Develop Reference

Manim: Is the a nice way to move and scale a bunch of mobjects?

I'm using the manim module in Python to display some decision trees. Fist I want to show Tree_1 as it is in the code below. Then I want it to scale it down and shift it to the left. Next I want Tree_2 to appear where Tree_1 is and them move to the upper right quadrant of the screen. Also the PURE_RED lines should move from tilted (as in Tree_1) to straight (as in Tree_2 in the code below). The same should then happen with Tree_3 just in the bottom right quadrant.
Now I could do it by figuring out all the points and then hardcode it. But I wanted to ask if there is a nicer way. Maybe one where I could define points in a local coordinate system and then I can just scale and move the whole tree.
Also I'm sorry if its is considered common knowledge, but I'm super new to manim.
from manim import *
class Tree_1(Scene):
def construct(self):
line_1 = Line([0,3,0], [-6,0,0])
line_2 = Line([0,3,0], [0,0,0])
line_3 = Line([0,3,0], [6,0,0])
self.play(
Create(line_1),
Create(line_2),
Create(line_3),
)
line_1l = Line([-6, 0, 0], [-7,-3, 0]).set_color(PURE_GREEN)
line_1r = Line([-6, 0, 0], [-5,-3, 0]).set_color(PURE_RED)
line_2l = Line([ 0, 0, 0], [-1,-3, 0]).set_color(PURE_GREEN)
line_2r = Line([ 0, 0, 0], [ 1,-3, 0]).set_color(PURE_RED)
line_3l = Line([ 6, 0, 0], [ 5,-3, 0]).set_color(PURE_GREEN)
line_3r = Line([ 6, 0, 0], [ 7,-3, 0]).set_color(PURE_RED)
self.play(
Create(line_1l),
Create(line_1r),
Create(line_2l),
Create(line_2r),
Create(line_3l),
Create(line_3r),
)
class Tree_2(Scene):
def construct(self):
line_1 = Line([0,3,0], [-6,0,0])
line_2 = Line([0,3,0], [0,0,0])
line_3 = Line([0,3,0], [6,0,0])
self.play(
Create(line_1),
Create(line_2),
Create(line_3),
)
line_4 = Line([-6, 0, 0], [-6,-3, 0]).set_color(PURE_RED)
line_5 = Line([ 0, 0, 0], [-0,-3, 0]).set_color(PURE_RED)
line_6 = Line([ 6, 0, 0], [ 6,-3, 0]).set_color(PURE_RED)
self.play(
Create(line_4),
Create(line_5),
Create(line_6),
)
class Tree_3(Scene):
def construct(self):
line_1 = Line([0,3,0], [-6,0,0])
line_2 = Line([0,3,0], [0,0,0])
line_3 = Line([0,3,0], [6,0,0])
self.play(
Create(line_1),
Create(line_2),
Create(line_3),
)
line_4 = Line([-6, 0, 0], [-6,-3, 0]).set_color(PURE_GREEN)
line_5 = Line([ 0, 0, 0], [-0,-3, 0]).set_color(PURE_GREEN)
line_6 = Line([ 6, 0, 0], [ 6,-3, 0]).set_color(PURE_GREEN)
self.play(
Create(line_4),
Create(line_5),
Create(line_6),
)

Yes there is, add them to a VGroup, here is the docs.

Select the right context for the OUTLINER using PYTHON and QT

I have the following code :
def make_override_editable():
for area in bpy.context.screen.areas:
if area.type == "OUTLINER":
ctx = bpy.context.copy()
ctx["area"] = area
with bpy.context.temp_override(area=area):
print(bpy.context.area.type)
bpy.ops.outliner.liboverride_operation(
type="OVERRIDE_LIBRARY_CREATE_HIERARCHY",
selection_set="SELECTED_AND_CONTENT",
)
The following line when is ran through the script :
print(bpy.context.area.type)
outputs OUTLINER
but I still get the error that I have the incorrect context :
RuntimeError: Operator bpy.ops.outliner.liboverride_operation.poll() failed, context is incorrect
which normally works in blender text editor with 3 lines :
bpy.context.area.type = 'OUTLINER'
bpy.ops.outliner.liboverride_operation(type="OVERRIDE_LIBRARY_CREATE_HIERARCHY",selection_set="SELECTED_AND_CONTENT")
bpy.context.area.type = 'TEXT_EDITOR'
I'm using python in a much more complex script with QT.
any suggestions ?
I'm trying to make a linked skeleton (Armature) editable so I can change it's pose.
I've also searched for a low level function that I can use, but did not had any success.
or perhaps is there another method to LINK and animation to a LINKED Armature ?
I've tried this :
def make_override_editable():
for area in bpy.context.screen.areas:
if area.type == "OUTLINER":
ctx = bpy.context.copy()
ctx["area"] = area
with bpy.context.temp_override(area=area):
print(bpy.context.area.type)
bpy.ops.outliner.liboverride_operation(
type="OVERRIDE_LIBRARY_CREATE_HIERARCHY",
selection_set="SELECTED_AND_CONTENT",
)
And was expecting to have this
bpy.ops.outliner.liboverride_operation(type="OVERRIDE_LIBRARY_CREATE_HIERARCHY",selection_set="SELECTED_AND_CONTENT")

this works even if the Armature is linked.
no need to use operators.
# Reset the Armature to the default POSE.
for n in bpy.context.object.pose.bones:
n.location = (0, 0, 0)
n.rotation_quaternion = (1, 0, 0, 0)
n.rotation_axis_angle = (0, 0, 1, 0)
n.rotation_euler = (0, 0, 0)
n.scale = (1, 1, 1)

Selecting Joints in hierarchy one at a time

I have this code
joint_name = cmds.ls(sl=1)[0]
circle_name = cmds.circle(name = joint_name + "_CTL", nr=(1, 0, 0) )
group_name = cmds.group(name = joint_name + "_OFFSET")
cmds.select(joint_name, group_name)temp_constraint = cmds.parentConstraint()
cmds.delete(temp_constraint)
cmds.select(circle_name, joint_name)
cmds.pointConstraint()
cmds.orientConstraint()
When you select a joint and run this code you will get a circle that will control that joint. While going down the hierarchy you have to select that joint and then run the code.
How would I be able to have all the joints to have circles controlling them without having to go through the outliner selection a joint?

run this on your chain of joint :
for x, joint_name in enumerate(cmds.ls(sl=1, dag=True, type='joint')):
circle_name = cmds.circle(name = '{}_CTL{:02d}'.format(joint_name,x), nr=(1, 0, 0) )
group_name = cmds.group(name = '{}_OFFSET{:02d}'.format(joint_name,x))
cmds.select(joint_name, group_name)
temp_constraint = cmds.parentConstraint()
cmds.delete(temp_constraint)
cmds.select(circle_name, joint_name)
cmds.pointConstraint()
cmds.orientConstraint()
Note that instead of use select, you could feed pointConstraint :
cmds.pointConstraint(circle_name, joint_name, n='something')

Example for vtkContourGrid

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()

3d image visualisation with numpy/vtk

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