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0a95064 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | import os
import os.path as osp
import numpy as np
import torch
import cv2
import json
import copy
from pycocotools.coco import COCO
from config import cfg
from utils.human_models import smpl_x
from utils.preprocessing import load_img, process_bbox, augmentation, process_db_coord, process_human_model_output, \
get_fitting_error_3D
from utils.transforms import world2cam, cam2pixel, rigid_align
from humandata import HumanDataset
class RICH(HumanDataset):
def __init__(self, transform, data_split):
super(RICH, self).__init__(transform, data_split)
self.use_cache = getattr(cfg, 'use_cache', False)
self.annot_path_cache = osp.join(cfg.data_dir, 'cache', 'rich_train_fix_betas.npz')
self.img_shape = None # (h, w)
self.cam_param = {}
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...')
if self.data_split == 'train':
filename = getattr(cfg, 'filename', 'rich_train_fix_betas.npz')
else:
raise ValueError('RICH test set is not support')
self.img_dir = osp.join(cfg.data_dir, 'RICH')
self.annot_path = osp.join(cfg.data_dir, 'preprocessed_datasets', filename)
# load data
self.datalist = self.load_data(
train_sample_interval=getattr(cfg, f'{self.__class__.__name__}_train_sample_interval', 1))
if self.use_cache:
self.save_cache(self.annot_path_cache, self.datalist)
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