static gboolean points_are_folded (GtsPoint * A,
GtsPoint * B,
GtsPoint * C,
GtsPoint * D,
gdouble max)
{
GtsVector AB, AC, AD;
GtsVector n1, n2;
gdouble nn1, nn2, n1n2;
gts_vector_init (AB, A, B);
gts_vector_init (AC, A, C);
gts_vector_init (AD, A, D);
gts_vector_cross (n1, AB, AC);
gts_vector_cross (n2, AD, AB);
nn1 = gts_vector_scalar (n1, n1);
nn2 = gts_vector_scalar (n2, n2);
if (nn1 >= AREA_RATIO_MAX2*nn2 || nn2 >= AREA_RATIO_MAX2*nn1)
return TRUE;
n1n2 = gts_vector_scalar (n1, n2);
if (n1n2 > 0.)
return FALSE;
if (n1n2*n1n2/(nn1*nn2) > max)
return TRUE;
return FALSE;
}
void gts_triangulate_convex_polygon(GtsSurface *s, GtsEdgePool *pool, GCList *p)
{
guint poly_length = g_clist_length(p);
while (poly_length > 2) {
while (poly_length > 3) {
GtsVector e1, e2;
GtsPoint *v1 = GTS_POINT(p->prev->data);
GtsPoint *v2 = GTS_POINT(p->data);
GtsPoint *v3 = GTS_POINT(p->next->data);
GtsPoint *v4 = GTS_POINT(p->next->next->data);
if (v1 == v3 || v2 == v4) {
g_debug("kill degenerated triangle");
GCList *p1 = p;
GCList *p2 = p->next;
p=g_clist_delete_link(p,p1);
p=g_clist_delete_link(p,p2);
poly_length -= 2;
continue;
}
gts_vector_init(e1, v1, v3);
gts_vector_init(e2, v2, v4);
if (gts_vector_scalar(e1, e1) < gts_vector_scalar(e2, e2)) {
add_face_from_polygon_corner(s, pool,
GTS_VERTEX(v1),
GTS_VERTEX(v2),
GTS_VERTEX(v3));
p = g_clist_delete_link(p, p);
} else {
add_face_from_polygon_corner(s, pool,
GTS_VERTEX(v2),
GTS_VERTEX(v3),
GTS_VERTEX(v4));
p = g_clist_delete_link(p, p->next);
}
--poly_length;
}
if (g_clist_length(p) > 2) {
add_face_from_polygon_corner(s, pool,
GTS_VERTEX(p->prev->data),
GTS_VERTEX(p->data),
GTS_VERTEX(p->next->data));
p = g_clist_delete_link(p, p);
--poly_length;
}
}
g_clist_free(p);
}
/**
* gts_bb_tree_triangle_distance:
* @tree: a bounding box tree.
* @t: a #GtsTriangle.
* @distance: a #GtsBBoxDistFunc.
* @delta: spatial scale of the sampling to be used.
* @range: a #GtsRange to be filled with the results.
*
* Given a triangle @t, points are sampled regularly on its surface
* using @delta as increment. The distance from each of these points
* to the closest object of @tree is computed using @distance and the
* gts_bb_tree_point_distance() function. The fields of @range are
* filled with the number of points sampled, the minimum, average and
* maximum value and the standard deviation.
*/
void gts_bb_tree_triangle_distance (GNode * tree,
GtsTriangle * t,
GtsBBoxDistFunc distance,
gdouble delta,
GtsRange * range)
{
GtsPoint * p1, * p2, * p3, * p;
GtsVector p1p2, p1p3;
gdouble l1, t1, dt1;
guint i, n1;
g_return_if_fail (tree != NULL);
g_return_if_fail (t != NULL);
g_return_if_fail (distance != NULL);
g_return_if_fail (delta > 0.);
g_return_if_fail (range != NULL);
gts_triangle_vertices (t,
(GtsVertex **) &p1,
(GtsVertex **) &p2,
(GtsVertex **) &p3);
gts_vector_init (p1p2, p1, p2);
gts_vector_init (p1p3, p1, p3);
gts_range_init (range);
p = GTS_POINT (gts_object_new (GTS_OBJECT_CLASS (gts_point_class ())));
l1 = sqrt (gts_vector_scalar (p1p2, p1p2));
n1 = l1/delta + 1;
dt1 = 1.0/(gdouble) n1;
t1 = 0.0;
for (i = 0; i <= n1; i++, t1 += dt1) {
gdouble t2 = 1. - t1;
gdouble x = t2*p1p3[0];
gdouble y = t2*p1p3[1];
gdouble z = t2*p1p3[2];
gdouble l2 = sqrt (x*x + y*y + z*z);
guint j, n2 = (guint) (l2/delta + 1);
gdouble dt2 = t2/(gdouble) n2;
x = t2*p1->x + t1*p2->x;
y = t2*p1->y + t1*p2->y;
z = t2*p1->z + t1*p2->z;
t2 = 0.0;
for (j = 0; j <= n2; j++, t2 += dt2) {
p->x = x + t2*p1p3[0];
p->y = y + t2*p1p3[1];
p->z = z + t2*p1p3[2];
gts_range_add_value (range,
gts_bb_tree_point_distance (tree, p, distance, NULL));
}
}
gts_object_destroy (GTS_OBJECT (p));
gts_range_update (range);
}
static gdouble angle_from_cotan (GtsVertex * vo,
GtsVertex * v1, GtsVertex * v2)
{
/* cf. Appendix B and the caption of Table 1 from [Meyer et al 2002] */
GtsVector u, v;
gdouble udotv, denom;
gts_vector_init (u, GTS_POINT (vo), GTS_POINT (v1));
gts_vector_init (v, GTS_POINT (vo), GTS_POINT (v2));
udotv = gts_vector_scalar (u, v);
denom = sqrt (gts_vector_scalar (u,u)*gts_vector_scalar (v,v)
- udotv*udotv);
/* Note: I assume this is what they mean by using atan2 (). -Ray Jones */
/* tan = denom/udotv = y/x (see man page for atan2) */
return (fabs (atan2 (denom, udotv)));
}
static gboolean angle_obtuse (GtsVertex * v, GtsFace * f)
{
GtsEdge * e = gts_triangle_edge_opposite (GTS_TRIANGLE (f), v);
GtsVector vec1, vec2;
gts_vector_init (vec1, GTS_POINT (v), GTS_POINT (GTS_SEGMENT (e)->v1));
gts_vector_init (vec2, GTS_POINT (v), GTS_POINT (GTS_SEGMENT (e)->v2));
return (gts_vector_scalar (vec1, vec2) < 0.0);
}
static gdouble cotan (GtsVertex * vo, GtsVertex * v1, GtsVertex * v2)
{
/* cf. Appendix B of [Meyer et al 2002] */
GtsVector u, v;
gdouble udotv, denom;
gts_vector_init (u, GTS_POINT (vo), GTS_POINT (v1));
gts_vector_init (v, GTS_POINT (vo), GTS_POINT (v2));
udotv = gts_vector_scalar (u, v);
denom = sqrt (gts_vector_scalar (u,u)*gts_vector_scalar (v,v) -
udotv*udotv);
/* denom can be zero if u==v. Returning 0 is acceptable, based on
* the callers of this function below. */
if (denom == 0.0) return (0.0);
return (udotv/denom);
}
static gboolean is_convex(GtsPoint *p0, GtsPoint *p1, GtsPoint *p2, GtsVector orientation)
{
g_assert(GTS_IS_POINT(p0));
g_assert(GTS_IS_POINT(p1));
g_assert(GTS_IS_POINT(p2));
GtsVector a;
GtsVector b;
GtsVector c;
gts_vector_init(a, p1, p0);
gts_vector_init(b, p1, p2);
gts_vector_cross(c, a, b);
return gts_vector_scalar(c, orientation) >= 0;
}
/**
* gts_bb_tree_segment_distance:
* @tree: a bounding box tree.
* @s: a #GtsSegment.
* @distance: a #GtsBBoxDistFunc.
* @delta: spatial scale of the sampling to be used.
* @range: a #GtsRange to be filled with the results.
*
* Given a segment @s, points are sampled regularly on its length
* using @delta as increment. The distance from each of these points
* to the closest object of @tree is computed using @distance and the
* gts_bb_tree_point_distance() function. The fields of @range are
* filled with the number of points sampled, the minimum, average and
* maximum value and the standard deviation.
*/
void gts_bb_tree_segment_distance (GNode * tree,
GtsSegment * s,
gdouble (*distance) (GtsPoint *,
gpointer),
gdouble delta,
GtsRange * range)
{
GtsPoint * p1, * p2, * p;
GtsVector p1p2;
gdouble l, t, dt;
guint i, n;
g_return_if_fail (tree != NULL);
g_return_if_fail (s != NULL);
g_return_if_fail (distance != NULL);
g_return_if_fail (delta > 0.);
g_return_if_fail (range != NULL);
p1 = GTS_POINT (s->v1);
p2 = GTS_POINT (s->v2);
gts_vector_init (p1p2, p1, p2);
gts_range_init (range);
p = GTS_POINT (gts_object_new (GTS_OBJECT_CLASS (gts_point_class())));
l = sqrt (gts_vector_scalar (p1p2, p1p2));
n = (guint) (l/delta + 1);
dt = 1.0/(gdouble) n;
t = 0.0;
for (i = 0; i <= n; i++, t += dt) {
p->x = p1->x + t*p1p2[0];
p->y = p1->y + t*p1p2[1];
p->z = p1->z + t*p1p2[2];
gts_range_add_value (range,
gts_bb_tree_point_distance (tree, p, distance, NULL));
}
gts_object_destroy (GTS_OBJECT (p));
gts_range_update (range);
}
/**
* gts_vertex_principal_directions:
* @v: a #GtsVertex.
* @s: a #GtsSurface.
* @Kh: mean curvature normal (a #GtsVector).
* @Kg: Gaussian curvature (a gdouble).
* @e1: first principal curvature direction (direction of largest curvature).
* @e2: second principal curvature direction.
*
* Computes the principal curvature directions at a point given @Kh
* and @Kg, the mean curvature normal and Gaussian curvatures at that
* point, computed with gts_vertex_mean_curvature_normal() and
* gts_vertex_gaussian_curvature(), respectively.
*
* Note that this computation is very approximate and tends to be
* unstable. Smoothing of the surface or the principal directions may
* be necessary to achieve reasonable results.
*/
void gts_vertex_principal_directions (GtsVertex * v, GtsSurface * s,
GtsVector Kh, gdouble Kg,
GtsVector e1, GtsVector e2)
{
GtsVector N;
gdouble normKh;
GSList * i, * j;
GtsVector basis1, basis2, d, eig;
gdouble ve2, vdotN;
gdouble aterm_da, bterm_da, cterm_da, const_da;
gdouble aterm_db, bterm_db, cterm_db, const_db;
gdouble a, b, c;
gdouble K1, K2;
gdouble *weights, *kappas, *d1s, *d2s;
gint edge_count;
gdouble err_e1, err_e2;
int e;
/* compute unit normal */
normKh = sqrt (gts_vector_scalar (Kh, Kh));
if (normKh > 0.0) {
N[0] = Kh[0] / normKh;
N[1] = Kh[1] / normKh;
N[2] = Kh[2] / normKh;
} else {
/* This vertex is a point of zero mean curvature (flat or saddle
* point). Compute a normal by averaging the adjacent triangles
*/
N[0] = N[1] = N[2] = 0.0;
i = gts_vertex_faces (v, s, NULL);
while (i) {
gdouble x, y, z;
gts_triangle_normal (GTS_TRIANGLE ((GtsFace *) i->data),
&x, &y, &z);
N[0] += x;
N[1] += y;
N[2] += z;
i = i->next;
}
g_return_if_fail (gts_vector_norm (N) > 0.0);
gts_vector_normalize (N);
}
/* construct a basis from N: */
/* set basis1 to any component not the largest of N */
basis1[0] = basis1[1] = basis1[2] = 0.0;
if (fabs (N[0]) > fabs (N[1]))
basis1[1] = 1.0;
else
basis1[0] = 1.0;
/* make basis2 orthogonal to N */
gts_vector_cross (basis2, N, basis1);
gts_vector_normalize (basis2);
/* make basis1 orthogonal to N and basis2 */
gts_vector_cross (basis1, N, basis2);
gts_vector_normalize (basis1);
aterm_da = bterm_da = cterm_da = const_da = 0.0;
aterm_db = bterm_db = cterm_db = const_db = 0.0;
weights = g_malloc (sizeof (gdouble)*g_slist_length (v->segments));
kappas = g_malloc (sizeof (gdouble)*g_slist_length (v->segments));
d1s = g_malloc (sizeof (gdouble)*g_slist_length (v->segments));
d2s = g_malloc (sizeof (gdouble)*g_slist_length (v->segments));
edge_count = 0;
i = v->segments;
while (i) {
GtsEdge * e;
GtsFace * f1, * f2;
gdouble weight, kappa, d1, d2;
GtsVector vec_edge;
if (! GTS_IS_EDGE (i->data)) {
i = i->next;
continue;
}
e = i->data;
/* since this vertex passed the tests in
* gts_vertex_mean_curvature_normal(), this should be true. */
g_assert (gts_edge_face_number (e, s) == 2);
/* identify the two triangles bordering e in s */
f1 = f2 = NULL;
j = e->triangles;
while (j) {
if ((! GTS_IS_FACE (j->data)) ||
(! gts_face_has_parent_surface (GTS_FACE (j->data), s))) {
j = j->next;
continue;
}
if (f1 == NULL)
f1 = GTS_FACE (j->data);
else {
f2 = GTS_FACE (j->data);
break;
}
j = j->next;
}
g_assert (f2 != NULL);
/* We are solving for the values of the curvature tensor
* B = [ a b ; b c ].
* The computations here are from section 5 of [Meyer et al 2002].
*
* The first step is to calculate the linear equations governing
* the values of (a,b,c). These can be computed by setting the
* derivatives of the error E to zero (section 5.3).
*
* Since a + c = norm(Kh), we only compute the linear equations
* for dE/da and dE/db. (NB: [Meyer et al 2002] has the
* equation a + b = norm(Kh), but I'm almost positive this is
* incorrect.)
*
* Note that the w_ij (defined in section 5.2) are all scaled by
* (1/8*A_mixed). We drop this uniform scale factor because the
* solution of the linear equations doesn't rely on it.
*
* The terms of the linear equations are xterm_dy with x in
* {a,b,c} and y in {a,b}. There are also const_dy terms that are
* the constant factors in the equations.
*/
/* find the vector from v along edge e */
gts_vector_init (vec_edge, GTS_POINT (v),
GTS_POINT ((GTS_SEGMENT (e)->v1 == v) ?
GTS_SEGMENT (e)->v2 : GTS_SEGMENT (e)->v1));
ve2 = gts_vector_scalar (vec_edge, vec_edge);
vdotN = gts_vector_scalar (vec_edge, N);
/* section 5.2 - There is a typo in the computation of kappa. The
* edges should be x_j-x_i.
*/
kappa = 2.0 * vdotN / ve2;
/* section 5.2 */
/* I don't like performing a minimization where some of the
* weights can be negative (as can be the case if f1 or f2 are
* obtuse). To ensure all-positive weights, we check for
* obtuseness and use values similar to those in region_area(). */
weight = 0.0;
if (! triangle_obtuse(v, f1)) {
weight += ve2 *
cotan (gts_triangle_vertex_opposite (GTS_TRIANGLE (f1), e),
GTS_SEGMENT (e)->v1, GTS_SEGMENT (e)->v2) / 8.0;
} else {
if (angle_obtuse (v, f1)) {
weight += ve2 * gts_triangle_area (GTS_TRIANGLE (f1)) / 4.0;
} else {
weight += ve2 * gts_triangle_area (GTS_TRIANGLE (f1)) / 8.0;
}
}
if (! triangle_obtuse(v, f2)) {
weight += ve2 *
cotan (gts_triangle_vertex_opposite (GTS_TRIANGLE (f2), e),
GTS_SEGMENT (e)->v1, GTS_SEGMENT (e)->v2) / 8.0;
} else {
if (angle_obtuse (v, f2)) {
weight += ve2 * gts_triangle_area (GTS_TRIANGLE (f2)) / 4.0;
} else {
weight += ve2 * gts_triangle_area (GTS_TRIANGLE (f2)) / 8.0;
}
}
/* projection of edge perpendicular to N (section 5.3) */
d[0] = vec_edge[0] - vdotN * N[0];
d[1] = vec_edge[1] - vdotN * N[1];
d[2] = vec_edge[2] - vdotN * N[2];
gts_vector_normalize (d);
/* not explicit in the paper, but necessary. Move d to 2D basis. */
d1 = gts_vector_scalar (d, basis1);
d2 = gts_vector_scalar (d, basis2);
/* store off the curvature, direction of edge, and weights for later use */
weights[edge_count] = weight;
kappas[edge_count] = kappa;
d1s[edge_count] = d1;
d2s[edge_count] = d2;
edge_count++;
/* Finally, update the linear equations */
aterm_da += weight * d1 * d1 * d1 * d1;
bterm_da += weight * d1 * d1 * 2 * d1 * d2;
cterm_da += weight * d1 * d1 * d2 * d2;
const_da += weight * d1 * d1 * (- kappa);
aterm_db += weight * d1 * d2 * d1 * d1;
bterm_db += weight * d1 * d2 * 2 * d1 * d2;
cterm_db += weight * d1 * d2 * d2 * d2;
const_db += weight * d1 * d2 * (- kappa);
i = i->next;
}
/* now use the identity (Section 5.3) a + c = |Kh| = 2 * kappa_h */
aterm_da -= cterm_da;
const_da += cterm_da * normKh;
aterm_db -= cterm_db;
const_db += cterm_db * normKh;
/* check for solvability of the linear system */
if (((aterm_da * bterm_db - aterm_db * bterm_da) != 0.0) &&
((const_da != 0.0) || (const_db != 0.0))) {
linsolve (aterm_da, bterm_da, -const_da,
aterm_db, bterm_db, -const_db,
&a, &b);
c = normKh - a;
eigenvector (a, b, c, eig);
} else {
/* region of v is planar */
eig[0] = 1.0;
eig[1] = 0.0;
}
/* Although the eigenvectors of B are good estimates of the
* principal directions, it seems that which one is attached to
* which curvature direction is a bit arbitrary. This may be a bug
* in my implementation, or just a side-effect of the inaccuracy of
* B due to the discrete nature of the sampling.
*
* To overcome this behavior, we'll evaluate which assignment best
* matches the given eigenvectors by comparing the curvature
* estimates computed above and the curvatures calculated from the
* discrete differential operators. */
gts_vertex_principal_curvatures (0.5 * normKh, Kg, &K1, &K2);
err_e1 = err_e2 = 0.0;
/* loop through the values previously saved */
for (e = 0; e < edge_count; e++) {
gdouble weight, kappa, d1, d2;
gdouble temp1, temp2;
gdouble delta;
weight = weights[e];
kappa = kappas[e];
d1 = d1s[e];
d2 = d2s[e];
temp1 = fabs (eig[0] * d1 + eig[1] * d2);
temp1 = temp1 * temp1;
temp2 = fabs (eig[1] * d1 - eig[0] * d2);
temp2 = temp2 * temp2;
/* err_e1 is for K1 associated with e1 */
delta = K1 * temp1 + K2 * temp2 - kappa;
err_e1 += weight * delta * delta;
/* err_e2 is for K1 associated with e2 */
delta = K2 * temp1 + K1 * temp2 - kappa;
err_e2 += weight * delta * delta;
}
g_free (weights);
g_free (kappas);
g_free (d1s);
g_free (d2s);
/* rotate eig by a right angle if that would decrease the error */
if (err_e2 < err_e1) {
gdouble temp = eig[0];
eig[0] = eig[1];
eig[1] = -temp;
}
e1[0] = eig[0] * basis1[0] + eig[1] * basis2[0];
e1[1] = eig[0] * basis1[1] + eig[1] * basis2[1];
e1[2] = eig[0] * basis1[2] + eig[1] * basis2[2];
gts_vector_normalize (e1);
/* make N,e1,e2 a right handed coordinate sytem */
gts_vector_cross (e2, N, e1);
gts_vector_normalize (e2);
}
