def test_horizon_events():
# using here MNI template affine 2009a
affine = np.array([[1., 0., 0., -98.],
[0., 1., 0., -134.],
[0., 0., 1., -72.],
[0., 0., 0., 1.]])
data = 255 * np.random.rand(197, 233, 189)
vox_size = (1., 1., 1.)
images = [(data, affine)]
# images = None
from dipy.segment.tests.test_bundles import setup_module
setup_module()
from dipy.segment.tests.test_bundles import f1
streamlines = f1.copy()
streamlines._data += np.array([-98., -134., -72.])
header = create_nifti_header(affine, data.shape, vox_size)
sft = StatefulTractogram(streamlines, header, Space.RASMM)
tractograms = [sft]
# select all centroids and expand and click everything else
# do not press the key shortcuts as vtk generates warning that
# blocks recording
fname = os.path.join(DATA_DIR, 'record_horizon.log.gz')
horizon(tractograms=tractograms, images=images, pams=None,
cluster=True, cluster_thr=5.0,
random_colors=False, length_gt=0, length_lt=np.inf,
clusters_gt=0, clusters_lt=np.inf,
world_coords=True, interactive=True, out_png='tmp.png',
recorded_events=fname)
def test_horizon():
s1 = 10 * np.array([[0, 0, 0], [1, 0, 0], [2, 0, 0], [3, 0, 0], [4, 0, 0]],
dtype='f8')
s2 = 10 * np.array([[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0], [0, 4, 0]],
dtype='f8')
s3 = 10 * np.array(
[[0, 0, 0], [1, 0.2, 0], [2, 0.2, 0], [3, 0.2, 0], [4, 0.2, 0]],
dtype='f8')
print(s1.shape)
print(s2.shape)
print(s3.shape)
streamlines = Streamlines()
streamlines.append(s1)
streamlines.append(s2)
streamlines.append(s3)
tractograms = [streamlines]
images = None
horizon(tractograms,
images=images,
cluster=True,
cluster_thr=5,
random_colors=False,
length_lt=np.inf,
length_gt=0,
clusters_lt=np.inf,
clusters_gt=0,
world_coords=False,
interactive=False)
affine = np.diag([2., 1, 1, 1]).astype('f8')
data = 255 * np.random.rand(150, 150, 150)
images = [(data, affine)]
horizon(tractograms,
images=images,
cluster=True,
cluster_thr=5,
random_colors=False,
length_lt=np.inf,
length_gt=0,
clusters_lt=np.inf,
clusters_gt=0,
world_coords=True,
interactive=False)
def test_horizon_events2():
affine = np.diag([2., 1, 1, 1]).astype('f8')
data = 255 * np.random.rand(150, 150, 150)
images = [(data, affine)]
from dipy.segment.tests.test_bundles import f1
streamlines = f1.copy()
tractograms = [streamlines]
fname = os.path.join(DATA_DIR, 'record_02.log.gz')
horizon(tractograms, images=images, cluster=False, cluster_thr=5,
random_colors=False, length_lt=np.inf, length_gt=0,
clusters_lt=np.inf, clusters_gt=0,
world_coords=True, interactive=True, recorded_events=fname)
def test_roi_images():
np.random.seed(42)
img1 = np.random.rand(5, 5, 5)
img2 = np.zeros((5, 5, 5))
img2[1, 1, 1] = 1
img3 = np.zeros((5, 5, 5))
img3[3, 3, 3] = 1
images = [(img1, np.eye(4)), (img2, np.eye(4)), (img3, np.eye(4))]
with TemporaryDirectory() as out_dir:
tmp_fname = os.path.join(out_dir, 'tmp_x.png')
# If roi_images=False, only the first non-binary image is loaded
horizon(images=images, interactive=False, out_png=tmp_fname)
npt.assert_equal(os.path.exists(tmp_fname), True)
# If roi_images=True, all th binary images are shown as contours
horizon(images=images, roi_images=True, interactive=False,
out_png=tmp_fname)
npt.assert_equal(os.path.exists(tmp_fname), True)
ss = load_image(tmp_fname)
npt.assert_equal(ss[150, 800, :], [147, 0, 0])
def test_horizon():
s1 = 10 * np.array([[0, 0, 0],
[1, 0, 0],
[2, 0, 0],
[3, 0, 0],
[4, 0, 0]], dtype='f8')
s2 = 10 * np.array([[0, 0, 0],
[0, 1, 0],
[0, 2, 0],
[0, 3, 0],
[0, 4, 0]], dtype='f8')
s3 = 10 * np.array([[0, 0, 0],
[1, 0.2, 0],
[2, 0.2, 0],
[3, 0.2, 0],
[4, 0.2, 0]], dtype='f8')
print(s1.shape)
print(s2.shape)
print(s3.shape)
streamlines = Streamlines()
streamlines.append(s1)
streamlines.append(s2)
streamlines.append(s3)
tractograms = [streamlines]
images = None
horizon(tractograms, images=images, cluster=True, cluster_thr=5,
random_colors=False, length_lt=np.inf, length_gt=0,
clusters_lt=np.inf, clusters_gt=0,
world_coords=False, interactive=False)
affine = np.diag([2., 1, 1, 1]).astype('f8')
data = 255 * np.random.rand(150, 150, 150)
images = [(data, affine)]
horizon(tractograms, images=images, cluster=True, cluster_thr=5,
random_colors=False, length_lt=np.inf, length_gt=0,
clusters_lt=np.inf, clusters_gt=0,
world_coords=True, interactive=False)
tractograms = []
horizon(tractograms, images=images, cluster=True, cluster_thr=5,
random_colors=False, length_lt=np.inf, length_gt=0,
clusters_lt=np.inf, clusters_gt=0,
world_coords=True, interactive=False)
def run(self,
input_files,
cluster=False,
cluster_thr=15.,
random_colors=False,
length_gt=0,
length_lt=1000,
clusters_gt=0,
clusters_lt=10**8,
native_coords=False,
stealth=False,
emergency_header='icbm_2009a',
bg_color=(0, 0, 0),
disable_order_transparency=False,
buan=False,
buan_thr=0.5,
buan_highlight=(1, 0, 0),
out_dir='',
out_stealth_png='tmp.png'):
""" Interactive medical visualization - Invert the Horizon!
Interact with any number of .trk, .tck or .dpy tractograms and anatomy
files .nii or .nii.gz. Cluster streamlines on loading.
Parameters
----------
input_files : variable string
cluster : bool, optional
Enable QuickBundlesX clustering.
cluster_thr : float, optional
Distance threshold used for clustering. Default value 15.0 for
small animal brains you may need to use something smaller such
as 2.0. The distance is in mm. For this parameter to be active
``cluster`` should be enabled.
random_colors : bool, optional
Given multiple tractograms have been included then each tractogram
will be shown with different color.
length_gt : float, optional
Clusters with average length greater than ``length_gt`` amount
in mm will be shown.
length_lt : float, optional
Clusters with average length less than ``length_lt`` amount in
mm will be shown.
clusters_gt : int, optional
Clusters with size greater than ``clusters_gt`` will be shown.
clusters_lt : int, optional
Clusters with size less than ``clusters_gt`` will be shown.
native_coords : bool, optional
Show results in native coordinates.
stealth : bool, optional
Do not use interactive mode just save figure.
emergency_header : str, optional
If no anatomy reference is provided an emergency header is
provided. Current options 'icbm_2009a' and 'icbm_2009c'.
bg_color : variable float, optional
Define the background color of the scene. Colors can be defined
with 1 or 3 values and should be between [0-1].
disable_order_transparency : bool, optional
Use depth peeling to sort transparent objects.
If True also enables anti-aliasing.
buan : bool, optional
Enables BUAN framework visualization.
buan_thr : float, optional
Uses the threshold value to highlight segments on the
bundle which have pvalues less than this threshold.
buan_highlight : variable float, optional
Define the bundle highlight area color. Colors can be defined
with 1 or 3 values and should be between [0-1].
For example, a value of (1, 0, 0) would mean the red color.
out_dir : str, optional
Output directory. (default current directory)
out_stealth_png : str, optional
Filename of saved picture.
References
----------
.. [Horizon_ISMRM19] Garyfallidis E., M-A. Cote, B.Q. Chandio,
S. Fadnavis, J. Guaje, R. Aggarwal, E. St-Onge, K.S. Juneja,
S. Koudoro, D. Reagan, DIPY Horizon: fast, modular, unified and
adaptive visualization, Proceedings of: International Society of
Magnetic Resonance in Medicine (ISMRM), Montreal, Canada, 2019.
"""
verbose = True
tractograms = []
images = []
pams = []
numpy_files = []
interactive = not stealth
world_coords = not native_coords
bundle_colors = None
mni_2009a = {}
mni_2009a['affine'] = np.array([[1., 0., 0.,
-98.], [0., 1., 0., -134.],
[0., 0., 1., -72.], [0., 0., 0., 1.]])
mni_2009a['dims'] = (197, 233, 189)
mni_2009a['vox_size'] = (1., 1., 1.)
mni_2009a['vox_space'] = 'RAS'
mni_2009c = {}
mni_2009c['affine'] = np.array([[1., 0., 0.,
-96.], [0., 1., 0., -132.],
[0., 0., 1., -78.], [0., 0., 0., 1.]])
mni_2009c['dims'] = (193, 229, 193)
mni_2009c['vox_size'] = (1., 1., 1.)
mni_2009c['vox_space'] = 'RAS'
if emergency_header == 'icbm_2009a':
hdr = mni_2009c
else:
hdr = mni_2009c
emergency_ref = create_nifti_header(hdr['affine'], hdr['dims'],
hdr['vox_size'])
io_it = self.get_io_iterator()
for input_output in io_it:
fname = input_output[0]
if verbose:
print('Loading file ...')
print(fname)
print('\n')
fl = fname.lower()
ends = fl.endswith
if ends('.trk'):
sft = load_tractogram(fname, 'same', bbox_valid_check=False)
tractograms.append(sft)
if ends('.dpy') or ends('.tck'):
sft = load_tractogram(fname, emergency_ref)
tractograms.append(sft)
if ends('.nii.gz') or ends('.nii'):
data, affine = load_nifti(fname)
images.append((data, affine))
if verbose:
print('Affine to RAS')
np.set_printoptions(3, suppress=True)
print(affine)
np.set_printoptions()
if ends(".pam5"):
pam = load_peaks(fname)
pams.append(pam)
if verbose:
print('Peak_dirs shape')
print(pam.peak_dirs.shape)
if ends(".npy"):
data = np.load(fname)
numpy_files.append(data)
if verbose:
print('numpy array length')
print(len(data))
if buan:
bundle_colors = []
for i in range(len(numpy_files)):
n = len(numpy_files[i])
pvalues = numpy_files[i]
bundle = tractograms[i].streamlines
indx = assignment_map(bundle, bundle, n)
ind = np.array(indx)
nb_lines = len(bundle)
lines_range = range(nb_lines)
points_per_line = [len(bundle[i]) for i in lines_range]
points_per_line = np.array(points_per_line, np.intp)
cols_arr = line_colors(bundle)
colors_mapper = np.repeat(lines_range, points_per_line, axis=0)
vtk_colors = numpy_to_vtk_colors(255 * cols_arr[colors_mapper])
colors = numpy_support.vtk_to_numpy(vtk_colors)
colors = (colors - np.min(colors)) / np.ptp(colors)
for i in range(n):
if pvalues[i] < buan_thr:
colors[ind == i] = buan_highlight
bundle_colors.append(colors)
if len(bg_color) == 1:
bg_color *= 3
elif len(bg_color) != 3:
raise ValueError('You need 3 values to set up backgound color. '
'e.g --bg_color 0.5 0.5 0.5')
order_transparent = not disable_order_transparency
horizon(tractograms=tractograms,
images=images,
pams=pams,
cluster=cluster,
cluster_thr=cluster_thr,
random_colors=random_colors,
bg_color=bg_color,
order_transparent=order_transparent,
length_gt=length_gt,
length_lt=length_lt,
clusters_gt=clusters_gt,
clusters_lt=clusters_lt,
world_coords=world_coords,
interactive=interactive,
buan=buan,
buan_colors=bundle_colors,
out_png=pjoin(out_dir, out_stealth_png))
def test_horizon_flow():
s1 = 10 * np.array([[0, 0, 0], [1, 0, 0], [2, 0, 0], [3, 0, 0], [4, 0, 0]],
dtype='f8')
s2 = 10 * np.array([[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0], [0, 4, 0]],
dtype='f8')
s3 = 10 * np.array(
[[0, 0, 0], [1, 0.2, 0], [2, 0.2, 0], [3, 0.2, 0], [4, 0.2, 0]],
dtype='f8')
affine = np.array([[1., 0., 0., -98.], [0., 1., 0., -134.],
[0., 0., 1., -72.], [0., 0., 0., 1.]])
data = 255 * np.random.rand(197, 233, 189)
vox_size = (1., 1., 1.)
streamlines = Streamlines()
streamlines.append(s1)
streamlines.append(s2)
streamlines.append(s3)
header = create_nifti_header(affine, data.shape, vox_size)
sft = StatefulTractogram(streamlines, header, Space.RASMM)
tractograms = [sft]
images = None
horizon(tractograms,
images=images,
cluster=True,
cluster_thr=5,
random_colors=False,
length_lt=np.inf,
length_gt=0,
clusters_lt=np.inf,
clusters_gt=0,
world_coords=True,
interactive=False)
buan_colors = np.ones(streamlines.get_data().shape)
horizon(tractograms,
buan=True,
buan_colors=buan_colors,
world_coords=True,
interactive=False)
data = 255 * np.random.rand(197, 233, 189)
images = [(data, affine)]
horizon(tractograms,
images=images,
cluster=True,
cluster_thr=5,
random_colors=False,
length_lt=np.inf,
length_gt=0,
clusters_lt=np.inf,
clusters_gt=0,
world_coords=True,
interactive=False)
with TemporaryDirectory() as out_dir:
fimg = os.path.join(out_dir, 'test.nii.gz')
ftrk = os.path.join(out_dir, 'test.trk')
fnpy = os.path.join(out_dir, 'test.npy')
save_nifti(fimg, data, affine)
dimensions = data.shape
nii_header = create_nifti_header(affine, dimensions, vox_size)
sft = StatefulTractogram(streamlines, nii_header, space=Space.RASMM)
save_tractogram(sft, ftrk, bbox_valid_check=False)
pvalues = np.random.uniform(low=0, high=1, size=(10, ))
np.save(fnpy, pvalues)
input_files = [ftrk, fimg]
npt.assert_equal(len(input_files), 2)
hz_flow = HorizonFlow()
hz_flow.run(input_files=input_files,
stealth=True,
out_dir=out_dir,
out_stealth_png='tmp_x.png')
npt.assert_equal(os.path.exists(os.path.join(out_dir, 'tmp_x.png')),
True)
npt.assert_raises(ValueError,
hz_flow.run,
input_files=input_files,
bg_color=(0.2, 0.2))
hz_flow.run(input_files=input_files,
stealth=True,
bg_color=[
0.5,
],
out_dir=out_dir,
out_stealth_png='tmp_x.png')
npt.assert_equal(os.path.exists(os.path.join(out_dir, 'tmp_x.png')),
True)
input_files = [ftrk, fnpy]
npt.assert_equal(len(input_files), 2)
hz_flow.run(input_files=input_files,
stealth=True,
bg_color=[
0.5,
],
buan=True,
buan_thr=0.5,
buan_highlight=(1, 1, 0),
out_dir=out_dir,
out_stealth_png='tmp_x.png')
npt.assert_equal(os.path.exists(os.path.join(out_dir, 'tmp_x.png')),
True)
npt.assert_raises(ValueError,
hz_flow.run,
input_files=input_files,
roi_colors=(0.2, 0.2))
hz_flow.run(input_files=input_files,
stealth=True,
roi_colors=[
0.5,
],
out_dir=out_dir,
out_stealth_png='tmp_x.png')
npt.assert_equal(os.path.exists(os.path.join(out_dir, 'tmp_x.png')),
True)
def test_horizon_flow():
s1 = 10 * np.array([[0, 0, 0], [1, 0, 0], [2, 0, 0], [3, 0, 0], [4, 0, 0]],
dtype='f8')
s2 = 10 * np.array([[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0], [0, 4, 0]],
dtype='f8')
s3 = 10 * np.array(
[[0, 0, 0], [1, 0.2, 0], [2, 0.2, 0], [3, 0.2, 0], [4, 0.2, 0]],
dtype='f8')
print(s1.shape)
print(s2.shape)
print(s3.shape)
streamlines = Streamlines()
streamlines.append(s1)
streamlines.append(s2)
streamlines.append(s3)
tractograms = [streamlines]
images = None
horizon(tractograms,
images=images,
cluster=True,
cluster_thr=5,
random_colors=False,
length_lt=np.inf,
length_gt=0,
clusters_lt=np.inf,
clusters_gt=0,
world_coords=False,
interactive=False)
#
affine = np.diag([2., 1, 1, 1]).astype('f8')
#
data = 255 * np.random.rand(150, 150, 150)
#
images = [(data, affine)]
horizon(tractograms,
images=images,
cluster=True,
cluster_thr=5,
random_colors=False,
length_lt=np.inf,
length_gt=0,
clusters_lt=np.inf,
clusters_gt=0,
world_coords=True,
interactive=False)
with TemporaryDirectory() as out_dir:
fimg = os.path.join(out_dir, 'test.nii.gz')
ftrk = os.path.join(out_dir, 'test.trk')
save_nifti(fimg, data, affine)
dimensions = data.shape
voxel_sizes = np.array([2, 1.0, 1.0])
nii_header = create_nifti_header(affine, dimensions, voxel_sizes)
sft = StatefulTractogram(streamlines, nii_header, space=Space.RASMM)
save_tractogram(sft, ftrk, bbox_valid_check=False)
input_files = [ftrk, fimg]
npt.assert_equal(len(input_files), 2)
hz_flow = HorizonFlow()
hz_flow.run(input_files=input_files,
stealth=True,
out_dir=out_dir,
out_stealth_png='tmp_x.png')
npt.assert_equal(os.path.exists(os.path.join(out_dir, 'tmp_x.png')),
True)
def test_horizon():
s1 = 10 * np.array([[0, 0, 0],
[1, 0, 0],
[2, 0, 0],
[3, 0, 0],
[4, 0, 0]], dtype='f8')
s2 = 10 * np.array([[0, 0, 0],
[0, 1, 0],
[0, 2, 0],
[0, 3, 0],
[0, 4, 0]], dtype='f8')
s3 = 10 * np.array([[0, 0, 0],
[1, 0.2, 0],
[2, 0.2, 0],
[3, 0.2, 0],
[4, 0.2, 0]], dtype='f8')
streamlines = Streamlines()
streamlines.append(s1)
streamlines.append(s2)
streamlines.append(s3)
affine = np.array([[1., 0., 0., -98.],
[0., 1., 0., -134.],
[0., 0., 1., -72.],
[0., 0., 0., 1.]])
data = 255 * np.random.rand(197, 233, 189)
vox_size = (1., 1., 1.)
streamlines._data += np.array([-98., -134., -72.])
header = create_nifti_header(affine, data.shape, vox_size)
sft = StatefulTractogram(streamlines, header, Space.RASMM)
# only tractograms
tractograms = [sft]
images = None
horizon(tractograms, images=images, cluster=True, cluster_thr=5,
random_colors=False, length_lt=np.inf, length_gt=0,
clusters_lt=np.inf, clusters_gt=0,
world_coords=True, interactive=False)
images = [(data, affine)]
# tractograms in native coords (not supported for now)
with npt.assert_raises(ValueError) as ve:
horizon(tractograms, images=images, cluster=True, cluster_thr=5,
random_colors=False, length_lt=np.inf, length_gt=0,
clusters_lt=np.inf, clusters_gt=0,
world_coords=False, interactive=False)
msg = 'Currently native coordinates are not supported for streamlines'
npt.assert_(msg in str(ve.exception))
# only images
tractograms = None
horizon(tractograms, images=images, cluster=True, cluster_thr=5,
random_colors=False, length_lt=np.inf, length_gt=0,
clusters_lt=np.inf, clusters_gt=0,
world_coords=True, interactive=False)
# no clustering tractograms and images
horizon(tractograms, images=images, cluster=False, cluster_thr=5,
random_colors=False, length_lt=np.inf, length_gt=0,
clusters_lt=np.inf, clusters_gt=0,
world_coords=True, interactive=False)
def run(self,
input_files,
cluster=False,
cluster_thr=15.,
random_colors=False,
length_gt=0,
length_lt=1000,
clusters_gt=0,
clusters_lt=10**8,
native_coords=False,
stealth=False,
out_dir='',
out_stealth_png='tmp.png'):
""" Highly interactive visualization - invert the Horizon!
Interact with any number of .trk, .tck or .dpy tractograms and anatomy
files .nii or .nii.gz. Cluster streamlines on loading.
Parameters
----------
input_files : variable string
cluster : bool
cluster_thr : float
random_colors : bool
length_gt : float
length_lt : float
clusters_gt : int
clusters_lt : int
native_coords : bool
stealth : bool
out_dir : string
out_stealth_png : string
References
----------
.. [Horizon_ISMRM19] Garyfallidis E., M-A. Cote, B.Q. Chandio,
S. Fadnavis, J. Guaje, R. Aggarwal, E. St-Onge, K.S. Juneja,
S. Koudoro, D. Reagan, DIPY Horizon: fast, modular, unified and
adaptive visualization, Proceedings of: International Society of
Magnetic Resonance in Medicine (ISMRM), Montreal, Canada, 2019.
"""
verbose = True
tractograms = []
images = []
pams = []
interactive = not stealth
world_coords = not native_coords
io_it = self.get_io_iterator()
for input_output in io_it:
fname = input_output[0]
if verbose:
print('Loading file ...')
print(fname)
print('\n')
fl = fname.lower()
ends = fl.endswith
if ends('.trk') or ends('.tck'):
streamlines = nib.streamlines.load(fname).streamlines
tractograms.append(streamlines)
elif ends('dpy'):
dpy_obj = Dpy(fname, mode='r')
streamlines = list(dpy_obj.read_tracks())
dpy_obj.close()
if ends('.nii.gz') or ends('.nii'):
data, affine = load_nifti(fname)
images.append((data, affine))
if verbose:
print('Affine to RAS')
np.set_printoptions(3, suppress=True)
print(affine)
np.set_printoptions()
if ends(".pam5"):
pam = load_peaks(fname)
pams.append(pam)
if verbose:
print('Peak_dirs shape')
print(pam.peak_dirs.shape)
horizon(tractograms=tractograms,
images=images,
pams=pams,
cluster=cluster,
cluster_thr=cluster_thr,
random_colors=random_colors,
length_gt=length_gt,
length_lt=length_lt,
clusters_gt=clusters_gt,
clusters_lt=clusters_lt,
world_coords=world_coords,
interactive=interactive,
out_png=pjoin(out_dir, out_stealth_png))
def run(self,
input_files,
cluster=False,
cluster_thr=15.,
random_colors=False,
length_gt=0,
length_lt=1000,
clusters_gt=0,
clusters_lt=10**8,
native_coords=False,
stealth=False,
emergency_header='icbm_2009a',
bg_color=(0, 0, 0),
disable_order_transparency=False,
out_dir='',
out_stealth_png='tmp.png'):
""" Interactive medical visualization - Invert the Horizon!
Interact with any number of .trk, .tck or .dpy tractograms and anatomy
files .nii or .nii.gz. Cluster streamlines on loading.
Parameters
----------
input_files : variable string
cluster : bool, optional
Enable QuickBundlesX clustering
cluster_thr : float, optional
Distance threshold used for clustering. Default value 15.0 for
small animal brains you may need to use something smaller such
as 2.0. The distance is in mm. For this parameter to be active
``cluster`` should be enabled
random_colors : bool, optional
Given multiple tractograms have been included then each tractogram
will be shown with different color
length_gt : float, optional
Clusters with average length greater than ``length_gt`` amount
in mm will be shown
length_lt : float, optional
Clusters with average length less than ``length_lt`` amount in
mm will be shown
clusters_gt : int, optional
Clusters with size greater than ``clusters_gt`` will be shown.
clusters_lt : int, optional
Clusters with size less than ``clusters_gt`` will be shown.
native_coords : bool, optional
Show results in native coordinates
stealth : bool, optional
Do not use interactive mode just save figure.
emergency_header : str, optional
If no anatomy reference is provided an emergency header is
provided. Current options 'icbm_2009a' and 'icbm_2009c'.
bg_color : variable float, optional
Define the background color of the scene. Colors can be defined
with 1 or 3 values and should be between [0-1].
Default is black (e.g --bg_color 0 0 0 or --bg_color 0).
disable_order_transparency : bool, optional
Default False. Use depth peeling to sort transparent objects.
If True also enables anti-aliasing.
out_dir : str, optional
Output directory. Default current directory.
out_stealth_png : str, optional
Filename of saved picture.
References
----------
.. [Horizon_ISMRM19] Garyfallidis E., M-A. Cote, B.Q. Chandio,
S. Fadnavis, J. Guaje, R. Aggarwal, E. St-Onge, K.S. Juneja,
S. Koudoro, D. Reagan, DIPY Horizon: fast, modular, unified and
adaptive visualization, Proceedings of: International Society of
Magnetic Resonance in Medicine (ISMRM), Montreal, Canada, 2019.
"""
verbose = True
tractograms = []
images = []
pams = []
interactive = not stealth
world_coords = not native_coords
mni_2009a = {}
mni_2009a['affine'] = np.array([[1., 0., 0.,
-98.], [0., 1., 0., -134.],
[0., 0., 1., -72.], [0., 0., 0., 1.]])
mni_2009a['dims'] = (197, 233, 189)
mni_2009a['vox_size'] = (1., 1., 1.)
mni_2009a['vox_space'] = 'RAS'
mni_2009c = {}
mni_2009c['affine'] = np.array([[1., 0., 0.,
-96.], [0., 1., 0., -132.],
[0., 0., 1., -78.], [0., 0., 0., 1.]])
mni_2009c['dims'] = (193, 229, 193)
mni_2009c['vox_size'] = (1., 1., 1.)
mni_2009c['vox_space'] = 'RAS'
if emergency_header == 'icbm_2009a':
hdr = mni_2009c
else:
hdr = mni_2009c
emergency_ref = create_nifti_header(hdr['affine'], hdr['dims'],
hdr['vox_size'])
io_it = self.get_io_iterator()
for input_output in io_it:
fname = input_output[0]
if verbose:
print('Loading file ...')
print(fname)
print('\n')
fl = fname.lower()
ends = fl.endswith
if ends('.trk'):
sft = load_tractogram(fname, 'same', bbox_valid_check=False)
tractograms.append(sft)
if ends('.dpy') or ends('.tck'):
sft = load_tractogram(fname, emergency_ref)
tractograms.append(sft)
if ends('.nii.gz') or ends('.nii'):
data, affine = load_nifti(fname)
images.append((data, affine))
if verbose:
print('Affine to RAS')
np.set_printoptions(3, suppress=True)
print(affine)
np.set_printoptions()
if ends(".pam5"):
pam = load_peaks(fname)
pams.append(pam)
if verbose:
print('Peak_dirs shape')
print(pam.peak_dirs.shape)
if len(bg_color) == 1:
bg_color *= 3
elif len(bg_color) != 3:
raise ValueError('You need 3 values to set up backgound color. '
'e.g --bg_color 0.5 0.5 0.5')
order_transparent = not disable_order_transparency
horizon(tractograms=tractograms,
images=images,
pams=pams,
cluster=cluster,
cluster_thr=cluster_thr,
random_colors=random_colors,
bg_color=bg_color,
order_transparent=order_transparent,
length_gt=length_gt,
length_lt=length_lt,
clusters_gt=clusters_gt,
clusters_lt=clusters_lt,
world_coords=world_coords,
interactive=interactive,
out_png=pjoin(out_dir, out_stealth_png))
def test_horizon_flow():
s1 = 10 * np.array([[0, 0, 0],
[1, 0, 0],
[2, 0, 0],
[3, 0, 0],
[4, 0, 0]], dtype='f8')
s2 = 10 * np.array([[0, 0, 0],
[0, 1, 0],
[0, 2, 0],
[0, 3, 0],
[0, 4, 0]], dtype='f8')
s3 = 10 * np.array([[0, 0, 0],
[1, 0.2, 0],
[2, 0.2, 0],
[3, 0.2, 0],
[4, 0.2, 0]], dtype='f8')
print(s1.shape)
print(s2.shape)
print(s3.shape)
streamlines = Streamlines()
streamlines.append(s1)
streamlines.append(s2)
streamlines.append(s3)
tractograms = [streamlines]
images = None
horizon(tractograms, images=images, cluster=True, cluster_thr=5,
random_colors=False, length_lt=np.inf, length_gt=0,
clusters_lt=np.inf, clusters_gt=0,
world_coords=False, interactive=False)
#
affine = np.diag([2., 1, 1, 1]).astype('f8')
#
data = 255 * np.random.rand(150, 150, 150)
#
images = [(data, affine)]
horizon(tractograms, images=images, cluster=True, cluster_thr=5,
random_colors=False, length_lt=np.inf, length_gt=0,
clusters_lt=np.inf, clusters_gt=0,
world_coords=True, interactive=False)
with TemporaryDirectory() as out_dir:
fimg = os.path.join(out_dir, 'test.nii.gz')
ftrk = os.path.join(out_dir, 'test.trk')
save_nifti(fimg, data, affine)
save_tractogram(ftrk, streamlines, affine)
input_files = [ftrk, fimg]
npt.assert_equal(len(input_files), 2)
hz_flow = HorizonFlow()
hz_flow.run(input_files=input_files, stealth=True,
out_dir=out_dir, out_stealth_png='tmp_x.png')
npt.assert_equal(os.path.exists(os.path.join(out_dir, 'tmp_x.png')),
True)
