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- """Example usage of remove_outliers() and compute_clustering()"""
- # Import the vedo library and numpy
- from vedo import np, Points, show
- # Generate 4 random sets of N points in 3D space
- N = 2000
- f = 0.6
- noise1 = np.random.rand(N, 3) * f + np.array([1, 1, 0])
- noise2 = np.random.rand(N, 3) * f + np.array([1, 0, 1.2])
- noise3 = np.random.rand(N, 3) * f + np.array([0, 1, 1])
- noise4 = np.random.randn(N, 3) * f / 8 + np.array([1, 1, 1])
- # Create a Points object from the noisy point sets
- noise4 = Points(noise4).remove_outliers(radius=0.05).coordinates
- pts = noise1.tolist() + noise2.tolist() + noise3.tolist() + noise4.tolist()
- pts = Points(pts)
- # Cluster the points to find back their original identity
- clpts = pts.compute_clustering(radius=0.1).print()
- # Set the color of the points based on their cluster ID using the 'jet' colormap
- clpts.cmap("jet", "ClusterId")
- show(clpts, __doc__, axes=1, viewup='z', bg='blackboard').close()
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