Source code for doctr.datasets.iiit5k

# Copyright (C) 2021-2022, 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, Dict, List, Tuple, Union

import numpy as np
import scipy.io as sio
from tqdm import tqdm

from .datasets import VisionDataset
from .utils import convert_target_to_relative

__all__ = ["IIIT5K"]


[docs]class IIIT5K(VisionDataset): """IIIT-5K character-level localization dataset from `"BMVC 2012 Scene Text Recognition using Higher Order Language Priors" <https://cdn.iiit.ac.in/cdn/cvit.iiit.ac.in/images/Projects/SceneTextUnderstanding/home/mishraBMVC12.pdf>`_. .. image:: https://doctr-static.mindee.com/models?id=v0.5.0/iiit5k-grid.png&src=0 :align: center >>> # NOTE: this dataset is for character-level localization >>> from doctr.datasets import IIIT5K >>> train_set = IIIT5K(train=True, download=True) >>> img, target = train_set[0] Args: train: whether the subset should be the training one use_polygons: whether polygons should be considered as rotated bounding box (instead of straight ones) recognition_task: whether the dataset should be used for recognition task **kwargs: keyword arguments from `VisionDataset`. """ URL = "https://cvit.iiit.ac.in/images/Projects/SceneTextUnderstanding/IIIT5K-Word_V3.0.tar.gz" SHA256 = "7872c9efbec457eb23f3368855e7738f72ce10927f52a382deb4966ca0ffa38e" def __init__( self, train: bool = True, use_polygons: bool = False, recognition_task: bool = False, **kwargs: Any, ) -> None: super().__init__( self.URL, None, file_hash=self.SHA256, extract_archive=True, pre_transforms=convert_target_to_relative if not recognition_task else None, **kwargs, ) self.train = train # Load mat data tmp_root = os.path.join(self.root, "IIIT5K") if self.SHA256 else self.root mat_file = "trainCharBound" if self.train else "testCharBound" mat_data = sio.loadmat(os.path.join(tmp_root, f"{mat_file}.mat"))[mat_file][0] self.data: List[Tuple[Union[str, np.ndarray], Union[str, Dict[str, Any]]]] = [] np_dtype = np.float32 for img_path, label, box_targets in tqdm(iterable=mat_data, desc="Unpacking IIIT5K", total=len(mat_data)): _raw_path = img_path[0] _raw_label = label[0] # File existence check if not os.path.exists(os.path.join(tmp_root, _raw_path)): raise FileNotFoundError(f"unable to locate {os.path.join(tmp_root, _raw_path)}") if recognition_task: self.data.append((_raw_path, _raw_label)) else: if use_polygons: # (x, y) coordinates of top left, top right, bottom right, bottom left corners box_targets = [ [ [box[0], box[1]], [box[0] + box[2], box[1]], [box[0] + box[2], box[1] + box[3]], [box[0], box[1] + box[3]], ] for box in box_targets ] else: # xmin, ymin, xmax, ymax box_targets = [[box[0], box[1], box[0] + box[2], box[1] + box[3]] for box in box_targets] # label are casted to list where each char corresponds to the character's bounding box self.data.append( (_raw_path, dict(boxes=np.asarray(box_targets, dtype=np_dtype), labels=list(_raw_label))) ) self.root = tmp_root def extra_repr(self) -> str: return f"train={self.train}"