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import os |
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import io |
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import datasets |
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from PIL import ImageOps, Image |
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_CITATION = """\ |
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@InProceedings{huggingface:dataset, |
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title = {Processed KHATT paragrpah dataset}, |
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author={Ahmed Alnaggar}, |
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year={2024} |
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} |
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""" |
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_DESCRIPTION = """\ |
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A curated version of KHATT paragraph dataset containing 3996 images and their crossponding Arabic paragraphs |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/a-alnaggar/khatt-paragraphs" |
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_LICENSE = "" |
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_REPO = "https://huggingface.co/datasets/a-alnaggar/khatt-paragraphs" |
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class KhattPara(datasets.GeneratorBasedBuilder): |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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'text': datasets.Value("string"), |
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'image': datasets.Image(), |
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} |
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), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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images_archive = dl_manager.download(f"{_REPO}/resolve/main/khatt-paragraphs-images.tar.gz") |
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image_iters = dl_manager.iter_archive(images_archive) |
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text_archive = dl_manager.download_and_extract(f"{_REPO}/resolve/main/khatt-paragraphs-text.tar.gz") |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"images": image_iters, |
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"text_archive_path": text_archive |
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} |
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), |
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] |
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def _generate_examples(self, images, text_archive_path): |
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"""Returns inverted image and Arabic text.""" |
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for idx, (filepath, image) in enumerate(images): |
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text_path = os.path.join("proc_text", str(os.path.basename(filepath)[:-4] + ".txt")) |
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text = self.read_arabic_text_file(os.path.join(text_archive_path,text_path)) |
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yield idx, { |
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"image": {"path": filepath, "bytes": image.read()}, |
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"text": text, |
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} |
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@staticmethod |
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def read_arabic_text_file(file_path): |
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with open(file_path, 'r', encoding='windows-1256') as file: |
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lines = file.readlines() |
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return ''.join(lines) |
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