text: # Copyright (c) Microsoft Corporation. All rights reserved.
  # Licensed under the MIT License.
  
  """BigEarthNet dataset."""
  
  import glob
  import json
  import os
  from typing import Callable, Dict, List, Optional
  
  import matplotlib.pyplot as plt
  import numpy as np
  import rasterio
  import torch
  from rasterio.enums import Resampling
  from torch import Tensor
  
  from .geo import VisionDataset
  from .utils import download_url, extract_archive, sort_sentinel2_bands
  
  
  class BigEarthNet(VisionDataset):
      """BigEarthNet dataset.
  
      The `BigEarthNet <http://bigearth.net/>`_
      dataset is a dataset for multilabel remote sensing image scene classification.
  
      Dataset features:
  
      * 590,326 patches from 125 Sentinel-1 and Sentinel-2 tiles
      * Imagery from tiles in Europe between Jun 2017 - May 2018
      * 12 spectral bands with 10-60 m per pixel resolution (base 120x120 px)
      * 2 synthetic aperture radar bands (120x120 px)
      * 43 or 19 scene classes from the 2018 CORINE Land Cover database (CLC 2018)
  
      Dataset format:
  
      * images are composed of multiple single channel geotiffs
      * labels are multiclass, stored in a single json file per image
      * mapping of Sentinel-1 to Sentinel-2 patches are within Sentinel-1 json files
      * Sentinel-1 bands: (VV, VH)
      * Sentinel-2 bands: (B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, B12)
      * All bands: (VV, VH, B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, B12)
      * Sentinel-2 bands are of different spatial resolutions and upsampled to 10m
  
      Dataset classes (43):
  
      0. Agro-forestry areas
      1. Airports
      2. Annual crops associated with permanent crops
      3. Bare rock
      4. Beaches, dunes, sands
      5. Broad-leaved forest
      6. Burnt areas
      7. Coastal lagoons
      8. Complex cultivation patterns
      9. Coniferous forest
      10. Construction sites
      11. Continuous urban fabric
      12. Discontinuous urban fabric
      13. Dump sites
      14. Estuaries
      15. Fruit trees and berry plantations
      16. Green urban areas
      17. Industrial or commercial units
      18. Inland marshes
      19. Intertidal flats
      20. Land principally occupied by agriculture, with significant
          areas of natural vegetation
      21. Mineral extraction sites
      22. Mixed forest
      23. Moors and heathland
      24. Natural grassland
      25. Non-irrigated arable land
      26. Olive groves
      27. Pastures
      28. Peatbogs
      29. Permanently irrigated land
      30. Port areas
      31. Rice fields
      32. Road and rail networks and associated land
      33. Salines
      34. Salt marshes
      35. Sclerophyllous vegetation
      36. Sea and ocean
      37. Sparsely vegetated areas
      38. Sport and leisure facilities
      39. Transitional woodland/shrub
      40. Vineyards
      41. Water bodies
      42. Water courses
  
      Dataset classes (19):
  
      0. Urban fabric
      1. Industrial or commercial units
      2. Arable land
      3. Permanent crops
      4. Pastures
      5. Complex cultivation patterns
      6. Land principally occupied by agriculture, with significant
         areas of natural vegetation
      7. Agro-forestry areas
      8. Broad-leaved forest
      9. Coniferous forest
      10. Mixed forest
      11. Natural grassland and sparsely vegetated areas
      12. Moors, heathland and sclerophyllous vegetation
      13. Transitional woodland, shrub
      14. Beaches, dunes, sands
      15. Inland wetlands
      16. Coastal wetlands
      17. Inland waters
      18. Marine waters
  
      If you use this dataset in your research, please cite the following paper:
  
      * https://doi.org/10.1109/IGARSS.2019.8900532
  
      """
  
      class_sets = {
          19: [
              "Urban fabric",
              "Industrial or commercial units",
              "Arable land",
              "Permanent crops",
              "Pastures",
              "Complex cultivation patterns",
              "Land principally occupied by agriculture, with significant areas of"
              " natural vegetation",
              "Agro-forestry areas",
              "Broad-leaved forest",
              "Coniferous forest",
              "Mixed forest",
              "Natural grassland and sparsely vegetated areas",
              "Moors, heathland and sclerophyllous vegetation",
              "Transitional woodland, shrub",
              "Beaches, dunes, sands",
              "Inland wetlands",
              "Coastal wetlands",
              "Inland waters",
              "Marine waters",
          ],
          43: [
              "Agro-forestry areas",
              "Airports",
              "Annual crops associated with permanent crops",
              "Bare rock",
              "Beaches, dunes, sands",
              "Broad-leaved forest",
              "Burnt areas",
              "Coastal lagoons",
              "Complex cultivation patterns",
              "Coniferous forest",
              "Construction sites",
              "Continuous urban fabric",
              "Discontinuous urban fabric",
              "Dump sites",
              "Estuaries",
              "Fruit trees and berry plantations",
              "Green urban areas",
              "Industrial or commercial units",
              "Inland marshes",
              "Intertidal flats",
              "Land principally occupied by agriculture, with significant areas of"
              " natural vegetation",
              "Mineral extraction sites",
              "Mixed forest",
              "Moors and heathland",
              "Natural grassland",
              "Non-irrigated arable land",
              "Olive groves",
              "Pastures",
              "Peatbogs",
              "Permanently irrigated land",
              "Port areas",
              "Rice fields",
              "Road and rail networks and associated land",
              "Salines",
              "Salt marshes",
              "Sclerophyllous vegetation",
              "Sea and ocean",
              "Sparsely vegetated areas",
              "Sport and leisure facilities",
              "Transitional woodland/shrub",
              "Vineyards",
              "Water bodies",
              "Water courses",
          ],
      }
  
      label_converter = {
          0: 0,
          1: 0,
          2: 1,
          11: 2,
          12: 2,
          13: 2,
          14: 3,
          15: 3,
          16: 3,
          18: 3,
          17: 4,
          19: 5,
          20: 6,
          21: 7,
          22: 8,
          23: 9,
          24: 10,
          25: 11,
          31: 11,
          26: 12,
          27: 12,
          28: 13,
          29: 14,
          33: 15,
          34: 15,
          35: 16,
          36: 16,
          38: 17,
          39: 17,
          40: 18,
          41: 18,
          42: 18,
      }
  
      splits_metadata = {
          "train": {
              "url": "https://git.tu-berlin.de/rsim/BigEarthNet-MM_19-classes_models/-/raw/master/splits/train.csv?inline=false",  # noqa: E501
              "filename": "bigearthnet-train.csv",
              "md5": "623e501b38ab7b12fe44f0083c00986d",
          },
          "val": {
              "url": "https://git.tu-berlin.de/rsim/BigEarthNet-MM_19-classes_models/-/raw/master/splits/val.csv?inline=false",  # noqa: E501
              "filename": "bigearthnet-val.csv",
              "md5": "22efe8ed9cbd71fa10742ff7df2b7978",
          },
          "test": {
              "url": "https://git.tu-berlin.de/rsim/BigEarthNet-MM_19-classes_models/-/raw/master/splits/test.csv?inline=false",  # noqa: E501
              "filename": "bigearthnet-test.csv",
              "md5": "697fb90677e30571b9ac7699b7e5b432",
          },
      }
      metadata = {
          "s1": {
              "url": "http://bigearth.net/downloads/BigEarthNet-S1-v1.0.tar.gz",
              "md5": "5a64e9ce38deb036a435a7b59494924c",
              "filename": "BigEarthNet-S1-v1.0.tar.gz",
              "directory": "BigEarthNet-S1-v1.0",
          },
          "s2": {
              "url": "http://bigearth.net/downloads/BigEarthNet-S2-v1.0.tar.gz",
              "md5": "5a64e9ce38deb036a435a7b59494924c",
              "filename": "BigEarthNet-S2-v1.0.tar.gz",
              "directory": "BigEarthNet-v1.0",
          },
      }
      image_size = (120, 120)
  
      def __init__(
          self,
          root: str = "data",
          split: str = "train",
          bands: str = "all",
          num_classes: int = 19,
          transforms: Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]] = None,
          download: bool = False,
          checksum: bool = False,
      ) -> None:
          """Initialize a new BigEarthNet dataset instance.
  
          Args:
              root: root directory where dataset can be found
              split: train/val/test split to load
              bands: load Sentinel-1 bands, Sentinel-2, or both. one of {s1, s2, all}
              num_classes: number of classes to load in target. one of {19, 43}
              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)
          """
          assert split in self.splits_metadata
          assert bands in ["s1", "s2", "all"]
          assert num_classes in [43, 19]
          self.root = root
          self.split = split
          self.bands = bands
          self.num_classes = num_classes
          self.transforms = transforms
          self.download = download
          self.checksum = checksum
          self.class2idx = {c: i for i, c in enumerate(self.class_sets[43])}
          self._verify()
          self.folders = self._load_folders()
  
      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
          """
          image = self._load_image(index)
          label = self._load_target(index)
          sample: Dict[str, Tensor] = {"image": image, "label": label}
  
          if self.transforms is not None:
              sample = self.transforms(sample)
  
          return sample
  
      def __len__(self) -> int:
          """Return the number of data points in the dataset.
  
          Returns:
              length of the dataset
          """
          return len(self.folders)
  
      def _load_folders(self) -> List[Dict[str, str]]:
          """Load folder paths.
  
          Returns:
              list of dicts of s1 and s2 folder paths
          """
          filename = self.splits_metadata[self.split]["filename"]
          dir_s1 = self.metadata["s1"]["directory"]
          dir_s2 = self.metadata["s2"]["directory"]
  
          with open(os.path.join(self.root, filename)) as f:
              lines = f.read().strip().splitlines()
              pairs = [line.split(",") for line in lines]
  
          folders = [
              {
                  "s1": os.path.join(self.root, dir_s1, pair[1]),
                  "s2": os.path.join(self.root, dir_s2, pair[0]),
              }
              for pair in pairs
          ]
          return folders
  
      def _load_paths(self, index: int) -> List[str]:
          """Load paths to band files.
  
          Args:
              index: index to return
  
          Returns:
              list of file paths
          """
          if self.bands == "all":
              folder_s1 = self.folders[index]["s1"]
              folder_s2 = self.folders[index]["s2"]
              paths_s1 = glob.glob(os.path.join(folder_s1, "*.tif"))
              paths_s2 = glob.glob(os.path.join(folder_s2, "*.tif"))
              paths_s1 = sorted(paths_s1)
              paths_s2 = sorted(paths_s2, key=sort_sentinel2_bands)
              paths = paths_s1 + paths_s2
          elif self.bands == "s1":
              folder = self.folders[index]["s1"]
              paths = glob.glob(os.path.join(folder, "*.tif"))
              paths = sorted(paths)
          else:
              folder = self.folders[index]["s2"]
              paths = glob.glob(os.path.join(folder, "*.tif"))
              paths = sorted(paths, key=sort_sentinel2_bands)
  
          return paths
  
      def _load_image(self, index: int) -> Tensor:
          """Load a single image.
  
          Args:
              index: index to return
  
          Returns:
              the raster image or target
          """
          paths = self._load_paths(index)
          images = []
          for path in paths:
              # Bands are of different spatial resolutions
              # Resample to (120, 120)
              with rasterio.open(path) as dataset:
                  array = dataset.read(
                      indexes=1,
                      out_shape=self.image_size,
                      out_dtype="int32",
                      resampling=Resampling.bilinear,
                  )
                  images.append(array)
          arrays = np.stack(images, axis=0)
          tensor: Tensor = torch.from_numpy(arrays)  # type: ignore[attr-defined]
          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 label
          """
          if self.bands == "s2":
              folder = self.folders[index]["s2"]
          else:
              folder = self.folders[index]["s1"]
  
          path = glob.glob(os.path.join(folder, "*.json"))[0]
          with open(path, "r") as f:
              labels = json.load(f)["labels"]
  
          # labels -> indices
          indices = [self.class2idx[label] for label in labels]
  
          # Map 43 to 19 class labels
          if self.num_classes == 19:
              indices = [
                  self.label_converter.get(idx) for idx in indices  # type: ignore[misc]
              ]
              indices = [idx for idx in indices if idx is not None]
  
          target: Tensor = torch.zeros(  # type: ignore[attr-defined]
              self.num_classes, dtype=torch.long  # type: ignore[attr-defined]
          )
          target[indices] = 1
          return target
  
      def _verify(self) -> None:
          """Verify the integrity of the dataset.
  
          Raises:
              RuntimeError: if ``download=False`` but dataset is missing or checksum fails
          """
          keys = ["s1", "s2"] if self.bands == "all" else [self.bands]
          urls = [self.metadata[k]["url"] for k in keys]
          md5s = [self.metadata[k]["md5"] for k in keys]
          filenames = [self.metadata[k]["filename"] for k in keys]
          directories = [self.metadata[k]["directory"] for k in keys]
          urls.extend([self.splits_metadata[k]["url"] for k in self.splits_metadata])
          md5s.extend([self.splits_metadata[k]["md5"] for k in self.splits_metadata])
          filenames_splits = [
              self.splits_metadata[k]["filename"] for k in self.splits_metadata
          ]
          filenames.extend(filenames_splits)
  
          # Check if the split file already exist
          exists = []
          for filename in filenames_splits:
              exists.append(os.path.exists(os.path.join(self.root, filename)))
  
          # Check if the files already exist
          for directory in directories:
              exists.append(os.path.exists(os.path.join(self.root, directory)))
  
          if all(exists):
              return
  
          # Check if zip file already exists (if so then extract)
          exists = []
          for filename in filenames:
              filepath = os.path.join(self.root, filename)
              if os.path.exists(filepath):
                  exists.append(True)
                  self._extract(filepath)
              else:
                  exists.append(False)
  
          if all(exists):
              return
  
          # Check if the user requested to download the dataset
          if not self.download:
              raise RuntimeError(
                  "Dataset not found in `root` directory and `download=False`, "
                  "either specify a different `root` directory or use `download=True` "
                  "to automaticaly download the dataset."
              )
  
          # Download and extract the dataset
          for url, filename, md5 in zip(urls, filenames, md5s):
              self._download(url, filename, md5)
              filepath = os.path.join(self.root, filename)
              self._extract(filepath)
  
      def _download(self, url: str, filename: str, md5: str) -> None:
          """Download the dataset.
  
          Args:
              url: url to download file
              filename: output filename to write downloaded file
              md5: md5 of downloaded file
          """
          if not os.path.exists(filename):
              download_url(
                  url, self.root, filename=filename, md5=md5 if self.checksum else None
              )
  
      def _extract(self, filepath: str) -> None:
          """Extract the dataset.
  
          Args:
              filepath: path to file to be extracted
          """
          if not filepath.endswith(".csv"):
              extract_archive(filepath)
  
      def _onehot_labels_to_names(
          self, label_mask: "np.typing.NDArray[np.bool_]"
      ) -> List[str]:
          """Gets a list of class names given a label mask.
  
          Args:
              label_mask: a boolean mask corresponding to a set of labels or predictions
  
          Returns
              a list of class names corresponding to the input mask
          """
          labels = []
          for i, mask in enumerate(label_mask):
              if mask:
                  labels.append(self.class_sets[self.num_classes][i])
          return labels
  
      def plot(
          self,
          sample: Dict[str, Tensor],
          show_titles: bool = True,
          suptitle: Optional[str] = None,
      ) -> plt.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 string to use as a suptitle
  
          Returns:
              a matplotlib Figure with the rendered sample
  
          Raises:
              ValueError: if ``self.bands`` is "s1"
  
          .. versionadded:: 0.2
          """
          if self.bands == "s2":
              image = np.rollaxis(sample["image"][[3, 2, 1]].numpy(), 0, 3)
              image = np.clip(image / 2000, 0, 1)
          elif self.bands == "all":
              image = np.rollaxis(sample["image"][[5, 4, 3]].numpy(), 0, 3)
              image = np.clip(image / 2000, 0, 1)
          elif self.bands == "s1":
              image = sample["image"][0].numpy()
  
          label_mask = sample["label"].numpy().astype(np.bool_)
          labels = self._onehot_labels_to_names(label_mask)
  
          showing_predictions = "prediction" in sample
          if showing_predictions:
              prediction_mask = sample["prediction"].numpy().astype(np.bool_)
              predictions = self._onehot_labels_to_names(prediction_mask)
  
          fig, ax = plt.subplots(figsize=(4, 4))
          ax.imshow(image)
          ax.axis("off")
          if show_titles:
              title = f"Labels: {', '.join(labels)}"
              if showing_predictions:
                  title += f"\nPredictions: {', '.join(predictions)}"
              ax.set_title(title)
  
          if suptitle is not None:
              plt.suptitle(suptitle)
          return fig
  