def notify(self, meta):
self.meta_df = meta.data_df
self.write_df_to_csv(self.name,
'LAST_TRUE') # Save new meta when updated
if DrawingParams.draw_accepted:
dict_format = pdu.from_dataframe_to_dict(self.meta_df)
visu.draw_partition(dict_format, int(os.environ['img_w']),
meta.info_name, self.curr_window_dir)
def final_merge_loop(self):
flag = True
check_ = 0
counter = 0
if not self.data_processing.pairs_to_consider:
return
while flag:
counter += 1
break_to_while = False
if not self.data_processing.pairs_to_consider:
break
for index, pair in enumerate(self.data_processing.pairs_to_consider):
self.cur_iter_name = str(counter) + '_' + str(pair)
logging.debug('pair {} '.format(pair))
if break_to_while:
check_ = 0
break
self.accepted = False
self.merge_move(final_merge=True,
pair=pair)
if self.proposed_partition is None:
if check_ >= len(self.data_processing.pairs_to_considercleaned) ** 2:
break_to_while = False
flag = False
break
else:
check_ += 1
continue
self.acc_obj.propose(self.proposed_partition[['frame_no', 'id']].values,
pdu.get_particular_states(self.proposed_partition,
self.change_track[
'new']))
accepted_count = self.acc_obj.analyse_acceptance(self.change_track)
ratio = max(list(
self.acc_obj.ratio.values()))
if DrawingParams.draw_every_iteration:
current = pdu.from_dataframe_to_dict(self.meta_partition.data_df)
visu.draw_partition(current, int(os.environ.get('img_w')),
'partitions_iteration_{}_{}_'.format(self.cur_iter_name,
"current_"),
self.files_.curr_window_dir)
if accepted_count >= AcceptanceParams.number_of_acc_for_acc:
self.acc_obj.accepted_(change_track=self.change_track)
self.meta_partition.data_df = self.proposed_partition
self.acc_update()
break_to_while = True
if break_to_while:
check_ = 0
break
check_ += 1
if not break_to_while and check_ >= len(self.data_processing.pairs_to_consider):
flag = False
def load_info(self):
files = os.listdir(self.files_dir)
for file in files:
file_path = os.path.join(args.files_dir, file)
if file.endswith('json'):
curr_file_info = load_and_process(file_path)
curr_file_info = sort_meta_by_key(curr_file_info)
elif file.endswith('.csv'):
meta_pandas = read_multiindex_pd(file_path)
meta_dict = from_dataframe_to_dict(meta_pandas)
curr_file_info = fixed_to_original_coordinate_system(
meta_dict, self.homography_dict,
int(os.environ.get('fixed_coordinate_resize_h')),
int(os.environ.get('fixed_coordinate_resize_w')),
self.height, self.width)
else:
raise ValueError('Check you input file formats!')
self.files_info[file] = curr_file_info
self.ids_counters[file] = []
def get_meta(self):
final_meta = self.analysis()
final_meta_dict = pdu.from_dataframe_to_dict(final_meta)
return final_meta_dict, self.homography