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import datasets
import pandas as pd
import os
import logging
_DESCRIPTION = """\
Arabic Handwritten dataset.
"""
_REPO = "https://huggingface.co/datasets/eDaraty/Handwritten_Khatt"
# logging.basicConfig(level=logging.INFO)
# logger = logging.getLogger(__name__)
class Khatt(datasets.GeneratorBasedBuilder):
"""Handwritten arabic image-text pairs"""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
'image': datasets.Image(),
'text': datasets.Value("string"),
}
),
# supervised_keys=None,
homepage=_REPO,
# citation=_CITATION,
)
def _split_generators(self, dl_manager):
train_archive = dl_manager.download(f"{_REPO}/resolve/main/data/train.zip")
test_archive = dl_manager.download(f"{_REPO}/resolve/main/data/test.zip")
val_archive = dl_manager.download(f"{_REPO}/resolve/main/data/validation.zip")
# split_metadata_paths = dl_manager.download(_METADATA_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"images": dl_manager.iter_archive(train_archive)
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"images": dl_manager.iter_archive(test_archive)
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"images": dl_manager.iter_archive(val_archive)
},
),
]
def _generate_examples(self, images):
""" This function returns the examples in the raw (text) form."""
df = pd.read_csv(f"{_REPO}/resolve/main/data/metadata.csv")
for idx, (filepath, image) in enumerate(images):
image_name = os.path.basename(filepath)
# logger.info(" filename of image is '%s' ", image_name)
description = df[df["file_name"] == image_name]['text'].values.tolist()[0]
# logger.info(" text of image is '%s' ", description)
# logger.info(" type of image is '%s' ", type(description))
yield idx, {
"image": {"path": filepath, "bytes": image.read()},
"text": description,
}
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