concave_hull
A very fast 2D concave hull algorithm.
Credits goes to:
Online document: https://concave-hull.readthedocs.io/en/latest/
Install
via pip
pip install -U concave_hull
from source
git clone --recursive https://github.com/cubao/concave_hull
pip install ./concave_hull
Or
pip install git+https://github.com/cubao/concave_hull.git
(you can build wheels for later reuse by pip wheel git+https://github.com/cubao/concave_hull.git)
Usage
Signature:
# import
from concave_hull import concave_hull, concave_hull_indexes
# get concave hull indexes
concave_hull_indexes(
points: Union[numpy.ndarray, List, Tuple],
*,
concavity: float = 2.0,
length_threshold: float = 0.0,
# you can just ignore "convex_hull_indexes"
convex_hull_indexes: numpy.ndarray[numpy.int32[m, 1]] = None,
) -> numpy.ndarray[numpy.int32[m, 1]]
# get concave hull points
concave_hull(
points: Union[numpy.ndarray, List, Tuple],
... # same as
) -> Union[numpy.ndarray, List, Tuple]
# P.S., we provide convex_hull (Graham scan)
from concave_hull import convex_hull, convex_hull_indexes
concavityis a relative measure of concavity. 1 results in a relatively detailed shape, Infinity results in a convex hull. You can use values lower than 1, but they can produce pretty crazy shapes.length_threshold: when a segment length is under this threshold, it stops being considered for further detalization. Higher values result in simpler shapes.
(document from https://github.com/mapbox/concaveman)
Example (see full code in test.py):
import matplotlib.pyplot as plt
import numpy as np
from scipy.spatial import ConvexHull
from concave_hull import concave_hull, concave_hull_indexes
points = []
c = np.array([250, 250])
for x in np.arange(100, 400, 5 * np.pi):
for y in np.arange(100, 400, 5 * np.pi):
if x > c[0] and y > c[1]:
continue
r = np.linalg.norm(c - [x, y])
if r > 150:
continue
points.append([x, y])
points = np.array(points)
convex_hull = ConvexHull(points[:, :2]) # it's already N-by-2, I'm just emphasizing
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.ConvexHull.html
plt.plot(points[:, 0], points[:, 1], "o")
for simplex in convex_hull.simplices:
plt.plot(points[simplex, 0], points[simplex, 1], "g-", alpha=0.5)
idxes = concave_hull_indexes(
points[:, :2],
length_threshold=50,
)
# you can get coordinates by `points[idxes]`
assert np.all(points[idxes] == concave_hull(points, length_threshold=50))
for f, t in zip(idxes[:-1], idxes[1:]): # noqa
seg = points[[f, t]]
plt.plot(seg[:, 0], seg[:, 1], "r-", alpha=0.5)
# plt.savefig('hull.png')
plt.show()
Tests
make python_install
make python_test
1 maintainer
Release files for concave-hull 0.1.2
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1dcb8dcee8168a1fc287b5327a70bfa8a273367cf925dac6467c6542c72c6a5d
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BLAKE2b-256 checksum How to use checksums |
2ecc3b6b54b930426ec3f3930a820eb7864e598589076fc1997b50d55eb7467a
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Yes |
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twine/6.1.0 CPython/3.13.7
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