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Source code for torchgeo.datasets.reforestree

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

"""ReforesTree dataset."""

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

import matplotlib.patches as patches
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_and_extract_archive, extract_archive


class ReforesTree(NonGeoDataset):
    """ReforesTree dataset.

    The `ReforesTree <https://github.com/gyrrei/ReforesTree>`__
    dataset contains drone imagery that can be used for tree crown detection,
    tree species classification and Aboveground Biomass (AGB) estimation.

    Dataset features:

    * 100 high resolution RGB drone images at 2 cm/pixel of size 4,000 x 4,000 px
    * more than 4,600 tree crown box annotations
    * tree crown matched with field measurements of diameter at breast height (DBH),
      and computed AGB and carbon values

    Dataset format:

    * images are three-channel pngs
    * annotations are csv file

    Dataset Classes:

    0. other
    1. banana
    2. cacao
    3. citrus
    4. fruit
    5. timber

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

    * https://arxiv.org/abs/2201.11192

    .. versionadded:: 0.3
    """

    classes = ["other", "banana", "cacao", "citrus", "fruit", "timber"]
    url = "https://zenodo.org/record/6813783/files/reforesTree.zip?download=1"

    md5 = "f6a4a1d8207aeaa5fbab7b21b683a302"
    zipfilename = "reforesTree.zip"

[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 ReforesTree 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.checksum = checksum self.download = download self._verify() try: import pandas as pd # noqa: F401 except ImportError: raise ImportError( "pandas is not installed and is required to use this dataset" ) self.files = self._load_files(self.root) self.annot_df = pd.read_csv(os.path.join(root, "mapping", "final_dataset.csv")) self.class2idx: Dict[str, int] = {c: i for i, c in enumerate(self.classes)}
[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 """ filepath = self.files[index] image = self._load_image(filepath) boxes, labels, agb = self._load_target(filepath) sample = {"image": image, "boxes": boxes, "label": labels, "agb": agb} 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) -> List[str]: """Return the paths of the files in the dataset. Args: root: root dir of dataset Returns: list of dicts containing paths for each pair of image, annotation """ image_paths = sorted(glob.glob(os.path.join(root, "tiles", "**", "*.png"))) return image_paths def _load_image(self, path: str) -> Tensor: """Load a single image. Args: path: path to the image Returns: the image """ with Image.open(path) as img: array: "np.typing.NDArray[np.uint8]" = np.array(img) tensor = torch.from_numpy(array) # Convert from HxWxC to CxHxW tensor = tensor.permute((2, 0, 1)) return tensor def _load_target(self, filepath: str) -> Tuple[Tensor, ...]: """Load boxes and labels for a single image. Args: filepath: image tile filepath Returns: dictionary containing boxes, label, and agb value """ tile_df = self.annot_df[self.annot_df["img_path"] == os.path.basename(filepath)] boxes = torch.Tensor(tile_df[["xmin", "ymin", "xmax", "ymax"]].values.tolist()) labels = torch.Tensor( [self.class2idx[label] for label in tile_df["group"].tolist()] ) agb = torch.Tensor(tile_df["AGB"].tolist()) return boxes, labels, agb def _verify(self) -> None: """Checks the integrity of the dataset structure. Raises: RuntimeError: if dataset is not found in root or is corrupted """ filepaths = [os.path.join(self.root, dir) for dir in ["tiles", "mapping"]] if all([os.path.exists(filepath) for filepath in filepaths]): return filepath = os.path.join(self.root, self.zipfilename) 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 # Check if the user requested to download the dataset if not self.download: raise RuntimeError( f"Dataset not found in `root={self.root}` and `download=False`, " "either specify a different `root` directory or use `download=True` " "to automatically download the dataset." ) # else download the dataset self._download() def _download(self) -> None: """Download the dataset and extract it. Raises: AssertionError: if the checksum does not match """ download_and_extract_archive( self.url, self.root, filename=self.zipfilename, md5=self.md5 if self.checksum else None, )
[docs] def plot( self, sample: Dict[str, Tensor], show_titles: bool = True, suptitle: Optional[str] = None, ) -> plt.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 Returns: a matplotlib Figure with the rendered sample """ image = sample["image"].permute((1, 2, 0)).numpy() ncols = 1 showing_predictions = "prediction_boxes" in sample if showing_predictions: ncols += 1 fig, axs = plt.subplots(ncols=ncols, figsize=(ncols * 10, 10)) if not showing_predictions: axs = [axs] axs[0].imshow(image) axs[0].axis("off") bboxes = [ patches.Rectangle( (bbox[0], bbox[1]), bbox[2] - bbox[0], bbox[3] - bbox[1], linewidth=1, edgecolor="r", facecolor="none", ) for bbox in sample["boxes"].numpy() ] for bbox in bboxes: axs[0].add_patch(bbox) if show_titles: axs[0].set_title("Ground Truth") if showing_predictions: axs[1].imshow(image) axs[1].axis("off") pred_bboxes = [ patches.Rectangle( (bbox[0], bbox[1]), bbox[2] - bbox[0], bbox[3] - bbox[1], linewidth=1, edgecolor="r", facecolor="none", ) for bbox in sample["prediction_boxes"].numpy() ] for bbox in pred_bboxes: axs[1].add_patch(bbox) if show_titles: axs[1].set_title("Predictions") if suptitle is not None: plt.suptitle(suptitle) return fig

© Copyright 2021, Microsoft Corporation. Revision 34680c94.

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