File size: 5,971 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 BRAINYBRAINConfig(datasets.BuilderConfig):
"""Builder Config for brainybrain"""
def __init__(self, data_urls, **kwargs):
"""
BuilderConfig for brainybrain.
Args:
data_urls: `dict`, name to url to download the zip file from.
**kwargs: keyword arguments forwarded to super.
"""
super(BRAINYBRAINConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
self.data_urls = data_urls
class BRAINYBRAIN(datasets.GeneratorBasedBuilder):
"""brainybrain object detection dataset"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
BRAINYBRAINConfig(
name="full",
description="Full version of brainybrain dataset.",
data_urls={
"train": "https://huggingface.co/datasets/disha07/brainybrain/resolve/main/data/train.zip",
"validation": "https://huggingface.co/datasets/disha07/brainybrain/resolve/main/data/valid.zip",
"test": "https://huggingface.co/datasets/disha07/brainybrain/resolve/main/data/test.zip",
},
),
BRAINYBRAINConfig(
name="mini",
description="Mini version of brainybrain dataset.",
data_urls={
"train": "https://huggingface.co/datasets/disha07/brainybrain/resolve/main/data/valid-mini.zip",
"validation": "https://huggingface.co/datasets/disha07/brainybrain/resolve/main/data/valid-mini.zip",
"test": "https://huggingface.co/datasets/disha07/brainybrain/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
|