analysis¶
Defines methods useful to analyse 3D meshes.
alignICP¶

vtkplotter.analysis.
alignICP
(source, target, iters=100, rigid=False)[source]¶ 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 leastsquare sense).
alignLandmarks¶
alignProcrustes¶

vtkplotter.analysis.
alignProcrustes
(sources, rigid=False)[source]¶ Return an
Assembly
of aligned source meshes with the Procrustes algorithm. The outputAssembly
is normalized in size.Procrustes algorithm takes N set of points and aligns them in a leastsquares sense to their mutual mean. The algorithm is iterated until convergence, as the mean must be recomputed after each alignment.
 Parameters
rigid (bool) – if True scaling is disabled.
booleanOperation¶
cluster¶
computeNormalsWithPCA¶

vtkplotter.analysis.
computeNormalsWithPCA
(mesh, n=20, orientationPoint=None, negate=False)[source]¶ Generate point normals using PCA (principal component analysis). Basically this estimates a local tangent plane around each sample point p by considering a small neighborhood of points around p, and fitting a plane to the neighborhood (via PCA).
 Parameters
n (int) – neighborhood size to calculate the normal
orientationPoint (list) – adjust the +/ sign of the normals so that the normals all point towards a specified point. If None, perform a traversal of the point cloud and flip neighboring normals so that they are mutually consistent.
negate (bool) – flip all normals
connectedPoints¶

vtkplotter.analysis.
connectedPoints
(mesh, radius, mode=0, regions=(), vrange=(0, 1), seeds=(), angle=0)[source]¶ 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.
 Parameters
radius (float) – radius variable specifying a local sphere used to define local point neighborhood
mode (int) –
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
regions (list) – a list of nonnegative regions id to extract
vrange (list) – scalar range to use to extract points based on scalar connectivity
seeds (list) – a list of nonnegative point seed ids
angle (list) – points are connected if the angle between their normals is within this angle threshold (expressed in degrees).
convexHull¶

vtkplotter.analysis.
convexHull
(mesh_or_list, alphaConstant=0)[source]¶ Create a 2D/3D Delaunay triangulation of input points.
 Parameters
mesh_or_list – can be either an
Mesh
or a list of 3D points.alphaConstant (float) – For a nonzero alpha value, only verts, edges, faces, or tetra contained within the circumsphere (of radius alpha) will be output. Otherwise, only tetrahedra will be output.
delaunay2D¶
delaunay3D¶
densifyCloud¶

vtkplotter.analysis.
densifyCloud
(mesh, targetDistance, closestN=6, radius=0, maxIter=None, maxN=None)[source]¶ 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.
dilateVolume¶

vtkplotter.analysis.
dilateVolume
(vol, neighbours=(2, 2, 2))[source]¶ Replace a voxel with the maximum over an ellipsoidal neighborhood of voxels. If neighbours of an axis is 1, no processing is done on that axis.
See example script: erode_dilate.py
erodeVolume¶

vtkplotter.analysis.
erodeVolume
(vol, neighbours=(2, 2, 2))[source]¶ Replace a voxel with the minimum over an ellipsoidal neighborhood of voxels. If neighbours of an axis is 1, no processing is done on that axis.
See example script: erode_dilate.py
euclideanDistanceVolume¶

vtkplotter.analysis.
euclideanDistanceVolume
(vol, anisotropy=False, maxDistance=None)[source]¶ Implementation of the Euclidean DT (Distance Transform) using Saito’s algorithm. The distance map produced contains the square of the Euclidean distance values. The algorithm has a O(n^(D+1)) complexity over nxnx…xn images in D dimensions.
Check out also: https://en.wikipedia.org/wiki/Distance_transform
 Parameters
See example script: euclDist.py
extractCellsByType¶
extractLargestRegion¶
extractSurface¶
extrude¶

vtkplotter.analysis.
extrude
(mesh, zshift=1, rotation=0, dR=0, cap=True, res=1)[source]¶ Sweep a polygonal data creating a “skirt” from free edges and lines, and lines from vertices. The input dataset is swept around the zaxis to create new polygonal primitives. For example, sweeping a line results in a cylindrical shell, and sweeping a circle creates a torus.
You can control whether the sweep of a 2D object (i.e., polygon or triangle strip) is capped with the generating geometry. Also, you can control the angle of rotation, and whether translation along the zaxis is performed along with the rotation. (Translation is useful for creating “springs”). You also can adjust the radius of the generating geometry using the “dR” keyword.
The skirt is generated by locating certain topological features. Free edges (edges of polygons or triangle strips only used by one polygon or triangle strips) generate surfaces. This is true also of lines or polylines. Vertices generate lines.
This filter can be used to model axisymmetric objects like cylinders, bottles, and wine glasses; or translational/rotational symmetric objects like springs or corkscrews.
Warning
Some polygonal objects have no free edges (e.g., sphere). When swept, this will result in two separate surfaces if capping is on, or no surface if capping is off.
fitLine¶
fitPlane¶

vtkplotter.analysis.
fitPlane
(points)[source]¶ Fits a plane to a set of points.
Extra info is stored in
mesh.info['normal']
,mesh.info['center']
,mesh.info['variance']
.Hint
Example: fitplanes.py
fitSphere¶
frequencyPassFilter¶

vtkplotter.analysis.
frequencyPassFilter
(volume, lowcutoff=None, highcutoff=None, order=1)[source]¶ Lowpass and highpass filtering become trivial in the frequency domain. A portion of the pixels/voxels are simply masked or attenuated. This function applies a high pass Butterworth filter that attenuates the frequency domain image with the function
The gradual attenuation of the filter is important. A simple highpass filter would simply mask a set of pixels in the frequency domain, but the abrupt transition would cause a ringing effect in the spatial domain.
 Parameters
Check out also this example:
geodesic¶
implicitModeller¶
interpolateToStructuredGrid¶

vtkplotter.analysis.
interpolateToStructuredGrid
(mesh, kernel=None, radius=None, bounds=None, nullValue=None, dims=None)[source]¶ 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.
interpolateToVolume¶

vtkplotter.analysis.
interpolateToVolume
(mesh, kernel='shepard', radius=None, bounds=None, nullValue=None, dims=(20, 20, 20))[source]¶ 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. Parameters
mesh2Volume¶
meshQuality¶

vtkplotter.analysis.
meshQuality
(mesh, measure=6)[source]¶ Calculate functions of quality of the elements of a triangular mesh. See class vtkMeshQuality for explanation.
 Parameters
measure (int) –
type of estimator
EDGE_RATIO, 0
ASPECT_RATIO, 1
RADIUS_RATIO, 2
ASPECT_FROBENIUS, 3
MED_ASPECT_FROBENIUS, 4
MAX_ASPECT_FROBENIUS, 5
MIN_ANGLE, 6
COLLAPSE_RATIO, 7
MAX_ANGLE, 8
CONDITION, 9
SCALED_JACOBIAN, 10
SHEAR, 11
RELATIVE_SIZE_SQUARED, 12
SHAPE, 13
SHAPE_AND_SIZE, 14
DISTORTION, 15
MAX_EDGE_RATIO, 16
SKEW, 17
TAPER, 18
VOLUME, 19
STRETCH, 20
DIAGONAL, 21
DIMENSION, 22
ODDY, 23
SHEAR_AND_SIZE, 24
JACOBIAN, 25
WARPAGE, 26
ASPECT_GAMMA, 27
AREA, 28
ASPECT_BETA, 29
normalLines¶
pcaEllipsoid¶

vtkplotter.analysis.
pcaEllipsoid
(points, pvalue=0.95, pcaAxes=False)[source]¶ Show the oriented PCA ellipsoid that contains fraction pvalue of points.
 Parameters
Extra info is stored in
mesh.info['sphericity']
,mesh.info['va']
,mesh.info['vb']
,mesh.info['vc']
(sphericity is equal to 0 for a perfect sphere).
pointCloudFrom¶
pointDensity¶

vtkplotter.analysis.
pointDensity
(mesh, dims=(30, 30, 30), bounds=None, radius=None, computeGradient=False)[source]¶ 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
pointSampler¶
probeLine¶
probePlane¶
probePoints¶
projectSphereFilter¶
recoSurface¶
rectilinearGridToTetrahedra¶

vtkplotter.analysis.
rectilinearGridToTetrahedra
(rgrid, tetraPerCell=6)[source]¶ 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.
removeOutliers¶
resampleArrays¶

vtkplotter.analysis.
resampleArrays
(source, target, tol=None)[source]¶ 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.
 Parameters
tol (float) – 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.
signedDistanceFromPointCloud¶
smoothMLS1D¶
smoothMLS2D¶
smoothMLS3D¶

vtkplotter.analysis.
smoothMLS3D
(meshs, neighbours=10)[source]¶ 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. Parameters
neighbours (int) – fixed nr. of neighbours in spacetime to take into account in the fit.
splitByConnectivity¶
streamLines¶

vtkplotter.analysis.
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)[source]¶ Integrate a vector field to generate streamlines.
The integration is performed using a specified integrator (RungeKutta). 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.
 Parameters
domain – the vtk object that contains the vector field
activeVectors (str) – name of the vector array
probe (Mesh,list) – the Mesh that probes the domain. Its coordinates will be the seeds for the streamlines, can also be an array of positions.
integrator (str) – RungeKutta integrator, either ‘rk2’, ‘rk4’ of ‘rk45’
initialStepSize (float) – initial step size of integration
maxPropagation (float) – maximum physical length of the streamline
maxSteps (int) – maximum nr of steps allowed
stepLength (float) – length of step integration.
extrapolateToBoundingBox (dict) –
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
surfaceConstrain (bool) – force streamlines to be computed on a surface
computeVorticity (bool) – Turn on/off vorticity computation at streamline points (necessary for generating proper streamribbons)
ribbons (int) – render lines as ribbons by joining them. An integer value represent the ratio of joining (e.g.: ribbons=2 groups lines 2 by 2)
tubes (dict) –
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
scalarRange (list) – specify the scalar range for coloring
surfaceIntersection¶
thinPlateSpline¶

vtkplotter.analysis.
thinPlateSpline
(mesh, sourcePts, targetPts, userFunctions=(None, None), sigma=1)[source]¶ Thin Plate Spline transformations describe a nonlinear warp transform defined by a set of source and target landmarks. Any point on the mesh close to a source landmark will be moved to a place close to the corresponding target landmark. The points in between are interpolated smoothly using Bookstein’s Thin Plate Spline algorithm.
Transformation object can be retrieved with
mesh.getTransform()
. Parameters
userFunctions – You may supply both the function and its derivative with respect to r.
volumeCorrelation¶

vtkplotter.analysis.
volumeCorrelation
(vol1, vol2, dim=2)[source]¶ Find the correlation between two volumetric data sets. Keyword dim determines whether the correlation will be 3D, 2D or 1D. The default is a 2D Correlation. The output size will match the size of the first input. The second input is considered the correlation kernel.
volumeFromMesh¶

vtkplotter.analysis.
volumeFromMesh
(mesh, bounds=None, dims=(20, 20, 20), signed=True, negate=False)[source]¶ 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. Parameters
See example script: volumeFromMesh.py
volumeOperation¶

vtkplotter.analysis.
volumeOperation
(volume1, operation, volume2=None)[source]¶ Perform operations with
Volume
objects.volume2 can be a constant value.
Possible operations are:
+
,
,/
,1/x
,sin
,cos
,exp
,log
,abs
,**2
,sqrt
,min
,max
,atan
,atan2
,median
,mag
,dot
,gradient
,divergence
,laplacian
.
volumeToPoints¶

vtkplotter.analysis.
volumeToPoints
(vol)[source]¶ Extract all image voxels as points. This function takes an input
Volume
and creates anMesh
that contains the points and the point attributes.See example script: vol2points.py
voronoi3D¶
warpMeshToPoint¶

vtkplotter.analysis.
warpMeshToPoint
(mesh, point, factor=0.1, absolute=True)[source]¶ Modify the mesh coordinates by moving the vertices towards a specified point.
 Parameters
 Example
from vtkplotter import * s = Cylinder(height=3).wireframe(1) pt = [4,0,0] w = warpMeshToPoint(s, pt, factor=0.5).wireframe(0) show(w,s, Point(pt), axes=1, bg='w')