cmd_args = parser.parse_args()
image_folder = cmd_args.image_folder
show = cmd_args.show
output_folder = cmd_args.output_folder
pause = cmd_args.pause
focal_length = cmd_args.focal_length
save_vis = cmd_args.save_vis
save_params = cmd_args.save_params
save_mesh = cmd_args.save_mesh
degrees = cmd_args.degrees
expose_batch = cmd_args.expose_batch
rcnn_batch = cmd_args.rcnn_batch
cfg.merge_from_file(cmd_args.exp_cfg)
cfg.merge_from_list(cmd_args.exp_opts)
cfg.datasets.body.batch_size = expose_batch
cfg.is_training = False
cfg.datasets.body.splits.test = cmd_args.datasets
use_face_contour = cfg.datasets.use_face_contour
set_face_contour(cfg, use_face_contour=use_face_contour)
with threadpool_limits(limits=1):
main(
image_folder,
cfg,
show=show,
demo_output_folder=output_folder,
def execute(args):
try:
logger.info('人物姿勢推定開始: {0}',
args.img_dir,
decoration=MLogger.DECORATION_BOX)
if not os.path.exists(args.img_dir):
logger.error("指定された処理用ディレクトリが存在しません。: {0}",
args.img_dir,
decoration=MLogger.DECORATION_BOX)
return False
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
parser = get_parser()
argv = parser.parse_args(args=[])
show = argv.show
pause = argv.pause
focal_length = argv.focal_length
save_vis = argv.save_vis
save_params = argv.save_params
save_mesh = argv.save_mesh
degrees = argv.degrees
expose_batch = argv.expose_batch
rcnn_batch = argv.rcnn_batch
cfg.merge_from_file(argv.exp_cfg)
cfg.merge_from_list(argv.exp_opts)
cfg.datasets.body.batch_size = expose_batch
cfg.is_training = False
cfg.datasets.body.splits.test = argv.datasets
use_face_contour = cfg.datasets.use_face_contour
set_face_contour(cfg, use_face_contour=use_face_contour)
output_folder = os.path.join(args.img_dir, "pose")
result = False
with threadpool_limits(limits=1):
result = main(
args,
cfg,
show=show,
output_folder=output_folder,
pause=pause,
focal_length=focal_length,
save_vis=save_vis,
save_mesh=save_mesh,
save_params=save_params,
degrees=degrees,
rcnn_batch=rcnn_batch,
)
logger.info('人物姿勢推定終了: {0}',
args.img_dir,
decoration=MLogger.DECORATION_BOX)
return result
except Exception as e:
logger.critical("姿勢推定で予期せぬエラーが発生しました。",
e,
decoration=MLogger.DECORATION_BOX)
return False