# 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