import os
import numpy as np
from PIL import Image
from typing import Any, Callable, List, Optional, Tuple, Union
import torch
from .vision import VisionDataset
from .utils import download_url
[docs]class PhotoTour(VisionDataset):
"""`Learning Local Image Descriptors Data <http://phototour.cs.washington.edu/patches/default.htm>`_ Dataset.
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(PhotoTour, self).__init__(root, transform=transform)
self.name = name
self.data_dir = os.path.join(self.root, name)
self.data_down = os.path.join(self.root, '{}.zip'.format(name))
self.data_file = os.path.join(self.root, '{}.pt'.format(name))
self.train = train
self.mean = self.means[name]
self.std = self.stds[name]
if download:
self.download()
if not self._check_datafile_exists():
raise RuntimeError('Dataset not found.' +
' You can use download=True to download it')
# load the serialized data
self.data, self.labels, self.matches = torch.load(self.data_file)
[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 = 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:
if self.train:
return self.lens[self.name]
return len(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('# Found cached data {}'.format(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('# Extracting data {}\n'.format(self.data_down))
import zipfile
with zipfile.ZipFile(fpath, 'r') as z:
z.extractall(self.data_dir)
os.unlink(fpath)
# process and save as torch files
print('# Caching data {}'.format(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:
return "Split: {}".format("Train" if self.train is True else "Test")
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, 1024, 64):
for x in range(0, 1024, 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.
"""
labels = []
with open(os.path.join(data_dir, info_file), 'r') 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), 'r') 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)
