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| ''' | |
| Copyright 2015 Matthew Loper, Naureen Mahmood and the Max Planck Gesellschaft. All rights reserved. | |
| This software is provided for research purposes only. | |
| By using this software you agree to the terms of the SMPL Model license here http://smpl.is.tue.mpg.de/license | |
| More information about SMPL is available here http://smpl.is.tue.mpg. | |
| For comments or questions, please email us at: smpl@tuebingen.mpg.de | |
| About this file: | |
| ================ | |
| This file defines the serialization functions of the SMPL model. | |
| Modules included: | |
| - save_model: | |
| saves the SMPL model to a given file location as a .pkl file | |
| - load_model: | |
| loads the SMPL model from a given file location (i.e. a .pkl file location), | |
| or a dictionary object. | |
| ''' | |
| __all__ = ['load_model', 'save_model'] | |
| import numpy as np | |
| import pickle | |
| import chumpy as ch | |
| from chumpy.ch import MatVecMult | |
| from .posemapper import posemap | |
| from .verts import verts_core | |
| def save_model(model, fname): | |
| m0 = model | |
| trainer_dict = {'v_template': np.asarray(m0.v_template),'J': np.asarray(m0.J),'weights': np.asarray(m0.weights),'kintree_table': m0.kintree_table,'f': m0.f, 'bs_type': m0.bs_type, 'posedirs': np.asarray(m0.posedirs)} | |
| if hasattr(model, 'J_regressor'): | |
| trainer_dict['J_regressor'] = m0.J_regressor | |
| if hasattr(model, 'J_regressor_prior'): | |
| trainer_dict['J_regressor_prior'] = m0.J_regressor_prior | |
| if hasattr(model, 'weights_prior'): | |
| trainer_dict['weights_prior'] = m0.weights_prior | |
| if hasattr(model, 'shapedirs'): | |
| trainer_dict['shapedirs'] = m0.shapedirs | |
| if hasattr(model, 'vert_sym_idxs'): | |
| trainer_dict['vert_sym_idxs'] = m0.vert_sym_idxs | |
| if hasattr(model, 'bs_style'): | |
| trainer_dict['bs_style'] = model.bs_style | |
| else: | |
| trainer_dict['bs_style'] = 'lbs' | |
| pickle.dump(trainer_dict, open(fname, 'w'), -1) | |
| def backwards_compatibility_replacements(dd): | |
| # replacements | |
| if 'default_v' in dd: | |
| dd['v_template'] = dd['default_v'] | |
| del dd['default_v'] | |
| if 'template_v' in dd: | |
| dd['v_template'] = dd['template_v'] | |
| del dd['template_v'] | |
| if 'joint_regressor' in dd: | |
| dd['J_regressor'] = dd['joint_regressor'] | |
| del dd['joint_regressor'] | |
| if 'blendshapes' in dd: | |
| dd['posedirs'] = dd['blendshapes'] | |
| del dd['blendshapes'] | |
| if 'J' not in dd: | |
| dd['J'] = dd['joints'] | |
| del dd['joints'] | |
| # defaults | |
| if 'bs_style' not in dd: | |
| dd['bs_style'] = 'lbs' | |
| def ready_arguments(fname_or_dict): | |
| if not isinstance(fname_or_dict, dict): | |
| dd = pickle.load(open(fname_or_dict)) | |
| else: | |
| dd = fname_or_dict | |
| backwards_compatibility_replacements(dd) | |
| want_shapemodel = 'shapedirs' in dd | |
| nposeparms = dd['kintree_table'].shape[1]*3 | |
| if 'trans' not in dd: | |
| dd['trans'] = np.zeros(3) | |
| if 'pose' not in dd: | |
| dd['pose'] = np.zeros(nposeparms) | |
| if 'shapedirs' in dd and 'betas' not in dd: | |
| dd['betas'] = np.zeros(dd['shapedirs'].shape[-1]) | |
| for s in ['v_template', 'weights', 'posedirs', 'pose', 'trans', 'shapedirs', 'betas', 'J']: | |
| if (s in dd) and not hasattr(dd[s], 'dterms'): | |
| dd[s] = ch.array(dd[s]) | |
| if want_shapemodel: | |
| dd['v_shaped'] = dd['shapedirs'].dot(dd['betas'])+dd['v_template'] | |
| v_shaped = dd['v_shaped'] | |
| J_tmpx = MatVecMult(dd['J_regressor'], v_shaped[:,0]) | |
| J_tmpy = MatVecMult(dd['J_regressor'], v_shaped[:,1]) | |
| J_tmpz = MatVecMult(dd['J_regressor'], v_shaped[:,2]) | |
| dd['J'] = ch.vstack((J_tmpx, J_tmpy, J_tmpz)).T | |
| dd['v_posed'] = v_shaped + dd['posedirs'].dot(posemap(dd['bs_type'])(dd['pose'])) | |
| else: | |
| dd['v_posed'] = dd['v_template'] + dd['posedirs'].dot(posemap(dd['bs_type'])(dd['pose'])) | |
| return dd | |
| def load_model(fname_or_dict): | |
| dd = ready_arguments(fname_or_dict) | |
| args = { | |
| 'pose': dd['pose'], | |
| 'v': dd['v_posed'], | |
| 'J': dd['J'], | |
| 'weights': dd['weights'], | |
| 'kintree_table': dd['kintree_table'], | |
| 'xp': ch, | |
| 'want_Jtr': True, | |
| 'bs_style': dd['bs_style'] | |
| } | |
| result, Jtr = verts_core(**args) | |
| result = result + dd['trans'].reshape((1,3)) | |
| result.J_transformed = Jtr + dd['trans'].reshape((1,3)) | |
| for k, v in dd.items(): | |
| setattr(result, k, v) | |
| return result | |