| """Minimal licensed-MANO runtime. Model files are discovered locally and never copied.""" |
| import os |
| from pathlib import Path |
| from types import SimpleNamespace |
| import numpy as np |
| import torch |
| import smplx |
| from freihand_common import DATASET_ROOT, RestrictedUnpickler |
|
|
| class StoredState: |
| def __setstate__(self, state): self.__dict__.update(state) |
|
|
| class ManoReader(RestrictedUnpickler): |
| def find_class(self, module, name): |
| if (module, name) in [("chumpy.ch", "Ch"), ("chumpy.reordering", "Select"), ("scipy.sparse.csc", "csc_matrix")]: |
| return StoredState |
| if (module, name) == ("__builtin__", "set"): return set |
| return super().find_class(module, name) |
|
|
| def model_dir(): |
| candidates = [] |
| if os.environ.get("MANO_MODEL_DIR"): candidates.append(Path(os.environ["MANO_MODEL_DIR"])) |
| candidates += [DATASET_ROOT / "tmp/models/mano", DATASET_ROOT / "tmp/pilot-models", Path.home() / ".mano/models"] |
| for path in candidates: |
| if (path / "MANO_RIGHT.pkl").is_file(): return path |
| raise FileNotFoundError("MANO_RIGHT.pkl not found. Set MANO_MODEL_DIR or place it in <dataset>/tmp/models/mano.") |
|
|
| def model_data(path): |
| with path.open("rb") as stream: data = ManoReader(stream, encoding="latin1").load() |
| shapedirs = data["shapedirs"] |
| if isinstance(shapedirs, StoredState): |
| data["shapedirs"] = shapedirs.a.x.reshape(-1)[shapedirs.idxs].reshape(shapedirs.preferred_shape) |
| sparse = data["J_regressor"] |
| if isinstance(sparse, StoredState): |
| dense = np.zeros(sparse._shape) |
| for col in range(sparse._shape[1]): |
| begin, end = sparse.indptr[col:col + 2]; dense[sparse.indices[begin:end], col] = sparse.data[begin:end] |
| data["J_regressor"] = dense |
| return data |
|
|
| class Mano: |
| def __init__(self): |
| directory = model_dir(); self.data = model_data(directory / "MANO_RIGHT.pkl") |
| self.layer = smplx.MANO(model_path=str(directory), data_struct=SimpleNamespace(**self.data), |
| is_rhand=True, use_pca=False, flat_hand_mean=True) |
| def geometry(self, pose, betas, translation=None): |
| pose = torch.as_tensor(np.asarray(pose).copy(), dtype=torch.float32).reshape(-1, 48) |
| betas = torch.as_tensor(np.asarray(betas).copy(), dtype=torch.float32).reshape(-1, 10) |
| with torch.no_grad(): output = self.layer(global_orient=pose[:, :3], hand_pose=pose[:, 3:], betas=betas) |
| vertices = output.vertices.numpy(); joints = output.joints.numpy()[:, :16] |
| if translation is not None: |
| delta = np.asarray(translation).reshape(-1, 1, 3); vertices += delta; joints += delta |
| keypoints = np.stack([joints[:,0],joints[:,13],joints[:,14],joints[:,15],vertices[:,744], |
| joints[:,1],joints[:,2],joints[:,3],vertices[:,320], |
| joints[:,4],joints[:,5],joints[:,6],vertices[:,443], |
| joints[:,10],joints[:,11],joints[:,12],vertices[:,555], |
| joints[:,7],joints[:,8],joints[:,9],vertices[:,672]], axis=1) |
| return vertices, keypoints, joints |
|
|
