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import datasets
from PIL import Image
import zipfile
class Beans(datasets.GeneratorBasedBuilder):
"""Bean disease dataset for classification of three classes: Angular Leaf Spot, Bean Rust, and Healthy leaves."""
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description="""The IBeans dataset contains leaf images representing three classes:
1) Healthy leaves, 2) Angular Leaf Spot, and 3) Bean Rust. Images are collected in Uganda for disease
classification in the field.""",
features=datasets.Features(
{
"image": datasets.Image(),
"label": datasets.ClassLabel(
names=["healthy", "angular_leaf_spot", "bean_rust"]
),
}
),
supervised_keys=("image", "label"),
license="MIT License",
citation="""@misc{makerere2020beans,
author = "{Makerere AI Lab}",
title = "{Bean Disease Dataset}",
year = "2020",
month = "January",
url = "https://github.com/AI-Lab-Makerere/ibean/"}"""
)
def _split_generators(self, dl_manager):
urls = {
"train": "https://storage.googleapis.com/ibeans/train.zip",
"test": "https://storage.googleapis.com/ibeans/test.zip",
"validation": "https://storage.googleapis.com/ibeans/validation.zip",
}
downloaded_files = dl_manager.download(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"zip_path": downloaded_files["train"]},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"zip_path": downloaded_files["test"]},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"zip_path": downloaded_files["validation"]},
),
]
def _generate_examples(self, zip_path):
with zipfile.ZipFile(zip_path, "r") as archive:
for file_name in archive.namelist():
if file_name.endswith(".jpg"):
with archive.open(file_name) as file:
image_data = Image.open(file)
label_name = file_name.split("/")[1]
label = self.info.features["label"].str2int(label_name)
yield file_name, {"image": image_data, "label": label}
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