def load_dwi_files_blinds(folder_name):
from dipy.core.gradients import gradient_table
from dipy.core.geometry import compose_matrix, decompose_matrix
for file in os.listdir(folder_name):
if file.endswith(".bvec"):
bvec_file = os.path.join(folder_name, file)
if file.endswith("labels.nii"):
labels_file_name = os.path.join(folder_name, file)
if file.endswith("WM.nii"):
wm_file_name = os.path.join(folder_name, file)
bval_file = bvec_file[:-4:]+'bval'
nii_file = bvec_file[:-4:]+'nii'
hardi_img = nib.load(nii_file)
data = hardi_img.get_fdata()
affine = hardi_img.affine
scale, shear, ang, trans, pre = decompose_matrix(affine)
shear = np.zeros(np.shape(shear))
affine = compose_matrix(scale, shear, ang, trans, pre)
gtab = gradient_table(bval_file, bvec_file)
#voxel_size = nib.affines.voxel_sizes(affine)
#data, affine1 = reslice(data, affine, voxel_size, (3., 3., 3.))
labels_img = nib.load(labels_file_name)
labels = labels_img.get_fdata()
#labels,affine1=reslice(labels,affine,voxel_size,(3., 3., 3.))
wm_img = nib.load(wm_file_name)
white_matter = wm_img.get_fdata()
#white_matter,affine1=reslice(white_matter,affine,voxel_size,(3., 3., 3.))
return gtab,data,affine,labels,white_matter,nii_file,bvec_file
def test_compose_decompose_matrix():
for translate in permutations(40 * np.random.rand(3), 3):
for angles in permutations(np.deg2rad(90 * np.random.rand(3)), 3):
for shears in permutations(3 * np.random.rand(3), 3):
for scale in permutations(3 * np.random.rand(3), 3):
mat = compose_matrix(translate=translate, angles=angles,
shear=shears, scale=scale)
sc, sh, ang, trans, _ = decompose_matrix(mat)
assert_array_almost_equal(translate, trans)
assert_array_almost_equal(angles, ang)
assert_array_almost_equal(shears, sh)
assert_array_almost_equal(scale, sc)
def compose_matrix44(t, dtype=np.double):
""" Compose a 4x4 transformation matrix
Parameters
-----------
t : ndarray
This is a 1D vector of affine transformation parameters with
size at least 3.
If the size is 3, t is interpreted as translation.
If the size is 6, t is interpreted as translation + rotation.
If the size is 7, t is interpreted as translation + rotation +
isotropic scaling.
If the size is 9, t is interpreted as translation + rotation +
anisotropic scaling.
If size is 12, t is interpreted as translation + rotation +
scaling + shearing.
Returns
-------
T : ndarray
Homogeneous transformation matrix of size 4x4.
"""
if isinstance(t, list):
t = np.array(t)
size = t.size
if size not in [3, 6, 7, 9, 12]:
raise ValueError('Accepted number of parameters is 3, 6, 7, 9 and 12')
MAX_DIST = 1e10
scale, shear, angles, translate = (None, ) * 4
translate = _threshold(t[0:3], MAX_DIST)
if size in [6, 7, 9, 12]:
angles = np.deg2rad(t[3:6])
if size == 7:
scale = np.array((t[6], ) * 3)
if size in [9, 12]:
scale = t[6:9]
if size == 12:
shear = t[9:12]
return compose_matrix(scale=scale,
shear=shear,
angles=angles,
translate=translate)
def test_rigid_partial_real_bundles():
static = fornix_streamlines()[:20]
moving = fornix_streamlines()[20:40]
static_center, shift = center_streamlines(static)
moving_center, shift2 = center_streamlines(moving)
print(shift2)
mat = compose_matrix(translate=np.array([0, 0, 0.]),
angles=np.deg2rad([40, 0, 0.]))
moved = transform_streamlines(moving_center, mat)
srr = StreamlineLinearRegistration()
srm = srr.optimize(static_center, moved)
print(srm.fopt)
print(srm.iterations)
print(srm.funcs)
moving_back = srm.transform(moved)
print(srm.matrix)
static_center = set_number_of_points(static_center, 100)
moving_center = set_number_of_points(moving_back, 100)
vol = np.zeros((100, 100, 100))
spts = np.concatenate(static_center, axis=0)
spts = np.round(spts).astype(np.int) + np.array([50, 50, 50])
mpts = np.concatenate(moving_center, axis=0)
mpts = np.round(mpts).astype(np.int) + np.array([50, 50, 50])
for index in spts:
i, j, k = index
vol[i, j, k] = 1
vol2 = np.zeros((100, 100, 100))
for index in mpts:
i, j, k = index
vol2[i, j, k] = 1
overlap = np.sum(np.logical_and(vol, vol2)) / float(np.sum(vol2))
assert_equal(overlap * 100 > 40, True)
def test_rigid_partial_real_bundles():
static = fornix_streamlines()[:20]
moving = fornix_streamlines()[20:40]
static_center, shift = center_streamlines(static)
moving_center, shift2 = center_streamlines(moving)
print(shift2)
mat = compose_matrix(translate=np.array([0, 0, 0.]),
angles=np.deg2rad([40, 0, 0.]))
moved = transform_streamlines(moving_center, mat)
srr = StreamlineLinearRegistration()
srm = srr.optimize(static_center, moved)
print(srm.fopt)
print(srm.iterations)
print(srm.funcs)
moving_back = srm.transform(moved)
print(srm.matrix)
static_center = set_number_of_points(static_center, 100)
moving_center = set_number_of_points(moving_back, 100)
vol = np.zeros((100, 100, 100))
spts = np.concatenate(static_center, axis=0)
spts = np.round(spts).astype(np.int) + np.array([50, 50, 50])
mpts = np.concatenate(moving_center, axis=0)
mpts = np.round(mpts).astype(np.int) + np.array([50, 50, 50])
for index in spts:
i, j, k = index
vol[i, j, k] = 1
vol2 = np.zeros((100, 100, 100))
for index in mpts:
i, j, k = index
vol2[i, j, k] = 1
overlap = np.sum(np.logical_and(vol, vol2)) / float(np.sum(vol2))
assert_equal(overlap * 100 > 40, True)
def compose_matrix44(t, dtype=np.double):
""" Compose a 4x4 transformation matrix
Parameters
-----------
t : ndarray
This is a 1D vector of of affine transformation parameters with
size at least 3.
If size is 3, t is interpreted as translation.
If size is 6, t is interpreted as translation + rotation.
If size is 7, t is interpreted as translation + rotation +
isotropic scaling.
If size is 9, t is interpreted as translation + rotation +
anisotropic scaling.
If size is 12, t is interpreted as translation + rotation +
scaling + shearing.
Returns
-------
T : ndarray
Homogeneous transformation matrix of size 4x4.
"""
if isinstance(t, list):
t = np.array(t)
size = t.size
if size not in [3, 6, 7, 9, 12]:
raise ValueError('Accepted number of parameters is 3, 6, 7, 9 and 12')
MAX_DIST = 1e10
scale, shear, angles, translate = (None, ) * 4
translate = _threshold(t[0:3], MAX_DIST)
if size in [6, 7, 9, 12]:
angles = np.deg2rad(t[3:6])
if size == 7:
scale = np.array((t[6],) * 3)
if size in [9, 12]:
scale = t[6: 9]
if size == 12:
shear = t[9: 12]
return compose_matrix(scale=scale, shear=shear,
angles=angles,
translate=translate)