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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 HyperSim_Multi(BaseMultiViewDataset):
def __init__(self, *args, split, ROOT, **kwargs):
self.ROOT = ROOT
self.video = True
self.is_metric = True
self.max_interval = 4
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 = []
start_img_ids = []
scene_img_list = []
j = 0
for scene_idx, scene in enumerate(subscenes):
scene_dir = osp.join(self.ROOT, scene)
rgb_paths = sorted([f for f in os.listdir(scene_dir) 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.scene_img_list = scene_img_list
self.start_img_ids = start_img_ids
def __len__(self):
return len(self.start_img_ids) * 10
def get_image_num(self):
return len(self.images)
def _get_views(self, idx, resolution, rng, num_views):
idx = idx // 10
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=16,
)
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])
rgb_path = self.images[view_idx]
depth_path = rgb_path.replace("rgb.png", "depth.npy")
cam_path = rgb_path.replace("rgb.png", "cam.npz")
rgb_image = imread_cv2(osp.join(scene_dir, rgb_path), cv2.IMREAD_COLOR)
depthmap = np.load(osp.join(scene_dir, depth_path)).astype(np.float32)
depthmap[~np.isfinite(depthmap)] = 0 # invalid
cam_file = np.load(osp.join(scene_dir, cam_path))
intrinsics = cam_file["intrinsics"].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
)
# 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="hypersim",
label=self.scenes[scene_id] + "_" + rgb_path,
instance=f"{str(idx)}_{str(view_idx)}",
is_metric=self.is_metric,
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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