in threestudio/data/uncond.py [0:0]
def __init__(self, cfg: Any, split: str) -> None:
super().__init__()
self.cfg: RandomCameraDataModuleConfig = cfg
self.split = split
if split == "val":
self.n_views = self.cfg.n_val_views
else:
self.n_views = self.cfg.n_test_views
azimuth_deg: Float[Tensor, "B"]
if self.split == "val":
# make sure the first and last view are not the same
azimuth_deg = torch.linspace(0, 360.0, self.n_views + 1)[: self.n_views]
else:
azimuth_deg = torch.linspace(0, 360.0, self.n_views)
elevation_deg: Float[Tensor, "B"] = torch.full_like(
azimuth_deg, self.cfg.eval_elevation_deg
)
camera_distances: Float[Tensor, "B"] = torch.full_like(
elevation_deg, self.cfg.eval_camera_distance
)
elevation = elevation_deg * math.pi / 180
azimuth = azimuth_deg * math.pi / 180
# convert spherical coordinates to cartesian coordinates
# right hand coordinate system, x back, y right, z up
# elevation in (-90, 90), azimuth from +x to +y in (-180, 180)
camera_positions: Float[Tensor, "B 3"] = 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,
)
# default scene center at origin
center: Float[Tensor, "B 3"] = torch.zeros_like(camera_positions)
# default camera up direction as +z
up: Float[Tensor, "B 3"] = torch.as_tensor([0, 0, 1], dtype=torch.float32)[
None, :
].repeat(self.cfg.eval_batch_size, 1)
fovy_deg: Float[Tensor, "B"] = torch.full_like(
elevation_deg, self.cfg.eval_fovy_deg
)
fovy = fovy_deg * math.pi / 180
light_positions: Float[Tensor, "B 3"] = camera_positions
lookat: Float[Tensor, "B 3"] = F.normalize(center - camera_positions, dim=-1)
right: Float[Tensor, "B 3"] = F.normalize(torch.cross(lookat, up), dim=-1)
up = F.normalize(torch.cross(right, lookat), dim=-1)
c2w3x4: Float[Tensor, "B 3 4"] = torch.cat(
[torch.stack([right, up, -lookat], dim=-1), camera_positions[:, :, None]],
dim=-1,
)
c2w: Float[Tensor, "B 4 4"] = torch.cat(
[c2w3x4, torch.zeros_like(c2w3x4[:, :1])], dim=1
)
c2w[:, 3, 3] = 1.0
# get directions by dividing directions_unit_focal by focal length
focal_length: Float[Tensor, "B"] = (
0.5 * self.cfg.eval_height / torch.tan(0.5 * fovy)
)
directions_unit_focal = get_ray_directions(
H=self.cfg.eval_height, W=self.cfg.eval_width, focal=1.0
)
directions: Float[Tensor, "B H W 3"] = directions_unit_focal[
None, :, :, :
].repeat(self.n_views, 1, 1, 1)
directions[:, :, :, :2] = (
directions[:, :, :, :2] / focal_length[:, None, None, None]
)
rays_o, rays_d = get_rays(
directions, c2w, keepdim=True, normalize=self.cfg.rays_d_normalize
)
self.proj_mtx: Float[Tensor, "B 4 4"] = get_projection_matrix(
fovy, self.cfg.eval_width / self.cfg.eval_height, 0.1, 1000.0
) # FIXME: hard-coded near and far
mvp_mtx: Float[Tensor, "B 4 4"] = get_mvp_matrix(c2w, self.proj_mtx)
self.rays_o, self.rays_d = rays_o, rays_d
self.mvp_mtx = mvp_mtx
self.c2w = c2w
self.camera_positions = camera_positions
self.light_positions = light_positions
self.elevation, self.azimuth = elevation, azimuth
self.elevation_deg, self.azimuth_deg = elevation_deg, azimuth_deg
self.camera_distances = camera_distances
self.fovy = fovy