Source code for torchgeo.datasets.utils
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""Common dataset utilities."""
import bz2
import collections
import contextlib
import gzip
import lzma
import os
import sys
import tarfile
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import (
Any,
Dict,
Iterable,
Iterator,
List,
Optional,
Sequence,
Tuple,
Union,
cast,
overload,
)
import numpy as np
import rasterio
import torch
from torch import Tensor
from torchvision.datasets.utils import check_integrity, download_url
from torchvision.utils import draw_segmentation_masks
__all__ = (
"check_integrity",
"download_url",
"download_and_extract_archive",
"extract_archive",
"BoundingBox",
"disambiguate_timestamp",
"working_dir",
"stack_samples",
"concat_samples",
"merge_samples",
"unbind_samples",
"rasterio_loader",
"sort_sentinel2_bands",
"draw_semantic_segmentation_masks",
"rgb_to_mask",
"percentile_normalization",
)
class _rarfile:
class RarFile:
def __init__(self, *args: Any, **kwargs: Any) -> None:
self.args = args
self.kwargs = kwargs
def __enter__(self) -> Any:
try:
import rarfile
except ImportError:
raise ImportError(
"rarfile is not installed and is required to extract this dataset"
)
# TODO: catch exception for when rarfile is installed but not
# unrar/unar/bsdtar
return rarfile.RarFile(*self.args, **self.kwargs)
def __exit__(self, exc_type: None, exc_value: None, traceback: None) -> None:
pass
class _zipfile:
class ZipFile:
def __init__(self, *args: Any, **kwargs: Any) -> None:
self.args = args
self.kwargs = kwargs
def __enter__(self) -> Any:
try:
# Supports normal zip files, proprietary deflate64 compression algorithm
import zipfile_deflate64 as zipfile
except ImportError:
# Only supports normal zip files
# https://github.com/python/mypy/issues/1153
import zipfile # type: ignore[no-redef]
return zipfile.ZipFile(*self.args, **self.kwargs)
def __exit__(self, exc_type: None, exc_value: None, traceback: None) -> None:
pass
def extract_archive(src: str, dst: Optional[str] = None) -> None:
"""Extract an archive.
Args:
src: file to be extracted
dst: directory to extract to (defaults to dirname of ``src``)
Raises:
RuntimeError: if src file has unknown archival/compression scheme
"""
if dst is None:
dst = os.path.dirname(src)
suffix_and_extractor: List[Tuple[Union[str, Tuple[str, ...]], Any]] = [
(".rar", _rarfile.RarFile),
(
(".tar", ".tar.gz", ".tar.bz2", ".tar.xz", ".tgz", ".tbz2", ".tbz", ".txz"),
tarfile.open,
),
(".zip", _zipfile.ZipFile),
]
for suffix, extractor in suffix_and_extractor:
if src.endswith(suffix):
with extractor(src, "r") as f:
f.extractall(dst)
return
suffix_and_decompressor: List[Tuple[str, Any]] = [
(".bz2", bz2.open),
(".gz", gzip.open),
(".xz", lzma.open),
]
for suffix, decompressor in suffix_and_decompressor:
if src.endswith(suffix):
dst = os.path.join(dst, os.path.basename(src).replace(suffix, ""))
with decompressor(src, "rb") as sf, open(dst, "wb") as df:
df.write(sf.read())
return
raise RuntimeError("src file has unknown archival/compression scheme")
def download_and_extract_archive(
url: str,
download_root: str,
extract_root: Optional[str] = None,
filename: Optional[str] = None,
md5: Optional[str] = None,
) -> None:
"""Download and extract an archive.
Args:
url: URL to download
download_root: directory to download to
extract_root: directory to extract to (defaults to ``download_root``)
filename: download filename (defaults to basename of ``url``)
md5: checksum for download verification
"""
download_root = os.path.expanduser(download_root)
if extract_root is None:
extract_root = download_root
if not filename:
filename = os.path.basename(url)
download_url(url, download_root, filename, md5)
archive = os.path.join(download_root, filename)
print("Extracting {} to {}".format(archive, extract_root))
extract_archive(archive, extract_root)
def download_radiant_mlhub_dataset(
dataset_id: str, download_root: str, api_key: Optional[str] = None
) -> None:
"""Download a dataset from Radiant Earth.
Args:
dataset_id: the ID of the dataset to fetch
download_root: directory to download to
api_key: the API key to use for all requests from the session. Can also be
passed in via the ``MLHUB_API_KEY`` environment variable, or configured in
``~/.mlhub/profiles``.
"""
try:
import radiant_mlhub
except ImportError:
raise ImportError(
"radiant_mlhub is not installed and is required to download this dataset"
)
dataset = radiant_mlhub.Dataset.fetch(dataset_id, api_key=api_key)
dataset.download(output_dir=download_root, api_key=api_key)
def download_radiant_mlhub_collection(
collection_id: str, download_root: str, api_key: Optional[str] = None
) -> None:
"""Download a collection from Radiant Earth.
Args:
collection_id: the ID of the collection to fetch
download_root: directory to download to
api_key: the API key to use for all requests from the session. Can also be
passed in via the ``MLHUB_API_KEY`` environment variable, or configured in
``~/.mlhub/profiles``.
"""
try:
import radiant_mlhub
except ImportError:
raise ImportError(
"radiant_mlhub is not installed and is required to download this collection"
)
collection = radiant_mlhub.Collection.fetch(collection_id, api_key=api_key)
collection.download(output_dir=download_root, api_key=api_key)
@dataclass(frozen=True)
class BoundingBox:
"""Data class for indexing spatiotemporal data."""
#: western boundary
minx: float
#: eastern boundary
maxx: float
#: southern boundary
miny: float
#: northern boundary
maxy: float
#: earliest boundary
mint: float
#: latest boundary
maxt: float
[docs] def __post_init__(self) -> None:
"""Validate the arguments passed to :meth:`__init__`.
Raises:
ValueError: if bounding box is invalid
(minx > maxx, miny > maxy, or mint > maxt)
.. versionadded:: 0.2
"""
if self.minx > self.maxx:
raise ValueError(
f"Bounding box is invalid: 'minx={self.minx}' > 'maxx={self.maxx}'"
)
if self.miny > self.maxy:
raise ValueError(
f"Bounding box is invalid: 'miny={self.miny}' > 'maxy={self.maxy}'"
)
if self.mint > self.maxt:
raise ValueError(
f"Bounding box is invalid: 'mint={self.mint}' > 'maxt={self.maxt}'"
)
# https://github.com/PyCQA/pydocstyle/issues/525
@overload
def __getitem__(self, key: int) -> float: # noqa: D105
pass
@overload
def __getitem__(self, key: slice) -> List[float]: # noqa: D105
pass
[docs] def __getitem__(self, key: Union[int, slice]) -> Union[float, List[float]]:
"""Index the (minx, maxx, miny, maxy, mint, maxt) tuple.
Args:
key: integer or slice object
Returns:
the value(s) at that index
Raises:
IndexError: if key is out of bounds
"""
return [self.minx, self.maxx, self.miny, self.maxy, self.mint, self.maxt][key]
[docs] def __iter__(self) -> Iterator[float]:
"""Container iterator.
Returns:
iterator object that iterates over all objects in the container
"""
yield from [self.minx, self.maxx, self.miny, self.maxy, self.mint, self.maxt]
[docs] def __contains__(self, other: "BoundingBox") -> bool:
"""Whether or not other is within the bounds of this bounding box.
Args:
other: another bounding box
Returns:
True if other is within this bounding box, else False
.. versionadded:: 0.2
"""
return (
(self.minx <= other.minx <= self.maxx)
and (self.minx <= other.maxx <= self.maxx)
and (self.miny <= other.miny <= self.maxy)
and (self.miny <= other.maxy <= self.maxy)
and (self.mint <= other.mint <= self.maxt)
and (self.mint <= other.maxt <= self.maxt)
)
[docs] def __or__(self, other: "BoundingBox") -> "BoundingBox":
"""The union operator.
Args:
other: another bounding box
Returns:
the minimum bounding box that contains both self and other
.. versionadded:: 0.2
"""
return BoundingBox(
min(self.minx, other.minx),
max(self.maxx, other.maxx),
min(self.miny, other.miny),
max(self.maxy, other.maxy),
min(self.mint, other.mint),
max(self.maxt, other.maxt),
)
[docs] def __and__(self, other: "BoundingBox") -> "BoundingBox":
"""The intersection operator.
Args:
other: another bounding box
Returns:
the intersection of self and other
Raises:
ValueError: if self and other do not intersect
.. versionadded:: 0.2
"""
try:
return BoundingBox(
max(self.minx, other.minx),
min(self.maxx, other.maxx),
max(self.miny, other.miny),
min(self.maxy, other.maxy),
max(self.mint, other.mint),
min(self.maxt, other.maxt),
)
except ValueError:
raise ValueError(f"Bounding boxes {self} and {other} do not overlap")
[docs] def intersects(self, other: "BoundingBox") -> bool:
"""Whether or not two bounding boxes intersect.
Args:
other: another bounding box
Returns:
True if bounding boxes intersect, else False
"""
return (
self.minx <= other.maxx
and self.maxx >= other.minx
and self.miny <= other.maxy
and self.maxy >= other.miny
and self.mint <= other.maxt
and self.maxt >= other.mint
)
def disambiguate_timestamp(date_str: str, format: str) -> Tuple[float, float]:
"""Disambiguate partial timestamps.
TorchGeo stores the timestamp of each file in a spatiotemporal R-tree. If the full
timestamp isn't known, a file could represent a range of time. For example, in the
CDL dataset, each mask spans an entire year. This method returns the maximum
possible range of timestamps that ``date_str`` could belong to. It does this by
parsing ``format`` to determine the level of precision of ``date_str``.
Args:
date_str: string representing date and time of a data point
format: format codes accepted by :meth:`datetime.datetime.strptime`
Returns:
(mint, maxt) tuple for indexing
"""
mint = datetime.strptime(date_str, format)
# TODO: This doesn't correctly handle literal `%%` characters in format
# TODO: May have issues with time zones, UTC vs. local time, and DST
# TODO: This is really tedious, is there a better way to do this?
if not any([f"%{c}" in format for c in "yYcxG"]):
# No temporal info
return 0, sys.maxsize
elif not any([f"%{c}" in format for c in "bBmjUWcxV"]):
# Year resolution
maxt = datetime(mint.year + 1, 1, 1)
elif not any([f"%{c}" in format for c in "aAwdjcxV"]):
# Month resolution
if mint.month == 12:
maxt = datetime(mint.year + 1, 1, 1)
else:
maxt = datetime(mint.year, mint.month + 1, 1)
elif not any([f"%{c}" in format for c in "HIcX"]):
# Day resolution
maxt = mint + timedelta(days=1)
elif not any([f"%{c}" in format for c in "McX"]):
# Hour resolution
maxt = mint + timedelta(hours=1)
elif not any([f"%{c}" in format for c in "ScX"]):
# Minute resolution
maxt = mint + timedelta(minutes=1)
elif not any([f"%{c}" in format for c in "f"]):
# Second resolution
maxt = mint + timedelta(seconds=1)
else:
# Microsecond resolution
maxt = mint + timedelta(microseconds=1)
mint -= timedelta(microseconds=1)
maxt -= timedelta(microseconds=1)
return mint.timestamp(), maxt.timestamp()
@contextlib.contextmanager
def working_dir(dirname: str, create: bool = False) -> Iterator[None]:
"""Context manager for changing directories.
Args:
dirname: directory to temporarily change to
create: if True, create the destination directory
"""
if create:
os.makedirs(dirname, exist_ok=True)
cwd = os.getcwd()
os.chdir(dirname)
try:
yield
finally:
os.chdir(cwd)
def _list_dict_to_dict_list(samples: Iterable[Dict[Any, Any]]) -> Dict[Any, List[Any]]:
"""Convert a list of dictionaries to a dictionary of lists.
Args:
samples: a list of dictionaries
Returns:
a dictionary of lists
.. versionadded:: 0.2
"""
collated = collections.defaultdict(list)
for sample in samples:
for key, value in sample.items():
collated[key].append(value)
return collated
def _dict_list_to_list_dict(sample: Dict[Any, Sequence[Any]]) -> List[Dict[Any, Any]]:
"""Convert a dictionary of lists to a list of dictionaries.
Args:
sample: a dictionary of lists
Returns:
a list of dictionaries
.. versionadded:: 0.2
"""
uncollated: List[Dict[Any, Any]] = [
{} for _ in range(max(map(len, sample.values())))
]
for key, values in sample.items():
for i, value in enumerate(values):
uncollated[i][key] = value
return uncollated
def stack_samples(samples: Iterable[Dict[Any, Any]]) -> Dict[Any, Any]:
"""Stack a list of samples along a new axis.
Useful for forming a mini-batch of samples to pass to
:class:`torch.utils.data.DataLoader`.
Args:
samples: list of samples
Returns:
a single sample
.. versionadded:: 0.2
"""
collated: Dict[Any, Any] = _list_dict_to_dict_list(samples)
for key, value in collated.items():
if isinstance(value[0], Tensor):
collated[key] = torch.stack(value)
return collated
def concat_samples(samples: Iterable[Dict[Any, Any]]) -> Dict[Any, Any]:
"""Concatenate a list of samples along an existing axis.
Useful for joining samples in a :class:`torchgeo.datasets.IntersectionDataset`.
Args:
samples: list of samples
Returns:
a single sample
.. versionadded:: 0.2
"""
collated: Dict[Any, Any] = _list_dict_to_dict_list(samples)
for key, value in collated.items():
if isinstance(value[0], Tensor):
collated[key] = torch.cat(value)
else:
collated[key] = value[0]
return collated
def merge_samples(samples: Iterable[Dict[Any, Any]]) -> Dict[Any, Any]:
"""Merge a list of samples.
Useful for joining samples in a :class:`torchgeo.datasets.UnionDataset`.
Args:
samples: list of samples
Returns:
a single sample
.. versionadded:: 0.2
"""
collated: Dict[Any, Any] = {}
for sample in samples:
for key, value in sample.items():
if key in collated and isinstance(value, Tensor):
# Take the maximum so that nodata values (zeros) get replaced
# by data values whenever possible
collated[key] = torch.maximum(collated[key], value)
else:
collated[key] = value
return collated
def unbind_samples(sample: Dict[Any, Sequence[Any]]) -> List[Dict[Any, Any]]:
"""Reverse of :func:`stack_samples`.
Useful for turning a mini-batch of samples into a list of samples. These individual
samples can then be plotted using a dataset's ``plot`` method.
Args:
sample: a mini-batch of samples
Returns:
list of samples
.. versionadded:: 0.2
"""
for key, values in sample.items():
if isinstance(values, Tensor):
sample[key] = torch.unbind(values)
return _dict_list_to_list_dict(sample)
def rasterio_loader(path: str) -> "np.typing.NDArray[np.int_]":
"""Load an image file using rasterio.
Args:
path: path to the image to be loaded
Returns:
the image
"""
with rasterio.open(path) as f:
array: "np.typing.NDArray[np.int_]" = f.read().astype(np.int32)
# VisionClassificationDataset expects images returned with channels last (HWC)
array = array.transpose(1, 2, 0)
return array
def sort_sentinel2_bands(x: str) -> str:
"""Sort Sentinel-2 band files in the correct order."""
x = os.path.basename(x).split("_")[-1]
x = os.path.splitext(x)[0]
if x == "B8A":
x = "B08A"
return x
def draw_semantic_segmentation_masks(
image: Tensor,
mask: Tensor,
alpha: float = 0.5,
colors: Optional[Sequence[Union[str, Tuple[int, int, int]]]] = None,
) -> "np.typing.NDArray[np.uint8]":
"""Overlay a semantic segmentation mask onto an image.
Args:
image: tensor of shape (3, h, w) and dtype uint8
mask: tensor of shape (h, w) with pixel values representing the classes and
dtype bool
alpha: alpha blend factor
colors: list of RGB int tuples, or color strings e.g. red, #FF00FF
Returns:
a version of ``image`` overlayed with the colors given by ``mask`` and
``colors``
"""
classes = torch.unique(mask)
classes = classes[1:]
class_masks = mask == classes[:, None, None]
img = draw_segmentation_masks(
image=image, masks=class_masks, alpha=alpha, colors=colors
)
img = img.permute((1, 2, 0)).numpy().astype(np.uint8)
return cast("np.typing.NDArray[np.uint8]", img)
def rgb_to_mask(
rgb: "np.typing.NDArray[np.uint8]", colors: List[Tuple[int, int, int]]
) -> "np.typing.NDArray[np.uint8]":
"""Converts an RGB colormap mask to a integer mask.
Args:
rgb: array mask of coded with RGB tuples
colors: list of RGB tuples to convert to integer indices
Returns:
integer array mask
"""
assert len(colors) <= 256 # we currently return a uint8 array, so the largest value
# we can map is 255
h, w = rgb.shape[:2]
mask: "np.typing.NDArray[np.uint8]" = np.zeros(shape=(h, w), dtype=np.uint8)
for i, c in enumerate(colors):
cmask = rgb == c
# Only update mask if class is present in mask
if isinstance(cmask, np.ndarray):
mask[cmask.all(axis=-1)] = i
return mask
def percentile_normalization(
img: "np.typing.NDArray[np.int_]",
lower: float = 2,
upper: float = 98,
axis: Optional[Union[int, Sequence[int]]] = None,
) -> "np.typing.NDArray[np.int_]":
"""Applies percentile normalization to an input image.
Specifically, this will rescale the values in the input such that values <= the
lower percentile value will be 0 and values >= the upper percentile value will be 1.
Using the 2nd and 98th percentile usually results in good visualizations.
Args:
img: image to normalize
lower: lower percentile in range [0,100]
upper: upper percentile in range [0,100]
axis: Axis or axes along which the percentiles are computed. The default
is to compute the percentile(s) along a flattened version of the array.
Returns
normalized version of ``img``
.. versionadded:: 0.2
"""
assert lower < upper
lower_percentile = np.percentile(img, lower, axis=axis)
upper_percentile = np.percentile(img, upper, axis=axis)
img_normalized: "np.typing.NDArray[np.int_]" = np.clip(
(img - lower_percentile) / (upper_percentile - lower_percentile), 0, 1
)
return img_normalized
