60 lines
1.4 KiB
Matlab
Executable File
60 lines
1.4 KiB
Matlab
Executable File
V = py.igl.eigen.MatrixXd();
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F = py.igl.eigen.MatrixXi();
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py.igl.read_triangle_mesh('../tutorial/shared/fertility.off', V, F);
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% Alternative discrete mean curvature
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HN = py.igl.eigen.MatrixXd();
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L = py.igl.eigen.SparseMatrixd();
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M = py.igl.eigen.SparseMatrixd();
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Minv = py.igl.eigen.SparseMatrixd();
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py.igl.cotmatrix(V,F,L)
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py.igl.massmatrix(V,F,py.igl.MASSMATRIX_TYPE_VORONOI,M)
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py.igl.invert_diag(M,Minv)
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% Laplace-Beltrami of position
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HN = -Minv*(L*V)
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% Extract magnitude as mean curvature
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H = HN.rowwiseNorm()
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% Compute curvature directions via quadric fitting
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PD1 = py.igl.eigen.MatrixXd()
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PD2 = py.igl.eigen.MatrixXd()
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PV1 = py.igl.eigen.MatrixXd()
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PV2 = py.igl.eigen.MatrixXd()
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py.igl.principal_curvature(V,F,PD1,PD2,PV1,PV2)
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% Mean curvature
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H = 0.5*(PV1+PV2)
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viewer = py.igl.viewer.Viewer()
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viewer.data.set_mesh(V, F)
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% Compute pseudocolor
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C = py.igl.eigen.MatrixXd()
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py.igl.parula(H,true,C)
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viewer.data.set_colors(C)
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% Average edge length for sizing
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avg = py.igl.avg_edge_length(V,F)
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% Draw a blue segment parallel to the minimal curvature direction
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red = py.iglhelpers.p2e(py.numpy.array([[0.8,0.2,0.2]]))
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blue = py.iglhelpers.p2e(py.numpy.array([[0.2,0.2,0.8]]))
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viewer.data.add_edges(V + PD1*avg, V - PD1*avg, blue)
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% Draw a red segment parallel to the maximal curvature direction
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viewer.data.add_edges(V + PD2*avg, V - PD2*avg, red)
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% Hide wireframe
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viewer.core.show_lines = false
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viewer.launch()
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