def main(params):
""" PIPELINE for new detections
:param {str: str} paths:
"""
logging.info('running...')
params = run_train.prepare_experiment_folder(params, FOLDER_EXPERIMENT)
# run_train.check_pathes_patterns(paths)
tl_expt.set_experiment_logger(params['path_expt'])
logging.info('COMPUTER: \n%s', repr(os.uname()))
logging.info(tl_expt.string_dict(params, desc='PARAMETERS'))
tl_expt.create_subfolders(params['path_expt'], LIST_SUBFOLDER)
path_csv = os.path.join(params['path_expt'], NAME_CSV_TRIPLES)
df_paths = get_csv_triplets(params['path_list'], path_csv,
params['path_images'], params['path_segms'],
force_reload=FORCE_RERUN)
dict_classif = seg_clf.load_classifier(params['path_classif'])
params_clf = dict_classif['params']
params_clf.update(params)
logging.info(tl_expt.string_dict(params, desc='UPDATED PARAMETERS'))
# perform on new images
df_stat = pd.DataFrame()
tqdm_bar = tqdm.tqdm(total=len(df_paths))
if params['nb_jobs'] > 1:
wrapper_detection = partial(load_compute_detect_centers, params=params_clf,
path_classif=params['path_classif'],
path_output=params['path_expt'])
mproc_pool = mproc.Pool(params['nb_jobs'])
for dict_center in mproc_pool.imap_unordered(wrapper_detection,
df_paths.iterrows()):
df_stat = df_stat.append(dict_center, ignore_index=True)
df_stat.to_csv(os.path.join(params['path_expt'], NAME_CSV_TRIPLES_TEMP))
tqdm_bar.update()
mproc_pool.close()
mproc_pool.join()
else:
classif = dict_classif['clf_pipeline']
for idx_row in df_paths.iterrows():
dict_center = load_compute_detect_centers(idx_row, params_clf, classif,
path_output=params['path_expt'])
df_stat = df_stat.append(dict_center, ignore_index=True)
df_stat.to_csv(os.path.join(params['path_expt'], NAME_CSV_TRIPLES_TEMP))
tqdm_bar.update()
df_stat.set_index(['image'], inplace=True)
df_stat.to_csv(os.path.join(params['path_expt'], NAME_CSV_TRIPLES))
logging.info('STATISTIC: \n %s', repr(df_stat.describe()))
logging.info('DONE')
def main(params):
""" PIPELINE for new detections
:param {str: str} paths:
"""
logging.info('running...')
params = run_train.prepare_experiment_folder(params, FOLDER_EXPERIMENT)
# run_train.check_pathes_patterns(paths)
tl_expt.set_experiment_logger(params['path_expt'])
logging.info('COMPUTER: \n%s', repr(os.uname()))
logging.info(tl_expt.string_dict(params, desc='PARAMETERS'))
tl_expt.create_subfolders(params['path_expt'], LIST_SUBFOLDER)
path_csv = os.path.join(params['path_expt'], NAME_CSV_TRIPLES)
df_paths = get_csv_triplets(params['path_list'],
path_csv,
params['path_images'],
params['path_segms'],
force_reload=FORCE_RERUN)
dict_classif = seg_clf.load_classifier(params['path_classif'])
params_clf = dict_classif['params']
params_clf.update(params)
logging.info(tl_expt.string_dict(params, desc='UPDATED PARAMETERS'))
# perform on new images
df_stat = pd.DataFrame()
wrapper_detection = partial(load_compute_detect_centers,
params=params_clf,
path_classif=params['path_classif'],
path_output=params['path_expt'])
iterate = tl_expt.WrapExecuteSequence(wrapper_detection,
df_paths.iterrows(),
nb_jobs=params['nb_jobs'])
for dict_center in iterate:
df_stat = df_stat.append(dict_center, ignore_index=True)
df_stat.to_csv(os.path.join(params['path_expt'],
NAME_CSV_TRIPLES_TEMP))
df_stat.set_index(['image'], inplace=True)
df_stat.to_csv(os.path.join(params['path_expt'], NAME_CSV_TRIPLES))
logging.info('STATISTIC: \n %s', repr(df_stat.describe()))
logging.info('DONE')