Delete loading script auxiliary file

cppe_5_backup.py

@@ -1,118 +0,0 @@
-#!/usr/bin/python3
-# -*- coding: utf-8 -*-
-from glob import glob
-import json
-import os
-from pathlib import Path
-
-import datasets
-from PIL import Image
-
-# _URL = "https://drive.google.com/uc?id=1MGnaAfbckUmigGUvihz7uiHGC6rBIbvr"
-
-_HOMEPAGE = "https://sites.google.com/view/cppe5"
-
-_LICENSE = "Unknown"
-
-_CATEGORIES = ["Coverall", "Face_Shield", "Gloves", "Goggles", "Mask"]
-
-_CITATION = """\
-@misc{dagli2021cppe5,
-      title={CPPE-5: Medical Personal Protective Equipment Dataset},
-      author={Rishit Dagli and Ali Mustufa Shaikh},
-      year={2021},
-      eprint={2112.09569},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-"""
-
-_DESCRIPTION = """\
-CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal
-to allow the study of subordinate categorization of medical personal protective equipments,
-which is not possible with other popular data sets that focus on broad level categories.
-"""
-
-
-class CPPE5(datasets.GeneratorBasedBuilder):
-    """CPPE - 5 dataset."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    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(
-                    feature=datasets.Features({
-                        "id": datasets.Value("int64"),
-                        "area": datasets.Value("int64"),
-                        "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
-                        "category": datasets.ClassLabel(names=_CATEGORIES),
-                    })
-                ),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        train_json = dl_manager.download("data/annotations/train.jsonl")
-        test_json = dl_manager.download("data/annotations/test.jsonl")
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "archive_path": train_json,
-                    "dl_manager": dl_manager,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "archive_path": test_json,
-                    "dl_manager": dl_manager,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, archive_path, dl_manager):
-        """Yields examples."""
-        archive_path = Path(archive_path)
-
-        idx = 0
-
-        with open(archive_path, "r", encoding="utf-8") as f:
-            for row in f:
-                sample = json.loads(row)
-
-                file_path = sample["image"]
-                file_path = dl_manager.download(file_path)
-
-                with open(file_path, "rb") as image_f:
-                    image_bytes = image_f.read()
-                    # image = Image.open(image_f)
-
-                yield idx, {
-                    "image_id": sample["image_id"],
-                    "image": {"path": file_path, "bytes": image_bytes},
-                    # "image": image,
-                    "width": sample["width"],
-                    "height": sample["height"],
-                    "objects": sample["objects"],
-                }
-                idx += 1
-
-
-if __name__ == '__main__':
-    pass