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# Copyright (C) 2021-2024, Mindee.
# This program is licensed under the Apache License 2.0.
# See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
import os
from random import sample
from typing import Any, List, Tuple
from tqdm import tqdm
from .datasets import AbstractDataset
__all__ = ["IIITHWS"]
class IIITHWS(AbstractDataset):
"""IIITHWS dataset from `"Generating Synthetic Data for Text Recognition"
<https://arxiv.org/pdf/1608.04224.pdf>`_ | `"repository" <https://github.com/kris314/hwnet>`_ |
`"website" <https://cvit.iiit.ac.in/research/projects/cvit-projects/matchdocimgs>`_.
>>> # NOTE: This is a pure recognition dataset without bounding box labels.
>>> # NOTE: You need to download the dataset.
>>> from doctr.datasets import IIITHWS
>>> train_set = IIITHWS(img_folder="/path/to/iiit-hws/Images_90K_Normalized",
>>> label_path="/path/to/IIIT-HWS-90K.txt",
>>> train=True)
>>> img, target = train_set[0]
>>> test_set = IIITHWS(img_folder="/path/to/iiit-hws/Images_90K_Normalized",
>>> label_path="/path/to/IIIT-HWS-90K.txt")
>>> train=False)
>>> img, target = test_set[0]
Args:
----
img_folder: folder with all the images of the dataset
label_path: path to the file with the labels
train: whether the subset should be the training one
**kwargs: keyword arguments from `AbstractDataset`.
"""
def __init__(
self,
img_folder: str,
label_path: str,
train: bool = True,
**kwargs: Any,
) -> None:
super().__init__(img_folder, **kwargs)
# File existence check
if not os.path.exists(label_path) or not os.path.exists(img_folder):
raise FileNotFoundError(f"unable to locate {label_path if not os.path.exists(label_path) else img_folder}")
self.data: List[Tuple[str, str]] = []
self.train = train
with open(label_path) as f:
annotations = f.readlines()
# Shuffle the dataset otherwise the test set will contain the same labels n times
annotations = sample(annotations, len(annotations))
train_samples = int(len(annotations) * 0.9)
set_slice = slice(train_samples) if self.train else slice(train_samples, None)
for annotation in tqdm(
iterable=annotations[set_slice], desc="Unpacking IIITHWS", total=len(annotations[set_slice])
):
img_path, label = annotation.split()[0:2]
img_path = os.path.join(img_folder, img_path)
self.data.append((img_path, label))
def extra_repr(self) -> str:
return f"train={self.train}"
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