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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 typing import Any, List, Tuple
from tqdm import tqdm
from .datasets import AbstractDataset
__all__ = ["MJSynth"]
class MJSynth(AbstractDataset):
"""MJSynth dataset from `"Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition"
<https://www.robots.ox.ac.uk/~vgg/data/text/>`_.
>>> # NOTE: This is a pure recognition dataset without bounding box labels.
>>> # NOTE: You need to download the dataset.
>>> from doctr.datasets import MJSynth
>>> train_set = MJSynth(img_folder="/path/to/mjsynth/mnt/ramdisk/max/90kDICT32px",
>>> label_path="/path/to/mjsynth/mnt/ramdisk/max/90kDICT32px/imlist.txt",
>>> train=True)
>>> img, target = train_set[0]
>>> test_set = MJSynth(img_folder="/path/to/mjsynth/mnt/ramdisk/max/90kDICT32px",
>>> label_path="/path/to/mjsynth/mnt/ramdisk/max/90kDICT32px/imlist.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`.
"""
# filter corrupted or missing images
BLACKLIST = [
"./1881/4/225_Marbling_46673.jpg\n",
"./2069/4/192_whittier_86389.jpg\n",
"./869/4/234_TRIASSIC_80582.jpg\n",
"./173/2/358_BURROWING_10395.jpg\n",
"./913/4/231_randoms_62372.jpg\n",
"./596/2/372_Ump_81662.jpg\n",
"./936/2/375_LOCALITIES_44992.jpg\n",
"./2540/4/246_SQUAMOUS_73902.jpg\n",
"./1332/4/224_TETHERED_78397.jpg\n",
"./627/6/83_PATRIARCHATE_55931.jpg\n",
"./2013/2/370_refract_63890.jpg\n",
"./2911/6/77_heretical_35885.jpg\n",
"./1730/2/361_HEREON_35880.jpg\n",
"./2194/2/334_EFFLORESCENT_24742.jpg\n",
"./2025/2/364_SNORTERS_72304.jpg\n",
"./368/4/232_friar_30876.jpg\n",
"./275/6/96_hackle_34465.jpg\n",
"./384/4/220_bolts_8596.jpg\n",
"./905/4/234_Postscripts_59142.jpg\n",
"./2749/6/101_Chided_13155.jpg\n",
"./495/6/81_MIDYEAR_48332.jpg\n",
"./2852/6/60_TOILSOME_79481.jpg\n",
"./554/2/366_Teleconferences_77948.jpg\n",
"./1696/4/211_Queened_61779.jpg\n",
"./2128/2/369_REDACTED_63458.jpg\n",
"./2557/2/351_DOWN_23492.jpg\n",
"./2489/4/221_snored_72290.jpg\n",
"./1650/2/355_stony_74902.jpg\n",
"./1863/4/223_Diligently_21672.jpg\n",
"./264/2/362_FORETASTE_30276.jpg\n",
"./429/4/208_Mainmasts_46140.jpg\n",
"./1817/2/363_actuating_904.jpg\n",
]
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:
img_paths = f.readlines()
train_samples = int(len(img_paths) * 0.9)
set_slice = slice(train_samples) if self.train else slice(train_samples, None)
for path in tqdm(iterable=img_paths[set_slice], desc="Unpacking MJSynth", total=len(img_paths[set_slice])):
if path not in self.BLACKLIST:
label = path.split("_")[1]
img_path = os.path.join(img_folder, path[2:]).strip()
self.data.append((img_path, label))
def extra_repr(self) -> str:
return f"train={self.train}"
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