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1 #!/usr/bin/env python
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2 import argparse
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3
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3 import shutil
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4
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5 import dipy.core.optimize as opt
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6 import dipy.tracking.life as life
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3
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7 from dipy.data import fetch_stanford_t1, read_stanford_labels, read_stanford_t1
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8 from dipy.viz import fvtk
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9 from dipy.viz.colormap import line_colors
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10
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11 import matplotlib
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12 import matplotlib.pyplot as plt
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13
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14 from mpl_toolkits.axes_grid1 import AxesGrid
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15
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16 import nibabel as nib
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17
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18 import numpy as np
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19
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20 parser = argparse.ArgumentParser()
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21 parser.add_argument('--input', dest='input', help='Track Visualization Header dataset')
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22 parser.add_argument('--output_life_candidates', dest='output_life_candidates', help='Output life candidates')
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23
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24 args = parser.parse_args()
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25
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1
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26 # We'll need to know where the corpus callosum is from these variables.
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27 hardi_img, gtab, labels_img = read_stanford_labels()
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28 labels = labels_img.get_data()
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29 cc_slice = labels == 2
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30 fetch_stanford_t1()
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31 t1 = read_stanford_t1()
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32 t1_data = t1.get_data()
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33 data = hardi_img.get_data()
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34
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35 # Read the candidates from file in voxel space:
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36 candidate_sl = [s[0] for s in nib.trackvis.read(args.input, points_space='voxel')[0]]
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37 # Visualize the initial candidate group of streamlines
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38 # in 3D, relative to the anatomical structure of this brain.
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39 candidate_streamlines_actor = fvtk.streamtube(candidate_sl, line_colors(candidate_sl))
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40 cc_ROI_actor = fvtk.contour(cc_slice, levels=[1], colors=[(1., 1., 0.)], opacities=[1.])
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41 vol_actor = fvtk.slicer(t1_data)
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42 vol_actor.display(40, None, None)
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43 vol_actor2 = vol_actor.copy()
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44 vol_actor2.display(None, None, 35)
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45 # Add display objects to canvas.
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46 ren = fvtk.ren()
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47 fvtk.add(ren, candidate_streamlines_actor)
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48 fvtk.add(ren, cc_ROI_actor)
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49 fvtk.add(ren, vol_actor)
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50 fvtk.add(ren, vol_actor2)
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51 fvtk.record(ren, n_frames=1, out_path="life_candidates.png", size=(800, 800))
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52 shutil.move("life_candidates.png", args.output_life_candidates)
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