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# Copyright (c), ETH Zurich and UNC Chapel Hill.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of
# its contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
from pathlib import Path
import pycolmap
from .utils import Dataset, SceneInfo
class _DatasetIMC(Dataset):
def __init__(
self,
data_path: Path,
categories: list[str],
scenes: list[Path],
run_path: Path,
run_name: str,
year: int,
):
super().__init__()
self.data_path = data_path
self.categories = categories
self.scenes = scenes
self.run_path = run_path
self.run_name = run_name
self.year = year
@property
def position_accuracy_gt(self):
return 0.02
def list_scenes(self):
folder_name = f"imc{self.year}"
scene_infos = []
for category_path in Path(
self.data_path / f"{folder_name}/train"
).iterdir():
if not category_path.is_dir() or (
self.categories and category_path.name not in self.categories
):
continue
category = category_path.name
for scene_path in category_path.iterdir():
if not scene_path.is_dir():
continue
scene = scene_path.name
if self.scenes and scene not in self.scenes:
continue
sfm_path = scene_path / "sfm"
if not sfm_path.exists():
pycolmap.logging.warning(
f"Skipping dataset=IMC{self.year}, "
f"category={category}, scene={scene}, "
"because the GT reconstruction is missing"
)
continue
workspace_path = (
self.run_path
/ self.run_name
/ folder_name
/ category
/ scene
)
image_path = scene_path / "images"
sparse_gt_path = scene_path / "sparse_gt"
scene_info = SceneInfo(
dataset=f"IMC{self.year}",
category=category,
scene=scene,
workspace_path=workspace_path,
image_path=image_path,
sparse_gt_path=sparse_gt_path,
camera_priors_from_sparse_gt=False,
colmap_extra_args=None,
)
scene_infos.append(scene_info)
return scene_infos
def prepare_scene(self, scene_info):
if scene_info.sparse_gt_path.exists():
return
scene_path = scene_info.image_path.parent
sfm_path = scene_path / "sfm"
train_image_names = set(
image.name for image in scene_info.image_path.iterdir()
)
sparse_sfm = pycolmap.Reconstruction(sfm_path)
sparse_gt = pycolmap.Reconstruction()
for image in sparse_sfm.images.values():
if image.name not in train_image_names:
continue
if image.camera_id not in sparse_gt.cameras:
sparse_gt.add_camera(image.camera)
image.reset_camera_ptr()
sparse_gt.add_image(image)
scene_info.sparse_gt_path.mkdir(exist_ok=True)
sparse_gt.write(scene_info.sparse_gt_path)
class DatasetIMC2023(_DatasetIMC):
def __init__(
self,
data_path: Path,
categories: list[str],
scenes: list[Path],
run_path: Path,
run_name: str,
):
super().__init__(
data_path=data_path,
categories=categories,
scenes=scenes,
run_path=run_path,
run_name=run_name,
year=2023,
)
class DatasetIMC2024(_DatasetIMC):
def __init__(
self,
data_path: Path,
categories: list[str],
scenes: list[Path],
run_path: Path,
run_name: str,
):
super().__init__(
data_path=data_path,
categories=categories,
scenes=scenes,
run_path=run_path,
run_name=run_name,
year=2024,
)
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