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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}" | |