def test_features_segmentation_mask(self, p):
input, expected = self.input_expected_image_tensor(p)
transform = transforms.RandomVerticalFlip(p=p)
actual = transform(features.SegmentationMask(input))
assert_equal(features.SegmentationMask(expected), actual)
def test_pil_image(self, p):
input, expected = self.input_expected_image_tensor(p, dtype=torch.uint8)
transform = transforms.RandomVerticalFlip(p=p)
actual = transform(to_pil_image(input))
assert_equal(expected, pil_to_tensor(actual))
def test_features_image(self, p):
input, expected = self.input_expected_image_tensor(p)
transform = transforms.RandomVerticalFlip(p=p)
actual = transform(features.Image(input))
assert_equal(features.Image(expected), actual)
def test_simple_tensor(self, p):
input, expected = self.input_expected_image_tensor(p)
transform = transforms.RandomVerticalFlip(p=p)
actual = transform(input)
assert_equal(expected, actual)
def test_features_bounding_box(self, p):
input = features.BoundingBox([0, 0, 5, 5], format=features.BoundingBoxFormat.XYXY, image_size=(10, 10))
transform = transforms.RandomVerticalFlip(p=p)
actual = transform(input)
expected_image_tensor = torch.tensor([0, 5, 5, 10]) if p == 1.0 else input
expected = features.BoundingBox.new_like(input, data=expected_image_tensor)
assert_equal(expected, actual)
assert actual.format == expected.format
assert actual.image_size == expected.image_size