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# Source code for torchvision.datasets.sbd

    import os
    import shutil
    from typing import Any, Callable, Optional, Tuple

    import numpy as np
    from PIL import Image

    from .utils import download_and_extract_archive, download_url, verify_str_arg
    from .vision import VisionDataset

    class SBDataset(VisionDataset):
        """`Semantic Boundaries Dataset <http://home.bharathh.info/pubs/codes/SBD/download.html>`_

        The SBD currently contains annotations from 11355 images taken from the PASCAL VOC 2011 dataset.

        .. note ::

            Please note that the train and val splits included with this dataset are different from
            the splits in the PASCAL VOC dataset. In particular some "train" images might be part of
            VOC2012 val.
            If you are interested in testing on VOC 2012 val, then use `image_set='train_noval'`,
            which excludes all val images.

        .. warning::

            This class needs `scipy <https://docs.scipy.org/doc/>`_ to load target files from `.mat` format.

        Args:
            root (string): Root directory of the Semantic Boundaries Dataset
            image_set (string, optional): Select the image_set to use, ``train``, ``val`` or ``train_noval``.
                Image set ``train_noval`` excludes VOC 2012 val images.
            mode (string, optional): Select target type. Possible values 'boundaries' or 'segmentation'.
                In case of 'boundaries', the target is an array of shape `[num_classes, H, W]`,
                where `num_classes=20`.
            download (bool, optional): If true, downloads the dataset from the internet and
                puts it in root directory. If dataset is already downloaded, it is not
                downloaded again.
            transforms (callable, optional): A function/transform that takes input sample and its target as entry
                and returns a transformed version. Input sample is PIL image and target is a numpy array
                if `mode='boundaries'` or PIL image if `mode='segmentation'`.
        """

        url = "https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz"
        md5 = "82b4d87ceb2ed10f6038a1cba92111cb"
        filename = "benchmark.tgz"

        voc_train_url = "http://home.bharathh.info/pubs/codes/SBD/train_noval.txt"
        voc_split_filename = "train_noval.txt"
        voc_split_md5 = "79bff800c5f0b1ec6b21080a3c066722"

        def __init__(
            self,
            root: str,
            image_set: str = "train",
            mode: str = "boundaries",
            download: bool = False,
            transforms: Optional[Callable] = None,
        ) -> None:

            try:
                from scipy.io import loadmat

                self._loadmat = loadmat
            except ImportError:
                raise RuntimeError("Scipy is not found. This dataset needs to have scipy installed: pip install scipy")

            super().__init__(root, transforms)
            self.image_set = verify_str_arg(image_set, "image_set", ("train", "val", "train_noval"))
            self.mode = verify_str_arg(mode, "mode", ("segmentation", "boundaries"))
            self.num_classes = 20

            sbd_root = self.root
            image_dir = os.path.join(sbd_root, "img")
            mask_dir = os.path.join(sbd_root, "cls")

            if download:
                download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.md5)
                extracted_ds_root = os.path.join(self.root, "benchmark_RELEASE", "dataset")
                for f in ["cls", "img", "inst", "train.txt", "val.txt"]:
                    old_path = os.path.join(extracted_ds_root, f)
                    shutil.move(old_path, sbd_root)
                download_url(self.voc_train_url, sbd_root, self.voc_split_filename, self.voc_split_md5)

            if not os.path.isdir(sbd_root):
                raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")

            split_f = os.path.join(sbd_root, image_set.rstrip("\n") + ".txt")

            with open(os.path.join(split_f)) as fh:
                file_names = [x.strip() for x in fh.readlines()]

            self.images = [os.path.join(image_dir, x + ".jpg") for x in file_names]
            self.masks = [os.path.join(mask_dir, x + ".mat") for x in file_names]

            self._get_target = self._get_segmentation_target if self.mode == "segmentation" else self._get_boundaries_target

        def _get_segmentation_target(self, filepath: str) -> Image.Image:
            mat = self._loadmat(filepath)
            return Image.fromarray(mat["GTcls"][0]["Segmentation"][0])

        def _get_boundaries_target(self, filepath: str) -> np.ndarray:
            mat = self._loadmat(filepath)
            return np.concatenate(
                [np.expand_dims(mat["GTcls"][0]["Boundaries"][0][i][0].toarray(), axis=0) for i in range(self.num_classes)],
                axis=0,
            )

        def __getitem__(self, index: int) -> Tuple[Any, Any]:
            img = Image.open(self.images[index]).convert("RGB")
            target = self._get_target(self.masks[index])

            if self.transforms is not None:
                img, target = self.transforms(img, target)

            return img, target

        def __len__(self) -> int:
            return len(self.images)

        def extra_repr(self) -> str:
            lines = ["Image set: {image_set}", "Mode: {mode}"]
            return "\n".join(lines).format(**self.__dict__)

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