def _run(self):
self.logger.info("Starting Up")
self.startup()
self.logger.info("Running")
if self.input_queue is None:
# this worker is a source, just run
run_and_catch_exceptions(self.logger, self.run)
run_and_catch_exceptions(self.logger, self.shutdown)
else:
# this worker is monitoring a queue, get each item and call run with it
for item in iter(self.input_queue.get, 'STOP'):
run_and_catch_exceptions(self.logger, self.run, item)
run_and_catch_exceptions(self.logger, self.shutdown)
self.logger.info("Done running")
vid_characteristics = pd.read_csv(os.path.join(conf.dirs.output, 'online_samples.csv'))
camera_list = vid_characteristics.camera.unique()
n_cameras = len(camera_list)
camera_to_idx = {camera_list[i]:i for i in range(n_cameras)}
analysis_group_cameras = [Analysis() for _ in range(n_cameras)]
analysis_group = Analysis()
for filename in os.listdir(conf.dirs.annotations):
if filename[-4:] != '.csv':
continue
vid_ref = filename[:-4]
logger.info(f'Starting to process the video {vid_ref}.')
analysis_group.pr_curves_onevid(vid_ref)
camera = vid_characteristics[vid_characteristics['video_segment_name'] == vid_ref]['camera'].squeeze()
analysis_group_cameras[camera_to_idx[camera]].pr_curves_onevid(vid_ref)
logger.info(f'Done processing {vid_ref}.')
analysis_group.pr_curves_agg()
for i in range(n_cameras):
try:
analysis_group_cameras[i].pr_curves_agg(camera_list[i])
except ValueError:
logger.info(f'No objects for camera {camera_list[i]}!')
if __name__ == "__main__":
script_name = os.path.basename(__file__).split(".")[0]
setup(script_name)
run_and_catch_exceptions(logger, main)