def categories(self):
label_cat = LabelCategories()
label_cat.add(VOC.VocLabel(1).name)
label_cat.add('non_voc_label')
return {
AnnotationType.label: label_cat,
}
def __iter__(self):
yield DatasetItem(id=1,
annotations=[
Bbox(2,
3,
4,
5,
label=self._label(
VOC.VocLabel(1).name),
id=1,
group=1,
attributes={
'truncated': False,
'difficult': False,
'occluded': False,
}),
Bbox(1,
2,
3,
4,
label=self._label('non_voc_label'),
id=2,
group=2,
attributes={
'truncated': False,
'difficult': False,
'occluded': False,
}),
])
def __iter__(self):
return iter([
DatasetItem(
id='2007_000001',
subset='train',
image=Image(path='2007_000001.jpg', size=(10, 20)),
annotations=[
Label(self._label(l.name))
for l in VOC.VocLabel if l.value % 2 == 1
] + [
Bbox(
1,
2,
2,
2,
label=self._label('cat'),
attributes={
'pose': VOC.VocPose(1).name,
'truncated': True,
'difficult': False,
'occluded': False,
},
id=1,
group=1,
),
Bbox(
4,
5,
2,
2,
label=self._label('person'),
attributes={
'truncated': False,
'difficult': False,
'occluded': False,
**{
a.name: a.value % 2 == 1
for a in VOC.VocAction
}
},
id=2,
group=2,
),
Bbox(5.5,
6,
2,
2,
label=self._label(VOC.VocBodyPart(1).name),
group=2),
Mask(
image=np.ones([5, 10]),
label=self._label(VOC.VocLabel(2).name),
group=1,
),
]),
DatasetItem(id='2007_000002',
subset='test',
image=np.ones((10, 20, 3))),
])
def __iter__(self):
return iter([
DatasetItem(id=1, subset='a', annotations=[
Mask(image=bit(x, y, shape=[10, 10]),
label=self._label(VOC.VocLabel(3).name),
group=10 * y + x + 1
)
for y in range(10) for x in range(10)
]),
])
def __iter__(self):
return iter([
DatasetItem(id='2007_000001', subset='train',
annotations=[
Mask(image=np.ones([5, 10]),
label=self._label(VOC.VocLabel(2).name),
group=1,
),
]
),
DatasetItem(id='2007_000002', subset='test')
])
