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import cv2
from pathlib import Path
import os
import argparse
import torch
import numpy as np
from tqdm import tqdm
import pyrender
from pathlib import Path
from scripts.pretrained_models import HMR2_DEFAULT_CKPT
if "PYOPENGL_PLATFORM" not in os.environ:
os.environ["PYOPENGL_PLATFORM"] = "egl"
from hmr2.models import HMR2, download_models, load_hmr2
# For Windows, remove PYOPENGL_PLATFORM to enable default rendering backend
sys_name = platform.system()
if sys_name == "Windows":
os.environ.pop("PYOPENGL_PLATFORM", None)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="transfer smpl")
parser.add_argument("--device", type=int, default=0, help="GPU device ID")
parser.add_argument(
"--driving_path",
type=str,
default="driving_videos/001",
help="Folder path to driving imgs sequence",
)
parser.add_argument(
"--reference_path",
type=str,
default="reference_imgs/images/ref.png",
help="Path to reference img",
)
parser.add_argument(
"--output_folder", type=str, default="output", help="Path to result imgs"
)
parser.add_argument(
"--figure_transfer",
dest="figure_transfer",
action="store_true",
default=False,
help="If true, transfer SMPL shape parameter.",
)
parser.add_argument(
"--view_transfer",
dest="view_transfer",
action="store_true",
default=False,
help="If true, transfer camera parameter.",
)
args = parser.parse_args()
os.makedirs(args.output_folder, exist_ok=True)
have_smpl_results = False
model, model_cfg = load_hmr2(HMR2_DEFAULT_CKPT)
model = model.to(args.device)
os.makedirs(os.path.join(args.output_folder), exist_ok=True)
os.makedirs(os.path.join(args.output_folder, "visualized_imgs"), exist_ok=True)
# os.makedirs(os.path.join(args.output_folder,"mesh"), exist_ok=True)
os.makedirs(os.path.join(args.output_folder, "mask"), exist_ok=True)
os.makedirs(os.path.join(args.output_folder, "semantic_map"), exist_ok=True)
os.makedirs(os.path.join(args.output_folder, "images"), exist_ok=True)
os.makedirs(os.path.join(args.output_folder, "normal"), exist_ok=True)
os.makedirs(os.path.join(args.output_folder, "depth"), exist_ok=True)
os.makedirs(os.path.join(args.output_folder, "smpl_results"), exist_ok=True)
driving_folder = args.driving_path
reference_file = args.reference_path
print(os.listdir(driving_folder))
if "smpl_results" in os.listdir(driving_folder):
have_smpl_results = True
driving_paths = os.listdir(os.path.join(driving_folder, "smpl_results"))
driving_paths = [
path
for path in driving_paths
if os.path.splitext(path)[1].lower() == ".npy"
]
driving_paths.sort(key=lambda x: int(x.split(".")[0]))
driving_paths = [
os.path.join(driving_folder, "smpl_results", path) for path in driving_paths
]
if not have_smpl_results:
print("No SMPLS found in driving folder.")
else:
reference_dict = np.load(str(reference_file), allow_pickle=True).item()
reference_path = Path(reference_file)
base_img_path = os.path.join(
reference_path.parent.parent,
"images",
reference_path.name.split(".")[0]
)
reference_img = None
for ext in [".png", ".jpg", ".jpeg"]:
if os.path.exists(base_img_path + ext):
reference_img = cv2.imread(base_img_path + ext)
break
if reference_img is None:
raise FileNotFoundError(f"找不到參考圖片 {base_img_path}.[png/jpg/jpeg]")
group_smpl_path = os.path.join(driving_folder, "smpl_results", "smpls_group.npz")
if os.path.exists(group_smpl_path):
result_dict_list = np.load(group_smpl_path, allow_pickle=True)
result_dict_first = np.load(driving_paths[0], allow_pickle=True).item()
i = 0
for smpl_outs, cam_t, file_path in tqdm(
zip(result_dict_list["smpl"], result_dict_list["camera"], driving_paths)
):
img_fn, _ = os.path.splitext(os.path.basename(file_path))
result_dict = {key: value for key, value in result_dict_first.items()}
result_dict["smpls"] = smpl_outs
result_dict["cam_t"] = cam_t
# 原始程式碼預期 npz 裡有 scaled_focal_length，但 generate_smpls.py 其實沒存進去。
# 不過這個焦距對於同一部影片都是固定的，所以直接從第一幀的 .npy (result_dict_first) 抓取即可！
result_dict["scaled_focal_length"] = result_dict_first["scaled_focal_length"]
if not args.figure_transfer:
result_dict["smpls"]["betas"] = reference_dict["smpls"]["betas"]
if args.view_transfer:
scaled_focal_length = reference_dict["scaled_focal_length"]
result_dict["cam_t"] = reference_dict["cam_t"]
result_dict["scaled_focal_length"] = scaled_focal_length
# transfer reference SMPL shape to driving SMPLs
if args.figure_transfer:
result_dict["smpls"]["betas"] = reference_dict["smpls"]["betas"]
smpl_output = model.smpl(
**{
k: torch.Tensor(v[[0]]).to(args.device).float()
for k, v in result_dict["smpls"].items()
},
pose2rot=False,
)
pred_vertices = smpl_output.vertices
result_dict["verts"][0] = (
pred_vertices.reshape(-1, 3).detach().cpu().numpy()
)
result_dict["render_res"] = reference_dict["render_res"]
if i == 0:
cv2.imwrite(
os.path.join(
args.output_folder, "reference_img", f"{img_fn}.png"
),
reference_img,
)
np.save(
str(
os.path.join(
args.output_folder, "smpl_results", f"{img_fn}.npy"
)
),
result_dict,
)
i += 1
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