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from typing import Dict
import torch
import kornia.geometry.epipolar as epi
def generate_two_view_random_scene(
device: torch.device = torch.device("cpu"), dtype: torch.dtype = torch.float32
) -> Dict[str, torch.Tensor]:
num_views: int = 2
num_points: int = 30
scene: Dict[str, torch.Tensor] = epi.generate_scene(num_views, num_points)
# internal parameters (same K)
K1 = scene['K'].to(device, dtype)
K2 = K1.clone()
# rotation
R1 = scene['R'][0:1].to(device, dtype)
R2 = scene['R'][1:2].to(device, dtype)
# translation
t1 = scene['t'][0:1].to(device, dtype)
t2 = scene['t'][1:2].to(device, dtype)
# projection matrix, P = K(R|t)
P1 = scene['P'][0:1].to(device, dtype)
P2 = scene['P'][1:2].to(device, dtype)
# fundamental matrix
F_mat = epi.fundamental_from_projections(P1[..., :3, :], P2[..., :3, :])
F_mat = epi.normalize_transformation(F_mat)
# points 3d
X = scene['points3d'].to(device, dtype)
# projected points
x1 = scene['points2d'][0:1].to(device, dtype)
x2 = scene['points2d'][1:2].to(device, dtype)
return dict(K1=K1, K2=K2, R1=R1, R2=R2, t1=t1, t2=t2, P1=P1, P2=P2, F=F_mat, X=X, x1=x1, x2=x2)
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