def select_largest(points, faces, exclude_labels=[-1], areas=None,
reindex=True):
"""
Select the largest segment (connected set of indices) in a mesh.
In case a surface patch is fragmented, we select the largest fragment,
remove extraneous triangular faces, and reindex indices.
Parameters
----------
points : list of lists of 3 floats
x,y,z coordinates for each vertex of the structure
faces : list of lists of 3 integers
3 indices to vertices that form a triangle on the mesh
exclude_labels : list of integers
background values to exclude
areas : numpy array or list of floats (or None)
surface area scalar values for all vertices
reindex : Boolean
reindex indices in faces?
Returns
-------
points : list of lists of 3 floats
x,y,z coordinates for each vertex of the structure
faces : list of lists of 3 integers
3 indices to vertices that form a triangle on the mesh
Examples
--------
>>> # Spectrum for one label (artificial composite), two fragments:
>>> import os
>>> import numpy as np
>>> from mindboggle.utils.io_vtk import read_scalars, read_vtk, write_vtk
>>> from mindboggle.utils.mesh import remove_faces
>>> from mindboggle.utils.segment import select_largest
>>> path = os.environ['MINDBOGGLE_DATA']
>>> label_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT31.manual.vtk')
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> exclude_labels = [-1]
>>> faces, lines, indices, points, u1, labels, u2,u3 = read_vtk(label_file,
>>> return_first=True, return_array=True)
>>> I19 = [i for i,x in enumerate(labels) if x==19] # pars orbitalis
>>> I22 = [i for i,x in enumerate(labels) if x==22] # postcentral
>>> I19.extend(I22)
>>> faces = remove_faces(faces, I19)
>>> areas, u1 = read_scalars(area_file, True, True)
>>> reindex = True
>>> #
>>> points, faces = select_largest(points, faces, exclude_labels, areas,
>>> reindex)
>>> # View:
>>> from mindboggle.utils.plots import plot_vtk
>>> scalars = np.zeros(np.shape(labels))
>>> scalars[I19] = 1
>>> vtk_file = 'test_two_labels.vtk'
>>> write_vtk(vtk_file, points, indices, lines, faces,
>>> scalars, scalar_names='scalars')
>>> plot_vtk(vtk_file)
"""
import numpy as np
from mindboggle.utils.mesh import find_neighbors, remove_faces, \
reindex_faces_points
from mindboggle.utils.segment import segment
# Areas:
use_area = False
if isinstance(areas, np.ndarray) and np.shape(areas):
use_area = True
elif isinstance(areas, list) and len(areas):
areas = np.array(areas)
use_area = True
# Check to see if there are enough points:
min_npoints = 4
npoints = len(points)
if npoints < min_npoints or len(faces) < min_npoints:
print("The input size {0} ({1} faces) should be much larger "
"than {2}". format(npoints, len(faces), min_npoints))
return None
else:
#---------------------------------------------------------------------
# Segment the indices into connected sets of indices:
#---------------------------------------------------------------------
# Construct neighbor lists:
neighbor_lists = find_neighbors(faces, npoints)
# Determine the indices:
indices = [x for sublst in faces for x in sublst]
# Segment:
segments = segment(indices, neighbor_lists, min_region_size=1,
seed_lists=[], keep_seeding=False, spread_within_labels=False,
labels=[], label_lists=[], values=[], max_steps='', verbose=False)
#---------------------------------------------------------------------
# Select the largest segment (connected set of indices):
#---------------------------------------------------------------------
unique_segments = [x for x in np.unique(segments)
if x not in exclude_labels]
if len(unique_segments) > 1:
select_indices = []
max_segment_area = 0
for segment_number in unique_segments:
segment_indices = [i for i,x in enumerate(segments)
if x == segment_number]
if use_area:
segment_area = np.sum(areas[segment_indices])
else:
segment_area = len(segment_indices)
if segment_area > max_segment_area:
select_indices = segment_indices
max_segment_area = len(select_indices)
print('Maximum size of {0} segments: {1} vertices'.
format(len(unique_segments), len(select_indices)))
#-----------------------------------------------------------------
# Extract points and renumber faces for the selected indices:
#-----------------------------------------------------------------
faces = remove_faces(faces, select_indices)
else:
select_indices = indices
# Alert if the number of indices is small:
if len(select_indices) < min_npoints:
print("The input size {0} is too small.".format(len(select_indices)))
return None
elif faces:
#-----------------------------------------------------------------
# Reindex indices in faces:
#-----------------------------------------------------------------
if reindex:
faces, points = reindex_faces_points(faces, points)
return points, faces
else:
points = np.array(points)
points = points[select_indices].tolist()
return points, faces
else:
return None
def plot_mask_surface(vtk_file, mask_file='', nonmask_value=-1,
masked_output='', remove_nonmask=False,
program='vtkviewer',
use_colormap=False, colormap_file=''):
"""
Use vtkviewer or mayavi2 to visualize VTK surface mesh data.
If a mask_file is provided, a temporary masked file is saved,
and it is this file that is viewed.
If using vtkviewer, can optionally provide colormap file
or set $COLORMAP environment variable.
Parameters
----------
vtk_file : string
name of VTK surface mesh file
mask_file : string
name of VTK surface mesh file to mask vtk_file vertices
nonmask_value : integer
nonmask (usually background) value
masked_output : string
temporary masked output file name
remove_nonmask : Boolean
remove vertices that are not in mask? (otherwise assign nonmask_value)
program : string {'vtkviewer', 'mayavi2'}
program to visualize VTK file
use_colormap : Boolean
use Paraview-style XML colormap file set by $COLORMAP env variable?
colormap_file : string
use colormap in given file if use_colormap==True? if empty and
use_colormap==True, use file set by $COLORMAP environment variable
Examples
--------
>>> import os
>>> from mindboggle.utils.plots import plot_mask_surface
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT31.manual.vtk')
>>> mask_file = os.path.join(path, 'test_one_label.vtk')
>>> nonmask_value = 0 #-1
>>> masked_output = ''
>>> remove_nonmask = True
>>> program = 'vtkviewer'
>>> use_colormap = True
>>> colormap_file = '' #'/software/mindboggle_tools/colormap.xml'
>>> plot_mask_surface(vtk_file, mask_file, nonmask_value, masked_output, remove_nonmask, program, use_colormap, colormap_file)
"""
import os
import numpy as np
from mindboggle.utils.mesh import remove_faces, reindex_faces_points
from mindboggle.utils.utils import execute
from mindboggle.utils.plots import plot_surfaces
from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars, \
read_vtk, write_vtk
#-------------------------------------------------------------------------
# Filter mesh with non-background values from a second (same-size) mesh:
#-------------------------------------------------------------------------
if mask_file:
mask, name = read_scalars(mask_file, True, True)
if not masked_output:
masked_output = os.path.join(os.getcwd(), 'temp.vtk')
file_to_plot = masked_output
#---------------------------------------------------------------------
# Remove nonmask-valued vertices:
#---------------------------------------------------------------------
if remove_nonmask:
#-----------------------------------------------------------------
# Load VTK files:
#-----------------------------------------------------------------
faces, lines, indices, points, npoints, scalars, scalar_names, \
o1 = read_vtk(vtk_file, True, True)
#-----------------------------------------------------------------
# Find mask indices, remove nonmask faces, and reindex:
#-----------------------------------------------------------------
Imask = [i for i,x in enumerate(mask) if x != nonmask_value]
mask_faces = remove_faces(faces, Imask)
mask_faces, points, \
original_indices = reindex_faces_points(mask_faces, points)
#-----------------------------------------------------------------
# Write VTK file with scalar values:
#-----------------------------------------------------------------
if np.ndim(scalars) == 1:
scalar_type = type(scalars[0]).__name__
elif np.ndim(scalars) == 2:
scalar_type = type(scalars[0][0]).__name__
else:
print("Undefined scalar type!")
write_vtk(file_to_plot, points, [], [], mask_faces,
scalars[original_indices].tolist(), scalar_names,
scalar_type=scalar_type)
else:
scalars, name = read_scalars(vtk_file, True, True)
scalars[mask == nonmask_value] = nonmask_value
rewrite_scalars(vtk_file, file_to_plot, scalars)
else:
file_to_plot = vtk_file
#-------------------------------------------------------------------------
# Display with vtkviewer.py:
#-------------------------------------------------------------------------
if program == 'vtkviewer':
plot_surfaces(file_to_plot, use_colormap=use_colormap,
colormap_file=colormap_file)
#-------------------------------------------------------------------------
# Display with mayavi2:
#-------------------------------------------------------------------------
elif program == 'mayavi2':
cmd = ["mayavi2", "-d", file_to_plot, "-m", "Surface", "&"]
execute(cmd, 'os')
def spectrum_per_label(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization='area', area_file='',
largest_segment=True):
"""
Compute Laplace-Beltrami spectrum per labeled region in a file.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
largest_segment : Boolean
compute spectrum only for largest segment with a given label?
Returns
-------
spectrum_lists : list of lists
first eigenvalues for each label's Laplace-Beltrami spectrum
label_list : list of integers
list of unique labels for which spectra are obtained
Examples
--------
>>> # Uncomment "if label==22:" below to run example:
>>> # Spectrum for Twins-2-1 left postcentral (22) pial surface:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT31.manual.vtk')
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> spectrum_size = 6
>>> exclude_labels = [0] #[-1]
>>> largest_segment = True
>>> spectrum_per_label(vtk_file, spectrum_size, exclude_labels, None,
>>> area_file, largest_segment)
([[6.3469513010430304e-18,
0.0005178862383467463,
0.0017434911095630772,
0.003667561767487686,
0.005429017880363784,
0.006309346984678924]],
[22])
"""
from mindboggle.utils.io_vtk import read_vtk, read_scalars
from mindboggle.utils.mesh import remove_faces, reindex_faces_points
from mindboggle.shapes.laplace_beltrami import fem_laplacian,\
spectrum_of_largest
# Read VTK surface mesh file:
faces, u1, u2, points, u4, labels, u5, u6 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
# Loop through labeled regions:
ulabels = []
[ulabels.append(int(x)) for x in labels if x not in ulabels
if x not in exclude_labels]
label_list = []
spectrum_lists = []
for label in ulabels:
#if label == 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
# Determine the indices per label:
Ilabel = [i for i,x in enumerate(labels) if x == label]
print('{0} vertices for label {1}'.format(len(Ilabel), label))
# Remove background faces:
pick_faces = remove_faces(faces, Ilabel)
pick_faces, pick_points, o1 = reindex_faces_points(pick_faces, points)
# Compute Laplace-Beltrami spectrum for the label:
if largest_segment:
exclude_labels_inner = [-1]
spectrum = spectrum_of_largest(pick_points, pick_faces,
spectrum_size,
exclude_labels_inner,
normalization, areas)
else:
spectrum = fem_laplacian(pick_points, pick_faces,
spectrum_size, normalization)
# Append to a list of lists of spectra:
spectrum_lists.append(spectrum)
label_list.append(label)
return spectrum_lists, label_list
def explode_scalars(input_indices_vtk, input_values_vtk='', output_stem='',
exclude_values=[-1], background_value=-1,
output_scalar_name='scalars',
remove_background_faces=True, reindex=True):
"""
Write out a separate VTK file for each integer (not in exclude_values)
in (the first) scalar list of an input VTK file.
Optionally write the values drawn from a second VTK file,
remove background values, and reindex indices.
Parameters
----------
input_indices_vtk : string
path of the input VTK file that contains indices as scalars
(assumes that the scalars are a list of floats or integers)
input_values_vtk : string
path of the input VTK file that contains values as scalars
output_stem : string
path and stem of the output VTK file
exclude_values : list or array
values to exclude
background_value : integer or float
background value in output VTK files
remove_background_faces : Boolean
remove all faces whose three vertices are not all a given index?
reindex : Boolean
reindex all indices in faces?
Examples
--------
>>> # Example 1: explode sulci with thickness values
>>> import os
>>> from mindboggle.utils.io_vtk import explode_scalars
>>> from mindboggle.utils.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> input_indices_vtk = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> input_values_vtk = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> output_stem = 'sulci_depth'
>>> #
>>> explode_scalars(input_indices_vtk, input_values_vtk, output_stem)
>>> #
>>> # View:
>>> example_vtk = os.path.join(os.getcwd(), output_stem + '0.vtk')
>>> plot_surfaces(example_vtk)
>>> #
>>> # Example 2: explode labels
>>> import os
>>> from mindboggle.utils.io_vtk import explode_scalars
>>> from mindboggle.utils.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> input_values_vtk = os.path.join(path, 'arno', 'labels',
>>> 'lh.labels.DKT25.manual.vtk')
>>> input_indices_vtk = input_values_vtk
>>> output_stem = 'label'
>>> exclude_values = [-1]
>>> background_value = -1,
>>> output_scalar_name = 'scalars'
>>> remove_background_faces = True
>>> reindex = True
>>> #
>>> explode_scalars(input_indices_vtk, input_values_vtk, output_stem,
>>> exclude_values, background_value,
>>> output_scalar_name, remove_background_faces, reindex)
>>> # View:
>>> example_vtk = os.path.join(os.getcwd(), output_stem + '2.vtk')
>>> plot_surfaces(example_vtk)
"""
import os
import numpy as np
from mindboggle.utils.io_vtk import read_scalars, read_vtk, write_vtk
from mindboggle.utils.mesh import reindex_faces_points, remove_faces
# Load VTK file:
faces, lines, indices, points, npoints, scalars, scalar_names, \
foo1 = read_vtk(input_indices_vtk, True, True)
print("Explode the scalar list in {0}".
format(os.path.basename(input_indices_vtk)))
if input_values_vtk != input_indices_vtk:
values, name = read_scalars(input_values_vtk, True, True)
print("Explode the scalar list of values in {0} "
"with the scalar list of indices in {1}".
format(os.path.basename(input_values_vtk),
os.path.basename(input_indices_vtk)))
else:
values = np.copy(scalars)
# Loop through unique (non-excluded) scalar values:
unique_scalars = np.unique(scalars)
if all(unique_scalars==np.round(unique_scalars)):
unique_scalars = [int(x) for x in unique_scalars
if x not in exclude_values]
else:
unique_scalars = [x for x in unique_scalars
if x not in exclude_values]
for scalar in unique_scalars:
# Remove background (keep only faces with the scalar):
if remove_background_faces:
scalar_indices = [i for i,x in enumerate(scalars) if x == scalar]
scalar_faces = remove_faces(faces, scalar_indices)
else:
scalar_faces = faces
# Reindex:
if reindex:
scalar_faces, select_points, \
o1 = reindex_faces_points(scalar_faces, points)
else:
select_points = points
# Create array and indices for scalar value:
if reindex:
len_indices = len(select_points)
select_values = scalar * np.ones(len_indices)
else:
select_values = np.copy(values)
select_values[scalars != scalar] = background_value
len_indices = len([i for i,x in enumerate(select_values)
if x != background_value])
print(" Scalar {0}: {1} vertices".format(scalar, len_indices))
# Write VTK file with scalar values (list of values):
if np.ndim(select_values) == 1:
scalar_type = type(select_values[0]).__name__
elif np.ndim(select_values) == 2:
scalar_type = type(select_values[0][0]).__name__
else:
print("Undefined scalar type!")
output_vtk = os.path.join(os.getcwd(),
output_stem + str(scalar) + '.vtk')
write_vtk(output_vtk, select_points, indices, lines, scalar_faces,
select_values.tolist(), output_scalar_name,
scalar_type=scalar_type)
def plot_mask_surface(vtk_file, mask_file='', nonmask_value=-1,
masked_output='', remove_nonmask=False,
program='vtkviewer',
use_colormap=False, colormap_file=''):
"""
Use vtkviewer or mayavi2 to visualize VTK surface mesh data.
If a mask_file is provided, a temporary masked file is saved,
and it is this file that is viewed.
If using vtkviewer, can optionally provide colormap file
or set $COLORMAP environment variable.
Parameters
----------
vtk_file : string
name of VTK surface mesh file
mask_file : string
name of VTK surface mesh file to mask vtk_file vertices
nonmask_value : integer
nonmask (usually background) value
masked_output : string
temporary masked output file name
remove_nonmask : Boolean
remove vertices that are not in mask? (otherwise assign nonmask_value)
program : string {'vtkviewer', 'mayavi2'}
program to visualize VTK file
use_colormap : Boolean
use Paraview-style XML colormap file set by $COLORMAP env variable?
colormap_file : string
use colormap in given file if use_colormap==True? if empty and
use_colormap==True, use file set by $COLORMAP environment variable
Examples
--------
>>> import os
>>> from mindboggle.utils.plots import plot_mask_surface
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT31.manual.vtk')
>>> mask_file = os.path.join(path, 'test_one_label.vtk')
>>> nonmask_value = 0 #-1
>>> masked_output = ''
>>> remove_nonmask = True
>>> program = 'vtkviewer'
>>> use_colormap = True
>>> colormap_file = '' #'/software/mindboggle_tools/colormap.xml'
>>> plot_mask_surface(vtk_file, mask_file, nonmask_value, masked_output, remove_nonmask, program, use_colormap, colormap_file)
"""
import os
import numpy as np
from mindboggle.utils.mesh import remove_faces, reindex_faces_points
from mindboggle.utils.utils import execute
from mindboggle.utils.plots import plot_surfaces
from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars, \
read_vtk, write_vtk
#-------------------------------------------------------------------------
# Filter mesh with non-background values from a second (same-size) mesh:
#-------------------------------------------------------------------------
if mask_file:
mask, name = read_scalars(mask_file, True, True)
if not masked_output:
masked_output = os.path.join(os.getcwd(), 'temp.vtk')
file_to_plot = masked_output
#---------------------------------------------------------------------
# Remove nonmask-valued vertices:
#---------------------------------------------------------------------
if remove_nonmask:
#-----------------------------------------------------------------
# Load VTK files:
#-----------------------------------------------------------------
faces, lines, indices, points, npoints, scalars, scalar_names, \
o1 = read_vtk(vtk_file, True, True)
#-----------------------------------------------------------------
# Find mask indices, remove nonmask faces, and reindex:
#-----------------------------------------------------------------
Imask = [i for i,x in enumerate(mask) if x != nonmask_value]
mask_faces = remove_faces(faces, Imask)
mask_faces, points, \
original_indices = reindex_faces_points(mask_faces, points)
#-----------------------------------------------------------------
# Write VTK file with scalar values:
#-----------------------------------------------------------------
if np.ndim(scalars) == 1:
scalar_type = type(scalars[0]).__name__
elif np.ndim(scalars) == 2:
scalar_type = type(scalars[0][0]).__name__
else:
print("Undefined scalar type!")
write_vtk(file_to_plot, points, [], [], mask_faces,
scalars[original_indices].tolist(), scalar_names,
scalar_type=scalar_type)
else:
scalars, name = read_scalars(vtk_file, True, True)
scalars[mask == nonmask_value] = nonmask_value
rewrite_scalars(vtk_file, file_to_plot, scalars)
else:
file_to_plot = vtk_file
#-------------------------------------------------------------------------
# Display with vtkviewer.py:
#-------------------------------------------------------------------------
if program == 'vtkviewer':
plot_surfaces(file_to_plot, use_colormap=use_colormap,
colormap_file=colormap_file)
#-------------------------------------------------------------------------
# Display with mayavi2:
#-------------------------------------------------------------------------
elif program == 'mayavi2':
cmd = ["mayavi2", "-d", file_to_plot, "-m", "Surface", "&"]
execute(cmd, 'os')
def spectrum_of_largest(points, faces, n_eigenvalues=6, exclude_labels=[-1],
normalization=None, areas=None):
"""
Compute Laplace-Beltrami spectrum on largest connected segment.
In case a surface patch is fragmented, we select the largest fragment,
remove extraneous triangular faces, and reindex indices.
Parameters
----------
points : list of lists of 3 floats
x,y,z coordinates for each vertex of the structure
faces : list of lists of 3 integers
3 indices to vertices that form a triangle on the mesh
n_eigenvalues : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
background values to exclude
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
areas : numpy array or list of floats (or None)
surface area scalar values for all vertices
Returns
-------
spectrum : list
first n_eigenvalues eigenvalues for Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for one label (artificial composite), two fragments:
>>> import os
>>> import numpy as np
>>> from mindboggle.utils.io_vtk import read_scalars, read_vtk, write_vtk
>>> from mindboggle.utils.mesh import remove_faces
>>> from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
>>> path = os.environ['MINDBOGGLE_DATA']
>>> label_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> n_eigenvalues = 6
>>> exclude_labels = [0] #[-1]
>>> normalization = None
>>> faces, lines, indices, points, foo1, labels, foo2, foo3 = read_vtk(label_file,
>>> return_first=True, return_array=True)
>>> I2 = [i for i,x in enumerate(labels) if x==2] # cingulate
>>> I22 = [i for i,x in enumerate(labels) if x==22] # postcentral
>>> I2.extend(I22)
>>> faces = remove_faces(faces, I2)
>>> areas, u1 = read_scalars(area_file, True, True)
>>> #
>>> spectrum_of_largest(points, faces, n_eigenvalues, exclude_labels,
>>> normalization, areas)
>>> #
>>> # View:
>>> from mindboggle.utils.plots import plot_vtk
>>> scalars = np.zeros(np.shape(labels))
>>> scalars[I2] = 1
>>> vtk_file = 'test_two_labels.vtk'
>>> write_vtk(vtk_file, points, indices, lines, faces,
>>> scalars, scalar_names='scalars')
>>> plot_vtk(vtk_file)
Load "Labels" scalars from lh.labels.DKT25.manual.vtk
Reduced 290134 to 29728 triangular faces
Load "scalars" scalars from lh.pial.area.vtk
2 segments
Reduced 29728 to 14498 triangular faces
Compute linear FEM Laplace-Beltrami spectrum
[-8.764053090852845e-18,
0.00028121452203987146,
0.0010941205613292243,
0.0017301461686759188,
0.0034244633555606295,
0.004280982704174599]
"""
from scipy.sparse.linalg import eigsh, lobpcg
import numpy as np
from mindboggle.utils.mesh import find_neighbors, remove_faces, \
reindex_faces_points
from mindboggle.utils.segment import segment
from mindboggle.shapes.laplace_beltrami import fem_laplacian
# Areas:
use_area = False
if isinstance(areas, np.ndarray) and np.shape(areas):
use_area = True
elif isinstance(areas, list) and len(areas):
areas = np.array(areas)
use_area = True
# Check to see if there are enough points:
min_npoints = n_eigenvalues
npoints = len(points)
if npoints < min_npoints or len(faces) < min_npoints:
print("The input size {0} ({1} faces) should be much larger "
"than n_eigenvalues {2}".
format(npoints, len(faces), n_eigenvalues))
return None
else:
#---------------------------------------------------------------------
# Segment the indices into connected sets of indices:
#---------------------------------------------------------------------
# Construct neighbor lists:
neighbor_lists = find_neighbors(faces, npoints)
# Determine the indices:
indices = [x for sublst in faces for x in sublst]
# Segment:
segments = segment(indices, neighbor_lists, min_region_size=1,
seed_lists=[], keep_seeding=False, spread_within_labels=False,
labels=[], label_lists=[], values=[], max_steps='', verbose=False)
#---------------------------------------------------------------------
# Select the largest segment (connected set of indices):
#---------------------------------------------------------------------
unique_segments = [x for x in np.unique(segments)
if x not in exclude_labels]
if len(unique_segments) > 1:
select_indices = []
max_segment_area = 0
for segment_number in unique_segments:
segment_indices = [i for i,x in enumerate(segments)
if x == segment_number]
if use_area:
segment_area = np.sum(areas[segment_indices])
else:
segment_area = len(segment_indices)
if segment_area > max_segment_area:
select_indices = segment_indices
max_segment_area = len(select_indices)
print('Maximum size of {0} segments: {1} vertices'.
format(len(unique_segments), len(select_indices)))
#-----------------------------------------------------------------
# Extract points and renumber faces for the selected indices:
#-----------------------------------------------------------------
faces = remove_faces(faces, select_indices)
else:
select_indices = indices
# Alert if the number of indices is small:
if len(select_indices) < min_npoints:
print("The input size {0} is too small.".format(len(select_indices)))
return None
elif faces:
#-----------------------------------------------------------------
# Reindex indices in faces:
#-----------------------------------------------------------------
faces, points = reindex_faces_points(faces, points)
#-----------------------------------------------------------------
# Compute spectrum:
#-----------------------------------------------------------------
spectrum = fem_laplacian(points, faces, n_eigenvalues,
normalization)
return spectrum
else:
return None
def spectrum_per_label(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization='area', area_file='',
largest_segment=True):
"""
Compute Laplace-Beltrami spectrum per labeled region in a file.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
largest_segment : Boolean
compute spectrum only for largest segment with a given label?
Returns
-------
spectrum_lists : list of lists
first eigenvalues for each label's Laplace-Beltrami spectrum
label_list : list of integers
list of unique labels for which spectra are obtained
Examples
--------
>>> # Uncomment "if label==22:" below to run example:
>>> # Spectrum for Twins-2-1 left postcentral (22) pial surface:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT31.manual.vtk')
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> spectrum_size = 6
>>> exclude_labels = [0] #[-1]
>>> largest_segment = True
>>> spectrum_per_label(vtk_file, spectrum_size, exclude_labels, None,
>>> area_file, largest_segment)
([[6.3469513010430304e-18,
0.0005178862383467463,
0.0017434911095630772,
0.003667561767487686,
0.005429017880363784,
0.006309346984678924]],
[22])
"""
from mindboggle.utils.io_vtk import read_vtk, read_scalars
from mindboggle.utils.mesh import remove_faces, reindex_faces_points
from mindboggle.shapes.laplace_beltrami import fem_laplacian,\
spectrum_of_largest
# Read VTK surface mesh file:
faces, u1, u2, points, u4, labels, u5, u6 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
# Loop through labeled regions:
ulabels = []
[ulabels.append(int(x)) for x in labels if x not in ulabels
if x not in exclude_labels]
label_list = []
spectrum_lists = []
for label in ulabels:
#if label == 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
# Determine the indices per label:
Ilabel = [i for i,x in enumerate(labels) if x == label]
print('{0} vertices for label {1}'.format(len(Ilabel), label))
# Remove background faces:
pick_faces = remove_faces(faces, Ilabel)
pick_faces, pick_points, o1 = reindex_faces_points(pick_faces, points)
# Compute Laplace-Beltrami spectrum for the label:
if largest_segment:
exclude_labels_inner = [-1]
spectrum = spectrum_of_largest(pick_points, pick_faces,
spectrum_size,
exclude_labels_inner,
normalization, areas)
else:
spectrum = fem_laplacian(pick_points, pick_faces,
spectrum_size, normalization)
# Append to a list of lists of spectra:
spectrum_lists.append(spectrum)
label_list.append(label)
return spectrum_lists, label_list