nalgebra/src/geometry/similarity_conversion.rs

160 lines
5.7 KiB
Rust

use alga::general::{Real, SubsetOf, SupersetOf};
use alga::linear::Rotation;
use base::{DefaultAllocator, MatrixN};
use base::dimension::{DimMin, DimName, DimNameAdd, DimNameSum, U1};
use base::allocator::Allocator;
use geometry::{Isometry, Point, Similarity, SuperTCategoryOf, TAffine, Transform, Translation};
/*
* This file provides the following conversions:
* =============================================
*
* Similarity -> Similarity
* Similarity -> Transform
* Similarity -> Matrix (homogeneous)
*/
impl<N1, N2, D: DimName, R1, R2> SubsetOf<Similarity<N2, D, R2>> for Similarity<N1, D, R1>
where
N1: Real + SubsetOf<N2>,
N2: Real + SupersetOf<N1>,
R1: Rotation<Point<N1, D>> + SubsetOf<R2>,
R2: Rotation<Point<N2, D>>,
DefaultAllocator: Allocator<N1, D> + Allocator<N2, D>,
{
#[inline]
fn to_superset(&self) -> Similarity<N2, D, R2> {
Similarity::from_isometry(self.isometry.to_superset(), self.scaling().to_superset())
}
#[inline]
fn is_in_subset(sim: &Similarity<N2, D, R2>) -> bool {
::is_convertible::<_, Isometry<N1, D, R1>>(&sim.isometry)
&& ::is_convertible::<_, N1>(&sim.scaling())
}
#[inline]
unsafe fn from_superset_unchecked(sim: &Similarity<N2, D, R2>) -> Self {
Similarity::from_isometry(
sim.isometry.to_subset_unchecked(),
sim.scaling().to_subset_unchecked(),
)
}
}
impl<N1, N2, D, R, C> SubsetOf<Transform<N2, D, C>> for Similarity<N1, D, R>
where
N1: Real,
N2: Real + SupersetOf<N1>,
C: SuperTCategoryOf<TAffine>,
R: Rotation<Point<N1, D>>
+ SubsetOf<MatrixN<N1, DimNameSum<D, U1>>>
+ SubsetOf<MatrixN<N2, DimNameSum<D, U1>>>,
D: DimNameAdd<U1> + DimMin<D, Output = D>, // needed by .determinant()
DefaultAllocator: Allocator<N1, D>
+ Allocator<N1, D, D>
+ Allocator<N1, DimNameSum<D, U1>, DimNameSum<D, U1>>
+ Allocator<N2, DimNameSum<D, U1>, DimNameSum<D, U1>>
+ Allocator<(usize, usize), D>
+ Allocator<N2, DimNameSum<D, U1>, DimNameSum<D, U1>>
+ Allocator<N2, D, D>
+ Allocator<N2, D>,
{
#[inline]
fn to_superset(&self) -> Transform<N2, D, C> {
Transform::from_matrix_unchecked(self.to_homogeneous().to_superset())
}
#[inline]
fn is_in_subset(t: &Transform<N2, D, C>) -> bool {
<Self as SubsetOf<_>>::is_in_subset(t.matrix())
}
#[inline]
unsafe fn from_superset_unchecked(t: &Transform<N2, D, C>) -> Self {
Self::from_superset_unchecked(t.matrix())
}
}
impl<N1, N2, D, R> SubsetOf<MatrixN<N2, DimNameSum<D, U1>>> for Similarity<N1, D, R>
where
N1: Real,
N2: Real + SupersetOf<N1>,
R: Rotation<Point<N1, D>>
+ SubsetOf<MatrixN<N1, DimNameSum<D, U1>>>
+ SubsetOf<MatrixN<N2, DimNameSum<D, U1>>>,
D: DimNameAdd<U1> + DimMin<D, Output = D>, // needed by .determinant()
DefaultAllocator: Allocator<N1, D>
+ Allocator<N1, D, D>
+ Allocator<N1, DimNameSum<D, U1>, DimNameSum<D, U1>>
+ Allocator<N2, DimNameSum<D, U1>, DimNameSum<D, U1>>
+ Allocator<(usize, usize), D>
+ Allocator<N2, DimNameSum<D, U1>, DimNameSum<D, U1>>
+ Allocator<N2, D, D>
+ Allocator<N2, D>,
{
#[inline]
fn to_superset(&self) -> MatrixN<N2, DimNameSum<D, U1>> {
self.to_homogeneous().to_superset()
}
#[inline]
fn is_in_subset(m: &MatrixN<N2, DimNameSum<D, U1>>) -> bool {
let mut rot = m.fixed_slice::<D, D>(0, 0).clone_owned();
if rot.fixed_columns_mut::<U1>(0)
.try_normalize_mut(N2::zero())
.is_some()
&& rot.fixed_columns_mut::<U1>(1)
.try_normalize_mut(N2::zero())
.is_some()
&& rot.fixed_columns_mut::<U1>(2)
.try_normalize_mut(N2::zero())
.is_some()
{
// FIXME: could we avoid explicit the computation of the determinant?
// (its sign is needed to see if the scaling factor is negative).
if rot.determinant() < N2::zero() {
rot.fixed_columns_mut::<U1>(0).neg_mut();
rot.fixed_columns_mut::<U1>(1).neg_mut();
rot.fixed_columns_mut::<U1>(2).neg_mut();
}
let bottom = m.fixed_slice::<U1, D>(D::dim(), 0);
// Scalar types agree.
m.iter().all(|e| SupersetOf::<N1>::is_in_subset(e)) &&
// The normalized block part is a rotation.
// rot.is_special_orthogonal(N2::default_epsilon().sqrt()) &&
// The bottom row is (0, 0, ..., 1)
bottom.iter().all(|e| e.is_zero()) && m[(D::dim(), D::dim())] == N2::one()
} else {
false
}
}
#[inline]
unsafe fn from_superset_unchecked(m: &MatrixN<N2, DimNameSum<D, U1>>) -> Self {
let mut mm = m.clone_owned();
let na = mm.fixed_slice_mut::<D, U1>(0, 0).normalize_mut();
let nb = mm.fixed_slice_mut::<D, U1>(0, 1).normalize_mut();
let nc = mm.fixed_slice_mut::<D, U1>(0, 2).normalize_mut();
let mut scale = (na + nb + nc) / ::convert(3.0); // We take the mean, for robustness.
// FIXME: could we avoid the explicit computation of the determinant?
// (its sign is needed to see if the scaling factor is negative).
if mm.fixed_slice::<D, D>(0, 0).determinant() < N2::zero() {
mm.fixed_slice_mut::<D, U1>(0, 0).neg_mut();
mm.fixed_slice_mut::<D, U1>(0, 1).neg_mut();
mm.fixed_slice_mut::<D, U1>(0, 2).neg_mut();
scale = -scale;
}
let t = m.fixed_slice::<D, U1>(0, D::dim()).into_owned();
let t = Translation::from_vector(::convert_unchecked(t));
Self::from_parts(t, ::convert_unchecked(mm), ::convert_unchecked(scale))
}
}