def test_whole_brain_slr():
streams, hdr = nib.trackvis.read(get_fnames('fornix'))
fornix = [s[0] for s in streams]
f = Streamlines(fornix)
f1 = f.copy()
f2 = f.copy()
# check translation
f2._data += np.array([50, 0, 0])
moved, transform, qb_centroids1, qb_centroids2 = whole_brain_slr(
f1, f2, x0='affine', verbose=True, rm_small_clusters=2,
greater_than=0, less_than=np.inf,
qbx_thr=[5, 2, 1], progressive=False)
# we can check the quality of registration by comparing the matrices
# MAM streamline distances before and after SLR
D12 = bundles_distances_mam(f1, f2)
D1M = bundles_distances_mam(f1, moved)
d12_minsum = np.sum(np.min(D12, axis=0))
d1m_minsum = np.sum(np.min(D1M, axis=0))
print("distances= ", d12_minsum, " ", d1m_minsum)
assert_equal(d1m_minsum < d12_minsum, True)
assert_array_almost_equal(transform[:3, 3], [-50, -0, -0], 2)
# check rotation
mat = compose_matrix44([0, 0, 0, 15, 0, 0])
f3 = f.copy()
f3 = transform_streamlines(f3, mat)
moved, transform, qb_centroids1, qb_centroids2 = slr_with_qbx(
f1, f3, verbose=False, rm_small_clusters=1, greater_than=20,
less_than=np.inf, qbx_thr=[2],
progressive=True)
# we can also check the quality by looking at the decomposed transform
assert_array_almost_equal(decompose_matrix44(transform)[3], -15, 2)
moved, transform, qb_centroids1, qb_centroids2 = slr_with_qbx(
f1, f3, verbose=False, rm_small_clusters=1, select_random=400,
greater_than=20, less_than=np.inf, qbx_thr=[2],
progressive=True)
# we can also check the quality by looking at the decomposed transform
assert_array_almost_equal(decompose_matrix44(transform)[3], -15, 2)
def test_slr_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()
f1_path = pjoin(out_dir, "f1.trk")
sft = StatefulTractogram(f1, data_path, Space.RASMM)
save_tractogram(sft, f1_path, bbox_valid_check=False)
f2 = f1.copy()
f2._data += np.array([50, 0, 0])
f2_path = pjoin(out_dir, "f2.trk")
sft = StatefulTractogram(f2, data_path, Space.RASMM)
save_tractogram(sft, f2_path, bbox_valid_check=False)
slr_flow = SlrWithQbxFlow(force=True)
slr_flow.run(f1_path, f2_path)
out_path = slr_flow.last_generated_outputs['out_moved']
npt.assert_equal(os.path.isfile(out_path), True)
def test_rb_no_neighb():
# what if no neighbors are found? No recognition
b = Streamlines(fornix)
b1 = b.copy()
b2 = b1[:20].copy()
b2._data += np.array([100, 0, 0])
b3 = b1[:20].copy()
b3._data += np.array([300, 0, 0])
b.extend(b3)
rb = RecoBundles(b, greater_than=0, clust_thr=10)
rec_trans, rec_labels = rb.recognize(model_bundle=b2,
model_clust_thr=5.,
reduction_thr=10)
if len(rec_trans) > 0:
refine_trans, refine_labels = rb.refine(model_bundle=b2,
pruned_streamlines=rec_trans,
model_clust_thr=5.,
reduction_thr=10)
assert_equal(len(refine_labels), 0)
assert_equal(len(refine_trans), 0)
else:
assert_equal(len(rec_labels), 0)
assert_equal(len(rec_trans), 0)
def test_rb_no_neighb():
# what if no neighbors are found? No recognition
b = Streamlines(fornix)
b1 = b.copy()
b2 = b1[:20].copy()
b2._data += np.array([100, 0, 0])
b3 = b1[:20].copy()
b3._data += np.array([300, 0, 0])
b.extend(b3)
rb = RecoBundles(b, greater_than=0, clust_thr=10)
rec_trans, rec_labels = rb.recognize(model_bundle=b2,
model_clust_thr=5.,
reduction_thr=10)
if len(rec_trans) > 0:
refine_trans, refine_labels = rb.refine(model_bundle=b2,
pruned_streamlines=rec_trans,
model_clust_thr=5.,
reduction_thr=10)
assert_equal(len(refine_labels), 0)
assert_equal(len(refine_trans), 0)
else:
assert_equal(len(rec_labels), 0)
assert_equal(len(rec_trans), 0)
def test_slr_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()
f1_path = pjoin(out_dir, "f1.trk")
save_trk(f1_path, Streamlines(f1), affine=np.eye(4))
f2 = f1.copy()
f2._data += np.array([50, 0, 0])
f2_path = pjoin(out_dir, "f2.trk")
save_trk(f2_path, Streamlines(f2), affine=np.eye(4))
slr_flow = SlrWithQbxFlow(force=True)
slr_flow.run(f1_path, f2_path)
out_path = slr_flow.last_generated_outputs['out_moved']
npt.assert_equal(os.path.isfile(out_path), True)
def test_slr_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()
f1_path = pjoin(out_dir, "f1.trk")
save_trk(f1_path, Streamlines(f1), affine=np.eye(4))
f2 = f1.copy()
f2._data += np.array([50, 0, 0])
f2_path = pjoin(out_dir, "f2.trk")
save_trk(f2_path, Streamlines(f2), affine=np.eye(4))
slr_flow = SlrWithQbxFlow(force=True)
slr_flow.run(f1_path, f2_path)
out_path = slr_flow.last_generated_outputs['out_moved']
npt.assert_equal(os.path.isfile(out_path), True)
def test_whole_brain_slr():
streams, hdr = nib.trackvis.read(get_data('fornix'))
fornix = [s[0] for s in streams]
f = Streamlines(fornix)
f1 = f.copy()
f2 = f.copy()
# check translation
f2._data += np.array([50, 0, 0])
moved, transform, qb_centroids1, qb_centroids2 = whole_brain_slr(
f1, f2, verbose=True, rm_small_clusters=2, greater_than=0,
less_than=np.inf, qb_thr=5, progressive=False)
# we can check the quality of registration by comparing the matrices
# MAM streamline distances before and after SLR
D12 = bundles_distances_mam(f1, f2)
D1M = bundles_distances_mam(f1, moved)
d12_minsum = np.sum(np.min(D12, axis=0))
d1m_minsum = np.sum(np.min(D1M, axis=0))
assert_equal(d1m_minsum < d12_minsum, True)
assert_array_almost_equal(transform[:3, 3], [-50, -0, -0], 3)
# check rotation
mat = compose_matrix44([0, 0, 0, 15, 0, 0])
f3 = f.copy()
f3 = transform_streamlines(f3, mat)
moved, transform, qb_centroids1, qb_centroids2 = slr_with_qb(
f1, f3, verbose=False, rm_small_clusters=1, greater_than=20,
less_than=np.inf, qb_thr=2, progressive=True)
# we can also check the quality by looking at the decomposed transform
assert_array_almost_equal(decompose_matrix44(transform)[3], -15, 2)
moved, transform, qb_centroids1, qb_centroids2 = slr_with_qb(
f1, f3, verbose=False, rm_small_clusters=1, select_random=400,
greater_than=20,
less_than=np.inf, qb_thr=2, progressive=True)
# we can also check the quality by looking at the decomposed transform
assert_array_almost_equal(decompose_matrix44(transform)[3], -15, 2)
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_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 setup_module():
global f, f1, f2, f3, fornix
fname = get_fnames('fornix')
fornix = load_tractogram(fname, 'same', bbox_valid_check=False).streamlines
f = Streamlines(fornix)
f1 = f.copy()
f2 = f1[:20].copy()
f2._data += np.array([50, 0, 0])
f3 = f1[200:].copy()
f3._data += np.array([100, 0, 0])
f.extend(f2)
f.extend(f3)
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)
import nibabel as nib
from numpy.testing import (assert_equal,
assert_almost_equal,
run_module_suite)
from dipy.data import get_fnames
from dipy.segment.bundles import RecoBundles
from dipy.tracking.distances import bundles_distances_mam
from dipy.tracking.streamline import Streamlines
from dipy.segment.clustering import qbx_and_merge
streams, hdr = nib.trackvis.read(get_fnames('fornix'))
fornix = [s[0] for s in streams]
f = Streamlines(fornix)
f1 = f.copy()
f2 = f1[:20].copy()
f2._data += np.array([50, 0, 0])
f3 = f1[200:].copy()
f3._data += np.array([100, 0, 0])
f.extend(f2)
f.extend(f3)
def test_rb_check_defaults():
rb = RecoBundles(f, greater_than=0, clust_thr=10)
def test_whole_brain_slr():
fname = get_fnames('fornix')
fornix = load_tractogram(fname, 'same', bbox_valid_check=False).streamlines
f = Streamlines(fornix)
f1 = f.copy()
f2 = f.copy()
# check translation
f2._data += np.array([50, 0, 0])
moved, transform, qb_centroids1, qb_centroids2 = whole_brain_slr(
f1,
f2,
x0='affine',
verbose=True,
rm_small_clusters=2,
greater_than=0,
less_than=np.inf,
qbx_thr=[5, 2, 1],
progressive=False)
# we can check the quality of registration by comparing the matrices
# MAM streamline distances before and after SLR
D12 = bundles_distances_mam(f1, f2)
D1M = bundles_distances_mam(f1, moved)
d12_minsum = np.sum(np.min(D12, axis=0))
d1m_minsum = np.sum(np.min(D1M, axis=0))
print("distances= ", d12_minsum, " ", d1m_minsum)
assert_equal(d1m_minsum < d12_minsum, True)
assert_array_almost_equal(transform[:3, 3], [-50, -0, -0], 2)
# check rotation
mat = compose_matrix44([0, 0, 0, 15, 0, 0])
f3 = f.copy()
f3 = transform_streamlines(f3, mat)
moved, transform, qb_centroids1, qb_centroids2 = slr_with_qbx(
f1,
f3,
verbose=False,
rm_small_clusters=1,
greater_than=20,
less_than=np.inf,
qbx_thr=[2],
progressive=True)
# we can also check the quality by looking at the decomposed transform
assert_array_almost_equal(decompose_matrix44(transform)[3], -15, 2)
moved, transform, qb_centroids1, qb_centroids2 = slr_with_qbx(
f1,
f3,
verbose=False,
rm_small_clusters=1,
select_random=400,
greater_than=20,
less_than=np.inf,
qbx_thr=[2],
progressive=True)
# we can also check the quality by looking at the decomposed transform
assert_array_almost_equal(decompose_matrix44(transform)[3], -15, 2)
import numpy as np
import nibabel as nib
from numpy.testing import (assert_equal, assert_almost_equal, run_module_suite)
from dipy.data import get_fnames
from dipy.segment.bundles import RecoBundles
from dipy.tracking.distances import bundles_distances_mam
from dipy.tracking.streamline import Streamlines
from dipy.segment.clustering import qbx_and_merge
streams, hdr = nib.trackvis.read(get_fnames('fornix'))
fornix = [s[0] for s in streams]
f = Streamlines(fornix)
f1 = f.copy()
f2 = f1[:20].copy()
f2._data += np.array([50, 0, 0])
f3 = f1[200:].copy()
f3._data += np.array([100, 0, 0])
f.extend(f2)
f.extend(f3)
def test_rb_check_defaults():
rb = RecoBundles(f, greater_than=0, clust_thr=10)
rec_trans, rec_labels = rb.recognize(model_bundle=f2,
model_clust_thr=5.,