def save_polydata(polydata, file_name, binary=False, color_array_name=None):
"""Save a vtk polydata to a supported format file.
Save formats can be VTK, FIB, PLY, STL and XML.
Parameters
----------
polydata : vtkPolyData
file_name : string
"""
# get file extension (type)
file_extension = file_name.split(".")[-1].lower()
if file_extension == "vtk":
writer = vtk.vtkPolyDataWriter()
elif file_extension == "fib":
writer = vtk.vtkPolyDataWriter()
elif file_extension == "ply":
writer = vtk.vtkPLYWriter()
elif file_extension == "stl":
writer = vtk.vtkSTLWriter()
elif file_extension == "xml":
writer = vtk.vtkXMLPolyDataWriter()
elif file_extension == "obj":
raise Exception("mni obj or Wavefront obj ?")
# writer = utils.set_input(vtk.vtkMNIObjectWriter(), polydata)
writer.SetFileName(file_name)
writer = utils.set_input(writer, polydata)
if color_array_name is not None:
writer.SetArrayName(color_array_name)
if binary:
writer.SetFileTypeToBinary()
writer.Update()
writer.Write()
def save_polydata(polydata, file_name, binary=False, color_array_name=None):
"""Save a vtk polydata to a supported format file.
Save formats can be VTK, FIB, PLY, STL and XML.
Parameters
----------
polydata : vtkPolyData
file_name : string
"""
# get file extension (type)
file_extension = file_name.split(".")[-1].lower()
if file_extension == "vtk":
writer = vtk.vtkPolyDataWriter()
elif file_extension == "fib":
writer = vtk.vtkPolyDataWriter()
elif file_extension == "ply":
writer = vtk.vtkPLYWriter()
elif file_extension == "stl":
writer = vtk.vtkSTLWriter()
elif file_extension == "xml":
writer = vtk.vtkXMLPolyDataWriter()
elif file_extension == "obj":
raise Exception("mni obj or Wavefront obj ?")
# writer = utils.set_input(vtk.vtkMNIObjectWriter(), polydata)
writer.SetFileName(file_name)
writer = utils.set_input(writer, polydata)
if color_array_name is not None:
writer.SetArrayName(color_array_name)
if binary:
writer.SetFileTypeToBinary()
writer.Update()
writer.Write()
def test_custom_interactor_style_events(recording=False):
print("Using VTK {}".format(vtk.vtkVersion.GetVTKVersion()))
filename = "test_custom_interactor_style_events.log.gz"
recording_filename = pjoin(DATA_DIR, filename)
renderer = window.Renderer()
# the show manager allows to break the rendering process
# in steps so that the widgets can be added properly
interactor_style = interactor.CustomInteractorStyle()
show_manager = window.ShowManager(renderer,
size=(800, 800),
reset_camera=False,
interactor_style=interactor_style)
# Create a cursor, a circle that will follow the mouse.
polygon_source = vtk.vtkRegularPolygonSource()
polygon_source.GeneratePolygonOff() # Only the outline of the circle.
polygon_source.SetNumberOfSides(50)
polygon_source.SetRadius(10)
# polygon_source.SetRadius
polygon_source.SetCenter(0, 0, 0)
mapper = vtk.vtkPolyDataMapper2D()
vtk_utils.set_input(mapper, polygon_source.GetOutputPort())
cursor = vtk.vtkActor2D()
cursor.SetMapper(mapper)
cursor.GetProperty().SetColor(1, 0.5, 0)
renderer.add(cursor)
def follow_mouse(iren, obj):
obj.SetPosition(*iren.event.position)
iren.force_render()
interactor_style.add_active_prop(cursor)
interactor_style.add_callback(cursor, "MouseMoveEvent", follow_mouse)
# create some minimalistic streamlines
lines = [
np.array([[-1, 0, 0.], [1, 0, 0.]]),
np.array([[-1, 1, 0.], [1, 1, 0.]])
]
colors = np.array([[1., 0., 0.], [0.3, 0.7, 0.]])
tube1 = actor.streamtube([lines[0]], colors[0])
tube2 = actor.streamtube([lines[1]], colors[1])
renderer.add(tube1)
renderer.add(tube2)
# Define some counter callback.
states = defaultdict(lambda: 0)
def counter(iren, obj):
states[iren.event.name] += 1
# Assign the counter callback to every possible event.
for event in [
"CharEvent", "MouseMoveEvent", "KeyPressEvent", "KeyReleaseEvent",
"LeftButtonPressEvent", "LeftButtonReleaseEvent",
"RightButtonPressEvent", "RightButtonReleaseEvent",
"MiddleButtonPressEvent", "MiddleButtonReleaseEvent"
]:
interactor_style.add_callback(tube1, event, counter)
# Add callback to scale up/down tube1.
def scale_up_obj(iren, obj):
counter(iren, obj)
scale = np.asarray(obj.GetScale()) + 0.1
obj.SetScale(*scale)
iren.force_render()
iren.event.abort() # Stop propagating the event.
def scale_down_obj(iren, obj):
counter(iren, obj)
scale = np.array(obj.GetScale()) - 0.1
obj.SetScale(*scale)
iren.force_render()
iren.event.abort() # Stop propagating the event.
interactor_style.add_callback(tube2, "MouseWheelForwardEvent",
scale_up_obj)
interactor_style.add_callback(tube2, "MouseWheelBackwardEvent",
scale_down_obj)
# Add callback to hide/show tube1.
def toggle_visibility(iren, obj):
key = iren.event.key
if key.lower() == "v":
obj.SetVisibility(not obj.GetVisibility())
iren.force_render()
interactor_style.add_active_prop(tube1)
interactor_style.add_active_prop(tube2)
interactor_style.remove_active_prop(tube2)
interactor_style.add_callback(tube1, "CharEvent", toggle_visibility)
if recording:
show_manager.record_events_to_file(recording_filename)
print(list(states.items()))
else:
show_manager.play_events_from_file(recording_filename)
msg = ("Wrong count for '{}'.")
expected = [('CharEvent', 6), ('KeyPressEvent', 6),
('KeyReleaseEvent', 6), ('MouseMoveEvent', 1652),
('LeftButtonPressEvent', 1), ('RightButtonPressEvent', 1),
('MiddleButtonPressEvent', 2),
('LeftButtonReleaseEvent', 1),
('MouseWheelForwardEvent', 3),
('MouseWheelBackwardEvent', 1),
('MiddleButtonReleaseEvent', 2),
('RightButtonReleaseEvent', 1)]
# Useful loop for debugging.
for event, count in expected:
if states[event] != count:
print("{}: {} vs. {} (expected)".format(
event, states[event], count))
for event, count in expected:
npt.assert_equal(states[event], count, err_msg=msg.format(event))
def line(lines, colors=None, opacity=1, linewidth=1,
spline_subdiv=None, lod=True, lod_points=10 ** 4, lod_points_size=3,
lookup_colormap=None):
""" Create an actor for one or more lines.
Parameters
------------
lines : list of arrays
colors : array (N, 3), list of arrays, tuple (3,), array (K,), None
If None then a standard orientation colormap is used for every line.
If one tuple of color is used. Then all streamlines will have the same
colour.
If an array (N, 3) is given, where N is equal to the number of lines.
Then every line is coloured with a different RGB color.
If a list of RGB arrays is given then every point of every line takes
a different color.
If an array (K, ) is given, where K is the number of points of all
lines then these are considered as the values to be used by the
colormap.
If an array (L, ) is given, where L is the number of streamlines then
these are considered as the values to be used by the colormap per
streamline.
If an array (X, Y, Z) or (X, Y, Z, 3) is given then the values for the
colormap are interpolated automatically using trilinear interpolation.
opacity : float, optional
Default is 1.
linewidth : float, optional
Line thickness. Default is 1.
spline_subdiv : int, optional
Number of splines subdivision to smooth streamtubes. Default is None
which means no subdivision.
lod : bool
Use vtkLODActor(level of detail) rather than vtkActor. Default is True.
Level of detail actors do not render the full geometry when the
frame rate is low.
lod_points : int
Number of points to be used when LOD is in effect. Default is 10000.
lod_points_size : int
Size of points when lod is in effect. Default is 3.
lookup_colormap : bool, optional
Add a default lookup table to the colormap. Default is None which calls
:func:`dipy.viz.actor.colormap_lookup_table`.
Returns
----------
v : vtkActor or vtkLODActor object
Line.
Examples
----------
>>> from dipy.viz import actor, window
>>> ren = window.Renderer()
>>> lines = [np.random.rand(10, 3), np.random.rand(20, 3)]
>>> colors = np.random.rand(2, 3)
>>> c = actor.line(lines, colors)
>>> ren.add(c)
>>> #window.show(ren)
"""
# Poly data with lines and colors
poly_data, is_colormap = lines_to_vtk_polydata(lines, colors)
next_input = poly_data
# use spline interpolation
if (spline_subdiv is not None) and (spline_subdiv > 0):
spline_filter = set_input(vtk.vtkSplineFilter(), next_input)
spline_filter.SetSubdivideToSpecified()
spline_filter.SetNumberOfSubdivisions(spline_subdiv)
spline_filter.Update()
next_input = spline_filter.GetOutputPort()
poly_mapper = set_input(vtk.vtkPolyDataMapper(), next_input)
poly_mapper.ScalarVisibilityOn()
poly_mapper.SetScalarModeToUsePointFieldData()
poly_mapper.SelectColorArray("Colors")
poly_mapper.Update()
# Color Scale with a lookup table
if is_colormap:
if lookup_colormap is None:
lookup_colormap = colormap_lookup_table()
poly_mapper.SetLookupTable(lookup_colormap)
poly_mapper.UseLookupTableScalarRangeOn()
poly_mapper.Update()
# Set Actor
if lod:
actor = vtk.vtkLODActor()
actor.SetNumberOfCloudPoints(lod_points)
actor.GetProperty().SetPointSize(lod_points_size)
else:
actor = vtk.vtkActor()
# actor = vtk.vtkActor()
actor.SetMapper(poly_mapper)
actor.GetProperty().SetLineWidth(linewidth)
actor.GetProperty().SetOpacity(opacity)
return actor
def slicer(data, affine=None, value_range=None, opacity=1.,
lookup_colormap=None, interpolation='linear'):
""" Cuts 3D scalar or rgb volumes into 2D images
Parameters
----------
data : array, shape (X, Y, Z) or (X, Y, Z, 3)
A grayscale or rgb 4D volume as a numpy array.
affine : array, shape (4, 4)
Grid to space (usually RAS 1mm) transformation matrix. Default is None.
If None then the identity matrix is used.
value_range : None or tuple (2,)
If None then the values will be interpolated from (data.min(),
data.max()) to (0, 255). Otherwise from (value_range[0],
value_range[1]) to (0, 255).
opacity : float
Opacity of 0 means completely transparent and 1 completely visible.
lookup_colormap : vtkLookupTable
If None (default) then a grayscale map is created.
interpolation : string
If 'linear' (default) then linear interpolation is used on the final
texture mapping. If 'nearest' then nearest neighbor interpolation is
used on the final texture mapping.
Returns
-------
image_actor : ImageActor
An object that is capable of displaying different parts of the volume
as slices. The key method of this object is ``display_extent`` where
one can input grid coordinates and display the slice in space (or grid)
coordinates as calculated by the affine parameter.
"""
if data.ndim != 3:
if data.ndim == 4:
if data.shape[3] != 3:
raise ValueError('Only RGB 3D arrays are currently supported.')
else:
nb_components = 3
else:
raise ValueError('Only 3D arrays are currently supported.')
else:
nb_components = 1
if value_range is None:
vol = np.interp(data, xp=[data.min(), data.max()], fp=[0, 255])
else:
vol = np.interp(data, xp=[value_range[0], value_range[1]], fp=[0, 255])
vol = vol.astype('uint8')
im = vtk.vtkImageData()
if major_version <= 5:
im.SetScalarTypeToUnsignedChar()
I, J, K = vol.shape[:3]
im.SetDimensions(I, J, K)
voxsz = (1., 1., 1.)
# im.SetOrigin(0,0,0)
im.SetSpacing(voxsz[2], voxsz[0], voxsz[1])
if major_version <= 5:
im.AllocateScalars()
im.SetNumberOfScalarComponents(nb_components)
else:
im.AllocateScalars(vtk.VTK_UNSIGNED_CHAR, nb_components)
# copy data
# what I do below is the same as what is commented here but much faster
# for index in ndindex(vol.shape):
# i, j, k = index
# im.SetScalarComponentFromFloat(i, j, k, 0, vol[i, j, k])
vol = np.swapaxes(vol, 0, 2)
vol = np.ascontiguousarray(vol)
if nb_components == 1:
vol = vol.ravel()
else:
vol = np.reshape(vol, [np.prod(vol.shape[:3]), vol.shape[3]])
uchar_array = numpy_support.numpy_to_vtk(vol, deep=0)
im.GetPointData().SetScalars(uchar_array)
if affine is None:
affine = np.eye(4)
# Set the transform (identity if none given)
transform = vtk.vtkTransform()
transform_matrix = vtk.vtkMatrix4x4()
transform_matrix.DeepCopy((
affine[0][0], affine[0][1], affine[0][2], affine[0][3],
affine[1][0], affine[1][1], affine[1][2], affine[1][3],
affine[2][0], affine[2][1], affine[2][2], affine[2][3],
affine[3][0], affine[3][1], affine[3][2], affine[3][3]))
transform.SetMatrix(transform_matrix)
transform.Inverse()
# Set the reslicing
image_resliced = vtk.vtkImageReslice()
set_input(image_resliced, im)
image_resliced.SetResliceTransform(transform)
image_resliced.AutoCropOutputOn()
# Adding this will allow to support anisotropic voxels
# and also gives the opportunity to slice per voxel coordinates
RZS = affine[:3, :3]
zooms = np.sqrt(np.sum(RZS * RZS, axis=0))
image_resliced.SetOutputSpacing(*zooms)
image_resliced.SetInterpolationModeToLinear()
image_resliced.Update()
if nb_components == 1:
if lookup_colormap is None:
# Create a black/white lookup table.
lut = colormap_lookup_table((0, 255), (0, 0), (0, 0), (0, 1))
else:
lut = lookup_colormap
x1, x2, y1, y2, z1, z2 = im.GetExtent()
ex1, ex2, ey1, ey2, ez1, ez2 = image_resliced.GetOutput().GetExtent()
class ImageActor(vtk.vtkImageActor):
def input_connection(self, output):
if vtk.VTK_MAJOR_VERSION <= 5:
self.SetInput(output.GetOutput())
else:
self.GetMapper().SetInputConnection(output.GetOutputPort())
self.output = output
self.shape = (ex2 + 1, ey2 + 1, ez2 + 1)
def display_extent(self, x1, x2, y1, y2, z1, z2):
self.SetDisplayExtent(x1, x2, y1, y2, z1, z2)
if vtk.VTK_MAJOR_VERSION > 5:
self.Update()
def display(self, x=None, y=None, z=None):
if x is None and y is None and z is None:
self.display_extent(ex1, ex2, ey1, ey2, ez2/2, ez2/2)
if x is not None:
self.display_extent(x, x, ey1, ey2, ez1, ez2)
if y is not None:
self.display_extent(ex1, ex2, y, y, ez1, ez2)
if z is not None:
self.display_extent(ex1, ex2, ey1, ey2, z, z)
def opacity(self, value):
if vtk.VTK_MAJOR_VERSION <= 5:
self.SetOpacity(value)
else:
self.GetProperty().SetOpacity(value)
def copy(self):
im_actor = ImageActor()
im_actor.input_connection(self.output)
im_actor.SetDisplayExtent(*self.GetDisplayExtent())
im_actor.opacity(opacity)
return im_actor
image_actor = ImageActor()
if nb_components == 1:
plane_colors = vtk.vtkImageMapToColors()
plane_colors.SetLookupTable(lut)
plane_colors.SetInputConnection(image_resliced.GetOutputPort())
plane_colors.Update()
image_actor.input_connection(plane_colors)
else:
image_actor.input_connection(image_resliced)
image_actor.display()
image_actor.opacity(opacity)
if interpolation == 'nearest':
image_actor.SetInterpolate(False)
else:
image_actor.SetInterpolate(True)
if major_version >= 6:
image_actor.GetMapper().BorderOn()
return image_actor
def streamtube(lines, colors=None, opacity=1, linewidth=0.1, tube_sides=9,
lod=True, lod_points=10 ** 4, lod_points_size=3,
spline_subdiv=None, lookup_colormap=None):
""" Uses streamtubes to visualize polylines
Parameters
----------
lines : list
list of N curves represented as 2D ndarrays
colors : array (N, 3), list of arrays, tuple (3,), array (K,), None
If None then a standard orientation colormap is used for every line.
If one tuple of color is used. Then all streamlines will have the same
colour.
If an array (N, 3) is given, where N is equal to the number of lines.
Then every line is coloured with a different RGB color.
If a list of RGB arrays is given then every point of every line takes
a different color.
If an array (K, ) is given, where K is the number of points of all
lines then these are considered as the values to be used by the
colormap.
If an array (L, ) is given, where L is the number of streamlines then
these are considered as the values to be used by the colormap per
streamline.
If an array (X, Y, Z) or (X, Y, Z, 3) is given then the values for the
colormap are interpolated automatically using trilinear interpolation.
opacity : float
Default is 1.
linewidth : float
Default is 0.01.
tube_sides : int
Default is 9.
lod : bool
Use vtkLODActor(level of detail) rather than vtkActor. Default is True.
Level of detail actors do not render the full geometry when the
frame rate is low.
lod_points : int
Number of points to be used when LOD is in effect. Default is 10000.
lod_points_size : int
Size of points when lod is in effect. Default is 3.
spline_subdiv : int
Number of splines subdivision to smooth streamtubes. Default is None.
lookup_colormap : vtkLookupTable
Add a default lookup table to the colormap. Default is None which calls
:func:`dipy.viz.actor.colormap_lookup_table`.
Examples
--------
>>> import numpy as np
>>> from dipy.viz import actor, window
>>> ren = window.Renderer()
>>> lines = [np.random.rand(10, 3), np.random.rand(20, 3)]
>>> colors = np.random.rand(2, 3)
>>> c = actor.streamtube(lines, colors)
>>> ren.add(c)
>>> #window.show(ren)
Notes
-----
Streamtubes can be heavy on GPU when loading many streamlines and
therefore, you may experience slow rendering time depending on system GPU.
A solution to this problem is to reduce the number of points in each
streamline. In Dipy we provide an algorithm that will reduce the number of
points on the straighter parts of the streamline but keep more points on
the curvier parts. This can be used in the following way::
from dipy.tracking.distances import approx_polygon_track
lines = [approx_polygon_track(line, 0.2) for line in lines]
Alternatively we suggest using the ``line`` actor which is much more
efficient.
See Also
--------
:func:``dipy.viz.actor.line``
"""
# Poly data with lines and colors
poly_data, is_colormap = lines_to_vtk_polydata(lines, colors)
next_input = poly_data
# Set Normals
poly_normals = set_input(vtk.vtkPolyDataNormals(), next_input)
poly_normals.ComputeCellNormalsOn()
poly_normals.ComputePointNormalsOn()
poly_normals.ConsistencyOn()
poly_normals.AutoOrientNormalsOn()
poly_normals.Update()
next_input = poly_normals.GetOutputPort()
# Spline interpolation
if (spline_subdiv is not None) and (spline_subdiv > 0):
spline_filter = set_input(vtk.vtkSplineFilter(), next_input)
spline_filter.SetSubdivideToSpecified()
spline_filter.SetNumberOfSubdivisions(spline_subdiv)
spline_filter.Update()
next_input = spline_filter.GetOutputPort()
# Add thickness to the resulting lines
tube_filter = set_input(vtk.vtkTubeFilter(), next_input)
tube_filter.SetNumberOfSides(tube_sides)
tube_filter.SetRadius(linewidth)
# TODO using the line above we will be able to visualize
# streamtubes of varying radius
# tube_filter.SetVaryRadiusToVaryRadiusByScalar()
tube_filter.CappingOn()
tube_filter.Update()
next_input = tube_filter.GetOutputPort()
# Poly mapper
poly_mapper = set_input(vtk.vtkPolyDataMapper(), next_input)
poly_mapper.ScalarVisibilityOn()
poly_mapper.SetScalarModeToUsePointFieldData()
poly_mapper.SelectColorArray("Colors")
poly_mapper.GlobalImmediateModeRenderingOn()
poly_mapper.Update()
# Color Scale with a lookup table
if is_colormap:
if lookup_colormap is None:
lookup_colormap = colormap_lookup_table()
poly_mapper.SetLookupTable(lookup_colormap)
poly_mapper.UseLookupTableScalarRangeOn()
poly_mapper.Update()
# Set Actor
if lod:
actor = vtk.vtkLODActor()
actor.SetNumberOfCloudPoints(lod_points)
actor.GetProperty().SetPointSize(lod_points_size)
else:
actor = vtk.vtkActor()
actor.SetMapper(poly_mapper)
actor.GetProperty().SetAmbient(0.1)
actor.GetProperty().SetDiffuse(0.15)
actor.GetProperty().SetSpecular(0.05)
actor.GetProperty().SetSpecularPower(6)
actor.GetProperty().SetInterpolationToPhong()
actor.GetProperty().BackfaceCullingOn()
actor.GetProperty().SetOpacity(opacity)
return actor
def line(lines,
colors=None,
opacity=1,
linewidth=1,
spline_subdiv=None,
lod=True,
lod_points=10**4,
lod_points_size=3,
lookup_colormap=None):
""" Create an actor for one or more lines.
Parameters
------------
lines : list of arrays
colors : array (N, 3), list of arrays, tuple (3,), array (K,), None
If None then a standard orientation colormap is used for every line.
If one tuple of color is used. Then all streamlines will have the same
colour.
If an array (N, 3) is given, where N is equal to the number of lines.
Then every line is coloured with a different RGB color.
If a list of RGB arrays is given then every point of every line takes
a different color.
If an array (K, ) is given, where K is the number of points of all
lines then these are considered as the values to be used by the
colormap.
If an array (L, ) is given, where L is the number of streamlines then
these are considered as the values to be used by the colormap per
streamline.
If an array (X, Y, Z) or (X, Y, Z, 3) is given then the values for the
colormap are interpolated automatically using trilinear interpolation.
opacity : float, optional
Default is 1.
linewidth : float, optional
Line thickness. Default is 1.
spline_subdiv : int, optional
Number of splines subdivision to smooth streamtubes. Default is None
which means no subdivision.
lod : bool
Use vtkLODActor(level of detail) rather than vtkActor. Default is True.
Level of detail actors do not render the full geometry when the
frame rate is low.
lod_points : int
Number of points to be used when LOD is in effect. Default is 10000.
lod_points_size : int
Size of points when lod is in effect. Default is 3.
lookup_colormap : bool, optional
Add a default lookup table to the colormap. Default is None which calls
:func:`dipy.viz.actor.colormap_lookup_table`.
Returns
----------
v : vtkActor or vtkLODActor object
Line.
Examples
----------
>>> from dipy.viz import actor, window
>>> ren = window.Renderer()
>>> lines = [np.random.rand(10, 3), np.random.rand(20, 3)]
>>> colors = np.random.rand(2, 3)
>>> c = actor.line(lines, colors)
>>> ren.add(c)
>>> #window.show(ren)
"""
# Poly data with lines and colors
poly_data, is_colormap = lines_to_vtk_polydata(lines, colors)
next_input = poly_data
# use spline interpolation
if (spline_subdiv is not None) and (spline_subdiv > 0):
spline_filter = set_input(vtk.vtkSplineFilter(), next_input)
spline_filter.SetSubdivideToSpecified()
spline_filter.SetNumberOfSubdivisions(spline_subdiv)
spline_filter.Update()
next_input = spline_filter.GetOutputPort()
poly_mapper = set_input(vtk.vtkPolyDataMapper(), next_input)
poly_mapper.ScalarVisibilityOn()
poly_mapper.SetScalarModeToUsePointFieldData()
poly_mapper.SelectColorArray("Colors")
poly_mapper.Update()
# Color Scale with a lookup table
if is_colormap:
if lookup_colormap is None:
lookup_colormap = colormap_lookup_table()
poly_mapper.SetLookupTable(lookup_colormap)
poly_mapper.UseLookupTableScalarRangeOn()
poly_mapper.Update()
# Set Actor
if lod:
actor = vtk.vtkLODActor()
actor.SetNumberOfCloudPoints(lod_points)
actor.GetProperty().SetPointSize(lod_points_size)
else:
actor = vtk.vtkActor()
# actor = vtk.vtkActor()
actor.SetMapper(poly_mapper)
actor.GetProperty().SetLineWidth(linewidth)
actor.GetProperty().SetOpacity(opacity)
return actor
def slicer(data,
affine=None,
value_range=None,
opacity=1.,
lookup_colormap=None,
interpolation='linear'):
""" Cuts 3D scalar or rgb volumes into 2D images
Parameters
----------
data : array, shape (X, Y, Z) or (X, Y, Z, 3)
A grayscale or rgb 4D volume as a numpy array.
affine : array, shape (4, 4)
Grid to space (usually RAS 1mm) transformation matrix. Default is None.
If None then the identity matrix is used.
value_range : None or tuple (2,)
If None then the values will be interpolated from (data.min(),
data.max()) to (0, 255). Otherwise from (value_range[0],
value_range[1]) to (0, 255).
opacity : float
Opacity of 0 means completely transparent and 1 completely visible.
lookup_colormap : vtkLookupTable
If None (default) then a grayscale map is created.
interpolation : string
If 'linear' (default) then linear interpolation is used on the final
texture mapping. If 'nearest' then nearest neighbor interpolation is
used on the final texture mapping.
Returns
-------
image_actor : ImageActor
An object that is capable of displaying different parts of the volume
as slices. The key method of this object is ``display_extent`` where
one can input grid coordinates and display the slice in space (or grid)
coordinates as calculated by the affine parameter.
"""
if data.ndim != 3:
if data.ndim == 4:
if data.shape[3] != 3:
raise ValueError('Only RGB 3D arrays are currently supported.')
else:
nb_components = 3
else:
raise ValueError('Only 3D arrays are currently supported.')
else:
nb_components = 1
if value_range is None:
vol = np.interp(data, xp=[data.min(), data.max()], fp=[0, 255])
else:
vol = np.interp(data, xp=[value_range[0], value_range[1]], fp=[0, 255])
vol = vol.astype('uint8')
im = vtk.vtkImageData()
if major_version <= 5:
im.SetScalarTypeToUnsignedChar()
I, J, K = vol.shape[:3]
im.SetDimensions(I, J, K)
voxsz = (1., 1., 1.)
# im.SetOrigin(0,0,0)
im.SetSpacing(voxsz[2], voxsz[0], voxsz[1])
if major_version <= 5:
im.AllocateScalars()
im.SetNumberOfScalarComponents(nb_components)
else:
im.AllocateScalars(vtk.VTK_UNSIGNED_CHAR, nb_components)
# copy data
# what I do below is the same as what is commented here but much faster
# for index in ndindex(vol.shape):
# i, j, k = index
# im.SetScalarComponentFromFloat(i, j, k, 0, vol[i, j, k])
vol = np.swapaxes(vol, 0, 2)
vol = np.ascontiguousarray(vol)
if nb_components == 1:
vol = vol.ravel()
else:
vol = np.reshape(vol, [np.prod(vol.shape[:3]), vol.shape[3]])
uchar_array = numpy_support.numpy_to_vtk(vol, deep=0)
im.GetPointData().SetScalars(uchar_array)
if affine is None:
affine = np.eye(4)
# Set the transform (identity if none given)
transform = vtk.vtkTransform()
transform_matrix = vtk.vtkMatrix4x4()
transform_matrix.DeepCopy(
(affine[0][0], affine[0][1], affine[0][2], affine[0][3], affine[1][0],
affine[1][1], affine[1][2], affine[1][3], affine[2][0], affine[2][1],
affine[2][2], affine[2][3], affine[3][0], affine[3][1], affine[3][2],
affine[3][3]))
transform.SetMatrix(transform_matrix)
transform.Inverse()
# Set the reslicing
image_resliced = vtk.vtkImageReslice()
set_input(image_resliced, im)
image_resliced.SetResliceTransform(transform)
image_resliced.AutoCropOutputOn()
# Adding this will allow to support anisotropic voxels
# and also gives the opportunity to slice per voxel coordinates
RZS = affine[:3, :3]
zooms = np.sqrt(np.sum(RZS * RZS, axis=0))
image_resliced.SetOutputSpacing(*zooms)
image_resliced.SetInterpolationModeToLinear()
image_resliced.Update()
if nb_components == 1:
if lookup_colormap is None:
# Create a black/white lookup table.
lut = colormap_lookup_table((0, 255), (0, 0), (0, 0), (0, 1))
else:
lut = lookup_colormap
x1, x2, y1, y2, z1, z2 = im.GetExtent()
ex1, ex2, ey1, ey2, ez1, ez2 = image_resliced.GetOutput().GetExtent()
class ImageActor(vtk.vtkImageActor):
def input_connection(self, output):
if vtk.VTK_MAJOR_VERSION <= 5:
self.SetInput(output.GetOutput())
else:
self.GetMapper().SetInputConnection(output.GetOutputPort())
self.output = output
self.shape = (ex2 + 1, ey2 + 1, ez2 + 1)
def display_extent(self, x1, x2, y1, y2, z1, z2):
self.SetDisplayExtent(x1, x2, y1, y2, z1, z2)
if vtk.VTK_MAJOR_VERSION > 5:
self.Update()
def display(self, x=None, y=None, z=None):
if x is None and y is None and z is None:
self.display_extent(ex1, ex2, ey1, ey2, ez2 // 2, ez2 // 2)
if x is not None:
self.display_extent(x, x, ey1, ey2, ez1, ez2)
if y is not None:
self.display_extent(ex1, ex2, y, y, ez1, ez2)
if z is not None:
self.display_extent(ex1, ex2, ey1, ey2, z, z)
def opacity(self, value):
if vtk.VTK_MAJOR_VERSION <= 5:
self.SetOpacity(value)
else:
self.GetProperty().SetOpacity(value)
def copy(self):
im_actor = ImageActor()
im_actor.input_connection(self.output)
im_actor.SetDisplayExtent(*self.GetDisplayExtent())
im_actor.opacity(opacity)
if interpolation == 'nearest':
im_actor.SetInterpolate(False)
else:
im_actor.SetInterpolate(True)
if major_version >= 6:
im_actor.GetMapper().BorderOn()
return im_actor
image_actor = ImageActor()
if nb_components == 1:
plane_colors = vtk.vtkImageMapToColors()
plane_colors.SetLookupTable(lut)
plane_colors.SetInputConnection(image_resliced.GetOutputPort())
plane_colors.Update()
image_actor.input_connection(plane_colors)
else:
image_actor.input_connection(image_resliced)
image_actor.display()
image_actor.opacity(opacity)
if interpolation == 'nearest':
image_actor.SetInterpolate(False)
else:
image_actor.SetInterpolate(True)
if major_version >= 6:
image_actor.GetMapper().BorderOn()
return image_actor
def streamtube(lines,
colors=None,
opacity=1,
linewidth=0.1,
tube_sides=9,
lod=True,
lod_points=10**4,
lod_points_size=3,
spline_subdiv=None,
lookup_colormap=None):
""" Uses streamtubes to visualize polylines
Parameters
----------
lines : list
list of N curves represented as 2D ndarrays
colors : array (N, 3), list of arrays, tuple (3,), array (K,), None
If None then a standard orientation colormap is used for every line.
If one tuple of color is used. Then all streamlines will have the same
colour.
If an array (N, 3) is given, where N is equal to the number of lines.
Then every line is coloured with a different RGB color.
If a list of RGB arrays is given then every point of every line takes
a different color.
If an array (K, ) is given, where K is the number of points of all
lines then these are considered as the values to be used by the
colormap.
If an array (L, ) is given, where L is the number of streamlines then
these are considered as the values to be used by the colormap per
streamline.
If an array (X, Y, Z) or (X, Y, Z, 3) is given then the values for the
colormap are interpolated automatically using trilinear interpolation.
opacity : float
Default is 1.
linewidth : float
Default is 0.01.
tube_sides : int
Default is 9.
lod : bool
Use vtkLODActor(level of detail) rather than vtkActor. Default is True.
Level of detail actors do not render the full geometry when the
frame rate is low.
lod_points : int
Number of points to be used when LOD is in effect. Default is 10000.
lod_points_size : int
Size of points when lod is in effect. Default is 3.
spline_subdiv : int
Number of splines subdivision to smooth streamtubes. Default is None.
lookup_colormap : vtkLookupTable
Add a default lookup table to the colormap. Default is None which calls
:func:`dipy.viz.actor.colormap_lookup_table`.
Examples
--------
>>> import numpy as np
>>> from dipy.viz import actor, window
>>> ren = window.Renderer()
>>> lines = [np.random.rand(10, 3), np.random.rand(20, 3)]
>>> colors = np.random.rand(2, 3)
>>> c = actor.streamtube(lines, colors)
>>> ren.add(c)
>>> #window.show(ren)
Notes
-----
Streamtubes can be heavy on GPU when loading many streamlines and
therefore, you may experience slow rendering time depending on system GPU.
A solution to this problem is to reduce the number of points in each
streamline. In Dipy we provide an algorithm that will reduce the number of
points on the straighter parts of the streamline but keep more points on
the curvier parts. This can be used in the following way::
from dipy.tracking.distances import approx_polygon_track
lines = [approx_polygon_track(line, 0.2) for line in lines]
Alternatively we suggest using the ``line`` actor which is much more
efficient.
See Also
--------
:func:`dipy.viz.actor.line`
"""
# Poly data with lines and colors
poly_data, is_colormap = lines_to_vtk_polydata(lines, colors)
next_input = poly_data
# Set Normals
poly_normals = set_input(vtk.vtkPolyDataNormals(), next_input)
poly_normals.ComputeCellNormalsOn()
poly_normals.ComputePointNormalsOn()
poly_normals.ConsistencyOn()
poly_normals.AutoOrientNormalsOn()
poly_normals.Update()
next_input = poly_normals.GetOutputPort()
# Spline interpolation
if (spline_subdiv is not None) and (spline_subdiv > 0):
spline_filter = set_input(vtk.vtkSplineFilter(), next_input)
spline_filter.SetSubdivideToSpecified()
spline_filter.SetNumberOfSubdivisions(spline_subdiv)
spline_filter.Update()
next_input = spline_filter.GetOutputPort()
# Add thickness to the resulting lines
tube_filter = set_input(vtk.vtkTubeFilter(), next_input)
tube_filter.SetNumberOfSides(tube_sides)
tube_filter.SetRadius(linewidth)
# TODO using the line above we will be able to visualize
# streamtubes of varying radius
# tube_filter.SetVaryRadiusToVaryRadiusByScalar()
tube_filter.CappingOn()
tube_filter.Update()
next_input = tube_filter.GetOutputPort()
# Poly mapper
poly_mapper = set_input(vtk.vtkPolyDataMapper(), next_input)
poly_mapper.ScalarVisibilityOn()
poly_mapper.SetScalarModeToUsePointFieldData()
poly_mapper.SelectColorArray("Colors")
poly_mapper.GlobalImmediateModeRenderingOn()
poly_mapper.Update()
# Color Scale with a lookup table
if is_colormap:
if lookup_colormap is None:
lookup_colormap = colormap_lookup_table()
poly_mapper.SetLookupTable(lookup_colormap)
poly_mapper.UseLookupTableScalarRangeOn()
poly_mapper.Update()
# Set Actor
if lod:
actor = vtk.vtkLODActor()
actor.SetNumberOfCloudPoints(lod_points)
actor.GetProperty().SetPointSize(lod_points_size)
else:
actor = vtk.vtkActor()
actor.SetMapper(poly_mapper)
actor.GetProperty().SetAmbient(0.1)
actor.GetProperty().SetDiffuse(0.15)
actor.GetProperty().SetSpecular(0.05)
actor.GetProperty().SetSpecularPower(6)
actor.GetProperty().SetInterpolationToPhong()
actor.GetProperty().BackfaceCullingOn()
actor.GetProperty().SetOpacity(opacity)
return actor
def contour_from_roi_smooth(data,
affine=None,
color=np.array([1, 0, 0]),
opacity=1,
smoothing=0):
"""Generates surface actor from a binary ROI.
Code from dipy, but added awesome smoothing!
Parameters
----------
data : array, shape (X, Y, Z)
An ROI file that will be binarized and displayed.
affine : array, shape (4, 4)
Grid to space (usually RAS 1mm) transformation matrix. Default is None.
If None then the identity matrix is used.
color : (1, 3) ndarray
RGB values in [0,1].
opacity : float
Opacity of surface between 0 and 1.
smoothing: int
Smoothing factor e.g. 10.
Returns
-------
contour_assembly : vtkAssembly
ROI surface object displayed in space
coordinates as calculated by the affine parameter.
"""
major_version = vtk.vtkVersion.GetVTKMajorVersion()
if data.ndim != 3:
raise ValueError('Only 3D arrays are currently supported.')
else:
nb_components = 1
data = (data > 0) * 1
vol = np.interp(data, xp=[data.min(), data.max()], fp=[0, 255])
vol = vol.astype('uint8')
im = vtk.vtkImageData()
if major_version <= 5:
im.SetScalarTypeToUnsignedChar()
di, dj, dk = vol.shape[:3]
im.SetDimensions(di, dj, dk)
voxsz = (1., 1., 1.)
# im.SetOrigin(0,0,0)
im.SetSpacing(voxsz[2], voxsz[0], voxsz[1])
if major_version <= 5:
im.AllocateScalars()
im.SetNumberOfScalarComponents(nb_components)
else:
im.AllocateScalars(vtk.VTK_UNSIGNED_CHAR, nb_components)
# copy data
vol = np.swapaxes(vol, 0, 2)
vol = np.ascontiguousarray(vol)
if nb_components == 1:
vol = vol.ravel()
else:
vol = np.reshape(vol, [np.prod(vol.shape[:3]), vol.shape[3]])
uchar_array = numpy_support.numpy_to_vtk(vol, deep=0)
im.GetPointData().SetScalars(uchar_array)
if affine is None:
affine = np.eye(4)
# Set the transform (identity if none given)
transform = vtk.vtkTransform()
transform_matrix = vtk.vtkMatrix4x4()
transform_matrix.DeepCopy(
(affine[0][0], affine[0][1], affine[0][2], affine[0][3],
affine[1][0], affine[1][1], affine[1][2], affine[1][3],
affine[2][0], affine[2][1], affine[2][2], affine[2][3],
affine[3][0], affine[3][1], affine[3][2], affine[3][3]))
transform.SetMatrix(transform_matrix)
transform.Inverse()
# Set the reslicing
image_resliced = vtk.vtkImageReslice()
set_input(image_resliced, im)
image_resliced.SetResliceTransform(transform)
image_resliced.AutoCropOutputOn()
# Adding this will allow to support anisotropic voxels
# and also gives the opportunity to slice per voxel coordinates
rzs = affine[:3, :3]
zooms = np.sqrt(np.sum(rzs * rzs, axis=0))
image_resliced.SetOutputSpacing(*zooms)
image_resliced.SetInterpolationModeToLinear()
image_resliced.Update()
# skin_extractor = vtk.vtkContourFilter()
skin_extractor = vtk.vtkMarchingCubes()
if major_version <= 5:
skin_extractor.SetInput(image_resliced.GetOutput())
else:
skin_extractor.SetInputData(image_resliced.GetOutput())
skin_extractor.SetValue(0, 100)
if smoothing > 0:
smoother = vtk.vtkSmoothPolyDataFilter()
smoother.SetInputConnection(skin_extractor.GetOutputPort())
smoother.SetNumberOfIterations(smoothing)
smoother.SetRelaxationFactor(0.1)
smoother.SetFeatureAngle(60)
smoother.FeatureEdgeSmoothingOff()
smoother.BoundarySmoothingOff()
smoother.SetConvergence(0)
smoother.Update()
skin_normals = vtk.vtkPolyDataNormals()
skin_normals.SetInputConnection(smoother.GetOutputPort())
skin_normals.SetFeatureAngle(60.0)
skin_mapper = vtk.vtkPolyDataMapper()
skin_mapper.SetInputConnection(skin_normals.GetOutputPort())
skin_mapper.ScalarVisibilityOff()
skin_actor = vtk.vtkActor()
skin_actor.SetMapper(skin_mapper)
skin_actor.GetProperty().SetOpacity(opacity)
skin_actor.GetProperty().SetColor(color[0], color[1], color[2])
return skin_actor
def test_custom_interactor_style_events(recording=False):
print("Using VTK {}".format(vtk.vtkVersion.GetVTKVersion()))
filename = "test_custom_interactor_style_events.log.gz"
recording_filename = pjoin(DATA_DIR, filename)
renderer = window.Renderer()
# the show manager allows to break the rendering process
# in steps so that the widgets can be added properly
interactor_style = interactor.CustomInteractorStyle()
show_manager = window.ShowManager(renderer, size=(800, 800), reset_camera=False, interactor_style=interactor_style)
# Create a cursor, a circle that will follow the mouse.
polygon_source = vtk.vtkRegularPolygonSource()
polygon_source.GeneratePolygonOff() # Only the outline of the circle.
polygon_source.SetNumberOfSides(50)
polygon_source.SetRadius(10)
polygon_source.SetRadius
polygon_source.SetCenter(0, 0, 0)
mapper = vtk.vtkPolyDataMapper2D()
vtk_utils.set_input(mapper, polygon_source.GetOutputPort())
cursor = vtk.vtkActor2D()
cursor.SetMapper(mapper)
cursor.GetProperty().SetColor(1, 0.5, 0)
renderer.add(cursor)
def follow_mouse(iren, obj):
obj.SetPosition(*iren.event.position)
iren.force_render()
interactor_style.add_active_prop(cursor)
interactor_style.add_callback(cursor, "MouseMoveEvent", follow_mouse)
# create some minimalistic streamlines
lines = [np.array([[-1, 0, 0.0], [1, 0, 0.0]]), np.array([[-1, 1, 0.0], [1, 1, 0.0]])]
colors = np.array([[1.0, 0.0, 0.0], [0.3, 0.7, 0.0]])
tube1 = actor.streamtube([lines[0]], colors[0])
tube2 = actor.streamtube([lines[1]], colors[1])
renderer.add(tube1)
renderer.add(tube2)
# Define some counter callback.
states = defaultdict(lambda: 0)
def counter(iren, obj):
states[iren.event.name] += 1
# Assign the counter callback to every possible event.
for event in [
"CharEvent",
"MouseMoveEvent",
"KeyPressEvent",
"KeyReleaseEvent",
"LeftButtonPressEvent",
"LeftButtonReleaseEvent",
"RightButtonPressEvent",
"RightButtonReleaseEvent",
"MiddleButtonPressEvent",
"MiddleButtonReleaseEvent",
]:
interactor_style.add_callback(tube1, event, counter)
# Add callback to scale up/down tube1.
def scale_up_obj(iren, obj):
counter(iren, obj)
scale = np.asarray(obj.GetScale()) + 0.1
obj.SetScale(*scale)
iren.force_render()
iren.event.abort() # Stop propagating the event.
def scale_down_obj(iren, obj):
counter(iren, obj)
scale = np.array(obj.GetScale()) - 0.1
obj.SetScale(*scale)
iren.force_render()
iren.event.abort() # Stop propagating the event.
interactor_style.add_callback(tube2, "MouseWheelForwardEvent", scale_up_obj)
interactor_style.add_callback(tube2, "MouseWheelBackwardEvent", scale_down_obj)
# Add callback to hide/show tube1.
def toggle_visibility(iren, obj):
key = iren.event.key
if key.lower() == "v":
obj.SetVisibility(not obj.GetVisibility())
iren.force_render()
interactor_style.add_active_prop(tube1)
interactor_style.add_active_prop(tube2)
interactor_style.remove_active_prop(tube2)
interactor_style.add_callback(tube1, "CharEvent", toggle_visibility)
if recording:
show_manager.record_events_to_file(recording_filename)
print(list(states.items()))
else:
show_manager.play_events_from_file(recording_filename)
msg = "Wrong count for '{}'."
expected = [
("CharEvent", 6),
("KeyPressEvent", 6),
("KeyReleaseEvent", 6),
("MouseMoveEvent", 1652),
("LeftButtonPressEvent", 1),
("RightButtonPressEvent", 1),
("MiddleButtonPressEvent", 2),
("LeftButtonReleaseEvent", 1),
("MouseWheelForwardEvent", 3),
("MouseWheelBackwardEvent", 1),
("MiddleButtonReleaseEvent", 2),
("RightButtonReleaseEvent", 1),
]
# Useful loop for debugging.
for event, count in expected:
if states[event] != count:
print("{}: {} vs. {} (expected)".format(event, states[event], count))
for event, count in expected:
npt.assert_equal(states[event], count, err_msg=msg.format(event))
def contour_actor(data, affine=None, levels=[50],
colors=[np.array([1.0, 0.0, 0.0])], opacities=[1]):
"""Take a volume and draw surface contours for any any number of
thresholds (levels) where every contour has its own color and opacity
Parameters
----------
data : array, shape (X, Y, Z) or (X, Y, Z, 3)
A grayscale or rgb 4D volume as a numpy array.
affine : array, shape (4, 4)
Grid to space (usually RAS 1mm) transformation matrix. Default is None.
If None then the identity matrix is used.
levels : array_like
Sequence of thresholds for the contours taken from image values needs
to be same datatype as `vol`.
colors : (N, 3) ndarray
RGB values in [0,1].
opacities : array_like
Opacities of contours.
Returns
-------
contour_assembly : vtkAssembly
An object that is capable of displaying an roi as contours in space
coordinates as calculated by the affine parameter.
"""
if data.ndim != 3:
if data.ndim == 4:
if data.shape[3] != 3:
raise ValueError('Only RGB 3D arrays are currently supported.')
else:
nb_components = 3
else:
raise ValueError('Only 3D arrays are currently supported.')
else:
nb_components = 1
vol = np.interp(data, xp=[data.min(), data.max()], fp=[0, 255])
vol = vol.astype('uint8')
im = vtk.vtkImageData()
if major_version <= 5:
im.SetScalarTypeToUnsignedChar()
di, dj, dk = vol.shape[:3]
im.SetDimensions(di, dj, dk)
voxsz = (1., 1., 1.)
# im.SetOrigin(0,0,0)
im.SetSpacing(voxsz[2], voxsz[0], voxsz[1])
if major_version <= 5:
im.AllocateScalars()
im.SetNumberOfScalarComponents(nb_components)
else:
im.AllocateScalars(vtk.VTK_UNSIGNED_CHAR, nb_components)
# copy data
# what I do below is the same as what is commented here but much faster
# for index in ndindex(vol.shape):
# i, j, k = index
# im.SetScalarComponentFromFloat(i, j, k, 0, vol[i, j, k])
vol = np.swapaxes(vol, 0, 2)
vol = np.ascontiguousarray(vol)
if nb_components == 1:
vol = vol.ravel()
else:
vol = np.reshape(vol, [np.prod(vol.shape[:3]), vol.shape[3]])
uchar_array = numpy_support.numpy_to_vtk(vol, deep=0)
im.GetPointData().SetScalars(uchar_array)
if affine is None:
affine = np.eye(4)
# Set the transform (identity if none given)
transform = vtk.vtkTransform()
transform_matrix = vtk.vtkMatrix4x4()
transform_matrix.DeepCopy((
affine[0][0], affine[0][1], affine[0][2], affine[0][3],
affine[1][0], affine[1][1], affine[1][2], affine[1][3],
affine[2][0], affine[2][1], affine[2][2], affine[2][3],
affine[3][0], affine[3][1], affine[3][2], affine[3][3]))
transform.SetMatrix(transform_matrix)
transform.Inverse()
# Set the reslicing
image_resliced = vtk.vtkImageReslice()
set_input(image_resliced, im)
image_resliced.SetResliceTransform(transform)
image_resliced.AutoCropOutputOn()
# Adding this will allow to support anisotropic voxels
# and also gives the opportunity to slice per voxel coordinates
rzs = affine[:3, :3]
zooms = np.sqrt(np.sum(rzs * rzs, axis=0))
image_resliced.SetOutputSpacing(*zooms)
image_resliced.SetInterpolationModeToLinear()
image_resliced.Update()
# code from fvtk contour function
skin_assembly = vtk.vtkAssembly()
for (i, l) in enumerate(levels):
skin_extractor = vtk.vtkContourFilter()
if major_version <= 5:
skin_extractor.SetInput(image_resliced)
else:
skin_extractor.SetInputData(image_resliced.GetOutput())
skin_extractor.SetValue(0, l)
skin_normals = vtk.vtkPolyDataNormals()
skin_normals.SetInputConnection(skin_extractor.GetOutputPort())
skin_normals.SetFeatureAngle(60.0)
skin_mapper = vtk.vtkPolyDataMapper()
skin_mapper.SetInputConnection(skin_normals.GetOutputPort())
skin_mapper.ScalarVisibilityOff()
skin_actor = vtk.vtkActor()
skin_actor.SetMapper(skin_mapper)
skin_actor.GetProperty().SetOpacity(opacities[i])
skin_actor.GetProperty().SetColor(colors[i][0], colors[i][1], colors[i][2])
skin_assembly.AddPart(skin_actor)
del skin_actor
del skin_mapper
del skin_extractor
return skin_assembly
def save_vtk_streamlines(streamlines, filename, to_lps=True, binary=False):
"""Save streamlines as vtk polydata to a supported format file.
File formats can be VTK, FIB
Parameters
----------
streamlines : list
list of 2D arrays or ArraySequence
filename : string
output filename (.vtk or .fib)
to_lps : bool
Default to True, will follow the vtk file convention for streamlines
Will be supported by MITKDiffusion and MI-Brain
binary : bool
save the file as binary
"""
if to_lps:
# ras (mm) to lps (mm)
to_lps = np.eye(4)
to_lps[0, 0] = -1
to_lps[1, 1] = -1
streamlines = transform_streamlines(streamlines, to_lps)
# Get the 3d points_array
nb_lines = len(streamlines)
points_array = np.vstack(streamlines)
# Get lines_array in vtk input format
lines_array = []
current_position = 0
for i in range(nb_lines):
current_len = len(streamlines[i])
end_position = current_position + current_len
lines_array.append(current_len)
lines_array.extend(range(current_position, end_position))
current_position = end_position
# Set Points to vtk array format
vtk_points = vtk.vtkPoints()
vtk_points.SetData(
ns.numpy_to_vtk(points_array.astype(np.float32), deep=True))
# Set Lines to vtk array format
vtk_lines = vtk.vtkCellArray()
vtk_lines.SetNumberOfCells(nb_lines)
vtk_lines.GetData().DeepCopy(
ns.numpy_to_vtk(np.array(lines_array), deep=True))
# Create the poly_data
polydata = vtk.vtkPolyData()
polydata.SetPoints(vtk_points)
polydata.SetLines(vtk_lines)
writer = vtk.vtkPolyDataWriter()
writer.SetFileName(filename)
writer = utils.set_input(writer, polydata)
if binary:
writer.SetFileTypeToBinary()
writer.Update()
writer.Write()
def contour_from_roi(data, affine=None,
color=np.array([1, 0, 0]), opacity=1):
"""Generates surface actor from a binary ROI.
The color and opacity of the surface can be customized.
Parameters
----------
data : array, shape (X, Y, Z)
An ROI file that will be binarized and displayed.
affine : array, shape (4, 4)
Grid to space (usually RAS 1mm) transformation matrix. Default is None.
If None then the identity matrix is used.
color : (1, 3) ndarray
RGB values in [0,1].
opacity : float
Opacity of surface between 0 and 1.
Returns
-------
contour_assembly : vtkAssembly
ROI surface object displayed in space
coordinates as calculated by the affine parameter.
"""
if data.ndim != 3:
raise ValueError('Only 3D arrays are currently supported.')
else:
nb_components = 1
data = (data > 0) * 1
vol = np.interp(data, xp=[data.min(), data.max()], fp=[0, 255])
vol = vol.astype('uint8')
im = vtk.vtkImageData()
if major_version <= 5:
im.SetScalarTypeToUnsignedChar()
di, dj, dk = vol.shape[:3]
im.SetDimensions(di, dj, dk)
voxsz = (1., 1., 1.)
# im.SetOrigin(0,0,0)
im.SetSpacing(voxsz[2], voxsz[0], voxsz[1])
if major_version <= 5:
im.AllocateScalars()
im.SetNumberOfScalarComponents(nb_components)
else:
im.AllocateScalars(vtk.VTK_UNSIGNED_CHAR, nb_components)
# copy data
vol = np.swapaxes(vol, 0, 2)
vol = np.ascontiguousarray(vol)
if nb_components == 1:
vol = vol.ravel()
else:
vol = np.reshape(vol, [np.prod(vol.shape[:3]), vol.shape[3]])
uchar_array = numpy_support.numpy_to_vtk(vol, deep=0)
im.GetPointData().SetScalars(uchar_array)
if affine is None:
affine = np.eye(4)
# Set the transform (identity if none given)
transform = vtk.vtkTransform()
transform_matrix = vtk.vtkMatrix4x4()
transform_matrix.DeepCopy((
affine[0][0], affine[0][1], affine[0][2], affine[0][3],
affine[1][0], affine[1][1], affine[1][2], affine[1][3],
affine[2][0], affine[2][1], affine[2][2], affine[2][3],
affine[3][0], affine[3][1], affine[3][2], affine[3][3]))
transform.SetMatrix(transform_matrix)
transform.Inverse()
# Set the reslicing
image_resliced = vtk.vtkImageReslice()
set_input(image_resliced, im)
image_resliced.SetResliceTransform(transform)
image_resliced.AutoCropOutputOn()
# Adding this will allow to support anisotropic voxels
# and also gives the opportunity to slice per voxel coordinates
rzs = affine[:3, :3]
zooms = np.sqrt(np.sum(rzs * rzs, axis=0))
image_resliced.SetOutputSpacing(*zooms)
image_resliced.SetInterpolationModeToLinear()
image_resliced.Update()
skin_extractor = vtk.vtkContourFilter()
if major_version <= 5:
skin_extractor.SetInput(image_resliced.GetOutput())
else:
skin_extractor.SetInputData(image_resliced.GetOutput())
skin_extractor.SetValue(0, 1)
skin_normals = vtk.vtkPolyDataNormals()
skin_normals.SetInputConnection(skin_extractor.GetOutputPort())
skin_normals.SetFeatureAngle(60.0)
skin_mapper = vtk.vtkPolyDataMapper()
skin_mapper.SetInputConnection(skin_normals.GetOutputPort())
skin_mapper.ScalarVisibilityOff()
skin_actor = vtk.vtkActor()
skin_actor.SetMapper(skin_mapper)
skin_actor.GetProperty().SetOpacity(opacity)
skin_actor.GetProperty().SetColor(color[0], color[1], color[2])
return skin_actor
