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import os
import pickle
import torch
import numpy as np
from psbody.mesh.sphere import Sphere
# to_params = lambda x: torch.from_numpy(x).float().to(self.device).requires_grad_(True)
# to_torch = lambda x: torch.from_numpy(x).float().to(self.device)
def to_params(x, device):
return x.to(device).requires_grad_(True)
def to_torch(x, device):
return torch.from_numpy(x).float().to(device)
def to_numpy(x):
return x.detach().cpu().numpy()
def load_smpl_seq(smpl_seq_path, gender=None, straighten_hands=False):
if not os.path.exists(smpl_seq_path):
raise Exception('Path does not exist: {}'.format(smpl_seq_path))
if smpl_seq_path.endswith('.pkl'):
data_dict = pickle.load(open(smpl_seq_path, 'rb'))
elif smpl_seq_path.endswith('.npz'):
data_dict = np.load(smpl_seq_path, allow_pickle=True)
if data_dict.files == ['pred_smpl_parms', 'verts', 'pred_cam_t']:
data_dict = data_dict['pred_smpl_parms'].item()# ['global_orient', 'body_pose', 'body_pose_axis_angle', 'global_orient_axis_angle', 'betas']
else:
data_dict = {key: data_dict[key] for key in data_dict.keys()} # convert to python dict
else:
raise Exception('Unknown file format: {}. Supported formats are .pkl and .npz'.format(smpl_seq_path))
# Instanciate a dictionary with the keys expected by the fitter
data_fixed = {}
# Get gender
if 'gender' not in data_dict:
assert gender is not None, f"The provided SMPL data dictionary does not contain gender, you need to pass it in command line"
data_fixed['gender'] = gender
elif not isinstance(data_dict['gender'], str):
# In some npz, the gender type happens to be: array('male', dtype='<U4'). So we convert it to string
data_fixed['gender'] = str(data_dict['gender'])
else:
data_fixed['gender'] = gender
# convert tensors to numpy arrays
for key, val in data_dict.items():
if isinstance(val, torch.Tensor):
data_dict[key] = val.detach().cpu().numpy()
# Get the SMPL pose
if 'poses' in data_dict:
poses = data_dict['poses']
elif 'body_pose_axis_angle' in data_dict and 'global_orient_axis_angle' in data_dict:
# assert 'global_orient' in data_dict and 'body_pose' in data_dict, f"Could not find poses in {smpl_seq_path}. Available keys: {data_dict.keys()})"
poses = np.concatenate([data_dict['global_orient_axis_angle'], data_dict['body_pose_axis_angle']], axis=1)
poses = poses.reshape(-1, 72)
elif 'body_pose' in data_dict and 'global_orient' in data_dict:
poses = np.concatenate([data_dict['global_orient_axis_angle'], data_dict['body_pose_axis_angle']], axis=-1)
else:
raise Exception(f"Could not find poses in {smpl_seq_path}. Available keys: {data_dict.keys()})")
if poses.shape[1] == 156:
# Those are SMPL+H poses, we remove the hand poses to keep only the body poses
smpl_poses = np.zeros((poses.shape[0], 72))
smpl_poses[:, :72-2*3] = poses[:, :72-2*3] # We leave params for SMPL joints 22 and 23 to zero as these DOF are not present in SMPLH
poses = smpl_poses
# Set SMPL joints 22 and 23 to zero as SKEL has rigid hands
if straighten_hands:
poses[:, 72-2*3:] = 0
data_fixed['poses'] = poses
# Translation
if 'trans' not in data_dict:
data_fixed['trans'] = np.zeros((poses.shape[0], 3))
else:
data_fixed['trans'] = data_dict['trans']
# Get betas
betas = data_dict['betas'][..., :10] # Keep only the 10 first betas
if len(betas.shape) == 1 and len(poses.shape) == 2:
betas = betas[None, :] # Add a batch dimension
data_fixed['betas'] = betas
for key in ['trans', 'poses', 'betas', 'gender']:
assert key in data_fixed.keys(), f'Could not find {key} in {smpl_seq_path}. Available keys: {data_fixed.keys()})'
out_dict = {}
out_dict['trans'] = data_fixed['trans']
out_dict['poses'] = data_fixed['poses']
out_dict['betas'] = data_fixed['betas']
out_dict['gender'] = data_fixed['gender']
return out_dict
def location_to_spheres(loc, color=(1,0,0), radius=0.02):
"""Given an array of 3D points, return a list of spheres located at those positions.
Args:
loc (numpy.array): Nx3 array giving 3D positions
color (tuple, optional): One RGB float color vector to color the spheres. Defaults to (1,0,0).
radius (float, optional): Radius of the spheres in meters. Defaults to 0.02.
Returns:
list: List of spheres Mesh
"""
cL = [Sphere(np.asarray([loc[i, 0], loc[i, 1], loc[i, 2]]), radius).to_mesh() for i in range(loc.shape[0])]
for spL in cL:
spL.set_vertex_colors(np.array(color))
return cL
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