def _get_direction_getter(args, mask_data):
sh_data = nib.load(args.sh_file).get_data().astype('float64')
sphere = HemiSphere.from_sphere(get_sphere(args.sphere))
theta = get_theta(args.theta, args.algo)
if args.algo in ['det', 'prob']:
if args.algo == 'det':
dg_class = DeterministicMaximumDirectionGetter
else:
dg_class = ProbabilisticDirectionGetter
return dg_class.from_shcoeff(shcoeff=sh_data,
max_angle=theta,
sphere=sphere,
basis_type=args.sh_basis,
relative_peak_threshold=args.sf_threshold)
# Code for type EUDX. We don't use peaks_from_model
# because we want the peaks from the provided sh.
sh_shape_3d = sh_data.shape[:-1]
npeaks = 5
peak_dirs = np.zeros((sh_shape_3d + (npeaks, 3)))
peak_values = np.zeros((sh_shape_3d + (npeaks, )))
peak_indices = np.full((sh_shape_3d + (npeaks, )), -1, dtype='int')
b_matrix = get_b_matrix(find_order_from_nb_coeff(sh_data), sphere,
args.sh_basis)
for idx in np.ndindex(sh_shape_3d):
if not mask_data[idx]:
continue
directions, values, indices = get_maximas(sh_data[idx], sphere,
b_matrix, args.sf_threshold,
0)
if values.shape[0] != 0:
n = min(npeaks, values.shape[0])
peak_dirs[idx][:n] = directions[:n]
peak_values[idx][:n] = values[:n]
peak_indices[idx][:n] = indices[:n]
dg = PeaksAndMetrics()
dg.sphere = sphere
dg.peak_dirs = peak_dirs
dg.peak_values = peak_values
dg.peak_indices = peak_indices
dg.ang_thr = theta
dg.qa_thr = args.sf_threshold
return dg
def get_maps(data, mask, args, npeaks=5):
nufo_map = np.zeros(data.shape[0:3])
afd_map = np.zeros(data.shape[0:3])
afd_sum = np.zeros(data.shape[0:3])
peaks_dirs = np.zeros(list(data.shape[0:3]) + [npeaks, 3])
order = find_order_from_nb_coeff(data)
sphere = get_sphere(args.sphere)
b_matrix = get_b_matrix(order, sphere, args.sh_basis)
for index in ndindex(data.shape[:-1]):
if mask[index]:
if np.isnan(data[index]).any():
nufo_map[index] = 0
afd_map[index] = 0
else:
maximas, afd, _ = get_maximas(
data[index], sphere, b_matrix, args.r_threshold, args.at)
# sf = np.dot(data[index], B.T)
n = min(npeaks, maximas.shape[0])
nufo_map[index] = maximas.shape[0]
if n == 0:
afd_map[index] = 0.0
nufo_map[index] = 0.0
else:
afd_map[index] = afd.max()
peaks_dirs[index][:n] = maximas[:n]
# sum of all coefficients, sqrt(power spectrum)
# sum C^2 = sum fODF^2
afd_sum[index] = np.sqrt(np.dot(data[index], data[index]))
# sum of all peaks contributions to the afd
# integral of all the lobes. Numerical sum.
# With an infinite number of SH, this should == to afd_sum
# sf[np.nonzero(sf < args.at)] = 0.
# afd_sum[index] = sf.sum()/n*4*np.pi/B.shape[0]x
return nufo_map, afd_map, afd_sum, peaks_dirs
def _get_direction_getter(args):
odf_data = nib.load(args.in_odf).get_fdata(dtype=np.float32)
sphere = HemiSphere.from_sphere(get_sphere(args.sphere))
theta = get_theta(args.theta, args.algo)
non_zeros_count = np.count_nonzero(np.sum(odf_data, axis=-1))
non_first_val_count = np.count_nonzero(np.argmax(odf_data, axis=-1))
if args.algo in ['det', 'prob']:
if non_first_val_count / non_zeros_count > 0.5:
logging.warning('Input detected as peaks. Input should be'
'fodf for det/prob, verify input just in case.')
if args.algo == 'det':
dg_class = DeterministicMaximumDirectionGetter
else:
dg_class = ProbabilisticDirectionGetter
return dg_class.from_shcoeff(
shcoeff=odf_data, max_angle=theta, sphere=sphere,
basis_type=args.sh_basis,
relative_peak_threshold=args.sf_threshold)
elif args.algo == 'eudx':
# Code for type EUDX. We don't use peaks_from_model
# because we want the peaks from the provided sh.
odf_shape_3d = odf_data.shape[:-1]
dg = PeaksAndMetrics()
dg.sphere = sphere
dg.ang_thr = theta
dg.qa_thr = args.sf_threshold
# Heuristic to find out if the input are peaks or fodf
# fodf are always around 0.15 and peaks around 0.75
if non_first_val_count / non_zeros_count > 0.5:
logging.info('Input detected as peaks.')
nb_peaks = odf_data.shape[-1] // 3
slices = np.arange(0, 15+1, 3)
peak_values = np.zeros(odf_shape_3d+(nb_peaks,))
peak_indices = np.zeros(odf_shape_3d+(nb_peaks,))
for idx in np.argwhere(np.sum(odf_data, axis=-1)):
idx = tuple(idx)
for i in range(nb_peaks):
peak_values[idx][i] = np.linalg.norm(
odf_data[idx][slices[i]:slices[i+1]], axis=-1)
peak_indices[idx][i] = sphere.find_closest(
odf_data[idx][slices[i]:slices[i+1]])
dg.peak_dirs = odf_data
else:
logging.info('Input detected as fodf.')
npeaks = 5
peak_dirs = np.zeros((odf_shape_3d + (npeaks, 3)))
peak_values = np.zeros((odf_shape_3d + (npeaks, )))
peak_indices = np.full((odf_shape_3d + (npeaks, )), -1, dtype='int')
b_matrix = get_b_matrix(
find_order_from_nb_coeff(odf_data), sphere, args.sh_basis)
for idx in np.argwhere(np.sum(odf_data, axis=-1)):
idx = tuple(idx)
directions, values, indices = get_maximas(odf_data[idx],
sphere, b_matrix,
args.sf_threshold, 0)
if values.shape[0] != 0:
n = min(npeaks, values.shape[0])
peak_dirs[idx][:n] = directions[:n]
peak_values[idx][:n] = values[:n]
peak_indices[idx][:n] = indices[:n]
dg.peak_dirs = peak_dirs
dg.peak_values = peak_values
dg.peak_indices = peak_indices
return dg