找到xy坐标的边界点
Find boundary points of xy coordinates
我有一个包含 xy 坐标的文本文件，名为 xy.txt。
29.66150677 -98.39336541
29.66150677 -98.39337576
29.66150651 -98.39336541
29.66150328 -98.39337576
29.66150677 -98.39336475
29.66150677 -98.39338611
29.66150393 -98.39338611
29.66150677 -98.39339646
29.66150659 -98.39339646
29.66150677 -98.39339693
29.66151576 -98.39334472
29.66151576 -98.39335506
29.66151511 -98.39334472
29.66151058 -98.39335506
29.66151576 -98.39334322
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29.66151576 -98.39337576
29.66151576 -98.39338611
29.66151576 -98.39339646
29.66151576 -98.39340681
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29.66151576 -98.39341515
29.66152475 -98.39332402
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29.66152443 -98.39332402
29.66151973 -98.39333437
29.66152475 -98.39332332
29.66152475 -98.39334472
29.66152475 -98.39335506
29.66152475 -98.39336541
29.66152475 -98.39337576
29.66152475 -98.39338611
29.66152475 -98.39339646
29.66152475 -98.39340681
29.66152475 -98.39341716
29.66151699 -98.39341716
29.66152475 -98.39342722
29.66153375 -98.39331367
29.66153375 -98.39332402
29.6615302 -98.39331367
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29.66153375 -98.39335506
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29.66160569 -98.39332402
29.66160569 -98.39333437
29.66160569 -98.39334472
29.66160569 -98.39335506
29.66160569 -98.39336541
29.66160569 -98.39337576
29.66160569 -98.39338611
29.66160569 -98.39339646
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29.66161468 -98.39334472
29.66161468 -98.39335506
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29.66163267 -98.39329297
29.66163267 -98.39330332
29.66163267 -98.39331367
29.66163267 -98.39332402
29.66163267 -98.39333437
29.66163267 -98.39334472
29.66163267 -98.39335506
29.66163267 -98.39336541
29.66163267 -98.39337576
29.66163267 -98.39338611
29.66163267 -98.39339646
29.66163267 -98.39340681
29.66163267 -98.39341716
29.66163267 -98.39341722
29.66163275 -98.39341716
29.66164166 -98.39325157
29.66164166 -98.39326192
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29.66164166 -98.39327227
29.66164166 -98.39328262
29.66164166 -98.39329297
29.66164166 -98.39330332
29.66164166 -98.39331367
29.66164166 -98.39332402
29.66164166 -98.39333437
29.66164166 -98.39334472
29.66164166 -98.39335506
29.66164166 -98.39336541
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29.66164166 -98.39339646
29.66164166 -98.39340681
29.66164166 -98.39341103
29.66164749 -98.39340681
29.66165066 -98.39325157
29.66165066 -98.39326192
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29.66165066 -98.39327227
29.66165066 -98.39328262
29.66165066 -98.39329297
29.66165066 -98.39330332
29.66165066 -98.39331367
29.66165066 -98.39332402
29.66165066 -98.39333437
29.66165066 -98.39334472
29.66165066 -98.39335506
29.66165066 -98.39336541
29.66165066 -98.39337576
29.66165066 -98.39338611
29.66165066 -98.39339646
29.66165066 -98.39340447
29.66165965 -98.39325157
29.66165965 -98.39326192
29.66165965 -98.39324479
29.66165965 -98.39327227
29.66165965 -98.39328262
29.66165965 -98.39329297
29.66165965 -98.39330332
29.66165965 -98.39331367
29.66165965 -98.39332402
29.66165965 -98.39333437
29.66165965 -98.39334472
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29.66166864 -98.39335506
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29.66167764 -98.39331367
29.66167764 -98.39332402
29.66167764 -98.39333437
29.66167764 -98.39334472
29.66167764 -98.39335506
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29.66168663 -98.39327227
29.66168663 -98.39328262
29.66168663 -98.39329297
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29.66169562 -98.39328262
29.66169562 -98.39329297
29.66169562 -98.39330332
29.66169562 -98.39331367
29.66169562 -98.39332402
29.66169562 -98.39333437
29.66169562 -98.39334472
29.66169562 -98.39335506
29.66169562 -98.39336541
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我使用
读取文件
import numpy as np
xy = np.loadtxt('xy.txt')
x, y = xy[:, 0], xy[:, 1]
我可以用
绘制点
import matplotlib.pyplot as plt
plt.plot(x, y, 'o', color='black', markersize=6)
plt.show()
视觉上数据如下所示：
我想检索构成形状边界的点的 xy 坐标。类似问题的答案建议使用 Concave Hull。在这个 blog 的帮助下，我编写了以下代码：
from scipy import spatial
hull = spatial.ConvexHull(xy, incremental=False, qhull_options='Qt')
hull_indices = hull.vertices
boundary_x = []
boundary_y = []
for i in range(len(hull_indices)):
index = hull_indices[i]
boundary_x.append(xy[index, 0].astype('float32'))
boundary_y.append(xy[index, 1].astype('float32'))
plt.plot(boundary_x, boundary_y, 'o', color='red', markersize=6)
plt.show()
输出看起来像
很明显红点不是边界点。有些被其他点包围，它们都与原始点不同。如何根据原始点定义边界点？
请指教
当我尝试重现您的代码时，convexHull 函数完美运行。我更改了您的代码，使黑色和红色圆圈的位置以相同的方式四舍五入。而且我减小了红色圆圈的半径，这样你就可以更好地看到是否一切都合适。
import numpy as np
import matplotlib.pyplot as plt
from scipy import spatial
xy = np.loadtxt('xy.txt')
x, y = xy[:, 0].astype('float64'), xy[:, 1].astype('float64')
hull = spatial.ConvexHull(xy, incremental=False, qhull_options='Qt')
hull_indices = hull.vertices
boundary_x = []
boundary_y = []
for i in range(len(hull_indices)):
index = hull_indices[i]
boundary_x.append(xy[index, 0].astype('float64'))
boundary_y.append(xy[index, 1].astype('float64'))
plt.plot(x, y, 'o', color='black', markersize=6)
plt.plot(boundary_x, boundary_y, 'o', color='red', markersize=4)
plt.show()
结果：
更新：如果你也需要边界的凹陷部分，你可以使用python包alphashape instead and calculate the alpha shape。你的测试数据中的点非常接近，所以我不得不标准化坐标并调整 alpha 值以获得合理的结果。
import numpy as np
import matplotlib.pyplot as plt
import alphashape
data = np.loadtxt('xy.txt')
xy = (data + [-29.0, 98.0]) * 10
x, y = xy[:, 0], xy[:, 1]
shape = alphashape.alphashape(xy, alpha=500.0)
shape_x, shape_y = shape.exterior.coords.xy
plt.plot(x, y, 'o', color='black', markersize=6)
plt.plot(shape_x, shape_y, 'o', color='red', markersize=4)
plt.show()
结果：
我有一个包含 xy 坐标的文本文件，名为 xy.txt。
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我使用
读取文件import numpy as np
xy = np.loadtxt('xy.txt')
x, y = xy[:, 0], xy[:, 1]
我可以用
绘制点import matplotlib.pyplot as plt
plt.plot(x, y, 'o', color='black', markersize=6)
plt.show()
视觉上数据如下所示：
我想检索构成形状边界的点的 xy 坐标。类似问题的答案建议使用 Concave Hull。在这个 blog 的帮助下，我编写了以下代码：
from scipy import spatial
hull = spatial.ConvexHull(xy, incremental=False, qhull_options='Qt')
hull_indices = hull.vertices
boundary_x = []
boundary_y = []
for i in range(len(hull_indices)):
index = hull_indices[i]
boundary_x.append(xy[index, 0].astype('float32'))
boundary_y.append(xy[index, 1].astype('float32'))
plt.plot(boundary_x, boundary_y, 'o', color='red', markersize=6)
plt.show()
输出看起来像
很明显红点不是边界点。有些被其他点包围，它们都与原始点不同。如何根据原始点定义边界点？ 请指教
当我尝试重现您的代码时，convexHull 函数完美运行。我更改了您的代码，使黑色和红色圆圈的位置以相同的方式四舍五入。而且我减小了红色圆圈的半径，这样你就可以更好地看到是否一切都合适。
import numpy as np
import matplotlib.pyplot as plt
from scipy import spatial
xy = np.loadtxt('xy.txt')
x, y = xy[:, 0].astype('float64'), xy[:, 1].astype('float64')
hull = spatial.ConvexHull(xy, incremental=False, qhull_options='Qt')
hull_indices = hull.vertices
boundary_x = []
boundary_y = []
for i in range(len(hull_indices)):
index = hull_indices[i]
boundary_x.append(xy[index, 0].astype('float64'))
boundary_y.append(xy[index, 1].astype('float64'))
plt.plot(x, y, 'o', color='black', markersize=6)
plt.plot(boundary_x, boundary_y, 'o', color='red', markersize=4)
plt.show()
结果：
更新：如果你也需要边界的凹陷部分，你可以使用python包alphashape instead and calculate the alpha shape。你的测试数据中的点非常接近，所以我不得不标准化坐标并调整 alpha 值以获得合理的结果。
import numpy as np
import matplotlib.pyplot as plt
import alphashape
data = np.loadtxt('xy.txt')
xy = (data + [-29.0, 98.0]) * 10
x, y = xy[:, 0], xy[:, 1]
shape = alphashape.alphashape(xy, alpha=500.0)
shape_x, shape_y = shape.exterior.coords.xy
plt.plot(x, y, 'o', color='black', markersize=6)
plt.plot(shape_x, shape_y, 'o', color='red', markersize=4)
plt.show()
结果：