Source code for gluoncv.data.pascal_aug.segmentation
"""Pascal Augmented VOC Semantic Segmentation Dataset."""
import os
import scipy.io
from PIL import Image
from ..segbase import SegmentationDataset
[docs]class VOCAugSegmentation(SegmentationDataset):
"""Pascal VOC Augmented Semantic Segmentation Dataset.
Parameters
----------
root : string
Path to VOCdevkit folder. Default is '$(HOME)/mxnet/datasplits/voc'
split: string
'train' or 'val'
transform : callable, optional
A function that transforms the image
Examples
--------
>>> from mxnet.gluon.data.vision import transforms
>>> # Transforms for Normalization
>>> input_transform = transforms.Compose([
>>> transforms.ToTensor(),
>>> transforms.Normalize([.485, .456, .406], [.229, .224, .225]),
>>> ])
>>> # Create Dataset
>>> trainset = gluoncv.data.VOCAugSegmentation(split='train', transform=input_transform)
>>> # Create Training Loader
>>> train_data = gluon.data.DataLoader(
>>> trainset, 4, shuffle=True, last_batch='rollover',
>>> num_workers=4)
"""
TRAIN_BASE_DIR = 'VOCaug/dataset/'
NUM_CLASS = 21
CLASSES = ("background", "airplane", "bicycle", "bird", "boat", "bottle",
"bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse",
"motorcycle", "person", "potted-plant", "sheep", "sofa", "train",
"tv")
def __init__(self, root=os.path.expanduser('~/.mxnet/datasets/voc'),
split='train', mode=None, transform=None, **kwargs):
super(VOCAugSegmentation, self).__init__(root, split, mode, transform, **kwargs)
# train/val/test splits are pre-cut
_voc_root = os.path.join(root, self.TRAIN_BASE_DIR)
_mask_dir = os.path.join(_voc_root, 'cls')
_image_dir = os.path.join(_voc_root, 'img')
if split == 'train':
_split_f = os.path.join(_voc_root, 'trainval.txt')
elif split == 'val':
_split_f = os.path.join(_voc_root, 'val.txt')
else:
raise RuntimeError('Unknown dataset split: {}'.format(split))
self.images = []
self.masks = []
with open(os.path.join(_split_f), "r") as lines:
for line in lines:
_image = os.path.join(_image_dir, line.rstrip('\n')+".jpg")
assert os.path.isfile(_image)
self.images.append(_image)
_mask = os.path.join(_mask_dir, line.rstrip('\n')+".mat")
assert os.path.isfile(_mask)
self.masks.append(_mask)
assert (len(self.images) == len(self.masks))
def __getitem__(self, index):
img = Image.open(self.images[index]).convert('RGB')
target = self._load_mat(self.masks[index])
# synchrosized transform
if self.mode == 'train':
img, target = self._sync_transform(img, target)
elif self.mode == 'val':
img, target = self._val_sync_transform(img, target)
else:
raise RuntimeError('unknown mode for dataloader: {}'.format(self.mode))
# general resize, normalize and toTensor
if self.transform is not None:
img = self.transform(img)
return img, target
def _load_mat(self, filename):
mat = scipy.io.loadmat(filename, mat_dtype=True, squeeze_me=True,
struct_as_record=False)
mask = mat['GTcls'].Segmentation
return Image.fromarray(mask)
def __len__(self):
return len(self.images)
@property
def classes(self):
"""Category names."""
return type(self).CLASSES