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_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 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 aseg_surf_conc_annot(self,
surf_path,
out_surf_path,
annot_path,
label_indices,
lut_path=None):
"""
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:
this_surf_path = surf_path + "-%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(
numpy.array(label_names)[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)
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 convert_fs_to_brain_visa(self, in_surf_path):
surface = IOUtils.read_surface(in_surf_path, False)
IOUtils.write_surface(in_surf_path + '.tri', surface)
def sample_vol_on_surf(self,
surf_path,
vol_path,
annot_path,
out_surf_path,
cras_path,
add_string='',
vertex_neighbourhood=1,
add_lbl=[],
lut_path=None):
"""
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')
def compute_region_details(atlas_suffix: AtlasSuffix, fs_color_lut: os.PathLike, t1: os.PathLike, lh_cort: os.PathLike,
rh_cort: os.PathLike, lh_cort_annot: os.PathLike, rh_cort_annot: os.PathLike,
lh_subcort: os.PathLike, rh_subcort: os.PathLike, lh_subcort_annot: os.PathLike,
rh_subcort_annot: os.PathLike):
annot_cort_lh = IOUtils.read_annotation(lh_cort_annot)
annot_cort_rh = IOUtils.read_annotation(rh_cort_annot)
annot_subcort_lh = IOUtils.read_annotation(lh_subcort_annot)
annot_subcort_rh = IOUtils.read_annotation(rh_subcort_annot)
mapping = MappingService(atlas_suffix, annot_cort_lh, annot_cort_rh, annot_subcort_lh, annot_subcort_rh)
mapping.generate_region_mapping_for_cort_annot(annot_cort_lh, annot_cort_rh)
mapping.generate_region_mapping_for_subcort_annot(annot_subcort_lh, annot_subcort_rh)
surface_service = SurfaceService()
surf_cort_lh = IOUtils.read_surface(lh_cort, False)
surf_cort_rh = IOUtils.read_surface(rh_cort, False)
full_cort_surface = surface_service.merge_surfaces([surf_cort_lh, surf_cort_rh])
surf_subcort_lh = IOUtils.read_surface(lh_subcort, False)
surf_subcort_rh = IOUtils.read_surface(rh_subcort, False)
full_subcort_surface = surface_service.merge_surfaces([surf_subcort_lh, surf_subcort_rh])
genericIO.write_list_to_txt_file(mapping.cort_region_mapping, AsegFiles.RM_CORT_TXT.value.replace("%s", atlas_suffix))
genericIO.write_list_to_txt_file(mapping.subcort_region_mapping,
AsegFiles.RM_SUBCORT_TXT.value.replace("%s", atlas_suffix))
vox2ras_file = "vox2ras.txt"
subprocess.call(["mri_info", "--vox2ras", t1, "--o", vox2ras_file])
surf_subcort_filename = "surface_subcort.zip"
IOUtils.write_surface(surf_subcort_filename, full_subcort_surface)
surf_cort_filename = "surface_cort.zip"
IOUtils.write_surface(surf_cort_filename, full_cort_surface)
os.remove(vox2ras_file)
cort_subcort_full_surf = surface_service.merge_surfaces([full_cort_surface, full_subcort_surface])
cort_subcort_full_region_mapping = mapping.cort_region_mapping + mapping.subcort_region_mapping
dict_fs_custom = mapping.get_mapping_for_connectome_generation()
genericIO.write_dict_to_txt_file(dict_fs_custom, AsegFiles.FS_CUSTOM_TXT.value.replace("%s", atlas_suffix))
region_areas = surface_service.compute_areas_for_regions(mapping.get_all_regions(), cort_subcort_full_surf,
cort_subcort_full_region_mapping)
genericIO.write_list_to_txt_file(region_areas, AsegFiles.AREAS_TXT.value.replace("%s", atlas_suffix))
region_centers = surface_service.compute_centers_for_regions(mapping.get_all_regions(), cort_subcort_full_surf,
cort_subcort_full_region_mapping)
cort_subcort_lut = mapping.get_entire_lut()
region_names = list(cort_subcort_lut.values())
with open(AsegFiles.CENTERS_TXT.value.replace("%s", atlas_suffix), "w") as f:
for idx, (val_x, val_y, val_z) in enumerate(region_centers):
f.write("%s %.2f %.2f %.2f\n" % (region_names[idx], val_x, val_y, val_z))
region_orientations = surface_service.compute_orientations_for_regions(mapping.get_all_regions(),
cort_subcort_full_surf,
cort_subcort_full_region_mapping)
lh_region_centers = surface_service.compute_centers_for_regions(mapping.get_lh_regions(), surf_cort_lh,
mapping.lh_region_mapping)
lh_region_orientations = surface_service.compute_orientations_for_regions(mapping.get_lh_regions(), surf_cort_lh,
mapping.lh_region_mapping)
with open(AsegFiles.LH_DIPOLES_TXT.value.replace("%s", atlas_suffix), "w") as f:
for idx, (val_x, val_y, val_z) in enumerate(lh_region_centers):
f.write("%.2f %.2f %.2f %.2f %.2f %.2f\n" % (
val_x, val_y, val_z, lh_region_orientations[idx][0], lh_region_orientations[idx][1],
lh_region_orientations[idx][2]))
rh_region_centers = surface_service.compute_centers_for_regions(mapping.get_rh_regions(), surf_cort_rh,
mapping.rh_region_mapping)
rh_region_orientations = surface_service.compute_orientations_for_regions(mapping.get_rh_regions(), surf_cort_rh,
mapping.rh_region_mapping)
with open(AsegFiles.RH_DIPOLES_TXT.value.replace("%s", atlas_suffix), "w") as f:
for idx, (val_x, val_y, val_z) in enumerate(rh_region_centers):
f.write("%.2f %.2f %.2f %.2f %.2f %.2f\n" % (
val_x, val_y, val_z, rh_region_orientations[idx][0], rh_region_orientations[idx][1],
rh_region_orientations[idx][2]))
numpy.savetxt(AsegFiles.ORIENTATIONS_TXT.value.replace("%s", atlas_suffix), region_orientations, fmt='%.2f %.2f %.2f')
annotation_service = AnnotationService()
lut_dict, _, _ = annotation_service.read_lut(fs_color_lut, "name")
rm_index_dict = mapping.get_mapping_for_aparc_aseg(lut_dict)
genericIO.write_dict_to_txt_file(rm_index_dict, AsegFiles.RM_TO_APARC_ASEG_TXT.value.replace("%s", atlas_suffix))
genericIO.write_list_to_txt_file(mapping.is_cortical_region_mapping(),
AsegFiles.CORTICAL_TXT.value.replace("%s", atlas_suffix))
def generate_surface_zip(in_file, out_file):
surface = IOUtils.read_surface(in_file, False)
IOUtils.write_surface(out_file, surface)
def merge_surfs(surf_lh, surf_rh, out_surf_path):
s_lh = IOUtils.read_surface(surf_lh, False)
s_rh = IOUtils.read_surface(surf_rh, False)
surf = surfaceService.merge_surfaces([s_lh, s_rh])
IOUtils.write_surface(out_surf_path, surf)
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 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
