File size: 2,287 Bytes
fa45621 8fdb331 fa45621 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
import os
import zipfile
from pathlib import Path
import datasets
class Photos(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features({
"image": datasets.Image(),
"label": datasets.ClassLabel(names=["Not Applicable", "Very Poor", "Poor", "Fair", "Good", "Excellent", "Exceptional"]),
}),
supervised_keys=("image", "label"),
)
def _split_generators(self, dl_manager):
# Define the URLs for the zip files
urls = {
'Not Applicable': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Not Applicable.zip",
'Very Poor': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Very Poor.zip",
'Poor': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Poor.zip",
'Fair': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Fair.zip",
'Good': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Good.zip",
'Excellent': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Excellent.zip",
'Exceptional': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Exceptional.zip"
}
# Download and extract the zip files
downloaded_files = dl_manager.download_and_extract(urls)
extracted_dirs = {label: Path(file).stem for label, file in downloaded_files.items()} # Remove .zip extension
# Here you would split the dataset into train, validation, and test sets
# For simplicity, we'll assume all images are used for training
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"extracted_dirs": extracted_dirs}),
]
def _generate_examples(self, extracted_dirs):
# Iterate over the images in the extracted directories and yield examples
for label, dir in extracted_dirs.items():
label_dir = os.path.join(self.config.data_dir, dir)
for img_path in Path(label_dir).glob('*.jpeg'):
yield str(img_path), {
"image": str(img_path),
"label": label,
}
|