def test_choose_create_out_dir():
with InTemporaryDirectory() as tmp_dir:
root_path = join(tmp_dir, 'fake_file.nii')
result_path = choose_create_out_dir('', root_path)
npt.assert_equal(result_path, tmp_dir)
rel_out_dir = 'test_dir'
result_path = choose_create_out_dir(rel_out_dir, root_path)
npt.assert_equal(result_path, join(tmp_dir, rel_out_dir))
result_path = choose_create_out_dir(tmp_dir, '')
npt.assert_equal(result_path, tmp_dir)
def median_otsu_flow(input_files,
out_dir='',
save_masked=False,
median_radius=4,
numpass=4,
autocrop=False,
vol_idx=None,
dilate=None):
""" Workflow wrapping the median_otsu segmentation method.
It applies median_otsu segmentation on each file found by 'globing'
``input_files`` and saves the results in a directory specified by
``out_dir``.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to process
multiple inputs at once.
out_dir : string, optional
Output directory (default input file directory)
save_masked : bool
Save mask
median_radius : int, optional
Radius (in voxels) of the applied median filter(default 4)
numpass : int, optional
Number of pass of the median filter (default 4)
autocrop : bool, optional
If True, the masked input_volumes will also be cropped using the
bounding box defined by the masked data. Should be on if DWI is
upsampled to 1x1x1 resolution. (default False)
vol_idx : string, optional
1D array representing indices of ``axis=3`` of a 4D `input_volume`
'None' (the default) corresponds to ``(0,)`` (assumes first volume in
4D array)
dilate : string, optional
number of iterations for binary dilation (default 'None')
Outputs
-------
mask : Nifti File
Binary volume representing the computed mask.
masked : Nifti File
Volume representing the masked input. This file is saved
save_masked is True.
"""
for fpath in glob(input_files):
print('')
print('Applying median_otsu segmentation on {0}'.format(fpath))
img = nib.load(fpath)
volume = img.get_data()
masked, mask = median_otsu(volume, median_radius, numpass, autocrop,
vol_idx, dilate)
fname, ext = splitext(basename(fpath))
if fname.endswith('.nii'):
fname, _ = splitext(fname)
ext = '.nii.gz'
mask_fname = fname + '_mask' + ext
out_dir_path = choose_create_out_dir(out_dir, fpath)
mask_img = nib.Nifti1Image(mask.astype(np.float32), img.get_affine())
mask_out_path = join(out_dir_path, mask_fname)
mask_img.to_filename(mask_out_path)
print('Mask saved as {0}'.format(mask_out_path))
if save_masked:
masked_fname = fname + '_bet' + ext
masked_img = nib.Nifti1Image(masked, img.get_affine(),
img.get_header())
masked_out_path = join(out_dir_path, masked_fname)
masked_img.to_filename(masked_out_path)
print('Masked volume saved as {0}'.format(masked_out_path))
def median_otsu_flow(input_files, out_dir='', save_masked=False,
median_radius=4, numpass=4, autocrop=False,
vol_idx=None, dilate=None):
""" Workflow wrapping the median_otsu segmentation method.
It applies median_otsu segmentation on each file found by 'globing'
``input_files`` and saves the results in a directory specified by
``out_dir``.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to process
multiple inputs at once.
out_dir : string, optional
Output directory (default input file directory)
save_masked : bool
Save mask
median_radius : int, optional
Radius (in voxels) of the applied median filter(default 4)
numpass : int, optional
Number of pass of the median filter (default 4)
autocrop : bool, optional
If True, the masked input_volumes will also be cropped using the
bounding box defined by the masked data. Should be on if DWI is
upsampled to 1x1x1 resolution. (default False)
vol_idx : string, optional
1D array representing indices of ``axis=3`` of a 4D `input_volume`
'None' (the default) corresponds to ``(0,)`` (assumes first volume in
4D array)
dilate : string, optional
number of iterations for binary dilation (default 'None')
Outputs
-------
mask : Nifti File
Binary volume representing the computed mask.
masked : Nifti File
Volume representing the masked input. This file is saved
save_masked is True.
"""
for fpath in glob(input_files):
print('')
print('Applying median_otsu segmentation on {0}'.format(fpath))
img = nib.load(fpath)
volume = img.get_data()
masked, mask = median_otsu(volume, median_radius,
numpass, autocrop,
vol_idx, dilate)
fname, ext = splitext(basename(fpath))
if fname.endswith('.nii'):
fname, _ = splitext(fname)
ext = '.nii.gz'
mask_fname = fname + '_mask' + ext
out_dir_path = choose_create_out_dir(out_dir, fpath)
mask_img = nib.Nifti1Image(mask.astype(np.float32), img.get_affine())
mask_out_path = join(out_dir_path, mask_fname)
mask_img.to_filename(mask_out_path)
print('Mask saved as {0}'.format(mask_out_path))
if save_masked:
masked_fname = fname + '_bet' + ext
masked_img = nib.Nifti1Image(
masked, img.get_affine(), img.get_header())
masked_out_path = join(out_dir_path, masked_fname)
masked_img.to_filename(masked_out_path)
print('Masked volume saved as {0}'.format(masked_out_path))