File size: 9,848 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
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
# Last modified: 2024-04-30
#
# 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 io
import os
import random
import tarfile
from enum import Enum
from typing import Union

import numpy as np
import torch
from PIL import Image
from torch.utils.data import Dataset
from torchvision.transforms import InterpolationMode, Resize

from src.util.depth_transform import DepthNormalizerBase


class DatasetMode(Enum):
    RGB_ONLY = "rgb_only"
    EVAL = "evaluate"
    TRAIN = "train"


class DepthFileNameMode(Enum):
    """Prediction file naming modes"""

    id = 1  # id.png
    rgb_id = 2  # rgb_id.png
    i_d_rgb = 3  # i_d_1_rgb.png
    rgb_i_d = 4


def read_image_from_tar(tar_obj, img_rel_path):
    image = tar_obj.extractfile("./" + img_rel_path)
    image = image.read()
    image = Image.open(io.BytesIO(image))


class EvaluateBaseDataset(Dataset):
    def __init__(
        self,
        mode: DatasetMode,
        filename_ls_path: str,
        dataset_dir: str,
        disp_name: str,
        min_depth: float,
        max_depth: float,
        has_filled_depth: bool,
        name_mode: DepthFileNameMode,
        depth_transform: Union[DepthNormalizerBase, None] = None,
        augmentation_args: dict = None,
        resize_to_hw=None,
        move_invalid_to_far_plane: bool = True,
        rgb_transform=lambda x: x / 255.0 * 2 - 1,  #  [0, 255] -> [-1, 1],
        **kwargs,
    ) -> None:
        super().__init__()
        self.mode = mode
        # dataset info
        self.filename_ls_path = filename_ls_path
        self.dataset_dir = dataset_dir
        assert os.path.exists(
            self.dataset_dir
        ), f"Dataset does not exist at: {self.dataset_dir}"
        self.disp_name = disp_name
        self.has_filled_depth = has_filled_depth
        self.name_mode: DepthFileNameMode = name_mode
        self.min_depth = min_depth
        self.max_depth = max_depth

        # training arguments
        self.depth_transform: DepthNormalizerBase = depth_transform
        self.augm_args = augmentation_args
        self.resize_to_hw = resize_to_hw
        self.rgb_transform = rgb_transform
        self.move_invalid_to_far_plane = move_invalid_to_far_plane

        # Load filenames
        with open(self.filename_ls_path, "r") as f:
            self.filenames = [
                s.split() for s in f.readlines()
            ]  # [['rgb.png', 'depth.tif'], [], ...]

        # Tar dataset
        self.tar_obj = None
        self.is_tar = (
            True
            if os.path.isfile(dataset_dir) and tarfile.is_tarfile(dataset_dir)
            else False
        )

    def __len__(self):
        return len(self.filenames)

    def __getitem__(self, index):
        rasters, other = self._get_data_item(index)
        if DatasetMode.TRAIN == self.mode:
            rasters = self._training_preprocess(rasters)
        # merge
        outputs = rasters
        outputs.update(other)
        return outputs

    def _get_data_item(self, index):
        rgb_rel_path, depth_rel_path, filled_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=filled_rel_path
            )
            rasters.update(depth_data)
            # valid mask
            rasters["valid_mask_raw"] = self._get_valid_mask(
                rasters["depth_raw_linear"]
            ).clone()
            rasters["valid_mask_filled"] = self._get_valid_mask(
                rasters["depth_filled_linear"]
            ).clone()

        other = {"index": index, "rgb_relative_path": rgb_rel_path}

        return rasters, other

    def _load_rgb_data(self, rgb_rel_path):
        # Read RGB data
        rgb = self._read_rgb_file(rgb_rel_path)
        rgb_norm = rgb / 255.0 * 2.0 - 1.0  #  [0, 255] -> [-1, 1]

        outputs = {
            "rgb_int": torch.from_numpy(rgb).int(),
            "rgb_norm": torch.from_numpy(rgb_norm).float(),
        }
        return outputs

    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]
        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

    def _get_data_path(self, index):
        filename_line = self.filenames[index]

        # Get data path
        rgb_rel_path = filename_line[0]

        depth_rel_path, filled_rel_path = None, None
        if DatasetMode.RGB_ONLY != self.mode:
            depth_rel_path = filename_line[1]
            if self.has_filled_depth:
                filled_rel_path = filename_line[2]
        return rgb_rel_path, depth_rel_path, filled_rel_path

    def _read_image(self, img_rel_path) -> np.ndarray:
        if self.is_tar:
            if self.tar_obj is None:
                self.tar_obj = tarfile.open(self.dataset_dir)
            image_to_read = self.tar_obj.extractfile("./" + img_rel_path)
            image_to_read = image_to_read.read()
            image_to_read = io.BytesIO(image_to_read)
        else:
            image_to_read = os.path.join(self.dataset_dir, img_rel_path)
        image = Image.open(image_to_read)  # [H, W, rgb]
        image = np.asarray(image)
        return image

    def _read_rgb_file(self, rel_path) -> np.ndarray:
        rgb = self._read_image(rel_path)
        rgb = np.transpose(rgb, (2, 0, 1)).astype(int)  # [rgb, H, W]
        return rgb

    def _read_depth_file(self, rel_path):
        depth_in = self._read_image(rel_path)
        #  Replace code below to decode depth according to dataset definition
        depth_decoded = depth_in

        return depth_decoded

    def _get_valid_mask(self, depth: torch.Tensor):
        valid_mask = torch.logical_and(
            (depth > self.min_depth), (depth < self.max_depth)
        ).bool()
        return valid_mask

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

        # Normalization
        rasters["depth_raw_norm"] = self.depth_transform(
            rasters["depth_raw_linear"], rasters["valid_mask_raw"]
        ).clone()
        rasters["depth_filled_norm"] = self.depth_transform(
            rasters["depth_filled_linear"], rasters["valid_mask_filled"]
        ).clone()

        # 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:
            resize_transform = Resize(
                size=self.resize_to_hw, interpolation=InterpolationMode.NEAREST_EXACT
            )
            rasters = {k: resize_transform(v) for k, v in rasters.items()}

        return rasters

    def _augment_data(self, rasters_dict):
        # lr flipping
        lr_flip_p = self.augm_args.lr_flip_p
        if random.random() < lr_flip_p:
            rasters_dict = {k: v.flip(-1) for k, v in rasters_dict.items()}

        return rasters_dict

    def __del__(self):
        if hasattr(self, "tar_obj") and self.tar_obj is not None:
            self.tar_obj.close()
            self.tar_obj = None

def get_pred_name(rgb_basename, name_mode, suffix=".png"):
    if DepthFileNameMode.rgb_id == name_mode:
        pred_basename = "pred_" + rgb_basename.split("_")[1]
    elif DepthFileNameMode.i_d_rgb == name_mode:
        pred_basename = rgb_basename.replace("_rgb.", "_pred.")
    elif DepthFileNameMode.id == name_mode:
        pred_basename = "pred_" + rgb_basename
    elif DepthFileNameMode.rgb_i_d == name_mode:
        pred_basename = "pred_" + "_".join(rgb_basename.split("_")[1:])
    else:
        raise NotImplementedError
    # change suffix
    pred_basename = os.path.splitext(pred_basename)[0] + suffix

    return pred_basename