最小包围盒算法 python

最小包围盒算法是一种用于计算点集或物体的最小矩形边界框的算法。在 Python 中，可以使用 scipy 库中的 spatial 模块来实现最小包围盒算法。具体实现步骤如下：

1. 导入库：`from scipy.spatial import ConvexHull, convex_hull_plot_2d`

2. 定义点集：`points = [(0, 0), (0, 1), (1, 1), (1, 0), (0.5, 0.5)]`

3. 计算凸包：`hull = ConvexHull(points)`

4. 获取凸包顶点：`vertices = hull.vertices`

5. 获取凸包边界点：`boundary_points = [points[vertex] for vertex in vertices]`

6. 计算最小包围盒：`from scipy.spatial import distance`

`min_x = min([point for point in boundary_points])`

`max_x = max([point for point in boundary_points])`

`min_y = min([point for point in boundary_points])`

`max_y = max([point for point in boundary_points])`

`width = distance.euclidean((min_x, min_y), (max_x, min_y))`

`height = distance.euclidean((min_x, min_y), (min_x, max_y))`

`print("最小包围盒宽度：", width)`

`print("最小包围盒高度：", height)`