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import math
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
import torch.nn.functional as F
import trimesh
from PIL import Image
from torch import BoolTensor, FloatTensor
LIST_TYPE = Union[list, np.ndarray, torch.Tensor]
def list_to_pt(
x: LIST_TYPE, dtype: Optional[torch.dtype] = None, device: Optional[str] = None
) -> torch.Tensor:
if isinstance(x, list) or isinstance(x, np.ndarray):
return torch.tensor(x, dtype=dtype, device=device)
return x.to(dtype=dtype)
def get_c2w(
elevation_deg: LIST_TYPE,
distance: LIST_TYPE,
azimuth_deg: Optional[LIST_TYPE],
num_views: Optional[int] = 1,
device: Optional[str] = None,
) -> torch.FloatTensor:
if azimuth_deg is None:
assert (
num_views is not None
), "num_views must be provided if azimuth_deg is None."
azimuth_deg = torch.linspace(
0, 360, num_views + 1, dtype=torch.float32, device=device
)[:-1]
else:
num_views = len(azimuth_deg)
azimuth_deg = list_to_pt(azimuth_deg, dtype=torch.float32, device=device)
elevation_deg = list_to_pt(elevation_deg, dtype=torch.float32, device=device)
camera_distances = list_to_pt(distance, dtype=torch.float32, device=device)
elevation = elevation_deg * math.pi / 180
azimuth = azimuth_deg * math.pi / 180
camera_positions = torch.stack(
[
camera_distances * torch.cos(elevation) * torch.cos(azimuth),
camera_distances * torch.cos(elevation) * torch.sin(azimuth),
camera_distances * torch.sin(elevation),
],
dim=-1,
)
center = torch.zeros_like(camera_positions)
up = torch.tensor([0, 0, 1], dtype=torch.float32, device=device)[None, :].repeat(
num_views, 1
)
lookat = F.normalize(center - camera_positions, dim=-1)
right = F.normalize(torch.cross(lookat, up, dim=-1), dim=-1)
up = F.normalize(torch.cross(right, lookat, dim=-1), dim=-1)
c2w3x4 = torch.cat(
[torch.stack([right, up, -lookat], dim=-1), camera_positions[:, :, None]],
dim=-1,
)
c2w = torch.cat([c2w3x4, torch.zeros_like(c2w3x4[:, :1])], dim=1)
c2w[:, 3, 3] = 1.0
return c2w
def get_projection_matrix(
fovy_deg: LIST_TYPE,
aspect_wh: float = 1.0,
near: float = 0.1,
far: float = 100.0,
device: Optional[str] = None,
) -> torch.FloatTensor:
fovy_deg = list_to_pt(fovy_deg, dtype=torch.float32, device=device)
batch_size = fovy_deg.shape[0]
fovy = fovy_deg * math.pi / 180
tan_half_fovy = torch.tan(fovy / 2)
projection_matrix = torch.zeros(
batch_size, 4, 4, dtype=torch.float32, device=device
)
projection_matrix[:, 0, 0] = 1 / (aspect_wh * tan_half_fovy)
projection_matrix[:, 1, 1] = -1 / tan_half_fovy
projection_matrix[:, 2, 2] = -(far + near) / (far - near)
projection_matrix[:, 2, 3] = -2 * far * near / (far - near)
projection_matrix[:, 3, 2] = -1
return projection_matrix
def get_orthogonal_projection_matrix(
batch_size: int,
left: float,
right: float,
bottom: float,
top: float,
near: float = 0.1,
far: float = 100.0,
device: Optional[str] = None,
) -> torch.FloatTensor:
projection_matrix = torch.zeros(
batch_size, 4, 4, dtype=torch.float32, device=device
)
projection_matrix[:, 0, 0] = 2 / (right - left)
projection_matrix[:, 1, 1] = -2 / (top - bottom)
projection_matrix[:, 2, 2] = -2 / (far - near)
projection_matrix[:, 0, 3] = -(right + left) / (right - left)
projection_matrix[:, 1, 3] = -(top + bottom) / (top - bottom)
projection_matrix[:, 2, 3] = -(far + near) / (far - near)
projection_matrix[:, 3, 3] = 1
return projection_matrix
@dataclass
class Camera:
c2w: Optional[torch.FloatTensor]
w2c: torch.FloatTensor
proj_mtx: torch.FloatTensor
mvp_mtx: torch.FloatTensor
cam_pos: Optional[torch.FloatTensor]
def __getitem__(self, index):
if isinstance(index, int):
sl = slice(index, index + 1)
elif isinstance(index, slice):
sl = index
else:
raise NotImplementedError
return Camera(
c2w=self.c2w[sl] if self.c2w is not None else None,
w2c=self.w2c[sl],
proj_mtx=self.proj_mtx[sl],
mvp_mtx=self.mvp_mtx[sl],
cam_pos=self.cam_pos[sl] if self.cam_pos is not None else None,
)
def to(self, device: Optional[str] = None):
if self.c2w is not None:
self.c2w = self.c2w.to(device)
self.w2c = self.w2c.to(device)
self.proj_mtx = self.proj_mtx.to(device)
self.mvp_mtx = self.mvp_mtx.to(device)
if self.cam_pos is not None:
self.cam_pos = self.cam_pos.to(device)
def __len__(self):
return self.c2w.shape[0]
def get_camera(
elevation_deg: Optional[LIST_TYPE] = None,
distance: Optional[LIST_TYPE] = None,
fovy_deg: Optional[LIST_TYPE] = None,
azimuth_deg: Optional[LIST_TYPE] = None,
num_views: Optional[int] = 1,
c2w: Optional[torch.FloatTensor] = None,
w2c: Optional[torch.FloatTensor] = None,
proj_mtx: Optional[torch.FloatTensor] = None,
aspect_wh: float = 1.0,
near: float = 0.1,
far: float = 100.0,
device: Optional[str] = None,
):
if w2c is None:
if c2w is None:
c2w = get_c2w(elevation_deg, distance, azimuth_deg, num_views, device)
camera_positions = c2w[:, :3, 3]
w2c = torch.linalg.inv(c2w)
else:
camera_positions = None
c2w = None
if proj_mtx is None:
proj_mtx = get_projection_matrix(
fovy_deg, aspect_wh=aspect_wh, near=near, far=far, device=device
)
mvp_mtx = proj_mtx @ w2c
return Camera(
c2w=c2w, w2c=w2c, proj_mtx=proj_mtx, mvp_mtx=mvp_mtx, cam_pos=camera_positions
)
def get_orthogonal_camera(
elevation_deg: LIST_TYPE,
distance: LIST_TYPE,
left: float,
right: float,
bottom: float,
top: float,
azimuth_deg: Optional[LIST_TYPE] = None,
num_views: Optional[int] = 1,
near: float = 0.1,
far: float = 100.0,
device: Optional[str] = None,
):
c2w = get_c2w(elevation_deg, distance, azimuth_deg, num_views, device)
camera_positions = c2w[:, :3, 3]
w2c = torch.linalg.inv(c2w)
proj_mtx = get_orthogonal_projection_matrix(
batch_size=c2w.shape[0],
left=left,
right=right,
bottom=bottom,
top=top,
near=near,
far=far,
device=device,
)
mvp_mtx = proj_mtx @ w2c
return Camera(
c2w=c2w, w2c=w2c, proj_mtx=proj_mtx, mvp_mtx=mvp_mtx, cam_pos=camera_positions
)
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