Source code for torchvision.datasets.phototour

import os
from pathlib import Path
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

[docs]class PhotoTour(VisionDataset): """`Multi-view Stereo Correspondence <>`_ 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 Args: root (str or ``pathlib.Path``): Root directory where images are. name (string): Name of the dataset to load. transform (callable, optional): A function/transform that takes in a 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": [ "", "", "69f8c90f78e171349abdf0307afefe4d", ], "yosemite_harris": [ "", "", "a73253d1c6fbd3ba2613c45065c00d46", ], "liberty_harris": [ "", "", "c731fcfb3abb4091110d0ae8c7ba182c", ], "notredame": [ "", "", "509eda8535847b8c0a90bbb210c83484", ], "yosemite": ["", "", "533b2e8eb7ede31be40abc317b2fd4f0"], "liberty": ["", "", "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: Union[str, Path], name: str, train: bool = True, transform: Optional[Callable] = None, download: bool = False, ) -> None: super().__init__(root, transform=transform) = 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: if not self._check_datafile_exists(): self.cache() # load the serialized data, self.labels, self.matches = torch.load(self.data_file, weights_only=True)
[docs] 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 =[index] if self.transform is not None: data = self.transform(data) return data m = self.matches[index] data1, data2 =[m[0]],[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( 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[][0] filename = self.urls[][1] md5 = self.urls[][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[]), 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:, 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 = 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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