Voronoi diagram: Difference between revisions
→{{header|Python}}: Added alternative Python implementation, using numpy/scipy.
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Superbatfish (talk | contribs) (→{{header|Python}}: Added alternative Python implementation, using numpy/scipy.) |
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=={{header|Python}}==
<syntaxhighlight lang="python">from PIL import Image
import random
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[[File:Voronoi_python.png|500px|thumb|center|Voronoi Diagram in Python]]
Alternatively, vectorized code leveraging numpy and scipy is 2x shorter and 10x faster:
<syntaxhighlight lang="python">
import numpy as np
from PIL import Image
from scipy.spatial import KDTree
def generate_voronoi_diagram(X, Y, num_cells):
# Random colors and points
colors = np.random.randint((256, 256, 256), size=(num_cells, 3), dtype=np.uint8)
points = np.random.randint((Y, X), size=(num_cells, 2))
# Construct a list of all possible (y,x) coordinates
idx = np.indices((Y, X))
coords = np.moveaxis(idx, 0, -1).reshape((-1, 2))
# Find the closest point to each coordinate
_d, labels = KDTree(points).query(coords)
labels = labels.reshape((Y, X))
# Export an RGB image
rgb = colors[labels]
Image.fromarray(rgb, mode='RGB').save('VoronoiDiagram.png', 'PNG')
return rgb
</syntaxhighlight>
=={{header|QB64}}==
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