曾经在这里被接受的答案是不正确的(它已经被不接受了，所以万岁!)它不能正确地消除所有非交点。简单地说，它可能有效，但也可能失败，特别是在0和1被认为对h有效的情况下。

考虑以下情况:

直线(4,1)-(5,1)和(0,0)-(0,2)

这两条垂线显然不重叠。

= (4,1) B =(5、1) C = (0, 0) D = (0, 2) E = (1) - (4,1) = (1,0) F = (0, 2) - (0, 0) = (0, 2) P = (0, 1) h =((4,1) -(0, 0))点(0,1)/((0,2)点(0,1))= 0

根据上面的答案，这两条线段在端点处相遇(值为0和1)。该端点为:

(0, 0) + (0, 2) * 0 = (0, 0)

So, apparently the two line segments meet at (0,0), which is on line CD, but not on line AB. So what is going wrong? The answer is that the values of 0 and 1 are not valid and only sometimes HAPPEN to correctly predict endpoint intersection. When the extension of one line (but not the other) would meet the line segment, the algorithm predicts an intersection of line segments, but this is not correct. I imagine that by testing starting with AB vs CD and then also testing with CD vs AB, this problem would be eliminated. Only if both fall between 0 and 1 inclusively can they be said to intersect.

如果你必须预测端点，我建议使用向量叉乘法。

-Dan

如果矩形的每条边都是一条线段，并且用户绘制的部分也是一条线段，那么您只需检查用户绘制的线段是否与四条边线段相交。这应该是一个相当简单的练习，给定每个段的起点和终点。

iMalc回答的Python版本:

def find_intersection( p0, p1, p2, p3 ) :

    s10_x = p1[0] - p0[0]
    s10_y = p1[1] - p0[1]
    s32_x = p3[0] - p2[0]
    s32_y = p3[1] - p2[1]

    denom = s10_x * s32_y - s32_x * s10_y

    if denom == 0 : return None # collinear

    denom_is_positive = denom > 0

    s02_x = p0[0] - p2[0]
    s02_y = p0[1] - p2[1]

    s_numer = s10_x * s02_y - s10_y * s02_x

    if (s_numer < 0) == denom_is_positive : return None # no collision

    t_numer = s32_x * s02_y - s32_y * s02_x

    if (t_numer < 0) == denom_is_positive : return None # no collision

    if (s_numer > denom) == denom_is_positive or (t_numer > denom) == denom_is_positive : return None # no collision


    # collision detected

    t = t_numer / denom

    intersection_point = [ p0[0] + (t * s10_x), p0[1] + (t * s10_y) ]


    return intersection_point

基于@Gareth Rees的回答，Python版本:

import numpy as np

def np_perp( a ) :
    b = np.empty_like(a)
    b[0] = a[1]
    b[1] = -a[0]
    return b

def np_cross_product(a, b):
    return np.dot(a, np_perp(b))

def np_seg_intersect(a, b, considerCollinearOverlapAsIntersect = False):
    # https://stackoverflow.com/questions/563198/how-do-you-detect-where-two-line-segments-intersect/565282#565282
    # http://www.codeproject.com/Tips/862988/Find-the-intersection-point-of-two-line-segments
    r = a[1] - a[0]
    s = b[1] - b[0]
    v = b[0] - a[0]
    num = np_cross_product(v, r)
    denom = np_cross_product(r, s)
    # If r x s = 0 and (q - p) x r = 0, then the two lines are collinear.
    if np.isclose(denom, 0) and np.isclose(num, 0):
        # 1. If either  0 <= (q - p) * r <= r * r or 0 <= (p - q) * s <= * s
        # then the two lines are overlapping,
        if(considerCollinearOverlapAsIntersect):
            vDotR = np.dot(v, r)
            aDotS = np.dot(-v, s)
            if (0 <= vDotR  and vDotR <= np.dot(r,r)) or (0 <= aDotS  and aDotS <= np.dot(s,s)):
                return True
        # 2. If neither 0 <= (q - p) * r = r * r nor 0 <= (p - q) * s <= s * s
        # then the two lines are collinear but disjoint.
        # No need to implement this expression, as it follows from the expression above.
        return None
    if np.isclose(denom, 0) and not np.isclose(num, 0):
        # Parallel and non intersecting
        return None
    u = num / denom
    t = np_cross_product(v, s) / denom
    if u >= 0 and u <= 1 and t >= 0 and t <= 1:
        res = b[0] + (s*u)
        return res
    # Otherwise, the two line segments are not parallel but do not intersect.
    return None

我从《多视图几何》这本书里读到了这些算法

以下文本使用

'作为转置符号

*作为点积

当用作算子时，X作为叉乘

1. 线的定义

点x_vec = (x, y)'在直线ax + by + c = 0上

标记L = (a, b, c)'，点为(x, y, 1)'为齐次坐标

直线方程可以写成

(x, y, 1)(a, b, c)' = 0或x' * L = 0

2. 直线交点

我们有两条直线L1=(a1, b1, c1)'， L2=(a2, b2, c2)'

假设x是一个点，一个向量，x = L1 x L2 (L1叉乘L2)。

注意，x始终是一个二维点，如果你对(L1xL2)是一个三元素向量，x是一个二维坐标感到困惑，请阅读齐次坐标。

根据三重积，我们知道

L1 * (L1 x L2) = 0, L2 * (L1 x L2) = 0，因为L1,L2共平面

我们用向量x代替L1*x，那么L1*x=0, L2*x=0，这意味着x在L1和L2上，x是交点。

注意，这里x是齐次坐标，如果x的最后一个元素是零，这意味着L1和L2是平行的。

有一个很好的方法来解决这个问题就是用向量叉乘。定义二维向量叉乘v × w为vx wy−vy wx。

假设这两条线段从p到p + r，从q到q + s。那么第一行上的任意点都可以表示为p + t r(对于标量参数t)，第二行上的任意点可以表示为q + u s(对于标量参数u)。

如果t和u满足以下条件，两条直线相交:

P + t r = q + u s

两边叉乘s，得到

(p + r) × s = (q + u s) × s

由于s × s = 0，这意味着

T (r × s) = (q−p) × s

因此，求解t:

T = (q−p) × s / (r × s)

同样地，我们可以解出u:

(p + r) × r = (q + u s) × r U (s × r) = (p−q) × r U = (p−q) × r / (s × r)

为了减少计算步骤，可以方便地将其重写为以下形式(记住s × r =−r × s):

U = q−p × r / (r × s)

现在有四种情况:

If r × s = 0 and (q − p) × r = 0, then the two lines are collinear. In this case, express the endpoints of the second segment (q and q + s) in terms of the equation of the first line segment (p + t r): t0 = (q − p) · r / (r · r) t1 = (q + s − p) · r / (r · r) = t0 + s · r / (r · r) If the interval between t0 and t1 intersects the interval [0, 1] then the line segments are collinear and overlapping; otherwise they are collinear and disjoint. Note that if s and r point in opposite directions, then s · r < 0 and so the interval to be checked is [t1, t0] rather than [t0, t1]. If r × s = 0 and (q − p) × r ≠ 0, then the two lines are parallel and non-intersecting. If r × s ≠ 0 and 0 ≤ t ≤ 1 and 0 ≤ u ≤ 1, the two line segments meet at the point p + t r = q + u s. Otherwise, the two line segments are not parallel but do not intersect.

来源:该方法是3D线相交算法的2维专门化，来自Ronald Goldman发表在Graphics Gems，第304页的文章“三条线在三维空间中的相交”。在三维空间中，通常的情况是直线是倾斜的(既不平行也不相交)，在这种情况下，该方法给出了两条直线最接近的点。