def test_can_write_and_parse_dataset_meta_file(self):
src_label_map = Cityscapes.CITYSCAPES_LABEL_MAP
with TestDir() as test_dir:
source_dataset = Dataset.from_iterable([],
categories=Cityscapes.make_cityscapes_categories(src_label_map))
CityscapesConverter.convert(source_dataset, test_dir,
save_dataset_meta=True)
dst_label_map = parse_meta_file(test_dir)
self.assertEqual(src_label_map, dst_label_map)
def test_can_import(self):
# is_crowd marks labels allowing to specify instance id
source_dataset = Dataset.from_iterable([
DatasetItem(id='defaultcity/defaultcity_000001_000031',
subset='test',
image=np.ones((1, 5, 3)),
annotations=[
Mask(np.array([[1, 1, 0, 0, 0]]), label=3,
attributes={'is_crowd': True}),
Mask(np.array([[0, 0, 1, 0, 0]]), id=1, label=27,
attributes={'is_crowd': False}),
Mask(np.array([[0, 0, 0, 1, 1]]), id=2, label=27,
attributes={'is_crowd': False}),
]
),
DatasetItem(id='defaultcity/defaultcity_000001_000032',
subset='test',
image=np.ones((1, 5, 3)),
annotations=[
Mask(np.array([[1, 1, 0, 0, 0]]), id=1, label=31,
attributes={'is_crowd': False}),
Mask(np.array([[0, 0, 1, 0, 0]]), label=12,
attributes={'is_crowd': True}),
Mask(np.array([[0, 0, 0, 1, 1]]), label=3,
attributes={'is_crowd': True}),
]
),
DatasetItem(id='defaultcity/defaultcity_000002_000045',
subset='train',
image=np.ones((1, 5, 3)),
annotations=[
Mask(np.array([[1, 1, 0, 1, 1]]), label=3,
attributes={'is_crowd': True}),
Mask(np.array([[0, 0, 1, 0, 0]]), id=1, label=24,
attributes={'is_crowd': False}),
]
),
DatasetItem(id='defaultcity/defaultcity_000001_000019',
subset = 'val',
image=np.ones((1, 5, 3)),
annotations=[
Mask(np.array([[1, 0, 0, 1, 1]]), label=3,
attributes={'is_crowd': True}),
Mask(np.array([[0, 1, 1, 0, 0]]), id=24, label=1,
attributes={'is_crowd': False}),
]
),
], categories=Cityscapes.make_cityscapes_categories())
parsed_dataset = Dataset.import_from(DUMMY_DATASET_DIR, 'cityscapes')
compare_datasets(self, source_dataset, parsed_dataset)
def test_can_import_with_train_label_map(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='defaultcity/defaultcity_000001_000031',
subset='test',
image=np.ones((1, 5, 3)),
annotations=[
Mask(np.array([[1, 1, 0, 0, 0]]), label=19,
attributes={'is_crowd': True}),
Mask(np.array([[0, 0, 1, 1, 1]]), label=14,
attributes={'is_crowd': True}),
]
),
DatasetItem(id='defaultcity/defaultcity_000001_000032',
subset='test',
image=np.ones((1, 5, 3)),
annotations=[
Mask(np.array([[1, 1, 0, 0, 0]]), label=16,
attributes={'is_crowd': True}),
Mask(np.array([[0, 0, 1, 0, 0]]), label=3,
attributes={'is_crowd': True}),
Mask(np.array([[0, 0, 0, 1, 1]]), label=19,
attributes={'is_crowd': True}),
]
),
DatasetItem(id='defaultcity/defaultcity_000002_000045',
subset='train',
image=np.ones((1, 5, 3)),
annotations=[
Mask(np.array([[1, 1, 0, 1, 1]]), label=19,
attributes={'is_crowd': True}),
Mask(np.array([[0, 0, 1, 0, 0]]), label=11,
attributes={'is_crowd': True}),
]
),
DatasetItem(id='defaultcity/defaultcity_000001_000019',
subset = 'val',
image=np.ones((1, 5, 3)),
annotations=[
Mask(np.array([[1, 1, 1, 1, 1]]), label=19,
attributes={'is_crowd': True}),
]
),
], categories=Cityscapes.make_cityscapes_categories(
label_map=TRAIN_CITYSCAPES_LABEL_MAP))
parsed_dataset = Dataset.import_from(DUMMY_TRAIN_DATASET_DIR, 'cityscapes')
compare_datasets(self, source_dataset, parsed_dataset)
def test_inplace_save_writes_only_updated_data(self):
src_mask_cat = MaskCategories.generate(2, include_background=False)
expected = Dataset.from_iterable([
DatasetItem(1, subset='a', image=np.ones((2, 1, 3)),
annotations=[
Mask(np.ones((2, 1)), label=2, id=1)
]),
DatasetItem(2, subset='a', image=np.ones((3, 2, 3))),
DatasetItem(2, subset='b', image=np.ones((2, 2, 3)),
annotations=[
Mask(np.ones((2, 2)), label=1, id=1)
]),
], categories=Cityscapes.make_cityscapes_categories(OrderedDict([
('a', src_mask_cat.colormap[0]),
('b', src_mask_cat.colormap[1]),
])))
with TestDir() as path:
dataset = Dataset.from_iterable([
DatasetItem(1, subset='a', image=np.ones((2, 1, 3)),
annotations=[
Mask(np.ones((2, 1)), label=1)
]),
DatasetItem(2, subset='b', image=np.ones((2, 2, 3)),
annotations=[
Mask(np.ones((2, 2)), label=0)
]),
DatasetItem(3, subset='c', image=np.ones((2, 3, 3)),
annotations=[
Mask(np.ones((2, 2)), label=0)
]),
], categories={
AnnotationType.label: LabelCategories.from_iterable(['a', 'b']),
AnnotationType.mask: src_mask_cat
})
dataset.export(path, 'cityscapes', save_images=True)
dataset.put(DatasetItem(2, subset='a', image=np.ones((3, 2, 3))))
dataset.remove(3, 'c')
dataset.save(save_images=True)
self.assertEqual({'a', 'b'},
set(os.listdir(osp.join(path, 'gtFine'))))
self.assertEqual({
'1_gtFine_color.png', '1_gtFine_instanceIds.png',
'1_gtFine_labelIds.png'
},
set(os.listdir(osp.join(path, 'gtFine', 'a'))))
self.assertEqual({
'2_gtFine_color.png', '2_gtFine_instanceIds.png',
'2_gtFine_labelIds.png'
},
set(os.listdir(osp.join(path, 'gtFine', 'b'))))
self.assertEqual({'a', 'b'},
set(os.listdir(osp.join(path, 'imgsFine', 'leftImg8bit'))))
self.assertEqual({'1_leftImg8bit.png', '2_leftImg8bit.png'},
set(os.listdir(osp.join(path, 'imgsFine', 'leftImg8bit', 'a'))))
self.assertEqual({'2_leftImg8bit.png'},
set(os.listdir(osp.join(path, 'imgsFine', 'leftImg8bit', 'b'))))
compare_datasets(self, expected,
Dataset.import_from(path, 'cityscapes'),
require_images=True, ignored_attrs=IGNORE_ALL)
def categories(self):
label_map = OrderedDict()
label_map['a'] = None
label_map['b'] = None
return Cityscapes.make_cityscapes_categories(label_map)
def categories(self):
label_map = OrderedDict()
label_map['background'] = (0, 0, 0)
label_map['label_1'] = (1, 2, 3)
label_map['label_2'] = (3, 2, 1)
return Cityscapes.make_cityscapes_categories(label_map)
def categories(self):
return Cityscapes.make_cityscapes_categories()
