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| 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 ETH3DDataset(BaseDataset): | |
| def __init__(self, cfg, phase, **kwargs): | |
| super(ETH3DDataset, self).__init__( | |
| cfg=cfg, | |
| phase=phase, | |
| **kwargs) | |
| self.metric_scale = cfg.metric_scale | |
| def __getitem__(self, idx): | |
| anno = self.annotations['files'][idx] | |
| curr_rgb_path = os.path.join(self.data_root, anno['rgb_path']) | |
| curr_depth_path = os.path.join(self.depth_root, anno['depth_path']) | |
| meta_data = self.load_meta_data(anno) | |
| ori_curr_intrinsic = [2000, 2000, 3024, 2016] #meta_data['cam_in'] | |
| curr_rgb = cv2.imread(curr_rgb_path) # [r, g, b] | |
| with open(curr_depth_path, 'r') as f: | |
| imgfile = np.fromfile(f, np.float32) | |
| curr_depth = imgfile.reshape((4032, 6048)) | |
| curr_depth[curr_depth>100] = 0 | |
| #curr_rgb, curr_depth = self.load_rgb_depth(curr_rgb_path, curr_depth_path) | |
| # curr_rgb = cv2.resize(curr_rgb, dsize=(3024, 2016), interpolation=cv2.INTER_LINEAR) | |
| # curr_depth = cv2.resize(curr_depth, dsize=(3024, 2016), interpolation=cv2.INTER_LINEAR) | |
| # ori_curr_intrinsic = [i//2 for i in ori_curr_intrinsic] | |
| ori_h, ori_w, _ = curr_rgb.shape | |
| # create camera model | |
| curr_cam_model = self.create_cam_model(curr_rgb.shape[0], curr_rgb.shape[1], ori_curr_intrinsic) | |
| # load tmpl rgb info | |
| # tmpl_annos = self.load_tmpl_annos(anno, curr_rgb, meta_data) | |
| # tmpl_rgb = tmpl_annos['tmpl_rgb_list'] # list of reference rgbs | |
| transform_paras = dict() | |
| rgbs, depths, intrinsics, cam_models, _, other_labels, transform_paras = self.img_transforms( | |
| images=[curr_rgb, ], | |
| labels=[curr_depth, ], | |
| intrinsics=[ori_curr_intrinsic,], | |
| cam_models=[curr_cam_model, ], | |
| transform_paras=transform_paras) | |
| # depth in original size | |
| depth_out = self.clip_depth(curr_depth) * self.depth_range[1] | |
| filename = os.path.basename(anno['rgb_path']) | |
| curr_intrinsic_mat = self.intrinsics_list2mat(intrinsics[0]) | |
| pad = transform_paras['pad'] if 'pad' in transform_paras else [0,0,0,0] | |
| scale_ratio = transform_paras['label_scale_factor'] if 'label_scale_factor' in transform_paras else 1.0 | |
| cam_models_stacks = [ | |
| torch.nn.functional.interpolate(cam_models[0][None, :, :, :], size=(cam_models[0].shape[1]//i, cam_models[0].shape[2]//i), mode='bilinear', align_corners=False).squeeze() | |
| for i in [2, 4, 8, 16, 32] | |
| ] | |
| raw_rgb = torch.from_numpy(curr_rgb) | |
| data = dict(input=rgbs[0], | |
| target=depth_out, | |
| intrinsic=curr_intrinsic_mat, | |
| filename=filename, | |
| dataset=self.data_name, | |
| cam_model=cam_models_stacks, | |
| ref_input=rgbs[1:], | |
| tmpl_flg=False, | |
| pad=pad, | |
| scale=scale_ratio, | |
| raw_rgb=raw_rgb, | |
| normal = np.zeros_like(curr_rgb.transpose((2,0,1))), | |
| #stereo_depth=torch.zeros_like(depth_out) | |
| ) | |
| return data | |
| def process_depth(self, depth): | |
| depth[depth>65500] = 0 | |
| depth /= self.metric_scale | |
| 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 = NYUDataset(cfg['Apolloscape'], 'train', **cfg.data_basic) | |
| print(dataset_i) | |