import os import os.path as osp import numpy as np import torch import cv2 import json import copy import csv from pycocotools.coco import COCO from config.config import cfg from util.human_models import smpl_x from util.transforms import world2cam, cam2pixel, rigid_align from humandata import HumanDataset from util.transforms import rigid_align, rigid_align_batch class EgoBody_Egocentric(HumanDataset): def __init__(self, transform, data_split): super(EgoBody_Egocentric, self).__init__(transform, data_split) if self.data_split == 'train': filename = 'data/preprocessed_npz/multihuman_data/egobody_egocentric_train_multi_080824.npz' self.annot_path_cache = 'data/preprocessed_npz/cache/egobody_egocentric_train_cache_080824.npz' self.sample_interval = 5 else: filename = 'data/preprocessed_npz/multihuman_data/egobody_egocentric_val_multi_080824.npz' self.annot_path_cache = 'data/preprocessed_npz/cache/egobody_egocentric_val_cache_080824.npz' self.sample_interval = 1 self.use_betas_neutral = getattr(cfg, 'egobody_fix_betas', False) self.img_dir = 'data/osx_data/EgoBody' self.annot_path = filename self.use_cache = getattr(cfg, 'use_cache', False) self.img_shape = (1080, 1920) # (h, w) self.cam_param = {} # check image shape img_path = osp.join(self.img_dir, np.load(self.annot_path)['image_path'][0]) img_shape = cv2.imread(img_path).shape[:2] assert self.img_shape == img_shape, 'image shape is incorrect: {} vs {}'.format( self.img_shape, img_shape) # load data or cache if self.use_cache and osp.isfile(self.annot_path_cache): print( f'[{self.__class__.__name__}] loading cache from {self.annot_path_cache}' ) self.datalist = self.load_cache(self.annot_path_cache) else: if self.use_cache: print( f'[{self.__class__.__name__}] Cache not found, generating cache...' ) self.datalist = self.load_data(train_sample_interval=getattr( cfg, f'{self.__class__.__name__}_train_sample_interval', self.sample_interval)) if self.use_cache: self.save_cache(self.annot_path_cache, self.datalist) def evaluate(self, outs, cur_sample_idx): annots = self.datalist sample_num = len(outs) eval_result = { 'pa_mpvpe_all': [], 'pa_mpvpe_l_hand': [], 'pa_mpvpe_r_hand': [], 'pa_mpvpe_hand': [], 'pa_mpvpe_face': [], 'mpvpe_all': [], 'mpvpe_l_hand': [], 'mpvpe_r_hand': [], 'mpvpe_hand': [], 'mpvpe_face': [] } vis = getattr(cfg, 'vis', False) vis_save_dir = cfg.vis_dir csv_file = f'{cfg.result_dir}/egobody_smplx_error.csv' file = open(csv_file, 'a', newline='') for n in range(sample_num): annot = annots[cur_sample_idx + n] out = outs[n] mesh_gt = out['smplx_mesh_cam_target'] mesh_out = out['smplx_mesh_cam'] ann_idx = out['gt_ann_idx'] img_path = [] for ann_id in ann_idx: img_path.append(annots[ann_id]['img_path']) eval_result['img_path'] = img_path eval_result['ann_idx'] = ann_idx # MPVPE from all vertices mesh_out_align = \ mesh_out - np.dot( smpl_x.J_regressor, mesh_out).transpose(1,0,2)[:, smpl_x.J_regressor_idx['pelvis'], None, :] + \ np.dot(smpl_x.J_regressor, mesh_gt).transpose(1,0,2)[:, smpl_x.J_regressor_idx['pelvis'], None, :] eval_result['mpvpe_all'].extend( np.sqrt(np.sum( (mesh_out_align - mesh_gt)**2, -1)).mean(-1) * 1000) mesh_out_align = rigid_align_batch(mesh_out, mesh_gt) eval_result['pa_mpvpe_all'].extend( np.sqrt(np.sum( (mesh_out_align - mesh_gt)**2, -1)).mean(-1) * 1000) # MPVPE from hand vertices mesh_gt_lhand = mesh_gt[:, smpl_x.hand_vertex_idx['left_hand'], :] mesh_out_lhand = mesh_out[:, smpl_x.hand_vertex_idx['left_hand'], :] mesh_gt_rhand = mesh_gt[:, smpl_x.hand_vertex_idx['right_hand'], :] mesh_out_rhand = mesh_out[:, smpl_x.hand_vertex_idx['right_hand'], :] mesh_out_lhand_align = \ mesh_out_lhand - \ np.dot(smpl_x.J_regressor, mesh_out).transpose(1,0,2)[:, smpl_x.J_regressor_idx['lwrist'], None, :] + \ np.dot(smpl_x.J_regressor, mesh_gt).transpose(1,0,2)[:, smpl_x.J_regressor_idx['lwrist'], None, :] mesh_out_rhand_align = \ mesh_out_rhand - \ np.dot(smpl_x.J_regressor, mesh_out).transpose(1,0,2)[:, smpl_x.J_regressor_idx['rwrist'], None, :] + \ np.dot(smpl_x.J_regressor, mesh_gt).transpose(1,0,2)[:, smpl_x.J_regressor_idx['rwrist'], None, :] eval_result['mpvpe_l_hand'].extend( np.sqrt(np.sum( (mesh_out_lhand_align - mesh_gt_lhand)**2, -1)).mean(-1) * 1000) eval_result['mpvpe_r_hand'].extend( np.sqrt(np.sum( (mesh_out_rhand_align - mesh_gt_rhand)**2, -1)).mean(-1) * 1000) eval_result['mpvpe_hand'].extend( (np.sqrt(np.sum( (mesh_out_lhand_align - mesh_gt_lhand)**2, -1)).mean(-1) * 1000 + np.sqrt(np.sum( (mesh_out_rhand_align - mesh_gt_rhand)**2, -1)).mean(-1) * 1000) / 2.) mesh_out_lhand_align = rigid_align_batch(mesh_out_lhand, mesh_gt_lhand) mesh_out_rhand_align = rigid_align_batch(mesh_out_rhand, mesh_gt_rhand) eval_result['pa_mpvpe_l_hand'].extend( np.sqrt(np.sum( (mesh_out_lhand_align - mesh_gt_lhand)**2, -1)).mean(-1) * 1000) eval_result['pa_mpvpe_r_hand'].extend( np.sqrt(np.sum( (mesh_out_rhand_align - mesh_gt_rhand)**2, -1)).mean(-1) * 1000) eval_result['pa_mpvpe_hand'].extend( (np.sqrt(np.sum( (mesh_out_lhand_align - mesh_gt_lhand)**2, -1)).mean(-1) * 1000 + np.sqrt(np.sum( (mesh_out_rhand_align - mesh_gt_rhand)**2, -1)).mean(-1) * 1000) / 2.) save_error=True if save_error: writer = csv.writer(file) new_line = [ann_idx[n], img_path[n], eval_result['mpvpe_all'][-1], eval_result['pa_mpvpe_all'][-1]] writer.writerow(new_line) return eval_result def print_eval_result(self, eval_result): print('======Egocentric======') print('PA MPVPE (All): %.2f mm' % np.mean(eval_result['pa_mpvpe_all'])) print('PA MPVPE (L-Hands): %.2f mm' % np.mean(eval_result['pa_mpvpe_l_hand'])) print('PA MPVPE (R-Hands): %.2f mm' % np.mean(eval_result['pa_mpvpe_r_hand'])) print('PA MPVPE (Hands): %.2f mm' % np.mean(eval_result['pa_mpvpe_hand'])) print('PA MPVPE (Face): %.2f mm' % np.mean(eval_result['pa_mpvpe_face'])) print() print('MPVPE (All): %.2f mm' % np.mean(eval_result['mpvpe_all'])) print('MPVPE (L-Hands): %.2f mm' % np.mean(eval_result['mpvpe_l_hand'])) print('MPVPE (R-Hands): %.2f mm' % np.mean(eval_result['mpvpe_r_hand'])) print('MPVPE (Hands): %.2f mm' % np.mean(eval_result['mpvpe_hand'])) print('MPVPE (Face): %.2f mm' % np.mean(eval_result['mpvpe_face'])) out_file = osp.join(cfg.result_dir,'Egocentric_val.txt') if os.path.exists(out_file): f = open(out_file, 'a+') else: f = open(out_file, 'w', encoding="utf-8") f.write('\n') f.write(f'{cfg.exp_name}\n') f.write(f'Egocentric dataset: \n') f.write('PA MPVPE (All): %.2f mm\n' % np.mean(eval_result['pa_mpvpe_all'])) f.write('PA MPVPE (L-Hands): %.2f mm\n' % np.mean(eval_result['pa_mpvpe_l_hand'])) f.write('PA MPVPE (R-Hands): %.2f mm\n' % np.mean(eval_result['pa_mpvpe_r_hand'])) f.write('PA MPVPE (Hands): %.2f mm\n' % np.mean(eval_result['pa_mpvpe_hand'])) f.write('PA MPVPE (Face): %.2f mm\n' % np.mean(eval_result['pa_mpvpe_face'])) f.write('MPVPE (All): %.2f mm\n' % np.mean(eval_result['mpvpe_all'])) f.write('MPVPE (L-Hands): %.2f mm\n' % np.mean(eval_result['mpvpe_l_hand'])) f.write('MPVPE (R-Hands): %.2f mm\n' % np.mean(eval_result['mpvpe_r_hand'])) f.write('MPVPE (Hands): %.2f mm\n' % np.mean(eval_result['mpvpe_hand'])) f.write('MPVPE (Face): %.2f mm\n' % np.mean(eval_result['mpvpe_face']))