Source code for torchgeo.datasets.xview
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""xView2 dataset."""
import glob
import os
from typing import Callable, Optional
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 check_integrity, draw_semantic_segmentation_masks, extract_archive
[docs]class XView2(NonGeoDataset):
"""xView2 dataset.
The `xView2 <https://xview2.org/>`__
dataset is a dataset for building disaster change detection. This dataset object
uses the "Challenge training set (~7.8 GB)" and "Challenge test set (~2.6 GB)" data
from the xView2 website as the train and test splits. Note, the xView2 website
contains other data under the xView2 umbrella that are _not_ included here. E.g.
the "Tier3 training data", the "Challenge holdout set", and the "full data".
Dataset format:
* images are three-channel pngs
* masks are single-channel pngs where the pixel values represent the class
Dataset classes:
0. background
1. no damage
2. minor damage
3. major damage
4. destroyed
If you use this dataset in your research, please cite the following paper:
* https://arxiv.org/abs/1911.09296
.. versionadded:: 0.2
"""
metadata = {
"train": {
"filename": "train_images_labels_targets.tar.gz",
"md5": "a20ebbfb7eb3452785b63ad02ffd1e16",
"directory": "train",
},
"test": {
"filename": "test_images_labels_targets.tar.gz",
"md5": "1b39c47e05d1319c17cc8763cee6fe0c",
"directory": "test",
},
}
classes = ["background", "no-damage", "minor-damage", "major-damage", "destroyed"]
colormap = ["green", "blue", "orange", "red"]
[docs] def __init__(
self,
root: str = "data",
split: str = "train",
transforms: Optional[Callable[[dict[str, Tensor]], dict[str, Tensor]]] = None,
checksum: bool = False,
) -> None:
"""Initialize a new xView2 dataset instance.
Args:
root: root directory where dataset can be found
split: one of "train" or "test"
transforms: a function/transform that takes input sample and its target as
entry and returns a transformed version
checksum: if True, check the MD5 of the downloaded files (may be slow)
"""
assert split in self.metadata
self.root = root
self.split = split
self.transforms = transforms
self.checksum = checksum
self._verify()
self.class2idx = {c: i for i, c in enumerate(self.classes)}
self.files = self._load_files(root, split)
[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["image1"])
image2 = self._load_image(files["image2"])
mask1 = self._load_target(files["mask1"])
mask2 = self._load_target(files["mask2"])
image = torch.stack(tensors=[image1, image2], dim=0)
mask = torch.stack(tensors=[mask1, mask2], dim=0)
sample = {"image": image, "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, root: str, split: str) -> list[dict[str, str]]:
"""Return the paths of the files in the dataset.
Args:
root: root dir of dataset
split: subset of dataset, one of [train, test]
Returns:
list of dicts containing paths for each pair of images and masks
"""
files = []
directory = self.metadata[split]["directory"]
image_root = os.path.join(root, directory, "images")
mask_root = os.path.join(root, directory, "targets")
images = glob.glob(os.path.join(image_root, "*.png"))
basenames = [os.path.basename(f) for f in images]
basenames = ["_".join(f.split("_")[:-2]) for f in basenames]
for name in sorted(set(basenames)):
image1 = os.path.join(image_root, f"{name}_pre_disaster.png")
image2 = os.path.join(image_root, f"{name}_post_disaster.png")
mask1 = os.path.join(mask_root, f"{name}_pre_disaster_target.png")
mask2 = os.path.join(mask_root, f"{name}_post_disaster_target.png")
files.append(dict(image1=image1, image2=image2, mask1=mask1, mask2=mask2))
return files
def _load_image(self, path: str) -> Tensor:
"""Load a single image.
Args:
path: path to the image
Returns:
the image
"""
filename = os.path.join(path)
with Image.open(filename) as img:
array: "np.typing.NDArray[np.int_]" = np.array(img.convert("RGB"))
tensor = torch.from_numpy(array)
# Convert from HxWxC to CxHxW
tensor = tensor.permute((2, 0, 1))
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 = tensor.to(torch.long)
return tensor
def _verify(self) -> None:
"""Verify the integrity of the dataset.
Raises:
RuntimeError: if checksum fails or the dataset is not downloaded
"""
# Check if the files already exist
exists = []
for split_info in self.metadata.values():
for directory in ["images", "targets"]:
exists.append(
os.path.exists(
os.path.join(self.root, split_info["directory"], directory)
)
)
if all(exists):
return
# Check if .tar.gz files already exists (if so then extract)
exists = []
for split_info in self.metadata.values():
filepath = os.path.join(self.root, split_info["filename"])
if os.path.isfile(filepath):
if self.checksum and not check_integrity(filepath, split_info["md5"]):
raise RuntimeError("Dataset found, but corrupted.")
exists.append(True)
extract_archive(filepath)
else:
exists.append(False)
if all(exists):
return
# Check if the user requested to download the dataset
raise RuntimeError(
"Dataset not found in `root` directory, either specify a different"
+ " `root` directory or manually download the dataset to this directory."
)
[docs] def plot(
self,
sample: dict[str, Tensor],
show_titles: bool = True,
suptitle: Optional[str] = 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
"""
ncols = 2
image1 = draw_semantic_segmentation_masks(
sample["image"][0], sample["mask"][0], alpha=alpha, colors=self.colormap
)
image2 = draw_semantic_segmentation_masks(
sample["image"][1], sample["mask"][1], alpha=alpha, colors=self.colormap
)
if "prediction" in sample: # NOTE: this assumes predictions are made for post
ncols += 1
image3 = draw_semantic_segmentation_masks(
sample["image"][1],
sample["prediction"],
alpha=alpha,
colors=self.colormap,
)
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 ncols > 2:
axs[2].imshow(image3)
axs[2].axis("off")
if show_titles:
axs[0].set_title("Pre disaster")
axs[1].set_title("Post disaster")
if ncols > 2:
axs[2].set_title("Predictions")
if suptitle is not None:
plt.suptitle(suptitle)
return fig
