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Update to include image affine #6

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Update to include image affine #6

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30 changes: 24 additions & 6 deletions run_dipy_cpu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,6 +49,9 @@
import nibabel as nib
from nibabel.orientations import aff2axcodes

import numpy.linalg as npl
from dipy.tracking.streamline import transform_streamlines

# Import custom module
import cuslines.cuslines as cuslines

Expand Down Expand Up @@ -88,6 +91,15 @@ def get_img(ep2_seq):
roi_data = roi.get_data()
mask = mask.get_data()

img_affine = img.affine

fa_threshold = 0.1
min_relative_peak = 0.25
min_peak_spacing = 0.7
min_peak_deg = 45
sm_lambda = 0
seed_density = 5

tenmodel = dti.TensorModel(gtab, fit_method='WLS')
print('Fitting Tensor')
tenfit = tenmodel.fit(data, mask)
Expand All @@ -96,26 +108,26 @@ def get_img(ep2_seq):
FA[np.isnan(FA)] = 0

# Setup tissue_classifier args
tissue_classifier = ThresholdStoppingCriterion(FA, 0.1)
tissue_classifier = ThresholdStoppingCriterion(FA, fa_threshold)
metric_map = np.asarray(FA, 'float64')

# Create seeds for ROI
seed_mask = utils.seeds_from_mask(roi_data, density=3, affine=np.eye(4))
seed_mask = utils.seeds_from_mask(roi_data, density=seed_density, affine=img_affine)

# Setup model
print('slowadcodf')
sh_order = 6
model = OpdtModel(gtab, sh_order=sh_order, min_signal=1)
sh_order = 4
model = OpdtModel(gtab, sh_order=sh_order, smooth=sm_lambda, min_signal=1)

# Setup direction getter args
print('Bootstrap direction getter')
boot_dg = BootDirectionGetter.from_data(data, model, max_angle=60., sphere=small_sphere)
boot_dg = BootDirectionGetter.from_data(data, model, max_angle=60., sphere=small_sphere, sh_order=sh_order, relative_peak_threshold=min_relative_peak, min_separation_angle=min_peak_deg)

print('streamline gen')
global_chunk_size = args.chunk_size
nchunks = (seed_mask.shape[0] + global_chunk_size - 1) // global_chunk_size

streamline_generator = LocalTracking(boot_dg, tissue_classifier, seed_mask, affine=np.eye(4), step_size=.5)
streamline_generator = LocalTracking(boot_dg, tissue_classifier, seed_mask, affine=img_affine, step_size=.5)

t1 = time.time()
streamline_time = 0
Expand All @@ -130,12 +142,18 @@ def get_img(ep2_seq):
seed_mask[idx*global_chunk_size:(idx+1)*global_chunk_size].shape[0],
te-ts))

# Invert streamline affine
inv_affine = npl.inv(img_affine)
streamlines = transform_streamlines(streamlines,inv_affine)


# Save tracklines file
if args.output_prefix:
fname = "{}.{}_{}.trk".format(args.output_prefix, idx+1, nchunks)
ts = time.time()
#save_tractogram(fname, streamlines, img.affine, vox_size=roi.header.get_zooms(), shape=roi_data.shape)
sft = StatefulTractogram(streamlines, args.nifti_file, Space.VOX)
sft.to_rasmm()
save_tractogram(sft, fname)
te = time.time()
print("Saved streamlines to {}, time {} s".format(fname, te-ts))
Expand Down