def load_image(
file_or_name: str,
*,
size: Optional[Union[int, Tuple[int, int]]],
device: torch.device,
) -> torch.Tensor:
file = os.path.abspath(os.path.expanduser(file_or_name))
name = file_or_name
images = demo.images()
image: _Image
if os.path.exists(file):
image = LocalImage(file)
elif name in images:
image = images[name]
else:
path = pathlib.Path(file_or_name)
could_be_name = not path.suffix and path.parent == pathlib.Path(".")
if could_be_name:
raise ValueError(
add_suggestion(
f"Unknown demo image '{name}'.",
word=name,
possibilities=images._images.keys(),
))
else:
raise ValueError(f"The file {file} does not exist.")
return image.read(size=size, device=device)
def test_guides_smoke(self, name, regions):
image = demo.images()[name]
assert isinstance(image.guides, data.DownloadableImageCollection)
names, _ = zip(*iter(image.guides))
assert set(names).issuperset(set(regions))
def test_smoke(self):
images = demo.images()
assert isinstance(images, data.DownloadableImageCollection)
names, _ = zip(*iter(images))
assert set(names) == {
"bird1",
"paint",
"bird2",
"mosaic",
"castle",
"church",
"cliff",
}
def test_file_starting_point(self, mock_execution_with):
image = demo.images()["bird2"]
expected = image.read()
file = pathlib.Path(pystiche.home()) / image.file
mock = self._mock_execution(
mock_execution_with, option="starting_point", value=str(file)
)
with exits(), pytest.warns(UserWarning):
main()
(input_image, perceptual_loss), _ = mock.call_args
ptu.assert_allclose(
input_image,
resize(expected, list(extract_image_size(perceptual_loss.content_image))),
)
def test_file(self, mock_execution_with, option):
image = demo.images()["bird2"]
# TODO: make this independent of the default size value
expected = image.read(size=500)
file = pathlib.Path(pystiche.home()) / image.file
mock = self._mock_execution(mock_execution_with, option=option, value=str(file))
with exits():
cli.main()
(input_image, perceptual_loss), _ = mock.call_args
if option == "content":
actual = perceptual_loss.content_image
else: # option == "style":
actual = perceptual_loss.style_image
ptu.assert_allclose(actual, expected)
def test_demo_image(self, mock_execution_with, option):
name = "bird2"
mock = self._mock_execution(mock_execution_with, option=option, value=name)
with exits():
cli.main()
(input_image, perceptual_loss), _ = mock.call_args
if option == "content":
image = perceptual_loss.content_image
elif option == "style":
image = perceptual_loss.style_image
else: # option == "starting_point"
image = input_image
ptu.assert_allclose(
image, demo.images()[name].read(size=extract_image_size(image)),
)
from os import path
import pytest
from pystiche import demo
from .utils import (
assert_downloads_correctly,
assert_is_downloadable,
rate_limited_urlopen,
retry,
)
from tests.mocks import make_mock_target
IMAGES_AND_IDS = [
(image, f"demo, {name}") for name, image in itertools.chain(demo.images(),)
]
IMAGE_PARAMETRIZE_KWARGS = dict(zip(("argvalues", "ids"), zip(*IMAGES_AND_IDS)))
def assert_image_is_downloadable(image, **kwargs):
assert_is_downloadable(image.url, **kwargs)
@pytest.mark.slow
@pytest.mark.parametrize("image", **IMAGE_PARAMETRIZE_KWARGS)
def test_image_download_smoke(image):
retry(lambda: assert_image_is_downloadable(image), times=2, wait=5.0)
######################################################################################## # We combine the ``content_loss`` and ``style_loss`` into a joined # :class:`~pystiche.loss.PerceptualLoss`, which will serve as optimization criterion. perceptual_loss = loss.PerceptualLoss(content_loss, style_loss).to(device) print(perceptual_loss) ######################################################################################## # Images # ------ # # We now load and show the images that will be used in the NST. The images will be # resized to ``size=500`` pixels. images = demo.images() images.download() size = 500 ######################################################################################## # .. note:: # # ``ìmages.download()`` downloads **all** demo images upfront. If you only want to # download the images for this example remove this line. They will be downloaded at # runtime instead. # # .. note:: # # If you want to work with other images you can load them with # :func:`~pystiche.image.read_image`: