def collapse_mask(mask, auto_label=True, custom_mask=None):
"""collapse separate masks into one mask with multiple integers
overlapping areas are ignored
Args:
mask: nibabel or Brain_Data instance
custom_mask: nibabel instance or string to file path; optional
Returns:
out: Brain_Data instance of a mask with different integers indicating
different masks
"""
from nltools.data import Brain_Data
if not isinstance(mask, Brain_Data):
if isinstance(mask, nib.Nifti1Image):
mask = Brain_Data(mask, mask=custom_mask)
else:
raise ValueError("Make sure mask is a nibabel or Brain_Data "
"instance.")
if len(mask.shape()) > 1:
if len(mask) > 1:
out = mask.empty()
# Create list of masks and find any overlaps
m_list = []
for x in range(len(mask)):
m_list.append(mask[x].to_nifti())
intersect = intersect_masks(m_list, threshold=1, connected=False)
intersect = Brain_Data(
nib.Nifti1Image(np.abs(intersect.get_data() - 1),
intersect.get_affine()),
mask=custom_mask,
)
merge = []
if auto_label:
# Combine all masks into sequential order
# ignoring any areas of overlap
for i in range(len(m_list)):
merge.append(
np.multiply(
Brain_Data(m_list[i], mask=custom_mask).data,
intersect.data) * (i + 1))
out.data = np.sum(np.array(merge).T, 1).astype(int)
else:
# Collapse masks using value as label
for i in range(len(m_list)):
merge.append(
np.multiply(
Brain_Data(m_list[i], mask=custom_mask).data,
intersect.data))
out.data = np.sum(np.array(merge).T, 1)
return out
else:
warnings.warn("Doesn't need to be collapased")
def create_sphere(coordinates, radius=5, mask=None):
""" Generate a set of spheres in the brain mask space
Args:
radius: vector of radius. Will create multiple spheres if
len(radius) > 1
centers: a vector of sphere centers of the form [px, py, pz] or
[[px1, py1, pz1], ..., [pxn, pyn, pzn]]
"""
from nltools.data import Brain_Data
if mask is not None:
if not isinstance(mask, nib.Nifti1Image):
if type(mask) is str:
if os.path.isfile(mask):
data = nib.load(mask)
else:
raise ValueError("mask is not a nibabel instance or a valid "
"file name")
else:
mask = nib.load(
os.path.join(get_resource_path(),
'MNI152_T1_2mm_brain_mask.nii.gz'))
dims = mask.get_data().shape
def sphere(r, p, mask):
""" create a sphere of given radius at some point p in the brain mask
Args:
r: radius of the sphere
p: point (in coordinates of the brain mask) of the center of the
sphere
"""
dims = mask.shape
m = [dims[0] / 2, dims[1] / 2,
dims[2] / 2] # JC edit: default value for centers
x, y, z = np.ogrid[-m[0]:dims[0] - m[0], -m[1]:dims[1] - m[1],
-m[2]:dims[2] - m[2]] #JC edit: creates sphere
# x, y, z = np.ogrid[-p[0]:dims[0]-p[0], -p[1]:dims[1]-p[1], -p[2]:dims[2]-p[2]]
mask_r = x * x + y * y + z * z <= r * r
activation = np.zeros(dims)
activation[mask_r] = 1
# JC edit shift mask to proper location
translation_affine = np.array([[1, 0, 0, p[0] - m[0]],
[0, 1, 0, p[1] - m[1]],
[0, 0, 1, p[2] - m[2]], [0, 0, 0, 1]])
# activation = np.multiply(activation, mask.get_data())
# activation = nib.Nifti1Image(activation, affine=np.eye(4))
activation = nib.Nifti1Image(activation, affine=translation_affine)
#return the 3D numpy matrix of zeros containing the sphere as a region of ones
# return activation.get_data(), translation_affine
return activation
# Initialize Spheres with options for multiple radii and centers of the spheres (or just an int and a 3D list)
# return sphere(radius,coordinates,mask)
if type(radius) is int:
radius = [radius]
if coordinates is None:
coordinates = [[dims[0] / 2, dims[1] / 2, dims[2] / 2] * len(radius)
] #default value for centers
elif type(coordinates) is list and type(
coordinates[0]) is int and len(radius) is 1:
coordinates = [coordinates]
if (type(radius)) is list and (type(coordinates) is
list) and (len(radius) == len(coordinates)):
A = np.zeros_like(mask.get_data())
A = Brain_Data(nib.Nifti1Image(A, affine=mask.affine), mask=mask)
for i in range(len(radius)):
A = A + Brain_Data(sphere(radius[i], coordinates[i], mask),
mask=mask)
A = A.to_nifti()
A.get_data()[A.get_data() > 0.5] = 1
A.get_data()[A.get_data() < 0.5] = 0
return A
else:
raise ValueError("Data type for sphere or radius(ii) or center(s) "
"not recognized.")
def create_sphere(coordinates, radius=5, mask=None):
"""Generate a set of spheres in the brain mask space
Args:
radius: vector of radius. Will create multiple spheres if
len(radius) > 1
centers: a vector of sphere centers of the form [px, py, pz] or
[[px1, py1, pz1], ..., [pxn, pyn, pzn]]
"""
from nltools.data import Brain_Data
if mask is not None:
if not isinstance(mask, nib.Nifti1Image):
if isinstance(mask, str):
if os.path.isfile(mask):
mask = nib.load(mask)
else:
raise ValueError("mask is not a nibabel instance or a valid "
"file name")
else:
mask = nib.load(resolve_mni_path(MNI_Template)["mask"])
def sphere(r, p, mask):
"""create a sphere of given radius at some point p in the brain mask
Args:
r: radius of the sphere
p: point (in coordinates of the brain mask) of the center of the
sphere
"""
dims = mask.shape
m = [dims[0] / 2, dims[1] / 2, dims[2] / 2]
x, y, z = np.ogrid[-m[0]:dims[0] - m[0], -m[1]:dims[1] - m[1],
-m[2]:dims[2] - m[2]]
mask_r = x * x + y * y + z * z <= r * r
activation = np.zeros(dims)
activation[mask_r] = 1
translation_affine = np.array([
[1, 0, 0, p[0] - m[0]],
[0, 1, 0, p[1] - m[1]],
[0, 0, 1, p[2] - m[2]],
[0, 0, 0, 1],
])
return nib.Nifti1Image(activation, affine=translation_affine)
if any(isinstance(i, list) for i in coordinates):
if isinstance(radius, list):
if len(radius) != len(coordinates):
raise ValueError("Make sure length of radius list matches"
"length of coordinate list.")
elif isinstance(radius, int):
radius = [radius] * len(coordinates)
out = Brain_Data(
nib.Nifti1Image(np.zeros_like(mask.get_data()),
affine=mask.affine),
mask=mask,
)
for r, c in zip(radius, coordinates):
out = out + Brain_Data(sphere(r, c, mask), mask=mask)
else:
out = Brain_Data(sphere(radius, coordinates, mask), mask=mask)
out = out.to_nifti()
out.get_data()[out.get_data() > 0.5] = 1
out.get_data()[out.get_data() < 0.5] = 0
return out
