def test_boxes_to_masks(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1,
image=np.zeros((5, 5, 3)),
annotations=[
Bbox(0, 0, 3, 3, z_order=1),
Bbox(0, 0, 3, 1, z_order=2),
Bbox(0, 2, 3, 1, z_order=3),
]),
])
target_dataset = Dataset.from_iterable([
DatasetItem(
id=1,
image=np.zeros((5, 5, 3)),
annotations=[
Mask(np.array(
[[1, 1, 1, 0, 0], [1, 1, 1, 0, 0], [1, 1, 1, 0, 0],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], ),
z_order=1),
Mask(np.array(
[[1, 1, 1, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], ),
z_order=2),
Mask(np.array(
[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [1, 1, 1, 0, 0],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], ),
z_order=3),
]),
])
actual = transforms.BoxesToMasks(source_dataset)
compare_datasets(self, target_dataset, actual)
def test_boxes_to_masks(self):
class SrcExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(id=1,
image=np.zeros((5, 5, 3)),
annotations=[
Bbox(0, 0, 3, 3, z_order=1),
Bbox(0, 0, 3, 1, z_order=2),
Bbox(0, 2, 3, 1, z_order=3),
]),
])
class DstExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(
id=1,
image=np.zeros((5, 5, 3)),
annotations=[
Mask(np.array([[1, 1, 1, 0, 0], [1, 1, 1, 0, 0],
[1, 1, 1, 0, 0], [0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]], ),
z_order=1),
Mask(np.array([[1, 1, 1, 0, 0], [0, 0, 0, 0, 0],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]], ),
z_order=2),
Mask(np.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0],
[1, 1, 1, 0, 0], [0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]], ),
z_order=3),
]),
])
actual = transforms.BoxesToMasks(SrcExtractor())
compare_datasets(self, DstExtractor(), actual)