# Align 2 shapes and for each vertex of the first draw
# and arrow to the closest point of the second.
# The source transformation is saved in actor.transform
# rigid=True doesn't allow scaling
#
from vtkplotter import Plotter, printc, mag2
from vtkplotter.analysis import align
vp = Plotter(verbose=0, axes=4)
limb = vp.load('data/270.vtk', alpha=0.3)
rim = vp.load('data/270_rim.vtk')
rim.color('r').lineWidth(4)
arim = align(rim, limb, iters=100, rigid=True)
arim.color('g').lineWidth(4)
vp.actors.append(arim)
d = 0
prim = arim.coordinates()
for p in prim:
cpt = limb.closestPoint(p)
vp.arrow(p, cpt, c='g')
d += mag2(p - cpt) # square of residual distance
printc('ave. squared distance =', d / len(prim), c='g')
vp.show()
a1 = vp.load('data/beethoven.ply', alpha=1)
coords1 = a1.coordinates()
pts1 = vp.points(coords1, r=4, c='g', legend='#points = ' + str(len(coords1)))
vp.show([a1, pts1], at=0)
a2 = a1.subdivide(method=0) # Increasing the number of points of the mesh
coords2 = a2.coordinates()
pts2 = vp.points(coords2, r=1, legend='#points = ' + str(len(coords2)))
vp.show([a2, pts2], at=1, interactive=True)
########################################################################################
# Draw a bunch of simple objects on separate parts of the rendering window:
# split window to best accomodate 9 renderers
vp = Plotter(N=9, title='basic shapes')
vp.sharecam = False # each object can be moved independently
vp.show(at=0, actors=vp.arrow([0, 0, 0], [1, 1, 1]), legend='arrow')
vp.show(at=1, actors=vp.line([0, 0, 0], [1, 1, 1]), legend='line')
vp.show(at=2, actors=vp.points([[0, 0, 0], [1, 1, 1]]), legend='points')
vp.show(at=3, actors=vp.text('Hello!'))
vp.show(at=4, actors=vp.sphere())
vp.show(at=5, actors=vp.cube(), legend='cube')
vp.show(at=6, actors=vp.ring(), legend='ring')
vp.show(at=7, actors=vp.helix(), legend='helix')
vp.show(at=8, actors=vp.cylinder(), legend='cylinder', interactive=1)
########################################################################################
# Draw a bunch of objects from various mesh formats. Loading is automatic.
vp = Plotter(shape=(3, 3),
title='mesh formats') # split window in 3 rows and 3 columns
vp.sharecam = False # each object can be moved independently
vp.show('data/beethoven.ply', at=0, c=0, axes=0,
# 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 plane.info['center'] and plane.info['normal'].
#
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
cn, v = plane.info['center'], plane.info['normal']
vp.arrow(cn, cn + v / 15, c='g')
variances.append(plane.info['variance'])
vp.histogram(variances, title='variance', c='g')
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()
# Retrieve the vtk transformation matrix.
#
from __future__ import division, print_function
from random import uniform as u
from vtkplotter import Plotter
from vtkplotter.analysis import align
vp = Plotter(shape=[1,2], verbose=0, axes=2)
N1 = 15 # number of points of first set
N2 = 10 # number of points of second set
x = 1. # add some randomness
pts1 = [ (u(0,x), u(0,x), u(0,x)+i) for i in range(N1) ]
pts2 = [ (u(0,x)+3, u(0,x)+i/2+2, u(0,x)+i+1) for i in range(N2) ]
act1 = vp.points(pts1, c='b', legend='source')
act2 = vp.points(pts2, c='r', legend='target')
vp.show(at=0)
# find best alignment between the 2 sets of points
alpts1 = align(act1, act2).coordinates()
for i in range(N1): #draw arrows to see where points end up
vp.arrow(pts1[i], alpts1[i], c='k', s=0.007, alpha=.1)
vp.show(at=1, interactive=1)
