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