Source code for torchgeo.datasets.ucmerced
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""UC Merced dataset."""
import os
from typing import Callable, Dict, Optional, cast
import matplotlib.pyplot as plt
import numpy as np
from torch import Tensor
from .geo import VisionClassificationDataset
from .utils import check_integrity, download_url, extract_archive
class UCMerced(VisionClassificationDataset):
"""UC Merced dataset.
The `UC Merced <http://weegee.vision.ucmerced.edu/datasets/landuse.html>`_
dataset is a land use classification dataset of 2.1k 256x256 1ft resolution RGB
images of urban locations around the U.S. extracted from the USGS National Map Urban
Area Imagery collection with 21 land use classes (100 images per class).
Dataset features:
* land use class labels from around the U.S.
* three spectral bands - RGB
* 21 classes
Dataset classes:
* agricultural
* airplane
* baseballdiamond
* beach
* buildings
* chaparral
* denseresidential
* forest
* freeway
* golfcourse
* harbor
* intersection
* mediumresidential
* mobilehomepark
* overpass
* parkinglot
* river
* runway
* sparseresidential
* storagetanks
* tenniscourt
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://dl.acm.org/doi/10.1145/1869790.1869829
"""
url = "http://weegee.vision.ucmerced.edu/datasets/UCMerced_LandUse.zip" # 318 MB
filename = "UCMerced_LandUse.zip"
md5 = "5b7ec56793786b6dc8a908e8854ac0e4"
base_dir = os.path.join("UCMerced_LandUse", "Images")
classes = [
"agricultural",
"airplane",
"baseballdiamond",
"beach",
"buildings",
"chaparral",
"denseresidential",
"forest",
"freeway",
"golfcourse",
"harbor",
"intersection",
"mediumresidential",
"mobilehomepark",
"overpass",
"parkinglot",
"river",
"runway",
"sparseresidential",
"storagetanks",
"tenniscourt",
]
splits = ["train", "val", "test"]
split_urls = {
"train": "https://storage.googleapis.com/remote_sensing_representations/uc_merced-train.txt", # noqa: E501
"val": "https://storage.googleapis.com/remote_sensing_representations/uc_merced-val.txt", # noqa: E501
"test": "https://storage.googleapis.com/remote_sensing_representations/uc_merced-test.txt", # noqa: E501
}
split_md5s = {
"train": "f2fb12eb2210cfb53f93f063a35ff374",
"val": "11ecabfc52782e5ea6a9c7c0d263aca0",
"test": "046aff88472d8fc07c4678d03749e28d",
}
[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 UC Merced 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)
Raises:
RuntimeError: if ``download=False`` and data is not found, or checksums
don't match
"""
assert split in self.splits
self.root = root
self.transforms = transforms
self.download = download
self.checksum = checksum
self._verify()
valid_fns = set()
with open(os.path.join(self.root, f"uc_merced-{split}.txt"), "r") 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.base_dir),
transforms=transforms,
is_valid_file=is_in_split,
)
def _check_integrity(self) -> bool:
"""Check integrity of dataset.
Returns:
True if dataset files are found and/or MD5s match, else False
"""
integrity: bool = check_integrity(
os.path.join(self.root, self.filename), self.md5 if self.checksum else None
)
return integrity
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.base_dir)
if os.path.exists(filepath):
return
# Check if zip file already exists (if so then extract)
if self._check_integrity():
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,
)
for split in self.splits:
download_url(
self.split_urls[split],
self.root,
filename=f"uc_merced-{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,
) -> 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 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
