Source code for torchgeo.datasets.resisc45
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""RESISC45 dataset."""
import os
from typing import Callable, Optional, cast
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.figure import Figure
from torch import Tensor
from .geo import NonGeoClassificationDataset
from .utils import download_url, extract_archive
[docs]class RESISC45(NonGeoClassificationDataset):
"""NWPU-RESISC45 dataset.
The `RESISC45 <https://doi.org/10.1109/jproc.2017.2675998>`__
dataset is a dataset for remote sensing image scene classification.
Dataset features:
* 31,500 images with 0.2-30 m per pixel resolution (256x256 px)
* three spectral bands - RGB
* 45 scene classes, 700 images per class
* images extracted from Google Earth from over 100 countries
* images conditions with high variability (resolution, weather, illumination)
Dataset format:
* images are three-channel jpgs
Dataset classes:
0. airplane
1. airport
2. baseball_diamond
3. basketball_court
4. beach
5. bridge
6. chaparral
7. church
8. circular_farmland
9. cloud
10. commercial_area
11. dense_residential
12. desert
13. forest
14. freeway
15. golf_course
16. ground_track_field
17. harbor
18. industrial_area
19. intersection
20. island
21. lake
22. meadow
23. medium_residential
24. mobile_home_park
25. mountain
26. overpass
27. palace
28. parking_lot
29. railway
30. railway_station
31. rectangular_farmland
32. river
33. roundabout
34. runway
35. sea_ice
36. ship
37. snowberg
38. sparse_residential
39. stadium
40. storage_tank
41. tennis_court
42. terrace
43. thermal_power_station
44. wetland
This dataset uses the train/val/test splits defined in the "In-domain representation
learning for remote sensing" paper:
* https://arxiv.org/abs/1911.06721
If you use this dataset in your research, please cite the following paper:
* https://doi.org/10.1109/jproc.2017.2675998
"""
url = "https://drive.google.com/file/d/1DnPSU5nVSN7xv95bpZ3XQ0JhKXZOKgIv"
md5 = "d824acb73957502b00efd559fc6cfbbb"
filename = "NWPU-RESISC45.rar"
directory = "NWPU-RESISC45"
splits = ["train", "val", "test"]
split_urls = {
"train": "https://storage.googleapis.com/remote_sensing_representations/resisc45-train.txt", # noqa: E501
"val": "https://storage.googleapis.com/remote_sensing_representations/resisc45-val.txt", # noqa: E501
"test": "https://storage.googleapis.com/remote_sensing_representations/resisc45-test.txt", # noqa: E501
}
split_md5s = {
"train": "b5a4c05a37de15e4ca886696a85c403e",
"val": "a0770cee4c5ca20b8c32bbd61e114805",
"test": "3dda9e4988b47eb1de9f07993653eb08",
}
[docs] def __init__(
self,
root: str = "data",
split: str = "train",
transforms: Optional[Callable[[dict[str, Tensor]], dict[str, Tensor]]] = None,
download: bool = False,
checksum: bool = False,
) -> None:
"""Initialize a new RESISC45 dataset instance.
Args:
root: root directory where dataset can be found
split: one of "train", "val", or "test"
transforms: a function/transform that takes input sample and its target as
entry and returns a transformed version
download: if True, download dataset and store it in the root directory
checksum: if True, check the MD5 of the downloaded files (may be slow)
"""
assert split in self.splits
self.root = root
self.download = download
self.checksum = checksum
self._verify()
valid_fns = set()
with open(os.path.join(self.root, f"resisc45-{split}.txt")) as f:
for fn in f:
valid_fns.add(fn.strip())
is_in_split: Callable[[str], bool] = lambda x: os.path.basename(x) in valid_fns
super().__init__(
root=os.path.join(root, self.directory),
transforms=transforms,
is_valid_file=is_in_split,
)
def _verify(self) -> None:
"""Verify the integrity of the dataset.
Raises:
RuntimeError: if ``download=False`` but dataset is missing or checksum fails
"""
# Check if the files already exist
filepath = os.path.join(self.root, self.directory)
if os.path.exists(filepath):
return
# Check if zip file already exists (if so then extract)
filepath = os.path.join(self.root, self.filename)
if os.path.exists(filepath):
self._extract()
return
# Check if the user requested to download the dataset
if not self.download:
raise RuntimeError(
"Dataset not found in `root` directory and `download=False`, "
"either specify a different `root` directory or use `download=True` "
"to automatically download the dataset."
)
# Download and extract the dataset
self._download()
self._extract()
def _download(self) -> None:
"""Download the dataset."""
download_url(
self.url,
self.root,
filename=self.filename,
md5=self.md5 if self.checksum else None,
)
for split in self.splits:
download_url(
self.split_urls[split],
self.root,
filename=f"resisc45-{split}.txt",
md5=self.split_md5s[split] if self.checksum else None,
)
def _extract(self) -> None:
"""Extract the dataset."""
filepath = os.path.join(self.root, self.filename)
extract_archive(filepath)
[docs] def plot(
self,
sample: dict[str, Tensor],
show_titles: bool = True,
suptitle: Optional[str] = None,
) -> Figure:
"""Plot a sample from the dataset.
Args:
sample: a sample returned by :meth:`NonGeoClassificationDataset.__getitem__`
show_titles: flag indicating whether to show titles above each panel
suptitle: optional string to use as a suptitle
Returns:
a matplotlib Figure with the rendered sample
.. versionadded:: 0.2
"""
image = np.rollaxis(sample["image"].numpy(), 0, 3)
label = cast(int, sample["label"].item())
label_class = self.classes[label]
showing_predictions = "prediction" in sample
if showing_predictions:
prediction = cast(int, sample["prediction"].item())
prediction_class = self.classes[prediction]
fig, ax = plt.subplots(figsize=(4, 4))
ax.imshow(image)
ax.axis("off")
if show_titles:
title = f"Label: {label_class}"
if showing_predictions:
title += f"\nPrediction: {prediction_class}"
ax.set_title(title)
if suptitle is not None:
plt.suptitle(suptitle)
return fig
