Datasets:
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import os
import random
import datasets
from datasets.tasks import ImageClassification
_HOMEPAGE = (
f"https://www.modelscope.cn/datasets/MuGemSt/{os.path.basename(__file__)[:-3]}"
)
_URL = f"{_HOMEPAGE}/resolve/master/images.zip"
_NAMES = ["Centromere", "Golgi", "Homogeneous", "NuMem", "Nucleolar", "Speckled"]
class HEp2(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features(
{
"image": datasets.Image(),
"label": datasets.features.ClassLabel(names=_NAMES),
}
),
supervised_keys=("image", "label"),
homepage=_HOMEPAGE,
license="mit",
version="0.0.1",
task_templates=[
ImageClassification(
task="image-classification",
image_column="image",
label_column="label",
)
],
)
def _ground_truth(self, id):
if id < 2495:
return "Homogeneous"
elif id < 5326:
return "Speckled"
elif id < 7924:
return "Nucleolar"
elif id < 10665:
return "Centromere"
elif id < 12873:
return "NuMem"
else:
return "Golgi"
def _split_generators(self, dl_manager):
data_files = dl_manager.download_and_extract(_URL)
files = dl_manager.iter_files([data_files])
dataset = []
for path in files:
file_name = os.path.basename(path)
if file_name.endswith(".png"):
dataset.append(
{
"image": path,
"label": self._ground_truth(int(file_name.split(".")[0])),
}
)
random.shuffle(dataset)
data_count = len(dataset)
p80 = int(data_count * 0.8)
p90 = int(data_count * 0.9)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"files": dataset[:p80],
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"files": dataset[p80:p90],
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"files": dataset[p90:],
},
),
]
def _generate_examples(self, files):
for i, path in enumerate(files):
yield i, path
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