Hugging Face's logo

Datasets:
GFM-Bench
/
SegMunich

Tasks:
Image Segmentation
ArXiv:
Dataset card Files Community
SegMunich
File size: 4,915 Bytes 
c25c4e6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
import os
import json
import shutil
import datasets
import tifffile

import pandas as pd
import numpy as np

S2_MEAN = [752.40087073, 884.29673756, 1144.16202635, 1297.47289228, 1624.90992062, 2194.6423161, 2422.21248945, 2581.64687018, 2368.51236873, 1805.06846033]

S2_STD = [1108.02887453, 1155.15170768, 1183.6292542, 1368.11351514, 1370.265037, 1355.55390699, 1416.51487101, 1439.3086061, 1455.52084939, 1343.48379601]

class SegMunichDataset(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")
    
    DATA_URL = "https://huggingface.co/datasets/GFM-Bench/SegMunich/resolve/main/SegMunich.zip"

    metadata = {
        "s2c": { 
            "bands": ["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8A", "B11", "B12"],
            "channel_wv": [442.7, 492.4, 559.8, 664.6, 704.1, 740.5, 782.8, 864.7, 1613.7, 2202.4],
            "mean": S2_MEAN,
            "std": S2_STD,
        },
        "s1": {
            "bands": None,
            "channel_wv": None,
            "mean": None,
            "std": None,
        }
    }

    SIZE = HEIGHT = WIDTH = 128

    spatial_resolution = 10 

    NUM_CLASSES = 13

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def _info(self):
        metadata = self.metadata
        metadata['size'] = self.SIZE
        metadata['num_classes'] = self.NUM_CLASSES
        metadata['spatial_resolution'] = self.spatial_resolution
        return datasets.DatasetInfo(
            description=json.dumps(metadata),
            features=datasets.Features({
                "optical": datasets.Array3D(shape=(10, self.HEIGHT, self.WIDTH), dtype="float32"),
                "label": datasets.Array2D(shape=(self.HEIGHT, self.WIDTH), dtype="int32"),
                "optical_channel_wv": datasets.Sequence(datasets.Value("float32")),
                "spatial_resolution": datasets.Value("int32"),
            }),
        )

    def _split_generators(self, dl_manager):
        if isinstance(self.DATA_URL, list):
            downloaded_files = dl_manager.download(self.DATA_URL)
            combined_file = os.path.join(dl_manager.download_config.cache_dir, "combined.tar.gz")        
            with open(combined_file, 'wb') as outfile:
                for part_file in downloaded_files:
                    with open(part_file, 'rb') as infile:
                        shutil.copyfileobj(infile, outfile)
            data_dir = dl_manager.extract(combined_file)
            os.remove(combined_file)
        else:
            data_dir = dl_manager.download_and_extract(self.DATA_URL)

        return [
            datasets.SplitGenerator(
                name="train",
                gen_kwargs={
                    "split": 'train',
                    "data_dir": data_dir, 
                },
            ),
            datasets.SplitGenerator(
                name="val",
                gen_kwargs={
                    "split": 'val',
                    "data_dir": data_dir,
                },
            ),
            datasets.SplitGenerator(
                name="test",
                gen_kwargs={
                    "split": 'test',
                    "data_dir": data_dir,
                },
            )
        ]

    def _generate_examples(self, split, data_dir):
        optical_channel_wv = self.metadata["s2c"]["channel_wv"]
        spatial_resolution = self.spatial_resolution

        data_dir = os.path.join(data_dir, "SegMunich")
        metadata = pd.read_csv(os.path.join(data_dir, "metadata.csv"))
        metadata = metadata[metadata["split"] == split].reset_index(drop=True)

        for index, row in metadata.iterrows():
            optical_path = os.path.join(data_dir, row.optical_path)
            optical = self._read_image(optical_path).astype(np.float32) # CxHxW

            label_path = os.path.join(data_dir, row.label_path)
            label = self._read_image(label_path).astype(np.int32)

            label[label == 21] = 1 
            label[label == 22] = 2
            label[label == 23] = 3
            label[label == 31] = 4
            label[label == 32] = 6
            label[label == 33] = 7
            label[label == 41] = 8
            label[label == 13] = 9
            label[label == 14] = 10

            sample = {
                "optical": optical,
                "optical_channel_wv": optical_channel_wv,
                "label": label,
                "spatial_resolution": spatial_resolution,
            }

            yield f"{index}", sample
    def _read_image(self, image_path):
        """Read tiff image from image_path
        Args:
            image_path: 
                Image path to read from

        Return:
            image: 
                C, H, W numpy array image
        """
        image = tifffile.imread(image_path)
        if len(image.shape) == 3:
            image = np.transpose(image, (2, 0, 1))

        return image