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Source code for torchgeo.datasets.cyclone

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

"""Tropical Cyclone Wind Estimation Competition dataset."""

import json
import os
from functools import lru_cache
from typing import Any, Callable, Dict, 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 check_integrity, download_radiant_mlhub_dataset, extract_archive


class TropicalCyclone(NonGeoDataset):
    """Tropical Cyclone Wind Estimation Competition dataset.

    A collection of tropical storms in the Atlantic and East Pacific Oceans from 2000 to
    2019 with corresponding maximum sustained surface wind speed. This dataset is split
    into training and test categories for the purpose of a competition.

    See https://www.drivendata.org/competitions/72/predict-wind-speeds/ for more
    information about the competition.

    If you use this dataset in your research, please cite the following paper:

    * https://doi.org/10.1109/JSTARS.2020.3011907

    .. note::

       This dataset requires the following additional library to be installed:

       * `radiant-mlhub <https://pypi.org/project/radiant-mlhub/>`_ to download the
         imagery and labels from the Radiant Earth MLHub

    .. versionchanged:: 0.4.0
        Class name changed from TropicalCycloneWindEstimation to TropicalCyclone
        to be consistent with TropicalCycloneDataModule.
    """

    collection_id = "nasa_tropical_storm_competition"
    md5s = {
        "train": {
            "source": "97e913667a398704ea8d28196d91dad6",
            "labels": "97d02608b74c82ffe7496a9404a30413",
        },
        "test": {
            "source": "8d88099e4b310feb7781d776a6e1dcef",
            "labels": "d910c430f90153c1f78a99cbc08e7bd0",
        },
    }
    size = 366

[docs] def __init__( self, root: str = "data", split: str = "train", transforms: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None, download: bool = False, api_key: Optional[str] = None, checksum: bool = False, ) -> None: """Initialize a new Tropical Cyclone Wind Estimation Competition Dataset. 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 api_key: a RadiantEarth MLHub API key to use for downloading the dataset checksum: if True, check the MD5 of the downloaded files (may be slow) Raises: AssertionError: if ``split`` argument is invalid RuntimeError: if ``download=False`` but dataset is missing or checksum fails """ assert split in self.md5s self.root = root self.split = split self.transforms = transforms self.checksum = checksum if download: self._download(api_key) if not self._check_integrity(): raise RuntimeError( "Dataset not found or corrupted. " + "You can use download=True to download it" ) output_dir = "_".join([self.collection_id, split, "source"]) filename = os.path.join(root, output_dir, "collection.json") with open(filename) as f: self.collection = json.load(f)["links"]
[docs] def __getitem__(self, index: int) -> Dict[str, Any]: """Return an index within the dataset. Args: index: index to return Returns: data, labels, field ids, and metadata at that index """ source_id = os.path.split(self.collection[index]["href"])[0] directory = os.path.join( self.root, "_".join([self.collection_id, self.split, "{0}"]), source_id.replace("source", "{0}"), ) sample: Dict[str, Any] = {"image": self._load_image(directory)} sample.update(self._load_features(directory)) 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.collection)
@lru_cache() def _load_image(self, directory: str) -> Tensor: """Load a single image. Args: directory: directory containing image Returns: the image """ filename = os.path.join(directory.format("source"), "image.jpg") with Image.open(filename) as img: if img.height != self.size or img.width != self.size: # Moved in PIL 9.1.0 try: resample = Image.Resampling.BILINEAR except AttributeError: resample = Image.BILINEAR img = img.resize(size=(self.size, self.size), resample=resample) array: "np.typing.NDArray[np.int_]" = np.array(img) if len(array.shape) == 3: array = array[:, :, 0] tensor = torch.from_numpy(array) return tensor def _load_features(self, directory: str) -> Dict[str, Any]: """Load features for a single image. Args: directory: directory containing image Returns: the features """ filename = os.path.join(directory.format("source"), "features.json") with open(filename) as f: features: Dict[str, Any] = json.load(f) filename = os.path.join(directory.format("labels"), "labels.json") with open(filename) as f: features.update(json.load(f)) features["relative_time"] = int(features["relative_time"]) features["ocean"] = int(features["ocean"]) features["label"] = int(features["wind_speed"]) return features def _check_integrity(self) -> bool: """Check integrity of dataset. Returns: True if dataset files are found and/or MD5s match, else False """ for split, resources in self.md5s.items(): for resource_type, md5 in resources.items(): filename = "_".join([self.collection_id, split, resource_type]) filename = os.path.join(self.root, filename + ".tar.gz") if not check_integrity(filename, md5 if self.checksum else None): return False return True def _download(self, api_key: Optional[str] = None) -> None: """Download the dataset and extract it. Args: api_key: a RadiantEarth MLHub API key to use for downloading the dataset Raises: RuntimeError: if download doesn't work correctly or checksums don't match """ if self._check_integrity(): print("Files already downloaded and verified") return download_radiant_mlhub_dataset(self.collection_id, self.root, api_key) for split, resources in self.md5s.items(): for resource_type in resources: filename = "_".join([self.collection_id, split, resource_type]) filename = os.path.join(self.root, filename) + ".tar.gz" extract_archive(filename, self.root)
[docs] def plot( self, sample: Dict[str, Any], show_titles: bool = True, suptitle: Optional[str] = None, ) -> plt.Figure: """Plot a sample from the dataset. Args: sample: a sample return 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 """ image, label = sample["image"], sample["label"] showing_predictions = "prediction" in sample if showing_predictions: prediction = sample["prediction"].item() fig, ax = plt.subplots(1, 1, figsize=(10, 10)) ax.imshow(image, cmap="gray") ax.axis("off") if show_titles: title = f"Label: {label}" if showing_predictions: title += f"\nPrediction: {prediction}" ax.set_title(title) if suptitle is not None: plt.suptitle(suptitle) return fig

© Copyright 2021, Microsoft Corporation. Revision 34680c94.

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