def main(params):
""" PIPELINE for new detections
:param dict params:
"""
params['path_expt'] = os.path.join(
params['path_output'], run_detect.FOLDER_EXPERIMENT % params['name'])
tl_expt.set_experiment_logger(params['path_expt'])
# tl_expt.create_subfolders(params['path_expt'], LIST_SUBDIRS)
logging.info(tl_expt.string_dict(params, desc='PARAMETERS'))
path_csv = os.path.join(params['path_expt'], NAME_CSV_TRIPLES)
df_paths = run_detect.get_csv_triplets(params['path_list'], path_csv,
params['path_images'],
params['path_segms'],
params['path_centers'],
FORCE_RELOAD)
df_eval = df_paths.copy(deep=True)
for stage in params['stages']:
df_eval = evaluate_detection_stage(df_eval, stage,
params['path_infofile'],
params['path_expt'],
params['nb_workers'])
if not df_eval.empty and 'image' in df_eval.columns:
df_eval.set_index('image', inplace=True)
df_eval.to_csv(os.path.join(params['path_expt'],
NAME_CSV_TRIPLES_STAT))
gc.collect()
time.sleep(1)
if not df_eval.empty:
df_stat = df_eval.describe().transpose()
logging.info('STATISTIC: \n %r', df_stat)
df_stat.to_csv(os.path.join(params['path_expt'], NAME_CSV_STATISTIC))
def main(params):
""" PIPELINE for new detections
:param {str: str} paths:
:param int nb_jobs:
"""
logging.info('running...')
# run_train.check_pathes_patterns(paths)
tl_expt.set_experiment_logger(params['path_output'])
# tl_expt.create_subfolders(params['path_output'], LIST_SUBDIRS)
logging.info(tl_expt.string_dict(params, desc='PARAMETERS'))
path_csv = os.path.join(params['path_output'], NAME_CSV_TRIPLES)
df_paths = run_detect.get_csv_triplets(params['path_list'], path_csv,
params['path_images'],
params['path_segms'],
params['path_centers'],
FORCE_RELOAD)
df_eval = df_paths.copy(deep=True)
for stage in params['stages']:
df_eval = evaluate_detection_stage(df_eval, stage,
params['path_infofile'],
params['path_output'],
params['nb_jobs'])
if len(df_eval) > 0 and 'image' in df_eval.columns:
df_eval.set_index('image', inplace=True)
df_eval.to_csv(
os.path.join(params['path_output'], NAME_CSV_TRIPLES_STAT))
gc.collect()
time.sleep(1)
if len(df_eval) > 0:
df_stat = df_eval.describe().transpose()
logging.info('STATISTIC: \n %s', repr(df_stat))
df_stat.to_csv(os.path.join(params['path_output'], NAME_CSV_STATISTIC))
logging.info('DONE')
