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Running
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Zero
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import torch
EPS = 1e-3
def normalize_calib(K: torch.Tensor, img_sizes: torch.Tensor) -> torch.Tensor:
"""Normalize the calibration matrices for fisheye cameras based on the image size
Args:
calib (torch.Tensor): B, n_views, 3, 3
img_sizes (torch.Tensor): B, n_views, 2
Returns:
torch.Tensor: B, n_views 7
"""
K[..., :2, :] = K[..., :2, :] / img_sizes.unsqueeze(-1) * 2.0
K[..., :2, 2] = K[..., :2, 2] - 1.0
return K
def unnormalize_calib(K: torch.Tensor, img_sizes: torch.Tensor) -> torch.Tensor:
"""Unnormalize the calibration matrices for fisheye cameras based on the image size
Args:
calib (torch.Tensor): B, n_views, 3, 3
img_sizes (torch.Tensor): B, n_views, 2
Returns:
torch.Tensor: B, n_views 7
"""
K[..., :2, 2] = K[..., :2, 2] + 1.0
K[..., :2, :] = K[..., :2, :] * img_sizes.unsqueeze(-1) / 2.0
return K
def pts_into_camera(pts: torch.Tensor, poses_w2c: torch.Tensor) -> torch.Tensor:
"""Project points from world coordinates into camera coordinate
Args:
pts (torch.Tensor): B, n_pts, 3
poses_w2c (torch.Tensor): B, n_view, 4, 4
Returns:
torch.Tensor: B, n_views, n_pts, 3
"""
# Add a singleton dimension to the input point cloud to match grid_f_poses_w2c shape
pts = pts.unsqueeze(1) # [B, 1, n_pts, 3]
ones = torch.ones_like(
pts[..., :1]
) ## Create a tensor of ones to add a fourth dimension to the point cloud for homogeneous coordinates
pts = torch.cat(
(pts, ones), dim=-1
) ## Concatenate the tensor of ones with the point cloud to create homogeneous coordinates
return (poses_w2c[:, :, :3, :]) @ pts.permute(0, 1, 3, 2)
def project_to_image(
pts: torch.Tensor, Ks: torch.Tensor
) -> tuple[torch.Tensor, torch.Tensor]:
"""Project pts in camera coordinates into image coordinates.
Args:
pts (torch.Tensor): B, n_views, n_pts, 3
Ks (torch.Tensor): B, n_views, 3, 3
Returns:
tuple[torch.Tensor, torch.Tensor]: (B, n_views, n_pts, 2), (B, n_views, n_pts, 1)
"""
pts = (Ks @ pts).permute(
0, 1, 3, 2
) ## Apply the intrinsic camera parameters to the projected points to get pixel coordinates
xy = pts[
:, :, :, :2
] ## Extract the x,y coordinates and depth value from the projected points
z_ = pts[:, :, :, 2:3]
xy = xy / z_.clamp_min(EPS)
return xy, z_
def outside_frustum(
xy: torch.Tensor,
z: torch.Tensor,
limits_x: tuple[float, float] | tuple[int, int] = (-1.0, 1.0),
limits_y: tuple[float, float] | tuple[int, int] = (-1.0, 1.0),
limit_z: float = EPS,
) -> torch.Tensor:
"""_summary_
Args:
xy (torch.Tensor): _description_
z (torch.Tensor): _description_
limits_x (tuple[float, float] | tuple[int, int], optional): _description_. Defaults to (-1.0, 1.0).
limits_y (tuple[float, float] | tuple[int, int], optional): _description_. Defaults to (-1.0, 1.0).
limit_z (float, optional): _description_. Defaults to EPS.
Returns:
torch.Tensor: _description_
"""
return (
(z <= limit_z)
| (xy[..., :1] < limits_x[0])
| (xy[..., :1] > limits_x[1])
| (xy[..., 1:2] < limits_y[0])
| (xy[..., 1:2] > limits_y[1])
)
|