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):
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)
# Set Mocap regressor
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)
args.out_dir = os.path.join(
"/output/annotations/",
args.input_path.replace("/data/sessions_processed/final_dataset/",
"").split(".")[0], f"hand_bboxes_0.2")
print(args.out_dir)
assert os.path.exists(args.out_dir)
# run
run_hand_mocap(args, bbox_detector, hand_mocap, visualizer)
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')
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 run_from_list(args):
assert os.path.exists(args.list)
with open(args.list, "r") as f:
lines = f.readlines()
args.save_frame = False
args.no_video_out = True
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)
# Set Mocap regressor
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)
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 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)
run_hand_mocap(args, bbox_detector)
