scripts/data_processors/smpl/smpl_transfer.py

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