Source code for torchgeo.datasets.gid15
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""GID-15 dataset."""
import glob
import os
from collections.abc import Callable
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, download_and_extract_archive
[docs]class GID15(NonGeoDataset):
"""GID-15 dataset.
The `GID-15 <https://captain-whu.github.io/GID15/>`__
dataset is a dataset for semantic segmentation.
Dataset features:
* images taken by the Gaofen-2 (GF-2) satellite over 60 cities in China
* masks representing 15 semantic categories
* three spectral bands - RGB
* 150 with 3 m per pixel resolution (6800x7200 px)
Dataset format:
* images are three-channel pngs
* masks are single-channel pngs
* colormapped masks are 3 channel tifs
Dataset classes:
1. background
2. industrial_land
3. urban_residential
4. rural_residential
5. traffic_land
6. paddy_field
7. irrigated_land
8. dry_cropland
9. garden_plot
10. arbor_woodland
11. shrub_land
12. natural_grassland
13. artificial_grassland
14. river
15. lake
16. pond
If you use this dataset in your research, please cite the following paper:
* https://doi.org/10.1016/j.rse.2019.111322
"""
url = "https://drive.google.com/file/d/1zbkCEXPEKEV6gq19OKmIbaT8bXXfWW6u"
md5 = "615682bf659c3ed981826c6122c10c83"
filename = "gid-15.zip"
directory = "GID"
splits = ["train", "val", "test"]
classes = [
"background",
"industrial_land",
"urban_residential",
"rural_residential",
"traffic_land",
"paddy_field",
"irrigated_land",
"dry_cropland",
"garden_plot",
"arbor_woodland",
"shrub_land",
"natural_grassland",
"artificial_grassland",
"river",
"lake",
"pond",
]
[docs] def __init__(
self,
root: str = "data",
split: str = "train",
transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
download: bool = False,
checksum: bool = False,
) -> None:
"""Initialize a new GID-15 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:
AssertionError: if ``split`` argument is invalid
DatasetNotFoundError: If dataset is not found and *download* is False.
"""
assert split in self.splits
self.root = root
self.split = split
self.transforms = transforms
self.checksum = checksum
if download:
self._download()
if not self._check_integrity():
raise DatasetNotFoundError(self)
self.files = self._load_files(self.root, self.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]
image = self._load_image(files["image"])
if self.split != "test":
mask = self._load_target(files["mask"])
sample = {"image": image, "mask": mask}
else:
sample = {"image": image}
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, val, test]
Returns:
list of dicts containing paths for each pair of image, mask
"""
image_root = os.path.join(root, "GID", "img_dir")
images = glob.glob(os.path.join(image_root, split, "*.tif"))
images = sorted(images)
if split != "test":
masks = [
image.replace("img_dir", "ann_dir").replace(".tif", "_15label.png")
for image in images
]
files = [dict(image=image, mask=mask) for image, mask in zip(images, masks)]
else:
files = [dict(image=image) for image in images]
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)).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 = tensor.to(torch.long)
return tensor
def _check_integrity(self) -> bool:
"""Checks the integrity of the dataset structure.
Returns:
True if the dataset directories and split files are found, else False
"""
filepath = os.path.join(self.root, self.directory)
if not os.path.exists(filepath):
return False
return True
def _download(self) -> None:
"""Download the dataset and extract it."""
if self._check_integrity():
print("Files already downloaded and verified")
return
download_and_extract_archive(
self.url,
self.root,
filename=self.filename,
md5=self.md5 if self.checksum else None,
)
[docs] def plot(self, sample: dict[str, Tensor], suptitle: str | None = None) -> Figure:
"""Plot a sample from the dataset.
Args:
sample: a sample return by :meth:`__getitem__`
suptitle: optional suptitle to use for figure
Returns:
a matplotlib Figure with the rendered sample
.. versionadded:: 0.2
"""
if self.split != "test":
image, mask = sample["image"], sample["mask"]
ncols = 2
else:
image = sample["image"]
ncols = 1
if "prediction" in sample:
ncols += 1
pred = sample["prediction"]
fig, axs = plt.subplots(nrows=1, ncols=ncols, figsize=(10, ncols * 10))
if self.split != "test":
axs[0].imshow(image.permute(1, 2, 0))
axs[0].axis("off")
axs[1].imshow(mask)
axs[1].axis("off")
if "prediction" in sample:
axs[2].imshow(pred)
axs[2].axis("off")
else:
if "prediction" in sample:
axs[0].imshow(image.permute(1, 2, 0))
axs[0].axis("off")
axs[1].imshow(pred)
axs[1].axis("off")
else:
axs.imshow(image.permute(1, 2, 0))
axs.axis("off")
if suptitle is not None:
plt.suptitle(suptitle)
return fig
