def smpl_from_skel(path, sub, out, skel3d, args):
config = CONFIG[args.body]
results3d, filenames = read_keypoints3d_all(skel3d)
pids = list(results3d.keys())
weight_shape = load_weight_shape(args.model, args.opts)
weight_pose = load_weight_pose(args.model, args.opts)
with Timer('Loading {}, {}'.format(args.model, args.gender)):
body_model = load_model(args.gender, model_type=args.model)
for pid, result in results3d.items():
body_params = smpl_from_keypoints3d(body_model,
result['keypoints3d'],
config,
args,
weight_shape=weight_shape,
weight_pose=weight_pose)
result['body_params'] = body_params
# write for each frame
for nf, skelname in enumerate(tqdm(filenames, desc='writing')):
basename = os.path.basename(skelname)
outname = join(out, basename)
res = []
for pid, result in results3d.items():
frames = result['frames']
if nf in frames:
nnf = frames.index(nf)
val = {'id': pid}
params = select_nf(result['body_params'], nnf)
val.update(params)
res.append(val)
write_smpl(outname, res)
def mv1pmf_smpl(dataset, args, weight_pose=None, weight_shape=None):
dataset.skel_path = args.skel
kp3ds = []
start, end = args.start, min(args.end, len(dataset))
keypoints2d, bboxes = [], []
dataset.no_img = True
for nf in tqdm(range(start, end), desc='loading'):
images, annots = dataset[nf]
keypoints2d.append(annots['keypoints'])
bboxes.append(annots['bbox'])
kp3ds = dataset.read_skeleton(start, end)
keypoints2d = np.stack(keypoints2d)
bboxes = np.stack(bboxes)
kp3ds = check_keypoints(kp3ds, 1)
# optimize the human shape
with Timer('Loading {}, {}'.format(args.model, args.gender),
not args.verbose):
body_model = load_model(gender=args.gender, model_type=args.model)
params = smpl_from_keypoints3d2d(body_model,
kp3ds,
keypoints2d,
bboxes,
dataset.Pall,
config=dataset.config,
args=args,
weight_shape=weight_shape,
weight_pose=weight_pose)
# write out the results
dataset.no_img = not (args.vis_smpl or args.vis_repro)
for nf in tqdm(range(start, end), desc='render'):
images, annots = dataset[nf]
param = select_nf(params, nf - start)
dataset.write_smpl(param, nf)
if args.vis_smpl:
vertices = body_model(return_verts=True,
return_tensor=False,
**param)
dataset.vis_smpl(vertices=vertices[0],
faces=body_model.faces,
images=images,
nf=nf,
sub_vis=args.sub_vis,
add_back=True)
if args.vis_repro:
keypoints = body_model(return_verts=False,
return_tensor=False,
**param)[0]
kpts_repro = projectN3(keypoints, dataset.Pall)
dataset.vis_repro(images, kpts_repro, nf=nf, sub_vis=args.sub_vis)
from easymocap.mytools import load_parser, parse_parser
parser = load_parser()
parser.add_argument('--skel', type=str, default=None,
help='path to keypoints3d')
parser.add_argument('--direct', action='store_true')
parser.add_argument('--video', action='store_true')
parser.add_argument('--gtK', action='store_true')
parser.add_argument('--normal', action='store_true',
help='set to use the normal of the mirror')
args = parse_parser(parser)
helps = '''
Demo code for single view and one person with mirror:
- Input : {}: [{}]
- Output: {}
- Body : {} => {}, {}
'''.format(args.path, ', '.join(args.sub), args.out,
args.model, args.gender, args.body)
print(helps)
with Timer('Loading {}, {}'.format(args.model, args.gender)):
body_model = load_model(args.gender, model_type=args.model)
with Timer('Loading SPIN'):
spin_model = SPIN(
SMPL_MEAN_PARAMS='data/models/smpl_mean_params.npz',
checkpoint='data/models/spin_checkpoint.pt',
device=body_model.device)
if args.video:
demo_1v1pmf_smpl_mirror(args.path, body_model, spin_model, args)
else:
demo_1v1p1f_smpl_mirror(args.path, body_model, spin_model, args)