def resample_to_average(tex_path, subject, side, verbose=False):
"""Resample a given texture to fsaverage
Parameters
==========
tex_path: string, path of the input texture
subject: string, subject id in the freesurfer database
side: string, on of ['left', 'right']
verbose: boolean, the verbosity mode
Returns
=======
resmapled: string, path of the result
"""
resampled = op.join(op.dirname(tex_path),
op.basename(tex_path)[:-4] + '_resampled.gii')
# convert the input to .mgz format
mgz = tex_path[:-4] + '.mgz'
tex = load_texture(tex_path)[np.newaxis, np.newaxis].T
mghformat.save(Nifti1Image(tex, np.eye(4)), mgz)
# run the resmapling using freesurfer tools
fs_comment = commands.getoutput(
'mri_surf2surf --srcsubject %s --srcsurfval %s --trgsubject ico --trgsurfval %s --hemi %sh --trgicoorder 7' % (
subject, mgz, resampled, side[0]))
if verbose:
print fs_comment
return resampled
def resample_to_subject(tex_path, subject, side, output_path, verbose=False):
"""Resample a given texture from fsaverage to subject space
Parameters
==========
tex_path: string, path of the input texture
subject: string, subject id in the freesurfer database
side: string, on of ['left', 'right']
outoput_path: string
verbose: boolean, the verbosity mode
"""
# convert the input to .mgz format
mgz = tex_path[:-4] + '.mgz'
tex = load_texture(tex_path)[np.newaxis, np.newaxis].T
mghformat.save(Nifti1Image(tex, np.eye(4)), mgz)
fs_comment = commands.getoutput(
'mri_surf2surf --trgsubject %s --trgsurfval %s --srcsubject ico --srcsurfval %s --hemi %sh --srcicoorder 7' % (
subject, output_path, mgz, side[0]))
if verbose:
print fs_comment
return output_path
imgs = np.expand_dims(imgs, axis=3)
return imgs
def preprocess_squeeze(imgs):
imgs = np.squeeze(imgs, axis=3)
return imgs
number_of_files = len(png_files)
image_rows = int(256)
image_cols = int(256)
image_depth = 5
print(' ---------------- image reconstruction in progress -----------------')
imgs_temp = np.ndarray((number_of_files, image_rows, image_cols),
dtype=np.uint8)
for i in range(0, number_of_files):
img = ((imread("mask/" + png_files[i])))
img = img.astype(np.uint8)
img = np.array([img])
imgs_temp[i] = img
imgs_temp = np.around(imgs_temp, decimals=0)
imgs_temp = (imgs_temp * 255).astype(np.uint8)
affine = np.diag([1, 2, 2, 1])
array_img = nib.Nifti1Image(imgs_temp, affine)
print(' ---------------- image reconstructed -----------------')
array_header = array_img.header
mgh.save(scaled_img, "img.mgz")
print(' ---------------- image saved to local drive -----------------')
