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1 #!/usr/bin/env python
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2 import argparse
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10
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3 import os
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3
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4 import shutil
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5
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6 import dipy.core.optimize as opt
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7 import dipy.tracking.life as life
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3
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8 from dipy.data import fetch_stanford_t1, read_stanford_labels, read_stanford_t1
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9 from dipy.viz import fvtk
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10 from dipy.viz.colormap import line_colors
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3
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11
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12 import matplotlib
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13 import matplotlib.pyplot as plt
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14
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15 from mpl_toolkits.axes_grid1 import AxesGrid
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3
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16
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17 import nibabel as nib
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18
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19 import numpy as np
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20
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21 parser = argparse.ArgumentParser()
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10
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22 parser.add_argument('--input_nifti1', dest='input_nifti1', help='Input nifti1 dataset')
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23 parser.add_argument('--input_nifti1_files_path', dest='input_nifti1_files_path', help='Input nifti1 extra files path')
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24 parser.add_argument('--input_nifti2', dest='input_nifti2', help='Input nifti2 dataset')
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25 parser.add_argument('--input_nifti2_files_path', dest='input_nifti2_files_path', help='Input nifti2 extra files path')
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26 parser.add_argument('--input_trackvis', dest='input_trackvis', help='Track Visualization Header dataset')
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27 parser.add_argument('--output_life_candidates', dest='output_life_candidates', help='Output life candidates')
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28
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29 args = parser.parse_args()
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30
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10
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31 # Get input data.
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32 # TODO: do not hard-code 'stanford_hardi'
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33 input_dir = 'stanford_hardi'
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34 os.mkdir(input_dir)
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35 # Copy the dRMI dataset (stanford_t1) files.
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36 for f in os.listdir(args.input_nifti1_files_path):
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37 shutil.copy(os.path.join(args.input_nifti1_files_path, f), input_dir)
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38 # Copy the dRMI dataset and label map (stanford_hardi) files.
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39 for f in os.listdir(args.input_nifti2_files_path):
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40 shutil.copy(os.path.join(args.input_nifti2_files_path, f), input_dir)
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41
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1
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42 # We'll need to know where the corpus callosum is from these variables.
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43 hardi_img, gtab, labels_img = read_stanford_labels()
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44 labels = labels_img.get_data()
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45 cc_slice = labels == 2
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46 t1 = read_stanford_t1()
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47 t1_data = t1.get_data()
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48 data = hardi_img.get_data()
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49
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50 # Read the candidates from file in voxel space:
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51 candidate_sl = [s[0] for s in nib.trackvis.read(args.input_trackvis, points_space='voxel')[0]]
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52 # Visualize the initial candidate group of streamlines
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53 # in 3D, relative to the anatomical structure of this brain.
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54 candidate_streamlines_actor = fvtk.streamtube(candidate_sl, line_colors(candidate_sl))
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55 cc_ROI_actor = fvtk.contour(cc_slice, levels=[1], colors=[(1., 1., 0.)], opacities=[1.])
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56 vol_actor = fvtk.slicer(t1_data)
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57 vol_actor.display(40, None, None)
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58 vol_actor2 = vol_actor.copy()
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59 vol_actor2.display(None, None, 35)
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60 # Add display objects to canvas.
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61 ren = fvtk.ren()
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62 fvtk.add(ren, candidate_streamlines_actor)
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63 fvtk.add(ren, cc_ROI_actor)
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64 fvtk.add(ren, vol_actor)
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65 fvtk.add(ren, vol_actor2)
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3
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66 fvtk.record(ren, n_frames=1, out_path="life_candidates.png", size=(800, 800))
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67 shutil.move("life_candidates.png", args.output_life_candidates)
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