def read_image(im_info, ids='segm/labels_memb_del', imtype='Label'):
""""Read a h5 dataset as Image object."""
fname = '{}_{}'.format(im_info['base'], im_info['postfix'])
fstem = os.path.join(im_info['datadir'], fname)
if imtype == 'Label':
im = LabelImage('{}.h5/{}'.format(fstem, ids))
elif imtype == 'Mask':
im = MaskImage('{}.h5/{}'.format(fstem, ids))
else:
im = Image('{}.h5/{}'.format(fstem, ids))
im.load(load_data=False)
if imtype == 'Label':
im.set_maxlabel()
return im
def write(out, outstem, postfix, ref_im, imtype='Image'):
"""Write an image to disk."""
outstem = outstem or gen_outpath(ref_im, '')
outpath = '{}{}'.format(outstem, postfix)
props = ref_im.get_props()
props['dtype'] = out.dtype
if imtype == 'Label':
mo = LabelImage(outpath, **props)
elif imtype == 'Mask':
mo = MaskImage(outpath, **props)
else:
mo = Image(outpath, **props)
mo.create()
mo.write(out)
if imtype == 'Label':
mo.set_maxlabel()
mo.ds.attrs.create('maxlabel', mo.maxlabel, dtype='uint32')
return mo
def csv_to_im(
image_in,
csv_path,
labelkey='label',
key='dapi',
name='',
maxlabel=0,
normalize=False,
scale_uint16=False,
replace_nan=False,
channel=-1,
outpath='',
):
"""Write segment backprojection."""
if isinstance(image_in, Image):
labels = image_in
else:
labels = LabelImage(image_in)
labels.load(load_data=False)
if not maxlabel:
labels.set_maxlabel()
maxlabel = labels.maxlabel
if csv_path.endswith('.csv'):
df = pd.read_csv(csv_path)
df = df.astype({labelkey: int})
elif csv_path.endswith('.h5ad'):
import scanpy as sc
adata = sc.read(csv_path)
if not csv_path.endswith('_nofilter.h5ad'):
adata.X = adata.raw.X
df = adata.obs[labelkey].astype(int)
df = pd.concat([df, adata[:, key].to_df()], axis=1)
# for key in keys: # TODO
fw = np.zeros(maxlabel + 1, dtype='float')
for index, row in df.iterrows():
fw[int(row[labelkey])] = row[key]
if replace_nan:
fw = np.nan_to_num(fw)
if normalize:
def normalize_data(data):
"""Normalize data between 0 and 1."""
data = data.astype('float64')
datamin = np.amin(data)
datamax = np.amax(data)
data -= datamin
data *= 1 / (datamax - datamin)
return data, [datamin, datamax]
fw_n, fw_minmax = normalize_data(fw)
fw_n *= 65535
fw = fw_n
elif scale_uint16: # for e.g. pseudotime / FA / etc / any [0, 1] vars
fw *= 65535
out = labels.forward_map(list(fw))
if outpath.endswith('.ims'):
mo = Image(outpath, permission='r+')
mo.load(load_data=False)
if channel >= 0 and channel < mo.dims[3]:
ch = channel
else:
mo.create()
ch = mo.dims[3] - 1
mo.slices[3] = slice(ch, ch + 1, 1)
mo.write(out.astype(mo.dtype)) # FIXME: >65535 wraps around
cpath = 'DataSetInfo/Channel {}'.format(ch)
name = name or key
mo.file[cpath].attrs['Name'] = np.array([c for c in name], dtype='|S1')
mo.close()
elif outpath.endswith('.nii.gz'):
props = labels.get_props()
if not labels.path.endswith('.nii.gz'):
props = transpose_props(props, outlayout='xyz')
out = out.transpose()
mo = write_output(outpath, out, props)
else:
outpath = outpath or gen_outpath(labels, key)
mo = write_output(outpath, out, labels.get_props())
return mo