import os
import sys
import tarfile
import collections
from .vision import VisionDataset
if sys.version_info[0] == 2:
import xml.etree.cElementTree as ET
else:
import xml.etree.ElementTree as ET
from PIL import Image
from .utils import download_url, check_integrity, verify_str_arg
DATASET_YEAR_DICT = {
'2012': {
'url': 'http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar',
'filename': 'VOCtrainval_11-May-2012.tar',
'md5': '6cd6e144f989b92b3379bac3b3de84fd',
'base_dir': 'VOCdevkit/VOC2012'
},
'2011': {
'url': 'http://host.robots.ox.ac.uk/pascal/VOC/voc2011/VOCtrainval_25-May-2011.tar',
'filename': 'VOCtrainval_25-May-2011.tar',
'md5': '6c3384ef61512963050cb5d687e5bf1e',
'base_dir': 'TrainVal/VOCdevkit/VOC2011'
},
'2010': {
'url': 'http://host.robots.ox.ac.uk/pascal/VOC/voc2010/VOCtrainval_03-May-2010.tar',
'filename': 'VOCtrainval_03-May-2010.tar',
'md5': 'da459979d0c395079b5c75ee67908abb',
'base_dir': 'VOCdevkit/VOC2010'
},
'2009': {
'url': 'http://host.robots.ox.ac.uk/pascal/VOC/voc2009/VOCtrainval_11-May-2009.tar',
'filename': 'VOCtrainval_11-May-2009.tar',
'md5': '59065e4b188729180974ef6572f6a212',
'base_dir': 'VOCdevkit/VOC2009'
},
'2008': {
'url': 'http://host.robots.ox.ac.uk/pascal/VOC/voc2008/VOCtrainval_14-Jul-2008.tar',
'filename': 'VOCtrainval_11-May-2012.tar',
'md5': '2629fa636546599198acfcfbfcf1904a',
'base_dir': 'VOCdevkit/VOC2008'
},
'2007': {
'url': 'http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar',
'filename': 'VOCtrainval_06-Nov-2007.tar',
'md5': 'c52e279531787c972589f7e41ab4ae64',
'base_dir': 'VOCdevkit/VOC2007'
}
}
[docs]class VOCSegmentation(VisionDataset):
"""`Pascal VOC <http://host.robots.ox.ac.uk/pascal/VOC/>`_ Segmentation Dataset.
Args:
root (string): Root directory of the VOC Dataset.
year (string, optional): The dataset year, supports years 2007 to 2012.
image_set (string, optional): Select the image_set to use, ``train``, ``trainval`` or ``val``
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.
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
transforms (callable, optional): A function/transform that takes input sample and its target as entry
and returns a transformed version.
"""
def __init__(self,
root,
year='2012',
image_set='train',
download=False,
transform=None,
target_transform=None,
transforms=None):
super(VOCSegmentation, self).__init__(root, transforms, transform, target_transform)
self.year = year
self.url = DATASET_YEAR_DICT[year]['url']
self.filename = DATASET_YEAR_DICT[year]['filename']
self.md5 = DATASET_YEAR_DICT[year]['md5']
self.image_set = verify_str_arg(image_set, "image_set",
("train", "trainval", "val"))
base_dir = DATASET_YEAR_DICT[year]['base_dir']
voc_root = os.path.join(self.root, base_dir)
image_dir = os.path.join(voc_root, 'JPEGImages')
mask_dir = os.path.join(voc_root, 'SegmentationClass')
if download:
download_extract(self.url, self.root, self.filename, self.md5)
if not os.path.isdir(voc_root):
raise RuntimeError('Dataset not found or corrupted.' +
' You can use download=True to download it')
splits_dir = os.path.join(voc_root, 'ImageSets/Segmentation')
split_f = os.path.join(splits_dir, image_set.rstrip('\n') + '.txt')
with open(os.path.join(split_f), "r") as f:
file_names = [x.strip() for x in f.readlines()]
self.images = [os.path.join(image_dir, x + ".jpg") for x in file_names]
self.masks = [os.path.join(mask_dir, x + ".png") for x in file_names]
assert (len(self.images) == len(self.masks))
[docs] def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is the image segmentation.
"""
img = Image.open(self.images[index]).convert('RGB')
target = Image.open(self.masks[index])
if self.transforms is not None:
img, target = self.transforms(img, target)
return img, target
def __len__(self):
return len(self.images)
[docs]class VOCDetection(VisionDataset):
"""`Pascal VOC <http://host.robots.ox.ac.uk/pascal/VOC/>`_ Detection Dataset.
Args:
root (string): Root directory of the VOC Dataset.
year (string, optional): The dataset year, supports years 2007 to 2012.
image_set (string, optional): Select the image_set to use, ``train``, ``trainval`` or ``val``
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.
(default: alphabetic indexing of VOC's 20 classes).
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, required): A function/transform that takes in the
target and transforms it.
transforms (callable, optional): A function/transform that takes input sample and its target as entry
and returns a transformed version.
"""
def __init__(self,
root,
year='2012',
image_set='train',
download=False,
transform=None,
target_transform=None,
transforms=None):
super(VOCDetection, self).__init__(root, transforms, transform, target_transform)
self.year = year
self.url = DATASET_YEAR_DICT[year]['url']
self.filename = DATASET_YEAR_DICT[year]['filename']
self.md5 = DATASET_YEAR_DICT[year]['md5']
self.image_set = verify_str_arg(image_set, "image_set",
("train", "trainval", "val"))
base_dir = DATASET_YEAR_DICT[year]['base_dir']
voc_root = os.path.join(self.root, base_dir)
image_dir = os.path.join(voc_root, 'JPEGImages')
annotation_dir = os.path.join(voc_root, 'Annotations')
if download:
download_extract(self.url, self.root, self.filename, self.md5)
if not os.path.isdir(voc_root):
raise RuntimeError('Dataset not found or corrupted.' +
' You can use download=True to download it')
splits_dir = os.path.join(voc_root, 'ImageSets/Main')
split_f = os.path.join(splits_dir, image_set.rstrip('\n') + '.txt')
with open(os.path.join(split_f), "r") as f:
file_names = [x.strip() for x in f.readlines()]
self.images = [os.path.join(image_dir, x + ".jpg") for x in file_names]
self.annotations = [os.path.join(annotation_dir, x + ".xml") for x in file_names]
assert (len(self.images) == len(self.annotations))
[docs] def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is a dictionary of the XML tree.
"""
img = Image.open(self.images[index]).convert('RGB')
target = self.parse_voc_xml(
ET.parse(self.annotations[index]).getroot())
if self.transforms is not None:
img, target = self.transforms(img, target)
return img, target
def __len__(self):
return len(self.images)
def parse_voc_xml(self, node):
voc_dict = {}
children = list(node)
if children:
def_dic = collections.defaultdict(list)
for dc in map(self.parse_voc_xml, children):
for ind, v in dc.items():
def_dic[ind].append(v)
voc_dict = {
node.tag:
{ind: v[0] if len(v) == 1 else v
for ind, v in def_dic.items()}
}
if node.text:
text = node.text.strip()
if not children:
voc_dict[node.tag] = text
return voc_dict
def download_extract(url, root, filename, md5):
download_url(url, root, filename, md5)
with tarfile.open(os.path.join(root, filename), "r") as tar:
tar.extractall(path=root)
