def main(path_landmarks,
path_dataset,
path_output,
scales,
nb_jobs=NB_THREADS):
assert path_landmarks != path_output, \
'this folder "%s" cannot be used as output' % path_output
assert path_dataset != path_output, \
'this folder "%s" cannot be used as output' % path_output
coll_dirs, _ = collect_triple_dir([path_landmarks],
path_dataset,
path_output,
scales=scales)
# filter existing
coll_dirs = [
d for d in coll_dirs
if os.path.isdir(d['images']) and os.path.isdir(d['landmarks'])
]
if not coll_dirs:
logging.info('No sub-folders collected.')
return 0
lnds_dirs = sorted([cd['landmarks'] for cd in coll_dirs])
logging.info('Collected %i sub-folder: \n%s', len(coll_dirs),
'\n'.join(lnds_dirs))
counts = list(
wrap_execute_parallel(export_visual_set_scale,
coll_dirs,
nb_jobs=nb_jobs,
desc='visualise'))
logging.info('Performed %i visualisations', sum(counts))
return counts
def main(path_annots,
path_dataset,
path_output,
nb_jobs=NB_THREADS,
visual=False):
coll_dirs, _ = collect_triple_dir([path_annots], '', '', with_user=True)
logging.info('Collected sub-folder: %i', len(coll_dirs))
user_names = sorted(
{parse_path_user_scale(d['landmarks'])[0]
for d in coll_dirs})
logging.info('Found users: %r', user_names)
if len(user_names) < 2:
logging.info('Not enough user annotations.')
_evaluate_user = partial(evaluate_user,
path_annots=path_annots,
path_dataset=path_dataset,
path_out=path_output,
visual=visual)
counts = list(
wrap_execute_parallel(_evaluate_user,
user_names,
nb_jobs=nb_jobs,
desc='evaluate'))
logging.info('Created %i statistics.', sum(counts))
return counts
def main(path_annots,
path_dataset,
path_output,
consensus='mean',
visual=False,
nb_jobs=NB_THREADS):
coll_dirs, _ = collect_triple_dir([path_annots], '', '', with_user=True)
logging.info('Collected sub-folder: %i', len(coll_dirs))
user_names = sorted(
{parse_path_user_scale(d['landmarks'])[0]
for d in coll_dirs})
logging.info('Found users: %r', user_names)
if len(user_names) < 2:
logging.info('Not enough user annotations.')
_evaluate_user = partial(evaluate_user,
path_annots=path_annots,
path_dataset=path_dataset,
path_out=path_output,
tp_consensus=consensus,
visual=visual)
dfs = list(
iterate_mproc_map(_evaluate_user,
user_names,
nb_workers=nb_jobs,
desc='evaluate'))
# aggregate results
df_all = pd.concat(dfs, sort=False)
df_all.to_csv(os.path.join(path_output, 'STATISTIC__partial.csv'))
df_short = pd.DataFrame()
for user, dfg in df_all.groupby('user'):
stat = dict(dfg['rTRE median'].describe().T[['mean', 'std', 'max']])
stat = {'%s [median rTRE]' % k: stat[k] for k in stat if k != 'count'}
stat.update({
'user': user,
'count': len(dfg),
})
df_short = df_short.append(stat, ignore_index=True)
df_short.set_index('user', inplace=True)
logging.info('OVERALL \n%s \n %s' % ('=' * 10, df_short))
df_short.to_csv(os.path.join(path_output, 'STATISTIC__overview.csv'))
logging.info('Created %i statistics.', len(df_all))
return len(df_all)