food.py

# coding=utf-8
# Copyright 2021 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Food dataset."""


import collections
import json
import os

import datasets


_CITATION = """\none"""

_DESCRIPTION = """\
A simple food dataset for personal study use. Structure follows the CPPE-5 dataset.
"""

_HOMEPAGE = ""

_LICENSE = "Unknown"

_URL = "https://drive.google.com/uc?id=1fXfOU8EyGn0oiZFclM-fe8FoCigDL41l"

_CATEGORIES = ["Broccoli", "Tomato", "Potato"]


class Food(datasets.GeneratorBasedBuilder):
    """Food 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(
                    {
                        "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):
        archive = dl_manager.download(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "annotation_file_path": "annotations/train.json",
                    "files": dl_manager.iter_archive(archive),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "annotation_file_path": "annotations/test.json",
                    "files": dl_manager.iter_archive(archive),
                },
            ),
        ]

    def _generate_examples(self, annotation_file_path, files):
        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
        # This loop relies on the ordering of the files in the archive:
        # Annotation files come first, then the images.
        for path, f in files:
            file_name = os.path.basename(path)
            if path == annotation_file_path:
                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)
                image_id_to_image = {annot["file_name"]: annot for annot in annotations["images"]}
            elif file_name in image_id_to_image:
                image = image_id_to_image[file_name]
                objects = [
                    process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
                ]
                yield idx, {
                    "image_id": image["id"],
                    "image": {"path": path, "bytes": f.read()},
                    "width": image["width"],
                    "height": image["height"],
                    "objects": objects,
                }
                idx += 1