'''
Work with vtkVolume objects and surface meshes
in the same rendering window.
'''
from vtkplotter import loadImageData, Plotter, Volume, Sphere, Text
vp = Plotter(axes=0, verbose=0, bg='w')
# Load a 3D voxel dataset (returns a vtkImageData object):
img = loadImageData('data/embryo.slc', spacing=[1, 1, 1])
# Build a vtkVolume object.
# A set of transparency values - of any length - can be passed
# to define the opacity transfer function in the range of the scalar.
# E.g.: setting alphas=[0, 0, 0, 1, 0, 0, 0] would make visible
# only voxels with value close to 98.5 (see print output).
vol = Volume(img, c='green', alphas=[0, 0.4, 0.9, 1]) # vtkVolume
# can relocate volume in space:
#vol.scale(0.3).pos([10,100,0]).rotate(90, axis=[0,1,1])
sph = Sphere(pos=[100, 100, 100], r=20) # add a dummy surface
doc = Text(__doc__, c='k')
# show both vtkVolume and vtkActor
vp.show([vol, sph, doc], zoom=1.4)
# Perform other simple mathematical operation between 3d images.
# Possible operations are: +, -, /, 1/x, sin, cos, exp, log, abs, **2, sqrt,
# min, max, atan, atan2, median, mag, dot, gradient, divergence, laplacian.
# Alphas defines the opacity transfer function in the scalar range.
#
from vtkplotter import Plotter, loadImageData
from vtkplotter import imageOperation, Volume
vp = Plotter(N=8, axes=4)
img0 = loadImageData('data/embryo.slc') # vtkImageData object
v0 = Volume(img0, c=0) # build a vtk.vtkVolume derived object
vp.show(v0, at=0)
img1 = imageOperation(img0, 'gradient')
img1 = imageOperation(img1, '+', 92.0)
v1 = Volume(img1, c=1, alphas=[0, 1, 0, 0, 0])
vp.show(v1, at=1)
img2 = imageOperation(img0, 'divergence')
v2 = Volume(img2, c=2)
vp.show(v2, at=2)
img3 = imageOperation(img0, 'laplacian')
v3 = Volume(img3, c=3, alphas=[0, 1, 0, 0, 1])
vp.show(v3, at=3)
img4 = imageOperation(img0, 'median')
v4 = Volume(img4, c=4)
vp.show(v4, at=4)
""" Perform other simple mathematical operation between 3d images. Possible operations are: +, -, /, 1/x, sin, cos, exp, log, abs, **2, sqrt, min, max, atan, atan2, median, mag, dot, gradient, divergence, laplacian. Alphas defines the opacity transfer function in the scalar range. """ print(__doc__) from vtkplotter import Plotter, loadImageData from vtkplotter import imageOperation, Volume, datadir vp = Plotter(N=8, axes=4, bg="w") img0 = loadImageData(datadir + "embryo.slc") # vtkImageData object v0 = Volume(img0, c=0) # build a vtk.vtkVolume derived object vp.show(v0, at=0) img1 = imageOperation(img0, "gradient") img1 = imageOperation(img1, "+", 92.0) v1 = Volume(img1, c=1, alphas=[0, 1, 0, 0, 0]) vp.show(v1, at=1) img2 = imageOperation(img0, "divergence") v2 = Volume(img2, c=2) vp.show(v2, at=2) img3 = imageOperation(img0, "laplacian") v3 = Volume(img3, c=3, alphas=[0, 1, 0, 0, 1]) vp.show(v3, at=3) img4 = imageOperation(img0, "median")
'''
Intersect a vtkImageData (voxel dataset) with planes.
'''
from vtkplotter import show, loadImageData, probeLine, vector, Text
img = loadImageData('data/embryo.slc')
pos = img.GetCenter()
lines = []
for i in range(60): # probe scalars on 60 parallel lines
step = (i - 30) * 2
p1, p2 = pos + vector(-100, step, step), pos + vector(100, step, step)
a = probeLine(img, p1, p2, res=200)
a.alpha(0.5).lineWidth(6)
lines.append(a)
#print(a.scalars(0)) # numpy scalars can be access here
#print(a.scalars('vtkValidPointMask')) # the mask of valid points
show(lines + [Text(__doc__)], axes=4, verbose=0, bg='w')
"""
Intersect a vtkImageData (voxel dataset) with planes
"""
from vtkplotter import show, loadImageData, probePlane, vector, Text, datadir
img = loadImageData(datadir + "embryo.slc")
planes = []
for i in range(6):
print("probing slice plane #", i)
pos = img.GetCenter() + vector(0, 0, (i - 3) * 25.0)
a = probePlane(img, origin=pos, normal=(0, 0, 1)).alpha(0.2)
planes.append(a)
# print(max(a.scalars(0))) # access scalars this way, 0 means first
show(planes + [Text(__doc__)], axes=4, verbose=0, bg="w")
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from vtkplotter import loadImageData, Volume, isosurface, show, datadir
maxradius = 0.2
neurons = 30
epochs = 20
image = loadImageData(datadir + "embryo.tif")
vmin, vmax = image.GetScalarRange()
nx, ny, nz = image.GetDimensions()
print("Scalar Range:", vmin, vmax, "Dimensions", image.GetDimensions())
visdata = np.zeros([nx, ny, nz])
datalist, scalars = [], []
lsx = np.linspace(0, 1, nx, endpoint=False)
lsy = np.linspace(0, 1, ny, endpoint=False)
lsz = np.linspace(0, 1, nz, endpoint=False)
for i, x in enumerate(lsx):
for j, y in enumerate(lsy):
for k, z in enumerate(lsz):
s = image.GetScalarComponentAsDouble(i, j, k, 0)
s = (s - vmin) / (vmax - vmin)
visdata[i, j, k] = s
datalist.append([x, y, z])
scalars.append(s)
datalist = np.array(datalist)
scalars = np.array(scalars)