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Grayscale Using Fieldler Vector
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| # %% | |
| import bpy | |
| from sklearn.cluster import KMeans # /path/to/blender's/python -m pip install scikit-learn | |
| import networkx as nx # /path/to/blender's/python -m pip install scipy networkx | |
| obj = bpy.data.objects['Armadillo'] | |
| mesh = obj.data | |
| edges = [(e.vertices[0], e.vertices[1]) for e in mesh.edges] | |
| G = nx.Graph(edges) | |
| S = [G.subgraph(c).copy() for c in nx.connected_components(G)] | |
| G = S[0] | |
| coordinates = [mesh.vertices[i].co for i in G.nodes] | |
| fiedler_vector = nx.fiedler_vector(G) | |
| pred = KMeans(n_clusters=11).fit_predict([(v, x, y, z) for v, (x, y, z) in zip(fiedler_vector, coordinates)]) | |
| colors = dict(zip(G.nodes, pred)) | |
| name = 'GrayscaleUsingFieldlerVector' | |
| if name not in mesh.vertex_colors: | |
| mesh.vertex_colors.new(name=name) | |
| vc = mesh.vertex_colors[name].data | |
| for polygon in mesh.polygons: | |
| for i, v in zip(polygon.loop_indices, polygon.vertices): | |
| if v in G.nodes: | |
| ratio = colors[v] * 0.1 | |
| vc[i].color = [ratio, ratio, ratio, 1] | |
| else: | |
| vc[i].color = [0, 0, 0, 0] |
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