Source code for torchgeo.datasets.etci2021

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

"""ETCI 2021 dataset."""

import glob
import os
from typing import Callable, Dict, List, Optional

import matplotlib.pyplot as plt
import numpy as np
import torch
from PIL import Image
from torch import Tensor

from .geo import NonGeoDataset
from .utils import download_and_extract_archive

class ETCI2021(NonGeoDataset):
    """ETCI 2021 Flood Detection dataset.

    The `ETCI2021 <>`_
    dataset is a dataset for flood detection

    Dataset features:

    * 33,405 VV & VH Sentinel-1 Synthetic Aperture Radar (SAR) images
    * 2 binary masks per image representing water body & flood, respectively
    * 2 polarization band images (VV, VH) of 3 RGB channels per band
    * 3 RGB channels per band generated by the Hybrid Pluggable Processing
      Pipeline (hyp3)
    * Images with 5x20m per pixel resolution (256x256) px) taken in
      Interferometric Wide Swath acquisition mode
    * Flood events from 5 different regions

    Dataset format:

    * VV band three-channel png
    * VH band three-channel png
    * water body mask single-channel png where no water body = 0, water body = 255
    * flood mask single-channel png where no flood = 0, flood = 255

    Dataset classes:

    1. no flood/water
    2. flood/water

    If you use this dataset in your research, please add the following to your
    acknowledgements section::

        The authors would like to thank the NASA Earth Science Data Systems Program,
        NASA Digital Transformation AI/ML thrust, and IEEE GRSS for organizing
        the ETCI competition.

    bands = ["VV", "VH"]
    masks = ["flood", "water_body"]
    metadata = {
        "train": {
            "filename": "",
            "md5": "1e95792fe0f6e3c9000abdeab2a8ab0f",
            "directory": "train",
            "url": "",
        "val": {
            "filename": "",
            "md5": "fd18cecb318efc69f8319f90c3771bdf",
            "directory": "test",
            "url": "",
        "test": {
            "filename": "",
            "md5": "da9fa69e1498bd49d5c766338c6dac3d",
            "directory": "test_internal",
            "url": "",

[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 ETCI 2021 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: AssertionError: if ``split`` argument is invalid RuntimeError: if ``download=False`` and data is not found, or checksums don't match """ assert split in self.metadata.keys() self.root = root self.split = split self.transforms = transforms self.checksum = checksum if download: self._download() if not self._check_integrity(): raise RuntimeError( "Dataset not found or corrupted. " + "You can use download=True to download it" ) self.files = self._load_files(self.root, self.split)
[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 """ files = self.files[index] vv = self._load_image(files["vv"]) vh = self._load_image(files["vh"]) water_mask = self._load_target(files["water_mask"]) if self.split != "test": flood_mask = self._load_target(files["flood_mask"]) mask = torch.stack(tensors=[water_mask, flood_mask], dim=0) else: mask = water_mask.unsqueeze(0) image =[vv, vh], dim=0) sample = {"image": image, "mask": mask} 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.files)
def _load_files(self, root: str, split: str) -> List[Dict[str, str]]: """Return the paths of the files in the dataset. Args: root: root dir of dataset split: subset of dataset, one of [train, val, test] Returns: list of dicts containing paths for each pair of vv, vh, water body mask, flood mask (train/val only) """ files = [] directory = self.metadata[split]["directory"] folders = sorted(glob.glob(os.path.join(root, directory, "*"))) folders = [os.path.join(folder, "tiles") for folder in folders] for folder in folders: vvs = sorted(glob.glob(os.path.join(folder, "vv", "*.png"))) vhs = sorted(glob.glob(os.path.join(folder, "vh", "*.png"))) water_masks = sorted( glob.glob(os.path.join(folder, "water_body_label", "*.png")) ) if split != "test": flood_masks = sorted( glob.glob(os.path.join(folder, "flood_label", "*.png")) ) for vv, vh, flood_mask, water_mask in zip( vvs, vhs, flood_masks, water_masks ): files.append( dict(vv=vv, vh=vh, flood_mask=flood_mask, water_mask=water_mask) ) else: for vv, vh, water_mask in zip(vvs, vhs, water_masks): files.append(dict(vv=vv, vh=vh, water_mask=water_mask)) return files def _load_image(self, path: str) -> Tensor: """Load a single image. Args: path: path to the image Returns: the image """ filename = os.path.join(path) with as img: array: "np.typing.NDArray[np.int_]" = np.array(img.convert("RGB")) tensor = torch.from_numpy(array) # Convert from HxWxC to CxHxW tensor = tensor.permute((2, 0, 1)) return tensor def _load_target(self, path: str) -> Tensor: """Load the target mask for a single image. Args: path: path to the image Returns: the target mask """ filename = os.path.join(path) with as img: array: "np.typing.NDArray[np.int_]" = np.array(img.convert("L")) tensor = torch.from_numpy(array) tensor = torch.clamp(tensor, min=0, max=1) tensor = return tensor def _check_integrity(self) -> bool: """Checks the integrity of the dataset structure. Returns: True if the dataset directories and split files are found, else False """ directory = self.metadata[self.split]["directory"] dirpath = os.path.join(self.root, directory) if not os.path.exists(dirpath): return False return True def _download(self) -> None: """Download the dataset and extract it. Raises: AssertionError: if the checksum of does not match """ if self._check_integrity(): print("Files already downloaded and verified") return download_and_extract_archive( self.metadata[self.split]["url"], self.root, filename=self.metadata[self.split]["filename"], md5=self.metadata[self.split]["md5"] if self.checksum else None, )
[docs] 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 """ vv = np.rollaxis(sample["image"][:3].numpy(), 0, 3) vh = np.rollaxis(sample["image"][3:].numpy(), 0, 3) mask = sample["mask"].squeeze(0) showing_flood_mask = mask.shape[0] == 2 showing_predictions = "prediction" in sample num_panels = 3 if showing_flood_mask: water_mask = mask[0].numpy() flood_mask = mask[1].numpy() num_panels += 1 else: water_mask = mask.numpy() if showing_predictions: predictions = sample["prediction"].numpy() num_panels += 1 fig, axs = plt.subplots(1, num_panels, figsize=(num_panels * 4, 3)) axs[0].imshow(vv) axs[0].axis("off") axs[1].imshow(vh) axs[1].axis("off") axs[2].imshow(water_mask) axs[2].axis("off") if show_titles: axs[0].set_title("VV") axs[1].set_title("VH") axs[2].set_title("Water mask") idx = 0 if showing_flood_mask: axs[3 + idx].imshow(flood_mask) axs[3 + idx].axis("off") if show_titles: axs[3 + idx].set_title("Flood mask") idx += 1 if showing_predictions: axs[3 + idx].imshow(predictions) axs[3 + idx].axis("off") if show_titles: axs[3 + idx].set_title("Predictions") idx += 1 if suptitle is not None: plt.suptitle(suptitle) return fig

© Copyright 2021, Microsoft Corporation. Revision 44fa4132.

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