def brainvisa_to_gifti(bv_mesh, gii_mesh):
""" Conversion of brainvisa mesh to a gifti mesh
Caveat
======
requires aims
"""
from gifti import GiftiEncoding, GiftiImage_fromarray, GiftiIntentCode
from soma import aims
mesh = aims.Reader().read(bv_mesh)
vertices = np.array(mesh.vertex())
poly = np.array([np.array(p) for p in mesh.polygon()])
def buildMeshImage(vertices,
faces,
encoding=GiftiEncoding.GIFTI_ENCODING_ASCII):
""" build a GiftiImage from arrays of vertices and faces
NOTE : this is doing the same as the gifti.GiftiImage_fromTriangles
function and is only redefined here for demonstration purpose"""
k = GiftiImage_fromarray(vertices)
k.arrays[0].intentString = "NIFTI_INTENT_POINTSET"
k.addDataArray_fromarray(faces, GiftiIntentCode.NIFTI_INTENT_TRIANGLE)
for a in k.arrays:
a.encoding = encoding
return k
buildMeshImage(vertices, poly).save(gii_mesh)
def execution(self, context):
from soma import aims
r = aims.Reader({'Volume': {'S16': 'Graph'}})
graph = r.read(self.read.fullPath())
graph['filename_base'] = '*'
aims.GraphManip.volume2Buckets(graph)
aims.write(graph, self.write.fullPath())
registration.getTransformationManager().copyReferential(self.read,
self.write)
if self.removeSource:
for f in self.read.fullPaths():
shelltools.rm(f)
def main(Primate1, Primate2, side):
"""
Main function that uses the text file returned by the Affine_transformations.py code in order to rescale the
Primate2's texture so we can map Primate1's textures onto Primate1's brain surface.
:param Primate1: string - name of Primate1
:param Primate2: string - name of Primate2
:param side: string - hemisphere 'L' or 'R'
:return: the rescaled Primate2's longitudinal and latitudinal textures
"""
nameLon = Primate2 + '_' + side + 'white_lon.gii'
nameLat = Primate2 + '_' + side + 'white_lat.gii'
print('reading coordinates')
r = aims.Reader()
texLatF = r.read(nameLat)
texLonF = r.read(nameLon)
texLat = np.array(texLatF[0])
texLon = np.array(texLonF[0])
print('reading affine transformations')
affine_model = 'affine_trans_' + Primate1 + '_to_' + Primate2 + '_' + side + '.txt'
int_lon, int_lat, lon_transform, lat_transform = read_affine(affine_model)
print('processing longitude')
newLon = rescale(texLon, lon_transform, int_lon)
print('processing latitude')
newLat = rescale(texLat, lat_transform, int_lat)
print('writing textures')
nv = texLat.size
newLatT = aims.TimeTexture_FLOAT(1, nv)
newLonT = aims.TimeTexture_FLOAT(1, nv)
for i in range(nv):
newLatT[0][i] = newLat[i]
newLonT[0][i] = newLon[i]
outLat = Primate1 + '_' + side + 'white_lat_to' + Primate2 + '.gii'
outLon = Primate1 + '_' + side + 'white_lon_to' + Primate2 + '.gii'
r = aims.Writer()
r.write(newLatT, outLat)
r.write(newLonT, outLon)
print('done')
def main(arguments):
if len(arguments) == 4:
myelinN = arguments[1]
surfaceN = arguments[2]
surfMapN = arguments[3]
r = aims.Reader()
myelin = r.read(myelinN)
surface = r.read(surfaceN)
surfMap, volOut = projectOnSurface(myelin, surface)
w = aims.Writer()
w.write(surfMap, surfMapN)
#w.write(volOut, 'midVoxels.nii')
else:
print 'Usage:'
print 'python projectT1T2onSurface.py myelinImage surface map'
def execution(self, context):
from soma import aims
reader = aims.Reader()
texture2 = reader.read(str(self.input[0]))
texture = aims.TimeTexture_FLOAT(len(self.input), len(texture2[0]))
# for i in six.moves.xrange(len(texture2[0])):
# texture[0][i] = texture2[0][i]
for i in six.moves.xrange(len(self.input)):
aux = reader.read(str(self.input[i]))
for j in six.moves.xrange(len(aux[0])):
texture[i][j] = aux[0][j]
context.write(texture.size())
writer = aims.Writer()
writer.write(texture, str(self.output))
context.write("Finished")
def cingular_project(parameters):
DATABASE, s_id = parameters
subj_dir = os.path.join(DATABASE, s_id)
for sd in ['L', 'R']:
file_white_mesh = os.path.join(subj_dir, "t1mri", "BL",
"default_analysis", "segmentation",
"mesh", s_id + "_" + sd + "white.gii")
pole_cingular = os.path.join(
subj_dir, "t1mri", "BL", "default_analysis", "segmentation",
"mesh", "surface_analysis",
s_id + "_" + sd + "white_pole_cingular.gii")
#aparc = os.path.join(subj_dir, "label",
# s_id+ "."+sd+".aparc.native.label.gii")
fs_cortex_label = os.path.join(subj_dir, "label",
sd.lower() + 'h.cortex.label')
re = aims.Reader()
ws = aims.Writer()
mesh = re.read(file_white_mesh)
nbv = len(mesh.vertex())
data = np.ones(nbv)
f = open(fs_cortex_label, 'r')
lines = f.readlines()
for a in lines[2:]:
a_spl = a.split()
data[int(a_spl[0])] = a_spl[4]
f.close()
#data_in = gio.read(file_aparc).darrays[0].data
#data = np.zeros_like(data_in)
#data[data_in==-1]=1
a, b = textureTls.textureTopologicalCorrection(mesh, data, 1)
cingular_tex_clean, cingular_tex_boundary = a, b
tex_out = aims.TimeTexture_S16()
tex_out[0].assign(cingular_tex_clean)
ws.write(tex_out, pole_cingular)
### =======================================================================
### Mesh perforator by a cylindre, centered on the lacune gravity center
### =======================================================================
mesh_perfo = aims.SurfaceGenerator.cylinder(perfo_ext1, perfo_ext2, .5, .5,
100, 1, 1)
aims.write(mesh_perfo, mesh_perfo__native_filename)
perfo_ext1_centered = perfo_ext1 - mass_center
perfo_ext2_centered = perfo_ext2 - mass_center
mesh_perfo_centered = aims.SurfaceGenerator.cylinder(
perfo_ext1_centered, perfo_ext2_centered, 1, 1, 100, 1, 1)
aims.write(mesh_perfo_centered, mesh_perfo__centered_filename)
### =======================================================================
### QC check center_of_mass == the one computed by AimsMoments
### =======================================================================
ima = aims.Reader().read(ima_filename)
mesh_lacune = aims.Reader().read(mesh_lacunes_filename)
mesh_xyz = np.array(mesh_lacune.vertex())
mesh_tri = np.array(mesh_lacune.polygon())
# t,z,y,x
arr = ima.arraydata().squeeze()
voxel_size = ima.header()['voxel_size'].arraydata()
# recompute mass_center from image to check that zyx*voxel_size == what is csv
# file
zyx = np.where(arr != 0)
xyz_mm = np.vstack([
zyx[2] * voxel_size[0], zyx[1] * voxel_size[1], zyx[0] * voxel_size[2]
]).T
xyz_mass_center_mm = xyz_mm.mean(axis=0)
# Check we retrive mass_center
def main(Primate1, Primate2, side):
nameLon = Primate2 + '_' + side + 'white_lon.gii'
nameLat = Primate2 + '_' + side + 'white_lat.gii'
print('reading models\' informations')
modelP1F = 'model_' + Primate1 + '_' + side + '.txt'
modelP2F = 'model_' + Primate2 + '_' + side + '.txt'
modelP1 = read_model(modelP1F)
modelP2 = read_model(modelP2F)
dimRect_P1, poles_lat_P1, longID_P1, latID_P1, sulci_lon_P1, sulci_lat_P1, lon_coor_P1, lat_coor_P1 = modelP1
dimRect_P2, poles_lat_P2, longID_P2, latID_P2, sulci_lon_P2, sulci_lat_P2, lon_coor_P2, lat_coor_P2 = modelP2
print('reading correspondences\' table')
name_corr = Primate1 + '_' + Primate2 + '_' + 'corr.txt'
if os.path.exists(name_corr):
corrTable = read_corr(name_corr)
else:
name_corr = Primate2 + '_' + Primate1 + '_' + 'corr.txt'
corrTable = read_corr(name_corr)
print('reading coordinates')
r = aims.Reader()
texLatF = r.read(nameLat)
texLonF = r.read(nameLon)
texLat = np.array(texLatF[0])
texLon = np.array(texLonF[0])
print('rescaling square coordinates to sphere coordinates')
sulciP1, sulciP2 = SquareToSphere(dimRect_P1, dimRect_P2,
[lon_coor_P1, lat_coor_P1],
[lon_coor_P2, lat_coor_P2], poles_lat_P1,
poles_lat_P2)
print('extracting correspondences')
assert (len(corrTable['lon_' + Primate1]) == len(corrTable['lon_' + Primate2]) and
len(corrTable['lat_' + Primate1]) == len(corrTable['lat_' + Primate2])), \
"Number of corresponding sulci do not match in corr text file."
Ncorr_lon = len(corrTable['lon_' + Primate1])
Ncorr_lat = len(corrTable['lat_' + Primate1])
long_corr = np.zeros((Ncorr_lon, 2)).astype('int')
lat_corr = np.zeros((Ncorr_lat, 2)).astype('int')
for i in range(Ncorr_lon):
long_corr[i][0] = longID_P1[sulci_lon_P1[corrTable['lon_' +
Primate1][i]]]
long_corr[i][1] = longID_P2[sulci_lon_P2[corrTable['lon_' +
Primate2][i]]]
for i in range(Ncorr_lat):
lat_corr[i][0] = latID_P1[sulci_lat_P1[corrTable['lat_' +
Primate1][i]]]
lat_corr[i][1] = latID_P2[sulci_lat_P2[corrTable['lat_' +
Primate2][i]]]
print('computing affine transformations')
long_transform, lat_transform = Affine_Transform(sulciP1, sulciP2,
long_corr, lat_corr,
poles_lat_P1,
poles_lat_P2)
print('processing longitude')
intervals_lon = np.concatenate(([0], sulciP2[0][long_corr[:, 1]], [360]))
intervals_lon = np.sort(intervals_lon)
newLon = rescale(texLon, long_transform, intervals_lon)
print('processing latitude')
intervals_lat = np.concatenate(([30], sulciP2[1][lat_corr[:, 1]], [150]))
intervals_lat = np.sort(intervals_lat)
newLat = rescale(texLat, lat_transform, intervals_lat)
print('writing textures')
nv = texLat.size
newLatT = aims.TimeTexture_FLOAT(1, nv)
newLonT = aims.TimeTexture_FLOAT(1, nv)
for i in range(nv):
newLatT[0][i] = newLat[i]
newLonT[0][i] = newLon[i]
outLat = Primate1 + '_' + side + 'white_lat_to' + Primate2 + '.gii'
outLon = Primate1 + '_' + side + 'white_lon_to' + Primate2 + '.gii'
r = aims.Writer()
r.write(newLatT, outLat)
r.write(newLonT, outLon)
print('done')
return None
def iterate(Primate1, Primate2, side, Primate_inter=None):
"""
Meant to be used on a database to iterate the reparametrization on a list of individuals of one species to the
coordinate system of Primate1.
:param Primate1: string - name of Primate1
:param Primate2: string - name of Primate2
:param side: string - hemisphere 'L' or 'R'
:return: the rescaled longitudinal and latitudinal textures for each individual of Primate2
"""
individuals = [i for i in os.listdir(Primate2) if '.' not in i]
print('reading affine transformations')
dir = Primate1 + '_to_' + Primate2
if Primate_inter is not None:
dir = Primate1 + '_to_' + Primate2 + '_via' + Primate_inter
affine_model = os.path.join(dir,
'affine_trans_' + dir + '_' + side + '.txt')
int_lon, int_lat, lon_transform, lat_transform = read_affine(affine_model)
for ind in individuals:
nameLon = os.path.join(Primate2, ind,
ind + '_' + side + 'white_lon.gii')
nameLat = os.path.join(Primate2, ind,
ind + '_' + side + 'white_lat.gii')
# print('reading coordinates')
r = aims.Reader()
texLatF = r.read(nameLat)
texLonF = r.read(nameLon)
texLat = np.array(texLatF[0])
texLon = np.array(texLonF[0])
# print('processing longitude')
newLon = rescale(texLon, lon_transform, int_lon)
# print('processing latitude')
newLat = rescale(texLat, lat_transform, int_lat)
# print('writing textures')
nv = texLat.size
newLatT = aims.TimeTexture_FLOAT(1, nv)
newLonT = aims.TimeTexture_FLOAT(1, nv)
for i in range(nv):
newLatT[0][i] = newLat[i]
newLonT[0][i] = newLon[i]
outLat = os.path.join(
dir, Primate1 + '_' + side + 'white_lat_to' + ind + '.gii')
outLon = os.path.join(
dir, Primate1 + '_' + side + 'white_lon_to' + ind + '.gii')
r = aims.Writer()
r.write(newLatT, outLat)
r.write(newLonT, outLon)
print('done')
in_file = os.path.join('/neurospin/brainomics/2016_AUSZ',"preproc_VBM","list_subjects","list_T1.txt")
out_file = os.path.join('/neurospin/brainomics/2016_AUSZ',"preproc_VBM","tissues-volumes-globals_spm-segmentation.csv")
suffix_im = "mprage_noface.nii"
inf=open(in_file, "r")
outf=open(out_file, "w")
outf.write("subject"+" "+"dimX"+" "+"dimY"+" "+"dimZ"+" "+
"voxsizeX"+" "+"voxsizeY"+" "+"voxsizeZ"+" "+
"nvoxGM_nomask"+" "+"nvoxWM_nomask"+" "+"nvoxCSF_nomask"+" "+
"nvoxGM"+" "+"nvoxWM"+" "+"nvoxCSF\n")
outf.flush()
r = aims.Reader()
for cur_anat_path in inf.readlines():
cur_anat_path = cur_anat_path.replace("\n","")
cur_dir = os.path.dirname(cur_anat_path)
image = os.path.basename(cur_anat_path)
image= image[:-3]
# read segmeted images
gm_im = r.read(os.path.join(cur_dir, "c1"+image+'nii'))
wm_im = r.read(os.path.join(cur_dir, "c2"+image+'nii'))
csf_im = r.read(os.path.join(cur_dir, "c3"+image+'nii'))
skull_im = r.read(os.path.join(cur_dir, "c4"+image+'nii'))
outside_im = r.read(os.path.join(cur_dir, "c5"+image+'nii'))
gm_arr = gm_im.arraydata().squeeze()
def csvMapGraph(options,
agraphs=None,
window=None,
displayProp=None,
palette=None,
propPrefix=None,
csvfilename=None,
filterCol=None,
filterExpr=None,
min=None,
max=None):
'''csvMapGraph( options, agraphs=None, window=None, displayProp=None,
palette=None, propPrefix=None, csvfilename=None )
read a CSV-like file, extract numeric figures on sulci from it and map it
on a sulci graph displayed in anatomist.
Paramters:
options: command-line parsing options of siCsvMapGraph (see the help of this
command)
agraphs: existing anatomist graph(s) on which to map data. If not specified, a
graph is read from the options.graphname parameter.
window: existing anatomist window in which the sulci graph will be displayed.
If not specified, a new window will be opened.
displayProp: when several data are read at the same time, specifies which
one will be currently displayed. If not specified, the first data column
will be used.
palette: color palette to apply to the graph display. May be either an
anatomist palette object, or a parameters dictionary, corresponding to the
parameters of the SetObjectPalette command (see
http://brainvisa.info/doc/anatomist-4.0/html/fr/programmation/commands.html#SetObjectPalette)
propPrefix: data read from the CSV file are stored in the graph nodes as
properties. The prefix is used in the properties names. If not specified,
'csv' is used
csvfilename: if specified, overrides options.csvfilename for convenience
Return values: aobjects, awindows
'''
if csvfilename is None:
csvfilename = options.csvfilename
if csvfilename is None:
parseOpts([sys.argv[0], '-h'])
sys.exit(1)
# read
ft = sigraph.FoldLabelsTranslator(options.transfile)
sigraph.si().setLabelsTranslPath(options.transfile)
labels, sulci_data, mode = read_csv(csvfilename,
options.columns,
operator=options.operator,
filterCol=filterCol,
filterExpr=filterExpr)
if options.summarycsvfilename:
write_summary_csv(options.summarycsvfilename, labels, sulci_data, mode)
# graph
if not options.graphname: return
from soma import aims
graphs = []
if agraphs:
if isinstance(agraphs, anatomist.Anatomist.AGraph):
agraphs = [agraphs]
graphs = [x.graph() for x in agraphs]
else:
r = aims.Reader(options={'subobjectsfilter': 1})
graphs = [r.read(f) for f in options.graphname]
for g in graphs:
ft.translate(g, options.label_attribute, options.label_attribute)
# mesh
for mfile in options.meshname:
m = aims.read(mfile)
else:
m = None
if propPrefix is None:
propPrefix = 'csv'
for g in graphs:
for v in g.vertices():
if v.getSyntax() != 'fold': continue
if mode in ('sulci', 'label', 'name'):
try:
l = v[options.label_attribute]
data = getdata(sulci_data, v[options.label_attribute])
except exceptions.KeyError:
continue
elif mode == 'nodes':
try:
data = getdata(sulci_data, str(int(v['index'])))
except exceptions.KeyError:
continue
for i, h in enumerate(labels):
v[propPrefix + '_mean_' + h] = data[0][i]
if options.log and data[0][i] != 0:
v[propPrefix + '_log_mean_' + h] = numpy.log(data[0][i])
elif options.log10 and data[0][i] != 0:
v[propPrefix + '_log_mean_' + h] = numpy.log10(data[0][i])
# add only no-null std
if data[1][i]: v[propPrefix + '_std_' + h] = data[1][i]
v[propPrefix + '_sum_' + h] = data[2][i]
a = anatomist.Anatomist()
aobjects = []
if not agraphs:
agraphs = [a.toAObject(g) for g in graphs]
aobjects = agraphs
if displayProp is None:
displayProp = propPrefix + '_mean_' + labels[0]
for ag in agraphs:
ag.setColorMode(ag.PropertyMap)
ag.setColorProperty(displayProp)
#modified to get the palette as imput argument
palette = a.getPalette(palette)
if palette is not None:
if isinstance(palette, a.APalette):
#modified to get min and max values as imputs
ag.setPalette(palette,
minVal=min,
maxVal=max,
absoluteMode=True)
#ag.setPalette( palette )
else: # dict
a.execute('SetObjectPalette', objects=[ag], **palette)
ag.notifyObservers()
if m:
am = a.toAObject(m)
aobjects.append(am)
if window is not None:
win = window
wins = []
else:
win = a.createWindow(wintype='3D')
wins = [win]
win.setHasCursor(0)
win.addObjects(aobjects, add_graph_nodes=True)
return aobjects, wins
def do_mesh_cluster_rendering(
title,
clust_mesh_file,
clust_texture_file,
brain_mesh_file,
anat_file,
palette_file="palette_signed_values_blackcenter",
#a = None,
check=False,
verbose=True):
""" Vizualization of signed stattistic map or weigths.
Parameters
----------
clust_mesh_file : str (mandatory)
a mesh file of interest.
clust_texture_file : str (mandatory)
an IMAGE from which to extract texture (for the mesh file).
brain_mesh_file : str (mandatory)
a mesh file of the underlying neuroanatomy.
anat_file : str (mandatory)
an IMAGE of the underlying neuroanatomy.
check : bool (default False)
manual check of the input parameters.
verbose : bool (default True)
print to stdout the function prototype.
Returns
-------
None
"""
#FunctionSummary(check, verbose)
global ANATOMIST
global VIEWS
global IMAGE
# instance of anatomist
if ANATOMIST is None:
ANATOMIST = anatomist.Anatomist()
# Add new SnapshotControl button
pix = QtGui.QPixmap('control.xpm')
anatomist.cpp.IconDictionary.instance().addIcon('Snap', pix)
ad = anatomist.cpp.ActionDictionary.instance()
ad.addAction('SnapshotAction', lambda: SnapshotAction())
cd = anatomist.cpp.ControlDictionary.instance()
cd.addControl('Snap', lambda: SnapshotControl(), 25)
cm = anatomist.cpp.ControlManager.instance()
cm.addControl('QAGLWidget3D', '', 'Snap')
a = ANATOMIST
# ------------
# load objects
# ------------
clust_mesh = a.loadObject(clust_mesh_file)
brain_mesh = a.loadObject(brain_mesh_file)
clust_texture = a.loadObject(clust_texture_file)
IMAGE = aims.Reader().read(clust_texture_file)
image_dim = IMAGE.header()['volume_dimension'].arraydata()[:3]
"""
cd /home/ed203246/mega/data/mescog/wmh_patterns/summary/cluster_mesh/tvl1l20001
from soma import aims
clust_texture_file = "clust_values.nii.gz"
IMAGE = aims.Reader().read(clust_texture_file)
image_dim = IMAGE.header()['volume_dimension'].arraydata()[:3]
trm_ima_mm2ref_mm = aims.Motion(ima.header()['transformations'][0])
trm_xyz_to_mm = ima_get_trm_xyz_to_mm(ima)
min_mm = trm_ima_mm2ref_mm.transform([0, 0, 0])
#a=ima.header()['volume_dimension']
ima_dim_xyz = ima.header()['volume_dimension'].arraydata()[:3]
max_mm = trm_ima_mm2ref_mm.transform(ima_dim_xyz)
trm_ima_mm2ref_mm = aims.Motion(ima.header()['transformations'][0])
ima_voxel_size = ima.header()['voxel_size'][:3]
xyz_to_mm(trm_ima_mm2ref_mm, ima_voxel_size)
"""
#print clust_texture.header()#['volume_dimension']
a_anat = a.loadObject(anat_file)
# mesh option
material = a.Material(diffuse=[0.8, 0.8, 0.8, 0.6])
brain_mesh.setMaterial(material)
# change palette
#palette_file = get_sample_data("brainvisa_palette").edouard
# bv_rgb_dir = os.path.join(os.environ["HOME"], ".anatomist", "rgb")
# if not os.path.isdir(bv_rgb_dir):
# os.makedirs(bv_rgb_dir)
# bv_rgb_file = os.path.join(bv_rgb_dir,
# os.path.basename(palette_file))
# if not os.path.isfile(bv_rgb_file):
# shutil.copyfile(palette_file, bv_rgb_file)
palette = a.getPalette(palette_file)
clust_texture.setPalette(palette, minVal=-10, maxVal=10, absoluteMode=True)
# view object
block = a.createWindowsBlock(2)
windows = list()
objects = list()
# --------------------------------
# 3D view = fusion 3D + brain_mesh
# --------------------------------
win3d = a.createWindow("3D", block=block)
# orientation front left
Q_rot = [np.cos(np.pi / 8.), np.sin(np.pi / 8.), 0, 0]
fusion3d = a.fusionObjects([clust_mesh, clust_texture], "Fusion3DMethod")
win3d.addObjects([fusion3d, brain_mesh])
a.execute("Fusion3DParams",
object=fusion3d,
step=0.1,
depth=5.,
sumbethod="max",
method="line_internal")
#define point of view
Q1 = win3d.getInfos()["view_quaternion"] # current point of view
Q = product_quaternion(Q1, Q_rot)
a.execute("Camera", windows=[win3d], view_quaternion=Q)
windows.append(win3d)
objects.append(fusion3d)
VIEWS[(title, "3d")] = [win3d, fusion3d]
# -------------------------------------
# Slices view = three VIEWS offusion 2D
# -------------------------------------
# fusion 2D
fusion2d = a.fusionObjects([a_anat, clust_texture], "Fusion2DMethod")
a.execute("Fusion2DMethod", object=fusion2d)
# change 2D fusion settings
a.execute("TexturingParams",
texture_index=1,
objects=[
fusion2d,
],
mode="Geometric") #, rate=0.1)
##############
# Coronal view
win_coronal = a.createWindow("3D", block=block)
print "win_coronal", win_coronal
# Fusion cut mesh
fusionmesh_coronal = a.fusionObjects([brain_mesh, fusion2d],
"FusionCutMeshMethod")
a.execute("FusionCutMeshMethod", object=fusionmesh_coronal)
fusionmesh_coronal.addInWindows(win_coronal)
# Coronal view
# Slice
#sclice_coronal(fusionmesh_coronal, -90)
sclice_coronal(fusionmesh_coronal, int(-(image_dim / 2)[1]))
# Store
windows.append(win_coronal)
#a.execute
objects.append([fusion2d, fusionmesh_coronal])
VIEWS[(title, "coronal")] = [win_coronal, fusionmesh_coronal]
##############
# Axial view
win_axial = a.createWindow("3D", block=block)
print "win_axial", win_axial, win_axial.getInfos()
# Fusion cut mesh
fusionmesh_axial = a.fusionObjects([brain_mesh, fusion2d],
"FusionCutMeshMethod")
a.execute("FusionCutMeshMethod", object=fusionmesh_axial)
fusionmesh_axial.addInWindows(win_axial)
# Axial view, orientation front top left
Q_rot = [np.cos(np.pi / 8.), np.sin(np.pi / 8.), 0, np.sin(np.pi / 8.)]
Q1 = win_axial.getInfos()["view_quaternion"] # current point of view
Q = product_quaternion(Q1, Q_rot)
a.execute("Camera", windows=[win_axial], view_quaternion=Q, zoom=0.8)
# Slice
sclice_axial(fusionmesh_axial, int(-(image_dim / 2)[2]))
# Store
windows.append(win_axial)
#a.execute
objects.append([fusion2d, fusionmesh_axial])
VIEWS[(title, "axial")] = [win_axial, fusionmesh_axial]
##############
#Sagital view
win_sagital = a.createWindow("3D", block=block)
print "win_sagital", win_sagital
# Fusion cut mesh
fusionmesh_sagital = a.fusionObjects([brain_mesh, fusion2d],
"FusionCutMeshMethod")
a.execute("FusionCutMeshMethod", object=fusionmesh_sagital)
fusionmesh_sagital.addInWindows(win_sagital)
# Sagital view: rotation -Pi/4 --> orientation right
Q_rot = [np.sqrt(2) / 2., -np.sqrt(2) / 2., 0, 0]
Q1 = win_sagital.getInfos()["view_quaternion"] # current point of view
Q = product_quaternion(Q1, Q_rot)
a.execute("Camera", windows=[win_sagital], view_quaternion=Q, zoom=0.9)
# Slice
sclice_sagital(fusionmesh_sagital, int(-(image_dim / 2)[0]))
# Store
windows.append(win_sagital)
#a.execute
objects.append([fusion2d, fusionmesh_sagital])
VIEWS[(title, "sagital")] = [win_sagital, fusionmesh_sagital]
# Global windows info
try:
block.widgetProxy().widget.setWindowTitle(str(title))
except:
print "could not set name"
return [
clust_mesh, brain_mesh, clust_texture, a_anat, material, palette,
block, windows, objects
]
[['moment_invariant_%i' % i, values[i]] for i in xrange(len(values))]
moment_invariant = False
cols, values = zip(*lacune_keys_values)
#if columns is None:
# columns = cols
#assert cols == columns
columns += list(cols)
moments += list(values)
# 3) Mesh file
out = os.popen('AimsMesh -i %s -o %s' % (ima_filename, prefix_mesh_filename))
# 4) Compute surface & volume from mesh
out = os.popen('AimsMeshArea -i %s' % mesh_filename)
columns += ["area_mesh", "vol_mesh"]
moments += [float(l.strip().split(" ")[1]) for l in out.readlines()]
# 5) Get perforator orientation
vol_aims = aims.Reader().read(perforator_filename)
# t,z,y,x
arr = vol_aims.arraydata().squeeze()
voxel_size = vol_aims.header()['voxel_size'].arraydata()
perfo_xyz1 = np.asarray(np.where(arr == 2)).squeeze()[::-1] * voxel_size
perfo_xyz2 = np.asarray(np.where(arr == 1)).squeeze()[::-1] * voxel_size
perfo_xyz_vec = (perfo_xyz1 - perfo_xyz2)
# perfo_zyx = perfo_ext1 - perfo_ext2
# perfo_ext1 = np.asarray(np.where(arr == 2)).squeeze()
# perfo_ext2 = np.asarray(np.where(arr == 1)).squeeze()
# perfo_zyx = perfo_ext1 - perfo_ext2
# perfo_xyz = (perfo_zyx * voxel_size[::-1])[::-1]
columns += ['perfo_orientation_%s' % xyz[i] for i in xrange(len(perfo_xyz_vec))]
moments += perfo_xyz_vec.tolist()
columns += ['perfo_ext1_%s' % xyz[i] for i in xrange(len(perfo_xyz1))]
moments += perfo_xyz1.tolist()
