def solve_x3ds_normals(x2ds, splits, labels, Ps, rays, robust=True):
x3ds, x3ds_labels, E, x2ds_labels = ISCV.solve_x3ds(
x2ds, splits, labels, Ps, robust)
x3ds_normals = np.ones_like(x3ds)
for xi, label in enumerate(x3ds_labels):
rayIndices = np.where(labels == label)[0]
x3ds_normals[xi] = np.sum(rays[rayIndices], axis=0)
# Normalise the ray directions
x3ds_normals /= (np.sum(x3ds_normals * x3ds_normals,
axis=1)**0.5).reshape(-1, 1)
return x3ds, x3ds_labels, x3ds_normals, E, x2ds_labels
def solve_x3ds(x2ds, splits, labels, Ps, robust=True):
"""
Given some labelled 2d points, generate labelled 3d positions for every multiply-labelled point and equations for
every single-labelled point.
Args:
x2ds (float[][2]): 2D Detections.
splits (int[]): Indices of ranges of 2Ds per camera.
labels (int[]: Labels of x2ds.
Ps (?): Projection matrices of the cameras?
robust (bool): Robustness Flag (requires more Rays to reconstruct). Default = True
Returns:
float[][3]: "x3ds" - the resulting 3D reconstructions.
int[]: "x3d_labels" - the labels for the 3D points.
??: "E[singles]" - Equations describing 2D detections not born of the 3D yet.
int[] "singles_labels" - labels for the 2D contributions.
Requires:
ISCV.solve_x3ds
"""
return ISCV.solve_x3ds(
x2ds, splits, labels, Ps,
robust) # x3ds, x3d_labels, E[single_rays], single_ray_labels