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# Last modified: 2024-02-26
#
# Copyright 2023 Bingxin Ke, ETH Zurich. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# --------------------------------------------------------------------------
# If you find this code useful, we kindly ask you to cite our paper in your work.
# Please find bibtex at: https://github.com/prs-eth/Marigold#-citation
# If you use or adapt this code, please attribute to https://github.com/prs-eth/marigold.
# More information about the method can be found at https://marigoldmonodepth.github.io
# --------------------------------------------------------------------------
import os
import tarfile
from io import BytesIO
import numpy as np
import torch
from .eval_base_dataset import EvaluateBaseDataset, DepthFileNameMode, DatasetMode
class DIODEDataset(EvaluateBaseDataset):
def __init__(
self,
**kwargs,
) -> None:
super().__init__(
# DIODE data parameter
min_depth=0.6,
max_depth=350,
has_filled_depth=False,
name_mode=DepthFileNameMode.id,
**kwargs,
)
def _read_npy_file(self, rel_path):
if self.is_tar:
if self.tar_obj is None:
self.tar_obj = tarfile.open(self.dataset_dir)
fileobj = self.tar_obj.extractfile("./" + rel_path)
npy_path_or_content = BytesIO(fileobj.read())
else:
npy_path_or_content = os.path.join(self.dataset_dir, rel_path)
data = np.load(npy_path_or_content).squeeze()[np.newaxis, :, :]
return data
def _read_depth_file(self, rel_path):
depth = self._read_npy_file(rel_path)
return depth
def _get_data_path(self, index):
return self.filenames[index]
def _get_data_item(self, index):
# Special: depth mask is read from data
rgb_rel_path, depth_rel_path, mask_rel_path = self._get_data_path(index=index)
rasters = {}
# RGB data
rasters.update(self._load_rgb_data(rgb_rel_path=rgb_rel_path))
# Depth data
if DatasetMode.RGB_ONLY != self.mode:
# load data
depth_data = self._load_depth_data(
depth_rel_path=depth_rel_path, filled_rel_path=None
)
rasters.update(depth_data)
# valid mask
mask = self._read_npy_file(mask_rel_path).astype(bool)
mask = torch.from_numpy(mask).bool()
rasters["valid_mask_raw"] = mask.clone()
rasters["valid_mask_filled"] = mask.clone()
other = {"index": index, "rgb_relative_path": rgb_rel_path}
return rasters, other
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