Python?? ??? ? ?? ?? ??? ???? ??? ?? 2D ? ?? ?? ??? ????? ???? ??? ?? ? ?? ??? ??????
Jan 18, 2025 am 07:26 AM
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import numpy as np
import cv2
import matplotlib.pyplot as plt
def find_holes(points, resolution=100):
"""Find holes in a set of 2D points.
Args:
points: A list of (x, y) tuples representing the points.
resolution: The resolution of the bitmap to use. Higher resolution
results in more accurate results, but is slower.
Returns:
A list of (x, y) tuples representing the vertices of the holes.
"""
# Create a bitmap of the points.
bitmap = np.zeros((resolution, resolution), dtype=np.uint8)
for point in points:
x, y = point
bitmap[int(y * resolution), int(x * resolution)] = 255
# Compute data density in the bitmap.
density = cv2.dilate(bitmap, np.ones((3, 3))) - cv2.erode(bitmap, np.ones((3, 3)))
# Identify unused areas in the bitmap.
unused_areas = np.where(density == 0)
# Segment the unused areas.
segmented_areas = cv2.watershed(density, np.zeros((resolution, resolution), dtype=np.int32), markers=unused_areas[0], mask=bitmap)
# Polygonize the segmented areas.
holes = []
for i in range(1, np.max(segmented_areas) + 1):
mask = np.zeros((resolution, resolution), dtype=np.uint8)
mask[segmented_areas == i] = 255
_, contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
hole = []
for point in contour:
x, y = point[0]
hole.append((x / resolution, y / resolution))
holes.append(hole)
return holes
# Example usage
# Generate a set of random points.
points = [(np.random.rand(), np.random.rand()) for _ in range(1000)]
# Find the holes in the set of points.
holes = find_holes(points, resolution=50)
# Plot the points and the holes.
plt.scatter([x for (x, y) in points], [y for (x, y) in points], s=1, c='black')
for hole in holes:
plt.plot([x for (x, y) in hole], [y for (x, y) in hole], c='red')
plt.show()
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