def test_colormap():
v = np.linspace(0., .5)
map1 = fvtk.create_colormap(v, 'bone', auto=True)
map2 = fvtk.create_colormap(v, 'bone', auto=False)
npt.assert_(not np.allclose(map1, map2))
npt.assert_raises(ValueError, fvtk.create_colormap, np.ones((2, 3)))
npt.assert_raises(ValueError, fvtk.create_colormap, v, 'no such map')
def test_colormap():
v = np.linspace(0., .5)
map1 = fvtk.create_colormap(v, 'bone', auto=True)
map2 = fvtk.create_colormap(v, 'bone', auto=False)
npt.assert_(not np.allclose(map1, map2))
npt.assert_raises(ValueError, fvtk.create_colormap, np.ones((2, 3)))
npt.assert_raises(ValueError, fvtk.create_colormap, v, 'no such map')
def show(imgtck, clusters, out_path):
colormap = fvtk.create_colormap(np.ravel(clusters.centroids), name='jet')
colormap_full = np.ones((len(imgtck.streamlines), 3))
for cluster, color in zip(clusters, colormap):
colormap_full[cluster.indices] = color
ren = fvtk.ren()
ren.SetBackground(1, 1, 1)
fvtk.add(ren, fvtk.streamtube(imgtck.streamlines, colormap_full))
fvtk.record(ren, n_frames=1, out_path=out_path, size=(600, 600))
fvtk.show(ren)
def renderCentroids(streamlines, clusters):
from dipy.viz import fvtk
import numpy as np
ren = fvtk.ren()
ren.SetBackground(0, 0, 0)
colormap = fvtk.create_colormap(np.arange(len(clusters)))
colormap_full = np.ones((len(streamlines), 3))
for cluster in clusters:
colormap_full[cluster.indices] = np.random.rand(3)
#fvtk.add(ren, fvtk.streamtube(streamlines, fvtk.colors.white, opacity=0.05))
fvtk.add(ren, fvtk.line(clusters.centroids, linewidth=0.4, opacity=1))
#fvtk.record(ren, n_frames=1, out_path='fornix_centroids.png', size=(600, 600))
fvtk.show(ren)
fvtk.clear(ren)
def renderCentroids(clusters):
from dipy.viz import fvtk
import numpy as np
ren = fvtk.ren()
ren.SetBackground(0, 0, 0)
colormap = fvtk.create_colormap(np.arange(len(clusters)))
colormap_full = np.ones((len(streamlines), 3))
for cluster in clusters:
colormap_full[cluster.indices] = np.random.rand(3)
#fvtk.add(ren, fvtk.streamtube(streamlines, fvtk.colors.white, opacity=0.05))
fvtk.add(ren, fvtk.line(clusters.centroids, linewidth=0.4, opacity=1))
#fvtk.record(ren, n_frames=1, out_path='fornix_centroids.png', size=(600, 600))
fvtk.show(ren)
fvtk.clear(ren)
def renderBundles(streamlines, clusters):
from dipy.viz import fvtk
import numpy as np
ren = fvtk.ren()
ren.SetBackground(0, 0, 0)
colormap = fvtk.create_colormap(np.arange(len(clusters)))
colormap_full = np.ones((len(streamlines), 3))
for cluster in clusters:
colormap_full[cluster.indices] = np.random.rand(3)
fvtk.add(ren, fvtk.line(streamlines, colormap_full))
#fvtk.record(ren, n_frames=1, out_path='fornix_clusters.png', size=(600, 600))
fvtk.show(ren)
fvtk.clear(ren)
def renderBundles(clusters):
from dipy.viz import fvtk
import numpy as np
ren = fvtk.ren()
ren.SetBackground(0, 0, 0)
colormap = fvtk.create_colormap(np.arange(len(clusters)))
colormap_full = np.ones((len(streamlines), 3))
for cluster in clusters:
colormap_full[cluster.indices] = np.random.rand(3)
fvtk.add(ren, fvtk.line(streamlines, colormap_full))
#fvtk.record(ren, n_frames=1, out_path='fornix_clusters.png', size=(600, 600))
fvtk.show(ren)
fvtk.clear(ren)
def show_bundles(streamlines, clusters, show_b=True):
# Color each streamline according to the cluster they belong to.
colormap = fvtk.create_colormap(np.arange(len(clusters)))
colormap_full = np.ones((len(streamlines), 3))
for cluster, color in zip(clusters, colormap):
colormap_full[cluster.indices] = color
ren = fvtk.ren()
ren.SetBackground(1, 1, 1)
fvtk.add(ren, fvtk.streamtube(streamlines, colormap_full))
fvtk.record(ren,
n_frames=1,
out_path='fornix_clusters_cosine.png',
size=(600, 600))
if show_b:
fvtk.show(ren)
and our `ArcLengthFeature`.
"""
from dipy.segment.clustering import QuickBundles
from dipy.segment.metric import SumPointwiseEuclideanMetric
metric = SumPointwiseEuclideanMetric(feature=ArcLengthFeature())
qb = QuickBundles(threshold=2., metric=metric)
clusters = qb.cluster(streamlines)
"""
We will now visualize the clustering result.
"""
# Color each streamline according to the cluster they belong to.
colormap = fvtk.create_colormap(np.ravel(clusters.centroids))
colormap_full = np.ones((len(streamlines), 3))
for cluster, color in zip(clusters, colormap):
colormap_full[cluster.indices] = color
ren = fvtk.ren()
ren.SetBackground(1, 1, 1)
fvtk.add(ren, fvtk.streamtube(streamlines, colormap_full))
fvtk.record(ren, n_frames=1, out_path='fornix_clusters_arclength.png', size=(600, 600))
"""
.. figure:: fornix_clusters_arclength.png
:align: center
**Showing the different clusters obtained by using the arc length**.
Perform QuickBundles clustering using the metric `SumPointwiseEuclideanMetric`
and our `ArcLengthFeature`.
"""
from dipy.segment.clustering import QuickBundles
from dipy.segment.metric import SumPointwiseEuclideanMetric
metric = SumPointwiseEuclideanMetric(feature=ArcLengthFeature())
qb = QuickBundles(threshold=2., metric=metric)
clusters = qb.cluster(streamlines)
"""
We will now visualize the clustering result.
"""
# Color each streamline according to the cluster they belong to.
colormap = fvtk.create_colormap(np.ravel(clusters.centroids))
colormap_full = np.ones((len(streamlines), 3))
for cluster, color in zip(clusters, colormap):
colormap_full[cluster.indices] = color
ren = fvtk.ren()
ren.SetBackground(1, 1, 1)
fvtk.add(ren, fvtk.streamtube(streamlines, colormap_full))
fvtk.record(ren,
n_frames=1,
out_path='fornix_clusters_arclength.png',
size=(600, 600))
"""
.. figure:: fornix_clusters_arclength.png
:align: center
""" ren = fvtk.ren() ren.SetBackground(1, 1, 1) fvtk.add(ren, fvtk.streamtube(streamlines, fvtk.colors.white)) fvtk.record(ren, n_frames=1, out_path='fornix_initial.png', size=(600, 600)) """ .. figure:: fornix_initial.png :align: center Initial Fornix dataset. Show the centroids of the fornix after clustering (with random colors): """ colormap = fvtk.create_colormap(np.arange(len(clusters))) fvtk.clear(ren) ren.SetBackground(1, 1, 1) fvtk.add(ren, fvtk.streamtube(streamlines, fvtk.colors.white, opacity=0.05)) fvtk.add(ren, fvtk.streamtube(clusters.centroids, colormap, linewidth=0.4)) fvtk.record(ren, n_frames=1, out_path='fornix_centroids.png', size=(600, 600)) """ .. figure:: fornix_centroids.png :align: center Showing the different QuickBundles centroids with random colors. Show the labeled fornix (colors from centroids). """
def interactive_viewer(streamlines, outlierness):
import vtk
from dipy.viz import fvtk, actor, window, widget
from dipy.data.fetcher import read_viz_icons
colormap_name = "jet"
stream_actor = actor.line(streamlines,
colors=fvtk.create_colormap(outlierness,
name=colormap_name))
stream_actor.SetPosition(-np.array(stream_actor.GetCenter()))
global threshold
threshold = 0.8
streamlines_color = np.zeros(len(streamlines), dtype="float32")
streamlines_color[outlierness < threshold] = 1
streamlines_color[outlierness >= threshold] = 0
lut = vtk.vtkLookupTable()
lut.SetNumberOfTableValues(2)
lut.Build()
lut.SetTableValue(0, tuple(fvtk.colors.orange_red) + (1, ))
lut.SetTableValue(1, tuple(fvtk.colors.green) + (1, ))
lut.SetTableRange(0, 1)
stream_split_actor = actor.line(streamlines,
colors=streamlines_color,
lookup_colormap=lut)
stream_split_actor.SetPosition(-np.array(stream_split_actor.GetCenter()))
hist_actor, hist_fig = create_hist_actor(outlierness,
colormap_name=colormap_name)
# Main renderder
bg = (0, 0, 0)
global screen_size
screen_size = (0, 0)
ren_main = window.Renderer()
ren_main.background(bg)
show_m = window.ShowManager(ren_main,
size=(1066, 600),
interactor_style="trackball")
show_m.window.SetNumberOfLayers(2)
ren_main.SetLayer(1)
ren_main.InteractiveOff()
# Outlierness renderer
ren_outlierness = window.Renderer()
show_m.window.AddRenderer(ren_outlierness)
ren_outlierness.background(bg)
ren_outlierness.SetViewport(0, 0.3, 0.5, 1)
ren_outlierness.add(stream_actor)
# ren_outlierness.reset_camera_tight()
ren_split = window.Renderer()
show_m.window.AddRenderer(ren_split)
ren_split.background(bg)
ren_split.SetViewport(0.5, 0.3, 1, 1)
ren_split.add(stream_split_actor)
ren_split.SetActiveCamera(ren_outlierness.GetActiveCamera())
# Histogram renderer
ren_hist = window.Renderer()
show_m.window.AddRenderer(ren_hist)
ren_hist.projection("parallel")
ren_hist.background(bg)
ren_hist.SetViewport(0, 0, 1, 0.3)
ren_hist.add(hist_actor)
ren_hist.SetInteractive(False)
def apply_threshold(obj, evt):
global threshold
new_threshold = np.round(obj.GetSliderRepresentation().GetValue(),
decimals=2)
obj.GetSliderRepresentation().SetValue(new_threshold)
if threshold != new_threshold:
threshold = new_threshold
streamlines_color = np.zeros(len(streamlines), dtype=np.float32)
streamlines_color[outlierness < threshold] = 1
streamlines_color[outlierness >= threshold] = 0
colors = []
for color, streamline in zip(streamlines_color, streamlines):
colors += [color] * len(streamline)
scalars = stream_split_actor.GetMapper().GetInput().GetPointData(
).GetScalars()
for i, c in enumerate(colors):
scalars.SetValue(i, c)
scalars.Modified()
threshold_slider_rep = vtk.vtkSliderRepresentation3D()
threshold_slider_rep.SetMinimumValue(0.)
threshold_slider_rep.SetMaximumValue(1.)
threshold_slider_rep.SetValue(threshold)
threshold_slider_rep.SetLabelFormat("%0.2lf")
threshold_slider_rep.SetLabelHeight(0.02)
threshold_slider_rep.GetPoint1Coordinate().SetCoordinateSystemToWorld()
x1, x2, y1, y2, z1, z2 = hist_actor.GetBounds()
threshold_slider_rep.GetPoint1Coordinate().SetValue(x1 * 1., y1 - 5, 0)
threshold_slider_rep.GetPoint2Coordinate().SetCoordinateSystemToWorld()
threshold_slider_rep.GetPoint2Coordinate().SetValue(x2 * 1., y1 - 5, 0)
threshold_slider_rep.SetEndCapLength(0.)
threshold_slider_rep.SetEndCapWidth(0.)
threshold_slider = vtk.vtkSliderWidget()
threshold_slider.SetInteractor(show_m.iren)
threshold_slider.SetRepresentation(threshold_slider_rep)
threshold_slider.SetCurrentRenderer(ren_hist)
threshold_slider.SetAnimationModeToJump()
threshold_slider.EnabledOn()
threshold_slider.AddObserver("InteractionEvent", apply_threshold)
#ren_main
def _place_buttons():
sz = 30.0
width, _ = ren_main.GetSize()
# bds = np.zeros(6)
# bds[0] = width - sz - 5
# bds[1] = bds[0] + sz
# bds[2] = 5
# bds[3] = bds[2] + sz
# bds[4] = bds[5] = 0.0
# save_button.GetRepresentation().PlaceWidget(bds)
def _window_callback(obj, event):
# ren_hist.reset_camera_tight(margin_factor=1.2)
_place_buttons()
show_m.add_window_callback(_window_callback)
show_m.initialize()
show_m.render()
show_m.start()
inliers = [
s for s, keep in zip(streamlines, outlierness < threshold) if keep
]
outliers = [
s for s, keep in zip(streamlines, outlierness >= threshold) if keep
]
return inliers, outliers
import numpy as np
from dipy.viz import fvtk
from dipy.segment.clustering import QuickBundles
from dipy.segment.metric import VectorOfEndpointsFeature
from dipy.segment.metric import CosineMetric
# Get some streamlines.
streamlines = get_streamlines() # Previously defined.
feature = VectorOfEndpointsFeature()
metric = CosineMetric(feature)
qb = QuickBundles(threshold=0.1, metric=metric)
clusters = qb.cluster(streamlines)
# Color each streamline according to the cluster they belong to.
colormap = fvtk.create_colormap(np.arange(len(clusters)))
colormap_full = np.ones((len(streamlines), 3))
for cluster, color in zip(clusters, colormap):
colormap_full[cluster.indices] = color
# Visualization
ren = fvtk.ren()
fvtk.clear(ren)
ren.SetBackground(0, 0, 0)
fvtk.add(ren, fvtk.streamtube(streamlines, colormap_full))
fvtk.record(ren, n_frames=1, out_path='cosine_metric.png', size=(600, 600))
"""
.. figure:: cosine_metric.png
:align: center
def interactive_viewer(streamlines, outlierness):
import vtk
from dipy.viz import fvtk, actor, window, widget
from dipy.data.fetcher import read_viz_icons
colormap_name = "jet"
stream_actor = actor.line(streamlines, colors=fvtk.create_colormap(outlierness, name=colormap_name))
stream_actor.SetPosition(-np.array(stream_actor.GetCenter()))
global threshold
threshold = 0.8
streamlines_color = np.zeros(len(streamlines), dtype="float32")
streamlines_color[outlierness < threshold] = 1
streamlines_color[outlierness >= threshold] = 0
lut = vtk.vtkLookupTable()
lut.SetNumberOfTableValues(2)
lut.Build()
lut.SetTableValue(0, tuple(fvtk.colors.orange_red) + (1,))
lut.SetTableValue(1, tuple(fvtk.colors.green) + (1,))
lut.SetTableRange(0, 1)
stream_split_actor = actor.line(streamlines, colors=streamlines_color, lookup_colormap=lut)
stream_split_actor.SetPosition(-np.array(stream_split_actor.GetCenter()))
hist_actor, hist_fig = create_hist_actor(outlierness, colormap_name=colormap_name)
# Main renderder
bg = (0, 0, 0)
global screen_size
screen_size = (0, 0)
ren_main = window.Renderer()
ren_main.background(bg)
show_m = window.ShowManager(ren_main, size=(1066, 600), interactor_style="trackball")
show_m.window.SetNumberOfLayers(2)
ren_main.SetLayer(1)
ren_main.InteractiveOff()
# Outlierness renderer
ren_outlierness = window.Renderer()
show_m.window.AddRenderer(ren_outlierness)
ren_outlierness.background(bg)
ren_outlierness.SetViewport(0, 0.3, 0.5, 1)
ren_outlierness.add(stream_actor)
ren_outlierness.reset_camera_tight()
ren_split = window.Renderer()
show_m.window.AddRenderer(ren_split)
ren_split.background(bg)
ren_split.SetViewport(0.5, 0.3, 1, 1)
ren_split.add(stream_split_actor)
ren_split.SetActiveCamera(ren_outlierness.GetActiveCamera())
# Histogram renderer
ren_hist = window.Renderer()
show_m.window.AddRenderer(ren_hist)
ren_hist.projection("parallel")
ren_hist.background(bg)
ren_hist.SetViewport(0, 0, 1, 0.3)
ren_hist.add(hist_actor)
ren_hist.SetInteractive(False)
def apply_threshold(obj, evt):
global threshold
new_threshold = np.round(obj.GetSliderRepresentation().GetValue(), decimals=2)
obj.GetSliderRepresentation().SetValue(new_threshold)
if threshold != new_threshold:
threshold = new_threshold
streamlines_color = np.zeros(len(streamlines), dtype=np.float32)
streamlines_color[outlierness < threshold] = 1
streamlines_color[outlierness >= threshold] = 0
colors = []
for color, streamline in zip(streamlines_color, streamlines):
colors += [color] * len(streamline)
scalars = stream_split_actor.GetMapper().GetInput().GetPointData().GetScalars()
for i, c in enumerate(colors):
scalars.SetValue(i, c)
scalars.Modified()
threshold_slider_rep = vtk.vtkSliderRepresentation3D()
threshold_slider_rep.SetMinimumValue(0.)
threshold_slider_rep.SetMaximumValue(1.)
threshold_slider_rep.SetValue(threshold)
threshold_slider_rep.SetLabelFormat("%0.2lf")
threshold_slider_rep.SetLabelHeight(0.02)
threshold_slider_rep.GetPoint1Coordinate().SetCoordinateSystemToWorld()
x1, x2, y1, y2, z1, z2 = hist_actor.GetBounds()
threshold_slider_rep.GetPoint1Coordinate().SetValue(x1*1., y1-5, 0)
threshold_slider_rep.GetPoint2Coordinate().SetCoordinateSystemToWorld()
threshold_slider_rep.GetPoint2Coordinate().SetValue(x2*1., y1-5, 0)
threshold_slider_rep.SetEndCapLength(0.)
threshold_slider_rep.SetEndCapWidth(0.)
threshold_slider = vtk.vtkSliderWidget()
threshold_slider.SetInteractor(show_m.iren)
threshold_slider.SetRepresentation(threshold_slider_rep)
threshold_slider.SetCurrentRenderer(ren_hist)
threshold_slider.SetAnimationModeToJump()
threshold_slider.EnabledOn()
threshold_slider.AddObserver("InteractionEvent", apply_threshold)
#ren_main
def _place_buttons():
sz = 30.0
width, _ = ren_main.GetSize()
# bds = np.zeros(6)
# bds[0] = width - sz - 5
# bds[1] = bds[0] + sz
# bds[2] = 5
# bds[3] = bds[2] + sz
# bds[4] = bds[5] = 0.0
# save_button.GetRepresentation().PlaceWidget(bds)
def _window_callback(obj, event):
ren_hist.reset_camera_tight(margin_factor=1.2)
_place_buttons()
show_m.add_window_callback(_window_callback)
show_m.initialize()
show_m.render()
show_m.start()
inliers = [s for s, keep in zip(streamlines, outlierness < threshold) if keep]
outliers = [s for s, keep in zip(streamlines, outlierness >= threshold) if keep]
return inliers, outliers
# print "Centroid of the last cluster:\n", clusters[-1].centroid
# show rhe initial dataset
ren = fvtk.ren()
ren.SetBackground(1, 1, 1)
fvtk.add(ren, fvtk.streamtube(streamlines, fvtk.colors.white))
fvtk.record(
ren,
n_frames=1,
out_path='/home/brain/workingdir/data/dwi/hcp/preprocessed/'
'response_dhollander/100206/result/CC_fib_remove_non_cc_z-10_x-min_10_jet.png',
size=(600, 600))
# fvtk.show(ren)
# show the centroids of the CC
colormap = fvtk.create_colormap(np.arange(len(clusters)), name='jet')
fvtk.clear(ren)
ren.SetBackground(1, 1, 1)
fvtk.add(ren, fvtk.streamtube(streamlines, fvtk.colors.white, opacity=0.05))
fvtk.add(ren, fvtk.streamtube(clusters.centroids, colormap, linewidth=0.4))
fvtk.record(
ren,
n_frames=1,
out_path='/home/brain/workingdir/data/dwi/hcp/preprocessed/'
'response_dhollander/100206/result/CC_fib1_remove_non_cc_z-10_x-min_10_jet.png',
size=(600, 600))
# fvtk.show(ren)
# show the label CC (colors form centroids)
colormap_full = np.ones((len(streamlines)), np.float64(3))
for clusters, color in zip(clusters, colormap):