File size: 3,132 Bytes
d378823 | 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 | """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
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