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