import numpy as np
import os
import warnings
from chainercv.chainer_experimental.datasets.sliceable import GetterDataset
from chainercv.datasets.sbd import sbd_utils
from chainercv.datasets.voc import voc_utils
from chainercv.utils import read_image
try:
import scipy
_available = True
except ImportError:
_available = False
def _check_available():
if not _available:
warnings.warn(
'SciPy is not installed in your environment,'
'so the dataset cannot be loaded.'
'Please install SciPy to load dataset.\n\n'
'$ pip install scipy')
[docs]class SBDInstanceSegmentationDataset(GetterDataset):
"""Instance segmentation dataset for Semantic Boundaries Dataset `SBD`_.
.. _`SBD`: http://home.bharathh.info/pubs/codes/SBD/download.html
Args:
data_dir (string): Path to the root of the training data. If this is
:obj:`auto`, this class will automatically download data for you
under :obj:`$CHAINER_DATASET_ROOT/pfnet/chainercv/sbd`.
split ({'train', 'val', 'trainval'}): Select a split of the dataset.
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]`"
:obj:`mask`, ":math:`(R, H, W)`", :obj:`bool`, --
:obj:`label`, ":math:`(R,)`", :obj:`int32`, \
":math:`[0, \#fg\_class - 1]`"
"""
def __init__(self, data_dir='auto', split='train'):
super(SBDInstanceSegmentationDataset, self).__init__()
_check_available()
if split not in ['train', 'trainval', 'val']:
raise ValueError(
'please pick split from \'train\', \'trainval\', \'val\'')
if data_dir == 'auto':
data_dir = sbd_utils.get_sbd()
id_list_file = os.path.join(
data_dir, '{}_voc2012.txt'.format(split))
self.ids = [id_.strip() for id_ in open(id_list_file)]
self.data_dir = data_dir
self.add_getter('img', self._get_image)
self.add_getter(('mask', 'label'), self._get_annotations)
def __len__(self):
return len(self.ids)
def _get_image(self, i):
data_id = self.ids[i]
img_file = os.path.join(
self.data_dir, 'img', data_id + '.jpg')
return read_image(img_file, color=True)
def _get_annotations(self, i):
data_id = self.ids[i]
label_img, inst_img = self._load_label_inst(data_id)
mask, label = voc_utils.image_wise_to_instance_wise(
label_img, inst_img)
return mask, label
def _load_label_inst(self, data_id):
label_file = os.path.join(
self.data_dir, 'cls', data_id + '.mat')
inst_file = os.path.join(
self.data_dir, 'inst', data_id + '.mat')
label_anno = scipy.io.loadmat(label_file)
label_img = label_anno['GTcls']['Segmentation'][0][0].astype(np.int32)
inst_anno = scipy.io.loadmat(inst_file)
inst_img = inst_anno['GTinst']['Segmentation'][0][0].astype(np.int32)
inst_img[inst_img == 0] = -1
inst_img[inst_img == 255] = -1
return label_img, inst_img