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import os.path as osp
import os
import sys
import itertools
sys.path.append(osp.join(osp.dirname(__file__), "..", ".."))
import cv2
import numpy as np
from dust3r.datasets.base.base_multiview_dataset import BaseMultiViewDataset
from dust3r.utils.image import imread_cv2
def stratified_sampling(indices, num_samples, rng=None):
if num_samples > len(indices):
raise ValueError("num_samples cannot exceed the number of available indices.")
elif num_samples == len(indices):
return indices
sorted_indices = sorted(indices)
stride = len(sorted_indices) / num_samples
sampled_indices = []
if rng is None:
rng = np.random.default_rng()
for i in range(num_samples):
start = int(i * stride)
end = int((i + 1) * stride)
# Ensure end does not exceed the list
end = min(end, len(sorted_indices))
if start < end:
# Randomly select within the current stratum
rand_idx = rng.integers(start, end)
sampled_indices.append(sorted_indices[rand_idx])
else:
# In case of any rounding issues, select the last index
sampled_indices.append(sorted_indices[-1])
return rng.permutation(sampled_indices)
class ARKitScenes_Multi(BaseMultiViewDataset):
def __init__(self, *args, split, ROOT, **kwargs):
self.ROOT = ROOT
self.video = True
self.is_metric = True
self.max_interval = 8
super().__init__(*args, **kwargs)
if split == "train":
self.split = "Training"
elif split == "test":
self.split = "Test"
else:
raise ValueError("")
self.loaded_data = self._load_data(self.split)
def _load_data(self, split):
with np.load(osp.join(self.ROOT, split, "all_metadata.npz")) as data:
self.scenes: np.ndarray = data["scenes"]
high_res_list = np.array(
[
d
for d in os.listdir(
os.path.join(
self.ROOT.rstrip("/") + "_highres",
split if split == "Training" else "Validation",
)
)
if os.path.join(self.ROOT + "_highres", split, d)
]
)
self.scenes = np.setdiff1d(self.scenes, high_res_list)
offset = 0
counts = []
scenes = []
sceneids = []
images = []
intrinsics = []
trajectories = []
groups = []
id_ranges = []
j = 0
for scene_idx, scene in enumerate(self.scenes):
scene_dir = osp.join(self.ROOT, self.split, scene)
with np.load(
osp.join(scene_dir, "new_scene_metadata.npz"), allow_pickle=True
) as data:
imgs = data["images"]
intrins = data["intrinsics"]
traj = data["trajectories"]
min_seq_len = (
self.num_views
if not self.allow_repeat
else max(self.num_views // 3, 3)
)
if len(imgs) < min_seq_len:
print(f"Skipping {scene}")
continue
collections = {}
assert "image_collection" in data, "Image collection not found"
collections["image"] = data["image_collection"]
num_imgs = imgs.shape[0]
img_groups = []
min_group_len = (
self.num_views
if not self.allow_repeat
else max(self.num_views // 3, 3)
)
for ref_id, group in collections["image"].item().items():
if len(group) + 1 < min_group_len:
continue
# groups are (idx, score)s
group.insert(0, (ref_id, 1.0))
group = [int(x[0] + offset) for x in group]
img_groups.append(sorted(group))
if len(img_groups) == 0:
print(f"Skipping {scene}")
continue
scenes.append(scene)
sceneids.extend([j] * num_imgs)
id_ranges.extend([(offset, offset + num_imgs) for _ in range(num_imgs)])
images.extend(imgs)
K = np.expand_dims(np.eye(3), 0).repeat(num_imgs, 0)
K[:, 0, 0] = [fx for _, _, fx, _, _, _ in intrins]
K[:, 1, 1] = [fy for _, _, _, fy, _, _ in intrins]
K[:, 0, 2] = [cx for _, _, _, _, cx, _ in intrins]
K[:, 1, 2] = [cy for _, _, _, _, _, cy in intrins]
intrinsics.extend(list(K))
trajectories.extend(list(traj))
# offset groups
groups.extend(img_groups)
counts.append(offset)
offset += num_imgs
j += 1
self.scenes = scenes
self.sceneids = sceneids
self.id_ranges = id_ranges
self.images = images
self.intrinsics = intrinsics
self.trajectories = trajectories
self.groups = groups
def __len__(self):
return len(self.groups)
def get_image_num(self):
return len(self.images)
def _get_views(self, idx, resolution, rng, num_views):
if rng.choice([True, False]):
image_idxs = np.arange(self.id_ranges[idx][0], self.id_ranges[idx][1])
cut_off = num_views if not self.allow_repeat else max(num_views // 3, 3)
start_image_idxs = image_idxs[: len(image_idxs) - cut_off + 1]
start_id = rng.choice(start_image_idxs)
pos, ordered_video = self.get_seq_from_start_id(
num_views,
start_id,
image_idxs.tolist(),
rng,
max_interval=self.max_interval,
video_prob=0.8,
fix_interval_prob=0.5,
block_shuffle=16,
)
image_idxs = np.array(image_idxs)[pos]
else:
ordered_video = False
image_idxs = self.groups[idx]
image_idxs = rng.permutation(image_idxs)
if len(image_idxs) > num_views:
image_idxs = image_idxs[:num_views]
else:
if rng.random() < 0.8:
image_idxs = rng.choice(image_idxs, size=num_views, replace=True)
else:
repeat_num = num_views // len(image_idxs) + 1
image_idxs = np.tile(image_idxs, repeat_num)[:num_views]
views = []
for v, view_idx in enumerate(image_idxs):
scene_id = self.sceneids[view_idx]
scene_dir = osp.join(self.ROOT, self.split, self.scenes[scene_id])
intrinsics = self.intrinsics[view_idx]
camera_pose = self.trajectories[view_idx]
basename = self.images[view_idx]
assert (
basename[:8] == self.scenes[scene_id]
), f"{basename}, {self.scenes[scene_id]}"
# print(scene_dir, basename)
# Load RGB image
rgb_image = imread_cv2(
osp.join(scene_dir, "vga_wide", basename.replace(".png", ".jpg"))
)
# Load depthmap
depthmap = imread_cv2(
osp.join(scene_dir, "lowres_depth", basename), cv2.IMREAD_UNCHANGED
)
depthmap = depthmap.astype(np.float32) / 1000.0
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="arkitscenes",
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(0.98, 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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