| | ''' |
| | Copyright 2017 Javier Romero, Dimitrios Tzionas, Michael J Black 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 MANO/SMPL+H Model license here http://mano.is.tue.mpg.de/license |
| | |
| | More information about MANO/SMPL+H is available at http://mano.is.tue.mpg.de. |
| | For comments or questions, please email us at: mano@tue.mpg.de |
| | |
| | |
| | About this file: |
| | ================ |
| | This file defines a wrapper for the loading functions of the MANO model. |
| | |
| | Modules included: |
| | - load_model: |
| | loads the MANO model from a given file location (i.e. a .pkl file location), |
| | or a dictionary object. |
| | |
| | ''' |
| | import os |
| | import cv2 |
| | import torch |
| | import numpy as np |
| | import pickle |
| | import chumpy as ch |
| | from chumpy.ch import MatVecMult |
| |
|
| |
|
| | class Rodrigues(ch.Ch): |
| | dterms = 'rt' |
| |
|
| | def compute_r(self): |
| | return cv2.Rodrigues(self.rt.r)[0] |
| |
|
| | def compute_dr_wrt(self, wrt): |
| | if wrt is self.rt: |
| | return cv2.Rodrigues(self.rt.r)[1].T |
| |
|
| |
|
| | def lrotmin(p): |
| | if isinstance(p, np.ndarray): |
| | p = p.ravel()[3:] |
| | return np.concatenate( |
| | [(cv2.Rodrigues(np.array(pp))[0] - np.eye(3)).ravel() |
| | for pp in p.reshape((-1, 3))]).ravel() |
| | if p.ndim != 2 or p.shape[1] != 3: |
| | p = p.reshape((-1, 3)) |
| | p = p[1:] |
| | return ch.concatenate([(Rodrigues(pp) - ch.eye(3)).ravel() |
| | for pp in p]).ravel() |
| |
|
| |
|
| | def posemap(s): |
| | if s == 'lrotmin': |
| | return lrotmin |
| | else: |
| | raise Exception('Unknown posemapping: %s' % (str(s), )) |
| |
|
| |
|
| | def ready_arguments(fname_or_dict, posekey4vposed='pose'): |
| | if not isinstance(fname_or_dict, dict): |
| | dd = pickle.load(open(fname_or_dict, 'rb'), encoding='latin1') |
| | else: |
| | dd = fname_or_dict |
| |
|
| | 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]) |
| |
|
| | assert (posekey4vposed in dd) |
| | 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 |
| | pose_map_res = posemap(dd['bs_type'])(dd[posekey4vposed]) |
| | dd['v_posed'] = v_shaped + dd['posedirs'].dot(pose_map_res) |
| | else: |
| | pose_map_res = posemap(dd['bs_type'])(dd[posekey4vposed]) |
| | dd_add = dd['posedirs'].dot(pose_map_res) |
| | dd['v_posed'] = dd['v_template'] + dd_add |
| |
|
| | return dd |
| |
|
| |
|
| |
|
| | def get_mano_pca_basis(ncomps=45, use_pca=True, side='right', mano_root='data/base_data/human_models/mano'): |
| | if use_pca: |
| | ncomps = ncomps |
| | else: |
| | ncomps = 45 |
| |
|
| | if side == 'right': |
| | mano_path = os.path.join(mano_root, 'MANO_RIGHT.pkl') |
| | elif side == 'left': |
| | mano_path = os.path.join(mano_root, 'MANO_LEFT.pkl') |
| | smpl_data = ready_arguments(mano_path) |
| | hands_components = smpl_data['hands_components'] |
| | selected_components = hands_components[:ncomps] |
| | th_selected_comps = selected_components |
| |
|
| | return torch.tensor(th_selected_comps, dtype=torch.float32) |
| |
|
| |
|
| |
|
| | def change_flat_hand_mean(hand_pose, remove=True, side='right', mano_root='data/base_data/human_models/mano'): |
| | if side == 'right': |
| | mano_path = os.path.join(mano_root, 'MANO_RIGHT.pkl') |
| | elif side == 'left': |
| | mano_path = os.path.join(mano_root, 'MANO_LEFT.pkl') |
| | smpl_data = ready_arguments(mano_path) |
| |
|
| | |
| | hands_mean = smpl_data['hands_mean'] |
| | hands_mean = hands_mean.copy() |
| |
|
| | if remove: |
| | hand_pose[3:] = hand_pose[3:] - hands_mean |
| | else: |
| | hand_pose[3:] = hand_pose[3:] + hands_mean |
| | return hand_pose |
