swap_end_count[rh_lh] += 1
end_count[inv_rh_lh] += 1
break
j += 1
new_lines += [new_line]
i += 1
stdout.write("\n done")
stdout.flush()
print "save intersection"
np.save(args.intersection, id_list)
np.save(args.surf_idx_inter, id_surf)
print "save as .fib"
lines_polydata = lines_to_vtk_polydata(new_lines, None, np.float32)
save_polydata(lines_polydata, args.output , True)
print "Do something with intersection !"
# Report
valid_count = [start_count[0] - bad_start_count[0] - bad_end_count[0] - bad_pft_count[0],
start_count[1] - bad_start_count[1] - bad_end_count[1] - bad_pft_count[1]]
if args.report is not None:
report = open(args.report,"w")
report.write("# Report : [ right , left ]\n")
report.write("start_count : " + str(start_count) + "\n")
report.write("bad_pft_count : " + str(bad_pft_count) + "\n")
report.write("bad_start_count : " + str(bad_start_count) + "\n")
report.write("end_count : " + str(end_count) + "\n")
report.write("bad_end_count : " + str(bad_end_count) + "\n")
#!/usr/bin/env python # [email protected] import argparse import numpy as np from trimeshpy.trimeshflow_vtk import lines_to_vtk_polydata from trimeshpy.trimesh_vtk import save_polydata, load_streamlines_poyldata, get_streamlines parser = argparse.ArgumentParser(description='lenghts stats') parser.add_argument('fibers', type=str, nargs='+', default=None, help='tractography fibers (.fib)') parser.add_argument('-o', type=str, default=None, help='merged tractography (.fib)') args = parser.parse_args() streamlines_list = [] for filename in args.fibers: streamlines_list.append(get_streamlines(load_streamlines_poyldata(filename))) print filename, len(streamlines_list[-1]) final_streamlines = [] for streamlines in streamlines_list: for line in streamlines: final_streamlines.append(line) print args.out, len(final_streamlines) lines_polydata = lines_to_vtk_polydata(final_streamlines, None, np.float32) save_polydata(lines_polydata, args.o , True)
def main():
parser = buildArgsParser()
args = parser.parse_args()
param = {}
if args.pft_theta is None and args.pft_curvature is None:
args.pft_theta = 20
if not np.any([args.nt, args.npv, args.ns]):
args.npv = 1
if args.theta is not None:
theta = gm.math.radians(args.theta)
elif args.curvature > 0:
theta = get_max_angle_from_curvature(args.curvature, args.step_size)
elif args.algo == 'prob':
theta = gm.math.radians(20)
else:
theta = gm.math.radians(45)
if args.pft_curvature is not None:
pft_theta = get_max_angle_from_curvature(args.pft_curvature, args.step_size)
else:
pft_theta = gm.math.radians(args.pft_theta)
if args.mask_interp == 'nn':
mask_interpolation = 'nearest'
elif args.mask_interp == 'tl':
mask_interpolation = 'trilinear'
else:
parser.error("--mask_interp has wrong value. See the help (-h).")
return
if args.field_interp == 'nn':
field_interpolation = 'nearest'
elif args.field_interp == 'tl':
field_interpolation = 'trilinear'
else:
parser.error("--sh_interp has wrong value. See the help (-h).")
return
param['random'] = args.random
param['skip'] = args.skip
param['algo'] = args.algo
param['mask_interp'] = mask_interpolation
param['field_interp'] = field_interpolation
param['theta'] = theta
param['sf_threshold'] = args.sf_threshold
param['pft_sf_threshold'] = args.pft_sf_threshold if args.pft_sf_threshold is not None else args.sf_threshold
param['sf_threshold_init'] = args.sf_threshold_init
param['step_size'] = args.step_size
param['max_length'] = args.max_length
param['min_length'] = args.min_length
param['is_single_direction'] = args.is_single_direction
param['nbr_seeds'] = args.nt if args.nt is not None else 0
param['nbr_seeds_voxel'] = args.npv if args.npv is not None else 0
param['nbr_streamlines'] = args.ns if args.ns is not None else 0
param['max_no_dir'] = int(math.ceil(args.maxL_no_dir / param['step_size']))
param['is_all'] = args.is_all
param['is_act'] = args.is_act
param['theta_pft'] = pft_theta
if args.not_is_pft:
param['nbr_particles'] = 0
param['back_tracking'] = 0
param['front_tracking'] = 0
else:
param['nbr_particles'] = args.nbr_particles
param['back_tracking'] = int(
math.ceil(args.back_tracking / args.step_size))
param['front_tracking'] = int(
math.ceil(args.front_tracking / args.step_size))
param['nbr_iter'] = param['back_tracking'] + param['front_tracking']
param['mmap_mode'] = None if args.isLoadData else 'r'
if args.isVerbose:
logging.basicConfig(level=logging.DEBUG)
logging.debug('Tractography parameters:\n{0}'.format(param))
if os.path.isfile(args.output_file):
if args.isForce:
logging.info('Overwriting "{0}".'.format(args.output_file))
else:
parser.error(
'"{0}" already exists! Use -f to overwrite it.'
.format(args.output_file))
include_dataset = Dataset(
nib.load(args.map_include_file), param['mask_interp'])
exclude_dataset = Dataset(
nib.load(args.map_exclude_file), param['mask_interp'])
if param['is_act']:
mask = ACT(include_dataset, exclude_dataset,
param['step_size'] / include_dataset.size[0])
else:
mask = CMC(include_dataset, exclude_dataset,
param['step_size'] / include_dataset.size[0])
dataset = Dataset(nib.load(args.sh_file), param['field_interp'])
field = SphericalHarmonicField(
dataset, args.basis, param['sf_threshold'],
param['sf_threshold_init'], param['theta'])
if args.algo == 'det':
tracker = deterministicMaximaTracker(field, param['step_size'])
elif args.algo == 'prob':
tracker = probabilisticTracker(field, param['step_size'])
else:
parser.error("--algo has wrong value. See the help (-h).")
return
pft_field = SphericalHarmonicField(
dataset, args.basis, param['pft_sf_threshold'],
param['sf_threshold_init'], param['theta_pft'])
pft_tracker = probabilisticTracker(pft_field, param['step_size'])
# ADD Seed input
# modify ESO
nib_mask = nib.load(args.map_include_file)
seed_points = np.load(args.seed_points)
seed_dirs = np.load(args.seed_dir)
rotation = nib_mask.get_affine()[:3,:3]
inv_rotation = np.linalg.inv(rotation)
translation = nib_mask.get_affine()[:3,3]
scale = np.array(nib_mask.get_header().get_zooms())
voxel_space = nib.aff2axcodes(nib_mask.get_affine())
print voxel_space
# seed points transfo
# LPS -> voxel_space
print scale
if voxel_space[0] != 'L':
print "flip X"
seed_points[:,0] = -seed_points[:,0]
if voxel_space[1] != 'P':
print "flip Y"
seed_points[:,1] = -seed_points[:,1]
if voxel_space[2] != 'S':
print "flip Z"
seed_points[:,2] = -seed_points[:,2]
# other transfo
seed_points = seed_points - translation
seed_points = seed_points.dot(inv_rotation)
seed_points = seed_points * scale
# seed dir transfo
seed_dirs[:,0:2] = -seed_dirs[:,0:2]
seed_dirs = seed_dirs.dot(inv_rotation)
seed_dirs = seed_dirs * scale
if args.inv_seed_dir:
seed_dirs = seed_dirs * -1.0
# Compute tractography
nb_seeds = len(seed_dirs)
if args.test is not None and args.test < nb_seeds:
nb_seeds = args.test
# end modify ESO
# tracker to modify
# modify ESO
start = time.time()
streamlines = []
for i in range(nb_seeds):
s = generate_streamline(tracker, mask, seed_points[i], seed_dirs[i], pft_tracker=pft_tracker, param=param)
streamlines.append(s)
stdout.write("\r %d%%" % (i*101//nb_seeds))
stdout.flush()
stdout.write("\n done")
stdout.flush()
stop = time.time()
# end modify ESO
# ADD save fiber output
# modify ESO
for i in range(len(streamlines)):
streamlines[i] = streamlines[i] / scale
streamlines[i] = streamlines[i].dot(rotation)
streamlines[i] = streamlines[i] + translation
# voxel_space -> LPS
if voxel_space[0] != 'L':
streamlines[i][:,0] = -streamlines[i][:,0]
if voxel_space[1] != 'P':
streamlines[i][:,1] = -streamlines[i][:,1]
if voxel_space[2] != 'S':
streamlines[i][:,2] = -streamlines[i][:,2]
lines_polydata = lines_to_vtk_polydata(streamlines, None, np.float32)
save_polydata(lines_polydata, args.output_file , True)
# end modify ESO
lengths = [len(s) for s in streamlines]
if nb_seeds > 0:
ave_length = (sum(lengths) / nb_seeds) * param['step_size']
else:
ave_length = 0
str_ave_length = "%.2f" % ave_length
str_time = "%.2f" % (stop - start)
print(str(nb_seeds) + " streamlines, with an average length of " +
str_ave_length + " mm, done in " + str_time + " seconds.")
def main():
np.random.seed(int(time.time()))
parser = buildArgsParser()
args = parser.parse_args()
param = {}
if args.algo not in ["det", "prob"]:
parser.error("--algo has wrong value. See the help (-h).")
if args.basis not in ["mrtrix", "dipy", "fibernav"]:
parser.error("--basis has wrong value. See the help (-h).")
#if np.all([args.nt is None, args.npv is None, args.ns is None]):
# args.npv = 1
if args.theta is not None:
theta = gm.math.radians(args.theta)
elif args.curvature > 0:
theta = get_max_angle_from_curvature(args.curvature, args.step_size)
elif args.algo == 'prob':
theta = gm.math.radians(20)
else:
theta = gm.math.radians(45)
if args.mask_interp == 'nn':
mask_interpolation = 'nearest'
elif args.mask_interp == 'tl':
mask_interpolation = 'trilinear'
else:
parser.error("--mask_interp has wrong value. See the help (-h).")
return
if args.field_interp == 'nn':
field_interpolation = 'nearest'
elif args.field_interp == 'tl':
field_interpolation = 'trilinear'
else:
parser.error("--sh_interp has wrong value. See the help (-h).")
return
param['algo'] = args.algo
param['mask_interp'] = mask_interpolation
param['field_interp'] = field_interpolation
param['theta'] = theta
param['sf_threshold'] = args.sf_threshold
param['sf_threshold_init'] = args.sf_threshold_init
param['step_size'] = args.step_size
param['max_length'] = args.max_length
param['min_length'] = args.min_length
param['is_single_direction'] = False
param['nbr_seeds'] = 0
param['nbr_seeds_voxel'] = 0
param['nbr_streamlines'] = 0
param['max_no_dir'] = int(math.ceil(args.maxL_no_dir / param['step_size']))
param['is_all'] = False
param['isVerbose'] = args.isVerbose
if param['isVerbose']:
logging.basicConfig(level=logging.DEBUG)
if param['isVerbose']:
logging.info('Tractography parameters:\n{0}'.format(param))
if os.path.isfile(args.output_file):
if args.isForce:
logging.info('Overwriting "{0}".'.format(args.output_file))
else:
parser.error(
'"{0}" already exists! Use -f to overwrite it.'
.format(args.output_file))
nib_mask = nib.load(args.mask_file)
mask = BinaryMask(
Dataset(nib_mask, param['mask_interp']))
dataset = Dataset(nib.load(args.sh_file), param['field_interp'])
field = SphericalHarmonicField(
dataset, args.basis, param['sf_threshold'], param['sf_threshold_init'], param['theta'])
if args.algo == 'det':
tracker = deterministicMaximaTracker(field, param['step_size'])
elif args.algo == 'prob':
tracker = probabilisticTracker(field, param['step_size'])
else:
parser.error("--algo has wrong value. See the help (-h).")
return
start = time.time()
# Etienne St-Onge
#load and transfo *** todo test with rotation and scaling
seed_points = np.load(args.seed_points)
seed_dirs = np.load(args.seed_dir)
rotation = nib_mask.get_affine()[:3,:3]
inv_rotation = np.linalg.inv(rotation)
translation = nib_mask.get_affine()[:3,3]
scale = np.array(nib_mask.get_header().get_zooms())
voxel_space = nib.aff2axcodes(nib_mask.get_affine())
print voxel_space
# seed points transfo
# LPS -> voxel_space
if voxel_space[0] != 'L':
print "flip X"
seed_points[:,0] = -seed_points[:,0]
if voxel_space[1] != 'P':
print "flip Y"
seed_points[:,1] = -seed_points[:,1]
if voxel_space[2] != 'S':
print "flip Z"
seed_points[:,2] = -seed_points[:,2]
# other transfo
seed_points = seed_points - translation
seed_points = seed_points.dot(inv_rotation)
seed_points = seed_points * scale
# seed dir transfo
seed_dirs[:,0:2] = -seed_dirs[:,0:2]
seed_dirs = seed_dirs.dot(inv_rotation)
seed_dirs = seed_dirs * scale
if args.inv_seed_dir:
seed_dirs = seed_dirs * -1.0
# Compute tractography
nb_seeds = len(seed_dirs)
if args.test is not None and args.test < nb_seeds:
nb_seeds = args.test
print args.algo," nb seeds: ", nb_seeds
streamlines = []
for i in range(nb_seeds):
s = generate_streamline(tracker, mask, seed_points[i], seed_dirs[i], pft_tracker=None, param=param)
streamlines.append(s)
stdout.write("\r %d%%" % (i*101//nb_seeds))
stdout.flush()
stdout.write("\n done")
stdout.flush()
# transform back
for i in range(len(streamlines)):
streamlines[i] = streamlines[i] / scale
streamlines[i] = streamlines[i].dot(rotation)
streamlines[i] = streamlines[i] + translation
# voxel_space -> LPS
if voxel_space[0] != 'L':
streamlines[i][:,0] = -streamlines[i][:,0]
if voxel_space[1] != 'P':
streamlines[i][:,1] = -streamlines[i][:,1]
if voxel_space[2] != 'S':
streamlines[i][:,2] = -streamlines[i][:,2]
lines_polydata = lines_to_vtk_polydata(streamlines, None, np.float32)
save_polydata(lines_polydata, args.output_file , True)
lengths = [len(s) for s in streamlines]
if nb_seeds > 0:
ave_length = (sum(lengths) / nb_seeds) * param['step_size']
else:
ave_length = 0
str_ave_length = "%.2f" % ave_length
str_time = "%.2f" % (time.time() - start)
print(str(nb_seeds) + " streamlines, with an average length of " +
str_ave_length + " mm, done in " + str_time + " seconds.")
#tri_mesh_flow.display_vertices_flow() mesh.set_vertices(lines[99]) #test = mesh.edge_triangle_normal_angle().max(1).toarray().squeeze() #test = mesh.vertices_gaussian_curvature(False) test = mesh.vertices_cotan_curvature(False) print "min =", test.min(), "max =", test.max() print test color = np.zeros_like(lines[0]) tmax = test.max() tmin = -test.min() color[:,0] = np.maximum(test,0).reshape((1,-1))*255/tmax color[:,2] = np.maximum(-test,0).reshape((1,-1))*255/tmin mesh.set_colors(color) mesh.display() """ """ line_to_save = np.swapaxes(lines, 0, 1) rend = fvtk.ren() fvtk.add(rend, mesh.get_vtk_actor()) fvtk.add(rend, fvtk.line(line_to_save)) fvtk.show(rend) """ """ # save fibers in .fib normal #line_to_save = streamline.compress_streamlines(np.swapaxes(lines, 0, 1)) line_to_save = np.swapaxes(lines, 0, 1) lines_polydata = lines_to_vtk_polydata(line_to_save, None, np.float32) save_polydata(lines_polydata, saved_fib, True) """
from trimeshpy.trimesh_vtk import load_polydata, save_polydata, load_streamlines_poyldata, get_streamlines from trimeshpy.trimeshflow_vtk import lines_to_vtk_polydata import numpy as np fib_file_name = "../data/tract.fib" save_file = "../data/tract.xml" #save_file = "../data/tract.stl" polydata = load_polydata(fib_file_name) save_polydata(polydata, save_file) ### load streamlines liste and save a a new smaller file """lines = get_streamlines(load_streamlines_poyldata(fib_file_name)) new_lines = lines[0:1000] lines_polydata = lines_to_vtk_polydata(new_lines, None, np.float32) save_polydata(lines_polydata, "../data/tract2.fib")"""