def setup_input(args):
"""
Input type can be
an image file
a video file
a folder with image files
a folder with bbox (json) files
"webcam"
"""
image_exts = ('jpg', 'png', 'jpeg', 'bmp')
video_exts = ('mp4', 'avi', 'mov')
# get type of input
input_type = __get_input_type(args)
if input_type == 'video':
cap = cv2.VideoCapture(args.input_path)
assert cap.isOpened(), f"Failed in opening video: {args.input_path}"
__video_setup(args)
return input_type, cap
elif input_type == 'webcam':
cap = cv2.VideoCapture(0) #webcam input
return input_type, cap
elif input_type == 'image_dir':
image_list = gnu.get_all_files(args.input_path, image_exts, "relative")
image_list = [
osp.join(args.input_path, image_name) for image_name in image_list
]
__img_seq_setup(args)
return input_type, image_list
elif input_type == 'bbox_dir':
__img_seq_setup(args)
json_files = gnu.get_all_files(args.input_path, '.json', "relative")
input_data = list()
for json_file in json_files:
json_path = osp.join(args.input_path, json_file)
image_path, body_bbox_list, hand_bbox_list = load_info_from_json(
json_path)
input_data.append(
dict(image_path=image_path,
hand_bbox_list=hand_bbox_list,
body_bbox_list=body_bbox_list))
return input_type, input_data
else:
assert False, "Unknown input type"
def main():
args = DemoOptions().parse()
# load pkl files
pkl_files = gnu.get_all_files(args.pkl_dir, ".pkl", "full")
# get smpl type
demo_type, smpl_type = __get_data_type(pkl_files)
# get smpl model
smpl_model = __get_smpl_model(demo_type, smpl_type)
# Set Visualizer
assert args.renderer_type in ['pytorch3d', 'opendr'], \
f"{args.renderer_type} not implemented yet."
from renderer.screen_free_visualizer import Visualizer
visualizer = Visualizer(args.renderer_type)
# load smpl model
visualize_prediction(args, demo_type, smpl_type, smpl_model, pkl_files,
visualizer)