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# borrow from optimization https://github.com/wangsen1312/joints2smpl
import os
import argparse
import pickle
import h5py
import natsort
import smplx
import torch
from mld.transforms.joints2rots import config
from mld.transforms.joints2rots.smplify import SMPLify3D
parser = argparse.ArgumentParser()
parser.add_argument("--pkl", type=str, default=None, help="pkl motion file")
parser.add_argument("--dir", type=str, default=None, help="pkl motion folder")
parser.add_argument("--num_smplify_iters", type=int, default=150, help="num of smplify iters")
parser.add_argument("--cuda", type=bool, default=True, help="enables cuda")
parser.add_argument("--gpu_ids", type=int, default=0, help="choose gpu ids")
parser.add_argument("--num_joints", type=int, default=22, help="joint number")
parser.add_argument("--joint_category", type=str, default="AMASS", help="use correspondence")
parser.add_argument("--fix_foot", type=str, default="False", help="fix foot or not")
opt = parser.parse_args()
print(opt)
if opt.pkl:
paths = [opt.pkl]
elif opt.dir:
paths = []
file_list = natsort.natsorted(os.listdir(opt.dir))
for item in file_list:
if item.endswith('.pkl') and not item.endswith("_mesh.pkl"):
paths.append(os.path.join(opt.dir, item))
else:
raise ValueError(f'{opt.pkl} and {opt.dir} are both None!')
for path in paths:
# load joints
if os.path.exists(path.replace('.pkl', '_mesh.pkl')):
print(f"{path} is rendered! skip!")
continue
with open(path, 'rb') as f:
data = pickle.load(f)
joints = data['joints']
# load predefined something
device = torch.device("cuda:" + str(opt.gpu_ids) if opt.cuda else "cpu")
print(config.SMPL_MODEL_DIR)
smplxmodel = smplx.create(
config.SMPL_MODEL_DIR,
model_type="smpl",
gender="neutral",
ext="pkl",
batch_size=joints.shape[0],
).to(device)
# load the mean pose as original
smpl_mean_file = config.SMPL_MEAN_FILE
file = h5py.File(smpl_mean_file, "r")
init_mean_pose = (
torch.from_numpy(file["pose"][:])
.unsqueeze(0).repeat(joints.shape[0], 1)
.float()
.to(device)
)
init_mean_shape = (
torch.from_numpy(file["shape"][:])
.unsqueeze(0).repeat(joints.shape[0], 1)
.float()
.to(device)
)
cam_trans_zero = torch.Tensor([0.0, 0.0, 0.0]).unsqueeze(0).to(device)
# initialize SMPLify
smplify = SMPLify3D(
smplxmodel=smplxmodel,
batch_size=joints.shape[0],
joints_category=opt.joint_category,
num_iters=opt.num_smplify_iters,
device=device,
)
print("initialize SMPLify3D done!")
print("Start SMPLify!")
keypoints_3d = torch.Tensor(joints).to(device).float()
if opt.joint_category == "AMASS":
confidence_input = torch.ones(opt.num_joints)
# make sure the foot and ankle
if opt.fix_foot:
confidence_input[7] = 1.5
confidence_input[8] = 1.5
confidence_input[10] = 1.5
confidence_input[11] = 1.5
else:
print("Such category not settle down!")
# ----- from initial to fitting -------
(
new_opt_vertices,
new_opt_joints,
new_opt_pose,
new_opt_betas,
new_opt_cam_t,
new_opt_joint_loss,
) = smplify(
init_mean_pose.detach(),
init_mean_shape.detach(),
cam_trans_zero.detach(),
keypoints_3d,
conf_3d=confidence_input.to(device)
)
# fix shape
betas = torch.zeros_like(new_opt_betas)
root = keypoints_3d[:, 0, :]
output = smplxmodel(
betas=betas,
global_orient=new_opt_pose[:, :3],
body_pose=new_opt_pose[:, 3:],
transl=root,
return_verts=True
)
vertices = output.vertices.detach().cpu().numpy()
floor_height = vertices[..., 1].min()
vertices[..., 1] -= floor_height
data['vertices'] = vertices
save_file = path.replace('.pkl', '_mesh.pkl')
with open(save_file, 'wb') as f:
pickle.dump(data, f)
print(f'vertices saved in {save_file}')
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