Source code for torchgeo.datasets.deepglobelandcover

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

"""DeepGlobe Land Cover Classification Challenge dataset."""

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 (

[docs]class DeepGlobeLandCover(NonGeoDataset): """DeepGlobe Land Cover Classification Challenge dataset. The `DeepGlobe Land Cover Classification Challenge <>`__ dataset offers high-resolution sub-meter satellite imagery focusing for the task of semantic segmentation to detect areas of urban, agriculture, rangeland, forest, water, barren, and unknown. It contains 1,146 satellite images of size 2448 x 2448 pixels in total, split into training/validation/test sets, the original dataset can be downloaded from `Kaggle < deepglobe-land-cover-classification-dataset>`__. However, we only use the training dataset with 803 images since the original test and valid dataset are not accompanied by labels. The dataset that we use with a custom train/test split can be downloaded from `Kaggle < datasets/geoap96/deepglobe2018-landcover-segmentation-traindataset>`__ (created as a part of Computer Vision by Deep Learning (CS4245) course offered at TU Delft). Dataset format: * images are RGB data * masks are RGB image with with unique RGB values representing the class Dataset classes: 0. Urban land 1. Agriculture land 2. Rangeland 3. Forest land 4. Water 5. Barren land 6. Unknown File names for satellite images and the corresponding mask image are id_sat.jpg and id_mask.png, where id is an integer assigned to every image. If you use this dataset in your research, please cite the following paper: * .. versionadded:: 0.3 """ filename = "" data_root = "data" md5 = "f32684b0b2bf6f8d604cd359a399c061" splits = ["train", "test"] classes = [ "Urban land", "Agriculture land", "Rangeland", "Forest land", "Water", "Barren land", "Unknown", ] colormap = [ (0, 255, 255), (255, 255, 0), (255, 0, 255), (0, 255, 0), (0, 0, 255), (255, 255, 255), (0, 0, 0), ]
[docs] def __init__( self, root: str = "data", split: str = "train", transforms: Optional[Callable[[dict[str, Tensor]], dict[str, Tensor]]] = None, checksum: bool = False, ) -> None: """Initialize a new DeepGlobeLandCover 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 checksum: if True, check the MD5 of the downloaded files (may be slow) Raises: DatasetNotFoundError: If dataset is not found. """ assert split in self.splits self.root = root self.split = split self.transforms = transforms self.checksum = checksum self._verify() if split == "train": split_folder = "training_data" else: split_folder = "test_data" self.image_fns = [] self.mask_fns = [] for image in sorted( os.listdir(os.path.join(root, self.data_root, split_folder, "images")) ): if image.endswith(".jpg"): id = image[:-8] image_path = os.path.join( root, self.data_root, split_folder, "images", image ) mask_path = os.path.join( root, self.data_root, split_folder, "masks", str(id) + "_mask.png" ) self.image_fns.append(image_path) self.mask_fns.append(mask_path)
[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 """ image = self._load_image(index) mask = self._load_target(index) 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.image_fns)
def _load_image(self, index: int) -> Tensor: """Load a single image. Args: index: index to return Returns: the image """ path = self.image_fns[index] with as img: array: "np.typing.NDArray[np.int_]" = np.array(img) tensor = torch.from_numpy(array) # Convert from HxWxC to CxHxW tensor = tensor.permute((2, 0, 1)).to(torch.float32) 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 mask """ path = self.mask_fns[index] with as img: array: "np.typing.NDArray[np.uint8]" = np.array(img) array = rgb_to_mask(array, self.colormap) tensor = torch.from_numpy(array) # Convert from HxWxC to CxHxW tensor = return tensor def _verify(self) -> None: """Verify the integrity of the dataset.""" # Check if the files already exist if os.path.exists(os.path.join(self.root, self.data_root)): return # Check if .zip file already exists (if so extract) filepath = os.path.join(self.root, self.filename) if os.path.isfile(filepath): if self.checksum and not check_integrity(filepath, self.md5): raise RuntimeError("Dataset found, but corrupted.") extract_archive(filepath) return raise DatasetNotFoundError(self)
[docs] def plot( self, sample: dict[str, Tensor], show_titles: bool = True, suptitle: Optional[str] = None, alpha: float = 0.5, ) -> 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 alpha: opacity with which to render predictions on top of the imagery Returns: a matplotlib Figure with the rendered sample """ ncols = 1 image1 = draw_semantic_segmentation_masks( sample["image"], sample["mask"], alpha=alpha, colors=self.colormap ) if "prediction" in sample: ncols += 1 image2 = draw_semantic_segmentation_masks( sample["image"], sample["prediction"], alpha=alpha, colors=self.colormap ) fig, axs = plt.subplots(ncols=ncols, figsize=(ncols * 10, 10)) if ncols > 1: (ax0, ax1) = axs else: ax0 = axs ax0.imshow(image1) ax0.axis("off") if ncols > 1: ax1.imshow(image2) ax1.axis("off") if show_titles: ax0.set_title("Ground Truth") if ncols > 1: ax1.set_title("Predictions") if suptitle is not None: plt.suptitle(suptitle) return fig

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