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1 #!/usr/bin/env python
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2 import argparse
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3 import os
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4 import shutil
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5
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6 from dipy.reconst import peaks, shm
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7 from dipy.tracking import utils
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8 from dipy.tracking.eudx import EuDX
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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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11
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12 import matplotlib.pyplot as plt
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13
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14 import nibabel as nib
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15
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16 import numpy as np
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17
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18 parser = argparse.ArgumentParser()
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12
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19 parser.add_argument('--input', dest='input', help='Input dataset')
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20 parser.add_argument('--input_extra_files_path', dest='input_extra_files_path', help='Input dataset extra files paths')
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21 parser.add_argument('--output_superiorfrontal_nifti', dest='output_superiorfrontal_nifti', help='Output superiorfrontal nifti1 dataset')
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22 parser.add_argument('--output_superiorfrontal_nifti_files_path', dest='output_superiorfrontal_nifti_files_path', help='Output superiorfrontal nifti1 extra files path')
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23 parser.add_argument('--output_trackvis_header', dest='output_trackvis_header', help='Output superiorfrontal track visualization header dataset')
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24
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25 args = parser.parse_args()
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26
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27 def move_directory_files(source_dir, destination_dir, copy=False, remove_source_dir=False):
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28 source_directory = os.path.abspath(source_dir)
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29 destination_directory = os.path.abspath(destination_dir)
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30 if not os.path.isdir(destination_directory):
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31 os.makedirs(destination_directory)
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32 for dir_entry in os.listdir(source_directory):
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33 source_entry = os.path.join(source_directory, dir_entry)
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34 if copy:
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35 shutil.copy(source_entry, destination_directory)
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36 else:
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37 shutil.move(source_entry, destination_directory)
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38 if remove_source_dir:
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39 os.rmdir(source_directory)
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40
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41 # Get input data.
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42 # TODO: do not hard-code 'stanford_hardi'
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43 input_dir = 'stanford_hardi'
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44 os.mkdir(input_dir)
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45 for f in os.listdir(args.input_extra_files_path):
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46 shutil.copy(os.path.join(args.input_extra_files_path, f), input_dir)
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47 hard_img, gtab = read_stanford_hardi()
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48 data = hardi_img.get_data()
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49 labels_file = os.path.join(input_dir, "aparc-reduced.nii.gz")
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50 labels_img = nib.load(labels_file)
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51 labels = labels_img.get_data()
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52
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53 white_matter = (labels == 1) | (labels == 2)
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54 csamodel = shm.CsaOdfModel(gtab, 6)
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55 csapeaks = peaks.peaks_from_model(model=csamodel, data=data, sphere=peaks.default_sphere, relative_peak_threshold=.8, min_separation_angle=45, mask=white_matter)
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56 seeds = utils.seeds_from_mask(white_matter, density=2)
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57 streamline_generator = EuDX(csapeaks.peak_values, csapeaks.peak_indices, odf_vertices=peaks.default_sphere.vertices, a_low=.05, step_sz=.5, seeds=seeds)
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58 affine = streamline_generator.affine
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59 streamlines = list(streamline_generator)
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60 cc_slice = labels == 2
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61 cc_streamlines = utils.target(streamlines, cc_slice, affine=affine)
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62 cc_streamlines = list(cc_streamlines)
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63
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64 lr_superiorfrontal_track = grouping[11, 54]
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65 shape = labels.shape
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66 dm = utils.density_map(lr_superiorfrontal_track, shape, affine=affine)
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67 # Save density map
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68 dm_img = nib.Nifti1Image(dm.astype("int16"), hardi_img.affine)
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69 dm_img.to_filename("lr-superiorfrontal-dm.nii")
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70 shutil.move('lr-superiorfrontal-dm.nii', args.output_superiorfrontal_nifti)
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71 move_directory_files(input_dir, args.output_superiorfrontal_nifti_files_path)
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72
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73 # Make a trackvis header so we can save streamlines
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74 voxel_size = labels_img.header.get_zooms()
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75 trackvis_header = nib.trackvis.empty_header()
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76 trackvis_header['voxel_size'] = voxel_size
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77 trackvis_header['dim'] = shape
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78 trackvis_header['voxel_order'] = "RAS"
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79 # Move streamlines to "trackvis space"
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80 trackvis_point_space = utils.affine_for_trackvis(voxel_size)
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81 lr_sf_trk = utils.move_streamlines(lr_superiorfrontal_track, trackvis_point_space, input_space=affine)
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82 lr_sf_trk = list(lr_sf_trk)
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83 # Save streamlines
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84 for_save = [(sl, None, None) for sl in lr_sf_trk]
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85 nib.trackvis.write("lr-superiorfrontal.trk", for_save, trackvis_header)
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86 shutil.move('lr-superiorfrontal.trk', args.output_trackvis_header)
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