def visual_coannotation(lnds_user, lnds_refs, path_dataset, path_user,
img_name, path_visu):
""" visualise the co-annotation
show consensus annotation and use annotation
:param DF lnds_user: loaded DataFrame
:param DF lnds_refs: loaded DataFrame
:param path_dataset: path to the image dataset (root)
:param name_set: name of the image set
:param name_user_scale: annotation folder containing user name and used scale
:param img_name: particular image/annotation/stain name
:param str|None path_visu: path to output
:return str: figure path
"""
name_set, name_user_scale = path_user.split(os.sep)[-2:]
user, scale = parse_path_user_scale(name_user_scale)
folder_scale = TEMPLATE_FOLDER_SCALE % scale
image = None
if path_dataset is not None and os.path.isdir(path_dataset):
path_dir = os.path.join(path_dataset, name_set, folder_scale)
paths_image = find_images(path_dir, img_name)
image = load_image(paths_image[0]) if paths_image else None
lnds_user = lnds_user * (scale / 100.)
lnds_refs = lnds_refs * (scale / 100.)
fig = figure_image_landmarks(lnds_refs, image, lnds_user, lnds2_name=user)
fig_name = NAME_FIGURE_COANNOT % img_name
path_dir = os.path.join(path_visu, name_set, name_user_scale)
create_folder_path(path_dir)
path_fig = os.path.join(path_dir, fig_name)
fig.savefig(path_fig)
plt.close(fig)
return path_fig
def scale_set_landmarks(path_set, scales=SCALES):
""" scale given set with landmarks to particular scales
:param str path_set: path to image/landmark set
:param [int] scales: selected output scales
:return {str: int}: collection of lengths
"""
logging.debug('> processing: %s', path_set)
path_scale100 = os.path.join(path_set, TEMPLATE_FOLDER_SCALE % 100)
if not os.path.isdir(path_scale100):
logging.error('missing base scale 100pc in "%s"', path_scale100)
return
list_csv = glob.glob(os.path.join(path_scale100, '*.csv'))
logging.debug('>> found landmarks: %i', len(list_csv))
dict_lnds = {
os.path.basename(p): pd.read_csv(p, index_col=0)
for p in list_csv
}
set_scales = {}
for sc in (sc for sc in scales if sc not in [100]): # drop the base scale
path_scale = os.path.join(path_set, TEMPLATE_FOLDER_SCALE % sc)
create_folder_path(path_scale)
for name in dict_lnds:
df_scale = dict_lnds[name] * (sc / 100.)
df_scale.to_csv(os.path.join(path_scale, name))
set_scales[sc] = len(dict_lnds)
dict_lens = {os.path.basename(path_set): set_scales}
return dict_lens
def generate_consensus_landmarks(path_set, path_dataset):
""" generate consensus landmarks for a particular image/landmark set
:param str path_set: path to the set with annotations
:param str path_dataset: output dataset path (root)
:return {str: int}:
"""
path_annots = list_sub_folders(path_set, '*_scale-*pc')
logging.debug('>> found annotations: %i', len(path_annots))
dict_lnds, dict_lens = create_consensus_landmarks(path_annots)
path_scale = os.path.join(path_dataset, os.path.basename(path_set),
TEMPLATE_FOLDER_SCALE % 100)
create_folder_path(path_scale)
for name in dict_lnds:
dict_lnds[name].to_csv(os.path.join(path_scale, name))
return {os.path.basename(path_set): dict_lens}