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a75bdfa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 | import platform
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")
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)
reference_img = cv2.imread(
os.path.join(
reference_path.parent.parent,
"images",
reference_path.name.split(".")[0] + ".png",
)
)
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, foc_len, file_path in tqdm(
zip(result_dict_list["smpl"], result_dict_list["camera"], result_dict_list["scaled_focal_length"], 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
result_dict["scaled_focal_length"] = foc_len
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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