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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 MP3D_Multi(BaseMultiViewDataset):
def __init__(self, *args, split, ROOT, **kwargs):
self.ROOT = ROOT
self.video = False
self.is_metric = True
super().__init__(*args, **kwargs)
self.loaded_data = self._load_data()
def _load_data(self):
scenes = os.listdir(self.ROOT)
offset = 0
overlaps = {scene: [] for scene in scenes}
scene_img_list = {scene: [] for scene in scenes}
images = []
j = 0
for scene in scenes:
scene_dir = osp.join(self.ROOT, scene)
rgb_dir = osp.join(scene_dir, "rgb")
basenames = sorted(
[f[:-4] for f in os.listdir(rgb_dir) if f.endswith(".png")]
)
overlap = np.load(osp.join(scene_dir, "overlap.npy"))
overlaps[scene] = overlap
num_imgs = len(basenames)
images.extend(
[(scene, i, basename) for i, basename in enumerate(basenames)]
)
scene_img_list[scene] = np.arange(num_imgs) + offset
offset += num_imgs
j += 1
self.scenes = scenes
self.scene_img_list = scene_img_list
self.images = images
self.overlaps = overlaps
def __len__(self):
return len(self.images)
def get_image_num(self):
return len(self.images)
def _get_views(self, idx, resolution, rng, num_views):
num_views_posible = 0
num_unique = num_views if not self.allow_repeat else max(num_views // 3, 3)
while num_views_posible < num_unique - 1:
scene, img_idx, _ = self.images[idx]
overlap = self.overlaps[scene]
sel_img_idx = np.where(overlap[:, 0] == img_idx)[0]
overlap_sel = overlap[sel_img_idx]
overlap_sel = overlap_sel[
(overlap_sel[:, 2] > 0.01) * (overlap_sel[:, 2] < 1)
]
num_views_posible = len(overlap_sel)
if num_views_posible >= num_unique - 1:
break
idx = rng.choice(len(self.images))
ref_id = self.scene_img_list[scene][img_idx]
ids = self.scene_img_list[scene][overlap_sel[:, 1].astype(np.int64)]
replace = False if not self.allow_repeat else True
image_idxs = rng.choice(
ids,
num_views - 1,
replace=replace,
p=overlap_sel[:, 2] / np.sum(overlap_sel[:, 2]),
)
image_idxs = np.concatenate([[ref_id], image_idxs])
ordered_video = False
views = []
for v, view_idx in enumerate(image_idxs):
scene, _, basename = self.images[view_idx]
scene_dir = osp.join(self.ROOT, scene)
rgb_path = osp.join(scene_dir, "rgb", basename + ".png")
depth_path = osp.join(scene_dir, "depth", basename + ".npy")
cam_path = osp.join(scene_dir, "cam", basename + ".npz")
rgb_image = imread_cv2(rgb_path, cv2.IMREAD_COLOR)
depthmap = np.load(depth_path).astype(np.float32)
depthmap[~np.isfinite(depthmap)] = 0 # invalid
cam_file = np.load(cam_path)
intrinsics = cam_file["intrinsics"]
camera_pose = cam_file["pose"]
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=[0.85, 0.1, 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="mp3d",
label=scene + "_" + rgb_path,
instance=f"{str(idx)}_{str(view_idx)}",
is_metric=self.is_metric,
is_video=ordered_video,
quantile=np.array(0.99, 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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