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import os.path as osp
import numpy as np
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 VirtualKITTI2_Multi(BaseMultiViewDataset):
def __init__(self, ROOT, *args, **kwargs):
self.ROOT = ROOT
self.video = True
self.is_metric = True
self.max_interval = 5
super().__init__(*args, **kwargs)
# loading all
self._load_data(self.split)
def _load_data(self, split=None):
scene_dirs = sorted(
[
d
for d in os.listdir(self.ROOT)
if os.path.isdir(os.path.join(self.ROOT, d))
]
)
if split == "train":
scene_dirs = scene_dirs[:-1]
elif split == "test":
scene_dirs = scene_dirs[-1:]
seq_dirs = []
for scene in scene_dirs:
seq_dirs += sorted(
[
os.path.join(scene, d)
for d in os.listdir(os.path.join(self.ROOT, scene))
if os.path.isdir(os.path.join(self.ROOT, scene, d))
]
)
offset = 0
scenes = []
sceneids = []
images = []
scene_img_list = []
start_img_ids = []
j = 0
for seq_idx, seq in enumerate(seq_dirs):
seq_path = osp.join(self.ROOT, seq)
for cam in ["Camera_0", "Camera_1"]:
basenames = sorted(
[
f[:5]
for f in os.listdir(seq_path + "/" + cam)
if f.endswith(".jpg")
]
)
num_imgs = len(basenames)
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(seq + "/" + cam)
scene_img_list.append(img_ids)
sceneids.extend([j] * num_imgs)
images.extend(basenames)
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_stats(self):
return f"{len(self)} groups of views"
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,
video_prob=1.0,
fix_interval_prob=0.9,
)
image_idxs = np.array(all_image_ids)[pos]
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])
basename = self.images[view_idx]
img = basename + "_rgb.jpg"
image = imread_cv2(osp.join(scene_dir, img))
depthmap = (
cv2.imread(
osp.join(scene_dir, basename + "_depth.png"),
cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH,
).astype(np.float32)
/ 100.0
)
camera_params = np.load(osp.join(scene_dir, basename + "_cam.npz"))
intrinsics = camera_params["camera_intrinsics"]
camera_pose = camera_params["camera_pose"]
sky_mask = depthmap >= 655
depthmap[sky_mask] = -1.0 # sky
image, depthmap, intrinsics = self._crop_resize_if_necessary(
image, depthmap, intrinsics, resolution, rng, info=(scene_dir, img)
)
# generate img mask and raymap mask
img_mask, ray_mask = self.get_img_and_ray_masks(
self.is_metric, v, rng, p=[0.85, 0.1, 0.05]
)
views.append(
dict(
img=image,
depthmap=depthmap,
camera_pose=camera_pose, # cam2world
camera_intrinsics=intrinsics,
dataset="VirtualKITTI2",
label=scene_dir,
is_metric=self.is_metric,
instance=scene_dir + "_" + img,
is_video=ordered_video,
quantile=np.array(1.0, 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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