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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 Scannet(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 load_all_scenes(self, base_dir):
self.folder = {'train': 'scans', 'val': 'scans', 'test': 'scans_test'}[self.split]
if self.test_id is None:
meta_split = osp.join(base_dir, 'splits', f'scannetv2_{self.split}.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 _get_views(self, idx, resolution, rng, attempts=0):
scene_id = self.scene_list[idx // self.num_seq]
# Load metadata
intri_path = osp.join(self.ROOT, self.folder, scene_id, 'intrinsic/intrinsic_depth.txt')
intri = np.loadtxt(intri_path).astype(np.float32)[:3, :3]
# Load image data
data_path = osp.join(self.ROOT, self.folder, scene_id, 'sensor_data')
num_files = len([name for name in os.listdir(data_path) if 'color' in name])
img_idxs_ = [f'{i:06d}' for i in range(num_files)]
imgs_idxs = self.sample_frame_idx(img_idxs_, rng, full_video=self.full_video)
imgs_idxs = deque(imgs_idxs)
views = []
while len(imgs_idxs) > 0:
im_idx = imgs_idxs.popleft()
# Load image data
impath = osp.join(self.ROOT, self.folder, scene_id, 'sensor_data', f'frame-{im_idx}.color.jpg')
depthpath = osp.join(self.ROOT, self.folder, scene_id, 'sensor_data', f'frame-{im_idx}.depth.png')
posepath = osp.join(self.ROOT, self.folder, scene_id, 'sensor_data', f'frame-{im_idx}.pose.txt')
rgb_image = imread_cv2(impath)
depthmap = imread_cv2(depthpath, cv2.IMREAD_UNCHANGED)
rgb_image = cv2.resize(rgb_image, (depthmap.shape[1], depthmap.shape[0]))
depthmap = np.nan_to_num(depthmap.astype(np.float32), 0.0) / 1000.0
camera_pose = np.loadtxt(posepath).astype(np.float32)
rgb_image, depthmap, intrinsics = self._crop_resize_if_necessary(
rgb_image, depthmap, intri, resolution, rng=rng, info=impath)
# Check if the image is valid
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='scannet',
label=osp.join(scene_id, im_idx),
instance=osp.split(impath)[1],
))
return views
if __name__ == "__main__":
num_frames=5
print('loading dataset')
dataset = Scannet(split='train', ROOT="./data/scannet_simple", resolution=224, num_seq=100, max_thresh=100)
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