# 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
# --------------------------------------------------------------------------

from .base_depth_dataset import BaseDepthDataset, DepthFileNameMode
import torch
from torchvision.transforms import InterpolationMode, Resize, CenterCrop
import torchvision.transforms as transforms

class DepthAnythingDataset(BaseDepthDataset):
    def __init__(
        self,
        **kwargs,
    ) -> None:
        super().__init__(
            # ScanNet data parameter
            min_depth=-1,
            max_depth=256,
            has_filled_depth=False,
            name_mode=DepthFileNameMode.id,
            **kwargs,
        )

    def _read_depth_file(self, rel_path):
        depth_in = self._read_image(rel_path)
        # Decode ScanNet depth
        # depth_decoded = depth_in / 1000.0
        return depth_in

    def _training_preprocess(self, rasters):
        # Augmentation
        if self.augm_args is not None:
            rasters = self._augment_data(rasters)

        # Normalization
        rasters["depth_raw_norm"] = rasters["depth_raw_linear"] / 255.0 * 2.0 - 1.0
        rasters["depth_filled_norm"] = rasters["depth_filled_linear"] / 255.0 * 2.0 - 1.0

        # Set invalid pixel to far plane
        if self.move_invalid_to_far_plane:
            if self.depth_transform.far_plane_at_max:
                rasters["depth_filled_norm"][~rasters["valid_mask_filled"]] = (
                    self.depth_transform.norm_max
                )
            else:
                rasters["depth_filled_norm"][~rasters["valid_mask_filled"]] = (
                    self.depth_transform.norm_min
                )

        # Resize
        if self.resize_to_hw is not None:
            T = transforms.Compose([
                Resize(self.resize_to_hw[0]),
                CenterCrop(self.resize_to_hw),
            ])
            rasters = {k: T(v) for k, v in rasters.items()}
        return rasters

    # def _load_depth_data(self, depth_rel_path, filled_rel_path):
    #     # Read depth data
    #     outputs = {}
    #     depth_raw = self._read_depth_file(depth_rel_path).squeeze()
    #     depth_raw_linear = torch.from_numpy(depth_raw).float().unsqueeze(0)  # [1, H, W]  [0, 255]
    #     outputs["depth_raw_linear"] = depth_raw_linear.clone()
    #
    #     if self.has_filled_depth:
    #         depth_filled = self._read_depth_file(filled_rel_path).squeeze()
    #         depth_filled_linear = torch.from_numpy(depth_filled).float().unsqueeze(0)
    #         outputs["depth_filled_linear"] = depth_filled_linear
    #     else:
    #         outputs["depth_filled_linear"] = depth_raw_linear.clone()
    #
    #     return outputs