Function epipolar_distance

core/loss.py:87–113

copy from kornia

(pts1, pts2, Fm, squared=False, eps = 1e-8)

    return (out + eps).sqrt()

def epipolar_distance(pts1, pts2, Fm, squared=False, eps = 1e-8):
    '''
    copy from kornia
    '''
    if not isinstance(Fm, torch.Tensor):
        raise TypeError("Fm type is not a torch.Tensor. Got {}".format(type(Fm)))

    if (len(Fm.shape) != 3) or not Fm.shape[-2:] == (3, 3):
        raise ValueError("Fm must be a (*, 3, 3) tensor. Got {}".format(Fm.shape))

    if pts1.size(-1) == 2:
        pts1 = kornia.geometry.convert_points_to_homogeneous(pts1)

    if pts2.size(-1) == 2:
        pts2 = kornia.geometry.convert_points_to_homogeneous(pts2)

    F_t: torch.Tensor = Fm.permute(0, 2, 1).contiguous()
    line1_in_2: torch.Tensor = pts1 @ F_t

    numerator: torch.Tensor = (pts2 * line1_in_2).sum(2).pow(2)

    denominator_inv: torch.Tensor = 1.0 / (line1_in_2[..., :2].norm(2, dim=2).pow(2) + eps)
    out: torch.Tensor = numerator * denominator_inv

    if squared:
        return out
    return (out + eps).sqrt()

def compute_transformation_loss(args, data, flow_est, type='BB1'):
    device = flow_est.device