prefixes = args.prefix
scale = args.scale
scale_min = args.scale_min
scale_max = args.scale_max
verbose = args.verbosity
au.setup_logger(verbose)
#checking number of files processed
nmasks = len(masklst)
nouts = 0
m = 0
for maskf in masklst:
if not scale:
ofname = au.features_str() + get_out_extension(otype)
else:
ofname = au.features_str() + '.' + au.scaled_str(
) + get_out_extension(otype)
if prefixes[m]:
ofname = prefixes[m] + '_' + ofname
oc = len(au.find(os.listdir(outdir), ofname))
nouts += oc
m += 1
if nouts >= nmasks:
au.log.debug('Nothing to do in ' + outdir + '. All files processed.')
return -1
else:
fmt='%s')
np.savetxt(outdir + os.path.sep + au.included_subjlabels_str(),
labels,
fmt='%i')
if excluf:
np.savetxt(outdir + os.path.sep + au.excluded_subjects_str(),
exclusubjs,
fmt='%s')
np.savetxt(outdir + os.path.sep + au.excluded_subjlabels_str(),
exclulabels,
fmt='%i')
#saving the feature matrix and labels in a binary file
filename = set_filename(outdir, prefix + '_' + au.features_str(), otype)
print('Creating ' + filename)
if otype == 'numpybin':
np.save(filename, feats)
elif otype == 'octave':
sio.savemat(filename, {au.feats_str(): feats, au.labels_str(): labels})
elif otype == 'svmperf':
labels[labels == 0] = -1
ae.write_svmperf_dat(filename, dataname, feats, labels)
if excluf:
exclulabels[exclulabels == 0] = -1
exclfilename = set_filename(
prefixes = args.prefix
scale = args.scale
scale_min = args.scale_min
scale_max = args.scale_max
verbose = args.verbosity
au.setup_logger(verbose)
# checking number of files processed
nmasks = len(masklst)
nouts = 0
m = 0
for maskf in masklst:
if not scale:
ofname = au.features_str() + get_out_extension(otype)
else:
ofname = au.features_str() + "." + au.scaled_str() + get_out_extension(otype)
if prefixes[m]:
ofname = prefixes[m] + "_" + ofname
oc = len(au.find(os.listdir(outdir), ofname))
nouts += oc
m += 1
if nouts >= nmasks:
au.log.debug("Nothing to do in " + outdir + ". All files processed.")
return -1
else:
au.log.debug("Processing to output in: " + outdir)
vold = nib.load(s)
vol = vold.get_data()
exclfeats[c,:] = vol[mask > 0]
c += 1
#saving description files
np.savetxt(outdir + os.path.sep + au.included_subjects_str(), subjs, fmt='%s')
np.savetxt(outdir + os.path.sep + au.included_subjlabels_str(), labels, fmt='%i')
if excluf:
np.savetxt(outdir + os.path.sep + au.excluded_subjects_str(), exclusubjs, fmt='%s')
np.savetxt(outdir + os.path.sep + au.excluded_subjlabels_str(), exclulabels, fmt='%i')
#saving the feature matrix and labels in a binary file
filename = set_filename (outdir, prefix + '_' + au.features_str(), otype)
print ('Creating ' + filename)
if otype == 'numpybin':
np.save (filename, feats)
elif otype == 'octave':
sio.savemat (filename, {au.feats_str(): feats, au.labels_str(): labels})
elif otype == 'svmperf':
labels[labels == 0] = -1
ae.write_svmperf_dat(filename, dataname, feats, labels)
if excluf:
exclulabels[exclulabels == 0] = -1
exclfilename = set_filename(outdir, prefix + '_' + au.excluded_str() + au.feats_str(), otype)