Source code for torchgeo.datasets.levircd

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

"""LEVIR-CD+ dataset."""

import glob
import os
from typing import Callable, Optional

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

from .geo import NonGeoDataset
from .utils import download_and_extract_archive

[docs]class LEVIRCDPlus(NonGeoDataset): """LEVIR-CD+ dataset. The `LEVIR-CD+ <>`__ dataset is a dataset for building change detection. Dataset features: * image pairs of 20 different urban regions across Texas between 2002-2020 * binary change masks representing building change * three spectral bands - RGB * 985 image pairs with 50 cm per pixel resolution (~1024x1024 px) Dataset format: * images are three-channel pngs * masks are single-channel pngs where no change = 0, change = 255 Dataset classes: 1. no change 2. change If you use this dataset in your research, please cite the following paper: * """ url = "" md5 = "1adf156f628aa32fb2e8fe6cada16c04" filename = "" directory = "LEVIR-CD+" splits = ["train", "test"]
[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 LEVIR-CD+ dataset instance. Args: root: root directory where dataset can be found split: one of "train" 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.splits 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] image1 = self._load_image(files["image1"]) image2 = self._load_image(files["image2"]) mask = self._load_target(files["mask"]) image = torch.stack(tensors=[image1, image2], 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, directory: str, split: str ) -> list[dict[str, str]]: """Return the paths of the files in the dataset. Args: root: root dir of dataset directory: sub directory LEVIR-CD+ split: subset of dataset, one of [train, test] Returns: list of dicts containing paths for each pair of image1, image2, mask """ files = [] images = glob.glob(os.path.join(root, directory, split, "A", "*.png")) images = sorted(os.path.basename(image) for image in images) for image in images: image1 = os.path.join(root, directory, split, "A", image) image2 = os.path.join(root, directory, split, "B", image) mask = os.path.join(root, directory, split, "label", image) files.append(dict(image1=image1, image2=image2, mask=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 """ for filename in self.splits: filepath = os.path.join(self.root,, filename) if not os.path.exists(filepath): 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.url, self.root, filename=self.filename, md5=self.md5 if self.checksum else None, )
[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 """ image1, image2, mask = (sample["image"][0], sample["image"][1], sample["mask"]) ncols = 3 if "prediction" in sample: prediction = sample["prediction"] ncols += 1 fig, axs = plt.subplots(nrows=1, ncols=ncols, figsize=(10, ncols * 5)) axs[0].imshow(image1.permute(1, 2, 0)) axs[0].axis("off") axs[1].imshow(image2.permute(1, 2, 0)) axs[1].axis("off") axs[2].imshow(mask) axs[2].axis("off") if "prediction" in sample: axs[3].imshow(prediction) axs[3].axis("off") if show_titles: axs[3].set_title("Prediction") if show_titles: axs[0].set_title("Image 1") axs[1].set_title("Image 2") axs[2].set_title("Mask") if suptitle is not None: plt.suptitle(suptitle) return fig

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