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
这大概是一个稍微不那么优化的C代码版本,它来自于这个页面。
我的c++版本使用std::vector<std::pair<double, double>>和两个double作为x和y。逻辑应该与原始C代码完全相同,但我发现我的更容易阅读。我不能为表演说话。
bool point_in_poly(std::vector<std::pair<double, double>>& verts, double point_x, double point_y)
{
bool in_poly = false;
auto num_verts = verts.size();
for (int i = 0, j = num_verts - 1; i < num_verts; j = i++) {
double x1 = verts[i].first;
double y1 = verts[i].second;
double x2 = verts[j].first;
double y2 = verts[j].second;
if (((y1 > point_y) != (y2 > point_y)) &&
(point_x < (x2 - x1) * (point_y - y1) / (y2 - y1) + x1))
in_poly = !in_poly;
}
return in_poly;
}
原始的C代码是
int pnpoly(int nvert, float *vertx, float *verty, float testx, float testy)
{
int i, j, c = 0;
for (i = 0, j = nvert-1; i < nvert; j = i++) {
if ( ((verty[i]>testy) != (verty[j]>testy)) &&
(testx < (vertx[j]-vertx[i]) * (testy-verty[i]) / (verty[j]-verty[i]) + vertx[i]) )
c = !c;
}
return c;
}
其他回答
这个问题很有趣。我有另一个可行的想法,不同于这篇文章的其他答案。其原理是利用角度之和来判断目标是在内部还是外部。也就是圈数。
设x为目标点。让数组[0,1,....N]是该区域的所有点。用一条线将目标点与每一个边界点连接起来。如果目标点在这个区域内。所有角的和是360度。如果不是,角度将小于360度。
参考这张图来对这个概念有一个基本的了解:
我的算法假设顺时针是正方向。这是一个潜在的输入:
[[-122.402015, 48.225216], [-117.032049, 48.999931], [-116.919132, 45.995175], [-124.079107, 46.267259], [-124.717175, 48.377557], [-122.92315, 47.047963], [-122.402015, 48.225216]]
下面是实现这个想法的python代码:
def isInside(self, border, target):
degree = 0
for i in range(len(border) - 1):
a = border[i]
b = border[i + 1]
# calculate distance of vector
A = getDistance(a[0], a[1], b[0], b[1]);
B = getDistance(target[0], target[1], a[0], a[1])
C = getDistance(target[0], target[1], b[0], b[1])
# calculate direction of vector
ta_x = a[0] - target[0]
ta_y = a[1] - target[1]
tb_x = b[0] - target[0]
tb_y = b[1] - target[1]
cross = tb_y * ta_x - tb_x * ta_y
clockwise = cross < 0
# calculate sum of angles
if(clockwise):
degree = degree + math.degrees(math.acos((B * B + C * C - A * A) / (2.0 * B * C)))
else:
degree = degree - math.degrees(math.acos((B * B + C * C - A * A) / (2.0 * B * C)))
if(abs(round(degree) - 360) <= 3):
return True
return False
简单的解决方案是将多边形划分为三角形,并按这里解释的那样对三角形进行测试
如果你的多边形是凸多边形,可能有更好的方法。把这个多边形看作是无限条线的集合。每一行将空间一分为二。对于每一个点,很容易判断它是在直线的一边还是另一边。如果一个点在所有直线的同一侧,那么它在多边形内。
nirg的c#版本的答案在这里:我只分享代码。这可能会节省一些时间。
public static bool IsPointInPolygon(IList<Point> polygon, Point testPoint) {
bool result = false;
int j = polygon.Count() - 1;
for (int i = 0; i < polygon.Count(); i++) {
if (polygon[i].Y < testPoint.Y && polygon[j].Y >= testPoint.Y || polygon[j].Y < testPoint.Y && polygon[i].Y >= testPoint.Y) {
if (polygon[i].X + (testPoint.Y - polygon[i].Y) / (polygon[j].Y - polygon[i].Y) * (polygon[j].X - polygon[i].X) < testPoint.X) {
result = !result;
}
}
j = i;
}
return result;
}
这大概是一个稍微不那么优化的C代码版本,它来自于这个页面。
我的c++版本使用std::vector<std::pair<double, double>>和两个double作为x和y。逻辑应该与原始C代码完全相同,但我发现我的更容易阅读。我不能为表演说话。
bool point_in_poly(std::vector<std::pair<double, double>>& verts, double point_x, double point_y)
{
bool in_poly = false;
auto num_verts = verts.size();
for (int i = 0, j = num_verts - 1; i < num_verts; j = i++) {
double x1 = verts[i].first;
double y1 = verts[i].second;
double x2 = verts[j].first;
double y2 = verts[j].second;
if (((y1 > point_y) != (y2 > point_y)) &&
(point_x < (x2 - x1) * (point_y - y1) / (y2 - y1) + x1))
in_poly = !in_poly;
}
return in_poly;
}
原始的C代码是
int pnpoly(int nvert, float *vertx, float *verty, float testx, float testy)
{
int i, j, c = 0;
for (i = 0, j = nvert-1; i < nvert; j = i++) {
if ( ((verty[i]>testy) != (verty[j]>testy)) &&
(testx < (vertx[j]-vertx[i]) * (testy-verty[i]) / (verty[j]-verty[i]) + vertx[i]) )
c = !c;
}
return c;
}
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;
}
推荐文章
- 确定记录是否存在的最快方法
- 阅读GHC核心
- Python: List vs Dict用于查找表
- 为什么MATLAB的矩阵乘法运算这么快?
- for循环和for-each循环在性能上有区别吗?
- 就性能而言,使用std::memcpy()还是std::copy()更好?
- 什么时候我应该(不)想要在我的代码中使用熊猫apply() ?
- 如何加速gwt编译器?
- MySQL OR与IN性能
- 应该……接住环内还是环外?
- 哪个更快/最好?SELECT *或SELECT columnn1, colum2, column3等
- 加快R中的循环操作
- INT和VARCHAR主键之间有真正的性能差异吗?
- c++标准是否要求iostreams的性能很差,或者我只是在处理一个糟糕的实现?
- 大概的成本访问各种缓存和主存储器?