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import os.path as osp
import cv2
import numpy as np
import itertools
import os
import sys
sys.path.append(osp.join(osp.dirname(__file__), "..", ".."))
from dust3r.datasets.base.base_multiview_dataset import BaseMultiViewDataset
from dust3r.utils.image import imread_cv2
class ScanNetpp_Multi(BaseMultiViewDataset):
def __init__(self, *args, ROOT, **kwargs):
self.ROOT = ROOT
self.video = True
self.is_metric = True
self.max_interval = 3
super().__init__(*args, **kwargs)
assert self.split == "train"
self.loaded_data = self._load_data()
def _load_data(self):
with np.load(osp.join(self.ROOT, "all_metadata.npz")) as data:
self.scenes = data["scenes"]
offset = 0
scenes = []
sceneids = []
images = []
intrinsics = []
trajectories = []
groups = []
id_ranges = []
j = 0
self.image_num = 0
for scene in self.scenes:
scene_dir = osp.join(self.ROOT, scene)
with np.load(
osp.join(scene_dir, "new_scene_metadata.npz"), allow_pickle=True
) as data:
imgs = data["images"]
self.image_num += len(imgs)
img_ids = np.arange(len(imgs)).tolist()
intrins = data["intrinsics"]
traj = data["trajectories"]
imgs_on_disk = sorted(os.listdir(osp.join(scene_dir, "images")))
imgs_on_disk = list(map(lambda x: x[:-4], imgs_on_disk))
dslr_ids = [
i + offset
for i in img_ids
if imgs[i].startswith("DSC") and imgs[i] in imgs_on_disk
]
iphone_ids = [
i + offset
for i in img_ids
if imgs[i].startswith("frame") and imgs[i] in imgs_on_disk
]
num_imgs = len(imgs)
assert max(dslr_ids) < min(iphone_ids)
assert "image_collection" in data
img_groups = []
img_id_ranges = []
for ref_id, group in data["image_collection"].item().items():
if len(group) + 1 < self.num_views:
continue
group.insert(0, (ref_id, 1.0))
sorted_group = sorted(group, key=lambda x: x[1], reverse=True)
group = [int(x[0] + offset) for x in sorted_group]
img_groups.append(sorted(group))
if imgs[ref_id].startswith("frame"):
img_id_ranges.append(dslr_ids)
else:
img_id_ranges.append(iphone_ids)
if len(img_groups) == 0:
print(f"Skipping {scene}")
continue
scenes.append(scene)
sceneids.extend([j] * num_imgs)
images.extend(imgs)
intrinsics.append(intrins)
trajectories.append(traj)
# offset groups
groups.extend(img_groups)
id_ranges.extend(img_id_ranges)
offset += num_imgs
j += 1
self.scenes = scenes
self.sceneids = sceneids
self.images = images
self.intrinsics = np.concatenate(intrinsics, axis=0)
self.trajectories = np.concatenate(trajectories, axis=0)
self.id_ranges = id_ranges
self.groups = groups
def __len__(self):
return len(self.groups) * 10
def get_image_num(self):
return self.image_num
def _get_views(self, idx, resolution, rng, num_views):
idx = idx // 10
image_idxs = self.groups[idx]
rand_val = rng.random()
image_idxs_video = self.id_ranges[idx]
cut_off = num_views if not self.allow_repeat else max(num_views // 3, 3)
start_image_idxs = image_idxs_video[: len(image_idxs_video) - cut_off + 1]
if rand_val < 0.7 and len(start_image_idxs) > 0:
start_id = rng.choice(start_image_idxs)
pos, ordered_video = self.get_seq_from_start_id(
num_views,
start_id,
image_idxs_video,
rng,
max_interval=self.max_interval,
video_prob=0.8,
fix_interval_prob=0.5,
block_shuffle=16,
)
image_idxs = np.array(image_idxs_video)[pos]
else:
ordered_video = True
# ordered video with varying intervals
num_candidates = len(image_idxs)
max_id = min(num_candidates, int(num_views * (2 + 2 * rng.random())))
image_idxs = sorted(rng.permutation(image_idxs[:max_id])[:num_views])
if rand_val > 0.75:
ordered_video = False
image_idxs = rng.permutation(image_idxs)
views = []
for v, view_idx in enumerate(image_idxs):
scene_id = self.sceneids[view_idx]
scene_dir = osp.join(self.ROOT, self.scenes[scene_id])
intrinsics = self.intrinsics[view_idx]
camera_pose = self.trajectories[view_idx]
basename = self.images[view_idx]
# Load RGB image
rgb_image = imread_cv2(osp.join(scene_dir, "images", basename + ".jpg"))
# Load depthmap
depthmap = imread_cv2(
osp.join(scene_dir, "depth", basename + ".png"), cv2.IMREAD_UNCHANGED
)
depthmap = depthmap.astype(np.float32) / 1000
depthmap[~np.isfinite(depthmap)] = 0 # invalid
rgb_image, depthmap, intrinsics = self._crop_resize_if_necessary(
rgb_image, depthmap, intrinsics, resolution, rng=rng, info=view_idx
)
# generate img mask and raymap mask
img_mask, ray_mask = self.get_img_and_ray_masks(
self.is_metric, v, rng, p=[0.75, 0.2, 0.05]
)
views.append(
dict(
img=rgb_image,
depthmap=depthmap.astype(np.float32),
camera_pose=camera_pose.astype(np.float32),
camera_intrinsics=intrinsics.astype(np.float32),
dataset="ScanNet++",
label=self.scenes[scene_id] + "_" + basename,
instance=f"{str(idx)}_{str(view_idx)}",
is_metric=self.is_metric,
is_video=ordered_video,
quantile=np.array(0.99, dtype=np.float32),
img_mask=img_mask,
ray_mask=ray_mask,
camera_only=False,
depth_only=False,
single_view=False,
reset=False,
)
)
assert len(views) == num_views
return views
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