def remesh(surfacefile, numbertriangles=40000, output="remeshed.vtp"):
"""
Remesh a surface mesh using using voronoi clustering. Source and module at https://pypi.org/project/pyacvd/
:param surfacefile: Surfacefile to be remeshed to a uniform triangulation.
:param numbertriangles: Number of triangles that the surface will have after the remeshing. Default:40000
:param output: output file name
:return: Nothing
"""
print("Remeshing surface.")
if surfacefile[-3:] == "vtp":
reader = vtk.vtkXMLPolyDataReader()
elif surfacefile[-3:] == "ply":
reader = vtk.vtkPLYReader()
else:
print("Input is not a ply or vtp file.")
return
reader.SetFileName(surfacefile)
reader.Update()
p = reader.GetOutput()
surf = pyvista.PolyData(p)
clus = pyacvd.Clustering(surf)
clus.subdivide(3)
clus.cluster(numbertriangles)
remesh = clus.create_mesh()
writer = vtk.vtkXMLPolyDataWriter()
writer.SetFileName(output)
writer.SetInputData(remesh)
writer.Write()
def test_cow():
# must be an all triangular mesh to sub-divide
cow.tri_filter(inplace=True)
# mesh is not dense enough for uniform remeshing
clus = pyacvd.Clustering(cow)
clus.subdivide(3)
clus.cluster(20000)
clus.plot(off_screen=True)
remesh = clus.create_mesh()
def test_cylinder():
cylinder = pv.Cylinder().tri_filter()
# cylinder.clean(inplace=True)
clus = pyacvd.Clustering(cylinder)
clus.subdivide(3)
nclus = 500
clus.cluster(nclus)
remesh = clus.create_mesh()
assert remesh.n_points == nclus
def mono_uniform_remeshing(data):
if VERBOSE: print(f"Remeshing: {data['meta_data']['name']}")
data = data["poly_data"].clean()
clus = pyacvd.Clustering(data)
try:
while len(clus.mesh.points) < 30000:
clus.subdivide(2)
except MemoryError as e:
print(f"Ups that a little too much memory! {e}")
clus.cluster(10000)
remesh = clus.create_mesh()
return remesh
def get_smooth_and_coarse_mesh_from_voxelization(img, sigma, npoints):
"""
Converts an image into a triangle mesh with even distributed points.
First we use a Gaussian kernel with size (sigma**3) to smooth the
input image. Next we apply marching cubes (vtkContourFilter) to obtain
a first mesh, which is used as input to a Voronoi-based clustering
that is responsible for remeshing. Details can be found here:
https://github.com/pyvista/pyacvd
Parameters
----------
img: np.array
Input image corresponding to the voxelized version of the original
average mesh.
sigma: float
Gaussian kernel size.
npoints: int
Number of points used to create the Voronoi clustering. The larger
this value the more points the final mesh will have.
Returns
-------
remesh_vtk: vtkPolyData
Triangle with even distirbuted points.
"""
rad = 5
img = np.pad(img, ((rad, rad), (rad, rad), (rad, rad)))
d, h, w = img.shape
img = skfilters.gaussian(img > 0, sigma=sigma, preserve_range=True)
imagedata = vtk.vtkImageData()
imagedata.SetDimensions([w, h, d])
imagedata.SetExtent(0, w - 1, 0, h - 1, 0, d - 1)
imagedata.AllocateScalars(vtk.VTK_UNSIGNED_CHAR, 1)
values = (255 * img).ravel().astype(np.uint8)
values = vtknp.numpy_to_vtk(values, deep=True, array_type=vtk.VTK_UNSIGNED_CHAR)
imagedata.GetPointData().SetScalars(values)
cf = vtk.vtkContourFilter()
cf.SetInputData(imagedata)
cf.SetValue(0, 255.0 / np.exp(1.0))
cf.Update()
mesh = cf.GetOutput()
pv_temp = pv.PolyData(mesh)
cluster = pyacvd.Clustering(pv_temp)
cluster.cluster(npoints)
remesh = cluster.create_mesh()
remesh_vtk = vtk.vtkPolyData()
remesh_vtk.SetPoints(remesh.GetPoints())
remesh_vtk.SetVerts(remesh.GetVerts())
remesh_vtk.SetPolys(remesh.GetPolys())
return remesh_vtk
def downsample(inp):
# surface = vtk.vtkSurface()
# surface.CreateFromPolyData(inp)
#
# areas = vtk.vtkDoubleArray()
# areas = surface.GetTrianglesAreas()
# surfaceArea = 0
#
# for i in range(0, areas.GetSize()):
# surfaceArea += areas.GetValue(i)
#
# clusterNumber = surfaceArea / 20
mesh = pyvista.PolyData(inp)
# Create clustering object
clus = pyacvd.Clustering(mesh)
# mesh is not dense enough for uniform remeshing
# clus.subdivide(3)
clus.cluster(3000)
Remesh = clus.create_mesh()
# print(Remesh)
# Remesh = vtk.vtkIsotropicDiscreteRemeshing()
# Remesh.SetInput(surface)
# Remesh.SetFileLoadSaveOption(0)
# Remesh.SetNumberOfClusters(clusterNumber)
# Remesh.SetConsoleOutput(0)
# Remesh.GetMetric().SetGradation(0)
# Remesh.SetDisplay(0)
# Remesh.Remesh()
# out = vtk.vtkPolyData()
# out.SetPoints(Remesh.GetOutput().GetPoints())
# out.SetPolys(Remesh.GetOutput().GetPolys())
return Remesh
# warp each point by the normal vectors
for i in range(1, int(args.distance) + 1):
print(f'Expanding: {i}')
shell = shell.compute_normals()
warp = vtk.vtkWarpVector()
warp.SetInputData(shell)
warp.SetInputArrayToProcess(0, 0, 0,
vtk.vtkDataObject.FIELD_ASSOCIATION_POINTS,
vtk.vtkDataSetAttributes.NORMALS)
warp.SetScaleFactor(2)
warp.Update()
shell = pv.wrap(warp.GetOutput())
expanded_mesh = shell.extract_surface()
clus = pyacvd.Clustering(expanded_mesh)
clus.subdivide(3)
clus.cluster(args.points)
shell = clus.create_mesh().extract_surface()
uniform = shell
p = pv.Plotter(notebook=False, shape=(1, 1))
p.add_mesh(shell)
p.add_points(np.asarray(uniform.points),
color="r",
point_size=8.0,
render_points_as_spheres=True)
p.add_mesh(expanded_mesh, smooth_shading=True)
p.link_views()
p.show_bounds()
p.show()
# timeout_s = 60)
# except ShellException as e:
# with open(LOG_DIR / (name + ".log"), "w") as log:
# log.write(str(e))
#-------------------------------------------------------------------------------
# uniform remeshing
#-------------------------------------------------------------------------------
print("uniform remeshing")
PLY_DIR3 = OUTPUT_DIR / "ply3" / experiment
os.makedirs(PLY_DIR3, exist_ok=True)
for f in PLY_DIR2.iterdir():
ply = pyvista.read(str(f))
clus = pyacvd.Clustering(ply)
clus.subdivide(4)
clus.cluster(ply.number_of_points * 11 // 10)
remesh = clus.create_mesh(flipnorm=False)
remesh.save(PLY_DIR3 / f.name)
# remove pyvtk header
fd = os.open(PLY_DIR3 / f.name, os.O_RDWR)
size = os.path.getsize(PLY_DIR3 / f.name)
mm = mmap.mmap(fd, size)
mm.readline(), mm.readline(), mm.readline()
byte = ("comment " + "s" *
(mm.find(b'\n') - mm.tell() - 8)).encode("latin-1")
mm.write(byte)
mm.flush()
############################################################################### # Using pyacvd # ~~~~~~~~~~~~ # # We can use `pyacvd <https://github.com/pyvista/pyacvd>`_ to create a # more uniform mesh using the mesh generated from ``pyvista``. We can # use the ``pyacvd`` module to generate a more uniform surface mesh # and then tetrahedralize that. # # Here we re-run the above example, except using a more uniform surface import pyacvd n_surf = 1000 clustered = pyacvd.Clustering(sphere) clustered.subdivide(2) clustered.cluster(n_surf) uniform_surf = clustered.create_mesh() # generate interior mesh and plot the surface of it tet = tetgen.TetGen(uniform_surf) tet.tetrahedralize(order=1, mindihedral=20, minratio=1.5) uniform_grid = tet.grid uniform_grid.plot(show_edges=True) ############################################################################### # Plot the cross section of the tetrahedralization cell_center = uniform_grid.cell_centers().points
def test_bunny():
clus = pyacvd.Clustering(bunny)
clus.cluster(5000)
remesh = clus.create_mesh()
assert remesh.n_points == 5000
screenshot='/home/alex/afrl/python/source/pyacvd/docs/images/cow.png')
cpos = [(7.927519161395299, 3.54223003919585, -4.1077249997544545),
(2.5251427740425236, 0.3910539874485469, 1.9812043586464985),
(-0.23846635120392892, 0.9325600395795517, 0.2710453318595791)]
cow.plot(show_edges=True, color='w', cpos=cpos,
screenshot='/home/alex/afrl/python/source/pyacvd/docs/images/cow_zoom.png')
# mesh is not dense enough for uniform remeshing
# must be an all triangular mesh to sub-divide
cow.tri_filter(inplace=True)
cow.subdivide(4, inplace=True)
clus = pyacvd.Clustering(cow)
clus.cluster(20000)
# plot clustered cow mesh
cpos = [(7.927519161395299, 3.54223003919585, -4.1077249997544545),
(2.5251427740425236, 0.3910539874485469, 1.9812043586464985),
(-0.23846635120392892, 0.9325600395795517, 0.2710453318595791)]
clus.plot(screenshot='/home/alex/afrl/python/source/pyacvd/docs/images/cow_clus.png',
cpos=cpos, cmap='bwr')
# remesh
remesh = clus.create_mesh()
# plot uniformly remeshed cow
remesh.plot(color='w', show_edges=True, cpos=cpos, smooth_shading=True,