Source code for torchgeo.datasets.oscd
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""OSCD dataset."""
import glob
import os
from collections.abc import Callable, Sequence
import matplotlib.pyplot as plt
import numpy as np
import torch
from matplotlib.figure import Figure
from PIL import Image
from torch import Tensor
from .geo import NonGeoDataset
from .utils import (
DatasetNotFoundError,
RGBBandsMissingError,
download_url,
draw_semantic_segmentation_masks,
extract_archive,
sort_sentinel2_bands,
)
[docs]class OSCD(NonGeoDataset):
"""OSCD dataset.
The `Onera Satellite Change Detection <https://rcdaudt.github.io/oscd/>`_
dataset addresses the issue of detecting changes between
satellite images from different dates. Imagery comes from
Sentinel-2 which contains varying resolutions per band.
Dataset format:
* images are 13-channel tifs
* masks are single-channel pngs where no change = 0, change = 255
Dataset classes:
0. no change
1. change
If you use this dataset in your research, please cite the following paper:
* https://doi.org/10.1109/IGARSS.2018.8518015
.. versionadded:: 0.2
"""
urls = {
"Onera Satellite Change Detection dataset - Images.zip": (
"https://partage.imt.fr/index.php/s/gKRaWgRnLMfwMGo/download"
),
"Onera Satellite Change Detection dataset - Train Labels.zip": (
"https://partage.mines-telecom.fr/index.php/s/2D6n03k58ygBSpu/download"
),
"Onera Satellite Change Detection dataset - Test Labels.zip": (
"https://partage.imt.fr/index.php/s/gpStKn4Mpgfnr63/download"
),
}
md5s = {
"Onera Satellite Change Detection dataset - Images.zip": (
"c50d4a2941da64e03a47ac4dec63d915"
),
"Onera Satellite Change Detection dataset - Train Labels.zip": (
"4d2965af8170c705ebad3d6ee71b6990"
),
"Onera Satellite Change Detection dataset - Test Labels.zip": (
"8177d437793c522653c442aa4e66c617"
),
}
zipfile_glob = "*Onera*.zip"
filename_glob = "*Onera*"
splits = ["train", "test"]
colormap = ["blue"]
all_bands = (
"B01",
"B02",
"B03",
"B04",
"B05",
"B06",
"B07",
"B08",
"B8A",
"B09",
"B10",
"B11",
"B12",
)
rgb_bands = ("B04", "B03", "B02")
[docs] def __init__(
self,
root: str = "data",
split: str = "train",
bands: Sequence[str] = all_bands,
transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
download: bool = False,
checksum: bool = False,
) -> None:
"""Initialize a new OSCD dataset instance.
Args:
root: root directory where dataset can be found
split: one of "train" or "test"
bands: bands to return (defaults to all bands)
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:
AssertionError: if ``split`` argument is invalid
DatasetNotFoundError: If dataset is not found and *download* is False.
"""
assert split in self.splits
assert set(bands) <= set(self.all_bands)
self.bands = bands
self.all_band_indices = [self.all_bands.index(b) for b in self.bands]
self.root = root
self.split = split
self.transforms = transforms
self.download = download
self.checksum = checksum
self._verify()
self.files = self._load_files()
[docs] def __getitem__(self, index: int) -> dict[str, Tensor]:
"""Return an index within the dataset.
Args:
index: index to return
Returns:
data and label at that index
"""
files = self.files[index]
image1 = self._load_image(files["images1"])
image2 = self._load_image(files["images2"])
mask = self._load_target(str(files["mask"]))
sample = {"image1": image1, "image2": image2, "mask": mask}
if self.transforms is not None:
sample = self.transforms(sample)
return sample
[docs] def __len__(self) -> int:
"""Return the number of data points in the dataset.
Returns:
length of the dataset
"""
return len(self.files)
def _load_files(self) -> list[dict[str, str | Sequence[str]]]:
regions = []
labels_root = os.path.join(
self.root,
f"Onera Satellite Change Detection dataset - {self.split.capitalize()} "
+ "Labels",
)
images_root = os.path.join(
self.root, "Onera Satellite Change Detection dataset - Images"
)
folders = glob.glob(os.path.join(labels_root, "*/"))
for folder in folders:
region = folder.split(os.sep)[-2]
mask = os.path.join(labels_root, region, "cm", "cm.png")
def get_image_paths(ind: int) -> list[str]:
return sorted(
glob.glob(
os.path.join(images_root, region, f"imgs_{ind}_rect", "*.tif")
),
key=sort_sentinel2_bands,
)
images1, images2 = get_image_paths(1), get_image_paths(2)
images1 = [images1[i] for i in self.all_band_indices]
images2 = [images2[i] for i in self.all_band_indices]
with open(os.path.join(images_root, region, "dates.txt")) as f:
dates = tuple(
line.split()[-1] for line in f.read().strip().splitlines()
)
regions.append(
dict(
region=region,
images1=images1,
images2=images2,
mask=mask,
dates=dates,
)
)
return regions
def _load_image(self, paths: Sequence[str]) -> Tensor:
"""Load a single image.
Args:
paths: paths to each image band
Returns:
the image
"""
images: list["np.typing.NDArray[np.int_]"] = []
for path in paths:
with Image.open(path) as img:
images.append(np.array(img))
array: "np.typing.NDArray[np.int_]" = np.stack(images, axis=0).astype(np.int_)
tensor = torch.from_numpy(array).float()
return tensor
def _load_target(self, path: str) -> Tensor:
"""Load the target mask for a single image.
Args:
path: path to the image
Returns:
the target mask
"""
filename = os.path.join(path)
with Image.open(filename) as img:
array: "np.typing.NDArray[np.int_]" = np.array(img.convert("L"))
tensor = torch.from_numpy(array)
tensor = torch.clamp(tensor, min=0, max=1)
tensor = tensor.to(torch.long)
return tensor
def _verify(self) -> None:
"""Verify the integrity of the dataset."""
# Check if the extracted files already exist
pathname = os.path.join(self.root, "**", self.filename_glob)
for fname in glob.iglob(pathname, recursive=True):
if not fname.endswith(".zip"):
return
# Check if the zip files have already been downloaded
pathname = os.path.join(self.root, self.zipfile_glob)
if glob.glob(pathname):
self._extract()
return
# Check if the user requested to download the dataset
if not self.download:
raise DatasetNotFoundError(self)
# Download the dataset
self._download()
self._extract()
def _download(self) -> None:
"""Download the dataset."""
for f_name in self.urls:
download_url(
self.urls[f_name],
self.root,
filename=f_name,
md5=self.md5s[f_name] if self.checksum else None,
)
def _extract(self) -> None:
"""Extract the dataset."""
pathname = os.path.join(self.root, self.zipfile_glob)
for zipfile in glob.iglob(pathname):
extract_archive(zipfile)
[docs] def plot(
self,
sample: dict[str, Tensor],
show_titles: bool = True,
suptitle: str | None = None,
alpha: float = 0.5,
) -> Figure:
"""Plot a sample from the dataset.
Args:
sample: a sample returned by :meth:`__getitem__`
show_titles: flag indicating whether to show titles above each panel
suptitle: optional string to use as a suptitle
alpha: opacity with which to render predictions on top of the imagery
Returns:
a matplotlib Figure with the rendered sample
Raises:
RGBBandsMissingError: If *bands* does not include all RGB bands.
"""
ncols = 2
try:
rgb_indices = [self.bands.index(band) for band in self.rgb_bands]
except ValueError as e:
raise RGBBandsMissingError() from e
def get_masked(img: Tensor) -> "np.typing.NDArray[np.uint8]":
rgb_img = img[rgb_indices].float().numpy()
per02 = np.percentile(rgb_img, 2)
per98 = np.percentile(rgb_img, 98)
rgb_img = (np.clip((rgb_img - per02) / (per98 - per02), 0, 1) * 255).astype(
np.uint8
)
array: "np.typing.NDArray[np.uint8]" = draw_semantic_segmentation_masks(
torch.from_numpy(rgb_img),
sample["mask"],
alpha=alpha,
colors=self.colormap,
)
return array
image1 = get_masked(sample["image1"])
image2 = get_masked(sample["image2"])
fig, axs = plt.subplots(ncols=ncols, figsize=(ncols * 10, 10))
axs[0].imshow(image1)
axs[0].axis("off")
axs[1].imshow(image2)
axs[1].axis("off")
if show_titles:
axs[0].set_title("Pre change")
axs[1].set_title("Post change")
if suptitle is not None:
plt.suptitle(suptitle)
return fig
