Source code for torchgeo.datasets.seco
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""Sentinel 2 imagery from the Seasonal Contrast paper."""
import os
from collections import defaultdict
from typing import Callable, Dict, List, Optional, cast
import numpy as np
import rasterio
import torch
from PIL import Image
from torch import Tensor
from .geo import VisionDataset
from .utils import download_url, extract_archive
class SeasonalContrastS2(VisionDataset):
"""Sentinel 2 imagery from the Seasonal Contrast paper.
The `Seasonal Contrast imagery <https://github.com/ElementAI/seasonal-contrast/>`_
dataset contains Sentinel 2 imagery patches sampled from different points in time
around the 10k most populated cities on Earth.
Dataset features:
* Two versions: 100K and 1M patches
* 12 band Sentinel 2 imagery from 5 points in time at each location
If you use this dataset in your research, please cite the following paper:
* https://arxiv.org/pdf/2103.16607.pdf
"""
ALL_BANDS = [
"B1",
"B2",
"B3",
"B4",
"B5",
"B6",
"B7",
"B8",
"B8A",
"B9",
"B11",
"B12",
]
RGB_BANDS = ["B4", "B3", "B2"]
urls = {
# 7.3 GB
"100k": "https://zenodo.org/record/4728033/files/seco_100k.zip?download=1",
# 36.3 GB
"1m": "https://zenodo.org/record/4728033/files/seco_1m.zip?download=1",
}
filenames = {
"100k": "seco_100k.zip",
"1m": "seco_1m.zip",
}
md5s = {
"100k": "ebf2d5e03adc6e657f9a69a20ad863e0",
"1m": "187963d852d4d3ce6637743ec3a4bd9e",
}
directory_names = {
"100k": "seasonal_contrast_100k",
"1m": "seasonal_contrast_1m",
}
[docs] def __init__(
self,
root: str = "data",
version: str = "100k",
bands: List[str] = RGB_BANDS,
transforms: Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]] = None,
download: bool = False,
checksum: bool = False,
) -> None:
"""Initialize a new SeCo dataset instance.
Args:
root: root directory where dataset can be found
version: one of "100k" or "1m" for the version of the dataset to use
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 ``version`` argument is invalid
RuntimeError: if ``download=False`` and data is not found, or checksums
don't match
"""
assert version in ["100k", "1m"]
for band in bands:
assert band in self.ALL_BANDS
self.root = root
self.bands = bands
self.url = self.urls[version]
self.filename = self.filenames[version]
self.md5 = self.md5s[version]
self.directory_name = self.directory_names[version]
self.transforms = transforms
self.download = download
self.checksum = checksum
self._verify()
# TODO: This is slow, I think this should be generated on download and then
# loaded in the constructor
self.scene_to_patches = defaultdict(list)
for root_directory, directories, fns in os.walk(
os.path.join(self.root, self.directory_name)
):
if len(directories) == 0 and len(fns) > 0:
root_directory, patch_name = os.path.split(root_directory)
_, scene_name = os.path.split(root_directory)
self.scene_to_patches[scene_name].append(patch_name)
self.scenes = sorted(self.scene_to_patches.keys())
for scene_name in self.scenes:
self.scene_to_patches[scene_name] = sorted(
self.scene_to_patches[scene_name]
)
[docs] def __getitem__(self, index: int) -> Dict[str, Tensor]:
"""Return an index within the dataset.
Args:
index: index to return
Returns:
sample with an "image" in 5xCxHxW format where the 5 indexes over the same
patch sampled from different points in time by the SeCo method
"""
scene_name = self.scenes[index]
patch_names = self.scene_to_patches[scene_name]
imagery = [
self._load_patch(scene_name, patch_name) for patch_name in patch_names
]
sample = {"image": torch.stack(imagery, dim=0)}
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.scenes)
def _load_patch(self, scene_name: str, patch_name: str) -> Tensor:
"""Load a single image patch.
Args:
scene_name: the name of the scene to load from, e.g. '019999'
patch_name: the name of the patch to load, e.g.
'20200713T075609_20200713T081050_T36QZH'
Returns:
the image with the subset of bands specified by ``self.bands``
"""
all_data = []
for band in self.bands:
fn = os.path.join(
self.root,
self.directory_name,
scene_name,
patch_name,
f"{band}.tif",
)
with rasterio.open(fn) as f:
band_data = f.read(1)
height, width = band_data.shape
assert height == width
size = height
if size < 264:
# TODO: PIL resize is much slower than cv2, we should check to see
# what could be sped up throughout later. There is also a potential
# slowdown here from converting to/from a PIL Image just to resize.
# https://gist.github.com/calebrob6/748045ac8d844154067b2eefa47de92f
pil_image = Image.fromarray(band_data)
band_data = np.array(
pil_image.resize((264, 264), resample=Image.BILINEAR)
)
all_data.append(band_data)
image = torch.from_numpy( # type: ignore[attr-defined]
np.stack(all_data, axis=0)
)
return cast(Tensor, image)
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
directory_path = os.path.join(self.root, self.directory_name)
if os.path.exists(directory_path):
return
# Check if the zip files have already been downloaded
zip_path = os.path.join(self.root, self.filename)
if os.path.exists(zip_path):
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,
filename=self.filename,
md5=self.md5 if self.checksum else None,
)
def _extract(self) -> None:
"""Extract the dataset."""
extract_archive(
os.path.join(self.root, self.filename),
)
