def update_mask(self, mask, positive_mask=True):
"""
Parameters
----------
mask: NIPY Image
A masking function which may be encoded as masked:=1 or
masked:=0. The coordinate mapping must be identical to the
original input Image (IE, the support of this function must
be the support of the original Image).
positive_mask: {True,False}
by default, the masking function will be taken as masked:=0
"""
# XYZ: CURRENTLY OBLITERATES OLD MASK! IS THIS DESIRABLE?
mdata = np.asarray(mask)
# convert mdata to boolean type negative-mask
if positive_mask:
if mdata.dtype.char is not 'B':
mdata = mdata.astype('B')
mdata = np.logical_not(mdata)
# now, if necessary resample the mask ...
if len(self.__resamp_kws):
m_resamp = ni_api.Image(mdata.astype('d'), mask.coordmap)
m_resamp = vu.resample_to_world_grid(
m_resamp, cval=1, **self.__resamp_kws
)
# ... and set mdata to wherever the mask goes towards 1
mdata = (np.asarray(m_resamp) > 0.5)
print 'new unmasked pts:', mdata.size - mdata.sum()
self.image_arr = np.ma.masked_array(np.ma.getdata(self.image_arr),
mask=mdata)
def __init__(self, image, bbox=None, mask=False,
grid_spacing=None, spatial_axes=None,
**interp_kws):
"""
Creates a new ResampledVolumeSlicer
Parameters
----------
image : a NIPY Image
The image to slice
bbox : iterable (optional)
The {x,y,z} limits of the enrounding volume box. If None, then
slices planes in the natural box of the image. This argument
is useful for overlaying an image onto another image's volume box
mask : bool or ndarray (optional)
A binary mask, with same shape as image, with unmasked points
marked as True (opposite of MaskedArray convention)
grid_spacing : sequence (optional)
New grid spacing for the sliced planes. If None, then the
natural voxel spacing is used.
spatial_axes : sequence (optional)
Normally the image will not be resampled as long as there is a
one-to-one correspondence from array axes to spatial axes. However,
a desired correspondence can be specified here. List in 'x, y, z'
order.
interp_kws : dict
Keyword args for the interpolating machinery.. eg:
* order -- spline order
* mode -- Points outside the boundaries of the input are filled
according to the given mode ('constant', 'nearest',
'reflect' or 'wrap'). Default is 'constant'.
* cval -- fill value if mode is 'constant'
"""
# XYZ: NEED TO BREAK API HERE FOR MASKED ARRAY
xyz_image = ni_api.Image(
image._data,
image.coordmap.reordered_range(xipy_ras)
)
native_spacing = vu.voxel_size(xyz_image.affine)
zoom_grid = grid_spacing is not None and \
(np.array(grid_spacing) != native_spacing).any()
# if no special instructions and image is somewhat aligned,
# don't bother with rotations
aligned = vu.is_spatially_aligned(image.coordmap) and not zoom_grid
self.__resamp_kws = dict()
if aligned:
# if aligned, double check that it is also aligned with
# spatial_axes (if present)
axes = vu.find_spatial_correspondence(image.coordmap)
if spatial_axes and axes != spatial_axes:
# XYZ: IF ARRAY IS ALIGNED IN SOME ORIENTATION, COULD
# RE-ALIGN WITH "spatial_axes" WITH A SIMPLE TRANSFORM
aligned = False
interp_kws['order'] = 0
else:
world_image = xyz_image
if not aligned:
print 'resampling entire Image volume'
self.__resamp_kws.update(interp_kws)
self.__resamp_kws.update(
dict(grid_spacing=grid_spacing, axis_permutation=spatial_axes)
)
world_image = vu.resample_to_world_grid(
image, **self.__resamp_kws
)
self.coordmap = world_image.coordmap
self.image_arr = np.asanyarray(world_image)
self.grid_spacing = vu.voxel_size(world_image.affine)
# take down the final bounding box; this will define the
# field of the overlay plot
self.bbox = vu.world_limits(world_image)
# now find the logical axis to array axis mapping
self._ax_lookup = vu.spatial_axes_lookup(self.coordmap)
w_shape = world_image.shape
# these planes are shaped as if the image_arr were
# sliced along a given axis
self.null_planes = [np.ma.masked_all((w_shape[0], w_shape[1]),'B'),
np.ma.masked_all((w_shape[0], w_shape[2]),'B'),
np.ma.masked_all((w_shape[1], w_shape[2]),'B')]
# finally, update mask if necessary
mask = np.ma.getmask(image._data)
if mask is not np.ma.nomask:
mask = ni_api.Image(mask, image.coordmap)
self.update_mask(mask, positive_mask=False)
