Initial data commit

Task/Voronoi-diagram/Python/voronoi-diagram-1.py

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+from PIL import Image
+import random
+import math
+
+def generate_voronoi_diagram(width, height, num_cells):
+	image = Image.new("RGB", (width, height))
+	putpixel = image.putpixel
+	imgx, imgy = image.size
+	nx = []
+	ny = []
+	nr = []
+	ng = []
+	nb = []
+	for i in range(num_cells):
+		nx.append(random.randrange(imgx))
+		ny.append(random.randrange(imgy))
+		nr.append(random.randrange(256))
+		ng.append(random.randrange(256))
+		nb.append(random.randrange(256))
+	for y in range(imgy):
+		for x in range(imgx):
+			dmin = math.hypot(imgx-1, imgy-1)
+			j = -1
+			for i in range(num_cells):
+				d = math.hypot(nx[i]-x, ny[i]-y)
+				if d < dmin:
+					dmin = d
+					j = i
+			putpixel((x, y), (nr[j], ng[j], nb[j]))
+	image.save("VoronoiDiagram.png", "PNG")
+        image.show()
+	
+generate_voronoi_diagram(500, 500, 25)

Task/Voronoi-diagram/Python/voronoi-diagram-2.py

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+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]
+    img = Image.fromarray(rgb, mode='RGB')
+    img.save('VoronoiDiagram.png', 'PNG')
+    img.show()
+    return rgb