def fit_NURBS(As, B1s, B2s, Cs, regularPoints, vertices, quads, fine_vertices,
parameters, fairnessWeight):
print "### Preprocessing ###"
# Throw away any datapoints which are NaN or outside the parameter range [0,1]
# since these cause trouble. (+ Scale the resulting ones so that max and min
# param values are 1 and 0 repspectively in both u and v)
[parameters, fine_vertices] = scaleAwayParameters(parameters,
fine_vertices)
# [As, B1s, B2s, Cs] = sortAB1B2VIndices(As, B1s, B2s, C) # not needed with new sorting of points
print "Done."
print "Calculating coefs."
coefs = createGlobalControlMeshCoefs(parameters, quads, As, B1s, B2s, Cs,
regularPoints)
print "Done."
print "Calculating fair coefs."
fair_coefs = createFairnessControlMeshCoefs(quads, As, B1s, B2s, Cs,
regularPoints)
print "fair_coefs.shape = " + str(fair_coefs.shape)
print "Done."
print "Sparsifying..."
sparse_coefs = sp.csr_matrix(coefs)
sparse_fair_coefs = sp.csr_matrix(fair_coefs)
print "Done."
print "Concatenating matrices...."
joined_verts = sp.vstack(
[scipy.array(fine_vertices),
scipy.zeros([fair_coefs.shape[0], 3])]).todense()
joined_coefs = sp.vstack(
[sparse_coefs, fairnessWeight * sparse_fair_coefs])
print "Done."
print "Solving least-squares problem..."
vertices = solve_least_squares_problem(joined_coefs, joined_verts)
print "Done."
#print "Writing files..."
#write_matrix_to_csv(vertices,'vertices.csv')
#write_matrix_to_asc(vertices,'vertices.asc')
#write_matrix_to_asc(fine_vertices,'vertices_fine.asc')
#print "Done."
print "Calculating NURBS control points..."
NURBSMatrix, NURBSIndices = createNURBSMatricesAllraised(
quads, As, B1s, B2s, Cs, regularPoints, vertices)
print "Done."
return NURBSMatrix, NURBSIndices
def fit_NURBS(As, B1s, B2s, Cs, regularPoints, vertices, quads, fine_vertices, parameters, fairnessWeight):
print "### Preprocessing ###"
# Throw away any datapoints which are NaN or outside the parameter range [0,1]
# since these cause trouble. (+ Scale the resulting ones so that max and min
# param values are 1 and 0 repspectively in both u and v)
[parameters, fine_vertices] = scaleAwayParameters(parameters, fine_vertices)
# [As, B1s, B2s, Cs] = sortAB1B2VIndices(As, B1s, B2s, C) # not needed with new sorting of points
print "Done."
print "Calculating coefs."
coefs = createGlobalControlMeshCoefs(parameters, quads, As, B1s, B2s, Cs, regularPoints)
print "Done."
print "Calculating fair coefs."
fair_coefs = createFairnessControlMeshCoefs(quads, As, B1s, B2s, Cs, regularPoints)
print "fair_coefs.shape = "+str(fair_coefs.shape)
print "Done."
print "Sparsifying..."
sparse_coefs = sp.csr_matrix(coefs)
sparse_fair_coefs = sp.csr_matrix(fair_coefs)
print "Done."
print "Concatenating matrices...."
joined_verts = sp.vstack([scipy.array(fine_vertices), scipy.zeros([fair_coefs.shape[0], 3])]).todense()
joined_coefs = sp.vstack([sparse_coefs, fairnessWeight * sparse_fair_coefs])
print "Done."
print "Solving least-squares problem..."
vertices = solve_least_squares_problem(joined_coefs, joined_verts)
print "Done."
print "Writing files..."
write_matrix_to_csv(vertices,'vertices.csv')
write_matrix_to_asc(vertices,'vertices.asc')
write_matrix_to_asc(fine_vertices,'vertices_fine.asc')
print "Done."
print "Calculating NURBS control points..."
NURBSMatrix, NURBSIndices = createNURBSMatricesAllraised(quads,As,B1s,B2s,Cs,regularPoints,vertices)
print "Done."
return NURBSMatrix, NURBSIndices
print "fair_coefs.shape = "+str(fair_coefs.shape)
print "Done."
print "Sparsify..."
sparse_coefs = sp.csr_matrix(coefs)
sparse_fair_coefs = sp.csr_matrix(fair_coefs)
print "Done."
print "Concatenating matrices."
fairnessWeight = 2.0
joined_verts = sp.vstack([scipy.array(fine_vertices), scipy.zeros([fair_coefs.shape[0], 3])]).todense()
joined_coefs = sp.vstack([sparse_coefs, fairnessWeight * sparse_fair_coefs])
print "Done."
print "Least squares..."
vertices = solve_least_squares_problem(joined_coefs, joined_verts)
print "Done."
write_matrix_to_csv(vertices, 'vertices.csv')
write_matrix_to_asc(vertices, 'vertices.asc')
#plotBezierSurfaceWhole(quads, newA, newB1, newB2, newC, regularPoints, vertices)
#[biqPatchPoints,biqIndices,bicPatchPoints,bicIndices] = createBezierPointMatrices(quads_Torus,newA,newB1,newB2,newC,regularPoints,otherVertices);
#csvwrite('biquadraticPatchPoints.csv',biqPatchPoints);
#csvwrite('biquadraticPatchIndices.csv',biqIndices);
#csvwrite('bicubicPatchPoints.csv',bicPatchPoints);
#csvwrite('bicubicPatchIndices.csv',bicIndices);
NURBSMatrix, NURBSIndices = createNURBSMatricesAllraised(quads, newA, newB1, newB2, newC, regularPoints, vertices)
#csvwrite('NURBSPatchPoints.csv',NURBSMatrix);
#csvwrite('NURBSPatchIndices.csv',NURBSIndices);
print "Sparsify..."
sparse_coefs = sp.csr_matrix(coefs)
sparse_fair_coefs = sp.csr_matrix(fair_coefs)
print "Done."
print "Concatenating matrices."
fairnessWeight = 2.0
joined_verts = sp.vstack(
[scipy.array(fine_vertices),
scipy.zeros([fair_coefs.shape[0], 3])]).todense()
joined_coefs = sp.vstack([sparse_coefs, fairnessWeight * sparse_fair_coefs])
print "Done."
print "Least squares..."
vertices = solve_least_squares_problem(joined_coefs, joined_verts)
print "Done."
write_matrix_to_csv(vertices, 'vertices.csv')
write_matrix_to_asc(vertices, 'vertices.asc')
#plotBezierSurfaceWhole(quads, newA, newB1, newB2, newC, regularPoints, vertices)
#[biqPatchPoints,biqIndices,bicPatchPoints,bicIndices] = createBezierPointMatrices(quads_Torus,newA,newB1,newB2,newC,regularPoints,otherVertices);
#csvwrite('biquadraticPatchPoints.csv',biqPatchPoints);
#csvwrite('biquadraticPatchIndices.csv',biqIndices);
#csvwrite('bicubicPatchPoints.csv',bicPatchPoints);
#csvwrite('bicubicPatchIndices.csv',bicIndices);
NURBSMatrix, NURBSIndices = createNURBSMatricesAllraised(
quads, newA, newB1, newB2, newC, regularPoints, vertices)
#csvwrite('NURBSPatchPoints.csv',NURBSMatrix);
