| import os | |
| import json | |
| import torch | |
| import torchvision.transforms as transforms | |
| import os.path | |
| import numpy as np | |
| import cv2 | |
| from torch.utils.data import Dataset | |
| import random | |
| from .__base_dataset__ import BaseDataset | |
| class DDADDataset(BaseDataset): | |
| def __init__(self, cfg, phase, **kwargs): | |
| super(DDADDataset, self).__init__( | |
| cfg=cfg, | |
| phase=phase, | |
| **kwargs) | |
| self.metric_scale = cfg.metric_scale | |
| #self.cap_range = self.depth_range # in meter | |
| def process_depth(self, depth, rgb): | |
| depth[depth>65500] = 0 | |
| depth /= 200.0 | |
| # depth[(depth>self.cap_range[1]) | (depth<self.cap_range[0])] = -1 | |
| # depth /= self.cap_range[1] | |
| return depth | |
| if __name__ == '__main__': | |
| from mmcv.utils import Config | |
| cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py') | |
| dataset_i = ApolloscapeDataset(cfg['Apolloscape'], 'train', **cfg.data_basic) | |
| print(dataset_i) | |