def test_can_save_voc_segm(self):
class TestExtractor(TestExtractorBase):
def __iter__(self):
return iter([
DatasetItem(id=1, subset='a', annotations=[
# overlapping masks, the first should be truncated
# the second and third are different instances
Mask(image=np.array([[0, 1, 1, 1, 0]]), label=4,
z_order=1),
Mask(image=np.array([[1, 1, 0, 0, 0]]), label=3,
z_order=2),
Mask(image=np.array([[0, 0, 0, 1, 0]]), label=3,
z_order=2),
]),
])
class DstExtractor(TestExtractorBase):
def __iter__(self):
return iter([
DatasetItem(id=1, subset='a', annotations=[
Mask(image=np.array([[0, 0, 1, 0, 0]]), label=4,
group=1),
Mask(image=np.array([[1, 1, 0, 0, 0]]), label=3,
group=2),
Mask(image=np.array([[0, 0, 0, 1, 0]]), label=3,
group=3),
]),
])
with TestDir() as test_dir:
self._test_save_and_load(TestExtractor(),
VocSegmentationConverter(label_map='voc'), test_dir,
target_dataset=DstExtractor())
def test_can_save_voc_segm_with_many_instances(self):
def bit(x, y, shape):
mask = np.zeros(shape)
mask[y, x] = 1
return mask
class TestExtractor(TestExtractorBase):
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),
z_order=10 * y + x + 1
)
for y in range(10) for x in range(10)
]),
])
class DstExtractor(TestExtractorBase):
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)
]),
])
with TestDir() as test_dir:
self._test_save_and_load(TestExtractor(),
VocSegmentationConverter(label_map='voc'), test_dir,
target_dataset=DstExtractor())