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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
class DL3DV_Multi(BaseMultiViewDataset):
def __init__(self, *args, split, ROOT, **kwargs):
self.ROOT = ROOT
self.video = True
self.max_interval = 20
self.is_metric = False
super().__init__(*args, **kwargs)
self.loaded_data = self._load_data()
def _load_data(self):
self.all_scenes = sorted(
[f for f in os.listdir(self.ROOT) if os.path.isdir(osp.join(self.ROOT, f))]
)
subscenes = []
for scene in self.all_scenes:
# not empty
subscenes.extend(
[
osp.join(scene, f)
for f in os.listdir(osp.join(self.ROOT, scene))
if os.path.isdir(osp.join(self.ROOT, scene, f))
and len(os.listdir(osp.join(self.ROOT, scene, f))) > 0
]
)
offset = 0
scenes = []
sceneids = []
images = []
scene_img_list = []
start_img_ids = []
j = 0
for scene_idx, scene in enumerate(subscenes):
scene_dir = osp.join(self.ROOT, scene, "dense")
rgb_paths = sorted(
[
f
for f in os.listdir(os.path.join(scene_dir, "rgb"))
if f.endswith(".png")
]
)
assert len(rgb_paths) > 0, f"{scene_dir} is empty."
num_imgs = len(rgb_paths)
cut_off = (
self.num_views if not self.allow_repeat else max(self.num_views // 3, 3)
)
if num_imgs < cut_off:
print(f"Skipping {scene}")
continue
img_ids = list(np.arange(num_imgs) + offset)
start_img_ids_ = img_ids[: num_imgs - cut_off + 1]
scenes.append(scene)
scene_img_list.append(img_ids)
sceneids.extend([j] * num_imgs)
images.extend(rgb_paths)
start_img_ids.extend(start_img_ids_)
offset += num_imgs
j += 1
self.scenes = scenes
self.sceneids = sceneids
self.images = images
self.start_img_ids = start_img_ids
self.scene_img_list = scene_img_list
def __len__(self):
return len(self.start_img_ids)
def get_image_num(self):
return len(self.images)
def _get_views(self, idx, resolution, rng, num_views):
start_id = self.start_img_ids[idx]
scene_id = self.sceneids[start_id]
all_image_ids = self.scene_img_list[scene_id]
pos, ordered_video = self.get_seq_from_start_id(
num_views,
start_id,
all_image_ids,
rng,
max_interval=self.max_interval,
block_shuffle=25,
)
image_idxs = np.array(all_image_ids)[pos]
views = []
for view_idx in image_idxs:
scene_id = self.sceneids[view_idx]
scene_dir = osp.join(self.ROOT, self.scenes[scene_id], "dense")
rgb_path = self.images[view_idx]
basename = rgb_path[:-4]
rgb_image = imread_cv2(
osp.join(scene_dir, "rgb", rgb_path), cv2.IMREAD_COLOR
)
depthmap = np.load(osp.join(scene_dir, "depth", basename + ".npy")).astype(
np.float32
)
depthmap[~np.isfinite(depthmap)] = 0 # invalid
cam_file = np.load(osp.join(scene_dir, "cam", basename + ".npz"))
sky_mask = (
cv2.imread(
osp.join(scene_dir, "sky_mask", rgb_path), cv2.IMREAD_UNCHANGED
)
>= 127
)
outlier_mask = cv2.imread(
osp.join(scene_dir, "outlier_mask", rgb_path), cv2.IMREAD_UNCHANGED
)
depthmap[sky_mask] = -1.0
depthmap[outlier_mask >= 127] = 0.0
depthmap = np.nan_to_num(depthmap, nan=0, posinf=0, neginf=0)
threshold = (
np.percentile(depthmap[depthmap > 0], 98)
if depthmap[depthmap > 0].size > 0
else 0
)
depthmap[depthmap > threshold] = 0.0
intrinsics = cam_file["intrinsic"].astype(np.float32)
camera_pose = cam_file["pose"].astype(np.float32)
rgb_image, depthmap, intrinsics = self._crop_resize_if_necessary(
rgb_image, depthmap, intrinsics, resolution, rng=rng, info=view_idx
)
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="dl3dv",
label=self.scenes[scene_id] + "_" + rgb_path,
instance=osp.join(scene_dir, "rgb", rgb_path),
is_metric=self.is_metric,
is_video=ordered_video,
quantile=np.array(0.9, dtype=np.float32),
img_mask=True,
ray_mask=False,
camera_only=False,
depth_only=False,
single_view=False,
reset=False,
)
)
return views
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