def label_with_dilation(self, to_label_nii_fname: os.PathLike, dilated_nii_fname: os.PathLike,
out_nii_fname: os.PathLike):
"""
Labels a volume using its labeled dilation. The dilated volume is labeled using scipy.ndimage.label function.
:param to_label_nii_fname: usually a CT-mask.nii.gz
:param dilated_nii_fname: dilated version of the to_label_nii_fname volume
"""
# TODO could make dilation with ndimage also.
mask = IOUtils.read_volume(to_label_nii_fname)
dil_mask = IOUtils.read_volume(dilated_nii_fname)
lab, n = scipy.ndimage.label(dil_mask.data)
# TODO: this change is from tvb-make. Keep it or not? It returns a different result than the old version.
lab_xyz = list(self.compute_label_volume_centers(lab, dil_mask.affine_matrix))
lab_sort = numpy.r_[:n + 1]
# sort labels along AP axis
for i, (val, _) in enumerate(sorted(lab_xyz, key=lambda t: t[1][1])):
lab_sort[val] = i
lab = lab_sort[lab]
mask.data *= lab
self.logger.info(
'%d objects found when labeling the dilated volume.', n)
IOUtils.write_volume(out_nii_fname, mask)
def test_remove_zero_connectivity():
service = VolumeService()
data = numpy.array([[[0, 0, 1], [2, 3, 0]], [[4, 0, 0], [0, 0, 0]]])
volume = Volume(data, [[1, 0, 0, 0], [0, 1, 0, 0],
[0, 0, 1, 0], [0, 0, 0, 1]], None)
volume_path = get_temporary_files_path("tdi_lbl.nii.gz")
IOUtils.write_volume(volume_path, volume)
in_connectivity = numpy.array(
[[10, 1, 0, 3], [0, 10, 0, 2], [0, 0, 0, 0], [0, 0, 0, 10]])
connectivity_path = get_temporary_files_path("conn.csv")
numpy.savetxt(connectivity_path, in_connectivity, fmt='%1d')
tract_lengths_path = get_temporary_files_path("tract_lengths.csv")
numpy.savetxt(tract_lengths_path, in_connectivity, fmt='%1d')
service.remove_zero_connectivity_nodes(
volume_path, connectivity_path, tract_lengths_path)
assert os.path.exists(os.path.splitext(connectivity_path)[0] + ".npy")
assert os.path.exists(os.path.splitext(tract_lengths_path)[0] + ".npy")
vol = IOUtils.read_volume(volume_path)
assert len(numpy.unique(vol.data)) == 4
conn = numpy.array(numpy.genfromtxt(connectivity_path, dtype='int64'))
assert numpy.array_equal(conn, [[20, 1, 3], [1, 20, 2], [3, 2, 20]])
def overlap_3_volumes(self, background_path: os.PathLike, overlay_1_path: os.PathLike,
overlay_2_path: os.PathLike, use_cc_point: bool,
snapshot_name: str=SNAPSHOT_NAME):
volume_background = IOUtils.read_volume(background_path)
volume_overlay_1 = IOUtils.read_volume(overlay_1_path)
volume_overlay_2 = IOUtils.read_volume(overlay_2_path)
if use_cc_point:
ras = self.generic_io.get_ras_coordinates(
self.read_t1_affine_matrix())
else:
ras = volume_background.get_center_point()
for projection in PROJECTIONS:
try:
x, y, background_matrix = volume_background.slice_volume(
projection, ras)
x1, y1, overlay_1_matrix = volume_overlay_1.slice_volume(
projection, ras)
x2, y2, overlay_2_matrix = volume_overlay_2.slice_volume(
projection, ras)
except IndexError:
new_ras = volume_background.get_center_point()
x, y, background_matrix = volume_background.slice_volume(
projection, new_ras)
x1, y1, overlay_1_matrix = volume_overlay_1.slice_volume(
projection, new_ras)
x2, y2, overlay_2_matrix = volume_overlay_2.slice_volume(
projection, new_ras)
self.logger.info("The volume center point has been used for %s snapshot of %s, %s and %s.", projection,
background_path, overlay_1_path, overlay_2_path)
self.writer.write_3_matrices(x, y, background_matrix, x1, y1, overlay_1_matrix, x2, y2, overlay_2_matrix,
self.generate_file_name(projection, snapshot_name))
def test_label_with_dilation():
service = VolumeService()
ct_mask_data = numpy.array(
[[[0, 0, 0], [0, 1, 0], [0, 1, 0]], [[1, 1, 1], [0, 0, 0], [0, 0, 0]], [[0, 0, 1], [0, 0, 0], [0, 0, 1]]])
ct_mask_volume = Volume(ct_mask_data, [[1, 0, 0, 0], [0, 1, 0, 0], [
0, 0, 1, 0], [0, 0, 0, 1]], None)
ct_mask_path = get_temporary_files_path("ct_mask.nii.gz")
IOUtils.write_volume(ct_mask_path, ct_mask_volume)
ct_dil_mask_data = numpy.array(
[[[0, 0, 0], [1, 1, 1], [0, 1, 0]], [[1, 1, 1], [0, 0, 0], [0, 0, 0]], [[0, 1, 1], [0, 0, 0], [0, 1, 1]]])
ct_dil_mask_volume = Volume(ct_dil_mask_data, [[1, 0, 0, 0], [
0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], None)
ct_dil_mask_path = get_temporary_files_path("ct_dil_mask.nii.gz")
IOUtils.write_volume(ct_dil_mask_path, ct_dil_mask_volume)
ct_result = get_temporary_files_path("ct_res.nii.gz")
service.label_with_dilation(ct_mask_path, ct_dil_mask_path, ct_result)
assert os.path.exists(ct_mask_path)
assert os.path.exists(ct_dil_mask_path)
assert os.path.exists(ct_result)
vol = IOUtils.read_volume(ct_result)
def overlap_volume_surfaces(self, volume_background: os.PathLike, surfaces_path: os.PathLike,
use_center_surface: bool, use_cc_point: bool, snapshot_name: str=SNAPSHOT_NAME):
volume = IOUtils.read_volume(volume_background)
if use_cc_point:
ras = self.generic_io.get_ras_coordinates(
self.read_t1_affine_matrix())
else:
ras = volume.get_center_point()
surfaces = [IOUtils.read_surface(os.path.expandvars(surface), use_center_surface) for surface in
surfaces_path]
for projection in PROJECTIONS:
try:
x, y, background_matrix = volume.slice_volume(projection, ras)
except IndexError:
ras = volume.get_center_point()
x, y, background_matrix = volume.slice_volume(projection, ras)
self.logger.info("The volume center point has been used for %s snapshot of %s and %s.", projection,
volume_background, surfaces_path)
clear_flag = True
for surface_index, surface in enumerate(surfaces):
surf_x_array, surf_y_array = surface.cut_by_plane(
projection, ras)
self.writer.write_matrix_and_surfaces(x, y, background_matrix, surf_x_array, surf_y_array,
surface_index, clear_flag)
clear_flag = False
self.writer.save_figure(
self.generate_file_name(projection, snapshot_name))
def test_write_volume():
in_file_path = get_data_file(
TEST_MODIF_SUBJECT, TEST_VOLUME_FOLDER, "T1.nii.gz")
volume = IOUtils.read_volume(in_file_path)
out_file_path = get_temporary_files_path('T1-out.nii.gz')
IOUtils.write_volume(out_file_path, volume)
assert os.path.exists(out_file_path)
def test_write_annotation(self):
file_path = get_data_file(
self.subject, self.annot_path, "lh.aparc.annot")
annotation = IOUtils.read_annotation(file_path)
out_annotation_path = self.temp_file_path("lh-test.aparc.annot")
IOUtils.write_annotation(out_annotation_path, annotation)
new_annotation = IOUtils.read_annotation(out_annotation_path)
self.assertEqual(annotation.region_names, new_annotation.region_names)
def test_write_write_brain_visa_surf():
surface_path = get_data_file(
TEST_FS_SUBJECT, TEST_SURFACE_FOLDER, "lh.pial")
out_path = get_temporary_files_path("lh.pial.tri")
surface = IOUtils.read_surface(surface_path, False)
IOUtils.write_surface(out_path, surface)
assert os.path.exists(out_path)
def test_write_fs_surface():
file_path = get_data_file(TEST_FS_SUBJECT, TEST_SURFACE_FOLDER, "lh.pial")
original_surface = IOUtils.read_surface(file_path, False)
triangles_number = len(original_surface.triangles)
output_file_path = get_temporary_files_path("lh-test.pial")
IOUtils.write_surface(output_file_path, original_surface)
new_surface = IOUtils.read_surface(output_file_path, False)
assert triangles_number == len(new_surface.triangles) == 327680
def simple_label_config(self, in_aparc_path: os.PathLike, out_volume_path: os.PathLike):
"""
Relabel volume to have contiguous values like Mrtrix' labelconfig.
:param in_aparc_path: volume voxel value is the index of the region it belongs to.
:return: writes the labeled volume to out_volume_path.
"""
aparc = IOUtils.read_volume(in_aparc_path)
aparc = self._label_config(aparc)
IOUtils.write_volume(out_volume_path, aparc)
def label_vol_from_tdi(self, tdi_volume_path: os.PathLike, out_volume_path: os.PathLike, lo: float=0.5):
"""
Creates a mask of the voxels with tract ends > lo and any other voxels become 0.
Labels each voxel different from 0 with integer labels starting from 1.
:param tdi_volume_path: volume voxel value is the sum of tract ends. Voxel without tract ends has value 0.
:param lo: tract ends threshold used for masking.
:return: writes labeled volume to :ut_volume_path.
"""
nii_volume = IOUtils.read_volume(tdi_volume_path)
tdi_volume = self._label_volume(nii_volume, lo)
IOUtils.write_volume(out_volume_path, tdi_volume)
def show_aparc_aseg_with_new_values(
self, aparc_aseg_volume_path: os.PathLike, region_values_path: os.PathLike,
background_volume_path: os.PathLike, use_cc_point: bool,
fs_to_conn_indices_mapping_path: os.PathLike=FS_TO_CONN_INDICES_MAPPING_PATH,
snapshot_name: str=SNAPSHOT_NAME):
"""
Parameters
----------
aparc_aseg_volume_path
region_values_path
background_volume_path
use_cc_point
fs_to_conn_indices_mapping_path
snapshot_name
Returns
-------
"""
aparc_aseg_volume = IOUtils.read_volume(aparc_aseg_volume_path)
fs_to_conn_indices_mapping = {}
with open(fs_to_conn_indices_mapping_path, 'r') as fd:
for line in fd.readlines():
key, _, val = line.strip().split()
fs_to_conn_indices_mapping[int(key)] = int(val)
len_fs_conn = len(fs_to_conn_indices_mapping)
conn_measure = np.loadtxt(region_values_path)
npad = len_fs_conn - conn_measure.size
conn_measure = np.pad( conn_measure, (0, npad), 'constant')
if use_cc_point:
ras = self.generic_io.get_ras_coordinates(
self.read_t1_affine_matrix())
else:
ras = aparc_aseg_volume.get_center_point()
background_volume = None
if background_volume_path:
background_volume = IOUtils.read_volume(background_volume_path)
for projection in PROJECTIONS:
self._aparc_aseg_projection(
aparc_aseg_volume, aparc_aseg_volume_path, projection, ras,
fs_to_conn_indices_mapping,
background_volume, background_volume_path,
snapshot_name, conn_measure
)
def remove_zero_connectivity_nodes(self, node_volume_path: os.PathLike, connectivity_matrix_path: os.PathLike,
tract_length_path: Optional[str]=None):
"""
It removes network nodes with zero connectivity from the volume and connectivity matrices.
The zero connectivity nodes will be labeled with 0 in the volume and the remaining labels will be updated.
The connectivity matrices will be symmetric.
:param node_volume_path: tdi_lbl.nii volume path
:param connectivity_matrix_path: .csv file, output of Mrtrix3 tck2connectome
:param tract_length_path: optional .csv tract lengths matrix
:return: overwrites the input volume and matrices with the processed ones. Also saves matrices as .npy.
"""
node_volume = IOUtils.read_volume(node_volume_path)
connectivity = numpy.array(numpy.genfromtxt(
connectivity_matrix_path, dtype='int64'))
connectivity = connectivity + connectivity.T
connectivity_row_sum = numpy.sum(connectivity, axis=0)
nodes_to_keep_indices = connectivity_row_sum > 0
connectivity = connectivity[nodes_to_keep_indices, :][
:, nodes_to_keep_indices]
numpy.save(os.path.splitext(connectivity_matrix_path)
[0] + NPY_EXTENSION, connectivity)
numpy.savetxt(connectivity_matrix_path, connectivity, fmt='%1d')
if os.path.exists(str(tract_length_path)):
connectivity = numpy.array(numpy.genfromtxt(
tract_length_path, dtype='int64'))
connectivity = connectivity[nodes_to_keep_indices, :][
:, nodes_to_keep_indices]
numpy.save(os.path.splitext(tract_length_path)
[0] + NPY_EXTENSION, connectivity)
numpy.savetxt(tract_length_path, connectivity, fmt='%1d')
else:
self.logger.warning("Path %s is not valid.", tract_length_path)
nodes_to_remove_indices, = numpy.where(~nodes_to_keep_indices)
nodes_to_remove_indices += 1
for node_index in nodes_to_remove_indices:
node_volume.data[node_volume.data == node_index] = 0
node_volume.data[node_volume.data > 0] = numpy.r_[
1:(connectivity.shape[0] + 1)]
IOUtils.write_volume(node_volume_path, node_volume)
def test_overlap_surface_annotation(self):
writer = ImageWriter(SNAPSHOTS_DIRECTORY)
surface_path = get_data_file(self.head2, "SurfaceCortical.h5")
surface = IOUtils.read_surface(surface_path, False)
annot_path = get_data_file(self.head2, "RegionMapping.h5")
annot = IOUtils.read_annotation(annot_path)
annot.region_names = ['reg1', 'reg2']
annot.regions_color_table = numpy.array(
[[200, 200, 200, 255, 30567], [100, 150, 200, 255, 30568]])
resulted_file_name = self.processor.generate_file_name(
'surface_annotation', SNAPSHOT_NAME)
writer.write_surface_with_annotation(surface, annot, resulted_file_name)
fname = '%s0' % (resulted_file_name, )
self._assert_writer_path_exists(fname)
def test_merge_surfaces(self,):
h5_surface_path = get_data_file("head2", "SurfaceCortical.h5")
h5_surface = IOUtils.read_surface(h5_surface_path, False)
vtx = h5_surface.vertices
tri = h5_surface.triangles
nv = vtx.size
nt = tri.size
nv2 = int(nv / 2)
nt2 = int(nt / 2)
lh_surface = Surface(vtx[:nv2], tri[:nt2])
rh_surface = Surface(vtx[nv2:], tri[nt2:])
# h5_region_mapping_path = get_data_file("head2", "RegionMapping.h5")
# annotation = IOUtils.read_annotation(h5_region_mapping_path)
#
# lh_region_mapping = annotation.region_mapping[:len(annotation.region_mapping) / 2]
# rh_region_mapping = annotation.region_mapping[len(annotation.region_mapping) / 2:]
out_surface = self.service.merge_surfaces([lh_surface, rh_surface])
self.assertEqual(len(out_surface.vertices),
len(lh_surface.vertices) + len(rh_surface.vertices))
self.assertEqual(len(out_surface.triangles),
len(lh_surface.triangles) + len(rh_surface.triangles))
def annot_to_conn_conf(self, annot_path, type, conn_conf_path, first_idx=0):
annotation_lh = IOUtils.read_annotation(os.path.join(annot_path, "lh." + type + ".annot"))
annotation_rh = IOUtils.read_annotation(os.path.join(annot_path, "rh." + type + ".annot"))
with open(conn_conf_path, 'w') as fd:
for id, name in enumerate(annotation_lh.region_names):
if type == "aparc" and name != "unknown":
name = "lh-" + name
fd.write('%d\t%s\n' % (id + first_idx, name))
first_idx += len(annotation_lh.region_names)
for id, name in enumerate(annotation_rh.region_names):
if (name == "unknown"):
first_idx -= 1
continue
if type == "aparc" and name != "unknown":
name = "rh-" + name
fd.write('%d\t%s\n' % (id + first_idx, name))
return first_idx + len(annotation_rh.region_names)
def create_tvb_dataset(atlas_suffix: AtlasSuffix, mri_direc: os.PathLike,
region_details_direc: os.PathLike,
weights_file: os.PathLike,
tracts_file: os.PathLike,
out_dir: os.PathLike,
bring_t1=False):
weights_matrix = numpy.loadtxt(str(weights_file), dtype='i', delimiter=' ')
weights_matrix += weights_matrix.T
tracts_matrix = numpy.loadtxt(str(tracts_file), dtype='f', delimiter=' ')
tracts_matrix += tracts_matrix.T
is_cortical_rm = numpy.genfromtxt(
os.path.join(region_details_direc, AsegFiles.CORTICAL_TXT.value.replace("%s", atlas_suffix)), usecols=[0],
dtype='i')
region_names = numpy.genfromtxt(
os.path.join(region_details_direc, AsegFiles.CENTERS_TXT.value.replace("%s", atlas_suffix)), usecols=[0],
dtype="str")
region_centers = numpy.genfromtxt(
os.path.join(region_details_direc, AsegFiles.CENTERS_TXT.value.replace("%s", atlas_suffix)), usecols=[1, 2, 3])
region_areas = numpy.genfromtxt(
os.path.join(region_details_direc, AsegFiles.AREAS_TXT.value.replace("%s", atlas_suffix)), usecols=[0])
region_orientations = numpy.genfromtxt(
os.path.join(region_details_direc, AsegFiles.ORIENTATIONS_TXT.value.replace("%s", atlas_suffix)),
usecols=[0, 1, 2])
rm_idx = numpy.genfromtxt(
os.path.join(region_details_direc, AsegFiles.RM_TO_APARC_ASEG_TXT.value.replace("%s", atlas_suffix)),
usecols=[0, 1], dtype='i')
rm_index_dict = dict(zip(rm_idx[:, 0], rm_idx[:, 1]))
print(rm_index_dict)
genericIO = GenericIO()
genericIO.write_connectivity_zip(out_dir, weights_matrix, tracts_matrix, is_cortical_rm, region_names,
region_centers, region_areas, region_orientations, atlas_suffix)
aparc_aseg_file = os.path.join(mri_direc, T1Files.APARC_ASEG_NII_GZ.value.replace("%s", atlas_suffix))
aparc_aseg_volume = IOUtils.read_volume(aparc_aseg_file)
volume_service = VolumeService()
aparc_aseg_cor_volume = volume_service.change_labels_of_aparc_aseg(atlas_suffix, aparc_aseg_volume, rm_index_dict,
weights_matrix.shape[0])
IOUtils.write_volume(os.path.join(out_dir, OutputConvFiles.APARC_ASEG_COR_NII_GZ.value.replace("%s", atlas_suffix)),
aparc_aseg_cor_volume)
if bring_t1:
shutil.copy2(os.path.join(mri_direc, "T1.nii.gz"), out_dir)
def test_read_transformation_matrix_from_gifti_metadata():
file_path = get_data_file(
TEST_MODIF_SUBJECT, TEST_SURFACE_FOLDER, "lh.pial.gii")
surface_io = IOUtils.surface_io_factory(file_path)
surf = surface_io.read(file_path, False)
matrix = surface_io.read_transformation_matrix_from_metadata(
surf.get_main_metadata())
assert matrix == [[-1, 0, 0, 0], [0, 0, 1, 0], [0, -1, 0, 0], [0, 0, 0, 1]]
def test_write_transformation_matrix_fs_metadata():
file_path = get_data_file(TEST_FS_SUBJECT, TEST_SURFACE_FOLDER, "lh.pial")
surface_io = IOUtils.surface_io_factory(file_path)
surf = surface_io.read(file_path, False)
surface_io.write_transformation_matrix(surf.get_main_metadata())
matrix = surface_io.read_transformation_matrix_from_metadata(
surf.get_main_metadata())
assert matrix.tolist() == [[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [
0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]]
def aseg_surf_conc_annot(self, surf_path: str, out_surf_path: str, annot_path: str,
label_indices: Union[numpy.ndarray, list], lut_path: Optional[str]=None) -> Surface:
"""
Concatenate surfaces of one specific label of interest each, to create a single annotated surface.
"""
lut_path = lut_path or default_lut_path()
label_names, color_table = self.annotation_service.lut_to_annot_names_ctab(lut_path=lut_path,
labels=label_indices)
label_indices = numpy.array(label_indices.split()).astype('i')
# verts tri area_mask cras
surfaces = []
out_annotation = Annotation([], [], [])
label_number = -1
for label_index in label_indices:
# TODO: This is hardcoded: /aseg-%06d here and also in pegasus dax generator
this_surf_path = surf_path + "/aseg-%06d" % int(label_index)
if os.path.exists(this_surf_path):
ind_l, = numpy.where(label_indices == label_index)
out_annotation.add_region_names_and_colors(
label_names[int(ind_l)],
color_table[ind_l, :])
label_number += 1
surfaces.append(IOUtils.read_surface(this_surf_path, False))
out_annotation.add_region_mapping(
label_number * numpy.ones((surfaces[-1].n_vertices,), dtype='int64'))
out_surface = self.merge_surfaces(surfaces)
# out_annotation.regions_color_table = numpy.squeeze(numpy.array(out_annotation.regions_color_table).astype('i'))
IOUtils.write_surface(out_surf_path, out_surface)
IOUtils.write_annotation(annot_path, out_annotation)
return out_surface
def compute_gdist_mat(self, surf_name: str='pial', max_distance: float=40.0) -> numpy.ndarray:
max_distance = float(max_distance) # in case passed from sys.argv
for h in 'rl':
subjects_dir = os.environ['SUBJECTS_DIR']
subject = os.environ['SUBJECT']
surf_path = '%s/%s/surf/%sh.%s' % (subjects_dir,
subject, h, surf_name)
surface = IOUtils.read_surface(surf_path, False)
mat_path = '%s/%s/surf/%sh.%s.gdist.mat' % (
subjects_dir, subject, h, surf_name)
mat = gdist.local_gdist_matrix(
surface.vertices, surface.triangles.astype('<i4'), max_distance=max_distance)
scipy.io.savemat(mat_path, {'gdist': mat})
return mat
def test_aseg_surf_conc_annot(self,):
out_surf_path = get_temporary_files_path("out_aseg")
out_annot_path = get_temporary_files_path("out_annot")
labels = "10 11"
colorLUT = get_data_file("colorLUT.txt")
self.service.aseg_surf_conc_annot(
data_path, out_surf_path, out_annot_path, labels, colorLUT)
self.assertTrue(os.path.exists(out_surf_path))
self.assertTrue(os.path.exists(out_annot_path))
surface_parser = FreesurferIO()
surface = surface_parser.read(out_surf_path, False)
self.assertEqual(len(surface.vertices), 5714)
self.assertEqual(len(surface.triangles), 11420)
annotation = IOUtils.read_annotation(out_annot_path)
assert_array_equal(
annotation.regions_color_table,
[[0, 118, 14, 0, 947712], [122, 186, 220, 0, 14465658]])
def con_vox_in_ras(self, ref_vol_path: os.PathLike) -> (numpy.ndarray, numpy.ndarray):
"""
This function reads a tdi_lbl volume and returns the voxels that correspond to connectome nodes,
and their coordinates in ras space, simply by applying the affine transform of the volume
:param ref_vol_path: the path to the tdi_lbl volume
:return: vox and voxxyz,
i.e., the labels (integers>=1) and the coordinates of the connnectome nodes-voxels, respectively
"""
# Read the reference tdi_lbl volume:
vollbl = IOUtils.read_volume(ref_vol_path)
vox = vollbl.data.astype('i')
# Get only the voxels that correspond to connectome nodes:
voxijk, = numpy.where(vox.flatten() > 0)
voxijk = numpy.unravel_index(voxijk, vollbl.dimensions)
vox = vox[voxijk[0], voxijk[1], voxijk[2]]
# ...and their coordinates in ras xyz space
voxxzy = vollbl.affine_matrix.dot(numpy.c_[voxijk[0], voxijk[1], voxijk[
2], numpy.ones(vox.shape[0])].T)[:3].T
return vox, voxxzy
def show_single_volume(self, volume_path: os.PathLike, use_cc_point: bool,
snapshot_name: os.PathLike=SNAPSHOT_NAME):
volume = IOUtils.read_volume(volume_path)
if use_cc_point:
ras = self.generic_io.get_ras_coordinates(
self.read_t1_affine_matrix())
else:
ras = volume.get_center_point()
for projection in PROJECTIONS:
try:
x_axis_coords, y_axis_coords, volume_matrix = volume.slice_volume(
projection, ras)
except IndexError:
new_ras = volume.get_center_point()
x_axis_coords, y_axis_coords, volume_matrix = volume.slice_volume(
projection, new_ras)
self.logger.info("The volume center point has been used for %s snapshot of %s.", projection,
volume_path)
self.writer.write_matrix(x_axis_coords, y_axis_coords, volume_matrix,
self.generate_file_name(projection, snapshot_name))
def test_parse_h5_annotation(self):
h5_path = get_data_file('head2', 'RegionMapping.h5')
annotation = IOUtils.read_annotation(h5_path)
self.assertEqual(annotation.region_mapping.size, 16)
def test_parse_not_annotation(self):
file_path = get_data_file(self.subject, "surf", "lh.pial")
annotation_io = IOUtils.annotation_io_factory(file_path)
self.assertRaises(ValueError, annotation_io.read, file_path)
def test_parse_not_existent_annotation(self):
file_path = "not_existent_annotation.annot"
annotation_io = IOUtils.annotation_io_factory(file_path)
self.assertRaises(IOError, annotation_io.read, file_path)
def test_parse_annotation(self):
file_path = get_data_file(
self.subject, self.annot_path, "lh.aparc.annot")
annot = IOUtils.read_annotation(file_path)
self.assertEqual(_expected_region_names, annot.region_names)
if __name__ == "__main__":
args = parse_arguments()
surface_path = os.path.expandvars(args.surface_path)
output_path = os.path.expandvars(args.output_path)
logger = get_logger(__name__)
image_processor = ImageProcessor(snapshots_directory=os.environ[SNAPSHOTS_DIRECTORY_ENVIRON_VAR],
snapshot_count=int(os.environ.get(SNAPSHOT_NUMBER_ENVIRON_VAR, 0)))
generic_io = GenericIO()
logger.info("The surface transformation process has began")
surface_io = IOUtils.surface_io_factory(surface_path)
surface = surface_io.read(surface_path, False)
if len(args.matrix_paths) is not 0:
transformation_matrices = []
for transform_matrix_path in args.matrix_paths:
transformation_matrices.append(
numpy.array(generic_io.read_transformation_matrix(os.path.expandvars(transform_matrix_path))))
for i in range(len(surface.vertices)):
for j in range(len(transformation_matrices)):
if len(transformation_matrices[j]) > 3:
vertex_coords = numpy.array(
[surface.vertices[i][0], surface.vertices[i][1], surface.vertices[i][2], 1])
else:
def annot_to_lut(self,
annot_path,
lut_path=None,
subject=None,
prefix=''):
"""
This function creates from an annotation a new lut_file, or adds new
entries to an existing lut file. In the latter case, new entries have
labels greater than the maximum alredy existing label inside the lut
file.
Parameters
----------
annot_path : str, os.PathLike
path to annotation.
lut_path : str, os.PathLike
path to existing or new LUT file.
subject : str, optional
subject name if provided, otherwise env var $SUBJECT is used
prefiw : str, optional
prefix for region names (i.e., "ctx-lh-")
"""
annotation = IOUtils.read_annotation(annot_path)
subject = subject or os.environ['SUBJECT']
# If this is an already existing lut file:
lut_path = lut_path or default_lut_path()
if os.path.isfile(lut_path):
# ...find the maximum label in it and add 1
add_lbl = 1 + \
numpy.max(self.read_lut(
lut_path=lut_path, key_mode='label')[0])
else:
# ...else, set it to 0
add_lbl = 0
with open(lut_path, 'a') as fd:
if add_lbl == 0:
# TODO: we should include an environment variable for
# freesurfer version, and print it here
fd.write("#$Id: %s %s\n\n" % (lut_path, datetime.now()))
fd.write('#No.\tLabel Name: \tR G B A \n')
else:
fd.write('\n')
fd.write("""
#Patient: {subject}
#User: {user}
#Annotation path: {annot_path}
#Time: {time}
""".format(
subject=subject,
user=os.path.split(os.path.expanduser('~'))[-1],
annot_path=annot_path,
time=datetime.now())
)
# TODO: align columns
# NOTE!!! that the fourth and fifth columns of color_table are not
# used in the lut file!!!
for name, (r, g, b, dummy1, dummy2), lbl in \
zip(annotation.region_names, annotation.regions_color_table,
list(range(len(annotation.region_names)))):
fd.write('%d\t%s\t%d %d %d %d\n' %
(lbl + add_lbl, prefix + name, r, g, b, 0))
def sample_vol_on_surf(self, surf_path: str, vol_path: str, annot_path: str, out_surf_path: str,
cras_path: str, add_string: str='', vertex_neighbourhood: int=1,
add_lbl: list=[], lut_path: Optional[str]=None) -> (Surface, Annotation):
"""
Sample a volume of a specific label on a surface, by keeping only those surface vertices, the nearest voxel of
which is of the given label (+ of possibly additional target labels, such as white matter).
Allow optionally for vertices within a given voxel distance vn from the target voxels.
"""
lut_path = lut_path or default_lut_path()
# Read the inputs
surface = IOUtils.read_surface(surf_path, False)
annotation = IOUtils.read_annotation(annot_path)
labels = self.annotation_service.annot_names_to_labels(annotation.region_names,
add_string=add_string, lut_path=lut_path)
region_mapping_indexes = numpy.unique(annotation.region_mapping)
volume_parser = VolumeIO()
volume = volume_parser.read(vol_path)
ras2vox_affine_matrix = numpy.linalg.inv(volume.affine_matrix)
cras = numpy.loadtxt(cras_path)
grid, n_grid = self.__prepare_grid(vertex_neighbourhood)
# Initialize the output mask:
verts_out_mask = numpy.repeat([False], surface.vertices.shape[0])
for label_index in range(len(region_mapping_indexes)):
self.logger.info("%s", add_string +
annotation.region_names[label_index])
# Get the indexes of the vertices corresponding to this label:
verts_indices_of_label, = numpy.where(
annotation.region_mapping[:] == region_mapping_indexes[label_index])
verts_indices_of_label_size = verts_indices_of_label.size
if verts_indices_of_label_size == 0:
continue
# Add any additional labels
all_labels = [labels[label_index]] + add_lbl
# get the vertices for current label and add cras to take them to
# scanner ras
verts_of_label = surface.vertices[verts_indices_of_label, :]
verts_of_label += numpy.repeat(numpy.expand_dims(
cras, 1).T, verts_indices_of_label_size, axis=0)
# Compute the nearest voxel coordinates using the affine transform
ijk = numpy.round(
ras2vox_affine_matrix.dot(numpy.c_[verts_of_label, numpy.ones(verts_indices_of_label_size)].T)[:3].T) \
.astype('i')
# Get the labels of these voxels:
surf_vxls = volume.data[ijk[:, 0], ijk[:, 1], ijk[:, 2]]
# Vertex mask to keep: those that correspond to voxels of one of
# the target labels
# surf_vxls==lbl if only one target label
verts_keep, = numpy.where(numpy.in1d(surf_vxls, all_labels))
verts_out_mask[verts_indices_of_label[verts_keep]] = True
if vertex_neighbourhood > 0:
# These are now the remaining indexes to be checked for
# neighboring voxels
verts_indices_of_label = numpy.delete(
verts_indices_of_label, verts_keep)
ijk = numpy.delete(ijk, verts_keep, axis=0)
for vertex_index in range(verts_indices_of_label.size):
# Generate the specific grid centered at the voxel ijk
ijk_grid = grid + \
numpy.tile(ijk[vertex_index, :], (n_grid, 1))
# Remove voxels outside the volume
indexes_within_limits = numpy.all([(ijk_grid[:, 0] >= 0), (ijk_grid[:, 0] < volume.dimensions[0]),
(ijk_grid[:, 1] >= 0), (ijk_grid[
:, 1] < volume.dimensions[1]),
(ijk_grid[:, 2] >= 0), (ijk_grid[:, 2] < volume.dimensions[2])],
axis=0)
ijk_grid = ijk_grid[indexes_within_limits, :]
surf_vxls = volume.data[
ijk_grid[:, 0], ijk_grid[:, 1], ijk_grid[:, 2]]
# If any of the neighbors is of the target labels include
# the current vertex
# surf_vxls==lbl if only one target label
if numpy.any(numpy.in1d(surf_vxls, all_labels)):
verts_out_mask[
verts_indices_of_label[vertex_index]] = True
# Vertex indexes and vertices to keep:
verts_out_indices, = numpy.where(verts_out_mask)
verts_out = surface.vertices[verts_out_indices]
# TODO maybe: make sure that all voxels of this label correspond to at least one vertex.
# Create a similar mask for faces by picking only triangles of which
# all 3 vertices are included
face_out_mask = numpy.c_[
verts_out_mask[surface.triangles[:, 0]], verts_out_mask[surface.triangles[:, 1]], verts_out_mask[
surface.triangles[:, 2]]].all(axis=1)
faces_out = surface.triangles[face_out_mask]
# The old vertices' indexes of faces have to be transformed to the new
# vrtx_out_inds:
for iF in range(faces_out.shape[0]):
for vertex_index in range(3):
faces_out[iF, vertex_index], = numpy.where(
faces_out[iF, vertex_index] == verts_out_indices)
surface.vertices = verts_out
surface.triangles = faces_out
# Write the output surfaces and annotations to files. Also write files
# with the indexes of vertices to keep.
IOUtils.write_surface(out_surf_path, surface)
annotation.set_region_mapping(
annotation.get_region_mapping_by_indices([verts_out_indices]))
IOUtils.write_annotation(out_surf_path + ".annot", annotation)
numpy.save(out_surf_path + "-idx.npy", verts_out_indices)
numpy.savetxt(out_surf_path + "-idx.txt", verts_out_indices, fmt='%d')
return surface, annotation
def convert_fs_to_brain_visa(self, in_surf_path: str, out_surf_path: Optional[str]=None):
surface = IOUtils.read_surface(in_surf_path, False)
if out_surf_path is None:
out_surf_path = in_surf_path + '.tri'
IOUtils.write_surface(out_surf_path, surface)
def create_tvb_dataset(atlas_suffix: AtlasSuffix,
mri_direc: os.PathLike,
region_details_direc: os.PathLike,
weights_file: os.PathLike,
tracts_file: os.PathLike,
out_dir: os.PathLike,
bring_t1=False):
weights_matrix = numpy.loadtxt(str(weights_file), dtype='i', delimiter=' ')
weights_matrix += weights_matrix.T
tracts_matrix = numpy.loadtxt(str(tracts_file), dtype='f', delimiter=' ')
tracts_matrix += tracts_matrix.T
is_cortical_rm = numpy.genfromtxt(os.path.join(
region_details_direc,
AsegFiles.CORTICAL_TXT.value.replace("%s", atlas_suffix)),
usecols=[0],
dtype='i')
region_names = numpy.genfromtxt(os.path.join(
region_details_direc,
AsegFiles.CENTERS_TXT.value.replace("%s", atlas_suffix)),
usecols=[0],
dtype="str")
region_centers = numpy.genfromtxt(os.path.join(
region_details_direc,
AsegFiles.CENTERS_TXT.value.replace("%s", atlas_suffix)),
usecols=[1, 2, 3])
region_areas = numpy.genfromtxt(os.path.join(
region_details_direc,
AsegFiles.AREAS_TXT.value.replace("%s", atlas_suffix)),
usecols=[0])
region_orientations = numpy.genfromtxt(os.path.join(
region_details_direc,
AsegFiles.ORIENTATIONS_TXT.value.replace("%s", atlas_suffix)),
usecols=[0, 1, 2])
rm_idx = numpy.genfromtxt(os.path.join(
region_details_direc,
AsegFiles.RM_TO_APARC_ASEG_TXT.value.replace("%s", atlas_suffix)),
usecols=[0, 1],
dtype='i')
rm_index_dict = dict(zip(rm_idx[:, 0], rm_idx[:, 1]))
print(rm_index_dict)
genericIO = GenericIO()
genericIO.write_connectivity_zip(out_dir, weights_matrix, tracts_matrix,
is_cortical_rm, region_names,
region_centers, region_areas,
region_orientations, atlas_suffix)
aparc_aseg_file = os.path.join(
mri_direc, T1Files.APARC_ASEG_NII_GZ.value.replace("%s", atlas_suffix))
aparc_aseg_volume = IOUtils.read_volume(aparc_aseg_file)
volume_service = VolumeService()
aparc_aseg_cor_volume = volume_service.change_labels_of_aparc_aseg(
atlas_suffix, aparc_aseg_volume, rm_index_dict,
weights_matrix.shape[0])
IOUtils.write_volume(
os.path.join(
out_dir,
OutputConvFiles.APARC_ASEG_COR_NII_GZ.value.replace(
"%s", atlas_suffix)), aparc_aseg_cor_volume)
if bring_t1:
shutil.copy2(os.path.join(mri_direc, "T1.nii.gz"), out_dir)