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''' |
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Copyright 2015 Matthew Loper, Naureen Mahmood and the Max Planck Gesellschaft. All rights reserved. |
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This software is provided for research purposes only. |
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By using this software you agree to the terms of the SMPL Model license here http://smpl.is.tue.mpg.de/license |
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More information about SMPL is available here http://smpl.is.tue.mpg. |
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For comments or questions, please email us at: smpl@tuebingen.mpg.de |
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About this file: |
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================ |
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This file defines the serialization functions of the SMPL model. |
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Modules included: |
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- save_model: |
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saves the SMPL model to a given file location as a .pkl file |
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- load_model: |
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loads the SMPL model from a given file location (i.e. a .pkl file location), |
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or a dictionary object. |
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''' |
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__all__ = ['load_model', 'save_model'] |
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import numpy as np |
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import pickle |
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import chumpy as ch |
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from chumpy.ch import MatVecMult |
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from posemapper import posemap |
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from verts import verts_core |
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def save_model(model, fname): |
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m0 = model |
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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)} |
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if hasattr(model, 'J_regressor'): |
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trainer_dict['J_regressor'] = m0.J_regressor |
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if hasattr(model, 'J_regressor_prior'): |
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trainer_dict['J_regressor_prior'] = m0.J_regressor_prior |
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if hasattr(model, 'weights_prior'): |
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trainer_dict['weights_prior'] = m0.weights_prior |
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if hasattr(model, 'shapedirs'): |
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trainer_dict['shapedirs'] = m0.shapedirs |
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if hasattr(model, 'vert_sym_idxs'): |
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trainer_dict['vert_sym_idxs'] = m0.vert_sym_idxs |
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if hasattr(model, 'bs_style'): |
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trainer_dict['bs_style'] = model.bs_style |
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else: |
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trainer_dict['bs_style'] = 'lbs' |
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pickle.dump(trainer_dict, open(fname, 'w'), -1) |
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def backwards_compatibility_replacements(dd): |
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if 'default_v' in dd: |
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dd['v_template'] = dd['default_v'] |
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del dd['default_v'] |
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if 'template_v' in dd: |
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dd['v_template'] = dd['template_v'] |
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del dd['template_v'] |
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if 'joint_regressor' in dd: |
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dd['J_regressor'] = dd['joint_regressor'] |
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del dd['joint_regressor'] |
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if 'blendshapes' in dd: |
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dd['posedirs'] = dd['blendshapes'] |
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del dd['blendshapes'] |
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if 'J' not in dd: |
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dd['J'] = dd['joints'] |
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del dd['joints'] |
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if 'bs_style' not in dd: |
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dd['bs_style'] = 'lbs' |
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def ready_arguments(fname_or_dict): |
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if not isinstance(fname_or_dict, dict): |
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dd = pickle.load(open(fname_or_dict, 'rb'), encoding='latin1') |
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else: |
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dd = fname_or_dict |
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backwards_compatibility_replacements(dd) |
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want_shapemodel = 'shapedirs' in dd |
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nposeparms = dd['kintree_table'].shape[1]*3 |
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if 'trans' not in dd: |
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dd['trans'] = np.zeros(3) |
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if 'pose' not in dd: |
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dd['pose'] = np.zeros(nposeparms) |
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if 'shapedirs' in dd and 'betas' not in dd: |
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dd['betas'] = np.zeros(dd['shapedirs'].shape[-1]) |
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for s in ['v_template', 'weights', 'posedirs', 'pose', 'trans', 'shapedirs', 'betas', 'J']: |
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if (s in dd) and not hasattr(dd[s], 'dterms'): |
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dd[s] = ch.array(dd[s]) |
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if want_shapemodel: |
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dd['v_shaped'] = dd['shapedirs'].dot(dd['betas'])+dd['v_template'] |
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v_shaped = dd['v_shaped'] |
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J_tmpx = MatVecMult(dd['J_regressor'], v_shaped[:,0]) |
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J_tmpy = MatVecMult(dd['J_regressor'], v_shaped[:,1]) |
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J_tmpz = MatVecMult(dd['J_regressor'], v_shaped[:,2]) |
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dd['J'] = ch.vstack((J_tmpx, J_tmpy, J_tmpz)).T |
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dd['v_posed'] = v_shaped + dd['posedirs'].dot(posemap(dd['bs_type'])(dd['pose'])) |
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else: |
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dd['v_posed'] = dd['v_template'] + dd['posedirs'].dot(posemap(dd['bs_type'])(dd['pose'])) |
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return dd |
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def load_model(fname_or_dict): |
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dd = ready_arguments(fname_or_dict) |
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args = { |
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'pose': dd['pose'], |
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'v': dd['v_posed'], |
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'J': dd['J'], |
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'weights': dd['weights'], |
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'kintree_table': dd['kintree_table'], |
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'xp': ch, |
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'want_Jtr': True, |
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'bs_style': dd['bs_style'] |
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} |
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result, Jtr = verts_core(**args) |
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result = result + dd['trans'].reshape((1,3)) |
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result.J_transformed = Jtr + dd['trans'].reshape((1,3)) |
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for k, v in dd.items(): |
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setattr(result, k, v) |
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return result |
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