File size: 2,247 Bytes
864ec44 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
# Last modified: 2024-02-08
#
# 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 torch
from .eval_base_dataset import DepthFileNameMode, EvaluateBaseDataset
class NYUDataset(EvaluateBaseDataset):
def __init__(
self,
eigen_valid_mask: bool,
**kwargs,
) -> None:
super().__init__(
# NYUv2 dataset parameter
min_depth=1e-3,
max_depth=10.0,
has_filled_depth=True,
name_mode=DepthFileNameMode.rgb_id,
**kwargs,
)
self.eigen_valid_mask = eigen_valid_mask
def _read_depth_file(self, rel_path):
depth_in = self._read_image(rel_path)
# Decode NYU depth
depth_decoded = depth_in / 1000.0
return depth_decoded
def _get_valid_mask(self, depth: torch.Tensor):
valid_mask = super()._get_valid_mask(depth)
# Eigen crop for evaluation
if self.eigen_valid_mask:
eval_mask = torch.zeros_like(valid_mask.squeeze()).bool()
eval_mask[45:471, 41:601] = 1
eval_mask.reshape(valid_mask.shape)
valid_mask = torch.logical_and(valid_mask, eval_mask)
return valid_mask
|