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import csv |
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import datasets |
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import requests |
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import os |
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from PIL import Image |
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from io import BytesIO |
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from datasets.tasks import ImageClassification |
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_HOMEPAGE = "https://huggingface.co/datasets/rshrott/renovation" |
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_CITATION = """\ |
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@ONLINE {renovationquality, |
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author="Your Name", |
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title="Renovation Quality Dataset", |
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month="Your Month", |
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year="Your Year", |
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url="https://huggingface.co/datasets/rshrott/renovation" |
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} |
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""" |
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_DESCRIPTION = """\ |
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This dataset contains images of various properties, along with labels indicating the quality of renovation - 'cheap', 'average', 'expensive'. |
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""" |
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_URL = "https://huggingface.co/datasets/rshrott/renovation/raw/main/labels.csv" |
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_NAMES = ["cheap", "average", "expensive"] |
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class RenovationQualityDataset(datasets.GeneratorBasedBuilder): |
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"""Renovation Quality Dataset.""" |
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VERSION = datasets.Version("1.0.0") |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"image_file_path": datasets.Value("string"), |
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"image": datasets.Image(), |
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"labels": datasets.features.ClassLabel(names=_NAMES), |
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} |
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), |
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supervised_keys=("image", "labels"), |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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task_templates=[ImageClassification(image_column="image", label_column="labels")], |
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) |
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def _split_generators(self, dl_manager): |
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csv_path = dl_manager.download(_URL) |
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with open(csv_path, "r") as f: |
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reader = csv.reader(f) |
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next(reader) |
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rows = list(reader) |
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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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"rows": rows[:int(0.9 * len(rows))], |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"rows": rows[int(0.9 * len(rows)):], |
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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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"rows": rows[int(0.9 * len(rows)):], |
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}, |
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), |
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] |
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def _generate_examples(self, rows): |
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def url_to_image(url): |
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response = requests.get(url) |
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img = Image.open(BytesIO(response.content)) |
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return img |
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for id_, row in enumerate(rows): |
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if len(row) < 2: |
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print(f"Row with id {id_} has less than 2 elements: {row}") |
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else: |
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image_file_path = str(row[0]) |
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image = url_to_image(image_file_path) |
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yield id_, { |
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'image_file_path': image_file_path, |
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'image': image, |
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'labels': row[1], |
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} |
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