renovation.py

import csv
import random
import datasets
import requests
import os
import py7zr
import numpy as np

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'.
"""

_URLS = {
    "cheap": "https://huggingface.co/datasets/rshrott/renovation/raw/main/cheap.7z",
    "average": "https://huggingface.co/datasets/rshrott/renovation/raw/main/average.7z",
    "expensive": "https://huggingface.co/datasets/rshrott/renovation/raw/main/expensive.7z",
}

_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):
            # Download and extract images
            image_paths = []
            for label, url in _URLS.items():
                extract_path = dl_manager.download_and_extract(url)
                print(f"Extracted files for label {label} to path: {extract_path}")
    
                # Get image paths
                for root, _, files in os.walk(extract_path):
                    for file in files:
                        if file.endswith(".jpeg"):  # Assuming all images are .jpeg
                            image_paths.append((os.path.join(root, file), label))
    
            print(f"Collected a total of {len(image_paths)} image paths.")
    
            # Shuffle image paths
            random.shuffle(image_paths)
    
            # 80% for training, 10% for validation, 10% for testing
            train_end = int(0.8 * len(image_paths))
            val_end = int(0.9 * len(image_paths))
    
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "rows": image_paths[:train_end],
                    },
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.VALIDATION,
                    gen_kwargs={
                        "rows": image_paths[train_end:val_end],
                    },
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.TEST,
                    gen_kwargs={
                        "rows": image_paths[val_end:],
                    },
                ),
            ]
    
        def _generate_examples(self, rows):
            def file_to_image(file_path):
                img = Image.open(file_path)
                return np.array(img)
    
            for id_, (image_file_path, label) in enumerate(rows):
                image = file_to_image(image_file_path)
                yield id_, {
                    'image_file_path': image_file_path,
                    'image': image,
                    'labels': label,
                }