def test_edge_to_image_size_vert_horz(self):
aspect_ratio = 2.0
edge_size = 2
actual = utils.edge_to_image_size(edge_size, aspect_ratio, edge="vert")
desired = (edge_size, round(edge_size * aspect_ratio))
self.assertTupleEqual(actual, desired)
actual = utils.edge_to_image_size(edge_size, aspect_ratio, edge="horz")
desired = (round(edge_size * aspect_ratio), edge_size)
self.assertTupleEqual(actual, desired)
def test_calculate_resized_image_size_long(self):
edge_size = 2
edge = "long"
aspect_ratio = 2.0
actual = utils.edge_to_image_size(edge_size, aspect_ratio, edge)
desired = (round(edge_size / aspect_ratio), edge_size)
self.assertTupleEqual(actual, desired)
aspect_ratio = 0.5
actual = utils.edge_to_image_size(edge_size, aspect_ratio, edge)
desired = (edge_size, round(edge_size * aspect_ratio))
self.assertTupleEqual(actual, desired)
def test_edge_to_image_size_short(self):
edge_size = 2
edge = "short"
aspect_ratio = 2.0
actual = utils.edge_to_image_size(edge_size, aspect_ratio, edge)
desired = (edge_size, round(edge_size * aspect_ratio))
self.assertTupleEqual(actual, desired)
aspect_ratio = 0.5
actual = utils.edge_to_image_size(edge_size, aspect_ratio, edge)
desired = (round(edge_size / aspect_ratio), edge_size)
self.assertTupleEqual(actual, desired)
def resize(
image: torch.Tensor,
size: Union[int, Tuple[int, int]],
edge: str = "short",
aspect_ratio: Optional[float] = None,
interpolation_mode: str = "bilinear",
) -> torch.Tensor:
r"""Resize an image as specified. See :class:`~pystiche.image.transforms.Resize`
for details.
"""
if aspect_ratio is None:
aspect_ratio = extract_aspect_ratio(image)
if is_image_size(size):
image_size = size
elif is_edge_size(size):
image_size = edge_to_image_size(cast(int, size), aspect_ratio, edge)
else:
raise RuntimeError
return interpolate(
image,
size=image_size,
scale_factor=None,
mode=interpolation_mode,
)
def test_read_image_resize_scalar(self):
edge_size = 200
image = self.load_image(backend="PIL")
aspect_ratio = utils.calculate_aspect_ratio(
(image.height, image.width))
image_size = utils.edge_to_image_size(edge_size, aspect_ratio)
actual = io.read_image(self.default_image_file(), size=edge_size)
desired = image.resize(image_size[::-1])
self.assertImagesAlmostEqual(actual, desired)
def resize(
image: torch.Tensor,
size: Union[int, Tuple[int, int]],
edge: str = "short",
aspect_ratio: Optional[float] = None,
interpolation_mode: str = "bilinear",
) -> torch.Tensor:
if aspect_ratio is None:
aspect_ratio = extract_aspect_ratio(image)
if is_image_size(size):
image_size = size
elif is_edge_size(size):
image_size = edge_to_image_size(cast(int, size), aspect_ratio, edge)
else:
raise RuntimeError
return interpolate(
image,
size=image_size,
scale_factor=None,
mode=interpolation_mode,
)