Source code for torchgeo.datasets.resisc45

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

"""RESISC45 dataset."""

import os
from typing import Callable, Optional, cast

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

from .geo import NonGeoClassificationDataset
from .utils import DatasetNotFoundError, download_url, extract_archive

[docs]class RESISC45(NonGeoClassificationDataset): """NWPU-RESISC45 dataset. The `RESISC45 <>`__ dataset is a dataset for remote sensing image scene classification. Dataset features: * 31,500 images with 0.2-30 m per pixel resolution (256x256 px) * three spectral bands - RGB * 45 scene classes, 700 images per class * images extracted from Google Earth from over 100 countries * images conditions with high variability (resolution, weather, illumination) Dataset format: * images are three-channel jpgs Dataset classes: 0. airplane 1. airport 2. baseball_diamond 3. basketball_court 4. beach 5. bridge 6. chaparral 7. church 8. circular_farmland 9. cloud 10. commercial_area 11. dense_residential 12. desert 13. forest 14. freeway 15. golf_course 16. ground_track_field 17. harbor 18. industrial_area 19. intersection 20. island 21. lake 22. meadow 23. medium_residential 24. mobile_home_park 25. mountain 26. overpass 27. palace 28. parking_lot 29. railway 30. railway_station 31. rectangular_farmland 32. river 33. roundabout 34. runway 35. sea_ice 36. ship 37. snowberg 38. sparse_residential 39. stadium 40. storage_tank 41. tennis_court 42. terrace 43. thermal_power_station 44. wetland This dataset uses the train/val/test splits defined in the "In-domain representation learning for remote sensing" paper: * If you use this dataset in your research, please cite the following paper: * """ url = "" md5 = "d824acb73957502b00efd559fc6cfbbb" filename = "NWPU-RESISC45.rar" directory = "NWPU-RESISC45" splits = ["train", "val", "test"] split_urls = { "train": "", # noqa: E501 "val": "", # noqa: E501 "test": "", # noqa: E501 } split_md5s = { "train": "b5a4c05a37de15e4ca886696a85c403e", "val": "a0770cee4c5ca20b8c32bbd61e114805", "test": "3dda9e4988b47eb1de9f07993653eb08", }
[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 RESISC45 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: DatasetNotFoundError: If dataset is not found and *download* is False. """ assert split in self.splits self.root = root = download self.checksum = checksum self._verify() valid_fns = set() with open(os.path.join(self.root, f"resisc45-{split}.txt")) as f: for fn in f: valid_fns.add(fn.strip()) is_in_split: Callable[[str], bool] = lambda x: os.path.basename(x) in valid_fns super().__init__( root=os.path.join(root,, transforms=transforms, is_valid_file=is_in_split, )
def _verify(self) -> None: """Verify the integrity of the dataset.""" # Check if the files already exist filepath = os.path.join(self.root, if os.path.exists(filepath): return # Check if zip file already exists (if so then extract) filepath = os.path.join(self.root, self.filename) if os.path.exists(filepath): self._extract() return # Check if the user requested to download the dataset if not raise DatasetNotFoundError(self) # Download and extract 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, ) for split in self.splits: download_url( self.split_urls[split], self.root, filename=f"resisc45-{split}.txt", md5=self.split_md5s[split] if self.checksum else None, ) def _extract(self) -> None: """Extract the dataset.""" filepath = os.path.join(self.root, self.filename) extract_archive(filepath)
[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:`NonGeoClassificationDataset.__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) label = cast(int, sample["label"].item()) label_class = self.classes[label] showing_predictions = "prediction" in sample if showing_predictions: prediction = cast(int, sample["prediction"].item()) prediction_class = self.classes[prediction] fig, ax = plt.subplots(figsize=(4, 4)) ax.imshow(image) ax.axis("off") if show_titles: title = f"Label: {label_class}" if showing_predictions: title += f"\nPrediction: {prediction_class}" ax.set_title(title) if suptitle is not None: plt.suptitle(suptitle) return fig

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