def test_recobundles_flow():
with TemporaryDirectory() as out_dir:
data_path = get_fnames('fornix')
fornix = load_tractogram(data_path, 'same',
bbox_valid_check=False).streamlines
f = Streamlines(fornix)
f1 = f.copy()
f2 = f1[:15].copy()
f2._data += np.array([40, 0, 0])
f.extend(f2)
f2_path = pjoin(out_dir, "f2.trk")
sft = StatefulTractogram(f2, data_path, Space.RASMM)
save_tractogram(sft, f2_path, bbox_valid_check=False)
f1_path = pjoin(out_dir, "f1.trk")
sft = StatefulTractogram(f, data_path, Space.RASMM)
save_tractogram(sft, f1_path, bbox_valid_check=False)
rb_flow = RecoBundlesFlow(force=True)
rb_flow.run(f1_path,
f2_path,
greater_than=0,
clust_thr=10,
model_clust_thr=5.,
reduction_thr=10,
out_dir=out_dir)
labels = rb_flow.last_generated_outputs['out_recognized_labels']
recog_trk = rb_flow.last_generated_outputs['out_recognized_transf']
rec_bundle = load_tractogram(recog_trk, 'same',
bbox_valid_check=False).streamlines
npt.assert_equal(len(rec_bundle) == len(f2), True)
label_flow = LabelsBundlesFlow(force=True)
label_flow.run(f1_path, labels)
recog_bundle = label_flow.last_generated_outputs['out_bundle']
rec_bundle_org = load_tractogram(recog_bundle,
'same',
bbox_valid_check=False).streamlines
BMD = BundleMinDistanceMetric()
nb_pts = 20
static = set_number_of_points(f2, nb_pts)
moving = set_number_of_points(rec_bundle_org, nb_pts)
BMD.setup(static, moving)
x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0]) # affine
bmd_value = BMD.distance(x0.tolist())
npt.assert_equal(bmd_value < 1, True)
def test_min_vs_min_fast_precision():
static = fornix_streamlines()[:20]
moving = fornix_streamlines()[:20]
static = [s.astype('f8') for s in static]
moving = [m.astype('f8') for m in moving]
bmd = BundleMinDistanceMatrixMetric()
bmd.setup(static, moving)
bmdf = BundleMinDistanceMetric()
bmdf.setup(static, moving)
x_test = [0.01, 0, 0, 0, 0, 0]
print(bmd.distance(x_test))
print(bmdf.distance(x_test))
assert_equal(bmd.distance(x_test), bmdf.distance(x_test))
def test_min_vs_min_fast_precision():
static = fornix_streamlines()[:20]
moving = fornix_streamlines()[:20]
static = [s.astype('f8') for s in static]
moving = [m.astype('f8') for m in moving]
bmd = BundleMinDistanceMatrixMetric()
bmd.setup(static, moving)
bmdf = BundleMinDistanceMetric()
bmdf.setup(static, moving)
x_test = [0.01, 0, 0, 0, 0, 0]
print(bmd.distance(x_test))
print(bmdf.distance(x_test))
assert_equal(bmd.distance(x_test), bmdf.distance(x_test))
def test_recobundles_flow():
with TemporaryDirectory() as out_dir:
data_path = get_fnames('fornix')
streams, hdr = nib.trackvis.read(data_path)
fornix = [s[0] for s in streams]
f = Streamlines(fornix)
f1 = f.copy()
f2 = f1[:15].copy()
f2._data += np.array([40, 0, 0])
f.extend(f2)
f2_path = pjoin(out_dir, "f2.trk")
save_trk(f2_path, f2, affine=np.eye(4))
f1_path = pjoin(out_dir, "f1.trk")
save_trk(f1_path, f, affine=np.eye(4))
rb_flow = RecoBundlesFlow(force=True)
rb_flow.run(f1_path,
f2_path,
greater_than=0,
clust_thr=10,
model_clust_thr=5.,
reduction_thr=10,
out_dir=out_dir)
labels = rb_flow.last_generated_outputs['out_recognized_labels']
recog_trk = rb_flow.last_generated_outputs['out_recognized_transf']
rec_bundle, _ = load_trk(recog_trk)
npt.assert_equal(len(rec_bundle) == len(f2), True)
label_flow = LabelsBundlesFlow(force=True)
label_flow.run(f1_path, labels)
recog_bundle = label_flow.last_generated_outputs['out_bundle']
rec_bundle_org, _ = load_trk(recog_bundle)
BMD = BundleMinDistanceMetric()
nb_pts = 20
static = set_number_of_points(f2, nb_pts)
moving = set_number_of_points(rec_bundle_org, nb_pts)
BMD.setup(static, moving)
x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0]) # affine
bmd_value = BMD.distance(x0.tolist())
npt.assert_equal(bmd_value < 1, True)
def evaluate_results(self, model_bundle, pruned_streamlines, slr_select):
""" Compare the similiarity between two given bundles, model bundle,
and extracted bundle.
Parameters
----------
model_bundle : Streamlines
pruned_streamlines : Streamlines
slr_select : tuple
Select the number of streamlines from model to neirborhood of
model to perform the local SLR.
Returns
-------
ba_value : float
bundle adjacency value between model bundle and pruned bundle
bmd_value : float
bundle minimum distance value between model bundle and
pruned bundle
"""
spruned_streamlines = Streamlines(pruned_streamlines)
recog_centroids = self._cluster_model_bundle(
spruned_streamlines,
model_clust_thr=1.25)
mod_centroids = self._cluster_model_bundle(
model_bundle,
model_clust_thr=1.25)
recog_centroids = Streamlines(recog_centroids)
model_centroids = Streamlines(mod_centroids)
ba_value = bundle_adjacency(set_number_of_points(recog_centroids, 20),
set_number_of_points(model_centroids, 20),
threshold=10)
BMD = BundleMinDistanceMetric()
static = select_random_set_of_streamlines(model_bundle,
slr_select[0])
moving = select_random_set_of_streamlines(pruned_streamlines,
slr_select[1])
nb_pts = 20
static = set_number_of_points(static, nb_pts)
moving = set_number_of_points(moving, nb_pts)
BMD.setup(static, moving)
x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0]) # affine
bmd_value = BMD.distance(x0.tolist())
return ba_value, bmd_value
def evaluate_results(self, model_bundle, pruned_streamlines, slr_select):
""" Comapare the similiarity between two given bundles, model bundle,
and extracted bundle.
Parameters
----------
model_bundle : Streamlines
pruned_streamlines : Streamlines
slr_select : tuple
Select the number of streamlines from model to neirborhood of
model to perform the local SLR.
Returns
-------
ba_value : float
bundle analytics value between model bundle and pruned bundle
bmd_value : float
bundle minimum distance value between model bundle and
pruned bundle
"""
spruned_streamlines = Streamlines(pruned_streamlines)
recog_centroids = self._cluster_model_bundle(
spruned_streamlines,
model_clust_thr=1.25)
mod_centroids = self._cluster_model_bundle(
model_bundle,
model_clust_thr=1.25)
recog_centroids = Streamlines(recog_centroids)
model_centroids = Streamlines(mod_centroids)
ba_value = ba_analysis(recog_centroids, model_centroids, threshold=10)
BMD = BundleMinDistanceMetric()
static = select_random_set_of_streamlines(model_bundle,
slr_select[0])
moving = select_random_set_of_streamlines(pruned_streamlines,
slr_select[1])
nb_pts = 20
static = set_number_of_points(static, nb_pts)
moving = set_number_of_points(moving, nb_pts)
BMD.setup(static, moving)
x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0]) # affine
bmd_value = BMD.distance(x0.tolist())
return ba_value, bmd_value
def test_recobundles_flow():
with TemporaryDirectory() as out_dir:
data_path = get_fnames('fornix')
streams, hdr = nib.trackvis.read(data_path)
fornix = [s[0] for s in streams]
f = Streamlines(fornix)
f1 = f.copy()
f2 = f1[:15].copy()
f2._data += np.array([40, 0, 0])
f.extend(f2)
f2_path = pjoin(out_dir, "f2.trk")
save_trk(f2_path, f2, affine=np.eye(4))
f1_path = pjoin(out_dir, "f1.trk")
save_trk(f1_path, f, affine=np.eye(4))
rb_flow = RecoBundlesFlow(force=True)
rb_flow.run(f1_path, f2_path, greater_than=0, clust_thr=10,
model_clust_thr=5., reduction_thr=10, out_dir=out_dir)
labels = rb_flow.last_generated_outputs['out_recognized_labels']
recog_trk = rb_flow.last_generated_outputs['out_recognized_transf']
rec_bundle, _ = load_trk(recog_trk)
npt.assert_equal(len(rec_bundle) == len(f2), True)
label_flow = LabelsBundlesFlow(force=True)
label_flow.run(f1_path, labels)
recog_bundle = label_flow.last_generated_outputs['out_bundle']
rec_bundle_org, _ = load_trk(recog_bundle)
BMD = BundleMinDistanceMetric()
nb_pts = 20
static = set_number_of_points(f2, nb_pts)
moving = set_number_of_points(rec_bundle_org, nb_pts)
BMD.setup(static, moving)
x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0]) # affine
bmd_value = BMD.distance(x0.tolist())
npt.assert_equal(bmd_value < 1, True)