if sph is None: continue
value = sph.info['radius']*10
color = colorMap(value, name='jet') # map value to a RGB color
n = norm(p-sph.info['center']) # unit vector from sphere center to p
vals.append(value)
cols.append(color)
pts1.append(p)
pts2.append(p+n/8)
if not i%500:
print(i,'/',s.N())
vp.points(pts1, c=cols)
vp.addScalarBar()
vp.lines(pts1, pts2, c='black 0.2')
vp.histogram(vals, title='values', bins=20, vrange=[0,1])
vp.show()
# In this example we fit a plane to regions of a surface defined by
# N points that are closest to a given point of the surface.
# For some of these point we show the fitting plane.
# Blue points are the N points used for fitting.
# Green histogram is the distribution of residuals from the fitting.
# Both plane center and normal can be accessed from the
# attribute actor.center and actor.normal (direction is arbitrary).
#
from __future__ import division, print_function
from vtkplotter import Plotter
from vtkplotter.analysis import fitPlane
vp = Plotter(verbose=0, axes=0)
s = vp.load('data/shapes/cow.vtk').alpha(0.3).subdivide() # remesh
variances = []
for i, p in enumerate(s.coordinates()):
if i % 100: continue # skip most points
pts = s.closestPoint(p, N=12) # find the N closest points to p
plane = fitPlane(pts, bc='r', alpha=0.3) # find the fitting plane
vp.actors.append(plane)
vp.points(pts) # blue points
vp.point(p, c='red 0.2') # mark in red the current point
vp.arrow(plane.center, plane.center + plane.normal / 15, c='g')
variances.append(plane.variance)
vp.histogram(variances, title='variance', c='g')
vp.show()
# Green histogram is the distribution of residuals from the fitting.
# Red histogram is the distribution of the curvatures (1/r**2).
# Fitted radius can be accessed from attribute actor.radius
from __future__ import division, print_function
from vtkplotter import Plotter
from vtkplotter.analysis import fitSphere
vp = Plotter(verbose=0, axes=0)
# load mesh and increase by a lot (N=2) the nr of surface vertices
s = vp.load('data/shapes/cow.vtk').alpha(0.3).subdivide(N=2)
reds, invr = [], []
for i, p in enumerate(s.coordinates()):
if i % 1000: continue # skip most points
pts = s.closestPoint(p, N=16) # find the N closest points to p
sph = fitSphere(pts, alpha=0.05) # find the fitting sphere
if sph is None:
continue # may fail if all points sit on a plane
vp.actors.append(sph)
vp.points(pts)
vp.line(sph.info['center'], p, lw=2)
reds.append(sph.info['residue'])
invr.append(1 / sph.info['radius']**2)
vp.histogram(reds, title='residue', bins=12, c='g', corner=3)
vp.histogram(invr, title='1/r**2', bins=12, c='r', corner=4)
vp.show()
