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# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# Dataloader for preprocessed BlendedMVS
# dataset at https://github.com/YoYo000/BlendedMVS
# See datasets_preprocess/preprocess_blendedmvs.py
# --------------------------------------------------------
import os.path as osp
import numpy as np
from dust3r.datasets.base.base_stereo_view_dataset import BaseStereoViewDataset
from dust3r.utils.image import imread_cv2
class BlendedMVS (BaseStereoViewDataset):
""" Dataset of outdoor street scenes, 5 images each time
"""
def __init__(self, *args, ROOT, split=None, **kwargs):
self.ROOT = ROOT
super().__init__(*args, **kwargs)
self._load_data(split)
def _load_data(self, split):
pairs = np.load(osp.join(self.ROOT, 'blendedmvs_pairs.npy'))
if split is None:
selection = slice(None)
if split == 'train':
# select 90% of all scenes
selection = (pairs['seq_low'] % 10) > 0
if split == 'val':
# select 10% of all scenes
selection = (pairs['seq_low'] % 10) == 0
self.pairs = pairs[selection]
# list of all scenes
self.scenes = np.unique(self.pairs['seq_low']) # low is unique enough
def __len__(self):
return len(self.pairs)
def get_stats(self):
return f'{len(self)} pairs from {len(self.scenes)} scenes'
def _get_views(self, pair_idx, resolution, rng):
seqh, seql, img1, img2, score = self.pairs[pair_idx]
seq = f"{seqh:08x}{seql:016x}"
seq_path = osp.join(self.ROOT, seq)
views = []
for view_index in [img1, img2]:
impath = f"{view_index:08n}"
image = imread_cv2(osp.join(seq_path, impath + ".jpg"))
depthmap = imread_cv2(osp.join(seq_path, impath + ".exr"))
camera_params = np.load(osp.join(seq_path, impath + ".npz"))
intrinsics = np.float32(camera_params['intrinsics'])
camera_pose = np.eye(4, dtype=np.float32)
camera_pose[:3, :3] = camera_params['R_cam2world']
camera_pose[:3, 3] = camera_params['t_cam2world']
image, depthmap, intrinsics = self._crop_resize_if_necessary(
image, depthmap, intrinsics, resolution, rng, info=(seq_path, impath))
views.append(dict(
img=image,
depthmap=depthmap,
camera_pose=camera_pose, # cam2world
camera_intrinsics=intrinsics,
dataset='BlendedMVS',
label=osp.relpath(seq_path, self.ROOT),
instance=impath))
return views
if __name__ == '__main__':
from dust3r.datasets.base.base_stereo_view_dataset import view_name
from dust3r.viz import SceneViz, auto_cam_size
from dust3r.utils.image import rgb
dataset = BlendedMVS(split='train', ROOT="data/blendedmvs_processed", resolution=224, aug_crop=16)
for idx in np.random.permutation(len(dataset)):
views = dataset[idx]
assert len(views) == 2
print(idx, view_name(views[0]), view_name(views[1]))
viz = SceneViz()
poses = [views[view_idx]['camera_pose'] for view_idx in [0, 1]]
cam_size = max(auto_cam_size(poses), 0.001)
for view_idx in [0, 1]:
pts3d = views[view_idx]['pts3d']
valid_mask = views[view_idx]['valid_mask']
colors = rgb(views[view_idx]['img'])
viz.add_pointcloud(pts3d, colors, valid_mask)
viz.add_camera(pose_c2w=views[view_idx]['camera_pose'],
focal=views[view_idx]['camera_intrinsics'][0, 0],
color=(idx * 255, (1 - idx) * 255, 0),
image=colors,
cam_size=cam_size)
viz.show()
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