caizhongang
47 lines
1.7 KiB
Python
47 lines
1.7 KiB
Python
import os
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import os.path as osp
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import numpy as np
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import torch
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import cv2
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import json
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import copy
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from pycocotools.coco import COCO
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from config import cfg
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from utils.human_models import smpl_x
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from utils.preprocessing import load_img, process_bbox, augmentation, process_db_coord, process_human_model_output, \
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get_fitting_error_3D
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from utils.transforms import world2cam, cam2pixel, rigid_align
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from humandata import HumanDataset
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class RICH(HumanDataset):
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def __init__(self, transform, data_split):
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super(RICH, self).__init__(transform, data_split)
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self.use_cache = getattr(cfg, 'use_cache', False)
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self.annot_path_cache = osp.join(cfg.data_dir, 'cache', 'rich_train_fix_betas.npz')
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self.img_shape = None # (h, w)
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self.cam_param = {}
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if self.use_cache and osp.isfile(self.annot_path_cache):
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print(f'[{self.__class__.__name__}] loading cache from {self.annot_path_cache}')
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self.datalist = self.load_cache(self.annot_path_cache)
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else:
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if self.use_cache:
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print(f'[{self.__class__.__name__}] Cache not found, generating cache...')
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if self.data_split == 'train':
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filename = getattr(cfg, 'filename', 'rich_train_fix_betas.npz')
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else:
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raise ValueError('RICH test set is not support')
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self.img_dir = osp.join(cfg.data_dir, 'RICH')
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self.annot_path = osp.join(cfg.data_dir, 'preprocessed_datasets', filename)
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# load data
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self.datalist = self.load_data(
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train_sample_interval=getattr(cfg, f'{self.__class__.__name__}_train_sample_interval', 1))
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if self.use_cache:
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self.save_cache(self.annot_path_cache, self.datalist)
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