Question

在Python中，平均表示多个多边形的点数组的最佳方法是什么？

我目前的代码是：

 

    poly1 = [(2, 2), (10, 0), (10, 10), (2, 10)]
    poly2 = [(4, 4), (20, 0), (20, 20), (4, 20)]

    polys = []
    polys.append(poly1)
    polys.append(poly2)

    x1 = []
    x2 = []
    x3 = []
    x4 = []
    y1 = []
    y2 = []
    y3 = []
    y4 = []
    for poly in polys:   
        x1.append(poly[0][0])
        y1.append(poly[0][1])

        x2.append(poly[1][0])
        y2.append(poly[1][1])

        x3.append(poly[2][0])
        y3.append(poly[2][1])

        x4.append(poly[3][0])
        y4.append(poly[3][1])

    avg_x1 = np.average(np.array(x1))
    avg_y1 = np.average(np.array(y1))

    avg_x2 = np.average(np.array(x2))
    avg_y2 = np.average(np.array(y2))

    avg_x3 = np.average(np.array(x3))
    avg_y3 = np.average(np.array(y3))

    avg_x4 = np.average(np.array(x4))
    avg_y4 = np.average(np.array(y4))

    new_poly = [(avg_x1, avg_y1),(avg_x2, avg_y2),(avg_x3, avg_y3),(avg_x4, avg_y4)]
    print(new_poly)

这给出了正确答案，

[(3.0, 3.0), (15.0, 0.0), (15.0, 15.0), (3.0, 15.0)]

但必须有一种更有效的方法。

Answer 1

使用numpy：使用numpy的矢量化Properties并使用map来转换回元组列表：

mean

没有numpy：使用嵌套列表理解/生成器表达式：

>>> list(map(tuple, np.mean(polys, axis=0)))
[(3.0, 3.0), (15.0, 0.0), (15.0, 15.0), (3.0, 15.0)]

Answer 2

使用numpy的简单而优雅的方式

data = np.array([poly1, poly2])
[tuple(l) for l in np.average(data, axis=0)]

输出：

[(3.0, 3.0), (15.0, 0.0), (15.0, 15.0), (3.0, 15.0)]

Python - 从点阵列计算平均多边形

2 个答案: