renovation.py

import os
import glob
import random

import datasets
from datasets.tasks import ImageClassification

_HOMEPAGE = "https://github.com/your-github/renovation"

_CITATION = """\
@ONLINE {renovationdata,
 author="Your Name",
 title="Renovation dataset",
 month="January",
 year="2023",
 url="https://github.com/your-github/renovation"
}
"""

_DESCRIPTION = """\
Renovations is a dataset of images of houses taken in the field using smartphone
cameras. It consists of 3 classes: cheap, average, and expensive renovations.
Data was collected by the your research lab.
"""

_URLS = {
 "cheap": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/cheap.zip",
 "average": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/average.zip",
 "expensive": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/expensive.zip",
}

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

class Renovations(datasets.GeneratorBasedBuilder):
 """Renovations house images dataset."""

 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):
 data_files = dl_manager.download_and_extract(_URLS)
 return [
 datasets.SplitGenerator(
 name=datasets.Split.TRAIN,
 gen_kwargs={
 "data_files": data_files,
 "split": "train",
 },
 ),
 datasets.SplitGenerator(
 name=datasets.Split.VALIDATION,
 gen_kwargs={
 "data_files": data_files,
 "split": "val",
 },
 ),
 datasets.SplitGenerator(
 name=datasets.Split.TEST,
 gen_kwargs={
 "data_files": data_files,
 "split": "test",
 },
 ),
 ]

 def _generate_examples(self, data_files, split):
 all_files_and_labels = []
 for label, path in data_files.items():
 files = glob.glob(path + '/*.jpeg', recursive=True)
 all_files_and_labels.extend((file, label) for file in files)
 
 random.shuffle(all_files_and_labels)
 
 num_files = len(all_files_and_labels)
 if split == "train":
 all_files_and_labels = all_files_and_labels[:int(num_files*0.7)]
 elif split == "val":
 all_files_and_labels = all_files_and_labels[int(num_files*0.7):int(num_files*0.85)]
 else:
 all_files_and_labels = all_files_and_labels[int(num_files*0.85):]
 
 for idx, (file, label) in enumerate(all_files_and_labels):
 yield idx, {
 "image_file_path": file,
 "image": file,
 "labels": label,
 }