def downsample_channel(image_in,
ch,
resolution_level=-1,
dsfacs=[1, 4, 4, 1, 1],
ismask=False,
output='',
report={}):
"""Downsample an image."""
ods = 'data' if not ismask else 'mask'
# return in case no mask provided
if not image_in:
return None, report, ''
if resolution_level != -1 and not ismask: # we should have an Imaris pyramid
image_in = '{}/DataSet/ResolutionLevel {}'.format(
image_in, resolution_level)
# load data
im = Image(image_in, permission='r')
im.load(load_data=False)
props = im.get_props()
if len(im.dims) > 4:
im.slices[im.axlab.index('t')] = slice(0, 1, 1)
props = im.squeeze_props(props, dim=4)
if len(im.dims) > 3:
im.slices[im.axlab.index('c')] = slice(ch, ch + 1, 1)
props = im.squeeze_props(props, dim=3)
data = im.slice_dataset()
im.close()
# downsample
dsfac = tuple(dsfacs[:len(data.shape)])
if not ismask:
data = downscale_local_mean(data, dsfac).astype('float32')
else:
data = block_reduce(data, dsfac, np.max)
# generate output
props['axlab'] = 'zyx' # FIXME: axlab returns as string-list
props['shape'] = data.shape
props['elsize'] = [es * ds for es, ds in zip(im.elsize[:3], dsfac)]
props['slices'] = None
mo = write_data(data, props, output, ods)
# report data
thr = 1000
meds_mask = data < thr
report['medians'][ods] = get_zyx_medians(data, meds_mask)
c_slcs = {dim: get_centreslice(mo, '', dim) for dim in 'zyx'}
report['centreslices'][ods] = c_slcs
return mo, report, meds_mask
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()