def get_centreslice(image_in, ids, dim='z', ch=0):
"""Return an image's centreslice for a dimension."""
if isinstance(image_in, Image):
im = image_in
else:
im = Image('{}/{}'.format(image_in, ids))
try:
im.load(load_data=False)
except KeyError:
print('dataset {} not found'.format(ids))
return None
if len(im.dims) > 3:
ch_idx = im.axlab.index('c')
im.slices[ch_idx] = slice(ch, ch + 1, 1)
dim_idx = im.axlab.index(dim)
cslc = int(im.dims[dim_idx] / 2)
slcs = [slc for slc in im.slices]
im.slices[dim_idx] = slice(cslc, cslc + 1, 1)
data = im.slice_dataset()
im.slices = slcs
return data
def extract_mean(im, dim='c', keep_dtype=True, output='', report={}):
"""Calculate mean over channels."""
ods = 'mean'
im.load(load_data=False)
props = im.get_props()
if len(im.dims) > 4:
props = im.squeeze_props(props, dim=4)
if len(im.dims) > 3:
props = im.squeeze_props(props, dim=3)
mo = Image(output.format(ods), **props)
mo.create()
zdim = im.axlab.index('z')
if im.chunks is not None:
nslcs = im.chunks[zdim]
else:
nslsc = 8
slc_thrs = []
for zstart in range(0, im.dims[zdim], nslcs):
zstop = min(im.dims[zdim], zstart + nslcs)
im.slices[zdim] = mo.slices[zdim] = slice(zstart, zstop, None)
data_mean = np.mean(im.slice_dataset(), axis=im.axlab.index(dim))
if keep_dtype:
data_mean = data_mean.astype(im.dtype)
mo.write(data_mean)
slc_thrs += list(np.median(np.reshape(data_mean, [data_mean.shape[0], -1]), axis=1))
mo.slices = None
mo.set_slices()
im.close()
c_slcs = {dim: get_centreslice(mo, '', dim) for dim in 'zyx'}
report['centreslices'][ods] = c_slcs
return mo, report, slc_thrs
def apply_bias_field_full(image_in,
bias_in,
dsfacs=[1, 64, 64, 1],
in_place=False,
write_to_single_file=False,
blocksize_xy=1280,
outputpath='',
channel=None):
"""single-core in ~200 blocks"""
perm = 'r+' if in_place else 'r'
im = Image(image_in, permission=perm)
im.load(load_data=False)
bf = Image(bias_in, permission='r')
bf.load(load_data=False)
if channel is not None:
im.slices[3] = slice(channel, channel + 1)
if write_to_single_file: # assuming single-channel copied file here
mo = Image(outputpath)
mo.load()
mo.slices[3] = slice(0, 1, 1)
mpi = wmeMPI(usempi=False)
mpi_nm = wmeMPI(usempi=False)
if blocksize_xy:
blocksize = [im.dims[0], blocksize_xy, blocksize_xy, 1, 1]
blockmargin = [0, im.chunks[1], im.chunks[2], 0, 0]
else:
blocksize = im.dims[:3] + [1, 1]
blockmargin = [0] * len(im.dims)
mpi.set_blocks(im, blocksize, blockmargin)
mpi_nm.set_blocks(im, blocksize)
mpi.scatter_series()
for i in mpi.series:
print(i)
block = mpi.blocks[i]
data_shape = list(im.slices2shape(block['slices']))
block_nm = mpi_nm.blocks[i]
it = zip(block['slices'], block_nm['slices'], blocksize, data_shape)
data_shape = list(im.slices2shape(block_nm['slices']))
data_slices = []
for b_slc, n_slc, bs, ds in it:
m_start = n_slc.start - b_slc.start
m_stop = m_start + bs
m_stop = min(m_stop, ds)
data_slices.append(slice(m_start, m_stop, None))
data_slices[3] = block['slices'][3]
data_shape = list(im.slices2shape(data_slices))
# get the fullres image block
im.slices = block['slices']
data = im.slice_dataset().astype('float')
# get the upsampled bias field
bias = get_bias_field_block(bf, im.slices, data.shape)
data /= bias
data = np.nan_to_num(data, copy=False)
if in_place:
im.slices = block_nm['slices']
data = data[tuple(data_slices[:3])].astype(im.dtype)
im.write(data)
elif write_to_single_file:
mo.slices = block_nm['slices']
mo.slices[3] = slice(0, 1, 1)
data = data[tuple(data_slices[:3])].astype(mo.dtype)
mo.write(data)
else:
props = im.get_props()
if len(im.dims) > 4:
props = im.squeeze_props(props, dim=4)
if len(im.dims) > 3:
props = im.squeeze_props(props, dim=3)
props['axlab'] = 'zyx' # FIXME: axlab return as string-list
props['shape'] = bias.shape
props['slices'] = None
props['dtype'] = bias.dtype
mo = Image(block['path'], **props) # FIXME: needs channel
mo.create(comm=mpi.comm)
mo.slices = None
mo.set_slices()
mo.write(data=bias)
mo.close()
im.close()
bf.close()
