Source code for torchgeo.datasets.landcoverai
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""LandCover.ai dataset."""
import glob
import hashlib
import os
from functools import lru_cache
from typing import Callable, Dict, Optional
import matplotlib.pyplot as plt
import numpy as np
import torch
from matplotlib.colors import ListedColormap
from PIL import Image
from torch import Tensor
from .geo import VisionDataset
from .utils import download_url, extract_archive, working_dir
class LandCoverAI(VisionDataset):
r"""LandCover.ai dataset.
The `LandCover.ai <https://landcover.ai/>`_ (Land Cover from Aerial Imagery)
dataset is a dataset for automatic mapping of buildings, woodlands, water and
roads from aerial images. This implementation is specifically for Version 1 of
Landcover.ai.
Dataset features:
* land cover from Poland, Central Europe
* three spectral bands - RGB
* 33 orthophotos with 25 cm per pixel resolution (~9000x9500 px)
* 8 orthophotos with 50 cm per pixel resolution (~4200x4700 px)
* total area of 216.27 km\ :sup:`2`
Dataset format:
* rasters are three-channel GeoTiffs with EPSG:2180 spatial reference system
* masks are single-channel GeoTiffs with EPSG:2180 spatial reference system
Dataset classes:
1. building (1.85 km\ :sup:`2`\ )
2. woodland (72.02 km\ :sup:`2`\ )
3. water (13.15 km\ :sup:`2`\ )
4. road (3.5 km\ :sup:`2`\ )
If you use this dataset in your research, please cite the following paper:
* https://arxiv.org/abs/2005.02264v3
.. note::
This dataset requires the following additional library to be installed:
* `opencv-python <https://pypi.org/project/opencv-python/>`_ to generate
the train/val/test split
"""
url = "https://landcover.ai/download/landcover.ai.v1.zip"
filename = "landcover.ai.v1.zip"
md5 = "3268c89070e8734b4e91d531c0617e03"
sha256 = "15ee4ca9e3fd187957addfa8f0d74ac31bc928a966f76926e11b3c33ea76daa1"
classes = ["Background", "Building", "Woodland", "Water", "Road"]
cmap = ListedColormap(
[
[0.63921569, 1.0, 0.45098039],
[0.61176471, 0.61176471, 0.61176471],
[0.14901961, 0.45098039, 0.0],
[0.0, 0.77254902, 1.0],
[0.0, 0.0, 0.0],
]
)
[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 LandCover.ai 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
RuntimeError: if ``download=False`` and data is not found, or checksums
don't match
"""
assert split in ["train", "val", "test"]
self.root = root
self.split = split
self.transforms = transforms
self.download = download
self.checksum = checksum
self._verify()
with open(os.path.join(self.root, split + ".txt")) as f:
self.ids = f.readlines()
[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
"""
id_ = self.ids[index].rstrip()
sample = {"image": self._load_image(id_), "mask": self._load_target(id_)}
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.ids)
@lru_cache()
def _load_image(self, id_: str) -> Tensor:
"""Load a single image.
Args:
id_: unique ID of the image
Returns:
the image
"""
filename = os.path.join(self.root, "output", id_ + ".jpg")
with Image.open(filename) as img:
array: "np.typing.NDArray[np.int_]" = np.array(img)
tensor = torch.from_numpy(array)
# Convert from HxWxC to CxHxW
tensor = tensor.permute((2, 0, 1))
return tensor
@lru_cache()
def _load_target(self, id_: str) -> Tensor:
"""Load the target mask for a single image.
Args:
id_: unique ID of the image
Returns:
the target mask
"""
filename = os.path.join(self.root, "output", id_ + "_m.png")
with Image.open(filename) as img:
array: "np.typing.NDArray[np.int_]" = np.array(img.convert("L"))
tensor = torch.from_numpy(array)
return tensor
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 extracted files already exist
jpg = os.path.join(self.root, "output", "*_*.jpg")
png = os.path.join(self.root, "output", "*_*_m.png")
if glob.glob(jpg) and glob.glob(png):
return
# Check if the zip file has already been downloaded
pathname = os.path.join(self.root, self.filename)
if os.path.exists(pathname):
self._extract()
return
# Check if the user requested to download the dataset
if not self.download:
raise RuntimeError(
f"Dataset not found in `root={self.root}` and `download=False`, "
"either specify a different `root` directory or use `download=True` "
"to automaticaly download the dataset."
)
# Download the dataset
self._download()
self._extract()
def _download(self) -> None:
"""Download the dataset."""
download_url(self.url, self.root, md5=self.md5 if self.checksum else None)
def _extract(self) -> None:
"""Extract the dataset.
Raises:
AssertionError: if the checksum of split.py does not match
"""
extract_archive(os.path.join(self.root, self.filename))
# Generate train/val/test splits
# Always check the sha256 of this file before executing
# to avoid malicious code injection
with working_dir(self.root):
with open("split.py") as f:
split = f.read().encode("utf-8")
assert hashlib.sha256(split).hexdigest() == self.sha256
exec(split)
[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:`__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)
mask = sample["mask"].numpy()
num_panels = 2
showing_predictions = "prediction" in sample
if showing_predictions:
predictions = sample["prediction"].numpy()
num_panels += 1
fig, axs = plt.subplots(1, num_panels, figsize=(num_panels * 4, 5))
axs[0].imshow(image)
axs[0].axis("off")
axs[1].imshow(mask, vmin=0, vmax=4, cmap=self.cmap, interpolation="none")
axs[1].axis("off")
if show_titles:
axs[0].set_title("Image")
axs[1].set_title("Mask")
if showing_predictions:
axs[2].imshow(
predictions, vmin=0, vmax=4, cmap=self.cmap, interpolation="none"
)
axs[2].axis("off")
if show_titles:
axs[2].set_title("Predictions")
if suptitle is not None:
plt.suptitle(suptitle)
return fig
