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from glob import glob |
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import json |
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import os |
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from pathlib import Path |
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import datasets |
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_HOMEPAGE = "https://sites.google.com/view/cppe5" |
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_LICENSE = "Unknown" |
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_CATEGORIES = ["Coverall", "Face_Shield", "Gloves", "Goggles", "Mask"] |
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_CITATION = """\ |
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@misc{dagli2021cppe5, |
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title={CPPE-5: Medical Personal Protective Equipment Dataset}, |
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author={Rishit Dagli and Ali Mustufa Shaikh}, |
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year={2021}, |
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eprint={2112.09569}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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""" |
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_DESCRIPTION = """\ |
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CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal |
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to allow the study of subordinate categorization of medical personal protective equipments, |
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which is not possible with other popular data sets that focus on broad level categories. |
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""" |
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class CPPE5(datasets.GeneratorBasedBuilder): |
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"""CPPE - 5 dataset.""" |
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VERSION = datasets.Version("1.0.0") |
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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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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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train_json = dl_manager.download("data/annotations/train.jsonl") |
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test_json = dl_manager.download("data/annotations/test.jsonl") |
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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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"archive_path": train_json, |
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"dl_manager": dl_manager, |
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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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"archive_path": test_json, |
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"dl_manager": dl_manager, |
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}, |
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), |
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] |
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def _generate_examples(self, archive_path, dl_manager): |
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"""Yields examples.""" |
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archive_path = Path(archive_path) |
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idx = 0 |
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with open(archive_path, "r", encoding="utf-8") as f: |
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for row in f: |
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sample = json.loads(row) |
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file_path = sample["image"] |
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file_path = os.path.join("data/images", file_path) |
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file_path = dl_manager.download(file_path) |
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with open(file_path, "rb") as image_f: |
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image_bytes = image_f.read() |
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yield idx, { |
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"image_id": sample["image_id"], |
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"image": {"path": file_path, "bytes": image_bytes}, |
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"width": sample["width"], |
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"height": sample["height"], |
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"objects": sample["objects"], |
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} |
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idx += 1 |
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if __name__ == '__main__': |
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pass |
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