Source code for torchgeo.datasets.patternnet
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""PatternNet dataset."""
import os
from typing import Callable, Dict, Optional, cast
import matplotlib.pyplot as plt
from torch import Tensor
from .geo import VisionClassificationDataset
from .utils import download_url, extract_archive
class PatternNet(VisionClassificationDataset):
"""PatternNet dataset.
The `PatternNet <https://sites.google.com/view/zhouwx/dataset>`_
dataset is a dataset for remote sensing scene classification and image retrieval.
Dataset features:
* 30,400 images with 6-50 cm per pixel resolution (256x256 px)
* three spectral bands - RGB
* 38 scene classes, 800 images per class
Dataset format:
* images are three-channel jpgs
Dataset classes:
0. airplane
1. baseball_field
2. basketball_court
3. beach
4. bridge
5. cemetery
6. chaparral
7. christmas_tree_farm
8. closed_road
9. coastal_mansion
10. crosswalk
11. dense_residential
12. ferry_terminal
13. football_field
14. forest
15. freeway
16. golf_course
17. harbor
18. intersection
19. mobile_home_park
20. nursing_home
21. oil_gas_field
22. oil_well
23. overpass
24. parking_lot
25. parking_space
26. railway
27. river
28. runway
29. runway_marking
30. shipping_yard
31. solar_panel
32. sparse_residential
33. storage_tank
34. swimming_pool
35. tennis_court
36. transformer_station
37. wastewater_treatment_plant
If you use this dataset in your research, please cite the following paper:
* https://doi.org/10.1016/j.isprsjprs.2018.01.004
"""
url = "https://drive.google.com/file/d/127lxXYqzO6Bd0yZhvEbgIfz95HaEnr9K"
md5 = "96d54b3224c5350a98d55d5a7e6984ad"
filename = "PatternNet.zip"
directory = os.path.join("PatternNet", "images")
[docs] def __init__(
self,
root: str = "data",
transforms: Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]] = None,
download: bool = False,
checksum: bool = False,
) -> None:
"""Initialize a new PatternNet dataset instance.
Args:
root: root directory where dataset can be found
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)
"""
self.root = root
self.download = download
self.checksum = checksum
self._verify()
super().__init__(root=os.path.join(root, self.directory), transforms=transforms)
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 automaticaly 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,
)
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,
) -> plt.Figure:
"""Plot a sample from the dataset.
Args:
sample: a sample returned by :meth:`VisionClassificationDataset.__getitem__`
show_titles: flag indicating whether to show titles above each panel
suptitle: optional suptitle to use for figure
Returns:
a matplotlib Figure with the rendered sample
.. versionadded:: 0.2
"""
image, label = sample["image"], cast(int, sample["label"].item())
showing_predictions = "prediction" in sample
if showing_predictions:
prediction = cast(int, sample["prediction"].item())
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
ax.imshow(image.permute(1, 2, 0))
ax.axis("off")
if show_titles:
title = f"Label: {self.classes[label]}"
if showing_predictions:
title += f"\nPrediction: {self.classes[prediction]}"
ax.set_title(title)
if suptitle is not None:
plt.suptitle(suptitle)
return fig