def test_io(subtests, test_image_file):
for backend1, backend2 in itertools.combinations(
pyimagetest.image_backends.values(), 2):
with subtests.test(backend1=backend1, backend2=backend2):
image1 = backend1.import_image(test_image_file)
image2 = backend2.import_image(test_image_file)
pyimagetest.assert_images_almost_equal(image1, image2)
def test_BottomRightCrop(test_image):
size = 200
transform = transforms.BottomRightCrop(size)
actual = transform(test_image)
desired = test_image[:, :, -size:, -size:]
pyimagetest.assert_images_almost_equal(actual, desired)
def test_TopLeftCrop(test_image):
size = 200
transform = transforms.TopLeftCrop(size)
actual = transform(test_image)
desired = test_image[:, :, :size, :size]
pyimagetest.assert_images_almost_equal(actual, desired)
def test_Crop(subtests, test_image):
origin = (200, 300)
size = (50, 30)
spatial_slices = {
("top", "left"): [
slice(origin[0], origin[0] + size[0]),
slice(origin[1], origin[1] + size[1]),
],
("bottom", "left"): [
slice(origin[0] - size[0], origin[0]),
slice(origin[1], origin[1] + size[1]),
],
("top", "right"): [
slice(origin[0], origin[0] + size[0]),
slice(origin[1] - size[1], origin[1]),
],
("bottom", "right"): [
slice(origin[0] - size[0], origin[0]),
slice(origin[1] - size[1], origin[1]),
],
}
for (vert_anchor, horz_anchor), spatial_slice in spatial_slices.items():
with subtests.test(vert_anchor=vert_anchor, horz_anchor=horz_anchor):
transform = transforms.Crop(origin,
size,
vert_anchor=vert_anchor,
horz_anchor=horz_anchor)
actual = transform(test_image)
desired = test_image[(slice(None), slice(None), *spatial_slice)]
pyimagetest.assert_images_almost_equal(actual, desired)
def test_CenterCrop():
image = torch.rand(1, 1, 100, 100)
size = 50
transform = transforms.CenterCrop(size)
actual = transform(image)
desired = image[:, :, size // 2:-size // 2, size // 2:-size // 2]
pyimagetest.assert_images_almost_equal(actual, desired)
def test_write_image(tmpdir):
torch.manual_seed(0)
image = torch.rand(3, 100, 100)
file = path.join(tmpdir, "tmp_image.png")
image_.write_image(image, file)
actual = image_.read_image(file=file)
desired = image
pyimagetest.assert_images_almost_equal(actual, desired)
def test_read_image_resize_scalar(test_image_file, test_image_pil):
edge_size = 200
aspect_ratio = image_.calculate_aspect_ratio(
(test_image_pil.height, test_image_pil.width)
)
image_size = image_.edge_to_image_size(edge_size, aspect_ratio)
actual = image_.read_image(test_image_file, size=edge_size)
desired = test_image_pil.resize(image_size[::-1])
pyimagetest.assert_images_almost_equal(actual, desired)
def test_ExportToPIL_multi_image(test_image, test_image_pil):
batch_size = 2
export_transform = transforms.ExportToPIL()
batched_image = test_image.repeat(2, 1, 1, 1)
actuals = export_transform(batched_image)
desired = test_image_pil
assert isinstance(actuals, tuple)
assert len(actuals) == batch_size
for actual in actuals:
pyimagetest.assert_images_almost_equal(actual, desired)
def test_ValidRandomCrop():
def randint(range):
return torch.randint(range + 1, ()).item()
image_size = (100, 100)
crop_size = (10, 20)
image = torch.rand(1, 1, *image_size)
image_height, image_width = image_size
crop_height, crop_width = crop_size
torch.manual_seed(0)
vert_origin = randint(image_height - crop_height)
horz_origin = randint(image_width - crop_width)
torch.manual_seed(0)
transform = transforms.ValidRandomCrop(crop_size)
actual = transform(image)
desired = image[:, :, vert_origin:vert_origin + crop_height,
horz_origin:horz_origin + crop_width, ]
pyimagetest.assert_images_almost_equal(actual, desired)
def test_ImportFromPIL_single_image(test_image, test_image_pil):
import_transform = transforms.ImportFromPIL(make_batched=False)
actual = import_transform(test_image_pil)
desired = image.make_single_image(test_image)
pyimagetest.assert_images_almost_equal(actual, desired)
def assert_transform_equality(pystiche_image, pil_image):
actual = pystiche_transform(pystiche_image)
desired = pil_transform(pil_image)
pyimagetest.assert_images_almost_equal(actual, desired, mae=mae)
def assert_is_identity_transform(transform, image=None, mae=1e-2):
if image is None:
image = read_image()
pyimagetest.assert_images_almost_equal(image, transform(image), mae=mae)
def test_read_image_resize(test_image_file, test_image_pil):
image_size = (200, 300)
actual = image_.read_image(test_image_file, size=image_size)
desired = test_image_pil.resize(image_size[::-1])
pyimagetest.assert_images_almost_equal(actual, desired)
def test_read_image(test_image_file, test_image):
actual = image_.read_image(test_image_file)
desired = test_image
assert image_.is_batched_image(actual)
pyimagetest.assert_images_almost_equal(actual, desired)
