125 lines
5.1 KiB
Python
125 lines
5.1 KiB
Python
![]() |
import os
|
||
|
import shutil
|
||
|
from pathlib import Path
|
||
|
from typing import Any, Callable, Optional, Tuple, Union
|
||
|
|
||
|
import numpy as np
|
||
|
from PIL import Image
|
||
|
|
||
|
from .utils import download_and_extract_archive, download_url, verify_str_arg
|
||
|
from .vision import VisionDataset
|
||
|
|
||
|
|
||
|
class SBDataset(VisionDataset):
|
||
|
"""`Semantic Boundaries Dataset <http://home.bharathh.info/pubs/codes/SBD/download.html>`_
|
||
|
|
||
|
The SBD currently contains annotations from 11355 images taken from the PASCAL VOC 2011 dataset.
|
||
|
|
||
|
.. note ::
|
||
|
|
||
|
Please note that the train and val splits included with this dataset are different from
|
||
|
the splits in the PASCAL VOC dataset. In particular some "train" images might be part of
|
||
|
VOC2012 val.
|
||
|
If you are interested in testing on VOC 2012 val, then use `image_set='train_noval'`,
|
||
|
which excludes all val images.
|
||
|
|
||
|
.. warning::
|
||
|
|
||
|
This class needs `scipy <https://docs.scipy.org/doc/>`_ to load target files from `.mat` format.
|
||
|
|
||
|
Args:
|
||
|
root (str or ``pathlib.Path``): Root directory of the Semantic Boundaries Dataset
|
||
|
image_set (string, optional): Select the image_set to use, ``train``, ``val`` or ``train_noval``.
|
||
|
Image set ``train_noval`` excludes VOC 2012 val images.
|
||
|
mode (string, optional): Select target type. Possible values 'boundaries' or 'segmentation'.
|
||
|
In case of 'boundaries', the target is an array of shape `[num_classes, H, W]`,
|
||
|
where `num_classes=20`.
|
||
|
download (bool, optional): If true, downloads the dataset from the internet and
|
||
|
puts it in root directory. If dataset is already downloaded, it is not
|
||
|
downloaded again.
|
||
|
transforms (callable, optional): A function/transform that takes input sample and its target as entry
|
||
|
and returns a transformed version. Input sample is PIL image and target is a numpy array
|
||
|
if `mode='boundaries'` or PIL image if `mode='segmentation'`.
|
||
|
"""
|
||
|
|
||
|
url = "https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz"
|
||
|
md5 = "82b4d87ceb2ed10f6038a1cba92111cb"
|
||
|
filename = "benchmark.tgz"
|
||
|
|
||
|
voc_train_url = "http://home.bharathh.info/pubs/codes/SBD/train_noval.txt"
|
||
|
voc_split_filename = "train_noval.txt"
|
||
|
voc_split_md5 = "79bff800c5f0b1ec6b21080a3c066722"
|
||
|
|
||
|
def __init__(
|
||
|
self,
|
||
|
root: Union[str, Path],
|
||
|
image_set: str = "train",
|
||
|
mode: str = "boundaries",
|
||
|
download: bool = False,
|
||
|
transforms: Optional[Callable] = None,
|
||
|
) -> None:
|
||
|
|
||
|
try:
|
||
|
from scipy.io import loadmat
|
||
|
|
||
|
self._loadmat = loadmat
|
||
|
except ImportError:
|
||
|
raise RuntimeError("Scipy is not found. This dataset needs to have scipy installed: pip install scipy")
|
||
|
|
||
|
super().__init__(root, transforms)
|
||
|
self.image_set = verify_str_arg(image_set, "image_set", ("train", "val", "train_noval"))
|
||
|
self.mode = verify_str_arg(mode, "mode", ("segmentation", "boundaries"))
|
||
|
self.num_classes = 20
|
||
|
|
||
|
sbd_root = self.root
|
||
|
image_dir = os.path.join(sbd_root, "img")
|
||
|
mask_dir = os.path.join(sbd_root, "cls")
|
||
|
|
||
|
if download:
|
||
|
download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.md5)
|
||
|
extracted_ds_root = os.path.join(self.root, "benchmark_RELEASE", "dataset")
|
||
|
for f in ["cls", "img", "inst", "train.txt", "val.txt"]:
|
||
|
old_path = os.path.join(extracted_ds_root, f)
|
||
|
shutil.move(old_path, sbd_root)
|
||
|
download_url(self.voc_train_url, sbd_root, self.voc_split_filename, self.voc_split_md5)
|
||
|
|
||
|
if not os.path.isdir(sbd_root):
|
||
|
raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")
|
||
|
|
||
|
split_f = os.path.join(sbd_root, image_set.rstrip("\n") + ".txt")
|
||
|
|
||
|
with open(os.path.join(split_f)) as fh:
|
||
|
file_names = [x.strip() for x in fh.readlines()]
|
||
|
|
||
|
self.images = [os.path.join(image_dir, x + ".jpg") for x in file_names]
|
||
|
self.masks = [os.path.join(mask_dir, x + ".mat") for x in file_names]
|
||
|
|
||
|
self._get_target = self._get_segmentation_target if self.mode == "segmentation" else self._get_boundaries_target
|
||
|
|
||
|
def _get_segmentation_target(self, filepath: str) -> Image.Image:
|
||
|
mat = self._loadmat(filepath)
|
||
|
return Image.fromarray(mat["GTcls"][0]["Segmentation"][0])
|
||
|
|
||
|
def _get_boundaries_target(self, filepath: str) -> np.ndarray:
|
||
|
mat = self._loadmat(filepath)
|
||
|
return np.concatenate(
|
||
|
[np.expand_dims(mat["GTcls"][0]["Boundaries"][0][i][0].toarray(), axis=0) for i in range(self.num_classes)],
|
||
|
axis=0,
|
||
|
)
|
||
|
|
||
|
def __getitem__(self, index: int) -> Tuple[Any, Any]:
|
||
|
img = Image.open(self.images[index]).convert("RGB")
|
||
|
target = self._get_target(self.masks[index])
|
||
|
|
||
|
if self.transforms is not None:
|
||
|
img, target = self.transforms(img, target)
|
||
|
|
||
|
return img, target
|
||
|
|
||
|
def __len__(self) -> int:
|
||
|
return len(self.images)
|
||
|
|
||
|
def extra_repr(self) -> str:
|
||
|
lines = ["Image set: {image_set}", "Mode: {mode}"]
|
||
|
return "\n".join(lines).format(**self.__dict__)
|