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  1. cppe-5.py +0 -134
cppe-5.py DELETED
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- # coding=utf-8
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- # Copyright 2021 The HuggingFace Datasets Authors and the current dataset script contributor.
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- #
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- # Licensed under the Apache License, Version 2.0 (the "License");
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- # you may not use this file except in compliance with the License.
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- # You may obtain a copy of the License at
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- #
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- # http://www.apache.org/licenses/LICENSE-2.0
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- #
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- # Unless required by applicable law or agreed to in writing, software
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- # distributed under the License is distributed on an "AS IS" BASIS,
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- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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- # See the License for the specific language governing permissions and
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- # limitations under the License.
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- """CPPE-5 dataset."""
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-
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-
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- import collections
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- import json
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- import os
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-
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- import datasets
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-
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-
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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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-
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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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-
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- _HOMEPAGE = "https://sites.google.com/view/cppe5"
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-
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- _LICENSE = "Unknown"
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-
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- _URL = "https://drive.google.com/uc?id=1MGnaAfbckUmigGUvihz7uiHGC6rBIbvr"
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-
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- _CATEGORIES = ["Coverall", "Face_Shield", "Gloves", "Goggles", "Mask"]
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-
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-
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- class CPPE5(datasets.GeneratorBasedBuilder):
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- """CPPE - 5 dataset."""
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-
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- VERSION = datasets.Version("1.0.0")
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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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- 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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-
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- def _split_generators(self, dl_manager):
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- archive = dl_manager.download(_URL)
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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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- "annotation_file_path": "annotations/train.json",
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- "files": dl_manager.iter_archive(archive),
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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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- "annotation_file_path": "annotations/test.json",
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- "files": dl_manager.iter_archive(archive),
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- },
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- ),
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- ]
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-
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- def _generate_examples(self, annotation_file_path, files):
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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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-
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- image_id_to_image = {}
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- idx = 0
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- # This loop relies on the ordering of the files in the archive:
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- # Annotation files come first, then the images.
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- for path, f in files:
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- file_name = os.path.basename(path)
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- if path == annotation_file_path:
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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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- image_id_to_image = {annot["file_name"]: annot for annot in annotations["images"]}
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- elif file_name in image_id_to_image:
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- image = image_id_to_image[file_name]
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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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- yield idx, {
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- "image_id": image["id"],
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- "image": {"path": path, "bytes": f.read()},
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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