def compute_statistic(path_user, path_refs, path_dataset=None, path_visu=None):
""" aggregate statistics over all his annotations
:param str path_user: path to user annotation
:param [str] path_refs: path to annotation of other users
:param str path_dataset: path to image dataset
:param str path_visu: path for visualisation (root)
:return [{}]: list of stat. dictionaries
"""
assert path_user and path_refs, 'missing user or reference annotation'
lnds_user, _ = create_consensus_landmarks([path_user])
lnds_refs, _ = create_consensus_landmarks(path_refs)
list_stats = []
name_set, name_user_scale = path_user.split(os.sep)[-2:]
for csv_name in lnds_user:
if csv_name not in lnds_refs:
continue
im_size = find_image_full_size(path_dataset, name_set,
os.path.splitext(csv_name)[0])
d_stat = compute_landmarks_statistic(lnds_refs[csv_name],
lnds_user[csv_name],
use_affine=False,
im_size=im_size)
d_stat.update({
'name_image_set': name_set,
'name_user': name_user_scale,
'landmarks': csv_name
})
list_stats.append(d_stat)
if path_visu is not None and os.path.isdir(path_visu):
img_name = os.path.splitext(csv_name)[0]
visual_coannotation(lnds_user[csv_name], lnds_refs[csv_name],
path_dataset, path_user, img_name, path_visu)
return list_stats
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}