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import argparse
import numpy as np
import pickle
from SMPLX.transfer_model.write_obj import write_obj
from SMPLX.transfer_model.utils import read_deformation_transfer
from SMPLX.transfer_model.data import build_dataloader
from SMPLX.transfer_model.transfer_model import run_fitting
from SMPLX.transfer_model.merge_output import merge
from SMPLX.smplx import build_layer
import os
import torch
from tqdm import tqdm
import subprocess
import platform
import time
def load_npz(path):
return np.load(path)
def load_pickle(path):
with open(path, "rb") as f:
res = pickle.load(f, encoding="latin1")
return res
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='transfer between smpls')
parser.add_argument('--source', default="smpl")
parser.add_argument("--target", default="smplh")
parser.add_argument("--model_path", default="/data/TTA/data/body_models")
parser.add_argument("--extra_dir", default="/data/TTA/data/extra_dir", help="https://smpl-x.is.tue.mpg.de/download.php")
parser.add_argument("--source_path", default="/data/TTA/data/humanact_smpl")
parser.add_argument("--target_path", default="/data/TTA/data/humanact_smplh")
parser.add_argument("--batch_size", default=500, type=int)
args = parser.parse_args()
device = "cuda"
if args.target == "smplx" or args.source == "smplx":
deformation_transfer_path = os.path.join(args.extra_dir, "{}2{}_deftrafo_setup.pkl".format(args.source, args.target))
else:
deformation_transfer_path = os.path.join(args.extra_dir, "{}2{}_def_transfer.pkl".format(args.source, args.target))
if args.target == "smplx":
model_params = {"betas":{"num":10}, "expression":{"num": 10}}
mask_ids_fname = os.path.join(args.extra_dir, "smplx_mask_ids.npy")
if os.path.exists(mask_ids_fname):
mask_ids = np.load(mask_ids_fname)
mask_ids = torch.from_numpy(mask_ids).to(device=device)
else:
print(f'Mask ids fname not found: {mask_ids_fname}')
elif args.target == "smplh" or args.target == "smpl":
model_params = {"betas":{"num":10}}
mask_ids_fname = ""
mask_ids = None
body_model_conf = {
"ext":"npz",
"model_type": args.target,
"folder": args.model_path,
"use_compressed": False,
args.target:model_params
}
if args.target == "smplx" or args.target == "smpl":
body_model_conf["use_face_contour"] = True
for root, dirs, files in os.walk(args.source_path):
for name in files:
curr_file = os.path.join(root, name)
new_root = os.path.join(args.target_path , "/".join(root.split("/")[:-2:-1]))
os.makedirs(new_root, exist_ok=True)
curr_target = os.path.join(new_root, name.replace(".npz", ".npy"))
if os.path.exists(curr_target):
print("%s has been competed"%(curr_target))
continue
if name.split(".")[-1] == "npz":
curr = load_npz(curr_file)
body_pose = None
elif name.split(".")[-1] == "pkl":
curr = load_pickle(curr_file)
body_pose = None
elif name.split(".")[-1] == "npy":
curr = np.load(curr_file)
body_pose = curr
else:
continue
if body_pose is None:
try:
body_pose = curr["poses"]
except:
print("Not Pose Data")
continue
gender = str(curr["gender"])
body_model_conf["gender"] = gender
else:
gender = "neutral"
body_model_conf["gender"] = gender
cid = name.split(".")[0]
save_folder1 = os.path.join("temp", "objs")
save_folder2 = os.path.join(new_root, str(time.time()))
os.makedirs(save_folder1, exist_ok=True)
os.makedirs(save_folder2, exist_ok=True)
write_obj(args.model_path, curr_file, save_folder1, args.source, gender, 10, 10, True, device)
body_model = build_layer(args.model_path, **body_model_conf)
body_model = body_model.to(device=device)
def_matrix = read_deformation_transfer(deformation_transfer_path, device=device)
datasets = {
"mesh_folder":{"data_folder":save_folder1},
"batch_size":args.batch_size
}
data_obj_dict = build_dataloader(datasets)
dataloader = data_obj_dict['dataloader']
for ii, batch in enumerate(tqdm(dataloader)):
for key in batch:
if torch.is_tensor(batch[key]):
batch[key] = batch[key].to(device=device)
var_dict = run_fitting(batch, body_model, def_matrix, mask_ids)
paths = batch['paths']
for ii, path in enumerate(paths):
_, fname = os.path.split(path)
output_path = os.path.join(
save_folder2, f'{os.path.splitext(fname)[0]}.pkl')
save_dict = {}
for key in var_dict.keys():
try:
save_dict[key] = var_dict[key][ii:ii+1]
except:
save_dict[key] = var_dict[key][ii:ii+1]
with open(output_path, "wb") as f:
pickle.dump(save_dict, f)
results = merge(save_folder2, gender)
np.save(curr_target, results)
cmd = "rm -r {}".format(save_folder1)
subprocess.call(cmd, shell=platform.system() != 'Windows')
cmd = "rm -r {}".format(save_folder2)
subprocess.call(cmd, shell=platform.system() != 'Windows')
del body_model
del dataloader
del data_obj_dict
torch.cuda.synchronize()
torch.cuda.empty_cache()
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