text: # 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
  
  import matplotlib.pyplot as plt
  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, percentile_normalization
  
  
  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"}
  
      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]
              )
  
      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
  
      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(np.stack(all_data, axis=0))
          return 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))
  
      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 the RGB bands are included in ``self.bands`` or the sample
                  contains a "prediction" key
  
          .. versionadded:: 0.2
          """
          if "prediction" in sample:
              raise ValueError("This dataset doesn't support plotting predictions")
  
          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")
  
          images = []
          for i in range(5):
              image = np.rollaxis(sample["image"][i, rgb_indices].numpy(), 0, 3)
              image = percentile_normalization(image, 0, 100)
              images.append(image)
  
          fig, axs = plt.subplots(ncols=5, figsize=(20, 4))
          for i in range(5):
              axs[i].imshow(images[i])
              axs[i].axis("off")
              if show_titles:
                  axs[i].set_title(f"t={i+1}")
  
          if suptitle is not None:
              plt.suptitle(suptitle)
          return fig
  