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

vedo.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¶

vedo.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.
The set of average points generated by the algorithm can be accessed with
algoutput.info['mean']
as a numpy array. Parameters
rigid (bool) – if True scaling is disabled.
booleanOperation¶
cluster¶
connectedPoints¶

vedo.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).
delaunay2D¶
densifyCloud¶

vedo.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.
fitLine¶
fitPlane¶

vedo.analysis.
fitPlane
(points)[source]¶ Fits a plane to a set of points.
Extra info is stored in
Plane.normal
,Plane.center
,Plane.variance
.Hint
Example: fitplanes.py
fitSphere¶
geodesic¶
implicitModeller¶
interpolateToStructuredGrid¶

vedo.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¶

vedo.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¶
normalLines¶
pcaEllipsoid¶

vedo.analysis.
pcaEllipsoid
(points, pvalue=0.95)[source]¶ Show the oriented PCA ellipsoid that contains fraction pvalue of points.
 Parameters
pvalue (float) – 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 inmesh.axis1
,mesh.axis12
andmesh.axis3
.
pointCloudFrom¶
pointDensity¶

vedo.analysis.
pointDensity
(mesh, dims=40, 40, 40, 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¶

vedo.analysis.
probeLine
(vol, p1, p2, res=100)[source]¶ 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’).
 Parameters
res (int) – nr of points along the line
probePlane¶
probePoints¶
projectSphereToPlane¶
recoSurface¶

vedo.analysis.
recoSurface
(pts, dims=250, 250, 250, radius=None, sampleSize=None, holeFilling=True, bounds=(), pad=0.1)[source]¶ Surface reconstruction from a scattered cloud of points.
 Parameters
dims (int) – number of voxels in x, y and z to control precision.
radius (float) – 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.
sampleSize (int) – if normals are not present they will be calculated using this sample size per point.
holeFilling (bool) – enables hole filling, this generates separating surfaces between the empty and unseen portions of the volume.
bounds (list) – region in space in which to perform the sampling in format (xmin,xmax, ymin,ymax, zim, zmax)
pad (float) – increase by this fraction the bounding box
removeOutliers¶
resampleArrays¶

vedo.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¶
smoothMLS3D¶

vedo.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.
streamLines¶

vedo.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 on a domain (a Mesh or other vtk datasets types) 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¶
visiblePoints¶

vedo.analysis.
visiblePoints
(mesh, area=(), tol=None, invert=False)[source]¶ Extract points based on whether they are visible or not. Visibility is determined by accessing the zbuffer of a rendering window. The position of each input point is converted into display coordinates, and then the zvalue at that point is obtained. If within the userspecified 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.
 Parameters
 Example
from vedo 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
volumeFromMesh¶

vedo.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