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import numpy as np
from .localtrack import local_tracker
from dipy.align import Bunch
from dipy.tracking import utils
# enum TissueClass (tissue_classifier.pxd) is not accessible
# from here. To be changed when minimal cython version > 0.21.
# cython 0.21 - cpdef enum to export values into Python-level namespace
class LocalTracking(object):
"""A streamline generator for local tracking methods"""
def _get_voxel_size(affine):
"""Computes the voxel sizes of an image from the affine.
Checks that the affine does not have any shear because local_tracker
assumes that the data is sampled on a regular grid.
lin = affine[:3, :3]
dotlin =, lin)
# Check that the affine is well behaved
if not np.allclose(np.triu(dotlin, 1), 0.):
msg = ("The affine provided seems to contain shearing, data must "
"be acquired or interpolated on a regular grid to be used "
"with `LocalTracking`.")
raise ValueError(msg)
return np.sqrt(dotlin.diagonal())
def __init__(self, direction_getter, tissue_classifier, seeds, affine,
step_size, max_cross=None, maxlen=500, fixedstep=True,
"""Creates streamlines by using local fiber-tracking.
direction_getter : instance of DirectionGetter
Used to get directions for fiber tracking.
tissue_classifier : instance of TissueClassifier
Identifies endpoints and invalid points to inform tracking.
seeds : array (N, 3)
Points to seed the tracking. Seed points should be given in point
space of the track (see ``affine``).
affine : array (4, 4)
Coordinate space for the streamline point with respect to voxel
indices of input data. This affine can contain scaling, rotational,
and translational components but should not contain any shearing.
An identity matrix can be used to generate streamlines in "voxel
coordinates" as long as isotropic voxels were used to acquire the
step_size : float
Step size used for tracking.
max_cross : int or None
The maximum number of direction to track from each seed in crossing
voxels. By default all initial directions are tracked.
maxlen : int
Maximum number of steps to track from seed. Used to prevent
infinite loops.
fixedstep : bool
If true, a fixed stepsize is used, otherwise a variable step size
is used.
return_all : bool
If true, return all generated streamlines, otherwise only
streamlines reaching end points or exiting the image.
self.direction_getter = direction_getter
self.tissue_classifier = tissue_classifier
self.seeds = seeds
if affine.shape != (4, 4):
raise ValueError("affine should be a (4, 4) array.")
self.affine = affine
self._voxel_size = self._get_voxel_size(affine)
self.step_size = step_size
self.fixed = fixedstep
self.max_cross = max_cross
self.maxlen = maxlen
self.return_all = return_all
def __iter__(self):
# Make tracks, move them to point space and return
track = self._generate_streamlines()
return utils.move_streamlines(track, self.affine)
def _generate_streamlines(self):
"""A streamline generator"""
N = self.maxlen
dg = self.direction_getter
tc = self.tissue_classifier
ss = self.step_size
fixed = self.fixed
max_cross = self.max_cross
vs = self._voxel_size
# Get inverse transform (lin/offset) for seeds
inv_A = np.linalg.inv(self.affine)
lin = inv_A[:3, :3]
offset = inv_A[:3, 3]
F = np.empty((N + 1, 3), dtype=float)
B = F.copy()
for s in self.seeds:
s =, s) + offset
directions = dg.initial_direction(s)
directions = directions[:max_cross]
for first_step in directions:
stepsF, tissue_class = local_tracker(dg, tc, s, first_step,
vs, F, ss, fixed)
if not (self.return_all or
tissue_class == TissueTypes.ENDPOINT or
tissue_class == TissueTypes.OUTSIDEIMAGE):
first_step = -first_step
stepsB, tissue_class = local_tracker(dg, tc, s, first_step,
vs, B, ss, fixed)
if not (self.return_all or
tissue_class == TissueTypes.ENDPOINT or
tissue_class == TissueTypes.OUTSIDEIMAGE):
if stepsB == 1:
streamline = F[:stepsF].copy()
parts = (B[stepsB-1:0:-1], F[:stepsF])
streamline = np.concatenate(parts, axis=0)
yield streamline