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Running
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Zero
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from math import sin, cos
import torch
from torch.cuda.amp import autocast
def transform_pts(pts: torch.Tensor, rel_pose: torch.Tensor) -> torch.Tensor:
"""Transform points by relative pose
Args:
pts (torch.Tensor): B, n_pts, 3
rel_pose (torch.Tensor): B, 4, 4
Returns:
torch.Tensor: B, n_pts, 3
"""
pts = torch.cat((pts, torch.ones_like(pts[..., :1])), dim=-1)
return (pts @ rel_pose.transpose(-1, -2))[..., :3]
# TODO: unify
def distance_to_z(depths: torch.Tensor, projs: torch.Tensor):
n, nv, h, w = depths.shape
device = depths.device
inv_K = torch.inverse(projs)
grid_x = (
torch.linspace(-1, 1, w, device=device).view(1, 1, 1, -1).expand(-1, -1, h, -1)
)
grid_y = (
torch.linspace(-1, 1, h, device=device).view(1, 1, -1, 1).expand(-1, -1, -1, w)
)
img_points = torch.stack((grid_x, grid_y, torch.ones_like(grid_x)), dim=2).expand(
n, nv, -1, -1, -1
)
cam_points = (inv_K @ img_points.view(n, nv, 3, -1)).view(n, nv, 3, h, w)
factors = cam_points[:, :, 2, :, :] / torch.norm(cam_points, dim=2)
return depths * factors
def z_to_distance(z: torch.Tensor, projs: torch.Tensor):
n, nv, h, w = z.shape
device = z.device
inv_K = torch.inverse(projs)
grid_x = (
torch.linspace(-1, 1, w, device=device).view(1, 1, 1, -1).expand(-1, -1, h, -1)
)
grid_y = (
torch.linspace(-1, 1, h, device=device).view(1, 1, -1, 1).expand(-1, -1, -1, w)
)
img_points = torch.stack((grid_x, grid_y, torch.ones_like(grid_x)), dim=2).expand(
n, nv, -1, -1, -1
)
cam_points = (inv_K @ img_points.view(n, nv, 3, -1)).view(n, nv, 3, h, w)
factors = cam_points[:, :, 2, :, :] / torch.norm(cam_points, dim=2)
return z / factors
def azimuth_elevation_to_rotation(azimuth: float, elevation: float) -> torch.Tensor:
rot_z = torch.tensor(
[
[cos(azimuth), -sin(azimuth), 0.0],
[sin(azimuth), cos(azimuth), 0.0],
[0.0, 0.0, 1.0],
]
)
rot_x = torch.tensor(
[
[1.0, 0.0, 0.0],
[0.0, cos(azimuth), -sin(azimuth)],
[0.0, sin(azimuth), cos(azimuth)],
]
)
return rot_x @ rot_z
def estimate_frustum_overlap(proj_source: torch.Tensor, pose_source: torch.Tensor, proj_target: torch.Tensor, pose_target: torch.Tensor, dist_lim=50):
device = proj_source.device
dtype = proj_source.dtype
# Check which camera has higher z value in target coordinate system
with autocast(enabled=False):
src2tgt = torch.inverse(pose_target) @ pose_source
for i in range(len(src2tgt)):
if src2tgt[i, 2, 3] < 0:
print("SWAP", i)
proj_ = proj_target[i].clone()
pose_ = pose_target[i].clone()
proj_target[i] = proj_source[i]
pose_target[i] = pose_source[i]
proj_source[i] = proj_
pose_source[i] = pose_
points = torch.tensor([[
[-1, 1, 1, 1],
[1, 1, 1, 1],
[1, -1, 1, 1],
[-1, -1, 1, 1],
]], device=device, dtype=dtype)
with autocast(enabled=False):
K_src_inv = torch.inverse(proj_source)
K_tgt_inv = torch.inverse(proj_target)
_ = K_src_inv.new_zeros(K_src_inv.shape[0], 4, 4)
_[:, 3, 3] = 1
_[:, :3, :3] = K_src_inv
K_src_inv = _
_ = K_tgt_inv.new_zeros(K_tgt_inv.shape[0], 4, 4)
_[:, 3, 3] = 1
_[:, :3, :3] = K_tgt_inv
K_tgt_inv = _
points_src = K_src_inv @ points.permute(0, 2, 1)
points_tgt = K_tgt_inv @ points.permute(0, 2, 1)
normals_tgt = torch.cross(points_tgt[..., :3, :], torch.roll(points_tgt[..., :3, :], shifts=-1, dims=-2), dim=-2)
normals_tgt = normals_tgt / torch.norm(normals_tgt, dim=-2, keepdim=True)
with autocast(enabled=False):
src2tgt = torch.inverse(pose_target) @ pose_source
base = src2tgt[:, :3, 3, None]
points_src_tgt = src2tgt @ points_src
dirs = points_src_tgt[..., :3, :] - base
# dirs = dirs / torch.norm(dirs, dim=-2) #dirs should have z length 1
dists = - (base[..., None] * normals_tgt[..., None, :]).sum(dim=-3) / (dirs[..., None] * normals_tgt[..., None, :]).sum(dim=-3).clamp_min(1e-4)
# print(dists)
# Ignore all non-positive
mask = (dists <= 0) | (dists > dist_lim)
dists[mask] = dist_lim
# print(dists)
dists = torch.min(dists, dim=-1)[0]
mean_dist = dists.mean(dim=-1)
# print(mean_dist, (torch.max(points_src[..., 0], dim=-1)[0] - torch.min(points_src[..., 0], dim=-1)[0]), (torch.max(points_src[..., 1], dim=-1)[0] - torch.min(points_src[..., 1], dim=-1)[0]))
volume_estimate = \
1/3 * \
(torch.max(points_src[..., 0], dim=-1)[0] - torch.min(points_src[..., 0], dim=-1)[0]) * mean_dist * \
(torch.max(points_src[..., 1], dim=-1)[0] - torch.min(points_src[..., 1], dim=-1)[0]) * mean_dist * \
mean_dist
return volume_estimate
def estimate_frustum_overlap_2(proj_source: torch.Tensor, pose_source: torch.Tensor, proj_target: torch.Tensor, pose_target: torch.Tensor, z_range=(3, 40), res=(8, 8, 16)):
device = proj_source.device
dtype = proj_source.dtype
with autocast(enabled=False):
K_src_inv = torch.inverse(proj_source)
n = proj_source.shape[0]
w, h, d = res
pixel_width = 2 / w
pixel_height = 2 / h
x = torch.linspace(-1 + .5 * pixel_width, 1 - .5 * pixel_width, w, dtype=dtype, device=device).view(1, 1, 1, w).expand(n, d, h, w)
y = torch.linspace(-1 + .5 * pixel_height, 1 - .5 * pixel_height, h, dtype=dtype, device=device).view(1, 1, h, 1).expand(n, d, h, w)
z = torch.ones_like(x)
xyz = torch.stack((x, y, z), dim=-1)
xyz = K_src_inv @ xyz.reshape(n, -1, 3).permute(0, 2, 1)
xyz = xyz.reshape(n, 3, d, h, w)
# xyz = xyz * (1 / torch.linspace(1 / z_range[0], 1 / z_range[1], d, dtype=dtype, device=device).view(1, 1, d, 1, 1).expand(n, 1, d, h, w))
xyz = xyz * torch.linspace(z_range[0], z_range[1], d, dtype=dtype, device=device).view(1, 1, d, 1, 1).expand(n, 1, d, h, w)
xyz = torch.cat((xyz, torch.ones_like(xyz[:, :1])), dim=1)
xyz = xyz.reshape(n, 4, -1)
with autocast(enabled=False):
src2tgt = torch.inverse(pose_target) @ pose_source
xyz = src2tgt @ xyz
# print(xyz)
xyz = proj_target @ xyz[:, :3, :]
xyz[:, :2] = xyz[:, :2] / xyz[:, 2:3, :]
# print(xyz)
valid = (xyz[:, 0].abs() < 1) & (xyz[:, 1].abs() < 1) & (xyz[:, 2].abs() > z_range[0])# & (xyz[:, 2].abs() < z_range[1])
# print(valid)
volume_estimate = valid.to(dtype).mean(-1)
return volume_estimate
def compute_occlusions(flow0, flow1):
n, _, h, w = flow0.shape
device = flow0.device
x = torch.linspace(-1, 1, w, device=device).view(1, 1, w).expand(1, h, w)
y = torch.linspace(-1, 1, h, device=device).view(1, h, 1).expand(1, h, w)
xy = torch.cat((x, y), dim=0).view(1, 2, h, w).expand(n, 2, h, w)
flow0_r = torch.cat((flow0[:, 0:1, :, :] * 2 / w , flow0[:, 1:2, :, :] * 2 / h), dim=1)
flow1_r = torch.cat((flow1[:, 0:1, :, :] * 2 / w , flow1[:, 1:2, :, :] * 2 / h), dim=1)
xy_0 = xy + flow0_r
xy_1 = xy + flow1_r
xy_0 = xy_0.view(n, 2, -1)
xy_1 = xy_1.view(n, 2, -1)
ns = torch.arange(n, device=device, dtype=xy_0.dtype)
nxy_0 = torch.cat((ns.view(n, 1, 1).expand(-1, 1, xy_0.shape[-1]), xy_0), dim=1)
nxy_1 = torch.cat((ns.view(n, 1, 1).expand(-1, 1, xy_1.shape[-1]), xy_1), dim=1)
mask0 = torch.zeros_like(flow0[:, :1, :, :])
mask0[nxy_1[:, 0, :].long(), 0, ((nxy_1[:, 2, :] * .5 + .5) * h).round().long().clamp(0, h-1), ((nxy_1[:, 1, :] * .5 + .5) * w).round().long().clamp(0, w-1)] = 1
mask1 = torch.zeros_like(flow1[:, :1, :, :])
mask1[nxy_0[:, 0, :].long(), 0, ((nxy_0[:, 2, :] * .5 + .5) * h).round().long().clamp(0, h-1), ((nxy_0[:, 1, :] * .5 + .5) * w).round().long().clamp(0, w-1)] = 1
return mask0, mask1 |