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Source code for torchgeo.datasets.zuericrop

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

"""ZueriCrop dataset."""

import os
from typing import Callable, Dict, Optional, Tuple

import torch
from torch import Tensor

from .geo import VisionDataset
from .utils import download_url


class ZueriCrop(VisionDataset):
    """ZueriCrop dataset.

    The `ZueriCrop <https://github.com/0zgur0/ms-convSTAR>`_
    dataset is a dataset for time-series instance segmentation of crops.

    Dataset features:

    * Sentinel-2 multispectral imagery
    * instance masks of 48 crop categories
    * nine multispectral bands
    * 116k images with 10 m per pixel resolution (24x24 px)
    * ~28k time-series containing 142 images each

    Dataset format:

    * single hdf5 dataset containing images, semantic masks, and instance masks
    * data is parsed into images and instance masks, boxes, and labels
    * one mask per time-series

    Dataset classes:

    * 48 fine-grained hierarchical crop
      `categories <https://github.com/0zgur0/ms-convSTAR/blob/master/labels.csv>`_

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

    * https://doi.org/10.1016/j.rse.2021.112603

    .. note::

       This dataset requires the following additional library to be installed:

       * `h5py <https://pypi.org/project/h5py/>`_ to load the dataset
    """

    urls = [
        "https://polybox.ethz.ch/index.php/s/uXfdr2AcXE3QNB6/download",
        "https://raw.githubusercontent.com/0zgur0/ms-convSTAR/master/labels.csv",
    ]
    md5s = ["1635231df67f3d25f4f1e62c98e221a4", "5118398c7a5bbc246f5f6bb35d8d529b"]
    filenames = ["ZueriCrop.hdf5", "labels.csv"]

[docs] def __init__( self, root: str = "data", transforms: Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]] = None, download: bool = False, checksum: bool = False, ) -> None: """Initialize a new ZueriCrop dataset instance. Args: root: root directory where dataset can be found 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: RuntimeError: if ``download=False`` and data is not found, or checksums don't match """ self.root = root self.transforms = transforms self.download = download self.checksum = checksum self.filepath = os.path.join(root, "ZueriCrop.hdf5") self._verify() try: import h5py # noqa: F401 except ImportError: raise ImportError( "h5py is not installed and is required to use this dataset" )
[docs] def __getitem__(self, index: int) -> Dict[str, Tensor]: """Return an index within the dataset. Args: index: index to return Returns: sample containing image, mask, bounding boxes, and target label """ image = self._load_image(index) mask, boxes, label = self._load_target(index) sample = {"image": image, "mask": mask, "boxes": boxes, "label": label} 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 """ import h5py with h5py.File(self.filepath, "r") as f: length: int = f["data"].shape[0] return length
def _load_image(self, index: int) -> Tensor: """Load a single image. Args: index: index to return Returns: the image """ import h5py with h5py.File(self.filepath, "r") as f: array = f["data"][index, ...] tensor: Tensor = torch.from_numpy(array) # type: ignore[attr-defined] # Convert from TxHxWxC to TxCxHxW tensor = tensor.permute((0, 3, 1, 2)) return tensor def _load_target(self, index: int) -> Tuple[Tensor, Tensor, Tensor]: """Load the target mask for a single image. Args: index: index to return Returns: the target mask and label for each mask """ import h5py with h5py.File(self.filepath, "r") as f: mask_array = f["gt"][index, ...] instance_array = f["gt_instance"][index, ...] mask_tensor = torch.from_numpy(mask_array) # type: ignore[attr-defined] instance_tensor = torch.from_numpy(instance_array) # type: ignore[attr-defined] # Convert from HxWxC to CxHxW mask_tensor = mask_tensor.permute((2, 0, 1)) instance_tensor = instance_tensor.permute((2, 0, 1)) # Convert instance mask of N instances to N binary instance masks instance_ids = torch.unique(instance_tensor) # type: ignore[attr-defined] # Exclude a mask for unknown/background instance_ids = instance_ids[instance_ids != 0] instance_ids = instance_ids[:, None, None] masks: Tensor = instance_tensor == instance_ids # Parse labels for each instance labels_list = [] for mask in masks: label = mask_tensor[mask[None, :, :]] label = torch.unique(label)[0] # type: ignore[attr-defined] labels_list.append(label) # Get bounding boxes for each instance boxes_list = [] for mask in masks: pos = torch.where(mask) # type: ignore[attr-defined] xmin = torch.min(pos[1]) # type: ignore[attr-defined] xmax = torch.max(pos[1]) # type: ignore[attr-defined] ymin = torch.min(pos[0]) # type: ignore[attr-defined] ymax = torch.max(pos[0]) # type: ignore[attr-defined] boxes_list.append([xmin, ymin, xmax, ymax]) masks = masks.to(torch.uint8) # type: ignore[attr-defined] boxes = torch.tensor(boxes_list).to(torch.float) # type: ignore[attr-defined] labels = torch.tensor(labels_list).to(torch.long) # type: ignore[attr-defined] return masks, boxes, labels def _verify(self) -> None: """Verify the integrity of the dataset. Raises: RuntimeError: if ``download=False`` but dataset is missing or checksum fails """ # Check if the files already exist exists = [] for filename in self.filenames: filepath = os.path.join(self.root, filename) exists.append(os.path.exists(filepath)) if all(exists): return # Check if the user requested to download the dataset if not self.download: raise RuntimeError( "Dataset not found in `root` directory and `download=False`, " "either specify a different `root` directory or use `download=True` " "to automaticaly download the dataset." ) # Download the dataset self._download() def _download(self) -> None: """Download the dataset.""" for url, filename, md5 in zip(self.urls, self.filenames, self.md5s): filepath = os.path.join(self.root, filename) if not os.path.exists(filepath): download_url( url, self.root, filename=filename, md5=md5 if self.checksum else None, )

© Copyright 2021, Microsoft Corporation. Revision c2b56148.

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