renovation.py

import os
import glob
import random

import datasets
from datasets.tasks import ImageClassification
from datasets import load_dataset
import os
from huggingface_hub import login
_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 7 classes: Not Applicable, Poor, Fair, Good, and Great renovations.
Data was collected by the your research lab.
"""

_URLS = {
 "Not Applicable": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/Not Applicable.zip",
 "Poor": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/Poor.zip",
 "Fair": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/Fair.zip",
 "Good": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/Good.zip",
 "Great": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/Great.zip",
 "Excellent": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/Excellent.zip"
}

_NAMES = ["Not Applicable", "Poor", "Fair", "Good", "Great", "Excellent"]
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):
 # Separate data by class
 data_by_class = {label: [] for label in _NAMES}
 allowed_extensions = {'.jpeg', '.jpg'}
 for label, path in data_files.items():
 files = [os.path.join(path, f) for f in os.listdir(path) if os.path.isfile(os.path.join(path, f)) and os.path.splitext(f)[1] in allowed_extensions]
 data_by_class[label].extend((file, label) for file in files)
 
 
 # Shuffle and split data for each class
 random.seed(43) # ensure reproducibility
 train_data, test_data, val_data = [], [], []
 for label, files_and_labels in data_by_class.items():
 random.shuffle(files_and_labels)
 num_files = len(files_and_labels)
 train_end = int(num_files * 0.85)
 test_end = int(num_files * 0.95)
 train_data.extend(files_and_labels[:train_end])
 test_data.extend(files_and_labels[train_end:test_end])
 val_data.extend(files_and_labels[test_end:])
 
 # Select the appropriate split
 if split == "train":
 data_to_use = train_data
 elif split == "test":
 data_to_use = test_data
 else: # "val" split
 data_to_use = val_data
 
 # Yield examples
 for idx, (file, label) in enumerate(data_to_use):
 yield idx, {
 "image_file_path": file,
 "image": file,
 "labels": label,
 }