我试图创建一个快速的2D点内多边形算法,用于命中测试(例如多边形.contains(p:点))。对有效技术的建议将不胜感激。
当前回答
Like David Segonds' answer suggests I use an approach of angle summation derived from my concave polygon drawing algorithm. It relies of adding up the approximate angles of subtriangles around the point to obtain a weight. A weight around 1.0 means the point is inside the triangle, a weight around 0.0 means outside, a weight around -1.0 is what happens when inside the polygon but in reverse order (like with one of the halves of a bowtie-shaped tetragon) and a weight of NAN if exactly on an edge. The reason it's not slow is that angles don't need to be estimated accurately at all. Holes can be handled by treating them as separate polygons and subtracting the weights.
typedef struct { double x, y; } xy_t;
xy_t sub_xy(xy_t a, xy_t b)
{
a.x -= b.x;
a.y -= b.y;
return a;
}
double calc_sharp_subtriangle_pixel_weight(xy_t p0, xy_t p1)
{
xy_t rot, r0, r1;
double weight;
// Rotate points (unnormalised)
rot = sub_xy(p1, p0);
r0.x = rot.x*p0.y - rot.y*p0.x;
r0.y = rot.x*p0.x + rot.y*p0.y;
r1.y = rot.x*p1.x + rot.y*p1.y;
// Calc weight
weight = subtriangle_angle_approx(r1.y, r0.x) - subtriangle_angle_approx(r0.y, r0.x);
return weight;
}
double calc_sharp_polygon_pixel_weight(xy_t p, xy_t *corner, int corner_count)
{
int i;
xy_t p0, p1;
double weight = 0.;
p0 = sub_xy(corner[corner_count-1], p);
for (i=0; i < corner_count; i++)
{
// Transform corner coordinates
p1 = sub_xy(corner[i], p);
// Calculate weight for each subtriangle
weight += calc_sharp_subtriangle_pixel_weight(p0, p1);
p0 = p1;
}
return weight;
}
因此,对于多边形的每一段,都形成一个子三角形,并计算点,然后旋转每个子三角形以计算其近似角度并添加到权重。
调用subtriangle_angle_approx(y, x)可以替换为atan2(y, x) / (2.*pi),但是一个非常粗略的近似值就足够精确了:
double subtriangle_angle_approx(double y, double x)
{
double angle, d;
int obtuse;
if (x == 0.)
return NAN;
obtuse = fabs(y) > fabs(x);
if (obtuse)
swap_double(&y, &x);
// Core of the approximation, a very loosely approximate atan(y/x) / (2.*pi) over ]-1 , 1[
d = y / x;
angle = 0.13185 * d;
if (obtuse)
angle = sign(d)*0.25 - angle;
return angle;
}
其他回答
计算点p与每个多边形顶点之间的有向角和。如果总倾斜角是360度,那么这个点在里面。如果总数为0,则点在外面。
我更喜欢这种方法,因为它更健壮,对数值精度的依赖更小。
计算交集数量的均匀性的方法是有限的,因为你可以在计算交集数量的过程中“击中”一个顶点。
编辑:顺便说一下,这种方法适用于凹凸多边形。
编辑:我最近在维基百科上找到了一篇关于这个话题的完整文章。
bobobobo引用的Eric Haines的文章真的很棒。特别有趣的是比较算法性能的表格;角度求和法和其他方法比起来真的很差。同样有趣的是,使用查找网格将多边形进一步细分为“in”和“out”扇区的优化可以使测试非常快,即使是在> 1000条边的多边形上。
不管怎样,现在还为时过早,但我的投票倾向于“交叉”方法,我认为这几乎就是Mecki所描述的。然而,我发现大卫·伯克(David Bourke)对它进行了最简洁的描述和编纂。我喜欢它不需要真正的三角函数,它适用于凸和凹,而且随着边数的增加,它的表现也相当不错。
顺便说一下,这是Eric Haines文章中的一个性能表,在随机多边形上进行测试。
number of edges per polygon
3 4 10 100 1000
MacMartin 2.9 3.2 5.9 50.6 485
Crossings 3.1 3.4 6.8 60.0 624
Triangle Fan+edge sort 1.1 1.8 6.5 77.6 787
Triangle Fan 1.2 2.1 7.3 85.4 865
Barycentric 2.1 3.8 13.8 160.7 1665
Angle Summation 56.2 70.4 153.6 1403.8 14693
Grid (100x100) 1.5 1.5 1.6 2.1 9.8
Grid (20x20) 1.7 1.7 1.9 5.7 42.2
Bins (100) 1.8 1.9 2.7 15.1 117
Bins (20) 2.1 2.2 3.7 26.3 278
没有什么比归纳定义问题更美好的了。为了完整起见,你在序言中有一个版本,它可能也澄清了光线投射背后的思想:
基于仿真的简化算法在http://www.ecse.rpi.edu/Homepages/wrf/Research/Short_Notes/pnpoly.html
一些helper谓词:
exor(A,B):- \+A,B;A,\+B.
in_range(Coordinate,CA,CB) :- exor((CA>Coordinate),(CB>Coordinate)).
inside(false).
inside(_,[_|[]]).
inside(X:Y, [X1:Y1,X2:Y2|R]) :- in_range(Y,Y1,Y2), X > ( ((X2-X1)*(Y-Y1))/(Y2-Y1) + X1),toggle_ray, inside(X:Y, [X2:Y2|R]); inside(X:Y, [X2:Y2|R]).
get_line(_,_,[]).
get_line([XA:YA,XB:YB],[X1:Y1,X2:Y2|R]):- [XA:YA,XB:YB]=[X1:Y1,X2:Y2]; get_line([XA:YA,XB:YB],[X2:Y2|R]).
给定两点a和B的直线(直线(a,B))方程为:
(YB-YA)
Y - YA = ------- * (X - XA)
(XB-YB)
It is important that the direction of rotation for the line is setted to clock-wise for boundaries and anti-clock-wise for holes. We are going to check whether the point (X,Y), i.e the tested point is at the left half-plane of our line (it is a matter of taste, it could also be the right side, but also the direction of boundaries lines has to be changed in that case), this is to project the ray from the point to the right (or left) and acknowledge the intersection with the line. We have chosen to project the ray in the horizontal direction (again it is a matter of taste, it could also be done in vertical with similar restrictions), so we have:
(XB-XA)
X < ------- * (Y - YA) + XA
(YB-YA)
Now we need to know if the point is at the left (or right) side of the line segment only, not the entire plane, so we need to restrict the search only to this segment, but this is easy since to be inside the segment only one point in the line can be higher than Y in the vertical axis. As this is a stronger restriction it needs to be the first to check, so we take first only those lines meeting this requirement and then check its possition. By the Jordan Curve theorem any ray projected to a polygon must intersect at an even number of lines. So we are done, we will throw the ray to the right and then everytime it intersects a line, toggle its state. However in our implementation we are goint to check the lenght of the bag of solutions meeting the given restrictions and decide the innership upon it. for each line in the polygon this have to be done.
is_left_half_plane(_,[],[],_).
is_left_half_plane(X:Y,[XA:YA,XB:YB], [[X1:Y1,X2:Y2]|R], Test) :- [XA:YA, XB:YB] = [X1:Y1, X2:Y2], call(Test, X , (((XB - XA) * (Y - YA)) / (YB - YA) + XA));
is_left_half_plane(X:Y, [XA:YA, XB:YB], R, Test).
in_y_range_at_poly(Y,[XA:YA,XB:YB],Polygon) :- get_line([XA:YA,XB:YB],Polygon), in_range(Y,YA,YB).
all_in_range(Coordinate,Polygon,Lines) :- aggregate(bag(Line), in_y_range_at_poly(Coordinate,Line,Polygon), Lines).
traverses_ray(X:Y, Lines, Count) :- aggregate(bag(Line), is_left_half_plane(X:Y, Line, Lines, <), IntersectingLines), length(IntersectingLines, Count).
% This is the entry point predicate
inside_poly(X:Y,Polygon,Answer) :- all_in_range(Y,Polygon,Lines), traverses_ray(X:Y, Lines, Count), (1 is mod(Count,2)->Answer=inside;Answer=outside).
下面是nirg给出的答案的c#版本,它来自RPI教授。请注意,使用来自RPI源代码的代码需要归属。
在顶部添加了一个边界框复选。然而,正如James Brown所指出的,主代码几乎和边界框检查本身一样快,所以边界框检查实际上会减慢整体操作,因为您正在检查的大多数点都在边界框内。所以你可以让边界框签出,或者另一种选择是预先计算多边形的边界框,如果它们不经常改变形状的话。
public bool IsPointInPolygon( Point p, Point[] polygon )
{
double minX = polygon[ 0 ].X;
double maxX = polygon[ 0 ].X;
double minY = polygon[ 0 ].Y;
double maxY = polygon[ 0 ].Y;
for ( int i = 1 ; i < polygon.Length ; i++ )
{
Point q = polygon[ i ];
minX = Math.Min( q.X, minX );
maxX = Math.Max( q.X, maxX );
minY = Math.Min( q.Y, minY );
maxY = Math.Max( q.Y, maxY );
}
if ( p.X < minX || p.X > maxX || p.Y < minY || p.Y > maxY )
{
return false;
}
// https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html
bool inside = false;
for ( int i = 0, j = polygon.Length - 1 ; i < polygon.Length ; j = i++ )
{
if ( ( polygon[ i ].Y > p.Y ) != ( polygon[ j ].Y > p.Y ) &&
p.X < ( polygon[ j ].X - polygon[ i ].X ) * ( p.Y - polygon[ i ].Y ) / ( polygon[ j ].Y - polygon[ i ].Y ) + polygon[ i ].X )
{
inside = !inside;
}
}
return inside;
}
以下是M. Katz基于Nirg方法的答案的JavaScript变体:
function pointIsInPoly(p, polygon) {
var isInside = false;
var minX = polygon[0].x, maxX = polygon[0].x;
var minY = polygon[0].y, maxY = polygon[0].y;
for (var n = 1; n < polygon.length; n++) {
var q = polygon[n];
minX = Math.min(q.x, minX);
maxX = Math.max(q.x, maxX);
minY = Math.min(q.y, minY);
maxY = Math.max(q.y, maxY);
}
if (p.x < minX || p.x > maxX || p.y < minY || p.y > maxY) {
return false;
}
var i = 0, j = polygon.length - 1;
for (i, j; i < polygon.length; j = i++) {
if ( (polygon[i].y > p.y) != (polygon[j].y > p.y) &&
p.x < (polygon[j].x - polygon[i].x) * (p.y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x ) {
isInside = !isInside;
}
}
return isInside;
}
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