下面是基于这个答案的一个简单的Python 3实现(反过来，它是基于已接受答案中提出的解决方案)

def is_clockwise(points):
    # points is your list (or array) of 2d points.
    assert len(points) > 0
    s = 0.0
    for p1, p2 in zip(points, points[1:] + [points[0]]):
        s += (p2[0] - p1[0]) * (p2[1] + p1[1])
    return s > 0.0

一个计算上更简单的方法，如果你已经知道多边形内的一个点:

从原始多边形中选择任意线段，按此顺序选择点及其坐标。 加上一个已知的“内部”点，形成一个三角形。 根据以上三点计算CW或CCW。

另一个解决方案是;

const isClockwise = (vertices=[]) => {
    const len = vertices.length;
    const sum = vertices.map(({x, y}, index) => {
        let nextIndex = index + 1;
        if (nextIndex === len) nextIndex = 0;

        return {
            x1: x,
            x2: vertices[nextIndex].x,
            y1: x,
            y2: vertices[nextIndex].x
        }
    }).map(({ x1, x2, y1, y2}) => ((x2 - x1) * (y1 + y2))).reduce((a, b) => a + b);

    if (sum > -1) return true;
    if (sum < 0) return false;
}

把所有的顶点作为一个数组;

const vertices = [{x: 5, y: 0}, {x: 6, y: 4}, {x: 4, y: 5}, {x: 1, y: 5}, {x: 1, y: 0}];
isClockwise(vertices);

虽然这些答案是正确的，但它们在数学上的强度比必要的要大。假设地图坐标，其中最北的点是地图上的最高点。找到最北的点，如果两个点相等，它是最北的，然后是最东的(这是lhf在他的答案中使用的点)。在你的观点中，

点[0]= (5,0)

点[1]= (6,4)

点[2]= (4,5)

点[3]= (1,5)

点[4]= (1,0)

If we assume that P2 is the most north then east point either the previous or next point determine clockwise, CW, or CCW. Since the most north point is on the north face, if the P1 (previous) to P2 moves east the direction is CW. In this case, it moves west, so the direction is CCW as the accepted answer says. If the previous point has no horizontal movement, then the same system applies to the next point, P3. If P3 is west of P2, it is, then the movement is CCW. If the P2 to P3 movement is east, it's west in this case, the movement is CW. Assume that nte, P2 in your data, is the most north then east point and the prv is the previous point, P1 in your data, and nxt is the next point, P3 in your data, and [0] is horizontal or east/west where west is less than east, and [1] is vertical.

if (nte[0] >= prv[0] && nxt[0] >= nte[0]) return(CW);
if (nte[0] <= prv[0] && nxt[0] <= nte[0]) return(CCW);
// Okay, it's not easy-peasy, so now, do the math
if (nte[0] * nxt[1] - nte[1] * nxt[0] - prv[0] * (nxt[1] - crt[1]) + prv[1] * (nxt[0] - nte[0]) >= 0) return(CCW); // For quadrant 3 return(CW)
return(CW) // For quadrant 3 return (CCW)

对于那些不想“重新发明轮子”的人，我认为值得一提的是，这个检查是在一个名为Shapely (github)的漂亮的Python包中实现的(它基于GEOS C/ c++库):

Shapely is a BSD-licensed Python package for manipulation and analysis of planar geometric objects. It is using the widely deployed open-source geometry library GEOS (the engine of PostGIS, and a port of JTS). Shapely wraps GEOS geometries and operations to provide both a feature rich Geometry interface for singular (scalar) geometries and higher-performance NumPy ufuncs for operations using arrays of geometries. Shapely is not primarily focused on data serialization formats or coordinate systems, but can be readily integrated with packages that are.

来源:https://shapely.readthedocs.io/en/stable/

一个给出OP坐标的小例子:

import numpy as np
from shapely.geometry import Polygon

points = np.array([
    (5,0),
    (6,4),
    (4,5),
    (1,5),
    (1,0)
])

P = Polygon(points)

这是新构造的多边形:

import matplotlib.pyplot as plt

x,y = P.exterior.coords.xy
plt.plot(x,y)
plt.axis('equal')
plt.grid()
plt.show()

你可以直接使用LinearRing的is_ccw属性来检查多边形是CW还是CCW:

type(P.exterior)
>: shapely.geometry.polygon.LinearRing

P.exterior.is_ccw
>: True

如果颠倒:

points = np.flipud(points)
points
>: 
array([[1, 0],
       [1, 5],
       [4, 5],
       [6, 4],
       [5, 0]])


P1 = Polygon(points)

P1.exterior.is_ccw
>: True

进一步阅读的文档和参考资料:

shaely is_ccw (github): https://github.com/shapely/shapely/blob/eba985c6e0170ecdd90c83592fd0afa7ae793cb8/shapely/predicates.py#L72-L108 Libgeos (github): https://github.com/libgeos/geos GEOS API参考:https://libgeos.org/doxygen/classgeos_1_1algorithm_1_1Orientation.html#a5af93795969b80f97d7997195974d7c8 GEOS实现(github): https://github.com/libgeos/geos/blob/ab0ce6dafdf7f75ec6d234b6c65bb209037dda17/src/algorithm/Orientation.cpp#L43-L133

The cross product measures the degree of perpendicular-ness of two vectors. Imagine that each edge of your polygon is a vector in the x-y plane of a three-dimensional (3-D) xyz space. Then the cross product of two successive edges is a vector in the z-direction, (positive z-direction if the second segment is clockwise, minus z-direction if it's counter-clockwise). The magnitude of this vector is proportional to the sine of the angle between the two original edges, so it reaches a maximum when they are perpendicular, and tapers off to disappear when the edges are collinear (parallel).

因此，对于多边形的每个顶点(点)，计算两条相邻边的叉乘大小:

Using your data:
point[0] = (5, 0)
point[1] = (6, 4)
point[2] = (4, 5)
point[3] = (1, 5)
point[4] = (1, 0)

把边连续地标为 edgeA是从point0到point1的段 点1到点2之间的edgeB .．. edgeE在point4和point0之间。

那么顶点A (point0)在两者之间 edgeE[从点4到点0] 从point0到' point1'

这两条边本身就是向量，它们的x坐标和y坐标可以通过减去它们的起点和终点的坐标来确定:

edgeE = point0 - point4 = (1,0) - (5,0) = (- 4,0) and edgeA = point1 - point0 = (6,4) - (1,0) = (5,4) and

这两个相邻边的外积是用下面矩阵的行列式来计算的，这个矩阵是通过将两个向量的坐标放在表示三个坐标轴的符号(i, j， & k)下面来构造的。第三个(零)值坐标在那里，因为外积概念是一个三维结构，所以我们将这些2-D向量扩展到3-D，以便应用外积:

 i    j    k 
-4    0    0
 1    4    0    

假设所有的叉乘都产生一个垂直于两个向量相乘平面的向量，上面矩阵的行列式只有一个k(或z轴)分量。 计算k轴或z轴分量大小的公式为 A1 *b2 - a2*b1 = -4* 4 - 0* 1 = -16

这个值的大小(-16)是两个原始向量夹角的正弦值，乘以两个向量大小的乘积。 实际上，它值的另一个公式是 A X B(叉乘)= |A| * |B| * sin(AB)。

为了得到角度的大小你需要用这个值(-16)除以两个向量大小的乘积。

|A| * |B| = 4 *根号(17)= 16.4924…

所以sin(AB) = -16 / 16.4924 = -.97014…

这是一个度量顶点后的下一段是否向左或向右弯曲，以及弯曲的程度。不需要取arcsin函数。我们只关心它的大小，当然还有它的符号(正的还是负的)!

对闭合路径周围的其他4个点都这样做，并将每个顶点的计算值相加。

如果最终的和是正的，就顺时针，负的，逆时针。