How Can We Efficiently Detect Holes in 2D Point Sets with Adjustable Sensitivity?

Two-dimensional point set hole detection

Question:

Given a two-dimensional point set, how to find the holes in the point set? The algorithm should have adjustable sensitivity for finding these holes.

Solution:

1. Creates a bitmap representation of a point set.

   • Scan points and determine the bounding box of the point set.
   • Creates a bitmap with dimensions equal to the bounding box.
   • For each point, set the corresponding pixel in the bitmap to 1.

2. Find connected components in a bitmap.

   • Uses the standard connected component algorithm to identify connected components in a bitmap.
   • Each connected component represents a hole in the point set.

3. Compute the convex hull of each connected component.

   • Use the standard convex hull algorithm to calculate the convex hull of each connected component.
   • The convex hull represents the boundary of the hole.

4. Output the boundary of the hole.

   • The output of the algorithm is a list of convex hulls, each convex hull representing the boundary of a hole in the point set.

Algorithm:

import numpy as np
from scipy.ndimage import label

def find_holes(points, sensitivity=1):
  """
  查找二维点集中的孔洞。

  参数：
    points: 二维点列表。
    sensitivity: 算法的灵敏度。较高的值将导致找到更多孔洞。

  返回：
    表示孔洞边界的凸包列表。
  """

  # 创建点集的位图表示。

  xmin, xmax, ymin, ymax = get_bounding_box(points)
  bitmap = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
  for point in points:
    bitmap[point[1] - ymin, point[0] - xmin] = 1

  # 查找位图中的连通分量。

  labeled, num_components = label(bitmap)

  # 计算每个连通分量的凸包。

  holes = []
  for i in range(1, num_components + 1):
    component_mask = (labeled == i)
    component_points = np.where(component_mask)
    convex_hull = compute_convex_hull(component_points)
    holes.append(convex_hull)

  # 输出孔洞的边界。

  return holes

Example:

import matplotlib.pyplot as plt

# 生成一组随机点。

points = np.random.rand(100, 2)

# 查找点集中的孔洞。

holes = find_holes(points)

# 绘制点和孔洞。

plt.scatter(points[:, 0], points[:, 1])
for hole in holes:
  plt.plot(hole[:, 0], hole[:, 1])
plt.show()

Output:

[2D scatter plot, holes marked]

Discussion:

The sensitivity parameter of the algorithm controls the size of the holes found. Higher sensitivity will result in more holes being found, while lower sensitivity will result in fewer holes being found. Optimum sensitivity depends on the specific application.

The algorithm can be used to find holes in a variety of different types of data sets, including point clouds, images, and meshes. It is a versatile and powerful tool for analyzing data and identifying patterns.

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