Source code for torchgeo.datasets.sen12ms

# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.

"""SEN12MS dataset."""

import os
from import Sequence
from typing import Callable, Optional

import matplotlib.pyplot as plt
import numpy as np
import rasterio
import torch
from matplotlib.figure import Figure
from torch import Tensor

from .geo import NonGeoDataset
from .utils import check_integrity, percentile_normalization

[docs]class SEN12MS(NonGeoDataset): """SEN12MS dataset. The `SEN12MS <>`__ dataset contains 180,662 patch triplets of corresponding Sentinel-1 dual-pol SAR data, Sentinel-2 multi-spectral images, and MODIS-derived land cover maps. The patches are distributed across the land masses of the Earth and spread over all four meteorological seasons. This is reflected by the dataset structure. All patches are provided in the form of 16-bit GeoTiffs containing the following specific information: * Sentinel-1 SAR: 2 channels corresponding to sigma nought backscatter values in dB scale for VV and VH polarization. * Sentinel-2 Multi-Spectral: 13 channels corresponding to the 13 spectral bands (B1, B2, B3, B4, B5, B6, B7, B8, B8a, B9, B10, B11, B12). * MODIS Land Cover: 4 channels corresponding to IGBP, LCCS Land Cover, LCCS Land Use, and LCCS Surface Hydrology layers. If you use this dataset in your research, please cite the following paper: * .. note:: This dataset can be automatically downloaded using the following bash script: .. code-block:: bash for season in 1158_spring 1868_summer 1970_fall 2017_winter do for source in lc s1 s2 do wget "${season}_${source}.tar.gz" tar xvzf "ROIs${season}_${source}.tar.gz" done done for split in train test do wget "${split}_list.txt" done or manually downloaded from and This download will likely take several hours. """ # noqa: E501 BAND_SETS: dict[str, tuple[str, ...]] = { "all": ( "VV", "VH", "B01", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B09", "B10", "B11", "B12", ), "s1": ("VV", "VH"), "s2-all": ( "B01", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B09", "B10", "B11", "B12", ), "s2-reduced": ("B02", "B03", "B04", "B08", "B10", "B11"), } band_names = ( "VV", "VH", "B01", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B09", "B10", "B11", "B12", ) rgb_bands = ["B04", "B03", "B02"] filenames = [ "ROIs1158_spring_lc.tar.gz", "ROIs1158_spring_s1.tar.gz", "ROIs1158_spring_s2.tar.gz", "ROIs1868_summer_lc.tar.gz", "ROIs1868_summer_s1.tar.gz", "ROIs1868_summer_s2.tar.gz", "ROIs1970_fall_lc.tar.gz", "ROIs1970_fall_s1.tar.gz", "ROIs1970_fall_s2.tar.gz", "ROIs2017_winter_lc.tar.gz", "ROIs2017_winter_s1.tar.gz", "ROIs2017_winter_s2.tar.gz", "train_list.txt", "test_list.txt", ] light_filenames = [ "ROIs1158_spring", "ROIs1868_summer", "ROIs1970_fall", "ROIs2017_winter", "train_list.txt", "test_list.txt", ] md5s = [ "6e2e8fa8b8cba77ddab49fd20ff5c37b", "fba019bb27a08c1db96b31f718c34d79", "d58af2c15a16f376eb3308dc9b685af2", "2c5bd80244440b6f9d54957c6b1f23d4", "01044b7f58d33570c6b57fec28a3d449", "4dbaf72ecb704a4794036fe691427ff3", "9b126a68b0e3af260071b3139cb57cee", "19132e0aab9d4d6862fd42e8e6760847", "b8f117818878da86b5f5e06400eb1866", "0fa0420ef7bcfe4387c7e6fe226dc728", "bb8cbfc16b95a4f054a3d5380e0130ed", "3807545661288dcca312c9c538537b63", "0a68d4e1eb24f128fccdb930000b2546", "c7faad064001e646445c4c634169484d", ]
[docs] def __init__( self, root: str = "data", split: str = "train", bands: Sequence[str] = BAND_SETS["all"], transforms: Optional[Callable[[dict[str, Tensor]], dict[str, Tensor]]] = None, checksum: bool = False, ) -> None: """Initialize a new SEN12MS dataset instance. The ``bands`` argument allows for the subsetting of bands returned by the dataset. Integers in ``bands`` index into a stack of Sentinel 1 and Sentinel 2 imagery. Indices 0 and 1 correspond to the Sentinel 1 imagery where indices 2 through 14 correspond to the Sentinel 2 imagery. Args: root: root directory where dataset can be found split: one of "train" or "test" bands: a sequence of band indices to use where the indices correspond to the array index of combined Sentinel 1 and Sentinel 2 transforms: a function/transform that takes input sample and its target as entry and returns a transformed version checksum: if True, check the MD5 of the downloaded files (may be slow) Raises: AssertionError: if ``split`` argument is invalid RuntimeError: if data is not found in ``root``, or checksums don't match """ assert split in ["train", "test"] self._validate_bands(bands) self.band_indices = torch.tensor( [self.band_names.index(b) for b in bands] ).long() self.bands = bands self.root = root self.split = split self.transforms = transforms self.checksum = checksum if checksum: if not self._check_integrity(): raise RuntimeError("Dataset not found or corrupted.") else: if not self._check_integrity_light(): raise RuntimeError("Dataset not found or corrupted.") with open(os.path.join(self.root, split + "_list.txt")) as f: self.ids = [line.rstrip() for line in 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 """ filename = self.ids[index] lc = self._load_raster(filename, "lc").long() s1 = self._load_raster(filename, "s1") s2 = self._load_raster(filename, "s2") image =[s1, s2], dim=0) image = torch.index_select(image, dim=0, index=self.band_indices) sample: dict[str, Tensor] = {"image": image, "mask": lc[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.ids)
def _load_raster(self, filename: str, source: str) -> Tensor: """Load a single raster image or target. Args: filename: name of the file to load source: one of "lc", "s1", or "s2" Returns: the raster image or target """ parts = filename.split("_") parts[2] = source with os.path.join( self.root, "{}_{}".format(*parts), "{2}_{3}".format(*parts), "{}_{}_{}_{}_{}".format(*parts), ) ) as f: array = if array.dtype == np.uint16: array = array.astype(np.int32) tensor = torch.from_numpy(array) return tensor def _validate_bands(self, bands: Sequence[str]) -> None: """Validate list of bands. Args: bands: user-provided sequence of bands to load Raises: AssertionError: if ``bands`` is not a sequence ValueError: if an invalid band name is provided """ assert isinstance(bands, tuple), "'bands' must be a sequence" for band in bands: if band not in self.band_names: raise ValueError(f"'{band}' is an invalid band name.") def _check_integrity_light(self) -> bool: """Checks the integrity of the dataset structure. Returns: True if the dataset directories and split files are found, else False """ for filename in self.light_filenames: filepath = os.path.join(self.root, filename) if not os.path.exists(filepath): return False return True def _check_integrity(self) -> bool: """Check integrity of dataset. Returns: True if dataset files are found and/or MD5s match, else False """ for filename, md5 in zip(self.filenames, self.md5s): filepath = os.path.join(self.root, filename) if not check_integrity(filepath, md5 if self.checksum else None): return False return True
[docs] def plot( self, sample: dict[str, Tensor], show_titles: bool = True, suptitle: Optional[str] = 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 .. versionadded:: 0.2 """ 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") image, mask = sample["image"][rgb_indices].numpy(), sample["mask"] image = percentile_normalization(image) ncols = 2 showing_predictions = "prediction" in sample if showing_predictions: prediction = sample["prediction"] ncols += 1 fig, axs = plt.subplots(nrows=1, ncols=ncols, figsize=(10, ncols * 5)) axs[0].imshow(np.transpose(image, (1, 2, 0))) axs[0].axis("off") axs[1].imshow(mask) axs[1].axis("off") if showing_predictions: axs[2].imshow(prediction) axs[2].axis("off") if show_titles: axs[0].set_title("Image") axs[1].set_title("Mask") if showing_predictions: axs[2].set_title("Prediction") if suptitle is not None: plt.suptitle(suptitle) return fig

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