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import numpy as np
import datasets
from PIL import Image
class KMNIST(datasets.GeneratorBasedBuilder):
"""Kuzushiji-MNIST and Kuzushiji-49 datasets."""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="kmnist", description="Kuzushiji-MNIST dataset with 10 classes."),
datasets.BuilderConfig(name="k49mnist", description="Kuzushiji-49 dataset with 49 classes."),
]
def _info(self):
if self.config.name == "kmnist":
num_classes = 10
else:
num_classes = 49
return datasets.DatasetInfo(
description="Kuzushiji-MNIST and Kuzushiji-49 datasets.",
features=datasets.Features({
"image": datasets.Image(), # Automatically converts to PIL.Image
"label": datasets.ClassLabel(num_classes=num_classes),
}),
supervised_keys=("image", "label"),
license="CC BY-SA 4.0",
homepage="https://github.com/rois-codh/kmnist",
citation="""
@online{clanuwat2018deep,
author = {Tarin Clanuwat and Mikel Bober-Irizar and Asanobu Kitamoto and Alex Lamb and Kazuaki Yamamoto and David Ha},
title = {Deep Learning for Classical Japanese Literature},
date = {2018-12-03},
year = {2018},
eprintclass = {cs.CV},
eprinttype = {arXiv},
eprint = {cs.CV/1812.01718},
}
"""
)
def _split_generators(self, dl_manager):
urls = {
"kmnist": {
"train_imgs": "http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-train-imgs.npz",
"train_labels": "http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-train-labels.npz",
"test_imgs": "http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-test-imgs.npz",
"test_labels": "http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-test-labels.npz",
},
"k49mnist": {
"train_imgs": "http://codh.rois.ac.jp/kmnist/dataset/k49/k49-train-imgs.npz",
"train_labels": "http://codh.rois.ac.jp/kmnist/dataset/k49/k49-train-labels.npz",
"test_imgs": "http://codh.rois.ac.jp/kmnist/dataset/k49/k49-test-imgs.npz",
"test_labels": "http://codh.rois.ac.jp/kmnist/dataset/k49/k49-test-labels.npz",
},
}
selected_urls = urls[self.config.name]
downloaded_files = dl_manager.download(selected_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"images_path": downloaded_files["train_imgs"],
"labels_path": downloaded_files["train_labels"]
}
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"images_path": downloaded_files["test_imgs"],
"labels_path": downloaded_files["test_labels"]
}
),
]
def _generate_examples(self, images_path, labels_path):
images = np.load(images_path)["arr_0"]
labels = np.load(labels_path)["arr_0"]
for idx, (image, label) in enumerate(zip(images, labels)):
# Convert each image to a PIL.Image object
image = Image.fromarray(image, mode="L") # Mode "L" for grayscale images
yield idx, {"image": image, "label": int(label)}
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