optional[Iterable[torch.Tensor, List, Tuple]], optional):
The plane of your z direction normal.
Should be a tuple or list containing two vectors of shape (N, 3).
Defaults to None.
dist (float, optional): distance to at.
Defaults to 1.0.
dist_speed (float, optional): distance moving speed.
Defaults to 1.0.
batch_size (int, optional): number of frames.
Defaults to 1.
z_vec (Union[torch.Tensor, List, Tuple], optional):
z direction of shape (-1, 3). If z_vec is not None, it will
override plane, dist, dist_speed.
Defaults to None.
y_vec (Union[torch.Tensor, List, Tuple], optional):
Will only be used when z_vec is used.
Defaults to (0, 1, 0).
convention (str, optional): Camera convention.
Defaults to 'pytorch3d'.
Returns:
Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: computed K, R, T.
"""
def norm_vec(vec):
return vec / torch.sqrt((vec * vec).sum())
if z_vec is None:
assert at is not None
at = torch.Tensor(at).view(-1, 3)
if eye is None:
assert plane is not None
dist = torch.linspace(dist, dist + batch_size * dist_speed,
batch_size)
vec1 = torch.Tensor(plane[0]).view(-1, 3)
norm_vec1 = norm_vec(vec1)
vec2 = torch.Tensor(plane[1]).view(-1, 3)
norm_vec2 = norm_vec(vec2)
norm = torch.cross(norm_vec1, norm_vec2)
normed_norm = norm_vec(norm)
eye = at + normed_norm * dist
else:
eye = torch.Tensor(eye).view(-1, 3)
norm = eye - at
normed_norm = norm_vec(norm)
z_vec = -normed_norm
else:
z_vec = torch.Tensor(z_vec).view(-1, 3)
z_vec = norm_vec(z_vec)
if eye is None:
assert at is not None
at = torch.Tensor(at).view(-1, 3)
dist = torch.linspace(dist, dist + batch_size * dist_speed,
batch_size)
eye = -z_vec * dist + at
eye = torch.Tensor(eye).view(-1, 3)
assert eye is not None
z_vec = norm_vec(z_vec)
normed_norm = -z_vec
z_vec = z_vec.view(-1, 3)
y_vec = torch.Tensor(y_vec).view(-1, 3)
y_vec = y_vec - torch.bmm(y_vec.view(-1, 1, 3), z_vec.view(-1, 3, 1)).view(
-1, 1) * z_vec
y_vec = norm_vec(y_vec)
x_vec = torch.cross(y_vec, z_vec)
R = torch.cat(
[x_vec.view(-1, 3, 1),
y_vec.view(-1, 3, 1),
z_vec.view(-1, 3, 1)], 1).view(-1, 3, 3)
T = eye
R = R.permute(0, 2, 1)
_, T = convert_world_view(R=R, T=T)
if K is None:
K = FoVPerspectiveCameras.get_default_projection_matrix(
batch_size=batch_size)
K, R, T = convert_camera_matrix(K=K,
R=R,
T=T,
is_perspective=True,
convention_src='pytorch3d',
convention_dst=convention)
return K, R, T
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