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"""Module to generate synthetic 3d scenes."""
import math
from typing import Dict
import torch
from kornia.geometry.conversions import angle_axis_to_rotation_matrix
from kornia.geometry.linalg import transform_points
from .projection import projection_from_KRt, random_intrinsics
def generate_scene(num_views: int, num_points: int) -> Dict[str, torch.Tensor]:
# Generate the 3d points
points3d = torch.rand(1, num_points, 3) # NxMx3
# Create random camera matrix
K = random_intrinsics(0.0, 100.0) # 1x3x3
# Create random rotation per view
ang = torch.rand(num_views, 1) * math.pi * 2.0
rvec = torch.rand(num_views, 3)
rvec = ang * rvec / torch.norm(rvec, dim=1, keepdim=True) # Nx3
rot_mat = angle_axis_to_rotation_matrix(rvec) # Nx3x3
# matches with cv2.Rodrigues -> yay !
# Create random translation per view
tx = torch.empty(num_views).uniform_(-0.5, 0.5)
ty = torch.empty(num_views).uniform_(-0.5, 0.5)
tz = torch.empty(num_views).uniform_(-1.0, 2.0)
tvec = torch.stack([tx, ty, tz], dim=1)[..., None]
# Make sure the shape is in front of the camera
points3d_trans = (rot_mat @ points3d.transpose(-2, -1)) + tvec
min_dist = torch.min(points3d_trans[:, 2], dim=1)[0]
tvec[:, 2, 0] = torch.where(min_dist < 0, tz - min_dist + 1.0, tz)
# compute projection matrices
P = projection_from_KRt(K, rot_mat, tvec)
# project points3d and backproject to image plane
points2d = transform_points(P, points3d.expand(num_views, -1, -1))
return dict(K=K, R=rot_mat, t=tvec, P=P, points3d=points3d, points2d=points2d)
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