def test_can_convert_polygons_to_mask(self):
label_categories = LabelCategories()
for i in range(10):
label_categories.add(str(i))
class SrcTestExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(id=1,
image=np.zeros((6, 10, 3)),
annotations=[
Polygon([0, 0, 4, 0, 4, 4],
label=3,
id=4,
group=4),
Polygon([5, 0, 9, 0, 5, 5],
label=3,
id=4,
group=4),
]),
])
def categories(self):
return {AnnotationType.label: label_categories}
class DstTestExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(
id=1,
image=np.zeros((6, 10, 3)),
annotations=[
Mask(
np.array(
[[0, 1, 1, 1, 0, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 1, 0, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
# only internal fragment (without the border),
# but not everywhere...
),
attributes={'is_crowd': True},
label=3,
id=4,
group=4),
],
attributes={'id': 1}),
])
def categories(self):
return {AnnotationType.label: label_categories}
with TestDir() as test_dir:
self._test_save_and_load(
SrcTestExtractor(),
CocoInstancesConverter(segmentation_mode='mask'),
test_dir,
target_dataset=DstTestExtractor())
def test_can_crop_covered_segments(self):
label_categories = LabelCategories()
for i in range(10):
label_categories.add(str(i))
class SrcTestExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(id=1,
image=np.zeros((5, 5, 3)),
annotations=[
Mask(np.array(
[[0, 0, 1, 1, 1], [0, 0, 1, 1, 1],
[1, 1, 0, 1, 1], [1, 1, 1, 0, 0],
[1, 1, 1, 0, 0]], ),
label=2,
id=1,
z_order=0),
Polygon([1, 1, 4, 1, 4, 4, 1, 4],
label=1,
id=2,
z_order=1),
]),
])
def categories(self):
return {AnnotationType.label: label_categories}
class DstTestExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(id=1,
image=np.zeros((5, 5, 3)),
annotations=[
Mask(np.array(
[[0, 0, 1, 1, 1], [0, 0, 0, 0, 1],
[1, 0, 0, 0, 1], [1, 0, 0, 0, 0],
[1, 1, 1, 0, 0]], ),
attributes={'is_crowd': True},
label=2,
id=1,
group=1),
Polygon([1, 1, 4, 1, 4, 4, 1, 4],
label=1,
id=2,
group=2,
attributes={'is_crowd': False}),
],
attributes={'id': 1}),
])
def categories(self):
return {AnnotationType.label: label_categories}
with TestDir() as test_dir:
self._test_save_and_load(SrcTestExtractor(),
CocoInstancesConverter(crop_covered=True),
test_dir,
target_dataset=DstTestExtractor())
def test_can_convert_masks_to_polygons(self):
label_categories = LabelCategories()
for i in range(10):
label_categories.add(str(i))
class SrcExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(id=1,
image=np.zeros((5, 10, 3)),
annotations=[
Mask(np.array([
[0, 1, 1, 1, 0, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 1, 0, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]),
label=3,
id=4,
group=4),
]),
])
def categories(self):
return {AnnotationType.label: label_categories}
class DstExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(
id=1,
image=np.zeros((5, 10, 3)),
annotations=[
Polygon([3.0, 2.5, 1.0, 0.0, 3.5, 0.0, 3.0, 2.5],
label=3,
id=4,
group=4,
attributes={'is_crowd': False}),
Polygon([5.0, 3.5, 4.5, 0.0, 8.0, 0.0, 5.0, 3.5],
label=3,
id=4,
group=4,
attributes={'is_crowd': False}),
],
attributes={'id': 1}),
])
def categories(self):
return {AnnotationType.label: label_categories}
with TestDir() as test_dir:
self._test_save_and_load(
SrcExtractor(),
CocoInstancesConverter(segmentation_mode='polygons'),
test_dir,
target_dataset=DstExtractor())
def test_can_merge_polygons_on_loading(self):
label_categories = LabelCategories()
for i in range(10):
label_categories.add(str(i))
categories = {AnnotationType.label: label_categories}
class TestExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(id=1,
image=np.zeros((6, 10, 3)),
annotations=[
Polygon([0, 0, 4, 0, 4, 4],
label=3,
id=4,
group=4),
Polygon([5, 0, 9, 0, 5, 5],
label=3,
id=4,
group=4),
]),
])
def categories(self):
return categories
class TargetExtractor(TestExtractor):
def __iter__(self):
items = list(super().__iter__())
items[0]._annotations = [
Mask(
np.array(
[[0, 1, 1, 1, 0, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 1, 0, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
# only internal fragment (without the border),
# but not everywhere...
),
label=3,
id=4,
group=4,
attributes={'is_crowd': False}),
]
return iter(items)
with TestDir() as test_dir:
self._test_save_and_load(
TestExtractor(),
CocoInstancesConverter(),
test_dir,
importer_args={'merge_instance_polygons': True},
target_dataset=TargetExtractor())
def test_can_save_and_load_instances(self):
label_categories = LabelCategories()
for i in range(10):
label_categories.add(str(i))
categories = {AnnotationType.label: label_categories}
class TestExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(
id=1,
subset='train',
image=np.ones((4, 4, 3)),
annotations=[
# Bbox + single polygon
Bbox(0,
1,
2,
2,
label=2,
group=1,
id=1,
attributes={'is_crowd': False}),
Polygon([0, 1, 2, 1, 2, 3, 0, 3],
attributes={'is_crowd': False},
label=2,
group=1,
id=1),
]),
DatasetItem(
id=2,
subset='train',
image=np.ones((4, 4, 3)),
annotations=[
# Mask + bbox
Mask(np.array([[0, 1, 0, 0], [0, 1, 0, 0],
[0, 1, 1, 1], [0, 0, 0, 0]], ),
attributes={'is_crowd': True},
label=4,
group=3,
id=3),
Bbox(1,
0,
2,
2,
label=4,
group=3,
id=3,
attributes={'is_crowd': True}),
]),
DatasetItem(
id=3,
subset='val',
image=np.ones((4, 4, 3)),
annotations=[
# Bbox + mask
Bbox(0,
1,
2,
2,
label=4,
group=3,
id=3,
attributes={'is_crowd': True}),
Mask(np.array([[0, 0, 0, 0], [1, 1, 1, 0],
[1, 1, 0, 0], [0, 0, 0, 0]], ),
attributes={'is_crowd': True},
label=4,
group=3,
id=3),
]),
])
def categories(self):
return categories
class DstExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(id=1,
subset='train',
image=np.ones((4, 4, 3)),
annotations=[
Polygon([0, 1, 2, 1, 2, 3, 0, 3],
attributes={'is_crowd': False},
label=2,
group=1,
id=1),
]),
DatasetItem(id=2,
subset='train',
image=np.ones((4, 4, 3)),
annotations=[
Mask(np.array(
[[0, 1, 0, 0], [0, 1, 0, 0],
[0, 1, 1, 1], [0, 0, 0, 0]], ),
attributes={'is_crowd': True},
label=4,
group=3,
id=3),
]),
DatasetItem(id=3,
subset='val',
image=np.ones((4, 4, 3)),
annotations=[
Mask(np.array(
[[0, 0, 0, 0], [1, 1, 1, 0],
[1, 1, 0, 0], [0, 0, 0, 0]], ),
attributes={'is_crowd': True},
label=4,
group=3,
id=3),
]),
])
def categories(self):
return categories
with TestDir() as test_dir:
self._test_save_and_load(TestExtractor(),
CocoInstancesConverter(),
test_dir,
target_dataset=DstExtractor())