def test_check_images():
data = np.zeros((91, 91, 60))
affine = np.eye(4)
affine[:, 3] = np.ones(4)
file1 = '/tmp/file1.nii'
file2 = '/tmp/file2.nii'
file3 = '/tmp/file3.nii'
nibabel.Nifti1Image(data, affine).to_filename(file1)
nibabel.Nifti1Image(data, affine * 3).to_filename(file2)
nibabel.Nifti1Image(data + 1, affine * 3).to_filename(file3)
_utils.check_images(file1, file1)
assert_raises(ValueError, _utils.check_images, file1, file2)
assert_raises(ValueError, _utils.check_images, file1, file3)
def test_check_images():
data = np.zeros((91, 91, 60))
affine = np.eye(4)
affine[:, 3] = np.ones(4)
file1 = '/tmp/file1.nii'
file2 = '/tmp/file2.nii'
file3 = '/tmp/file3.nii'
nibabel.Nifti1Image(data, affine).to_filename(file1)
nibabel.Nifti1Image(data, affine * 3).to_filename(file2)
nibabel.Nifti1Image(data + 1, affine * 3).to_filename(file3)
_utils.check_images(file1, file1)
assert_raises(ValueError, _utils.check_images, file1, file2)
assert_raises(ValueError, _utils.check_images, file1, file3)
def apply_mask(in_file, mask_file, mask_value=np.nan):
"""Masks input with a binary mask_file.
"""
# Load images
image = nibabel.load(in_file)
data = image.get_data()
mask_image = nibabel.load(mask_file)
mask_data = mask_image.get_data()
# Check shapes and affines
check_images(in_file, mask_file)
# Compute the masked image
data[mask_data == 0] = mask_value
out_image = nibabel.Nifti1Image(data, image.get_affine(),
image.get_header())
out_file = add_prefix('masked_', in_file)
nibabel.save(out_image, out_file)
return out_file
def apply_mask(in_file, mask_file, mask_value=np.nan, out_file=None):
"""Masks input with a binary mask_file.
Parameters
----------
in_file : str
Path to the 3D image
mask_file : str
Path to the binary mask image
mask_value : float, optional
Value to allocate to masked voxels.
out_file : str or None, optional
Path to the masked image
Returns
-------
out_file : str
Path to the masked image
"""
# Load images
image = nibabel.load(in_file)
data = image.get_data()
mask_image = nibabel.load(mask_file)
mask_data = mask_image.get_data()
# Check shapes and affines
check_images(in_file, mask_file)
# Compute the masked image
data[mask_data == 0] = mask_value
out_image = nibabel.Nifti1Image(data, image.get_affine(),
image.get_header())
if out_file is None:
out_file = _add_prefix('masked_', in_file)
nibabel.save(out_image, out_file)
return out_file
