File size: 1,195 Bytes
78c0046 |
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 |
import torch
from torch.utils.data import Dataset
import glob
import numpy as np
import os
from tqdm import tqdm
class Robo360(Dataset):
def __init__(self, datadir, downsample=4):
self.root_dir = datadir
self.downsample = downsample
self.read_meta()
def read_meta(self):
poses_bounds = np.load(os.path.join(self.root_dir, 'poses_bounds.npy')) # (N_cams, 17)
poses = poses_bounds[:, :15].reshape(-1, 3, 5) # (N_images, 3, 5)
self.near_fars = poses_bounds[:, -2:] # (N_images, 2)
# Step 1: rescale focal length according to training resolution
H, W, _ = poses[0, :, -1]
self.focal = poses[:, -1, -1]
self.img_wh = np.array([int(W / self.downsample), int(H / self.downsample)])
self.focal = self.focal * self.img_wh[0] / W
# Step 2: correct poses
# Original poses has rotation in form "down right back", change to "right up back"
# See https://github.com/bmild/nerf/issues/34
self.poses = np.concatenate([poses[..., 1:2], -poses[..., :1], poses[..., 2:4]], -1)
def __len__(self):
return 0
def __getitem__(self, idx):
return None |