这似乎在R中工作(为丑陋道歉，希望看到更好的版本!)。

pnpoly <- function(nvert,vertx,verty,testx,testy){
          c <- FALSE
          j <- nvert 
          for (i in 1:nvert){
              if( ((verty[i]>testy) != (verty[j]>testy)) && 
   (testx < (vertx[j]-vertx[i])*(testy-verty[i])/(verty[j]-verty[i])+vertx[i]))
            {c <- !c}
             j <- i}
   return(c)}

For graphics, I'd rather not prefer integers. Many systems use integers for UI painting (pixels are ints after all), but macOS, for example, uses float for everything. macOS only knows points and a point can translate to one pixel, but depending on monitor resolution, it might translate to something else. On retina screens half a point (0.5/0.5) is pixel. Still, I never noticed that macOS UIs are significantly slower than other UIs. After all, 3D APIs (OpenGL or Direct3D) also work with floats and modern graphics libraries very often take advantage of GPU acceleration.

现在你说速度是你最关心的，好吧，让我们追求速度。在运行任何复杂的算法之前，先做一个简单的测试。在多边形周围创建一个轴对齐的包围框。这是非常简单，快速的，已经可以节省你很多计算。这是怎么做到的呢?遍历多边形的所有点，找到X和Y的最小/最大值。

如你有点(9/1),(4/3),(2/7),(8/2),(3/6)。这意味着Xmin是2,Xmax是9,Ymin是1,Ymax是7。矩形外有两条边(2/1)和(9/7)的点不可能在多边形内。

// p is your point, p.x is the x coord, p.y is the y coord
if (p.x < Xmin || p.x > Xmax || p.y < Ymin || p.y > Ymax) {
    // Definitely not within the polygon!
}

这是对任意点运行的第一个测试。正如你所看到的，这个测试非常快，但也非常粗糙。要处理边界矩形内的点，我们需要更复杂的算法。有几种计算方法。哪种方法有效还取决于多边形是否有孔或始终是固体。以下是实体的例子(一个凸面，一个凹面):

这里有一个洞:

绿色的中间有个洞!

最简单的算法，可以处理上述三种情况，并且仍然非常快，叫做射线投射。该算法的思想非常简单:从多边形外的任何地方绘制一条虚拟光线到你的点，并计算它击中多边形一侧的频率。如果命中次数是偶数，则在多边形外，如果是奇数，则在多边形内。

圈数算法是另一种选择，它对非常接近多边形线的点更准确，但也慢得多。由于有限的浮点精度和舍入问题，光线投射可能会因为太靠近多边形一侧的点而失败，但在现实中这几乎不是问题，因为如果一个点靠近一侧，在视觉上甚至不可能让观看者识别它是否已经在内部或仍然在外部。

还记得上面的边界框吗?只需在边界框外选择一个点，并将其用作射线的起点。例如，点(Xmin - e/p.y)肯定在多边形外。

But what is e? Well, e (actually epsilon) gives the bounding box some padding. As I said, ray tracing fails if we start too close to a polygon line. Since the bounding box might equal the polygon (if the polygon is an axis aligned rectangle, the bounding box is equal to the polygon itself!), we need some padding to make this safe, that's all. How big should you choose e? Not too big. It depends on the coordinate system scale you use for drawing. If your pixel step width is 1.0, then just choose 1.0 (yet 0.1 would have worked as well)

现在我们有了光线的起始坐标和结束坐标，问题从“是多边形内的点”转移到“光线与多边形边相交的频率”。因此，我们不能像以前那样只处理多边形点，现在我们需要实际的边。一条边总是由两点来定义。

side 1: (X1/Y1)-(X2/Y2)
side 2: (X2/Y2)-(X3/Y3)
side 3: (X3/Y3)-(X4/Y4)
:

你需要从各个方面测试光线。假设射线是一个矢量，每条边都是一个矢量。光线必须恰好击中每边一次，否则就永远不会。它不可能击中同一侧两次。二维空间中的两条直线总是只相交一次，除非它们是平行的，在这种情况下，它们永远不会相交。然而，由于向量的长度是有限的，两个向量可能不平行，也永远不会相交，因为它们太短而无法相遇。

// Test the ray against all sides
int intersections = 0;
for (side = 0; side < numberOfSides; side++) {
    // Test if current side intersects with ray.
    // If yes, intersections++;
}
if ((intersections & 1) == 1) {
    // Inside of polygon
} else {
    // Outside of polygon
}

到目前为止一切顺利，但是如何检验两个向量是否相交呢?下面是一些C代码(未测试)，应该可以做到:

#define NO 0
#define YES 1
#define COLLINEAR 2

int areIntersecting(
    float v1x1, float v1y1, float v1x2, float v1y2,
    float v2x1, float v2y1, float v2x2, float v2y2
) {
    float d1, d2;
    float a1, a2, b1, b2, c1, c2;

    // Convert vector 1 to a line (line 1) of infinite length.
    // We want the line in linear equation standard form: A*x + B*y + C = 0
    // See: http://en.wikipedia.org/wiki/Linear_equation
    a1 = v1y2 - v1y1;
    b1 = v1x1 - v1x2;
    c1 = (v1x2 * v1y1) - (v1x1 * v1y2);

    // Every point (x,y), that solves the equation above, is on the line,
    // every point that does not solve it, is not. The equation will have a
    // positive result if it is on one side of the line and a negative one 
    // if is on the other side of it. We insert (x1,y1) and (x2,y2) of vector
    // 2 into the equation above.
    d1 = (a1 * v2x1) + (b1 * v2y1) + c1;
    d2 = (a1 * v2x2) + (b1 * v2y2) + c1;

    // If d1 and d2 both have the same sign, they are both on the same side
    // of our line 1 and in that case no intersection is possible. Careful, 
    // 0 is a special case, that's why we don't test ">=" and "<=", 
    // but "<" and ">".
    if (d1 > 0 && d2 > 0) return NO;
    if (d1 < 0 && d2 < 0) return NO;

    // The fact that vector 2 intersected the infinite line 1 above doesn't 
    // mean it also intersects the vector 1. Vector 1 is only a subset of that
    // infinite line 1, so it may have intersected that line before the vector
    // started or after it ended. To know for sure, we have to repeat the
    // the same test the other way round. We start by calculating the 
    // infinite line 2 in linear equation standard form.
    a2 = v2y2 - v2y1;
    b2 = v2x1 - v2x2;
    c2 = (v2x2 * v2y1) - (v2x1 * v2y2);

    // Calculate d1 and d2 again, this time using points of vector 1.
    d1 = (a2 * v1x1) + (b2 * v1y1) + c2;
    d2 = (a2 * v1x2) + (b2 * v1y2) + c2;

    // Again, if both have the same sign (and neither one is 0),
    // no intersection is possible.
    if (d1 > 0 && d2 > 0) return NO;
    if (d1 < 0 && d2 < 0) return NO;

    // If we get here, only two possibilities are left. Either the two
    // vectors intersect in exactly one point or they are collinear, which
    // means they intersect in any number of points from zero to infinite.
    if ((a1 * b2) - (a2 * b1) == 0.0f) return COLLINEAR;

    // If they are not collinear, they must intersect in exactly one point.
    return YES;
}

输入值是向量1 (v1x1/v1y1和v1x2/v1y2)和向量2 (v2x1/v2y1和v2x2/v2y2)的两个端点。有2个向量，4个点，8个坐标。YES和NO很清楚。“是”增加了交叉路口，“否”什么都不做。

What about COLLINEAR? It means both vectors lie on the same infinite line, depending on position and length, they don't intersect at all or they intersect in an endless number of points. I'm not absolutely sure how to handle this case, I would not count it as intersection either way. Well, this case is rather rare in practice anyway because of floating point rounding errors; better code would probably not test for == 0.0f but instead for something like < epsilon, where epsilon is a rather small number.

如果你需要测试更多的点，你当然可以通过在内存中保留多边形边的线性方程标准形式来加快整个过程，这样你就不必每次都重新计算这些点。这将在每次测试中为您节省两次浮点乘法和三次浮点减法，以换取在内存中为每个多边形边存储三个浮点值。这是一个典型的内存与计算时间的权衡。

Last but not least: If you may use 3D hardware to solve the problem, there is an interesting alternative. Just let the GPU do all the work for you. Create a painting surface that is off screen. Fill it completely with the color black. Now let OpenGL or Direct3D paint your polygon (or even all of your polygons if you just want to test if the point is within any of them, but you don't care for which one) and fill the polygon(s) with a different color, e.g. white. To check if a point is within the polygon, get the color of this point from the drawing surface. This is just a O(1) memory fetch.

Of course this method is only usable if your drawing surface doesn't have to be huge. If it cannot fit into the GPU memory, this method is slower than doing it on the CPU. If it would have to be huge and your GPU supports modern shaders, you can still use the GPU by implementing the ray casting shown above as a GPU shader, which absolutely is possible. For a larger number of polygons or a large number of points to test, this will pay off, consider some GPUs will be able to test 64 to 256 points in parallel. Note however that transferring data from CPU to GPU and back is always expensive, so for just testing a couple of points against a couple of simple polygons, where either the points or the polygons are dynamic and will change frequently, a GPU approach will rarely pay off.

我知道这是旧的，但这里是一个在Cocoa实现的光线投射算法，如果有人感兴趣的话。不确定这是最有效的方法，但它可能会帮助别人。

- (BOOL)shape:(NSBezierPath *)path containsPoint:(NSPoint)point
{
    NSBezierPath *currentPath = [path bezierPathByFlatteningPath];
    BOOL result;
    float aggregateX = 0; //I use these to calculate the centroid of the shape
    float aggregateY = 0;
    NSPoint firstPoint[1];
    [currentPath elementAtIndex:0 associatedPoints:firstPoint];
    float olderX = firstPoint[0].x;
    float olderY = firstPoint[0].y;
    NSPoint interPoint;
    int noOfIntersections = 0;

    for (int n = 0; n < [currentPath elementCount]; n++) {
        NSPoint points[1];
        [currentPath elementAtIndex:n associatedPoints:points];
        aggregateX += points[0].x;
        aggregateY += points[0].y;
    }

    for (int n = 0; n < [currentPath elementCount]; n++) {
        NSPoint points[1];

        [currentPath elementAtIndex:n associatedPoints:points];
        //line equations in Ax + By = C form
        float _A_FOO = (aggregateY/[currentPath elementCount]) - point.y;  
        float _B_FOO = point.x - (aggregateX/[currentPath elementCount]);
        float _C_FOO = (_A_FOO * point.x) + (_B_FOO * point.y);

        float _A_BAR = olderY - points[0].y;
        float _B_BAR = points[0].x - olderX;
        float _C_BAR = (_A_BAR * olderX) + (_B_BAR * olderY);

        float det = (_A_FOO * _B_BAR) - (_A_BAR * _B_FOO);
        if (det != 0) {
            //intersection points with the edges
            float xIntersectionPoint = ((_B_BAR * _C_FOO) - (_B_FOO * _C_BAR)) / det;
            float yIntersectionPoint = ((_A_FOO * _C_BAR) - (_A_BAR * _C_FOO)) / det;
            interPoint = NSMakePoint(xIntersectionPoint, yIntersectionPoint);
            if (olderX <= points[0].x) {
                //doesn't matter in which direction the ray goes, so I send it right-ward.
                if ((interPoint.x >= olderX && interPoint.x <= points[0].x) && (interPoint.x > point.x)) {  
                    noOfIntersections++;
                }
            } else {
                if ((interPoint.x >= points[0].x && interPoint.x <= olderX) && (interPoint.x > point.x)) {
                     noOfIntersections++;
                } 
            }
        }
        olderX = points[0].x;
        olderY = points[0].y;
    }
    if (noOfIntersections % 2 == 0) {
        result = FALSE;
    } else {
        result = TRUE;
    }
    return result;
}

这个问题的大多数答案并没有很好地处理所有的极端情况。以下是一些微妙的极端情况: 这是一个javascript版本，所有角落的情况都得到了很好的处理。

/** Get relationship between a point and a polygon using ray-casting algorithm
 * @param {{x:number, y:number}} P: point to check
 * @param {{x:number, y:number}[]} polygon: the polygon
 * @returns -1: outside, 0: on edge, 1: inside
 */
function relationPP(P, polygon) {
    const between = (p, a, b) => p >= a && p <= b || p <= a && p >= b
    let inside = false
    for (let i = polygon.length-1, j = 0; j < polygon.length; i = j, j++) {
        const A = polygon[i]
        const B = polygon[j]
        // corner cases
        if (P.x == A.x && P.y == A.y || P.x == B.x && P.y == B.y) return 0
        if (A.y == B.y && P.y == A.y && between(P.x, A.x, B.x)) return 0

        if (between(P.y, A.y, B.y)) { // if P inside the vertical range
            // filter out "ray pass vertex" problem by treating the line a little lower
            if (P.y == A.y && B.y >= A.y || P.y == B.y && A.y >= B.y) continue
            // calc cross product `PA X PB`, P lays on left side of AB if c > 0 
            const c = (A.x - P.x) * (B.y - P.y) - (B.x - P.x) * (A.y - P.y)
            if (c == 0) return 0
            if ((A.y < B.y) == (c > 0)) inside = !inside
        }
    }

    return inside? 1 : -1
}

David Segond's answer is pretty much the standard general answer, and Richard T's is the most common optimization, though therre are some others. Other strong optimizations are based on less general solutions. For example if you are going to check the same polygon with lots of points, triangulating the polygon can speed things up hugely as there are a number of very fast TIN searching algorithms. Another is if the polygon and points are on a limited plane at low resolution, say a screen display, you can paint the polygon onto a memory mapped display buffer in a given colour, and check the color of a given pixel to see if it lies in the polygons.

像许多优化一样，这些优化是基于特定情况而不是一般情况，并且基于摊销时间而不是单次使用产生效益。

在这个领域工作，我发现约瑟夫·奥鲁克斯的《计算几何》在C' ISBN 0-521-44034-3是一个很大的帮助。

我认为这是迄今为止所有答案中最简洁的一个。

例如，假设我们有一个多边形，它带有多边形凹，看起来像这样:

大多边形顶点的二维坐标为

[[139, 483], [227, 792], [482, 849], [523, 670], [352, 330]]

方框顶点的坐标为

[[248, 518], [336, 510], [341, 614], [250, 620]]

空心三角形顶点的坐标为

[[416, 531], [505, 517], [495, 616]]

假设我们想要测试两个点[296,557]和[422,730]，如果它们在红色区域内(不包括边缘)。如果我们定位这两个点，它将是这样的:

显然，[296,557]不在读取区域内，而[422,730]在。

我的解决方案是基于圈数算法。下面是我只使用numpy的4行python代码:

def detect(points, *polygons):
    import numpy as np
    endpoint1 = np.r_[tuple(np.roll(p, 1, 0) for p in polygons)][:, None] - points
    endpoint2 = np.r_[polygons][:, None] - points
    p1, p2 = np.cross(endpoint1, endpoint2), np.einsum('...i,...i', endpoint1, endpoint2)
    return ~((p1.sum(0) < 0) ^ (abs(np.arctan2(p1, p2).sum(0)) > np.pi) | ((p1 == 0) & (p2 <= 0)).any(0))

要测试实现:

points = [[296, 557], [422, 730]]
polygon1 = [[139, 483], [227, 792], [482, 849], [523, 670], [352, 330]]
polygon2 = [[248, 518], [336, 510], [341, 614], [250, 620]]
polygon3 = [[416, 531], [505, 517], [495, 616]]

print(detect(points, polygon1, polygon2, polygon3))

输出:

[False  True]