File size: 5,859 Bytes
4eb56a7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import collections
import json
import os

import datasets


_HOMEPAGE = "https://universe.roboflow.com/brain-leisons/brain-lesion-kmiz9/dataset/1"
_LICENSE = "CC BY 4.0"
_CITATION = """\
@misc{
                            brain-lesion-kmiz9_dataset,
                            title = { Brain lesion  Dataset },
                            type = { Open Source Dataset },
                            author = { brain leisons },
                            howpublished = { \\url{ https://universe.roboflow.com/brain-leisons/brain-lesion-kmiz9 } },
                            url = { https://universe.roboflow.com/brain-leisons/brain-lesion-kmiz9 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { apr },
                            note = { visited on 2024-04-21 },
                            }
"""
_CATEGORIES = ['tumor']
_ANNOTATION_FILENAME = "_annotations.coco.json"


class DISHA07Config(datasets.BuilderConfig):
    """Builder Config for disha07"""

    def __init__(self, data_urls, **kwargs):
        """
        BuilderConfig for disha07.

        Args:
          data_urls: `dict`, name to url to download the zip file from.
          **kwargs: keyword arguments forwarded to super.
        """
        super(DISHA07Config, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
        self.data_urls = data_urls


class DISHA07(datasets.GeneratorBasedBuilder):
    """disha07 object detection dataset"""

    VERSION = datasets.Version("1.0.0")
    BUILDER_CONFIGS = [
        DISHA07Config(
            name="full",
            description="Full version of disha07 dataset.",
            data_urls={
                "train": "https://huggingface.co/datasets/disha07/resolve/main/data/train.zip",
                "validation": "https://huggingface.co/datasets/disha07/resolve/main/data/valid.zip",
                "test": "https://huggingface.co/datasets/disha07/resolve/main/data/test.zip",
            },
        ),
        DISHA07Config(
            name="mini",
            description="Mini version of disha07 dataset.",
            data_urls={
                "train": "https://huggingface.co/datasets/disha07/resolve/main/data/valid-mini.zip",
                "validation": "https://huggingface.co/datasets/disha07/resolve/main/data/valid-mini.zip",
                "test": "https://huggingface.co/datasets/disha07/resolve/main/data/valid-mini.zip",
            },
        )
    ]

    def _info(self):
        features = datasets.Features(
            {
                "image_id": datasets.Value("int64"),
                "image": datasets.Image(),
                "width": datasets.Value("int32"),
                "height": datasets.Value("int32"),
                "objects": datasets.Sequence(
                    {
                        "id": datasets.Value("int64"),
                        "area": datasets.Value("int64"),
                        "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
                        "category": datasets.ClassLabel(names=_CATEGORIES),
                    }
                ),
            }
        )
        return datasets.DatasetInfo(
            features=features,
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE,
        )

    def _split_generators(self, dl_manager):
        data_files = dl_manager.download_and_extract(self.config.data_urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "folder_dir": data_files["train"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "folder_dir": data_files["validation"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "folder_dir": data_files["test"],
                },
            ),
]

    def _generate_examples(self, folder_dir):
        def process_annot(annot, category_id_to_category):
            return {
                "id": annot["id"],
                "area": annot["area"],
                "bbox": annot["bbox"],
                "category": category_id_to_category[annot["category_id"]],
            }

        image_id_to_image = {}
        idx = 0

        annotation_filepath = os.path.join(folder_dir, _ANNOTATION_FILENAME)
        with open(annotation_filepath, "r") as f:
            annotations = json.load(f)
        category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
        image_id_to_annotations = collections.defaultdict(list)
        for annot in annotations["annotations"]:
            image_id_to_annotations[annot["image_id"]].append(annot)
        filename_to_image = {image["file_name"]: image for image in annotations["images"]}

        for filename in os.listdir(folder_dir):
            filepath = os.path.join(folder_dir, filename)
            if filename in filename_to_image:
                image = filename_to_image[filename]
                objects = [
                    process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
                ]
                with open(filepath, "rb") as f:
                    image_bytes = f.read()
                yield idx, {
                    "image_id": image["id"],
                    "image": {"path": filepath, "bytes": image_bytes},
                    "width": image["width"],
                    "height": image["height"],
                    "objects": objects,
                }
                idx += 1