import numpy as np
vp1 = Plotter(shape=(1, 4), axes=4, bg="w")
act = vp1.load("data/shapes/bunny.obj").normalize().subdivide()
act.color("k").alpha(0.05).wire(True)
pts = act.coordinates(copy=True) # pts is a copy of the points not a reference
pts += (np.random.randn(len(pts), 3) / 40
) # add noise, will not mess up the original points
#################################### smooth cloud with MLS
# build the points actor
s0 = Points(pts, c="blue", r=3).legend("point cloud")
vp1.show(s0, at=0)
s1 = s0.clone().color("dg") # a dark green copy of s0
# project s1 points into a smooth surface of points
# return a demo actor showing 30 regressions at random points
mls1 = smoothMLS2D(s1, f=0.5, showNPlanes=30) # first pass
vp1.show(mls1, at=1, legend="first pass")
mls2 = smoothMLS2D(s1, f=0.3, showNPlanes=30) # second pass
vp1.show(mls2, at=2, legend="second pass")
mls3 = smoothMLS2D(s1, f=0.1) # third pass
vp1.show(s1, at=3, legend="third pass", zoom=1.3)
#################################### draw errors
vp2 = Plotter(pos=(200, 400), shape=(1, 2), axes=4, bg="w")
4. a triangular mesh is extracted from
this set of sparse Points, 'bins' is the
number of voxels of the subdivision
'''
from __future__ import division, print_function
from vtkplotter import Plotter, recoSurface, smoothMLS2D, Points, Text
import numpy as np
vp = Plotter(shape=(1, 5), axes=0)
vp.show(Text(__doc__), at=4)
act = vp.load('data/shapes/pumpkin.vtk')
vp.show(act, at=0)
noise = np.random.randn(act.N(), 3) * 0.05
act_pts0 = Points(act.coordinates() + noise, r=3) # add noise
act_pts1 = act_pts0.clone() # make a copy to modify
vp.show(act_pts0, at=1, legend='noisy cloud')
smoothMLS2D(act_pts1, f=0.4) # smooth cloud, input actor is modified
print('Nr of points before cleaning polydata:', act_pts1.N())
act_pts1.clean(tol=0.01) # impose a min distance among mesh points
print(' after cleaning polydata:', act_pts1.N())
vp.show(act_pts1, at=2, legend='smooth cloud')
act_reco = recoSurface(act_pts1, bins=128) # reconstructed from points
vp.show(act_reco, at=3, axes=7, interactive=1, legend='surf reco')
apos = Points(positions, r=2)
# for p in apos.points(): ####### Uncomment to fix some points.
# if abs(p[2]-5) > 4.999: # differences btw RBF and thinplate
# sources.append(p) # will become much smaller.
# deltas.append(np.zeros(3))
sources = np.array(sources)
deltas = np.array(deltas)
src = Points(sources, c="r", r=12)
trs = Points(sources + deltas, c="v", r=12)
arr = Arrows(sources, sources + deltas)
################################################# Thin Plate Splines
warped = apos.clone().thinPlateSpline(sources, sources + deltas)
warped.alpha(0.4).color("lg").pointSize(10)
allarr = Arrows(apos.points(), warped.points())
set1 = [apos, warped, src, trs, arr, __doc__]
vp = show([set1, allarr], N=2, bg='bb') # returns the Plotter class
################################################# RBF
from scipy.interpolate import Rbf
x, y, z = sources[:, 0], sources[:, 1], sources[:, 2]
dx, dy, dz = deltas[:, 0], deltas[:, 1], deltas[:, 2]
itrx = Rbf(x, y, z, dx) # Radial Basis Function interpolator:
itry = Rbf(x, y, z, dy) # interoplate the deltas in each separate
itrz = Rbf(x, y, z, dz) # cartesian dimension
'''
Using 1D Moving Least Squares to skeletonize a surface.
'''
print(__doc__)
from vtkplotter import Plotter, smoothMLS1D, Points
N=10 # nr of iterations
f=0.1 # fraction of neighbours
vp = Plotter(N=N, axes=0)
pts = vp.load('data/shapes/man.vtk').decimate(0.1).coordinates()
#pts = vp.load('data/shapes/spider.ply').coordinates()
#pts = vp.load('data/shapes/magnolia.vtk').subdivide().coordinates()
#pts = vp.load('data/shapes/pumpkin.vtk').coordinates()
#pts = vp.load('data/shapes/teapot.vtk').coordinates()
a = Points(pts)
for i in range(N):
vp.show(a, at=i, elevation=-5)
a = a.clone().color(i)
smoothMLS1D(a, f)
vp.show(interactive=1)