text: # Copyright (C) 2021-2022, Mindee.
  
  # This program is licensed under the Apache License version 2.
  # See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0.txt> for full license details.
  
  import csv
  import os
  from pathlib import Path
  from typing import Any, Dict, List, Tuple
  
  import numpy as np
  
  from .datasets import AbstractDataset
  from .utils import convert_target_to_relative
  
  __all__ = ["IC13"]
  
  
  class IC13(AbstractDataset):
      """IC13 dataset from `"ICDAR 2013 Robust Reading Competition" <https://rrc.cvc.uab.es/>`_.
  
      Example::
          >>> # NOTE: You need to download both image and label parts from Focused Scene Text challenge Task2.1 2013-2015.
          >>> from doctr.datasets import IC13
          >>> train_set = IC13(img_folder="/path/to/Challenge2_Training_Task12_Images",
          >>>                  label_folder="/path/to/Challenge2_Training_Task1_GT")
          >>> img, target = train_set[0]
          >>> test_set = IC13(img_folder="/path/to/Challenge2_Test_Task12_Images",
          >>>                 label_folder="/path/to/Challenge2_Test_Task1_GT")
          >>> img, target = test_set[0]
  
      Args:
          img_folder: folder with all the images of the dataset
          label_folder: folder with all annotation files for the images
          use_polygons: whether polygons should be considered as rotated bounding box (instead of straight ones)
          **kwargs: keyword arguments from `AbstractDataset`.
      """
  
      def __init__(
          self,
          img_folder: str,
          label_folder: str,
          use_polygons: bool = False,
          **kwargs: Any,
      ) -> None:
          super().__init__(img_folder, pre_transforms=convert_target_to_relative, **kwargs)
  
          # File existence check
          if not os.path.exists(label_folder) or not os.path.exists(img_folder):
              raise FileNotFoundError(
                  f"unable to locate {label_folder if not os.path.exists(label_folder) else img_folder}")
  
          self.data: List[Tuple[Path, Dict[str, Any]]] = []
          np_dtype = np.float32
  
          img_names = os.listdir(img_folder)
  
          for img_name in img_names:
  
              img_path = Path(img_folder, img_name)
              label_path = Path(label_folder, "gt_" + Path(img_name).stem + ".txt")
  
              with open(label_path, newline='\n') as f:
                  _lines = [
                      [val[:-1] if val.endswith(",") else val for val in row]
                      for row in csv.reader(f, delimiter=' ', quotechar="'")
                  ]
              labels = [line[-1] for line in _lines]
              # xmin, ymin, xmax, ymax
              box_targets = np.array([list(map(int, line[:4])) for line in _lines], dtype=np_dtype)
              if use_polygons:
                  # (x, y) coordinates of top left, top right, bottom right, bottom left corners
                  box_targets = np.array(
                      [
                          [
                              [coords[0], coords[1]],
                              [coords[2], coords[1]],
                              [coords[2], coords[3]],
                              [coords[0], coords[3]],
                          ] for coords in box_targets
                      ], dtype=np_dtype
                  )
              self.data.append((img_path, dict(boxes=box_targets, labels=labels)))
  