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import collections |
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import json |
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import os |
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import datasets |
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_HOMEPAGE = "https://universe.roboflow.com/brain-leisons/brain-lesion-kmiz9/dataset/1" |
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_LICENSE = "CC BY 4.0" |
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_CITATION = """\ |
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@misc{ |
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brain-lesion-kmiz9_dataset, |
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title = { Brain lesion Dataset }, |
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type = { Open Source Dataset }, |
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author = { brain leisons }, |
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howpublished = { \\url{ https://universe.roboflow.com/brain-leisons/brain-lesion-kmiz9 } }, |
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url = { https://universe.roboflow.com/brain-leisons/brain-lesion-kmiz9 }, |
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journal = { Roboflow Universe }, |
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publisher = { Roboflow }, |
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year = { 2024 }, |
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month = { apr }, |
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note = { visited on 2024-04-21 }, |
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} |
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""" |
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_CATEGORIES = ['tumor'] |
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_ANNOTATION_FILENAME = "_annotations.coco.json" |
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class BRAINYBRAINConfig(datasets.BuilderConfig): |
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"""Builder Config for brainybrain""" |
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def __init__(self, data_urls, **kwargs): |
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""" |
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BuilderConfig for brainybrain. |
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Args: |
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data_urls: `dict`, name to url to download the zip file from. |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(BRAINYBRAINConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs) |
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self.data_urls = data_urls |
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class BRAINYBRAIN(datasets.GeneratorBasedBuilder): |
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"""brainybrain object detection dataset""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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BRAINYBRAINConfig( |
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name="full", |
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description="Full version of brainybrain dataset.", |
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data_urls={ |
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"train": "https://huggingface.co/datasets/disha07/brainybrain/resolve/main/data/train.zip", |
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"validation": "https://huggingface.co/datasets/disha07/brainybrain/resolve/main/data/valid.zip", |
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"test": "https://huggingface.co/datasets/disha07/brainybrain/resolve/main/data/test.zip", |
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}, |
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), |
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BRAINYBRAINConfig( |
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name="mini", |
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description="Mini version of brainybrain dataset.", |
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data_urls={ |
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"train": "https://huggingface.co/datasets/disha07/brainybrain/resolve/main/data/valid-mini.zip", |
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"validation": "https://huggingface.co/datasets/disha07/brainybrain/resolve/main/data/valid-mini.zip", |
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"test": "https://huggingface.co/datasets/disha07/brainybrain/resolve/main/data/valid-mini.zip", |
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}, |
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) |
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] |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"image_id": datasets.Value("int64"), |
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"image": datasets.Image(), |
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"width": datasets.Value("int32"), |
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"height": datasets.Value("int32"), |
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"objects": datasets.Sequence( |
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{ |
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"id": datasets.Value("int64"), |
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"area": datasets.Value("int64"), |
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"bbox": datasets.Sequence(datasets.Value("float32"), length=4), |
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"category": datasets.ClassLabel(names=_CATEGORIES), |
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} |
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), |
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} |
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) |
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return datasets.DatasetInfo( |
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features=features, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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license=_LICENSE, |
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) |
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def _split_generators(self, dl_manager): |
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data_files = dl_manager.download_and_extract(self.config.data_urls) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"folder_dir": data_files["train"], |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"folder_dir": data_files["validation"], |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"folder_dir": data_files["test"], |
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}, |
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), |
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] |
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def _generate_examples(self, folder_dir): |
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def process_annot(annot, category_id_to_category): |
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return { |
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"id": annot["id"], |
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"area": annot["area"], |
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"bbox": annot["bbox"], |
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"category": category_id_to_category[annot["category_id"]], |
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} |
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image_id_to_image = {} |
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idx = 0 |
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annotation_filepath = os.path.join(folder_dir, _ANNOTATION_FILENAME) |
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with open(annotation_filepath, "r") as f: |
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annotations = json.load(f) |
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category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]} |
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image_id_to_annotations = collections.defaultdict(list) |
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for annot in annotations["annotations"]: |
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image_id_to_annotations[annot["image_id"]].append(annot) |
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filename_to_image = {image["file_name"]: image for image in annotations["images"]} |
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for filename in os.listdir(folder_dir): |
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filepath = os.path.join(folder_dir, filename) |
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if filename in filename_to_image: |
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image = filename_to_image[filename] |
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objects = [ |
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process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]] |
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] |
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with open(filepath, "rb") as f: |
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image_bytes = f.read() |
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yield idx, { |
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"image_id": image["id"], |
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"image": {"path": filepath, "bytes": image_bytes}, |
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"width": image["width"], |
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"height": image["height"], |
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"objects": objects, |
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} |
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idx += 1 |
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