Related
I am trying to export a VTK rendering as an OBJ file to be viewed in Meshlab. Everything appears normal until I open the .obj file in Meshlab, where only the spheres have been exported and not any of the tubes I have connecting the spheres. I have tried exporting the rendering in different file types but only the spheres are exported for some reason. Could it be because the tube filter doesn't actually create a 3D object? Looking for any solutions to this problem! The writer is at the bottom of the code. Thanks
np={}
for n in G.nodes():
np[n]=node_pos[n]
nodePoints = vtk.vtkPoints()
i=0
for (x,y,z) in np.values():
nodePoints.InsertPoint(i, x, y, z)
i=i+1
# Create a polydata to be glyphed.
inputData = vtk.vtkPolyData()
inputData.SetPoints(nodePoints)
# Use sphere as glyph source.
balls = vtk.vtkSphereSource()
balls.SetRadius(.1)
balls.SetPhiResolution(20)
balls.SetThetaResolution(20)
glyphPoints = vtk.vtkGlyph3D()
glyphPoints.SetInputData(inputData)
glyphPoints.SetSourceData(balls.GetOutput())
glyphMapper = vtk.vtkPolyDataMapper()
glyphMapper.SetInputData(glyphPoints.GetOutput())
glyph = vtk.vtkActor()
glyph.SetMapper(glyphMapper)
glyph.GetProperty().SetDiffuseColor(plum)
glyph.GetProperty().SetSpecular(.3)
glyph.GetProperty().SetSpecularPower(30)
# Generate the polyline for the spline.
points = vtk.vtkPoints()
edgeData = vtk.vtkPolyData()
# Edges
lines = vtk.vtkCellArray()
i=0
for e in G.edges:
u=e[0]
v=e[1]
lines.InsertNextCell(2)
for n in (u,v):
(x,y,z)=node_pos[n]
points.InsertPoint(i, x, y, z)
lines.InsertCellPoint(i)
i=i+1
edgeData.SetPoints(points)
edgeData.SetLines(lines)
Tubes = vtk.vtkTubeFilter()
Tubes.SetNumberOfSides(16)
Tubes.SetInputData(edgeData)
Tubes.SetRadius(0.05) # edge RADIUS
profileMapper = vtk.vtkPolyDataMapper()
profileMapper.SetInputData(Tubes.GetOutput())
balls.Update()
glyphPoints.Update()
Tubes.Update()
profile = vtk.vtkActor()
profile.SetMapper(profileMapper)
profile.GetProperty().SetDiffuseColor(banana)
profile.GetProperty().SetSpecular(.3)
profile.GetProperty().SetSpecularPower(30)
ren = vtk.vtkRenderer()
renWin = vtk.vtkRenderWindow()
renWin.AddRenderer(ren)
iren = vtk.vtkRenderWindowInteractor()
iren.SetRenderWindow(renWin)
ren.AddActor(profile)
ren.AddActor(glyph)
renWin.SetSize(1000, 1000)
iren.Initialize()
renWin.Render()
iren.Start()
writer = vtk.vtkOBJExporter()
writer.SetFilePrefix('test')
writer.SetInput(renWin)
writer.Write()
What I want to do:
I made a dynamic map using bokeh, pandas and geopandas. The data to be displayed is loaded from a table, then mapped to a country per year. The year to be displayed is determined by a bokeh slider. You can also hover over a country and get its value. I now want to be able to change the data source by selecting a radio button. To display the data correctly, I want to change the color palette, rescale it (a range from e.g. 50 to 100 instead of 0 to 4.5), update the scaling on the slider to the new lowest year to highest year, and then display the world-map with the new data. I also want to update the title of the map from e.g. "Fertility" to "Life expectancy".
What I already have:
I have a working dynamic map with Slider and Hover tool. I also have a list from which the data to be used is taken (datapath, title to be used, color palette to be used, highest and lowest year, highest and lowest value). I have a radio button group, with three different data sources to choose from. All paths are relative, the data is provided with a consistent structure. I had the map changing the data below and displaying the new stuff, but than I did something and it stopped working. I also had the Hover tool display the right values, but with a wrong (old) description.
What I need help with:
Updating the color bar to accomodate the new palette and the new range
Updating the slider to accomodate a changed range
Updating the title displayed to show what's actually displayed
What I already tried:
I've put the whole loading and displaying in the function executed when the radio button group is changed. The first thing this function does is clearing the layout and then rebuilding it. Unfortunately, this is neither efficient, nor working, since I only get the radio button Group and an empty space below, no matter what I do. I've searched for a solution, but all I found (and tried) didn't do what I needed.
I can provide the actual code, if needed (though some of the variables and documentations are in German), but since I'm pretty new to the whole python thing, I don't now, what exactly of that about 300 lines of code you need. Just let me now, and I'll try and provide.
Hope you can help me with that.
Thanks in advance,
Asd789
EDIT: As correctly pointed out in the comments, some code to help understand what I did.
I'll cut all the imports for the sake of brevity, as a mistake there would have shown up as error in my terminal. Also leaving out comments.
geoFrame #dataframe for geopanda shapefile
configList = [0, "Fertility", 'YlGnBu', 'Year' ]
df #dataframe for the .csv file
higStep = df['step'].max()
lowStep = df['step'].min()
configList.append(higStep)
higVal = df['valueInterest'].max()
merged = geoFrame.merge(df, left_on = 'country_code', right_on = 'code')
merged_json = json.loads(merged.to_json())
json_data = json.dumps(merged_json)
geosource = GeoJSONDataSource(geojson = json_data)
palette = brewer[configList[2]][8]
color_mapper = LinearColorMapper(palette = palette, low = 0, high = 4)
color_bar = ColorBar(color_mapper=color_mapper, label_standoff=8,width = 500, height = 20,
border_line_color=None,location = (0,0), orientation = 'horizontal')
p = figure(title = configList[1], plot_height = 600 , plot_width = 950, toolbar_location = None)
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
p.patches('xs','ys', source = geosource,fill_color = {'field' :'valueInterest', 'transform' : color_mapper},
line_color = 'black', line_width = 0.25, fill_alpha = 1)
p.add_layout(color_bar, 'below')
df_curr = df[df['step'] == higStep]
color_mapper = LinearColorMapper(palette = palette, low = 0, high = 40, nan_color = '#d9d9d9')
def json_data(selectedStep):
st = selectedStep
df_st = df[df['step'] == st]
merged = geoFrame.merge(df_st, left_on = 'country_code', right_on = 'code', how = 'left')
merged_json = json.loads(merged.to_json())
json_data = json.dumps(merged_json)
return json_data
geosource = GeoJSONDataSource(geojson = json_data(higStep))
palette = brewer[configList[2]][8]
palette = palette[::-1]
color_mapper = LinearColorMapper(palette = palette, low = 0, high = higVal/2, nan_color = '#d9d9d9')
hover = HoverTool(tooltips = [ ('Country/region','#country'),(configList[1], '#valueInterest')])
color_bar = ColorBar(color_mapper=color_mapper, label_standoff=8,width = 500, height = 20,
border_line_color=None,location = (0,0), orientation = 'horizontal')
p = figure(title = configList[1], plot_height = 600 , plot_width = 950, toolbar_location = None, tools = [hover])
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
p.patches('xs','ys', source = geosource,fill_color = {'field' :'valueInterest', 'transform' : color_mapper},
line_color = 'black', line_width = 0.25, fill_alpha = 1)
p.add_layout(color_bar, 'below')
def update_plot(attr, old, new):
st = slider.value
new_data = json_data(st)
geosource.geojson = new_data
p.title.text = configList[1] %st
slider = Slider(title = configList[3],start = 1950, end = 2015, step = 1, value = 2015)
slider.on_change('value', update_plot)
def radHandler(attr, new, old):
if radio.active == 0:
datapath = os.path.join(dataloc, 'children-per-woman-UN.csv')
configList = [0, "Fertility", 'YlGnBu', 'Year']
elif radio.active == 1:
#see above with diferent data
elif radio.active == 2:
#see above with different data
curdoc().clear()
higStep = df['step'].max()
lowStep = df['step'].min()
configList.append(higStep)
update_plot(attr, new, old)
hover = HoverTool(tooltips = [ ('Country/region','#country'),(configList[1], '#valueInterest')])
palette = brewer[configList[2]][8]
palette = palette[::-1]
olor_mapper = LinearColorMapper(palette = palette, low = 0, high = higVal/2, nan_color = '#d9d9d9')
color_bar = ColorBar(color_mapper=color_mapper, label_standoff=8,width = 500, height = 20,
border_line_color=None,location = (0,0), orientation = 'horizontal'
p = figure(title = configList[1]+' '+str(configList[4]), plot_height = 600 , plot_width = 950, toolbar_location = None, tools = [hover])
layout = column(widgetbox(radio),p,widgetbox(slider))
curdoc().add_root(layout)
radio = RadioButtonGroup(labels=['Fertility', 'Life expectancy', 'Covid-19 total cases'], active=0)
radio.on_change('active',radHandler)
layout = column(p, widgetbox(radio),widgetbox(slider))
curdoc().title = configList[1]
curdoc().add_root(layout)
Sorry I could'nt cook it down further, but I don't know what's essential and what's just fancy stuff around.
This code works until I touch the radio button group. After that, the plot itself is just blank, without any prompts anywhere. The code itself is not entirely my fault, I got it to fix and maybe expand on it, the expansion being the ability to switch between data sources as decribed above.
I am trying to make contour plot from unstructured grid. My code does not work and I could not find an example to follow. I did not include the input file, hoping that a sample unstructured grid is easy to find. I managed to make work on Paraview by converting cell data to point data and then contouring "p" scalar. But I cannot do it with vtk. What's wrong in my code?
from vtk import *
file_name = "results.vtk"
reader = vtkUnstructuredGridReader()
reader.SetFileName(file_name)
reader.Update()
output = reader.GetOutput()
scalar_range = output.GetScalarRange()
c2p = vtkCellDataToPointData()
c2p.SetInputData(output)
contours = vtkContourGrid()
contours.SetInputData(c2p.GetOutput())
contours.SetValue(0, 0.007009505294263363)
gridmapper = vtkDataSetMapper()
gridmapper.SetInputData(output)
gridmapper.GetInput().GetCellData().SetActiveScalars("p")
gridmapper.SetScalarVisibility(1)
gridmapper.SetScalarRange(scalar_range)
mapper = vtkPolyDataMapper()
#mapper = vtkDataSetMapper()
mapper.SetInputConnection(contours.GetOutputPort())
actor = vtkActor()
actor.SetMapper(mapper)
gridactor = vtkActor()
gridactor.SetMapper(gridmapper)
gridactor.GetProperty().EdgeVisibilityOn()
renderer = vtkRenderer()
renderer.AddActor(actor)
renderer.AddActor(gridactor)
#renderer.SetBackground(1, 1, 1) # Set background to white
renderer_window = vtkRenderWindow()
renderer_window.AddRenderer(renderer)
interactor = vtkRenderWindowInteractor()
interactor.SetRenderWindow(renderer_window)
interactor.Initialize()
interactor.Start()
I have a basic question as i am new to VTK. I have to draw live point cloud data in VTK. I have modified the code given in How to display point cloud in vtk in different colors?.
The pointcloud should update for number of times given in iterations(here 30). I have used Initialize() to avoid blocking control flow as mentioned in some solutions, in every iteration the point cloud is updated, and render() is called so that it can update the window with new data.
I cannot figure out why this is blocking the control flow, and the data is not updated. Only once the iterations are over, after renderWindowInteractor.Start() is called, the interaction is enabled.
import vtk
from numpy import random
class VtkPointCloud:
def __init__(self, zMin=-10.0, zMax=10.0, maxNumPoints=1e6):
self.maxNumPoints = maxNumPoints
self.vtkPolyData = vtk.vtkPolyData()
self.clearPoints()
mapper = vtk.vtkPolyDataMapper()
mapper.SetInputData(self.vtkPolyData)
mapper.SetColorModeToDefault()
mapper.SetScalarRange(zMin, zMax)
mapper.SetScalarVisibility(1)
self.vtkActor = vtk.vtkActor()
self.vtkActor.SetMapper(mapper)
def addPoint(self, point):
if self.vtkPoints.GetNumberOfPoints() < self.maxNumPoints:
pointId = self.vtkPoints.InsertNextPoint(point[:])
self.vtkDepth.InsertNextValue(point[2])
self.vtkCells.InsertNextCell(1)
self.vtkCells.InsertCellPoint(pointId)
else:
r = random.randint(0, self.maxNumPoints)
self.vtkPoints.SetPoint(r, point[:])
self.vtkCells.Modified()
self.vtkPoints.Modified()
self.vtkDepth.Modified()
def clearPoints(self):
self.vtkPoints = vtk.vtkPoints()
self.vtkCells = vtk.vtkCellArray()
self.vtkDepth = vtk.vtkDoubleArray()
self.vtkDepth.SetName('DepthArray')
self.vtkPolyData.SetPoints(self.vtkPoints)
self.vtkPolyData.SetVerts(self.vtkCells)
self.vtkPolyData.GetPointData().SetScalars(self.vtkDepth)
self.vtkPolyData.GetPointData().SetActiveScalars('DepthArray')
def func(pointCloud):
# Renderer
renderer = vtk.vtkRenderer()
renderer.AddActor(pointCloud.vtkActor)
renderer.SetBackground(.2, .3, .4)
renderer.ResetCamera()
# Render Window
renderWindow = vtk.vtkRenderWindow()
renderWindow.AddRenderer(renderer)
# Interactor
renderWindowInteractor = vtk.vtkRenderWindowInteractor()
renderWindowInteractor.SetRenderWindow(renderWindow)
# Begin Interaction
renderWindow.Render()
renderWindowInteractor.Initialize()
return renderWindow,renderWindowInteractor
def main(iter):
while iter > 0:
pointCloud = VtkPointCloud()
for k in xrange(10000):
point = 20*(random.rand(3)-0.5)
pointCloud.addPoint(point)
pointCloud.addPoint([0,0,0])
pointCloud.addPoint([0,0,0])
pointCloud.addPoint([0,0,0])
pointCloud.addPoint([0,0,0])
if iter == 30:
renderWindow,renderWindowInteractor = func(pointCloud)
else:
#pointCloud.vtkPolyData.Modified()
renderWindow.Render()
iter -= 1
renderWindowInteractor.Start()
main(30)
So you want to do an animation.
A better practice is to follow this sample explaining how to do use a TimerEvent.
Here is what it would look like with your code:
import vtk
from numpy import random
class VtkPointCloud:
def __init__(self, zMin=-10.0, zMax=10.0, maxNumPoints=1e6):
self.maxNumPoints = maxNumPoints
self.vtkPolyData = vtk.vtkPolyData()
self.clearPoints()
mapper = vtk.vtkPolyDataMapper()
mapper.SetInputData(self.vtkPolyData)
mapper.SetColorModeToDefault()
mapper.SetScalarRange(zMin, zMax)
mapper.SetScalarVisibility(1)
self.vtkActor = vtk.vtkActor()
self.vtkActor.SetMapper(mapper)
def addPoint(self, point):
if self.vtkPoints.GetNumberOfPoints() < self.maxNumPoints:
pointId = self.vtkPoints.InsertNextPoint(point[:])
self.vtkDepth.InsertNextValue(point[2])
self.vtkCells.InsertNextCell(1)
self.vtkCells.InsertCellPoint(pointId)
else:
r = random.randint(0, self.maxNumPoints)
self.vtkPoints.SetPoint(r, point[:])
self.vtkCells.Modified()
self.vtkPoints.Modified()
self.vtkDepth.Modified()
def clearPoints(self):
self.vtkPoints = vtk.vtkPoints()
self.vtkCells = vtk.vtkCellArray()
self.vtkDepth = vtk.vtkDoubleArray()
self.vtkDepth.SetName('DepthArray')
self.vtkPolyData.SetPoints(self.vtkPoints)
self.vtkPolyData.SetVerts(self.vtkCells)
self.vtkPolyData.GetPointData().SetScalars(self.vtkDepth)
self.vtkPolyData.GetPointData().SetActiveScalars('DepthArray')
class AddPointCloudTimerCallback():
def __init__(self, renderer, iterations):
self.iterations = iterations
self.renderer = renderer
def execute(self, iren, event):
if self.iterations == 0:
iren.DestroyTimer(self.timerId)
pointCloud = VtkPointCloud()
self.renderer.AddActor(pointCloud.vtkActor)
pointCloud.clearPoints()
for k in xrange(10000):
point = 20*(random.rand(3)-0.5)
pointCloud.addPoint(point)
pointCloud.addPoint([0,0,0])
pointCloud.addPoint([0,0,0])
pointCloud.addPoint([0,0,0])
pointCloud.addPoint([0,0,0])
iren.GetRenderWindow().Render()
if self.iterations == 30:
self.renderer.ResetCamera()
self.iterations -= 1
if __name__ == "__main__":
# Renderer
renderer = vtk.vtkRenderer()
renderer.SetBackground(.2, .3, .4)
renderer.ResetCamera()
# Render Window
renderWindow = vtk.vtkRenderWindow()
renderWindow.AddRenderer(renderer)
# Interactor
renderWindowInteractor = vtk.vtkRenderWindowInteractor()
renderWindowInteractor.SetRenderWindow(renderWindow)
renderWindowInteractor.Initialize()
# Initialize a timer for the animation
addPointCloudTimerCallback = AddPointCloudTimerCallback(renderer, 30)
renderWindowInteractor.AddObserver('TimerEvent', addPointCloudTimerCallback.execute)
timerId = renderWindowInteractor.CreateRepeatingTimer(10)
addPointCloudTimerCallback.timerId = timerId
# Begin Interaction
renderWindow.Render()
renderWindowInteractor.Start()
Note that I renamed iter to iterations because iter is a reserved name in Python.
I'm trying to display further images (ct-scan) using numpy/vtk as describe in this sample code (http://www.vtk.org/Wiki/VTK/Examples/Python/vtkWithNumpy) but I don't get it and don't know why.
If someone could help me it would be kind.
Here's my code :
import vtk
import numpy as np
import os
import cv, cv2
import matplotlib.pyplot as plt
import PIL
import Image
DEBUG =True
directory="splitted_mri/"
w = 226
h = 186
d = 27
stack = np.zeros((w,d,h))
k=-1 #add the next picture in a differente level of depth/z-positions
for file in os.listdir(directory):
k+=1
img = directory + file
im = Image.open(img)
temp = np.asarray(im, dtype=int)
stack[:,k,:]= temp
print stack.shape
#~ plt.imshow(test)
#~ plt.show()
print type(stack[10,10,15])
res = np.amax(stack)
res1 = np.amin(stack)
print res, type(res)
print res1, type(res1)
#~ for (x,y,z), value in np.ndenumerate(stack):
#~ stack[x,y,z]=np.require(stack[x,y,z],dtype=np.int16)
#~ print type(stack[x,y,z])
stack = np.require(stack,dtype=np.uint16)
print stack.dtype
if DEBUG : print stack.shape
dataImporter = vtk.vtkImageImport()
data_string = stack.tostring()
dataImporter.CopyImportVoidPointer(data_string, len(data_string))
dataImporter.SetDataScalarTypeToUnsignedChar()
dataImporter.SetNumberOfScalarComponents(1)
dataImporter.SetDataExtent(0, w-1, 0, 1, 0, h-1)
dataImporter.SetWholeExtent(0, w-1, 0, 1, 0, h-1)
essai = raw_input()
alphaChannelFunc = vtk.vtkPiecewiseFunction()
colorFunc = vtk.vtkColorTransferFunction()
for i in range (0,255):
alphaChannelFunc.AddPoint(i, 0.9)
colorFunc.AddRGBPoint(i,i,i,i)
volumeProperty = vtk.vtkVolumeProperty()
volumeProperty.SetColor(colorFunc)
#volumeProperty.ShadeOn()
volumeProperty.SetScalarOpacity(alphaChannelFunc)
# This class describes how the volume is rendered (through ray tracing).
compositeFunction = vtk.vtkVolumeRayCastCompositeFunction()
# We can finally create our volume. We also have to specify the data for it, as well as how the data will be rendered.
volumeMapper = vtk.vtkVolumeRayCastMapper()
volumeMapper.SetVolumeRayCastFunction(compositeFunction)
volumeMapper.SetInputConnection(dataImporter.GetOutputPort())
# The class vtkVolume is used to pair the preaviusly declared volume as well as the properties to be used when rendering that volume.
volume = vtk.vtkVolume()
volume.SetMapper(volumeMapper)
volume.SetProperty(volumeProperty)
# With almost everything else ready, its time to initialize the renderer and window, as well as creating a method for exiting the application
renderer = vtk.vtkRenderer()
renderWin = vtk.vtkRenderWindow()
renderWin.AddRenderer(renderer)
renderInteractor = vtk.vtkRenderWindowInteractor()
renderInteractor.SetRenderWindow(renderWin)
# We add the volume to the renderer ...
renderer.AddVolume(volume)
# ... set background color to white ...
renderer.SetBackground(1, 1, 1)
# ... and set window size.
renderWin.SetSize(400, 400)
# A simple function to be called when the user decides to quit the application.
def exitCheck(obj, event):
if obj.GetEventPending() != 0:
obj.SetAbortRender(1)
# Tell the application to use the function as an exit check.
renderWin.AddObserver("AbortCheckEvent", exitCheck)
#to quit, press q
renderInteractor.Initialize()
# Because nothing will be rendered without any input, we order the first render manually before control is handed over to the main-loop.
renderWin.Render()
renderInteractor.Start()
I finally find out what was wrong
here's my new code
import vtk
import numpy as np
import os
import matplotlib.pyplot as plt
import PIL
import Image
DEBUG =False
directory="splitted_mri/"
l = []
k=0 #add the next picture in a differente level of depth/z-positions
for file in os.listdir(directory):
img = directory + file
if DEBUG : print img
l.append(img)
# the os.listdir function do not give the files in the right order
#so we need to sort them
l=sorted(l)
temp = Image.open(l[0])
h, w = temp.size
d = len(l)*5 #with our sample each images will be displayed 5times to get a better view
if DEBUG : print 'width, height, depth : ',w,h,d
stack = np.zeros((w,d,h),dtype=np.uint8)
for i in l:
im = Image.open(i)
temp = np.asarray(im, dtype=int)
for i in range(5):
stack[:,k+i,:]= temp
k+=5
#~ stack[:,k,:]= temp
#~ k+=1
if DEBUG :
res = np.amax(stack)
print 'max value',res
res1 = np.amin(stack)
print 'min value',res1
#convert the stack in the right dtype
stack = np.require(stack,dtype=np.uint8)
if DEBUG :#check if the image have not been modified
test = stack [:,0,:]
plt.imshow(test,cmap='gray')
plt.show()
if DEBUG : print 'stack shape & dtype' ,stack.shape,',',stack.dtype
dataImporter = vtk.vtkImageImport()
data_string = stack.tostring()
dataImporter.CopyImportVoidPointer(data_string, len(data_string))
dataImporter.SetDataScalarTypeToUnsignedChar()
dataImporter.SetNumberOfScalarComponents(1)
#vtk uses an array in the order : height, depth, width which is
#different of numpy (w,h,d)
w, d, h = stack.shape
dataImporter.SetDataExtent(0, h-1, 0, d-1, 0, w-1)
dataImporter.SetWholeExtent(0, h-1, 0, d-1, 0, w-1)
alphaChannelFunc = vtk.vtkPiecewiseFunction()
colorFunc = vtk.vtkColorTransferFunction()
for i in range(256):
alphaChannelFunc.AddPoint(i, 0.2)
colorFunc.AddRGBPoint(i,i/255.0,i/255.0,i/255.0)
# for our test sample, we set the black opacity to 0 (transparent) so as
#to see the sample
alphaChannelFunc.AddPoint(0, 0.0)
colorFunc.AddRGBPoint(0,0,0,0)
volumeProperty = vtk.vtkVolumeProperty()
volumeProperty.SetColor(colorFunc)
#volumeProperty.ShadeOn()
volumeProperty.SetScalarOpacity(alphaChannelFunc)
# This class describes how the volume is rendered (through ray tracing).
compositeFunction = vtk.vtkVolumeRayCastCompositeFunction()
# We can finally create our volume. We also have to specify the data for
# it, as well as how the data will be rendered.
volumeMapper = vtk.vtkVolumeRayCastMapper()
# function to reduce the spacing between each image
volumeMapper.SetMaximumImageSampleDistance(0.01)
volumeMapper.SetVolumeRayCastFunction(compositeFunction)
volumeMapper.SetInputConnection(dataImporter.GetOutputPort())
# The class vtkVolume is used to pair the preaviusly declared volume as
#well as the properties to be used when rendering that volume.
volume = vtk.vtkVolume()
volume.SetMapper(volumeMapper)
volume.SetProperty(volumeProperty)
# With almost everything else ready, its time to initialize the renderer and window,
# as well as creating a method for exiting the application
renderer = vtk.vtkRenderer()
renderWin = vtk.vtkRenderWindow()
renderWin.AddRenderer(renderer)
renderInteractor = vtk.vtkRenderWindowInteractor()
renderInteractor.SetRenderWindow(renderWin)
# We add the volume to the renderer ...
renderer.AddVolume(volume)
# ... set background color to white ...
renderer.SetBackground(1, 1, 1)
# ... and set window size.
renderWin.SetSize(550, 550)
renderWin.SetMultiSamples(4)
# A simple function to be called when the user decides to quit the application.
def exitCheck(obj, event):
if obj.GetEventPending() != 0:
obj.SetAbortRender(1)
# Tell the application to use the function as an exit check.
renderWin.AddObserver("AbortCheckEvent", exitCheck)
#to auit, press q
renderInteractor.Initialize()
# Because nothing will be rendered without any input, we order the first
# render manually before control is handed over to the main-loop.
renderWin.Render()
renderInteractor.Start()
If you are ok with a solution not using VTK, you could use Matplotlib imshow and interactive navigation with keys.
This tutorial shows how:
https://www.datacamp.com/community/tutorials/matplotlib-3d-volumetric-data
https://github.com/jni/mpl-volume-viewer
and here an implementation for viewing RTdose files:
https://github.com/pydicom/contrib-pydicom/pull/19
See also:
https://github.com/napari/napari