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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']))
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