Source code for vtkplotter.analysis

from __future__ import division, print_function
import vtkplotter.docs as docs
import vtk
import numpy as np
from vtk.util.numpy_support import numpy_to_vtk, vtk_to_numpy

import vtkplotter.utils as utils
import vtkplotter.colors as colors
import vtkplotter.shapes as shapes
from vtkplotter.assembly import Assembly
from vtkplotter.mesh import Mesh
from vtkplotter.volume import Volume

__doc__ = (
    """
Defines methods useful to analyse 3D meshes.
"""
    + docs._defs
)


__all__ = [
    "delaunay2D",
    "delaunay3D",
    "normalLines",
    "alignLandmarks",
    "alignICP",
    "alignProcrustes",
    "fitLine",
    "fitPlane",
    "fitSphere",
    "pcaEllipsoid",
    "smoothMLS3D",
    "booleanOperation",
    "surfaceIntersection",
    "probePoints",
    "probeLine",
    "probePlane",
    "resampleArrays",
    "recoSurface",
    "cluster",
    "removeOutliers",
    "pointSampler",
    "geodesic",
    "convexHull",
    "mesh2Volume",
    "projectSphereFilter",
    "voronoi3D",
    "connectedPoints",
    "interpolateToVolume",
    "interpolateToStructuredGrid",
    "streamLines",
    "densifyCloud",
    "implicitModeller",
    "signedDistanceFromPointCloud",
    "volumeFromMesh",
    "pointDensity",
    "rectilinearGridToTetrahedra",
    "extractCellsByType",
    "pointCloudFrom",
    "pointDensity",
    "visiblePoints",
]


[docs]def delaunay2D(plist, mode='xy', tol=None): """ Create a mesh from points in the XY plane. If `mode='fit'` then the filter computes a best fitting plane and projects the points onto it. |delaunay2d| |delaunay2d.py|_ """ pd = vtk.vtkPolyData() vpts = vtk.vtkPoints() vpts.SetData(numpy_to_vtk(np.ascontiguousarray(plist), deep=True)) pd.SetPoints(vpts) delny = vtk.vtkDelaunay2D() delny.SetInputData(pd) if tol: delny.SetTolerance(tol) if mode=='fit': delny.SetProjectionPlaneMode(vtk.VTK_BEST_FITTING_PLANE) delny.Update() return Mesh(delny.GetOutput())
[docs]def delaunay3D(mesh, alpha=0, tol=None, boundary=False): """Create 3D Delaunay triangulation of input points.""" deln = vtk.vtkDelaunay3D() if utils.isSequence(mesh): pd = vtk.vtkPolyData() vpts = vtk.vtkPoints() vpts.SetData(numpy_to_vtk(np.ascontiguousarray(mesh), deep=True)) pd.SetPoints(vpts) deln.SetInputData(pd) else: deln.SetInputData(mesh.GetMapper().GetInput()) deln.SetAlpha(alpha) if tol: deln.SetTolerance(tol) deln.SetBoundingTriangulation(boundary) deln.Update() return deln.GetOutput()
[docs]def normalLines(mesh, ratio=1, atCells=True, scale=1): """ Build an ``Mesh`` made of the normals at cells shown as lines. if `atCells` is `False` normals are shown at vertices. """ poly = mesh.computeNormals().polydata() if atCells: centers = vtk.vtkCellCenters() centers.SetInputData(poly) centers.Update() poly = centers.GetOutput() maskPts = vtk.vtkMaskPoints() maskPts.SetInputData(poly) maskPts.SetOnRatio(ratio) maskPts.RandomModeOff() maskPts.Update() ln = vtk.vtkLineSource() ln.SetPoint1(0, 0, 0) ln.SetPoint2(1, 0, 0) ln.Update() glyph = vtk.vtkGlyph3D() glyph.SetSourceData(ln.GetOutput()) glyph.SetInputData(maskPts.GetOutput()) glyph.SetVectorModeToUseNormal() b = poly.GetBounds() sc = max([b[1] - b[0], b[3] - b[2], b[5] - b[4]]) / 50 *scale glyph.SetScaleFactor(sc) glyph.OrientOn() glyph.Update() glyphActor = Mesh(glyph.GetOutput()) glyphActor.mapper().SetScalarModeToUsePointFieldData() glyphActor.PickableOff() prop = vtk.vtkProperty() prop.DeepCopy(mesh.GetProperty()) glyphActor.SetProperty(prop) return glyphActor
[docs]def alignLandmarks(source, target, rigid=False): """ Find best matching of source points towards target in the mean least square sense, in one single step. """ lmt = vtk.vtkLandmarkTransform() ss = source.polydata().GetPoints() st = target.polydata().GetPoints() if source.N() != target.N(): colors.printc('~times Error in alignLandmarks(): Source and Target with != nr of points!', source.N(), target.N(), c=1) raise RuntimeError() lmt.SetSourceLandmarks(ss) lmt.SetTargetLandmarks(st) if rigid: lmt.SetModeToRigidBody() lmt.Update() tf = vtk.vtkTransformPolyDataFilter() tf.SetInputData(source.polydata()) tf.SetTransform(lmt) tf.Update() mesh = Mesh(tf.GetOutput()) mesh.transform = lmt pr = vtk.vtkProperty() pr.DeepCopy(source.GetProperty()) mesh.SetProperty(pr) return mesh
[docs]def alignICP(source, target, iters=100, rigid=False): """ Return a copy of source mesh which is aligned to target mesh through the `Iterative Closest Point` algorithm. The core of the algorithm is to match each vertex in one surface with the closest surface point on the other, then apply the transformation that modify one surface to best match the other (in the least-square sense). .. hint:: |align1.py|_ |align2.py|_ |align1| |align2| """ prop = None if isinstance(source, Mesh): prop = vtk.vtkProperty() prop.DeepCopy(source.GetProperty()) source = source.polydata() if isinstance(target, Mesh): target = target.polydata() icp = vtk.vtkIterativeClosestPointTransform() icp.SetSource(source) icp.SetTarget(target) icp.SetMaximumNumberOfIterations(iters) if rigid: icp.GetLandmarkTransform().SetModeToRigidBody() icp.StartByMatchingCentroidsOn() icp.Update() icpTransformFilter = vtk.vtkTransformPolyDataFilter() icpTransformFilter.SetInputData(source) icpTransformFilter.SetTransform(icp) icpTransformFilter.Update() poly = icpTransformFilter.GetOutput() mesh = Mesh(poly) if prop: mesh.SetProperty(prop) # mesh.info['transform'] = icp.GetLandmarkTransform() # not working! # do it manually... sourcePoints = vtk.vtkPoints() targetPoints = vtk.vtkPoints() for i in range(10): p1 = [0, 0, 0] source.GetPoints().GetPoint(i, p1) sourcePoints.InsertNextPoint(p1) p2 = [0, 0, 0] poly.GetPoints().GetPoint(i, p2) targetPoints.InsertNextPoint(p2) # Setup the transform landmarkTransform = vtk.vtkLandmarkTransform() landmarkTransform.SetSourceLandmarks(sourcePoints) landmarkTransform.SetTargetLandmarks(targetPoints) if rigid: landmarkTransform.SetModeToRigidBody() mesh.transform = landmarkTransform return mesh
[docs]def alignProcrustes(sources, rigid=False): """ Return an ``Assembly`` of aligned source meshes with the `Procrustes` algorithm. The output ``Assembly`` is normalized in size. `Procrustes` algorithm takes N set of points and aligns them in a least-squares sense to their mutual mean. The algorithm is iterated until convergence, as the mean must be recomputed after each alignment. :param bool rigid: if `True` scaling is disabled. |align3| |align3.py|_ """ group = vtk.vtkMultiBlockDataGroupFilter() for source in sources: if sources[0].N() != source.N(): colors.printc("~times Procrustes error in align():", c=1) colors.printc(" sources have different nr of points", c=1) raise RuntimeError() group.AddInputData(source.polydata()) procrustes = vtk.vtkProcrustesAlignmentFilter() procrustes.StartFromCentroidOn() procrustes.SetInputConnection(group.GetOutputPort()) if rigid: procrustes.GetLandmarkTransform().SetModeToRigidBody() procrustes.Update() acts = [] for i, s in enumerate(sources): poly = procrustes.GetOutput().GetBlock(i) mesh = Mesh(poly) mesh.SetProperty(s.GetProperty()) acts.append(mesh) assem = Assembly(acts) assem.transform = procrustes.GetLandmarkTransform() return assem
################################################### working with point clouds
[docs]def fitLine(points): """ Fits a line through points. Extra info is stored in ``Line.slope``, ``Line.center``, ``Line.variances``. |fitline| |fitline.py|_ """ if isinstance(points, Mesh): points = points.points() data = np.array(points) datamean = data.mean(axis=0) uu, dd, vv = np.linalg.svd(data - datamean) vv = vv[0] / np.linalg.norm(vv[0]) # vv contains the first principal component, i.e. the direction # vector of the best fit line in the least squares sense. xyz_min = points.min(axis=0) xyz_max = points.max(axis=0) a = np.linalg.norm(xyz_min - datamean) b = np.linalg.norm(xyz_max - datamean) p1 = datamean - a * vv p2 = datamean + b * vv l = shapes.Line(p1, p2, lw=1) l.slope = vv l.center = datamean l.variances = dd return l
[docs]def fitPlane(points): """ Fits a plane to a set of points. Extra info is stored in ``Plane.normal``, ``Plane.center``, ``Plane.variance``. .. hint:: Example: |fitplanes.py|_ """ if isinstance(points, Mesh): points = points.points() data = np.array(points) datamean = data.mean(axis=0) res = np.linalg.svd(data - datamean) dd, vv = res[1], res[2] xyz_min = points.min(axis=0) xyz_max = points.max(axis=0) s = np.linalg.norm(xyz_max - xyz_min) n = np.cross(vv[0], vv[1]) pla = shapes.Plane(datamean, n, s, s) pla.normal = n pla.center = datamean pla.variance = dd[2] return pla
[docs]def fitSphere(coords): """ Fits a sphere to a set of points. Extra info is stored in ``Sphere.radius``, ``Sphere.center``, ``Sphere.residue``. .. hint:: Example: |fitspheres1.py|_ |fitspheres2| |fitspheres2.py|_ """ if isinstance(coords, Mesh): coords = coords.points() coords = np.array(coords) n = len(coords) A = np.zeros((n, 4)) A[:, :-1] = coords * 2 A[:, 3] = 1 f = np.zeros((n, 1)) x = coords[:, 0] y = coords[:, 1] z = coords[:, 2] f[:, 0] = x * x + y * y + z * z C, residue, rank, sv = np.linalg.lstsq(A, f) # solve AC=f if rank < 4: return None t = (C[0] * C[0]) + (C[1] * C[1]) + (C[2] * C[2]) + C[3] radius = np.sqrt(t)[0] center = np.array([C[0][0], C[1][0], C[2][0]]) if len(residue): residue = np.sqrt(residue[0]) / n else: residue = 0 s = shapes.Sphere(center, radius, c=(1,0,0)).wireframe(1) s.radius = radius # used by fitSphere s.center = center s.residue = residue return s
[docs]def pcaEllipsoid(points, pvalue=0.95): """ Show the oriented PCA ellipsoid that contains fraction `pvalue` of points. :param float pvalue: ellypsoid will contain the specified fraction of points. Extra can be calculated with ``mesh.asphericity()``, ``mesh.asphericity_error()`` (asphericity is equal to 0 for a perfect sphere). Axes can be accessed in ``mesh.va``, ``mesh.vb``, ``mesh.vc``. End point of the axes are stored in ``mesh.axis1``, ``mesh.axis12`` and ``mesh.axis3``. .. hint:: Examples: |pca.py|_ |cell_colony.py|_ |pca| |cell_colony| """ from scipy.stats import f if isinstance(points, Mesh): coords = points.points() else: coords = points if len(coords) < 4: colors.printc("Warning in pcaEllipsoid(): not enough points!", c='y') return None P = np.array(coords, ndmin=2, dtype=float) cov = np.cov(P, rowvar=0) # covariance matrix U, s, R = np.linalg.svd(cov) # singular value decomposition p, n = s.size, P.shape[0] fppf = f.ppf(pvalue, p, n-p)*(n-1)*p*(n+1)/n/(n-p) # f % point function cfac = 1 + 6/(n-1) # correction factor for low statistics ua, ub, uc = np.sqrt(s*fppf)/cfac # semi-axes (largest first) center = np.mean(P, axis=0) # centroid of the hyperellipsoid elli = shapes.Ellipsoid((0,0,0), (1,0,0), (0,1,0), (0,0,1), alpha=0.2) matri = vtk.vtkMatrix4x4() matri.DeepCopy((R[0][0] * ua*2, R[1][0] * ub*2, R[2][0] * uc*2, center[0], R[0][1] * ua*2, R[1][1] * ub*2, R[2][1] * uc*2, center[1], R[0][2] * ua*2, R[1][2] * ub*2, R[2][2] * uc*2, center[2], 0, 0, 0, 1)) vtra = vtk.vtkTransform() vtra.SetMatrix(matri) # assign the transformation elli.SetScale(vtra.GetScale()) elli.SetOrientation(vtra.GetOrientation()) elli.SetPosition(vtra.GetPosition()) elli.GetProperty().BackfaceCullingOn() elli.nr_of_points = n elli.va = ua elli.vb = ub elli.vc = uc elli.axis1 = vtra.TransformPoint([1,0,0]) elli.axis2 = vtra.TransformPoint([0,1,0]) elli.axis3 = vtra.TransformPoint([0,0,1]) elli.transformation = vtra elli.name = "pcaEllipsoid" return elli
#def smoothMLS1D(mesh, f=0.2, radius=None, showNLines=0): # """ # Smooth mesh or points with a `Moving Least Squares` variant. # The list ``mesh.info['variances']`` contain the residue calculated for each point. # Input mesh's polydata is modified. # # :param float f: smoothing factor - typical range is [0,2]. # :param int showNLines: build a mesh showing the fitting line for N random points. # # .. hint:: |moving_least_squares1D.py|_ |skeletonize.py|_ # # |moving_least_squares1D| |skeletonize| # """ # coords = mesh.points() # ncoords = len(coords) # Ncp = int(ncoords * f / 10) # nshow = int(ncoords) # if showNLines: # ndiv = int(nshow / showNLines) # # if not radius and Ncp < 4: # colors.printc("smoothMLS1D: Please choose a fraction higher than " + str(f), c=1) # Ncp = 4 # # variances, newline, acts = [], [], [] # for i, p in enumerate(coords): # # points = mesh.closestPoint(p, N=Ncp, radius=radius) # if len(points) < 4: # continue # # points = np.array(points) # pointsmean = points.mean(axis=0) # plane center # uu, dd, vv = np.linalg.svd(points - pointsmean) # newp = np.dot(p - pointsmean, vv[0]) * vv[0] + pointsmean # variances.append(dd[1] + dd[2]) # newline.append(newp) # # if showNLines and not i % ndiv: # fline = fitLine(points) # fitting plane # iapts = shapes.Points(points) # blue points # acts += [fline, iapts] # # pcloud = shapes.Points(newline, c="r", alpha=0.5) # pcloud.GetProperty().SetPointSize(mesh.GetProperty().GetPointSize()) # # if showNLines: # asse = Assembly([pcloud] + acts) # asse.info["variances"] = np.array(variances) # return asse # NB: a demo mesh is returned # else: # pcloud.info["variances"] = np.array(variances) # return pcloud # #def smoothMLS2D(mesh, f=0.2, radius=None, decimate=1, showNPlanes=0): # """ # Smooth mesh or points with a `Moving Least Squares` algorithm variant. # The list ``mesh.info['variances']`` contains the residue calculated for each point. # # :param float f: smoothing factor - typical range is [0,2]. Ignored if ``radius`` is set. # :param float radius: radius search in absolute units. If set then ``f`` is ignored. # :param int decimate: decimation integer factor. # :param showNPlanes: build a demo object showing the fitting plane for N random points. # # .. hint:: |moving_least_squares2D.py|_ |recosurface.py|_ # # |moving_least_squares2D| |recosurface| # """ # coords = mesh.points() # ncoords = len(coords) # Ncp = int(ncoords * f / 100) # nshow = int(ncoords / decimate) # decimate = int(decimate) # if showNPlanes: # ndiv = int(nshow / showNPlanes * decimate) # # if radius: # print("smoothMLS2D: Searching radius, #pt:", radius, ncoords) # else: # if Ncp < 5: # colors.printc("~target Please choose a fraction higher than " + str(f), c=1) # Ncp = 5 # print("smoothMLS2D: Searching #neighbours, #pt:", Ncp, ncoords) # # variances, newpts, acts = [], [], [] # pb = utils.ProgressBar(0, ncoords) # for i, p in enumerate(coords): # pb.print("smoothing mesh ...") # if i % decimate: # continue # # points = mesh.closestPoint(p, N=Ncp, radius=radius) # if radius and len(points) < 5: # continue # # pointsmean = points.mean(axis=0) # plane center # uu, dd, vv = np.linalg.svd(points - pointsmean) # a, b, c = np.cross(vv[0], vv[1]) # normal # d, e, f = pointsmean # plane center # x, y, z = p # t = a * d - a * x + b * e - b * y + c * f - c * z # /(a*a+b*b+c*c) # variances.append(dd[2]) # newpts.append((x + t*a, y + t*b, z + t*c)) # # if showNPlanes and not i % ndiv: # plane = fitPlane(points).alpha(0.3) # fitting plane # iapts = shapes.Points(points) # blue points # acts += [plane, iapts] # # pcloud = shapes.Points(newpts, c="r", alpha=0.5, r=2) # pcloud.GetProperty().SetPointSize(mesh.GetProperty().GetPointSize()) # # if showNPlanes: # asse = Assembly([pcloud] + acts) # asse.info["variances"] = np.array(variances) # return asse # NB: a demo Assembly is returned # else: # pcloud.info["variances"] = np.array(variances) # # return pcloud
[docs]def smoothMLS3D(meshs, neighbours=10): """ A time sequence of point clouds (Mesh) is being smoothed in 4D (3D + time) using a `MLS (Moving Least Squares)` algorithm variant. The time associated to an mesh must be specified in advance with ``mesh.time()`` method. Data itself can suggest a meaningful time separation based on the spatial distribution of points. :param int neighbours: fixed nr. of neighbours in space-time to take into account in the fit. |moving_least_squares3D| |moving_least_squares3D.py|_ """ from scipy.spatial import KDTree coords4d = [] for a in meshs: # build the list of 4d coordinates coords3d = a.points() n = len(coords3d) pttimes = [[a.time()]] * n coords4d += np.append(coords3d, pttimes, axis=1).tolist() avedt = float(meshs[-1].time() - meshs[0].time()) / len(meshs) print("Average time separation between meshes dt =", round(avedt, 3)) coords4d = np.array(coords4d) newcoords4d = [] kd = KDTree(coords4d, leafsize=neighbours) suggest = "" pb = utils.ProgressBar(0, len(coords4d)) for i in pb.range(): mypt = coords4d[i] # dr = np.sqrt(3*dx**2+dt**2) # iclosest = kd.query_ball_Point(mypt, r=dr) # dists, iclosest = kd.query(mypt, k=None, distance_upper_bound=dr) dists, iclosest = kd.query(mypt, k=neighbours) closest = coords4d[iclosest] nc = len(closest) if nc >= neighbours and nc > 5: m = np.linalg.lstsq(closest, [1.0] * nc)[0] # needs python3 vers = m / np.linalg.norm(m) hpcenter = np.mean(closest, axis=0) # hyperplane center dist = np.dot(mypt - hpcenter, vers) projpt = mypt - dist * vers newcoords4d.append(projpt) if not i % 1000: # work out some stats v = np.std(closest, axis=0) vx = round((v[0] + v[1] + v[2]) / 3, 3) suggest = "data suggest dt=" + str(vx) pb.print(suggest) newcoords4d = np.array(newcoords4d) ctimes = newcoords4d[:, 3] ccoords3d = np.delete(newcoords4d, 3, axis=1) # get rid of time act = shapes.Points(ccoords3d) act.pointColors(ctimes, cmap="jet") # use a colormap to associate a color to time return act
def extractSurface(volume, radius=0.5): """Generate the zero-crossing isosurface from truncated signed distance volume in input. Output is an ``Mesh`` object. """ img = _getinput(volume) fe = vtk.vtkExtractSurface() fe.SetInputData(img) fe.SetRadius(radius) fe.Update() return Mesh(fe.GetOutput())
[docs]def recoSurface(pts, dims=(250,250,250), radius=None, sampleSize=None, holeFilling=True, bounds=(), pad=0.1): """ Surface reconstruction from a scattered cloud of points. :param int dims: number of voxels in x, y and z to control precision. :param float radius: radius of influence of each point. Smaller values generally improve performance markedly. Note that after the signed distance function is computed, any voxel taking on the value >= radius is presumed to be "unseen" or uninitialized. :param int sampleSize: if normals are not present they will be calculated using this sample size per point. :param bool holeFilling: enables hole filling, this generates separating surfaces between the empty and unseen portions of the volume. :param list bounds: region in space in which to perform the sampling in format (xmin,xmax, ymin,ymax, zim, zmax) :param float pad: increase by this fraction the bounding box |recosurface| |recosurface.py|_ """ if not utils.isSequence(dims): dims = (dims,dims,dims) if isinstance(pts, Mesh): polyData = pts.polydata() else: polyData = shapes.Points(pts).polydata() sdf = vtk.vtkSignedDistance() if len(bounds)==6: sdf.SetBounds(bounds) else: x0, x1, y0, y1, z0, z1 = polyData.GetBounds() sdf.SetBounds(x0-(x1-x0)*pad, x1+(x1-x0)*pad, y0-(y1-y0)*pad, y1+(y1-y0)*pad, z0-(z1-z0)*pad, z1+(z1-z0)*pad) if polyData.GetPointData().GetNormals(): sdf.SetInputData(polyData) else: normals = vtk.vtkPCANormalEstimation() normals.SetInputData(polyData) if not sampleSize: sampleSize = int(polyData.GetNumberOfPoints()/50) normals.SetSampleSize(sampleSize) normals.SetNormalOrientationToGraphTraversal() sdf.SetInputConnection(normals.GetOutputPort()) #print("Recalculating normals with sample size =", sampleSize) if radius is None: b = polyData.GetBounds() diagsize = np.sqrt((b[1]-b[0])**2 + (b[3]-b[2])**2 + (b[5]-b[4])**2) radius = diagsize / (sum(dims)/3) * 5 #print("Calculating mesh from points with radius =", radius) sdf.SetRadius(radius) sdf.SetDimensions(dims) sdf.Update() surface = vtk.vtkExtractSurface() surface.SetRadius(radius * 0.99) surface.SetHoleFilling(holeFilling) surface.ComputeNormalsOff() surface.ComputeGradientsOff() surface.SetInputConnection(sdf.GetOutputPort()) surface.Update() return Mesh(surface.GetOutput())
[docs]def cluster(points, radius): """ Clustering of points in space. `radius` is the radius of local search. Individual subsets can be accessed through ``mesh.clusters``. |clustering| |clustering.py|_ """ if isinstance(points, vtk.vtkActor): poly = points.GetMapper().GetInput() else: src = vtk.vtkPointSource() src.SetNumberOfPoints(len(points)) src.Update() vpts = src.GetOutput().GetPoints() for i, p in enumerate(points): vpts.SetPoint(i, p) poly = src.GetOutput() cluster = vtk.vtkEuclideanClusterExtraction() cluster.SetInputData(poly) cluster.SetExtractionModeToAllClusters() cluster.SetRadius(radius) cluster.ColorClustersOn() cluster.Update() idsarr = cluster.GetOutput().GetPointData().GetArray("ClusterId") Nc = cluster.GetNumberOfExtractedClusters() sets = [[] for i in range(Nc)] for i, p in enumerate(points): sets[idsarr.GetValue(i)].append(p) acts = [] for i, aset in enumerate(sets): acts.append(shapes.Points(aset, c=i)) asse = Assembly(acts) asse.info["clusters"] = sets print("Nr. of extracted clusters", Nc) if Nc > 10: print("First ten:") for i in range(Nc): if i > 9: print("...") break print("Cluster #" + str(i) + ", N =", len(sets[i])) print("Access individual clusters through attribute: obj.info['cluster']") return asse
[docs]def removeOutliers(points, radius): """ Remove outliers from a cloud of points within the specified `radius` search. |clustering| |clustering.py|_ """ isactor = False if isinstance(points, vtk.vtkActor): isactor = True poly = points.GetMapper().GetInput() else: src = vtk.vtkPointSource() src.SetNumberOfPoints(len(points)) src.Update() vpts = src.GetOutput().GetPoints() for i, p in enumerate(points): vpts.SetPoint(i, p) poly = src.GetOutput() removal = vtk.vtkRadiusOutlierRemoval() removal.SetInputData(poly) removal.SetRadius(radius) removal.SetNumberOfNeighbors(5) removal.GenerateOutliersOff() removal.Update() rpoly = removal.GetOutput() print("# of removed outlier points: ", removal.GetNumberOfPointsRemoved(), '/', poly.GetNumberOfPoints()) outpts = [] for i in range(rpoly.GetNumberOfPoints()): outpts.append(list(rpoly.GetPoint(i))) outpts = np.array(outpts) if not isactor: return outpts return shapes.Points(outpts)
[docs]def booleanOperation(mesh1, operation, mesh2): """Volumetric union, intersection and subtraction of surfaces. :param str operation: allowed operations: ``'plus'``, ``'intersect'``, ``'minus'``. |boolean| |boolean.py|_ """ bf = vtk.vtkBooleanOperationPolyDataFilter() poly1 = mesh1.computeNormals().polydata() poly2 = mesh2.computeNormals().polydata() if operation.lower() == "plus" or operation.lower() == "+": bf.SetOperationToUnion() elif operation.lower() == "intersect": bf.SetOperationToIntersection() elif operation.lower() == "minus" or operation.lower() == "-": bf.SetOperationToDifference() #bf.ReorientDifferenceCellsOn() bf.SetInputData(0, poly1) bf.SetInputData(1, poly2) bf.Update() mesh = Mesh(bf.GetOutput(), c=None) return mesh
[docs]def surfaceIntersection(mesh1, mesh2, tol=1e-06): """Intersect 2 surfaces and return a line mesh. .. hint:: |surfIntersect.py|_ """ bf = vtk.vtkIntersectionPolyDataFilter() poly1 = mesh1.GetMapper().GetInput() poly2 = mesh2.GetMapper().GetInput() bf.SetInputData(0, poly1) bf.SetInputData(1, poly2) bf.Update() mesh = Mesh(bf.GetOutput(), "k", 1) mesh.GetProperty().SetLineWidth(3) return mesh
def _getinput(obj): if isinstance(obj, (vtk.vtkVolume, vtk.vtkActor)): return obj.GetMapper().GetInput() else: return obj
[docs]def probePoints(vol, pts): """ Takes a ``Volume`` (or any other vtk data set) and probes its scalars at the specified points in space. Note that a mask is also output with valid/invalid points which can be accessed with `mesh.getPointArray('vtkValidPointMask')`. """ if isinstance(pts, Mesh): pts = pts.points() def readPoints(): output = src.GetPolyDataOutput() points = vtk.vtkPoints() for p in pts: x, y, z = p points.InsertNextPoint(x, y, z) output.SetPoints(points) cells = vtk.vtkCellArray() cells.InsertNextCell(len(pts)) for i in range(len(pts)): cells.InsertCellPoint(i) output.SetVerts(cells) src = vtk.vtkProgrammableSource() src.SetExecuteMethod(readPoints) src.Update() img = _getinput(vol) probeFilter = vtk.vtkProbeFilter() probeFilter.SetSourceData(img) probeFilter.SetInputConnection(src.GetOutputPort()) probeFilter.Update() poly = probeFilter.GetOutput() pm = Mesh(poly) pm.name = 'ProbePoints' return pm
[docs]def probeLine(vol, p1, p2, res=100): """ Takes a ``Volume`` (or any other vtk data set) and probes its scalars along a line defined by 2 points `p1` and `p2`. Note that a mask is also output with valid/invalid points which can be accessed with `mesh.getPointArray('vtkValidPointMask')`. :param int res: nr of points along the line |probeLine1| |probeLine1.py|_ |probeLine2.py|_ """ line = vtk.vtkLineSource() line.SetResolution(res) line.SetPoint1(p1) line.SetPoint2(p2) img = _getinput(vol) probeFilter = vtk.vtkProbeFilter() probeFilter.SetSourceData(img) probeFilter.SetInputConnection(line.GetOutputPort()) probeFilter.Update() poly = probeFilter.GetOutput() lnn = Mesh(poly) lnn.name = 'ProbeLine' return lnn
[docs]def probePlane(vol, origin=(0, 0, 0), normal=(1, 0, 0)): """ Takes a ``Volume`` (or any other vtk data set) and probes its scalars on a plane defined by a point and a normal. """ img = _getinput(vol) plane = vtk.vtkPlane() plane.SetOrigin(origin) plane.SetNormal(normal) planeCut = vtk.vtkCutter() planeCut.SetInputData(img) planeCut.SetCutFunction(plane) planeCut.Update() poly = planeCut.GetOutput() cutmesh = Mesh(poly) return cutmesh
[docs]def resampleArrays(source, target, tol=None): """Resample point and cell data of a dataset on points from another dataset. It takes two inputs - source and target, and samples the point and cell values of target onto the point locations of source. The output has the same structure as the source but its point data have the resampled values from target. :param float tol: set the tolerance used to compute whether a point in the target is in a cell of the source. Points without resampled values, and their cells, are be marked as blank. """ rs = vtk.vtkResampleWithDataSet() rs.SetInputData(source.polydata()) rs.SetSourceData(target.polydata()) rs.SetPassPointArrays(True) rs.SetPassCellArrays(True) if tol: rs.SetComputeTolerance(False) rs.SetTolerance(tol) rs.Update() return rs.GetOutput()
def thinPlateSpline(*args, **kwargs): """Obsolete! Use mesh.thinPlateSpline() and mesh.applyTransform() instead.""" colors.printc("WARNING: thinPlateSpline(mesh) is obsolete!", c=1) colors.printc(" : Use mesh.thinPlateSpline() and mesh.applyTransform() instead.", c=1) raise RuntimeError()
[docs]def connectedPoints(mesh, radius, mode=0, regions=(), vrange=(0,1), seeds=(), angle=0): """ Extracts and/or segments points from a point cloud based on geometric distance measures (e.g., proximity, normal alignments, etc.) and optional measures such as scalar range. The default operation is to segment the points into "connected" regions where the connection is determined by an appropriate distance measure. Each region is given a region id. Optionally, the filter can output the largest connected region of points; a particular region (via id specification); those regions that are seeded using a list of input point ids; or the region of points closest to a specified position. The key parameter of this filter is the radius defining a sphere around each point which defines a local neighborhood: any other points in the local neighborhood are assumed connected to the point. Note that the radius is defined in absolute terms. Other parameters are used to further qualify what it means to be a neighboring point. For example, scalar range and/or point normals can be used to further constrain the neighborhood. Also the extraction mode defines how the filter operates. By default, all regions are extracted but it is possible to extract particular regions; the region closest to a seed point; seeded regions; or the largest region found while processing. By default, all regions are extracted. On output, all points are labeled with a region number. However note that the number of input and output points may not be the same: if not extracting all regions then the output size may be less than the input size. :param float radius: radius variable specifying a local sphere used to define local point neighborhood :param int mode: - 0, Extract all regions - 1, Extract point seeded regions - 2, Extract largest region - 3, Test specified regions - 4, Extract all regions with scalar connectivity - 5, Extract point seeded regions :param list regions: a list of non-negative regions id to extract :param list vrange: scalar range to use to extract points based on scalar connectivity :param list seeds: a list of non-negative point seed ids :param list angle: points are connected if the angle between their normals is within this angle threshold (expressed in degrees). """ # https://vtk.org/doc/nightly/html/classvtkConnectedPointsFilter.html cpf = vtk.vtkConnectedPointsFilter() cpf.SetInputData(mesh.polydata()) cpf.SetRadius(radius) if mode == 0: # Extract all regions pass elif mode == 1: # Extract point seeded regions cpf.SetExtractionModeToPointSeededRegions() for s in seeds: cpf.AddSeed(s) elif mode == 2: # Test largest region cpf.SetExtractionModeToLargestRegion() elif mode == 3: # Test specified regions cpf.SetExtractionModeToSpecifiedRegions() for r in regions: cpf.AddSpecifiedRegion(r) elif mode == 4: # Extract all regions with scalar connectivity cpf.SetExtractionModeToLargestRegion() cpf.ScalarConnectivityOn() cpf.SetScalarRange(vrange[0], vrange[1]) elif mode == 5: # Extract point seeded regions cpf.SetExtractionModeToLargestRegion() cpf.ScalarConnectivityOn() cpf.SetScalarRange(vrange[0], vrange[1]) cpf.AlignedNormalsOn() cpf.SetNormalAngle(angle) cpf.Update() return Mesh(cpf.GetOutput())
[docs]def pointSampler(mesh, distance=None): """ Algorithm to generate points the specified distance apart. """ poly = mesh.polydata(True) pointSampler = vtk.vtkPolyDataPointSampler() if not distance: distance = mesh.diagonalSize() / 100.0 pointSampler.SetDistance(distance) # pointSampler.GenerateVertexPointsOff() # pointSampler.GenerateEdgePointsOff() # pointSampler.GenerateVerticesOn() # pointSampler.GenerateInteriorPointsOn() pointSampler.SetInputData(poly) pointSampler.Update() umesh = Mesh(pointSampler.GetOutput()) prop = vtk.vtkProperty() prop.DeepCopy(mesh.GetProperty()) umesh.SetProperty(prop) return umesh
[docs]def geodesic(mesh, start, end): """ Dijkstra algorithm to compute the graph geodesic. Takes as input a polygonal mesh and performs a single source shortest path calculation. :param start: start vertex index or close point `[x,y,z]` :type start: int, list :param end: end vertex index or close point `[x,y,z]` :type start: int, list |geodesic| |geodesic.py|_ """ dijkstra = vtk.vtkDijkstraGraphGeodesicPath() if utils.isSequence(start): cc = mesh.points() pa = shapes.Points(cc) start = pa.closestPoint(start, returnIds=True) end = pa.closestPoint(end, returnIds=True) dijkstra.SetInputData(pa.polydata()) else: dijkstra.SetInputData(mesh.polydata()) dijkstra.SetStartVertex(start) dijkstra.SetEndVertex(end) dijkstra.Update() weights = vtk.vtkDoubleArray() dijkstra.GetCumulativeWeights(weights) length = weights.GetMaxId() + 1 arr = np.zeros(length) for i in range(length): arr[i] = weights.GetTuple(i)[0] dmesh = Mesh(dijkstra.GetOutput()) prop = vtk.vtkProperty() prop.DeepCopy(mesh.GetProperty()) prop.SetLineWidth(3) prop.SetOpacity(1) dmesh.SetProperty(prop) dmesh.info["CumulativeWeights"] = arr return dmesh
[docs]def convexHull(mesh_or_list, alphaConstant=0): """ Create a 2D/3D Delaunay triangulation of input points. :param mesh_or_list: can be either an ``Mesh`` or a list of 3D points. :param float alphaConstant: For a non-zero alpha value, only verts, edges, faces, or tetra contained within the circumsphere (of radius alpha) will be output. Otherwise, only tetrahedra will be output. |convexHull| |convexHull.py|_ """ if utils.isSequence(mesh_or_list): mesh = shapes.Points(mesh_or_list) else: mesh = mesh_or_list apoly = mesh.clean().polydata() triangleFilter = vtk.vtkTriangleFilter() triangleFilter.SetInputData(apoly) triangleFilter.Update() poly = triangleFilter.GetOutput() if np.count_nonzero(mesh.points()[:,2]): delaunay = vtk.vtkDelaunay3D() # Create the convex hull of the pointcloud else: delaunay = vtk.vtkDelaunay2D() if alphaConstant: delaunay.SetAlpha(alphaConstant) delaunay.SetInputData(poly) delaunay.Update() surfaceFilter = vtk.vtkDataSetSurfaceFilter() surfaceFilter.SetInputConnection(delaunay.GetOutputPort()) surfaceFilter.Update() return Mesh(surfaceFilter.GetOutput())
[docs]def mesh2Volume(mesh, spacing=(1, 1, 1)): """ Convert a mesh it into a ``Volume`` where the foreground (exterior) voxels value is 1 and the background (interior) voxels value is 0. Internally the ``vtkPolyDataToImageStencil`` class is used. |mesh2volume| |mesh2volume.py|_ """ # https://vtk.org/Wiki/VTK/Examples/Cxx/PolyData/PolyDataToImageData pd = mesh.polydata() whiteImage = vtk.vtkImageData() bounds = pd.GetBounds() whiteImage.SetSpacing(spacing) # compute dimensions dim = [0, 0, 0] for i in [0, 1, 2]: dim[i] = int(np.ceil((bounds[i * 2 + 1] - bounds[i * 2]) / spacing[i])) whiteImage.SetDimensions(dim) whiteImage.SetExtent(0, dim[0] - 1, 0, dim[1] - 1, 0, dim[2] - 1) origin = [0, 0, 0] origin[0] = bounds[0] + spacing[0] / 2 origin[1] = bounds[2] + spacing[1] / 2 origin[2] = bounds[4] + spacing[2] / 2 whiteImage.SetOrigin(origin) whiteImage.AllocateScalars(vtk.VTK_UNSIGNED_CHAR, 1) # fill the image with foreground voxels: inval = 255 count = whiteImage.GetNumberOfPoints() for i in range(count): whiteImage.GetPointData().GetScalars().SetTuple1(i, inval) # polygonal data --> image stencil: pol2stenc = vtk.vtkPolyDataToImageStencil() pol2stenc.SetInputData(pd) pol2stenc.SetOutputOrigin(origin) pol2stenc.SetOutputSpacing(spacing) pol2stenc.SetOutputWholeExtent(whiteImage.GetExtent()) pol2stenc.Update() # cut the corresponding white image and set the background: outval = 0 imgstenc = vtk.vtkImageStencil() imgstenc.SetInputData(whiteImage) imgstenc.SetStencilConnection(pol2stenc.GetOutputPort()) imgstenc.ReverseStencilOff() imgstenc.SetBackgroundValue(outval) imgstenc.Update() return Volume(imgstenc.GetOutput())
[docs]def projectSphereFilter(mesh): """ Project a spherical-like object onto a plane. |projectsphere| |projectsphere.py|_ """ poly = mesh.polydata() psf = vtk.vtkProjectSphereFilter() psf.SetInputData(poly) psf.Update() return Mesh(psf.GetOutput())
[docs]def voronoi3D(nuclei, bbfactor=1, tol=None): """Generate 3D Voronio tasselization with the `Voro++ <http://math.lbl.gov/voro++/>`_ package. |voronoi3d| |voronoi3d.py|_ """ from vtkplotter import settings import os # run voro++ if os.path.isfile(settings.voro_path+'/voro++') or settings.voro_path=='': outF = open('voronoi3d.txt', "w") for i,p in enumerate(nuclei): outF.write(str(i)+' '+str(p[0])+' '+str(p[1])+' '+str(p[2])+'\n') outF.close() ncl = shapes.Points(nuclei) b = np.array(ncl.GetBounds())*bbfactor bbstr = str(b[0])+' '+str(b[1])+' '+str(b[2])+' '+str(b[3])+' '+str(b[4])+' '+str(b[5]) print('Using Voro++ in :', settings.voro_path) os.system(settings.voro_path+'/voro++ -c "%F %v %w %P %t" -v '+bbstr+' voronoi3d.txt') f = open('voronoi3d.txt.vol', "r") lines = f.readlines() f.close() else: print('Cannot find Voro++ installation in:', settings.voro_path) print('Download and install Voro++ from http://math.lbl.gov/voro++/download') print('Then add:') print('from vtkplotter import settings"') print('settings.voro_path="path_to_voro++_executable"') raise RuntimeError() # build polydata sourcePoints = vtk.vtkPoints() sourcePolygons = vtk.vtkCellArray() cells, areas, volumes = [], [], [] for l in lines: # each line corresponds to an input point ls = l.split() area = float(ls[0]) volu = float(ls[1]) n = int(ls[2]) ids = [] for i in range(3, n+3): p = tuple(map(float, ls[i][1:-1].split(','))) aid = sourcePoints.InsertNextPoint(p[0], p[1], p[2]) if tol: bp = np.array([p[0]-b[0], p[0]-b[1], p[1]-b[2], p[1]-b[3], p[2]-b[4], p[2]-b[5]]) bp = np.abs(bp) < tol if np.any(bp): ids.append(None) else: ids.append(aid) else: ids.append(aid) # fill polygon elements if None in ids: continue faces = [] for j in range(n+3, len(ls)): face = tuple(map(int, ls[j][1:-1].split(','))) ele = vtk.vtkPolygon() ele.GetPointIds().SetNumberOfIds(len(face)) elems = [] for k,f in enumerate(face): ele.GetPointIds().SetId(k, ids[f]) elems.append(ids[f]) sourcePolygons.InsertNextCell(ele) faces.append(elems) cells.append(faces) areas.append(area) volumes.append(volu) poly = vtk.vtkPolyData() poly.SetPoints(sourcePoints) poly.SetPolys(sourcePolygons) voro = Mesh(poly).alpha(0.5) voro.info['cells'] = cells voro.info['areas'] = areas voro.info['volumes'] = volumes return voro
[docs]def extractCellsByType(obj, types=(7,)): """Extract cells of a specified type. Given an input vtkDataSet and a list of cell types, produce an output containing only cells of the specified type(s). Find `here <https://vtk.org/doc/nightly/html/vtkCellType_8h_source.html>`_ the list of possible cell types. """ ef = vtk.vtkExtractCellsByType() for ct in types: ef.AddCellType(ct) ef.Update() return Mesh(ef.GetOutput())
[docs]def pointCloudFrom(obj, useCellData=False): """ Build a `Mesh` object from any VTK dataset as a point cloud. :param bool useCellData: if True cell data is interpolated at point positions. """ from vtk.numpy_interface import dataset_adapter if useCellData: c2p = vtk.vtkCellDataToPointData() c2p.SetInputData(obj) c2p.Update() obj = c2p.GetOutput() wrapped = dataset_adapter.WrapDataObject(obj) ptdatanames = wrapped.PointData.keys() vpts = obj.GetPoints() poly = vtk.vtkPolyData() poly.SetPoints(vpts) for name in ptdatanames: arr = obj.GetPointData().GetArray(name) poly.GetPointData().AddArray(arr) return Mesh(poly, c=None)
[docs]def interpolateToVolume(mesh, kernel='shepard', radius=None, bounds=None, nullValue=None, dims=(20,20,20)): """ Generate a ``Volume`` by interpolating a scalar or vector field which is only known on a scattered set of points or mesh. Available interpolation kernels are: shepard, gaussian, voronoi, linear. :param str kernel: interpolation kernel type [shepard] :param float radius: radius of the local search :param list bounds: bounding box of the output Volume object :param list dims: dimensions of the output Volume object :param float nullValue: value to be assigned to invalid points |interpolateVolume| |interpolateVolume.py|_ """ if isinstance(mesh, vtk.vtkPolyData): output = mesh else: output = mesh.polydata() # Create a probe volume probe = vtk.vtkImageData() probe.SetDimensions(dims) if bounds is None: bounds = output.GetBounds() probe.SetOrigin(bounds[0],bounds[2],bounds[4]) probe.SetSpacing((bounds[1]-bounds[0])/(dims[0]-1), (bounds[3]-bounds[2])/(dims[1]-1), (bounds[5]-bounds[4])/(dims[2]-1)) if radius is None: radius = min(bounds[1]-bounds[0], bounds[3]-bounds[2], bounds[5]-bounds[4])/3 locator = vtk.vtkPointLocator() locator.SetDataSet(output) locator.BuildLocator() if kernel == 'shepard': kern = vtk.vtkShepardKernel() kern.SetPowerParameter(2) kern.SetRadius(radius) elif kernel == 'gaussian': kern = vtk.vtkGaussianKernel() kern.SetRadius(radius) elif kernel == 'voronoi': kern = vtk.vtkVoronoiKernel() elif kernel == 'linear': kern = vtk.vtkLinearKernel() kern.SetRadius(radius) else: print('Error in interpolateToVolume, available kernels are:') print(' [shepard, gaussian, voronoi, linear]') raise RuntimeError() interpolator = vtk.vtkPointInterpolator() interpolator.SetInputData(probe) interpolator.SetSourceData(output) interpolator.SetKernel(kern) interpolator.SetLocator(locator) if nullValue is not None: interpolator.SetNullValue(nullValue) else: interpolator.SetNullPointsStrategyToClosestPoint() interpolator.Update() return Volume(interpolator.GetOutput())
[docs]def interpolateToStructuredGrid(mesh, kernel=None, radius=None, bounds=None, nullValue=None, dims=None): """ Generate a volumetric dataset (vtkStructuredData) by interpolating a scalar or vector field which is only known on a scattered set of points or mesh. Available interpolation kernels are: shepard, gaussian, voronoi, linear. :param str kernel: interpolation kernel type [shepard] :param float radius: radius of the local search :param list bounds: bounding box of the output vtkStructuredGrid object :param list dims: dimensions of the output vtkStructuredGrid object :param float nullValue: value to be assigned to invalid points """ if isinstance(mesh, vtk.vtkPolyData): output = mesh else: output = mesh.polydata() if dims is None: dims = (20,20,20) if bounds is None: bounds = output.GetBounds() # Create a probe volume probe = vtk.vtkStructuredGrid() probe.SetDimensions(dims) points = vtk.vtkPoints() points.Allocate(dims[0] * dims[1] * dims[2]) deltaZ = (bounds[5]-bounds[4]) / (dims[2] - 1) deltaY = (bounds[3]-bounds[2]) / (dims[1] - 1) deltaX = (bounds[1]-bounds[0]) / (dims[0] - 1) for k in range(dims[2]): z = bounds[4] + k * deltaZ kOffset = k * dims[0] * dims[1] for j in range(dims[1]): y = bounds[2] + j * deltaY jOffset = j * dims[0] for i in range(dims[0]): x = bounds[0] + i * deltaX offset = i + jOffset + kOffset points.InsertPoint(offset, [x,y,z]) probe.SetPoints(points) if radius is None: radius = min(bounds[1]-bounds[0], bounds[3]-bounds[2], bounds[5]-bounds[4])/3 locator = vtk.vtkPointLocator() locator.SetDataSet(output) locator.BuildLocator() if kernel == 'gaussian': kern = vtk.vtkGaussianKernel() kern.SetRadius(radius) elif kernel == 'voronoi': kern = vtk.vtkVoronoiKernel() elif kernel == 'linear': kern = vtk.vtkLinearKernel() kern.SetRadius(radius) else: kern = vtk.vtkShepardKernel() kern.SetPowerParameter(2) kern.SetRadius(radius) interpolator = vtk.vtkPointInterpolator() interpolator.SetInputData(probe) interpolator.SetSourceData(output) interpolator.SetKernel(kern) interpolator.SetLocator(locator) if nullValue is not None: interpolator.SetNullValue(nullValue) else: interpolator.SetNullPointsStrategyToClosestPoint() interpolator.Update() return interpolator.GetOutput()
[docs]def rectilinearGridToTetrahedra(rgrid, tetraPerCell=6): """Create a tetrahedral mesh from a ``vtkRectilinearGrid``. The tetrahedra can be 5 per cell, 6 per cell, or a mixture of 5 or 12 per cell. The resulting mesh is consistent, meaning that there are no edge crossings and that each tetrahedron face is shared by two tetrahedra, except those tetrahedra on the boundary. All tetrahedra are right handed. """ r2t = vtk.vtkRectilinearGridToTetrahedra() r2t.SetInputData(rgrid) if tetraPerCell == 5: r2t.SetTetraPerCellTo5() if tetraPerCell == 6: r2t.SetTetraPerCellTo6() if tetraPerCell == 12: r2t.SetTetraPerCellTo12() r2t.Update() return r2t.GetOutput()
[docs]def streamLines(domain, probe, activeVectors='', integrator='rk4', direction='forward', initialStepSize=None, maxPropagation=None, maxSteps=10000, stepLength=None, extrapolateToBoundingBox=(), surfaceConstrain=False, computeVorticity=True, ribbons=None, tubes={}, scalarRange=None, lw=None, ): """ Integrate a vector field on a domain (a Mesh or other vtk datasets types) to generate streamlines. The integration is performed using a specified integrator (Runge-Kutta). The length of a streamline is governed by specifying a maximum value either in physical arc length or in (local) cell length. Otherwise, the integration terminates upon exiting the field domain. :param domain: the vtk object that contains the vector field :param str activeVectors: name of the vector array :param Mesh,list probe: the Mesh that probes the domain. Its coordinates will be the seeds for the streamlines, can also be an array of positions. :param str integrator: Runge-Kutta integrator, either 'rk2', 'rk4' of 'rk45' :param float initialStepSize: initial step size of integration :param float maxPropagation: maximum physical length of the streamline :param int maxSteps: maximum nr of steps allowed :param float stepLength: length of step integration. :param dict extrapolateToBoundingBox: Vectors defined on a surface are extrapolated to the entire volume defined by its bounding box - kernel, (str) - interpolation kernel type [shepard] - radius (float)- radius of the local search - bounds, (list) - bounding box of the output Volume - dims, (list) - dimensions of the output Volume object - nullValue, (float) - value to be assigned to invalid points :param bool surfaceConstrain: force streamlines to be computed on a surface :param bool computeVorticity: Turn on/off vorticity computation at streamline points (necessary for generating proper stream-ribbons) :param int ribbons: render lines as ribbons by joining them. An integer value represent the ratio of joining (e.g.: ribbons=2 groups lines 2 by 2) :param dict tubes: dictionary containing the parameters for the tube representation: - ratio, (int) - draws tube as longitudinal stripes - res, (int) - tube resolution (nr. of sides, 12 by default) - maxRadiusFactor (float) - max tube radius as a multiple of the min radius - varyRadius, (int) - radius varies based on the scalar or vector magnitude: - 0 - do not vary radius - 1 - vary radius by scalar - 2 - vary radius by vector - 3 - vary radius by absolute value of scalar :param list scalarRange: specify the scalar range for coloring .. hint:: Examples: |streamlines1.py|_ |streamribbons.py|_ |office.py|_ |streamlines2.py|_ |streamlines2| |office| |streamribbons| |streamlines1| """ if isinstance(domain, vtk.vtkActor): if len(extrapolateToBoundingBox): grid = interpolateToStructuredGrid(domain, **extrapolateToBoundingBox) else: grid = domain.polydata() else: grid = domain if activeVectors: grid.GetPointData().SetActiveVectors(activeVectors) b = grid.GetBounds() size = (b[5]-b[4] + b[3]-b[2] + b[1]-b[0])/3 if initialStepSize is None: initialStepSize = size/100. if maxPropagation is None: maxPropagation = size if utils.isSequence(probe): pts = probe else: pts = probe.clean().points() src = vtk.vtkProgrammableSource() def readPoints(): output = src.GetPolyDataOutput() points = vtk.vtkPoints() for x, y, z in pts: points.InsertNextPoint(x, y, z) output.SetPoints(points) src.SetExecuteMethod(readPoints) src.Update() st = vtk.vtkStreamTracer() st.SetInputDataObject(grid) st.SetSourceConnection(src.GetOutputPort()) st.SetInitialIntegrationStep(initialStepSize) st.SetComputeVorticity(computeVorticity) st.SetMaximumNumberOfSteps(maxSteps) st.SetMaximumPropagation(maxPropagation) st.SetSurfaceStreamlines(surfaceConstrain) if stepLength: st.SetStepLength(stepLength) if 'f' in direction: st.SetIntegrationDirectionToForward() elif 'back' in direction: st.SetIntegrationDirectionToBackward() elif 'both' in direction: st.SetIntegrationDirectionToBoth() if integrator == 'rk2': st.SetIntegratorTypeToRungeKutta2() elif integrator == 'rk4': st.SetIntegratorTypeToRungeKutta4() elif integrator == 'rk45': st.SetIntegratorTypeToRungeKutta45() else: colors.printc("Error in streamlines, unknown integrator", integrator, c=1) st.Update() output = st.GetOutput() if ribbons: scalarSurface = vtk.vtkRuledSurfaceFilter() scalarSurface.SetInputConnection(st.GetOutputPort()) scalarSurface.SetOnRatio(int(ribbons)) scalarSurface.SetRuledModeToPointWalk() scalarSurface.Update() output = scalarSurface.GetOutput() if len(tubes): streamTube = vtk.vtkTubeFilter() streamTube.SetNumberOfSides(12) streamTube.SetRadius(tubes['radius']) if 'res' in tubes: streamTube.SetNumberOfSides(tubes['res']) # max tube radius as a multiple of the min radius streamTube.SetRadiusFactor(50) if 'maxRadiusFactor' in tubes: streamTube.SetRadius(tubes['maxRadiusFactor']) if 'ratio' in tubes: streamTube.SetOnRatio(int(tubes['ratio'])) if 'varyRadius' in tubes: streamTube.SetVaryRadius(int(tubes['varyRadius'])) streamTube.SetInputData(output) vname = grid.GetPointData().GetVectors().GetName() streamTube.SetInputArrayToProcess(1, 0, 0, vtk.vtkDataObject.FIELD_ASSOCIATION_POINTS, vname) streamTube.Update() sta = Mesh(streamTube.GetOutput(), c=None) scals = grid.GetPointData().GetScalars() if scals: sta.mapper().SetScalarRange(scals.GetRange()) if scalarRange is not None: sta.mapper().SetScalarRange(scalarRange) sta.GetProperty().BackfaceCullingOn() sta.phong() return sta sta = Mesh(output, c=None) if lw is not None and len(tubes)==0 and not ribbons: sta.lw(lw) scals = grid.GetPointData().GetScalars() if scals: sta.mapper().SetScalarRange(scals.GetRange()) if scalarRange is not None: sta.mapper().SetScalarRange(scalarRange) return sta
[docs]def densifyCloud(mesh, targetDistance, closestN=6, radius=0, maxIter=None, maxN=None): """Adds new points to an input point cloud. The new points are created in such a way that all points in any local neighborhood are within a target distance of one another. The algorithm works as follows. For each input point, the distance to all points in its neighborhood is computed. If any of its neighbors is further than the target distance, the edge connecting the point and its neighbor is bisected and a new point is inserted at the bisection point. A single pass is completed once all the input points are visited. Then the process repeats to the limit of the maximum number of iterations. .. note:: Points will be created in an iterative fashion until all points in their local neighborhood are the target distance apart or less. Note that the process may terminate early due to the limit on the maximum number of iterations. By default the target distance is set to 0.5. Note that the TargetDistance should be less than the Radius or nothing will change on output. .. warning:: This class can generate a lot of points very quickly. The maximum number of iterations is by default set to =1.0 for this reason. Increase the number of iterations very carefully. Also, `maxN` can be set to limit the explosion of points. It is also recommended that a N closest neighborhood is used. """ src = vtk.vtkProgrammableSource() def readPoints(): output = src.GetPolyDataOutput() points = vtk.vtkPoints() pts = mesh.points() for p in pts: x, y, z = p points.InsertNextPoint(x, y, z) output.SetPoints(points) src.SetExecuteMethod(readPoints) dens = vtk.vtkDensifyPointCloudFilter() dens.SetInputConnection(src.GetOutputPort()) dens.InterpolateAttributeDataOn() dens.SetTargetDistance(targetDistance) if maxIter: dens.SetMaximumNumberOfIterations(maxIter) if maxN: dens.SetMaximumNumberOfPoints(maxN) if radius: dens.SetNeighborhoodTypeToRadius() dens.SetRadius(radius) elif closestN: dens.SetNeighborhoodTypeToNClosest() dens.SetNumberOfClosestPoints(closestN) else: colors.printc("Error in densifyCloud: set either radius or closestN", c=1) raise RuntimeError() dens.Update() pts = vtk_to_numpy(dens.GetOutput().GetPoints().GetData()) return shapes.Points(pts, c=None).pointSize(3)
[docs]def implicitModeller(mesh, distance=0.05, res=(110,40,20), bounds=(), maxdist=None, outer=True): """Finds the surface at the specified distance from the input one""" if not len(bounds): bounds = mesh.bounds() if not maxdist: maxdist = mesh.diagonalSize()/2 imp = vtk.vtkImplicitModeller() imp.SetInputData(mesh.polydata()) imp.SetSampleDimensions(res) imp.SetMaximumDistance(maxdist) imp.SetModelBounds(bounds) contour = vtk.vtkContourFilter() contour.SetInputConnection(imp.GetOutputPort()) contour.SetValue(0, distance) contour.Update() poly = contour.GetOutput() if outer: return Mesh(poly).extractLargestRegion().c('lb') return Mesh(poly, c='lb')
[docs]def signedDistanceFromPointCloud(mesh, maxradius=None, bounds=None, dims=(20,20,20)): """ Compute signed distances over a volume from an input point cloud. The output is a ``Volume`` object whose voxels contains the signed distance from the cloud. :param float maxradius: how far out to propagate distance calculation :param list bounds: volume bounds. :param list dims: dimensions (nr. of voxels) of the output volume. """ if bounds is None: bounds = mesh.GetBounds() if maxradius is None: maxradius = mesh.diagonalSize()/10. dist = vtk.vtkSignedDistance() dist.SetInputData(mesh.polydata(True)) dist.SetRadius(maxradius) dist.SetBounds(bounds) dist.SetDimensions(dims) dist.Update() return Volume(dist.GetOutput())
[docs]def volumeFromMesh(mesh, bounds=None, dims=(20,20,20), signed=True, negate=False): """ Compute signed distances over a volume from an input mesh. The output is a ``Volume`` object whose voxels contains the signed distance from the mesh. :param list bounds: bounds of the output volume. :param list dims: dimensions (nr. of voxels) of the output volume. See example script: |volumeFromMesh.py|_ """ if bounds is None: bounds = mesh.GetBounds() sx = (bounds[1]-bounds[0])/dims[0] sy = (bounds[3]-bounds[2])/dims[1] sz = (bounds[5]-bounds[4])/dims[2] img = vtk.vtkImageData() img.SetDimensions(dims) img.SetSpacing(sx, sy, sz) img.SetOrigin(bounds[0], bounds[2], bounds[4]) img.AllocateScalars(vtk.VTK_FLOAT, 1) imp = vtk.vtkImplicitPolyDataDistance() imp.SetInput(mesh.polydata()) b4 = bounds[4] r2 = range(dims[2]) for i in range(dims[0]): x = i*sx+bounds[0] for j in range(dims[1]): y = j*sy+bounds[2] for k in r2: v = imp.EvaluateFunction((x, y, k*sz+b4)) if signed: if negate: v = -v else: v = abs(v) img.SetScalarComponentFromFloat(i,j,k, 0, v) return Volume(img)
[docs]def pointDensity(mesh, dims=(40,40,40), bounds=None, radius=None, computeGradient=False): """Generate a density field from a point cloud. Output is a ``Volume``. The local neighborhood is specified as a `radius` around each sample position (each voxel). The density is normalized to the upper value of the scalar range. See example script: |pointDensity.py|_ """ if not utils.isSequence(dims): dims = (dims,dims,dims) pdf = vtk.vtkPointDensityFilter() pdf.SetInputData(mesh.polydata()) pdf.SetSampleDimensions(dims) pdf.SetDensityEstimateToFixedRadius() pdf.SetDensityFormToVolumeNormalized() pdf.SetDensityFormToNumberOfPoints () if radius is None: radius = mesh.diagonalSize()/20 pdf.SetRadius(radius) pdf.SetComputeGradient(computeGradient) if bounds is None: bounds = mesh.GetBounds() pdf.SetModelBounds(bounds) pdf.Update() img = pdf.GetOutput() vol = Volume(img) return vol
[docs]def visiblePoints(mesh, area=(), tol=None, invert=False): """Extract points based on whether they are visible or not. Visibility is determined by accessing the z-buffer of a rendering window. The position of each input point is converted into display coordinates, and then the z-value at that point is obtained. If within the user-specified tolerance, the point is considered visible. Associated data attributes are passed to the output as well. This filter also allows you to specify a rectangular window in display (pixel) coordinates in which the visible points must lie. :param list area: specify a rectangular region as (xmin,xmax,ymin,ymax) :param float tol: a tolerance in normalized display coordinate system :param bool invert: select invisible points instead. :Example: .. code-block:: python from vtkplotter import Ellipsoid, show, visiblePoints s = Ellipsoid().rotateY(30) #Camera options: pos, focalPoint, viewup, distance, # clippingRange, parallelScale, thickness, viewAngle camopts = dict(pos=(0,0,25), focalPoint=(0,0,0)) show(s, camera=camopts, offscreen=True) m = visiblePoints(s) #print('visible pts:', m.points()) # numpy array show(m, newPlotter=True, axes=1) # optionally draw result """ # specify a rectangular region from vtkplotter import settings svp = vtk.vtkSelectVisiblePoints() svp.SetInputData(mesh.polydata()) svp.SetRenderer(settings.plotter_instance.renderer) if len(area)==4: svp.SetSelection(area[0],area[1],area[2],area[3]) if tol is not None: svp.SetTolerance(tol) if invert: svp.SelectInvisibleOn() svp.Update() m = Mesh(svp.GetOutput()).pointSize(5) return m