def loading_full_tractograpy(self, tracpath=None):
"""
Loading full tractography and creates StreamlineLabeler to
show it all.
"""
# load the tracks registered in MNI space
self.tracpath = tracpath
basename = os.path.basename(self.tracpath)
tracks_basename, tracks_format = os.path.splitext(basename)
if tracks_format == '.dpy':
dpr = Dpy(self.tracpath, 'r')
print "Loading", self.tracpath
self.T = dpr.read_tracks()
dpr.close()
self.T = np.array(self.T, dtype=np.object)
elif tracks_format == '.trk':
streams, self.hdr = nib.trackvis.read(self.tracpath,
points_space='voxel')
print "Loading", self.tracpath
self.T = np.array([s[0] for s in streams], dtype=np.object)
print "Removing short streamlines"
self.T = np.array([t for t in self.T if length(t) >= 15],
dtype=np.object)
tracks_directoryname = os.path.dirname(self.tracpath) + '/.temp/'
general_info_filename = tracks_directoryname + tracks_basename + '.spa'
# Check if there is the .spa file that contains all the
# computed information from the tractography anyway and try to
# load it
try:
print "Looking for general information file"
self.load_info(general_info_filename)
except (IOError, KeyError):
print "General information not found, recomputing buffers"
self.update_info(general_info_filename)
# create the interaction system for tracks,
self.streamlab = StreamlineLabeler(
'Bundle Picker',
self.buffers,
self.clusters,
vol_shape=self.dims,
affine=np.copy(self.affine),
clustering_parameter=len(self.clusters),
clustering_parameter_max=len(self.clusters),
full_dissimilarity_matrix=self.full_dissimilarity_matrix)
self.scene.add_actor(self.streamlab)
def loading_full_tractograpy(self, tracpath=None):
"""
Loading full tractography and creates StreamlineLabeler to
show it all.
"""
# load the tracks registered in MNI space
self.tracpath=tracpath
basename = os.path.basename(self.tracpath)
tracks_basename, tracks_format = os.path.splitext(basename)
if tracks_format == '.dpy':
dpr = Dpy(self.tracpath, 'r')
print "Loading", self.tracpath
self.T = dpr.read_tracks()
dpr.close()
self.T = np.array(self.T, dtype=np.object)
elif tracks_format == '.trk':
print "Loading", self.tracpath
# Old nibabel API:
# streams, self.hdr = nib.trackvis.read(self.tracpath, points_space='voxel')
# self.T = np.array([s[0] for s in streams], dtype=np.object)
# New nibabel API
tmp = nib.streamlines.load(self.tracpath)
streams = tmp.tractogram.apply_affine(np.linalg.inv(tmp.affine)).streamlines
self.header = tmp.header
self.T = np.array(streams, dtype=np.object)
# The following code has been commented out to avoid
# misalignment between original streamlines IDs and final IDs.
# print "Removing short streamlines"
# self.T = np.array([t for t in self.T if length(t)>= 15], dtype=np.object)
tracks_directoryname = os.path.dirname(self.tracpath) + '/.temp/'
general_info_filename = tracks_directoryname + tracks_basename + '.spa'
# Check if there is the .spa file that contains all the
# computed information from the tractography anyway and try to
# load it
try:
print "Looking for general information file"
self.load_info(general_info_filename)
except (IOError, KeyError):
print "General information not found, recomputing buffers"
self.update_info(general_info_filename)
# create the interaction system for tracks,
self.streamlab = StreamlineLabeler('Bundle Picker',
self.buffers, self.clusters,
vol_shape=self.dims,
affine=np.copy(self.affine),
clustering_parameter=len(self.clusters),
clustering_parameter_max=len(self.clusters),
full_dissimilarity_matrix=self.full_dissimilarity_matrix)
self.scene.add_actor(self.streamlab)
class Tractome(object):
"""
"""
def __init__(self):
"""
Initializing the class that contains that manipulates the
scene, actors and all the logic of Tractome.
"""
self.scene = Scene(scenename = 'Main Scene', activate_aabb = False)
self.d_active_ROIS = {}
self.list_ROIS = []
self.list_oper_ROIS = []
def loading_structural(self, structpath = None):
"""
Loading structural data.
"""
# load structural volume
print "Loading structural information file"
self.structpath = structpath
self.img = nib.load(self.structpath)
data = self.img.get_data()
self.affine = self.img.get_affine()
self.dims = data.shape[:3]
# verifying if structural is color_fa created by TrackVis :: Diffusion Toolkit
if data.dtype == [('R', '|u1'), ('G', '|u1'), ('B', '|u1')]:
data1 = data.view((np.uint8, len(data.dtype.names)))
data = data1
del data1
# Create the Guillotine object
data = (np.interp(data, [data.min(), data.max()], [0, 255]))
self.guil = Guillotine('Volume Slicer', data, np.copy(self.affine))
self.scene.add_actor(self.guil)
def loading_full_tractograpy(self, tracpath=None):
"""
Loading full tractography and creates StreamlineLabeler to
show it all.
"""
# load the tracks registered in MNI space
self.tracpath=tracpath
basename = os.path.basename(self.tracpath)
tracks_basename, tracks_format = os.path.splitext(basename)
if tracks_format == '.dpy':
dpr = Dpy(self.tracpath, 'r')
print "Loading", self.tracpath
self.T = dpr.read_tracks()
dpr.close()
self.T = np.array(self.T, dtype=np.object)
elif tracks_format == '.trk':
print "Loading", self.tracpath
# Old nibabel API:
# streams, self.hdr = nib.trackvis.read(self.tracpath, points_space='voxel')
# self.T = np.array([s[0] for s in streams], dtype=np.object)
# New nibabel API
tmp = nib.streamlines.load(self.tracpath)
streams = tmp.tractogram.apply_affine(np.linalg.inv(tmp.affine)).streamlines
self.header = tmp.header
self.T = np.array(streams, dtype=np.object)
# The following code has been commented out to avoid
# misalignment between original streamlines IDs and final IDs.
# print "Removing short streamlines"
# self.T = np.array([t for t in self.T if length(t)>= 15], dtype=np.object)
tracks_directoryname = os.path.dirname(self.tracpath) + '/.temp/'
general_info_filename = tracks_directoryname + tracks_basename + '.spa'
# Check if there is the .spa file that contains all the
# computed information from the tractography anyway and try to
# load it
try:
print "Looking for general information file"
self.load_info(general_info_filename)
except (IOError, KeyError):
print "General information not found, recomputing buffers"
self.update_info(general_info_filename)
# create the interaction system for tracks,
self.streamlab = StreamlineLabeler('Bundle Picker',
self.buffers, self.clusters,
vol_shape=self.dims,
affine=np.copy(self.affine),
clustering_parameter=len(self.clusters),
clustering_parameter_max=len(self.clusters),
full_dissimilarity_matrix=self.full_dissimilarity_matrix)
self.scene.add_actor(self.streamlab)
def load_segmentation(self, segpath=None):
"""
Loading file containing a previous segmentation
"""
print "Loading saved session file"
segm_info = pickle.load(open(segpath))
state = segm_info['segmsession']
self.structpath=segm_info['structfilename']
self.tracpath=segm_info['tractfilename']
# load T1 volume registered in MNI space
print "Loading structural information file"
self.loading_structural(self.structpath)
# load tractography
self.loading_full_tractograpy(self.tracpath)
self.streamlab.set_state(state)
self.scene.update()
def max_num_clusters(self):
"""
"""
n_clusters = len(self.streamlab.streamline_ids)
if (len(self.streamlab.streamline_ids) < 1e5) and (len(self.streamlab.streamline_ids)>= 50):
default = 50
else:
default = len(self.streamlab.streamline_ids)
return n_clusters, default
def recluster(self, n_clusters):
"""
Re-cluster current selected set of streamlines
"""
# MBKM:
self.streamlab.recluster(n_clusters, data=self.full_dissimilarity_matrix)
self.set_streamlines_clusters()
def loading_mask(self, filename, color):
"""
Loads a mask
"""
print "Loading mask"
img = nib.load(filename)
mask = img.get_data()
itemindex = np.where(mask!=0)
self.ROIMask(os.path.basename(filename), itemindex, color)
def ROIMask(self, nameroi, mask, color):
"""
Create actor for ROI from loaded mask and add it to the scene.
"""
coords_streamlines, index_streamlines = self.compute_dataforROI()
self.list_ROIS.append(nameroi)
self.d_active_ROIS[nameroi] = False
self.list_oper_ROIS.append('and')
#create Mask actor and add it to scene
mask = Mask(nameroi, color.getRgbF(), color.name(), coords_streamlines, mask, index_streamlines, self.affine, self.dims)
self.scene.add_actor(mask)
def max_coordinates(self):
"""
Computing maximum value of each coordinate from the whole
tractography.
"""
max= [np.amax(t,axis=0).tolist() for t in self.T]
coords_max = np.amax(max,axis=0)
return coords_max
def save_info(self, filepath):
"""
Saves all the information from the tractography required for
the whole segmentation procedure.
"""
info = {'initclusters':self.clusters, 'buff':self.buffers, 'dismatrix':self.full_dissimilarity_matrix,'nprot':self.num_prototypes, 'kdtree':self.kdt}
print "Saving information of the tractography for the segmentation"
print filepath
filedir = os.path.dirname(filepath)
if not os.path.exists(filedir):
os.makedirs(filedir)
pickle.dump(info, open(filepath,'w'), protocol=pickle.HIGHEST_PROTOCOL)
def load_info(self, filepath):
"""
Loads all the information from the tractography required for
the whole segmentation procedure.
"""
print "Loading general information file"
general_info = pickle.load(open(filepath))
self.full_dissimilarity_matrix = general_info['dismatrix']
self.num_prototypes = general_info['nprot']
self.buffers = general_info['buff']
self.clusters = general_info['initclusters']
self.kdt = general_info['kdtree']
def update_info(self, filepath):
"""
Compute missing or inconsistent information in the cache.
"""
save = False
try:
self.buffers
except AttributeError:
print "Computing buffers."
self.buffers = compute_buffers(self.T, alpha=1.0, save=False)
save = True
try:
self.num_prototypes
except AttributeError:
print "Defining number of prototypes"
self.num_prototypes = 40
save = True
try:
self.full_dissimilarity_matrix
except AttributeError:
print "Computing dissimilarity matrix"
self.full_dissimilarity_matrix = compute_dissimilarity(self.T, distance=bundles_distances_mam, prototype_policy='sff', num_prototypes=self.num_prototypes)
save = True
try:
assert(self.full_dissimilarity_matrix.shape[0] == len(self.T))
except AssertionError:
print "Re-computing dissimilarity matrix."
self.num_prototypes = 40
self.full_dissimilarity_matrix = compute_dissimilarity(self.T, distance=bundles_distances_mam, prototype_policy='sff', num_prototypes=self.num_prototypes)
save = True
try:
self.clusters
except AttributeError:
print "Computing MBKM"
size_T = len(self.T)
if size_T > 150:
n_clusters = 150
else:
n_clusters = size_T
streamlines_ids = np.arange(size_T, dtype=np.int)
self.clusters = mbkm_wrapper(self.full_dissimilarity_matrix, n_clusters, streamlines_ids)
save = True
try:
self.kdt
except AttributeError:
print "Computing KDTree"
self.compute_kdtree()
save=True
if save: self.save_info(filepath)
def save_segmentation(self, filename):
"""
Saves the information of the segmentation result from the
current session.
"""
print "Save segmentation result from current session"
filename = filename[0]+'.seg'
state = self.streamlab.get_state()
seg_info={'structfilename':self.structpath, 'tractfilename':self.tracpath, 'segmsession':state}
pickle.dump(seg_info, open(filename,'w'), protocol=pickle.HIGHEST_PROTOCOL)
def save_trk(self, filename):
"""
Save current streamlines in .trk file.
"""
filename = filename[0]+'.trk'
streamlines_ids = list(self.streamlab.streamline_ids)
# Old nibabel API:
# hdr = nib.trackvis.empty_header()
# hdr['voxel_size'] = self.img.get_header().get_zooms()[:3]
# hdr['voxel_order'] = 'LAS'
# hdr['dim'] = self.dims
# hdr['vox_to_ras'] = self.affine
# streamlines = [(s, None, None) for s in self.T[streamlines_ids]]
# nib.trackvis.write(filename, streamlines, hdr, points_space = 'voxel')
# New nibabel API:
tmp = nib.streamlines.Tractogram(self.T[streamlines_ids], affine_to_rasmm=self.affine)
nib.streamlines.save(tmp, filename=filename, header=self.header)
def compute_kdtree(self):
"""
Compute kdtree from tactography, for ROIs and extensions.
"""
self.kdt=KDTree(self.full_dissimilarity_matrix)
def compute_kqueries(self, k):
"""
Makes the query to find the knn of the current streamlines on the scene
"""
if k==0:
if self.streamlab.knnreset == True:
self.streamlab.reset_state('knn')
else:
if len(self.streamlab.streamline_ids) == len(self.T):
raise TractomeError("Cannot enlarge clusters. The whole tractography is being used as input.")
else:
nn = k+1
if self.streamlab.save_init_set==True:
self.streamlines_before_knn = self.streamlab.streamline_ids.copy()
a2 = self.kdt.query(self.full_dissimilarity_matrix[list( self.streamlines_before_knn)],k=nn, return_distance = False)
b2 = set(a2.flat)
# Finding difference between the initial set of streamlines and those from the kdt query. This will give us the new streamlines
b2.difference_update(self.streamlines_before_knn)
if len(b2)>0:
self.streamlab.set_streamlines_knn(list(b2))
def set_streamlines_clusters(self):
"""
The actual composition of clusters will be set as reference to compute the new neighbors.
"""
self.streamlab.save_init_set = True
self.streamlab.hide_representatives = False
def compute_dataforROI(self):
"""
Compute info from tractography to provide it to ROI.
"""
coords = np.vstack(self.T)
index = np.concatenate([i*np.ones(len(s)) for i,s in enumerate(self.T)]).astype(np.int)
return coords, index
def create_ROI_sphere(self, nameroi, coordx, coordy, coordz, radius, method, color, colorname):
"""
Create actor for ROI sphere and add it to the scene.
"""
coords_streamlines, index_streamlines = self.compute_dataforROI()
self.list_ROIS.append(nameroi)
self.d_active_ROIS[nameroi] = False
self.list_oper_ROIS.append('and')
#create Sphere actor and add it to scene
sphere = SphereTractome(nameroi, coordx, coordy, coordz, radius, color, colorname, method, coords_streamlines, index_streamlines, self.affine, self.dims)
self.scene.add_actor(sphere)
def update_ROI(self, nameroi, newname = None, coordx=None, coordy=None, coordz=None, radius=None, color=None, method = None, rebuild = False, pos_activeroi = None):
"""
Updates any parameter of the specified ROI.
"""
if coordx is not None:
self.scene.actors[nameroi].update_xcoord(coordx)
if coordy is not None:
self.scene.actors[nameroi].update_ycoord(coordy)
if coordz is not None:
self.scene.actors[nameroi].update_zcoord(coordz)
if radius is not None:
self.scene.actors[nameroi].update_radius(radius)
if color is not None:
self.scene.actors[nameroi].update_color (color)
if method is not None:
value_dict = self.d_active_ROIS[nameroi]
del self.d_active_ROIS[nameroi]
actor_roi = self.scene.actors[str(nameroi)]
self.scene.remove_actor(str(nameroi))
self.create_ROI_sphere(str(nameroi), actor_roi.coordinates[0], actor_roi.coordinates[1], actor_roi.coordinates[2], actor_roi.radius, method, actor_roi.color, actor_roi.colorname)
if rebuild:
self.compute_streamlines_ROIS()
if newname is not None:
value_dict = self.d_active_ROIS[nameroi]
del self.d_active_ROIS[nameroi]
self.d_active_ROIS[newname] = value_dict
self.list_ROIS[pos_activeroi] = newname
actor_roi = self.scene.actors[nameroi]
actor_roi.name = newname
self.scene.remove_actor(str(nameroi))
self.scene.add_actor(actor_roi)
def activation_ROIs(self, pos_activeroi, activate, operator=None):
"""
Activates or deactivates the specified ROI. In case it is
activated, the operator to be applied is also specified.
"""
self.d_active_ROIS[self.list_ROIS[pos_activeroi]] = activate
if operator is not None:
self.list_oper_ROIS[pos_activeroi] = operator
def compute_streamlines_ROIS(self):
"""
Obtain set of streamlines that pass through the specified
ROIs.
"""
streamlines_ROIs = []
last_chkd = -1
for pos in range(0, len(self.list_ROIS)):
name_roi = self.list_ROIS[pos]
if self.d_active_ROIS[name_roi]:
if last_chkd == -1:
streamlines_ROIs = set(self.scene.actors[name_roi].streamlines)
else:
current_roi_streamlines = set(self.scene.actors[name_roi].streamlines)
if self.list_oper_ROIS[last_chkd] == 'and':
streamlines_ROIs.intersection_update(current_roi_streamlines)
elif self.list_oper_ROIS[last_chkd] == 'or':
streamlines_ROIs.update(current_roi_streamlines)
last_chkd =pos
if last_chkd == -1:
self.streamlab.reset_state()
else:
if len(streamlines_ROIs) > 0:
self.streamlab.set_streamlines_ROIs(streamlines_ROIs)
else:
self.streamlab.set_empty_scene()
def information_from_ROI(self, name_roi):
"""
Returns general information from the specified ROI.
"""
roi = self.scene.actors[name_roi]
xcoord = roi.coordinates[0]
ycoord = roi.coordinates[1]
zcoord = roi.coordinates[2]
radius = roi.radius
color = roi.colorname
return xcoord, ycoord, zcoord, radius, color
def clear_all(self):
"""
Actors of scene will be removed in order to load new ones.
"""
self.d_active_ROIS = {}
self.list_ROIS = []
self.list_oper_ROIS = []
self.scene.actors.clear()
self.scene.update()
def clear_actor(self, name):
"""
Specified actor will be removed from the scenename
"""
self.scene.remove_actor(name)
def show_hide_actor(self, name, state):
"""
Show or hide the specified actor
"""
if state:
self.scene.actors[name].show()
else:
self.scene.actors[name].hide()
self.scene.update()
class Tractome(object):
"""
"""
def __init__(self):
"""
Initializing the class that contains that manipulates the
scene, actors and all the logic of Tractome.
"""
self.scene = Scene(scenename='Main Scene', activate_aabb=False)
self.d_active_ROIS = {}
self.list_ROIS = []
self.list_oper_ROIS = []
def loading_structural(self, structpath=None):
"""
Loading structural data.
"""
# load structural volume
print "Loading structural information file"
self.structpath = structpath
self.img = nib.load(self.structpath)
data = self.img.get_data()
self.affine = self.img.get_affine()
self.dims = data.shape[:3]
# verifying if structural is color_fa created by TrackVis :: Diffusion Toolkit
if data.dtype == [('R', '|u1'), ('G', '|u1'), ('B', '|u1')]:
data1 = data.view((np.uint8, len(data.dtype.names)))
data = data1
del data1
# Create the Guillotine object
data = (np.interp(data, [data.min(), data.max()], [0, 255]))
self.guil = Guillotine('Volume Slicer', data, np.copy(self.affine))
self.scene.add_actor(self.guil)
def loading_full_tractograpy(self, tracpath=None):
"""
Loading full tractography and creates StreamlineLabeler to
show it all.
"""
# load the tracks registered in MNI space
self.tracpath = tracpath
basename = os.path.basename(self.tracpath)
tracks_basename, tracks_format = os.path.splitext(basename)
if tracks_format == '.dpy':
dpr = Dpy(self.tracpath, 'r')
print "Loading", self.tracpath
self.T = dpr.read_tracks()
dpr.close()
self.T = np.array(self.T, dtype=np.object)
elif tracks_format == '.trk':
streams, self.hdr = nib.trackvis.read(self.tracpath,
points_space='voxel')
print "Loading", self.tracpath
self.T = np.array([s[0] for s in streams], dtype=np.object)
print "Removing short streamlines"
self.T = np.array([t for t in self.T if length(t) >= 15],
dtype=np.object)
tracks_directoryname = os.path.dirname(self.tracpath) + '/.temp/'
general_info_filename = tracks_directoryname + tracks_basename + '.spa'
# Check if there is the .spa file that contains all the
# computed information from the tractography anyway and try to
# load it
try:
print "Looking for general information file"
self.load_info(general_info_filename)
except (IOError, KeyError):
print "General information not found, recomputing buffers"
self.update_info(general_info_filename)
# create the interaction system for tracks,
self.streamlab = StreamlineLabeler(
'Bundle Picker',
self.buffers,
self.clusters,
vol_shape=self.dims,
affine=np.copy(self.affine),
clustering_parameter=len(self.clusters),
clustering_parameter_max=len(self.clusters),
full_dissimilarity_matrix=self.full_dissimilarity_matrix)
self.scene.add_actor(self.streamlab)
def load_segmentation(self, segpath=None):
"""
Loading file containing a previous segmentation
"""
print "Loading saved session file"
segm_info = pickle.load(open(segpath))
state = segm_info['segmsession']
self.structpath = segm_info['structfilename']
self.tracpath = segm_info['tractfilename']
# load T1 volume registered in MNI space
print "Loading structural information file"
self.loading_structural(self.structpath)
# load tractography
self.loading_full_tractograpy(self.tracpath)
self.streamlab.set_state(state)
self.scene.update()
def max_num_clusters(self):
"""
"""
n_clusters = len(self.streamlab.streamline_ids)
if (len(self.streamlab.streamline_ids) < 1e5) and (len(
self.streamlab.streamline_ids) >= 50):
default = 50
else:
default = len(self.streamlab.streamline_ids)
return n_clusters, default
def recluster(self, n_clusters):
"""
Re-cluster current selected set of streamlines
"""
# MBKM:
self.streamlab.recluster(n_clusters,
data=self.full_dissimilarity_matrix)
self.set_streamlines_clusters()
def loading_mask(self, filename, color):
"""
Loads a mask
"""
print "Loading mask"
img = nib.load(filename)
mask = img.get_data()
itemindex = np.where(mask != 0)
self.ROIMask(os.path.basename(filename), itemindex, color)
def ROIMask(self, nameroi, mask, color):
"""
Create actor for ROI from loaded mask and add it to the scene.
"""
coords_streamlines, index_streamlines = self.compute_dataforROI()
self.list_ROIS.append(nameroi)
self.d_active_ROIS[nameroi] = False
self.list_oper_ROIS.append('and')
#create Mask actor and add it to scene
mask = Mask(nameroi, color.getRgbF(), color.name(), coords_streamlines,
mask, index_streamlines, self.affine, self.dims)
self.scene.add_actor(mask)
def max_coordinates(self):
"""
Computing maximum value of each coordinate from the whole
tractography.
"""
max = [np.amax(t, axis=0).tolist() for t in self.T]
coords_max = np.amax(max, axis=0)
return coords_max
def save_info(self, filepath):
"""
Saves all the information from the tractography required for
the whole segmentation procedure.
"""
info = {
'initclusters': self.clusters,
'buff': self.buffers,
'dismatrix': self.full_dissimilarity_matrix,
'nprot': self.num_prototypes,
'kdtree': self.kdt
}
print "Saving information of the tractography for the segmentation"
print filepath
filedir = os.path.dirname(filepath)
if not os.path.exists(filedir):
os.makedirs(filedir)
pickle.dump(info,
open(filepath, 'w'),
protocol=pickle.HIGHEST_PROTOCOL)
def load_info(self, filepath):
"""
Loads all the information from the tractography required for
the whole segmentation procedure.
"""
print "Loading general information file"
general_info = pickle.load(open(filepath))
self.full_dissimilarity_matrix = general_info['dismatrix']
self.num_prototypes = general_info['nprot']
self.buffers = general_info['buff']
self.clusters = general_info['initclusters']
self.kdt = general_info['kdtree']
def update_info(self, filepath):
"""
Compute missing or inconsistent information in the cache.
"""
save = False
try:
self.buffers
except AttributeError:
print "Computing buffers."
self.buffers = compute_buffers(self.T, alpha=1.0, save=False)
save = True
try:
self.num_prototypes
except AttributeError:
print "Defining number of prototypes"
self.num_prototypes = 40
save = True
try:
self.full_dissimilarity_matrix
except AttributeError:
print "Computing dissimilarity matrix"
self.full_dissimilarity_matrix = compute_dissimilarity(
self.T,
distance=bundles_distances_mam,
prototype_policy='sff',
num_prototypes=self.num_prototypes)
save = True
try:
assert (self.full_dissimilarity_matrix.shape[0] == len(self.T))
except AssertionError:
print "Re-computing dissimilarity matrix."
self.num_prototypes = 40
self.full_dissimilarity_matrix = compute_dissimilarity(
self.T,
distance=bundles_distances_mam,
prototype_policy='sff',
num_prototypes=self.num_prototypes)
save = True
try:
self.clusters
except AttributeError:
print "Computing MBKM"
size_T = len(self.T)
if size_T > 150:
n_clusters = 150
else:
n_clusters = size_T
streamlines_ids = np.arange(size_T, dtype=np.int)
self.clusters = mbkm_wrapper(self.full_dissimilarity_matrix,
n_clusters, streamlines_ids)
save = True
try:
self.kdt
except AttributeError:
print "Computing KDTree"
self.compute_kdtree()
save = True
if save: self.save_info(filepath)
def save_segmentation(self, filename):
"""
Saves the information of the segmentation result from the
current session.
"""
print "Save segmentation result from current session"
filename = filename[0] + '.seg'
state = self.streamlab.get_state()
seg_info = {
'structfilename': self.structpath,
'tractfilename': self.tracpath,
'segmsession': state
}
pickle.dump(seg_info,
open(filename, 'w'),
protocol=pickle.HIGHEST_PROTOCOL)
def save_trk(self, filename):
"""
Save current streamlines in .trk file.
"""
filename = filename[0] + '.trk'
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = self.img.get_header().get_zooms()[:3]
hdr['voxel_order'] = 'LAS'
hdr['dim'] = self.dims
hdr['vox_to_ras'] = self.affine
streamlines_ids = list(self.streamlab.streamline_ids)
streamlines = [(s, None, None) for s in self.T[streamlines_ids]]
nib.trackvis.write(filename, streamlines, hdr, points_space='voxel')
def compute_kdtree(self):
"""
Compute kdtree from tactography, for ROIs and extensions.
"""
self.kdt = KDTree(self.full_dissimilarity_matrix)
def compute_kqueries(self, k):
"""
Makes the query to find the knn of the current streamlines on the scene
"""
if k == 0:
if self.streamlab.knnreset == True:
self.streamlab.reset_state('knn')
else:
if len(self.streamlab.streamline_ids) == len(self.T):
raise TractomeError(
"Cannot enlarge clusters. The whole tractography is being used as input."
)
else:
nn = k + 1
if self.streamlab.save_init_set == True:
self.streamlines_before_knn = self.streamlab.streamline_ids.copy(
)
a2 = self.kdt.query(self.full_dissimilarity_matrix[list(
self.streamlines_before_knn)],
k=nn,
return_distance=False)
b2 = set(a2.flat)
# Finding difference between the initial set of streamlines and those from the kdt query. This will give us the new streamlines
b2.difference_update(self.streamlines_before_knn)
if len(b2) > 0:
self.streamlab.set_streamlines_knn(list(b2))
def set_streamlines_clusters(self):
"""
The actual composition of clusters will be set as reference to compute the new neighbors.
"""
self.streamlab.save_init_set = True
self.streamlab.hide_representatives = False
def compute_dataforROI(self):
"""
Compute info from tractography to provide it to ROI.
"""
coords = np.vstack(self.T)
index = np.concatenate(
[i * np.ones(len(s)) for i, s in enumerate(self.T)]).astype(np.int)
return coords, index
def create_ROI_sphere(self, nameroi, coordx, coordy, coordz, radius,
method, color, colorname):
"""
Create actor for ROI sphere and add it to the scene.
"""
coords_streamlines, index_streamlines = self.compute_dataforROI()
self.list_ROIS.append(nameroi)
self.d_active_ROIS[nameroi] = False
self.list_oper_ROIS.append('and')
#create Sphere actor and add it to scene
sphere = SphereTractome(nameroi, coordx, coordy, coordz, radius, color,
colorname, method, coords_streamlines,
index_streamlines, self.affine, self.dims)
self.scene.add_actor(sphere)
def update_ROI(self,
nameroi,
newname=None,
coordx=None,
coordy=None,
coordz=None,
radius=None,
color=None,
method=None,
rebuild=False,
pos_activeroi=None):
"""
Updates any parameter of the specified ROI.
"""
if coordx is not None:
self.scene.actors[nameroi].update_xcoord(coordx)
if coordy is not None:
self.scene.actors[nameroi].update_ycoord(coordy)
if coordz is not None:
self.scene.actors[nameroi].update_zcoord(coordz)
if radius is not None:
self.scene.actors[nameroi].update_radius(radius)
if color is not None:
self.scene.actors[nameroi].update_color(color)
if method is not None:
value_dict = self.d_active_ROIS[nameroi]
del self.d_active_ROIS[nameroi]
actor_roi = self.scene.actors[str(nameroi)]
self.scene.remove_actor(str(nameroi))
self.create_ROI_sphere(str(nameroi), actor_roi.coordinates[0],
actor_roi.coordinates[1],
actor_roi.coordinates[2], actor_roi.radius,
method, actor_roi.color,
actor_roi.colorname)
if rebuild:
self.compute_streamlines_ROIS()
if newname is not None:
value_dict = self.d_active_ROIS[nameroi]
del self.d_active_ROIS[nameroi]
self.d_active_ROIS[newname] = value_dict
self.list_ROIS[pos_activeroi] = newname
actor_roi = self.scene.actors[nameroi]
actor_roi.name = newname
self.scene.remove_actor(str(nameroi))
self.scene.add_actor(actor_roi)
def activation_ROIs(self, pos_activeroi, activate, operator=None):
"""
Activates or deactivates the specified ROI. In case it is
activated, the operator to be applied is also specified.
"""
self.d_active_ROIS[self.list_ROIS[pos_activeroi]] = activate
if operator is not None:
self.list_oper_ROIS[pos_activeroi] = operator
def compute_streamlines_ROIS(self):
"""
Obtain set of streamlines that pass through the specified
ROIs.
"""
streamlines_ROIs = []
last_chkd = -1
for pos in range(0, len(self.list_ROIS)):
name_roi = self.list_ROIS[pos]
if self.d_active_ROIS[name_roi]:
if last_chkd == -1:
streamlines_ROIs = set(
self.scene.actors[name_roi].streamlines)
else:
current_roi_streamlines = set(
self.scene.actors[name_roi].streamlines)
if self.list_oper_ROIS[last_chkd] == 'and':
streamlines_ROIs.intersection_update(
current_roi_streamlines)
elif self.list_oper_ROIS[last_chkd] == 'or':
streamlines_ROIs.update(current_roi_streamlines)
last_chkd = pos
if last_chkd == -1:
self.streamlab.reset_state()
else:
if len(streamlines_ROIs) > 0:
self.streamlab.set_streamlines_ROIs(streamlines_ROIs)
else:
self.streamlab.set_empty_scene()
def information_from_ROI(self, name_roi):
"""
Returns general information from the specified ROI.
"""
roi = self.scene.actors[name_roi]
xcoord = roi.coordinates[0]
ycoord = roi.coordinates[1]
zcoord = roi.coordinates[2]
radius = roi.radius
color = roi.colorname
return xcoord, ycoord, zcoord, radius, color
def clear_all(self):
"""
Actors of scene will be removed in order to load new ones.
"""
self.d_active_ROIS = {}
self.list_ROIS = []
self.list_oper_ROIS = []
self.scene.actors.clear()
self.scene.update()
def clear_actor(self, name):
"""
Specified actor will be removed from the scenename
"""
self.scene.remove_actor(name)
def show_hide_actor(self, name, state):
"""
Show or hide the specified actor
"""
if state:
self.scene.actors[name].show()
else:
self.scene.actors[name].hide()
self.scene.update()
def loading_full_tractograpy(self, tracpath=None):
"""
Loading full tractography and creates StreamlineLabeler to
show it all.
"""
# load the tracks registered in MNI space
self.tracpath=tracpath
basename = os.path.basename(self.tracpath)
tracks_basename, tracks_format = os.path.splitext(basename)
if tracks_format == '.dpy':
dpr = Dpy(self.tracpath, 'r')
print "Loading", self.tracpath
self.T = dpr.read_tracks()
dpr.close()
self.T = np.array(self.T, dtype=np.object)
elif tracks_format == '.trk':
streams, self.hdr = nib.trackvis.read(self.tracpath, points_space='voxel')
print "Loading", self.tracpath
self.T = np.array([s[0] for s in streams], dtype=np.object)
print "Removing short streamlines"
self.T = np.array([t for t in self.T if length(t)>= 15], dtype=np.object)
tracks_directoryname = os.path.dirname(self.tracpath) + '/.temp/'
general_info_filename = tracks_directoryname + tracks_basename + '.spa'
# Check if there is the .spa file that contains all the
# computed information from the tractography anyway and try to
# load it
try:
print "Looking for general information file"
self.load_info(general_info_filename)
except IOError:
print "General information not found, recomputing buffers"
print "Computing buffers."
self.buffers = compute_buffers(self.T, alpha=1.0, save=False)
print "Computing dissimilarity matrix"
self.num_prototypes = 40
self.full_dissimilarity_matrix = compute_dissimilarity(self.T, distance=bundles_distances_mam, prototype_policy='sff', num_prototypes=self.num_prototypes)
# compute initial MBKM with given n_clusters
print "Computing MBKM"
size_T = len(self.T)
if size_T > 150:
n_clusters = 150
else:
n_clusters = size_T
streamlines_ids = np.arange(size_T, dtype=np.int)
self.clusters = mbkm_wrapper(self.full_dissimilarity_matrix, n_clusters, streamlines_ids)
print "Saving computed information from tractography"
if not os.path.exists(tracks_directoryname):
os.makedirs(tracks_directoryname)
self.save_info(general_info_filename)
# create the interaction system for tracks,
self.streamlab = StreamlineLabeler('Bundle Picker',
self.buffers, self.clusters,
vol_shape=self.dims,
affine=np.copy(self.affine),
clustering_parameter=len(self.clusters),
clustering_parameter_max=len(self.clusters),
full_dissimilarity_matrix=self.full_dissimilarity_matrix)
self.scene.add_actor(self.streamlab)
