def test_api_repair(image, size):
src = PIL.ImageOps.mirror(image)
repair = Repair(target=image.convert("RGBA"), source=src)
for r in process_octaves(repair, size=size):
assert len(r.images) == 1
assert isinstance(r.images, torch.Tensor)
assert r.images.shape[2:] == (size[1] // r.scale, size[0] // r.scale)
def test_patch_single(image, size=(64, 48)):
remix = Remix(image(size), mode="patch")
for r in process_octaves(remix, octaves=2, size=size, threshold=1e-3):
assert len(r.images) == 1
assert isinstance(r.images, torch.Tensor)
assert r.images.shape[2:] == (size[1] // r.scale, size[0] // r.scale)
assert r.loss < 5e-0
def test_hist_single(image, size=(32, 48)):
remix = Remix(image(size), mode="hist")
for r in process_octaves(remix, octaves=2, size=size):
assert len(r.images) == 1
assert isinstance(r.images, torch.Tensor)
assert r.images.shape[2:] == (size[1] // r.scale, size[0] // r.scale)
assert r.loss < 1e-1
def test_gram_single(image, size=(96, 88)):
remix = Remix(image(size), mode="gram")
for r in process_octaves(remix, octaves=2, size=size):
assert len(r.images) == 1
assert isinstance(r.images, torch.Tensor)
assert r.images.shape[2:] == (size[1] // r.scale, size[0] // r.scale)
assert abs(r.iteration) < 200
assert r.loss < 5e-1
def test_gram_single(image, size=(96, 88)):
for _, loss, images in process_octaves(image(size),
octaves=2,
size=size,
mode="gram"):
assert len(images) == 1
assert all(isinstance(img, PIL.Image.Image) for img in images)
assert all(img.size == size for img in images)
assert loss < 5e-2
def test_patch_single(image, size=(64, 48)):
for _, loss, images in process_octaves(image(size),
octaves=2,
size=size,
mode="patch",
threshold=1e-3):
assert len(images) == 1
assert all(isinstance(img, PIL.Image.Image) for img in images)
assert all(img.size == size for img in images)
assert loss < 5.0
def test_gram_variations(image, size=(72, 64)):
for _, loss, images in process_octaves(image(size),
variations=2,
octaves=2,
size=size,
mode="gram"):
assert len(images) == 2
assert all(isinstance(img, PIL.Image.Image) for img in images)
assert all(img.size == size for img in images)
assert loss < 5e-1
def test_api_expand(image, size):
expand = Expand(target=image, source=image, factor=(2.0, 2.0))
for r in process_octaves(expand, size=size):
assert len(r.images) == 1
assert isinstance(r.images, torch.Tensor)
assert r.images.shape[2:] == (size[1] // r.scale, size[0] // r.scale)
def test_api_enhance(image, size):
mashup = Enhance(target=image, source=image, zoom=2)
for r in process_octaves(mashup, size=size):
assert len(r.images) == 1
assert isinstance(r.images, torch.Tensor)
assert r.images.shape[2:] == (size[1] // r.scale, size[0] // r.scale)
def test_api_mashup(image, size):
mashup = Mashup(sources=[image, image])
for r in process_octaves(mashup, size=size):
assert len(r.images) == 1
assert isinstance(r.images, torch.Tensor)
assert r.images.shape[2:] == (size[1] // r.scale, size[0] // r.scale)
def test_api_remake(image, size):
remake = Remake(target=image, source=image)
for r in process_octaves(remake, size=size):
assert len(r.images) == 1
assert isinstance(r.images, torch.Tensor)
assert r.images.shape[2:] == (size[1] // r.scale, size[0] // r.scale)