Spaces:
Sleeping
Sleeping
File size: 6,918 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 |
import os
import cv2
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 BlendMVS(BaseManyViewDataset):
def __init__(self, num_seq=100, num_frames=5,
min_thresh=10, max_thresh=100,
test_id=None, full_video=False,
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
# load all scenes
self.load_all_scenes(ROOT)
def __len__(self):
return len(self.scene_list) * self.num_seq
def sample_pairs(self, pairs_path, rng, max_trials=10):
cluster_lines = open(pairs_path).read().splitlines()
image_num = int(cluster_lines[0])
trials = 0
while trials < max_trials:
trials += 1
sample_idx = rng.choice(image_num)
ref_idx = int(cluster_lines[2 * sample_idx + 1])
cluster_info = cluster_lines[2 * sample_idx + 2].split()
total_view_num = int(cluster_info[0])
if total_view_num > self.num_frames-1:
list_idx = ['{:08d}.jpg'.format(ref_idx)]
sample_cidx = rng.choice(total_view_num, self.num_frames-1, replace=False)
for cidx in sample_cidx:
list_idx.append('{:08d}.jpg'.format(int(cluster_info[2 * cidx + 1])))
if rng.choice([True, False]):
list_idx.reverse()
return list_idx
return None
def load_all_scenes(self, base_dir):
if self.test_id is None:
meta_split = osp.join(base_dir, f'{self.split}_list.txt')
if not osp.exists(meta_split):
raise FileNotFoundError(f"Split file {meta_split} not found")
with open(meta_split) as f:
self.scene_list = f.read().splitlines()
print(f"Found {len(self.scene_list)} scenes in split {self.split}")
else:
if isinstance(self.test_id, list):
self.scene_list = self.test_id
else:
self.scene_list = [self.test_id]
print(f"Test_id: {self.test_id}")
def load_cam_mvsnet(self, f, interval_scale=1):
""" read camera txt file """
# f = open(file)
RT = np.loadtxt(f, skiprows=1, max_rows=4, dtype=np.float32)
assert RT.shape == (4, 4)
# RT = np.linalg.inv(RT) # world2cam to cam2world
K = np.loadtxt(f, skiprows=2, max_rows=3, dtype=np.float32)
assert K.shape == (3, 3)
return K, RT
def _get_views(self, idx, resolution, rng, attempts=0):
scene_id = self.scene_list[idx // self.num_seq]
image_path = osp.join(self.ROOT, scene_id, 'blended_images')
depth_path = osp.join(self.ROOT, scene_id, 'rendered_depth_maps')
cam_path = osp.join(self.ROOT, scene_id, 'cams')
pairs_path = osp.join(self.ROOT, scene_id, 'cams', 'pair.txt')
if not self.full_video:
img_idxs = self.sample_pairs(pairs_path, rng)
else:
img_idxs = sorted(os.listdir(image_path))
img_idxs = img_idxs[::self.kf_every]
if img_idxs is None:
new_idx = rng.integers(0, self.__len__()-1)
return self._get_views(new_idx, resolution, rng)
imgs_idxs = deque(img_idxs)
views = []
max_depth_min = 1e8
max_depth_max = 0.0
max_depth_first = None
while len(imgs_idxs) > 0:
im_idx = imgs_idxs.popleft()
impath = osp.join(image_path, im_idx)
depthpath = osp.join(depth_path, im_idx.replace('.jpg', '.pfm'))
campath = osp.join(cam_path, im_idx.replace('.jpg', '_cam.txt'))
rgb_image = imread_cv2(impath)
depthmap = imread_cv2(depthpath, cv2.IMREAD_UNCHANGED)
depthmap = np.nan_to_num(depthmap.astype(np.float32), 0.0)
cur_intrinsics, camera_pose = self.load_cam_mvsnet(open(campath, 'r'))
intrinsics = cur_intrinsics[:3, :3]
camera_pose = np.linalg.inv(camera_pose)
H, W = rgb_image.shape[:2]
cx, cy = intrinsics[:2, 2].round().astype(int)
min_margin_x = min(cx, W-cx)
min_margin_y = min(cy, H-cy)
if min_margin_x <= W/5 or min_margin_y <= H/5:
new_idx = rng.integers(0, self.__len__()-1)
return self._get_views(new_idx, resolution, rng)
rgb_image, depthmap, intrinsics = self._crop_resize_if_necessary(
rgb_image, depthmap, intrinsics, resolution, rng=rng, info=impath)
input_depth_max = depthmap.max()
if input_depth_max> max_depth_max:
max_depth_max = input_depth_max
if input_depth_max < max_depth_min:
max_depth_min = input_depth_max
if max_depth_first is None:
max_depth_first = input_depth_max
num_valid = (depthmap > 0.0).sum()
if num_valid == 0 or (not np.isfinite(camera_pose).all()):
if self.full_video:
print(f"Warning: No valid depthmap found for {impath}")
continue
else:
if attempts >= 5:
new_idx = rng.integers(0, self.__len__()-1)
return self._get_views(new_idx, resolution, rng)
return self._get_views(idx, resolution, rng, attempts+1)
views.append(dict(
img=rgb_image,
depthmap=depthmap,
camera_pose=camera_pose,
camera_intrinsics=intrinsics,
dataset='blendmvs',
label=osp.join(scene_id, im_idx),
instance=osp.split(impath)[1],
))
if max_depth_max / max_depth_min > 100. or max_depth_max / max_depth_first > 10.:
print(f"Warning: Depthmap range too large: {max_depth_max} {max_depth_min} {max_depth_first}")
new_idx = rng.integers(0, self.__len__()-1)
return self._get_views(new_idx, resolution, rng)
return views
|