Source code for torchgeo.datasets.chabud
# Copyright (c) TorchGeo Contributors. All rights reserved.
# Licensed under the MIT License.
"""ChaBuD dataset."""
import os
from collections.abc import Callable, Sequence
from typing import ClassVar
import einops
import matplotlib.pyplot as plt
import numpy as np
import torch
from matplotlib.figure import Figure
from torch import Tensor
from .errors import DatasetNotFoundError
from .geo import NonGeoDataset
from .utils import Path, download_url, lazy_import, percentile_normalization
[docs]class ChaBuD(NonGeoDataset):
"""ChaBuD dataset.
`ChaBuD <https://huggingface.co/spaces/competitions/ChaBuD-ECML-PKDD2023>`__
is a dataset for Change detection for Burned area Delineation and is used
for the ChaBuD ECML-PKDD 2023 Discovery Challenge.
Dataset features:
* Sentinel-2 multispectral imagery
* binary masks of burned areas
* 12 multispectral bands
* 356 pairs of pre and post images with 10 m per pixel resolution (512x512 px)
Dataset format:
* single hdf5 dataset containing images and masks
Dataset classes:
0. no change
1. burned area
If you use this dataset in your research, please cite the following paper:
* https://doi.org/10.1016/j.rse.2021.112603
.. note::
This dataset requires the following additional library to be installed:
* `h5py <https://pypi.org/project/h5py/>`_ to load the dataset
.. versionadded:: 0.6
"""
all_bands = (
'B01',
'B02',
'B03',
'B04',
'B05',
'B06',
'B07',
'B08',
'B8A',
'B09',
'B11',
'B12',
)
rgb_bands = ('B04', 'B03', 'B02')
folds: ClassVar[dict[str, list[int]]] = {'train': [1, 2, 3, 4], 'val': [0]}
url = 'https://hf.co/datasets/chabud-team/chabud-ecml-pkdd2023/resolve/de222d434e26379aa3d4f3dd1b2caf502427a8b2/train_eval.hdf5'
filename = 'train_eval.hdf5'
md5 = '15d78fb825f9a81dad600db828d22c08'
[docs] def __init__(
self,
root: Path = '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 ChaBuD dataset instance.
Args:
root: root directory where dataset can be found
split: one of "train" or "val"
bands: the subset of bands to load
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`` or ``bands`` arguments are invalid.
DatasetNotFoundError: If dataset is not found and *download* is False.
DependencyNotFoundError: If h5py is not installed.
"""
lazy_import('h5py')
assert split in self.folds
assert set(bands) <= set(self.all_bands)
self.root = root
self.split = split
self.bands = bands
self.transforms = transforms
self.download = download
self.checksum = checksum
self.filepath = os.path.join(root, self.filename)
self.band_indices = [self.all_bands.index(b) for b in bands]
self._verify()
self.uuids = self._load_uuids()
[docs] def __getitem__(self, index: int) -> dict[str, Tensor]:
"""Return an index within the dataset.
Args:
index: index to return
Returns:
sample containing image and mask
"""
image = self._load_image(index)
mask = self._load_target(index)
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.uuids)
def _load_uuids(self) -> list[str]:
"""Return the image uuids for the given split.
Returns:
the image uuids
"""
h5py = lazy_import('h5py')
uuids = []
with h5py.File(self.filepath, 'r') as f:
for k, v in f.items():
if v.attrs['fold'] in self.folds[self.split] and 'pre_fire' in v:
uuids.append(k)
uuids = sorted(uuids)
return uuids
def _load_image(self, index: int) -> Tensor:
"""Load a single image.
Args:
index: index to return
Returns:
the image
"""
h5py = lazy_import('h5py')
uuid = self.uuids[index]
with h5py.File(self.filepath, 'r') as f:
pre_array = f[uuid]['pre_fire'][:]
post_array = f[uuid]['post_fire'][:]
# index specified bands and concatenate
pre_array = pre_array[..., self.band_indices]
post_array = post_array[..., self.band_indices]
array = np.stack([pre_array, post_array]).astype(np.float32)
tensor = torch.from_numpy(array)
tensor = einops.rearrange(tensor, 't h w c -> t c h w')
return tensor
def _load_target(self, index: int) -> Tensor:
"""Load the target mask for a single image.
Args:
index: index to return
Returns:
the target mask
"""
h5py = lazy_import('h5py')
uuid = self.uuids[index]
with h5py.File(self.filepath, 'r') as f:
array = f[uuid]['mask'][:].astype(np.int32).squeeze(axis=-1)
tensor = torch.from_numpy(array)
tensor = tensor.to(torch.long)
# VideoSequential requires time dimension
return einops.rearrange(tensor, 'h w -> () h w')
def _verify(self) -> None:
"""Verify the integrity of the dataset."""
# Check if the files already exist
if os.path.exists(self.filepath):
return
# Check if the user requested to download the dataset
if not self.download:
raise DatasetNotFoundError(self)
# Download the dataset
self._download()
def _download(self) -> None:
"""Download the dataset."""
if not os.path.exists(self.filepath):
download_url(
self.url,
self.root,
filename=self.filename,
md5=self.md5 if self.checksum else None,
)
[docs] def plot(
self,
sample: dict[str, Tensor],
show_titles: bool = True,
suptitle: str | None = None,
) -> 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 suptitle to use for figure
Returns:
a matplotlib Figure with the rendered sample
"""
rgb_indices = []
for band in self.rgb_bands:
if band in self.bands:
rgb_indices.append(self.bands.index(band))
else:
raise ValueError("Dataset doesn't contain some of the RGB bands")
mask = sample['mask'].numpy()[0]
image_pre = sample['image'][0][rgb_indices].numpy()
image_post = sample['image'][1][rgb_indices].numpy()
image_pre = percentile_normalization(image_pre)
image_post = percentile_normalization(image_post)
ncols = 3
showing_predictions = 'prediction' in sample
if showing_predictions:
prediction = sample['prediction'][0]
ncols += 1
fig, axs = plt.subplots(nrows=1, ncols=ncols, figsize=(10, ncols * 5))
axs[0].imshow(einops.rearrange(image_pre, 'c h w -> h w c'))
axs[0].axis('off')
axs[1].imshow(einops.rearrange(image_post, 'c h w -> h w c'))
axs[1].axis('off')
axs[2].imshow(mask)
axs[2].axis('off')
if showing_predictions:
axs[3].imshow(prediction)
axs[3].axis('off')
if show_titles:
axs[0].set_title('Image Pre')
axs[1].set_title('Image Post')
axs[2].set_title('Mask')
if showing_predictions:
axs[3].set_title('Prediction')
if suptitle is not None:
plt.suptitle(suptitle)
return fig
