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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 tqdm import tqdm
from dust3r.datasets.base.base_multiview_dataset import BaseMultiViewDataset
from dust3r.utils.image import imread_cv2
class PointOdyssey_Multi(BaseMultiViewDataset):
def __init__(self, *args, ROOT, **kwargs):
self.ROOT = ROOT
self.video = True
self.is_metric = True
self.max_interval = 4
super().__init__(*args, **kwargs)
assert self.split in ["train", "test", "val"]
self.scenes_to_use = [
# 'cab_h_bench_3rd', 'cab_h_bench_ego1', 'cab_h_bench_ego2',
"cnb_dlab_0215_3rd",
"cnb_dlab_0215_ego1",
"cnb_dlab_0225_3rd",
"cnb_dlab_0225_ego1",
"dancing",
"dancingroom0_3rd",
"footlab_3rd",
"footlab_ego1",
"footlab_ego2",
"girl",
"girl_egocentric",
"human_egocentric",
"human_in_scene",
"human_in_scene1",
"kg",
"kg_ego1",
"kg_ego2",
"kitchen_gfloor",
"kitchen_gfloor_ego1",
"kitchen_gfloor_ego2",
"scene_carb_h_tables",
"scene_carb_h_tables_ego1",
"scene_carb_h_tables_ego2",
"scene_j716_3rd",
"scene_j716_ego1",
"scene_j716_ego2",
"scene_recording_20210910_S05_S06_0_3rd",
"scene_recording_20210910_S05_S06_0_ego2",
"scene1_0129",
"scene1_0129_ego",
"seminar_h52_3rd",
"seminar_h52_ego1",
"seminar_h52_ego2",
]
self.loaded_data = self._load_data(self.split)
def _load_data(self, split):
root = os.path.join(self.ROOT, split)
self.scenes = []
offset = 0
scenes = []
sceneids = []
scene_img_list = []
images = []
start_img_ids = []
j = 0
for scene in tqdm(os.listdir(root)):
if scene not in self.scenes_to_use:
continue
scene_dir = osp.join(root, scene)
rgb_dir = osp.join(scene_dir, "rgb")
basenames = sorted(
[f[:-4] for f in os.listdir(rgb_dir) if f.endswith(".jpg")]
)
num_imgs = len(basenames)
img_ids = list(np.arange(num_imgs) + offset)
cut_off = (
self.num_views if not self.allow_repeat else max(self.num_views // 3, 3)
)
start_img_ids_ = img_ids[: num_imgs - cut_off + 1]
# start_img_ids_ = img_ids[:-self.num_views+1]
if num_imgs < cut_off:
print(f"Skipping {scene}")
continue
start_img_ids.extend(start_img_ids_)
sceneids.extend([j] * num_imgs)
images.extend(basenames)
scenes.append(scene)
scene_img_list.append(img_ids)
# offset groups
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]
all_image_ids = self.scene_img_list[self.sceneids[start_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=1.0,
)
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.split, self.scenes[scene_id])
rgb_dir = osp.join(scene_dir, "rgb")
depth_dir = osp.join(scene_dir, "depth")
cam_dir = osp.join(scene_dir, "cam")
basename = self.images[view_idx]
# Load RGB image
rgb_image = imread_cv2(osp.join(rgb_dir, basename + ".jpg"))
# Load depthmap
depthmap = np.load(osp.join(depth_dir, basename + ".npy"))
depthmap[~np.isfinite(depthmap)] = 0 # invalid
depthmap[depthmap > 1000] = 0.0
cam = np.load(osp.join(cam_dir, basename + ".npz"))
camera_pose = cam["pose"]
intrinsics = cam["intrinsics"]
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=[0.9, 0.05, 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="PointOdyssey",
label=self.scenes[scene_id] + "_" + basename,
instance=osp.join(rgb_dir, basename + ".jpg"),
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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