Spaces:
Sleeping
Sleeping
File size: 4,434 Bytes
e4bf056 |
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 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 |
import os
import cv2
import json
import numpy as np
import os.path as osp
from collections import deque
from dust3r.utils.image import imread_cv2
from .base_many_view_dataset import BaseManyViewDataset
class NRGBD(BaseManyViewDataset):
def __init__(self, num_seq=1, num_frames=5,
min_thresh=10, max_thresh=100,
test_id=None, full_video=False,
tuple_path=None, seq_id=None,
kf_every=1, *args, ROOT, **kwargs):
self.ROOT = ROOT
super().__init__(*args, **kwargs)
self.num_seq = num_seq
self.num_frames = num_frames
self.max_thresh = max_thresh
self.min_thresh = min_thresh
self.test_id = test_id
self.full_video = full_video
self.kf_every = kf_every
self.seq_id = seq_id
# load all scenes
self.load_all_tuples(tuple_path)
self.load_all_scenes(ROOT)
def __len__(self):
if self.tuple_list is not None:
return len(self.tuple_list)
return len(self.scene_list) * self.num_seq
def load_all_tuples(self, tuple_path):
if tuple_path is not None:
with open(tuple_path) as f:
self.tuple_list = f.read().splitlines()
else:
self.tuple_list = None
def load_all_scenes(self, base_dir):
scenes = os.listdir(base_dir)
if self.test_id is not None:
self.scene_list = [self.test_id]
else:
self.scene_list = scenes
print(f"Found {len(self.scene_list)} sequences in split {self.split}")
def load_poses(self, path):
file = open(path, "r")
lines = file.readlines()
file.close()
poses = []
valid = []
lines_per_matrix = 4
for i in range(0, len(lines), lines_per_matrix):
if 'nan' in lines[i]:
valid.append(False)
poses.append(np.eye(4, 4, dtype=np.float32).tolist())
else:
valid.append(True)
pose_floats = [[float(x) for x in line.split()] for line in lines[i:i+lines_per_matrix]]
poses.append(pose_floats)
return np.array(poses, dtype=np.float32), valid
def _get_views(self, idx, resolution, rng):
if self.tuple_list is not None:
line = self.tuple_list[idx].split(" ")
scene_id = line[0]
img_idxs = line[1:]
else:
scene_id = self.scene_list[idx // self.num_seq]
num_files = len(os.listdir(os.path.join(self.ROOT, scene_id, 'images')))
img_idxs = [f'{i}' for i in range(num_files)]
img_idxs = self.sample_frame_idx(img_idxs, rng, full_video=self.full_video)
fx, fy, cx, cy = 554.2562584220408, 554.2562584220408, 320, 240
intrinsics_ = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]], dtype=np.float32)
posepath = osp.join(self.ROOT, scene_id, f'poses.txt')
camera_poses, valids = self.load_poses(posepath)
imgs_idxs = deque(img_idxs)
views = []
while len(imgs_idxs) > 0:
im_idx = imgs_idxs.popleft()
impath = osp.join(self.ROOT, scene_id, 'images', f'img{im_idx}.png')
depthpath = osp.join(self.ROOT, scene_id, 'depth',f'depth{im_idx}.png')
rgb_image = imread_cv2(impath)
depthmap = imread_cv2(depthpath, cv2.IMREAD_UNCHANGED)
depthmap = np.nan_to_num(depthmap.astype(np.float32), 0.0) / 1000.0
depthmap[depthmap>10] = 0
depthmap[depthmap<1e-3] = 0
rgb_image = cv2.resize(rgb_image, (depthmap.shape[1], depthmap.shape[0]))
camera_pose = camera_poses[int(im_idx)]
# gl to cv
camera_pose[:, 1:3] *= -1.0
rgb_image, depthmap, intrinsics = self._crop_resize_if_necessary(
rgb_image, depthmap, intrinsics_, resolution, rng=rng, info=impath)
views.append(dict(
img=rgb_image,
depthmap=depthmap,
camera_pose=camera_pose,
camera_intrinsics=intrinsics,
dataset='nrgbd',
label=osp.join(scene_id, im_idx),
instance=osp.split(impath)[1],
))
return views
|