|
import json |
|
import os |
|
import datasets |
|
|
|
|
|
_CITATION = """\ |
|
@article{seker2022generalized, title={A generalized framework for recognition of expiration dates on product packages using fully convolutional networks}, author={Seker, Ahmet Cagatay and Ahn, Sang Chul}, journal={Expert Systems with Applications}, pages={117310}, year={2022}, publisher={Elsevier} } |
|
""" |
|
|
|
_DESCRIPTION = """\ |
|
The dataset for Date detection in the proposed framework aims to provide annotated images that are relevant for training and evaluating models tasked with detecting dates within product labels or similar contexts. |
|
""" |
|
|
|
_HOMEPAGE = "https://acseker.github.io/ExpDateWebsite/" |
|
|
|
_LICENSE = "https://licenses.nuget.org/AFL-3.0" |
|
|
|
_URLs = { |
|
"products_synth": "https://huggingface.co/datasets/dimun/ExpirationDate/resolve/main/Products-Synth.zip?download=true", |
|
"products_real": "https://huggingface.co/datasets/dimun/ExpirationDate/resolve/main/Products-Real.zip?download=true", |
|
} |
|
|
|
|
|
def has_extension(file_path, extensions): |
|
_, file_extension = os.path.splitext(file_path) |
|
return file_extension.lower() in extensions |
|
|
|
|
|
logger = datasets.logging.get_logger(__name__) |
|
|
|
|
|
class ExpirationDate(datasets.GeneratorBasedBuilder): |
|
VERSION = datasets.Version("0.0.1") |
|
CATEGORIES = ["prod", "date", "due", "code"] |
|
|
|
def _info(self): |
|
features = datasets.Features( |
|
{ |
|
"id": datasets.Value("string"), |
|
"transcriptions": datasets.Sequence(datasets.Value("string")), |
|
"bboxes_block": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))), |
|
"categories": datasets.Sequence(datasets.features.ClassLabel(names=self.CATEGORIES)), |
|
"image_path": datasets.Value("string"), |
|
"width": datasets.Value("int32"), |
|
"height": datasets.Value("int32") |
|
} |
|
) |
|
|
|
return datasets.DatasetInfo( |
|
|
|
description=_DESCRIPTION, |
|
|
|
features=features, |
|
|
|
homepage=_HOMEPAGE, |
|
|
|
license=_LICENSE, |
|
|
|
citation=_CITATION, |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
"""Returns SplitGenerators.""" |
|
|
|
|
|
|
|
archive_path = dl_manager.download_and_extract(_URLs) |
|
|
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, |
|
|
|
gen_kwargs={ |
|
"filepath": os.path.join(archive_path["products_synth"], "Products-Synth/"), |
|
"split": "", |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.VALIDATION, |
|
|
|
gen_kwargs={ |
|
"filepath": os.path.join(archive_path["products_real"], "Products-Real/"), |
|
"split": "evaluation", |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TEST, |
|
|
|
gen_kwargs={ |
|
"filepath": os.path.join(archive_path["products_real"], "Products-Real/"), |
|
|
|
"split": "train", |
|
}, |
|
), |
|
] |
|
|
|
def _generate_examples(self, filepath, split): |
|
logger.info( |
|
f"⏳ Generating examples from = {filepath} to the split {split}") |
|
ann_file = os.path.join(filepath, split, "annotations.json") |
|
|
|
|
|
with open(ann_file, "r", encoding="utf8") as f: |
|
features_map = json.load(f) |
|
|
|
img_dir = os.path.join(filepath, split, "images") |
|
img_listdir = os.listdir(img_dir) |
|
|
|
for guid, filename in enumerate(img_listdir): |
|
if filename.endswith(".jpg"): |
|
image_features = features_map[filename] |
|
image_ann = image_features.get("ann") |
|
|
|
transcriptions = [box.get("transcription", "") |
|
for box in image_ann] |
|
bboxes_block = [box.get("bbox") for box in image_ann] |
|
categories = [box.get("cls") if box.get( |
|
"cls") in self.CATEGORIES else "date" for box in image_ann] |
|
|
|
|
|
image_path = os.path.join(img_dir, filename) |
|
|
|
yield guid, { |
|
"id": filename, |
|
"transcriptions": transcriptions, |
|
"bboxes_block": bboxes_block, |
|
"categories": categories, |
|
"image_path": image_path, |
|
"width": image_features.get("width"), |
|
"height": image_features.get("height"), |
|
} |
|
|