File size: 2,604 Bytes
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 48 49 50 51 52 53 54 55 56 57 58 59 | 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
# 'talkshow_smplx_chemistry_path.npz' zipfile.BadZipFile: File is not a zip file
# ['talkshow_smplx_conan.npz',
# 'talkshow_smplx_oliver_path.npz', 'talkshow_smplx_seth.npz']:
class Talkshow(HumanDataset):
def __init__(self, transform, data_split):
super(Talkshow, self).__init__(transform, data_split)
sample_rate = getattr(cfg, 'Talkshow_train_sample_interval', 1)
self.use_cache = getattr(cfg, 'use_cache', False)
self.annot_path_cache = osp.join(cfg.data_dir, 'cache', 'talkshow_smplx.npz')
self.img_shape = None # (h, w)
self.cam_param = {}
# 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 = []
for pre_prc_file in ['talkshow_smplx_chemistry.npz', 'talkshow_smplx_conan.npz',
'talkshow_smplx_oliver.npz', 'talkshow_smplx_seth.npz']:
if self.data_split == 'train':
filename = getattr(cfg, 'filename', pre_prc_file)
else:
raise ValueError('Talkshow test set is not support')
self.img_dir = osp.join(cfg.data_dir, 'Talkshow')
self.annot_path = osp.join(cfg.data_dir, 'preprocessed_datasets', filename)
# 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
datalist_slice = self.load_data(sample_rate)
self.datalist.extend(datalist_slice)
if self.use_cache:
self.save_cache(self.annot_path_cache, self.datalist) |