AnaChikashua commited on
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Create handwriting.py

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  1. handwriting.py +75 -0
handwriting.py ADDED
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+ import struct
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+
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+ import numpy as np
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+
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+ import datasets
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+ from datasets.tasks import ImageClassification
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+
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+
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+ _CITATION = """\
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+ @article{lecun2010mnist,
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+ title={MNIST handwritten digit database},
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+ author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
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+ journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},
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+ volume={2},
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+ year={2010}
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+ }
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+ """
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+
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+ _DESCRIPTION = """\
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+ The MNIST dataset consists of 70,000 28x28 black-and-white images in 10 classes (one for each digits), with 7,000
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+ images per class. There are 60,000 training images and 10,000 test images.
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+ """
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+ _URL = "https://huggingface.co/datasets/AnaChikashua/handwriting/resolve/main/handwriting_dataset.zip"
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+ _NAMES = ['ა', 'ბ', 'გ', 'დ', 'ე', 'ვ', 'ზ', 'თ', 'ი', 'კ', 'ლ', 'მ', 'ნ', 'ო', 'პ', 'ჟ', 'რ', 'ს', 'ტ', 'უ', 'ფ', 'ქ', 'ღ', 'ყ', 'შ', 'ჩ', 'ც', 'ძ', 'წ', 'ჭ', 'ხ', 'ჯ', 'ჰ']
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+
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+
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+ class MNIST(datasets.GeneratorBasedBuilder):
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+ """MNIST Data Set"""
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+
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+ BUILDER_CONFIGS = [
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+ datasets.BuilderConfig(
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+ name="data",
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+ version=datasets.Version("1.0.0"),
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+ description=_DESCRIPTION,
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+ )
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+ ]
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+
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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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+ "image": datasets.Image(),
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+ "label": datasets.features.ClassLabel(names=_NAMES),
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+ }
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+ ),
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+ supervised_keys=("image", "label"),
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+ citation=_CITATION,
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+ task_templates=[
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+ ImageClassification(
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+ image_column="image",
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+ label_column="label",
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+ )
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+ ],
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ path = dl_manager.dowload(_URL)
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+ image_iters = dl_manager.iter_archive(path)
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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={"images": image_iters}
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+ ),
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+ ]
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+
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+ def _generate_examples(self, images):
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+ """This function returns the examples in the raw form."""
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+ for idx, filepath, image in enumerate(images):
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+ # extract the text from the filename
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+ text = [c for c in str(filepath) if not 0 <= ord(c) <= 127][0]
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+ yield idx, {
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+ "label": text,
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+ "image": {"path": filepath, "bytes": image.read()}
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+ }