def main():
args = DemoOptions().parse()
args.use_smplx = True
device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu')
assert torch.cuda.is_available(), "Current version only supports GPU"
hand_bbox_detector = HandBboxDetector('third_view', device)
#Set Mocap regressor
body_mocap = BodyMocap(args.checkpoint_body_smplx,
args.smpl_dir,
device=device,
use_smplx=True)
hand_mocap = HandMocap(args.checkpoint_hand, args.smpl_dir, device=device)
# Set Visualizer
if args.renderer_type in ['pytorch3d', 'opendr']:
from renderer.screen_free_visualizer import Visualizer
else:
from renderer.visualizer import Visualizer
visualizer = Visualizer(args.renderer_type)
run_frank_mocap(args, hand_bbox_detector, body_mocap, hand_mocap,
visualizer)
def main():
args = DemoOptions().parse()
args.use_smplx = True
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
assert torch.cuda.is_available(), "Current version only supports GPU"
hand_bbox_detector = HandBboxDetector('third_view', device)
#Set Mocap regressor
body_mocap = BodyMocap(args.checkpoint_body_smplx, args.smpl_dir, device = device, use_smplx= True)
hand_mocap = HandMocap(args.checkpoint_hand, args.smpl_dir, device = device)
run_frank_mocap(args, hand_bbox_detector, body_mocap, hand_mocap)
def __init__(self):
self.args = DemoOptions().parse()
self.args.use_smplx = True
self.args.save_pred_pkl = True
self.device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu')
self.bbox_detector = HandBboxDetector(self.args.view_type, self.device)
self.hand_mocap = HandMocap(self.args.checkpoint_hand,
self.args.smpl_dir,
device=self.device)
self.visualizer = Visualizer(self.args.renderer_type)
def main():
args = DemoOptions().parse()
args.use_smplx = True
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
#Set Mocap regressor
if not torch.cuda.is_available():
hand_bbox_detector = HandBboxDetector_cpu("third_view", device)
body_mocap = BodyMocap_cpu(args.checkpoint_body_smplx, args.smpl_dir, device = device, use_smplx= True)
hand_mocap = HandMocap_cpu(args.checkpoint_hand, args.smpl_dir, device = device)
# Set Visualizer
if args.renderer_type in ['pytorch3d', 'opendr']:
from renderer.screen_free_visualizer import Visualizer
else:
from renderer.visualizer import Visualizer
visualizer = Visualizer(args.renderer_type)
run_frank_mocap_cpu(args, hand_bbox_detector, body_mocap, hand_mocap,visualizer)
else:
print("This is the CPU beta version")
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)
def main():
args = DemoOptions().parse()
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
assert torch.cuda.is_available(), "Current version only supports GPU"
# Set bbox detector
body_bbox_detector = BodyPoseEstimator()
# Set mocap regressor
use_smplx = args.use_smplx
checkpoint_path = args.checkpoint_body_smplx if use_smplx else args.checkpoint_body_smpl
print("use_smplx", use_smplx)
body_mocap = BodyMocap(checkpoint_path, args.smpl_dir, device, use_smplx)
# Set Visualizer
if args.renderer_type in ['pytorch3d', 'opendr']:
from renderer.screen_free_visualizer import Visualizer
else:
from renderer.visualizer import Visualizer
visualizer = Visualizer(args.renderer_type)
run_body_mocap(args, body_bbox_detector, body_mocap, visualizer)
args.out_dir = os.path.join(output, args.input_path.replace(root, "").split(".")[0])
args.out_dir = os.path.join(args.out_dir, f"hand_bboxes_{args.th_hands}")
if not os.path.exists(args.out_dir):
os.makedirs(args.out_dir)
elif not args.replace and os.path.exists(os.path.join(args.out_dir, f"finished.marker")):
print(f"[{i}/{len(lines)-1}] Already processed video -> '{args.out_dir}'")
continue
print(f"[{i}/{len(lines)-1}] Processing video '{args.input_path}' -> '{args.out_dir}'")
run_hand_mocap(args, bbox_detector)
print(f"[FINISHED] Time: {time.time() - t0} s")
if __name__ == '__main__':
args = DemoOptions().parse()
args.use_smplx = True
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
assert torch.cuda.is_available(), "Current version only supports GPU"
#Set Bbox detector
bbox_detector = HandBboxDetector(args.view_type, device, th=args.th_hands)
if args.list:
run_from_list(args, bbox_detector)
else:
root = "/data/sessions_processed/final_dataset/"
args.out_dir = os.path.join(args.out_dir, args.input_path.replace(root, "").split(".")[0], f"hand_bboxes_{args.th_hands}")
if not os.path.exists(args.out_dir):
os.makedirs(args.out_dir)
visualizer = Visualizer(args.renderer_type)
for i in range(1, len(lines)):
t0 = time.time()
path = lines[i].replace("\n", "")
session, task = (path.split(".")[0]).split("/")[-2:]
args.input_path = path
args.out_dir = f"/output/annotations/{session}/{task}/hand_bboxes_0.2/"
if not args.replace and os.path.exists(
os.path.join(args.out_dir, MARKER)):
print(
f"[{i}/{len(lines)-1}] Landmarks already extracted for this video -> '{args.out_dir}'"
)
continue
print(
f"[{i}/{len(lines)-1}] Extracting hand landmarks of '{session} - {task}'"
)
# run
run_hand_mocap(args, bbox_detector, hand_mocap, visualizer)
print(f"[FINISHED] Time: {time.time() - t0} s")
if __name__ == '__main__':
args = DemoOptions().parse()
if args.list:
run_from_list(args)
else:
main(args)
