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import os
import datasets
from datasets.tasks import ImageClassification
_HOMEPAGE = ""
_CITATION = ""
_DESCRIPTION = """\
This is a dataset of particle samples to be classified for the ancient mortars project.
"""
_URLS = {
"train": "https://huggingface.co/datasets/apetulante/mortars_test/resolve/main/data/train.zip",
"validation": "https://huggingface.co/datasets/apetulante/mortars_test/resolve/main/data/valid.zip",
"test": "https://huggingface.co/datasets/apetulante/mortars_test/resolve/main/data/test.zip",
}
#names_list = open("https://huggingface.co/datasets/apetulante/mortars_test/resolve/main/data/particle_names.txt","r").read().split("\n")
_NAMES = ["kurkar", "sand", "soil", "chert", "obsidian", "arch_18", "kurkar_nahal","sand_beach","volcanicash_pozzuoli"]
class MortarsTest(datasets.GeneratorBasedBuilder):
"""Ancient particles 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={
"files": dl_manager.iter_files([data_files["train"]]),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"files": dl_manager.iter_files([data_files["validation"]]),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"files": dl_manager.iter_files([data_files["test"]]),
},
),
]
def _generate_examples(self, files):
for i, path in enumerate(files):
file_name = os.path.basename(path)
if file_name.endswith(".bmp"):
yield i, {
"image_file_path": path,
"image": path,
"labels": os.path.basename(file_name).lower().split('-')[0].split('_')[0],
} |