Mercurial > repos > greg > linear_fascile_evaluation

comparison linear_fascile_evaluation.py @ 10:2de70534993d draft

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author greg
date Thu, 30 Nov 2017 11:28:22 -0500
parents eb03934e044f
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9:6ad10978f14d 10:2de70534993d
1 #!/usr/bin/env python 1 #!/usr/bin/env python
2 import argparse 2 import argparse
3 import os
3 import shutil 4 import shutil
4 5
5 import dipy.core.optimize as opt 6 import dipy.core.optimize as opt
6 import dipy.tracking.life as life 7 import dipy.tracking.life as life
7 from dipy.data import fetch_stanford_t1, read_stanford_labels, read_stanford_t1 8 from dipy.data import fetch_stanford_t1, read_stanford_labels, read_stanford_t1
16 import nibabel as nib 17 import nibabel as nib
17 18
18 import numpy as np 19 import numpy as np
19 20
20 parser = argparse.ArgumentParser() 21 parser = argparse.ArgumentParser()
21 parser.add_argument('--input', dest='input', help='Track Visualization Header dataset') 22 parser.add_argument('--input_nifti1', dest='input_nifti1', help='Input nifti1 dataset')
23 parser.add_argument('--input_nifti1_files_path', dest='input_nifti1_files_path', help='Input nifti1 extra files path')
24 parser.add_argument('--input_nifti2', dest='input_nifti2', help='Input nifti2 dataset')
25 parser.add_argument('--input_nifti2_files_path', dest='input_nifti2_files_path', help='Input nifti2 extra files path')
26 parser.add_argument('--input_trackvis', dest='input_trackvis', help='Track Visualization Header dataset')
22 parser.add_argument('--output_life_candidates', dest='output_life_candidates', help='Output life candidates') 27 parser.add_argument('--output_life_candidates', dest='output_life_candidates', help='Output life candidates')
23 28
24 args = parser.parse_args() 29 args = parser.parse_args()
30
31 # Get input data.
32 # TODO: do not hard-code 'stanford_hardi'
33 input_dir = 'stanford_hardi'
34 os.mkdir(input_dir)
35 # Copy the dRMI dataset (stanford_t1) files.
36 for f in os.listdir(args.input_nifti1_files_path):
37 shutil.copy(os.path.join(args.input_nifti1_files_path, f), input_dir)
38 # Copy the dRMI dataset and label map (stanford_hardi) files.
39 for f in os.listdir(args.input_nifti2_files_path):
40 shutil.copy(os.path.join(args.input_nifti2_files_path, f), input_dir)
25 41
26 # We'll need to know where the corpus callosum is from these variables. 42 # We'll need to know where the corpus callosum is from these variables.
27 hardi_img, gtab, labels_img = read_stanford_labels() 43 hardi_img, gtab, labels_img = read_stanford_labels()
28 labels = labels_img.get_data() 44 labels = labels_img.get_data()
29 cc_slice = labels == 2 45 cc_slice = labels == 2
30 fetch_stanford_t1()
31 t1 = read_stanford_t1() 46 t1 = read_stanford_t1()
32 t1_data = t1.get_data() 47 t1_data = t1.get_data()
33 data = hardi_img.get_data() 48 data = hardi_img.get_data()
34 49
35 # Read the candidates from file in voxel space: 50 # Read the candidates from file in voxel space:
36 candidate_sl = [s[0] for s in nib.trackvis.read(args.input, points_space='voxel')[0]] 51 candidate_sl = [s[0] for s in nib.trackvis.read(args.input_trackvis, points_space='voxel')[0]]
37 # Visualize the initial candidate group of streamlines 52 # Visualize the initial candidate group of streamlines
38 # in 3D, relative to the anatomical structure of this brain. 53 # in 3D, relative to the anatomical structure of this brain.
39 candidate_streamlines_actor = fvtk.streamtube(candidate_sl, line_colors(candidate_sl)) 54 candidate_streamlines_actor = fvtk.streamtube(candidate_sl, line_colors(candidate_sl))
40 cc_ROI_actor = fvtk.contour(cc_slice, levels=[1], colors=[(1., 1., 0.)], opacities=[1.]) 55 cc_ROI_actor = fvtk.contour(cc_slice, levels=[1], colors=[(1., 1., 0.)], opacities=[1.])
41 vol_actor = fvtk.slicer(t1_data) 56 vol_actor = fvtk.slicer(t1_data)