Source code for torchgeo.datasets.loveda

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

"""LoveDA 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 LoveDA(NonGeoDataset): """LoveDA dataset. The `LoveDA <>`__ datataset is a semantic segmentation dataset. Dataset features: * 2713 urban scene and 3274 rural scene HSR images, spatial resolution of 0.3m * image source is Google Earth platform * total of 166768 annotated objects from Nanjing, Changzhou and Wuhan cities * dataset comes with predefined train, validation, and test set * dataset differentiates between 'rural' and 'urban' images Dataset format: * images are three-channel pngs with dimension 1024x1024 * segmentation masks are single-channel pngs Dataset classes: 1. background 2. building 3. road 4. water 5. barren 6. forest 7. agriculture No-data regions assigned with 0 and should be ignored. If you use this dataset in your research, please cite the following paper: * .. versionadded:: 0.2 """ scenes = ["urban", "rural"] splits = ["train", "val", "test"] info_dict = { "train": { "url": "", "filename": "", "md5": "de2b196043ed9b4af1690b3f9a7d558f", }, "val": { "url": "", "filename": "", "md5": "84cae2577468ff0b5386758bb386d31d", }, "test": { "url": "", "filename": "", "md5": "a489be0090465e01fb067795d24e6b47", }, } classes = [ "background", "building", "road", "water", "barren", "forest", "agriculture", "no-data", ]
[docs] def __init__( self, root: str = "data", split: str = "train", scene: list[str] = ["urban", "rural"], transforms: Optional[Callable[[dict[str, Tensor]], dict[str, Tensor]]] = None, download: bool = False, checksum: bool = False, ) -> None: """Initialize a new LoveDA dataset instance. Args: root: root directory where dataset can be found split: one of "train", "val", or "test" scene: specify whether to load only 'urban', only 'rural' or both 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 AssertionError: if ``scene`` argument is invalid RuntimeError: if ``download=False`` and data is not found, or checksums don't match """ assert split in self.splits assert set(scene).intersection( set(self.scenes) ), "The possible scenes are 'rural' and/or 'urban'" assert len(scene) <= 2, "There are no other scenes than 'rural' or 'urban'" self.root = root self.split = split self.scene = scene self.transforms = transforms self.checksum = checksum self.url = self.info_dict[self.split]["url"] self.filename = self.info_dict[self.split]["filename"] self.md5 = self.info_dict[self.split]["md5"] = os.path.join(self.root, split.capitalize()) self.scene_paths = [ os.path.join(, s.capitalize()) for s in self.scene ] if download: self._download() if not self._check_integrity(): raise RuntimeError( "Dataset not found at root directory or corrupted. " + "You can use download=True to download it" ) self.files = self._load_files(self.scene_paths, self.split)
[docs] def __getitem__(self, index: int) -> dict[str, Tensor]: """Return an index within the dataset. Args: index: index to return Returns: image and mask at that index with image of dimension 3x1024x1024 and mask of dimension 1024x1024 """ files = self.files[index] image = self._load_image(files["image"]) if self.split != "test": mask = self._load_target(files["mask"]) sample = {"image": image, "mask": mask} else: sample = {"image": image} if self.transforms is not None: sample = self.transforms(sample) return sample
[docs] def __len__(self) -> int: """Return the number of datapoints in the dataset. Returns: length of dataset """ return len(self.files)
def _load_files(self, scene_paths: list[str], split: str) -> list[dict[str, str]]: """Return the paths of the files in the dataset. Args: scene_paths: contains one or two paths, depending on whether user has specified only 'rural', 'only 'urban' or both split: subset of dataset, one of [train, val, test] """ images = [] for s in scene_paths: images.extend(glob.glob(os.path.join(s, "images_png", "*.png"))) images = sorted(images) if self.split != "test": masks = [image.replace("images_png", "masks_png") for image in images] files = [ dict(image=image, mask=mask) for image, mask, in zip(images, masks) ] else: files = [dict(image=image) for image in images] return files def _load_image(self, path: str) -> Tensor: """Load a single image. Args: path: path to the image Returns: the loaded 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).float() # Convert from HxWxC to CxHxW tensor = tensor.permute((2, 0, 1)) return tensor def _load_target(self, path: str) -> Tensor: """Load a single mask corresponding to image. Args: path: path to the mask Returns: the mask of the image """ 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 = return tensor def _check_integrity(self) -> bool: """Check the integrity of the dataset structure. Returns: True if the dataset directories and split files are found, else False """ for s in self.scene_paths: if not os.path.exists(s): 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], suptitle: Optional[str] = None) -> Figure: """Plot a sample from the dataset. Args: sample: a sample return by :meth:`__getitem__` suptitle: optional suptitle to use for figure Returns: a matplotlib Figure with the rendered sample """ if self.split != "test": image, mask = sample["image"], sample["mask"] ncols = 2 else: image = sample["image"] ncols = 1 fig, axs = plt.subplots(nrows=1, ncols=ncols, figsize=(10, ncols * 10)) if self.split != "test": axs[0].imshow(image.permute(1, 2, 0)) axs[0].axis("off") axs[1].imshow(mask) axs[1].axis("off") else: axs.imshow(image.permute(1, 2, 0)) axs.axis("off") if suptitle is not None: plt.suptitle(suptitle) return fig

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