def load_labelled_volume(data, vmin=0, alpha=1, **kwargs):
"""
Load volume image from .nrrd file.
It assume that voxels with value = 0 are empty while voxels with values > 0
are labelles (e.g. to indicate the location of a brain region in a reference atlas)
:param data: str, path to file with volume data or 3d numpy array
:param vmin: float, values below this numner will be assigned an alpha=0 and not be visualized
:param **kwargs: kwargs to pass to the Volume class from vtkplotter
:param alpha: float in range [0, 1], transparency [for the part of volume with value > vmin]
"""
# Load/check volumetric data
if isinstance(data, str): # load from file
if not os.path.isfile(data):
raise FileNotFoundError(f'Volume data file {data} not found')
try:
data = brainio.load_any(data)
except:
raise ValueError(f"Could not load volume data from file: {data}")
elif not isinstance(data, np.ndarray):
raise ValueError(
f"Data should be a filepath or np array, not: {data.__type__}")
# Create volume and set transparency range
vol = Volume(data, alpha=alpha, **kwargs)
otf = vol.GetProperty().GetScalarOpacity()
otf.RemoveAllPoints()
otf.AddPoint(vmin, 0) # set to transparent
otf.AddPoint(vmin + .1, alpha) # set to opaque
otf.AddPoint(data.max(), alpha)
return vol
def add_volume(self,
volume,
cmap='afmhot_r',
alpha=1,
add_colorbar=True,
**kwargs):
"""
Renders intensitdata from a 3D numpy array as a lego volumetric actor.
:param volume: np 3D array with number of dimensions = those of the 100um reference space.
:param cmap: str with name of colormap to use
:param alpha: float, transparency
:param add_colorbar: if True a colorbar is added to show the values of the colormap
"""
# Parse kwargs
line_width = kwargs.pop('line_width', 1)
if cmap == 'random' or not cmap or cmap is None:
cmap = get_random_colormap()
# Get vmin and vmax threshold for visualisation
vmin = kwargs.pop('vmin', 0.000001)
vmax = kwargs.pop('vmax', np.nanmax(volume))
# Check values
if np.max(volume) > vmax:
print(
"While rendering mapped projection some of the values are above the vmax threshold."
+ "They will not be displayed." +
f" vmax was {vmax} but found value {round(np.max(volume), 5)}."
)
if vmin > vmax:
raise ValueError(
f'The vmin threhsold [{vmin}] cannot be larger than the vmax threshold [{vmax}'
)
if vmin < 0: vmin = 0
# Get 'lego' actor
vol = Volume(volume)
lego = vol.legosurface(vmin=vmin, vmax=vmax, cmap=cmap)
# Scale and color actor
lego.alpha(alpha).lw(line_width).scale(self.voxel_size)
lego.cmap = cmap
# Add colorbar
if add_colorbar:
lego.addScalarBar(vmin=vmin,
vmax=vmax,
horizontal=1,
c='k',
pos=(0.05, 0.05),
titleFontSize=40)
# Add to scene
actor = self.scene.add_vtkactor(lego)
return actor
def load_volume_file(filepath, **kwargs):
"""
Load a volume file (e.g., .nii) and return vtk actor
:param filepath: path to file
:param **kwargs:
"""
from vtkplotter import Volume, load
if not os.path.isfile(filepath): raise FileNotFoundError(filepath)
if ".x3d" in filepath.lower():
raise ValueError(
"brainrender cannot use .x3d data as they are not supported by vtkplotter"
)
elif "nii" in filepath.lower() or ".label" in filepath.lower():
import nibabel as nb
data = nb.load(filepath)
d = data.get_fdata()
act = Volume(d, **kwargs)
else:
act = load(filepath, **kwargs)
if act is None:
raise ValueError("Could not load {}".format(filepath))
return act
def load_mesh_from_file(filepath, *args, **kwargs):
"""
Load a a mesh or volume from files like .obj, .stl, ...
:param filepath: path to file
:param **kwargs:
"""
if not os.path.isfile(filepath):
raise FileNotFoundError(filepath)
try:
actor = load(filepath, *args, **kwargs)
except:
actor = Volume(load_volume_file(filepath, *args, **kwargs))
return actor
# Create the reader for the data.
reader = vtk.vtkXMLImageDataReader()
reader.SetFileName(datadir+"vase.vti")
reader.Update()
img = reader.GetOutput() # vtkImageData object
# specify the data array in the file to process
# img.GetPointData().SetActiveAttribute('SLCImage', 0)
# NB: all the above lines could be reduced to:
#img = load(datadir+"vase.vti").imagedata()
#################################
from vtkplotter import Volume, show, Text
# can set colors and transparencies along the scalar range
# from minimum to maximum value. In this example voxels with
# the smallest value will be completely transparent (and white)
# while voxels with highest value of the scalar will get alpha=0.8
# and color will be=(0,0,1)
vol1 = Volume(img,
mode=0, # composite rendering
c=["white", "fuchsia", "dg", (0,0,1)],
alpha=[0.0, 0.2, 0.3, 0.8])
# mode = 1 is maximum-projection volume rendering
vol2 = load(datadir+"vase.vti").mode(1).addPos(60,0,0)
# show command creates and returns an instance of class Plotter
show(vol1, vol2, Text(__doc__), bg="w", axes=1)
model.add(Dense(neurons, activation="relu"))
model.add(Dense(neurons, activation="relu"))
model.add(Dense(1, activation="sigmoid"))
model.compile(optimizer="rmsprop", loss="mse", metrics=["mae"])
model.fit(datalist, scalars, epochs=50, batch_size=64)
predicted_scalars = model.predict(datalist)
model.summary()
idx = 0
vispred = np.zeros([n, n, n])
for i, x in enumerate(ls):
for j, y in enumerate(ls):
for k, z in enumerate(ls):
vispred[i, j, k] = predicted_scalars[idx]
idx += 1
v1 = Volume(visdata)
v2 = Volume(vispred)
s1 = v1.isosurface(threshold=[t for t in arange(0, 1, 0.1)])
s1.alpha(0.5)
s2 = v2.isosurface(threshold=[t for t in arange(0, 1, 0.1)])
s2.alpha(0.5)
show([[v1, s1], s2], N=2, axes=8)
model = Sequential()
model.add(Dense(neurons, activation="relu", input_dim=3))
model.add(Dense(neurons, activation="relu"))
model.add(Dense(neurons, activation="relu"))
model.add(Dense(1, activation="sigmoid"))
model.compile(optimizer="rmsprop", loss="mse", metrics=["mae"])
model.fit(shuffled_datalist,
shuffled_scalars,
epochs=epochs,
batch_size=max(nx, ny, nz))
predicted_scalars = model.predict(datalist)
model.summary()
idx = 0
vispred = np.zeros([nx, ny, nz])
for i, x in enumerate(lsx):
for j, y in enumerate(lsy):
for k, z in enumerate(lsz):
vispred[i, j, k] = predicted_scalars[idx]
idx += 1
v1 = Volume(visdata)
v2 = Volume(vispred)
s1 = v1.isosurface(threshold=0).alpha(0.8)
s2 = v2.isosurface(threshold=0).alpha(0.8)
show(v1, v2, s1, s2, N=4, axes=8, bg="w")
iteration = 4
size = 3**iteration
voxels = np.ones((size, size, size))
def iterate(length, x, y, z):
nl = length // 3
if nl < 1: return
margin = (nl - 1) // 2
voxels[z - margin:z + margin + 1, y - margin:y + margin + 1, :] = 0
voxels[z - margin:z + margin + 1, :, x - margin:x + margin + 1] = 0
voxels[:, y - margin:y + margin + 1, x - margin:x + margin + 1] = 0
for ix, iy, iz in np.ndindex((3, 3, 3)):
if (1 if ix != 1 else 0) + (1 if iy != 1 else 0) + (1 if iz != 1 else
0) != 2:
iterate(nl, x + (ix - 1) * nl, y + (iy - 1) * nl,
z + (iz - 1) * nl)
iterate(size, size // 2, size // 2, size // 2)
print('voxels min, max =', np.min(voxels), np.max(voxels))
vol = Volume(voxels)
lego = vol.legosurface(-0.1, 1.1, cmap='afmhot_r')
show(vol, lego, N=2, bg='w')
scals = np.abs(coords[:, 2]) # let the scalar be the z of point itself
fact = 1. / (bins - 1) # conversion factor btw the 2 ranges
vp = Plotter(verbose=0)
vp.ztitle = 'z == scalar value'
cloud = vp.points(coords)
# fill the vtkImageData object
pb = ProgressBar(0, bins, c=4)
for iz in pb.range():
pb.print()
for iy in range(bins):
for ix in range(bins):
pt = vector(ix, iy, iz) * fact
closestPointsIDs = cloud.closestPoint(pt, N=5, returnIds=True)
num, den = 0, 0
for i in closestPointsIDs: # work out RBF manually on N points
invdist = 1 / (mag2(coords[i] - pt) + 1e-06)
num += scals[i] * invdist
den += invdist
img.SetScalarComponentFromFloat(ix, iy, iz, 0, num / den)
#vp.write(img, 'imgcube.tif') # or .vti
# set colors and transparencies along the scalar range
vol = Volume(img, c=['r', 'g', 'b'], alphas=[0.4, 0.8]) #vtkVolume
act = vp.points(coords / fact)
vp.show([vol, act], viewup='z')
model = Sequential()
model.add(Dense(neurons, activation="relu", input_dim=3))
model.add(Dense(neurons, activation="relu"))
model.add(Dense(neurons, activation="relu"))
model.add(Dense(1, activation="sigmoid"))
model.compile(optimizer="rmsprop", loss="mse", metrics=["mae"])
model.fit(shuffled_datalist,
shuffled_scalars,
epochs=epochs,
batch_size=max(nx, ny, nz))
predicted_scalars = model.predict(datalist)
model.summary()
idx = 0
vispred = np.zeros([nx, ny, nz])
for i, x in enumerate(lsx):
for j, y in enumerate(lsy):
for k, z in enumerate(lsz):
vispred[i, j, k] = predicted_scalars[idx]
idx += 1
v1 = Volume(visdata)
v2 = Volume(vispred)
s1 = isosurface(v1, threshold=0).alpha(0.8)
s2 = isosurface(v2, threshold=0).alpha(0.8)
show([v1, v2, s1, s2], N=4, axes=8, bg="w")
"""Create a Volume from a numpy array""" import numpy as np data_matrix = np.zeros([75, 75, 75], dtype=np.uint8) # all voxels have value zero except: data_matrix[0:35, 0:35, 0:35] = 1 data_matrix[35:55, 35:55, 35:55] = 2 data_matrix[55:74, 55:74, 55:74] = 3 from vtkplotter import Volume, Text2D, show vol = Volume(data_matrix, c=['white', 'b', 'g', 'r']) show(vol, Text2D(__doc__), axes=1)
'''
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)
"""
Generate the isosurfaces corresponding to a set of thresholds.
(These surfaces are not separate meshes).
"""
from vtk import vtkQuadric, vtkSampleFunction
# Quadric definition. This is a type of implicit function.
quadric = vtkQuadric()
quadric.SetCoefficients(0.5, 1, 0.2, 0, 0.1, 0, 0, 0.2, 0, 0)
# the vtkSampleFunction evaluates quadric over a volume
sample = vtkSampleFunction()
sample.SetSampleDimensions(40, 40, 40)
sample.SetImplicitFunction(quadric)
sample.Update()
img = sample.GetOutput() # vtkImageData
print("Scalar Range", img.GetScalarRange(), "\ntry press shift-x.")
########################
from vtkplotter import show, Text, Volume
# generate an isosurface the volume for each thresholds
ts = [0.1, 0.25, 0.4, 0.6, 0.75, 0.9]
# Use c=None to use the default vtk color map. isos is of type Actor
isos = Volume(img).isosurface(threshold=ts)
show(isos, Text(__doc__))
# Using normal vtk commands to load a xml vti file
# then use vtkplotter to show the resulting 3d image.
#
import vtk
# Create the reader for the data.
reader = vtk.vtkXMLImageDataReader()
reader.SetFileName('data/vase.vti')
reader.Update()
img = reader.GetOutput()
# specify the data array in the file to process
#img.GetPointData().SetActiveAttribute('SLCImage', 0)
#################################
from vtkplotter import Volume, load, show
# can set colors and transparencies along the scalar range
vol = Volume(img,
c=['gray', 'fuchsia', 'dg', (0, 0, 1)],
alphas=[0.1, 0.2, 0.3, 0.8])
# load command returns an isosurface (vtkActor) of the 3d image
iso = load('data/vase.vti', threshold=140).wire(True).alpha(0.1)
# show command creates and returns an instance of class Plotter
show([vol, iso], verbose=0)
"""
Using normal vtk commands to load a xml vti file
then use vtkplotter to show the resulting 3d image.
"""
import vtk
from vtkplotter import datadir
# Create the reader for the data.
reader = vtk.vtkXMLImageDataReader()
reader.SetFileName(datadir + "vase.vti")
reader.Update()
img = reader.GetOutput()
# specify the data array in the file to process
# img.GetPointData().SetActiveAttribute('SLCImage', 0)
#################################
from vtkplotter import Volume, load, show, Text
# can set colors and transparencies along the scalar range
vol = Volume(img,
c=["gray", "fuchsia", "dg", (0, 0, 1)],
alpha=[0.1, 0.2, 0.3, 0.8])
# load command returns an isosurface (vtkActor) of the 3d image
iso = load(datadir + "vase.vti", threshold=140).wire(True).alpha(0.1)
# show command creates and returns an instance of class Plotter
show(vol, iso, Text(__doc__), bg="w")
"""Create a Volume from a numpy array""" import numpy as np data_matrix = np.zeros([75, 75, 75], dtype=np.uint8) # all voxels have value zero except: data_matrix[0:35, 0:35, 0:35] = 1 data_matrix[35:55, 35:55, 35:55] = 2 data_matrix[55:74, 55:74, 55:74] = 3 from vtkplotter import Volume, show vol = Volume(data_matrix, c=['white', 'b', 'g', 'r']) vol.addScalarBar3D() show(vol, __doc__, axes=1)
def addCutterTool(actor):
"""Create handles to cut away parts of a mesh.
.. hint:: |cutter| |cutter.py|_
"""
if isinstance(actor, vtk.vtkVolume):
return _addVolumeCutterTool(actor)
elif isinstance(actor, vtk.vtkImageData):
from vtkplotter import Volume
return _addVolumeCutterTool(Volume(actor))
vp = settings.plotter_instance
if not vp.renderer:
save_int = vp.interactive
vp.show(interactive=0)
vp.interactive = save_int
vp.clickedActor = actor
apd = actor.polydata()
planes = vtk.vtkPlanes()
planes.SetBounds(apd.GetBounds())
clipper = vtk.vtkClipPolyData()
clipper.GenerateClipScalarsOn()
clipper.SetInputData(apd)
clipper.SetClipFunction(planes)
clipper.InsideOutOn()
clipper.GenerateClippedOutputOn()
clipper.Update()
act0 = Actor(clipper.GetOutput())
# act0.mapper = actor.mapper
# act0.mapper.Modified()
# print(actor.mapper.GetLookupTable())
# print(actor.polydata().GetPointData().GetScalars())
# print(act0.polydata().GetCellData().GetScalars().GetRange())
# print(act0.mapper.GetScalarRange())
# act0.mapper.SetScalarRange(act0.polydata().GetCellData().GetScalars().GetRange())
# act0.cellColors('pointColors_seismic', vmin=40, vmax=130)
act1Mapper = vtk.vtkPolyDataMapper() # the part which is cut away
act1Mapper.SetInputConnection(
clipper.GetClippedOutputPort()) # needs OutputPort??
act1 = vtk.vtkActor()
act1.SetMapper(act1Mapper)
act1.GetProperty().SetOpacity(0.02)
act1.GetProperty().SetRepresentationToWireframe()
act1.VisibilityOn()
vp.renderer.AddActor(act0)
vp.renderer.AddActor(act1)
vp.renderer.RemoveActor(actor)
def SelectPolygons(vobj, event):
vobj.GetPlanes(planes)
boxWidget = vtk.vtkBoxWidget()
boxWidget.OutlineCursorWiresOn()
boxWidget.GetSelectedOutlineProperty().SetColor(1, 0, 1)
boxWidget.GetOutlineProperty().SetColor(0.1, 0.1, 0.1)
boxWidget.GetOutlineProperty().SetOpacity(0.8)
boxWidget.SetPlaceFactor(1.05)
boxWidget.SetInteractor(vp.interactor)
boxWidget.SetInputData(apd)
boxWidget.PlaceWidget()
boxWidget.AddObserver("InteractionEvent", SelectPolygons)
boxWidget.On()
vp.cutterWidget = boxWidget
vp.clickedActor = act0
ia = vp.actors.index(actor)
vp.actors[ia] = act0
colors.printc("Mesh Cutter Tool:", c="m", invert=1)
colors.printc(" Move gray handles to cut away parts of the mesh", c="m")
colors.printc(" Press X to save file to: clipped.vtk", c="m")
vp.interactor.Start()
boxWidget.Off()
vp.widgets.append(boxWidget)
vp.interactor.Start() # allow extra interaction
return act0
iteration = 4
size = 3**iteration
voxels = np.ones((size, size, size))
def iterate(length, x, y, z):
nl = length // 3
if nl < 1: return
margin = (nl - 1) // 2
voxels[z - margin:z + margin + 1, y - margin:y + margin + 1, :] = 0
voxels[z - margin:z + margin + 1, :, x - margin:x + margin + 1] = 0
voxels[:, y - margin:y + margin + 1, x - margin:x + margin + 1] = 0
for ix, iy, iz in np.ndindex((3, 3, 3)):
if (1 if ix != 1 else 0) + (1 if iy != 1 else 0) + (1 if iz != 1 else
0) != 2:
iterate(nl, x + (ix - 1) * nl, y + (iy - 1) * nl,
z + (iz - 1) * nl)
iterate(size, size // 2, size // 2, size // 2)
print('voxels min, max =', np.min(voxels), np.max(voxels))
vol = Volume(voxels)
lego = legosurface(vol, -0.1, 1.1, cmap='afmhot_r')
show(vol, lego, N=2, bg='w')
import numpy as np data_matrix = np.zeros([75, 75, 75], dtype=np.uint8) # all voxels have value zero except: data_matrix[0:35, 0:35, 0:35] = 1 data_matrix[35:55, 35:55, 35:55] = 2 data_matrix[55:74, 55:74, 55:74] = 3 from vtkplotter import Volume vol = Volume(data_matrix, c=['white', 'b', 'g', 'r']) vol.show(axes=1)
# 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)
"""Modify a Volume dataset and colorize voxels individually """ from vtkplotter import Text2D, Volume, show import numpy as np vol = Volume(shape=(10,11,12), mode=0) vol.alpha(0.8).jittering(False) vol.interpolation(0) # nearest neighbour interpolation type # Overwrite the (sofar empty) voxel data with new data vol.imagedata().AllocateScalars(3, 4) # type 3 corresponds to np.uint8 arr = vol.getPointArray() arr[:] = np.random.randint(0,255, (10*11*12,4)).astype(np.uint8) # For 4 component data, the first 3 directly represent RGB, no lookup table. # (see https://vtk.org/doc/nightly/html/classvtkVolumeProperty.html): vol.GetProperty().IndependentComponentsOff() show(vol, Text2D(__doc__), axes=1)
reader.SetFileName(datadir + "vase.vti")
reader.Update()
img = reader.GetOutput() # vtkImageData object
# NB: the above lines could be reduced to:
#img = load(datadir+"vase.vti").imagedata()
#################################
from vtkplotter import Volume, show, Text
# can set colors and transparencies along the scalar range
# from minimum to maximum value. In this example voxels with
# the smallest value will be completely transparent (and white)
# while voxels with highest value of the scalar will get alpha=0.8
# and color will be=(0,0,1)
vol1 = Volume(img, mode=0) # composite rendering
vol1.color(["white", "fuchsia", "dg", (0, 0, 1)])
#vol1.color('jet') # a matplotlib colormap name is also accepted
vol1.alpha([0.0, 0.2, 0.3, 0.8])
# a transparency for the GRADIENT of the scalar can also be set:
# in this case when the scalar is ~constant the gradient is ~zero
# and the voxel are made transparent:
vol1.alphaGradient([0.0, 0.5, 0.9])
# mode = 1 is maximum-projection volume rendering
vol2 = load(datadir + "vase.vti").mode(1).addPos(60, 0, 0)
# show command creates and returns an instance of class Plotter
show(vol1, vol2, Text(__doc__), bg="w", axes=1)
###################################### getActors
print('Test getMeshes')
assert len(asse.unpack()) == 2
assert asse.unpack(0) == cone
assert asse.unpack(1) == sphere
assert 4.1 < asse.diagonalSize() < 4.2
############################################################################ Volume
X, Y, Z = np.mgrid[:30, :30, :30]
scalar_field = ((X - 15)**2 + (Y - 15)**2 + (Z - 15)**2) / 225
print('Test Volume, scalar min, max =', np.min(scalar_field),
np.max(scalar_field))
vol = Volume(scalar_field)
volarr = vol.getPointArray()
assert volarr.shape[0] == 27000
assert np.max(volarr) == 3
assert np.min(volarr) == 0
###################################### isosurface
print('Test isosurface')
iso = vol.isosurface(threshold=1.0)
print('area', iso.area())
assert 2540 < iso.area() < 3000
#lego = vol.legosurface(vmin=0.3, vmax=0.5)
#show(lego)
#print('lego.N()', lego.N())
"""Load and render a 3D Volume.
mode=0, composite rendering
mode=1, maximum-projection rendering
"""
import vtk
from vtkplotter import datadir, load, Volume, show
# Create the reader for the data.
reader = vtk.vtkXMLImageDataReader()
reader.SetFileName(datadir + "vase.vti")
reader.Update()
img = reader.GetOutput() # vtkImageData object
vol1 = Volume(img, mode=0) # mode=0, composite rendering
# --- NB: THE ABOVE LINES COULD BE REDUCED TO:
#vol1 = load(datadir+"vase.vti")
# -------------------------------------------
# can set colors and transparencies along the scalar range
# from minimum to maximum value. In this example voxels with
# the smallest value will be completely transparent (and white)
# while voxels with highest value of the scalar will get alpha=0.8
# and color will be=(0,0,1)
vol1.color(["white", "fuchsia", "dg", (0, 0, 1)])
#vol1.color('jet') # a matplotlib colormap name is also accepted
vol1.alpha([0.0, 0.2, 0.3, 0.8])
# a transparency for the GRADIENT of the scalar can also be set:
# in this case when the scalar is ~constant the gradient is ~zero