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# Source code for torchvision.datasets.phototour

    import os
    from typing import Any, Callable, List, Optional, Tuple, Union

    import numpy as np
    import torch
    from PIL import Image

    from .utils import download_url
    from .vision import VisionDataset

    class PhotoTour(VisionDataset):
        """`Multi-view Stereo Correspondence <http://matthewalunbrown.com/patchdata/patchdata.html>`_ Dataset.

        .. note::

            We only provide the newer version of the dataset, since the authors state that it

                is more suitable for training descriptors based on difference of Gaussian, or Harris corners, as the
                patches are centred on real interest point detections, rather than being projections of 3D points as is the
                case in the old dataset.

            The original dataset is available under http://phototour.cs.washington.edu/patches/default.htm.

        Args:
            root (string): Root directory where images are.
            name (string): Name of the dataset to load.
            transform (callable, optional): A function/transform that  takes in an PIL image
                and returns a transformed version.
            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.

        """

        urls = {
            "notredame_harris": [
                "http://matthewalunbrown.com/patchdata/notredame_harris.zip",
                "notredame_harris.zip",
                "69f8c90f78e171349abdf0307afefe4d",
            ],
            "yosemite_harris": [
                "http://matthewalunbrown.com/patchdata/yosemite_harris.zip",
                "yosemite_harris.zip",
                "a73253d1c6fbd3ba2613c45065c00d46",
            ],
            "liberty_harris": [
                "http://matthewalunbrown.com/patchdata/liberty_harris.zip",
                "liberty_harris.zip",
                "c731fcfb3abb4091110d0ae8c7ba182c",
            ],
            "notredame": [
                "http://icvl.ee.ic.ac.uk/vbalnt/notredame.zip",
                "notredame.zip",
                "509eda8535847b8c0a90bbb210c83484",
            ],
            "yosemite": ["http://icvl.ee.ic.ac.uk/vbalnt/yosemite.zip", "yosemite.zip", "533b2e8eb7ede31be40abc317b2fd4f0"],
            "liberty": ["http://icvl.ee.ic.ac.uk/vbalnt/liberty.zip", "liberty.zip", "fdd9152f138ea5ef2091746689176414"],
        }
        means = {
            "notredame": 0.4854,
            "yosemite": 0.4844,
            "liberty": 0.4437,
            "notredame_harris": 0.4854,
            "yosemite_harris": 0.4844,
            "liberty_harris": 0.4437,
        }
        stds = {
            "notredame": 0.1864,
            "yosemite": 0.1818,
            "liberty": 0.2019,
            "notredame_harris": 0.1864,
            "yosemite_harris": 0.1818,
            "liberty_harris": 0.2019,
        }
        lens = {
            "notredame": 468159,
            "yosemite": 633587,
            "liberty": 450092,
            "liberty_harris": 379587,
            "yosemite_harris": 450912,
            "notredame_harris": 325295,
        }
        image_ext = "bmp"
        info_file = "info.txt"
        matches_files = "m50_100000_100000_0.txt"

        def __init__(
            self, root: str, name: str, train: bool = True, transform: Optional[Callable] = None, download: bool = False
        ) -> None:
            super().__init__(root, transform=transform)
            self.name = name
            self.data_dir = os.path.join(self.root, name)
            self.data_down = os.path.join(self.root, f"{name}.zip")
            self.data_file = os.path.join(self.root, f"{name}.pt")

            self.train = train
            self.mean = self.means[name]
            self.std = self.stds[name]

            if download:
                self.download()

            if not self._check_datafile_exists():
                self.cache()

            # load the serialized data
            self.data, self.labels, self.matches = torch.load(self.data_file)

        def __getitem__(self, index: int) -> Union[torch.Tensor, Tuple[Any, Any, torch.Tensor]]:
            """
            Args:
                index (int): Index

            Returns:
                tuple: (data1, data2, matches)
            """
            if self.train:
                data = self.data[index]
                if self.transform is not None:
                    data = self.transform(data)
                return data
            m = self.matches[index]
            data1, data2 = self.data[m[0]], self.data[m[1]]
            if self.transform is not None:
                data1 = self.transform(data1)
                data2 = self.transform(data2)
            return data1, data2, m[2]

        def __len__(self) -> int:
            return len(self.data if self.train else self.matches)

        def _check_datafile_exists(self) -> bool:
            return os.path.exists(self.data_file)

        def _check_downloaded(self) -> bool:
            return os.path.exists(self.data_dir)

        def download(self) -> None:
            if self._check_datafile_exists():
                print(f"# Found cached data {self.data_file}")
                return

            if not self._check_downloaded():
                # download files
                url = self.urls[self.name][0]
                filename = self.urls[self.name][1]
                md5 = self.urls[self.name][2]
                fpath = os.path.join(self.root, filename)

                download_url(url, self.root, filename, md5)

                print(f"# Extracting data {self.data_down}\n")

                import zipfile

                with zipfile.ZipFile(fpath, "r") as z:
                    z.extractall(self.data_dir)

                os.unlink(fpath)

        def cache(self) -> None:
            # process and save as torch files
            print(f"# Caching data {self.data_file}")

            dataset = (
                read_image_file(self.data_dir, self.image_ext, self.lens[self.name]),
                read_info_file(self.data_dir, self.info_file),
                read_matches_files(self.data_dir, self.matches_files),
            )

            with open(self.data_file, "wb") as f:
                torch.save(dataset, f)

        def extra_repr(self) -> str:
            split = "Train" if self.train is True else "Test"
            return f"Split: {split}"

    def read_image_file(data_dir: str, image_ext: str, n: int) -> torch.Tensor:
        """Return a Tensor containing the patches"""

        def PIL2array(_img: Image.Image) -> np.ndarray:
            """Convert PIL image type to numpy 2D array"""
            return np.array(_img.getdata(), dtype=np.uint8).reshape(64, 64)

        def find_files(_data_dir: str, _image_ext: str) -> List[str]:
            """Return a list with the file names of the images containing the patches"""
            files = []
            # find those files with the specified extension
            for file_dir in os.listdir(_data_dir):
                if file_dir.endswith(_image_ext):
                    files.append(os.path.join(_data_dir, file_dir))
            return sorted(files)  # sort files in ascend order to keep relations

        patches = []
        list_files = find_files(data_dir, image_ext)

        for fpath in list_files:
            img = Image.open(fpath)
            for y in range(0, img.height, 64):
                for x in range(0, img.width, 64):
                    patch = img.crop((x, y, x + 64, y + 64))
                    patches.append(PIL2array(patch))
        return torch.ByteTensor(np.array(patches[:n]))

    def read_info_file(data_dir: str, info_file: str) -> torch.Tensor:
        """Return a Tensor containing the list of labels
        Read the file and keep only the ID of the 3D point.
        """
        with open(os.path.join(data_dir, info_file)) as f:
            labels = [int(line.split()[0]) for line in f]
        return torch.LongTensor(labels)

    def read_matches_files(data_dir: str, matches_file: str) -> torch.Tensor:
        """Return a Tensor containing the ground truth matches
        Read the file and keep only 3D point ID.
        Matches are represented with a 1, non matches with a 0.
        """
        matches = []
        with open(os.path.join(data_dir, matches_file)) as f:
            for line in f:
                line_split = line.split()
                matches.append([int(line_split[0]), int(line_split[3]), int(line_split[1] == line_split[4])])
        return torch.LongTensor(matches)

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