def test_deprecated():
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", DeprecationWarning)
scene = window.Renderer()
npt.assert_equal(scene.size(), (0, 0))
npt.assert_equal(len(w), 1)
npt.assert_(issubclass(w[-1].category, DeprecationWarning))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", DeprecationWarning)
scene = window.renderer(background=(0.0, 1.0, 0.0))
npt.assert_equal(scene.size(), (0, 0))
npt.assert_equal(len(w), 1)
npt.assert_(issubclass(w[-1].category, DeprecationWarning))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", DeprecationWarning)
scene = window.ren()
npt.assert_equal(scene.size(), (0, 0))
npt.assert_equal(len(w), 2)
npt.assert_(issubclass(w[-1].category, DeprecationWarning))
scene = window.Scene()
with warnings.catch_warnings(record=True) as l_warn:
warnings.simplefilter("always", DeprecationWarning)
obj = actor.axes(scale=(1, 1, 1))
window.add(scene, obj)
arr = window.snapshot(scene)
report = window.analyze_snapshot(arr)
npt.assert_equal(report.objects, 3)
window.rm(scene, obj)
arr = window.snapshot(scene)
report = window.analyze_snapshot(arr)
npt.assert_equal(report.objects, 0)
window.add(scene, obj)
window.rm_all(scene)
arr = window.snapshot(scene)
report = window.analyze_snapshot(arr)
npt.assert_equal(report.objects, 0)
window.add(scene, obj)
window.clear(scene)
report = window.analyze_renderer(scene)
npt.assert_equal(report.actors, 0)
deprecated_warns = [
w for w in l_warn if issubclass(w.category, DeprecationWarning)
]
npt.assert_equal(len(deprecated_warns), 7)
npt.assert_(issubclass(l_warn[-1].category, DeprecationWarning))
def my_visu(sf, sphere, rot=True, norm=True, scale=True, title="Modeling"):
ren = window.renderer(background=window.colors.white)
sf_actor = actor.odf_slicer(sf,
sphere=sphere,
colormap='jet',
scale=0.4,
norm=norm,
radial_scale=scale)
if rot:
sf_actor.RotateX(90)
ren.add(sf_actor)
window.show(ren, title=title, size=WINDOW_SIZE)
ren.rm(sf_actor)
window.rm_all(ren)
def test_streamtube_and_line_actors():
renderer = window.renderer()
line1 = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2.]])
line2 = line1 + np.array([0.5, 0., 0.])
lines = [line1, line2]
colors = np.array([[1, 0, 0], [0, 0, 1.]])
c = actor.line(lines, colors, linewidth=3)
window.add(renderer, c)
c = actor.line(lines, colors, spline_subdiv=5, linewidth=3)
window.add(renderer, c)
# create streamtubes of the same lines and shift them a bit
c2 = actor.streamtube(lines, colors, linewidth=.1)
c2.SetPosition(2, 0, 0)
window.add(renderer, c2)
arr = window.snapshot(renderer)
report = window.analyze_snapshot(arr,
colors=[(255, 0, 0), (0, 0, 255)],
find_objects=True)
npt.assert_equal(report.objects, 4)
npt.assert_equal(report.colors_found, [True, True])
# as before with splines
c2 = actor.streamtube(lines, colors, spline_subdiv=5, linewidth=.1)
c2.SetPosition(2, 0, 0)
window.add(renderer, c2)
arr = window.snapshot(renderer)
report = window.analyze_snapshot(arr,
colors=[(255, 0, 0), (0, 0, 255)],
find_objects=True)
npt.assert_equal(report.objects, 4)
npt.assert_equal(report.colors_found, [True, True])
def test_bundle_maps():
renderer = window.renderer()
bundle = fornix_streamlines()
bundle, shift = center_streamlines(bundle)
mat = np.array([[1, 0, 0, 100],
[0, 1, 0, 100],
[0, 0, 1, 100],
[0, 0, 0, 1.]])
bundle = transform_streamlines(bundle, mat)
# metric = np.random.rand(*(200, 200, 200))
metric = 100 * np.ones((200, 200, 200))
# add lower values
metric[100, :, :] = 100 * 0.5
# create a nice orange-red colormap
lut = actor.colormap_lookup_table(scale_range=(0., 100.),
hue_range=(0., 0.1),
saturation_range=(1, 1),
value_range=(1., 1))
line = actor.line(bundle, metric, linewidth=0.1, lookup_colormap=lut)
window.add(renderer, line)
window.add(renderer, actor.scalar_bar(lut, ' '))
report = window.analyze_renderer(renderer)
npt.assert_almost_equal(report.actors, 1)
# window.show(renderer)
renderer.clear()
nb_points = np.sum([len(b) for b in bundle])
values = 100 * np.random.rand(nb_points)
# values[:nb_points/2] = 0
line = actor.streamtube(bundle, values, linewidth=0.1, lookup_colormap=lut)
renderer.add(line)
# window.show(renderer)
report = window.analyze_renderer(renderer)
npt.assert_equal(report.actors_classnames[0], 'vtkLODActor')
renderer.clear()
colors = np.random.rand(nb_points, 3)
# values[:nb_points/2] = 0
line = actor.line(bundle, colors, linewidth=2)
renderer.add(line)
# window.show(renderer)
report = window.analyze_renderer(renderer)
npt.assert_equal(report.actors_classnames[0], 'vtkLODActor')
# window.show(renderer)
arr = window.snapshot(renderer)
report2 = window.analyze_snapshot(arr)
npt.assert_equal(report2.objects, 1)
# try other input options for colors
renderer.clear()
actor.line(bundle, (1., 0.5, 0))
actor.line(bundle, np.arange(len(bundle)))
actor.line(bundle)
colors = [np.random.rand(*b.shape) for b in bundle]
actor.line(bundle, colors=colors)
def test_slicer():
renderer = window.renderer()
data = (255 * np.random.rand(50, 50, 50))
affine = np.eye(4)
slicer = actor.slicer(data, affine)
slicer.display(None, None, 25)
renderer.add(slicer)
renderer.reset_camera()
renderer.reset_clipping_range()
# window.show(renderer)
# copy pixels in numpy array directly
arr = window.snapshot(renderer, 'test_slicer.png', offscreen=True)
import scipy
print(scipy.__version__)
print(scipy.__file__)
print(arr.sum())
print(np.sum(arr == 0))
print(np.sum(arr > 0))
print(arr.shape)
print(arr.dtype)
report = window.analyze_snapshot(arr, find_objects=True)
npt.assert_equal(report.objects, 1)
# print(arr[..., 0])
# The slicer can cut directly a smaller part of the image
slicer.display_extent(10, 30, 10, 30, 35, 35)
renderer.ResetCamera()
renderer.add(slicer)
# save pixels in png file not a numpy array
with InTemporaryDirectory() as tmpdir:
fname = os.path.join(tmpdir, 'slice.png')
# window.show(renderer)
window.snapshot(renderer, fname, offscreen=True)
report = window.analyze_snapshot(fname, find_objects=True)
npt.assert_equal(report.objects, 1)
npt.assert_raises(ValueError, actor.slicer, np.ones(10))
renderer.clear()
rgb = np.zeros((30, 30, 30, 3))
rgb[..., 0] = 1.
rgb_actor = actor.slicer(rgb)
renderer.add(rgb_actor)
renderer.reset_camera()
renderer.reset_clipping_range()
arr = window.snapshot(renderer, offscreen=True)
report = window.analyze_snapshot(arr, colors=[(255, 0, 0)])
npt.assert_equal(report.objects, 1)
npt.assert_equal(report.colors_found, [True])
lut = actor.colormap_lookup_table(scale_range=(0, 255),
hue_range=(0.4, 1.),
saturation_range=(1, 1.),
value_range=(0., 1.))
renderer.clear()
slicer_lut = actor.slicer(data, lookup_colormap=lut)
slicer_lut.display(10, None, None)
slicer_lut.display(None, 10, None)
slicer_lut.display(None, None, 10)
slicer_lut.opacity(0.5)
slicer_lut.tolerance(0.03)
slicer_lut2 = slicer_lut.copy()
npt.assert_equal(slicer_lut2.GetOpacity(), 0.5)
npt.assert_equal(slicer_lut2.picker.GetTolerance(), 0.03)
slicer_lut2.opacity(1)
slicer_lut2.tolerance(0.025)
slicer_lut2.display(None, None, 10)
renderer.add(slicer_lut2)
renderer.reset_clipping_range()
arr = window.snapshot(renderer, offscreen=True)
report = window.analyze_snapshot(arr, find_objects=True)
npt.assert_equal(report.objects, 1)
renderer.clear()
data = (255 * np.random.rand(50, 50, 50))
affine = np.diag([1, 3, 2, 1])
slicer = actor.slicer(data, affine, interpolation='nearest')
slicer.display(None, None, 25)
renderer.add(slicer)
renderer.reset_camera()
renderer.reset_clipping_range()
arr = window.snapshot(renderer, offscreen=True)
report = window.analyze_snapshot(arr, find_objects=True)
npt.assert_equal(report.objects, 1)
npt.assert_equal(data.shape, slicer.shape)
renderer.clear()
data = (255 * np.random.rand(50, 50, 50))
affine = np.diag([1, 3, 2, 1])
from dipy.align.reslice import reslice
data2, affine2 = reslice(data, affine, zooms=(1, 3, 2),
new_zooms=(1, 1, 1))
slicer = actor.slicer(data2, affine2, interpolation='linear')
slicer.display(None, None, 25)
renderer.add(slicer)
renderer.reset_camera()
renderer.reset_clipping_range()
# window.show(renderer, reset_camera=False)
arr = window.snapshot(renderer, offscreen=True)
report = window.analyze_snapshot(arr, find_objects=True)
npt.assert_equal(report.objects, 1)
npt.assert_array_equal([1, 3, 2] * np.array(data.shape),
np.array(slicer.shape))
def test_contour_from_roi():
# Render volume
renderer = window.renderer()
data = np.zeros((50, 50, 50))
data[20:30, 25, 25] = 1.
data[25, 20:30, 25] = 1.
affine = np.eye(4)
surface = actor.contour_from_roi(data, affine,
color=np.array([1, 0, 1]),
opacity=.5)
renderer.add(surface)
renderer.reset_camera()
renderer.reset_clipping_range()
# window.show(renderer)
# Test binarization
renderer2 = window.renderer()
data2 = np.zeros((50, 50, 50))
data2[20:30, 25, 25] = 1.
data2[35:40, 25, 25] = 1.
affine = np.eye(4)
surface2 = actor.contour_from_roi(data2, affine,
color=np.array([0, 1, 1]),
opacity=.5)
renderer2.add(surface2)
renderer2.reset_camera()
renderer2.reset_clipping_range()
# window.show(renderer2)
arr = window.snapshot(renderer, 'test_surface.png', offscreen=True)
arr2 = window.snapshot(renderer2, 'test_surface2.png', offscreen=True)
report = window.analyze_snapshot(arr, find_objects=True)
report2 = window.analyze_snapshot(arr2, find_objects=True)
npt.assert_equal(report.objects, 1)
npt.assert_equal(report2.objects, 2)
# test on real streamlines using tracking example
from dipy.data import read_stanford_labels
from dipy.reconst.shm import CsaOdfModel
from dipy.data import default_sphere
from dipy.direction import peaks_from_model
from dipy.tracking.local import ThresholdTissueClassifier
from dipy.tracking import utils
from dipy.tracking.local import LocalTracking
from fury.colormap import line_colors
hardi_img, gtab, labels_img = read_stanford_labels()
data = hardi_img.get_data()
labels = labels_img.get_data()
affine = hardi_img.affine
white_matter = (labels == 1) | (labels == 2)
csa_model = CsaOdfModel(gtab, sh_order=6)
csa_peaks = peaks_from_model(csa_model, data, default_sphere,
relative_peak_threshold=.8,
min_separation_angle=45,
mask=white_matter)
classifier = ThresholdTissueClassifier(csa_peaks.gfa, .25)
seed_mask = labels == 2
seeds = utils.seeds_from_mask(seed_mask, density=[1, 1, 1], affine=affine)
# Initialization of LocalTracking.
# The computation happens in the next step.
streamlines = LocalTracking(csa_peaks, classifier, seeds, affine,
step_size=2)
# Compute streamlines and store as a list.
streamlines = list(streamlines)
# Prepare the display objects.
streamlines_actor = actor.line(streamlines, line_colors(streamlines))
seedroi_actor = actor.contour_from_roi(seed_mask, affine, [0, 1, 1], 0.5)
# Create the 3d display.
r = window.Renderer()
r2 = window.Renderer()
r.add(streamlines_actor)
arr3 = window.snapshot(r, 'test_surface3.png', offscreen=True)
report3 = window.analyze_snapshot(arr3, find_objects=True)
r2.add(streamlines_actor)
r2.add(seedroi_actor)
arr4 = window.snapshot(r2, 'test_surface4.png', offscreen=True)
report4 = window.analyze_snapshot(arr4, find_objects=True)
# assert that the seed ROI rendering is not far
# away from the streamlines (affine error)
npt.assert_equal(report3.objects, report4.objects)
def qa_tractography(stream_path, qa_out_path, brain_path):
"""
Visualize the streamlines,
then using window.record to get nine snapshots of the 3D streamslines.
Parameters
----------
stream_path : str
Path for the input streamline.trk file(s).
qa_out_path_path : str
Path for the output QA imgae file(s)
brain_path : str
Path for the reference brain scan, in order to get the scan volume.
"""
#Use window to visualize the streamlines
r = window.renderer()
#Load the streamline.trk file
streamlines_mni_load = nib.streamlines.load(stream_path).streamlines
streamlines_mni_in = Streamlines(streamlines_mni_load)
streamlines_actor = actor.line(
streamlines_mni_in,
colormap.line_colors(streamlines_mni_in),
lod_points=10000,
depth_cue=True,
linewidth=0.2,
fake_tube=True,
opacity=0.3,
)
r.add(streamlines_actor)
#window.show(r)
showmng = window.ShowManager(r)
#window.record function can rotate the 3D-image, then get the snapshot of the specific angle.
window.record(r,
cam_pos=(70.03, 64.97, 269.80),
cam_view=(0, 1, 0),
path_numbering=True,
out_path=qa_out_path + '/Rotate_Z_axis_',
az_ang=120,
n_frames=3,
reset_camera=True,
size=(600, 600))
window.record(r,
cam_pos=(70.03, 64.97, 269.80),
cam_view=(1, 0, 0),
path_numbering=True,
out_path=qa_out_path + '/Rotate_Y_axis_',
az_ang=120,
n_frames=3,
reset_camera=True,
size=(600, 600))
window.record(r,
cam_pos=(70.03, 64.97, 269.80),
cam_view=(0, 0, 1),
path_numbering=True,
out_path=qa_out_path + '/Rotate_X_axis_',
az_ang=120,
n_frames=3,
reset_camera=True,
size=(600, 600))
showmng.exit()
combine_plot(qa_out_path, brain_path)
