import glob
import os
from chainercv.chainer_experimental.datasets.sliceable import GetterDataset
from chainercv.datasets.ade20k.ade20k_utils import get_ade20k
from chainercv.utils import read_image
root = 'pfnet/chainercv/ade20k'
url = 'http://data.csail.mit.edu/places/ADEchallenge/release_test.zip'
[docs]class ADE20KTestImageDataset(GetterDataset):
"""Image dataset for test split of `ADE20K`_.
This is an image dataset of test split in ADE20K dataset distributed at
MIT Scene Parsing Benchmark website. It has 3,352 test images.
.. _`MIT Scene Parsing Benchmark`: http://sceneparsing.csail.mit.edu/
Args:
data_dir (string): Path to the dataset directory. The directory should
contain the :obj:`release_test` dir. If :obj:`auto` is given, the
dataset is automatically downloaded into
:obj:`$CHAINER_DATASET_ROOT/pfnet/chainercv/ade20k`.
This dataset returns the following data.
.. csv-table::
:header: name, shape, dtype, format
:obj:`img`, ":math:`(3, H, W)`", :obj:`float32`, \
"RGB, :math:`[0, 255]`"
"""
def __init__(self, data_dir='auto'):
super(ADE20KTestImageDataset, self).__init__()
if data_dir is 'auto':
data_dir = get_ade20k(root, url)
img_dir = os.path.join(data_dir, 'release_test', 'testing')
self.img_paths = sorted(glob.glob(os.path.join(img_dir, '*.jpg')))
self.add_getter('img', self._get_image)
self.keys = 'img' # do not return tuple
def __len__(self):
return len(self.img_paths)
def _get_image(self, i):
return read_image(self.img_paths[i])