renovation.py

import csv
import datasets
import requests
import os

from PIL import Image
from io import BytesIO
from datasets.tasks import ImageClassification


_HOMEPAGE = "https://huggingface.co/datasets/rshrott/renovation"

_CITATION = """\
@ONLINE {renovationquality,
    author="Your Name",
    title="Renovation Quality Dataset",
    month="Your Month",
    year="Your Year",
    url="https://huggingface.co/datasets/rshrott/renovation"
}
"""

_DESCRIPTION = """\
This dataset contains images of various properties, along with labels indicating the quality of renovation - 'cheap', 'average', 'expensive'.
"""

_URL = "https://huggingface.co/datasets/rshrott/renovation/raw/main/labels.csv"

_NAMES = ["cheap", "average", "expensive"]

class RenovationQualityDataset(datasets.GeneratorBasedBuilder):
    """Renovation Quality Dataset."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "image_file_path": datasets.Value("string"),
                    "image": datasets.Image(),
                    "labels": datasets.features.ClassLabel(names=_NAMES),
                }
            ),
            supervised_keys=("image", "labels"),
            homepage=_HOMEPAGE,
            citation=_CITATION,
            task_templates=[ImageClassification(image_column="image", label_column="labels")],
        )

    def _split_generators(self, dl_manager):
        csv_path = dl_manager.download(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": csv_path,
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": csv_path,
                    "split": "validation",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": csv_path,
                    "split": "test",
                },
            ),
        ]

    def _generate_examples(self, filepath, split):
        def url_to_image(url):
            response = requests.get(url)
            img = Image.open(BytesIO(response.content))
            return img
    
        with open(filepath, "r") as f:
            reader = csv.reader(f)
            next(reader)  # skip header
            rows = list(reader)
            if split == 'train':
                rows = rows[:int(0.8 * len(rows))]
            elif split == 'validation':
                rows = rows[int(0.8 * len(rows)):int(0.9 * len(rows))]
            else: # test
                rows = rows[int(0.9 * len(rows)):]
    
            for id_, row in enumerate(rows):
                if len(row) < 2:
                    print(f"Row with id {id_} has less than 2 elements: {row}")
                else:
                    image_file_path = str(row[0])
                    image = url_to_image(image_file_path)
                    yield id_, {
                        'image_file_path': image_file_path,
                        'image': image,
                        'labels': row[1],
                    }